Software Engineering Perspectives in Intelligent Systems: Proceedings of 4th Computational Methods in Systems and Software 2020, Vol.1 [1st ed.] 9783030633219, 9783030633226

Table of contents :
Front Matter ....Pages i-xix
Smart City Technology Investment Solution Support System Accounting Multi-factories (V. Lakhno, V. Malyukov, O. Kryvoruchko, A. Desiatko, Y. Shestak)....Pages 1-11
Institutionalization of Intelligent Digital Customs (Viktor Makrusev, Valentin Vakhrushev, Amir Nasibullin)....Pages 12-19
Implementation of the Internet of Things Application Based on Spring Boot Microservices and REST Architecture (Satish Reddy Modugu, Hassan Farhat)....Pages 20-31
Modelling and Simulation of Scrum Team Strategies: A Multi-agent Approach (Zhe Wang)....Pages 32-63
Development of a Scheme of a Hardware Accelerator of Quantum Computing for Correction Quantum Types of Errors (Sergey Gushanskiy, Valery Pukhovskiy, Viktor Potapov, Alexander Kozlovskiy)....Pages 64-73
Personalized Information Representation to Anonymous Users: Digital Signage Case (Nikolay Shilov, Nikolay Teslya)....Pages 74-86
Development of a Web Application of Facilitate Multidisciplinary Rehabilitation of Children with Cleft Lip and Palate (O. V. Dudnik, Ad. A. Mamedov, A. B. Maclennan, Y. O. Volkov, G. E. Odzhaggulieva, S. -M. A. Akhmetkhanov et al.)....Pages 87-101
Quality Assessment Method for GAN Based on Modified Metrics Inception Score and Fréchet Inception Distance (Artem Obukhov, Mikhail Krasnyanskiy)....Pages 102-114
On Systematics of the Information Security of Software Supply Chains (Alexander Barabanov, Alexey Markov, Valentin Tsirlov)....Pages 115-129
Comprehensive Intelligent Information Security Management System (CIISMS) for Supply Networks: The Actor-Network Perspective (Yury Iskanderov, Mikhail Pautov)....Pages 130-142
3D Face Capture for Rehabilitation Progress Assessment After Brain Surgery (Jakub Tomeš, Jan Kohout, Jan Mareš)....Pages 143-149
An Ontological Approach to the Text Sample Size Adaptation for the False Pseudonyms Detection (I. S. Korovin, A. B. Klimenko, I. B. Safronenkova)....Pages 150-158
It Was Never About the Language: Paradigm Impact on Software Design Decisions (Laura M. Castro)....Pages 159-169
Deep Neural Network Acoustic Model Baseline for Character-Level Transcription of Naturally Spoken Czech Language (Martin Vejvar)....Pages 170-185
Problems of Software Developing for the Automation of Scientific Activities (Alexander V. Solovyev, Irina V. Tumanova)....Pages 186-199
E- Learning Readiness Frameworks and Models (Irene Kolo, Tranos Zuva)....Pages 200-211
Arabic Question Answering System Using Graph Ontology (Mohamed S. Zeid, Nahla A. Belal, Yasser El-Sonbaty)....Pages 212-224
Stability Study of a Protection Structure by Stacking GSC Geosynthetics: Application to the Port of Corisco (Equatorial Guinea) (Mustapha Mouhid, Laila Mouakkir, Soumia Mordane, Mohamed Loukili, Mohamed Chagdali, Brahim El Bouni)....Pages 225-241
Comparative Analysis of Products for Testing Software (Alexander Fedosov, Dina Eliseeva, Nina Khodakova, Olga Mnatsakanyan, Natalia Kulikova)....Pages 242-252
A Novel Adaptive Web-Based Environment to Help Deafblind Individuals in Accessing the Web and Lifelong Learning (Samaa M. Shohieb, Ceymi Doenyas, Shaibou Abdoulai Haji)....Pages 253-266
Impact of Agile Methodology Use on Project Success in Organizations - A Systematic Literature Review (Makoena Moloto, Anneke Harmse, Tranos Zuva)....Pages 267-280
Fingerprint Alteration Classification Using Convolutional Neural Network (Shayekh Mohiuddin Ahmed Navid, Umme Kulsum Ritu, Nabiul Hoque Khandakar, Ishrat Jahan Ananya, Shawan Shurid, Nabeel Mohammed et al.)....Pages 281-291
Numerical Modeling of the Wave-Structure Interaction Using the Boundary Element Method (Laila El Aarabi, Laila Mouakkir, Soumia Mordane)....Pages 292-303
Transition from Serverfull to Serverless Architecture in Cloud-Based Software Applications (Oliviu Matei, Pawel Skrzypek, Robert Heb, Alexandru Moga)....Pages 304-314
Comparison of Document Generation Algorithms Using the Docs-as-Code Approach and Using a Text Editor (Marina Igorevna Ozerova, Ilya Evgenievich Zhigalov, Vitatliy Vasilievich Vershinin)....Pages 315-326
A Proactive University Library Book Recommender System (Tadesse Zewdu Mekonnen, Tranos Zuva)....Pages 327-335
Conflict Resolution in Process Models Merging (Asma Hachemi, Mohamed Ahmed-Nacer)....Pages 336-345
Medical Chatbot Techniques: A Review (Andrew Reyner Wibowo Tjiptomongsoguno, Audrey Chen, Hubert Michael Sanyoto, Edy Irwansyah, Bayu Kanigoro)....Pages 346-356
A Model for Effectively Teaching Information Technology (Leila Goosen)....Pages 357-371
Identifying Wood Types Using Convolutional Neural Network ( Rostina, P. H. Gunawan, Esa Prakasa)....Pages 372-381
Mathematical Model of Heat and Mass Transfer in a Colloidal Suspension with Nanoparticles (Sergey Smagin, Polina Vinoogradova, Ilya Manzhula, Alber Livashvili)....Pages 382-392
Review of Current Data Mining Techniques Used in the Software Effort Estimation (Julius Olufemi Ogunleye)....Pages 393-408
Dispatching GPU Distributed Computing When Modeling Large Network Communities of Agents (Donat Ivanov, Eduard Melnik)....Pages 409-418
OIDC Authentication for Educational Purposes and Solving Problems for Localization of Faults in Combinational Circuits (Barish Yumerov, Galina Ivanova)....Pages 419-429
Users Activity Time Series Features on Social Media (Andrey M. Fedorov, Igor O. Datyev, Andrey L. Shchur)....Pages 430-441
Architecture of the Decision Support System for Personnel Security of the Regional Mining and Chemical Cluster (V. V. Bystrov, D. N. Khaliullina, S. N. Malygina)....Pages 442-463
Fault-Tolerant Management for the Edge Devices on the Basis of Consensus with Elected Leader (Melnik E. V., Klimenko A. B., Korobkin V. V.)....Pages 464-474
Simulation of Digital Logic Principles Using DCBLPy with IoT in Packet Tracer (Lukas Hapl, Hashim Habiballa)....Pages 475-481
An Algorithm for Constructing an Efficient Investment Portfolio (Vera Ivanyuk, Dmitry Berzin)....Pages 482-490
Development of an Intelligent Ensemble Forecasting System (Vera Ivanyuk, Andrey Sunchalin, Anna Sunchalina)....Pages 491-500
Intelligent Methods for Predicting Financial Time Series (Vera Ivanyuk, Kirill Levchenko)....Pages 501-509
ASC-Analysis of the Dependence of Volume and Structure of Highly Productive Dairy Cattle Incidence in Krasnodar Region (E. V. Lutsenko, V. A. Grin, K. A. Semenenko, M. P. Semenenko, E. V. Kuzminova, N. D. Kuzminov)....Pages 510-526
Detecting the Abrupt Change in the Bandwidth of a Fast-Fluctuating Gaussian Random Process (Oleg Chernoyarov, Serguei Dachian, Tatiana Demina, Alexander Makarov, Alexandra Salnikova)....Pages 527-541
Cognitive Interaction of Robot Communities, Simulation Modeling (G. V. Gorelova, E. V. Melnik, A. B. Klimenko, I. B. Safronenkova)....Pages 542-554
Construction of an Automated Process Control System for the Exploitation of Oil and Gas Fields in a Heterogeneous Information Environment (Gritsenko Yury, Senchenko Pavel, Sidorov Anatoly)....Pages 555-570
Mobile Robots Groups Use for Monitoring and Data Collection in Continuous Missions with Limited Communications (Donat Ivanov)....Pages 571-581
The Analysis of EEG Signal and Comparison of Classification Algorithms Using Machine Learning Methods (Andrea Nemethova, Dmitrii Borkin, Martin Nemeth)....Pages 582-590
The Analysis of EEG Signal and Finding Correlations Between Right-Handed and Left-Handed People (Martin Nemeth, Andrea Nemethova, Dmitrii Borkin)....Pages 591-597
Research of Data Analysis Techniques for Vibration Monitoring of Technological Equipment (Vladimir Bukhtoyarov, Danil Zyryanov, Vadim Tynchenko, Kirill Bashmur, Eduard Petrovsky)....Pages 598-605
Using the Mathematical Modeling Method for Forecasting Severe Bronchial Obstruction Syndrome with ARVI in Children (L. V. Kramar, T. Yu. Larina)....Pages 606-614
Recognition Recipes with Deep Machine Learning (Sergey V. Ulyanov, Andrey Filipyev, Kirill Koshelev)....Pages 615-622
Computer Simulation of the Structural Properties of Energetic Materials Using High Performance Computing (Igor A. Fedorov, Tatyana S. Reyn, Sergei N. Karabtsev)....Pages 623-632
Some New Approaches to Comparative Evaluation of Algorithms for Calculating Distances Between Genomic Sequences (Boris Melnikov, Marina Trenina, Anastasia Nichiporchuk, Elena Melnikova, Mikhail Abramyan)....Pages 633-642
Basis Finite Automata in Some Minimization Problems. Part I: Introduction and the General Description of the Algorithms (Boris Melnikov, Aleksandra Melnikova)....Pages 643-651
Effect of Bitcoin Volatility on Altcoins Pricing (Artur Meynkhard)....Pages 652-664
Development of Image Dataset Using Hand Gesture Recognition System for Progression of Sign Language Translator (Arifa Ashrafi, Victor Sergeevich Mokhnachev, Yuriy Nikolaevich Philippovich, Lyubov Petrovna Tsilenko)....Pages 665-675
Component of Decision Support Subsystem for Monitoring and Predicting of Hazardous Processes at the Base of Analysis of Macro Zoobenthos Communities of Azov Sea (E. V. Melnik, N. I. Bulysheva, M. V. Orda-Zhigulina, D. V. Orda-Zhigulina)....Pages 676-687
Cognitive Model for Monitoring and Predicting Dangerous Natural Processes for Hydro Ecosystem Analysis (D. V. Orda-Zhigulina, M. V. Orda-Zhigulina, A. A. Rodina)....Pages 688-695
Application of the GERT Method to Visualize the Process of Managing Receivables and Payables of an Enterprise (Svetlana B. Globa, Vladimir P. Maslovsky, Nina M. Butakova, Viktoria V. Berezovaya)....Pages 696-708
Predicting Default Probability of Bank’s Corporate Clients in the Czech Republic. Comparison of Generalized Additive Models and Support Vector Machine Approaches (Mariya Oleynik, Tomáš Formánek)....Pages 709-722
Data Harmonization for Heterogeneous Datasets in Big Data - A Conceptual Model (Ganesh Kumar, Shuib Basri, Abdullahi Abubakar Imam, Abdullateef Oluwagbemiga Balogun)....Pages 723-734
A Productivity Optimising Model for Improving Software Effort Estimation (Vo Van Hai, Ho Le Thi Kim Nhung, Huynh Thai Hoc)....Pages 735-746
AdamOptimizer for the Optimisation of Use Case Points Estimation (Huynh Thai Hoc, Vo Van Hai, Ho Le Thi Kim Nhung)....Pages 747-756
An Evaluation of Technical and Environmental Complexity Factors for Improving Use Case Points Estimation (Ho Le Thi Kim Nhung, Huynh Thai Hoc, Vo Van Hai)....Pages 757-768
Gestural Interface to Support Car Drivers Interacting with Smartphone: A Systematic Literature Review (Jayasankari Ganasan, Ahmad Sobri Hashim)....Pages 769-783
The Price Determinants of Bitcoin as a New Digital Form of Money (Vladislav Rutskiy, Sarfaraz Javed, Shahzool Hazimin Azizam, Nikita Chudopal, Kirill Zhigalov, Roman Kuzmich et al.)....Pages 784-792
Cyber Safety Awareness Framework for South African Schools (Dorothy Scholtz, Elmarie Kritzinger, Adele Botha)....Pages 793-808
Decision-Making-Based Modeling of Auxiliary Diagnosis of Ischemic Stroke in Recovery Period (Dongxue Zhang, Zhihui Huang, Hui Wang, Xiaomin Zhu)....Pages 809-820
The Proposal of Customized Convolutional Neural Network Using for Image Blur Recognition (Dmitrii Borkin, Martin Nemeth, Andrea Nemethova)....Pages 821-828
Statistical Characteristics of Decisions Made by a Neural Network Molecule with Quadrant Quantization and a Molecule with Data Quantization by Two Ellipses (Aleksandr Ivanov, Tatyana Zolotareva)....Pages 829-835
Development of E-Insurance Through Market Institutions: The Example of Digital Compulsory Third-Party Motor Insurance (Vladislav Rutskiy, Ekaterina Konovalova, Younes El Amrani, Svetlana Kapustina, Oleg Ikonnikov, Natalia Bystrova et al.)....Pages 836-843
Using an Expert Panel to Validate the Malaysian SMEs-Software Process Improvement Model (MSME-SPI) (Malek A. Almomani, Shuib Basri, Omar Almomani, Luiz Fernando Capretz, Abdullateef Balogun, Moath Husni et al.)....Pages 844-859
Single Disease DRGs Based on Hospitalization Costs of Hypertensive Patients (Hui Wang, Zhihui Huang, Xiaomin Zhu, Xin Deng)....Pages 860-870
Burst Detection in Social Media Communities (Andrey M. Fedorov, Igor O. Datyev, Andrey L. Shchur)....Pages 871-882
Assessment of Biomedical Risk Factors Associated with Adverse Pregnancy Outcomes (Natalia Lukyanova, Olga Melnikova)....Pages 883-893
NGBoost Interpretation Using LIME for Alcoholic EEG Signal Based on GLDM Feature Extraction (Dandi Trianta Barus, Fikhri Masri, Achmad Rizal)....Pages 894-904
Modeling of Product Heating at the Stage of Beam Input in the Process of Electron Beam Welding Using the COMSOL Multiphysics System (Sergei Kurashkin, Daria Rogova, Vadim Tynchenko, Vyacheslav Petrenko, Anton Milov)....Pages 905-912
Breaking Microsoft Azure Information Protection Viewer Using Memory Dump (Ján Mojžiš, àtefan Balogh)....Pages 913-920
Comparison of Various NoSQL Databases for Unstructured Industrial Data (Andrea Vaclavova, Michal Kebisek)....Pages 921-930
Topic Clustering of Social Media Using Multilayer Text Analysis (V. V. Dikovitsky, A. M. Fedorov)....Pages 931-938
Searching the Hyper-heuristic for the Traveling Salesman Problem with Time Windows by Genetic Programming (Václav Hrbek, Jan Merta)....Pages 939-946
A Multi-criteria Model Application in the Prioritization of Processes for Automation in the Scenario of Intelligence and Investigation Units (Gleidson Sobreira Leite, Adriano Bessa Albuquerque, Plácido Rogério Pinheiro)....Pages 947-965
On the Scheduling of Industrial IoT Tasks in a Fog Computing Environment (Elarbi Badidi)....Pages 966-978
IMC Strategy Using Neural Networks for 3D Printer Bed Temperature Control (Dominik Stursa, Libor Havlicek, Libor Kupka, Petr Dolezel)....Pages 979-989
A Comparison of Processes and Threads Creation (Martin Sysel)....Pages 990-997
Planning and Approving Corporate Resource Development (Yuri Kondrashov, Olga Glushkova, Dmitry Kobzev)....Pages 998-1010
Fast-Growing Firms – “Gazelles” in Modern Russia: An Empirical Study of Growth Factors (Vladislav Rutskiy, Marina Solodova, Roman Tsarev, Irina Yarygina, Omer Faruk Derindag)....Pages 1011-1022
Combining Earth Remote Sensing and Land Wireless Sensor Networks Data in Smart Agriculture Information Products (Ilya Ginzburg, Sergey Padalko, Maxim Terentiev)....Pages 1023-1031
A Model for the Operating Management of the Aircraft Maintenance Composition (Andrey Stankevich)....Pages 1032-1041
The Problem of Rational Allocation of Resources for Replacing Aircraft (Vladimir A. Sudakov, Tatiana V. Sivakova)....Pages 1042-1050
Data Science Around the Indexed Literature Perspective (Mahyuddin K. M. Nasution, Opim Salim Sitompul, Erna Budhiarti Nababan, Esther S. M. Nababan, Emerson P. Sinulingga)....Pages 1051-1065
Comprehensive Assessment of a Student Using Neural Network Algorithms for Students of Technical Specialities and Areas of Training (Vladimir Simonov, Dina Eliseeva)....Pages 1066-1072
A Review on Intenet of Things Smart Homes, Challenges, Open Issues and Countermeasures (Bryan David Julies, Tranos Zuva)....Pages 1073-1089
Applying Bayesian Network to Assess the Levels of Skills Mastering in Adaptive Dynamic OER-Systems (Igor Nekhaev, Ilya Zhuykov, Suren Manukyants, Artyom Maslennikov)....Pages 1090-1116
Clustering Large DataSet’ to Prediction Business Metrics (Rahmad Syah, Marischa Elveny, Mahyuddin K. M. Nasution)....Pages 1117-1127
Multiple Radiotechnical System for the Takeoff and Landing Zones of Unmanned Aerial Vehicles (A. R. Bestugin, V. A. Zavyalov, S. G. Petukhov, I. A. Kirshina, O. M. Filonov)....Pages 1128-1136
Patterns of Long-Term Dynamics of World Gold Production (R. I. Dzerjinski, E. N. Pronina, M. R. Dzerjinskaya)....Pages 1137-1145
Back Matter ....Pages 1147-1150

Citation preview

Advances in Intelligent Systems and Computing 1294

Radek Silhavy Petr Silhavy Zdenka Prokopova   Editors

Software Engineering Perspectives in Intelligent Systems Proceedings of 4th Computational Methods in Systems and Software 2020, Vol.1

Advances in Intelligent Systems and Computing Volume 1294

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Nikhil R. Pal, Indian Statistical Institute, Kolkata, India Rafael Bello Perez, Faculty of Mathematics, Physics and Computing, Universidad Central de Las Villas, Santa Clara, Cuba Emilio S. Corchado, University of Salamanca, Salamanca, Spain Hani Hagras, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK László T. Kóczy, Department of Automation, Széchenyi István University, Gyor, Hungary Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA Chin-Teng Lin, Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan Jie Lu, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia Patricia Melin, Graduate Program of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro, Rio de Janeiro, Brazil Ngoc Thanh Nguyen , Faculty of Computer Science and Management, Wrocław University of Technology, Wrocław, Poland Jun Wang, Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. Indexed by SCOPUS, DBLP, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/11156

Radek Silhavy Petr Silhavy Zdenka Prokopova •

•

Editors

Software Engineering Perspectives in Intelligent Systems Proceedings of 4th Computational Methods in Systems and Software 2020, Vol.1

123

Editors Radek Silhavy Faculty of Applied Informatics Tomas Bata University in Zlín Zlín, Czech Republic

Petr Silhavy Faculty of Applied Informatics Tomas Bata University in Zlín Zlín, Czech Republic

Zdenka Prokopova Faculty of Applied Informatics Tomas Bata University in Zlín Zlín, Czech Republic

ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-3-030-63321-9 ISBN 978-3-030-63322-6 (eBook) https://doi.org/10.1007/978-3-030-63322-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This book constitutes the refereed proceedings of the Computational Methods in Systems and Software 2020 (CoMeSySo 2020), held in October 2020. CoMeSySo 2020 conference intends to provide an international forum for the discussion of the latest high-quality research results in all areas related to intelligent systems. The addressed topics are the theoretical aspects and applications of software engineering, computational methods or artificial intelligence. The papers address topics as software engineering, cybernetics and automation control theory, econometrics, mathematical statistics or artificial. CoMeSySo 2020 has received (all sections) 308 submissions, 184 of them were accepted for publication. The volume Software Engineering Perspectives in Intelligent Systems brings the discussion of new approaches and methods to real-world problems. Furthermore, the exploratory research that describes novel approaches in the software engineering and informatics in the scope of the intelligent systems is presented. The editors believe that readers will find the following proceedings interesting and useful for their research work. September 2020

Radek Silhavy Petr Silhavy Zdenka Prokopova

v

Organization

Program Committee Program Committee Chairs Petr Silhavy

Radek Silhavy

Zdenka Prokopova

Krzysztof Okarma

Roman Prokop Viacheslav Zelentsov

Lipo Wang Silvie Belaskova

Department of Computers and Communication Systems, Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic Department of Computers and Communication Systems, Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic Department of Computers and Communication Systems, Tomas Bata University in Zlin, Czech Republic Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, Poland Department of Mathematics, Tomas Bata University in Zlin, Czech Republic Doctor of Engineering Sciences, Chief Researcher of St. Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS), Russian Federation School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore Head of Biostatistics, St. Anne's University Hospital Brno, International Clinical Research Center, Czech Republic

vii

viii

Roman Tsarev

Organization

Department of Informatics, Siberian Federal University, Krasnoyarsk, Russia

International Program Committee Members Pasi Luukka

Ondrej Blaha

Izabela Jonek-Kowalska

Maciej Majewski

Alena Vagaska

Boguslaw Cyganek Piotr Lech

Monika Bakosova

Pavel Vaclavek

Miroslaw Ochodek Olga Brovkina

Elarbi Badidi

Gopal Sakarkar

President of North European Society for Adaptive and Intelligent Systems & School of Business and School of Engineering Sciences Lappeenranta University of Technology, Finland Louisiana State University Health Sciences Center New Orleans, New Orleans, United States of America Faculty of Organization and Management, The Silesian University of Technology, Poland Department of Engineering of Technical and Informatic Systems, Koszalin University of Technology, Koszalin, Poland Department of Mathematics, Informatics and Cybernetics, Faculty of Manufacturing Technologies, Technical University of Kosice, Slovak Republic Department of Computer Science, University of Science and Technology, Krakow, Poland Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, Poland Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology, Bratislava, Slovak Republic Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic Faculty of Computing, Poznan University of Technology, Poznan, Poland Global Change Research Centre Academy of Science of the Czech Republic, Brno, Czech Republic College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates Shri. Ramdeobaba College of Engineering and Management, Republic of India

Organization

V. V. Krishna Maddinala Anand N. Khobragade Abdallah Handoura

ix

GD Rungta College of Engineering and Technology, Republic of India Scientist, Maharashtra Remote Sensing Applications Centre, Republic of India Computer and Communication Laboratory, Telecom Bretagne – France

Organizing Committee Chair Radek Silhavy

Tomas Bata University in Zlin, Faculty of Applied Informatics, Email: [email protected]

Conference Organizer (Production) Silhavy s.r.o. Web: http://comesyso.openpublish.eu Email: [email protected]

Conference website, Call for Papers http://comesyso.openpublish.eu

Contents

Smart City Technology Investment Solution Support System Accounting Multi-factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Lakhno, V. Malyukov, O. Kryvoruchko, A. Desiatko, and Y. Shestak Institutionalization of Intelligent Digital Customs . . . . . . . . . . . . . . . . . . Viktor Makrusev, Valentin Vakhrushev, and Amir Nasibullin

1 12

Implementation of the Internet of Things Application Based on Spring Boot Microservices and REST Architecture . . . . . . . . . . . . . Satish Reddy Modugu and Hassan Farhat

20

Modelling and Simulation of Scrum Team Strategies: A Multi-agent Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhe Wang

32

Development of a Scheme of a Hardware Accelerator of Quantum Computing for Correction Quantum Types of Errors . . . . . . . . . . . . . . Sergey Gushanskiy, Valery Pukhovskiy, Viktor Potapov, and Alexander Kozlovskiy Personalized Information Representation to Anonymous Users: Digital Signage Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nikolay Shilov and Nikolay Teslya Development of a Web Application of Facilitate Multidisciplinary Rehabilitation of Children with Cleft Lip and Palate . . . . . . . . . . . . . . . O. V. Dudnik, Ad. A. Mamedov, A. B. Maclennan, Y. O. Volkov, G. E. Odzhaggulieva, S. -M. A. Akhmetkhanov, N. V. Gorlova, and Ma Guopei

64

74

87

Quality Assessment Method for GAN Based on Modified Metrics Inception Score and Fréchet Inception Distance . . . . . . . . . . . . . . . . . . . 102 Artem Obukhov and Mikhail Krasnyanskiy

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Contents

On Systematics of the Information Security of Software Supply Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Alexander Barabanov, Alexey Markov, and Valentin Tsirlov Comprehensive Intelligent Information Security Management System (CIISMS) for Supply Networks: The Actor-Network Perspective . . . . . . 130 Yury Iskanderov and Mikhail Pautov 3D Face Capture for Rehabilitation Progress Assessment After Brain Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Jakub Tomeš, Jan Kohout, and Jan Mareš An Ontological Approach to the Text Sample Size Adaptation for the False Pseudonyms Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 I. S. Korovin, A. B. Klimenko, and I. B. Safronenkova It Was Never About the Language: Paradigm Impact on Software Design Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Laura M. Castro Deep Neural Network Acoustic Model Baseline for Character-Level Transcription of Naturally Spoken Czech Language . . . . . . . . . . . . . . . 170 Martin Vejvar Problems of Software Developing for the Automation of Scientific Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 Alexander V. Solovyev and Irina V. Tumanova E- Learning Readiness Frameworks and Models . . . . . . . . . . . . . . . . . . 200 Irene Kolo and Tranos Zuva Arabic Question Answering System Using Graph Ontology . . . . . . . . . . 212 Mohamed S. Zeid, Nahla A. Belal, and Yasser El-Sonbaty Stability Study of a Protection Structure by Stacking GSC Geosynthetics: Application to the Port of Corisco (Equatorial Guinea) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Mustapha Mouhid, Laila Mouakkir, Soumia Mordane, Mohamed Loukili, Mohamed Chagdali, and Brahim El Bouni Comparative Analysis of Products for Testing Software . . . . . . . . . . . . 242 Alexander Fedosov, Dina Eliseeva, Nina Khodakova, Olga Mnatsakanyan, and Natalia Kulikova A Novel Adaptive Web-Based Environment to Help Deafblind Individuals in Accessing the Web and Lifelong Learning . . . . . . . . . . . . 253 Samaa M. Shohieb, Ceymi Doenyas, and Shaibou Abdoulai Haji Impact of Agile Methodology Use on Project Success in Organizations - A Systematic Literature Review . . . . . . . . . . . . . . . . 267 Makoena Moloto, Anneke Harmse, and Tranos Zuva

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Fingerprint Alteration Classification Using Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Shayekh Mohiuddin Ahmed Navid, Umme Kulsum Ritu, Nabiul Hoque Khandakar, Ishrat Jahan Ananya, Shawan Shurid, Nabeel Mohammed, and Sifat Momen Numerical Modeling of the Wave-Structure Interaction Using the Boundary Element Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 Laila El Aarabi, Laila Mouakkir, and Soumia Mordane Transition from Serverfull to Serverless Architecture in Cloud-Based Software Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 304 Oliviu Matei, Pawel Skrzypek, Robert Heb, and Alexandru Moga Comparison of Document Generation Algorithms Using the Docs-as-Code Approach and Using a Text Editor . . . . . . . . . . . . . . 315 Marina Igorevna Ozerova, Ilya Evgenievich Zhigalov, and Vitatliy Vasilievich Vershinin A Proactive University Library Book Recommender System . . . . . . . . . 327 Tadesse Zewdu Mekonnen and Tranos Zuva Conflict Resolution in Process Models Merging . . . . . . . . . . . . . . . . . . . 336 Asma Hachemi and Mohamed Ahmed-Nacer Medical Chatbot Techniques: A Review . . . . . . . . . . . . . . . . . . . . . . . . . 346 Andrew Reyner Wibowo Tjiptomongsoguno, Audrey Chen, Hubert Michael Sanyoto, Edy Irwansyah, and Bayu Kanigoro A Model for Effectively Teaching Information Technology . . . . . . . . . . 357 Leila Goosen Identifying Wood Types Using Convolutional Neural Network . . . . . . . 372 Rostina, P. H. Gunawan, and Esa Prakasa Mathematical Model of Heat and Mass Transfer in a Colloidal Suspension with Nanoparticles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 Sergey Smagin, Polina Vinoogradova, Ilya Manzhula, and Alber Livashvili Review of Current Data Mining Techniques Used in the Software Effort Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Julius Olufemi Ogunleye Dispatching GPU Distributed Computing When Modeling Large Network Communities of Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 Donat Ivanov and Eduard Melnik OIDC Authentication for Educational Purposes and Solving Problems for Localization of Faults in Combinational Circuits . . . . . . . 419 Barish Yumerov and Galina Ivanova

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Users Activity Time Series Features on Social Media . . . . . . . . . . . . . . . 430 Andrey M. Fedorov, Igor O. Datyev, and Andrey L. Shchur Architecture of the Decision Support System for Personnel Security of the Regional Mining and Chemical Cluster . . . . . . . . . . . . . . . . . . . . 442 V. V. Bystrov, D. N. Khaliullina, and S. N. Malygina Fault-Tolerant Management for the Edge Devices on the Basis of Consensus with Elected Leader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 Melnik E. V., Klimenko A. B., and Korobkin V. V. Simulation of Digital Logic Principles Using DCBLPy with IoT in Packet Tracer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 Lukas Hapl and Hashim Habiballa An Algorithm for Constructing an Efficient Investment Portfolio . . . . . 482 Vera Ivanyuk and Dmitry Berzin Development of an Intelligent Ensemble Forecasting System . . . . . . . . . 491 Vera Ivanyuk, Andrey Sunchalin, and Anna Sunchalina Intelligent Methods for Predicting Financial Time Series . . . . . . . . . . . . 501 Vera Ivanyuk and Kirill Levchenko ASC-Analysis of the Dependence of Volume and Structure of Highly Productive Dairy Cattle Incidence in Krasnodar Region . . . . . . . . . . . . 510 E. V. Lutsenko, V. A. Grin, K. A. Semenenko, M. P. Semenenko, E. V. Kuzminova, and N. D. Kuzminov Detecting the Abrupt Change in the Bandwidth of a FastFluctuating Gaussian Random Process . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Oleg Chernoyarov, Serguei Dachian, Tatiana Demina, Alexander Makarov, and Alexandra Salnikova Cognitive Interaction of Robot Communities, Simulation Modeling . . . . 542 G. V. Gorelova, E. V. Melnik, A. B. Klimenko, and I. B. Safronenkova Construction of an Automated Process Control System for the Exploitation of Oil and Gas Fields in a Heterogeneous Information Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 Gritsenko Yury, Senchenko Pavel, and Sidorov Anatoly Mobile Robots Groups Use for Monitoring and Data Collection in Continuous Missions with Limited Communications . . . . . . . . . . . . . 571 Donat Ivanov The Analysis of EEG Signal and Comparison of Classification Algorithms Using Machine Learning Methods . . . . . . . . . . . . . . . . . . . . 582 Andrea Nemethova, Dmitrii Borkin, and Martin Nemeth

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The Analysis of EEG Signal and Finding Correlations Between Right-Handed and Left-Handed People . . . . . . . . . . . . . . . . . . . . . . . . . 591 Martin Nemeth, Andrea Nemethova, and Dmitrii Borkin Research of Data Analysis Techniques for Vibration Monitoring of Technological Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 598 Vladimir Bukhtoyarov, Danil Zyryanov, Vadim Tynchenko, Kirill Bashmur, and Eduard Petrovsky Using the Mathematical Modeling Method for Forecasting Severe Bronchial Obstruction Syndrome with ARVI in Children . . . . . . . . . . . 606 L. V. Kramar and T. Yu. Larina Recognition Recipes with Deep Machine Learning . . . . . . . . . . . . . . . . . 615 Sergey V. Ulyanov, Andrey Filipyev, and Kirill Koshelev Computer Simulation of the Structural Properties of Energetic Materials Using High Performance Computing . . . . . . . . . . . . . . . . . . . 623 Igor A. Fedorov, Tatyana S. Reyn, and Sergei N. Karabtsev Some New Approaches to Comparative Evaluation of Algorithms for Calculating Distances Between Genomic Sequences . . . . . . . . . . . . . 633 Boris Melnikov, Marina Trenina, Anastasia Nichiporchuk, Elena Melnikova, and Mikhail Abramyan Basis Finite Automata in Some Minimization Problems. Part I: Introduction and the General Description of the Algorithms . . . . . . . . . 643 Boris Melnikov and Aleksandra Melnikova Effect of Bitcoin Volatility on Altcoins Pricing . . . . . . . . . . . . . . . . . . . . 652 Artur Meynkhard Development of Image Dataset Using Hand Gesture Recognition System for Progression of Sign Language Translator . . . . . . . . . . . . . . . 665 Arifa Ashrafi, Victor Sergeevich Mokhnachev, Yuriy Nikolaevich Philippovich, and Lyubov Petrovna Tsilenko Component of Decision Support Subsystem for Monitoring and Predicting of Hazardous Processes at the Base of Analysis of Macro Zoobenthos Communities of Azov Sea . . . . . . . . . . . . . . . . . . 676 E. V. Melnik, N. I. Bulysheva, M. V. Orda-Zhigulina, and D. V. Orda-Zhigulina Cognitive Model for Monitoring and Predicting Dangerous Natural Processes for Hydro Ecosystem Analysis . . . . . . . . . . . . . . . . . . . . . . . . 688 D. V. Orda-Zhigulina, M. V. Orda-Zhigulina, and A. A. Rodina Application of the GERT Method to Visualize the Process of Managing Receivables and Payables of an Enterprise . . . . . . . . . . . . 696 Svetlana B. Globa, Vladimir P. Maslovsky, Nina M. Butakova, and Viktoria V. Berezovaya

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Predicting Default Probability of Bank’s Corporate Clients in the Czech Republic. Comparison of Generalized Additive Models and Support Vector Machine Approaches . . . . . . . . . . . . . . . . . . . . . . . 709 Mariya Oleynik and Tomáš Formánek Data Harmonization for Heterogeneous Datasets in Big Data - A Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723 Ganesh Kumar, Shuib Basri, Abdullahi Abubakar Imam, and Abdullateef Oluwagbemiga Balogun A Productivity Optimising Model for Improving Software Effort Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735 Vo Van Hai, Ho Le Thi Kim Nhung, and Huynh Thai Hoc AdamOptimizer for the Optimisation of Use Case Points Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 Huynh Thai Hoc, Vo Van Hai, and Ho Le Thi Kim Nhung An Evaluation of Technical and Environmental Complexity Factors for Improving Use Case Points Estimation . . . . . . . . . . . . . . . . . . . . . . . 757 Ho Le Thi Kim Nhung, Huynh Thai Hoc, and Vo Van Hai Gestural Interface to Support Car Drivers Interacting with Smartphone: A Systematic Literature Review . . . . . . . . . . . . . . . . 769 Jayasankari Ganasan and Ahmad Sobri Hashim The Price Determinants of Bitcoin as a New Digital Form of Money . . . 784 Vladislav Rutskiy, Sarfaraz Javed, Shahzool Hazimin Azizam, Nikita Chudopal, Kirill Zhigalov, Roman Kuzmich, Alexander Pupkov, and Roman Tsarev Cyber Safety Awareness Framework for South African Schools . . . . . . 793 Dorothy Scholtz, Elmarie Kritzinger, and Adele Botha Decision-Making-Based Modeling of Auxiliary Diagnosis of Ischemic Stroke in Recovery Period . . . . . . . . . . . . . . . . . . . . . . . . . . 809 Dongxue Zhang, Zhihui Huang, Hui Wang, and Xiaomin Zhu The Proposal of Customized Convolutional Neural Network Using for Image Blur Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821 Dmitrii Borkin, Martin Nemeth, and Andrea Nemethova Statistical Characteristics of Decisions Made by a Neural Network Molecule with Quadrant Quantization and a Molecule with Data Quantization by Two Ellipses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829 Aleksandr Ivanov and Tatyana Zolotareva

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Development of E-Insurance Through Market Institutions: The Example of Digital Compulsory Third-Party Motor Insurance . . . . 836 Vladislav Rutskiy, Ekaterina Konovalova, Younes El Amrani, Svetlana Kapustina, Oleg Ikonnikov, Natalia Bystrova, and Roman Tsarev Using an Expert Panel to Validate the Malaysian SMEs-Software Process Improvement Model (MSME-SPI) . . . . . . . . . . . . . . . . . . . . . . . 844 Malek A. Almomani, Shuib Basri, Omar Almomani, Luiz Fernando Capretz, Abdullateef Balogun, Moath Husni, and Abdul Rehman Gilal Single Disease DRGs Based on Hospitalization Costs of Hypertensive Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 860 Hui Wang, Zhihui Huang, Xiaomin Zhu, and Xin Deng Burst Detection in Social Media Communities . . . . . . . . . . . . . . . . . . . . 871 Andrey M. Fedorov, Igor O. Datyev, and Andrey L. Shchur Assessment of Biomedical Risk Factors Associated with Adverse Pregnancy Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 883 Natalia Lukyanova and Olga Melnikova NGBoost Interpretation Using LIME for Alcoholic EEG Signal Based on GLDM Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 894 Dandi Trianta Barus, Fikhri Masri, and Achmad Rizal Modeling of Product Heating at the Stage of Beam Input in the Process of Electron Beam Welding Using the COMSOL Multiphysics System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905 Sergei Kurashkin, Daria Rogova, Vadim Tynchenko, Vyacheslav Petrenko, and Anton Milov Breaking Microsoft Azure Information Protection Viewer Using Memory Dump . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 913 Ján Mojžiš and Štefan Balogh Comparison of Various NoSQL Databases for Unstructured Industrial Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 921 Andrea Vaclavova and Michal Kebisek Topic Clustering of Social Media Using Multilayer Text Analysis . . . . . 931 V. V. Dikovitsky and A. M. Fedorov Searching the Hyper-heuristic for the Traveling Salesman Problem with Time Windows by Genetic Programming . . . . . . . . . . . . . . . . . . . . 939 Václav Hrbek and Jan Merta

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A Multi-criteria Model Application in the Prioritization of Processes for Automation in the Scenario of Intelligence and Investigation Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 947 Gleidson Sobreira Leite, Adriano Bessa Albuquerque, and Plácido Rogério Pinheiro On the Scheduling of Industrial IoT Tasks in a Fog Computing Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 966 Elarbi Badidi IMC Strategy Using Neural Networks for 3D Printer Bed Temperature Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 979 Dominik Stursa, Libor Havlicek, Libor Kupka, and Petr Dolezel A Comparison of Processes and Threads Creation . . . . . . . . . . . . . . . . . 990 Martin Sysel Planning and Approving Corporate Resource Development . . . . . . . . . . 998 Yuri Kondrashov, Olga Glushkova, and Dmitry Kobzev Fast-Growing Firms – “Gazelles” in Modern Russia: An Empirical Study of Growth Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1011 Vladislav Rutskiy, Marina Solodova, Roman Tsarev, Irina Yarygina, and Omer Faruk Derindag Combining Earth Remote Sensing and Land Wireless Sensor Networks Data in Smart Agriculture Information Products . . . . . . . . . . 1023 Ilya Ginzburg, Sergey Padalko, and Maxim Terentiev A Model for the Operating Management of the Aircraft Maintenance Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1032 Andrey Stankevich The Problem of Rational Allocation of Resources for Replacing Aircraft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1042 Vladimir A. Sudakov and Tatiana V. Sivakova Data Science Around the Indexed Literature Perspective . . . . . . . . . . . . 1051 Mahyuddin K. M. Nasution, Opim Salim Sitompul, Erna Budhiarti Nababan, Esther S. M. Nababan, and Emerson P. Sinulingga Comprehensive Assessment of a Student Using Neural Network Algorithms for Students of Technical Specialities and Areas of Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1066 Vladimir Simonov and Dina Eliseeva A Review on Intenet of Things Smart Homes, Challenges, Open Issues and Countermeasures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1073 Bryan David Julies and Tranos Zuva

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Applying Bayesian Network to Assess the Levels of Skills Mastering in Adaptive Dynamic OER-Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090 Igor Nekhaev, Ilya Zhuykov, Suren Manukyants, and Artyom Maslennikov Clustering Large DataSet’ to Prediction Business Metrics . . . . . . . . . . . 1117 Rahmad Syah, Marischa Elveny, and Mahyuddin K. M. Nasution Multiple Radiotechnical System for the Takeoff and Landing Zones of Unmanned Aerial Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1128 A. R. Bestugin, V. A. Zavyalov, S. G. Petukhov, I. A. Kirshina, and O. M. Filonov Patterns of Long-Term Dynamics of World Gold Production . . . . . . . . 1137 R. I. Dzerjinski, E. N. Pronina, and M. R. Dzerjinskaya Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1147

Smart City Technology Investment Solution Support System Accounting Multi-factories V. Lakhno1 , V. Malyukov1 , O. Kryvoruchko2 A. Desiatko2(&) , and Y. Shestak2 1

2

,

Faculty of Information Security, Computer Science and Communication, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine [email protected], [email protected] Faculty of Information Technologies, Kyiv National University of Trade and Economics, Kyiv, Ukraine {kryvoruchko_ev,desyatko,shestack}@knute.edu.ua

Abstract. This article proposes a model for decision support system (DSS) in the process of financing into information technology (IT) for Smart City. Proposed model has the capacity to find a solution in analytical approach for the problem using means of bilinear differential quality games with several terminal surfaces. The new class of bilinear differential games was considered during the research course of optimal financing strategies into IT for Smart City. The resulting approach along with a new class of equations made it possible to adequately describe financing processes taking into consideration the multifactorial nature of the problem statement. The software product DSS “IT Invest” was developed, with help of which it became possible to reduce the discrepancy between forecasting data and the real productivity of investments into IT for Smart City. Moreover it become possible to concurrently optimize investment strategies among all investment parties. Keywords: Smart City
Rational investment system
Decision support
Differential game
Software product
Information-driven systems
Information systems

1 Introduction At the present days many investment market players are looking for new promising technologies for investing their financial resources. Digital technologies sphere considered as one of the most promising area for investment. Nowadays, innovative digital technologies determine the pace of development not only in the field of IT, but also development and modernization trends of traditional industries and economic sectors [1]. Moreover, not all investors realize that investing in IT is a risky, albeit promising area [1, 2]. According to statistics, after the end of the active investment stage, not more than 11–16% of IT projects will be able to achieve self-sufficiency and bring profit to the investor.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 1–11, 2020. https://doi.org/10.1007/978-3-030-63322-6_1

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Thus, investors who are thinking about choosing among directions for investing into IT and needing support in deciding on preferred strategies for investing their financial resources are much like gold diggers. That is, until the investor is able to find the coveted nugget, he has to review a huge amount of empty ore. In many research papers [3, 4] authors noted that, in order to increase the evaluation effectiveness and efficiency of such investment projects, it is advisable to use the potential of various computerized decision support systems (DSS). It is especially valuable when it is about investing in large IT projects. For example, during the predictive assessment, which informational technology has more priority and is more promising for the investor when investing financial resources in Smart City. Investors can have a diametrically different vision of the initial points for investment of their resources among new directions of the postulates of classical urbanism and digitalization of all aspects of urban activity convergence. For example, some investors consider the development of energy-saving technologies within the framework of Smart City as a priority, while others primarily want to develop technologies related to the safety of residents and monitoring of the rule of law [5, 6]. Along the way, we note that in this situation, the divergence of the invested financial resources productivity rate for investors will be different. And this, in turn, dictates the need to find rational strategies for investing in such complex projects as Smart City. And it is quite difficult for the investors to manage it without appropriate computer support in making decisions on choosing strategies. All of the above written has predetermined the relevance of the topic of our study. One of the priority tasks of which is the development of new models and an appropriate software product that is useful for investors in the search for rational strategies for investing in Smart City IT. Also, such as DSS will be useful in predicting the real investment productivity in IT Smart City.

2 Literature Review According to a range of authors [7–9], the development of such a direction as mathematical support for decision-making during the selection of a rational strategy for investing in IT (Smart City in particular) should be accompanied by a synthesis of new models and methods. These new methods and models form the computing core of computer DSS, which is designed to simplify the task for investors. Note that there are quite different approaches from the point of view of the mathematical apparatus used in such models. For example, the authors of [10, 11] describe the application of classical economic and mathematical models. However, in most situations, these models do not take into account many parameters of investing in Smart City in the context of obtaining predictive assessment of the appropriateness of a particular financial resource investment strategy by the investor. In [12–14], authors note that in the relation to this class of problems, the most adequate models describing the behavior of a complex system are models based on game theory. As an analysis of studies in this area showed, most of the models and algorithms presented in [15–17] do not contain real recommendations and predictive estimates for Smart City investors. Existing DSSs are not suitable for the purposes of real choice

Smart City Technology Investment Solution Support System

3

decision making of strategies by the investor when investing financial resources in Smart City and are not very informative for evaluating real investment projects and investor options. These circumstances necessitate the relevance of new models and software products focused on cross-platform applications development, that would be capable to support decision making procedures in search for rational strategies of continuous investment into IT Smart City.

3 Goals and Objectives of the Study This research aims to develop a model based on the use of bilinear differential quality game means with several terminal surfaces for a decision support system in the process of financing information technologies for Smart City. Research objectives: – development of a model and algorithms for a decision support system for the search of rational continuous investment strategies into Smart City IT; – testing of the model during a computational experiment and development of the decision support system for choosing rational continuous investment strategies into IT Smart City.

4 Methods and Models 4.1

Problem Statement

The problem of investing into IT that ensures the functioning of Smart City is the key to the paradigm of innovative development of both megacities and medium-sized cities. This is due to the fact that IT has recently begun to determine the development of many urban infrastructures, for example, such as urban logistics, security, energy, water supply, etc. [17–19]. For development Smart City IT needs not only to have financial resources (FR - cFinR), but also to be able to use them correctly [20, 21]. However, the proper use of financial resources requires the development of appropriate tools. As noted above, one of the most effective tool is the use of DSS. DSS with the core of the gaming-oriented computational model allows constructively determining rational strategies for allocating financial resources for the development of IT for Smart City. There is no universal model that takes into account all the factors corresponding to the problem under consideration. This means that for its solution it is necessary to develop a set of models that make it possible to optimize the procedure for finding rational strategies for distributing financial resources for IT for Smart City.

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4.2

A Model Based on the Use of Means of Bilinear Differential Quality Games with Several Terminal Surfaces

The following is a model that takes into account factors such as the multiplicity of IT for Smart City technologies and the continuity of the investment process by investors. We formulate the model. Note that the statement of the problem itself continues our previous research in this direction [9, 14]. Two investors (players) manage a dynamic system in multidimensional spaces. It is defined by a set of bilinear differential equations with dependent motions. Sets of strategies (U) and (V) of players and terminal surfaces S0 , F0 . The goal of the first player (hereinafter Inv1) to bring a dynamic system with the help of his control strategies on the terminal surface S0 no matter how the second player acts (hereinafter Inv2). The goal of Inv2 is to bring a dynamic system using its control strategies to the terminal surface F0 , no matter how Inv1 acts. The problem under consideration generates two subtasks from the point of view of the first ally player and from the point of view of the second ally player [9, 17]. The article considers the subtask from the point of view of the first ally player, since the subtask from the point of view of the second ally player is symmetrical. The solution is to find the set of initial states of the players and their strategies. These strategies will allow objects to bring the system to one or another surface. In subtask 1, an ally player is treated for Inv1, an adversary is treated for Inv2. And vice versa - in subtask 2, the ally player is treated as Inv2, and the opposing player is treated as Inv1. Both players try to invest their FR in IT. We assume that Inv1 has a set hð0Þ ¼ ðh1 ð0Þ; . . .; hn ð0ÞÞ of financial resources (hi ð0Þ - FR for the development of i new technology for Smart City). Accordingly, Inv2 has f ð0Þ ¼ ðf1 ð0Þ; . . .; fn ð0ÞÞ (fi ð0Þ - FR for the development of i new technology for Smart City). These sets determine the predicted, at the moment t ¼ 0, size of FinR of the players for each new IT for Smart City). We describe the dynamics of FinR change for players as following: dhðtÞ=dt ¼ hðtÞ þ B1  hðtÞ þ ½ðA1 þ R1 Þ  E  UðtÞ  B1  hðtÞ  ½ðA2 þ R2 Þ  E  VðtÞ  B2  f ðtÞ; df ðtÞ=dt ¼ f ðtÞ þ B2  f ðtÞ  ½ðA2 þ R2 Þ  E  VðtÞ  B2  f ðtÞ  ½ðA1 þ R1 Þ  E  UðtÞ  B1
hðtÞ;

where hi ðtÞ 2 Rn ; f ðtÞ 2 Rn ; UðtÞ; VðtÞ – square n - order matrices with positive elements ui ðtÞ; vi ðtÞ 2 ½0; 1 on the diagonals of diagonal matrices UðtÞ; VðtÞ, respectively; B1 ; B2 – transformation matrices FinR Inv1 and Inv2 upon their successful implementation in IT for Smart City, which are square matrices order with positive elements gij1 ; gi2 ; respectively; A1 ; R1 - diagonal matrices with positive elements that characterize the interest rate of Inv2 for financial investments and the share of return on investment of Inv2 in relation to Inv1 investments in IT for Smart City; A2 ; R2 - diagonal matrices with positive elements that characterize the interest rate of Inv1 for financial investments and the share of return on investment of Inv1 in relation to Inv2 investments in IT for Smart City;

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E unit n -order matrix The interaction ends when the following conditions are met: ðhðtÞ; f ðtÞÞ 2 S0 ;

ð1Þ

ðhðtÞ; f ðtÞÞ 2 F0 :

ð2Þ

Assuming that S0 ¼

n [

fðh; f Þ : ðh; f Þ 2 R2
n ; h  0; fi ¼ 0g; F0

i¼1

¼

n [

fðh; f Þ : ðh; f Þ 2 R2
n ; f  0; hi ¼ 0g:

i¼1

If condition (1) is fulfilled, we believe that the IT financing procedure for Smart City has been completed. That is, Inv2 didn’t have enough FinR for at least one of IT to continue the continuous investment process (for example, the investor does not have enough funds to develop LoRa technology, but enough funds to develop Smart grid technologies, video surveillance in Smart City, etc.). If condition (2) is fulfilled, then we believe that the continuous IT investment procedure for Smart City has also been completed. That is, Inv1 did not have enough FinR to continue the continuous investment process for at least one of the IT. If both conditions (1) and (2) are not fulfilled, we believe that the continuous investment process for Smart City continues. The process of continuous IT investment procedure for Smart City was considered as part of a positional differential game scheme with full information [9, 17]. 4.3

The Solution to the Problem of Finding the Sets of “Preference” of the Investor of His Optimal Strategies

The solution to subtask 1 is to find the sets of “preference” for Inv1 and its optimal strategies. Similarly, the task is posed from the point of view Inv2. Here are the conditions under which the solution to the game is found, i.e. range of “preferences” W1 and optimal strategies for Inv1. These conditions will be given by matrix inequalities. 1) 2) 3) 4) 5)

ðA1  R1 Þ  E  0; ðA2  R2 Þ  E  0; ðA1  R1 Þ  E  0; ðA2  R2 Þ  E  0; ðA1  R1 Þ  E  0; ðA2  R2 Þ  E  0; ðA1  R1 Þ  E  0; ðA2  R2 Þ  E  0: All other options for the relations of the elements of these matrices.

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Let’s introduce the notation. n X

Gi ¼ Si ¼

j¼1 n X

½ðA2 þ R2 Þ  B2 ij ;

n n X X f½ðA1 þ R1 Þ  E  B1 ih g=f f½ðA1 þ R1 Þ  Egij  f½ðA2 þ R2 Þ  E  B2 ghj g;

h¼1 n X

Gi1 ¼ Si1 ¼ Fi ¼ Hi ¼ H1i ¼ F1i ¼

h¼1 n X j¼1 n X

f½ðA1 þ R1 Þ  Egij 

½ðA1 þ R1 Þ  B1 hj g;

j¼1

f½ðA1 þ R1 Þ  E  B1 ij g; ½ðA1 þ R1 Þ  B1 ij ;

h¼1 n X

h¼1

f½ðA1 þ R1 Þ  E  B1 ih g=f

j¼1 n X

j¼1

j¼1 n X

j¼1 n X

j¼1 n X

f½ðA2 þ R2 Þ  E  B2 ih g=f

n X

f½ðA2 þ R2 Þ  Egij 

j¼1

n X

f½ðA1 þ R1 Þ  E  B1 ghj g;

j¼1

f½ðA2 þ R2 Þ  E  B2 ij g; n n X X f½ðA2 þ R2 Þ  E  B2 ih g=f f½ðA2 þ R2 Þ  Egij  ½ðA2 þ R2 Þ  B2 hj g; j¼1

j¼1

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðq Þi ¼ ½Gi  Gi1 =½2  Si1  þ f½Gi  Gi1 =½2  Si1 g2 þ ½Si =Si1 ; qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðu Þi ¼ ½F i  F1i =½2  H1i  þ f½F i  F1i =½2  H1i g2 þ ½H i =H1i :

In the framework of these notations for case 1), the set of preference W1 is defined as following: W1 ¼ W1i ¼

n [ i¼1 n [

W1i ; fðhð0Þ; f ð0ÞÞ 2 R2n þ ; ðq Þi  fi ð0Þ

i¼1

8i ¼ 1; . . .; n; ðu Þi  hi ð0Þ 

n X h¼1

n X h¼1

f½ðA1 þ R1 Þ  Eih =

f½ðA2 þ R2 Þ  E  B2 ih =f

n X

½ðA1 þ R1 Þ  Eij g  hh ð0Þ;

j¼1 n X

½ðA2 þ R2 Þ  E  B2 ij g  fh ð0Þg:

j¼1

The first player’s optimal strategy would be U ðtÞ ¼ E: For cases 2)–5), the preference sets of the first player and his optimal strategies are found similarly. In the same way, the solution of the subtask can be also found by the second ally player.

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5 Computational Experiment Computational experiments were performed in the Maple environment, see Fig. 1, 2. As the initial data were taken from investment projects into Smart City technology in large cities of Ukraine - Kiev, Kharkov, Lviv, Zaporozhye. Figures 1 and 2 show the results for two calculation tests during a computational experiment. As a result of the experiment, many investor strategies were identified. Two cases were considered. First case assumed that the first investor manages investments, which are described by two variables. It was assumed that the equations that specify the changes in the variables of the first player are the same. The second investor manages the investments described by one variable. In the second case, the situation is similar, except for the assumption that the equations defining the changes in the variables of the first investor are the same. They are different. There are many initial states of objects and their strategies during the experiment These strategies allow objects to bring the system to one or another terminal surface S0 , F0 . On the plane (Fig. 1), the vertical axis is Inv2 financial resources. Two horizontal axes (x, y) - Inv1 financial resources. The area under both hyperplanes at the same time (“below” them) is W1 (the area of “preference” for Inv1). On Fig. 1, numeral 1 denotes a hyperplane, which defines the area located below the hyperplane, in which the first investor guarantees himself the preservation of his financial resources for both components. Numeral 2 denotes a hyperplane that defines the area located below this hyperplane, in which the first investor is able to ensure the loss of financial resources of the second investor.

Fig. 1. Results of a computational experiment 1

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Fig. 2. The results of computational experiment 2

On Fig. 2 the vertical axis also denotes Inv2 financial resources and the two horizontal axes (x, y) - Inv1 financial resources. The area under the three hyperplanes at the same time (“below” them) is W1 (the area of “preference” for Inv1). On Fig. 2 numerals 1 and 2 indicate hyperplanes that define the area below these hyperplanes, in which the first investor guarantees himself the preservation of his financial resources for both components. Numeral 3 denotes a hyperplane that defines the area located below this hyperplane, in which the first investor has the opportunity to ensure the loss of financial resources of the second investor. Obtained results demonstrate the effectiveness of the proposed approach. During the testing of the model in the Maple environment, as well as the Invest Smart City software product, the correctness of the results was established.

6 IT Invest Software Based on the model and data obtained during the computational experiments, the software system IT Invest was implemented. DSS allows investor to graphically visualize the conclusions of the system about the advisability of choosing one or another version of the strategies for continuous investment in Smart City IT. DSS was implemented in the C # programming language. A general view of the DSS interface after receiving the system output and the corresponding surface graph is shown in Fig. 3. An example of the DSS output is shown at the bottom of the form window to visualize the graphical interpretation of the solution. Interpretation of the decision in DSS: in this case, the first player, applying his optimal strategy, achieves the goal in cooperation. This means that the second player will lose his financial resource, despite his opposition to the first player.

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Fig. 3. General view of the DSS decision output window and the system output about the appropriateness of the strategy used by the investor

DSS “IT Invest” was simulating cases when the strategies of the players bring them to the corresponding terminal surfaces. The visualization window of the DSS “IT Invest” (Fig. 3.) shows: abscissa axis – financial resources of the first investor (H); ordinate axis – financial resources of the second investor (Q). At the bottom of the window (Fig. 3) the output generated by the DSS is shown. Currently, the DSS interface is only implemented in Russian and Ukrainian localization.

7 Discussion of the Results of Computational Experiments and the Work of DSS “IT Invest” Figures 1 and 2 show that if the parameters determining the investment process of the parties satisfy condition 1), then the first investor in his area of preference has a strategy in which he will not lose his financial resources. Along with the positive characteristics and advantages of the considered model, it also has drawbacks. So, the drawback of the model is the fact that the predictive assessment data obtained using the IT Invest DSS when choosing investment strategies in Smart City did not always coincide with the actual data. Model and software product DSS “IT Invest” can be scaled up for other investment into information technology tasks, for example to create information and control systems for trading enterprises. However, in comparison with the available models, the proposed solution improves the efficiency and predictability for the investor by an average of 9–12% [8, 12]. Further prospects for the development of this study, presented in the framework of the article, is the transfer of accumulated experience to the actual practice of optimizing investment policy in technology for Smart City.

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8 Conclusion A model for a decision support system in the process of financing in information technology (IT) for Smart City was proposed. Unlike the existing, this model made it possible to analytically find a solution to the problem using the means of bilinear differential quality games with several terminal surfaces; it is a very difficult task for such differential games in multidimensional spaces to find a solution in an analytical form. In the course of finding optimal strategies for financing IT for Smart City problem solving, a new class of bilinear differential games was considered. The resulting solution and a new class of equations made it possible to adequately describe the financing processes taking into account multifactorial nature in the statement of the problem; The “IT Invest” software product was developed in the Visual Studio 2019 environment. The “IT Invest” product reduces the discrepancies between forecasting data and the real return on investment in IT for Smart City. At the same time, it makes possible the optimization of IT investment strategies for Smart City by all parties of the investment process.

References 1. Naym, J., Hossain, M.A.: Does investment in information and communication technology lead to higher economic growth: evidence from Bangladesh. Int. J. Bus. Manag. 11(6), 302 (2016) 2. Khallaf, A., Omran, M.A., Zakaria, T.: Explaining the inconsistent results of the impact of information technology investments on firm performance: a longitudinal analysis. J. Acc. Organ. Change 13(3), 359–380 (2017) 3. Mohammed, A.N.N.A.M., Hu, W., Obrenovic, B., Aina, A.A.M.: An empirical assessment of the link between decision support system (DSS) capabilities, competencies and firm performance: a mediating role of absorptive capacity. In: Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences, pp. 193–198. ACM, January 2017 4. Sandström, J., Kyläheiko, K., Collan, M.: Managing uncertainty in long life cycle investments: unifying investment planning, management, and post-audit with a fuzzy DSS. Int. J. Bus. Innov. Res. 11(1), 133–145 (2016) 5. Albino, V., Berardi, U., Dangelico, R.M.: Smart cities: definitions, dimensions, performance, and initiatives. J. Urban Technol. 22(1), 3–21 (2015) 6. Angelidou, M.: Smart cities: a conjuncture of four forces. Cities 47, 95–106 (2015) 7. Irani, Z., Sharif, A., Kamal, M.M., Love, P.E.: Visualising a knowledge mapping of information systems investment evaluation. Expert Syst. Appl. 41(1), 105–125 (2014) 8. Lakhno, V., Malyukov, V., Bochulia, T., Hipters, Z., Kwilinski, A., Tomashevska, O.: Model of managing of the procedure of mutual financial investing in information technologies and smart city systems. Int. J. Civ. Eng. Technol. (IJCIET) 9(8), 1802–1812 (2018) 9. Altuntas, S., Dereli, T.: A novel approach based on DEMATEL method and patent citation analysis for prioritizing a portfolio of investment projects. Expert Syst. Appl. 42(3), 1003– 1012 (2015)

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10. Gottschlich, J., Hinz, O.: A decision support system for stock investment recommendations using collective wisdom. Decis. Support Syst. 59, 52–62 (2014) 11. Strantzali, E., Aravossis, K.: Decision making in renewable energy investments: a review. Renew. Sustain. Energy Rev. 55, 885–898 (2016) 12. Cascetta, E., Carteni, A., Pagliara, F., Montanino, M.: A new look at planning and designing transportation systems: a decision-making model based on cognitive rationality, stakeholder engagement and quantitative methods. Transp. Policy 38, 27–39 (2015) 13. Akhmetov, B.B., Lakhno, V.A., et al.: The choice of protection strategies during the bilinear quality game on cyber security financing. Bull. Natl. Acad. Sci. Repub. Kaz. (3), 6–14 (2018) 14. Smit, H.T., Trigeorgis, L.: Flexibility and games in strategic investment. Multinational Finance J. 14(1/2), 125–151 (2010) 15. Arasteh, A.: Considering the investment decisions with real options games approach. Renew. Sustain. Energy Rev. 72, 1282–1294 (2017) 16. Akhmetov, B.S., Akhmetov, B.B., et al.: Adaptive model of mutual financial investment procedure control in cybersecurity systems of situational transport centers. News Natl. Acad. Sci. Repub. Kaz. Ser. Geol. Tech. Sci. 3(435), 159–172 (2019) 17. Lee, J.H., Phaal, R., Lee, S.H.: An integrated service-device-technology roadmap for smart city development. Technol. Forecast. Soc. Change 80(2), 286–306 (2013) 18. Paskaleva, K.A.: Enabling the smart city: the progress of city e-governance in Europe. Int. J. Innov. Reg. Dev. 1(4), 405–422 (2009) 19. Vadgama, C.V., Khutwad, A., Damle, M., Patil, S.: Smart funding options for developing smart cities: a proposal for India. Indian J. Sci. Technol. 8(34), 1–12 (2015) 20. Washburn, D., Sindhu, U., Balaouras, S., Dines, R.A., Hayes, N., Nelson, L.E.: Helping CIOs understand “smart city” initiatives. Growth 17(2), 1–17 (2009)

Institutionalization of Intelligent Digital Customs Viktor Makrusev1 , Valentin Vakhrushev2(&) and Amir Nasibullin1,2

,

1

2

Russian Customs Academy, Komsomolsky Prospect 4, Lyubertsy 140015, Russia Peoples’ Friendship University of Russia, Miklukho-maklaya St., Moscow 117198, Russia [email protected]

Abstract. The article identifies the most relevanttheoretical and applied problems of the digital transformation of the customs regulation institute. The integrative paradigm of the development of the institution of customs regulation and its current state are outlined. Definitions of the concepts “digital customs” and “intellectualdigital customs” are formulated. The concept, processes and methodology of intellectualization of the information environment of the customs system, the processes of customs regulation and management are described. The methodological basis of the presented research is a holistic evolutionary approach, the idea of which is the unity of the elements of the customs system model, reflecting its features in the conditions of evolution, as well as the conditions and dynamics of its institutional environment. #COMESYSO1120 Keywords: Digital economy
Institute of Customs Regulation
Digital customs
Intellectualization of customs regulation and management processes
Intellectual digital customs
Cognitive management theory
Metatechnology of holistic evolutionary intellectualization

1 Introduction The global digitalization of the world economy, the dominance of a qualitatively new digital development paradigm, all this fundamentally changes the essence of the product and its properties as a key factor in meeting people’s needs [1]. The instrumental environment for the production of goods, their movement and provision to the consumer is also changing. In world trade, a new product, the «product–figure» is becoming more and more dominant. Ultimately, this is 1) a tangible or intangible item (figure-subject) or 2) a service provided through information technology (figure-service). The first product is created, placed and transferred to the consumer on computer tools and other digital information media; for example, a program, an idea, an invention, a project. The second is a special type of service provided through

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 12–19, 2020. https://doi.org/10.1007/978-3-030-63322-6_2

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information tools, systems or environments - in the modern classification of information technology services. In the context of the specifics of the digital product, the specifics of the technology of its production and the provision to the consumer, the problems of sectoral and comprehensive studies of digitalization processes in all areas of the economy and management (including public administration) in the context of the further development of information and cognitive (intelligent) systems become most relevant. Such studies are especially relevant for the customs sector. The emergence of a new type of goods transported across the customs border in the electronic environment requires the search for adequate solutions for its identification, declaration and customs control. In a broader setting - the development of the concept of customs regulation and management institutions is required under the conditions of digitalization of world economic processes; development of fundamentally new methods and tools of customs administration is necessary [2]. The article presents the results of research on institutional aspects of customs administration transformation in the Eurasian economic Union, including: the key analytical characteristics of the initial position of customs institutions in modern conditions are determined; an integrative paradigm for the development of customs institutions of the Eurasian economic Union and the development of the concept of the digital Institute of customs is formulated; the paper presents conceptual provisions and theoretical and methodological problems of intelligent digital customs based on cognitive methodology.

2 Analytical Characteristics of the Starting Position Modern customs is a unique, complex, developing and historically determined phenomenon, expressed at different historical stages in various institutional forms, but always with politically and economically justified content [3]. Customs as a systemic socio-economic phenomenon covers a complex set of social relations arising in the movement of goods, vehicles and other values across the customs border, as well as relations arising as a result of obligations caused by these movements. The term “customs case” has various meanings. From a systemic point of view, customs is a sphere of state policy, a special area of state interests, a specific area of government activity in regulating, administering and controlling economic processes, primarily foreign trade and foreign economic activity. The main mechanisms for the implementation of customs in general, the state customs policy, in particular, are customs regulation and customs administration, which are based on tariff and non-tariff methods of regulating foreign trade and customs control over the operations of participants in foreign economic activity (FEA) [4]. This position is fully consistent with the Constitution of Russia and the Customs Code of the Eurasian Economic Union. It is in the Code, as the normative legal code

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of provisions and rules that sets the legal field for the activities of customs authorities and businesses, the main content of customs affairs is disclosed through customs regulation [5]. In other words, at the present stage, customs is represented as an institution regulating the processes of movement of goods within the customs territory of the Eurasian Economic Union. And just such an institution is a system-forming element, an active regulator of foreign trade and foreign economic activity of the member states of the Eurasian Economic Union. The Institute of Customs Regulation directly or indirectly affects political, economic, legal, energy, information and other communications in the field of foreign trade and foreign economic relations both within the Eurasian Economic Union and outside with the world community. Thus, such an institution is designed to influence the stability and predictability of global and regional processes in the field of foreign economic activity and the economy as a whole. In a broad sense, by the institution of customs regulation we propose to understand a system of education that regulates commodity flows and the interaction of participants in economic relations in the field of foreign economic activity and combines the activities of state structures to achieve a socially significant goal - protecting the economic security of the member states of the Eurasian Economic Union. The customs regulation institute (customs institute) in the narrow sense is a set of ideas, functions, rules and mechanisms determining, forming or developing a customs organization, or the organization itself (customs administration, system of customs authorities, customs service, customs) as an open evolving system.

3 Integrative Paradigm of Development of Customs Institutions. Digital Customs Institute At the present stage, the development of customs institutions is represented in the form of a change in regulatory and managerial paradigmsand corresponding models. On the foreseeable horizon of evolution, their main content can be determined by the following paradigms characterized by the corresponding qualitative parameters of the environment of the customs authorities, and models of the management system [6, 7]: • functional paradigm: the environment of the customs authority is functional, the management model is functionally oriented; • process paradigm: the environment of activity is a process, a process-oriented management model; • service paradigm: business environment - service (services), marketing management model; • digital paradigm: business environment - digital, information and logistics management model; • cognitive paradigm: the environment of activity is intellectual, the cognitiveproductive management model (based on elements of artificial (hybrid) intelligence). We give a brief description of each of the paradigms.

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The functional paradigm and the corresponding functionally-oriented management model implement the principles of rational bureaucracy, division and specialization of labor; In accordance with these principles, traditional customs strategies and methods of customs administration are built. In the process paradigm, the initial concept of activity and the meta-technology for solving managerial problems are based on the identification and coordination of business processes, each of which proceeds in conjunction with other business processes in the organization and/or in the external environment. A rational (optimal) set of business processes of the customs structure is a hierarchical network structure that covers all processes of the organization: management processes, production cycle processes, resource supply processes [6]. The concept of service-oriented administration offers innovative solutions to achieve a qualitatively new level of customs control [8]. The most relevant goals of the concept are to ensure security and facilitate trade in customs methods and means. It is its large-scale implementation that positions promising customs as a service one, puts forward customs in a cluster of the main regulators (controllers) of goods flows in the field of foreign economic activity. The digital paradigm (the paradigm of the digital economy) is a conceptual platform that allows you to create qualitatively new models of business, trade, logistics, production, change the format of public administration, information communications and infrastructures, determine a new model for the development of the state, economy, and foreign trade. The Institute of Customs Regulation, in turn, must be adapted to the new realities of the economy, must contain appropriate technologies and tools for customs administration and control of goods produced and moved in the digital economy [6]. In a strategic perspective, the so-called cognitive (informative) approach is the fundamental basis of qualitative integrative changes in the customs institution. In the customs authorities, this trend is most clearly manifested in the search for technologies for the formation of knowledge (the cognitive component of customs regulation and management) and their use (productive component), taking into account the specifics of customs activity [9]. It can be argued that, in general, the development of the customs institute is multivector, while the greatest success is achieved in the process of creating and developing an innovative customs system known as “digital customs”. The term “digital customs” was introduced by the World Customs Organization in 2016. Since that time, customs authorities must actively demonstrate the use of information and communication technologies in order to collect and provide guarantees for the payment of customs duties, to control the movement of goods, people, vehicles and money, as well as to ensure the safety of cross-border trade [10]. It can be argued that the emergence of the digital customs institute (digital customs) was predetermined by the objectively existing, rapidly developing international economic phenomenon under the general name digital economy. According to the interpretation of the Oxford Dictionary, the digital economy is an economy that operates mainly through digital technologies, in particular electronic transactions made via the Internet. Digitizing “everything” creates new intellectual digital networks that fundamentally change traditional trade.

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Moreover, the term “digital customs” in the interpretation of world-famous dictionaries is missing. However, one of the editions of WCO News defined the term “digital customs” as the use of digital systems to ensure and guarantee the payment of customs duties, control the flow of goods, people, vehicles and money, as well as protect international trade from crime, including international terrorism, which continues spread all over the world. At the WCO, the problems of creating a digital customs institute are presented in 3 blocks: the concept of a digital customs institute, the maturity model of digital customs, and a work plan for creating a digital customs. The absence of a legend or explanation for each of the elements of the model makes their interpretation difficult, but most of the terms presented in the model are used in the practice of customs administration of the WCO member states [11].

4 Intellectual Digital Customs: Conceptual Provisions and Theoretical and Methodological Problems In our opinion, the prospect of a customs institute is most clearly manifested within the framework of a multidisciplinary development model that combines and harmonizes all the above paradigms. Elements of such a model are already being implemented in practice - in the activities of customs authorities. This thesis is also confirmed by the analysis of modern approaches and international practice of solving the problems of customs regulation in the context of the implementation of the digital economy paradigm [12]. The methodological basis for the formation and practical implementation of a multidisciplinary development model is the holistic evolutionary approach that we are developing. This approach considers the customs institution as a continuously evolving system and provides the formation, accumulation and use of knowledge about the evolution of the integrity of the system in the conditions of its functioning and development. At its core, it is an integrative methodology for the formation and development of an intelligent digital institution, combining system, process and cognitive approaches for the formation and management of knowledge [13]. The main content of the process of formation, accumulation and use of knowledge on the basis of such a methodology is disclosed in the course of a continuous learning process (knowledge) and knowledge management. In the future, this phenomenon will be called the intellectualization of the system (institute, customs, knowledge base, etc.). To implement such an intellectualization process in the environment of a real system, a special metotechnology is formed, called namimetate intellectualization technology [14]. The main conceptual provisions of intelligent digital customs reveal the following points. Intellectual customs is a customs that implements the process of training, intellectualization. As a result of such a process, a knowledge base is formed and updated that is adequate to a multidisciplinary model of the functioning and development of the customs institution. Such a base is an integrator of cognitive and productive models (knowledge) of decision-making on customs regulation, administration and control of

Institutionalization of Intelligent Digital Customs

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goods and services in the field of foreign economic activity, as well as models (knowledge) for preparing and making decisions on customs management. The instrumental basis for the formation and management of knowledge is the corresponding metate technology - a set of cognitive and productive technologies that implement the intellectual functions of the formation and management of knowledge. Cognitive models (technologies) - models (technologies) on the basis of which productive models (technologies) or productive knowledge are formed. Productive models (technologies) - models (technologies) used to resolve practical situations arising in the process of customs regulation, administration and control of goods and services or for customs management.

5 Theoretical and Methodological Problems of the Formation and Implementation of Intellectual Digital Customs The creation of intellectual digital customs is primarily a large-scale task of synthesizing a theory that reflects and combines the progressive directions of evolution in public administration and management, promising trends in international practice of foreign economic activity and customs regulation, technological innovations in the production and provision of goods and services [15]. Identification of key areas and solving problematic issues of creating such a theory is the main goal of the further development of applied tools (methodologies, technologies and tools) of customs. In our opinion, the most urgent problematic issues in the theory and practice of the creation and operation of intellectual digital customs, requiring priority attention in the digital economy, are as follows: – development of the concept of hybrid intellectualization of customs technologies for regulation, administration and control of goods and services in the field of foreign economic activity, as well as training and decision-making technologies for customs management; – formation and development of the idea of an intellectual digital customs institute, reflecting the essence of hybrid intellectualization of customs processes and technologies and a roadmap (project) for its implementation in the digital economy; – standardization of customs regulation methods in the context of the emergence of a new type of goods and services in the digital environment of foreign trade in accordance with international agreements and requirements; – development of the fundamentals of the theory of customs regulation and management based on knowledge - the intellectualization theory of the digital customs institute (cognitive theory); – development of cognitive (intellectual) technologies of electronic regulation, administration and control (e-regulation, e-administration and e-control) taking into account the requirements of the WCO protocols and framework standards for ensuring security and facilitating trade; – development of a complex of intellectual functions (training, self-training, adaptation, self-organization, etc.), relevant models and methods for solving problems in the formation of knowledge in the field of customs regulation and management;

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– the creation of a unified information platform, integrated knowledge bases, an intellectual interface, methodological tools for analyzing and preparing solutions in the environment of intellectual digital customs (intellectual DC) and knowledge management tools on a unified methodological basis; – the creation of a coherent and balanced system of goals, indicators and criteria for evaluating the activities of customs authorities and the quality of services provided in the environment of intellectual digital customs; – development of a methodological and software-technical base for ensuring the security of information systems and environments, software and hardware tools of intellectual digital customs; – solving legal, infrastructural, organizational issues and training issues. It can be assumed that it is the intellectualization of digital customs that will make it possible to modernize customs authorities in such a way that they become both a reliable support for participants in foreign economic activity and an effective mechanism for achieving the state’s goals in a strategic perspective of the digital economy.

6 Conclusion In modern conditions, the customs sphere is important not only in ensuring national security, but also in the development of foreign economic activity of the state. The dynamic environment and integration processes show new patterns in the formation and development of customs institutions, which consist in the need to move to a flexible and intellectually secure informative infrastructure that can solve problems in the digital economy. As the analysis shows, in general, the problems of the development of the theory and practice of customs regulation in the context of the formation of a digital economy are complex and cover all levels that are characteristic of special theories. At the same time, the key problems include the creation and modernization of technologies and systems of customs regulation, administration and control in the digital environment, and the problems of creating intellectual digital customs, the search for adequate models, methods and technologies for the formation and management of knowledge.

References 1. Edronova, V.N.: Digital economy: analysis of statistics on the volume of Internet markets. Econ. Anal. Theory Pract. 18(9, 492), 1596–1612(2019) 2. Makrusev,V.V., Lobas, E.V., Lyubkina, E.O.: The institutional theory of customs regulation in the digital economy. Econ. Anal. Theory Pract. № 12 (2019) 3. Makrusev, V.V., Suglobov, A.E.: Customs management. p. 348. ITC “Dashkov and K” (2017). M 4. Makrusev, V.V., Pchelintsev, N.V.: Management of the development of the customs authorities of Russia: monograph. Publishing House of the Russian Customs Academy (2013). M

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5. Yusupova, S.Y., Makrusev, V.V., Boykova, M.V.: Innovation in the controlling system. Manage. Econ. Syst. Electron. Sci. J. 2(96), 13 (2017) 6. Makrusev, V.V.: The concept of a theoretical model of a multidisciplinary institute of customs regulation. Eur. Soc. Sci. J. 12(1), 8–15 (2017) 7. Masloboev, A.V., Gorokhov, A.V.: A problem-oriented agent platform for creating polymodel complexes for supporting regional security management. Sci. Tech. J. Inf. Technol. Mech. Optics 2(78), 60–65 (2012) 8. Makrusev, V.V.: Customs services marketing. Prospect (2017). M 9. Makrusev, V.V.: Service-oriented customs regulation: ideas, institutions, management. Competitiveness in the global world: economics, science, technology. vol. 12, no. part 19, pp. 1239–1242 (2017) 10. Boykova, M.V.: Foreign experience of customs administration: a textbook. Publishing House of RTA (2017). M 11. Moser, C.V.: Improving the legal institute of digital customs: analysis of the WCO maturity model. http://customs-academy.net/?p=12336. Accessed 15 Jun 2019 12. Makrusev, V., Lyubkina, E., Vakhrushev, V.: The adaptive model of customs management. compilation. In: MATEC Web of Conferences 2018, p. 01018 (2018) 13. Bukatova, I.L., Makrusev, V.V.: A holistic evolutionary process of cognition: basic concepts and a computer perspective. XI International Conference: Logic, Methodology, Philosophy of Science. t.2. pp. 104–108. Institute of Philosophy of the Russian Academy of Sciences. Obninsk (1995) 14. Bukatova, I.L., Makrusev, V.V.: Intensive informatization of socio-economic systems based on holistic cognitive representations. Analysis and optimization of cybernetic systems. GIFTP RAS (1996). M 15. Andreev, A.F., Makrusev, V.V.: Fundamentals of CONTROL THEORY: a course of lectures. p. 164. Publishing House of the Russian Customs Academy (2009). M 16. Plesner, U., Husted, E.: Digital Organizing: Revisiting Themes in Organization Studies. pp. 50–130. MacMillan Education, London (2019) 17. Leonard-Barton, D.: Harvard Business Review on Knowledge Management. pp. 10–54. Harvard Business Press, Boston (1998) 18. Senge, P.M.: The fifth discipline: the art & practice of the learning organization. Crown Business, pp. 187–200 (2006)

Implementation of the Internet of Things Application Based on Spring Boot Microservices and REST Architecture Satish Reddy Modugu(&) and Hassan Farhat University of Nebraska at Omaha, Omaha, NE 68124, USA {smodugu,hfaraht}@unomaha.edu

Abstract. Internet of Things is a field of Computer Science that is gaining extensive popularity. In this paper, we incorporate the concepts of full stack development using Spring Boot, Angular.js and Java technologies in implementation of Internet of Things applications. We signify the advantages and applicability of these technologies in the implementation. We particularly emphasize the design of REST Architecture based microservices development using Spring Boot for the IoT applications. All elements, required for the development of an end-to-end IoT system, are discussed in detail. The paper considers a use case of inventory management in industrial IoT and provides an innovate architecture solution. We present a prototype using the developed architecture and discuss the advantages offered by our architecture. Keywords: Internet of Things services


IoT
Microservices
Spring Boot
IoT

1 Introduction Internet of Things is an emerging topic. There are many advancements made in this field due to its increasing applications. It has been estimated that by the end of 2020 there would be more than 50 Billion IoT devices that are connected worldwide as a part of IoT ecosystem [1]. All IoT devices in a system use specific services to communicate with the internet or other IoT devices. In most of IoT devices, there would be no direct communication amongst themselves. Instead, these devices depend on middleware services for communication and data transfer. As a result, there is a need for the development of efficient services for IoT devices that are secure, fast and fault tolerant. This paper examines the use of Spring Boot development technology for the development of micro services. It makes use of microservices based architecture in the development of IoT device services. These IoT services should be scalable, service extensible, secure and interoperable [2]. All these requirements can be supported by the microservices architecture. Recently there has been a lot of acceptance of microservices architecture in the software industry, many companies are migrating the architecture of their software products from the monolithic to microservice architecture because of the advantages it has. A microservice can be defined as a single small application that has a single © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 20–31, 2020. https://doi.org/10.1007/978-3-030-63322-6_3

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responsibility which can be deployed independently, scaled independently and tested independently [3]. In the IoT context, microservices provide many advantages such as loosely coupled services, containerization, load balancing and decentralization. By utilizing microservices architecture, IoT system can be fully distributed microservice based system, instead of just a thing-based system. In addition, this architecture ensures other non-functional requirements such as stability, reliability, scalability, fault tolerance, monitoring and performance [4]. In this paper, we consider an IoT use case, i.e. inventory management in Industrial Internet of Things (IIoT). We have built a prototype of inventory management system that has the sensors that measure the inventory level and use the microservices for updating the data to edge servers. Low cost rapid prototyping devices, such as Arduino or Raspberry Pi connected with needed sensors and communication devices, are used for the creation of the sensor network and the system prototype. The paper is organized as follows. Section 2 reviews some of the already implemented related work in the microservices based IoT architectures. Section 3 describes the use of microservices for IoT system development and the advantages it offers. Section 4 describes the use of Spring-Boot for the microservices development from IoT point of view, gives an overview of hardware devices used in our implementation and gives detailed explanation of the considered IoT use case. Section 5 gives the implementation and hardware details of the developed prototype Sect. 6 gives the summary of the implementation details and presents the advantages of the microservices architecture when compared with the monolithic architecture. The conclusion and future work is given in Sect. 7.

2 Related Work There has been ongoing research on choosing the architecture to develop IoT systems. Most of the research on IoT classifies systems into monolithic architecture and microservice architectures [5]. In the monolithic architecture-based system [6], the entire software unit runs independently as it is deployed as a single system. Such systems have the advantage of independency, but they have less reliability and are not fault tolerant. In microservice architecture, the system is fully divided into small units and categorized into the specific groups that makes the system reusable. The services can be deployed at multiple instances making the system fault tolerant and distributed. Microservice architecture is more frequently adopted because of the drawbacks and limitations of the monolithic architecture. In IoT systems, in general, we have multiple data points to process. The response time to a user request of processed data is an important metric. The bottlenecks such as, low response due to multiple requests, possible faults, and scalability constraints caused by a monolithic, can be addresses with microservice architecture. A detailed comparison between monolithic and microservices architecture has been presented in [7]. The authors conclude both architectures have similar performance under minimal load, under 100 users. For larger loads, microservices architectures outperforms monolithic. Since most IoT based systems use a large number of sensors, a microservices architecture would be preferred in terms of performance alone.

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Due to above, there has been a move in IoT systems towards microservices architecture. For example, [8] discusses the advantages of microservices in IOT. [2] used microservices based safety monitoring system in public buildings. [9] presented application of microservices in designing an IoT based smart city. [1] presented a data centric IoT architecture based on microservices. [10] presented 3 cloud microservices: contextual triggering microservice, visualization microservice, and anomaly detection and root cause microservice. The purpose is to accelerate and facilitate the development of context and location-based applications. In above, most of the authors considered a specific application and demonstrated how microservices can be applied to them. We feel there is a need to demonstrate what technology can be used for implementation of IoT architecture which can, easily, be applied to any use case. As a result, we have considered the use of opensource technologies such as using the Spring Boot technology stack for developing an IoT system.

3 Microservices for IoT Apart from the advantages microservices architecture has as compared to monolithic architecture, there are many similar requirements of an IoT system and microservices, thus making the microservices architecture approach a good fit for the development of the IoT system. Some of the characteristics of microservices that are good fit for IoT applications are given below. 3.1

Lightweight Communication

Generally, in the microservices frameworks, there would be services running across the different instances in a distributed manner. These services communicate between themselves using any inter-process communication protocols, typically HTTP based communication is used in the microservices framework. Microservices commonly follow the concepts of smart endpoints and dumb pipes, a design pattern that avoids complex integration platforms [11]. Microservices simply follows event driven HTTPREST communication and lightweight asynchronous messaging for the communicating the updates within the microservices [12]. When IoT system uses microservices architecture, we can have synchronous communication mechanism avoiding complex integration. 3.2

Decentralization

Two of the main principles of the microservices are Decentralized Governance and Decentralized Data Management [13]. These principles separate and distribute the processing and data handling. Each service has its own database which reduces the processing time. Often centralized based architectures lead to the heavy load on resources and high traffic as everything is centralized at one point. Whereas a decentralized approach, because of its distributed nature, decreases traffic and resource load. [14] has performed a study on IoT network architectures. It suggested centralized approaches would face challenges if the IoT devices or number of users and request

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increases. As a result, there is a high need of the decentralization in the IoT system which can be achieved if we use the microservices framework. 3.3

Interoperability

In general, interoperability is a measure of the ability for systems, or components of systems, to communicate with each other irrespective of their type. In an IoT system, interoperability is among the most important measures as there are different types of devices, each with its own set of standards. These devices have to understand each other when there is a need of communication between them. A detailed study of use of microservices for interoperability is presented in [15]. The authors have used microservices approach to build IoT platform that hides the complexities when the system has heterogeneous IoT devices bringing the interoperability to the system. By following microservices architecture pattern, IoT applications run in their own independent process and all the inter service communications in between them are based on the TCP/UDP based application protocols. 3.4

Other Features

Apart from items above, service-oriented architecture based on the microservice architectural style offers many other features that are suitable for IoT applications. These include monitoring the devices, uploading data to the cloud, better reusability, better scalability, and faster development and deployment time.

4 Implementation Details of the IoT Services 4.1

Spring Boot for IoT Service

We have considered developing our services using Java technology and for developing the services, we have used Spring Boot framework [16], which is an open source Javabased framework that makes the development of microservice simple. Spring Boot is initially designed by the Pivotal Team is predominantly used in the development of spring-based applications. Using Spring Boot for IoT services offers several advantages like, Spring Boot takes care of all the complex XML configuration using the autoconfiguration and makes the development of the Spring applications simple. Spring Boot has support to many inbuilt libraries that reduces the development time and makes the developed application to run independently. Spring Boot is open source with minimal configuration and is highly effective. The Annotation based coding offered by the Spring Boot makes it easy to deal with databases. It is easy to implement functionalities needed for any IoT devices such as REST endpoints for the sensors to POST values, REST endpoints to get data to reviewing and analysis. Spring Boot framework also supports security mechanisms such as tokens and authorization using the Spring Security which makes the sensors data secure. Another

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main advantage of using Spring Boot is its ability to integrate with cloud using Spring Cloud by which we can secure the sensor data and processing in the cloud. 4.2

Sensors and Hardware Used

We have used Raspberry Pi as an edge device to which sensors are connected and the services are deployed to read data from sensors. We used Raspberry Pi 3. It is a low cost minicomputer. It consumes less than 5 W of power and runs on Open Source Linux OS. Some features of the Raspberry Pi are: it has BCM2835 SoC, USB 2.0 port, HDMI port, 10/100-BaseT Ethernet port, 256 MB RAM, consists of GPIO pins, micro USB power, and micro SD card. There are existing java libraries provided by the project Pi4J [17] which is a project that provides object-oriented input/output API to access the complete input/output capabilities of Raspberry Pi platform. One of the sensors that we have used in our project implementation is an ultrasonic sensor. The ultrasonic sensor used was HC-SR04. The ultrasonic sensor is a transceiver module (Transmitter + Receiver). The sensor sends out a sound wave at a specific frequency. It then listens for that specific sound wave to bounce back from the object. The distance is calculated as a product of time and velocity. The code for this is in java using the Pi4J libraries.

5 IoT REST Microservices Design and Implementation To demonstrate the use of restful microservices for an IoT system design, we have considered a use case study. In this study, we constantly monitor the quantity of an item in an inventory that is measured using ultrasonic sensor. The logged changes in data are sent as an email to administrators when the data has reached below a given threshold value. The following services are developed in the implementation of the system. 5.1

Sensor Data Retrieval Microservice

This service gets the data from the sensors, when it is deployed using ultrasonic sensor, it returns the measured value of the sensor. The measured value includes value in container along with the other details like timestamp, device ID and state of the device. This data is returned in the JSON format [18]. The working model of the service is demonstrated in Fig. 1. As shown in the figure, the client application sends the request in the form of REST URL with the needed information (sensor device ID) to the server in which the application is hosted. The controller of the application maps the request and sends it to the corresponding method that interacts with the sensor, the method then returns the value to the controller which converts the data into the JSON format and sends it back to the requested client. The service that we have developed is reusable, same service can be used for different sensors, all we need to do is to configure the pin connection details of the sensors to the corresponding ID of the sensor. When the consumer calls the service, the request should have the ID of the sensor as the input parameter, the

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Fig. 1. Sensor data retrieval microservice

controller logic of the service gets the corresponding pins to call. The call gets the value of the requested sensors from the pins of the sensors. 5.2

Data Monitoring Microservice

This service calls the sensor data retrieval microservice to get sensor data and checks if there are any abnormalities in the retrieved data. One major need for separating the monitoring service from the data retrieval service is the low memory constraint available at the IoT device. This monitoring service need not be deployed at the IoT devices. Instead, this can be deployed anywhere else in the overall IoT system (either in cloud or another local server). A local server is preferred over cloud when low latency is a requirement. A working model of the monitoring microservice is demonstrated in Monitoring microservice calls the data retrieval microservice using the REST template of Spring Boot. JsonParser is used to parse the JSON data retrieved from the data retrieval microservice. The data obtained is sent to the data analyzer method that compares the collected sensor data with a preset threshold value. If the data collected from the sensor is above the preset threshold value, it logs the data by sending the request to the data processing microservice (explained in the next section) for future data analysis (such as computing the rate of data change). If the data is below the preset threshold value, along with logging the data, the admin or appropriate person managing the IoT system is notified that the data has reached below the set threshold through email using Java Mail API (Fig. 2). 5.3

Data Processing and Storage Microservice

The collected data has to be preprocessed before storing the data in the database. Preprocessing the data includes cleaning the data, validating the data and avoiding the duplication of the data. Data is sent to this service using the HTTP-PUT request [19]. This microservice first checks if the data received has all the necessary attributes, if yes, it sends the data to the data processing method, else it sends back an error response stating the data sent is not valid. The data processing method compares the data with

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Fig. 2. Monitoring microservice

the last updated value in the database, if the data is same, storing such data cause duplication, in such instances the data is ignored. If the data received is not the same as the last updated value in the database, such data is updated in the database as there is a change in the collected data. Spring Boot has a very good support to integrate and work with different kind of databases. In our implementation we have used Spring Data JPA library to access the MySQL database. This is just a choice of one of many possible choices, Spring Boot has a good support for almost all the popular relational and non-relational databases such as Oracle, DB2, PostgreSQL, and MongoDB. Spring Boot also has good support to use the cloud-based databases for cloud storage services such as AWS and Azure. 5.4

Gateway and Load Balancing Mechanisms

In many cases, multiple sensors are connected to a device such as raspberry Pi where sensor data retrieval microservice is deployed. When multiple data retrieval requests are simultaneously sent to an IoT device with only one instance of the service running, the system would become inefficient. All incoming requests are handled by just one instance of the service. As a result, there is a need to scale the data retrieving microservice by deploying multiple instances of it across different ports. When multiple instances of a service are running, load balancing is a mechanism to distribute the incoming requests in such a way that all the running instances of the service gets

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approximately equal load. We implement load balancing using the open source Spring Boot ZUUL API gateway developed by Netflix [20]. ZUUL API acts as a load balancer and a gateway to route the incoming requests. ZUUL API uses Ribbon load balancer [21] to equally distribute the incoming requests and making the application robust when there are higher number of requests. When the same service is replicated for achieving higher availability, trying to configure all these instances manually is impractical. Spring Boot has the Service discovery Eureka Client [22]. When microservices are registered with Eureka service discovery and when multiple instances of the services are deployed, Eureka service discovery have all the details of the deployed services. If a request is a hit on ZUUL API gateway, this gate way gets the details of the available instances of the requested service. It then uses its ribbon load balancer to forward the request to one of the running instances of the microservice while maintaining equal load distribution among the instances of the microservice. Figure 3 shows our implementation. All incoming requests are directed to the ZUUL gateway. ZUUL gateway gets the details of the running instances of the requested service from discover service. The discover service acts as a registry of all instances of running microservices. ZUUL gateway then routes the request to the corresponding instance of the data retrieval microservice ensuring equal distribution of load between running instances. ZUUL gateway and discovery service has to be deployed where the microservice is running. In our case, these are deployed in the IoT device, i.e. Raspberry Pi where the instances of data retrieval microservices are running.

Fig. 3. Load balancing mechanism

5.5

Securing the Microservices and Communication

In an IoT system, we make the devices connect to the internet. If such internet connected devices are not properly protected, it results in serious vulnerabilities. Such security impacts may not only impact the IoT devices, but it may also impact the whole network to which these devices are connected. Attackers can easily get access to the

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network with the unprotected IoT devices. [23] gives an overview of the security issues in IoT network. Hence, when building an IoT application, one should always give high priority to the security aspects. All devices and communications among devices in the IoT system must be secure. Spring Boot framework has good security support provided by spring security starter API. The API starter can be used to make our developed microservices secure. In our Implementation, ZULL API gateway acts as a single-entry point through which HTTP requests go through to all the microservices. We enable spring security at ZULL API gateway and make it as a gatekeeper that passes only the requests that are authenticated to the IoT network. Here we use JSON Web Tokens (JWT) for authenticating the incoming requests [24]. JWT is an open standard that defines a compact and self-contained way for securely transmitting information between parties as a JSON object. This information can be verified and trusted because it is digitally signed. JWTs can be signed using encoding given by the HMAC algorithm or by a public/private key pair using RSA or ECDSA [25]. The server sends the JWT tokens to the verified clients, here the clients must register themselves with the server in-order to get the JWT token and the token is stored locally at the client side. When the client makes HTTP requests to the protected URL’s, it should include this token in the authentication header of the request. When the server (API gateway in our case) receives request with JWT token in it, the server validates the token by checking if the JWT is well formed and by validating the signature. In our implemented IoT system, using Spring Boot security libraries, we added token generation mechanisms at sensor data retrieval microservices and ZULL API gateway. For any client application to request data from the sensor data retrieval microservice, first the client application has to register itself and get the JWT token from the server. Then the client application must use that JWT token while requesting data which is validated at the ZULL API gateway. Here, the ZULL API gateway rejects the incoming HTTP requests if the JWT token validation fails. The gateway allows microservices access only to verified clients. To further secure the system, Spring Security also provides an option to allow the requests only if they come from the same IP address of the server. In order to send request, client has to be either in the same network of server or it has to virtually connect to the server network through virtual port connections.

6 Summary In our built system, we have completely utilized the advantages of Spring Boot. With our implemented proof of concept of IoT system, we conclude that using microservices architecture for building IoT systems provide greater benefits. These benefits include making the system distributed, scalable, flexible for future changes, greater availability and increased performance. In particular, we have implemented our system in a distributed way by modularizing the application to the maximum. Figure 4 shows an overview of our implemented distributed architecture. Here, in our implementation as shown in Fig. 4, all the major functionalities of the application are separated in a distributed way. By separating the data sensing service, the IoT device now has just the data sensing service. This service will always run and

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Fig. 4. Distributed approach

doesn’t have to wait for other services. The data of the sensors are always available through these data sensing services. Given below is a sample JSON response that is returned by the data retrieval services.

Now the above data is available to all the other services that use the sensor data. Monitoring service is deployed and run independently, as this service always monitors the values returned by the sensors. When the active sensors to monitor becomes high, we can easily scale this service by deploying multiple instances of this service when there is a high data to be monitored. Here we achieve scalability of the needed functionality which is not possible in the monolithic architecture. When the client applications are requesting data from the IoT system, the requests have to go through the gateway application. The gateway application validates the input requests and allow only the request that are authenticated, making the system secure. Generally, IoT devices have low memory that makes it hard for the application developers to implement rigid security mechanisms at the device side. But here we have separated the gateway from the device, and our device end points are never exposed to the client applications, IoT device data must be accessed through gateway. Thus, we are securing all our IoT devices without consuming any extra memory at the device side. The other benefit that we have achieved through this architecture is greater availability of the system. In our implementation, only time when the system goes down is when there is a hardware level failure of sensors or IoT device. Other than that, the system never goes down as all the services can be deployed redundantly to achieve fault tolerance. Even if any service completely goes down it doesn’t show affect on

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other working functionalities. This is the one main advantage of following the microservice architecture. As an example, if the data monitoring service has some issues and is not working, this has no impact on data handling services that send the sensor data to the client applications. Only affected data monitoring service is fixed and deployed without making changes to other working services. This not possible when we followed monolithic architecture.

7 Conclusion In this paper we successfully implemented microservices based architecture for an IoT system. We have developed a REST based service for each functionality of the considered IoT application that acts as a microservice. In our work, we used the advantages of microservices such as lite weight communication, load balancing, scalability, interoperability and security. We have evaluated the use of Spring Boot technology for developing microservices for IoT based application and could successfully use Spring Boot technology stack in our implementation. We found that Spring Boot technology stack perfectly fits developing IoT microservices because of its diverse API’s such as load balancing, Spring Security and Spring Cloud. Spring Boot not only makes the microservice development easy but also makes the development process faster because of its features like auto configuration and build tools among others. In future work, we perform performance comparative study of our work with alternative monolithic implementation.

References 1. Datta, S.K., Bonnet, C.: Next-generation, data centric and end-to-end IoT architecture based on microservices. In: IEEE International Conference on Consumer Electronics - Asia (ICCEAsia), pp. 206–212 (2018) 2. Mongiello, M., Nocera, N., Parchitelli, A., Riccardi, L., Avena, L., Patrono, L., Sergi, I., Rametta, P.: A microservices-based IoT monitoring system to improve the safety in public building. In: 3rd International Conference on Smart and Sustainable Technologies (SpliTech), pp. 1–6 (2018) 3. Thönes, J.: Microservices. IEEE Softw. 32(1), 116 (2017) 4. Butzin, B., Golatowski, F., Timmermann, D.: Microservices approach for the Internet of Things. In: IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, pp. 1–6 (2016) 5. Sun, L., Li, Y., Memon, R.A.: An open IoT framework based on microservices architecture. China Commun. 14(2), 154–162 (2017) 6. Namiot, D., Sneps-Sneppe, M.: On microservices architecture. Int. J. Open Inf. Technol. 2(9), 24–27 (2014) 7. Al-Debagy, O., Martinek, P.: A comparative review of microservices and monolithic architectures. In: EEE 18th International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, Hungary, pp. 000149–000154 (2018)

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8. Butzin, B., Golatowski, F., Timmermann, D.: Microservices approach for the Internet of Things. In: IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, 2016, pp. 1–6 (2016) 9. Krylovskiy, A., Jahn, M., Patti, E.: Designing a smart city Internet of Things platform with microservice architecture. In: 3rd International Conference on Future Internet of Things and Cloud, pp. 25–30 (2015) 10. Bak, P., Melamed, R., Moshkovich, D., Nardi, Y., Ship, H., Yaeli, A.: Location and contextbased microservices for mobile and Internet of Things workloads. In: IEEE International Conference on Mobile Services, pp. 1–8 (2015) 11. What is smart endpoints and dumb pipes. https://simplicable.com/new/smart-endpoints-anddumb-pipes. Accessed 03 Sept 2020 12. Communication in a microservice architecture. https://docs.microsoft.com/en-us/dotnet/ architecture/microservices/architect-microservice-container-applications/communication-inmicroservice-architecture. Accessed 03 Sept 2020 13. Microservices. https://martinfowler.com/articles/microservices.html. Accessed 03 Sept 2020 14. Verma, P.K., Verma, R., Prakash, A., Agrawal, A., Naik, K., Tripathi, R., Alsabaan, M., Khalifa, T., Abdelkader, T., Abogharaf, A., et al.: Machine-to-machine (M2M) communications: a survey. J. Netw. Comput. Appl. 66, 83– 105 (2018). ISSN 1084-8045 15. Vresk, T., Avrak, I.: Architecture of an Interoperable IoT platform based on microservices. In: 39th International Convention on Information and Communication Technology, Electronics and Microelectonics (MIPRO), pp. 1196–1201 (2016) 16. Spring boot introduction. https://www.tutorialspoint.com/spring_boot/spring_boot_introduction.htm. Accessed 03 Sept 2020 17. The Pi4J project. https://pi4j.com/1.2/index.html. Accessed 03 Sept 2020 18. JSON. https://www.json.org/json-en.html. Accessed 03 Sept 2020 19. Hypertext transfer protocol – HTTP/1.1. https://www.w3.org/Protocols/rfc2616/rfc2616sec9.html. Accessed 03 Sept 2020 20. The Netflix tech blog, announcing zuul. https://netflixtechblog.com/announcing-zuul-edgeservice-in-the-cloud-ab3af5be08ee. Accessed 03 Sept 2020 21. Spring cloud, client side load balancer: ribbon. https://cloud.spring.io/spring-cloud-netflix/ multi/multi_spring-cloud-ribbon.html. Accessed 03 Sept 2020 22. Spring cloud, service discovery: eureka clients. https://cloud.spring.io/spring-cloud-netflix/ multi/multi__service_discovery_eureka_clients.html. Accessed 03 Sept 2020 23. Sprig, spring security reference. https://docs.spring.io/spring-security/site/docs/current/ reference/html5/. Accessed 03 Sept 2020 24. JSON web token (JWT). https://tools.ietf.org/html/rfc7519. Accessed 03 Sept 2020 25. Introduction to JSON web tokens. https://jwt.io/introduction/. Accessed 03 Sept 2020

Modelling and Simulation of Scrum Team Strategies: A Multi-agent Approach Zhe Wang(&) Lincoln University, Lincoln, New Zealand [email protected]

Abstract. Scrum is a type of agile process that incrementally, iteratively and continuously deliver software based on sprint time box. It is composed by User Stories, product backlog, sprint backlog, scrum team and sprints. Scrum team take user stories from product backlog into sprint backlog to start each sprint and deliver products at the end of each sprint. Sprint retrospective and review occurs at the end of each sprint to evaluate the delivered products and team performance. Based on the Scrum guide, scrum is easy to be understood but hard to be measured. Especially, it is depended largely on the performance of team dynamics referring to team compositions and task allocations, as its optimization make big impact on each sprint result. This paper investigating how solo and pair programming can make impact on scrum team performance based on several designed innovative team working strategies for both solo and pair programming by using agent-based modelling. Such innovative team working strategies for solo and pair programming have not been designed or applied into scrum context for investigation and evaluation based on agent-based modelling. As Scrum is a very complex environment, so that different strategies can be compared through modelling and simulation under various scrum context, such as scrum has different team composition, different task distribution and some randomly events to occur. A simulation tool is also designed and developed to carry on scrum team modelling and simulation which has realized all the designed strategies to carry on experiments and evaluation. The tool can simulate all types of scrum context and team composition to test designed strategies under various what-if assumptions in agent-based modelling. Keywords: Scrum
Team dynamics
Agent-based modelling
Multi-agent system
Team strategies
Solo programming
Pair programming

1 Introduction Since computer scientists invented the first computer in 1946 through the United States Strategic military plan in the second world-war. Computer software has become the most critical part of the information system. (Wang et al. 2015; Wang and Chalmers 2013; Zhe et al. 2013; Zhe Wang et al. 2013; Zhe Wang and Cheng 2015a, 2015b; Zhe et al. 2013; Zhe et al. 2012) has proposed an ideal of software evolution which means software should always adapt itself to be compatible with the changing environment in order to meet the new requirement or demand from humans. Software evolution is an © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 32–63, 2020. https://doi.org/10.1007/978-3-030-63322-6_4

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advanced project, as software will continue evolve in its all life-cycle. The software and its evolution process are fully supported by agile based software development process, for its continuous adopt new requirements and features. Scrum which is type of agile process, it can always adopt new reequipments and features into the product backlog and provide it to the scrum team for next sprint planning and estimating. The dynamics of scrum process is fully supported by the dynamics of scrum team, as how team performance can affect the software delivery. A good team dynamic (refers to team composition and task allocation) should support the evolution of scrum process to make it better and better. The team should be able to delivery software effectively to achieve the scrum goal. We should think the agile and scrum process as an evolutionary software development process that all factors inside this process are dynamic and measurable, especially for its team dynamics. Software is created by people, technologies and tools which are integrated in a process known as the software development process life cycle (SDLC). They are two major approaches in software development, the waterfall model and the Agile Model. The waterfall model is a non-iterative sequential design process which progresses from the requirement analysis, system design, coding, testing and maintenance. Such approach provides limited interaction between customers and software team during the software development process due to its sequential software development process where interactions only happen in the requirement analysis part. Agile is a concept of creating software through iterative and incremental process. (Beck et al. 2020) describes agile as “Individuals and interactions over processes and tools; Working software over comprehensive documentation; Customer collaboration over contract negotiation; Responding to change over following a plan.” Scrum is one of the agile processes that realizes the agile manifesto where it splits user stories into several parts and aims to achieve part of them within a time period which can last from one day to thirty days as a sprint. Within each sprint, the process is like the waterfall model which is also composed of requirement analysis, system design, coding, testing and maintenance. However, there are more opportunities for scrum team and customer (product owner) to discuss on the software during the process because there are several sprints in an agile project and each sprint contains requirement analysis which may be refined at each sprint. Each sprint is able to deliver a higher project success rate compared to the waterfall approach because the size of software is smaller, its design goal is clearer, and the Scrum team is fully focused on the project. Scrum is composed by user stories, tasks, sprints, sprint meeting, deliverable software and Scrum team. User stories are designed based on user requirements. Each user story contains one or more tasks that need to be completed by the Scrum team in one sprint or several sprints. Scrum team will have daily sprint meeting to discuss what has been done and what needs to be done. Scrum team is composed of several members, which have different skills and capabilities, such as designer, developer and tester. Scrum team members need to interact and collaborate with each other to achieve the goal of the team incrementally or iteratively through sprints. Although the Agile Scrum approach is an improvement over the waterfall model, it still suffers from several problems. One such problem is the team dynamics, which largely affect the quality, risk and value of the process. Team dynamics refers to team composition, task allocation, interactions between team members and how they work

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together. (Song et al. 2015) define effective team dynamics according to the following criteria indicated by (Nadler et al. 1979), there are: 1. team performance (i.e., the product of teamwork meets the expectations of those who use it); 2. member satisfaction (i.e., each team member’s experience contributes to his or her personal well-being and development); and 3. team adaptation (i.e., the team experience enhances each member’s capability to work and learn together in the future) A team consisting of experienced and highly skilled members will normally perform better than a junior team that is less experienced and skilled. In an Agile Scrum environment, the composition of a team will greatly affect the performance of the team, because the scrum team needs higher level of cooperation among the team members to achieve the sprint goal. Skills, experience and capabilities of the team members affect the performance of the team. Varying methods of tasks allocation may result in different outcomes and may affect the delivery of the software. It would be useful to investigate what kind of team dynamics leads to a more efficient and high-quality software delivery because team dynamics is affected by several factors such as capability, skills, roles and responsibilities and how well the members work together. Our study investigates how team composition, task allocations and team strategies can be used to improve the performance of the team in delivering a timely and high-quality software. Software development process is a complex system, because it contains various unanticipated changes and uncertainties during the process. It involves complex interaction between people, tools and technologies. In order to reduce the risk and enhance the success rate of software project, software process simulation is proposed. Software process simulations can be used to evaluate people, process and management of software projects and decision making. It is the preferred method as it is less costly and risky compared to conducting experiment in the real-world scenario. This project will simulate team dynamics in Scrum using multi-agent based modelling. Multi-agent based modelling is widely used to simulate social interactions indicated in (Wooldridge 1995). It can model human behaviours, group collaborations and conflicts. It uses discrete modelling rather than continuous modelling. The multi-agent based modelling can be used to model and capture the dynamic interaction between members of the team, model and capture each time step for analysis, and to perform dynamic estimation instead of static estimation. Agent based modelling can model the uncertain and unpredictable behaviour of human, such as expert leaving and joining to another company, that will greatly affect the software project. Such uncertainly and unpredictable behaviour should be considered in the simulation. To the best of our knowledge, there are works that have been done in modelling software development process. However, there is no such work using multi-agent based simulation to investigate team composition and team dynamics in scrum context and that an exploration into this will be useful.

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Moreover, no detailed study has been conducted to analyses the impact of team composition and team dynamics on the performance of a scrum team which is directly related to the successful delivery of the project. This project will investigate the impact of team dynamics on the performance of scrum team in a software development process setting.

2 Literature Review 2.1

Scrum

(Ali et al. 2019; Çetin et al. 2019; Freire et al. 2018; Loaiza and De León, 2019; Lunesu et al. 2018; Zainab Masood et al. 2018; Wang 2018; Zhe Wang 2018, 2019a, 2019b) indicate scrum has gained a lots attention by researchers during these years, especially for its capability of solving high complex software delivery problems, because the most serious problems for modern software delivery is its dynamics features, which user requirements are constantly changed from time to time for its new business environment. Based on the current modern research on scrum we find that those related work on scrum, such as (Alsalemi and Yeoh 2015; Anderson et al. 2012; Bassi 2016; Cocco et al. 2011; de O. Melo et al. 2013; Dingsøyr et al. 2012; Dybå and Dingsøyr 2008; Farid and Mitropoulos 2013a, 2013b; Gren et al. 2017; Griffith et al. 2014; Gunga et al. 2013; Hoda and Murugesan 2016; Khmelevsky et al. 2017; Košinár and Štrba 2013; Lei et al. 2017; Lin et al. 2014; Lin et al. 2015; López-Martínez et al. 2016; Mahnič and Hovelja 2012; Maxim et al. 2016; Moe et al. 2010; Moe and Dingsøyr 2008; Orłowski et al. Orlowski et al. 2014; Perkusich et al. 2013; Quaglia and Tocantins 2011; Ramanujam and Lee 2011; Scott et al. 2014, Scott et al. 2016 Shiohama et al. 2012; Stray et al. 2016; Tamburri et al. 2012; von Wangenheim et al. 2013; Zualkernan et al. 2008) are all real world scrum project based research, compare with scrum guide which is the most important guide document on explaining what is scrum framework, the realworld scrum project can no guarantee its fully obey the rules indicated in the scrum guide, even it provides sufficient practice based on the scrum guide. In this thesis, all the scrum concept is only based on the standard scrum guide, not based on any of those scrum projects. Software products can be very complex because of complexity in development, requirement analysis, technology adoption, functional complexity. Complexity mainly comes from the user requirements, which need to be addressed by the development team in conjunction with the software user who is the product owner. SCRUM is one of the more popular agile methods which can deal with complex software system production. It is a software development process that was developed by Ken Schwaber and Jeff Sutherland in the United States which has been widely adopted and has become a common software development method (Scrum Guide 2017) SCRUM is defined as “A framework within which people can address complex adaptive problems, while productively and creatively delivering products of the highest possible value.” This section describes Scrum and its processes in detail as shown in Fig. 1.

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Fig. 1. Scrum model in brief https://www.scruminc.com/the-3-5-3-of-scrum/

2.2

Task Allocation Research

(Masood et al. 2017) analyzed real case in software companies, it found that majority developer in the software team consider their own skills and matching with task allocated, the whole team’s goal is much neglected by the team, which prevent the other team member to shows its option and make influence on each other in a positive way. (Lin 2013) proposed a task allocation algorithm for the scrum team based on context aware methods, this method balance the workload of task and agent status, such as the requirement of task quality, completion efficiency and agent’s psychology and pressure. (Lin et al. 2014a) proposed a survey-based research on the developer’s selfassessment on the confidence in completion the task. In order to find whether the correlation between the confidence and completion time of the task is correct or not for its task allocation decision making adjustment. The confidence is scale from 0 to 11 points so as to indicate no confidence and very confidence. (Lin et al. 2014b) analysis the task allocation in scrum context by consider the developer’s morale. 2.3

Pair Programming

Pair programming is a type of team dynamics, it is proposed in the extreme programming which allows two personal working together. This ideal can be used in scrum team to evaluate the performance of the team compare to solo programming. However, strategies in pair programming or solo programming has not been designed for scrum team. 2.4

Simulation on Agile Team Dynamics

(Shiohama et al. 2012) conduct an experiment on estimating the appropriate iteration length that needed for agile development, the focus of the estimation is to provide cost predication for the agile project, the less cost the better of the plan for conducting the iteration length. The problem of this kind of research is people don’t know how to

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measure scrum, as scrum is just a framework, the way to apply scrum can be very various, the motivation of this research is to solve the problem of how to apply scrum by using modelling and simulation so as to know the relation between the iteration length and cost of the agile project. Each iteration is a sprint in the agile project. Based on this study, it was found that when the user stories change frequently then the sprint length should be shorter, if the user stories complexity is higher than expected, the sprint length should be longer. (Cao et al. 2010) using system dynamics approach to modelling agile development process and validation on the effectiveness of pair programming in agile team. Here is the system dynamic modelling on the agile process. It simulates the agile process through system dynamic methods and validate the model through the real data collected from real software project, based on solo programming and pair programming methods. The simulation tells us that by using pair programming the quality of the software is enhanced which reduce the effort of refactoring, in its experiment, the project that using pair programming only needs two sessions of refactoring, while the project using solo programming needs three sessions of refactoring, Which cause higher cost and effort than pair programming. (Zualkernan et al. 2008) worked on an agent-based simulation project on agile scrum. He simulated the process of agile scrum in order to train students to understand the correct steps of agile scrum, such as the role of members and the progress of agile scrum. The training is very useful as it can help those students to avoid making fundamental mistakes when applying Scrum in their project. This also a type of modelling and simulation, which is used for training purpose, this research finds the training system can help people to know what scrum is and how to practice it as most of the problems for scrum are because of adoption of scrum. There are similar work in the same purpose such as the research (Zualkernan et al. 2008) practice the concept of scrum through the analysis of role, such as correct person performance the correct role, for example who should preparing the product backlog? The scrum master or the product owner? The scrum activity, such as should the team performance daily meeting or not? The sequence of the scrum activity, such as the estimation of the user stories points should happen before or after the product backlog preparation? The location of the scrum activity, should some of the distributed team hold their meeting online? The motivation of the scrum activity, does the scrum daily meeting been hold just because the scrum master does not trust the scrum team? The quality of the scrum activities, such as irrelevant topics occupy the scrum daily meeting. The estimation correctness, such as the scrum team velocity been over estimated or not? Those questions help the scrum practitioners to fully understand scrum concepts. (Lin 2013) addressed task allocation in agile scrum where he designed an algorithm to allocate tasks to team members and then to verify the time delay of the project based on its designed task allocation methods. He found that the task allocation should be both average and focused, which means, all the team member should be working instead of idling. However, the strong member should not work too much, so as not to make any further pressure on the others, which can enhance the team’s working efficiency. When the scrum process is completed, the delay rate of the tasks is calculated and compared to the accept-when-requested task allocation. It was found that the algorithm is more stable and performed better in the task delay rate in which most tasks

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were completed within the estimated time limitation. The system was constructed using multi-agent based simulation. This type of research is working on how to measure the scrum team task allocation, which is the only related work find in the area. It wants to balance the quality of task which seeking to allocate this task to highly capable agent, and the on-time completion of the task, which there should be low level agent idle, however this is still not multi-agent based simulation, which each agent cannot influence the other in the agile team. (Noori and Kazemifard 2015) developed an agent-based model on pair programming in agile project. They argued that the different personalities and characters will affect the work of pair programming. They modelled the developer through agents and found that some good combinations of specific personalities are well suited for pair programming. The results showed that personality plays an important role in the formation and utility of a pair. For example, when the expertise of both individuals is high, the best pairing is introvert-extrovert. When both individuals are extrovert, the best pairing is low-high or medium-high expertise. This work focused on the team selection in agile scrum. (Joslin and Poole 2005) proposed several key ideas of how to apply agent-based simulation for software projects. The goal of agent-based software project simulation is for decision support in software project management. The agent can be used to simulate how developers work and how resources are allocated. The agents’ strategy can be separated from its algorithm design. Its strategy is mainly focused on high level planning while the algorithm is more focused on specific realization, such as the agent behaviours and activities. The search strategy can be optimized using genetic algorithms. There should be initial simulation parameters, which will be updated at each sprint. The duration of the sprint can be defined based on the Scrum master and the development team. At the end of each sprint, those initial estimated parameters will get a final value. They also indicated that the task allocation for multi-agents is a very critical part of software development process. (Zualkernan et al. 2008) This is a process-based simulation game that help students to well understand scrum by practice on the platform, to do correct activities instead of wrong works during adopting scrum in real world. The research (practice the concept of scrum through the analysis of role, such as correct person performance the correct role, for example who should preparing the product backlog? The scrum master or the product owner? The scrum activity, such as should the team performance daily meeting or not? The sequence of the scrum activity, such as the estimation of the user stories points should happen before or after the product backlog preparation? The location of the scrum activity, should some of the distributed team hold their meeting online? The motivation of the scrum activity, does the scrum daily meeting been hold just because the scrum master does not trust the scrum team? The quality of the scrum activities, such as irrelevant topics occupy the scrum daily meeting. The estimation correctness, such as the scrum team velocity been over estimated or not? Those questions help the scrum practitioners to fully understand scrum concepts. The research find that this type of training system is very important to teach students to practice scrum. (Tamburri et al. 2012) This research explores the simulation of various time zonebased Scrum and how time can affect the task completion, obviously, the time zone will affect the task that assigned to the team on different locations. The delay or errors on the

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task caused by communication are affected by the time zone which needs to be considerate and optimized through Scrum that adapted to be distributed in worldwide team performance. There are task3 is in Vancouver, task5 is in Washington, task4 is in Washington, task1 and task2 are in los Angeles. Task8 is in Berlin, task6 and task7 are in Rome. And most importantly, the stakeholder seems to be in another place that even don’t know where he/she is. We can find those tasks are have dependence on each other, which makes the delivery process even more complex, the time zone gap is one of the major difficulties that prevent team members to communicate effectively and timely fashion. An error occurred in one task may be informed or detected by other team in other place, two teams can be affected by the error that shared between tasks. (Griffith et al. 2014) provides a discrete-event based simulation of the scrum agile process with particular focus on defects and technique debt creation. It updated the scrum process by consideration technique debt creation. The purpose of this simulation is to investigate how technique debt could affect the scrum process, including the impact on the delivered software products, as the team effort is a limited resource, however, the technique debt can be almost unlimited depends on the criteria about what is technique debt. The above estimation of the time needed to complete a task is based on the developer capability, which specifically defined junior as 2.0-time, middle level as 1 time and senior take 0.5 time to complete a given task. It shows that the more tech debt the lower the team performance, if the error size is just the same as the system size then the team productivity is zero. The less the error size the higher the team productivity. It focusses on the effect of technique debt on agile scrum. It further simulate the debt process into agile scrum process, and it think technique debt needs to be solved before project move on, but the agile team cannot pay too much attention on every debt, otherwise the budget of the project will be increased, and the whole project cannot be completed within in time and budget, there is a balance in selection which technique debt should be solved priority than others. (Maxim et al. 2016) In this research, a game is developed based on AI that support training on student to understand the scrum process, student needs to select correct activities in order to win the game, so as to complete the scrum project, it helps student to remember the correct scrum process based on the options designed above which is the system working flow. This research finds that the game-based training can help student understand scrum. This paper (Lunesu et al. 2018) analysis a way of using event-based simulation to model and simulation software projects delivery through scrum based on the collaboration in the cloud’s development platform. It compares the size of the project and the number of developers to predict the effort the and time scale to complete that project through event-based simulation and it also compare the difference between scrum and Kanban model in delivering software through clouds platform. The simulation results in the following major findings: Project teams face problems regarding communication and organization of distributed projects affecting the teams’ productivity and/or increasing the time required to achieve the project goals. This paper (Orłowski et al. 2014) proposed a framework to simulate the scrum process, The aim of this article is to present the project framework for constructing a Software Process Simulation Modelling (SPSM) system. SPSM systems can be used as a virtual environment for the selection of methods and tools of project management in

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IT support organizations. The constructed system simulates the Scrum methodology, including the management processes and the project roles. For the implementation of Scrum processes, the Scrum ontology is proposed and for the competences of the roles of project team members, a fuzzy-logic representation. As a result, they present the hybrid fuzzy-ontological system. The framework of the design processes proposed in the article was verified based on real courses of project management processes in a large IT company. This paper (Košinár and Štrba 2013) used an event-based simulation to provide estimation on time and effort needed to complete healthcare information projects through scrum modelling and simulation. It simulates the number of tasks and the size of the development team in order to estimation on the completing time of the project through Burndown chart. So far, the tests of the simulation tools and formal model they build prove that this approach works well even it still has some insufficiencies and not all simulations are precise enough compared to real world. However, these deviations are usually caused by external factors like unpredicted obstacles or changes to team.

3 Design and Implementation of the Multi-agent Simulation System 3.1

System Design Diagram in UML Including Ontology Design

Scrum Master Agent Design The scrum master agent as shown in Fig. 2 is designed to help the scrum working agents to take tasks, allocate tasks and manage the scrum board. The scrum master agent can communicate with all working agents to identify its working status is busy or idle, know its preference value on the task, allocate task to each agent, form the pair based on task requirement and allocate the task to the pair. In the initial start of the simulation system, there is only a scrum master agent is running in the run-time, however the scrum master agent is designed to have an interface to interact with human, so that we can create as many working agents as we need, and deploys user stories into the product backlog through that interface, those setup facility can trigger the startup of the simulation system. During the run-time of the simulation, the scrum master agent will send tasks to working agents and identify who is idle at each time tick, if there is no agent is idle or can get task allocate, the scrum master agent will send working time tick order. Each working agent receiving that working time tick will work on the task and report its personal status and task status to the scrum master agent at each time tick. Any agent completed the task will be allocated new tasks before next working time tick send by the scrum master agent, unless that agent is not able to work on those tasks. For solo programming, each task is allocated to one agent, so that the scrum master agent compares each agent’s preference value on the task and make decision based on solo strategy. For the pair programming, each task could be worked by two paired agents, the scrum master agent is responsible to choose two most appropriate agent to work on the task based on pair strategy.

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Fig. 2. Scrum agent design

Working Agent Design The working agent as shown in Fig. 3 will receive order from the scrum agent, those orders are “please pick up task”, “please aware this is working time tick order”, “this is the task allocated information”, “ please tell me your status is busy or idle”. The scrum master agent communicates with all working agents through the message above to organize the whole scrum team working progress. Such as when should send tasks for choose, when should send time tick to work, when should give the task allocation information.

Fig. 3. Working agent and its ontology design

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Working Agent Ontology Design Each agent has its own ontology to present its own information, the working agent’s ontology is different from its agent instance. The ontology is the description on the agent, it is not the run-time object that can do any behavior in the simulation. The description on the agent includes the agent name, capability level and status. That information can be sent to the scrum master agent by the working agent itself during the task selection, so that the scrum master agent knows the agent profile to make task allocation decision based on the strategy defined. Task Ontology Design The task ontology as shown in Fig. 4 is the same as the task modelling, as task do not have behavior, it is the information about the task, such as task complexity, task size and which user story this task is belongs to.

Fig. 4. Task ontology design

3.2

The Working Agent Choose Task Process Function

The working agent choose task strategy is different during pair programming and solo programming, we have several types of task choosing strategy in each type. However, in this section we just give the basic task choosing strategy for the working agent under solo and pair context.

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• Solo Programming During solo programming, each agent will choose task based on its relatively compare of task complexity and agent capability, only those whose capability is not lower than −3 can choose the task to work on. Otherwise, this agent is not allowed to choose the task that so much higher than its capability required. After choosing the task, each agent will send its selected task list to the scrum master agent. That task list include all the tasks that this agent can work on, by providing its preference value on each task. • Pair Programming The pair programming strategy is different from the solo, it will be given more details in the next chapter. In the pair programming, the working agent can only choose task based on the rules that defined. The basic rules are as following: a) b) c) d) e) 3.3

Any agent can choose easy task Intermediate agent can choose intermediate task Expert agent can choose intermediate task expert agent can choose complex task Intermediate agent can choose complex task The Task Allocation Function Design in the Scrum Agent

• Solo programming The scrum master agent will receive all the corresponding agents’ selected task list and preference value on each task. The scrum agent will pick up the highest prioritized task and choose the agent whose preference value is the best match with the solo strategy defined to work on the task. The scrum master agent behavior as the global agent to understand and acquire all the information regards to the team, so as to make final decision based on the rules defined by the solo strategy. • Pair Programming The pair programming task allocation for each task is much more complex than solo programming, because it needs to take two agents working on a task. Normally, the scrum master agent will still receive all the corresponding agents’ preference value on the task list, and the scrum master agent should take the task which has the highest priority to be allocated first. The scrum master agent will choose the most appropriate pair for the task. Such as the complex task can be worked by expert-novice pair, expertintermediate pair or intermediate-intermediate pair. The scrum master should choose expert-novice pair as the best choice as an example. If there are more than two experts available, the scrum master agent should compare those experts based on its preference

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value on the same task and pick the most appropriate expert. Then the scrum mater agent can pair the chosen expert with the novice to work on the task. For complex tasks, the scrum master agent should always get the high-level agent choose first than the low-level agent to form the pair, because without the high-level agent, this complex task will not be allocated even there are novice agent available. 3.4

Conclusion

In this section, the conclusion is very clear, I have designed and implemented a very innovative and advanced multi-agent based simulation software for scrum and its team modelling && simulation. such system can model the solo programming as well as pair programming. This system is innovative and critical before we move into next chapter which all strategies are running based on this designed && implemented software system. This software system is the pre-requirement to do any testing and strategy design. It is this software system that realized the real multi-agent based modelling and simulation on scrum and its team dynamics. (Zhe Wang 2019a)

4 Strategies 4.1

Must Pair Programming Strategies Design I

4.1.1 Agent Decision Strategy In this agent Strategy design, all agent will still choice task based on its own preference, and choice partner after it get the task, however, as this is a must pair strategy, which means every agent must be paired to work on any task. There is no solo programming in this strategy, even some of the pair may cause conflict and harmful, we will still pair those agents to work. Each agent will choose its partner based on the maximum benefit of pairing, however, if there is no benefit pair and some other agents are still available, then it must pair with the available agents even such pair is harmful. We know such pairing is not feasible, but for research purpose, we want to see such must pair strategy performance compare with solo programming and the intelligent pair. Those harmful pairing including expert with expert pair, expert with intermediate to work on intermediate/easy task, intermediate with intermediate to work on intermediate/easy task. Table 1 shows the agent motivation of pairing based on the task and partner availability.

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Table 1. Agent decision strategy for must pair strategy Agent Novice

task Easy Intermediate Complex

Intermediate

Easy Intermediate Complex

Expert

Easy Intermediate Complex

Best choice Novice-novice pair Intermediatenovice pair Expert-novice pair Intermediate novice pair Intermediate novice pair Expertintermediate pair Expert- novice pair Expert-novice pair Expertintermediate pair

Second choice Novice-any agent pair Expert-novice pair Intermediatenovice pair Intermediate-any agent pair Intermediate-any agent pair Intermediateintermediate pair Expert-any agent pair Expert-any agent pair Expert-novice pair

Third choice No No No No No Intermediate-any agent pair No No Expert-any agent pair

4.1.2 Task Allocation (a) The task allocation in this process is based on choose the first agent to work on the task first based on the comparisons of the preference value as shown in Table 2 and 3. We will still be allowed novice to work on easy task first. Intermediate to work on intermediate task first and expert to work on complex task first. However, as this is a must pair strategy, after the first agent has been decided then that agent must choose an agent to pair with and work on the task, there is no solo working under this strategy. The first agent is still trying to get the best benefit of pairing as the priority, however, if it cannot find such good way of pairing, then any pair will be the only choose to get any available agent as a random selection from those idle agents. We will set each harmful pairing and negative impact on the team, but still set positive pair impact on benefit pairing.

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First choice Any Agent Novice Intermediate Intermediate Novice Complex Expert Intermediate

4.1.3

Second choice Any Agent Any agent Intermediate Any agent Expert Novice

Third choice Forth choice

Expert Novice Expert Any agent

Expert Any agent Intermediate Intermediate

Task Allocation (B)

Table 3. Must pair I type B Task

First choice

Third choice

Forth choice

Fifth choice

Intermediate

Any Agent Novice Intermediate

Second choice Any Agent Any agent Intermediate

Easy

Expert

Expert

Complex

Novice Expert

Any agent expert

Novice Expert

Any agent Intermediate

Intermediate

Novice

Any agent

Intermediate

Any agent gap value >=−3 Any agent Any agent gap value >=−3 Any agent

4.2 4.2.1

Must Pair Programming Strategies Design II Task Allocation (a)

• Must Pair II (even negative agent (novice and intermediate) can be the leader of the team), as shown in Table 4 and 5. • Negative novice can lead an easy task to pair • Negative intermediate can lead an intermediate task to pair

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Table 4. Must pair II type a Task Easy

First choice Novice Novice Intermediate Intermediate Novice Complex Expert Novice

4.2.2

Second choice Novice Any agent Expert Novice Expert Intermediate

Third choice Any agent Any agent Intermediate Any agent Expert Any agent

Forth choice

Expert Any agent Intermediate Intermediate

Task Allocation (B) Table 5. Must pair II type B

Task

First choice

Fifth choice

Intermediate

Third choice Any agent Any agent Intermediate

Forth choice

Novice Novice Intermediate

Second choice Novice Any agent Expert

Easy

Expert

Complex

Novice Expert

Novice Expert

Any agent Expert

Any agent Intermediate

Novice

Intermediate

Any agent

Intermediate

Any agent gap value >= −3 Any agent Any agent gap value >= −3 Any agent

4.3 1. 2. 3. 4. 5.

Penalty on the Pair if two experts working together if two intermediate working on intermediate task if expert and intermediate working on intermediate task if intermediate and expert working on easy task. If two intermediate working on easy task

each will get −15% impact on the team performance when pair. Based on the new design of intelligent pair which would not use those penaltybased pair programming we can also have two type of intelligent pair I and II.

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4.4

Conclusion

In this section, the conclusion is very clear I have designed three types of strategies for scrum team, there are solo strategies, intelligent pair strategies and must pair strategies. Each strategy has its sub types, it can be classified into this table.

5 Experiments 5.1

Pure Random

Table 6. Testing Scope In the pure random Scrum team Strategies Type1

solo

Must pair

Intelligent pair

Run time

Must pair II type A Must pair II type B

Intelligent pair I Type A Intelligent pair II Type A Intelligent pair II Type B

100 times repeated random running 100 times repeated random running 100 times repeated random running

Solo pvalue > -4

Type2 Type3

In Table 6 indicate the testing scope for this experiment. All the experiments are conducted randomly, which give totally random distribution of task complexity and agent capabilities, which means those task can be any kind of complexity and team can be composed by any level of agents, both task and agent has ten levels from 1 to 10, however, the team size are classified as 4 agent, 5 agents, 6 agents, 7 agents and 8 agents. Each strategy is tested for pure randomly 100 times running to collect those data, such as completion time, effort time, number of tasks, workload, idle time and efficiency value. The efficiency is calculated as: Working efficiency = completion time/(workload/number of agents), the lower the value the higher the efficiency. The lower the value the better the performance. Equation 1 5.1.1 1. 2. 3. 4. 5. 6.

1 2 3 4 5 6

4 Agents Random MUST PAIR II A INTELLIGENT PAIR II A MUST PAIR II B INTELLIGENT PAIR II B SOLO INTELLIGENT PIAR I A

Modelling and Simulation of Scrum Team Strategies Table 7. 4 agents random testing result Must

Intelligent

Must PairII Intelligent

PairIIA

pairIIA

B

pairII B

Solo

Intelligent pairIA

legend

1

2

3

4

5

6

Average

189.3

148.3

155.7

150.5

142.7

133.6

270

166

221

195

189

240

134

95

111

97

81

54

392.2

350.3

341.1

355.9

344.1

297.9

75

67

66.5

67.5

69

56.5

576.6

500.5

521.7

525.1

500.2

410.9

1.92

1.71

1.83

1.72

1.69

1.75

168.6

79.1

89.7

55.7

49.1

106

complete time Maximum complete time

Minimum complete time Average Worked load Average Task number Average effort time Efficiency(individual) Average idle time

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Fig. 5. 4 agents random testing result

In the above diagram Table 7 and Fig. 5 we can see the intelligent pair II type B seems to performance the best among all the strategies, however, the intelligent pair II type A performance the most stable and consistent. Both solo and intelligent Pair II type B is not stable and only performance occasionally good, however, the intelligent pair II type A performance consistently good.

Modelling and Simulation of Scrum Team Strategies

5.1.2

5 Agents Random

Table 8. 5 agents random testing result Must

Intelligent

PairIIA

pairIIA

Must pair II B Intelligent

Solo

legend

1

2

3

4

5

6

Average

158.9

115.4

160.3

117.8

153.3

123.7

198

143

220

165

223

187

110

83

112

71

99

89

382.6

374.4

347.8

361.6

400.4

350.4

72.5

70.5

68.5

70.5

76

69

533.7

513.5

528.3

516.2

620

474.8

2.07

1.54

2.32

1.65

1.92

1.80

245.9

48.1

256.8

48.9

104.8

115.3

Pair II B

Intelligent pairIA

complete time Maximum complete time Minimum complete time Average Worked load Average Task number Average effort time Efficiency(individual) Average time

idle

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Fig. 6. 5 agents random testing result

From the above diagram Table 8 and Fig. 6, we can observe that the intelligent pair II A and intelligent pair II B performance the best and most stable among all strategies. The intelligent pair II A is even more stable. The must pair II A and must pair II B does not performance good and it is unstable and low efficiency. Must pair II B is the worst.

Modelling and Simulation of Scrum Team Strategies

5.1.3

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6 Agents Random Table 9. 6 agents random testing result Must

Intelligent

Must pair II Intelligent pair Solo

Intelligent pair

PairIIA

pairIIA

B

II B

IA

legend

1

2

3

4

5

6

Average

113.7

104.2

119.7

100.6

102.8

93.2

146

126

187

133

126

136

78

76

73

72

85

47

377.1

354.2

355.6

380.6

356.3

368

71

63.5

70

73

69

70.5

545.2

527

544.8

551.2

522.7

489.9

1.81

1.78

2.03

1.6

1.82

1.51

116.4

74.7

148.2

26

51.7

45.3

complete time

Maximum complete time

Minimum complete time

Average Worked load

Average Task number

Average effort time Efficiency(individ ual)

Average idle time

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Fig. 7. 6 agents random testing result

As shown in Table 9 and Fig. 7 Intelligent pair II B and intelligent pair I A performance the best. The intelligent pair I A performance even more effective and stable. Must pair II B performance the worst.

Modelling and Simulation of Scrum Team Strategies

5.1.4

7 Agents Random

Table 10. 7 agents random testing result Must PairIIA

Intelligent

Must pair II B Intelligent pair Solo

pairIIA

II B

Intelligent pairIA

legend

1

2

3

4

5

6

Average

111.5

86.4

111

82.3

94.3

87.9

138

100

150

118

152

143

74

60

79

55

43

52

389.1

367.3

362.9

354.5

351.1

377.4

74.5

68

70

70.5

68

74.5

562.7

534.1

554.8

516.1

524.6

522.4

2.00

1.65

2.15

1.63

1.91

1.61

199.6

39.6

193.7

27

84.3

63.5

complete time Maximum complete time Minimum complete time Average Worked load Average Task number Average effort time Efficiency(individua l) Average idle time

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Fig. 8. 7 agents random testing result

As shown in Table 10 and Fig. 8 The intelligent pair II A, the intelligent pair II B and the intelligent pair I A performance the best.

Modelling and Simulation of Scrum Team Strategies

5.1.5

8 Agents Random

Table 11. 8 agents random testing result Must PairII A

Intelligent

Must PairII Intelligent

pairII A

B

pairII B

Solo

Intelligent pairI A

legend

1

2

3

4

5

6

Average

83.1

78.2

72.6

69.7

87.3

79.2

103

93

94

93

124

119

78

61

59

39

41

42

352.5

378.3

309.2

312.4

359.9

358.4

67

71

61

61.5

70.5

71.5

531.2

544.3

464.4

480.3

539.8

509.7

1.89

1.65

1.89

1.9

2.03

1.75

108

56

85.2

44.9

106.9

89.5

complete time Maximum complete time Minimum complete time Average Worked load Average Task number Average effort time Efficiency(individual ) Average idle time

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Fig. 9. 8 agents random testing result

As shown in Table 11 and Fig. 9, The intelligent pair II TYPE A and the intelligent pair I TYPE A performance the best. 5.2

Summary

In the current testing on the solo strategy, I need to explain the agent in this strategy can do task which higher than its capability required, if it is not higher than 4. Which means the agent whose capability is 4 which is a novice can do task level at 7 which is the highest intermediate task. And in this solo strategy, the agent whose capability is 7 which is a intermediate can work on complex task level at 10. The above design result different affect compares with the intelligent pair with any pair strategy, those pairing strategies never allow novice agent to work on the intermediate task alone or intermediate agent to work on the complex task alone. It they want to work on higher level task, they have to pair with another agent which is intermediate agent or expert agent. This makes the agent utility in the solo and pair strategy are very different. The solo strategy performance difference compares with paring strategies in task allocation, this result the following testing. The solo strategy never performance better than intelligent pair when the team size is bigger than 4. However, the solo strategy does performance better than the intelligent pair when the team size is 4 agents’ team. This is a very interesting finding. And the solo performance the worst when the team size is 8 agents. The solo strategy only works when the team size is smaller than 4. As we already define the team dynamics in the research proposal as team composition and task allocation, we should continue use this definition, we can see these aspects affect the scrum team performance significantly than other factors. And pair

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programming is also task delivery oriented, we should link the team with the task in the team dynamics definition for our research. Intelligent pair is still the best in the scrum team performance in all those strategies. I have done the testing on the Intelligent pair I strategy, by using totally random testing, it shows the intelligent Pair I performance almost the same with the Intelligent pair II in the overall team performance, but less effective in the idle time in most of the situation, sometimes it’s even better in reduce the idle time. The intelligent pair I always performance far better than must pair II. The intelligent pair I and the intelligent pair II both performance the best compare with other strategies, Based on the literature review, the pair which should not happen will get penalty, 15% penalty on the inappropriate pair is the lowest penalty, and 15% increasing on the appropriate pair is the lowest increasing. Which means the intelligent pair could be even better if the pair is good and very good, and the must pair can be even worse if the pair is bad or even bad. The performance tendency between the intelligent pair and must pair be just the opposite. We should clarify in this thesis, we only considerate the lowest performance of the intelligent pair, which it could performance even better in the real world. And we only considerate the highest performance of the must pair, which it could performance even worse in the real world.

6 Conclusion The three strategies are all independent form each other in the insightful thinking of design, solo, must pair and intelligent pair do not have any relation with each other. The reason is their design purpose are different. And those three strategies are all not existing in the exiting scrum team dynamics. They are all innovative and new. The solo strategy is designed to solve the problem of idle agent and can avoid too much long time working on a task, because of the agent is novice. It always keen to matching the best agent and task as much as possible, unless there is no better agent, it will find the second best and so on, until the task get allocated. The solo strategy considerate the efficiency of team dynamics as well as the effective delivery of sprint. I need to emphasise that this strategy is innovative, on one has even thinking in this type of work before. Scrum guide doesn’t have this working strategy. The must pair strategy is designed to pair two agents working together at every time they are working; however, this strategy is not random pair, random pair is used in real world and it’s not work well. This is advanced than random pair, because it always tries to maximum the benefit of pair. most importantly, the strategy is based on the solo strategy’s p-value design, which define the most appropriate first agent before getting the second agent choose. This strategy only allowed the first agent to choose the second agent. Which already show the ideal that this strategy also try its best to get the benefit of pair, not the worse of pair as the random strategy. The intelligent pair strategy is not an evolution from the must pair strategy, this strategy is designed earlier than the must pair strategy, because it is based on consideration the benefit of solo and pair at the same time, and only get all its benefit and avoid any loss of either solo or pair. this strategy is expected as the best strategy, because it maximum the team dynamics benefit to the maximum value. It shares the

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designed though p-value based solo strategy to get the first agent selected, and then let the first agent to choose the second agent, without compulsory pair when that pair is harmful. It allows agent to work in solo or pair just based on the context. The design of the above strategies is based on different insightful thinking, they are all p-value (Zhe Wang 2019a; Wang 2019) based task allocation but working in various manner.

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Wang, Z.: Teamworking strategies of scrum team: a multi-agent based simulation. In: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, Shenzhen, China (2018) Wang, Z.: The compare of solo programming strategies in a scrum team: a multi-agent simulation tool for Scrum team Dynamics, Cham (2019a) Wang, Z.: Estimating Productivity in a Scrum team: a multi-agent simulation. In: Proceedings of the 11th International Conference on Computer Modeling and Simulation, North Rockhampton, QLD, Australia (2019b) Wang, Z.: P-value based task allocation in a Scrum team: a multi-agent simulation. In: 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), 18–20 October 2019 (2019) Wang, Z., Chalmers, K.: Evolution feature oriented model driven product line engineering approach for synergistic and dynamic service evolution in clouds: four kinds of schema. Procedia Computer Science 19, 889–894 (2013). https://doi.org/10.1016/j.procs.2013.06.120 Wang, Z., Chalmers, K., Liu, X.: Evolution pattern verification for services evolution in clouds with model driven architecture (2013) Wang, Z., Cheng, G.: An approach to synergistic and dynamic service evolution in clouds. IJCC 4, 177–198 (2015) Wang, Z., Cheng, G.: Service evolution in clouds for dementia patient monitoring system usability enhancement (2015b) Wooldridge, M., Jennings, N.R.: Agent theories, architectures, and languages: a survey. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-58855-8_1 Zhe, W., Chalmers, K., Liu, X.: Evolution feature oriented Model Driven product line engineering approach for synergistic and dynamic service Evolution in Clouds: AO4BPEL3.0 proposal. In: International Conference on Information Society (i-Society 2013), 24–26 June 2013 (2013) Zhe, W., Liu, X., Chalmers, K., Cheng, G.: Evolution pattern for service evolution in clouds. In: 2012 International Conference for Internet Technology and Secured Transactions, 10–12 Dec. 2012 (2012) Zualkernan, I.A., Darmaki, H.A., Shouman, M.: A methodology for building simulation-based elearning environments for Scrum. In: 2008 International Conference on Innovations in Information Technology, 16–18 December 2008 (2008)

Development of a Scheme of a Hardware Accelerator of Quantum Computing for Correction Quantum Types of Errors Sergey Gushanskiy, Valery Pukhovskiy, Viktor Potapov(&), and Alexander Kozlovskiy Department of Computer Engineering, Southern Federal University, Taganrog, Russia {smgushanskiy,vpuhovskiy,vpotapov, kozlovskiy}@sfedu.ru

Abstract. This work is a study of the influence of the medium on the quantum system of qubits. The article assumes a description of the fundamentals of the quantum theory of information, as well as a place in it of the concept of quantum entanglement. A technique has been developed for correcting two basic types of quantum errors, based on the implementation and implementation of certain quantum schemes. The main difficulties in ensuring the protection of the quantum channel from various types of errors are analyzed and considered. The dependences of data distortion on noise and measures of decoherence on noise in one qubit are demonstrated. This article is devoted to solving the problem of research and development of corrective codes for correcting several types of quantum errors that appear during computational processes in quantum algorithms and models of quantum computing devices. The aim of the work is to study existing methods for correcting various types and types of quantum errors and to create a 3-qubit corrective code for quantum error correction. The work touches upon the tasks of research and development of the functioning methods of quantum circuits and models of quantum computing devices. The relevance of these studies lies in the mathematical and software modeling and implementation of corrective codes for correcting several types of quantum errors as part of the development and implementation of quantum algorithms for solving classes of classical problems. The scientific novelty of this area is expressed in the exclusion of one of the shortcomings of the quantum computing process. Currently, in many advanced countries of the world, intensive research is being carried out to develop and create quantum computers and their software, and there has been a rapid increase in interest in quantum computers. A large number of articles and monographs are published. The paper presents the main theoretical and practical results in the field of quantum computing. Keywords: Quantum register
Quantum computer simulator plane
Qubit
Quantum algorithm
Phase amplitude

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 64–73, 2020. https://doi.org/10.1007/978-3-030-63322-6_5


Complex

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1 Introduction Currently, the greatest prospect in ultrafast parallel computing is a quantum computer [1]. The idea of creating this type of device that processes information using the mechanisms of quantum mechanics was put forward by the American physicist R. Feynman in 1982. A quantum computer is able to effectively cope with tasks that are impossible for an acceptable time on a classical computer. Currently there are working prototypes of a quantum computer, however, not all quantum algorithms [2] can be implemented on their basis. For such algorithms, computer simulations with classical architecture are used. Effective simulation of quantum computing is not possible on classical computers because the process of mathematical modeling itself has an exponential growth, with an increase in the quantum system for which this simulation is performed. In turn, in 1994 P. Shor showed how, using a hypothetically existing quantum computer, it is possible to decompose huge numbers into prime factors in polynomial time. This event created a huge impetus in the field of quantum computing [3] and can be called a starting point, from which ideas on modeling quantum computing began to gain popularity. Another factor that arouses interest in this area is that, according to Moore’s law [4], the size of transistors in computer chips is becoming smaller every year. Moreover, the decrease is in exponential progression. This means that in a few years, the size of the transistor will be comparable to the size of an atom, where the usual laws of physics no longer apply and you have to use the elements of quantum mechanics. However, these implementations do not yet represent the possibility of computing serious problems on them, such as the mentioned Shore factorization algorithm [5]. At the same time, modeling of quantum computing represents a huge field of research, since new quantum algorithms need to be tested for their effective operation on a quantum computer. For this purpose, simulators of a quantum computer are created, however, not all of the models are effective in terms of performance and the amount of memory used on classical computers. The field of modeling of quantum computing today uses all possible resources to achieve the greatest efficiency when simulating the processes of quantum computing and affects such approaches as: – modeling of quantum computing on multiprocessor computing systems; – simulation of quantum computing using video cards; – FPGA simulation [6]. As you can see, these approaches to modeling go deeper into the hardware. Therefore, the development of a methodology for creating a hardware accelerator for modeling quantum computing is relevant and promising, since it is obviously clear that when using a problem-oriented processor, or in this case an accelerator, the task for which it is created is solved many times faster. The concept of an accelerator, not a processor, comes out of the fact that a quantum processor is theoretically designed not to replace classical CPUs, but only to increase the speed of work for a certain kind of tasks.

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2 Mathematical Model of Quantum Computations 2.1

Quantum Bits

Currently, there are methods for modeling quantum computing, which mainly use the mathematical apparatus for representing quantum computing [7]. A pure quantum state (any possible state that a quantum system can be in) can be described using the equation of the wave function [8]: |w(t) > = R w(x,t)*|x > *dx. The physical meaning of the wave function is in that the probability density of a particle at a given point in the configuration space at a given time is considered equal to the square of the absolute value of the wave function of this state in the coordinate representation. In matrix mechanics, a pure state is described using a state vector [9] (when modeling this vector is also called a quantum register), or a complete set of quantum numbers [10] for a particular system. In matrix mechanics, it is believed that a physical system can be in one of a discrete set of states n or in a superposition of these states, therefore, in general, the state of a quantum-mechanical system P is determined by a state vector: a finite or infinite set of complex numbers w(t) = Cn * wn. If we say that the data manipulation mechanisms are different from the classical calculation scheme and that any classical arithmetic system is not suitable for the description of quantum processes, then we should use another concept that allow us to describe the elementary unit of information in a quantum computer. Such a unit is qubit [11] (quantum bit). Physically, a qubit can be realized using electrons, ion traps or molecules. Information is stored using the spin or polarization of these particles. The main difference between such a bit and the classical one is that it is capable of being in the boundary states of zero and one. Or we can say that the state of a qubit is described through the probability at a certain moment to be zero or one. However, such a concept as probability refers to a qubit only indirectly, namely: the states of zero and unity are described in terms of amplitudes and phases, since this happens in the Schrödinger equation. Moreover, this equation can be represented both in the form of a differential equation, and in the matrix representation. Now most models of quantum computing use exactly the matrix model, since working with differential equations is difficult using digital circuitry. To simulate and describe the states of a qubit, the Hilbert space [12] mechanism is used. 2.2

State Register

One qubit, which is described using the equation of the wave function, in matrix form can be represented as a column vector of the form. It is worth noting here that for calculations it is only necessary to know the amplitudes of states. The state column vector is a quantum register in the simulation. If the quantum register [13] contains more than one qubit, the equation, for example, describing a quantum system of 2 qubits, is as follows: |w > = |w1 > *|w2 > = k1|00 > + k2|01 > + k3|10 > + k4|11> . This opens the main problem that exists in the simulation of quantum computing. To efficiently model quantum algorithms, a quantum register with more than 128 qubits is needed. However, even with 40 qubits, there are difficulties with the memory capacity, since a volume of 1 TB is needed only to store the state vector. The amount of required

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memory in the simulation is taken from the calculation of N = 2^n. Such a calculation is due to the fact that in the calculations there are entangled states [14], in which we cannot separately model the effects on each or a pair of qubits. 2.3

Confusion

From the point of view of the physical system of a real quantum computer, a pair of qubits begins to behave in such a way that when it affects one qubit, the state of the second qubit changes, with which the first was confused. A characteristic feature of entangled or inseparable states is that their creation requires nonlocal unitary transformations, that is, acting simultaneously on different qubits. Entanglement, arising due to some interaction between qubits, can persist even when this interaction becomes insignificant. The main problem that arises in modelling in the case of entangled states is that due to such states, when simulating the behaviour of a quantum computer, one has to work with the transformation matrices of the quantum register model (column vector) and store the entire state vector (byte for n-qubit). Moreover, we are not able to first apply operations on qubits and only then perform the calculation of states as in the equation to save computer memory. The transformation matrix will occupy an even larger place in memory equal to 2^n * 2^n. Entanglement can occur in a real physical system and is used in most quantum algorithms; therefore, this possibility cannot be excluded from the simulator, although this approach leads to an exponential increase in the spent memory. However, at the moment there are algorithms that reduce the memory used in the simulation and the optimization of the calculation process will be shown in the following chapters.

3 Quantum Errors: Cause and Effect Before we start discussing the details of quantum correcting codes, it is necessary to study some common sources of errors in information processing and contextualize what they mean for calculation. There are two trivial quantum algorithms with one qubit. The first algorithm is a calculation consisting of one qubit, initialized to the state |0 > , undergoing N transformations. If this algorithm runs correctly, then ultimately, jwifinal ¼

N Y

Ii j0i ¼ j0i;

i

where I  rI are the identical gates. The measurement of qubits in the basis |0 > ,|1 > , therefore, will give the result 0 with a probability of unity. Second illustration - three gate algorithm 1 1 jwifinal ¼ HIH j0i ¼ HI pffiffiffi ðj0i þ j1iÞ ¼ H pffiffiffi ðj0i þ j1iÞ ¼ j0i: 2 2 This algorithm ideally gives the result | 0 > with probability 1 when measured in the basis | 0 > ,| 1 > . This algorithm implements two Hadamard [15] operations,

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separated by a waiting stage, represented by the first gate. Errors that exist in any quantum system depend on the specific physical mechanisms that control the system.

4 Development of a Correction Scheme for Quantum Errors The fundamental unit of quantum information is qubit. Unlike the classical bit, a qubit can exist in a coherent superposition of its two states, denoted as |0 > and |1 >. In quantum information, unitary transformations and measurements are usually decomposed into quantum circuits, which are sequences of quantum gates (gates): unitary transformations that act only on one or two qubits at a time. There are standard sets of quantum gates [16] that are widely used. On one qubit, common gates include specific Hadamard gates (H) and Phases (S). Using these simple elements, coding schemes for inverted bit and phase reversal codes can be implemented. The identification of error syndromes can also be performed by a quantum scheme. Two CNOT gates are checked for parity in one of the two additional qubits below (called auxiliary qubits [17]), which is then measured in the standard Z basis. There are no corrections made for the syndrome (+1, +1); for (+1, −1) X is applied to qubit 3; for (−1, + 1) X is applied to qubit 1; and for (−1, −1) X is applied to qubit 2. Two CNOT gates are checked for parity in one of the two additional qubits at the bottom (called auxiliary qubits), which is then measured in the standard basis Z. The result of these measurements is an error that says that correction should be applied. There are no corrections made for the syndrome (+1, +1); for (+ 1, −1) X is applied to qubit 3; for (−1, +1) X is applied to qubit 1; and for (−1, −1) X is applied to qubit 2 (Fig. 2).

Fig. 1. Coding scheme for a 3-qubit inverted code involving two CNOT gates

Fig. 2. Coding scheme for a 3-qubit phase reversal code

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Fig. 3. Scheme of correction of phase error

The coding includes two CNOT gates and three Hadamard gates. Correction of Phase Errors. The classical analogue of the phase error does not exist, but the phase error can be transformed into the classical one. Consider the basis | +> = 1/2 * (|0 > + |1 >),|– > = 1/2 * (|0 > –|1 >). The operator X acts in a similar way in the basis|0,|1 > . Using this fact, this type of error can be corrected or corrected using the three-qubit [18] encoding |0 > – > |+++> and |1 > – > |– – – >. Using the correction scheme, it is easy to draw up a phase error correction scheme (Fig. 3). The described types of quantum errors occur both in real quantum computing devices and in simulators based on quantum mechanical effects and phenomena that are as close as possible to real devices. This is due to the fact that the described errors occur at the most basic level of units of quantum information - qubits. The development of a quantum accelerator also contains all the variety of quantum errors.

5 General Scheme of a Hardware Accelerator of Quantum Computing This technique is proposed under the influence of those factors that now the simulation of quantum computing is increasingly starting to use non-standard equipment to increase the productivity of models. Combining the computing capabilities of a problem-oriented hardware and optimization algorithms, which allow minimizing the number of processed states of a quantum register model, a technique is proposed that allows you to take into account such features of a quantum computer model as: – work with complex numbers; – matrix and vector operations (conversion using quantum gates); – parallelism of calculations. The general hardware accelerator circuit is shown in Fig. 4. Blocks “Control Unit” (CU) and “Memory microprograms” (MM) are standard in the implementation of accelerators. The main functions of the CU are the implementation of data initialization, organization of sampling and execution of a command from the memory microprograms. Control unit also needs to receive data from the outside and process it correctly. However, since there are a large number of interfaces, the interface controller circuit is not considered here and we will conditionally assume that the data comes from the X bus, the data on which is formed by the accelerator interface controller. The following are blocks specific to the accelerator of quantum computing, namely, “Block for generating pairs of state indices” (BGPSI) and “Control unit for ALU and state samples from RAM”.

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Block for generating state index pairs

Control device

X

Firmware memory

ALU control unit and state samples from RAM

RAM block (quantum register model)

State permutation scheme for n-qubit gates

Block ALU for single qubit gates

Y

Fig. 4. General diagram of a hardware accelerator of quantum computing

Real part N-1

N-2

Symbol

Whole part

N-3

Imaginary part 0

Fraction

N-1

N-2

Symbol Whole part

N-3

0

Fraction

Fig. 5. Representation of the complex number in the accelerator

BGPSI implements an algorithm that searches for a sequence of states, depending on the operation (single-qubit or multi-qubit) and qubit (s) to which this effect will be applied. The sampling signals from the BGPSI are fed to the input, which determines how to most efficiently extract data from the “RAM block”, since there may be several RAMs to increase productivity and organize parallelism of calculations. The “Block of parallel ALUs” (BPA) and the “State permutation scheme” (SPS) directly perform operations on data extracted from RAM. At the end of the calculations, the UE sends a signal to the RAM unit to output the result to the Y bus. As in the case of receiving data, the Y bus can be considered as an interface controller through which communication with the PC is carried out. The methodology for constructing such an accelerator consists of the following points. a) Creating a data format that will reduce the number of operations spent on arithmetic operations. An example of such a format was illustrated in Fig. 5. b) Application of the optimization algorithm to reduce the actions associated with the construction of the transformation matrix of the quantum gate. Such an algorithm was used in order to reduce the required operations when calculating on supercomputers. This algorithm is implemented in BGPSI (Fig. 4). c) Creation of BPA and ATP blocks (Fig. 4), which are the main computing schemes in the structure of the accelerator. d) Creation of an ALU control circuit and a selection of states from RAM, which will make it possible to make a pair selection of states from the RAM block. You can use a cascade of two port RAM to simultaneously fetch state pairs.

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e) “Control Unit” and “Memory microprograms” blocks (Fig. 4) are common for various types of accelerators. The creation of these blocks is typical, namely: when the calculations are started, the control unit starts reading the micro-command from the memory microprograms and then, after decrypting it, transmits control signals to the BGPSI. f) Initialization of the quantum register model. During initialization, the quantum register model, in our case, RAM or a cascade of RAM, takes the form as shown in Fig. 1. Therefore, as the initial data that is necessary to start the calculations, you should pass the state number through the interface (X bus in Fig. 4), which will be 1. The remaining states should be initialized with zeros. g) The implementation of the reading of information can occur both on the accelerator itself and outside it. When reading, according to the physics of the quantum computing process, the wave function collapses, an example of modeling of this process is shown in Fig. 5. Therefore, depending on the amount of remaining space on the chip (FPGA or hard logic), you can offer a circuit that performs a sequence of actions as in Fig. 5, or transfer all the data in the form of state amplitudes via the interface to a control computer. The first thing that needs to be implemented in the synthesis of the accelerator is to determine the format of the data used. As shown earlier, work in the calculator takes place on complex numbers. Complex numbers in programming can be represented as a record of a pair of real numbers. However in vhdl by. However, VHDL does not by default provide for working with real data types. Therefore, the question arises of implementing your own data type according to criteria that were not explicitly discussed in previous chapters. The main question that arises at this stage is: which of the two possible hardware real data types to choose: floating or fixed point. The floating-point types that are used in the coprocessors of most desktop computers have 32, 64, or 72 bits. There are IEEEE specifications for them, and it also provides support for working with them in most modern CAD systems. Another fixed-point method is most preferred in our case. Since the amplitude of a particular state of the quantum register cannot be greater than unity, from here we can present the following format scheme with a fixed point for a complex number. It is also worth noting the possibility of accelerating arithmetic operations when entering this type, since the usual vector of bits was taken as the basis for a real number. That is, with multiplication, addition, and other arithmetic operations, it is possible to speed up operations by introducing a parallel architecture (for example, accelerated transfer schemes). Another important advantage of this representation of the data format is the reduced type resolution, compared to standard floating-point IEEE types. Therefore, to store one complex number with the real and complex parts of 8 is only 2 bytes, while storing a complex number when represented by real numbers with single precision will require 8 bytes. Due to the specifics of the calculations, this factor is critical, since most modern systems for modeling quantum computing are faced with the problem of storing quantum register states.

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6 Conclusion Correcting errors is one of the main tasks facing quantum computing devices. And without solving this problem, further successful developments in this promising field will become ineffective. In this paper, codes for correcting various types of errors are numerically modeled. The main obstacles and difficulties in protecting the channel from noise are analyzed, and some methods for overcoming them are proposed. Correction schemes for two main types of quantum errors were implemented. The dependences of data distortion on noise and decoherence measures on noise in one qubit are shown, as well as the dependence of error on the measure and purity of entanglement. Acknowledgments. The reported study was funded by RFBR according to the research project № 20-07-00916.

References 1. Sukachev, D.D., Sipahigil, A., Lukin, M.D.: Silicon-vacancy spin qubit in diamond: a quantum memory exceeding 10 ms with single-shot state readout. Phys. Rev. Lett. 119(22), 223602 (2017) 2. Lukin, M.D.: Probing many-body dynamics on a 51-atom quantum simulator. Nature, vol. 551 (2013) 3. Potapov, V., Gushansky, S., Guzik, V., Polenov, M.: Architecture and software implementation of a quantum computer model. In: Silhavy, R., Senkerik, R., Oplatkova, Z.K., Silhavy, P., Prokopova, Z. (eds.) Software Engineering Perspectives and Application in Intelligent Systems. AISC, vol. 465, pp. 59–68. Springer, Cham (2016). https://doi.org/10.1007/978-3319-33622-0_6 4. Raedt, K.D., Michielsen, K., De Raedt, H., Trieu, B., Arnold, G., Arnold, G., Marcus, R., Lippert, T., Watanabe, H., Ito, N.: Massively parallel quantum computer simulator. Comput. Phys. Commun. 176, 121–136 (2007) 5. Boixo, S., Isakov, S.V., Smelyanskiy, V.N., Babbush, R., Ding, N., Jiang, Z., Martinis, J.M., Neven, H.: Characterizing quantum supremacy in near-term devices. arXiv preprint arXiv: 1608.00263 (2016) 6. Stierhoff, G.C., Davis, A.G.: A history of the IBM systems journal. IEEE Ann. History Comput. 20(1), 29–35 (1998) 7. Lipschutz, S., Lipson, M.: Linear Algebra (Schaum’s Outlines). 4th ed. McGraw Hill, (2009) 8. Collier, D.: The Comparative Method. In: Finifter, A.W. (ed.) Political Sciences: The State of the Discipline II, pp. 105–119. American Science Association, Washington, DC (1993) 9. Vectorization. https://en.wikipedia.org/w/index.php?title=Vectorization&ldid=829988201 10. Williams, C.P.: Explorations in Quantum Computing. Texts in Computer Science, Chapter 2. Quantum Gates, pp. 51–122. Springer (2011) 11. Olukotun, K.: Chip Multiprocessor Architecture – Techniques to Improve Throughput and Latency. Morgan and Claypool Publishers (2007) 12. Potapov, V., Guzik, V., Gushanskiy, S., Polenov, M.: Complexity estimation of quantum algorithms using entanglement properties In: Informatics, Geoinformatics and Remote Sensing (Proceedings of 16-th International Multidisciplinary Scientific Geoconference, SGEM 2016, Bulgaria), vol. 1, pp. 133–140. STEF92 Technology Ltd. (2016)

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13. Inverter (logic gate). https://en.wikipedia.org/w/index.php?title=Inverter_(logic_gate) &oldid=844691629 14. Lachowicz, P.: Walsh – Hadamard Transform and Tests for Randomness of Financial Return-Series. http://www.quantatrisk.com/2015/04/07/walsh-hadamard-transform-pythontests-for-randomness-of-financial-return-series/ (2015) 15. Potapov, V., Gushanskiy, S., Guzik, V., Polenov, M.: The computational structure of the quantum computer simulator and its performance evaluation. In: Silhavy, R. (ed.) CSOC2018 2018. AISC, vol. 763, pp. 198–207. Springer, Cham (2019). https://doi.org/ 10.1007/978-3-319-91186-1_21 16. Zwiebach, B.A.: First Course in String Theory. Cambridge University Press, England (2009) 17. Potapov, V., Gushanskiy, S., Samoylov, A., Polenov, M.: The quantum computer model structure and estimation of the quantum algorithms complexity. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) CoMeSySo 2018. AISC, vol. 859, pp. 307–315. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00211-4_27 18. Universe of Light: What is the Amplitude of a Wave Regents of the University of California. http://cse.ssl.berkeley.edu/light/measure_amp.html 19. Sternberg, R.J., Sternberg, K.: Cognitive Psychology, 6th edn. Wadsworth, Cengage Learning (2012)

Personalized Information Representation to Anonymous Users: Digital Signage Case Nikolay Shilov1(&)

and Nikolay Teslya2

1

2

SPIIRAS, 39, 14 Line, St. Petersburg, Russia [email protected] ITMO University, 49, Kronverksky Pr., St. Petersburg, Russia [email protected]

Abstract. Providing personalized information to a wide audience, e.g., digital signage requires approaches and technologies different from those used for individual users. Such systems have to take care of user confidentiality and dynamically assess their interests. The paper developed earlier presented works on this topic and concentrates on the description of the ontological modeling of user interests and preferences as well as major components of such system. The developed ontological model intensively uses various types of relationships in order to provide for quantitative evaluation the user interest in a particular topic and semantic proximity of interests. It also enables generalization of interests, which is helpful for finding common interests for small amount of people, who are not related to each other and unlikely have much in common. Proposed structure of granular anonymous user profiles makes it possible to take care of the user confidentiality and to dynamically adapt to new information. Keywords: Ontological modelling of preferences
Anonymous user profiling
Personalized digital signage

1 Introduction Today, personalization and contextualization of the information provided to users is a common practice. Targeted advertisement is a good illustrative example of this trend, which is very intensively applied by Internet-oriented companies including Amazon or Google [1]. However, in case of targeted advertisement in Web-browsing the advertisement is viewed by one particular user and is targeted to her/him. Providing information to a wider audience, e.g., digital signage [2, 3] (“the provision of content (advertisements, news, assistance) on electronic scoreboards or large displays in places where many people are present or passing by” [4]) requires different technologies and approaches. First of all, the attention has to be paid to laws regulating work with personal data (such as Russian Federal Law No. 152-ФЗ “On Personal Data” dated July 27, 2006 [5], the General Data Protection Regulation in Europe-GDPR [6] and others). Except for special cases not related to this research, this law obliges one to register with corresponding authorities when working with personal data, as well as to obtain written consents of the personal data owners to process their personal data. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 74–86, 2020. https://doi.org/10.1007/978-3-030-63322-6_6

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Digital signage is currently widely used and can be found in various places including both places with access limited to certain people (e.g., offices, condominiums) and places with unlimited access (exhibitions, shopping malls, city streets). It is used for delivering both non-commercial information and advertisements. The specific features of this technology were elaborated in [7, 8] and include: 1) Users Anonymity. This requirement is caused by the necessity to follow the laws. The laws by themselves do not require the anonymity, but a permission to use personal information from its owner. This can be solved, for example, if digital signage is installed in an office building and the employees provide the permission for usage of their personal data. However, in the generic case when digital signage ca be viewed by unrestricted community collecting such permissions is impossible [9, 10]. As a result, this research is focused on operating anonymized (depersonalized) data. 2) Tacit assessment of user interests. Since storing personal preferences and interests of the users is not possible for digital signage, the system has to evaluate these on a continuous basis. However, explicit feedback collecting is not possible either since regular people unlikely would approach a digital signage screen to fill out questionnaires, even if the questionnaires are really short (contain just one question). As a result, the system has to derive user interests from the feedback collected in a tacit way through evaluation of users behavior in front of the digital signage screen (e.g., if they watch the advertisement or pass without paying attention to it). 3) User clustering. Since saving personal data is not allowed, the only possible way to separate users is to form generic user types with certain preferences and interests. This can be done via application of clustering techniques. The clusters then can be assigned with typical characteristics identifiable by the system (e.g., visual characteristics such as age, gender, mood, etc.). 4) Storage & processing of structured user preferences and interests. Since user preferences and interests have to be processed there have to be developed corresponding models of their representation in the personalized digital signage system. 5) Situational awareness. This is rather a recommendation than a requirement since the information about current situation (context) can increase the interests of the users to the information shown but this is not mandatory. The context may include such external information as time, date, holidays and events. For example, information about currently going on soccer championship can be used to attract attention of the users to the digital signage. The paper addresses the problem of personalizing digital signage taking into account legislative restrictions that can be used not only for advertisement but also for delivering various important information, for example, in office buildings or in smart city environments. The next section presents the state-of-the-art review in the related areas. It is followed by the description of the developed methodology of presenting personalized information to a group of anonymous users. Designed ontological model of user interests and preferences is presented in Sect. 4. Then, details of the major system components are discussed. The main results are summarized in the conclusion.

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2 State-of-the-Art Review There are multiple works on personalization of digital signage but almost all of them are based on identification of people located in front of the screen [11–13]. The identification is either done via face recognition or recognition of personal mobile device (smartphone, smartwatch, etc.) via WiFi or Bluetooth. In this work we aim at personalization without identification of people and any prior registration of them or their personal mobile devices but we could not find any works on the same task. As a result, the review of the state of the art below emphasizes research results related to this area and particular subtasks that have to be solved. Profiling is a key component of personalization. The task of user profiling is not new. There are many works that allow one to create effective user profiles for various tasks, e.g., [14–16]. Currently, there are two main categories of social profiling – Individual and group profiling [17]. An individual profile describes attributes that belong to one person. Such profiling is carried out either directly (obtained from the person her/himself or based on the analysis of available data about her/him), or indirectly (obtained as a result of the analysis of data from a large population based on data categorization and generalization) [18]. Group profiling describes people who have one or more common attributes in a group. Depending on the common attributes, group profiling can be divided into distributive (assigning a group with properties, which are considered to belong to all members of this group) and non-distributive (formulated in terms of probabilities, average values or significant deviations from other groups) [19]. However, the task of creating user profiles or user groups with regard to confidentiality is only beginning to attract the interest of the scientific community [20, 21]. Another important aspect of the problem under consideration is representation of interests/preferences. The carried out analysis of the literature related to the area of classifying preferences and interests of users has resulted in quite a few works. The work reported in [22] “is one of the starting points for preference profiles that are” close “to well-structured.” A significant attention is paid to this problem in [23], but it is more devoted to the study of the categorization “like - dislike” and not of preference classification as such. An approach aimed at interest classification using Wikipedia is presented in [24], which seems to be an efficient approach. In addition, the authors propose a method for assessing the closeness of Wikipedia article categories, which can also be useful for preference and interest analysis. Determining user preferences is also quite a challenge because it is very difficult to “force” people to manually set preferences in an application or on a web page; and in view of the need to support the anonymity of the data being processed, the use of social networks to search for a specific person and identify his/her preferences is not acceptable. The authors of [25] proposed a method for dynamic evaluation of how interesting the information provided to the user is. For this purpose, the method of intelligent video analytics (IVA) is used, which helps to determine how attentively a person looks at the screen while it displays certain information (for example, looks carefully, glances occasionally, or ignores). This method seems to be very interesting

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for assessing the interests of users. For anonymous data collection, the authors use the methods of anonymous video analytics (AVA), which is a subclass of IVA. In [26], the level of interest of viewers to the displayed information is also analyzed and the following parameters are evaluated: distance to a person, time spent near the screen, number of viewers, time spent viewing the information, number of screen calls, and gender and age viewer group. Since at present, AI-based image recognition algorithms allow one to accurately determine the gender, age, and mood of a person [27–29], the application of this method may allow to identify target groups and reveal their interests, as well as to refine these through analyzing the correlation between the attention of users of a particular type and information shown.

3 Methodology of Personalized Information Presentation to a Group of Anonymous Users Based on the earlier defined requirements and state of the art review, principles of operation of personalized digital signage system have been identified: 1) Information collection without interaction with users. Since, as it was mentioned before, interaction with users aimed at entering information about interests by themselves or tracking their activities is not possible, the system has to collect this information itself, without any efforts from the user side through analyzing their behavior in front of the digital signage screen (e.g., evaluate, if the user was interested by a certain information piece or not). 2) Application of anonymous profiles describing user interests and preferences. This principle assumes that personalized digital signage system would form user profiles that cannot be used for identification of them. Such anonymous profile can be viewed as a model of a generic user with certain interests, preferences and characteristics. However, actual anonymous profiles need to have more complex structure since interests of users with the same characteristics are not necessarily the same. It is also important to note that on the one hand having detailed data is beneficial for personalization, on the other hand the level of details should not be high enough so that a particular user could be identified (cf. [30]). 3) User reaction-based self-learning and adaptation. The essence of this principle is continuous monitoring and analysis of user’s reaction to the information shown for indirect evaluation of the interests of the users related to the particular information piece. An example of such reaction could be time spent by people looking at the screen, e.g., if it is interesting people would look at the screen for several seconds, if not – they will pass by without making a glance) or change of their emotional state. 4) Ontological modeling of user interests and preferences. In the area of information technologies, ontologies are usually used for unified description of knowledge of a certain problem area. As for decision support, ontologies are widely used to support the semantic integrity of the collected information & personalization and show themselves as a reliable and effective tool, e.g., [31–33]. Though state of the art review showed only ontologies of interests for specific narrow areas they still can be considered as an efficient mechanism for user interest generalization and semantic proximity evaluation [34].

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5) Context management techniques for the current situation assessment. Just like application of the ontologies, context-based information representation is not novel. However, it cannot be skipped since as it was noted before taking into account this information can significantly improve the attractiveness of the information shown on digital signage to the users. Based on these principles the following methodology is proposed. The methodology is based on the feedback loop for refining the information to be shown on the digital signage and anonymous user profiles (Fig. 1):

2. Anonymous user profile assignment

1. Identification of users

3. Current situation analysis and context formation

4. Forming the list of preferences for the current group of users 5. Presentation of information according to the preference list

6. User interest analysis

7. Refinement of anonymous profiles

Fig. 1. Generic scheme of operation of personalized digital signage systems.

1) At the first stage images of people located in front of the digital signage screen are captured and analyzed. The visual characteristics of each person (age, gender, mood, and others) are identified. 2) For each user located in front of the digital signage screen an anonymous user profile is assigned based on the matching of identified visual characteristics and those of the anonymous profile. At this stage, several anonymous profiles can be assigned to a user. 3) Information about the current situation is collected and the context is formed. Stage 3 and stages 1–2 can be done simultaneously. 4) Based on the list of identified anonymous profiles and the context information the list of interests of for the current group of the users is formed (the ontology-based method of identifying interests common to several users was described in [34]).

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5) Selection of the pieces of information that match the formed list of interests and showing these on the digital signage screen. 6) Monitoring of the users in front of the digital screen to capture their behavior (how much attention each user pays to the information shown) and assessing their interest in the information shown on the screen. This step is the feedback capturing step. 7) Based on the captured feedback the anonymous profiles are refined and shown information pieces are replaced with new ones. Steps 4–7 are repeated. If the group of users changes (some of the users leave the area in front of the screen and new users enter it), the process starts from step 1.

4 Ontological Modeling of User Interests and Preferences Ontological modelling of user interests and preferences in the area of information personalization for a group of people has certain specifics. As it was mentioned, one of the main problems in identifying common preferences is the lack of such for a relatively small number (from 3 to 10 people) of users who are not related to each other (for example, [35]). This greatly complicates the search for information that would be of interest to the entire group of users, or at least to most of them. To address this issue it is proposed for the ontological model of user preferences to include not only associative relationships (for example, the Kazan Cathedral in St. Petersburg can be associated with the monuments of Kutuzov and Barclay de Tolly by sculptor Boris Orlovsky), but also hierarchical relationships “is a” (for example, interest “Architecture” is the parent class for the classes “Architecture of the XVII century” and “Architecture of the XVI century”) and “part of” (“Carlo di Giovanni Rossi” and “St. Basil’s Cathedral” can also be combined by the interest of “Architecture”). The presence of such relationships enables generalization of preferences and interests. For example, objects of the class “Architecture” can be interesting to both a user with an interest in Carlo di Giovanni Rossi and a user with an interest in Architecture of the 16th century; at the same time, searching for an intersection their interests in a usual way would produce no result. The developed ontological model conforms a top-level ontology that has so-called meta-classes [36] that define such concepts as “time”, “space”, “object”, “event”, “moment”, etc. Thus, several hierarchies with the relationships “is a” and “part of” can be formed in the ontological model simultaneously. For example, the class “Carlo di Giovanni Rossi” is at the same time a part of the classes “Late Classicism”, “Architecture of St. Petersburg”, “XIX Century” and others. On one hand, this requirement complicates the ontological model, but on the other hand, it opens up wide possibilities for semantic proximity search between various classes. It is obvious that certain events and phenomena are tied to one or another time period (for example, Moscow was the capital from the second half of the 13th century to 1712 and from 1918 till now). Such issues are addressed via inclusion of temporal logic elements to the ontological model. An ontological model with temporal logic makes it possible to determine when a statement (relation, value) is relevant. Moreover,

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temporal constraints can also be used to describe the preferences of users taking into account that they can change over time [37, 38]. For this, the OWL2 (Web Ontology Language version 2 [39]) was chosen for the ontological model development since it is currently widely used for creation of various ontologies and the possibility of integration of the developed model with existing ontological models of various problem areas was considered as an important issue. This allowed to significantly simplify and speed up the process of creating the model, as well as its further extension. For example, some information from such open sources as TripAdvisor [40] and Wikipedia [41] was imported in a semi-automatic way [42] resulting in about 400 classes describing cultural and historical sites of St. Petersburg and its suburbs. Temporal restrictions have been set manually for a number of objects (for example, the Summer Garden is not of interest for tourists in April, since at that time it is closed for drying). The CIDOC-CRM (Conceptual Reference Model developed by Comité International pour la Documentation/ International Committee for Documentation) standard [43] was partially used to form the upper level ontology. In addition to cultural and historical objects, this ontology includes visual characteristics of users, which enables establishing correspondences between objects of interest and visual characteristics of users (gender, age, mood, presence of recognizable visual elements). In addition, the ontology includes context elements, which also allow one to specify the correspondence between objects of interest and user characteristics by taking into account the current situation. Compared to generally accepted representation of user preferences in the form of ontology, which usually do not have associative and “part of” relationships (“is a” relationships are used in nearly all ontological representations), the developed model has several advantages. This representation enables quantitative evaluation of the possible interest of a user in a particular topic, as well as of the semantic proximity of interests. Such quantitative evaluation can be calculated on the basis of a weighted graph, the vertices of which are interests (classes of the ontological model), and the arcs are the relationships between them. Different types of relationships (associative, “part of”, “is a”) can be assigned weights that can differ depending on the direction of the relationship (the semantic proximity between the interest of “Carlo di Giovanni Rossi” and “Architecture” is higher than that between “Architecture” and “Carlo di Giovanni Rossi”). However, the definition of the algorithm for calculating semantic proximity on the graph of the ontological model is still a subject of future research. The developed model does not require describing all interests of a user. It is enough to identify only the key ones and the rest can be calculated based on the above described graph of the ontological model. This is especially important for digital signage systems when information about users is very limited. Possibility to generalize interests in the developed model allows ensuring the confidentiality of information about the preferences of specific users. In the event that a particular preference is confidential, it still can be possible to provide information about classes that are not directly related to this particular preference, but are “remoted” on the graph of the ontological model to a certain “distance” calculated based on the weights of the edges. However, the development of an algorithm for calculating such a distance and its possible limitations are out of the scope of the presented research.

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5 Development of the System Components 5.1

Anonymous Profiles

For representing user preferences, it was proposed to use so-called anonymous profiles of interests and preferences of users that do not require identification of users and tracking of their actions. The characteristics of users that can be identified based on the image analysis (gender, age, mood and recognizable visual elements such as trademark symbols) are used. Thus, anonymous profiles can look similar to profile templates that describe groups of users with similar visual characteristics. However, users with similar visual characteristics do not always have same preferences. In this regard, the anonymous profiles are organized on a granular principle, where granules are closely related pieces of information about an abstract user, including his/her visual characteristics, preferences, contextual elements, as well as the numerical value of the relationship from the lower threshold (for example, 0.5) to 1, where 1 means that the given information fragments are inseparably related (for example, the person with at least 2 logotypes of companies producing sportswear is always interested in sportswear), and 0.5 means presence of the relation in about 50% of cases. The presence of relations and the numerical values of relationships are calculated based on the analysis of historical data. Further, based on this data, the personal preferences are calculated (e.g., as it is done in work [44] aimed at the search for common interests in user communities). The specific feature of the granular information presentation is that the granules can be combined into larger granules, which makes it possible to flexibly create and update dynamically changing anonymous profiles when new user-related information appears in the system. Presented structure of the anonymous profiles in some extent is close to the usage of collaborative filtering [45, 46], however, the proposed model searches for “similar” users at the stage of filling out the profile, rather than when alternative solutions are evaluated. Such implementation is more efficient for smaller dimension of the user characteristic space (visual characteristics, interests and preferences), while the conventional collaborative filtering methods are usually oriented to thousands or millions of dimensions. 5.2

Context Model

Context can have a significant impact on user preferences (for example, the same user in different situations may have different preferences and interests), and its consideration can significantly increase the accuracy of the personalization of the information provided (e.g., [47]). The context is described in the terminology and formalism of the presented above ontological model and includes concepts such as date, day of the week, time, weather and its forecast, user location, and the presence of special conditions, in other words, the characteristics that can affect the interests of users. Date, day of the week, time, weather and its forecast are generally accepted characteristics of the current situation and do not need a description [48, 49].

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The user location is also a fairly common feature. However, it is worth noting that in scenarios associated with digital signage, this characteristic can be used very efficiently for contextual advertising (for example, interests associated with objects located near the screen may be assigned higher weights). Special conditions are various events and phenomena that occur during the provision of information or in the near future. For example, a music festival can cause assigning higher weight to interests related to music, and a soccer championship increases interests related to soccer and sports in general. These conditions are also included in the ontological model of user preferences and are associated with their respective interests. 5.3

Association of Information Fragments with User Preferences

The developed original method of association of information fragments with user preferences is based on the usage of weights and elements of temporal logic. The association is carried out mainly in the granules of anonymous user profiles, and also partially in the framework of the ontological model based on defining relationships between its elements. In general, these relations are ternary (“interest – user characteristic – context element”) with assigned weights and temporal constraints: n o ck ¼ fOi g; fOu g; fOc g; wtk1 ; tk1 ; fOi g; fOu g; fOc g; wtk2 ; tk2 ; . . .; fOi g; fOu g; fOc g; wtkn ; tkn ;

where ck is the k-th correspondence relationship, tk1 ; tk2 ; tkn are the first, the second and n-th temporal constraints on the k-th relationship, wtk1 ; wtk2 ; wtkn are the weights of the k-th relationship when the first, second and n-th temporal constraints are fulfilled, fOi g is one interest or a set of interests, fOu g is one user characteristic or a set of user characteristics, fOc g is one context element or a set of context elements. This means that in general case, if there is a relationship between a user characteristics and an interest (or several interests) for a certain context value (one or several context elements), several constraints can be set with different weights for different time intervals defined by temporal constraints. If any of the elements is not important (for example, there is no temporal restriction), this element is replaced with the value “True”. Thus, the correspondence relationships between n interests in the ontological o model can also be described via the same method as

fOi g; True; True; wtk1 ; tk1

ffOi g; True; True; wk ; Trueg if temporal restriction is absent.

or

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6 Conclusion The paper presents results of the work on presenting personalized information to anonymous users. Digital signage was selected as a case study. The paper concentrates on the description of the ontological modeling of user interests and preferences as well as major components of the system implementing the presented approach. Unlike commonly used representation of user preferences in the form of ontology the developed ontological model has associative and “part of” relationships, what gives some advantages, such as possibility of quantitative evaluation the user interest in a particular topic and semantic proximity of interests, describing all interests of a user; possibility to identify only key interests (the rest can be calculated based on the ontological model); possibility to generalize interests ensuring confidentiality of information about the preferences of specific users. An alternative approach could be to apply deep learning techniques that do not require feature engineering. However, such models require large volumes of data and have a problem of cold start that can be solved by the presented approach. Besides, such models are interpretable and accumulating of large amount of information may cause such problems as described in [30]. Since personal information is sensitive, we assume that explicitly defined features and control over the process of user identification are of high importance. However, we do not exclude usage of machine learning techniques for, for example, matching of information pieces to anonymous profiles. If such black box approaches cannot be applied to smart billboards/digital signage, then it would still seem that that many approaches could be taken. Planned future work includes definition of the algorithm for calculating semantic proximity on the graph of the ontological model and development of a complete testbed for validation of the approach as a whole (prototypes of some components of the system have been described in previous publications [7, 8, 34]). The cold start problem is going to be solved through defining initial anonymous profiles via interviewing a limited group of people including members of the team carrying out this research. Acknowledgements. The development of the presented methodology and models (Sects. 3–5) have been supported by the grant from RFBR (project number 18-07-01201). The motivation and general framework (Sect. 1) are due to the State Research, project number 0073-2019-0005. The state-of-the-art analysis (Sect. 2) is due to the grant of the Government of Russian Federation (grant 08-08).

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36. Vlachidis, A., Bikakis, A., Terras, M., Naudet, Y., Deladiennee, L., Manessi, D., Vasilakaki, E., Triantafyllou, I., Padfield, J., Kontiza, K.: Upper-level Cultural Heritage Ontology (2018) 37. Debnath, M., Tripathi, P.K., Biswas, A.K., Elmasri, R.: Preference aware travel route recommendation with temporal influence. In: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks – LocalRec 2018. pp. 1–9. ACM Press, New York, USA (2018). https://doi.org/10.1145/ 3282825.3282829 38. Oguego, C.L.L., Augusto, J.C.C., Muñoz, A., Springett, M.: Using argumentation to manage users’ preferences. Futur. Gener. Comput. Syst. 81, 235–243 (2018). https://doi.org/10.1016/ j.future.2017.09.040 39. W3C: OWL 2 Web Ontology Language Document Overview (Second Edition). https:// www.w3.org/TR/owl2-overview/ Accessed 25 Dec. 2015 40. TripAdvisor LLC: TripAdvisor, https://www.tripadvisor.ru, last accessed 2019/12/30 41. Wikimedia Foundation: Wikipedia, the Free Encyclopedia. https://wikipedia.org/wiki Accessed 09 Jan 2020 42. Vairavasundaram, S.R.L.: Applying semantic relations for automatic topic ontology construction. In: Developments and Trends in Intelligent Technologies and Smart Systems. pp. 48–77. IGI Global (2018). https://doi.org/10.4018/978-1-5225-3686-4.ch004 43. Doerr, M.: The CIDOC conceptual reference module: an ontological approach to semantic interoperability of metadata. AI Mag. 24, 75–92 (2003). https://doi.org/10.1609/aimag. v24i3.1720 44. Zheng, J., Wang, S., Li, D., Zhang, B.: Personalized recommendation based on hierarchical interest overlapping community. Inf. Sci. (Ny) 479, 55–75 (2019). https://doi.org/10.1016/j. ins.2018.11.054 45. Thakkar, P., Varma, K., Ukani, V., Mankad, S., Tanwar, S.: Combining user-based and item-based collaborative filtering using machine learning. In: Satapathy, S.C., Joshi, A. (eds.) Information and Communication Technology for Intelligent Systems. SIST, vol. 107, pp. 173–180. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1747-7_17 46. Nilashi, M., Ibrahim, O., Bagherifard, K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl. 92, 507–520 (2018). https://doi.org/10.1016/j.eswa.2017.09.058 47. Papneja, S., Sharma, K., Khilwani, N.: Context aware personalized content recommendation using ontology based spreading activation. Int. J. Inf. Technol. 10(2), 133–138 (2018). https://doi.org/10.1007/s41870-017-0052-5 48. Otebolaku, A.M., Andrade, M.T.: Context-aware personalization for mobile services. In: Encyclopedia of Information Science and Technology, Fourth Edition. pp. 6031–6042. IGI Global (2018). https://doi.org/10.4018/978-1-5225-2255-3.ch524 49. Smirnov, A., Shilov, N., Gusikhin, O.: Context-dependent guided tours: approach and technological framework. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds.) IITI’18 2018. AISC, vol. 874, pp. 43–50. Springer, Cham (2019). https://doi.org/ 10.1007/978-3-030-01818-4_4

Development of a Web Application of Facilitate Multidisciplinary Rehabilitation of Children with Cleft Lip and Palate O. V. Dudnik(&) , Ad. A. Mamedov , A. B. Maclennan , Y. O. Volkov , G. E. Odzhaggulieva , S.-M.A. Akhmetkhanov N. V. Gorlova , and Ma Guopei

,

Department of Pediatric Dentistry and Orthodontics, FSAEI of HE I.M. Sechenov First Mocow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia [email protected]

Abstract. In recent months the priority area of modern medicine has become on-line informatization and computerization. The most promising development has been the use of information and computer support in an integrated diagnostic system for patients with cleft lip and palate. In our research we aim to developed an autonomous web application that allows practitioners to determine the tactics of multidisciplinary diagnosis and treatment based on data integrated into a specific web application in order to increase the efficiency of the treatment of children with cleft lip and palate of different age groups. Based on more than 45 years of clinical and scientific experience in diagnosis and treatment of patients with cleft lip and palate we developed a web application “ADI” (Application of Digital Imaging). The developed web application “ADI” is a system for processing, accumulating and analyzing information on the rehabilitation of patients with cleft lip and palate by type of pathology and age, allowing doctors to obtain structured information about the stages of the necessary methods of interdisciplinary cleft lip and palate diagnosis and treatment. One of the main advantages of “ADI” web application is the ability to quickly exchange information between specialists in various fields of knowledge. Such complex information is the basis for combining scientific ideas, analysis and exchange of experience of various specialists, thereby making it possible to create a unified system of interdisciplinary rehabilitation of children with congenital pathology of the maxillofacial region #COMESYSO1120. Keywords: Web application
Information technology Dentistry
Orthodontic treatment
Surgical treatment


Cleft lip and palate


1 Introduction Considering the latest developments with the introduction of the new COVID-19 viral infection and development of a world pandemic, modern medicine have experienced an acute need for the development of new ways of communication and treatment protocols for patients with different medical problems. More than a thirty thousand children with © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 87–101, 2020. https://doi.org/10.1007/978-3-030-63322-6_7

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congenital and hereditary diseases are born annually in the Russian Federation. Of these, about 2500 children are born annually with cleft lip and palate in Russia [1, 2]. Cleft lip and palate (CLP) is one of the most common congenital malformations of the maxillofacial region. The number of children born with this pathology, on average, is from 1: 500 to 1: 1000 newborns. According to the results of monitoring in 2018, in the structure of all congenital malformations of CLP amounted to 16.2% and took first place among other malformations of the maxillofacial area [1–3]. According to the existing statistics children with congenital and hereditary pathologies in Russian Federation are being treated in specialized centers, such as the Moscow Center for Pediatric Maxillofacial Surgery (headed by Professor V.V. Roginsky), the Institute of Neurosurgery named after N.N. Burdenko (Doctor of Medical Sciences, L.A. Satanin) the Department of Surgical Dentistry and Oral and Maxillofacial Surgery, Moscow State Medical University A.I. Evdokimova (led by professor O.Z. Topolnitsky), the Department of Pediatric Dentistry and Orthodontics, First Moscow State Medical University Sechenov (headed by Professor Ad.A. Mamedov). Half of these patients are children with complex craniofacial diseases [1, 2, 4]. Children with CLP are in need of comprehensive treatment involving health professionals of various specialities: ultrasound - specialist, neonatologist, pediatrician, maxillofacial surgeon, orthodontist dentist, ENT specialist, speech therapist, geneticist and other related specialists [1, 3, 5–7]. One of the priorities modern medicine at present is the informatization and computerization of the country’s health care system on all levels. A modern computer system provides a health professional with a number of qualitatively new technical capabilities of material, registration, processing, archiving, analysis and display of useful information [8–10]. The most promising task is the use of information and computer support in an integrated diagnostic system for patients with CLP for its subsequent integration into a range of rehabilitation measures, including the participation of health professionals of vide range of specialities (psychologist, teacher, pediatrician, surgeon, orthodontist, speech therapist, etc.) [1, 3, 5]. This allows for an interdisciplinary structural phased approach in the methods of diagnosis and treatment planning, therefore, it enhances rehabilitation efficiency for patients with this congenital pathology in a specialized center. The use of computer technology allows us to ensure not only effective information exchange between specialists involved in the comprehensive rehabilitation process, but also on the basis of the joint efforts of doctors specializing in surgery, orthodontics, pediatrics, psychology, speech therapy, computer science and a number of others, it allows to develop and quickly implement the whole range of new comprehensive rehabilitation methods for children with CLP into practice [1]. All things considered, it seems relevant to develop a web application for an interdisciplinary approach in the diagnosis and treatment of children with different types of CLP of different age groups for computers and portable devices. This will allow doctors to choose the best types of diagnostic procedures and effective treatment methods at every stage of planning and treatment. All these aspects we consider to be the basis for this scientific research.

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The aim of the study was to create a web application that allows practitioners to determine the tactics of multidisciplinary diagnosis and treatment based on the data of the “web application” program which will increase the effectiveness of diagnosis and treatment of children with cleft lip and palate in different ages groups.

2 Materials and Methods Based on more than 45 years of clinical and scientific observation, treated by more than 6,000 patients with various forms of CLP, proposed by professor Mamedov Ad.A. and employees of the Department of Pediatric Dentistry and Orthodontics of the Institute of Dentistry. E.V. Borovsky First MGMU them. I.M. Sechenova’s “Interdisciplinary database for the diagnosis and treatment of children with congenital malformations of the maxillofacial region” was transformed, together with IT specialists, into the web application “ADI” (Application of Digital Imaging), which allows dentists and doctors of related specialties to determine the tactics of interdisciplinary diagnostics online, analysis and treatment of children with cleft lip and palate at different age periods. “ADI” web application was created using the standardized mark-up language for HTML documents, the formal language for describing the appearance of a CSS document, the programming languages PHP, Java Script and the MySQL database management system. To improve the security features of “ADI”, an SSL certificate has been activated [11–14]. The SSL certificate does not allow fraudsters to intercept or replace user personal data. A secure connection is established between the client’s browser and the web site. Using HTML and CSS allowed for adaptive layout, allowing to use “ADI” not only on desktop computers, but also on portable or mobile devices (see Fig. 1 a, b, c).

Fig. 1. Image of the main page of the “ADI” web application on a smartphone (a), a tablet (b) and a desktop computer (c).

PHP (English PHP: Hypertext Preprocessor - “PHP - hypertext preprocessor”) is a general-purpose programming language that is often used to develop web applications and web sites. PHP is currently supported by most hosting providers and is one of the

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leaders among the programming languages used to create dynamic web applications, such as “ADI”. JavaScript is a programming language that supports object-oriented, imperative, and functional styles. One of the key advantages of this software product is support by almost all well-known and most popular browsers [13, 14]. MySQL is a free database management system. MySQL is a solution for small to medium sized applications. It is part of the WAMP, AppServ, LAMP servers and the portable server assemblies Denver, XAMPP. Typically, MySQL is used as a server accessed by local or remote clients, but the distribution includes an internal server library that allows you to include MySQL in stand-alone programs [14]. Thus, the choice of PHP, JavaScript and MySQL for the development of the “ADI” web application is due to ease of use, stability, cross-platform compatibility, a promising opportunity with a large number of extensions. The “ADI” application contains information intended for medical professionals in specific medical areas; it doesn’t give access to information to persons who do not have a medical education. Based on this requirement, we decided to establish two levels of access to the application: for users and for administrator. In user mode, you can enter “ADI” and use its interface: quick access to the diagnosis and treatment plan for a specific pathology (on request), which are accompanied by a short text, pictures or video diagnostics and stages of the treatment plan. In administrative mode, it is possible to publish any information, add and/or edit materials. 2.1

Admin Panel

The admin panel is a WordPress console. The WordPress console is the most popular Content Management System in the world today [15]. More than 30% of web sites around the world are based on WordPress and this number is constantly growing [16]. A content management system is a web application that allows site owners, editors, authors to manage their sites and publish content without any programming knowledge. WordPress uses PHP and MySQL, they are supported by almost all hosting providers [15]. One of the features of WordPress is its intuitive and simple interface for creating content. WordPress Benefits: 1. Simple installation and update process - unlike many other content management systems, WordPress requires minimal configuration, it is easy to upgrade to the latest version. 2. Easy to manage - no programming knowledge is needed for everyday tasks such as writing and editing publications, downloading and editing images, managing users, adding menus, installing plugins and themes. 3. Individual design - the ability to easily create your own individual design. 4. Custom functions - the ability to use plugins to extend the standard WordPress features. In the address bar of the browser, after the address https://adi-program.ru/, type in admin. An authorization window appears on the screen, enter the administrator login and password in the appropriate fields and click the Login button.

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After authorization, get into the admin panel - the WordPress console. The upper right corner of the console displays a greeting from an authorized user. Also, in the upper right corner of the console there are buttons to go to the site, update the topic, and you can also quickly go on to add a record, media file, page, user (see Fig. 2).

Fig. 2. The window of the administrative panel.

The main window contains a quick draft. Instead of opening a full visual editor, it is possible to quickly record a headline and a few notes. WordPress will save them as a draft, then you can continue to work at any convenient time. On the left side of the panel is a menu that contains the following items: Home - brings you to the console home page. Materials - contains the submenu List, Add, Categories, Tags. Media - contains the submenu Library, Add new. Pages - contains the submenu All Pages, Add New. Users - contains a submenu All users, Add new, your profile. Minimize the menu - collapses the menu into a more compact view, by clicking on the button again, the menu expands to the normal view. We consider separately the functions and capabilities of each submenu. Materials menu, open the List. Here you can see all the entries (Materials). By clicking on the Published tab, the administrator can see all published entries by clicking on the Recycle Bin tab and deleted entries in the recycle bin. Hovering over each entry, it becomes possible to change it, see its properties, delete it, and also go to it to view it in user mode. It is possible to filter entries by date or by category. In the dropdown list of dates, select the one you need, then click the Filter button. Same must be done if one needs to filter by category. From the drop-down list, select the desired category, then click the Filter button. Materials menu, open Add. The window for adding a new record opens (see Fig. 3). In the Add Title field, the title is entered as a new entry.

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Fig. 3. The menu window “Materials”.

Button Add media file - this button allows you to add images, videos, sound files to the recording. Next, select the Upload Files tab. Then click on the button Select files. Select a file on the computer. Click on the button Insert to record. Add the text of the publication in the input field, above this field there is a small panel for editing text. In the Headings field, mark the headings in which we want to post the publication. Add tags to the Tags field. If needed a thumbnail for recording can be set by clicking Set image of recording. The button is located below the label block. Upload Files tab must be selected by clicking on button Select files. Then a file on the computer can be selected. Click Open, then click Set Record Image. The record is ready for publication, click Publish. This functionality provides the ability to create pending publications. Next to Publish text Change button can be used. Next, the desired date and time for the publication can also be selected. Publish selected material by clicking the Publish button. A new record on the ADI website is published or will be published automatically at the specified date and time. Materials menu, select Categories. The window for adding a heading opens (see Fig. 4). To add a new category. In the Name field, enter the name of the new section. We select the parent category in the Parent column drop-down list if it was supposed to create a sub-heading, or No if intending to only create a new heading. A short description can be edited in the Description field. On the right, the entire list of headings and subheadings is displayed. At the top are the Actions, Apply, page-bypage navigation buttons, the input field and the Search button for quick search by category. Pointing to each section, can be changed, its properties can be seen, it can be deleted, and also viewed in user mode. Materials menu, Tags can be selected. The window for adding a tag (Tag) opens. To add a new tag, in the Name field, the name of the new tag must be entered. A short description can also be added in the Description field.

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Fig. 4. Window “Headings”.

The entire list of tags is displayed on the right. At the top are the Actions, Apply, page-by-page navigation buttons, the input field and the Search button for quick search by tags. When choosing each label, it can be change, its properties can be seen, it can be deleted, and also viewed in user mode. Menu Media, select Library. The entire library of loaded media opens by selecting Library (see Fig. 5). These are all images, videos, sound files that were previously downloaded. Media files can be filtered by type and date of addition, for this, select the desired type in the drop-down list or/ and select the desired date, click the Multiple selection button. Using the buttons on the panel, the display of files can also be changes. It can be a table or large icons.

Fig. 5. Window “File Library”.

When opening each file, they can be edited, for example, changes can be made in the signature or the file can be deleted.

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Media files menu, Add media file can be selected. In the window for adding a media file, button Select files allows to select files on the computer by clicking on the Open button. Pages menu, All pages can be selected. The window displays a list of all pages of the “ADI” program (see Fig. 6). By clicking on the Published tab, the administrator can see all published pages. The Draft tab contains pages that have been created but not published. When hovering over each page, it becomes possible to change it, see its properties, delete it, and also go to it to view it in user mode.

Fig. 6. The “Pages” window of the “ADI” web application.

Pages menu, select Add New. The window for adding a new page opens. In the Add Title field, the title of the new page can be entered. Button Add media file. This button allows to add images, videos, sound files to the page. When the Upload Files tab is selected the option of clicking on the button Select files is presented. It allows to select file on the computer. To select that file the Add to Page button is used. In the input field, the text that will be placed on the new page is added. Above this field there is a small panel for editing text. If needed, the image for the page can be set by clicking Set page image. The button is located below the Publish block. Select the Upload Files tab. Click on the button Select files. Select a file on the computer. Click Open, then click Set page image. To publish the page, click the Publish button. As well as when creating records there is the possibility of delayed publication. Users menu, select All users. Displays a list of all site users. Here one can delete the user, change his rights, see his records. In the upper right corner there is a quick search. Thus, it is necessary to note the simplicity and ease in managing the admin panel. This aspect, for doctors, was one of the main, since it should be available to any doctor who knows how to work with a computer. No special programming knowledge is needed for the daily tasks of an administrator (doctor) such as writing and editing

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publications, downloading and editing images, managing users, adding menus, installing plugins, and so on. Thus, having developed the “ADI” web application, we get a system for processing and accumulating information and analyzing (theoretical and practical knowledge) diagnostics and treatment of patients with CLP by the type of pathology and according to the child’s age. 2.2

User Panel

The system has a common user account, the login and password for this account can be obtained from the application administrator. The initial page contains fields for entering the login, password and the Login button (see Fig. 7).

Fig. 7. The start page of the “ADI” application. Fields for entering login and password.

The username and password previously obtained from the administrator must be entered, then, the Login button clicked. Login data and password are stored in browser cookies for 7 days. After 7 days or after clearing the browser history, the user need to re-authorize. Then the user gets to the main page after login (see Fig. 8). In the upper right corner is the search panel. In the upper left corner is the application logo. When user clicks on the logo, it takes him to the Home page. Under the logo is a menu with the items Home, About Us, Our Team, Our Clinical Bases, Media. In the central part of the page is a classifier. The classification is carried out according to the type of pathology, for clarity, each pathology corresponds to an image. After clicking on the image of the pathology, a window for selecting the patient’s age appears (Fig. 9). Next, the user selects the desired age and receives help on the selected pathology. The certificate contains diagnostics, dynamic observation and treatment plan shown on a specific clinical case. By clicking on the heading or the Details button, doctors receive complete information on the necessary methods for multidisciplinary diagnosis and treatment of the chosen pathology and patient age (see Fig. 10).

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Fig. 8. The main page of the “ADI” application.

Fig. 9. Window for selecting the patient’s age.

Fig. 10. Window “Diagnosis and treatment of bilateral cleft lip and palate in newborn period.”

3 Results Based on our Interdisciplinary data base approach for the diagnosis and treatment of children with congenital malformations of the maxillofacial area, the program was designed in a way to give easy access to the a multidisciplinary team to standard protocols for treating children with CLP.

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This digital app was specifically divided by flowing age groups: prenatal period newborns (from 0–1 months) infants (from 1 month to 1 year) nursery group (1-3 years) preschool group (3–7 years) school age (7–18 years). In that section any member of multidisciplinary cleft team with is able to find the following protocol for prenatal management. For all pregnant women: ultrasound diagnostics (from 16 weeks). For risk groups: genetic counseling, ultrasound diagnostics (from 16 weeks). In identifying the pathology of the craniofacial region genetic counseling; medical and psychological support of the family by specialist specialists (psychologist, geneticist, craniofacial surgeon, neurosurgeon, orthodontist). If a combined pathology of the body is detected, consultations of specialized specialists. In the next age group “Newborn period” (0–1 months) examination and counseling by specialists - neonatologist, psychologist, geneticist, orthodontist, craniofacial surgeon, neurosurgeon, ENT specialist, audiologist. Social support of the family through social security agencies. For the infant group (from 1 month to 1 year) Orthodontic treatment: - orthodontic treatment using removable orthodontic appliances; - orthodontic treatment with the use of fixed maxillary distraction orthodontic appliances in order to prepare for the initial surgical intervention; surgical treatment: - primary cheiloplasty, chelorinoplasty, chelorhinoperiosteoplasty; - primary one-stage, two-stage uranoplasty - stage I - plastic within the soft palate - uvuloplasty, veloplasty; - primary elimination of oblique, lateral, transverse (macrostomy) and other forms of cleft faces; - congenital cleft palate (full, partial); - Pierre Robin Syndrome, Respiratory Obstruction Syndrome: Distraction Osteosynthesis. In the “Nursery group” (1–3 years) orthodontic treatment using various types of orthodontic techniques Speech therapy - speech therapy training (the formation of the psychomotor sphere as a pedagogical condition for preventing speech underdevelopment) according to the age of the child. Surgical treatment: - primary one-stage, twostage uranoplasty; - two-stage uranoplasty (stage II - plastic within the hard palate by various approaches); - rehabilitation of ENT organs; - cranioplasty; - reconstruction of the nasorbital region; - when combined with CLP - elimination of defects and deformities of the lower jaw in the syndrome of I – II gill arches (all types of osteoplastic reconstructions, distraction osteosynthesis). Consultations and supervision by specialists: - pediatrician, ENT specialist, audiologist, ophthalmologist, neuropathologist, psychologist, psychotherapist, teacher, pediatric dentist. In a “Preschool group” section (3–7 years) orthodontic treatment using various types of removable orthodontic equipment Speech therapy Surgical treatment: - reconstructive surgery of defects and deformations that arose after initial surgical interventions; reconstructive surgery of defects and deformations of soft tissues; speech-improving operations (velopharyngoplasty, pharyngoplasty; - prevention of hearing impairment (tympanostomy), hearing-enhancing operations; - distraction osteosynthesis in complex syndromes; - elimination of defects and deformities of the auricle; Consultations and observation by specialists: - pediatrician, otorhinolaryngologist, neurologist, ophthalmologist, psychologist, psychotherapist, teacher, pediatric dentist - oral sanitation. For “School age children with CLP (7–18 years) orthodontic treatment using various types of removable, fixed, orthodontic appliances. Speech therapy training. Surgical treatment: - reconstructive surgery of defects and deformations of soft tissues

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that arose after primary surgical interventions; - surgical removal of defects and deformations of the facial skeleton; - orthodontic and surgical repair of defects and deformations of the facial skeleton using orthodontic techniques and technology of distraction osteosynthesis. Consultations and supervision by specialists: pediatrician, otorhinolaryngologist, audiologist, ophthalmologist, neuropathologist, psychologist, psychotherapist, teacher, pediatric dentist - oral rehabilitation, orthopedic dentist (cosmetic prosthetics). Aesthetic plastic reconstructive surgery: the use of various approaches using reconstructive operations to eliminate the psychological discomfort of a patient with congenital malformation and anomaly in the development of the craniofacial area. The web-based application “ADI” (Application of Digital Imaging) has been developed, which allows dentists and doctors of related specialties to determine the tactics of multidisciplinary diagnosis, analysis and treatment of children with RHN online. Web application “ADI” allows doctors to obtain structured information about the stages of the necessary methods for interdisciplinary diagnosis and treatment of all types of CLP pathologies in children at different age periods.

4 Discussion In the context of the intensification of programs for creating a unified information and educational space, problems arise associated with the comprehensive CLP patients rehabilitation, severe speech impairment, caused by insufficiency of the palatopharyngeal ring (PPR) after previously performed uranoplasty, and other types of complications that narrow specialists profile such as: orthodontist, pediatric dentist, ENT doctor, pediatric psychologist and others [1, 7, 17]. Rehabilitation of patients with CLP is a poorly structured area that requires systematization of all available knowledge and experience into a specific system or algorithm for diagnosing and treatment [18]. Nowadays, traditional sources of knowledge contain poor or randomly structured medical information material. It should be emphasized once again that the rehabilitation of patients with CLP should be distinguished by an integrated approach to the necessary methods of diagnosis and treatment. Nonetheless, there are no unified recommendations regarding the tactics of diagnosing and treating children with different types of CLP at different age periods [2, 3, 5, 19]. The “Interdisciplinary diagnostics and treatment database for children with congenital malformations of the maxillofacial region” developed by us allows the multidisciplinary team of specialist to determine tactics of comprehensive diagnosis and treatment for children with CLP from birth to 18 years of age. In modern conditions, a comprehensive solution to treating children with CLP requires the summarizing and analysing large in volumes of information, timely and high-quality processing of which is impossible without the widespread use of an automized information space [11].

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However, to date, no program has been proposed that allows doctors to determine the tactics of comprehensive diagnosis and treatment of children with CLP at different age periods. Computer methods of data analysis in the field of comprehensive rehabilitation for patients with CLP and speech impairment are a highly demanding area for medical practitioners. Data analysis methods are implemented various types of computers as application packages. These packages include the well-known dispersion, correlation, regression, factor, discriminant and cluster analysis procedures, as well as other procedures of multivariate applied statistics [8, 20, 21]. The developed program “ADI” is a multi-platform web application with an accessible and clear interface. The choice of such an application is due to the ease of installation and maintenance, namely, special knowledge in the field of programming is not required for the administrative work of the doctor. To control the admin panel, doctor needs a workstation, a mobile or portable device that has an Internet browser installed and has an Internet connection. In modern medical practice there is certainly high demand for a program that allows us to describe new studies in certain medical fields, results of each type of examination and the ability to assess the patient at every stage of rehabilitation; with the possibility of building a graph during the examination and dynamic observation, creating special “windows” with a multimedia image (for example, a diagnosis at birth, a diagnosis during the examination) [8, 10]. The developed web application “ADI” is a system for processing, accumulating and analyzing rehabilitation information for patients with CLP by type of pathology and age, allowing doctors to obtain structured information about the stages of the necessary methods for interdisciplinary diagnosis and treatment of all types of CLP in different age groups. One of the main advantages of creating an “ADI” web application is the ability to quickly exchange information between specialists in various fields of knowledge. Such complex information is the basis for combining scientific ideas and exchanging experiences of various specialists, thereby making it possible to create a unified system of interdisciplinary rehabilitation of children with congenital pathology of the maxillofacial region. The “ADI” web application allows not only the effective information exchange of accumulated knowledge between specialists involved in the comprehensive rehabilitation of patients with this pathology, but also based on the joint efforts of doctors specializing in surgery, orthodontics, psychology, speech therapy, computer science and a number of others - to develop and quickly implement the whole range of new methods of rehabilitation of children with CLP. The creation of the “ADI” web application is a combination of creative thoughts, theoretical knowledge and practical skills of specialists involved in the treatment of children with CLP, which enables specialists to provide early highly qualified, specialized, interdisciplinary medical care, which in turn leads to early medical and psychological, pedagogical and social rehabilitation of patients with this pathology.

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5 Conclusion Therefore, our suggested algorithm for diagnosis, treatment and comprehensive rehabilitation of patients with cleft lip and palate, the development and implementation of a digital program (web application) will make it possible to “lay” the basis of the program may provide optimal treatment results for children, taking into account the competence of each specialist in a compact and informative form.

References 1. Mamedov, Ad.A., Maclennan, A.B., Admakin, O.I., Morozova, N.S., Mazurina, L.A.: Multidiciplinary approach to cleft lip and palate treatment of children in a new born period. In: 59th Annual meeting of the Japanese Teratological Society; 13th World congress of the International Cleft Lip and Palate Foundation, p. 124, Japan (2019) 2. Mamedov, A.A., Morozova, N.S., Maklennan, A.B., Volkov, U.O., Mazurina, L.A., Stebeleva, U.V.: Surgical treatment protocol of early surgical reconstruction in patients with cleft lip and palate in newborn period. In: Scientific Program Ist International Scientific Congress of Azerbaijan Society of Oral and Maxillofacial Surgeons, p. 57, Azerbaijan (2019) 3. Arsenina, O.I., Malashenkova, E.I., Paschenko, S.A.: Algorithm of orthodontic treatment of patients with congenital cleft lip, palate and alveolar process before and after bone autoplasty. Dentistry 5, 62–65 (2017). https://doi.org/10.17116/stomat201796562-65 4. Mamedov, Ad.A.: Rendering specialized assistance to children with congenital cleft lip and palate in modern conditions of healthcare development. In: Materials of the Scientific and Practical Conference of Dentists and Maxillofacial Surgeons of the Central Federal District of the Russian Federation with International Participation “Technologies of the XXI Century in Dentistry and Maxillofacial Surgery”, pp. 224–229, Russia (2008) 5. Bimbas, E.S., Blokhina, S.I., Menshikova, E.V., Yershova, O.Y.: Application of modern orthodontic and surgical technologies in complex rehabilitation of children with congenital cleft of the upper lip, alveolar process and palate. The Problem of Dentistry 17(4), 71–76 (2018) 6. Eriguchi, M., Watanabe, A., Suga, K., Nakano, Y., Sakamoto, T., Sueishi, K., et al.: Growth of palate in unilateral cleft lip and palate patients undergoing two-stage palatoplasty and orthodontic treatment. Bull Tokyo Dent. Coll. 59(3), 183–191 (2018). https://doi.org/10. 2209/tdcpublication.2017-0014 7. Stock, N.M., Ridley, M., Guest, E.: Teachers’ Perspectives on the impact of the Cleft lip and/or palate during the school years. The Cleft palate-craniofacial journal: official publication of the American Cleft Palate-Craniofacial Association 56(2), 204–209 (2019). https://doi.org/10.1177/1055665618770191 8. Gusev, A.V., Zarubina, T.V.: Support for medical decision making in medical information systems of a medical organization. Doctor Inf. Technol. 2, 60–72 (2017) 9. Belyshev, D.V., Kallistov, D.Y., Mikheev, A.E., Romanov, A.I., Khatkevich, M.I.: Information system of medical rehabilitation in the digital ecosystem of medical care. Doctor Inf. Technol. 5, 34–45 (2018) 10. Belyshev, D.V., Guliev, Y.I., Mikheev, A.E.: Digital ecosystem of medical care. Doctor Inf. Technol. 5, 4–17 (2018) 11. Date, C.J.: An Introduction to database systems. 8th edn. Williams, M., Moscow (2006)

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12. Bryan, P., Steven, M.S., Chuck White, Bill, K.: HTML, XHTML and CSS Bible. 3rd edn. Dialectics, M., Moscow (2007) 13. Flanagan, D.: JavaScript. Pocket Reference. 6th edn. SPb: The Plus Symbol, St. Petersburg (2012) 14. Nixon, R.: Learning PHP, MySQL, JavaScript, and CSS, 5th edn. SPb Piter, St. Petersburg (2019) 15. Sergeev, A.N.: Creating websites based on WordPress. M.: “Lan”, Moscow (2015) 16. W3TechS Homepage. https://w3techs.com/technologies/overview/content_management Accessed 15 May 2020 17. Recaman, M.: Cleft lip and palate. Plastic Surg. 43(4), 442–510 (2006) 18. Freitas, J.A.S., Garib, D.G., Oliveira, M., et al.: Rehabilitative treatment of cleft lip and palate: experience of the hospital for rehabilitation of craniofacial anomalies-usp-part 2: pediatric dentistry and orthodontics. J. Appl. Oral Sci. 20, 268–281 (2012). https://doi.org/ 10.1590/S1678-77572012000200024 19. Costa, B., et al.: Parent’s experiences of diagnostics and care following the birth of a child with cleft lip and palate. British J. Midwifery 27(3), 151–160 (2019) 20. Girod, S., Tescher, M., Schrell, U.: Computer-aided 3D simulation and prediction of craniofacial surgery: a new approach. J. Cranio-maxillofacial Surg. 29(3), 156–158 (2001). https://doi.org/10.1054/jcms.2000.0203 21. Gusev, A.V., Pliss, M.A., Levin, M.B., Novitsky, R.E.: Trends and forecasts of medical information systems development in Russia. Doctor Inf. Technol. 2, 38–49 (2019)

Quality Assessment Method for GAN Based on Modified Metrics Inception Score and Fréchet Inception Distance Artem Obukhov(&)

and Mikhail Krasnyanskiy

Tambov State Technical University, 392000 Tambov, Russian Federation [email protected]

Abstract. The article examines the problem of quality assessment for generative adversarial networks (GANs). There is no unified and universal metric to compare and evaluate GAN. Well-known approaches for the GAN quality assessment are focused on images generating neural networks. This paper considers the problem of the quality determination of arbitrary GAN operating with various data sets. For problem solution, a quality assessment method of arbitrary GAN is proposed, which differs by the modification of the calculation formulas Inception Score and Fréchet Inception Distance. The included changes allow the use of these metrics to assess and compare arbitrary GANs. The developed method was tested during experiments on the objects generation from marked (MNIST) and unmarked (Human Activity Recognition Using Smartphones and Epileptic Seizure Recognition) datasets. The obtained results confirm the possibility of applying the modified metrics Inception Score and Fréchet Inception Distance to assess the quality of arbitrary GANs. Keywords: Neural networks
Generative adversarial networks Score
Fréchet Inception Distance
GAN assessment


Inception

1 Introduction Machine learning technologies and neural networks open the new possibilities for the information analysis, processing and generation. One of the quickly developing areas is the development and application of generative adversarial networks (GAN). The principle of their work is based on “competition” between the generator, forming some objects, and the discriminator that evaluates the generated objects for correspondence with the given properties or classes [1]. Thus, for example, the following problems are solved such as improvement of images and video quality [2], photorealistic images generation [3], three-dimensional models obtaining from two-dimensional projections [4], image processing in the medical industry [5] and physics [6]. The range of problems solved with the help of GAN is constantly expanding. However, it is necessary to note a significant drawback of GAN. They are extremely difficult to train. Since in the training process there is a “competition” of two neural networks, the problem stops to be a search for the extremum of the loss function (for example, MSE), but turns into a search for a saddle point [7, 8]. Also, if during the © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 102–114, 2020. https://doi.org/10.1007/978-3-030-63322-6_8

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training process the discriminator becomes too effective, it will return values very close to 0 or 1, so the generator will have difficulty reading the gradient. On the contrary, if the generator exceeds the discriminator, it will constantly use the disadvantages of the discriminator and generate incorrect output. In order to eliminate this problem, significant work was done to increase the sustainability of GAN learning process [9, 10]. One of the topical problems in the implementation and application of GAN is the problem of quality assessment of the generated data, comparison of various GAN architectures. One of the first ways to solve it is an expert assessment of the generated GAN objects. This is a person, having experience and intelligence, who can really evaluate how much the generated objects are similar to real ones. On the other hand, this approach consumes a lot of time, cannot be applied automatically in the training process, is subjective and completely depends on the expert’s qualifications. Therefore, the new methods for GAN quality assessment are constantly appearing. A comparative analysis of 24 quantitative and 5 qualitative metrics was performed in paper [11]. Despite so many different approaches, there is currently no consensus about which metric reflects best the quality of GAN models and should be used for objective comparison of models. It is necessary to note that the central attention in GAN and their assessment is paid to quality measuring of the generated images [12], but the capabilities of GAN are not limited only to the generation and transformation of images. Their application is possible for the formation and processing of various information objects. Therefore, the following scientific problem is posed in this study: analysis of existing methods for assessing the quality of GAN and their modification to use in arbitrary GAN that work with various data types and structures. The structure of the article includes an introduction with analysis of the state of the issue of application and evaluation of GAN. The following is an implementation of the two most common GAN metrics - Inception Score and Fréchet Inception Distance. The main part of the article presents a method for arbitrary GAN assessment based on modification of these metrics. The proposed method was tested in experimental studies. The article concludes with a discussion of the results and conclusions.

2 Methods 2.1

Analysis of Assessment Implementation for Inception Score and Fréchet Inception Distance

The Inception Score (IS), proposed in paper [13], is one of the ways to objectively evaluate the quality of the generated images. Therefore, this metric is also applicable for objective and automatic assessment of GAN quality. IS metric, as shown by numerous experiments, correlates well with subjective assessments of experts. IS is based on the use of the previously trained model of the deep learning neural network Inception v3 [14]. This model is applied to classify the generated images, after which the probability of the images belonging to each of 1000 classes is estimated. Forecasts are summarized and form the final IS score. The initial assessment takes values from 1 to N, where N is the number of classes. A high-quality GAN must

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generate images x, that belong to a particular class y with a high conditional probability pðyjxÞ. Under these requirements, the total probability of all images will have low entropy. Also, GAN must provide a variety of generated images, and not just correspondence to their specified class. In order to fulfill this condition, the limit integral must have high entropy: Z pðyjx ¼ GAN ðzÞÞdz:

ð1Þ

We get two key conditions that ensure GAN high quality. In order to combine them, IS metric developers propose calculating the Kullback–Leibler divergence [15] KLðCjjM Þ where C is the conditional distribution, M is the limit distribution. The calculation of the initial assessment for the set of generated images includes the determination of the conditional probability for each image pðyjxÞ. Next, the critic probability is calculated as the average value of the conditional probabilities for the images in the group pðyÞ. Then, for the calculation of IS for each image x, it is necessary to calculate KLx by the following formula: KLx ¼ pðyjxÞ
ðlogðpðyjxÞÞ  logðpð yÞÞÞ:

ð2Þ

Then KLx is summarized over all images and averaged over all classes N. The obtained average value KLi is used to obtain the final assessment: N P

IS ¼

eKLi

i¼1

N

:

ð3Þ

IS metric is currently used to assess the GAN quality, including the automatic determination of their structure, in which it is necessary to adjust the parameters and number of layers without using a subjective expert assessment [16]. Besides IS, Fréchet Inception Distance (FID) metric [17] is very popular, which represents a further development of the idea of an objective assessment of GAN. The idea of FID metric is to compare the characteristics of the generated and real images. FID also uses the trained Inception v3 network with the exception of the last layer, which allows using not specific class marks, but specific features of images obtained from the values of the activation functions of the penultimate layer of the model. For real and generated images, a multidimensional normal distribution is calculated based on the average value and covariance of activations of the penultimate layer. The distance between the two distributions is defined as FID. A low FID value corresponds to high quality images and vice versa. The addition of noise and image distortion increases FID, which confirms the correlation between FID value and image quality. We consider the classic FID implementation. The loaded sets of real and generated images are processed by Inception v3 neural network with the removed last layer, which gives a features vector a of 2048 output values of the global spatial pool layer activation functions. Then FID is determined by the following formula:

Quality Assessment Method for GAN Based on Modified Metrics

FID ¼ jjmu1  mu2 jj2 þ TrðC1 þ C2  2
2048 P

mui ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi C1
C2 Þ;

105

ð4Þ

aij

j¼1

2048

; Ci ¼ covðai Þ; i ¼ 1; 2;

ð5Þ

where mu1 , mu2 - arithmetic mean of the values of feature vectors (activations) for real a1 and generated images a2 , determined by the formula (5); C1 , C2 - covariance matrices of values of feature vectors for real a1 and generated a2 images, determined by the formula (5); jjmu1  mu2 jj2 - difference of the sum of squares between the means of two vectors. Tr - trace operation (the sum of the elements of the main diagonal). In accordance with formulas (1)–(4), the quality assessment methods of generated GAN images in various libraries (Keras, Tensorflow, Pytorch, etc.) were successfully implemented [18]. However, in all methods only color images are analyzed, since Inception V3 network is trained on them. This network is applied in IS and FID calculating. In addition, a number of researchers, for example [19], note that IS is not perfect enough and has a number of significant disadvantages, which confirms the relevance of the problem of further research and improvement of these metrics, increasing their objectivity, finding new approaches to their application to solve problems of the quality assessment of neural networks. 2.2

Method for Assessing the Quality of Arbitrary GANs Based on Modification of IS and FID Metrics

The analysis showed that a significant limit of IS and FID metrics application for GAN assessment is their focus solely on image analysis. For black-and-white images and color images of different size, the use of IS and FID is possible due to the transformation of the source data. However, such transformations show distortions into the data structure when changing the number of color channels and scaling The application of IS and FID is impossible for objects represented by vectors of integral or real values of arbitrary size. Inception V3 neural network, used to calculate IS and FID, does not support input data of arbitrary formats and is focused on image analysis from 75  75 to 299  299 pixels in size. Even its modification with the addition of more layers at the input will not allow you to get the correct result because in Inception V3 structure there is a large number of convolution layers Convolution, MaxPooling and others that will not be found in the initial data of those signs that can be detected in the images. Therefore, such network cannot form a correct output feature vector. The classification of data using such a network will also not be optimal. Then the following method for the quality assessment of arbitrary GAN is proposed, which differs by using neural networks (decoder and classifier) for calculating IS and FID with specified parameters and architecture instead of Inception V3. We formalize the main stages of the method.

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The vector of initial data X ¼ ðx1 ; . . .; xn Þ of arbitrary size XN is given, for which it is necessary to obtain an output vector Y ¼ ðy1 ; . . .; yn Þ of the same size. In order to solve this problem, you need to select a structure and train the GAN NNGAN that implements the mapping X ! Y, and if the initial data belongs to a certain class (X 2 Z1 ), then the output data also belongs to it (Y 2 Z1 ). In order to obtain the optimal result of problem solving, it is necessary to achieve such structure of a neural network in which the metrics of its quality reach the best values: ISðNNGAN Þ ! max; FIDðNNGAN Þ ! min; LOSSðNNGAN Þ ! min;

ð6Þ

where IS- IS metrics calculation for any network NNGAN based on the formula (3); FID - FID metrics calculation for any network NNGAN based on the formula (4); LOSS - loss function calculation for any network NNGAN , defined as binary entropy. We introduce the following modifications, which will allow us to use IS and FID assessments for any initial data. 1. The conversion from Inception V3 network to an arbitrary classifier for IS calculation when defined pðyjxÞ in formulas (1) and (2). Two options are possible: - For real data, there are class marks, total N classes. Then the neural network NNC is trained, which implements the classification through display X ! fyk jk ¼ 1::Ng. The type of network depends on the initial data. For images, a sequence of convolutional layers is used, dense multilayer networks are used for arbitrary vectors with numerical values, and recurrent networks are used for classification of time series or tokens. - For real data, there are no class marks, or data from one class (N ¼ 1) is presented. Then an arbitrary number of categories (clusters) N [ 1 is set, and NNC clustering algorithm is trained based on K-means, which implements the display X ! fyk jk ¼ 1::Ng. 2. The conversion from Inception V3 network to an arbitrary auto-encoder NNAE to calculate FID determining the values of the feature vectors a1 and a2 for both real and generated data, respectively. The auto encoder NNAE performs the following conversion: X ! HV ! X, where HV is the hidden layer between the decoder and the encoder with length H. Then, in the formula (5), the feature vectors a1 and a2 will be replaced by the vectors corresponding to the real and generated data HV1 and HV2 : H P

mui ¼

HVi

j¼1

H

; Ci ¼ covðHVi Þ; i ¼ 1; 2:

ð7Þ

Thus, these modifications within the considered method of the quality assessment of GAN will allow the use of IS and FID metrics for arbitrary data sets regardless of their structure and type.

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3 Results In order to verify the adequacy of the proposed method, a number of studies were performed on two types of data. In the first case, a marked set of image data MNIST is generated. In the second experiment, unmarked datasets are examined: - Human Activity Recognition Using Smartphones (HAR): 7767 records with 561 values, contains information about human activities using the data of smartphones [20]; - Epileptic Seizure Recognition (ESR): 11500 records with 179 values, contains information about the detection of epileptic seizures [21]. The experimental scheme is organized in a following way. An acceptable GAN structure is selected, after which the dependence of the modified IS/FID metrics on the training time (number of epochs) is studied. Additionally, the dependence of the metric values on the selected parameters H and N is checked. 3.1

Experiment on Marked Data

During the first experiment, a multilayer convolutional GAN was built using the layers Conv1D, Conv2D and BatchNormalization to normalize the intermediate values of activation. Since the set of MNIST is marked in 10 classes, then N ¼ 10. We will take H ¼ 100 as part of this experiment, having received a mapping of 768-pixel values into 100 attributes. GAN training is carried out over 15 eras on a set of 50,000 training images. The results are presented in Fig. 1.

Fig. 1. GAN training process on dataset MNIST.

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The graphs of the modified IS and FID metrics adequately reflect the progress in GAN training (Fig. 2): from the initially fuzzy image with noise (at IS = 7.25, FID = 3.27), a fairly high-quality result was obtained (IS = 8.52, FID = 1.36).

Fig. 2. Generated objects of dataset MNIST (1 and 15 epochs).

Within this experiment, the dependence of FID value was also studied (for which an arbitrary size of the feature vector H was chosen). By varying the values of H in the range from 5 to 800, rather contradictory results were obtained (Fig. 3).

Fig. 3. Dependence of FID value from length H.

The analysis showed that FID value is directly proportional to the length of the feature vector H. This does not allow us to accept this assessment as absolutely objective. Indeed, changing H, we will always get different metric values, which will not allow us to correctly compare different GAN structures.

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We make the following assumption: in order to obtain a more objective assessment of FID, we normalize it with respect to the length of the vector H. Having performed this operation, the following dependence is obtained (Fig. 4).

Fig. 4. Dependence of normalized value FID from length H.

From graph (Fig. 4) it was found that the smallest spread in FID assessment is in the range H  ¼ ½90; 250. This confirms the dispersion values for this area: D ¼ 3:46e  06. Whereas for the remaining areas the dispersion varies from 3:77e  06 to 1:47e  05, the total disperse of the entire roe is D ¼ 2:30e  05. Thus, choosing any FID value at H 2 H  and normalizing it with respect to length H, we obtain a more stable and objective GAN assessment. 3.2

Experiment on Unmarked Data

The second experiment involves the generation of numerical data based on unmarked datasets. Therefore, in accordance with the proposed method, it is necessary to cluster the initial data into N categories. First, we consider GAN training and assessment for HAR dataset. Experimental studies showed a high error in the estimates of IS and FID for GAN when generating unmarked data of arbitrary size. For IS, a series of experiments was conducted with a different number N of categories: from 10 to 1500 (Fig. 5). GAN training process was carried out during 1000 epochs, IS measurement was carried out every 10 epochs at various N. This allowed us to avoid the error which is possible with repeated training of GAN. The obtained results led to the following conclusions: when N  25 the learning process is not evaluated correctly. IS in these ranges either fluctuates in the same value, or falls, which does not allow its use in GAN quality assessing. On the other hand, if

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Fig. 5. IS benchmarks with various N at GAN training for dataset HAR.

N  25 the learning process is correctly reflected: with an increase in the number of iterations, IS assessment grows along with a decrease in the errors of discriminator and generator. Moreover, the correctness of IS estimate does not fall even with N [ 1500. However, we note that with N [ 500 an increase in the value of IS is very insignificant. This can be explained by the excess of clusters having small differences among themselves. The excess of clusters also leads to an increase in computational load. Thus, the best results are obtained with N 500. We put forward the following hypothesis: IS for an arbitrary unallocated data set X ¼ fxi g, where each xi is represented by XN elements, is determined based on the distribution of data into N clusters, where N is selected based on the condition: 1þ

XN  N  1 þ XN: 20

ð8Þ

The obtained results satisfy this condition. The left and right sides of the double inequality (8) are obtained on the basis of the analysis of experimental data (Fig. 5). Moreover, the right side of the inequality is defined by a decrease in the computational load and does not affect the accuracy of IS determining. In order to check the hypothesis of N value, an experiment was additionally conducted on ESR data set (11500 records with 179 values). Based on condition (8), the number of clusters for unmarked data should be in the interval 10  N  180. The

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experimental results (Fig. 6) confirm the considered hypothesis. When N\10 the value of the assessment of IS drops significantly. The optimal value is achieved at N ! 180.

Fig. 6. IS measurements at different N when GAN training for ESR dataset.

To sum up the calculation of IS for arbitrary data, the size N for marked data corresponds to the number of classes, for unmarked data it is selected based on condition (8). Next, we consider the possibility of using FID to assess the GAN quality for arbitrary data sets. When calculating IS on HAR and ESR datasets, FID was also calculated in parallel for various H. The final results are presented in Fig. 7 for both datasets. The first experiment showed that FID estimate is proportional to H value, in this case, the same pattern is observed. The tests showed that for arbitrary data in accordance with modifications (7), FID calculation is possible, however, its values do not allow characterizing the GAN quality. In some cases, FID value increased with the number of epochs, which did not correspond to the learning process. For both datasets, the change in FID at the end of training relative to its beginning was no more than 12%, on average from 6 to 10%. For comparison, the FID was reduced by almost 60% in the first experiment.

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Fig. 7. FID measurements at various N (GAN training on the HAR and ESR datasets).

4 Discussion The proposed method for GAN quality assessment for arbitrary data was successfully tested during three experiments on MNIST, HAR, and ESR datasets. Modified IS and FID metrics were used for each experiment. Acceptable results were obtained during the first experiment on the marked MNIST dataset. During GAN training, the quality of the generated samples is greatly improved. The behavior of IS and FID metrics corresponds to planned in the stated problem (6): IS increases with increasing quality of samples, and FID decreases. The following regularity was found for FID: an increase in the length of the feature vector H proportionally increases the value of FID metric; therefore, its regulation is recommended. The second and third experiments showed that for unlabeled data, the choice of the number of clusters N should be carried out in accordance with condition (8). This allows IS metric to match the progress in GAN training. It should also be noted that condition (8) sets an optional restriction on the maximum value N, after which an increase in the number of clusters is impractical from the point of growth of the calculative load. It is recommended that the number of selected clusters approximately equal to the size of the input data vector XN. FID metric, despite the introduction of the necessary modifications (7), shows itself ineffectively on unmarked data, is not precise and objective enough. The use of FID as an objective function in GAN optimization is also considered impractical. This is due to the high dispersion of the obtained FID values in comparison with the first experiment, as well as a little improvement in FID values in the learning process.

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5 Conclusion The paper considers the problem of GAN quality assessment for arbitrary data sets. Currently, such popular metrics as IS and FID are applied only for the generated images quality assessment. In this study, we developed a method for arbitrary GAN assessment based on modifications of IS and FID metrics, which allowed us to apply them to various data sets, not just images. In the course of experimental studies, the effectiveness of the method for marked data was established. The modified IS metric can also be applied to evaluate GAN quality for unmarked data. FID metric needs to be improved and refined, which will be the subject of further study. The obtained modified IS and FID assessments can be used to compare the arbitrary GANs quality. This work was supported by the Ministry of Science and Higher Education of the Russian Federation within the grant of the President of the Russian Federation, MK74.2020.9

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On Systematics of the Information Security of Software Supply Chains Alexander Barabanov1 , Alexey Markov1(&) and Valentin Tsirlov2 1

,

Bauman Moscow State Technical University, 105005 Moscow, Russia [email protected] 2 NPO Echelon, JSC, 107023 Moscow, Russia

Abstract. This paper presents the systematization results of measures used to protect information systems against threats of attacks on software supply chains and, particularly, attacks related to an unauthorized impact on the open-source libraries/components used to develop software. The paper analyzes terminology related to software supply chains security and formulates the main properties of the notions “supply chain” and “supply chain attack”. The existing models of threats associated with software supply chain attacks are analyzed. Potential attack vectors are identified and analyzed for the attacks attributed to the unauthorized impact on open-source libraries/components used to develop the software. Limitations are identified in the models of threats associated with software supply chain attacks. Measures required for protection against threats associated with attacks on software supply chains are identified and classified based on the analysis of available regulatory documents and guidelines pertaining to the supply chain security, and best practices of secure software development. Classification of protective measures is provided for threats associated with an unauthorized impact on the open-source libraries/components used to develop software. Classification criteria were suggested, namely, security mechanisms, information protection methods, software development life cycle (according to ISO/IEC 12207). The conclusion has been drawn on the need to develop international legislative regulations and legal norms pertaining to the information security of software supply chains, and possible areas of improvement were described #COMESYSO1120. Keywords: Supply chain attacks
Information security
Software supply
Safe supply chain management
Security threats taxonomy
Systematics of information protection measures

1 Introduction The topicality of information security of software supply chains arises out of two phenomena: an objective increase in the number of borrowed components (modules, libraries) and the development of IT product distribution and logistics networks. Thus, current statistics demonstrates that:

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 115–129, 2020. https://doi.org/10.1007/978-3-030-63322-6_9

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• More than half of software development companies outsource software development [1]. • Over 70% of software developers use free software components [2]. • As is well known, the embedded (microprogram) software used in software and hardware complexes originates from multiple foreign countries (the countries of origin are often not clearly ascertained). As for the modern distribution and logistics systems, unfortunately, over the last ten years it has become common practice for the existing integrators, who depend on the hierarchy of third-party companies (contractors and subcontractors), to be unable to control the intermediate chains of security subsystems and their bottlenecks [3, 4]. Considering the end user’s current requirements for information and cyber security, these aspects introduce the problem related to new, yet poorly known types of the information security risks associated with [5]: • Appearance of different types of multiple mutual accesses to the information resources of the end user, integrators, multiple subcontractors and suppliers. • Software that has vulnerabilities and non-declared features of the borrowed components delivered to end users. • Delivery to end users of IT products and systems that have fragments of malicious logic introduced intentionally at the multiple poorly controlled stages of implementation and delivery. It should be noted that at present the number of supply chain attacks is steadily growing, especially the number of attacks on the lower hierarchical levels related to freelancers, as well as IoT attacks [6–9] and attacks on electric power facilities [10–12]. By the way, the importance of this topic is recognized at the highest international level in Resolution of UNO General Assembly A/RES/73/27 [13]. Table 1 shows the examples of popular supply chain attacks. It is remarkable that the ShadowHammer attack is also known for the fact that about 200 MAC-addresses of devices located in Russia were identified in the deciphered fragment of the code. One can remember that the well-known Stuxnet attack is also classified as a supply chain attack [14]. Table 1. Known software supply chain attacks. Name of attack Triada The Big Hack ShadowHammer

Brief description Implementation of malware during software installation on smartphones Insertion of an instrument bug in motherboards Malware distribution through ASUS Live Update utility. The malicious code was implemented during software compilation

Years 2016– 2019 2018 2018, 2019

The attacks related to third-party libraries/components from freeware repositories [15] used by the developers should be noted separately (Table 2).

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Table 2. Known software supply chain attacks related to freeware repositories. Name of attack Python Package Highjacking [16]

Brief description Attackers created a library named almost the same as a widely used library and uploaded it in a freeware repository. In addition to the standard functionality of the original library, the library included malicious code Malicious logic in npm The attacker was admitted (as a new developer who package Event-Stream [17] joined the development team) to the creation of a popular (over 2 million users) npm-package Event Stream that executes utilities for data stream operations. In the course of modification, the package was expanded with an embedded flatmap-stream library that contained malicious logic performing unauthorized acquisition of the information about the user’s Bitcoin wallet Insertion of malicious logic in The study [18] showed that the freeware repository RubyGems repository RubyGems includes 11 libraries with intentionally introduced malicious logic related to cryptocurrency mining using the users’ computational resources

Years 2017

2018

2019

It should be noted that the international scientific community is deeply concerned about this problem, which can be seen in a number of topic-related regulatory documents and reviews being published (for instance, [19–21]). The study of these issues is the subject of this paper.

2 Statement of the Research Problem The object of the research in this paper is software supply chains in the context of the information security. The subject of the research includes the elements of classification of the information security threats (which are related to potential computer attacks on software supply chains), as well as the administrative and technical measures required to protect the information from these threats. The aim of the research is to systematize the existing information security measures taken to prevent the threats associated with supply chain attacks and to formulate suggestions for their improvement. To this end, the following tasks were solved within the scope of the study: • Terminology analysis. • Review of the existing models of threats associated with software supply chain attacks, including the supply chain attacks related to free software repositories. • Review and systematization of the information security measures taken to prevent supply chain attacks including the software supply chain attacks related to free software repositories. • Development of recommendations on the improvement of appropriate information security measures.

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Definition of Supply Chain Terms

According to the definitions provided by MITRE, NIST SP 800-161, ISO 27036-1 and ISO 28000, a software supply chain generally means a system of its participants with an interconnected set of resources and processes involved in the life cycle of software movement from the developer to the end user, namely, design, development, manufacturing, supply, implementation, support of programs and associated services. The following key characteristics of a supply chain should be identified (see Fig. 1): • The goal of the supply chain creation is to deliver a software product or service to the end users (for example, on Platform-as-a-Service or Software-as-a-Service basis). • Existence of relationships (documented in an agreement) between different organizations (developers, logistic centers, distribution and assembly centers) which act as a supplier and/or a customer. • Existence of two material/service streams: the streams connected with the product creation using third-party components (Upstream) and streams associated with the product delivery to end users through the distribution network (Downstream). • In case of a multilink supply chain, which is spread most widely, the same company can act both as a customer (with regard to the lower level company) and as a supplier (with regard to the higher level company). • The user’s poor ability to monitor the quality of the delivered products and services across the entire supply chain in case of a multilink supply chain. In general, the “supplier-customer” relationship is focused on providing the customer with: • The software components the customer will use to form a product (a service or a system) which will be transferred to the end user or further along the supply chain. • The service the customer will use to form a product (a service or a system) which will be transferred to the end user or further along the supply chain.

Fig. 1. Typical structure of a software supply chain.

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Compromising malicious attacks on the processes and resources of supply chains in the computer environment are commonly referred to as computer supply chain attacks. Generally, the attacker mainly aims to: • Add (insert) non-claimed features, malware, malicious hardware (inserts, implants), or false (invalid) information to the supply chain objects. • Replace trusted elements (software, documentation, configuration files) with untrusted ones. • Modify the delivered objects in an unauthorized way. Speaking about attacks aimed to add (insert) non-claimed features and malicious logic to supply chain objects one should mention the widely spread type of attack that affects the open source libraries/components used to develop software products (see Fig. 2).

Fig. 2. Graph of software dependencies on libraries/components.

Software developers create their own components/libraries using free software components from various repositories (Maven, NPM) thus building direct and indirect dependencies of the software and its components on the components of such libraries. A software vulnerability existing in the dependent component renders the entire software product vulnerable. It should be noted that as a rule, these indirectly dependent vulnerable components are the most difficult to identify. The State of Open Source Security Report 2019 [22] study shows that about 80% of software vulnerabilities are concerned with the vulnerabilities in indirectly dependent components. 2.2

Overview of the Existing Models of Threats Related to Supply Chain Attacks

Several studies performed by international scientific institutes were devoted to the task of enumerating the threats related to supply chain attacks. The paper [21] provides the threat modeling results attributed or software supply chains for the US Department of Defense facilities. The list of typical threats was

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formed using the methods for analysis and systematization of the information related to real information security incidents occurred due to supply chain attacks. In addition to the systematized list (including 41 names), the paper provides a threat description method and a list of recommended counteractions aimed to neutralize the identified threats. The threats identified in the paper are related to software supply chain attacks or intentional malicious actions of the adversaries in the software development environment; the threats associated with unintentional actions of employees of the software development company were not considered. It should be noted that the Supply Chain category of the Common Attack Pattern Enumeration and Classification, CAPEC [23], contains the list of supply chain attacks developed based on the publication [21]. NIST, the US National Institute of Standards and Technology, develops lists of threats for the US state information system operators. Paper [24] includes the list of threats generally relevant for information systems. A separate group of five threats is connected with embedding software that contains vulnerabilities or non-claimed features into the information system as a result of supply chain attacks (see Table 3). It should be noted that the threat classification provided in the document does not take into account the peculiarities of the information systems used as the software development environment. Table 3. Vectors of software supply chain attacks. Attack vector

Brief description

Creation and utilization of decoy companies in order The attacker sets up decoy companies which to introduce malicious components to the supply simulate legitimate suppliers and are involved in the chain supply life cycle to compromise the information system components in the supply chain Introduction of pirate or counterfeited hardware to The attacker intercepts the hardware of legitimate the supply chain suppliers to perform illegal replacement or modification Implementation of counterfeited critical components The attacker uses an insider and/or the supply chain in the organization system to introduce illegal changes to critical components of the information systems Supply chain attacks aimed to use critical hardware, The intruder attacks the operating information software or embedded software systems by installing malware, embedded software and hardware that performs critical functions for the company Coordination of the cyber-attack considering the The attacker carries out continuous (iterative) external and internal (insider) capabilities and coordinated attacks using all three potential attack compromised supply chain vectors (external attacks, internal attacks, supplier attacks)

A separate area of NIST activities is enumeration of threats pertaining to the use of mobile devices in the information systems is [19]. NISTIR 8144 publication provides general information about the classification of such threats, describes the procedure for making a threat list used by NIST specialists, and suggests a pattern for threat description by various characteristics. The threat catalogue itself is available in NIST’s information system on the Web [25]. One of the threat categories includes threats posed

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to supply chains for the software of mobile devices and the mobile devices used in the information system (Supply Chain category). As such, the list of threats constituting this category is an adapted list of mobile threats defined in the paper [21] and describes 22 threats. The paper does not address any threats relating to unintentional actions of developers or suppliers of mobile device applications (for example, due to errors or incorrect application of secure software development practices). Some threats relating to the software development environment are described in development environment security targets [26] i.e. the documents the international certification system Common Criteria uses to evaluate the certified objects. As a rule, the list of threats provided in such documents is not structured and threats associated with unintentional actions of the software developers or suppliers are not considered. The analysis of the latest information security incidents allowed defining the vectors of attacks aimed at open source libraries/components [27] (see Table 4). Table 4. Vectors of attacks aimed at open source libraries/components. Attack vector

Brief description

Attacks induced by misprints in library names

For this type of attach, the attacker creates a library which contains a software insert and has a name that differs from the name of the standard library only by a few symbols (for instance, “epxress”). If the developer makes a misprint while importing an external library, the product becomes vulnerable The attack is based on the fact that internal and external components (which are available in external repositories) may have the same names. If the repository contains different versions of components, the software assembler will give preference to the later version and will use it to assemble the software. If the attacker can insert malicious software in the external component and assign the maximum version to this component, it will be used during the software assembly, and the software will contain the software insert The attacker obtains access privileges and can modify the library due to the faults of the access management system and hired developers check procedure The adversary searches for popular libraries/packages which are imported at the level of the program source code. If the resources that host the downloaded libraries have vulnerabilities and can be controlled by the attacker, the attacker may use the resources to place the versions of libraries with malicious logic The attacker can insert malicious logic in the libraries used to assemble software by: - Obtaining unauthorized access to the developer’s account; - Sending a request comprising malware for changing the source code, which is accepted due to organizational errors during the verification of change requests; - Installing software on developers’ laptops; - Exploiting vulnerabilities in the software assembly line (CI/CD processes)

Library masking

Transfer of the access right

Attacks caused by taking control of resources, i.e. library/component sources

Introduction of malicious logic into software components used for assembly

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Some Russian studies are worth mentioning. The information source Database of Data Security Threats is maintained by the Russian Federal Service for Technical and Export Control and contains a regularly updated (also based on the analysis of real incidents) classified list of threats the operators and developers of information systems use for simulation of information security threats. The threats of attacks on software supply chains are not explicitly described in the database. Russian standard GOST R 58412-2019 Information Protection. Secure Software Development. Software Development Life Cycle Threats [28, 29] contains a list and description of information security threats including those associated with the software developer infrastructure attacks. This list has the following peculiarities: • It explicitly shows the threats related to software development environment which may lead to the introduction of vulnerabilities into the software or disclosure of sensitive information. • It takes into account unintentional actions of software developers. • All threat models considered can have the following limitations [28]: • Since threats associated with software development do not typically arise out of physical phenomena, only anthropogenic threats are taken into account, while threats caused by natural disasters, natural phenomena and information leakage through technical channels are not considered. • As a rule, the lists of threats provided are not exhaustive and should be defined during threat identification for the specific software or information system development environment. 2.3

Overview of Threat Protection Measures Against Supply Chain Attacks

At present the supply chain security researches performed by some of the national and international standardization committees such as US NIST, UK NCSC and ISO can be found most easily. Thus, NIST publication SP 800-161 Supply Chain Risk Management Practices for Federal Information Systems and Organizations describes the approach to identification, assessment, selection and implementation of the information security risk management process relating to software supply chains [30]. The document mainly consists of: • Guidelines for implementation of the risk management process (harmonized with NIST SP 800-39 and NIST SP 800-30) relating to information security threats in software supply chains. • Information protection measures (according to NIST SP 800-53 notation) ensuring protection from the identified threats.

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The risk-oriented approach suggested in NIST SP 800-161 to assess the risks of threats relating to attacks on supply chains is defined by a set of phases, namely [31]: • Structure definition: requirements capture, determination of the scope of supply chain security measures. • Assessment: threat identification, information security risks assessment. • Development of countermeasures that neutralize the critical threats. • Monitoring aimed to define the effectiveness of implemented countermeasures. In 2014 and 2018, NIST [32] is interviewed the representatives of leading software development companies in order to identify and systematize the practices they used to protect supply chains and formulated the main principles in its publication NISTIR 8276 [33] (Table 5).

Table 5. Key practices according to NISTIR 8276 publication. Practice Integrate C-SCRM across the organization Establish a formal program to ensure supply chain security

Brief description

Use of Cyber Supply Chain Risk Management (C-SCRM) approach developed by NIST [30] Availability of a formal program ensures responsibility of the management for supply chain security and allows using formal management tools Know and manage your critical Critical suppliers are those whose errors may affect the business suppliers severely. Managing critical suppliers also means making a balanced decision on providing access to the company’s infrastructure and information for these suppliers Understand your supply chain The company should identify and describe in as much detail as possible the supply chains it uses. While doing so, it shall identify both direct and indirect suppliers involved in a multilink chain Closely collaborate with your key Close interaction with key suppliers will improve coordination suppliers and facilitate the supply chain management Include key suppliers in your resilience The company should take into account the (key) suppliers while and improvement activities implementing supply chain security practices, for instance, include suppliers in threat simulation processes, development of the fault recovery plan, audit of the delivered software source code Assess and monitor throughout supplier Evaluation of a potential supplier before signing an agreement, relationship establishment of a service level agreement, regular monitoring of current suppliers for compliance with information security requirements Plan for the full lifecycle Elaboration of a business continuity plan to be followed in case the supply chain threat is realized

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Technical committees of the United Kingdom develop regulatory and instructional documents relating to supply chain security intended both for commercial and state organizations. The paper Supply Chain Security Guidance [34] describes 12 supply chain protection principles suggested by NCSC (UK National Cyber Security Center) including the following: improvement of supply chain protection awareness, integration of information security measures in contractual obligations, development of supply chain incident response measures. The document of the Ministry of Defense of the United Kingdom, Defence Cyber Protection Partnership Cyber Security Model Industry Buyer and Supplier Guide [35], includes the instructions, which are based on the risk-oriented approach, the users and suppliers should follow to protect the supply chains and prevent disclosure of the information pertaining to the defensive potential of the United Kingdom. The international standards of ISO/IEC 27036 series include regulatory and instructional requirements and guidelines for information protection in supplier-customer relationships. Thus, ISO/IEC 27036-2 sets forth the top-level requirements for information security to be complied with when hiring subcontractors. The standard suggests using the risk-oriented approach to create the list of information security measures. ISO/IEC 27036-3 specifies the requirements to be followed when procuring services or software components from subcontractors. The standard describes the measures taken to protect the supply chains; the standard is harmonized with ISO/IEC 27001, ISO/IEC 15288 and ISO/IEC 12207 as the measures are defined considering the process of software and system development based on ISO/IEC 15288 and ISO/IEC 12207. It should be noted that along with the traditional information security measures, blockchain-based approaches to software developing have been developed recently [36–38]. The state of the Russian legislative and regulatory framework that addresses the issues of supply chain security should be described in a few words. For example, the doctrinal documents of the Russian Federation describe the technological threats from foreign countries but (for example, in contrast to the US National Cyber Security Strategy) they do not cover the issue of logistics chain security. Similar situation can be seen in the current versions of regulatory and legal acts devoted to the state information systems. The Russian standard GOST R 56939 includes the requirements for secure development, and the new provision on information security means certification tightens up the requirements for foreign products certification. Nevertheless, in an explicit form, the documents address the issues of supply chain security quite concisely [29]. In view of the above, based on the completed research, the authors can present the following classification of information security measures to the readers (see Fig. 3).

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Fig. 3. Systematization of information security measures to prevent supply chain attack threats.

The measures taken to protect supply chains from open source libraries/components attacks have been classified considering the software development process according to ISO/IEC 12207 (see Table 6). Table 6. Systematization of information security measures to prevent supply chain attacks aimed at open source libraries/components. Processes of software development life cycle Security measures to be used Analysis of software requirements - Use multifactor authentication for persons involved in setting software requirements - Set security requirements for third-party components/libraries used during the development Software architecture design - Use multifactor authentication for persons involved in the software architecture design - Document third-party components/libraries (including open source) used for software development (continued)

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Processes of software development life cycle Security measures to be used Software construction and complexation - Use multifactor authentication for software developers - Use digital (cryptographic) signature to sign any modification introduced into original code - Use a change tracking system that enables an audit of the events that relate to modification of the initial code and configuration files - Use a configuration management system that provides a unique marking of each software version delivered to the end user - Use instrumental tools to identify known vulnerabilities in components borrowed from third-party developers Software qualification test - Use multifactor authentication for persons involved in software tests - Test the developed software for security, including tests for any non-claimed features and malicious logic Program installation and support of software - Use multifactor authentication for persons acceptance and software troubleshooting in involved in software assembly and support the course of operation processes - Use instrumental tools to manage the software dependencies and to track their vulnerabilities - Use digital (cryptographic) signature to sign the supplied software distribution package - Test the software development infrastructure security

3 Conclusions and Discussion Universal use of software supply chains and the growing number of information security incidents relating to supply chain attacks call for the development of regulatory and instructional guidelines and approaches to software supply chain security (or adaptation of the existing guidelines and approaches). Within the scope of this paper the authors made an attempt to systematize the measures of information protection against threats associated with supply chain attacks. This attempt differs from those made by US NIST, UK NCSC and ISO, as it takes into account the measures of supply chain protection against open sources library/component attacks. On the international scale, the following improvements can be made: • Unification of laws pertaining to the fight against cybercrime that creates risks of attacks on supply chains. • Formulation of ethical norms for attacks on supply chains for military purposes.

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• Creation of data exchange protocols to exchange information about unfair software suppliers and supply chain attacks, and creation of national centers that serve to exchange information and security measures among the countries participating in military and political alliances, customs unions etc. [39]. • Creation of science-based methods and procedures for supply chain protection • Development of an international security certification system, including unification of certification test procedures, mutual recognition of certificates, rules of code disclosure etc. • Development of the international information security risk management system relating to supply chain attacks with the involvement of science and industry representatives. • Creation and updating of an international register of threats relating to supply chain attacks. • Creation and maintenance of a single data repository of bona fide software suppliers. • Implementation of actions aimed to raise companies’ awareness of threats to supply chains (holding thematic conferences, creating information resources, development of training programs [40]).

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Comprehensive Intelligent Information Security Management System (CIISMS) for Supply Networks: The Actor-Network Perspective Yury Iskanderov1(&)

and Mikhail Pautov2

1

2

The St. Petersburg Institute for Informatics and Automation of RAS, 39, 14-Th Line, St. Petersburg, Russia [email protected] Foscote Group, 23A Spetson St 102A, Mesa Geitonia 4000 Limassol, Cyprus [email protected]

Abstract. Actor-network theory based concept of spatialities was applied to the comprehensive intelligent information security management system composed of the three blocks: technical, psychological and legal. The incoming information security threats and the relevant system responses generated through the interaction of the system blocks were considered as enacting the three Law’s spaces: the space of regions, the space of networks and the space of fluids. The system stability in the space of networks is considered a precondition for its successful performance in the space of regions, and its resilience in the space of fluids gained through the dynamic knowledge formation helps overcome the adverse effects of the fluidity. We further reflect on the intentionality/unintentionality of the information security threats and reactivity/proactivity of the relevant system responses. The ANT-based topological approach presented in this paper in combination with other relevant methods has a potential of developing into an efficient theoretical tool for future research in the fields of information systems and information security. Keywords: Actor-network theory
Spatialities
Space of regions
Space of networks
Space of fluids
Comprehensive intelligent information security management system

1 Introduction In [1] we have suggested a 3-block model of comprehensive intelligent information security management system (CIISMS) for supply chains and networks. Here we talk of supply networks to escalate our consideration to a more generic level where a supply chain is seen as a special instance of a supply network. The suggested model is based on the three fundamental blocks (legal, technical and psychological) responsible for management of the pertinent data and information security risks across a supply network and allows for selection of a relevant method of challenging and eliminating the threats as soon as these threats enter the system (or proactively, wherever it is possible). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 130–142, 2020. https://doi.org/10.1007/978-3-030-63322-6_10

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Algorithms of assessment and analysis of the information security threats as well as the relevant protective measures are extracted from specific knowledge bases (KB) of individual actors (enterprises) interacting within a supply network. The suggested CIISMS for supply networks is considered an integrator for thoroughly designed special tools like the risk management program, DLP, SIEM, personnel profiling and others, and is sought to provide for the following [1, 15, 16]: – – – – – –

Determined steady sequence and speed of information security control operations; Reasonability and certitude of the final solutions; Smooth and confidential interactions between the actors within a supply network; Minimization of various resource expenditures needed for efficient interaction; Homogeneity and simplicity of the user interface; Prevention of possible conflicts between supply network actors.

The generalized architecture of CIISMS for supply networks is presented on Fig. 1. In this paper we suggest an extended actor-network theory (ANT) inspired outlook on the security of information processes in general, and on the comprehensive intelligent information security management system (CIISMS) for supply networks introduced in [1]. We use the approach focused on the topologies of Law’s spaces suggested in [2] as a theoretical framework for our 3-block model. In [2] the authors’ reflections revolve around “malware” understood to a certain extent as a hard- and software bound group of threats represented in the technical block of our model architecture [1]. However, their approach with no theoretical restrictions in regard to the legal or psychological aspects of information security can be applied to the entire 3-block CIISMS for supply networks suggested in [1]. Here we also refer to the discussion on the “dark side” of computing presented in [10], where the authors presume intentionality of cyber-threats. Similar vision is shared by the authors of [2] where cyber incidents are defined as “deliberate disruptions of normalized cyber-security practices by malware, leading to different effects on political imaginations and interventions” [2]. They highlight the deliberate elements to differentiate them from ‘failures’ and ‘accidents’ leaving those beyond consideration. Though we agree that the majority of information related threats are caused by intentional malicious behavior of humans or automated nonhuman actors initially programmed and launched by humans (that is why we have included the psychological block in the CIISMS and emphasize its significance), however for the sake of comprehensiveness of our model we cannot discriminate the cases where the threats are produced by unintentional/unconscious natural or technological (f)actors. We deem that the instances of unintentional (arbitrary) information tampering must inevitably be considered in the framework of the ANT-driven semiotic investigation of dependencies between the level/quality of protection of the texts moved across the network and the network situations caused by protected/unchanged texts on the one hand, and deliberately or arbitrarily changed texts on the other hand [3]. Even in [2] where the intentional character of cyber-threats is presupposed the ANT-based approach provides malware (understood as mediator) with the “agency” of its own, detached from the intent of the person(s) who wrote the code [2]. Understanding of intentionality/arbitrariness of information security threats leads us directly to investigation of reactivity/proactivity of relevant system responses. In [1] we partly agree with the authors of [2] who admit that “quasi all the immediate responses to malware are

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Fig. 1. Generalized architecture of a comprehensive intelligent information security management system (CIISMS) for supply chains and networks.

reactive: the malware’s performance always comes first” since the intent of the person causing the cyber-incident is almost impossible to know with enough certainty in advance [2]. However describing the psychological approach to information security in [1] we refer to [8] where the authors tackle the problem of proactive detection of the malicious behavior by suggesting a combination of the two research areas: (a) Structural Anomaly Detection (SA) extracting information from the social networks, messages, internet visits and other arrays of information and then defining notions of similarity between individuals, normal patterns and structural anomalies with the use of graph analysis, dynamic tracking and machine learning, and (b) Psychological Profiling (PP) allowing to create a dynamic psychological model from behavioral patterns through construction of psychological profiles and detection of psychological anomalies providing semantics for information network data [1]. The authors of [2] aim to understand the effects of information security threats on politics, in particular their role in shaping threat perceptions and relevant policy responses [2], which justifies the

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links between the Legal block and the other two blocks in our model and helps shed new theoretical light on these links. The fundamentals of the actor-network theory dealing with interaction of heterogeneous elements and formation and stabilization of alliances between them were discussed in our earlier work [3, 9]. Here we suggest a deeper insight into the actor-network approach as it is used in the information systems and cyber-security studies. NB. While working on this paper we came across multiple reports stating that unidentified hackers in an attempt to take advantage of the COVID-19 pandemic proliferation launched worldwide attacks on the supply networks through massive distribution of fraudulent messages asking to make payments to different bank accounts due to “administrative changes caused by the pandemic”. Many enterprises were affected. This situation clearly demonstrates how vulnerable the information systems become when global calamities challenge the world, and the “disembodied adversaries” take advantage of these vulnerabilities through the anonymity provided by information networks [2].

2 ANT as a Theoretical Framework for Information Systems and Cyber-Security Studies Actor-network theory (ANT) is increasingly used as an analytical framework in the information systems and cyber-security studies. According to Latour and Law, an information system must be viewed through the socio-technical lens [10] as a product of interactions of heterogeneous (human and nonhuman) actors. ANT thus plays a role of the theoretical integrator between the technical dimension focused primarily on the technology and technical solutions to benefit the information systems and cybersecurity concerns on the one hand, and the socio-behavioral dimension dealing with human behaviors that impact information systems [10] and their security on the other hand, which is essential for the ambitions of comprehensiveness of the CIISMS model considered in this study. Information systems researchers using an ANT approach normally concentrate their attention on the processes of network formation, investigating the human and nonhuman alliances and networks established by the actors involved [12]. They focus on the actor negotiations that allow a network to be configured by the enrolement of heterogeneous (human and nonhuman) allies, and consider parameters and characteristics of the built system only as “network effects resulting from association” [12]. In ANT (which is often referred to as “the theory that gives voice to technological artifacts” [11]) interactions and associations between actors are all equally important, and actors are understood as the sum of their interactions with other actors [12]. In the context of the information systems studies ANT has attracted attention as breaking the symbolic boundary between humans and information technologies addressing the problem in a way that IT and users were defined in their relational networks [11]. However, some authors claim that the potential of the theory in the information related research is still underestimated since it continues to be used as an “interpretative lens for the analysis of the effects of technology on relational networks and vice versa” rather than an ontology to inform information systems and cyber-security research [11].

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The ANT based method still lacks due formalization and its applied potential in various fields is not fully discovered. Therefore, we can foresee the trajectory of evolution of the actor-network approach from the descriptive theory created (and further revised) by its protagonists, through its formalization and integration with other relevant methods of information systems research, towards eventual conversion into a full-fledged applied tool for modelling and simulation of information systems [9].

3 Topology of CIISMS for Supply Networks in Law’s Spaces 3.1

Spatial Worldview in ANT

John Law sees the network of inter-object relations as a topological system, a “spatiality”. Spatiality establishes an order of objects perceived as “intersections” of relations. Objects as well as the spatialities enacted by them are subject to changes when their relations are changed. Based on this spatial vision Law has initiated a research program known as “social topology” [13], where the following three basic principles were postulated. (1) The formation of objects has spatial implications. The forms of spatiality (spaces) are multiple [14]. (2) Objects determine the spatial limits of their existence (i.e. conditions making possible their existence in a specific space). The spatialities are generated and enacted with the objects they contain. The forms of spatiality include regions, networks and fluids [14]. (3) The forms of spatiality and objects filling respective spaces are unconformable, there is a tension between them, that is to say, “they are Other to one another” [14]. In Law’s terms the constitution of objects is indistinguishable from the performance of the spatial relations [14]. The objects are measured in spaces they inhabit by their coordinates, trajectories, positions vis-à-vis other objects and distances from them, continuity of their “shapes” in these spaces. In the world of information security the spaces of regions, networks and fluids come with distinct metrics of order/disorder and, hence, distinct notions of threats and relevant system responses, based on the relations between the objects enacting the respective space [2]. At the same time, these three forms of spatiality are fundamentally intertwined: while any of the three spaces is positioned as the “other” vis-à-vis the other two, it is inextricably linked to them [2]. Adopting the approach used in [2] we consider the information security threats and respective system responses (where the three blocks of CIISMS are triggered) as circulating within the spaces of regions, networks and fluids thereby co-creating them [2]. Based on the concept of agency postulated in ANT the information security should be understood both as a process and as an outcome of the topologies through which the threats to the information security are enacted [2]. In the following three sections we suggest an insight into the spaces of regions, networks and fluids as those are performed by CIISMS for supply networks.

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Space of Regions (Euclidean)

The space of regions (Euclidean or Cartesian) is the most obvious form of space given in actu. It is naturally perceived as an ideal model of the physical space where the material processes evolve. An object remains homeostatic through its evolution in Euclidean space-time (i.e. retains its continuity and singularity), if the relations within the set of the Euclidean coordinates describing this object remain stable within the limits determined for this object. In [14] Law gives the following eloquent example: “a vessel remains a vessel, the same vessel if it holds together physically as it moves around the seas: topologically speaking, if the set of relative co-ordinates which describe its shape as an object occupying Euclidean space are not disrupted” [14]. In the same manner trajectories of objects in Euclidean space-time and their spatial relations with other objects are similarly defined in terms of Euclidean coordinates. The Euclidean space embeds the objects and, at the same time, stable and continuous spatial (in terms of Euclidean space) performance of objects helps to perform a Euclidean space [14]. In terms of topology, the space and the objects it embeds are interrelated (if not identical). In topology definition of objects (or shapes) and their spatial continuity (homeostasis) in movement is interchangeable with the definition of the spatial conditions of their existence [14]. Spatiality of a software code is its inherent characteristic according to [26]. Code is used to retrieve data from a storage location at a given place in the space of regions, process these data elsewhere following a set of predefined instructions, and then send the data back to the original or different storage [26]. The space of regions perfectly manifests itself in the Internet of Things paradigm where the interacting mobile intelligent actors are continuously de/re-territorialized. The performance of regions in data and information security in supply networks is linked mainly to the manifestation of various threats in physical space [2], i.e. inside information storing, processing and transmitting devices, or other material components and processes in supply networks situated and performed in clearly identifiable geographic locations, within particular territories [2]. Even talking of the “virtual” elements of the information systems, we still perceive them as “fundamentally grounded in physical reality” [2], i.e. in the framework of a “real” geography [2, 4]. The channels of data breach in supply networks (e-communication channels, phone/fax, postal and courier channels, direct physical contact with data sources etc. [1]) are obviously inscribed in the space of regions (Euclidian space). Any “hard” or “soft” supply network breach as suggested by Shackleford [17] is embedded in the space of regions (see Fig. 2). Attacks on sensitive data and information drift through a supply network before they become visible first through their (technical) effect on the material infrastructure and processes in the supply network, and second through various information security techniques aimed at preventing, detecting, and removing these attacks [2] (see Fig. 1 above). In the next two sections we will demonstrate how the space of regions interacts with the other two Law’s spaces (the space of networks and the space of fluids) and the effect of this interaction on the CIISMS stability.

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MATERIALS

Physical disrupon (the, tampering)

ENTERPRISE

• Physical compromise (policy violaon) • Physical disrupon (product tampering)

CUSTOMER

Service interrupon

SOFT STREAM

INFRASTRUCTUREAS-ASERVICE

Virtual disrupon (DDoS, MiM etc.)

ENTERPRISE

• Malware (command & control) • Insider threat (IP loss)

CUSTOMER

Compromise (infiltraon/ exfiltraon)

Fig. 2. “Anatomy” of a supply network breach as per [17].

3.3

Space of Networks (Semiotic)

Any successful movement of an object from point A to point B in Euclidean space-time (space of regions) must be preconditioned by stability of a set of links with other entities connected with this object through networks of relations of multiple orders. These networks of functional links form a space of networks. An object is stable in the space of regions only if its functional links are (syntactically or semiotically) stable in the space of networks [14]. If the stability in the space of networks is lost (i.e. when the functional relations constituting semiotic Object X are changed) the material homologue of semiotic Object X in the space of regions loses its homeostasis and identity and transforms into something different. Such spatial shifts are known as “singularities” or “catastrophes” in topology. Topologically speaking, immutability (invariability) of an object in the space of networks is a precondition for its persistence and homeomorphous evolution in the space of regions. We can thus hypothesize topological equivalence between the space of networks and the space of regions. Referring to the Law’s remarkable example of a ship: “hull, spars, sails, stays, stores, rudder, crew, water, winds, all of these entities (and many others) have to be held in place, so to speak functionally, if we are to be able to point to an object and call it a… properly working ship” [14]. Capacity of a software code to alter scale (distances) between local and global is based on its ability to retrieve, process and distribute data across local and global topologies of geographically distributed interconnected devices. “For example, a server located in Paris can have the same network distance (or sometimes less) when sending/receiving data to/from another server in New York than it would if it were communicating with a server located in the basement of the same building” [26]. Measurement of transaction time is based more on network configurations rather than on the physical distance between the servers [26] in the space of regions. In information security management systems the space of networks is seen as stabilizing security practices. Like in the space of regions security in the space of networks is defined as “stability and immutable continuity” [2]. Continuity

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(homeostasis) of regions and networks is preserved through identifying crucial “points of vulnerability” perceived as the obligatory passage points (OPP) where special protection against intrusions is required [2]. An OPP is one of the fundamentals of the actor-network theory introduced by Callon in [18]. In CIISMS network spaces are performed by similar information security practices – this creates zones of stable relations between the functional elements of the system. There are three overlapping network spaces in CIISMS for supply networks that emerge [2]: (1) a network of incoming threats and affected or targeted supply network infrastructure, (2) a network of CIISMS response to threats generated in the Technical block, and (3) networks of behavioral security norms as those are generated in the Psychological and Legal blocks. Unlike the authors of [2] who define the first network as woven around elements with the same vulnerability and/or type of infection, we consider the entire flow of heterogeneous threats entering the supply network as a complex infection producing the “network of sameness” [2]. Indeed, though the heterogeneous threats come from different sources and target different pieces of information or elements of material infrastructure they are united in their clandestine character since they normally happen without prior knowledge of or consent by the supply network actors. The second network performed by the information security threats is a network of CIISMS response and threat removal. The practices of assessment and analysis of the information security threats and selection of the relevant algorithms of protective actions aimed at the elimination of the threats [1] (see Fig. 1 above) form the basis for in-security measurement creating the data required for the performance of regions [2] as described in the previous section. A variety of different in-security measurement methodologies aimed at the identification and characterization of heterogeneous information security threats and their effects (e.g. DLP, SIEM, UTM, SA, PP described in [1]) make the same measurement network [2]. Standardization of the information assurance practices also plays an important role in performance of networks [2]. The national and international standards underly the information security policies and governance included in the Legal block of CIISMS for supply networks [1]. For instance, ISO/IEC 27001 guidelines [19] help formalize and standardize contractual regulations concerning informational support of cooperation (creation of information, use, transfer, access etc.). The General Data Protection Regulation (GDPR) implemented in the European Union [20] sets standards for integration of data security into supplier governance, definition and classification of suppliers and data, appointment of data protection officers, supplier audition etc. The third group of networks is performed by the threat makers with “orderly strategic and operational vision, logistics and deployment” [2, 5]. These actors driven by their individual interests become integral part of the computer underground ecosystem once incorporated into it through the translation process [10]. Summarizing, we emphasize that the creation of stability is a paramount goal in the information security network space, and successful performance of CIISMS in the space of regions is provided by the stability of network practices [2]. The best security practices must be defined, measured, selected, stabilized and standardized.

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Space of Fluids

What is seen as the loss of homeostasis and identity of an object in the space of networks is not considered as such in the topological system defined by Law as the space of fluids. In this new space the variability of functional relations is the basis of object constitution [13]. Topological relation between the space of fluids and the space of networks is symmetric to the relation between the space of networks and the space of regions, meaning that the immutability (invariability) of an object in the space of fluids is a precondition for its homeomorphous evolution in the space of networks with new functional relations coming into being [14]. If an object evolving in the space of regions while staying immutable in the space of networks is perceived as an “immutable mobile” in Latourian terms, an object performing fluids should be understood as a “mutable mobile” [22]. One of the most successful metaphoric examples of a “fluid object” found in the literature is that of the Zimbabwe bush pump [21]: a device demonstrating flexibility and adaptability to the changing environment and responsiveness to the changing social demands while traveling from settlement to settlement across savannah. This modest and vital device has proven its capability to live through detachment/replacement of its various bits and pieces to the extent of its almost complete reconstruction with the use of barely fitting parts taken from occasional sources, changes to the methods of its use and the use of the water it produces, as well as the changes to the social role it plays while keeping its identity as a water pump [21]. Security of a supply network as a whole is to a significant extent about “keeping everything in its place” [2, 6]. In this context any event or actor should be perceived as actually or potentially dangerous if it disrupts the functional integrity of the supply network, i.e. a coordinated flow of multiple processes through a multifaceted spacetime system [2, 7]. If this happens, the functional (semiotic) relations holding the supply network and its information security management system stable become subject to permanent changes creating variability [2]. In this permanently changing environment supply networks perform the space of fluids. Supply networks performing fluids are known as resilient since the space of fluids is much more resilient to the changes than the space of networks [2]. A resilient supply network can rapidly respond to unforeseen changes and chaotic disruptions, bounce back and forward “with speed, grace, determination and precision” [23] by aligning its strategy, operations, management systems, governance structure, and decision-support tools to adjust to permanently changing risks, live through disruptions and create advantages over less flexible and less adaptive competitors [25]. Resilience in the modern supply network research is regarded as the newest phase in the evolution of traditional enterprise structures as those are performed in the space of regions and the space of networks towards highly virtualized fluid structures enabling human and nonhuman actors to effectively function anytime and anywhere [23, 24]. The fluid space is about system resilience to various uncertainties, fuzziness and destabilizing factors affecting the system both from outside and from inside. The temporarily stable algorithms incorporated in the Block of analysis and assessment of security threats and in the Block of selection of relevant algorithms of protective

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actions (see Fig. 1 above) become fluid when the knowledge extracted from the knowledge base feeding these blocks appears irrelevant if the system encounters an unknown/unidentifiable threat. This threat – not recognizable by the system – enacts fluid space through the fuzziness of its effect on the supply network (i.e. the effect is visible, but its reasons and aftermaths cannot be assessed with certainty). The authors of [2] state that the information security threats disintegrate knowledge about “the other”, and thus make “fluid the boundaries between the threatening and the threatened”. Therefore, knowledge engineering plays a critical role in maintaining security system stability. Not only the knowledge bases of the individual supply network actors (feeding the united knowledge base of the supply network) must be permanently updated to keep on par with the latest world knowledge on the information security threats affecting supply networks to be able to effectively uncover the purposes of intrusions and forecast damages. It is equally important to identify the system vulnerabilities targeted by the new threats so that those vulnerabilities be fixed in order to prevent their future misuse [2]. Since the vast majority of information security threats are the outcome of intentional malicious activity of human actors, the investigation of intentions plays an important role in the knowledge creation. The techniques of psychological profiling (PP) and structural anomaly detection (SA) incorporated in the Psychological block of CIISMS are sought to be mobilized for the detection of potential insider attacks [1], however possibility of the system proactivity aimed at the prevention of outsider intrusions remains highly questioned. Earlier we have mentioned that for the sake of comprehensiveness the CIISMS for supply networks deals with both intended and unintended security threats. But even in cases where a threat is generated intentionally and targets particular pieces of information or elements of the material infrastructure, the mode of its manifestation in different topological spaces is not always fully programmed, manageable or desired by the threat creator [2]. Malware and other types of information security threats united in their clandestine character and purpose to steal or tamper with data are usually uncovered due to defects in their code or flaws in the behavior of the threat creators. The actions of various threats in a supply network may have unintended technical and political consequences (e.g. uncontrollably self-replicating malware [2]). To prevent the unwanted consequences of the fluidity enacted by the information security threats [2] ending up in inadequate political responses of supply network decision-makers it is critical to keep the functional links between the Legal block and the other two blocks of CIISMS well balanced and resilient to disruptions. Summarizing this section, it is important to emphasize that fluidity being a permanent challenge to the stability of networks, at the same time makes possible “the reestablishment of regions and networks” [2] with their desired stability.

4 Conclusions This paper places the information security concerns in the broader context of supply network security management. Presenting CIISMS for supply networks as a comprehensive information security management system we do not restrict our model to the intentional threats alone

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(though we certainly recognize the leading role of the intent in tampering with the supply network data and information) by including unintended breaches and disruptions into consideration. We further reflect on possible system proactivity to prevent threat formation. The actor-network approach was used to show that the information security threats affecting supply networks are topologically multiple. Their identity can thus be understood as an intersection between different versions of topological invariance [14] in the spaces they enact: regions, networks and fluids. The performance of regions in CIISMS for supply networks is related to the manifestation of various threats inside hardware or other material elements linked to particular regions located in Euclidean space. CIISMS performed in the space of networks creates zones of stable relations between the functional elements of the system so that the successful performance of CIISMS in the space of regions is provided by the stability of network practices [2]. One of the basic goals of this work is to familiarize the ICT research community with the language and transdisciplinary theoretical framework provided by the actornetwork theory, its spatial concepts and their application in information systems studies. Despite a number of publications on the use of actor-network approach in the information systems (e.g. [11, 12]) and cyber-security (e.g. [2, 10]) research, the method remains widely ignored by the researchers working in these fields. The space and thematic limits of this paper do not allow us to give a wide panoramic overview of ANT and deeper insight into its fundamentals, therefore we encourage the readers to refer to our earlier publications on the method [3, 9] and other literature cited in this work. Our forthcoming publications are devoted to the application of ANT-based approach in intelligent logistics systems studies. We look forward to further development of the ANT based topological approach to the investigation of information systems and information security management through formalization of this method and its integration with other relevant approaches.

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3D Face Capture for Rehabilitation Progress Assessment After Brain Surgery Jakub Tomeˇs, Jan Kohout(B) , and Jan Mareˇs Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technick´ a 1905/5, 166 28 Praha 6, Prague, Czech Republic [email protected]

Abstract. The aim of this article is to show how modern sensors can be used in rehabilitation monitoring after a specific brain surgery that affects the facial muscles. Rehabilitation takes several months, it is important for both doctors and patients to be able to monitor progress quantitatively. To evaluate a patient’s facial disorders, the stereo-vision camera is used to detect and capture precise facial movements, which the subject is asked to perform. From these videos, specific patterns can be extracted for the rehabilitation process description. In this paper, we focused on a 3D analysis app for mobile phones. Keywords: Unity3D · ARKit Image processing · Mobile

1

· Rehabilitation · Virtual reality ·

Introduction

The topic of facial mimics analysis (after certain types of diseases) is a very actual topic in the last decades. One of these diseases is the so-called Vestibular Schwannoma, a benign tumor that originates from the Schwann cells of the vestibular nerve [8], [6]. It is the most common tumor affecting the temporal bone and the cerebellopontine angle [6], [7]. For effective rehabilitation planning, which typically lasts several months, it is crucial to correctly evaluate the improvement of the muscle function. Describing the rehabilitation process depends on the subjective opinion and expertise of the doctor. For this reason, we want to monitor the work of muscles objectively in the long term. For a precise analysis of the mimic muscles, we developed a complex system for 3D scanning, modeling, and analysis. This system is now placed in the Clinic of Otorhinolaryngology for clinical testing. [5] [4] The aim of this work is to extend the system by a supporting system – a 3D analysis app for mobile phones. This app uses a smartphone and digital camera for scanning and reconstruction. Since the vast majority of the population has a smartphone, this system can be used for home rehabilitation and practicing. Patients can see the progress in real-time, without having to visit the practitioner. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 143–149, 2020. https://doi.org/10.1007/978-3-030-63322-6_11

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3D Scanning

The TrueDepth camera is primarily used for secure user authentication on certain of Apple’s smartphones and tablets, however, it can also be accessed by other applications via various SDKs. The SDK of choice for this project was the ARKit, which provides access to pre-processed data in the form of facial mesh. The Unity3D engine then provides a way of accessing the facial mesh data along with other necessities such as Frontal Camera feed, 3D Scene, or UI Canvas. The main goal is to store this mesh in real-time and export it via native sharing features of iOS as an archive containing a 3D model of the recorded face for every captured frame. The TrueDepth sensor scans the environment by projecting more than 30,000 dots in front of the device and then captures the resulting overlaid image by an infrared camera. This provides the device with the means to produce raw depth data for the captured scene. The measuring error can reach 5 % in the worst conditions, however, measurements at around 300 mm provide the best trade-off between captured details and accuracy for raw data capture. [1] Advanced VR applications are able to display a cross-section of the 3D model in real-time. This process requires the latest graphical card. With the latest hardware, it is possible to create a very realistic scene where the shadows of the object are displayed, while the brightness and contrast could be regulated by the user. [9]

2

Materials and Methods

The Unity3D engine in version 2019.2.17f was used as a starting point for this project in order to avoid the complexities of game engine design and to ensure future compatibility with other methods of Face Capture on Android devices. A standard 3D project was used along with the AR Foundation [10] and ARKit Face Tracking [11] packages that were imported via the Package Manager. The NativeSharing [12], pb Stl [3] and SharpZipLib [2] were imported as standard plugins into the Assets folder. 2.1

Scene Structure

The scene contains a Canvas GameObject with two buttons that are used to start and stop the face capture. Two AR Session Origin and AR Session GameObject were added from the XR tab located in the context menu. AR Face Manager has then been added to the AR Session Origin GameObject. 2.2

Face Capture Implementation

The whole face capture was implemented in the ARFaceCapture component. The component exposes two functions, StartRecording and StopRecording, and two UnityEvents, onRecordingStarted and onRecordingFinished.

3D Face Capture for Rehabilitation Progress Assessment

Fig. 1. Screenshot from the mobile application

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Parameters

Camera

12MP wide-angle and telephoto cameras Wide-angle: f/1.8 aperture Telephoto: f/2.4 aperture Dual optical image stabilization Six-element lens Autofocus with Focus Pixels

TrueDepth Camera 7MP camera f/2.2 aperture Body and face detection Auto image stabilization Chip

A11 Bionic chip with 64-bit architecture Neural engine Embedded M11 motion coprocessor

Display

5.8-inch (diagonal) all-screen OLED Multi-Touch display 2436 × 1125px - 458dpi

Size and Weight

143.6 × 70.9 × 7.7mm 174g

Fig. 2. Model captured on the 15th (left) and 78th (right) frame of recording

When StartRecording is called, a frame containing current face mesh is stored in memory sixty times per second. After the StopRecording call, all frames are sequentially converted into meshes and then to STL files. The STL files are then archived and stored in the cache, the onRecordingFished event is then invoked, which in turn calls Share function of the ShareResult component, that uses the NativeSharing plugin to open the sharing context menu.

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Fig. 3. Screenshot from Virtual reality

3

Results

The mobile application provides one button, as can be seen in Fig. 1, which is a way to accurately record user’s facial expressions and movements into multiple STL files as demonstrated in Fig. 2. The file size of each frame ranges from 300 to 500 KB; this results in 23.44 MB of uncompressed size per second of recording at 60fps. The exported models can be converted to Vertex Animation and played back by external programs such as Blender or shown in virtual reality as shown in Fig. 3. The disadvantage of this approach is the high price of Apple devices, see Table 2.

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J. Tomeˇs et al. Table 2. Price of Apple’s Iphones Name Iphone XR

Type

64 GB 256 GB 64 GB Iphone 11 128 GB 256 GB 64 GB Iphone 11 Pro 256 GB 512 GB Iphone 11 Pro Max 64 GB 256 GB 512 GB

4

Price [$] 599 649 699 749 849 999 1149 1349 1099 1249 1449

Conclusion

The aim of this article is to show how modern sensors can be used in rehabilitation monitoring after a specific brain surgery affecting the muscles of the face. We introduce a 3D analysis app for mobile phones. This app uses a smartphone and a digital camera for scanning and 3D reconstruction. While it may not provide as accurate results as the Kinect sensor (developed in the previous project), it provides a way for patients to track their own progress more effectively without the need for expensive medical equipment. The main advantages of this approach are: 1. modularity 2. availability 3. low-cost solution in comparison to standard medical approaches. Acknowledgements. Financial support from specific university research (A1 FCHI 2020 002) and LTAIN19007 Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation. The financial support is gratefully acknowledged.

References 1. Breitbarth, A., Schardt, T., Kind, C., Brinkmann, J., Dittrich, P.G., Notni, G.: Measurement accuracy and dependence on external influences of the iphone x truedepth sensor. In: Photonics and Education in Measurement Science 2019, vol. 11144, p. 1114407. International Society for Optics and Photonics (2019) 2. karasusan: Sharpziplib. https://github.com/karasusan/UPM.SharpZipLib. Accessed 21 Apr 2020 3. karl: pb stl. https://github.com/karl-/pb Stl. Accessed 21 Apr 2020

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4. Kohout, J., Crha, J., Trnkova, K., Sticha, K., Mares, J., Chovanec, M.: Robotbased image analysis for evaluating rehabilitation after brain surgery. MENDEL 24, 159–164 (2018) 5. Kohout, J., Egermaier, J., Tylov´ a, N., Mareˇs, J.: Preprocessing CT images for virtual reality based on matlab. In: Proceedings of the Computational Methods in Systems and Software. pp. 49–57. Springer (2019) 6. Nam, G.S., Jung, C.M., Kim, J.H., Son, E.J.: Relationship of vertigo and postural instability in patients with vestibular schwannoma. Clin. Exper. otorhinolaryngol. 11(2), 102 (2018) 7. Rosahl, S., Bohr, C., Lell, M., Hamm, K., Iro, H.: Diagnostics and therapy of vestibular schwannomas–an interdisciplinary challenge. GMS Curr. Top. Otorhinolaryngol. Head Neck Surg. 16 (2017) 8. Sagers, J.E., Brown, A.S., Vasilijic, S., Lewis, R.M., Sahin, M.I., Landegger, L.D., Perlis, R.H., Kohane, I.S., Welling, D.B., Patel, C.J., et al.: Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma. Sci. Rep. 8(1), 1–12 (2018) ´ 9. Torner Rib´e, J., G´ omez Gonz´ alez, S., Alpiste Penalba, F., Brigos Hermida, M.A.: Virtual reality application applied to biomedical models reconstructed from CT scanning. In: Computer Science Research Notes [CSRN], pp. 21–24 (2016) 10. Unity3D: Unity docs: AR foundation. https://docs.unity3d.com/Packages/com. [email protected]/manual. Accessed 20 Apr 2020 11. Unity3D: Unity docs: Arkit face tracking. https://docs.unity3d.com/Packages/ [email protected]/manual. Accessed 20 Apr 2020 12. yasirkula: Nativesharing. https://github.com/yasirkula/UnityNativeShare, Accessed 20 Apr 2020

An Ontological Approach to the Text Sample Size Adaptation for the False Pseudonyms Detection I. S. Korovin1, A. B. Klimenko1(&), and I. B. Safronenkova2 1

Scientific Research Institute of Multiprocessor Computer Systems of Southern Federal University, 2, Chekhov Street, 347922 Taganrog, Russian Federation [email protected] 2 Federal Research Centre, The Southern Scientific Centre of the Russian Academy of Sciences, 41, Chekhov Street, 344006 Rostov-on-Don, Russian Federation

Abstract. The paper deals with the problem of text feature vector forming under the conditions of heterogeneous sources of training sets. To create a service of victim behavior prevention it is necessary to process huge volumes of information, including the processing of text samples to create the text feature vectors. We propose to use the ontological approach to select the shortest possible text samples and to use the appropriate text processing methods. We examined the wide range of literature presented in this area, and conducted an ontology, which describes the relations between the data source types and text processing approaches. Such ontology makes it possible to select the optimal method of the text processing in terms of time and computational resources consumption. Keywords: Ontology
Authorship attribution
False pseudonyms
Stylometric analysis
Text sample size
Text feature vector
On-line messaging

1 Introduction Fake accounts, or false pseudonyms, play a significant role in cyberbulling (a form of bullying or harassment with the usage of the Internet Social Media) and are mostly used by the potential perpetrators. For example, fake Instagram accounts gain the popularity nowadays [1]: “A ‘finsta’ is a secret or fake Instagram account that people use to post content that’s different to their real Instagram account, that may be more spontaneous, intimate or revealing”. Besides, fake accounts or false pseudonyms are used by victims to avoid the acts of harassment [2]. They also can tend to gain an access to some private data or to participate various communities producing destructive content. Some victims also take part in online bullying and trolling relating to the other social network users with unpredictable consequences up to the suicide commitment. So, the problem of false pseudonyms detection is a topical one and is under concern. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 150–158, 2020. https://doi.org/10.1007/978-3-030-63322-6_12

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The current work is an extension of [3, 4]. In works [3, 4] the architecture of cognitive assistant on the basis of text stylometric analysis with a focus on resourceconsuming operations decreasing was proposed. It is important to note that in this respect a text feature vector forming is one of the resource-consuming operations and needs to be optimized in terms of time consumption. A text feature vector forming is a non-trivial and rather time-consuming task. Among existing works, which are devoted to the author identification, the work [5] is worth noticing in a greater degree. In this work, a set of features, which is involved in the vector representing a writing style, was presented. The research [6] also refers to a set of features from [5]. However, as referred to in [5, 6], not less than 1000 various text features have been examined now. Herewith, a consensus about the best text features, which allow to identify the style, has not reached yet. Some works examine the author identification effectiveness for different domain. For example, for on-line author identification the most efficient features are lexical, syntactic and structural ones [7]. Meanwhile, the effectiveness of different methods of text samples forming can also be estimated by users. The authors of the current work presuppose that a text sample size adaptation, depending of data source type and users’ feedbacks, is expedient.

2 The System Response Time and the Text Sample Size As is mentioned in previous section, a text sample size for feature vector forming can be recommended on the basis of Internet resource types analysis. Moreover, a training sample size can be regulated on the basis of cognitive assistant (CA) user feedbacks of duplicate account identification. The adaptation of text sample size, which is used for text feature vector forming, affects the time of vector forming in a certain way as it shown in Fig. 1.

Fig. 1. The influence of text sample size on time of text feature vector forming

Consider a model, which represents the effect of text sample size adaptation, depending of users’ feedbacks or Internet source types.

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Consider c – a number of features (vector dimension), i – feature number. Then, the complexity of text processing for i-th feature getting is evaluated as: xi ¼ ni Vt

ð1Þ

where Vt – a text sample size (words); ni - is a ratio, which determines the complexity of text processing for xi getting. Then, the complexity of text feature vector forming is formulated as follows: X¼

c X

xi ¼

i¼1

c X

ni Vt

ð2Þ

i¼1

The time of vector forming is defined as: Tc ¼ aX

ð3Þ

where a is a ratio, which is determined by a node performance where the processing is taking place. The total time of feature vector forming integrates the following parts, assuming that a text sample is collected of: text sample delivery time to a device, where text feature vector is going to be formed, the time of feature vector forming, the result delivery time to a user. It is reasonable to model this procedure considering the location of a node, computing a vector, in reference to the source of a text sample as it is shown in Eq. 4: T0 ¼ Vt k þ Tc þ kgc

ð4Þ

where T0 – the total time of text feature vector forming by a user according to the text sample size; k – a coefficient, which determines a data transmission rate considering the number of network hops; gc - text feature vector size (Fig. 2). Text sample size adaptability assumption bases on the following works: – in [8], a text sample including 50–1000 tweets is proposed; – in [9], a text sample length is 200–400 messages for social nets with shot messages is proposed; – in [10], the problem of authorship attribution for sms-messages is examined. In this case, a training set includes not less than 50 messages; – in [11], for sources with simplified content authors used text samples containing no more than 400 characters. While for data source containing extended texts, training set includes up to 10000 words [12]. Besides a relationship between the text sample size and the source data type existence, there is a relationship between the authorship identification methods and the text sample size [13].

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Fig. 2. “Text sample size-time” diagram shows the dependency between the text sample size and the time for text processing

Hence, it is reasonable to examine the works, which represent a relationship between the text sample size, the data source types and authorship attribution methods, in details. Besides the presented relationship, a text sample size can be regulated by the way of users’ pseudonym search feedbacks. The sub-system of training set size adaptation on the basis of users’ feedback analysis is implemented by means of distributed ledger. The record format is presented as follows: . Assuming that there is a limited set of variables, which describes a “data_source_type”, data source types can be presented in terms of ontological description of resources according with message length feature (as was mentioned above there are different text sample sizes and different author identification methods for different resources). “Text_sample_size” – is a length of text sample for which it is possible to esteem the CA output. It is represented in “user’s_feedback”. As statistic is being collected, the certain range of resources begins from text sample size choice each next search, which is necessary for text feature vector forming. For this purpose the following steps are conducted: 1. 2. 3. 4.

«data_source_type» selection is performed from the distributed ledger; the records with available text feature forming size and methods are selected; from the formed subset the records with the best user feedbacks are selected; On the basis of selected values the text sample size is formed, calculated as an arithmetical average.

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3 The Data Sources Ontology As it is defined in [14], an ontology encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many or all domains of discourse. For the purposes of discussion an ontology is a formal explicit description of concepts (classes) in a domain of discourse, properties of each concept describing various features and attributes of the concept (slot), restrictions on slots (facets), a set of individual instances of classes [15]. It is therefore possible to formulate that ontology constitutes a frame of a knowledge base. There is a common methodology of ontology development [15], which includes the following steps: – – – –

defining classes in the ontology; arranging the classes in a taxonomic; defining slots and describing allowed values for these slots; filling in the values for slots for instances.

For the purposes of the current research and on the basis of literature survey the ontology includes the following hierarchy of classes (super-sub-classes), as it is presented in Fig. 3.

Fig. 3. Class hierarchy of the ontology

A considerable part of essential works devoted to the problem of author identification in various data sources such as e-mails, forums, tweets, blogs, Quora and newspapers, were examined for the purpose of current ontology development [16–22]. In the majority of existing works [6, 23–27] authors proposed to use various set of stylometric features (bag-of-words, content specific, lexical, structural, misspelling errors, etc.). Herewith, authors used different classification methods (supervised training, unsupervised learning, reinforcement learning) [17, 28] or their sequential combinations [25]. As previously described, text sample sizes is essential for text

An Ontological Approach to the Text Sample Size Adaptation

Fig. 4. The data sources ontology

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feature vector forming. Text sample sizes take various values, depending of the data source types. For example, for e-mail author identification text training length includes 23,000 words [29]. In case of forum author identification – 30,600 words [30], for tweets – 10,000 words [21, 27]. On the basis of literature survey the following ontology was developed in Protégé 5.0 (Fig. 4). The developed ontology demonstrates that for different data sources (e-mails, blogs, forums, etc.) various author identification methods on the basis of different text sample sizes can be applied. If there is an opportunity, a minimal text training set, which provides acceptable quality, should be chosen.

4 Conclusion In this work the problem of text sample size adaptation, depending on data source types and users’ feedbacks, was considered. The proposed approach is focused on the computational resources consumption optimization, which is needed for fake accounts identification. The authors of this paper propose to use the ontological analysis to decrease the time consumption of the text feature vector forming. We examined relatively wide range of publications and concluded that for different information sources types the sufficient text sample size is different. According to this fact the special ontology was developed. It allows to adapt the text sample size to the information source type and so to minimize the time of text sample size processing and the text feature vector forming. Acknowledgments. The paper has been prepared within the RFBR projects 18-29-22093, 2004-60485.

References 1. The disturbing new cyber-bullying trend gaining popularity with students. https://www. news.com.au/technology/online/social/the-disturbing-new-cyberbullying-trend-gainingpopularity-with-students/news-story/b03a9e27ef8ef00d4d9195867fc2adfd. Accessed 28 May 2019 2. The 10 forms of Cyberbullying. https://cyberforpeople.com/the-10-forms-of-cyberbullying/. Accessed 28 May 2019 3. Melnik, E., Korovin, I., Klimenko, A.: A cognitive assistant functional model and architecture for the social media victim behavior prevention. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol. 985, pp. 51–61. Springer, Cham (2019) 4. Melnik, E., Korovin, I., Klimenko A.: The improvement of the stylometry-based cognitive assistant performance in conditions of big data analysis. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing 5. Abbasi, A., Chen, H.: Writeprints: a stylometric approach to identity-level identification and similarity detection in cyberspace. ACM Trans. Inf. Syst. 26(2), 1–29 (2008)

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6. Afroz, S., Caliskan, A.I., Ariel Stolerman, Greenstadt R., McCoy D.: Doppelgänger finder: taking stylometry to the underground. In: Proceedings of the 2014 IEEE Symposium on Security and Privacy Proceedings, pp. 212–226. IEEE, NJ (2014) 7. Zheng, R., LI, J., Huang, Z., Andchen, H.: A framework for authorship analysis of online messages: Writing-style features and techniques. J. Am. Soc. Inf. Sci. Technol. 57(3), 378– 393 (2006) 8. Melnik, E.V., Radchenko, S.A., Gerasimenko, I.S.: Application of the documents processing method based on cognitive analysis for improving the internet information retrieval efficiency. Vestnik Komp’iuternykh i Informatsionnykh Tekhnologii. 11(77), 33–39 (2010) 9. Schwartz, R., Tsur, O., Rappoport, A., Koppel, M.: Authorship attribution of micromessages. In: 2013 Conference on Empirical Methods in Natural Language Processing Proceedings, USA, pp. 1880–1891. Association for Computational Linguistics (2013) 10. Nilan, S., Pratyush, D., Himadri, S.: Authorship attribution of short texts using multi layer perceptron. Int. J. Appl. Pattern Recogn. 5(3), 251–259 (2017) 11. Roshan, R., Pramod, H., Upul, S.: Authorship detection of SMS messages using unigrams. In: Proceedings of 2013 IEEE 8th International Conference on Industrial and Information Systems, Sri Lanka, pp. 387–392. IEEE (2014) 12. Rexha, A., Kröll, M., Kern, R., Ziak, H.: Authorship identification of documents with high content similarity. Scientometrics 115(1), 223–237 (2018) 13. Altamimi, A., Alotaibi, S., Alruban, A.: Surveying the development of authorship identification of text messages. Int. J. Intell. Comput. Res. 10(1), 953–966 (2019) 14. Asuncion, G.P., Mariano, F.L., Oscar, C.: Ontological Engineering: with Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web, 1st edn. SpringerVerlag, London (2004) 15. Natalya, F. Noy, Deborah, L.: Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880 (2001) 16. Corney, M., De Vel, O., Anderson A., Mohay, G.: Gender-preferential text mining of e-mail discourse. In: 18th Annual Computer Security Applications Conference Proceedings, California, pp. 282–289. IEEE (2002) 17. Iqbal, F., Binsalleeh, H., Fung, B.C., Debbabi, M.: Mining writeprints from anonymous emails for forensic investigation. Digit. Invest. 7, 56–64 (2010) 18. Yuta, T.: Authorship identification for heterogeneous documents (2002) 19. Martijn, S., Femke K., Gijs K., Mark, S.: Authorship analysis on dark marketplace forums. In: Brynielsson, J. and Yap, M.H. (eds.) IEEE European Intelligence & Security Informatics Conference, Los Alamitos, pp. 1–8. IEEE (2015) 20. Narayanan, A., Paskov, H., Gong, N. Z., Bethencourt, J., Stefanov, E., Shin, E.C.R., Song, D.: On the feasibility of internet-scale author identification. In: Proceedings of IEEE Symposium on Security and Privacy (SP), 2012 IEEE, pp. 300–314. IEEE, Los Alamitos (2012) 21. Green, R.M., Sheppard, J.W.: Comparing frequency-and style-based features for twitter author identification. In: Twenty-Sixth International Florida Artificial Intelligence Research Society Conference. Association for the Advancement of Artificial, Florida (2013) 22. Conrad, S., Günter S.: Short text authorship attribution via sequence kernels, markov chains and author unmasking: an investigation. In: International Conference on Empirical Methods in Natural Language Processing Proceedings, USA, pp. 482–491. Association for Computational Linguistics (2006) 23. Schmid, M., Iqbal, F., Benjamin, F.: E-mail authorship attribution using customized associative classification. Digit. Invest. 14, S116–S126 (2015)

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24. De Vel, O., Anderson, A., Corney, M., Mohay, G.: Mining e-mail content for author identification forensics. ACM SIGMOD Rec. 30(4), 55–64 (2001) 25. Sangita, R.P., Thamar, S.: Authorship attribution of web forum posts. In: eCrime Researchers Summit Proceedings, pp. 1–7. IEEE, Los Alamitos (2010) 26. Overdorf, R., Greenstadt, R.: Blogs, twitter feeds, and reddit comments: cross-domain authorship attribution. Proc. Privacy Enhanc. Technol. 3, 155–171 (2016) 27. Castro, A., Lindauer, B.: Author Identification on Twitter (2012) 28. George, K.M.: Authorship Attribution and Gender Identification in Greek Blogs (2013) 29. Corney, M., Anderson, A., Mohay G., De Vel, O.: Identifying the authors of suspect e-mail. Comput. Secur. (2001) 30. Abbasi, A., Chen, H.: Applying authorship analysis to extremist-group web forum messages. IEEE Intell. Syst. 20(5), 67–75 (2005)

It Was Never About the Language: Paradigm Impact on Software Design Decisions Laura M. Castro(B) Universidade da Coru˜ na Centro de Investigaci´ on en TIC (CITIC), A Coru˜ na, Spain [email protected] Abstract. Programming languages development has intensified in recent years. New ones are created; new features, often cross-paradigm, are featured in old ones. This new programming landscape makes language selection a more complex decision, both from the companies points of view (technical, recruiting) and from the developers point of view (career development). In this paper, however, we argue that programming languages have a secondary role in software development design decisions. We illustrate, based on a practical example, how the main influencer are higher-level traits: those traditionally assigned with programming paradigms. Following this renovated perspective, concerns about language choice are shifted for all parties. Beyond particular syntax, grammar, execution model or code organization, the main consequence of the predominance of one paradigm or another in the mind of the developer is the way solutions are designed. Keywords: Programming languages · Imperative programming · Object-oriented programming · Declarative programming · Functional programming · Programming paradigm · Software design

1

Introduction

The current socio-economic context is increasingly considering programming as a key competence in children education [12], and predicting a high demand of professionals with programming skills in the short term [13]. However, no matter the age, the previous experience, or the formal/informal approach, any person who wants to learn to code is immediately confronted with the question of which programming language to choose. A similar uncertainty is present in IT and software development companies, which struggle in risk evaluation and cost-of-opportunity assessments with regard to sticking to the languages they know and master versus (early) adopting or embracing new languages and technologies. And last but not least, it affects c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 159–169, 2020. https://doi.org/10.1007/978-3-030-63322-6_13

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practitioners as well at a personal level, in terms of career decisions for advance and improvement. In the last decade, a handful of languages have been created that can already be considered mainstream, like Swift [31], Elixir [29], Kotlin [10] or Rust [22]; a remarkable achievement in such a short time [6]. Same happened in the2000 s, with (by now) well-established names such as Go [24], C# [3], F# [36], Scala [28], Clojure [15], or VB.NET [37]. Table 1. Popular programming languages which are less than 20 years old. Language Year of creation Main paradigm C#

2000

Object-oriented

Clojure

2007

Functional

Elixir

2011

Functional

F#

2005

Functional

Go

2009

Functional

Kotlin

2011

Object-oriented

Swift

2014

Object-oriented

Rust

2010

Imperative

Scala

2004

Functional

VB.NET

2001

Object-oriented

While many of the languages in Table 1 are described as multi-paradigm, it is remarkable that half of them can be classified as functional languages. Programming paradigms are the most commonly used means of classification for programming languages, based on their features. The main programming paradigms, imperative and declarative, have been largely considered antagonistic, and it is commonly acknowledged that changing from one domain to the other takes significant mental energy [2,32], same as mastering the corresponding set of features. In this paper, we argue that the actual impact of the paradigm-in-mind goes beyond the execution model, code organization, syntax and grammar (Sect. 2). We show, by means of a simple yet complete and illustrative example, that the influence of the programming language a developer is more used to or intends to use as target reflects on the actual design of the software in a structural and organizational way, and that this affects non-functional characteristics of the software result, such as stability, robustness, or fault-tolerance. We also compare our position to previous analysis of the impact of paradigm or language choice in software products and their qualities (Sect. 3). Our main contribution is a new perspective on language selection, meaningful both at the individual and the organizational level: the actual language is less relevant than its main paradigm. In other words, we show how it is the

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paradigm underneath that drives the design decisions that developers will make, and consequently the key aspect to consider. This realization can alleviate the decision burden of learning or adopting a new programming language, given that paradigm is preserved. It can also motivate technology change, with the goal of shifting the approach to software design.

2

Methods

For decades, the programming landscape was dominated by imperative languages. The imperative paradigm understands programming as a series of commands, instructions to be given to a computer in a specific order. As a result of the execution of said instructions, the computer state is altered and the desired behaviour is obtained. In the early and mid-1990s, the prevalence of imperative programming shifted in favour of object-orientation. However, for software developers, this still meant thinking in terms of commands in certain order (for object-orientation is still a subtype of imperative programming): but the instructions and the data over which they operate now where shaped into procedures and data fields, with accessibility restrictions of the first over the later (and the first amongst each other) depending on which “object” they were associated to. It has only been in the new millennium that the functional paradigm has broken its own entry barrier into industry [25,26,30], even if it had been around for much longer. Often considered in contrast to imperative programming, the functional paradigm understands programming as the composition of functions which do not alter state, but rather offer a return value (which can itself be another function). This deterministic nature is one of the big differences with imperative procedures, which often have side effects due to state alteration. The first-class citizenship of functions and the restriction of side effects have given solid ground for the argumentation that the functional paradigm favours programs which are easier to reason about, debug and test [17]. In the age of parallelism and concurrency, this has been seen as an enormous advantage, and is possibly behind the adoption of “functional traits” by languages that identify mainly as imperative or object-oriented [14,18], as well as the current popularity of functional languages [16]. However, the perspective that the impact of paradigm choice restricts to the programming levels is very limited. On the contrary, our argument is that said impact is much broader, extending to the higher design of the software, its very own conception. By impacting the software design, paradigm choice affects, for instance, the number and responsibilities of the components that will integrate the solution, its scalability and fault-tolerance. 2.1

Practical Illustration of How “Paradigm Thinking” Impacts Software Design

To illustrate this central point, we will use a simple example taken from a real project. Let us consider a college environment, and think specifically of last-year

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students. At most universities, these students would need to carry out a final degree project before graduating. Now, the final degree project assignment may vary greatly from institution to institution, even within the same country or state. It might be the case that the student must come up with their own project, or that a set of project proposals are offered and the students request them, with the assignment being made using some objective criteria like average mark on their student record. If we were to design a system to automatically assign project proposals to students based on certain criteria, the user story that would drive the system is shown in Table 2. Table 2. User story of the automatic project assignment system. AS A student I WANT TO introduce my preferences of project proposals SO THAT the system can assign me one

Let us assume that information like the list of available project proposals is to be retrieved by integration with a different system (possibly, a software used by teachers to create, update and delete said proposals, and also by the corresponding academic committee that would oversee them all), same as the academic information concerning each student (possibly, the enrolment system, which holds not only data on the average marking, but also the concentration that each student is doing, the number of credits the student has passed, etc.), which may play a role as assignment constraints. If so, the overall architecture of the solution will be depicted as shown in Fig. 1 in C4 format [4]. In the upcoming subsections we analyse the internals of the Project Assignment component to see how paradigm choice affects software design. The Imperative Approach A series of commands is nothing else but an algorithm. When developers approach a software problem with an imperative paradigm mindset, they will focus on the algorithm that will solve it. This will reflect in the design of few, very powerful components that: – have unlimited access to all data they need to carry out their task – embed the complete logic of the solution, in a centralised fashion An example of imperative design for the project assignment example is shown in Fig. 2. Aside from the functional aspect, it is worth noting that failure is to be held under the same conditions as the rest of the logic: as part of the algorithm. This means that any fault-tolerance and resilience properties need to be incorporated into the system in one of two ways: either by allowing the system to simply “run again” if something goes wrong (with the consequential waste of time and resources), or to incorporate fault-tolerance management into the problem-solving logic (with the consequential complexity increase that this lack of separation of concerns brings [19]).

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Fig. 1. Architectural representation of motivation example (C4 context level)

Fig. 2. Imperative approach design (C4 component level)

The Object-Oriented Approach. Using objects to encapsulate data and procedures will reflect in the structure of the design by favouring the appearance of more components (objects), each of which is responsible for its own data, both in terms of ownership and in terms of operating with it. However, it is not so straightforward to know how to distribute responsibilities with regard to business logic as it is to do so with regard to data and relatively small tasks on that data. Even more when object-orientation, for quite some time, did not come hand-in-hand with asynchrony, rather the opposite [8]. Some have even used the term agent to differentiate it from the classical object to reflect this [27] (cf. “agent-oriented programming” [33]).

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An example of object-oriented design for the project assignment example is shown in Fig. 3. The structure is not as linear as in the imperative approach (cf. Fig. 2): a main orchestrator (control) component will implement a higherlevel version of the algorithm, in which object-specific (i.e. student, assignment) logic is delegated1 . Similarly, error handling with take place in two levels: internal to the objects, taken care by the objects themselves, and at the algorithm level, which again will be, if present, mixed with the functional logic.

Fig. 3. Object-oriented approach design (C4 component level)

The Functional Approach. If used to capture business logic in the shape of composable functions, a functional developer will approach our project assignment example in a radically different manner. The focus is shifted from data (students, assignments) to processes (proposals, requests). This will drive, instead of a sequential approximation that goes over the data in order to make a decision, an iterative approximation where partial solutions are offered until a stable one (i.e. a consistent output) is reached. An example of this functional design is depicted in Fig. 4, where the iteration loop that replaces the centralised control of the two previous approaches is shown. Additional advantages of this solution include the ability of reaching a partial solution in the presence of errors, without explicitly coding error management logic that intertwines with the business logic. If a request or proposal are invalid or become unavailable or corrupt, only the calculation (i.e. function call) that concerns them will be affected. But given that there is no central control, and that computations are independent and side-effect free, the rest of the execution will still take place. 1

In this particular case, these two abstractions will likely hide from the master control the particulars of interacting with the corresponding external systems featured in Fig. 1.

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Fig. 4. Functional approach design (C4 component level)

Some functional technologies have taken advantage of this to incorporate powerful fault-tolerance mechanisms that do not interfere with business logic, such as supervision. An enhanced version of the diagram in Fig. 4 is presented in Fig. 5, where supervisors transparently provide the ability to retry failed computations. Moreover, independence of computations and freedom from side-effects also means that operations may take place in different orderings, and even locations, making concurrency and distribution more straightforward, since the sort of data or flow dependencies that typically make them difficult [5] are not present by

Fig. 5. Functional approach with supervision (C4 component level)

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design. In our project assignment example, given that the decision making is based not on a temporal criteria (which requests are made first) but on the basis of quantifiable data (i.e. input information), the order will effectively not affect the result. This, together with the absence of a centralized control, would mean we could approach domains that we do not fully understand, and iterate towards a solution in an incremental manner, by refining the behaviour of simpler functions, rather than a single, large and complex algorithm. Last but not least, the absence of a single, main, sequential algorithm must not mean the absence of clear and transparent explainability [21]. Once a stable situation is reached (i.e. constant outputs that show no further changes), the system should have a means to show how that situation was reached, both for demonstrability and for auditing purposes. Fig. 6 shows a last version of the functional approach that embodies such logging for accountability purposes.

3

Discussion

That programming languages have an effect on software development has been discussed, both in academia and in more informal forums, for decades [2].

Fig. 6. Functional approach with explainability (C4 component level)

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However, we can argue that the focus of the debate has been on the internal aspects, both of the developers [35] (e.g. the skill set to acquire) and of the code itself [17,20] (e.g.. its legibility, maintainability, testability, etc.), but not so much on the external aspects of the software that is created (e.g.. its architecture), or even the effect on developers minds and way of thinking and approaching problems. At a moment in time where soft skills are getting more and more attention [1], the problem-solving capabilities that software professionals have become much broader than dominating a particular programming language. Similarly, a company’s competitive advantage goes beyond collaborative and organizational tools [9]. In this context, it is very relevant to ask which one is the key influencer: the programming language or the paradigm? Programming language popularity has been shown to be hardly related to its internal characteristics [11,23], rather its application area or business environment [7]. Also, the programming habits and thought-shaping that programming paradigms can have, have been analysed in the context of paradigm change [34], but not so much for the possible benefits of maintaining their guidelines regardless of the particular implementation (i.e. language).

4

Conclusions

In this paper, we have argued, in a previously unexplored dimension, that is not the programming language that is primarily relevant in terms of software development, but the paradigm. We have used a simple yet realistic example how this can be the case, but not at code-level, but at much higher abstraction level: that of the software architecture. We expect that these reflections will open new perspectives, both individual and collective, when it comes to language adoption and technology change. Of course, the preliminary insights presented in this paper could and should be explored in both analytical and empirical ways, either via developer surveys or analysing the combination of architectural patterns and programming paradigms of open source projects. We intend to continue this line of research in the short term.

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Deep Neural Network Acoustic Model Baseline for Character-Level Transcription of Naturally Spoken Czech Language Martin Vejvar(&) University of Chemistry and Technology, Prague, Czechia [email protected]

Abstract. Heavy portion of previous work in automated speech recognition (ASR) conducts their research and provides results exclusively for English spoken language, for which there are many ready-to-use datasets publicly available. ASR research for other less widely utilized languages suffers from the lack of high-quality speech datasets and insufficient experimental results to compare with new research. In this article, we aim to remedy this problem for the Czech language by proposing Deep Neural Network-based Acoustic Model (AM) architecture as well as providing experimental results for character-level transcription of naturally spoken utterances. The AM architecture was developed by utilizing working solutions from modern English language ASR research and further tailored for better performance on Czech language datasets by conducting a comparative experimental study for several versions of the AM. The models were trained on up to 331 h of naturally spoken Czech language utterances from the PDTSC 1.0 and ORAL2013 datasets and validated on a 5.5hour excerpt from the PDTSC 1.0 dataset. The results show that our final AM architecture can reach an average of 26:6% transcript character error rate (CER) on the validation set when trained with all of the available training data. We believe that the final AM architecture presented in this paper and the experimental results can serve as a baseline for further Czech language ASR research. Keywords: Speech recognition
Czech language speech recognition
Acoustic Model
Character-level transcription
Experimental baseline study Deep Neural Networks
Connectionist temporal classification




1 Introduction Speech is one of the main means of communication between humans and serves as a natural way to exchange information in everyday life. Thus, allowing machines to receive, process and return information through speech has understandably been the focus of research for several decades. With the emergence of Deep Learning in 2006, Deep Neural Networks were found effective for automatic speech recognition (ASR) tasks and have been at the top of research ever since [1]. Typical examples of successful DNN deployment in ASR are the Deep Speech research conducted by Baidu [2–4] or the Wav2Letter [5, 6] open-source architecture and speech recognition system © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 170–185, 2020. https://doi.org/10.1007/978-3-030-63322-6_14

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by Facebook AI. Most of the research utilizing DNNs so far has been conducted and published for the English language, for which there are many high-quality datasets publicly available. Lesser-known languages suffer from lack of datasets with enough variability, good audio recordings and well-transcribed labels. That prolongs the stages of collecting and preparing enough data for training the DNNs, which hinders the research. Czech language is one of these languages, for which no recent ASR research has been conducted and hence there are no comparable results available as a baseline for other researchers. Our article focuses on exploring the application of modern, DNN based, techniques in speech recognition on natural Czech language and thus providing a working model and a performance baseline for any subsequent research. Reaching this goal required several stages of development. In the Dataset Collection stage, we searched for available Czech language spoken conversation datasets with alphabetic transcripts. Namely, we chose to use the PDTSC 1.0 [7] and ORAL2013 [8] datasets. The process of Dataset Collection is unimportant for the purposes of this article. Therefore, only the utilization of datasets during experimentation is later introduced in Sect. 4.1. In the following Feature Extraction stage (Sect. 2.1), we preprocessed the audio recordings into more training-friendly spectral representations called Mel-frequency Cepstral (resp. Spectral) Coefficients, shortly MFCC (resp. MFSC). In the Acoustic Model stage (Sect. 2.2), we researched and developed an Acoustic Model, a DNN-based system, which transforms audio utterances into their transcripts. Our Acoustic Model heavily utilizes Deep Neural Networks (Sect. 2.3) with a combination of Fully-Connected (Dense), Convolutional and Bidirectional Recurrent Layers, which can be trained as a classifier of elementary speech units in the utterances to produce raw probability distributions over all possible alphabetic characters for each sample of the utterance. These raw distributions serve as an input to the Connectionist Temporal Classification function, which reduces the distributions into their final transcript predictions and calculates training loss value for the gradient optimizer (Sect. 2.5). As another stage, we chose to implement Batch Normalization layers (Sect. 2.4) into the DNN model, which promise to increase training efficiency and stability [9]. In the final stages, we proposed and analysed different layouts and configurations of the classification part of the Acoustic Model to achieve competitive validation results and provide a good baseline. We present the best performing Acoustic Model configuration (Sect. 3) before the Experimental Study (Sect. 4) itself, as we believe that the architecture can be instrumental for other Czech language speech recognition research.

2 Theoretical Basis The following sections provide a brief introduction and motivation for the use of important concepts to the research as they were mentioned in the Introduction section. 2.1

Feature Extraction

Feature extraction marks a process of transforming raw input data into a more appropriate form for the task at hand. A form in which the important parts of the data

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are more easily accessible than from the raw data itself. Although trainable feature extraction from raw audio signals (utterances) have already been successfully utilized in recent work [10–12], we used a more traditional approach of explicitly defined transformation of signals to Mel-Frequency scaled coefficients. Specifically, we have chosen to compare the more compact and more frequently used Mel-Frequency Cepstral Coefficients (MFCC) with the less processed Mel-Frequency Spectral Coefficients (MFSC), which drop the Discrete Cosine Transformation at the end of MFCC to prevent information loss. 2.2

Acoustic Model

An Acoustic Model (AM) is a term used for a system (typically with trainable parameters) that transforms preprocessed audio signals of speech into their textual transcripts. For character-level transcription of speech that usually describes a structure that can model the elementary sound units, which are forming speech in a given language, called phonemes. By serialization of these phonemes a phonetic transcript of an utterance is obtained, which then must be transformed into alphabetic representations by feeding it into a manually/automatically crafted pronunciation dictionary. The process of crafting said dictionary must be tailored to the given language and requires ample time and linguistic expertise [13]. In our project, as successfully done before for other languages (e.g. [13–15]), we chose to go a step further and design the AM to directly output alphabetic characters (graphemes) by training it on not phonetic, but alphabetic transcripts of the speech utterances. For achieving this task, our AM utilizes Deep Neural Networks (DNN) and Connectionist Temporal Classification (CTC), which are discussed in the following sections. The DNN serves as a trainable classifier that transforms the input audio utterances into sequences of distributions over all possible alphabetic character classes while the CTC then reduces those into the final transcript. 2.3

Deep Neural Networks

Deep Neural Network (DNN) is a broadly accepted term used for neural network structures containing more than 2 stacked computational layers of varying architectures. The usual layer architecture groups divide into Feed-Forward layers (FF), Convolutional Network layers (CNL) and Recurrent Network layers (RNL). In our article, whenever we mention FF or CNL, we mean their most common interpretations in the field unless specified otherwise. FF as a Fully-Connected layer of neurons and CNL as a 2D Convolutional layer with adjustable number of filters, filter sizes and strides. Recurrent Neural Layers. The RNL is a crucial part of a speech recognition system as it can model temporal dependencies between samples in the audio utterances (i.e. it can learn to understand the context of the spoken sentences and utilize it for predicting the next character). The choice of RNL implementation was argued between the LSTM [16] and the GRU [17] cell architectures. These were designed to address the vanishing gradient [18], a common problem with serializing many layers/computations in which

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the gradients computed are so small that the layer parameters fail to be updated during backpropagation, resulting in very slow and inefficient training process especially for long-term time dependencies in the data [19]. The LSTM is by its nature more computationally complex than the GRU and provides comparable, if not worse, results to the GRU architecture as shown in [20]. Subsequently, we selected the GRU as our architecture of choice in the RNL parts of the AM. Bidirectional Recurrent Neural Layers. Regarding Recurrent Neural Network (RNN) utilization in automatic speech recognition, one key structure, dubbed Bidirectional RNN (BRNN) and proposed by Schuster and Paliwal [21], has been very effective throughout the years [22–27]. BRNN layer is built from two parallel RNN layers running in opposite directions timewise, i.e. forward-RNN layer runs forward in time (from start to current time step) and provides context from previous samples in the utterance, while backward-RNN layer runs backwards in time (from end to current time step) and thus provides context from future samples in the utterance. The outputs from forward-RNN and backward-RNN are then concatenated together, resulting in the final BRNN output in which each time-step was calculated with the consideration of every other time-sample of the utterance (as opposed to only the previous time-samples in the usual unidirectional RNN). As the BRNN is essentially two typical RNN layers running in parallel, any type of RNN cell architecture can also be used in BRNN variant. For reasons described above, we utilized BRNN layers with GRU cell architecture for our models, which we will refer to as bidirectional GRU or BGRU from this point forward. Activation Function. Each layer is typically followed by an activation function, which normalizes/truncates the layer output value and allows for a non-linear transformation throughout the layers. The choice of activation function affects the networks ability to propagate gradients throughout the layers and therefore affects its aptitude to learn during the training process. In our models, we used so-called Leaky Rectified Linear Units (LReLU) as the main type of activation function. The Leaky ReLU activation function was proposed in a paper by Maas et al. [28] for addressing a problem with neurons that stay at zero value throughout the training (essentially dead neurons) and thus do not contribute to the training process at all when the classical Rectified Linear Unit activation function is used. Therefore, the LReLU is defined as  aLReLU ¼

y y[0 for a
y y0

ð1Þ

where a is a hyperparameter with small value between 0 and 1 controlling the slope in the negative part of the domain of the LReLU function. This small nonzero slope prevents the occurrence of these dead neurons. 2.4

Batch Normalization

Batch Normalization (BN), proposed by Ioffe and Szegedy [9], is an effective regularization technique, which reduces layer input distribution changes due to shift in

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parameters of previous layers during training (referred as internal covariate shift). As stated in the article [9], reducing internal covariate shift between layers increases parameter stability (allowing for higher learning rates) and therefore reduces the number of steps needed for convergence of trainable parameters in the network. Subsequently, BN also reduces the need for correct parameter initialization and further minimizes the vanishing gradient problem, enabling deeper models to be trained. In its essence, Batch Normalization is based on a similar idea as network input normalization, which also acts as a regularization technique. The difference being that BN is implemented directly to the training pipeline as a BN layer and can be applied after every network layer. 2.5

Connectionist Temporal Classification

Published by Graves et al. [29], Connectionist Temporal Classification (CTC) was designed for labelling unsegmented sequences of speech utterances or other data with similar character. Before CTC, the input utterances had to be manually/automatically segmented by adding timestamps where each word/character starts and ends in the audio. This process, dubbed also as Forced Alignment (FA), was heavily researched and countless methods have been developed ensuring its automation and improvement [30–34]. Consequently, if the AM requires segmented data, FA acts as an additional necessary step before the training process itself and requires a thorough study of the subject matter to select the best implementation for the available data. Using CTC eliminates this step entirely as the labels do not have to be pre-aligned with the utterances. Instead, the CTC acts as a middleman that ensures the classification layer output of the AM, which is usually many times longer than the desired number of characters in its label, is correctly reduced into the desired transcript and then compared with the true label of the utterance.

Fig. 1. Typical Acoustic Model (AM) pipeline with Connectionist Temporal Classification (CTC) transforming utterance “apple” preprocessed as MFCC features into a final character-level transcript. The number to character (n2c) transformation is added only for clarity.

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As an example, seen in Fig. 1, an audio utterance of the word “apple” is first processed by a simple classifier consisting of Bidirectional Recurrent Layer with a classification layer output (BRNL). The classifier does not implement any kind of timewise sample reduction (e.g. striding, pooling, etc.), which means that the resulting classification output has the same sample-length as the audio utterance. The CTC has to be able to reduce this output into the final alphabetic transcript, which can be many times shorter than the original number of samples in the utterance (depending on the sampling frequency of the audio). One of the allowed output classes of the classifier is the blank character, which exists only to be deleted by the CTC and in its essence signifies parts of the utterance where nothing of import has been detected. The CTC removes subsequent duplicate characters first and blank characters afterwards, producing the final decoded utterance transcript. For a more in-depth description of the CTC process, please refer to the original article [29].

3 Proposed Acoustic Model Our Acoustic Model (AM) is partly based on architectures that were successfully implemented for different languages in previous research and partly on an experimental development process, focused on compatibility for Czech language datasets, which will be described in detail in Sect. 4. The AM can be separated into two overarching parts: 1. The Classifier 2. The CTC transformation function 3.1

The Classifier

The Classifier is a DNN, which compounds of several serialized neural layers. The final version of the Classifier we propose consists of four convolutional layers (for additional feature extraction and compression) combined with Leaky ReLU (LRELU) activation followed by two bidirectional GRU layers (for capturing long and short-time dependencies between inputs) and finalized by two Feed-Forward layers (for adding more depth and training capacity to the model). Batch Normalization (BN) layers were implemented after every layer to speed-up and stabilize the training process as well. The terminal layer of the DNN is a classification layer, a Feed-Forward layer with 44 units (number of classes, i.e. unique characters for Czech alphabet plus a space and a blank character) followed by a Softmax [35] transformation function to produce a probability distribution across the allowed classes. A detailed architecture of the final version of the Classifier is provided in Fig. 2.

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Fig. 2. Final Classifier architecture utilizing Convolutional layers (CNL) with Leaky ReLU (LReLU) activation and Batch Normalization (BN) followed by two bidirectional GRU (BGRU) layers with BN and two Feed-Forward (FF) layers with LReLU and BN. The final layer is the classification layer with Softmax output for providing probability distribution over the character classes, which the CTC can then decode into transcripts. R stands for the number of time frames in an utterance. Black values show the size of layer inputs. Red values mark CNL filter sizes or number of units in BGRU and FF layers. Blue values signify strides of the CNL filters in their respective dimension.

3.2

The CTC Transformation Function

The Classifier is followed by a Connectionist Temporal Classification (CTC) transformation function, which decodes the classification layer outputs into time-independent alphabetic transcripts of the input speech utterances as explained in Sect. 2.5. During training, CTC loss [29] is computed by comparing decoded transcripts with the true labels, allowing for gradient calculation and model parameter updates.

4 Experimental Study The resulting Classifier architecture presented in the previous section is a product of an experimental study conducted on datasets containing naturally spoken utterances from two Czech language corpora, which are introduced in Sect. 4.1. 4.1

Utilized Datasets

To carry out the experiments, the training and validation sets had to be created by preprocessing and pruning the two following corpora:

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1. PDTSC 1.0 [7]: corpus with approx. 122 h of spoken Czech dialogues and their literal manual transcriptions taken as part of the Malach [36] and Companions [37] projects. As many of the recordings and their transcripts were sampled up to several minutes long with no pause, the AM was unable to learn from the full length of the dataset during the training process. Hence, any utterance shorter than 1 s (noise filtering) and longer than 30 s had to be pruned from the dataset, resulting in 55% of the corpus being unused. After pruning, the fully utilized length of the corpus approaches 55 h. 2. ORAL2013 [8]: corpus with approx. 291 h of spontaneous Czech conversations between two or more speakers with close relations and informal manner. The corpus is focused on balance in speaker gender and age and includes literal transcripts of all of the 1297 unique speakers from every region in the Czech Republic. Unlike PDTSC, the ORAL dataset was made to be easily segmented into utterances with a maximum of 15 word-long transcripts. Thus, only 3% of the corpus had to be removed due to extreme length. The full length of the utilized corpus is 282 h. The validation set was created from 10% excerpt of the PDTSC dataset (not used during training) with a focus on equal distribution of utterance lengths. The rest of PDTSC was taken as the training set. The ORAL dataset was utilized after a good Classifier architecture was found with the PDTSC dataset to ensure that the model can capitalize on more data. To elaborate, there are two sets of training data • PDTSC training set with 49.5 h (90% of the utilized PDTSC 1.0 corpus) of audio utterances • COMBINED training set with 331.5 h (90% of the utilized PDTSC 1.0 corpus and 100% of the utilized ORAL2013 corpus) and one universal set of validation data • PDTSC validation set with 5.5 h (10% of the utilized PDTSC 1.0 corpus) of audio utterances used for experiments. The audio utterances in training and validation sets were then converted into two distinct feature representations. Namely the popular and compact Mel-Frequency Cepstral Coefficients (MFCC) employing lossy Discrete Cosine Transform compression and the Mel-Frequency Spectral Coefficients (MFSC), which are less compact but carry full information about the original audio. With the datasets prepared and their audio converted into feature representations, we could begin the experimental phase. 4.2

Acoustic Model Classifier Experimental Results

Because performance of DNN based classifiers for the Czech language natural speech was never publicly documented before, we developed, trained and tested the accuracy of several Classifier architectures using different feature types and datasets. Throughout

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the experimentation process, four important stages of Classifier architecture changes were made. These are later provided with a numeric identifier (such as #1, #2, etc.) ordered chronologically as they were implemented for a simple distinction between them. The transcript inaccuracy for validation dataset was measured by calculating the Character Error Rates (CER) based on Levenshtein edit distance [38], which can be described as the minimum number of character-level edit operations (ne ) needed for transforming the predicted transcript into the true label transcript for the respective utterance. To normalize the error rate into a percentage representation, the edit distance is divided by the length of the true label transcripts. The CER is thus calculated as CER ¼

ne l

ð2Þ

where ne is the edit distance and l is the true label transcript length. As a more intuitive (although only approximate) explanation, the CER value can also be understood as a percentage of characters being wrong in the predicted transcript. In other words, CER value of 0:0 (0%) signifies that there is no wrong character in the predicted transcript, therefore the prediction is entirely correct and fully corresponds with the true label transcript, while CER value of 1:0 (100%) would imply that every character in the prediction is incorrect. Each experiment was repeated five times (excluding first, which was only repeated three times due to technical difficulties) and the resulting value of CER on the validation set was recorded. To prevent overfitting, early stopping was implemented with a patience of 3 epochs. Mean value and variance of CER are calculated for each experiment to investigate overall precision and stability. The final column signifies the relative accuracy increase percentage over the previous experiment configuration gained as  arel ¼

CERi 1 CERi1


100

ð3Þ

where CERi is the mean Character Error Rate from the current experiment configuration and CERi1 is the mean Character Error Rate from the previous experiment configuration. In Table 1, all important experimental results are summarized. The table provides information about the dataset and feature type being used for training as well as an overview of the Classifier id and architecture, the overall number of trainable parameters and the average time needed for one training epoch.

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Table 1. Dataset, feature type and Classifier architecture settings and Character Error Rate (CER) results for conducted experiments. CNL marks 2D convolutional layers, FF stands for Fully-Connected Feed-Forward (Dense) layers and BGRU for bidirectional Gated Recurrent Unit layers. CNL and FF layers are also implicitly followed be Leaky ReLU activation, which was omitted for compactness. If -BN is included, each respective layer is followed by Batch Normalization layer. Important changes from previous rows are highlighted in Bold red text. Dataset

Feature type

Classifier Architecture ID and layers

Trainable Mean CER parameters epochs min

CER CER rel. acc. mean variance increase arel (%)

PDTSC

MFCC

#1 3 CNL-BN 2 BGRU

17 384 108

20

0.424 3.6E-06

PDTSC

MFSC

#1 3 CNL-BN 2 BGRU

19 758 892

18

PDTSC

MFSC

#2 3 CNL-BN 2 BGRU-BN

19 762 988

8

PDTSC

MFSC

#3 3 CNL-BN 2 BGRU-BN 2 FF-BN

3 480 972

7

PDTSC

MFSC

#4 4 CNL-BN 2 BGRU-BN 2 FF-BN

9 254 668 12

COMBINED MFSC

#4 4 CNL-BN 2 BGRU-BN 2 FF-BN

9 254 668

9

COMBINED

#3 3 CNL-BN 2 BGRU-BN 2 FF-BN

1 769 484

6

MFSC

0.427 0.423 0.423 0.387 0.386 0.395 0.396 0.373 0.382 0.353 0.352 0.372 0.346 0.331 0.326 0.344 0.341 0.348 0.309 0.309 0.316 0.311 0.309 0.265 0.268 0.251 0.274 0.273 0.352 0.352 0.357 0.350 0.348

0.387 6.8E-05

8.7

0.361 1.9E-04

6.8

0.338 6.8E-05

6.4

0.311 7.4E-06

8.0

0.266 7.0E-05

14.4

0.352 8.8E-06 -32.2

Classifier #1 is built with 3 Convolutional layers with LReLU activations and Batch Normalization (CNL-BN) followed by two bidirectional GRU (BGRU) layers. The convolutional layers provide additional trainable feature extraction and selection as well as an effective method for reducing the computational complexity of the model

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utilizing the stride parameter. The stride parameter controls the step of CNL filters throughout the input structure. Setting stride to a value of 2 in the time axis results in halving the length of the CNL input, thus stride of 2 for two subsequent CN layers provide 22 times reduction in the number of time frames in the final CNL output compared to the Classifier input (feature representation of the utterance) while still keeping the essential information about the utterance. We also observed that the reduction of time-length higher than 4 times resulted in poor performance of the model on the validation set, which is likely caused by insufficient time-length of the Classifier outputs for the CTC to reduce into final transcript prediction. For this reason, only the first two CN layers were set to stride 2. The following BGRU layers are important for discerning time dependencies between frames of the utterances but suffer from high computational and memory requirements. The reduced time-length from CNL layers helps to eliminate this bottleneck. Classifier #1 was first tested on the MFCC transformed PDTSC dataset, which reached the mean CER value of 0:424 on the validation set, meaning that about 42:4% of the characters in predicted transcripts had to be edited to match the true label transcripts. Consequently, we tested if the Classifiers accuracy increases by using the MFSC feature representations instead of the MFCC. The resulting mean CER of 0:387 shows a relative 8:7% accuracy improvement over the MFCC features, which means that the deep model can utilize the additional information present in the uncompressed MFSC features to reach better generalization during training. MFSC features were thus considered beneficial for our DNN classifier and was used as the main feature representation from now on. The negative aspect of using MFSC representations is about 315% increase in drive space requirements, which can prove difficult to store for larger datasets. Classifier #2 is inspired by Deep Speech 2 model by Amodei et al. [3] where BN layers, while originally only designed for feed-forward and convolutional layers [9], were tweaked to be implemented after recurrent layers. The motivation was to increase layer parameter stability and thus decrease the number of training epochs needed for model parameter convergence, which was reported successful. Although our Classifier #1 was stable enough to converge, the number of epochs for convergence was high and training was time-consuming. Therefore, we opted to implement BGRU compatible Batch Normalization layers and created Classifier #2, which has BN layers after both BGRU layers. The results were noticeable, as the mean number of training epochs was more than halved compared to Classifier #1 (from 18 to 8). The BGRU-BN layers also proved to increase training stability as the learning rate of Classifier #2 could be increased tenfold over Classifier #1. The mean CER value of 0:361 (#2) also shows a relative 6:8% increase in accuracy over the 0.387 (#1) with the same data and comparable number of trainable parameters. These results reveal that implementing BGRUBN also serves as a regularization technique and allows Classifier #2 to better generalize on identical training data. Classifier #3 explores the idea of expanding the model with Feed-Forward layers with Batch Normalization after the BGRU-BN layers. These FF-BN layers are implemented to allow the Classifier to further post-process the BGRU-BN layer outputs and reduce the layer dimensionality more gently than directly feeding the thousands of values from BGRU-BN into the 44-value classification layer. FF-BN layers also serve

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to increase the depth and therefore the training capacity of the model significantly, which is important for the model to utilize bigger datasets. Consequently, the increase in depth also allowed us to greatly reduce the number of trainable parameters while not experiencing underfitting and still reaching better results than Classifier #2 on the PDTSC MFSC dataset. The Classifier #3 averaged at 0:338 CER, which is another 6:4% relative increase in accuracy over the 0:361 CER of Classifier #2. The experiments also revealed that in the early epochs of training the validation set CER values displayed high variability dependant on parameter initialization and random data shuffling between individual experiment runs, signifying possible stability issues. Nonetheless, the optimizer was always able to reach similar validation CER values. Classifier #4 expands on the idea of increasing model depth to enhance training capacity by adding one more CNL-BN layer at the beginning of the model. The strides of CNL are still set so that only 4 times reduction in time-length is achieved. The additional CNL-BN layer allows for finding more complex structures in the utterance feature representations, which further helps to increase generalization. The number of trainable parameters was also increased almost threefold (from 3.5 million to 9.3 million) because lower values resulted in training instabilities. With this configuration, Classifier #4 was able to achieve a mean CER of 0:311 compared to 0:338 CER of Classifier #3 (relative accuracy increase of 8:0%) on the PDTSC MFSC dataset. However, on average 5 more epochs were needed for the convergence of parameters during the training compared to Classifier #3, which might be caused by vanishing gradient being more prominent as the number of layers increases. This suspicion was further supported by doing tests on deeper architectures by adding more FF-BN and CNN-BN layers to Classifier #4, which lead to the optimizer being unable to reach stable competitive CER values to the previously tested Classifier architectures. The four classifier’s layer architecture overview as they were developed can be seen and compared in Fig. 3. Further down follow the experiments with the COMBINED dataset, which was obtained by combining the PDTSC training set with the ORAL training set as it was explained in Sect. 4.1. Training Classifier #4 with COMBINED dataset was conducted to determine how well the classifier fares on bigger training sets with architecture and settings unchanged from the previous experiment. Overall time length of the COMBINED training set amounts to almost 7 times the time length of the PDTSC training set. Unexpectedly, one training epoch took only 4 times longer to finish, which was later attributed to the lower average length of the utterances in the ORAL dataset (compared to the PDTSC) and the model being able to process shorter utterances more efficiently. With the COMBINED dataset, the mean CER values have dropped significantly from 0.311 to 0.266 (approx. 14.4% relative accuracy increase). The average number of epochs to convergence has also lowered from 12 to 9. Training Classifier #3 with COMBINED dataset was a final experiment for evaluating smaller classifier performance with low number of trainable parameters on bigger training sets. The resulting CER values averaged around 0.352, meaning a 32; 2% decrease in relative accuracy compared to Classifier #4 on the COMBINED dataset. The small classifier was unable to effectively learn a complex enough transformation to generalize on the whole training dataset and although the Classifier #3 is

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smaller, the average training times per epoch slightly increased. A possible explanation is that the classifier itself is not the computational bottleneck in the system.

Fig. 3. Classifier architecture comparison. Pink colour marks neural layers such as FeedForward (FF), Convolutional (CNL) and bidirectional GRU (BGRU). Green signifies activation functions, namely Leaky ReLU and Softmax. Blue is reserved for Batch Normalization layers. Each group of structures (marked by right-hand brackets) can repeat in the layer series for several (N) times before the next group continues, which is signified by the Nx on the right side of the brackets.

5 Conclusions Research on Czech language automatic speech recognition (ASR) was conducted with the utilization of an Acoustic Model based on a Deep Neural Network Classifier with Connectionist Temporal Classification transformation to produce character-level transcript predictions from audio utterances. Acoustic Model architecture for Czech language ASR was proposed in Sect. 3, which draws from previous research for other languages and was developed based on the experimental research conducted in Sect. 4 on the PDTSC 1.0 and ORAL2013 datasets. Section 4 describes the process of preparing the PDTSC and COMBINED training and validation sets (Sect. 4.1) and gradually developing 4 Classifiers (Sect. 4.2), each building on the knowledge gained in the previous version. The full configurations and results of all the experiments can be seen in Table 1. Classifier #1 had 3 convolutional layers with Leaky ReLU activations and Batch Normalization (CNL-BN) followed by 2-layer bidirectional recurrent layers with GRU cell architecture (BGRU). By training the Classifier on PDTSC dataset with both MFCC and MFSC feature representations, we determined that the MFSC is better suited for our DNN-based classifier (8:7% relative accuracy increase on the validation set). Further training was done with MFSC feature representations exclusively. In Classifier #2 we implemented BGRU compatible BN layers after each BGRU layer from Classifier #1. From the results it is safe to say

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that implementing Batch Normalization after Recurrent Layers benefits the classifiers accuracy (6:8% relative accuracy increase) and parameter stability, allowing for higher learning rates and fewer epochs needed for parameter convergence during training. Classifier #3 was enriched by 2 Feed-Forward-BN layers placed after the BGRU-BN layers. The layers increased classifier depth and BGRU post-processing, which enabled to significantly lower the number of trainable parameters while still keeping the same training capacity and achieving better results than Classifier #2 (6:4% relative accuracy increase). In the final Classifier (#4), the training capacity and generalization capability of the model was further expanded by increasing the number of CNL-BN layers from 3 to 4 and rising the number of trainable parameters, resulting in further 8:0% accuracy increase. Consequently, the number of training epochs needed to converge slightly increased. This phenomenon was reasoned to be caused by the vanishing gradient effect being more prominent in deeper structures and further increase of classifier depth was not advised. Subsequently, Classifier #4 was trained on the COMBINED training set, achieving another 14:4% relative accuracy improvement on the validation set. Finally, Classifier #3 with a reduced number of trainable parameters was trained on the COMBINED dataset but it was unable to achieve comparable results to Classifier #4. Based on the experimental results in Sect. 4, Classifier #4 and therefore the whole Acoustic Model architecture and configuration explained in Sect. 3 was determined to be a competitive baseline for further Czech language speech recognition research and sets the results of 31:1% CER on a relatively small naturally spoken Czech dataset with 49:5 hours of audio utterances (PDTSC training set) and 26:6% CER on large naturally spoken Czech language dataset with 331.5 h of audio utterances (COMBINED training set).

References 1. Nassif, A.B., Shahin, I., Attili, I., Azzeh, M., Shaalan, K.: Speech recognition using deep neural networks: a systematic review. IEEE Access. 7, 19143–19165 (2019). https://doi.org/ 10.1109/ACCESS.2019.2896880 2. Hannun, A., Case, C., Casper, J., Catanzaro, B., Diamos, G., Elsen, E., Prenger, R., Satheesh, S., Sengupta, S., Coates, A., Ng, A.Y.: Deep speech: Scaling up end-to-end speech recognition. ArXiv14125567 Cs. (2014) 3. Amodei, D., Anubhai, R., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Chen, J., Chrzanowski, M., Coates, A., Diamos, G., Elsen, E., Engel, J., Fan, L., Fougner, C., Han, T., Hannun, A.Y., Jun, B., LeGresley, P., Lin, L., Narang, S., Ng, A.Y., Ozair, S., Prenger, R., Raiman, J., Satheesh, S., Seetapun, D., Sengupta, S., Wang, Y., Wang, Z., Wang, C., Xiao, B., Yogatama, D., Zhan, J., Zhu, Z.: Deep speech 2: end-to-end speech recognition in English and Mandarin. CoRR. abs/1512.02595 (2015) 4. Battenberg, E., Chen, J., Child, R., Coates, A., Li, Y.G.Y., Liu, H., Satheesh, S., Sriram, A., Zhu, Z.: Exploring neural transducers for end-to-end speech recognition. In: 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 206–213. IEEE (2017) 5. Collobert, R., Puhrsch, C., Synnaeve, G.: Wav2Letter: an End-to-End ConvNet-based Speech Recognition System. CoRR. abs/1609.03193 (2016)

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Problems of Software Developing for the Automation of Scientific Activities Alexander V. Solovyev(&) and Irina V. Tumanova Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 44/2 Vavilova Street, Moscow 119333, Russian Federation [email protected]

Abstract. The article discusses the problems of software development for the automation of scientific activities. The analysis of the complex problem of automation of scientific research. The history of the creation of such systems is briefly reviewed. A review of the main automation systems is made, the basic algorithm for the functioning of such systems is determined. A system of indicators of the effectiveness of automation of scientific activity is considered. The reasons for the complexity of creating software are described. The main business process of scientific activity is determined. This process is the study and analysis of scientific information in scientific databases, databases of patents and inventions, scientific libraries. The tasks that require automation to improve the effectiveness of the scientific activities of the organization are considered. Such tasks include tasks: experiment management, the activities of scientists and dissertation councils, the preparation of scientific events, editorial and publishing activities, the expert evaluation of scientific projects, the work of postgraduate and doctoral departments, and a number of other tasks. For each type of automated activity, an approximate composition of the functional is determined. The software structure is defined. It is concluded that the development of such a system is most effective and expedient at the level of not the organization, but the industry as a whole because of the complexity of such an automation system. The prospects for further research on the topic of software development for the automation of scientific activities are outlined. #COMESYSO1120. Keywords: Software development
Automation of scientific activities Automated systems for scientific research




1 Literature and Problem Review 1.1

Introduction

This article analyzes the main processes of scientific activity, identifies their specific features, describes those that can be automated in order to increase the efficiency of scientific research. Of course, to automate scientific activity, it is necessary to create special software that allows you to automate both the main stages of scientific research and the activities of the scientific organization as a whole.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 186–199, 2020. https://doi.org/10.1007/978-3-030-63322-6_15

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However, the development of such software is faced with a number of significant difficulties. This article is devoted to the analysis of the problems of developing software for the automation of scientific activity and possible solutions. 1.2

A Brief Overview of the Problem of Automation of Scientific Activity

There is no doubt the following statement that the main result of any scientific activity is the acquisition of new knowledge. We formulate the problem from this point of scientific organization: the production of new knowledge, presented in the form of content and proven information, executed in the form of the results of scientific work (articles, monographs, reports on scientific research, methodical manuals, etc.). It should also be noted that the faster and more reliable the reliability of the acquired knowledge can be confirmed by testing it in practice or at least for the consistency of models and methods, the more effective the scientific activity. An exception may be fundamental research, the reflection and verification of which may take years or even decades. Of course, from the above it is impossible to unequivocally derive the formula for the effectiveness of scientific research. The fact is that scientific activity is in many respects a difficult to predict creative process, the result of which may differ from the originally planned or supposed. This, on the one hand, complicates the automation of scientific activity, on the other hand, makes the human factor (or Human Reliability Assessment – HRA) influence on the result of scientific activity significant. In many respects, the result depends on perseverance, qualifications, attentiveness and, possibly, intuition of the scientist conducting the research. For example, the system of economic performance indicators proposed in GOST 24.702-85 “Unified system of standards for automated control systems. The effectiveness of automated control systems” is hardly suitable as universal for evaluating the effectiveness of automation of scientific research. Such a conclusion can be made, if only on the basis that the developments in the field of fundamental science cannot give an immediate economic effect in annual terms. The indicator system proposed in GOST 34.003-90 “Automated Systems” is much more flexible in terms of evaluating the effectiveness of scientific research automation. According to this document, the effectiveness of an automated system is understood as “a property characterized by the degree of achievement of the goals set during its creation.” Then in the general case, the following set of indicators can be taken as performance criteria: – completeness of automation of functions of scientific activity; – timeliness (response time) of scientific functions; – the accuracy of the analysis of experimental results. Economic and social indicators are not given here, since they do not have such universality and their purpose will depend on the specific decision on the automation of scientific activity. It should be noted that automation of scientific research began in the USSR: in 1980, a document was issued by the USSR State Committee for Science and Technology (SCST) “Industry-wide guidance methodological materials on the creation of

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automated research systems and integrated testing of new equipment samples” (SCST of the USSR, Moscow, 1980). The document, in particular, said that “The use of automated systems for scientific research and integrated testing of samples of new technology (ASSR) is most effective in those modern areas of science and technology that deal with the use of large amounts of information”. 40 years ago, the fields of science and technology dealing with large volumes of information (big data) included: physics (in particular, nuclear, geophysics, astronomy, space research, etc.), energy, medicine (diagnostics, microbiology), research chemical, technological, transport processes. Thus, ASSR were primarily focused on introducing into those areas of science where it is possible to operate with digital data obtained during experiments, summarizing and analyzing them. In fact, the review of ASSR systems given in [1] confirms that systems can be effectively implemented in areas where the main business process of scientific research includes the following stages (see Fig. 1).

Fig. 1. Typical automated research process in modern ASSR.

Of course, each of these stages is a separate business process. So, for example, the design of the experiment will include strategic and tactical planning, preparation and adjustment of the design of the experiment, etc.

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The review given in [1] shows that the most successful and brought to the stage of industrial solutions of ASSR are precisely in the field of experimental physics: EPICS [2], TANGO [3], SCADA [4]. In the latter, however, revealed significant problems with information security [5]. With some stretch, an example system may be ASSR Open-notebook science [6, 7] are designed to open publication of the results of experiments and the experimental data, and to search them. It is more of a system containing information for researchers than a system designed to automate scientific activities. Systems developed in the Russian Federation that can be assigned with some assumption to the ASSR class are systems based on “1C: Enterprise” [8], in [9–11], concepts and approaches to the automation of scientific activity that confirm its necessity are considered and relevance. The closest analogue of ASSR can be ERPsystems, which are primarily designed to automate the management of production and labor resources, processes, financial management and asset and material resources, but do not automate the actual business processes of scientific activity. Recently, quite successful private solutions to hotel scientific problems of educational institutions (see, for example, [12]) and the creation of systems for evaluating scientific activity (see [13]) have appeared. There are also attempts to solve the problem of creating a science automation system using the Content Management System (CMS) [14]. However, this approach, in fact, turns the system into a huge warehouse of scientific materials, the work in which with the growth of volume becomes extremely difficult. The solution to the problem of automation of science only on the basis of an electronic document management system [15] also does not have universality, because such a system is primarily focused on the operational movement of documents within the organization, but it cannot solve the problem of quickly finding the necessary scientific information.

2 Development of Software for the Automation of Scientific Activities 2.1

Automation of the Main Activities of a Scientific Organization

Automation of the creative process of acquiring new knowledge is complex for a number of reasons, including: 1. versatility of scientific activity: a variety of areas of knowledge, description languages and conceptual apparatus, a variety of temporal and spatial scales of research. All this makes it difficult to create a universal business process that automates scientific research regardless of the field of knowledge, scale and nature of research; 2. a high level of uncertainty in the duration and results of research, and a negative result is also a result, makes it difficult to apply generally accepted technology for strict planning and control of deadlines to the area under consideration;

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3. interdisciplinarity of scientific activity: recently, an increasing number of results are achieved at the junction of scientific disciplines. However, a fundamental problem arises here: the complexity of the transfer of knowledge between representatives of different fields of science that exist within the framework of different paradigms. Recently, a lot of attention has been paid to this problem in the scientific community (see, for example, [16–19]); 4. a huge amount of scientific information requiring study and analysis by scientists; 5. the influence of the HRA on the results of the study: ASSR may, on occasion, better conduct and control the experiment (the reason is that the machine makes fewer mistakes when setting the parameters of the experiment and taking statistics, unlike a person, confirmation of this is the widespread use of systems such as EPICS for processing the data of experimental results), process the obtained results, but cannot engage in creative activity. In addition, the timing and quality of the study, not to mention the conclusions made, directly depend on the abilities of the person conducting them (see [20, 21]), and this, in turn, introduces additional unpredictability to the result of scientific research -giving. The main objective of scientific activity is to obtain new knowledge. It follows from this that when automating the main activity of a scientific organization, it is necessary to facilitate as much as possible for a scientist-researcher access to information about other studies conducted on this topic. The document “Industry-wide guidance materials for creating automated systems for scientific research and integrated testing of new equipment samples” (State Committee for Science and Technology of the USSR, Moscow, 1980) states that ASSR are applicable to areas of knowledge with a lot of information. However, in modern conditions, a huge amount of information accumulates in any field of science. Therefore, the main task in automating the activities of a scientific organization has changed and consists in reducing the time of access to scientific information on research problems. Currently, the number of articles published by the world scientific community is so large that even a cursory review of them becomes an impossible task. This raises the problem of processing the so-called “big data”, and in conditions of an avalanche-like growth in their volume. Therefore, specific methods should be applied to them, namely, intellectual analysis in order to search for similar research topics, contradictions and repetitions (duplication), as well as analysis of annotations in order to identify the essence of the knowledge gained. With this formulation of the problem, the main business process of scientific activity is logically simple on the one hand, and extremely difficult to implement on the other. In essence, we are talking about the automation of work with scientific databases (Scopus, Web of Science, RSCI, Google Scholar, etc.), databases of patents and inventions (for example, the database of the Federal Institute of Industrial Property (FIPS) has an open part with the ability to search), scientific libraries, etc. Moreover, for effective work there should be the ability to search for information from different sources through a single interface, the ability to create analytical requests for information, including annotations and texts of publications.

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The complexity of the implementation of such a process is due to the fact that scientific databases are difficult to access, obtaining texts of articles can be paid, each database has its own search system. Thus, the development of a common search information system is required, which will make the search independent of a specific scientific base. Although the idea of creating such a system is not new in itself. So a few years ago, initiatives appeared in different countries, the purpose of which was to create open repositories of scientific information (see, for example, “Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities” (http://www.zim.mpg.de/ openaccess-berlin/berlin_declaration.pdf), “Budapest Open Access Initiative” (http:// www.budapestopenaccessinitiative.org/), “International Federation of Library Associations and Institutions” (http://www.ifla.org/)). A separate issue is the complexity of creating such a search engine, because it should support not only keyword searches, but also a complex intellectual analysis of the texts of scientific publications. An example of such a query system is the Exactus Expert system (http://expert.exactus.ru/). However, the problem of developing and using such a system will be the need to create an index base of scientific publications, which will require significant technical costs for a scientific organization. In addition, such a system should also be equipped with automated translation options to reduce the time for acquaintance with scientific literature in the native language, as well as to translate search queries into other languages. The solution here could be to create a common search system at the level of the Academy of Sciences or the Ministry of Science and Higher Education of the Russian Federation. 2.2

Complex Automation of Scientific Activities of the Organization

In addition to the task of working with scientific publications, inventions and discoveries, libraries, the activities of a scientific organization include a number of business processes that can be automated, thereby increasing their efficiency. Such automated tasks include: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Experiment management automation. Scientific council automation. Automation of the preparation of scientific events. Publishing automation. Scientific library automation. Automation of dissertation councils. Automation of postgraduate and doctoral departments. Automation of the activity of scientific and educational centers. Automation of expert evaluation of scientific projects. Automation of reporting. Staff recruitment automation. Automation of the evaluation of scientific activities of scientists. Other types of automated activities of a scientific organization, which are also specific in terms of work on scientific projects. These types include contract

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management activities with other organizations, as well as document management and paperwork. A brief description of the functionality of the software to automate the scientific activities of the organization is possible using the following Table 1. Table 1. Description of the functionality of the software for automation of scientific activities of the organization. Activities

Description of program functionality 1. Experiment – determination of the parameters management of the experimental setup; – determination of external factors affecting the course and results of ongoing experiments; – determination of levels and ranges of factor values; – identification of possible interference; – experiment planning; – conducting an experiment; – collection and processing of experimental results with their subsequent generalization, visualization; – organization of storage of the obtained experimental data. 2. Scientific council – planning the work of the council; activities – collection of necessary information materials; – distribution of materials and agendas; – automation of meetings; – control over the implementation of council decisions; – search for materials related to the activities of scientific councils; – organization of archival storage of materials, minutes of meetings, decisions and reports on their implementation, video recordings of meetings.

Comments Most modern ASSRs automate precisely this activity (TANGO, ECLIPS, SCADA).

These types of activities can be automated by special means of electronic document management systems and supplemented by means of conferences.

(continued)

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Table 1. (continued) Activities

Description of program functionality 3. Preparation of – event planning; scientific events – collection of proposals and compilation of programs; – sending invitations to the event; – collection of applications from participants; – preparation and reviewing of received materials, including texts of reports; – selection of reviewers for a given topic of the event; – preparation of draft decisions; – search for event materials; – organization of control over the implementation of decisions; – storage of event materials; – reports on the events carried out. 4. Publishing – collection and review of articles; – maintaining a database of reviewers; – automated selection of reviewers in accordance with the subject of the article; – organization of storage of articles (including their versions and reviews), materials and collections; – interaction with authors of articles and publications (sending out notifications, reviews, reviews, collecting articles, etc.) – search for materials, articles, publications; – layout of issues, including the formation of original mock-ups taking into account restrictions on the volume. 5. Scientific library Automation of activities implies, in management essence, the creation of an electronic library system (ELS), which includes a search system, as well as bibliographic card design programs.

Comments By scientific events we mean the holding of seminars, conferences, round tables, etc. Automation of this activity can be solved using a specialized electronic document management system, supplemented by an analytical expert search system.

As automation tools, the use of publishing systems with the possibility of typesetting original layouts can be suggested. Additionally, we need an analytical system for searching reviewers, as well as a system for electronic interaction with authors and reviewers (like CRM (Customer relationship management) systems).

ELS - is a database containing publications of scientific, methodological and other literature used in scientific activities.

(continued)

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Activities 6. Activity of dissertation councils

7. Activities of postgraduate and doctoral departments

8. Activities of scientific and educational centers

Description of program functionality – acceptance of documents; – selection of opponents and expert groups on the dissertation in accordance with its specialization; – publication of the text of the dissertation on the organization’s website; – automatic tracking of deadlines for submitting documents; – pre-defense and consideration of dissertations with the formation of an appropriate set of documents; – the formation of the case; – preparation and sending of documents to the Higher Attestation Commission; – holding meetings of dissertation councils; – preparation of council decisions; – carrying out defenses of dissertations; – distribution of documents to applicants, council members; – storage and retrieval of materials; – maintaining an archive of materials. – acceptance of applications and documents from applicants; – preparation of training programs; – preparation and conduct of exams; – organization of electronic interaction with graduate students and doctoral students; – control of individual plans; – preparation, search and storage of various postgraduate documents. A business process may include tasks such as: – drawing up and sending invitations to teachers; – automation of thematic lecture courses; – preparation of necessary documents; – performance monitoring, etc.

Comments Automation requires a specialized software solution.

Automation requires a specialized software solution.

Automation requires a specialized software solution.

(continued)

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Table 1. (continued) Activities

Description of program functionality 9. Expert evaluation – acceptance of applications for of scientific projects examination; – maintaining a database of experts; – search for experts in accordance with the subject of the project; – processing expert opinions and assessing the quality of their work. 10. Reporting In addition to their main activity, scientists have to spend quite a lot of time on: – preparation of applications for grants; – reports on completed research; – maintaining a list of publications; – drawing up research plans, etc. 11. Staff The specificity of this business recruitment process for a scientific organization is that scientists are, one might say, “piece goods”. Typical HR agencies and services may not identify the right qualifications, as usually do not have the appropriate competencies. When evaluating candidates, it is necessary to analyze the data on the candidate taking into account the requirements for him, including an automated analysis of the results of scientific activities. The personnel selection automation subsystem can be an addition to the organization’s personnel system, or, together with the personnel system, it can be a specialized HRM solution. 12. Assessment of The complexity of the assessment scientific activity lies in the need to create a system of criteria to most fully evaluate the results of a scientist’s scientific activity. In addition, the assessment should be carried out automatically, then all the results of scientific activities should be stored in the ASSR, automatically taken into account, classified and evaluated.

Comments Similar interaction models were developed by the author of the study for the task of selecting experts [22].

It is necessary to develop a specialized reporting system to minimize the scientist’s time for routine work.

Human Resources Management (HRM) – information systems designed for human resource management, aimed at providing the organization with high-quality personnel capable of performing the labor functions assigned to it, and its optimal use.

It is necessary to develop a specialized software solution.

(continued)

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Activities Analysis of scientific information

Description of program functionality Automation of work with scientific databases, databases of patents and inventions, scientific libraries. Search for information from various sources through a single interface, the ability to create analytical queries of information, including annotations and texts of publications. Automated translation of scientific literature to reduce the time for acquaintance with scientists, as well as automatic translation of search queries into other languages.

Comments Automation can hardly be solved at the organization level due to the large amounts of data. It is necessary to create a common system at the level of the Academy of Sciences or the Ministry of Science and Higher Education of the Russian Federation.

Then the structure of the ASSR software can be represented as follows (see Fig. 2).

Fig. 2. ASSR layered software structure.

Not all business processes that are characteristic of the work of a scientific organization are listed here, but even such a short list provides a voluminous picture of the diversity and heterogeneity of the problem of creating software for automating scientific activities.

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3 Conclusion As can be seen from the materials of the article, the development of software for the automation of scientific activity is an extremely difficult task. So, for example, in [23, 24] it was rather convincingly proved that in the digital economy, to improve the functioning of a scientific organization, it is necessary not so much a unified system of automation of scientific activity as an integrated management system that automates all aspects of the organization’s life. Moreover, by the nature of the activity, this system may have the functions of ERP, SRM, CRM, HRM, EBS, EDMS, electronic archive. However, such a creation of a monster system within the organization will lead to the fact that there is guaranteed not enough money to develop a really powerful and necessary system that can analyze the “big data” of scientific research. The development of private solutions and their complex integration will not lead to the necessary increase in the effectiveness of scientific research. Thus, the creation of a powerful automated information system is possible only at the industry level. That is, the point is that ASSR can be developed and exist within the framework of the scientific industry as a whole. Perform their functions in order to increase the effectiveness of scientific research within the state as a whole. Only in this case, according to the authors, is it possible to create a similar system, and, therefore, bring scientific research to a qualitatively new level. In the same article, the main aspects of the task of automating scientific activities and developing specialized software for this are considered. In the future, the authors plan to describe in detail business processes that are automated within the framework of each activity of a scientific organization described in the article. It is also planned to develop mathematical models for evaluating the effectiveness of the functioning of automation software for scientific activities. In the future, it is also planned to develop mathematical models for assessing the scientific activity of scientists.

References 1. Fomichev, N.I.: Automated research systems [Avtomatizirovannyye sistemy nauchnykh issledovaniy]. Yaroslavl State University, Yaroslavl (2001). ISBN 5-8397-0156-4 2. Experimental physics and industrial control system. Aragone national laboratory. https:// epics.anl.gov. Accessed 01 May 2020 3. What is Tango Controls? TANGO.https://www.tango-controls.org/what-tango-controls/. Accessed 03 May 2020 4. Supervisory control and data acquisition. Encyclopedia. https://www.pcmag.com/ encyclopedia/term/50832/scada. Accessed 03 May 2020 5. Unpatched vulnerabilities detected in SCADA systems [V SCADA-sistemakh obnaruzheny nepodlezhashchiye ispravleniyu uyazvimosti] SecurityLab.ru. https://www.securitylab.ru/ news/482454.php. Accessed 04 May 2020 6. Researcher is an open book: first to share lab notes in real time. University of Toronto faculty of medicine. https://medicine.utoronto.ca/news/researcher-open-book-first-share-lab-notesreal-time. Accessed 06 May 2020 7. Webcast: open lab notebooks: an extreme open science initiative, SGC channel, 19 Jan 2018. https://www.youtube.com/watch?v=vxoxKVUWsUY. Accessed 07 May 2020

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8. Automation of scientific and educational organizations. Infocom-S [Avtomatizatsiya nauchnykh i obrazovatel’nykh organizatsiy. Infokom-S]. https://infocom-s.ru/solution/autoedu/science/. Accessed 05 May 2020 9. Karnaukhov, A.M.: Prospects for the digitalization of research activities in geological exploration [Perspektivy tsifrovizatsii issledovatel’skoy deyatel’nosti v geologorazvedke], Oil and gas geology. Theory and practice [Neftegazovaya geologiya. Teoriya i praktika], vol. 12, no. 4 (2017). http://www.ngtp.ru/rub/3/44_2017.pdf. Accessed 07 May 2020 https://doi. org/10.17353/2070-5379/44_2017 10. Rudnitskii, G.: Digitalization of scientific knowledge. IT-Manager [Tsifrovizatsiya nauchnykh znaniy]. https://www.it-world.ru/cionews/management/140747.html. Accessed 08 May 2020 11. Kiseleva, M.: What to expect from the digitalization of science in Russia. Siberian science news [Chego zhdat’ ot tsifrovizatsii nauki v Rossii. Novosti sibirskoy nauki]. http://www. sib-science.info/ru/news/uchenyy-bolshe-ne-sidit-v-29122017. Accessed 07 May 2020 12. Patskan, M.Y., Nikulikhin, V.G.: The principle of synergy in the integration of systems. Features of integration of the university’s own automated system with “1C” [Printsip sinergii pri integratsii sistem. Osobennosti integratsii sobstvennoy avtomatizirovannoy sistemy vuza s «1S»]. In: Chistov, D.V. (ed.) 18th International Scientific and Practical Conference. New information technologies in education: the use of 1C technologies for the development of competencies in the digital economy 2018, pp. 161–162. “1-C Publishing@ Ltd. Moscow (2018) 13. Egorova, D.V., Kuklina, E.A.: Development of an information system for evaluating the scientific activity of an employee of a research and production enterprise [Razrabotka informatsionnoy sistemy otsenki nauchnoy deyatel’nosti rabotnika nauchnoproizvodstvennogo predpriyatiya]. In: XVII International Scientific and Practical Conference World Science: Problems and InnovationS, pp. 70–72. ICSN “Science and Education”, Penza (2018) 14. Maican, C., Lixandroiu, R.: A system architecture based on open source enterprise content management systems for supporting educational institutions. Int. J. Inf. Manage. 36(2), 207– 214 (2016) 15. Gridina, E.G., Ezhov, E.A., Murasheva, O.V.: Features of creating an information system for managing the university’s activities in terms of indicators and organization of an electronic document management system [Osobennosti sozdaniya informatsionnoy sistemy upravleniya deyatel’nost’yu universiteta po pokazatelyam i organizatsii sistemy elektronnogo dokumentooborota]. Theoretical and applied issues of modern information technology [Teoreticheskiye i prikladnyye voprosy sovremennykh informatsionnykh tekhnologiy], pp. 6–10 (2015) 16. Mihalcea, A.D.: Knowledge transfer in organization of future. In: International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, vol. 17, no. 53, pp. 555–562 (2017) 17. Wang, C., Zuo, M., An, X.: Differential influences of perceived organizational factors on younger employees’ participation in offline and online intergenerational knowledge transfer. Int. J. Inf. Manage. 37(6), 650–663 (2017) 18. Bailey, M.: Absorptive capacity, international business knowledge transfer, and local adaptation. Australian Econ. History Rev. 57(2), 194–216 (2017) 19. Solovyev, A.V., Farsobina, V.V.: Assessment of the quality of knowledge transfer between carriers of different paradigms. In: Proceedings of the ISA RAS, vol. 69, no. 1, pp. 96–104 (2019). https://doi.org/10.14357/20790279190109

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E- Learning Readiness Frameworks and Models Irene Kolo(&)

and Tranos Zuva

Vaal University of Technology, Vanderbijlpark, South Africa [email protected], [email protected]

Abstract. E-readiness is considered as one of the critical characteristics for accomplishing successful implementation of e-learning in higher education. Without e-learning readiness, the benefits of e-learning such as time saving, flexible education delivery and training from anywhere at any time will not be reaped. The probability of failure in the adoption of the e-learning is very high. Therefore, this study researched on models and/or frameworks in literature to measure e-learning readiness. The models were selected using the inclusion and exclusion criteria. Keywords were used to search for relevant articles and only those published in or after the year 2000 were used in this study. Reputable academic research databases were used to retrieve relevant journal and conference articles. The results show that there are core factors that are common in almost all the reviewed models. The study found that technology, people, content, resources and, social and cultural are the factors that are used in most models/frameworks reviewed. This re-search helps researchers to find appropriate models or frameworks to use in determining the e-learning readiness in educational environment. Keywords: E-learning


Frameworks
Models

1 Introduction It was confirmed by [1] that “E-Readiness is recognized as one of the most critical aspects for achieving successful implementation of e-learning in higher education.” To build an effective e-learning environment many things need to be prepared, such as the readiness of technology, the readiness of educational institutions, and the readiness of the community. These preparations are important because it will affect the quality of elearning programs later on [2]. [3] says: “e-learning also includes advantages which are not found in traditional learning, such as: time for digesting the information and responding, enhanced communication among the learners, both with regard to quality and urgency, knowledge being acquired and transferred among the learners themselves, the ability to conduct an open discussion, where each learner gets more of an equal standing than in a face-toface discussion, access to information and to discussion ability, responses may be made around the clock with no restrictions, a higher motivation and involvement in the process on the part of the learners.”

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 200–211, 2020. https://doi.org/10.1007/978-3-030-63322-6_16

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There are many benefits offered by e-learning such as time saving, flexible education delivery and training from anywhere at any time. Last but not least is reduced costs of commuting from residences to the institution. The utmost importance of elearning is that every learner can study anywhere using their own without their educators. It was mentioned by [4] that “e-learning promotes learner-centered learning and enhances activities that promote collaboration, communication and interaction, and gives learners better experience and education effect [5].” To introduce e-learning in the schools it is imperative to look at the model that can best measure e-learning readiness. With the world facing the pandemic of Covid 19 in the current year, e-learning is the priority one in all the schools for the learners to catchup and interact with the educators and the classmates. The best model is needed hence readiness is important to avoid failure of e-learning implementation and the utilization of the resources. The arrangement of the paper is as follow: Sect. 2 is methodology, Sect. 3 is the related work where existing e-learning models are reviewed, while the results are discussed in Sect. 4. Conclusion is Sect. 5 and the last section is references.

2 Methodology The paper is explaining the different e-learning models. A systematic approach for the review of the literature on the e-learning models was selected. A systematic literature review was defined by [6] as “a process of identifying, assessing, and interpreting all available research evidence.” E-learning readiness and adoption frameworks or models were studied, only journals, international journals and primary studies were used. The models chosen are with both technology and/or content factors. The below table is the inclusion and exclusion criteria Table 1. Table 1. Selection table

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3 Reviewed Papers [8] mentioned that “culture must be supportive of learning, self-directed training and development are to be observed, and organization goals are to be aligned with elearning.” The proposed model consists of the organization’s culture, individual learners and technology. Figure 1. above was developed by [9] for e-learning readiness assessment in Kenya’s higher education institutions. There is one factor known as technology “that can be used to adapt a technological innovation in an organization [7].” It will not be easy or almost not possible to implement e-learning without the easy accessibility of the equipment [10]. Institutions are required to value e-learning in terms of cultural readiness [11].

Fig. 1. E-learning readiness model by [9]

Content readiness is “the driving engine of any system; for the purpose of this study, the readiness of e-learning is determined by the measurement of content readiness. That is, to determine easy availability of well-structured and reusable content [12].” Demographical factors, namely, gender, age and education level will be collected from all the participants [13]. [7] confirmed that “the people factor deals with the characteristics of all human resources of a company as individuals with a level of higher education are more likely to adopt an innovation than others.” As per [14], “The four main parameters that are used to develop the hybrid model are; technological readiness” [13, 15], culture readiness [16, 17], content readiness [12, 15, 16], and demographics factors [13].

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Figure 2. above is a model used to assess e-learning readiness in Kenya secondary schools. The model was adapted by [14] from the paper written by [5]. The model has three distinct phases, readiness, acceptance and training. There are components of elearning implementation studied by [14] i.e. people and technology. Students’ and teachers’ readiness to accept and use e-learning were measured. The readiness in this model include people (attitude, confidence and experience), technology (stability, software and hardware), content and school management. Then acceptance include perceived usefulness and perceived ease of use while training includes the teacher and the learner.

Fig. 2. E-learning readiness model [14]

Figure 3. is a model developed by [18], to assess Iran’s higher education e-learning readiness. The factors identified were equipment, policy, management, networks, content, standard, financial and human resource sources, security and culture. According to [19], “Policies and standards are essential to make any system or operation work successfully. Managing e-learning methods include the preparation of the required rules together with the design of an operative management system. With regard to the network, clear assessment should be done for e-learning to be successful. Culture is a factor that contributes to the e-learning environment. Equipment is critical, as it will make the method be operational in terms of technological equipment.” Figure 4. is model developed by [5]. This model was used to measure student elearning readiness of HEIs in Turkey. There are three phases including readiness, acceptance and training respectively. The readiness phase reflects four aspects, namely, the technology, content, people and institution. The aspects in readiness phase, based on people, was about attitude, confidence and experience, technology aspects were about stability, hardware and software, institutional aspects were about university,

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Fig. 3. E-learning readiness frameworks by [18]

faculty and department while content was about theory and practice. The acceptance phase included perceived ease of use and perceived usefulness. Ford, Ford [20] mentioned that,” when there is an implementation of a change, some problems are experienced, but it will be accepted at the later stage.” The third phase is training of leaners and teachers after accepting the e-learning (Fig. 5). According to [19], “there was a study conducted by [21] to identify the factors that affect the e-learning readiness process implemented by HEIs in Uganda. [21] anticipated ways to encourage the use of e-learning systems to improve the level of education in Uganda. Their analysis revealed that the factors that affect the implementation of e-learning systems and that are imperative to consider include awareness, culture and technology along with pedagogy and content. Regardless of the particular country in which the e-learning is to be implemented, these factors are significant and need to be considered and managed well in order to reduce the resistance level and to increase the results that are to be reached.” Table 2 below summarizes the developed models used to measure/assess the readiness of e-learning in different countries. “Most of the models were developed for use in business organizations, universities or higher education institutions. In addition, they were designed for use in developed countries whose e-maturity is high. Every system, (organization, culture, country and individual) has its own norms, for that measurement instruments that work in one country might not work for organizations in other countries [9].”

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Fig. 4. E-learning readiness model by [5]

Fig. 5. E-learning readiness model by [21]

There are many benefits offered by e-learning such as time saving, flexible education delivery and training from anywhere at any time. Last but not least is reduced costs of commuting from residence to the institution.

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4 Results This paper discusses the different e-learning readiness models from different countries. The papers from a systematic review of e-learning readiness models research published from 2000 on e-learning readiness in different countries. 4.1

Summarizing the Models

The below Table 3 is summarizing the models together with the factors showing the total of factors used in different models. Table 3. Summary table

4.2

Network Analysis of Models

Figure 6. is the analysis of the results from Table 3. The yellow nodes indicate the models, the red nodes and the green nodes represent factors used in the models. The green nodes indicate the factors that are common in at least two or more models. The green nodes indicate factors which are found in only one model. The sizes of the yellow nodes on the diagram are of different sizes, there are bigger nodes and smaller nodes. The bigger the yellow node, the more the number of factors

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Fig. 6. Analyzing figure

found in that model. Smaller nodes indicate that least number of factors were used in the model. When looking at the diagram, one will notice that diagram that [18] has the biggest yellow node. This simply means the [18] model used large number of factors than all other models in the diagram. The diagram above shows smaller yellow nodes on the three models. The models by [8, 12, 24] are the three models with the least number of factors used, each having three factors. There are 10 factors with red nodes, which each was used only once by one model, they are innovation, strategy, demographic, laws and regulation, policy, standards, security, network, pedagogy, and awareness. Even though those 10 factors were each used in one model, five of those 10 factors were used in the model by [18]. In spite the model by [18] having the number of factors than any other models above, the model is the only one not connected to the technology factor. 4.3

Comparing the Factors of the Models

“Technology is one of the factors that can be effectively used to adapt a technological innovation in an organization [7].” Figure six above also shows that technology is the key to the organization, it is the highest factor used in different models at 12 out of 13 models. Content is second important factor. Social and culture and people are equally important factors as can be seen in figure six above and they are third important factors after the content. Institution is the fourth factor. Institution is alone as the fifth factor.

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Fig. 7. Comparison of models

Training, financial, HR and resources are the sixth important factors, followed by acceptance of e-learning, management and equipment. Last factors are demographic, policy, network, laws and regulations, standards, security, pedagogy, standards, strategy and innovation.

5 Conclusion In this paper, e-learning readiness models were reviewed. The most common variables used in e-learning are technology, followed by content. Third position is taken by people together with social and cultural factors. Institution is the fourth important factor. It is clear that technology and content are the most critical factors in e-learning readiness model. And the rest of the factors are important but not critical as they occur in some and not occurring in some models.

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References 1. Rohayani, A.H., Kurniabudi, Sharipuddin, A.: Literature review: readiness factors to measuring e-learning readiness in higher education. In: International Conference on Computer Science and Computational Intelligence (ICCSCI 2015), P.C. Science, Editor. Elsevier B.V: Jambi 36138, Indonesia, pp. 230 – 234 (2015) 2. Maulida, I.F., Lo, J.: E-learning readiness in senior high school in Banda Aceh. J. Inf. Technol. Appl. 7(4), 122–132 (2013) 3. Rashty, D.: Traditional learning vs. eLearning. (2000) 4. Du, Z., et al.: Interactive and collaborative e learning platform with integrated social software and learning management systems. In: Information Technology and software Engineering & Digital Media Technology, pp. 11–19 (2013) 5. Akaslan, D., Law, C.: Measuring student e-learning readiness: a case about the subject of electricity in higher education institutions in Turkey. In: International Conference on WebBased Learning, United Kingdom, pp. 209–218 (2011) 6. Kitchenham, B., Charters, S.: Guidelines for performing systematic literature reviews in software engineering (2007) 7. Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2003) 8. Engholm, P., McLean, J.: What determines an organisation’s readiness for elearning? (2001). http://www.x-konsult.se/academia/Thesis%20FINAL.htm 9. Oketch, H.A., Njihia, J.M., Wausi, A.N.: E-learning readiness assessment model in Kenyas’ higher education institutions: a case study of university of Nairobi. Int. J. Sci. Knowl. Comput. Inf. Technol. 5(6) (2014) 10. Oliver, R., Towers, S.: Up time: Information communication technology: literacy and access for tertiary students in Australia. In: Training and Youth Affairs, Department of Education, Canberra (2000) 11. Ettinger, A., Holton, V., Blass, E.: E-learner experiences: key questions to ask when considering implementing e-learning (2006) 12. Psycharis, S.: Presumptions and actions affecting an e-learning adoption by the educational system. implementation using virtual private networks. Eur. J. Open Distance Learn. 8(2) (2005) 13. Aydin, C.H., Tasci, D.: Measuring readiness for e-learning: reflections from an emerging country. Educ. Technol. Soc. 8(4), 244–257 (2005) 14. Ouma, O., Awuor, M.F., Kyambo, B.: E-learning readiness in public secondary school in Kenya. Eur. J. Open Distance e-Learning 16(2), 97–100 (2013) 15. Chapnick, S., Are you ready for elearning? J. Learn. Circ. ASoTD’s online magazine all about ELearning (2000) 16. Borotis, S., Poulymenakou, A.: E-learning readiness components: key issues to consider before adopting e-learning interventions. In: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2004, Washington, DC, USA (2004) 17. Kaur, K., Abas, Z.W.: An assessment of e-learning readiness at the open university Malaysia. In: International Conference on Computers in Education (ICCE2004), Melbourne, Australia (2004) 18. Darab, B., Montazer, G.: An eclectic model for assessing e-learning readiness in the Iranian universities. Comput. Educ. 56(3), 900–910 (2011) 19. Mosa, A., Mahrin, N., Ibrrahim, R.: Technological aspects of E-learning readiness in higher education: a review of the literature. Computer and Information Science 9(1), 113 (2016) 20. Ford, J., Ford, L., D’Amelio, A.: Resistance to change: the rest of the story. Acad. Manage. Rev. 33(2), 362–377 (2008)

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21. Omoda, G., Lubega, J.: E-learning readiness assessment model: a case study of higher institutions of learning in Uganda hybrid learning, pp. 200–211 (2011) 22. Lopes, C.T.: Evaluating e-learning readiness in a health sciences higher education institution (2008) 23. Alshaher, A.A.-F.: The McKinsey 7S model framework for e-learning system readiness assessment. Int. J. Adv. Eng. Technol. 6(5), 1948–1966 (2013) 24. Keramati, A., Afshari-Mofrad, M., Kamrani, A.: The role of readiness factors in E-learning outcomes: an empirical study. Comput. Educ. 57(3), 1919–1929 (2011)

Arabic Question Answering System Using Graph Ontology Mohamed S. Zeid(&), Nahla A. Belal, and Yasser El-Sonbaty College of Computing and Information Technology, Arab Academy for Science Technology and Maritime Transport, Alexandria, Egypt [email protected], {nahlabelal,yasser}@aast.edu

Abstract. The search engines are doing a great effort in getting answers for questions, especially, sophisticated answers. Question answering systems are used as modules of the search engines to enrich the search mechanism. This paper aims to present an Arabic question answering system using graph ontology, by using multiple semantic techniques. Graph ontology is used as the main source of getting answers, in addition to a web search API as an alternative path to get answers and enrich the ontology. The proposed system is tested on three datasets. The system achieved an accuracy (C@1) of 0.846 with an increase of 0.486 over similar systems and a recall of 0.958 in the second experiment, which is less than the compared systems by 0.008. Keywords: Natural language processing
Question answering system
Information retrieval
Query expansion
Question classification
Answer extraction

1 Introduction Due to the day by day growth in the amount of information available through the internet, the need for quick and precise answers, especially for Arabic native speakers, and the shallow results by the search engines according to ranking approaches, a new approach of Arabic question answering systems in custom architecture needs to be designed. Arabic language is one of the most important languages worldwide and its rank is the fifth most spoken language of the most spoken languages and about 422 million speakers around the world speak Arabic. The Arabic language is also a Holy language for Islamic countries because it is the language of the Holy Quran. All those reasons justify the need to create a question answering system (QAS) to have precise, brief, and accurate answers in Arabic. Question-answering is a challenging field in general. It entails many fields, among which are Information Retrieval (IR), Natural Language Processing (NLP), question analysis, and choosing passage. Natural language processing generally involves many categories. Those categories include, sentiment analysis with its two techniques, the first using a lexicon ruleset, and the second using machine learning [1]. Sentiment analysis is also used in approaches to gather statistics in certain subject areas or social media [2]. Another category in natural language processing is Arabic stemming. Stemming is the process of restoring the © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 212–224, 2020. https://doi.org/10.1007/978-3-030-63322-6_17

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word’s root [3], and the Arabic language stemming has two approaches, the first approach uses roots of words, and the second approach uses a set of templates to convey the meaning of the word [4]. Arabic relation extraction is the task of extracting relations between entities. Relations can be binary or higher-order relations [5]. Arabic relation extraction has many preprocesses like stemming, semantic expansion, ranking and finding negative dependencies [6]. This paper is concerned with the category of Arabic question answering systems. To create a QAS for the Arabic language some challenges would be faced. Morphological analysis is a very hard task of the Arabic language because Arabic is an inflectional and derivational language, as illustrated in the examples below.

As shown, and are the same word, meaning players, but spelling changed according to inflection. Another example (verb) and (object) are the same word but the derivation is different. Having no diacritics in the written text may change its meaning.

From the previous example, the word in the first sentence means Egypt but in the second sentence, it is a verb meaning insist. Unlike English, Arabic is written from right to left. Arabic characters have many shapes based on their location in words. Names and abbreviations cannot be easily identified because there is no capitalization [7]. The Arabic language also has a few gazetteers and corpora. QAS consists of three fundamental components: a component to classify questions, another component for information-retrieval, and the third component is for extracting answers [8]. Classifying questions is a key operation in the system, as it categorizes questions based on their question marks: who, when, where, what and how much. The information retrieval component finds the most relevant answers by searching the information stores for matching keywords. Finally, extracting the answer is done by selecting the most relevant answers and ranking them. The answer extraction module is also an important component because it contains the user interface. Arabic question answering systems can be divided into two domain types. The first domain is the closed domain question answering systems, which deals with questions in a specific domain. This domain is easier to apply QAS to because it has a specific domain to search in, hence, specific domain ontology can be applied. The second domain is the open domain question answering systems, which deals with general questions in any topic and relies on general ontologies and world knowledge. Ontology is defined as “a formal explicit description of concepts in a domain of discourse (classes). Properties of each concept describe various features and attributes of the concept (slots), and restrictions on slots (facets). Ontologies together with a set of individual instances of classes constitutes a knowledge base” [9].

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This paper aims to propose an Arabic question answering system that uses graph ontology. The core of the proposed system is to get a question from the user and perform some semantic operations to create an expanded query statement. The system then uses this query to look for data into a custom structured graph ontology. The proposed system aims to enhance searching, ranking, storage, processing, and forming a structure of Arabic question answering systems. Having 24 random questions about diseases, the proposed system achieved promising results by correctly answering 16 questions. The system was tested using datasets in areas other than healthcare and was able to adapt to other areas by forming the ontology in the same manner. Two more experiments were tested and also achieved promising results. In the first experiment, the proposed system achieved a C@1 of 0.681, where C@1 is an accuracy measure that counts the answered and unanswered questions, in addition to questions with no response. The system also achieved a precision of 0.76, a recall of 0.8 and a F-measure of 0.615. In the second experiment, the proposed system achieved a C@1 of 0.846 with an increase about 0.486 over the system proposed in [10]. Moreover, in the third experiment, the proposed system achieved a recall of 0.958, which is lower by 0.008 than the system in [11]. The general approach used in the proposed system can be applied to other languages. However, the system was designed to specifically handle the Arabic language character encoding in data storage and the search API which is a more specific application. The remaining sections of this paper are organized as follows. Section 2 covers the related work. In Sect. 3, the architecture of the proposed system is presented. Section 4 explains the datasets, experiments, and results. Finally, the conclusion is in Sect. 5.

2 Related Work A lot of work has been carried out in languages other than Arabic. Although Arabic is one of the most widely spoken and written languages, it still generally lacks in this research area. The related work is categorized below by the language of the system developed. Languages considered in the related work are the English, German, and Arabic languages. 2.1

English Language

An ontology-based question answering method is presented in [12]. The system uses query template, OWL ontology, and protégé to form a dining ontology which involves address contact details of the restaurant, beverages and cuisine. Another graph-based approach for a question answering system on entrance exams is presented in [13]. The system in this paper relies on XML format documents, where the question is entered in natural language, and based on the question mark, the passage is retrieved. The question also passes through a lot of semantic steps to form the final query. Their proposed system was able to get an F-measure of 0.87. In [14], the authors developed a question answering system for translation of holy Quran, based on question expansion

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and neural networks. The system also uses WordNet to get synonyms and semantic operations to get answers for Surah Al-Baqarah [15]. This system was able to get a C@1 of 0.375. 2.2

German Language

A system for classifying German questions according to ontology-based answer types is presented in [16]. The paper used a merged dataset of 2000 questions from a smart web project, 1400 Wikipedia and another 500 questions from the corpus. The system uses n-gram, question marks, classification algorithms such as decision tree, naïve bayes and k-nearest neighbor to find the requested answer. The reported accuracy was 0.60. 2.3

Arabic Language

A system designed by A. Abiatha [9] uses RDF ontology and semantic algorithms to retrieve answers using SPARQL queries. Their system was able to correctly answer 28 questions out of 30 questions. An enhanced Arabic question answering system was proposed by A. Kamal et al. [11]. The system uses passage retrieval according to question mark, query expansion and other semantic algorithms, and was able to achieve a recall of 0.966. Another Arabic question answering system using ontology was proposed by K. Shaaban et al. [17]. The proposed system is an enhancement for A. Abiatha [9] system, as it used the same technology RDF with SPARQL queries and was able to achieve a maximum f-measure of 0.86. Furthermore, AL QASIM by A. Ezzeldin et al. [10] used data documents as an ontology to retrieve data, and it also incorporated passage retrieval and semantic algorithms to retrieve answers. AL QASIM was able to get a C@1 of 0.36. In contrast to the related work, the proposed system uses stemming, stop words removal, query expansion, graph dataset, passage retrieval and alternative source of information (web search API) to retrieve answers to questions. A step further after retrieving the answer, the reverse feedback of information approach is applied to enrich the ontology.

3 The Proposed System In this section, the general framework of the proposed system is presented, followed by the architecture of the system. 3.1

General View of the Framework

Figure 1 shows a block diagram illustrating the components of the system to get the verified answer to an input question. The system consists of three main components, namely, question, document, and answer processing. The following subsections discuss the components of the system in detail.

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3.1.1 Question Processing Question processing is done through three stages as explained in Fig. 1. The first stage is to analyze which semantic is the most important to form the query. This is done through stemming and NLP techniques, discussed in later sections. The second stage is the processing of a passage taken to get the most suitable answer, this is carried out through a corpus that classifies the question [18]. The last stage is to get word synonyms, using Arabic word net framework, and reformulate the query to get more possibilities to get an answer.

Fig. 1. Framework of the ArQA (Arabic question answering) system

3.1.2 Document Processing Document processing is done through three stages as explained in Fig. 1. The three stages are retrieval, filtering, and ordering. The retrieval stage retrieves a set of ranked nodes that are relevant to the submitted question as a sub-graph which includes the connected nodes and relations that are most relevant to the asked question. The filtering stage reduces the candidate nodes returned by the retrieval system. Finally, the ordering stage orders the candidate nodes to get a set of ranked nodes list. 3.1.3 Answer Extraction Answer extraction is likewise executed through three stages. First, the module identifies the answer candidates in the filtered ordered nodes list. Then, the answer is extracted by choosing words or phrases that answer the submitted question. And finally, the answer is validated by providing confidence in the correctness of the answer.

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217

Implementation and Architecture

The system architecture is shown in Fig. 2. The system starts by receiving a question from the user entered in natural language. The question analysis module then starts by removing stop words. After that, it segments each word of the question into separate categories. The system gets the root of each word using the Khoja stemmer [19]. Then the system adds each root to the corresponding word’s category, and gets each word synonym, using the Arabic word net (AWN). Khoja stemmer and AWN were used because they are easy to use and are most widely applied in the field, therefore, it will allow results to be accurately compared. Also, the system adds those synonyms to each word’s corresponding category. The result of the previous preprocessing steps is the reformulated question which is ready to use to query the ontology. The next stage in the system architecture is to query the ontology and calculate the part of the graph ontology with the highest match. Matching is done between each word using the cypher language used to execute queries on Neo4j graph databases per category, as well as nodes properties and labels in the graph ontology. A decision is made whether the answer can be found in the ontology or not based on a certain threshold entered by the user, then-candidate answers are acquired and presented to the user. In the case of failing to find an answer in the graph ontology, the system searches the web using Google search API and returns the answer to the user.

Fig. 2. System architecture

To avoid searching the web every time an answer is needed for the same question, the user can feedback on the ontology with new additions to make the ontology richer for further use. For either paths, ontology or web, the answer is sent back to the user ranked in descending order, according to term frequency-inverse. Term frequencyinverse is a weighting factor that indicates how important a word or group of words is to a paragraph or a document. This frequency is used to give a weight to answers

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retrieved from the system to indicate which answer is more likely to be correct. The term frequency-inverse is calculated as follows [20]: tf ðt; d Þ ¼ count of t in d=number of words in d idf ðtÞ ¼ logðN=ðdf þ 1ÞÞ tf -idf ðt; d Þ ¼ tf ðt; d Þ  idf ðtÞ where t is the term evaluated, d is a single document, N is the total number of documents and df is the number of t in all documents.

4 Datasets and Experimental Results This section presents the experiments carried out and the results obtained to test the performance of the proposed system. Firstly, the experimental setup is demonstrated, then the datasets used are explained, followed by the metrics used for the evaluation of the system. Finally, the experimental results and discussion are presented. 4.1

Experimental Setup

The experiments were executed on a laptop with an intel i3 processor, 8 GB RAM and SSD hard disk. The software used in the experiments includes Windows 10 pro, Neo4j 3.5 [21, 22], Java JDK 1.8, Khoja stemmer, Arabic word net, Google search API, Anercorp, and NetBeans. 4.2

Datasets

Three datasets were imported to test the system: The first dataset belongs to the Saudi Arabia Ministry of health, and it is available on their website (Diseases section) [23]. The dataset consists of 43 diseases, each disease has eight attributes, namely, introduction, brief, types, causes, symptoms, complications, prevention methods, and cure. Figure 3 shows the Arabic description of the disease attributes. Each disease node has attribute nodes connected through a relation as shown in Fig. 4 and the relation has properties that carry the data. In Fig. 4, an example is explained for the question: What are the symptoms of Tuberculosis? The right node represents the disease Tuberculosis and the left one represents the symptoms. The relation between the two nodes has attributes that carry the actual symptoms of Tuberculosis to be able to retrieve the answer. An example is shown in Fig. 4 link 2. Table 1 shows statistics about the dataset. The dataset contains 43 diseases, where each of those diseases has 8 attributes, as illustrated in Fig. 3. The number of the relations between disease nodes and properties is 774 relations.

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Fig. 3. Disease attributes

Fig. 4. Example of building the dataset Table 1. Dataset statistics Object Count Number of diseases 43 Number of disease attributes 8 Number of relations 774

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The second dataset is Trec and CLEF [24, 25], imported from 31525 text files, where each text file has the question and the corresponding answer. A new graph was created with the same method discussed earlier in Fig. 4. After the graph was created, 300870 nodes and 299847 relations were added and connected. The third dataset is Fatwaa Corpus (Islamic Religion datasets) [11]. The data was imported from the SQL database. The dataset contains 200 rows of questions and corresponding answers used to build the new graph ontology. The system was tested with 120 questions. 4.3

Experimental Results

The following performance metrics, illustrated in Eqs. 1–5, were used to test the performance of the proposed system: C@1: An extension of accuracy with the advantage of not only measuring answered and unanswered questions, but also the non-responding factor is considered [26]. Let A represent the number of questions that are answered, U, the number of questions that are unanswered, W, the number of questions with wrong answers, NA, questions with no answer, and total number of questions is N. C@1 ¼ ð1=NÞ  ðA þ ðU=NÞÞ

ð1Þ

U ¼ W þ NA

ð2Þ

Precision (P): gives a measure of the number of correctly predicted positive class predictions [27]. P ¼ A=ðA þ WÞ

ð3Þ

Recall (R): gives the number of positively predicted samples made out of all positive examples in the dataset [27]. R ¼ A=N

ð4Þ

F-Measure: gives one number relating precision and recall [27]. F-Measure ¼ ð2  P  RÞ=ðP þ RÞ

4.4

ð5Þ

Experimental Results and Discussion

In this paper, three experiments were conducted to test the performance of the proposed system. The first experiment was carried out using the Saudi health ministry dataset. In this experiment, a full test of the system was performed to test how the system will respond against new and random questions. Hence, the web search module was enabled and a random set of questions was acquired from a sample test of an eighth-grade exam translated to Arabic [28].

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The first experiment has 24 questions about diseases. At a threshold of 20%, the system was able to answer 5 questions of 24, at 50% threshold, the system was able to answer 8 out of 24, and at the threshold of 80%, the system was able to answer 16 out of 24 questions. Table 2 shows the number of answered, incorrectly answered, nonanswered questions, as well as whether the system used ontology or the web search for each threshold value, and the C@1 factor, the precision, recall, and F-measure. The thresholds in Table 2 refer to the chosen threshold of accuracy entered by the user. If the threshold fulfills the question existence in the ontology, then the answer will be found in the local ontology, otherwise the system will search the web for the answer and return it back to the user [29]. Table 2. Results of the first experiment Total number of 24 questions about diseases T Ontology Web A NA W U c@1 P R F-measure 20% 16 8 5 8 11 19 0.241 0.5 0.385 0.435 50% 13 11 8 6 10 16 0.361 0.566 0.571 0.533 80% 2 22 16 4 4 8 0.681 0.76 0.8 0.615 U: Total unanswered questions T: Threshold A: Correctly answered questions P: Precision R: Recall NA: Not answered questions W: Incorrectly answered questions

Table 2 shows results for three thresholds of 20, 50 and 80%. The user can enter this threshold using the GUI. For example, in a 50% threshold, if the question’s and synonym’s query fulfill 50% or more of matched words in the ontology, then the system is more likely to find the answer in the ontology and no need to use the search API. Table 2 shows an increase in the C@1 factor when increasing the threshold. At 80% the system achieves a C@1 of 0.681. Moreover, the result shows good precision, recall and F-measure, which confirms the performance of the proposed system, considering the chosen questions are random. In the case of using the web search path to retrieve answers, when the system fails to find the answer in the ontology, reverse feed to ontology is embedded in the system to enrich the ontology with the correct answers for future searches. The second experiment was carried out using the imported text files for news. Each text file has a question and answer imported to graph ontology as mentioned in Sect. 4.1. Table 3 shows the results of the proposed system on the second dataset in comparison to the results reported in [10]. The proposed system achieved an accuracy of 0.84 and C@1 of 0.846, outperforming other systems results. The experiment was carried out by selecting 25 questions, where the system was able to answer 21 questions, with 2 unanswered and 2 incorrectly answered questions.

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Accuracy 0.13 0.19 0.53 0.31 0.84

C@1 0.21 0.19 0.65 0.36 0.846

The third experiment was carried out on the imported SQL database for Fatwaa [11]. Each row in the fatwa SQL table has a question column and answer columns imported to the graph ontology as mentioned in Sect. 4.1. The results in Table 4 show that the proposed system achieved a recall of 0.958 by testing 120 questions, where 115 questions were correctly answered, 2 questions unanswered, and 3 incorrectly answered. The proposed system could perform better if the web search was activated. Table 4. Results of the third experiment Systems Questions AnswerBus (multilingual) [14] 200 QArabPro [33] 335 ArQA [34] 120 Enhanced ArQA [11] 120 Proposed system 120

Recall 0.7052 0.8118 0.9083 0.9660 0.9580

5 Conclusion In this Paper, Arabic language challenges are discussed, the problem of Arabic question answering systems is broken down into three main components, namely, question processing, document processing and answer extraction. The paper also discusses different approaches of the Arabic question answering systems. The proposed open domain Arabic question answering system based on graph ontology allows the user to ask a question in natural language then the system preprocesses this question using stop words removal, stemming, tagging, normalization, and query expansion. The system expands the query search a step further after the graph ontology, if the answer is not found then it searches the web. In the case of finding multiple answers, the system ranks the answers in descending order using term frequency-inverse document frequency. The system allows the user to update the ontology after using the web search to enrich it for further use. The experimental results were obtained based on three ontologies created for testing. The first ontology was created from a dataset on the Saudi health ministry’s site, with random 24 questions about diseases and pathology.

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The system achieved an accuracy C@1 of 0.681. The second experiment used Trec and CLEF news database, with 25 questions and the proposed system achieved C@1 of 0.846. The third experiment used the Fatwaa corpus, with 120 questions and the proposed system achieved a recall of 0.958.

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16. Davidescu, A., Heyl, A., Kazalski, S., Cramer, I., Klakow, D.: Classifying German questions according to ontology-based answer types. In: Decker, R., Lenz, H.-J. (eds.) Advances in Data Analysis, pp. 603–610. Springer, Heidelberg (2007) 17. Albarghothi, A., Khater, F., Shaalan, K.: Arabic question answering using ontology. Procedia Comput. Sci. 117, 183–191 (2017) 18. ANERcorp - Cohen Courses. http://curtis.ml.cmu.edu/w/courses/index.php/ANERcorp. Accessed 03 Apr 2020 19. Saad, M.: Motazsaad/Khoja-stemmer-command-line. https://github.com/motazsaad/khojastemmer-command-line. Accessed 03 Apr 2020 20. Robertson, S.: Understanding inverse document frequency: on theoretical arguments for IDF. J. Doc. 60(5), 503–520 (2004) 21. Needham, M., Hodler, A.E.: Graph Algorithms, 1st edn, p. 257. O’Reilly Media Inc. (2019) 22. Neo4j Graph Platform – The Leader in Graph Databases. Neo4j Graph Database Platform. https://neo4j.com/. Accessed 03 Apr 2020 23. . https://www.moh.gov.sa/HealthAwareness/EducationalContent/Diseases/Pages/default.aspx. Accessed 01 May 2020 24. About CLEF. http://www.clef-campaign.org/. Accessed 03 May 2020 25. Text REtrieval Conference (TREC). https://trec.nist.gov/. Accessed 03 May 2020 26. Pe, A.: A simple measure to assess non-response. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 1415–1424 (2011) 27. Chalabi, H.M.A.: Question processing for Arabic question answering system, p. 62. The British University in Dubai (2015) 28. Disease Test Review- 8th - Quiz. https://quizizz.com/admin/quiz/58b30bb7ba7e5e34252d 3b6f/disease-test-review-8th. Accessed 21 May 2020 29. Custom Search JSON API. Google Developers. https://developers.google.com/customsearch/v1/overview. Accessed 03 Apr 2020 30. Abouenour, L., Bouzoubaa, K., Rosso, P.: IDRAAQ: new Arabic question answering system based on query expansion and passage retrieval. In: CLEF 2012 Workshop on Question Answering for Machine Reading Evaluation, QA4MRE (2012) 31. Trigui, O., Belguith, L.H., Rosso, P., Amor, H.B., Gafsaoui, B.: Arabic question answering for machine reading evaluation. In: CLEF 2012 Workshop on Question Answering for Machine Reading Evaluation, QA4MRE (2012) 32. Bhaskar, P., Pakray, P., Banerjee, S., Banerjee, S., Bandyopadhyay, S., Gelbukh, A.: Question answering system for QA4MRE@CLEF 2012. In: CLEF 2012 Workshop on Question Answering for Machine Reading Evaluation, QA4MRE (2012) 33. Akour, M., Abufadeh, S., Magel, K., Al-Radaideh, Q.: QArabPro: a rule based question answering system for reading comprehension tests in Arabic. Am. J. Appl. Sci. 8(6), 652– 661 (2011) 34. Hamadene, A., Mohamed, O., Shaheen, M.: High performance question answering system for Arabic language (ArQA). Arab Academy for Science and Technology and Maritime Transport College of Computing and Information Technology, Egypt (2012)

Stability Study of a Protection Structure by Stacking GSC Geosynthetics: Application to the Port of Corisco (Equatorial Guinea) Mustapha Mouhid1,2(&), Laila Mouakkir1, Soumia Mordane1, Mohamed Loukili1, Mohamed Chagdali1, and Brahim El Bouni2 1

Polymer Physics and Critical Phenomena Laboratory, Faculty of Sciences Ben M’sik, University Hassan II, P.O. BOX 7955, Casablanca, Morocco [email protected] 2 SOMAGEC, Angle Rue Mohamed El Mesfioui & Corbi - Oukacha, CP 20580 Casablanca, Morocco

Abstract. The port of Corisco Island is located on the north-east coast of the island in an area with sufficient sea depths at an acceptable distance from the coast. In order to avoid erosion of the coasts adjacent to the port site and not to hinder the important sedimentological transit of the island, we are interested in this paper to highlight the realization of protective structures using sand-filled geotubes. For that, a modeling of the wave propagation on the Island of Corisco is essential, this modeling was carried out by the SWAN (Simulating Waves Nearshore) model. This model has generally been designed for wave simulations in near-shore regions. The reference swell data off the port of Corisco are derived from studies carried out by SOMAGEC’s research department and data provided by GlobOceon. The results undertaken in this study have shown that the use of geotubes filled with sand is a very suitable solution for the CORISCO site, in order to avoid the environmental impact caused by conventional solutions based on the use of materials from rock quarries. Keywords: Equatorial guinea
Port
Island
Sand Swell
SWAN model
Seawall
Dimensioning


Geosynthetic GSC


1 Introduction Corisco Island is located approximately 29 km southwest of the Rio Muni River (Cogo Estuary, Equatorial Guinea – see Fig. 1). Its area is 14 km2 and its highest point is 35 m above sea level. This island is directly influenced by the Rio Muni, which carries large amounts of alluvium during the rainy season. It is characterized by a stretch of white sand (200–300 lm) which covers the entire coastline and ends with a “Comet tail” exactly to the south-east of the island, where the ocean waves of the West meet after having turned around the island by the North and the South. The project involves the construction of a half-moon sand island; the central part is a protected basin for port use. The protective dikes are formed by tubes stacked from a bottom at −6 m to a level of +5 m which is high enough to be protected from the risk of flooding. The tubes are placed on a pre-tapping mat anchored on the bottom. Our © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 225–241, 2020. https://doi.org/10.1007/978-3-030-63322-6_18

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contribution to the project is to provide the structural dimensions for the structure in terms of its stability, protection against agitation and the risk of flooding. For this, modeling of wave propagation in the study area is essential. In this paper, we have used the SWAN model (Simulating Wave Near-shore). It’s a wave model, widely used appropriate for shelf seas, coastal and nearshore areas. It simulates wave generation, propagation and dissipation and includes the effects of refraction, shoaling, and blocking in wave propagation [1]. The reference swell data off the port of Corisco are derived from studies carried out by SOMAGEC’s [2] research department and data provided by GlobOceon [3].

Fig. 1. (a) Location of the Corisco port project, (b) port of Corisco, general plan.

2 Model Description The SWAN model is a numerical third-generation wave model that provides realistic estimates of wave parameters in open seas, coastal areas, lakes, and estuaries from given wind, bottom, and current conditions [4]. In the SWAN model [5], for the control equation for wave description, the dynamic spectrum balance equation is adopted based on the theory of linear and random surface gravity waves. In the flow field, the random waves are presented in two-dimensional dynamic spectral density rather than twodimensional energy spectral density. Dynamic spectral density N(r, h) is the ratio of the spectral energy density E(r, h) to the intrinsic representative wave frequencys. The control equation is:   Sen @N @ @
@ @
þ Cy N þ Cyh N ¼ ðC x N Þ þ ðC r N Þ þ @t @x @y @r @h r

ð1Þ

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N (r, h): Wave action density (= qg.E(q, h)/r[J.s/m2]). E (r, h): spectral variance. r: relative angular frequency (relative to current) [Hz] Cx, Cy, Ch, Cq: propagation speeds according to x, y and h and angular frequency Sen: term of energy sources [J/m2] where x and y are horizontal Cartesian coordinates, t is time, h is the propagation direction of each wave component, Cx, Cy, Cr, and Ch are the propagation velocity in xspace, y-space, r-space, and h-space respectively, and Stott represents the energy source. The first term on the left-hand side of Eq. (1) is the rate of change of action density in time, the second and third terms are the propagation of action in physical space. The fourth and fifth terms show the shifting of the relative frequency and the refraction due to variations in depth and currents. The item S on the right side of this equation includes dissipations, quadruplet interaction, and triad interaction caused by wind input, white capping, bottom friction, and depth-induced wave breaking. Details of these processes can be found in the SWAN technical documentation manual [6].

3 Model Simulation 3.1

Bathymetric Data

The boundaries of the SWAN approach model are defined off the island of Corisco, extending 24.2 km from West to East and 36.4 km from North to South. These limits are justified in particular for examining the propagation of swells in the Southwest sector (see Fig. 3). – SHOM Board No. 6183 “Corisco Bay” – the bathymetric survey carried by the survey team (SOMAGEC GE).

Fig. 2. Bathymetry of port Corisco.

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Swell and Agitation Conditions

They are presented in Figs. 2, 3, 4 and 5. We note that there is a damping of the SW offshore swell between 75% and 80%, before reaching the site of the project. The characteristics of the incident swell for different periods of return are shown in a summary table. The maximum values are 1.1 m for a 50-year return period. As regards the agitation generated by the SE wind seas in the inland water body, it can reach 1 m in annual condition (Table 1). Table 1. Estimated of residual wave heights Tr (year) Dir (°N) Tp (s) H1/10 (m) Hs (m) Ho (m) Estimation of residual swell heights P1 P2 P3 P4 P5 P6 P7 1 10 20 50 100

220 220 220 220 220

12 14 14 13 15

3,3 3,9 4,2 4,4 4,6

2,6 3,1 3,3 3,46 3,6

2,00 2,00 2,20 2,25 2,35

0,5 0,7 0,9 1,0 1,1

0,6 0,7 1,0 1,1 1,1

0,6 0,7 1,0 1,0 1,1

0,6 0,7 1,0 1,0 1,1

0,5 0,7 0,9 1,0 1,0

0,5 0,6 0,9 0,9 1,0

0,5 0,6 0,9 0,9 1,0

Tr: Return period, or recurrence interval (year) Dir: Mean direction of waves, usually to grid north [(rad or (°)] Tp: Wave period (s) H1/10: Mean height of highest 1/10 fraction of waves Hs: Significant wave height, Hs = H1/3 (Mean height of highest 1/3 fraction of waves) Ho: Stability number, Ho = Ns = Hs/(DDn50) [Dn50: Median nominal diameter] The characteristics of SW sector incident swells off Corisco near the project area [the average input depth of the close model (Z), the incidence of swell (hz), attenuator/amplifier coefficient (Caz)] calculated, were recorded in 7 points represented in the following figure:

Fig. 3. Location of the reading points of the wave characteristics around the project area.

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229

Simulation Results

In this study, the SWAN cycle III version 41.20 was used for wave simulations. The model was executed in the third generation and stationary mode with Cartesian coordinates on a spatial resolution of 0.5 km. The spectral directions cover the full circle. The resolution in h-space is 10° for the variable h and 32 frequency. The lowest frequency is fixed at 0.0521 Hz and the highest at 1 Hz. The wave simulations were carried by respecting the same initial conditions of the SOMAGEC STUDY. By applying the SWAN model taking into account the site conditions, we obtained the results of the simulations which are presented in the form of 8 boards representing the significant height with a color gradient (the wave heights gradually increasing from blue to the red) and the swell trajectory (arrows) on each zone (Figs. 6, 7, 8, 9, 10, 11):

Fig. 4. Annual SW swell [220 °N; Tp = 12 s; ho = 2,00 m].

Fig. 5. A 10-year return period swell [(220 °N, Tp = 13 s); ho = 2,00 m].

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Fig. 6. Decennial swell (SW) [220 °N; Tp = 14 s; ho = 2,00 m].

Fig. 7. Twenty swell (SW) [220 °N; Tp = 14 s; ho = 2,20 m].

Fig. 8. Fiftieth swell (SW) [220 °N; Tp = 14 s; ho = 2,25 m].

Stability Study of a Protection Structure by Stacking GSC Geosynthetics

Fig. 9. Centennial swell (SW) [220 °N; Tp = 15 s; ho = 2,35 m].

Fig. 10. Project - annual swell from SE, Tp = 4 s.

Fig. 11. Project- annual swell from SE NW Tp = 12 s.

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Approaching the coast, we noticed the following results: – Strong dampening of the offshore swell SW sector (220 °N) variating from 76% to 81% for wave periods ranging from 12 s to 15 s. – a variation of the SW Offshore swell sector (220 °N), Orientation, from 112° to 148° thus reaching the project area with an orientation NW to N (332° to 08 °N) – Residual swell heights range are from 0.5 m to 1.1 m maximum for SW offshore swells (220 °N) and for periods of swell ranging from 12 s to 15 s.

4 GSC Pre-sizing The study of the stability of geotextile tubes is complex. Indeed, their behavior is conditioned unparticular by: – – – – – –

The The The The The The

water depth and swell conditions to which they are subjected; non-rigidity of the structure; dimensions of the structure and the filling rate; dimensions of the structure and the filling rate; filling material; mechanical characteristics of geotextile (…).

As far as we know, there are no formulations in the literature to integrate all of these parameters. However, several publications make it possible to estimate the stability of a geotextile tube/container according to its dimensions and the wave or current characteristics to which it is subjected. Two procedures are implemented: – From the desired degree of filling and the size of the empty tube, the geometrical characteristics of the filled tube and an initial approximation of the maximum permissible hydrodynamic conditions according to the reference [7] will be determined (see Fig. 13); – From the dimensions defined above, stability criteria will be applied to ensure, according to different formulations, that the tube will be stable according to the hydrodynamic conditions to which it will be subjected (Fig. 12). According to the reference [7], the characteristics of the tube used can be determined from the known dimensions of the geotextile (here D = 2R = 5 m) and the degree of filling (Table 2).  pffiffiffiffiffiffiffiffiffiffiffi h  1  1  f :D Bhþ

1 :p:ðD  hÞ 2

ð2Þ ð3Þ

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Fig. 12. Dimensions of a tube in geotextile for a degree of filling f.

f [-] degree of filling h [m] height of the tube B [m] width of the tube D [m] diameter of the tube with f = 1

Table 2. Estimated dimensions of the tube in function of the degree of filling f[-] 1 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6

h[m] 4.50 3.58 3.20 2.90 2.63 2.41 2.21 2.00 1.82

B[-] 4.50 5.13 5.40 5.60 5.76 5.92 6.05 6.17 6.28

S[m] 14.14 14.04 14.33 14.20 14.06 14.25 14.33 13.74 13.74

A[m2] 15.90 14.96 14.54 13.62 12.64 12.04 11.33 10.04 9.30

In the first approach, it is also possible to estimate (3, 4) the critical values of the swell heights or currents (uniforms) below which a geotextile tube of known dimensions will be stable: Hs 1 Dt :h

ð4Þ

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us pffiffiffiffiffiffiffiffiffiffiffiffi  g:Dt :h



1 0:5 to 1

SCHIERECK ½8 PILARCZYK ½9

ð5Þ

Considering a degree of filling of 0,8, the critical value of permissible wave height is Hs < 2,4 m and the critical speed of current is estimated at: ucr < 2,4 m/s. The next objective is to be able to determine the critical width of the structure for a given height and length. A balance of the forces applied to the tubes is made like the method used in the reference [10]. In this analysis, the deformation effects of the tubes on their hydrodynamic stability are not taken into account. These forces are listed below: Drag force: FD ¼ 0:5qx u2 FCD As

ð6Þ

Inertial force: FM ¼ qx CM V

@u @t

ð7Þ

Lifting Force: FL ¼ 0:5qx u2 CL AT

ð8Þ

FGSC ¼ ðqs  qx Þu2 gV

ð9Þ

Relative weight GSC:

These forces therefore depend on the velocity, the acceleration of the fluid particles, the dimensions of the GSC and coefficients reflecting the influence of the structures on the flow. They are a function of the flow regime but also of the geometry of the structure. The main difficulty in estimating critical lengths of stability lies in estimating these coefficients. Although they are quite well known in the case of batteries [11] because they are widely used offshore, they are not well informed in the case of geotextile tubes. In the reference [12] makes it possible to estimate these coefficients for various configurations of GSC stacks. The calculation is carried out for a stack of three GSC (two joints placed on the natural ground and one above). The results are from channel tests with a Reynolds number less than 106. The critical length of the GSC is calculated in two ways, considering the critical slip length (see Fig. 14) and the critical roll-over length (see Fig. 15). The maximum value of these lengths shall be retained as the minimum design basis. The calculation formulas used to determine the critical design width were adapted from the reference [10] to match the project. For this, we will consider a height of 2.7 m and a width of 5.7 m. The formulations become: l0sðslidingÞ  l2

CD þ l:2;5CL lDg  CM @u @t

ð10Þ

Stability Study of a Protection Structure by Stacking GSC Geosynthetics

l0sðoverturningÞ  l2

0:1CD þ 1;5CL 0;5Dg  0:1CM @u @t

with: l: friction coefficient, and D ¼ qsqqw w u: velocity of fluid particles qx : water density qs : sediment density AS : GSC surface normal to wave direction AT : GSC surface in wave direction V: le volume du GSC CD : drag coefficient related to the form of GCS CM : inertia coefficient related to the form of GCS CL : lifting coefficient related to the form of GCS

Fig. 13. Diagram of definition of the sliding stability of GSC.

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ð11Þ

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Fig. 14. Diagram of definition of the overturning stability of GSC.

The swell conditions used for the calculation are shown in Table 3. The objective is to consider the extreme water levels considered according to the position on the island. For both cases, the aim is to check the stability of the GST according to their dimensions and in response to swells likely to occur in the sector.

Table 3. Conditions of the selected swells for the calculation of lc Case Swell characteristic h B(m) T(s) H(m) 1 2,7 5,7 14 1 2 2,7 5,7 15 1 3 2,7 5,7 14 1 4 2,7 5,7 15 1

L(m) 306 351 306 351

L(m) 126 136 103 112

d(m) 6,5 + 6,5 + 3,5 + 3,5 +

2,25 2,35 2,25 2,35

The results are presented in Table 4. The critical lengths are halved to obtain the critical width of the GST. The speed and acceleration results calculated at half height of the GSC (1.2 m) are given for information purposes.

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Table 4. Minimum GST width results Case 1 2 3 4

lc(sliding)/2 (m) lc(overturning)/2 (m) u(m/s) 0,21 0,11 0, 64 0,20 0,11 0,63 0,32 0,17 0,78 0,30 0,17 0,77

a(m/s2) 0,29 0,26 0,35 0,32

The dimensions initially envisaged in terms of length and width therefore meet the stability criteria in the case of a rigid structure. By way of comparison, four other criteria for the stability of structures composed of GSC present in the literature whose geometric configurations are close to the project are: – Buyze [13], for the calculation of a stability criterion under the action of a uniform current; – Pilarczyk [9], for the calculation of a stability criterion for geotextile tubes; – Oumeraci [14], for the estimation of the critical characteristic width of GST in the case of an impassable seawall type structure; – Grüne [15], for estimating the critical characteristic width of GST.

Table 5. Critical width of geotubes; comparison of stability formulas Case

1 2 1 2

Ucr/ (gD)0.5 * 0,5– 1 [13] 0,11 1,11 0,13 1,13

b = (1,1 * 1,2) HB < 1

l = H¾T½/1,74 (qGSC/ qw − 1)

l = (10Vqs)1/3

[9] 0,3 0,3 0,3 0,3

[14] 3,1 3,2 3,1 3,2

[15] 4,8 4,8 4,8 4,8

The results of the various stability criteria are given in Table 5. It appears that all the formulations conclude that the stability of the GSC for the 4 proposed cases. The characteristics of the geotextile must be adapted to the strength of the membrane to filling material, and its permeability. In contrast to non-woven geotextiles, woven geotextiles have a high tensile strength (40 < Tm < 300 kN/m) with a low maximum permissible deformation level (1% < Ɛm < 15%). The permeability as well as the size of the geotextile filtration opening are two determining characteristics in the choice of geotextile. The particle size of the filler materials was adapted to the selected woven GST (O90 = 425 µm).

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5 The Protective Seawall For the section of the structure for the geotextile protection seawall, a variant is proposed for the inner profile: – A solid base with 2 geotextile tubes whose positioning is alternated or shifted to each other; – A slope using geotextile geotubes with a slope a = 30° (the shoulder of these geotubes is formed by sand or hydraulic fill) – A geotextile tube in the upper part (out of water) which ensures the stability of the rear side at the desired height.

Fig. 15. GSC protection profile for the inner part.

The external profile chosen for the project is shown in Fig. 16. Considering the angle of the embankment made up of stacks of tubes, we recommend keeping an angle of 30° corresponding to the friction angle of the filling sediment. In order to limit the premature use of geotextile tubes in the face of UV exposure or wear generated by wave conditions, an additional geotextile mat (Flexmat – see Fig. 17), on which concrete blocks are attached, covers the structure. The natural terrain varies between −3.5 and −6.5 m CM. The arase level of the structure should be determined based on water levels and agitation: – – – – –

The water level at open sea VE: +2.35 m; The water level at open sea VE: +2.35 m; Excess generated by storm conditions: +0.5 m; Sea level rise due to climate change: +0.1 m; Run-up calculations performed show that the run-up does not exceed 4 m for a 100year swell: Ru2%3.8 m.

In the light of all these factors, a value za for the arase level of the structure is equal to or greater than +6.75 m CM.

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Fig. 16. Schematic diagram for a natural ground level at −6 m and a 50-year return period water level.

Considering these results and the reached water heights defined in the previous section, we propose the following breakdown according to two geographical areas with the arase levels of the structure according to the waves of different return periods (see Fig. 17).

Fig. 17. Minimum arase levels function of Tr (return period).

6 Conclusion The project: construction of a port in Corisco island, is part of the development and its opening up. The port aims to receive cargo boats up to 5 m of draft carrying goods to and from Corisco; as well as pleasure boats of various sizes (10 to 50 m in length).

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The objective of this study is, to design the protective structures that will be constructed by stacking GSC (Geosynthetics Sand Containers). Areal knowledge of hydrodynamics in the studied site is essential, initially we modelled the wave propagation using the SWAN model, this simulation allowed us to estimate wave parameters such as significant height, direction and period. In a second time, we were interested in the realization of the geotubes. Indeed, for a degree of filling at 80%, a tube height at 2.6 m and a width of 5.8 m, the tubes are stable under hydrodynamic constraints (Hs < 2 m) and at a critical current speed of 2.4 m/s. Concerning the choice of geotextile, the installation of a protective geotextile filter of type Flexmat Robusta above the structure is recommended to ensure this protection against the risks of external degradation (carting, navigation…). The granulometry of the filling material (d50#400 lm) and the water/sediment mixture of 20% filling are recommended. Two dyke profiles are proposed. One for the exterior part and one for the interior part of the island. For the exterior part, the arase rating is fixed at +6.75 m corresponding to the centennial return period. This is the project rating with a 30° slope and at the base an assembly of 3 pyramid tubes. For the inner part, less exposed to the hustle and bustle a sandy beach is proposed to dampen the cushion effect and enhance the space of the tourist area. This concept of a sand island developed in the Emirates can also be considered in other environments (tropical or equatorial) provided that it is adequately protected from hydrodynamic agents, to have good quality sand reserves and sufficiently low depths.

References 1. Booij, N., Ris, R.C., Holthuijsen, L.H.: A third-generation wave model for coastal regions: 1. Model description and validation. J. Geophys. Res. 104, 7649–7666 (1999) 2. SOMAGEC: Stémaghrébienne de génie civil. www.somagec.ma 3. GLOBOCEAN: Climatologie des états de mer et du vent au large de BATA-GUINEE EQUATORIALE. réf. R012-013-A (2012) 4. Zijlema, M., Van der Westhuysen, A.J.: On convergence behaviour and numerical accuracy in stationary SWAN simulations of nearshore wind wave spectra. Coast. Eng. 52(3), 237– 256 (2005) 5. Liang, B., Gao, H., Shao, Z.: Characteristics of global waves based on the third-generation wave model SWAN. Mar. Struct. 64, 35–53 (2019) 6. Swan Scientific and Technical Documentation: Delft University of Technology, Faculty of Civil Engineering and Geosciences, Environmental Fluid Mechanics Section, The Netherlands. http://swanmodel.sourceforge.net/download/zip/swantech.pdf 7. Bezuijen, A., Vastenburg, E.W.: Geosystems: Design Rules and Applications. Deltares, Delft (2012). Edited by CRC Press 8. Schiereck, G.J.: Introduction to bed, bank and shore protection. DUP Blue Print, Delft (2004) 9. Pilarczyk, K.: Geosynthetics and Geosystems in Hydraulic and Coastal Engineering. A.A. Balkema, Rotterdam (2000) 10. Recio, J., Oumeraci, H.: Process based stability formulae for coastal structures made of geotextile sand containers. Coast. Eng. 56, 632–658 (2009)

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11. Morison, J.R., Johnson, J.W., O’brien, M.P.: Experimental studies of forces on piles. Coast. Eng. Proc. 1(4), 340–370 (1953) 12. Recio, J., Oumeraci, H.: Effect of deformations on the hydraulic stability of coastal structures made of geotextile sand containers. Geotext. Geomembr. 25, 278–292 (2007) 13. Buyze, J.G., Schram, A.R.: Stabiliteit van grondkribben en onderwatergolfbrekers opgebouwd uit zandworsten. TU-Delft, Studentarbeit. Master student project report (1990) 14. Oumeraci, H., Hinz, M., Bleck, M., Kubler, S.: Large-scale model test for hydraulic stability of geotextile sand containers under wave attack. Leichtweiß-Institute for Hydraulic Engineering and Water Resources. LWI-report no. 878 (2002) 15. Grüne, J., Sparboom, U., Schmidt-Koppenhagen, R., Wang, Z., Oumeraci, H.: Stability tests of geotextile sand containers for monopile scour protection. In: Proceedings of the 30th International Conference Coastal Engineering (ICCE), vol. 5, pp. 5093–5105 (2006)

Comparative Analysis of Products for Testing Software Alexander Fedosov1(&), Dina Eliseeva1, Nina Khodakova2, Olga Mnatsakanyan1, and Natalia Kulikova3 1

3

Russian State Social University, Moscow, Russia [email protected], [email protected], [email protected] 2 Moscow City Pedagogical University, Moscow, Russia [email protected] National Research University “Moscow Power Engineering Institute”, Moscow, Russia [email protected]

Abstract. Software testing is a critical step in its development. The combination of a number of stages of testing, from the analysis of a software product, the development of a testing strategy and the planning of quality control procedures, the creation of test documentation and to the testing of a prototype, stabilization - provides the necessary level of quality and efficient use of software. It allows you to optimize the costs of its design and market launch, and companies to save time, provides ease of use of appropriate software for personnel and increases productivity. The fundamental principles of testing software products: the presence of defects, dependence on the context, delusion in the absence of errors in the program, the so-called “pesticide paradox” and others, allow you to identify errors in the operation of information systems and products at the stage of development and operation. In this case, various approaches to software testing can be used - statistical and dynamic testing, research (ad hoc and exploratory testing). Many companies use special software for testing. The article presents the results of a market research of tools for testing software products, a comprehensive analysis of their functionality, the need for an integrated system for testing software #COMESYSO1120. Keywords: Software


Testing
Test cases
Bug tracking system

1 Introduction The rapid development and complication of hardware and software, information systems, an unprecedented expansion of the scale of introduction of new information technologies and the spectrum of their possible consequences in all areas of practice necessitates a more thorough verification of complex software systems in information systems [1]. Testing is widely used in various areas of life. So, for example, the learning process in teaching information-oriented disciplines in the higher education system can be built using various distance learning systems, including modules for testing students’ knowledge. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 242–252, 2020. https://doi.org/10.1007/978-3-030-63322-6_19

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Due to the developed methods of analyzing the dialogue with the user, modern information and communication technologies can greatly contribute to adapting the process of knowledge transfer to the needs and capabilities of a particular student. This creates the prerequisites for a significant approximation of the general level of quality of e-learning to individual learning and allows you to successfully create and implement various methods of adaptive knowledge control and rational methods of knowledge correction in the educational process at the university [2]. The development of technology and the increase in proposals in the computer equipment market have led to the need for a more responsible attitude towards the choice of software for solving various system and applied problems. An essential feature of the information field is that annually there are many new technologies, hardware and software products [3]. A thorough check of the quality of the product before its acquisition and implementation served as the basis for the widespread introduction of software testing procedures in the development process, which has actually become a mandatory stage today. In the general case, a program in information systems is an extremely complex multi-level object, the analysis of which is significantly difficult, and in most cases unrealistic. This is due to the fact that for any program the answer about the correctness of its execution depends on its complexity, the available time resource, and also on the number of inputs (i.e., input options) for which you can establish the correctness of the outputs, etc. It is necessary to verify cases that are likely to occur in practice [1].

2 Theoretical Framework Software testing is the identification of errors of a different nature in the development of software; one of the quality control techniques, including activities for planning work (Test Management), designing tests (Test Design), performing tests (Test Execution) and analyzing the results (Test Analysis). Figure 1 shows a diagram of the software life cycle with an emphasis on the testing phase.

Fig. 1. Software life cycle diagram

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Software testing is carried out in order to identify all possible defects of the program for their subsequent elimination and bringing the software product to the relevant direct and indirect requirements of the customer. Figure 2a shows the life cycle of the testing process, reflecting all its main stages, and Fig. 2b shows the life cycle of the defect. In the classical, if it can be called this way, interpretation, there are two main testing methods: with open (whitebox testing) and with closed (blackbox testing) code. Some translators literally translate these terms as “black box testing” and “white box testing” [4, 5]. White box testing has the ability to view internal mechanisms and software components, in contrast to a black box, in which there is no access to the source code. Gray box involves a combination of white and black box.

a – Test Life Cycle

b – Defect life cycle

Fig. 2. Life cycles of the testing process and software product defect

These types of testing allow us to consider the internal components of the system software, or vice versa, the influence of the external environment on its work, which allows us to conduct various tests to determine the correct operation of the system in general. In addition, here are the following groups of types of software testing: functional, non-functional, and associated with changes. The functional group includes: – system testing involves checking the entire system as a whole to establish the compliance of the system with the original requirements of the customer; – unit testing - checking individual software components for errors, it means checking a module or several modules of the program source code, due to the ability to “drown” the various functions performed by the program, which allows you to evaluate the operation of certain components; – integration testing - a type of testing that is aimed at checking the operation of the modules among themselves, meaning their connections, the modules are combined and tested in a group;

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– acceptance testing - final testing before delivery to the customer, determines the level of readiness for subsequent operation; it is usually carried out by an employee from the side of the customer (it can be an ordinary user or a tester). The non-functional group includes: – load testing - checks the operability of the software at the nth load, meaning if there are users on the resource; – stress testing - a type of testing when a software product is exposed to conditions that go beyond normal working conditions; – stability and reliability testing - software security verification; – volumetric testing is a check on the use of system resources while increasing the amount of data, the result is an assessment of system performance. Testing with changes: – regression testing - carried out after changes in the program code; – sanitary testing - implies the purposefulness of regression testing, but has a smaller volume, as it is aimed at testing a specific function or functionality that is critical for verification. In addition to knowing the types and methods, it is necessary to develop qualitatively and thoughtfully: test scripts, test cases, think over test designs, test plans, carefully record the presence of errors in the program. It is high-quality software testing that gives a great guarantee of the success of the developed software product. And, vice versa, if an error is detected already during the operation of the software, the organization that performed the development task receives serious fines from the customer. Automation, however strange it may sound, begins with manual testing. To be more precise, with the documentation written for such testing. That means, in order to start the testing automation process, you need to know exactly what and how you are going to do [6]. Automation of the testing process consists of three large blocks: – library of functions (function library) - a set of functions that use scripts; – library of objects (object repository) - describes all graphic objects used in the program; – library of scripts (script repository) - scripts that automate testing (the core of the whole process). In addition to the software part, documentation (a description of the infrastructure, performance matrix, etc.) and a data set for automatic testing are also needed to automate testing processes. This set should include the expected return values, data entered during the execution of tests, etc. [6]. There are many resources for conducting and planning project activities, but not for conducting all testing processes in one available resource.

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3 Results and Discussion We will conduct a comparative analysis of the four most common systems: TestRail and Bugzilla, which cover only a fraction of what is needed for testing, and Jira and Mantis are designed to create tasks for the project. The most popular Jira system is a commercial error tracking system designed to organize user interactions and is partially used for project management; developed by Atlassian [7]. It has a number of advantages: nice design, convenient menu location, automatic preparation of task status for the future, the ability to attach a file, the presence of an intuitive interface, there are task management templates, task and defect creation modes, functions for fixing the responsible person for the task, assigning an executor, deadlines and importance of the task, as well as labels for the task, the ability to leave comments. Figure 3 shows the form for creating a request in Jira.

Fig. 3. Request creation in Jira

The system has an advantage over other systems - a mobile version, thanks to which you can observe and use some groups of functions to perform tasks without being near a computer. The most significant drawback of the system is its high cost. In addition, Jira was created for project activities, including testing, which does not fully reflect the capabilities necessary specifically for testing. Test TestRail is a software for creating test cases and conducting them. It is popular among companies involved in software development, as it allows you to store projects in itself. It has a significant advantage - it can be integrated with Jira. This system has several tabs for the formation of the project. It includes project selection, then project steps, project steps, etc. The system is built not only for writing and saving information,

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but also for its output to statistics. You can observe the percentage of the project as a whole, as well as individual stages of implementation. It has statistics of passable and impassable test steps with statuses (accompanied by color): failed, passed, blocked. There is a pie chart that clearly shows the ratio of test steps. Figure 4 shows an example of statistics on completed test cases in the system [8].

Fig. 4. Passing test cases Test TestRail

Test TestRail provides the ability to create test cases with the name, description, steps and expected result. If only test cases are needed, then the system is well suited for writing them, but when the question arises about defects, a second system is needed to fix them – which is inconvenient. General access to all projects is shown in Fig. 5. The next development is Mantis (see Fig. 6), a freeware bug tracking system for software products. It provides interaction between developers and users. It allows users to start error messages and track the further process of work on them by developers. The system is a web application; therefore, it does not require special software from the user. The system has flexible configuration options and the following advantages: convenient menu location, the ability to assign the person responsible for the task and its terms, the presence of visible dates of the task change, the presence of importance labels, color indication of the status of the incident (bug), user-configurable fields, convenient filters, acceptable speed, the ability to send notifications by e-mail, the ability to print a list of tasks, the ability to export data to MS Excel [9].

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Fig. 5. Project selection and statistics Test TestRail

Fig. 6. System menu Mantis

A significant drawback of the system is that its functions are limited to creating and editing tasks, it is impossible to make significant changes to the settings through the web interface, there is no possibility to edit the status list. BugZilla (see Fig. 7) – a free web-based bug tracking system since 2012, developed by the Mozilla Foundation [10].

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Fig. 7. Menu BugZilla

The system is focused on creating errors and tracking them. The main function is defect management and description. It has a simplified design and a basic interface. It contains functions for adding a defect, has the date of the last change, responsible for the correction, description and additional comment, which is filled as necessary. Tables 1–3 show a comparative analysis of Jira and Bugzilla on the main functions of task tracking, project management and Agile project management. Table 1 shows the comparison between the two systems for task tracking functions: a clear dominance of the Jira system relative to Mantis is revealed, because of the eleven possible basic functions, Mantis has only six. The most important drawback is the investment/attachment of files to the created task, since because of the additional information due to screenshots or files, it is possible to describe the task more specifically and clearly. Table 1. Comparative analysis Jira and Mantis Task tracking Dashboards Office appointment Knowledge base Email notifications FAQ Task management Time tracking Access control Ticket statuses Attachments Discussions

Jira + + + + + + + + + + +

Mantis + + + + – + + – – – –

If we are talking about creating a defect, then there is a need for a full description with the attached files, since for the developer, you need to know all the information necessary for him to eliminate the error found by the tester. Otherwise, the defect will be sent back as an incorrectly described defect.

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A. Fedosov et al. Table 2. Comparison of project management functions Project management Jira Gantt chart + Timetables + Reports + Cloud storage (GB) + Notifications + Comments on the task + Attachments of files to a task + Filters + Task delegation + Access setting + Percentage tracking +

Mantis – – – – – – – – – – –

Table 2 compares the project management functions: the second system, Mantis, does not have any functions that Jira has. Project management is a very important aspect in the process of fulfilling tasks, it includes such aspects as: risk assessment, calculation of functional points, calculation of the project’s labor intensity, project efficiency assessment, calculation of the project’s optimal laboriousness, assessment of project metrics, quality assessment. This allows you to track at what stage the project is, how much time will be needed for each stage of development, what task should be paid attention to and more time should be devoted to another task. Responsible for the task in the project are appointed. Material costs and the cost of equipment costs are indicated and attached to tasks and stages. Table 3. Comparative analysis of the possibilities of project management Agile project management Milestone management Sprint management Backlog management Project status assessment Task combustion chart Time management

Jira + + + + + +

Mantis – – – – – –

Table 3 presents the comparative analysis of project management: you can see the clear advantage of the Jira product. Mantis has a number of advantages in creating and maintaining tasks, many labels and export options, but, unfortunately, does not have the necessary functions that other systems have.

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The Jira system is convenient to use, and also attractive in design, is the latest development and has a huge number of advantages relative to other products. It has many platforms and features in accessibility, which indicates its competitiveness. TestRail is a convenient system for creating and maintaining test cases, but it does not have the ability to create defects inside the same system, which is inconvenient. BugZilla has limited testing functionality and specializes specifically in fixing bugs, but does not cover the entire testing process, as well as other developments reviewed with it. Most beginner testers go through the stages of professional development for a long time on the basis of self-study due to the fact that the testing approach is often personal - to understand the basics of testing, you need to study and analyze a large amount of often scattered information and video content, synthesize knowledge. That is why it is relevant to develop software for testing, which includes the necessary templates for maintaining documentation, examples of their use: test cases, bug tracking system, etc.; reference theoretical materials and options for using various techniques so that beginning testers, without leaving the program, can turn to theory. Among other things, knowledge of the documentation process, as well as “various types of program documentation and existing Russian and international standards, the knowledge of which is necessary for a technical writer” [11] and a software tester.

4 Conclusion Summing up the analysis of existing developments, we can state the fact that there is still no software that will allow testing and record its results in full. For software developers, there are many different environments presented on various platforms with a diverse interface, design, using different programming languages, but for software testers there is no complete software today, which is a problem for the implementation of the most important stage of design and implementation of a software product. If you need to quickly prepare new versions of software without quality degradation, testing as part of the development should not lag behind. This requires a transition from slow, labor-intensive testing methods to faster and more fully-automated testing technologies [12]. We can confidently say that the development of information technology and software contributes to the widespread use of software testing and this area will only expand, requiring an increasing number of professionals. Rapid advances in hardware and innovative experiments with software occur simultaneously… for two main reasons: 1) exponential growth; 2) when it comes to software, progress may seem slow, but then insight can instantly change the speed of movement… in fact, this is just one system setup so that it becomes 1000 times more efficient and scales up to the human level of intelligence [13].

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References 1. Kulikova, N.L.: Development and research of logical methods for testing software systems in information systems. The Dissertation for the Degree of Candidate of Technical Sciences/Moscow (2000) (in Russian) 2. Fedosov, A., Eliseeva, D., Karnaukhova, A.: The use of machine translation system for component development of adaptive computer system for individual testing of students’ knowledge. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O., Musabirov, I. (eds.) Digital Transformation and Global Society. DTGS 2019. Communications in Computer and Information Science, vol. 1038. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37858-5_40 3. Gdansky, N.I., Rysin, M.L., Altimentova Yu, D.: Features of teaching programming in the modern labor market in IT-technologies. Sci. Notes Russ. State Soc. Univ. 2(5 (120)), 73–76 (2013). (in Russian) 4. Majchrzak, T.A.: Software testing. In: Improving Software Testing. Springer Briefs in Information Systems. Springer, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3642-27464-0_2 5. Fraser, G., Rojas, J.M.: Software testing. In: Cha, S., Taylor, R., Kang, K. (eds.) Handbook of Software Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00262-6_4 6. Vinnichenko, I.V.: Automation of Testing Processes. Peter Publishing House (2015) (in Russian) 7. Atlassian Software Development and Cjllabjration Tools. https://www.atlassian.com 8. TestRail: Comprehensive Test Case Management for QA and Development Teams. Access Mode. https://www.gurock.com 9. MantisHab. Hassle free bug & issue tracking. Access Mode. https://www.mantishub.com 10. Russian Mozilla Team. Access Mode. https://mozilla-russia.org/products/bugzilla/ 11. Makarovskikh, T.A., Lenand, M.: Documentation of software. To help a technical writer (2015) (in Russian) 12. Dustin, E., Rashka, J., Paul, J.: Automated Software Testing: Introduction, Management and Performance. Addison Wesley, Boston (1999) 13. Urban, T.: The AI Revolution: The Road to Superintelligence. Access Mode. https:// waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html

A Novel Adaptive Web-Based Environment to Help Deafblind Individuals in Accessing the Web and Lifelong Learning Samaa M. Shohieb1(&) , Ceymi Doenyas2 and Shaibou Abdoulai Haji3

,

1

2

Department of Computer Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt [email protected] Koç University Research Center for Translational Medicine, Istanbul, Turkey [email protected] 3 Institute of Educational Research, Korea University, Seoul, South Korea [email protected]

Abstract. Deafblindness is a combination of hearing and sight impairment that affects how a person accesses information and communicates with others. This paper presents the design, implementation, and validation of a creative adaptive web-based environment to support deafblind individuals in their web accessibility and life-long learning processes. This system adapts to deafblind users’ degree of loss of senses. It retrieves the web page content using dumbing of DOM (Document Object Model) technique and converts the retrieved content into an appropriate format. If the user can hear, the text is transformed into speech. If they have some sight sense, the content is transformed to screen-based Picture Exchange Communication System (PECS) presentation technique. However, if the user has an entirely dual sensory loss, the web page content is transformed into a tactile presentation technique (Moon code or Braille) that can be printed with a special embosser printer on swelled paper or touched using a display device. In the tests performed to deafblind users, this system obtained about 85% user satisfaction. This novel adaptable system that has been tested with users offers the benefit of easing at least some of the daily challenges faced by these individuals and of aiding them in web accessibility and lifelong learning content. Keywords: Deafblind
Dumbing DOM accessibility
Web content retrieving


Life-Long learning
Web

1 Introduction Deafblindness (also known as multi-sensory impairment or dual-sensory impairment) is a combination of sight and hearing impairment that impacts on how the person accesses information and communicates with people around him [1, 2]. There are two types of deafblindness; acquired deafblindness and congenital deafblindness [3]. Acquired deafblindness is an expression used when a person loses hearing and sight later in life. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 253–266, 2020. https://doi.org/10.1007/978-3-030-63322-6_20

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This may happen as a result of ageing, an accident, or illness. On the other hand, congenital deafblindness refers to cases where a person is born with a hearing and sight impairment [4, 5]. This may be due to medical complications during pregnancy or birth, cerebral palsy, premature birth, sensory loss because of ageing, accidents, illness, or range of syndromes including CHARGE syndrome [6], Usher syndrome [6], Down syndrome [7], and congenital Rubella syndrome [6]. The word “deafblind” does not mean that a person is entirely blind and entirely deaf. Most people who are deafblind have a combination of hearing and vision impairments [8, 9]. They commonly have some help but not eternally depending on hearing and vision. Deafblind individuals communicate with others in different ways [10–16]. A deafblind person who has some vision uses sign language that is adapted to conform to their visual field [10], or Picture Exchange Communication System (PECS) where PECS cards with enlarged and embossed pictures created on special raised lined drawing paper are utilized [11]. Furthermore, deafblind people who have some hearing sense use speech reading and speech in communication. Others who lose their vision and hearing completely can use tracking, tactile sign language, print on the palm, tactile fingerspelling, Braille, Tadoma, or Moon code [11, 14, 17]. The communication method differs for each person, depending on the reasons for their combined hearing and vision loss, their education, and their background [17]. The right to lifelong learning for disabled people is included in the United Nations Disabilities’ rights [18]. Lifelong learning means improving one’s competencies, skills, and knowledge by engaging in learning activities on an ongoing basis throughout a person’s life [19, 20]. The importance of lifelong learning lies in considerably increasing the capability of communities and individuals to adapt to economic, technological, environmental, social or political changes and renew their skills and knowledge [21]. Lifelong learning, in the precedent technological revolution, can be obtained with accessing the web (called web accessibility in the context of individuals with disabilities) [22–30]. Some research has been conducted to facilitate learning processes for deafblind people. Hartmann and Weismer [31] performed a comprehensive study using three models and frameworks dedicated for using technology with learning environments: 1. UDL (universal design for learning), a framework to enhance learning and teaching based on creative utilisation of digital technologies and the new ideas from the learning sciences [32]. 2. SETT (student, environment, task, tools), a tool that helps groups organize and collect information that can be used to lead collaborative decisions about services that consolidate the educational success of disabled students [33]. 3. SAMR (substitution, augmentation, modification, and redefinition) offers a technique to see how computer technology may affect learning and teaching [34]. The mentioned instructional and assistive technology frameworks were shown to be useful if they are used to provide deafblind children with accessibility and learning rights [31]. Other researchers developed assistive technologies and systems to help deafblind individuals to communicate and merge with the community. However, these assistive technologies are not dedicated to learning processes; instead, they are designed to be used to increase a deafblind individual’s knowledge and experiences. Under the PARLOMA

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project, Air`o Farulla et al. proposed a technology that uses hand tracking to transmit signs from tactile sign languages to enable remote communication for deafblind individuals [35]. Hatakeyama et al. [36] developed an assistive technology to help deafblind people to communicate with other people without the help of an interpreter. That system gives feedback from the tablet computer as vibratory stimuli as either a message from the person’s own tablet or another Bluetooth device. Also, Khambadkar and Folmer [37] developed the “AUTOSEM” that defines a set of semaphores that represent an alphabet by utilizing combinations of different directions of both hands. Furthermore, Spiers and Dollar [38] developed a device called “The Animotus” that is capable of altering its forms so that it facilitates communication of information. Based on the available literature review presented, the researchers aim to provide ways to facilitate communication for deaf-blind people. Since no tool exists to facilitate web accessibility and, therefore, the lifelong learning processes for deaf-blind people, especially for those with acquired deaf-blindness, this paper presents the design, implementation, and testing of a novel adaptive web-based system that helps deafblind individuals in their web accessibility and lifelong learning processes. Our system is based on retrieving the content of the required web page based on the dumbing of DOM technique and converting the retrieved content into a format that fits the deafblind user’s level of sensory loss [39–41].

2 The Adaptive Environment for the Deafblind People Most acquired deafblind users can turn on the computer, launch the browser, and input the required URL independently or semi-independently. The deafblind user, in the beginning, only copies the URL of the required web page and pastes it in our online website. Then, the system adapts to the degree of their disability and retrieves the main content from the required web page. Finally, the system presents the retrieved content in a format that fits the user. The system was implemented in the object-oriented PHP language, and the database was in MySQL. The proposed system consists of four consequent layers that are described in the following sections and is shown in Fig. 1. 2.1

The Adaptation Layer

This layer adapts the system according to the user’s loss of senses (i.e. loss of hearing sense with some helpful sight, loss of sight sense with some hearing, or loss of both sight and hearing senses). Also, this layer adapts the system to the user’s preferred language (either English or Arabic). In this layer, the system presents a loud spoken sentence for the user “If you can hear me, please click the ‘ENTER’ button”. Moreover, the same sentence is spoken in Arabic, asking the user to press the “SPACE” button. If the user clicks the button, the system switches to the next layer; the content retrieving the required page. If the user has not responded within five seconds, the system automatically presents a sentence in big font “If you can see me, please click the ‘ENTER’ button”. Moreover, the same sentence is presented in Arabic, asking the user to press the “SPACE” button. If the user clicks the button, the system switches to the next layer.

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Finally, if the user has not responded within five seconds, the system automatically considers the user as completely dual sensory impaired and switches to the next layer.

Fig. 1. The block diagram of the adaptive environment for the deafblind individuals

The user can easily, with or without a little external assistance, register to our web site. Both the preferred language and the type of senses loss will be saved to their profile. Consequently, the user will not be required to adapt the system each time after the initial registration. However, there is an availability to change the settings later, if required, from the profile area. 2.2

The Content Retrieving Layer

The Document Object Model (DOM) can be defined as a language-independent and cross-platform convention for interacting with and representing objects in HTML, XML, and XHTML documents. The DOM is an application programming interface (API) for documents. It is based on a hierarchical object structure that carefully matches the structure of the documents it redacts. The first group to use the “retrieving web

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content form DOM” technique was Gupta et al. [40], who tried to remove links and advertisements from web content. Their approach was based on the ratio of non-linked words in a DOM-tree and the number of links. Also, Fernandes et al. [39] proposed an algorithm for information retrieval that works at the block level. They proposed that important computing values of blocks based on the occurrence of the term in blocks (not on the whole page) gives much better results. Their technique proposed a Noise Detector (ND) that was able to declare the blocks that form a template in a website that would later be used to derive the DOM structure blocks. Yet, our system was based on the work of Solórzano et al. [41] in which we made certain modifications to be in line with the final goal of the system. In this layer, all types of tags are extracted one by one. When dealing with images, texts are extracted from captions under the images (“alt” attribute in < img > tag). Algorithm for Content Retrieving Input: URL of the web page Output: Pure text that represents the content of the web page begin //Create DOM Structure from the entered URL var html = get-html-from-file(URL'); // Find every block inarticle foreach(html->find ('div. article') as var article) { article->find('div.title', 0)->plain_text = item ['title']; article->find('div.intro', 0)->plain_text= item ['intro'] ; article->find('div.details', 0)->plain_text = item ['details'] ; article[] = item; } //find HTML elements foreach(html->find(element) as var element { { // Find all elements, returns an array of objects returns = html->find('a'); // Find every“id” attribute included in returns = html->find('div[id]'); // Find every including the alt attribute returns = html->find(img[alt]'); description_of_img= returns; } //Dumping contents of DOM object from the internal DOM tree and save it into a string var str= save(html); Next_layer > > @u > < @z 

2  2 @u @u @u 1 þ þ þ gg > @t 2 @x @t > > > : @u @g @g @u @z  @t  @x @x

¼ 0 ¼ 0

in D at z ¼ 0

¼ 0

at z ¼ g

¼ 0

at z ¼ g

ð1Þ

Other boundary conditions should be considered named the wave generation conditions upstream and the absorption conditions downstream [5]. The two-dimensional problem of nonlinear wave reduces to determine a complex solutions u(x, z, t) and η(x, t). The time-dependence of these variables is harmonic. Indeed, the potential u and the elevation of the free surface η can be written as: 8 < uðx; z; tÞ

¼

: gðx; tÞ

¼

Xn Xin i

Un ðx; zÞeinxt gn ðxÞeinxt

ð2Þ

where i is the complex number (i2 = −1), w represents the pulsation of the incident wave and t is the temporal variable. The free surface is considered as an unknown boundary which leads the integration of the motion equations more complex. To overcome this difficulty and facilitate the imposition of the boundary conditions, we project the free surface on the middle line by expressing the potential u and its derivatives by a Taylor series expansion at z = H, as presented in the theory of Stokes [6]. The different operators involved in the equations of motion are written in the following form:

Numerical Modeling of the Wave-Structure Interaction

8    > @u @u @ @u > > ¼ þg þ > @t  > @t H @z @t H > H þg > >   > < @u @u @ @u  ¼ þg þ @x H þ g @x H @z @x H > > > >    > > @u @ @u > @u > ¼ þ g þ > : @z  @z H @z @z H H þg

 g2 @ 2 @u þ... 2 @z2 @t H  g2 @ 2 @u þ... 2 @z2 @x H  g2 @ 2 @u þ... 2 @z2 @z 

295

ð3Þ

H

Taking into account the expression (2) the different operators in (3) become:    8 @u @U1  2ixt > ixt > ¼ ixU j e þ ix 2U þ g e þ... 1 H > 1 > @t H þ g @z H > > >     > < @u @U1  ixt @U2 @ @U1  2ixt  þ g2 ¼ e þ e þ... ð4Þ @z @x H @x > @x H þ g @x H > >     > > > @u @U1  ixt @U2 @ @U1  2ixt > > þ g ¼ e þ e þ... :  2 @z @z @z  @z  @z H þg

H

H

The expressions (4) are replaced in combined Eqs. (1c) and (1d) and by injecting the series (2) in Eqs. (1a) and (1b), one obtains, after identification term by term according to the increasing powers of einxt , a succession of well-posed linear problems verified by the terms Un and gn . Problem at order 1 (Linear problem): 8 DU1 ¼ 0 in D > > > < @U1 ¼ 0 at z ¼ 0 @z ð5Þ @U1 x2  U ¼ 0 at z ¼ g > 1 @z g > > :g ¼ ix 1 g U1 Problem at order 2: 8 DU2 > > > @U2 < @z @U

2 > @z  > > :g

2

4x2 g

U2

¼ 0 ¼ 0 ¼ 2i xg S22  S21 ¼ 1g S22  2 ix g U2

2 1 1 @U1 Where: S21 ¼ g1 @@zU21  @g @x @x and S2 ¼ 2 2

 @U 2 1

@x

þ

in D at z ¼ 0 at z ¼ g


@U1 @z

2 

ð6Þ

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Problem at order 3: 8 DU3 > > > @U3 < @z @U

3 > @z  > > :g

9x2 g

U3

3

¼ 0 ¼ 0 ¼ 3i xg S32  S31 ¼ 1g S32  3 ix g U3

in D at z ¼ 0 at z ¼ g

ð7Þ

Where: 8 @g @U2 @g2 @U1 @g @ 2 U1 > > S31 ¼  1   g1 1 þ > > > @x @x @x @x @x @x@y > > > > > 1 2 @ 3 U1 @ 2 U2 @ 2 U1 > > g1 þ g1 þ g2 < 3 2 2 @y @y @y2 > @U1 @U2 @U1 @ 2 U1 > >  g1  S32 ¼  > > > @x @x @x @x@y > > > > > @U1 @U2 @U1 @ 2 U1 > :  g1 @y @y @y @y2

ð8Þ

3 Boundary Element Method In order to solve the Laplace equation with all boundary conditions, the Green function is applied and the governing equation can be transformed to the boundary integral equation shown as: cUðx; zÞ ¼

Z



@D

 @Uðx0 ; z0 Þ @Gðr Þ G ðr Þ  Uðx0 ; z0 Þ ds @n @n

ð9Þ

Where: c ¼ 0 if the coordinate points ðx; zÞ 62 D [ @D c ¼ 0:5 if the coordinate points ðx; zÞ 2 @D c ¼ 1 if the coordinate points ðx; zÞ 2 D. 1 G is the Green’s function given by: GðrÞ ¼  2p lnðrÞ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 Where r ¼ ðx  x0 Þ þ ðz  z0 Þ is the distance between a field point and a boundary point. Then the boundary surface is discretized with of finite number N segments (See Fig. 2). We subdivide the bottom boundary into N1 elements, the upstream boundary into N2 elements, the free surface boundary of N3 elements and the downstream boundary of N4 elements, where N = N1 + N2 + N3 + N4. We suppose that on each segment, the potential and its normal derivative are constant and we note the values of these latter in the middle of segment i. This approach makes it possible to obtain the system in the following matrix form:

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cUi ¼

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 ð10Þ

The influence matrices H and E have explicit form:

the following

R @ 1 For i 6¼ j: Hij ¼ R @G DS dS ¼ ln and E ¼ GdS ¼ ln 1r DSj j ij @n @n r

For i = j: Eij ¼ 2p and Hij ¼ 2 1  LogDSj DSj The new matrix form of The relation (10) therefore written: fUg ¼ ½K 

@U @n

ð11Þ

This system is of N equations with 2 N unknowns, which are respectively the potential and its normal derivative on the boundaries of the field of study. The N additional equations are obtained by writing the boundary conditions (2-b), (2-c), (2-d), (2-e)and (2-f). These latter can be expressed by relations between the two unknowns of the form:

0

½0i¼1;N1 0 B B With: [F] = B 0 @ 0

@U @n

¼ ½F fUg þ fSN g 0

½ - iki¼N1 þ 1;N2 0 0

0 0

h 2i x g i¼N2 þ 1;N3 0

ð12Þ 0 0 0 ½iki¼N3 þ 1;N4

1 C C C A

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1 f0gi¼1;N1 B f2ikf(y)gi¼N1 þ 1;N2 C C fSNg = B @ f0gi¼N2 þ 1;N3 A f0gi¼N3 þ 1;N4 ag ch(ky) x ch(kH) and k is the wave vector. By injecting the relation (12) into the relation (11), the determination of the potential U therefore comes down to the resolution of the following system: Where f(y) =

4 Applications The first step of this work is the validation of the numerical model to solve the problem of wave propagation in the presence of obstacles, then the validation of the theoretical formula of the reflection coefficient in comparison with the experimental result. 4.1

Validity of the Numerical Code (Case of a Flat Bottom)

We consider a monochromatic incident wave of small amplitude propagating in thepresence of the flat bottom of a wave tank such as length L = 30 m, height H = 2.5 m and amplitude a = 0.01 m. We present in Fig. 3 a comparison of the numerical solution and the Stokes analytical solution to order 1. We notice that there is a good agreement between the two solutions, so the adopted numerical approach is valid.

0,04

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Elévation de la surface libre

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Fig. 3. Numerical and analytica l (Stokesfirst order) elevation of the free surface

To evaluate the effectiveness of downstream numerical absorption conditions, the relationship of the numerical reflection coefficient calculation was exploited. Figure 4 shows the results of the calculation of the reflection coefficient in the channel in the absence of obstacles as a function of kH (H being the water depth of the channel).

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Fig. 4. Reflection coefficient R as a function of Kh

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Case of a Rectangular Obstacle Placed on the Bottom

We consider a monochromatic incident wave of small amplitude propagating in the presence of a rectangular obstacle fixed on the bottom of a numerical wave tank (See Fig. 5).l is the length of the obstacle, h = 1.25 m its immersion and H = 2.5 m the water depth.

Z

Ƞ(x,t) Resting level h

H

→

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Fig. 5. Sketch of numerical wave tank of a rectangular obstacle on the bottom.

Figure 6-a present the elevation of the free surface for a moment t = T, and Fig. 6-b shows the elevation of the free surface along the wave tank for different moments in the presence of a rectangular obstacle on the bottom, for an incident amplitude a = 0.01 m, h = 1.25 m and l = 5 m. Figure 7 shows the results of the numerical calculation of the reflection coefficient as a function of kh for an immersion (h/H) of 50%. These results are compared with experimental results in a laboratory channel [7]. A good agreement between the numerical results is observed and experimental measurements are approximated.

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Fig. 7. Numerical and experimental reflection coefficient for an obstacle as a function of Kh with 50% immersion.

4.3

Case of Two Spaced Rectangular Obstacles Placed on the Bottom

We consider a monochromatic incident wave of small amplitude, propagating in the presence of two obstacles of the same length l = 0.25 m, of the same height h = 1.25 m and spaced at a distance d fixed on the bottom of a numerical wave tank (NWT) (see Fig. 8). Figure 9 shows the elevation of the free surface along the canal for an incident amplitude a = 0.01 m, a wavelength ʎ = 15 m and a height h = 1.25 m. A wavelength range was scanned for direct reflection coefficient calculation. The results obtained numerically are shown in Fig. 10 for different spacing of the two obstacles.

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Z Free surface

Ƞ(x,t) h

→

Wave maker

Restinglevel H

DownStream

d l

BoƩom

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Fig. 8. Numerical wave tank in the presence of two rectangular obstacles.

0,015

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Fig. 9. a. Elevation of the numerical free surface as a function of the length of the tank. b. Elevation of the numerical free surface a function of the length of the tank at different times.

We found that: – For all spacings, the maximum reflection coefficient is in the area around kh = 0,5. The attenuation effect in this zone is important when the spacing is of the order of the length of the obstacle. – There is a shadow zone for which the reflection coefficient is minimal. Figure 11 present the variation of the reflection coefficient, calculated numerically, as a function of Kh with an immersion (h/H) of 50%. These results are compared with experimental results, there is a good agreement between the numerical results and experimental measurements [8].

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Fig. 10. Numerical reflection coefficient as a function of kh for 2 obstacle d = l, d = 3 l/4, d = l/2, d = l/4

0,6

R numerical R experimental

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Fig. 11. Numerical and experimental reflection coefficient for 2 obstacle as a function of Kh with 50% immersion.

5 Conclusion The objective of the work presented here was to validate the numerical computational code for the Boundary Element Method (BEM) for the resolution of the wave problem. The use of this tool has allowed us on the one hand to achieve our objectives, and on the other hand to open a path towards understanding the processes of wave dissipation through rectangular obstacles. Indeed, the numerical calculation code developed proves to be efficient for the calculation of the reflection coefficient during the passage of a

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monochromatic wave on an obstacle made up of one or two rectangular steps fixed on the bottom of a tank. This assertion is based on good agreement with the experimental results. The introduction of non-linearity is done by a combination of a trigonometric development procedure and a Boundary Element Method (BEM) solution, in which the problems of the different orders have the same shape, only the second member changes, thus saving a lot of computing time, hence a significant gain in computing time.

References 1. Bonnet, M.: Équations intégrales et éléments de frontières: Applications en mécanique des solides et des fluides (in French). Sciences et Techniques de l’ingénieur, CNRS Editions/Editions Eyrolles (1995) 2. Kellog, O.D.: Fundations of potential theory. Berlin, (1979) 3. Gunther, N.M.: La théorie du potentiel et ses applications aux problèmes fondamentaux de la physique mathématique (in French). Gauthier-Villars (1934) 4. Grilli, S.T.: Lecture on the boundary element method. Private lecture given at laboratory St-Venant for Hydraulics, Chatou (France) (2010) 5. Orlansky, I.: A simple boundary condition for unbonded hyperbolic flows. J. Comput. Phys 3, 251–269 (1976) https://doi.org/10.1016/0021-9991(76)90023-1 6. Mordane, S.: Calcul du problème de la houle non-linéaireetinstationnaire par une méthode asymptotique-numérique.thèse de 3ème cycle.Université Hassan II- Mohammedia, Faculté des Sciences Ben M’Sik, Casablanca, Maroc (1995) 7. Mordane, S., Chahine, C., Naasse, S., Chagdali, M.: Propagation d’une onde de gravité en présence de deux obstacles fixés sur le fond d’un canal .3éme Rencontre Hydrodynamique marine Casablanca, Maroc (1999) 8. Mordane, S., Naasse, C., Chahine, M., Chagdali, M.: Propagation de la houle en présence d’obstacles: Etude théorique, numérique et expérimentale, les cahiers de la recherche. 3, 161–176 (2001) 9. Loukili, M., Mordane, S.: Modélisation de l’interaction houle-marche rectangulaire la méthode des solutions fondamentales. In: 13ème Congrès de MécaniqueMeknès, Maroc (2017) 10. Loukili, M., El Aarabi, L., Mordane, S.: Computation of nonlinear free-surface flows using the method of fundamental solutions. In: Silhavy, R. (ed.) Software Engineering and Algorithms in Intelligent Systems. CSOC 2018. Advances in Intelligent Systems and Computing, vol. 763. Springer, Cham (2019)

Transition from Serverfull to Serverless Architecture in Cloud-Based Software Applications Oliviu Matei1(B) , Pawel Skrzypek2 , Robert Heb3 , and Alexandru Moga3 1

Department of Electrical Engineering, North University of Baia Mare, Str. V. Babes, 430083 Baia Mare, Romania [email protected] 2 7bulls, Warszaw, Poland [email protected] 3 HOLISUN, Baia Mare, Romania {robert.heb,alexandru.moga}@holisun.com

Abstract. This paper makes a practical comparison between serverfull and serverless architectures for the same specific cloud application used for face recognition. It turns out that both approaches have advantages and disadvantages and their specific use needs to be carefully assessed in the design phase of the software life cycle.

Keywords: Cloud

1

· Serverless computing · Software architecture

Introduction

In the spehere of cloud world, a new paradigm arises, namely serverless computing, which is an execution model based on small components which start or die based on their needs and usage. In such case, the cloud resources are dynamically allocated. Pricing is based on the actual amount of resources consumed by an application, rather than on pre-purchased units of capacity [1]. In many specific application, serverless computing is the optimal solution which allows very easily scaling, capacity planning and maintenance of the application. No full servers are needed anymore and the resources are not stuck for ever for one platform [2]. This should not be confused with computing or networking models that do not require an actual server to function, such as peer-to-peer (P2P). Almost all serverless vendors provide function as a service (FaaS) platforms, which execute application logic but do not store data. In 2008, Google released Google App Engine, which featured metered billing for applications that used a custom Python framework, but could not execute arbitrary code [3]. Kubeless and Fission are two Open Source FaaS platforms which run with Kubernetes [4]. The first public cloud vendor providing serverless facilities was c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 304–314, 2020. https://doi.org/10.1007/978-3-030-63322-6_24

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Amazon with its AWS Lambda, in 2014 [5]. Several additional AWS serverless tools such as AWS Serverless Application Model (AWS SAM) Amazon CloudWatch, and others accompany the serverless service. Google Cloud Platform offers Google Cloud Functions since 2016 [6]. IBM provides IBM Cloud Functions in the public IBM Cloud since 2016 [7]. Microsoft Azure offers Azure Functions, either in the Azure public cloud or on-premises via Azure Stack [8].

2

Functionizer Project

The main goal of the project is to create a unique platform that will optimize and manage the deployment of serverless applications in multi-cloud environment. This approach is an outcome of a growing market of data-intensive applications that constantly pursue better resource- and cost-efficiency. Serverless computing is positioned to change the state-of-play for cloud applications. The business case must validate the Functionizer platform [9], by demonstrating that serverless computing can be effectively incorporated in the multi-cloud domain and demonstrate how Functionizer makes deployment and management of multi-cloud data-intensive applications faster, simpler and cheaper. The business case focuses on a certain software solution, which takes an audio/video streaming from wearable devices (namely smart glasses) and processes it on a server for face or image recognition. It has applications is several fields, such as: – Industry. As a company there are situations that require a specialist to be present at various interventions. You can ship a pair of glasses anywhere you need and have an employee ready to be equipped with them. The glasses will transmit a live feed of whatever the employee is watching, back to a support center where your expert will be able to provide the much needed assistance. – Medicine and Emergency response. Doctors and paramedics can be coordinated by a specialist from the Emergency Room during resuscitation maneuvers. – Training. The lecturer can perform live demonstrations (presenting equipment, performing surgery or health and safety), while students can see exactly what he is doing in real time, classes can be recorded and all that while the lecturers are using both their hands. The features employed by the solutions include: – Audio/video streaming: The engineer and the technician can talk to each other and see what the other sees. The stream is encoded using H.264 encoder. If the device supports hardware acceleration, this feature is also used for better video quality. The default frame rate is 30 fps, and the default image resolution is 640 × 480. They resolution may vary between 320 × 240 and 1028 × 768, depending on the bandwidth. The unidirectional stream bandwidth is 1 Mb/s at a resolution of 640 × 480. As the communication is bidirectional, the reasonably needed bandwidth is 2 Mb. If there is a multi-user conference with n users, the bandwidth should be 2xn Mb.

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– Hands free: While the technician has the support of the engineer and streaming back what he sees, he also has his hands free and available for using the tools he needs. Therefore, the efficiency is not affected in any way. – Chat: This can be used when the audio stream is bad, or the user needs to send a model or serial number. The technician can send the inventory codes of the assets or their models or serial numbers. Moreover, the chat is the base communication means for drawings and file transfer. Of course, they are not visible in the chat form. – Drawings: The engineer can draw simple shapes, such as rectangles, circles, polylines, and lines for spotting points of interest and making himself clearer in designating things. More complex shapes are useless, because if the glasses wearer moves her head, the shapes may not focus on the desired spot. – File transfer: The engineer can send maps of the pipelines, maintenance manuals, and designs of the specific equipment or pipes to be repaired directly to the technician. The file can be opened on the glasses if there is a viewer installed. The most often transferred files are AutoCAD, pdf, images, doc, and txt files. – Platform independency: We tested on the following OS: Android 5.1+, ReticleOS, iOS, and Windows. The devices used are desktops/notebooks, smart glasses (ODG R7 (HL) and Epson Moverio BT-300), and smart phones with the indicated OS. This means that the engineer can provide his support even when he is mobile (whether using Android or iPhone). – Snapshots: These are possible from the support center (desktop/notebook) for documenting interventions. If a solution is not available because of very specific configuration of the pipe or equipment, the engineer can take a snapshot and further research the possible solutions. – Recording: This is similar to snapshots. The recordings are stored when they are ended. This means that during a teleconference there can be more recordings (only of the important phases). The engineer can decide which parts of the conversation are worth being recorded and later stored. This improves the quantity of institutional knowledge in the company, which usually is lost when a technician leaves the company. – Multi-user video-conferencing: This is an option if they are all available from the beginning. For each user, some basic information (e.g. GPS coordinate and the browser) is available.

3

Serverfull Architecture

The serverfull architecture of AR Assistance follows client-server and is described in Fig. 1. The client side is accessed by the user and consists of smart glasses, physically. The server side is, of course, more complex and assumes all functionalities already provided by AR Assistance as well as the ones to be implemented during the Functionizer project. In the architecture in Fig. 1, different kinds of application components are represented with different colours:

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Fig. 1. The serverfull architecture of the application

– The gray components are the two tiers of the application - the client and the server. – The green components are already implemented in a classical manner (i.e., statefully). – The mauve components are to be deployed in the cloud (e.g. AWS). – The ochre components represent the third party modules used within the development process. The server-side modules include: – User management - needed for managing the users (add, remove, modify and grant rights), their related info and meta-data. – File management - needed for storing the files to be transferred within the application. – Stream management offers the largest and most complex functionalities, related to the management of audio-video streams, such as:

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• • • • 3.1

Pairing the ends of the conversation (two or more clients); Video streaming; Sending text from one end to the other; Sending graphic info from one end to the other. Sequence Diagram of the Serverless Approach

The sequence diagram of the serverful approach is depicted in Fig. 2.

Fig. 2. The sequence diagram of the serverfull approach

Besides the modules already provisioned in the serverless approach and depicted in Fig. 8, this serverfull exhibits some extras: – The Application server, containing the specific components (λ1 , λ2 and λ3 ) modules; – All Application server, λ1 , λ2 and λ3 are on forever; – All calls of λ1 , λ2 and λ3 pass through the Application server.

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309

Serverless Architecture

The image/face recognition related modules are very complex, therefore we present the second level decomposition of AR Assistance architecture related to them and are depicted in Fig. 3.

Fig. 3. The serverless architecture of the application

The colours of the components have the following meanings: – The gray components represent the clients (dark grey is the web client and the light grey depicts the wearable client). – The green components are already serverfull components. – The ochre components cloud side components.

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The architecture consists of: – 1st tier (the client) – 2nd tier (the business logic) consisting of: • Serverful components are deployed in the cloud and refer to:  Stream management  server (Kurento Media Server)  Database (DB) (Maria DB) • Serverless components:  3 lambda functions: Recognize from past, Faces training and Faces recognize  3rd party component for file storage (e.g. Amazon S3 service). 3.3

Sequence Diagram

The sequence diagram for AR Assistance is depicted in Fig. 4. The red life lines are serverful (belonging to components living indeterminable), whereas the green life lines belong to serverless components and are usually relatively short.

Fig. 4. The serverless sequence diagram

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The sequence diagram depicted in Fig. 4 has two lines - online (from the wearable client) and off-line (from the web client), split with a horizontal bar. All use cases start from the client - web for training, respectively wearable for production. – The wearable client connects to media manager and establishes a communication session. In turn, media manager captures the frames from the Media Server (which is connected to the wearable via WebRTC) and transfers them to λ3 (for face recognition), which returns the metadata of the recognized person (if it is the case) to Media Manager and from there to the client. For face recognitions, λ3 uses the images stored in File Storage. – In the same time, the video stream stored for further detailed processing and historical augmentation. – Off-line, the face recognitions can be run on stored videos. The action is initiated from the web client. – However, the face recognition algorithm needs to be trained for running properly. This sequence has been depicted as last one because it is less important in the economy of the application (as it runs less than the former ones). Communication Modes. The underlying communication mechanisms of the sequence diagram in Fig. 4 are depicted in Fig. 5. The client invokes the 2nd tier by socket connections. The serverless components are called using a specific protocol by an SDK specific for each cloud.

Fig. 5. The communication modes of the serverless approach

AWS Lambda supports synchronous and asynchronous invocation of a Lambda function, called on-demand invocation [10]. When the AWS service is used as a trigger, the invocation type is predetermined for each service.

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Discussions

In many cases, applications comprise application servers which could orchestrate the execution of certain application functionalities offered as a service usually within the same host as the application server. We advocate that through serverless computing, it is possible to either totally remove the application server (e.g., when it just plays the role of a proxy) or to replace it with a light serverless function compositor (e.g., when it does involve some orchestration logic). Through this transformation, it can then be possible to reduce costs as: – the serverless components live only when executed; – they map to a smaller operation cost; – they can be elastically scaled in quite high instance numbers in contrast to the case of application servers (and respective services) which might be restrained based on the elasticity bounds enforced by the hosting cloud providers. Table 1 sumarizes the comparison between the serverfull and serverless architectures or AR Assistance. Table 1. Comparison between serverfull and serverless architectures Serverfull

Serverless

Architecturally The application server, which hosts and controls the specific components is architecturally defined

The serverless components are dynamically created on demand and very atomic. That allows a large flexibility of deployment

Interaction and lifetime The calls of the specific components pass The calls of the specific through the application server components go directly to them with no imposed proxies The specific components live only The application server along with the specific components live indefinitely long as long as they are running Deployment The components, hosted by the application server, run on the same infrastructure

5

The serverless components may run in various clouds (various infrastructures)

Conclusions

Serverless computing is a new concept more and more used in the cloud. It has its own advantages and disadvantages.

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The advantages consist of: – Serverless can be more cost efficient than renting full underused servers. There is significant research on optimizing the cost function in multi-cloud deployment [11–13]. – Serverless architecture are very flexible and very scalable as they use only needed resources, no extra resources being allocated apriori. – Serverless components are architecturally simple and the developer does not have to worry about multithreading or directly handling HTTP requests in their code. – Serverless paradigm is very suitable in the context of Internet of Things [14,15]. On the other hand, there are also associated disadvantages: – The serverless code infrequently used may show greater response latency until is starts, comparing with a serverfull. – Serverless computing is not suited for resource intensive computation efforts, e.g. high-performance computing, because it would likely be cheaper to use servers. – Serverless architectures lack of deep debugging and monitoring facilities. – The amount of code vulnerable at cyber attacks is significantly larger compared to traditional architectures. – The portability of serverless components may be an issue when changing the cloud providers, although it is covered by International Data Center Authority (IDCA) in their Framework AE360. Acknowledgement. This work has received funding from the Functionizer Eurostars project and the EUs Horizon 2020 research and innovation programme under Grant Agreement No. 731664.

References 1. Miller, R.: AWS Lambda makes serverless applications a reality. TechCrunch. 10, (2016) 2. Raines, G., Lawrence, P.: Platform as a service: A 2010 marketplace analysis. MITRE Technicel Paper (2010) 3. Zahariev, A.: Google app engine. Helsinki University of Technology 1–5 (2009) 4. Brewer, E.A.: Kubernetes and the path to cloud native. In: Proceedings of the Sixth ACM Symposium on Cloud Computing (2015) 5. Villamizar, M., et al.: Infrastructure cost comparison of running web applications in the cloud using AWS lambda and monolithic and microservice architectures. In: 16th IEEE/ACM International Symposium on Cluster, p. 2016. Cloud and Grid Computing (CCGrid), IEEE (2016) 6. Lynn, T., et al.: A preliminary review of enterprise serverless cloud computing (function-as-a-service) platforms. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE (2017)

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7. Cash, S., et al.: Managed infrastructure with IBM cloud OpenStack services. IBM J. Res. Develop. 60(2–3), 1–6 (2016) 8. Sreeram, P.K.: Azure Serverless Computing Cookbook. Packt Publishing Ltd (2017) 9. Kritikos, K., et al.: Towards the modelling of hybrid cloud applications. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE (2019) 10. Kiran, M., Peter, M., Inder, M., Jon, D., Sartaj, S.B.: Lambda architecture for cost-effective batch and speed big data processing. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2785–2792. IEEE (2015) 11. Horn, G., Skrzypek, P.: MELODIC: utility based cross cloud deployment optimisation. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), Krakow, pp. 360–367 (2018) https://doi. org/10.1109/WAINA.2018.00112. 12. Horn, G., R´ oza´ nska, M.: Affine scalarization of two-dimensional utility using the pareto front. In: 2019 IEEE International Conference on Autonomic Computing (ICAC), Umea, Sweden, pp. 147–156 (2019)https://doi.org/10.1109/ICAC.2019. 00026. 13. Matei, O.: Evolutionary computation: principles and practices. Risoprint (2008) 14. Matei, O., et al.: Collaborative data mining for intelligent home appliances. In: Working Conference on Virtual Enterprises. Springer, Cham (2016) 15. Di Orio, G., et al.: A platform to support the product servitization. Int. J. Adv. Comput. Sci. Appl. IJACSA 7(2) (2016)

Comparison of Document Generation Algorithms Using the Docs-as-Code Approach and Using a Text Editor Marina Igorevna Ozerova(&) , Ilya Evgenievich Zhigalov and Vitatliy Vasilievich Vershinin

,

Vladimir State University, 66-40 Anniversary St, Vladimir 600031, Russian Federation [email protected], [email protected], [email protected] Abstract. This article describes the process of generating requirements and the problems that system engineers face during documenting requirements. A comparison is made of two approaches to documentation with the formulation of a computational experiment and the calculation of the index of the complexity of documenting an equivalent document using different approaches #CSOC1120. The process of generating requirements imposes many problems during document phase for system engineers. We compare two approaches to documentation using a computational experiment in order to calculate the complexity index #CSOC1120. Keywords: Requirements documentation


Docs-as-Code

1 Introduction The creation of an information system, like any other system, begins with the formation of requirements for it. Requirements formed at several main stages: the vision stage, the business requirements stage and the system requirements stage. Passing through the above stages, the idea expands and acquires details, which ultimately turns it (the idea) into a product. This is a long and hard way. To achieve success, you must make every effort at every stage of development. To increase work efficiency, you need to use the most appropriate tools and approaches. Analysts mainly form requirements for the system. The results of the work of analysts are documents containing the generated functional requirements that are transferred to the next stage and it is a kind of instruction, for implementation. Therefore, the question arises: what is the most efficient way to document requirements? In addition, this is not about the wording, the rules for writing requirements, but about the seemingly familiar and so familiar work with the file. There is more than one approach to solving this problem. Accordingly, in order to make a choice, it is necessary to carry out multi criteria analysis, for which it is necessary to determine a number of important criteria and from its results to determine which approach to give preference. Like any system, the creation of an information one starts with requirement collection. Development pipeline of requirements can be broken down to several stages: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 315–326, 2020. https://doi.org/10.1007/978-3-030-63322-6_25

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the concept, the business requirements and the system requirements. As we move forward the pipeline, the idea becomes more complex and detailed, eventually transforming into a product (but this is an ideal case). This is a long and hard way. To achieve success, one must make maximal effort at every stage of development. To make work more efficient, one needs to use the most appropriate tools and approaches. Usually analysts form requirements for the system. The results of analyst work are documents containing the generated functional requirements that are transferred to the next stage serving a kind of instruction for implementation. Hence the question arises of how to document requirements in the most efficient way? Apparently there are many approaches to deal with this problem. In order to make a proper choice, one has to carry out multi criteria analysis. Therefore it is necessary to determine a number of important criteria, thus making it possible to determine the most preferred approach. 1.1

Place of Creation of Requirements in the Process of Creating an Information System

Creation of requirements and there it stands in the process of building an information system. Creating an information system is a multi-stage and not simple process. The development of requirements is at its very beginning and is its foundation. It is worth noting that the formation of requirements is an iterative process. Requirements can be specified, added and deleted. The paper discusses the process of documenting system requirements. This level of requirements characterized by the most detailed requirements and maximum proximity to the development and testing phase. The diagram (see Fig. 1) shows a simplified process of converting ideas into requirements [1]. Creating an information system is a multi-stage complex process. The development of requirements lies at it’s core. One should bear in mind that the formation of requirements is an iterative process. Requirements can be specified, added and deleted. The paper discusses the process of documenting system requirements. This level of

Idea Business Requirements System requirements Subsystem Requirements Component Requirements Fig. 1. The process of forming requirements.

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requirements is characterized by the most detailed requirements and maximum affinnity to the development and testing phase. The diagram (see Fig. 1) shows a simplified process of converting ideas into requirements [1]. Going down below on the scheme, the requirements are gaining more and more technical details. The last two blocks determined directly by technicians, not system engineers. Undoubtedly, it will be a plus if the analyst outlines how the subsystems work, but one should not go too deep into technical details. The process of forming requirements defined and then it is worth paying attention to how work done with documents, becomes the main object of the described process. As one goes down below on the scheme, the requirements are gaining more and more technical details. Two final blocks are determined directly by technicians instead of system engineers. Undoubtedly, it will be for the better if the analyst outlines how the subsystems work, but one should not go too deep into technical details. 1.2

Documentation Process History

The 20th century has come and the advent of computers has pushed the requirements for the transfer from paper format to digital. Of course, this happened at different speeds in different parts of the world [2]. However, requirements like other documents, more and more often presented in the form of files of various extensions on users’ devices. Now, you can view documents not only at the desktop computer, but also from the screen of a smartphone, tablet [3]. However, would like to note one point: no matter how the media and forms of presentation of information change, the principle remains the same - to keep it in order to refer to it later. Each of the forms of presentation of information has become more perfect than the previous ones. However, some problems remained. The transition from paper to digital format was no exception. With the advance in computer technologies a shift began from paper format to digital. Of course, this shift had different pace in different parts of the world [2]. Nowadays requirements, just like any other document, are used in the form of files with various formats on users’ devices. There are many ways to read document nowadays – using desktop computer, tablet, smartphone and so on [3]. One point is worth noting, however. No matter how the media and forms of presentation of information change, the principle remains the same - to keep it in order to refer to it later. Each of the forms of presentation of information was an obvious step forward. However, some problems still remained. The transition from paper to digital format was no exception. 1.3

Challenges to a Traditional Approach to Documenting Requirements

Now the era of digital technology has come, we can create documents on our computer, but how much has changed compared to using sheets of paper? Now that the era of digital technology is on its rise, we can create documents using a computer, but how much has changed compared to using paper sheets? A simple example: a document created, say, in the Microsoft Office Word editor. There is the subject of our study - the requirements. Now the problem arises: development is a collective process, which means that to ensure proper quality it would be nice to review this document [4]. We are still in the digital age and it does not cost anything to send

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this document to reviewers. Alternatively, act even more universally - to place this document in the public domain for all interested parties. Then each of them will be able to download a version of the document. Moreover, it is always relevant. Let’s say we have a document created in the Microsoft Office Word editor. There is the subject of our study – the requirements. Now the problem arises: document writing is a collective process, which means that to ensure proper quality it would be nice to review this document [4]. In the digital age the cost of sending this document to reviewers is close to zero. (чтo знaчит wearestillinthedigitalage? Цифpoвoй вeк чтo, cкopo зaкoнчитcя, нacтyпит aпoкaлипcиc?) An even more universal and bold alternative would be to place this document in the public domain available for all the parties concerned. Thus each of the parties will be able to download a document version. As a side effect, it is always relevant. After reviewers have rated the requirements, it is totally possible they have some comments for the author [5]. A proper communication process is required here. For example, you can write in person, indicating the problem location up to the character number in the document. You can leave a comment on the page where the document placed. You can leave comments directly in the document. Modern editors allow this. Now the requirements author needs to reduce all the reviews into a single list. This is easy in case of a single reviewer. Difficulties begin for multiple revievers. The author has to look at two different versions of the document and in the same time correct the third. It may seem inconvenient, but in practice, everything is much worse. This is similar to using paper documents. Nevertheless, this problem was also solved - online versions of documents appeared. The same Microsoft Office Word only in the browser window. All interested parties have access to the document. We see a solution to the problem for a paper documents. Reviewers leave comments directly in the file, and the author is relieved of the need to look at a stack of documents and reduce corrections of comments into one [6]. Nevertheless, this problem was also solved by online versions of documents. This can be view as Microsoft Office Word but in the browser tab. All parties involved have access to the document. We consider this a solution to the problem for a paper documents. Reviewers leave comments directly in the file, and the author gets rid of the need to look at a stack of documents and correct all comments in one document [6]. As a result, a new problem appears control over the changes made. The author be-gins to correct the text of the requirements according to the comments. However, where is the guarantee that the author will make the changes exactly as needed? Suppose this happened. This problem solved as follows: the author has the opportunity to return to any of the previous saved versions. However, he will lose all the changes that a made between the initial and final versions. This change to review process brought up new problems. The author begins to correct the text of the requirements according to the comments. However, there is no guarantee that the author will make the changes exactly as needed. Suppose this happened. This problem has an obvious solution - the author always can return to any of the previous saved versions. However, he will lose all the changes made between the initial and final versions. There is no way to exclude the changes made pointwise. This problem is illustrated in Fig. 2.

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IniƟal version

A version where some of the requirements are not corrected as needed.

A version where some of the requirements are not corrected as needed.

A version where some of the requirements are fixed as expected.

A version where some of the requirements are fixed as expected.

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Fig. 2. The problem of versioning documents as files in the public domain.

In addition, that is not all. Let us complicate the task. The document exists for a long time and contains many already implemented requirements. Market conditions are changing, the system must be competitive and it needs modernization. And that is not all. Let us complicate the task. The document exists for a long time and contains a lot of requirements already implemented. Market conditions are changing, the system must be competitive and it needs updates. For example, happened that two system engineers are working on one document. This is necessary to accelerate the implementation of modernization. In addition, if one of the engineers finished work on his part of the document earlier than the other, then he can get the approval of reviewers, go through all stages of approval and up-date the current version of the document. What situation is the second engineer in now? He has an irrelevant version of the document, and work has already begun. How to be? Download the current document again; make your changes to it. However, the problem here is in designing the structure of the document: you should not have made it so big. Usually no one protected from errors, this may be justified by the complexity of the description process, and dividing a document into parts will only make everything more confusing. In total, we have two main problems, which entail the following consequences: waste of the system engineer’s time and possible errors when manually transferring changes. This leads to an increase in labor costs, which reflected in the costs of the customer and the timing of the transition of the system into commercial operation. In addition, this is not all the consequences that usually grow like a snowball. Let’s say two system engineers are working on the same document. This is necessary to accelerate the implementation of update. Furthermore, if one of the engineers finished work on his part of the document earlier than the other, then he can get the approval of reviewers, go through all stages of approval and update the current version of the document. But now there is a problem with second engineer. He made some changes to an obsolete version of the document He can download the current document again and reproduce his changes. However, the problem here is in designing the

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structure of the document, that should have been divided in several parts from the start. Since no one is protected from errors, this structure may be justified by the complexity of the description process, and dividing a document into parts will only add more confusion. In total, we have two essential problems, which result in waste of the system engineer’s time and possible errors during manual change transfer. This causes an increase in labor costs, ending in the cost of the customer order and the time of the system transition into commercial operation. Furthermore, a lot more problems arise due to these problems. 1.4

Possible Solutions to the Problems of the Generally Accepted Approach to Documentation

The advent of computers has provided not only the ability to work with documents in digital form. The software used to operate computers written using programming languages, which are text, albeit highly formalized. There are some similarities with the requirements? Writing code is like writing requirements - this is the result of teamwork. The wishes of programmers and analysts are similar: it is convenient to work with text. Someone with the text of the programs, someone with the text of the requirements. True, programmers have a convenient tool for working with source code. This is a GIT version control system [7]. The version control system keeps a history of file changes and uses the concept of branches. There is a master branch - the source of truth. It is stored in a specially formed section, which developers have access to. The remaining branches are stored there, with various changes. Branches made up of commits - that same point, atomic changes to the source code. The set of commits is a branch [7]. The presence of branches allows, quite simply, adding something new to the program. The developer, being in the master branch, makes his copy of the master branch and makes his changes there. Now we have two parallel versions of the source code. To add your method to the master branch, the developer needs to “merge” his changes into the general branch [7]. This can done directly, or can done with a preliminary review by other developers. The proposed changes are available for viewing by other project participants and represents the difference between the initial state of the file and the current one, with the changes. Reminds the process of reviewing a document: interested developers/reviewers can evaluate the changes made, point out inaccuracies and shortcomings, leaving comments and after universal approval commits with the new code will added to the master branch. In this case, the developer does not need manually transfer their changes to the master branch. A version control system will do this for him [8]. It seems that two quite serious problems have just solved when working with familiar documents. If we could work with the requirements text as well as with the code, there would be even less problems. In other words, requirements mast written in the same way as writing the source code of pro-grams. This approach includes the same tools similar to those used by programmers. This approach called docs-as-code [9]. The text of the requirement in this approach is written using a markup language, which, passing through the post-processing process, is converted into a document familiar to us. For example, a pdf file [10]. The possibilities of trans forming the “source code

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requirements” are much greater. At the exit, you can get the requirements in the form of a web page or even a site. It all depends on how much effort has put into writing the “requirements code”.

2 Comparative Characteristics of the Use of Approaches to Writing Documents To conduct a computational experiment to evaluate the effectiveness of the docs-ascode approach, it is necessary to operate with input requirements that are the same in complexity. To understand the algorithms for creating a document, two schemes were created that describe the process using different approaches docs-as-code and Microsoft Office Word (as a representative of text editors). Algorithm schemes are presented at the end of the article (Fig. 4, 5). To assess the complexity of a document describing system requirements, the approach of functionally oriented metrics was used [10]. Since the document describes the requirements for an information system, this imposes certain conditions for calculating the coefficient of complexity of using the approach. Requirements usually describe the input data for the system, their quantity, structure. Algorithms for data processing, calls to other systems and processing of responses from them, work with data warehouses are also described. The sum of the above elements is the value of the inputs for documents. To calculate functionally oriented document metrics, an initial set of information characteristics taken [10]. The formed set is a special case of the initial set, reflects the specifics of the subject area and contains the following characteristics: 1. Inputs 1.1. Request for IP 1.2. IP response 1.3. IP request validation 1.4. Request IP to data warehouse 1.5. IP request for integration 1.6. Validation of the response from integration 1.7. Sort Description 1.8. Filtration Description 1.9. Description of Business Rules 2. The number of blocks in which the algorithm for working with the document passes through all inputs 3. Number of reviewers 4. Number of reviewers comments 5. Possibility of parallel review

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To determine the complexity of creating a document with requirements, we use the following ratio: y ¼ x1 x2 þ x3 x4 þ x3 x5

ð1Þ

Where: x1 - number of inputs x2 - the number of blocks in which the algorithm for working with the document passes through all inputs x3 - number of reviewers x4 - number of reviewers comments x5 - parallel review option To evaluate the effectiveness of the approaches, the above formula used and a computational experiment conducted. The calculation results shown in Table 1.The smaller the value of the resulting variable, the more effective the approach is (see Table 1). Table 1. The results of calculations of the complexity of creating a document. Docs-as-code x1 x2 x3 x4 x5 y 1 10 0 1 5 0 5 2 11 0 1 10 0 10 3 12 0 2 5 0 10 4 13 0 2 5 0 10 5 14 0 3 5 0 15 6 15 0 3 5 0 15 7 16 0 4 5 0 20 8 17 0 4 5 0 20 9 18 0 5 5 0 25 10 19 0 6 5 0 30 Microsoft Office Word x1 x2 x3 x4 x5 y 1 10 1 1 5 1 16 2 11 1 1 10 1 22 3 12 1 2 5 1 24 4 13 1 2 5 1 25 5 14 1 3 5 1 32 6 15 1 3 5 1 33 7 16 1 4 5 1 40 8 17 1 4 5 1 41 9 18 1 5 5 1 48 10 19 1 6 5 1 55

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For a visual comparison of the results of a computational experiment, the results summarized in accordance with Fig. 3.

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As a result of a computational experiment, the docs-as-code approach proved to be more profitable than using a text editor using the example of Microsoft Office Word.

3 Conclusion The use of modern technology in documenting the requirements for information systems under development provides a qualitative improvement in the result. This suggests the need for their use at all stages of the life cycle of system development. In addition, where there are similar tasks of the formation and maintenance of documentation. In addition, the docs-as-code solution is effective to use where there are similar tasks in the formation and maintenance of documentation. The docs-as-code approach reduces the complexity of the process of maintaining documentation up to date by an average of 2.5 times. The indicated decrease is due to the following factors: Introducing a unified process of working on documentation; The exclusion of time-consuming operations to track changes made by all coauthors of the document; Additionally, it is possible to maintain versioning of documentation and automatic generation of the final document. Document generation algorithm using the docs-ascode approach see in Fig. 4.

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Fig. 4. Document generation algorithm using the docs-as-code approach.

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Fig. 5. Algorithm for generating a document using a text editor.

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References 1. Hull, J K.D.J.E.: Requirements Engineering, Kent: Springer London Berlin Heidelberg (2011) 2. Jim, G.: Data management: past, present, and future. IEEE Comput. 29(10), 38–46 (1996) 3. WordTeam, « Word Online update: Comments, list improvements, and footnotes now available! » 14 April 2014. https://www.microsoft.com/en-us/microsoft-365/blog/2014/04/ 14/word-online-update-comments-list-improvements-and-footnotes-now-available/ Accessed 21 Dec 2019 4. WordTeam, « Insert, delete, or change a comment ». https://support.office.com/en-us/article/ insert-delete-or-change-a-comment-5cb1af25–4dfe-4484-9713-2c80391ecf12 Accessed 21 Dec 2019 5. Dove, J.: « 5 Word Processing Apps for Smartphones and Tablets » 18 April 2018. https:// www.businessnewsdaily.com/6121-word-processing-apps-smartphone-and-tablet.html Accessed 21 Dec 2019 6. Gildred, J.: « The Ultimate Guide On How To Share Files on Google Drive » 23 August 2018. [B Интepнeтe]. https://www.cloudwards.net/how-to-share-files-on-google-drive/ Accessed 21 Dec 2019 7. Scott Chacon, B.S.: Pro Git, Apress (2018) 8. Holscher, E.: « Docs as Code » (2017). [B Интepнeтe]. http://www.writethedocs.org/guide/ docs-as-code/Accessed 21 Dec 2019 9. Dan Allen, S.W.: « Asciidoctor PDF » (2018). https://asciidoctor.org/docs/asciidoctor-pdf/ Accessed 21 Dec 2019 10. Albrecht, A.J.: Measuring application, development productivity. In: Share/Guide Application Development Symposium, pp. 83–92 (1979)

A Proactive University Library Book Recommender System Tadesse Zewdu Mekonnen1(&) and Tranos Zuva2 1

Vaal University of Technology, Vanderbijlpark 1900, South Africa [email protected] 2 Vaal University of Technology, Andries Potgieter Blvd, Vanderbijlpark 1900, South Africa [email protected]

Abstract. Too many options on internet are the reason for information overload problem to obtain relevant information. Recommender system is a technique that filters information from large sets of data and recommends the most relevant ones based on people’s preference. Collaborative and content-based techniques are the core techniques used to implement recommender system. A combined use of both collaborative and content-based techniques called hybrid technique provides relatively good recommendations by avoiding common problems from each techniques. In this research, a pro-active University library book recommender system has been proposed in which Hybrid filtering is used for enhanced and more accurate recommendations. The prototype designed was able to recommend highest 10 books for each user. We evaluated the accuracy of the results using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). A measure value of 0.84904 MAE and 0.9579 RMSE found by our system shows that the combined use of both techniques can give an improved prediction accuracy of university library book recommender system. Keywords: Recommender system filtering
Content based filtering


Hybrid recommendation
Collaborative

1 Introduction There are many options on the internet that cause information overload. Information overload makes it difficult for many internet users to obtain relevant information on time. It is necessary to filter, prioritize and recommend relevant information to solve this problem. Even if search engines such as Google and yahoo have solved some of the problems, prioritization and personalization are still needed to get a more accurate and relevant information to individuals. Therefore, there is a need for more research in this area. This has increased the demand for recommender systems. Recommender systems are machine-learning algorithms that has the ability to predict user’s preferences based on their profile. By taking user’s online experiences input set, the Recommender system generates a probable recommendation for the user. It provides users a prediction closer to reality. Companies like Amazon.com use the © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 327–335, 2020. https://doi.org/10.1007/978-3-030-63322-6_26

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recommender system for recommending products; Facebook for recommending people you may know and Netflix recommending content to watch. Although there are a few book recommender systems on the web such as Radgeek, What should I read next, Bookish, Jelly books, My independent bookshop, Shelfari, Librarything, Amazon, Goodreads and Getglue, all of them recommend books for commercial activities. Similar systems of recommendation of books can be helpful if applied for University Libraries. Different libraries in South Africa have well-built automated library systems which is very helpful for library users to get information about books in the library. These libraries use search engines to access books using keywords and users are depend on the search engine to retrieve items (or books) in the collection. Library users have to try different keywords repeatedly to get what they need in the library store until the relevant items are found. Even if it is very helpful for the users of library to get different books, a search engine only is not enough for users to find all books they want in university library efficiently. Library users need a better assistant to access the books which they may be interested in. Therefore, there must is a need for optimum recommender system for university library books. The recommender system assists by increasing the visibility of available books. This paper proposed a Pro-active University library book recommender system which provides library books recommendation based on a hybrid of collaborative and content-based filtering techniques. Proactive book recommendation system decides which available book is most likely relevant to the user after it retrieve large quantities of books and it gives recommendation without user’s request. The existence of too many different books in one small library building results in increasing difficulty for the users in searching and finding what they want in a manner which best meets their requirements. Increase in availability of digital information causes information overload. Users may not have the title and the key words of all books required for their studies due to an information gap. Even though library users may have a title of a book which they need for their studies, the other related books which are probably more important for their studies will remain undiscovered while the books are still available in the library (Isinkaye et al. 2015). The paper is arranged as follows: Sect. 2 literature review, 3 Methodology, 4 results and finally 5 conclusion.

2 Literature Review (Chen and Chen 2007) used Clustering Algorithm from collaborative technique and association rules to propose personalized recommendatory framework engineering to empower personalized services and management in a campus computerized library. They designed a two-phase data mining process and gave recommendations. The method took consideration of the connections within the users’ cluster and the affiliations among the data gotten to. The suggestion given by this pro-posed framework nearly met users’ needs. Mooney and Roy (2000) proposed a content-based book recommending system using learning for text categorization. They mentioned that the capacity to recommend

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books based on users’ general interests instead of particular request is an imperative benefit of digital libraries. It was stated that content-based recommender is able to successfully recommend books which are not rated and give effective recommendations to users who have unique tastes. They used 10-Fold cross validation, classification accuracy, recall, precision, rating of top 3, rating of top 10 and Rank Correlation evaluation methods. Even if they solve the problem of collaborative approach which fails to recommend unrated and unpopular items, the proposed system has a limitation of finding more information from other users’ ratings and comments about the content of the book. Hence content-based approach does not consider most rated by other users. (Liao et al. 2010) proposed a library recommender system based on a personal ontology model and collaborative filtering technique for English collections. A collaborative filtering approach with personal ontology was adopted by utilizing the use of key-words of gadgets in the library’s collections to assess the inclination of every user. This minimizes information scarcity; improves accuracy and solves cold start of new coming items caused by collaborative filtering strategy. The proposed system has been used by National Chung Hsing University and the personal ontology approach lacked personal interest since keywords only are not enough to know somebody’s choices. (Bhure and Adhe 2015) proposed a system for book recommendation called Book Recommendation System Using Opinion Mining Technique. This system recommends books to the user by collecting the feedback and comments. The system used opinion mining techniques to analyze the data. A collaborative method of Commtrust and normalization algorithm is used. Normalization contains the ranking of books, based on the weights assigned to them. The evaluation method used was Normalized opinion score (NS). NS = T/M where, T = Sum of total weights assigned to the book M = Sum of the maximum weights that can be assigned to each feature of the book. Even if the algorithm used for collaborative methods is a good one, the system still has a problem of cold start since it used only collaborative method. Bhure and Adhe (2015) proposed book recommendation system using opinion mining Technique. The system collects the inputs and comments and recommends books to the users by analyzing data using opinion mining techniques. A collaborative strategy of Commtrust and normalization algorithm was utilized. Books were ranked according to the weights relegated to them under Normalization algorithm. Evaluation method called Normalized opinion score was utilized. Normalized opinion was calculated as a division of the summation of total relegated to the book and the summation of the maximum weights that can be allotted to each feature of the book. Even if the algorithm used for collaborative methods is a good one, the system still has a problem of cold start since it used only collaborative method. O’Mahony and Smyth (2007) proposed a course recommender system for on-line enrolment application at University College. This paper out-lined the elements that impacts the preference of the students and outlined arrangements to address a few of the key contemplations that are recognized. A collaborative filtering style that proposes elective modules based on the past preferences of students was used. They also used content-based recommender technique to recommend similar modules by utilizing similar keywords. Recall and coverage were used to evaluate the system. Rana and Jain (2012) used time based content filtering to propose a book recommender system. They introduced an approach called temporal dimension. Temporal

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dimension considers number times a book is liked with some duration of time and stored as counter every time a book is liked or checked by a user. The probability for a book to be recommended increased with increase amount of counter count. The users are asked to rate the recommendations for recommendation evaluation. The system is limited to finding more information from other users’ ratings and comments about the content of the book; hence content based approach does not consider most rated by other users. Ghadling et al. (2015) proposed digital library using hybrid book recommender engine which applied Collaborative, content based and demographic recommending techniques. By using a hybrid of those techniques, they could propose a book recommender system for the digital library. The Hybrid book recommendation engine was very helpful to eliminate the weak side of each technique. The proposed system can be used for college libraries, for public libraries and private online libraries.

3 Methodology 3.1

Proposed Hybrid Approach

The research study used Hybrid filtering approach which encompasses both collaborative filtering technique and content-based filtering technique as shown in Fig. 1. The use of combination of both techniques helps to reduce the drawbacks of both techniques which make the recommendation more accurate. Hybrid filtering method is used to make a more accurate recommendation by avoiding common problems like cold start problem from collaborative filtering method and lack of information on the book from other users’ ratings from content-based filtering method. Hybrid recommendation is demonstrated in the Fig. 1.

Fig. 1. Hybrid recommendation

The system computes the similarity between different library book users and then predicts probable ratings for unrated books by the users which enable the system to give a good recommendation for users. The system also used vector space model to determine which books are more similar to each other and to the user profile.

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Content Based Filtering Method

Content-based recommender method analyzes book details, pics out and recommends items which the users are interested in. The product of term-frequency and inverse document frequency (TF*IDF) and vector space representation were used. The term frequency (tf) is the frequency of a word which counts how many times a word appears in a document. The inverse document frequency (idf) measures how significant a term is in the whole corpus. The TF*IDF algorithm is used to weigh a keyword in a content and checks the relevance of the keyword in the whole corpus. TF*IDF was utilized since it refutes the impact of tall recurrence words and deter-mines the significance of a book. TF*IDF weight was used to put the terms in order. The more the terms weight elevated, the more the terms happened more regularly in that book than in another other books. The values found from term frequency calculation make the attribute vector of each book. Moreover, by utilizing the logarithm converse of document recurrence from the complete corpus, we found inverse document frequency. TF*IDF weight w(t,d) is calculated using the equations below. ( wðt; d Þ ¼

1 þ log10 tft;d ; if tft;d [ 0 0; otherwise

ð1Þ

where w(t,d) represents TF*IDF weight, t is term, d is a document function, (tft,d) is term frequency in the document, (dft) is number of documents that contain the term and N represents the number of documents in the whole collection (Michael J. Pazzani and Billsus 2007). Vector Space Model is utilized to decide which books are more similar to the other books and to user profile. Vector space model is a numeric model which represents text documents as vectors and it is used to filter, retrieve, index and rank significance of data by computing the vicinity based on the angle between vectors. Books were saved as a vector of their attributes and the points between the vectors were calculated to decide the closeness between vectors. Users profile vectors have been made based on users’ activities on past traits of books. The closeness between a book and a user are also calculated in a similar manner. Cosine value of an angle between the user profile vector and the document vector was used to measure users’ like and detest. Vectors’ length are measured as the square root of the summation of squared values of every attribute in the vector. Vector normalizing is done finally by dividing term vector by length of vector, the cosine values of books are considered as the similarity measures between books. The cosine value of books is calculated as a sum product of normalized term from both books. Cosine is used because the cosine value decreases with the increasing value of the angle which shows less similarity and vice versa. 3.3

Collaborative Filtering Method

Collaborative filtering (CF) method was used to filter and predict the users’ preferences by using other users’ preferences. In this method, similarity measures are used to predict ratings of users. This was achieved through finding nearest similar neighbors and then recommending items. Cosine similarity algorithm was applied for calculating

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the similarity between users. Cosine similarity algorithm was chosen over other algorithms because it is very efficient to evaluate the results obtained. Cosine similarity algorithm calculates the measure of cosine value of two vectors. The more the value is close to one (1) the more the vectors are similar to each other since the value of cosine approximates to one (1) when the angles between two vectors are smaller. The equation to calculate cosine similarity is given below. Pn i¼1 V1i V2j cosðV1 ; V2 Þ ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qP ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn n 2 2 i¼1 V1i i¼1 V2i

ð2Þ

where V1 represents Vector one for user1 (U1) and V2 represent vector two for user2 (U2). A zero angle with cosine value one (1) means total similarity while a ninety (90)° angle with cosine value of 0 means no similarity. Pearson Correlation was used to determine the prediction rating of the active user ‘a’ for book item ‘i’. Predictions were computed as the weighted average of deviations from the neighbor’s mean. The equation to calculate Prediction is given in the Eq. 3. Pn Pa;i ¼ ra þ

u¼1

  ru;i  ru  sima;u Pn u¼1 Pa;u

ð3Þ

where pa,i is the prediction for the active user ‘a’ for item ‘i’; ru,i is the rating made by user u to item i; ra represents the mean rating made by user a; pa,u represents the similarity between users ‘u’ and ‘a’; and n represents number of users in that neighborhood (Melville et al. 2002). 3.4

Evaluation Metrics

In this study Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the accuracy of the recommendation system. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were chosen over other methods because they are able to find the exact value of the difference between actual ratings given by users and estimated ratings. MAE measures the average of the absolute deviance between the actual rating given by the users and the predicted rating. Mean Absolute Error (MAE) is calculated using Eq. 4. Pn MAE =

i¼1

Pi  qi n

ð4Þ

Where p1…pn are predicated ratings; q1…qn are actual ratings and n is amount of ratings. Lower the MAE, the more accurate the prediction is (Parvatikar and Joshi 2015). RMSE measures the average size of the error by calculating and finding the square root of the average of the squared differences between prediction given by the prototype and actual rating given by user. RMSE gives a relatively bigger weight amount to

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large errors because the errors are squared before they are averaged. RMSE is calculated using Eq. 3.

RMSE =

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 i¼1 ðpi  qi Þ n

ð5Þ

Where p1…pn are predicated ratings; q1…qn are actual ratings and n is amount of ratings (Najafi and Salam, 2016).

4 Recommendation We used a data set called book crossing data set which has 278,858 users and 271,379 books. In book crossing data set 1,149,780 ratings were used for training data and conducting the experiment. The data set was found from University of Freiburg department of computer science website (http://www2.informatik.uni-freiburg.de/ *cziegler/BX/). Explicit ratings were gathered on the range from 0 to 10 based on users’ appreciation to the books. A 70% of the data were taken for training and then evaluated by 30% data points which were used as the test set. The top ten (10) similar books (obtained from content-based method) with the highest predicted ratings (from collaborative method) were recommended to the users. The top ten book recommendations for user 278222 is demonstrated on the Fig. 2.

Fig. 2. Top ten book recommendation of user 278222

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We evaluated the accuracy of the prototype system by comparing the predicted ratings directly with the actual ratings given by the users. The Mean Absolute Error (MAE) of our system is 0.84904 and Root Mean Squared Error (RMSE) is 0.9579. We concluded that the developed prototype has a better performance compared to previous works done by other researchers. Our results are comparable to the results obtained by Tashkandi et al. (2017) who achieved 1.953229033 RMSE and (Chai and Draxler, 2014) who achieved 0.8 MAE and 1.0 RMSE.

5 Conclusions This study shows that using Hybrid method approach to recommend books to university libraries can increase the value of recommending books by increasing the visibility and availability of books. Most of existed recommender systems recommend books for commercial purposes. Our research showed that similar systems of recommendation of books can be applied to University libraries for helping University libraries users to discover related books of their own interest easily and timely. The developed prototype is useful to solve some problems of information overload in the existing book collection management and retrieval systems. It specifically helps users to get recommendation of appropriate books in a library which contains the topics of their interest. The created prototype is valuable to unravel some of issues of data overload problems in the existing book collection management and retrieval systems. The prototype helps users to urge recommendation of suitable books in a library which contains the titles of their intrigued. In this paper, the hybrid recommender technique that is used to pro-actively recommend university library books is demonstrated. We firstly explained the similarity algorithms. Then, we explained an algorithm used for predictions of ratings. The evaluation results on our dataset finally showed that the algorithm used performs better than the previous ones done by other researchers mentioned in Sect. 4 when it comes to the prediction accuracy.

References Tewari, A.S., and Priyanka, K.: Book recommendation system based on collaborative filtering and association rule mining for college students. International Conference 2014, Mysore, India, pp. 135–138. Contemporary Computing and Informatics (IC3I) (2014) Anderson, C.: The long Tail: Why the Future of Business Is Selling Less of More. Hyperion, United States (2006) Rana, C., Jain, S.K.: Building a book recommender system using time based content filtering. WSEAS Trans. Comput. 11(2), 2224–2872 (2012) Chen, C., Chen, A.P.: Using data mining technology to provide arecommendation service in the digital library. Electron. Libr. 25, 711–724 (2007) Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16, 261–273 (2015)

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Li, H., Cai, F., Liao, Z.: Content-based filtering recommendation algorithm using HMM computational and information sciences (ICCIS. In: 2012 Fourth International Conference, Chongqing, China, pp. 275–277 (2012) Liao, I.E., Hsu, W.C., Cheng, M.S., Chen, L.P.: A library recommender system based on a personal ontology model and collaborative filtering technique for English collections. Electron. Libr. 28, 386–400 (2010) Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems, pp. 225–273. Heidelberg, Germany (2007) O’mahony, M.P., Smyth, B.A.: Recommender system for on-line course enrolment: an initial study. conference. In: Proceedings of the 2007 ACM Conference on Recommender Systems, Minneapolis, MN, USA, pp. 133–136 (2007) Bhure, P., Adhe, N.: Book recommendation system using opinion mining technique. Int. J. Res. Eng. Technol. 04, 332–334 (2015) Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Eighteenth national conference on Artificial intelligence 2002, Edmonton, Alberta, Canada. Menlo Park, CA, USA, pp. 187–192 (2002) Ghadling, S., Belavadi, K., Bhegade, S., Ghojage, P., Kamble, S.: Digital library: using hybrid book recommendation engine. Int. J. Eng. Comput. Sci. 4, 01–02 (2015) Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. DL ‘00 Proceedings of the fifth ACM conference on Digital libraries 2000, San Antonio, Texas, USA, pp. 195–204 (2000) Parvatikar, S., Joshi, D.B.: Online book recommendation system by using collaborative filtering and association mining. In: Computational Intelligence and Computing Research (ICCIC) IEEE International Conference 2015, Madurai, India, pp. 1–4 (2015)

Conflict Resolution in Process Models Merging Asma Hachemi(&)

and Mohamed Ahmed-Nacer

Computer Systems Laboratory, Computer Science Department, USTHB, Algiers, Algeria [email protected]

Abstract. Process models consist mainly of process elements that are linked by process relations, as well as control elements and control relations (called control parts). These later facilitate the readability of a process model or control some of its fragments. In many cases of process models merging, control parts can cause conflicts (called control conflicts), that must be identified and resolved to ensure the reliability of the merged process model. We study in this article a control conflict, related to the control element Choice along with a task outflows, when merging process models. We propose the resolution of this conflict in the various scenarios that can arise, in order to ensure a reliable merging result. Keywords: Process model improvement
Model merging
Conflict resolution
Process model reliability
Control element
Control relation
Choice element
Process element
Process relation
Process model
Process meta model

1 Introduction Software development process models (that we call in this paper process models) are mainly constitute of process elements, that are linked together by process relations. For a better expressiveness, process models are generally enriched by control elements and control relations, called control parts. The aim of the control parts is to facilitate the readability of a process model, or to control some of its fragments. For instance, control parts make it possible to introduce branches of choice after a task, or to clearly mention the beginning and the end of a process model. In many situations, specially for process model improvement, process models merging is operated. Control parts can however cause conflicts when merging models. Such conflicts, called control conflicts, must be identified and solved in order to ensure the reliability of the resulting process model. We have already discussed in [1] the conflicts management in the process models merging, as part of the process patterns reuse [2, 3]. We were however limited in that work to process models consisting of process elements and process relations only (without control parts). Through the present work we want to expand this management, to cover control conflicts when merging process models. We target in this paper one of these conflicts, namely the conflict related to the control element Choice along with a task outflows. Our aim is to propose its resolution in the various scenarios where it can arise. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 336–345, 2020. https://doi.org/10.1007/978-3-030-63322-6_27

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The rest of this article is organized as follows. Section 2 describes the meta model governing our approach, in sub-Sect. 2.1. The control conflict is studied in subSect. 2.2. Section 3 presents our contribution to resolve the three scenarios of the conflict that can arise. Section 4 compares our approach with related work, and finishes the paper with conclusion and open lines for future research.

2 Method We proposed in [1] an approach that manages conflicts when merging process models. This approach merges two models by applying parameters substitution. Indeed, one of the models to be merged will contain formal parameters, that will be substituted by the effective parameters from the second model to be merged. However, this approach does not support control parts within process models, and therefore does not handle control conflicts. Our aim in the present work is to extend the conflicts management when merging process models, by: (a) enriching the meta model already proposed in [1] to support the control parts; and (b) identifying and managing a control conflict, namely the conflict related to the element Choice along with a task outflows. For this purpose, we extend the meta model proposed in [1] with control parts, as explained in sub-Sect. 2.1. Then, we study a control conflict in sub-Sect. 2.2. In fact, several control conflicts are likely to arise when merging process models. To limit the scope of the present work, we select the conflict concerning the control element Choice along with a task outflows to be studied in this paper. We consider this conflict within a process model resulting from the merging of two coherent process models. 2.1

The Meta Model

Our meta model shown in Fig. 1 has its origin in the meta model proposed in [1]. It is based on commonly adopted concepts, that makes it easier to understand and use. Indeed, it is based on the most important elements of process models, namely Task, Role and Product, as well as the process relations linking them. A process relation is a binary link between two process elements, which are the source and the target of the relation. A process relation may be one of the following kinds: TaskPerformance that has a role as source and a task as a target, and that is of kind Performer or Assistant; ProductResponsibility that has a role as source and a product as a target; TaskParameter that has a task as source and a product as a target, and that is of kind in, out or in_out; TaskPrecedence which links two tasks; ProductImpact which links two products; Aggregation and Refinement, both of them having a source and a target of the same nature. Our meta model is however extended to the control parts (control elements and control relations), which are inspired by those of UML 2.5 SuperStructure [9]. The control elements are: • InitialActivity: indicates the beginning of the process model. It cannot be preceded by any element, and it is unique within à process model.

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Fig. 1. Our meta model.

• FinalActivity: indicates the end of the process model. It cannot be followed by any element, and there may be one or more FinalActivity within a process model. • Choice: allows giving several possibilities after the evaluation of a condition. It has an inbound link, and at least two outbound links along with guard conditions. Control relations are links between either control elements or between a process element and a control element. In our meta model, only one control relation is defined, namely the relation Link. Link: allows introducing a guard condition, so that if this condition is verified, the link will be passed in the direction of the next element in the process model. 2.2

The Control Conflict

A control conflict is an inconsistency within a process model that implies a control part. Several control conflicts are likely to arise when merging process models. We are selecting one of them in this paper, giving its details and explaining its resolution. The selected conflict concerns the control element Choice along with a task outflows. It is considered within a process model resulting from the merging of two coherent process models. Let’s have a task called T. In the context of this article, the notion of task outflow defines one of the following points: (a) a process relation of kind TaskPrecedence, having T as predecessor; (b) a control relation of kind Link, linking T to a FinalActivity; (c) a control relation of kind Link, linking T to a Choice that has T as predecessor.

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With respect to these outflows of the task T, we call respectively the outflow target element: (a) the task T’ which is the successor of T; (b) the FinalActivity; (c) the Choice which is the successor of T. In a process model resulting from the merging, a task will have at most two outflows. Indeed, such task comes either from one of the two merged models, that we initially assume coherent (so in each of these models, a task has a single outflow), or comes from the both merged models (a common task). In this last case, the task may have two outflows, if in each model it possesses a different outflow target. The possible outflows targets of a task in the merging result are: • • • • • •

Two successor tasks, Two control elements of kind FinalActivity, Two control elements of kind Choice, A successor task and a FinalActivity, A successor task and a Choice, A FinalActivity and a Choice.

Let’s have a process model resulting from the merging, where a task T has two outflows. The fact that a task has more than one outflow is a conflict. The resolution of such conflict depends on the existence or not of a Choice among the outflows targets. Three scenarios arise then, that we will discuss in the following section: (a) there is no Choice among these outflows targets. (b) There is one Choice among these outflows targets. (c) Both outflows targets are Choices.

3 Result Three scenarios of the conflict can arise when a task has two outflows. We will explain each scenario in what follows. 3.1

No Choice Among the Outflows Targets

In this scenario the T outflows are elements of kind Task or FinalActivity. The solution is to introduce a Choice after the task T, and to attach the two outflows of T to this Choice while specifying appropriate conditions. An example of this first scenario is given in Fig. 2. Sub-Figs. 2.a and 2.b show the two process models to be merged. The merging proceeds the following substitutions: (TA, T1) (RA, R1) (PA, P1) (IA, I1) (FA, F1). The merging result without conflicts management is shown in sub-Fig. 2.c. This result presents a conflict; the task T1 has two outflows, so we do not know what to do after T1 (go to TB or to F1). The sub-Fig. 2.d shows the merging result with conflicts management, where the problem is resolved. Indeed, after T1 a Choice is introduced, to which the two outflows of T1 are attached with the respective conditions [C1] and [C2]. Thus only one outflow will be considered after T1, according to the satisfied condition ([C1] or [C2]).

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Fig. 2. Example of the first scenario.

3.2

One Choice Among the Outflows Targets

In this scenario the two following cases are possible: (a) the outflow of T (other than the one leading to Choice) leads to an element already linked to the same Choice. In this case, the conflict resolution consists on deleting this outflow (other than the one leading to the Choice). (b) The outflow of T (other than the one leading to Choice) leads to an element X which is not linked to the same Choice. In this case, the conflict resolution consists on replacing that outflow by a new one linking the target X to the Choice, while specifying the appropriate condition. An example of this second scenario is given in Fig. 3. Sub-Figs. 3.a and 3.b show the two process models to be merged. The merging proceeds the following substitutions: (TA, T1) (RA, R1) (IA, I1) (TB, T2) (RB, R2).). The merging result without conflicts management is shown in sub-Fig. 3.c. This result presents a conflict; task T2 has two outflows, one leading to TC, and the other to Choice C whose outflows lead to T3 and T4. No outflow of TC leads to C, so this is the case (b). The sub-Fig. 3.d shows the merging result with conflicts management, where the conflict is resolved. Indeed, the T2 outflow leading to TC has been replaced by the one linking TC to Choice C, with the condition [C3].

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(a)

(c)

341

(b)

(d)

Fig. 3. Example of the second scenario (case b).

3.3

Both Outflows Targets are Choices

In this scenario the two following cases are possible: (a) these two Choices leads to totally different elements. The conflict resolution in this case is to merge all the target elements of the two Choices into one Choice. (b) Both Choices target one or more common elements. The conflict resolution in this case is to merge all the target elements of the two Choices into one Choice; each common element must appear once; the condition of a common element in the result will be the merge of its two conditions in the two merged process models. An example of the case (b) is shown in Fig. 4. Sub-Figs. 4.a and 4.b show the two process models to be merged. The merging proceeds the following substitutions: (T1, T1) (R1, R1) (I1, I1). The merging result without conflicts management is shown in subFig. 4.c. This result presents a conflict; task T1 has two outflows leading to CA and to CB. The sub-Fig. 4.d shows the merging result with conflicts management, where the conflict is resolved. Indeed, a single outflow is linked to T1; this is Choice C. All the elements previously linked to CA or to CB are attached to C, with their respective conditions. The common element to CA and CB, namely T4, is attached only once to C.

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(a)

(b)

(c)

(d) Fig. 4. Example of the third scenario (case b).

4 Discussion 4.1

Related Work

The control conflict we are studying is due to the merging of process models. So we did a literature review on models merging. We found that most of works in literature concern model versioning [4–6], where the challenges are: identifying the changes between model versions, and merging those changes to create a consolidated version [4, 7, 8]. However, most of these works focused only on detecting conflicts when merging versions [5], not on resolving them [4].

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A major work in the field of model versions merging is [10]. It merges versions automatically if they are free of conflicts. In the case of conflicts, this work considers that “repairing an inconsistency can create more inconsistencies (cascading)” [4]. So, it notifies software engineers, and computes compromises for resolving conflicts. The merging procedure in [10] calculates the difference set between the versions to be merged, and then detects conflicts using an incremental inconsistency checker. However, the work [10] is limited to the merging of two model versions, generated from the same original model. It cannot thus be applied to merge two independent models. Besides, it is not dedicated to process models, but is rather applicable to arbitrary modeling languages, as long as they follow a well defined meta model. This implies that it must be defined, for each modeling language, a set of consistency and well formedness rules to check when merging; a fact that is very constraining, knowing that the work [10] does not allow the automatic generation of such set of rules. Another important work on versions merging is [11]. It is a formal approach to merge models in the EMF framework [12]. The approach may be applied to instances of arbitrary Ecore models. The approach offers a merging algorithm which produces a consistent model from consistent input models. The work [11] is however limited to the general level of EMF model instances, and guarantees only consistency with the underlying Ecore model. The approach [13] is also concerned with versions merging. It considers the construction of the union of multiple models, which is called merged model. Merged model is intended specially to analysts who wish to create a model that subsumes variants of the same process model, with the aim of replacing the variants with the merged model. The work [13] is however limited to Business Process Models, and is intended specially to merge versions of the same original model. As part of our study, we also conducted a literature review on process patterns reuse in the cases of process models modification [3]. The reason is that this form of reuse merges two process models: the process model to be modified and the solution of the reused process pattern. The approach [14] proposes the operator PatternApplying, that merges a process pattern and a process model. The operator links each element of the process pattern specified as a formal parameter, with an existing element in the process model specified as an actual parameter. PatternApplying may however generate conflicts that are neither detected nor treated in the approach [14], a fact that prevents the exploitation of this operator. Another work in this field is [15, 16]. It transforms the process pattern along with the process model to be modified into StructureTrees, merges them (both in the form of StructureTrees), and then transforms the resulting StructureTree into the usual form of processes. However, the meta model of [15, 16], does not cover all possible relations between process elements. This meta model is hence inadequate to represent process models and patterns that may be concerned by the merging. Furthermore, this work does not provide a concrete procedure to transform a process model into a StructureTree, and

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conversely a StructureTree into a process model; the transformation cannot thus be automated. Summing up, the major drawback of this work remains the lack of a concrete merging algorithm. The approach [15, 16] cannot then be operated automatically. 4.2

Conclusion

We discussed in this paper a control conflict, related to the control element Choice along with a task outflows. We have first expanded the meta model presented in [1] to the control parts. Then, we have studied all cases where the considered conflict is likely to arise, and have proposed its resolution in each of that cases. Compared to the state of the art, our approach has several positive points. Our approach is not limited to the versions of the same original model, unlike [4, 7, 10] and [11]. Indeed, our approach is able to merge process models regardless to their origin. Unlike [13] which is limited to Business Process models, or [11] which is limited to EMF model instances, our approach detects and resolves conflicts without being limited to a category of process models. Our approach is specially dedicated to process models. Unlike [10], it does not require any definition of specific rules to deal with process models. Compared to the merging procedures defined in pattern reuse works, our approach is not just about merging models, it detects conflicts and resolves them when merging (unlike the approach [14]). Moreover, our approach uses a complete meta model that covers the main elements and relations, unlike [15, 16], which uses a limited meta model. Finally, the approach proposed in this article is not perfect. Several perspectives remain possible to improve it. Our approach needs to be completed with an automatic mean for condition merging. Such mean will be used in the resolution of the conflict mentioned in the case (b) of subSect. 3.3. As next step, we will identify the remaining control conflicts and propose their resolution. We will also propose an automatic operator, to proceed the merging along with the control conflicts detection and resolution.

References 1. Hachemi, A., Ahmed-Nacer, M.: Reusing process patterns in software process models modification. J. Softw. Evol. Process; Gerardo C., Darren D., David R., (eds.), e1938, (2018). https://doi.org/10.1002/smr.1938 2. Hachemi, A., Ahmed-Nacer, M.: Software process patterns: a roadmap. In: Proceeding of the 14th ACS/IEEE International Conference on Computer Systems and Applications Tunisia, pp. 887–894 (2017). IEEEXplore, ISBN: 978-1-5386-3581-0, ISSN: 2161-5330, https://doi. org/10.1109/aiccsa.2017.197 3. Hachemi, A.: Software developement process modeling with patterns. In: Proceeding of the 2nd World Symposium on Software Engineering (WSSE 2020), Chengdu, China (2020). https://doi.org/10.1145/3425329.3425339 4. Dam, H.K., et al.: Consistent merging of model versions. J. Syst. Softw. 112, 137–155 (2016)

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5. Brosch, P., Kappel, G., Langer, P., Seidl, M., Wieland, K., Wimmer, M.: An introduction to model versioning. In: Bernardo, M. et al. (eds.) Formal Methods for Model-Driven Engineering, Lecture Notes in Computer Science, vol. 7320, pp. 336–398, Springer (2012) 6. Bibliography on Comparison and Versioning of Software Models. http://pi.informatik.unisiegen.de/CVSM/. Accessed 18 Apr 2020 7. Rubin, J., Chechik, M.: N-way model merging. In: Proceedings of the 9th Joint Meeting on Foundations of Software Engineering, New York, USA, pp. 301–311. ACM (2013) 8. Kessentini, M., Werda, W., Langer, P., Wimmer, M.: Search-based model merging. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, New York, USA, pp. 1453–1460. ACM (2013) 9. OMG, UML 2.5 Superstructure Specification. http://www.omg.org/spec/UML/2.5/. Accessed 16 Apr 2020 10. Dam, H.K., Rede, A., Egyed, A.: Inconsistency resolution in merging versions of architectural models. In: Proceeding of the 11th IEEE/IFIP Conference on Software Architecture, Washington, USA, pp. 153–162. IEEE (2014) 11. Westfechtel, B.: Merging of EMF models- formal foundations. Softw. Syst. Mod. 13, 757– 788 (2014). https://doi.org/10.1007/s10270-012-0279-3 12. Steinberg, D., Budinsky, F., Paternostro, M., Merks, E.: EMF Eclipse Modeling Framework. The Eclipse Series. Addison-Wesley, Upper Saddle River (2009) 13. La Rosa, M., Dumas, M., Uba, R., Dijkman, R.: Business process model merging: an approach to business process consolidation. ACM Trans. Softw. Eng. Method. (TOSEM) 22(2), 1–42 (2013) 14. Tran, H.N., Coulette, B., Dong, B.T.: Broadening the use of process patterns for modeling processes. In: Proceeding of the 9th International Conference on Software Engineering and Knowledge Engineering, Boston, USA (2007) 15. Wang, Y., He, X., Guo, J., Jiang, J.: Software process reuse by pattern weaving. In: Proceeding of the 22nd International Conference on Software Engineering & Knowledge Engineering, San Francisco, USA (2010) 16. He, X., Wang, Y., Guo, J., Zhou, W., Ma, J.: Weaving process patterns into software process models. In: Proceeding of the 21st International Conference on Software Engineering & Knowledge Engineering, Boston, USA (2009)

Medical Chatbot Techniques: A Review Andrew Reyner Wibowo Tjiptomongsoguno, Audrey Chen, Hubert Michael Sanyoto, Edy Irwansyah(B) , and Bayu Kanigoro School of Computer Science, Bina Nusantara University, Jakarta, Indonesia {eirwansyah,bkanigoro}@binus.edu Abstract. In the current world situation, people are more concerned about their health. Unfortunately, nowadays the doctor human resource is lesser than the patient. These circumstances make a lot of people who seek treatment are unhandled. Many studies can solve this problem with some kind of chatbot or health assistant. In this paper, we want to explore and deepen more about chatbots that could help people to get the same and proper treatment as a doctor would do. Keywords: Chatbot

1

· Health service

Introduction

In recent years, people get addicted to the internet in obtaining information for every problem they face. This not only yet people to seek knowledge about general topics but also their health concerns [15]. However, people are afraid of misinterpretation when they googled their symptoms since most search end up with creating unnecessary paranoid to the users and may sometimes inaccurate. Based on that needs, people start to develop several technologies to help people get the most accurate results on their disease. One of them is by creating a yes-no answer questionnaire system. It certainly helps, however, due to some disease have almost the same symptoms as the other, we can’t rely on this yes-no system since more information need to be elaborated to obtain accuracy. Another one is creating website whereas according to Aswini [3], a medical website plays a vital role in today’s digital world and a lot of forum is available for answering the queries provided by the user. The need for a reliable and accurate diagnosis wakes the rise of a new generation of healthcare technology called the Medical Chatbot. The main idea of creating this chatbot is to replicate a person’s discussion [7]. This helps people to learn more about their symptoms and give them the most accurate diagnosis possible. The chatbot is also drawing upon the ever-growing medical question range, to broaden its already significant wealth of medical expertise. Many seemingly static scenes contain subtle changes that are invisible to the naked human eye. However, it is possible to pull out these small changes from videos through the use of algorithms via motion magnification [29]. Motion magnification gives a way to visualize these small changes by amplifying them and to pull out interesting signals from these videos, such as the human pulse [29]. Artificial intelligence (AI) c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 346–356, 2020. https://doi.org/10.1007/978-3-030-63322-6_28

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is an umbrella term for computer software consisting of a complex mathematical algorithm that processes input information to produce any specific pre-defined outputs, which lead to relevant outcomes [19]. AI systems, which utilize large datasets, can be designed to enhance decision-making and analytical processes while imitating human cognitive functions. AI has been applied in medicine and various healthcare services such as diagnostic imaging and genetic diagnosis, as well as a clinical laboratory, screening, and health communications [19].

2

Theoretical Background

For the technology, Bohle et al. [6] aims to create an “empathetic” embodied AI chatbot to search, retrieve, analyze, and communicate medical information and to interact with health care providers in natural language and “voice” using 3D facial expressions and gestures. There also a medical chatbot which used A Webbased text messaging application, Bonobot, was built as a research prototype to deliver the sequence in a conversation [20]. Kumar et al. [14] proposes a chatbot with system: Input gathering and data pre-processing, medical terminology detection, mapping relevant document, and generating answers and solutions. Raj et al. [22] use NLP and NLU. NLP Text converted into structured data that is used to select a probable answer. There are several steps, Sentiment Analysis, Tokenization, Named Entity Recognition, Normalization, Dependency Parsing. NLTK library used to break sentences into words and reducing words to their stem. Multinomial Na¨ıve Bayes used for text classification. This classifier treats every word independently later organized into two dictionaries, corpus words, class words. Each word is tokenized, stemmed, and lowercased and transformed into training data. Each class generates a total score for the number of words that match. Lots of information can be lost if given the wrong training data. Dharwadkar [7] classifies the test image into the class with highest distance up to the neighboring point in the training. SVM training algorithm built a model that predict whether the test image fall into this class or another. SVM necessitate a vast training data to decide a decision boundary and computing cost is very high. The data which cannot be distinguished the input is mapped to high-dimensional attribute space where they can be separated by a hyper plane. SVM classifiers is faster to train. The accuracy of the SVM is greater than Na¨ıve Bayes and KNN method which is near about 94% greater. Other text processing proposed by [12] is seq2seq and apriori algorithm. The seq2seq model consists of two RNN, an encoder and a decoder. The encoder takes a sentence as input and processes one word one at a time. The decoder generates words one by one in each time step of the decoder’s iteration. After one complete iteration, the output is generated. The apriori algorithm is used for finding frequent item sets in a dataset for Boolean association rules. The apriori principle can reduce the number of items sets we need to examine. The algorithm uses bottom up approach where frequent subsets are extended one at a time, known as candidate generation and group of candidates are tested against the data. It states that if an item set is infrequent, then all its supersets must also

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be infrequent. This means that if pale eyes was found to be infrequent, we can expect pale eyes, cold to be equally or even more infrequent, so in consolidation the list of popular item sets, we need not consider pale eyes, cold, nor any other item set configuration that contains pale eyes. CARO [9] uses Facebook AI Empathetic Dialogue dataset and Medical Question Answering dataset. The Empathetic Response Generator consists of four parallel LSTM followed by Concatenation and Dense layers. It considers the previous two utterances along with the current user input to maintain the context of the conversation. The model determines the emotion of the current user-text and prepends that to the text before passing it to the model. For both the generators, we have used teacher forcing as a method of training. In this method, the output at each time instance is generated based on what the model has generated in the previous time steps where the sentence started. However, LSTM cannot detect the keywords from a sentence [5]. Bao [5] proposed HHH using knowledge graph and Hierarchical Bi-Directional Attention. The knowledge graph is developed by Neo4j with data from the Health Navigator New Zealand, common illnesses and symptom and common diseases and conditions. When a user’s question is given as input, it can be processed by two QA retrieval modules. (1) The information from “Web Interface Interaction” will be transferred into the information retrieval module. If the answer can be extracted directly from the knowledge graph dataset, the information retrieval module can retrieve and return the answer. (2) If, on the other hand, the required answer cannot be found from the knowledge graph. In this case, the question will be transferred into the question-answer pair retrieval module. HBAM will be used to check the semantic similarity of the user’s question and the questions from the question-answer pair dataset. The top-k most similar questions will be returned as the answer set. The last is Ensemble Learning [4]. Ensemble Learning depends on the presumption that diverse models trained autonomously are probably going to be useful for various reasons. Each model looks at marginally different parts of the data to make predictions, and getting some portion of reality however not every last bit of it. The popular methods of combining the classifiers in ensemble learning are mixture of experts, majority voting ensemble, boosting, bagging and stacking. Majority voting ensemble is actually a combiner method that can be used alongside stacking based ensemble learning. Stacking is based on a heterogeneous set of weak learners. Every classifier is trained autonomously, and final choice is made by a majority vote, averaging the result. Since the results are derived using ensemble learning of all classifiers and not a single classifier that could possibly dominate, it is a simple and efficient approach to combine weak and/or dominant classifiers while providing a good balanced output. In this paper, we take 27 other paper as a foundation about the chatbot, especially our paper discusses medical chatbot techniques. The papers are shown in Table 1.

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Table 1. Recent works of medical chatbot techniques. No Title

Methodology Method

Pros and Cons Algorithm, Architecture, Model, etc.

Dataset

Support vector machine, Natural language processing

Heart-disease dataset

Advantage: SVM can solve more complex classification and faster training. Disadvantage: NLP misinterpret

1

A medical chatbot Support vector [7] machine algorithm, NLP, Word Order similarity among sentences

2

Diabot: a Ensemble learning Classifier trained predictive medical autonomously and chatbot using final choice is ensemble learning made by a [4] majority vote, stacking-based ensemble learning with majority voting ensemble as combiner

General health dataset and the Pima Indian diabetes dataset

Advantage: No Dominating classifiers. Disadvantage: Computation and design time are high

3

HHH: An online medical chatbot system based on knowledge graph and hierarchical bi-directional attention [5]

Architecture: hybrid QA model framework, combines a knowledge graph to manage a medical dataset and HBAM to understand the text

3,500 entities (which include 675 diseases and 2825 symptoms) and 4,500 relationships. The relationship includes the relationship between the diseases, symptoms, and the other 6 properties

Advantage: Utilizes structured storage so that it may help easy maintenance and retrieval of domain-specific knowledge. While the advantage of the attention model utilizes deep learning to represent better and comprehend natural language questions. Disadvantage: Complex work

4

Chatbot for NLTK medical treatment using NLTK Lib [11]

Breaking words, POS tagging, dot product to measure similarity

Wordnet, NLTK collection reader, a word database for English

Advantages: Easier to make. Disadvantages: There are some cases where the output hasvery low cosine similarity, and the answer may or may not be an exact match

5

Emergency patient care system using chatbot [22]

Corpus words, class words

Advantage: Simple to build. Disadvantage: No data provided for disease

6

A personalized RNN, NLP, medical assistant Speech to text chatbot: MediBot [12]

The model has been trained on the Cornell Movie Corpus dialogue dataset, se. The model is trained on dataset available from the New York Presbyterian Hospital

Advantage: The apriori principle can reduce the number of items sets we need to examine. Disadvantage: Lack of correct and accurate medical dataset, There is one more big challenge that the seq2seq model requires a lot of time for training even though the hardware is capable of handling it (continued)

Knowledge graph and hierarchical bi-directional attention

NLU, NLTP, Sentiment Multinomial Naive Analysis, Bayes Tokenization, Named Entity Recognition, Normalization, Dependency Parsing Sequence-toSequence Model, Apriori

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No Title

Methodology

7

CARO: An Teacher forcing empathetic health conversational chatbot for people with major depression [9]

8

9

Pros and Cons Facebook AI Empathetic Dialogue [3] dataset and Medical Question Answering dataset [2]

Advantage: Accuracy of intent classifier was 98.5% and that of emotion classifer was 92.4% Disadvantage: Poor model performance and instability

What’s up, doc? a Text Mining with GloVe vectors, medical diagnosis Wit.ai and use APIMedic bot [1] APIMedic

A survey of demographic information, a natural language description of symptoms, further elaboration on the symptoms, and the presumed diagnosis and ApiMedic database

Advantage: Complete dataset from API medic and easier to check symptom. Disadvantage: Not accurate result

Clinical medical knowledge extraction using crowdsourcing techniques [3]

10 Automated medical chatbot [23]

A medical advice generator, and a general empathetic conversation generator with four parallel LSTM layers followed by Concatenation and Dense layers

MKE system

Truth discovery method, Trustworthy calculation

Guided interview, Advantage: Variety of data. Disadvantage: Noisy dataset,

Detect pattern using AIML

NLP and Classification

Medical QA Forum

Can capture long chat, provides solution directly

11 A conversational Kowledge-graph chatbot based on for factoid kowledge-graphs medical question for factoid medical questions [16]

Natural language RDF Data Intrepeter, Dialog Manager, Natural Language Generator

Efficient handle the dialog, ask missing information, generate more precise and contextualized response

12 “Plutchik”: artificial intelligence chatbot for searching NCBI databases

Customized programming using AIML and LSL

Tensor Flow NCBI suite of Algorithms and databases Data Visualization

Voice enabled

13 SHIHbot: a facebook chatbot for sexual health information on HIV/AIDS

Use NPCEditor to Classification and Online Survey, drive chatbot NLP QA in SHIHbot responses, a Domain dialogue manager, and plugins to Facebook

14 A survey on Using NLTK chatbot implementation in health care using NLTK [25]

NLP

QA Record

The live conversations will exhibit SHIHbot’s ability to understand new questions, the chatbot’s ability to cope with being asked questions outside of the domain knowledge, and the overall flow of dialogue User Friendly, Can be used by any person who knows how to type in their own language in mobile app or desktop version, Provides personalized diagnoses based on symptoms (continued)

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Table 1. (continued) No Title

Methodology

Pros and Cons

15 Conception and Conversation realization of a SDK, TaskQueue, chatbotsystem to ConversationCase support psychological and medical procedures [28]

SVM, with some Chat History pre training by IBM. Entities use a fuzzy matching algorithm

Only possible to implement the mobile application that is used to test the developed frameworks, This application allows the user to interact with it by asking questions about a specific topic, It represents the virtual assistant that can be used by patients and experts

16 Designing a Motivational chatbot for a brief interviewing motivational interview on stress management: qualitative case study [20]

Braun and Survey of Clarke’s thematic demographic method information and perceived stress and a semistructured interview

The bot give encouragement

17 Quro: facilitating Using UMLS user symtom check using a personalised chatbot - oriented dialogue system [8]

NLP and Data mining

Medical triage data

Provides a pre-assessment of probable conditions using learning algorithms across 7 million medical entities and patterns over a large-scale knowledge graph

18 Sanative chatbot for health seekers [13]

Input gathering and data pre-processing, Medical terminology detection, Mapping relevant document, Generating answers and solutions

Comparing the medical keywords in the query

Internet history search, Medical report

Relevant keyword Selection process, Handle large-scale data

19 Self- diagnosis medical chat-bot using artificial intelligence [24]

Implementing NLP to analyse human language

Natural language processing

Literature Survey NLP can be wrong in answering questions

20 Chatbots meet eHealth: automatizing healthcare [2]

Case study

Using IBM Patient records Watson from C.M.O Conversation APIs center by understanding natural language, Machine learning algorithm using Spark

21 Intelligent healthbot for transforming healthcare [10]

Study of existing system

NLP, Machine learning

Adaptable

HealtData site

(continued)

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A. R. W. Tjiptomongsoguno et al. Table 1. (continued)

3

No Title

Methodology

22

Smart medical chatbot with integrated contactless vital sign monitor [29]

Database and automated diagnosis, Motion magnification, Proposed algorithm pipeline, Contactless vital sign monitor

23

Pros and Cons Proposed algorithm

Clinical data set

High possibility to get accurate results.

Chatbot in mental Implementing health care [26] NLP

NLP

Online survey

Flexible but not able to show human emotions.

24

Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: a mixed-methods study [19]

Design, Data collection, Data analysis

AI hesitancy

Semi-structured interviews by online survey

Easy to analyze

25

Trust Me, I’m a Chatbot: How artificial intelligence in health care fails the turing test [21]

Data collection, Data analysis

Artificial neural network

Image scans

Easy to analyze

26

Trust in health chatbots [27]

Objectives, Data collection, Data analysis, System requirement

Neural Network

Medical dataset

Requires a lot of data

27

Mobile-based medical health application MediChat App [17]

General Firebase for objectives, backend, Android Specific for frontend objectives, System requirement specification, Functional requirements, Non-functional requirements

Survey on similar system

It’s easy to get under everywhere because it’s mobile

Methodology

This paper uses PRISMA checklist methodology [18] as the model of systematic review. We have chosen the PRISMA method because PRISMA is the recognized standard for reporting evidence in systematic reviews and meta-analyses. Figure 1 provides an overview of the number of journals, papers and articles that have been reviewed for the usage of writing this paper.

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Fig. 1. PRISMA checklist methodology.

4

Result and Discussion

There several techniques used for a chatbot. The techniques that they use can we can classify as NLP, Machine Learning, Braun and Clarke’s, Compare Keyword, and Data Mining. Ghosh et al. [8] have a research chatbot using data mining and natural language processing. NLP generation for user responses is based on predefined templates and system initiative to prompt easily interpretable responses from the user. Gajendra et al. [12] are also using NLP on his chatbot project. Based on his research we know that the goal of NLP is to take the unstructured output of the text input that is given as input to their chatbot system. Lastly, Raj et al. [22] also use NLP as on their chatbot system. Based on their research we can get the sentiment analysis of user experience with a chatbot. NLP also used to tokenize user input (string) to pieces or token so it can be processed by the system. Paper that uses machine learning is [4], which implements machine

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learning and the key contributions of their work are ensemble learning. We learn that ensemble learning is going to be useful for various reasons such as each model looks at marginally different parts of the data to make predictions, getting some portion of reality however not every bit of it. Other papers, [7] also using machine learning with a support vector machine algorithm. From this paper, we learn SVM can distinguish two classes and discover the finest distinguishing hyperplane which minimizes the error for an unseen pattern. This constitutes for RQ1: What technique preferred for a chatbot? The algorithm that matches medical chatbot is machine learning and natural language processing. There are some of the different algorithms used at machine learning such as ensemble learning, supervised and unsupervised learning, artificial neural network, binary regression, and classification. The NLP technique is for process the raw input from the user to a token that the machine learning can understand. Gajendra et al. [12] uses NLP to take the unstructured output of the Google API, which text input, is given as input to their chatbot system. After the text input is processed, the chatbot will respond with a series of questions to understand the situation of the user better. So basically, they use NLP to extract the keyword from the user input so it can be processed by the machine learning. Bali et al. [4] used Ensemble Learning to predict user disease base on the user symptoms that are given in the user input in the format of the token or processed string. This constitutes for RQ2: What technique preferred for a medical chatbot?

5

Conclusion

For short our paper discussed all the studies that related to a chatbot, especially medical chatbot. We learn and research the paper about how to make a chatbot, what kind algorithm the chatbot uses, and how to get the data set to train the chatbot. We see that there is a lot of algorithms we can use to make a chatbot like natural language processing, machine learning, Braun and Clarke’s algorithm, compare keyword, and data mining. From those algorithms, we have seen that the most match algorithm for a chatbot is natural language processing and machine learning. The major papers use natural language processing techniques to process the user input, that usually formatted as a string, to a format that the program can process. The raw input (string) can’t be processed by the program or the architecture. The string format usually processed with the NLP method becomes a tokenized format. The tokenize format can be processed easily for the program rather than the string format. After the user inputs are tokenized, it can be processed with machine learning such as classification to process the symptoms and match to the disease that available in the classification training. So the most suitable algorithm to make a chatbot from our point of view are NLP and Machine Learning.

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References 1. Agrawal, M., Cheng, J., Tran, C.: What’s up, doc? a medical diagnosis bot 2. Amato, F., Marrone, S., Moscato, V., Piantadosi, G., Picariello, A., Sansone, C.: Chatbots meet ehealth: Automatizing healthcare. In: Workshop on Artificial Intelligence with Application in Health, Bari, Ital, pp. 40–49 (2017) 3. Aswini, D.: Clinical medical knowledge extraction using crowdsourcing techniques. Int. Res. J. Eng. Technol. 6 (2019) 4. Bali, M., Mohanty, S., Chatterjee, S., Sarma, M., Puravankara, R.: Diabot: A predictive medical chatbot using ensemble learning 5. Bao, Q., Ni, L., Liu, J.: Hhh: an online medical chatbot system based on knowledge graph and hierarchical bi-directional attention. In: Proceedings of the Australasian Computer Science Week Multiconference, pp. 1–10 (2020) 6. Bohle, S.: ”Plutchik” artificial intelligence chatbot for searching NCBI databases. J. Med. Libr. Assoc. JMLA 106(4), 501 (2018) 7. Dharwadkar, R., Deshpande, N.A.: A medical chatbot. Int. J. Comput. Trends Technol. (IJCTT) 60, 41–45 (2018) 8. Ghosh, S., Bhatia, S., Bhatia, A.: Quro: facilitating user symptom check using a personalised chatbot-oriented dialogue system. Stud. Health Technol. Inform. 252, 51–56 (2018) 9. Harilal, N., Shah, R., Sharma, S., Bhutani, V.: CARO: an empathetic health conversational chatbot for people with major depression. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, pp. 349–350 (2020) 10. Kadu, O., Sihasane, S., Naik, S., Katariya, V., Gutte, V.S.: Intelligent healthbot for transforming healthcare. Int. J. Trend Sci. Res. Dev. (IJTSRD) 3(3), 1576–1579 (2019) 11. Kalla, D., Samiuddin, V.: Chatbot for medical treatment using NLTK Lib. IOSR J. Comput. Eng. 22 (2020) 12. KC, G.P., Ranjan, S., Ankit, T., Kumar, V.: A personalized medical assistant chatbot: Medibot. Int. J. Sci. Technol. Eng. 5(7) (2019) 13. Keerthana, V.M.K.A., Madhumitha, M., Valliammai, S., Vinithasri, V.: Sanative chatbot for health seekers. Int. J. Eng. Comput. Sci. 5(3) (2016) 14. Kumar, M.N., Chandar, P.L., Prasad, A.V., Sumangali, K.: Android based educational chatbot for visually impaired people. In: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–4. IEEE (2016) 15. Kumar, V.M., Keerthana, A., Madhumitha, M., Valliammai, S., Vinithasri, V.: Sanative chatbot for health seekers. Int. J. Eng. Comput. Sci. 5(03), 16022–16025 (2016) 16. Minutolo, A., Esposito, M., De Pietro, G.: A conversational chatbot based on kowledge-graphs for factoid medical questions. In: SoMeT, pp. 139–152 (2017) 17. Mohammed, M.A., Bright, A.S., Ashigbe, F.D., Somuah, C.: Mobile-based medical health application-medichat app. Int. J. Sci. Technol. Res. 6 (2017) 18. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L.A., et al.: Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015 statement. Syst. Rev. 4(1), 1 (2015) 19. Nadarzynski, T., Miles, O., Cowie, A., Ridge, D.: Acceptability of artificial intelligence (ai)-led chatbot services in healthcare: A mixed-methods study. Digit. Health 5, 2055207619871808 (2019)

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20. Park, S., Choi, J., Lee, S., Oh, C., Kim, C., La, S., Lee, J., Suh, B.: Designing a chatbot for a brief motivational interview on stress management: qualitative case study. J. Med. Internet Res. 21(4), e12231 (2019) 21. Powell, J.: Trust me, i’ma chatbot: How artificial intelligence in health care fails the turing test. J. Med. Internet Res. 21(10), e16222 (2019) 22. Raj, P., Murali Krishna, R., Krishna, S.M., Vardhan, K.H., Rao, K.: Emergency patient care system using chatbot. Int. J. Technol. Res. Eng. 6 (2019) 23. Rarhi, K., Bhattacharya, A., Mishra, A., Mandal, K.: Automated medical chatbot. SSRN (2017) 24. Shifa, G., Sabreen, S., Bano, S.T., Fakih, A.H.: Self-diagnosis medical chat-bot using artificial intelligence. Easychair (2020) 25. Sophia, J.J., Kumar, D.A., Arutselvan, M., Ram, S.B.: A survey on chatbot implementation in health care using NLTK. Int. J. Comput. Sci. Mob. Comput. 9 (2020) 26. Vijayarani, M., Balamurugan, G., et al.: Chatbot in mental health care. Indian J. Psychiatr. Nurs. 16(2), 126 (2019) 27. Wang, W., Siau, K.: Trust in health chatbots. Thirty ninth International Conference on Information Systems (2018) 28. Winkler, J.: Conception and Realization of a Chatbot-System to support Psychological and Medical Procedures. Ph.D. thesis, Ulm University (2019) 29. Zaki, W.M.A.W., Shakhih, M.F.M., Ramlee, M.H., Ab Wahab, A.: Smart medical chatbot with integrated contactless vital sign monitor. J. Phys. Conf. Ser. 1372, 012025 (2019)

A Model for Effectively Teaching Information Technology Leila Goosen(&) University of South Africa, 0003 Pretoria, South Africa [email protected]

Abstract. The purpose of this research was to achieve objectives related to developing a theoretical model for the teaching of Information Technology (IT), determining which teaching strategies and methods were being used to teach the various components of IT, which adjustments needed to be made with regard to the teaching strategies and methods of IT, and what the implications of these adjustments would be for the training of IT teachers. The use of direct and problem-based instruction, as well as discovery and cooperative learning, was explored theoretically towards the development of the model for the effective teaching of Information Technology. The empirical research undertaken, using questionnaires, determined how IT was taught in classrooms. The questionnaires were composed based on a literature study and revised after a pilot test. The research found that teachers viewed demonstrations, individualized teaching and lectures with practice as the most effective strategies. Learners found that they obtained the best results when they researched parts of the work. Class situations where the teacher showed learners how to work, and learners then practice the work, and learners helping each other, obtained results almost as good. The greatest need for teacher training in the subject was in graphics, desktop publishing and communication skills, such as the Internet. Some results obtained from the empirical research were also offered to the subject advisor and didactical trainers of the subject for comment: there are courses available for teachers to better train themselves in order to teach IT effectively. Keywords: Effective teaching


Information Technology
Secondary school

1 Introduction These days, things like the security aspects of studies into the impact of digital transformation via Unified Communication and Collaboration (UC&C) technologies on the productivity and innovation of global enterprises make the headlines daily [1]. Both learners at basic (school) and students at Higher Education and Training (HET) levels are indicating that they want access to e-learning opportunities [2]. Educational technologies are therefore used more and more for growing innovative eschools in the 21st century [3], with, for example, trans-disciplinary approaches to Action Research [4], and often in the form of community engagement projects and/or in the context of Information and Communications Technology for Development (ICT4D) [5, 6]. The combination of Information Systems (IS) and technologies and © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 357–371, 2020. https://doi.org/10.1007/978-3-030-63322-6_29

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research on technology-supported teaching and learning is also e.g. opening new worlds for learning to children with Autism Spectrum Disorders (ASDs) [7, 8]. It is therefore apparent that Information and Communications Technologies (ICTs) can play a dramatically major role, especially “in the education sector, by improving the quality, effectiveness and efficiency of teaching, learning, research and educational management around the world” [9, p. 304]. On the other side of the coin, observations by Egba, Oko, Egba, Achimugu and Achimugu [10, p. 3622] suggested an “increasing lack of interest in teaching and learning of subjects as a result of the use of teaching” strategies and methods “devoid of ICT compliance. The nature of” Information Technology (IT) as a subject “makes it almost imperative to imbibe ICT as the effective tool for achieving the” objectives thereof.

2 Purpose/Objectives of the Paper Towards the end of the previous century, the “value of computer programming courses in secondary schools” was being questioned [11, p. 96]. At the same time, educators were attempting to create learning opportunities identical to those identified as exemplary in this regard [12]. Since this is, unfortunately, still very much the case today, the purpose of the research reported in this paper was achieving objectives related to: • developing a theoretical model for the teaching of Information Technology in secondary schools, • and determining – which teaching strategies and methods were being used in secondary schools to teach the various components of Information Technology, – which adjustments needed to be made with regard to choosing the ‘best’ teaching strategies and methods for first programming languages and Information Technology in secondary schools, and – what the implications of these adjustments were for the training of Information Technology teachers. 2.1

Research Questions

This research was thus undertaken to answer the following research questions: • How could a theoretical model be developed for teaching Information Technology in secondary schools? • Which teaching strategies and methods were being used in secondary schools to teach the various components of Information Technology? • Which adjustments needed to be made with regard to the teaching strategies and methods for Information Technology in secondary schools? • What were the implications of these adjustments for the training of Information Technology teachers?

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3 Conceptual/Theoretical Background or Framework “There have been a number of ideas on how” teaching strategies and methods can be effectively applied, such as in the research by Somawati, Astuti, Kanca, Widanta and Ardika [13, p. 1]. The latter authors also attempted to investigate how teaching “should appropriately be implemented in” classrooms. The paper by Angolia and Pagliari [14, p. 457] assessed “the effectiveness of the adoption of curriculum content developed and supported by a global academic university-industry alliance sponsored by one of the world’s largest information technology software providers”, while Vorster and Goosen [15] provided a framework for university partnerships, which promote the continued support of e-schools. The study by Nwanewezi [16, p. 278] “was undertaken to identify the factors hindering the effective teaching of ICT courses in Office Technology and Management” (OTM) programs. The need for the latter “study arose because of the observed ineffective teaching of ICT courses in OTM which in turn defeats the” purpose of said programs. With some aspect similarities to the latter studies, the use of direct and problembased instruction, as well as discovery and cooperative learning [17], was explored theoretically in the research reported on this paper. This was done towards the development of a model and pedagogy to strengthen teaching Information Technology effectively within an evolving landscape [14, 18], by combining the following components [19]: 1. 2. 3. 4. 5. 6.

Planning The use of IT textbooks in teaching and learning The handling of new information from the computer field Programming Problem solving Feedback from learners. This model will now be further illustrated with practical examples.

3.1

Planning

The departure point for education is that which learners should be able to do, that they could not do previously, after they had been exposed to a certain learning situation [20]. Such a learning “situation is an important part of the design of classroom teaching, as well as a platform for students to realize knowledge construction. The effective creation of teaching situations is helpful to stimulate students’ learning interest and curiosity” [21]. “Where a number of key features of innovative teaching and” cooperative planning with learners are brought together effectively, a start can be made towards making the curriculum real to them [22, p. 164]. Conceptual Development. Although the integration of different themes is important, the teacher should also think about types of activities and what to teach, by organizing concepts carefully, in order “to be able to teach … concepts and address students’ misconceptions” [23, p. 43].

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Role of Outcomes. Both short and longer-term outcomes should be considered when compiling a profile of each learner with regard to general computer competence, as well as specific areas where learners already show competence – this should also be done in a non-intimidating way and individualized as far as possible. Learners can further discuss with small group of their peers what each of them want to achieve with the subject Information Technology. Learners thus not only develop explicit goals, but a class climate of mutual respect is also built. What do Learners Bring to the Computer Class? The learning of Information Technology definitely occurs within the context of the computer knowledge that learners build in their immediate environment - at home, at the Internet-café, or, for example, by making use of the information systems architecture supplied by the government or other enterprise information technologies at a regulatory institution, made available to the community. They bring certain competencies, knowledge and perspectives to the classroom and this will influence how they make sense of new knowledge [24]. Knowledge is more accessible and will therefore more likely be transferred to new situations, when it forms a central and integral part of a person’s cognitive structure. Interconnectedness of Knowledge. There should be interaction between learners and new knowledge, and interaction amongst the learners themselves. Learners should be provided with the opportunity to give meaning to the new knowledge, connect it to their existing knowledge and to test their comprehension thereof against other meanings, while they are also exposed to a number of different viewpoints. It is especially important when topics, which learners are required to understand, are taught that the teacher constantly checks throughout the lesson that learners is understanding the content thus far. In general, during planning it should be ensured that the objectives, which are meant to be achieved during a specific lesson, as well as the assessment procedures to be used to determine whether the former are being achieved, are set out explicitly. Active Learning. Metacognition refers to learners’ awareness and knowledge of their own cognitive processes, their associated products, and how these are related to learning outcomes and the ability to evaluate and control the thought processes [25]. Learners should possess the metacognitive awareness and managing strategies to actively monitor and direct their own studies and problem solving [11]. The use of appropriate materials, the principles of learning, and feedback “are key when teaching and confirming understanding in any educational setting” [26, p. 251]. “For education to be truly effective though, steps must also be taken to” ensure that “the learner will take action” by actually using what they have been taught. Getting Started. The start of an effective lesson consists of planning a learning activity, which will generate and support the discussion and doing of IT by learners [27]. When an IT lesson is being planned, the special nature of the lesson topic and objectives, resources available, size of the class, time available and the developmental stage(s) of leaners should be considered [28]. Planning the lesson sequence consists of:

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• Determining what existing knowledge learners need to proceed with this specific lesson; • How can be determined whether learners have this existing knowledge; and • How the new topic can be introduced. In an effective lesson, the teacher would typically set up a meaningful situation (for example, an activity for learner exploration), which generate and support learning activities amongst small groups of learners [27]. In order to be effective, the situation should motivate learners, sustain learners’ interest and involve learners in meaningfully talking about, and engaging with, learning content. Together with these, effective discussions should be used, which are based on an activity in which learners work together to discuss what they are doing. It is also necessary to ensure that the teacher checks whether all learners understand the model used in the lesson and will be using that model when completing written exercises. Affective Aspects. Goosen [25] indicated that another approach that can be used profitably in the class is the intertwining of the affective aspects of a specific topic with the cognitive objectives of the lesson, so that affective level responses are provoked in learners as part of that class session [29]. 3.2

The Use of Information Technology Textbooks in Teaching and Learning

The teaching and learning of Information Technology as discussed in this paper takes place “under the guidance of the” South African National Curriculum Statement (NCS) for the Further Education and Training (FET) phase [30]; [31, p. 207]. In a similar milieu, Yue, Liu and Yang [31] discussed how to utilize learning environments and methods created for Information Technology [32], to promote the effective teaching thereof by analyzing the discipline ontology. In general, IT textbooks act as a handy source of subject knowledge and can be used by learners for revision purposes [28]. With regard to the so-called ‘theory’ chapters, the teacher should explain clearly to learners what it is that they should be able to do at the end of each chapter: should they be able to compute sums similar to those in the chapter, or should they know definitions and be able to compare different computer generations? The questions that appear at the end of certain chapters could act to indicate the level of knowledge required, or the teacher could consider supplying learners with a list of questions at the required level. Especially in Grade 10, when learners are first getting to know the subject, it might be worth the while to introduce learners to specific learning techniques, which are applicable to certain chapters and provide them with opportunities to practice these. Experience shows that many learners, even at Grade 10 level, still do not have access to effective techniques for learning.

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Handling New Information from the Computer Field

Especially chapters in IT textbooks on, for example, Information Systems (IS) architecture and technology security aspects relating to the usability attributes and evaluation methods of mobile commerce websites [33], quickly become outdated and it is essential that the teacher, together with the learners, aim to stay up to date with the latest trends with regard to the field [34]. In light of the relationship between the Information Technology skills acquired by teachers and their utilization of the Internet for effective teaching [35], information from the Internet, as well as newspaper and magazine articles, can be useful in this regard. These new pieces of information could be displayed on a notice board and be updated frequently, and learners can also use it in the completion of their research projects. When learners present their research projects to the class, other learners also obtain access to such information, while the teacher is provided with an additional way of ensuring that this is indeed the learner’s own work. 3.4

Programming

Guided Exploration. Faculty’s attitude towards the adoption in the teaching process of educational practices to be used for Information Technology “should bring overall teaching and learning to a higher level” [36, p. 234]. Contrary to some indications to allow learners to learn programming on their own, Mayer [37] quoted educational researchers, who found that when learners work on their own in non-directive, handson teaching programs, they are often not successful in the discovery of even the fundamentals of programming. In comparison, teaching that emphasizes structure and mediated guidance, obtains better results for transmission and learning. Structuring ensures that the learner receives the basic information in a useful order, while the mediation of the teacher ensures that the learner connects the presented information with relevant existing knowledge. The overwhelming consensus is that, for most learners, a practical discovery environment should be complemented by instruction that emphasizes structure, interventional facilitation by the teacher, and direct instruction in the programming language [25]. Chapters in textbooks that are used in the learning of programming usually consist of: • • • •

definitions and concept structures that have to be learned; sections that explain work like a teacher would; examples that learners work through themselves; and exercises that should be completed by learners [28].

The first two of these categories mentioned can be handled by the teachers themselves, after which the learners can then consult the section in the textbook for revision, or if they do not understand something. It is, however, not enough that the teacher just presents the content: irrespective of how well the content is presented, the more interference there is in terms “of teaching information during transmission, the less the students” are likely to take from the lesson [38, p. 155]. “It is impossible to effectively” teach for students’ understanding, and it “is clear that in spite of” the advantages of

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application, it will not assist if the learners do not personally use the content to address and understand important aspects of their learning environment [39]. For effective learning to take place, it is necessary for the leaner to discover as large a part of the learning content, as is possible within the circumstances, for her/himself, (s)he should be interested in the material to be learnt and should ideally find joy in the learning activity [40]. An exploratory phase should precede the phase of verbalization and concept forming and the material, which should be learnt, should be assimilated with, and contribute to, the integral thinking attitude of the learner. Learners should work through examples themselves. Learners, who are able to discover the general concept on their own, even if it requires careful hints from the teacher, display improved recall and transfer, compared to learners, who were merely told about the concept [39]. Under these circumstances, while the examples are being carried out, the teacher acts as a facilitator, leading the learners in discussion [41], and by being available for questions, helping with problems, for example, with hardware, but still insisting that learners understand the example themselves, by working with it. Exercises act to practice the new skills and should be chosen in such a way that the transferability with regard to the use of a particular programming structure within a wide variety of environments and applications can be obtained. Teacher-learner Conferences. The teacher could indicate to learners the programming assignments that should be completed, and then spend time with each learner to discuss her/his efforts, with special attention to two areas of competency and two areas where they can improve. Notes can also be made with regard to additional teaching needed and/or possible next assignments. Portfolios and Projects. Learners should be provided with opportunities to work on projects, research papers and group activities, instead of a mountain of summative tests [42]. A ‘contract’ can be drawn up with regard to what is expected of the learner, and when it should be completed. Changes, extensions and flexibility should, however, be possible. There should also be no upper ceiling for what can be achieved, and learners should clearly understand that commitment and responsibility is expected. Developing Programming Mastery. It should be indicated very clearly to learners that one can not only learn for programming tests and examinations ‘the night before the time’, but that this type of knowledge is rather built up gradually, as the learner works on her/his own programs. It is therefore essential that each learner writes and debugs her/his own programs. Learners should be thoroughly introduced to the debugging section of the programming environment and should use it regularly. However, where learners make logical errors, especially the teacher can help by explicitly explaining why the error occurred and how it can be rectified. Memory Picture of the Computer System. In order to address some of the difficulties, which may constrain the learners’ level of programming skill, and thereby improve learners’ ability to use a programming language to solve problems, instructional methods that help the learner to build a memory image of the computer system should be used. Some of the areas and operations for which models need to be constructed include:

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• a simplified version of the main areas where processing is carried out; • the objects that operations are carried out on; and • the actions that are executed within the computer. Learning IT will be more effective if learners are provided with opportunities to talk about their own ideas, describe and explain their thought processes and make predictions, which can be tested, based on their personal experiences. There should also be opportunities for learners to discuss and think about these experiences, so that they are able to make sense of the learning content, which they encounter. 3.5

Problem Solving

Choosing a Problem. To teach problem solving effectively, more should be done than just presenting learners with problems [25]. It is the task of the teacher, as ‘salesperson’ of knowledge, to convince learners that what the point being discussed is indeed interesting and that the problem that they are trying to solve deserves their efforts [40]. For these reasons, the teacher should pay attention to the choice, formulation and acceptable representation of the problem that (s)he wants to present. The problem should be meaningful in, and as relevant as is possible from, the learner’s viewpoint, it should be related to the every-day world of the learner, and should be introduced using a paradox, a joke or any other situation that will draw learners’ attention. As an alternative, the teacher can start from information that is quite familiar to learners, and therefore has meaning to learners, or can be applied. Learners’ Contribution. If learners are allowed to actively contribute to the formulation of the problem that they are going to solve, they are not only motivated to work harder, but they also learn a meaningful attitude towards thinking [40]. Opportunities for Developing Skills. To ensure that an atmosphere is created in the learning process where learners can develop into problem solvers, it should be ensured that they get enough opportunities to: • • • • • •

observe and discuss patterns; predict solutions; experiment with ideas; discuss and compare problem solving strategies; judge the progress that is being made in their own learning process; and communicate what they already know, compared to those sections where they are still experiencing problems in the learning situation [27].

Similarities Between Problem-solving and Learning. The similarities that the problem-solving process shows with the learning process leads to it that the active application of one of these activities could also possibly lead to advantages being obtained in terms of the other.

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Algorithms vs. Problem Solving. Learners should keep up a list of standard algorithms for sections of the work, which had already been completed, for example, counting the number of entries in loops, or a search algorithm for arrays or files. These algorithms act as building blocks for larger projects and learners should practice breaking up complicated problem in terms of familiar algorithms. The education of learners requires that they should learn more than just being able to simply employ standard algorithms for a limited number of applications, but, rather, that they are enabled to act as genuine problem solvers in the real and cyber worlds. 3.6

Feedback from Learners

Learners could benefit from listing the factors that prevent them from doing well, or better, in Information Technology, and then make suggestions of what they can do about these problems themselves [43]. This aspect was addressed in the learner questionnaires (see later) and provided a lot of useful information, which can be applied by the teacher. Large improvements with regard to attitude, attendance, completion of assignments, and willingness to participate in class were observed for learners, who were requested to complete such a assignment.

4 Methods/Techniques ICTs have become a buzz word in society with reference to data collection, data processing and generating information [44] and using ICTs to facilitate teaching and learning has become inevitable, based on the development of Information Technology [45]. These aspects did, however, not receive attention in the study reported on in this paper. Rather, the empirical research for this study “was carried out to investigate the extent of … effective teaching and learning in public secondary schools” of Information Technology, with the research questions used for the study as indicated earlier in this paper [46, p. 95]. This was undertaken, mainly by using questionnaires, which were composed based on a literature study and revised after a small pilot test. 4.1

Research Design

“A descriptive research design of the survey type was adopted” [47, p. 210] to gather data, using questionnaires, aimed at teachers and learners of the subject. In other research, case studies were also used to investigate how innovative educational technologies, technology-supported teaching and learning and research methods were being used by educators [48] to improve students’ access to an ICT4D Massive Open Online Course (MOOC) in the 21st century [5, 6, 49]. 4.2

Data Collection Instrument(S)

The questionnaires were designed by the researcher and was initially piloted with a small number of volunteers, who did not form part of the population. The revised instrument was distributed and completed at a two-day gathering of provincial teachers

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organized by the subject advisor at the time. A number of teachers, who did not attend, were sent a questionnaire and returned the completed ones The teacher questionnaire consisted of 75 items, divided into five sections. 4.3

Data Analysis

Ethical data management and research integrity in the context of e-schools and community engagement, as also described in Goosen [50] and [51], were maintained with regard to data analysis activities.

5 Results/Findings In the empirical research, the following was found: • Demonstrations, individualized teaching and lectures with practice were viewed by teachers as the most effective strategies for teaching Information Technology (see Table 1). • Learners found that they obtained the best results in Information Technology when they researched parts of the work themselves. Class situations where the teacher showed learners how to do the work, and learners were then provided with opportunities to practice the work, and learners that helped each other with their work, obtained results that are almost as good (see Table 2). • The greatest need for training of teachers of the subject was in the areas of graphics, desktop publishing and communication skills, such as the Internet (see Table 3). 5.1

Discussion

Some results obtained from the empirical research were also offered to the subject advisor and didactical trainers of the subject for comment: there are courses available for teachers to better train themselves in order to teach Information Technology effectively. Table 1. Averages and standard deviations regarding class activities. Class activities Demonstrations Individualized teaching Lectures with practice

Averages 2.65 2.58 2.33

Standard deviations 0.97 1.17 1.11

Table 2. Ordering of class activities according to efficiency and amount of use in the class. Class activities Teacher shows how, then practice the work Learners research parts of the work themselves Learners help each other with their work

Efficiency 1 2 2

Amount of use 8 4 5

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Table 3. How do IT teachers see their ability to teach different topics? Sections Word processing Spreadsheets Databases Operating systems Desktop publishing Graphics Communication Boole algebra Problem solving Programming

Inadequate 0 1 (2%) 6 (13%) 4 (9%) 8 (18%) 11 (24%) 6 (14%) 14 (31%) 5 (11%) 13 (30%)

Poor 3 (7%) 6 (13%) 11 (24%) 3 (7%) 17 (39%) 17 (38%) 16 (36%) 2 (4%) 10 (22%) 1 (2%)

Adequate 22 (48%) 18 (40%) 13 (29%) 22 (49%) 13 (30%) 14 (31%) 16 (36%) 8 (18%) 13 (29%) 10 (23%)

Advanced 21 (46%) 20 (44%) 15 (33%) 16 (36%) 6 (14%) 3 (7%) 6 (14%) 21 (47%) 17 (38%) 20 (45%)

6 Conclusion ICTs have “offered great potential, especially as an aid to every aspect of human endeavor” [52, p. 77]. Despite the role played by ICTs, especially in effective teaching and meaningful learning, however, “there are still some impediments to its effective utilization by” academic staff. In the model for the effective teaching of Information Technology proposed, attention was therefore paid to aspects with regard to effectively planning teaching opportunities. The use of theory textbooks in terms of learning content chapters and teaching calculations from these were discussed next. It is recommended that the large amounts of new information, which literally appears every day from the computer field, be handled by using publication media, as well as by addressing this by way of research projects. With regard to programming, a practical exploration environment is recommended, complemented by direct instruction and demonstrations by the teacher. It remains important that the learner discovers as much as is possible for her/himself, as well as completing programming exercises by themselves. The role, which algorithms and memory representations of computer processing, could play, was also explained. Within the IT classroom, an atmosphere should be created in which learners can develop into problem solvers, by, for example, the choice of applicable problems to solve and the promotion of discussion between learners about their learning experiences and feedback. In terms of originality and a contribution to the field, this paper shows how sustainable and inclusive quality education can be made possible through research informed practice on Information Technology in education [53]. An appropriate depth of research was achieved by citing a wide variety of resources spanning four decades, including several seminal ones, but also incorporating a majority of recent sources, published within the last five years.

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When teachers’ management and control of Information Technology for effective teaching and learning in secondary school classrooms [54] are strengthened towards promoting students’ meaningful learning [55], and “instruction is taken beyond establishing facts and practicing skills to an approach using more openness, investigation, problem solving and critical discussion, there will be more emphasis upon shared interpretation and evaluation of what goes on in” classrooms [56, p. 105]. By modelling course-design characteristics, self-regulated learning and the mediating effect of knowledge-sharing behavior, teachers can thus become drivers of learners’ individual innovative behavior [57].

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Identifying Wood Types Using Convolutional Neural Network Rostina1,2 , P. H. Gunawan1,2(B) , and Esa Prakasa1,2 1 School of Computing, Telkom University, Jl. Telekomunikasi no 1, Terusan Buah Batu, Bandung 40275, Indonesia [email protected], [email protected] 2 Computer Vision Research Group, Research Center for Informatics, Indonesian Institute of Sciences (LIPI), Bandung, Indonesia [email protected]

Abstract. This paper explains about identifying wood types using a macroscopic image on wood surfaces which have specfic characteristics, such as cross-section, radial, and tangential. Generally, on the identification process of wood types, traders and carpenters only do the checking which focuses on the cross-section part, it happened because of the difficulty of identifying the radial and the tangential wood surfaces. By using the convolutional neural network method, it can extract images with several layers, so that it is possible to do an identification process on all three wood surfaces. There are approximately 3,000 images which consist of 3 species of wood with each cross-section, radial and tangential surfaces. Identification results showed great potential even though there was a small amount of misclassification caused by similarities in different species and differences in similar species. Within the process, classification results obtained by the amount training accuracy 89% and testing accuracy 96% for the cross-section, 79% for the radial and 88% for the tangential planes. Thus, the identification of wood surfaces with high accuracy result was at the cross-section surface. abstract environment. Keywords: Macroscopic · Image · Convolutional neural network Cross section · Radial · Tangential

1

·

Introduction

Wood is one of human need that has been recognized for thousands of years until nowadays. The use of wood at the present moment develops from time to time and turns into more unique, begins from the creation of building materials, household appliances, miniatures, and others. The process of identifying wood species was performed by wood anatomists, traders, and carpenters through manual assessment. Digital identification might also be conducted, but the procedure only focuses on the cross-section area. Hence, in this study, the identification process was carried out on all three wood surfaces which have specific characteristics, such as cross-section, radial and tangential. By using the Convolutional Neural Network (CNN) method, it can c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 372–381, 2020. https://doi.org/10.1007/978-3-030-63322-6_30

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extract images with fewer layers and more efficient. Convolutional Neural Network method has been implemented in classification of image widely [7]. This method is widely applied for imagery analysis with better accuracy values than other Deep Learning methods [12]. Wood has characteristics in identification, including using macroscopic and microscopic. Each characteristics has various level of difficulty for identification task, therefore some experts still have any trouble for identifying wood species in a brief time [5,6]. For the macroscopic characteristic, it is physical that can be checked directly and manually, whereas, for the microscopic characteristic, it is structured which can be done with the help of a microscope, including pores, Parenchyma, Resin channels, and others. Each type of wood has specific characteristics for each type of surface. Identification using macroscopic can make a type of wood surface can be seen directly in plain view or with the help of a mobile phone camera. By using macroscopic images, it can obtain datasets from wood surfaces (cross-section, radial and tangential). Macroscopic image of a wood surface sample, Shown in Fig. 1.

Fig. 1. Sample of macroscopic wood image

Previous researches explain about the process of identifying wood, but there are differences on the identification process provided in paper [4,9,11]. Paper [9] describes the identification method on Japanese Fagaceae wood using microscopic imagery with the implementation of Daubechies Wavelet (DW) and Local Binary Pattern (LBP) algorithms. Paper [4] explains about the development of wood identification systems using Local Binary Pattern (LBP) and Hough Transform (HT) as methods for extraction from wood patterns and then will be classified using the Support Vector Machine (SVM). Whereas paper [11] is developing identification of wood species by Oriented Gradient (HOG) and SVM method with extracting the feature of image into Histogram. A large number of wood species in Indonesia make traders or carpenters find it challenging to identify, even for experienced scientists. Thus, paper [1] describes a new approach to the introduction of automatic wood species through multidimensional texture analysis. The classification rate is 91% in the CrossSection part, with a total of twelve wood species and approximately 4,200 wood pictures (from cross-section, radial and tangential pieces from typical wood structures). Paper [6] suggests that CNN is a suitable methodology for developing efficient computing models. The results show excellent performance of seven wood

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species with an accuracy of 94% and a precision and sensitivity value of around 90%. In the papers [5,13], they describe that in the last few years, the CNN had become a popular technique for image recognition because of its advantages on shifts and distortions for the image, with lesser necessity and efficient use of memory that has been tested for accuracy with an accuracy rate of 97%. The CNN method is widely applied to imagery analysis with better accuracy than other deep learning methods [12]. According to [14], it can be concluded that the CNN method has the classification ability, which is intended for image data. The macroscopic image was used to obtain a dataset of wood images which were grouped into training, validation and testing data. After that, the data were modelled into the CNN method. The identification process using macroscopic made it able to see types of wood surfaces directly with bare eyes with the help of a mobile phone camera. In this study, the author assumed that wood surface characteristics (radial and tangential) are more challenging to recognize compared to Cross Section. Thus, testing was carried out by each wood surface (cross-section, radial, tangential) in order to identify type of species that mostly influences the three wood surfaces. The identification results showed great potential, although there were a small number of misclassifications caused by similarities in different species and differences in the same species. The results of testing proved that the crosssection was superior compared to the radial and tangential.

2 2.1

Research Methodology Convolutional Neural Network (CNN)

Before getting into the CNN, firstly, it is important to understand that machine learning is apart from artificial intelligence that functions to solve the problem by finding the best solution. Machine Learning has a branch called Deep Learning. Deep Learning is a scientific field that is used to classify objects in images [2]. Implementation of deep learning methods that is often used in classifying the image is called Convolutional Neural Network (CNN). The deep neural Network architecture was used in this research. It was based on models that achieved high levels of accuracy on object classification tasks [2]. Illustration of CNN architecture, shown in Fig. 2. – Convolutional Layer : Convolutional layer is the core of CNN building blocks. Where most of the computing is done at this layer. Generally, Convolution Layer in CNN architecture uses more than one filter [6]. – ReLU Layer : Rectified Linear Units Layer is a layer that applies the activity of function f (x) = max (0, x). Serves to increase representation model, knowing nonlinearity and the network as a whole without affecting receptive sections on the convolution layer [12].

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Fig. 2. The deep CNN architecture.

– Pooling Layer : Pooling Layer performs to maintain the size of data during convolution by performing down-sampling (sample reduction). With pooling, we can present a smaller, manageable, and easy to control overfitting data. This layer has the function of reducing the sample in a non-linear way [6]. The commonly used pooling process is max pooling. – Fully Connected Layer : This layer is fully connected; every neuron have full connection to all activities against the previous layer [12]. This layer is similar to CNN, and all neurons are connected from one layer to the next layer using the unshared weights [6]. – Softmax : Softmax is used to get better and faster classification results with several classes. Receiving input from the previous process to determine features that are most correlated with a certain class and unites all the nodes into one dimension [13]. 2.2

Wood Dataset

The wood dataset is created from the image acquisition of wood samples. The samples consist of three types of commercial woods, namely Jati, Merbau, and Sengon. These samples are collected from a local timber shop in Bandung area. The image dataset were acquired from several wood samples that have been cut and sliced. Therefore, the wood surface can be clearly observed. A Vivo V7+ mobile camera at magnification level of 4.0 and a convex lens mounted on the smartphone camera, are used to collect the wood images. This procedure is

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defined to enable acquisition of good quality images. Several collected images are displayed in Table 1. Table 1. Samples of Cross-section, Radial, Tangential of the 3 species in the dataset: Jati, Merbau and Sengon. Name

Wood Surface

Jati

Merbau

Sengon

Cross-section Section.jpeg

Radial

Tangential

– The cross-section surface is found in the section which is created by cutting the stem, which is perpendicular to the vertical axis of the stem. The vertical axis is the exact line through the centre of the circle and perpendicular to the section of the line latitude. – The radial surface is observed in the section which is created by cutting wood according to the vertical axis of the rod. More precisely, logs cut or parallel to the radius of the wood. – The tangential surface can be obtained from the section when the tree is split by a random plane parallel to the axis of the rod, but not through it and perpendicular to wrong one wooden radius. 2.3

Performance Evaluation

The evaluation of performance was performed for this paper in identifying wood species using a confusion matrix. Meanwhile, for classification with more than

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two classes, the formula that was used is multiclass. The formulas for calculating evaluation are given as follow: Accuracy(A) =

TP + TN , TP + TN + FP + FN

TP , TP + FP TP , Sensitivity(S) = TP + FN P recision(P ) =

F 1 − Score =

2(P × S) , P +S

(1) (2) (3) (4)

where, – – – –

TP = True Positive, TN = True Negative, FP = False Positive, FN = False Negative.

The detail about these notations can be found in [8]. 2.4

Image Representation

Image is an imitation of the actual pictures, or in other words, it is a twodimensional function that has a coordinate point f (x, y). The f function on each coordinate (x, y) is the intensity that leads to the grey level of the image at the coordinate points. According to [3] Image is a representation, similarity or imitation of an object or thing. To define a colour on an object, there are three types of images including RGB (Red Green Blue), Grayscale and Binary are images that only have two intensity values that are 0 (black) and 1 (white). According to [10] in image processing, a grayscale image is commonly used. Because the RGB image (which is coloured) has three matrix layers that surely take a longer time for the next image processing. However, the RGB image is used in this study.

3 3.1

Results and Discussion Implementation of Convolution Neural Network

Based on the implementation process, the data set is divided into three parts, namely training, testing and validation. These three data have different tasks and the number of data sets in the ratio of 70:20:10 for each training, testing and validation. The training stage takes responsibility for training the network on CNN by studying preprocessing data which consists of training and validation data. If the training results from the network model give good results, then network model will be tested using dataset testing.

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The CNN model consists of an input layer in the form of images, five convolution layers followed by the max-pooling layer and the ReLU activation function, 2 fully connected layers, and the output layer with softmax activation function. Convolution Neural Network structure model that was formed, shown in Table 2. Table 2. Structure model of CNN. Layer

Pixel Size

Input

256 × 256 × 3

Conv2d1

3 × 3 × 32

MaxPooling1 2 × 2 Conv2d2

3 × 3 × 64

MaxPooling2 2 × 2 Conv2d3

3 × 3 × 128

MaxPooling3 2 × 2 Conv2d4

3 × 3 × 256

MaxPooling4 2 × 2 Conv2d15

3 × 3 × 512

Output Shape 256, 256, 32 128, 128, 32 128, 128, 64 64, 64, 64 64, 64, 128 32, 32, 128 32, 32, 256 16, 16, 256 16, 16, 512

MaxPooling5 2 × 2

8, 8, 512

FC6

150

FC7

256

FC8-n(Class)

3

Output

256 × 256 × 3

The used dataset on the input layer was training and validation data. Then, the data was processed at the first convolution layer by using max-pooling and ReLU activation function. The first convolution process continued until the fifth convolution. After that, the results of first to the fifth convolution were filtered out in 2 fully connected layers. Then, features that have correlations with a particular class with the softmax activation function were determined. Based on the model implementation, there were 3 classes with each comparison of the dataset. They were 70% of data for training, 20% of data for testing and 10% of data for validation. However, we can find out that to obtain optimal performance. More data was required to improve its performance. For this reason, it is necessary to augment the data to become greater without losing the core or the essence of the following data. Input that went into layers was sized of 256 × 256 pixels and 3 arrays because the picture was in colour (RGB). Input that was entered in the layer was the same as the output, which was 256×256×3. Based on the model that was formed, the results can be shown to the training and testing data with more than 20 epochs. The accuracy results for training 89% with 26% loss while for validation 78% and loss validation 55% were obtained. Graph for the implementation results of training and validation data, shown in Fig. 3.

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Fig. 3. Accuracy and loss curves during CNN training and validation

3.2

Evaluation of CNN

After training, the model that was formed was continued to do a performance testing of CNN using the testing dataset with 256 × 256 pixel size. A confusion matrix with calculation of accuracy (1), precision (2), sensitivity (3) and F1score (4) was obtained. Results from the confusion matrix, shown in Table 3.

Table 3. Performance evaluation of confusion matrix Wood surface Table column confusion matrix Species Precision Recall F1-Score Accuracy Cross section Sengon 95% Merbau 93% Jati 100%

100% 100% 91%

97% 96% 95%

96%

Radial

Sengon Merbau Jati

63% 95% 77%

80% 97% 60%

70% 96% 67%

79%

Tangential

Sengon Merbau Merbau

86% 92% 82%

92% 98% 63%

89% 95% 71%

88%

From all three tests, the highest accuracy results were cross-section with 96%, then Tangential with 88% and finally Radial with 79% accuracy. The high accuracy was found at the cross-section due to wood surfaces characteristics (radial and tangential) that were difficult to recognize especially in Jati and Merbau species. It is difficult because they have a similarity on the Radial and Tangential section, or because the images from Radial and Tangential section were almost similar caused by defects in cutting and cleaning the surface during the shooting process. The results of the test, shown in Fig. 4.

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Fig. 4. Confusion Matrix with data of the test set: (a) Cross-section, (b) Radial, (c) Tangential.

Confusion matrix resulted in 140 datasets for Cross-section, 110 datasets for Radial section and 130 datasets for the Tangential section. All three testing datasets have been carried out consisting of Cross-section as the first test than testing both Radial section and the last test is Tangential section. Consists of 3 classes species ranging from 0 to 2. For class 0 is a Sengon species, class 1 is for Merbau species, and class 2 is for Jati species. The identification results showed little potential for CNN method, which might be caused by a small number of misclassification. The inaccurate result caused by surface similarities, such as the radial and tangential sections of Jati and Merbau. However, the accuracy results still can be improved by using an architecture that has been provided by CNN or by increasing the number of used epochs during training.

4

Conclusion

In this paper, the identification of wood species using CNN method on wood surfaces has been trained and tested. Work evaluation of CNN architecture on the training process obtained an accuracy of 89%. It was carried out three times for each surface of the wood including 96% for the cross-section, 79% for the Radial section and 88% for the Tangential section. The results were obtained for the three superior tests in cross-section. That was because of the difficulty of identifying the radial and tangential sections, both of them have a similar image. So that, for identification of wood with good accuracy results are cross-section

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wood surface, followed by Tangential section, and finally the wood surface of the radial. Although the accuracy was no better than the one in the primary reference, this paper achieved a good performance in identifying wood surfaces which each surface have different characteristics.

References 1. Barmpoutis, P., Dimitropoulos, K., Barboutis, I., Grammalidis, N., Lefakis, P.: Wood species recognition through multidimensional texture analysis. Comput. Electron. Agricul. 144, 241–248 (2018) R 2. Deng, L., Yu, D., et al.: Deep learning: methods and applications. Found. Trends Sign. Process. 7(3–4), 197–387 (2014) 3. Gonzalez, R.C.: Richard e. woods. Digit. Image Process. 2, 550–570 (2002) 4. Hadiwidjaja, M.L., Gunawan, P.H., Prakasa, E., Rianto, Y., Sugiarto, B., Wardoyo, R., Damayanti, R., Sugiyanto, K., Dewi, L.M., Astutiputri, V.F.: Developing wood identification system by local binary pattern and hough transform method. In: Journal of Physics: Conference Series. vol. 1192, p. 012053. IOP Publishing (2019) 5. Hafemann, L.G., Oliveira, L.S., Cavalin, P.: Forest species recognition using deep convolutional neural networks. In: 2014 22nd International Conference on Pattern Recognition, pp. 1103–1107. IEEE (2014) 6. Lainez, M.P.E.A., Bustamante, S.G.H., Orellana, G.C.: Deep learning applied to identification of commercial timber species from peru. In: 2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1–4. IEEE (2018) 7. Liang, G., Hong, H., Xie, W., Zheng, L.: Combining convolutional neural network with recursive neural network for blood cell image classification. IEEE Access 6, 36188–36197 (2018) 8. Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of machine learning. MIT press (2018) 9. Salma, Gunawan, P.H., Prakasa, E., Damayanti, R., Sugiyama, J., et al.: Classification of Japanese fagaceae wood based on microscopic image analysis. In: 2019 7th International Conference on Information and Communication Technology (ICoICT), pp. 1–6. IEEE (2019) 10. Salma, Gunawan, P.H., Prakasa, E., Sugiarto, B., Wardoyo, R., Rianto, Y., Damayanti, R., Dewi, L.M., et al.: Wood identification on microscopic image with daubechies wavelet method and local binary pattern. In: 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 23–27. IEEE (2018) 11. Sugiarto, B., Prakasa, E., Wardoyo, R., Damayanti, R., Dewi, L.M., Pardede, H.F., Rianto, Y., et al.: Wood identification based on histogram of oriented gradient (HOG) feature and support vector machine (svm) classifier. In: 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 337–341. IEEE (2017) 12. Suyanto: Machine Learning Tingkat Dasar dan Lanjutan. Informatika (2018) 13. Yusof, R., Ahmad, A., Khairuddin, A.S.M., Khairuddin, U., Azmi, N.M.A.N., Rosli, N.R.: Transfer learning approach in automatic tropical wood recognition system. In: International Conference on Computational & Experimental Engineering and Sciences, pp. 1225–1233. Springer (2019) 14. Zufar, M., Setiyono, B., et al.: Convolutional neural networks untuk pengenalan wajah secara real-time. Jurnal Sains dan Seni ITS 5(2), 128862 (2016)

Mathematical Model of Heat and Mass Transfer in a Colloidal Suspension with Nanoparticles Sergey Smagin1

, Polina Vinoogradova1,2 , Ilya Manzhula1(&) and Alber Livashvili2

,

1

2

Information Technology Laboratory, Computing Center FEB RAS, 680000 Khabarovsk, Russia [email protected] Department of Higher Mathematics, Far Eastern State Transport University, 680000 Khabarovsk, Russia

Abstract. Information technology has affected almost all areas of human activity. The solution of increasingly complex technical problems leads to a constant complication of electronic devices by which these tasks are solved. For the normal operation of powerful semiconductor devices and high-performance computing systems, it is necessary to ensure their effective cooling. The possibility of using nanofluids to remove excess heat in such systems is one of the main reasons for the increased interest in them around the world. The approach used in this work to the study of properties and processes in nanofluids is based on the use of mathematical modeling methods. A mathematical model of heat and mass transfer in a liquid-phase medium with nanoparticles under the influence of a light field is studied taking into account one-dimensional concentration convection in the form of an initial-boundary-value problem for a system of nonlinear partial differential equations of the second order. A finitedifference algorithm for the numerical simulation of such processes is developed and software implemented. The results can be used in the development and study of effective methods for controlling heat transfer processes. Keywords: Digital technologies
Electronic devices
Heat and mass transfer
Thermodiffusion
Nanofluid
Numerical modelling
Coefficient of thermal conductivity

1 Introduction Digital technologies are widely used in many types of human activities. Starting from providing information about various events (news feeds, transport schedules, weather forecasts), and ending with the diagnosis, maintenance and management of infrastructure facilities of varying complexity (automated enterprise management systems, putting into orbit and correction of spacecraft trajectories). The solution of increasingly complex technical problems through digital technology leads to a continuous complication of electronic devices through which these tasks are solved. An analysis of the development of electronic technologies shows that over about 10 years, the complexity © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 382–392, 2020. https://doi.org/10.1007/978-3-030-63322-6_31

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of electronic devices has increased by about 10 times. During the same time, their performance and energy consumption are growing significantly. With the development of energy-saturated electronic technologies, the need arises to create effective cooling systems and control large heat fluxes [1]. Very promising are developments related to molecular computers, which are based on switchable bistable molecules or their aggregates [2]. One of the ways to intensify heat transfer is to improve the thermophysical characteristics of the coolant. This can be achieved by increasing the thermal conductivity of the liquid by adding solid particles with high thermal conductivity. At the same time, along with thermal conductivity, other thermophysical properties of the liquid also change. An important characteristic when using flow cooling systems is the viscosity of the cooling substance. Of particular interest in the creation of such systems are nanofluids. A nanofluid is a two-phase medium consisting of a liquid and particles of a solid phase uniformly distributed in it in the nanometer size range. This term was first coined by Choi at the Argonne National Laboratory (USA) in 1995 [3]. A characteristic feature of nanofluids is a significant change in the thermophysical properties of the base fluid even at a low concentration of nanoparticles. Unlike micron-sized particles, nanoparticles are more slowly precipitated in liquids and do not lead to clogging and wear of the channels. The possibility of their use in creating effective materials to remove excess heat in cooling systems is one of the main reasons for conducting numerous studies in laboratories around the world. The use of nanofluids as an effective coolant is currently being considered as a promising method for creating new heat power plants, thermal energy transport plants, and various microelectromechanical systems. As a dispersing medium in such nanofluids, common liquid coolants can be used - water, alcohols, machine oils. As solid inclusions - metal particles - copper, iron, silver, gold and non-metallic - CuO, Al2O3, TiO2, SiO2, Fe3O4, carbon nanotubes. It should be noted that at present, technologies based on nanoparticles from phase-change materials are also actively developing. This material is a substance with a high heat of fusion, which, melting and hardening at a certain temperature, is able to store and release the heat of phase transition [4]. The creation of such a material as core-shell particles (particles consisting of a core and one or several shells) has significantly expanded the scope of nanofluids widely used in various modern technologies. For example, magnetic fluids are used to polish optical components [5], while suspensions of silicon dioxide particles in liquid crystals significantly improve the characteristics of optical storage devices [6]. Nanofluids based on such materials occupy a special place in biology and medicine, where they are used in technologies for the transport of drugs and hyperthermia [3–5]. We also note their use in chemical processes (catalysis), in the creation of lubricants, etc. Methods for producing nanofluids can be divided into two groups - one-stage and two-stage [7, 8]. In the first case, nanoparticles are obtained and distributed directly in a liquid by chemical methods or by evaporation in a liquid. In the second case, nanoparticles are first obtained by various methods, then dispersed in a carrier fluid using ultrasound or a magnetic field. Each method has its advantages and disadvantages, however, the two-stage methods of production are of practical interest, since they can be implemented on an industrial scale.

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Despite numerous studies over the past two decades, many experimental data obtained to date for the thermophysical properties of nanofluids have a significant scatter and often contradict each other. This is due to the complexity of the description of processes occurring in nanofluids. For example, during the experiment it is difficult to say about the degree of uniformity of the distribution of particles and the number of particles stuck together or settled. It is also important to take into account the rheology of nanofluids, since a large concentration of particles, a non-spherical shape, or a small diameter, individually or in combination, can lead to a transition from Newtonian to non-Newtonian fluid behavior. Difficulties also arise in the theoretical description of the thermophysical properties of nanofluids. At present, many models have been proposed to describe the thermal conductivity of nanofluids, but the question of the exact description of the thermal conductivity mechanism has not yet been solved. To describe the thermal properties of suspensions based on larger particles, the classical Maxwell theory [9] is used. This theory was proposed for suspensions based on fixed spherical micro- and macro particles of low concentration. For nanofluids, there are a number of additional factors affecting their thermal conductivity, which casts doubt on the possibility of using this theory to describe them. Maxwell’s theory is a classic description of the thermal conductivity of suspensions. Maxwell solved the Laplace equations for temperature fields, neglecting the interaction between particles, and proposed a model that predicts that the effective thermal conductivity of suspensions containing fixed spherical particles increases with increasing volume concentration of solid particles [9]. In a number of publications [10–13], the dynamics of nanoparticles in a colloidal suspension is located under the influence of a light field was studied. In these works, various aspects of non-stationary problems of heat and mass transfer were considered. We note that the approaches used by the authors of these works were limited by the framework of small perturbations and linear approximation. The presence of the above problems was an incentive to write this work. It is organized as follows. The section “Problem statement” presents the statement of the initial-boundary-value problem for the nonlinear diffusion equation without taking into account concentration convection. The section “Results of a numerical experiment” is devoted to numerical modeling of the evolution of the concentration of nanoparticles in a liquid exposed to a light field, taking into account the nonlinear thermodiffusion term, and visualization of the results of computational experiments. In conclusion, a discussion is held of the results and conclusions are formulated.

2 Statement of the Problem The paper considers a liquid-phase medium with nanoparticles irradiated with a light beam. We believe that the light wavelength is significantly larger than the characteristic sizes of the nanoparticles. Thus, we do not consider the processes of diffraction and light scattering, we also exclude the processes of sedimentation of particles. As is known, as a result of the action of a light field in the medium, temperature and concentration gradients arise, which determine the processes of heat and mass transfer. These phenomena are described by a nonlinear system of balance equations for

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medium temperature T and particle concentration C, which in the case of a single spatial variable x 2 (0, L) have the form [14, 15]: V


 @T @ @T ¼ k þ aI; @t @x @x

ð1Þ


 @C @ @C @T @I ¼ D þ DT Cð1  CÞ  cC : @t @x @x @x @x

ð2Þ

The following notation is used here: t - time, 0\t  T, V ¼ ð1  C Þql Cl þ Cqp Cp , Cl , ql , Cp , qp - heat capacity and density of the accommodating liquid and nanoparticle material, k - heat conductivity coefficient, I ¼ I ð xÞ - light flux intensity, D; DT - dif4pbD , c - speed of light in vacuum, nef fusion and thermal diffusion coefficients, c ¼ cn ef kT effective refractive index of a medium, k - Boltzmann constant, b - particle polarize e ability, b ¼ el ep pþ 2el l a3p , ep ; el - dielectric constant particles and liquids, ap is the linear

size of the nanoparticle, a is the absorption coefficient of the light wave, depending on the chemical nature medium and the wavelength of transmitted light. We consider Eq. (2) in more detail. The first term in its right-hand side is responsible for the particle flux caused by molecular diffusion, the second term is the particle flux caused by thermal diffusion, the last term is the particle flux due to the action of the gradient force from the side of the light field (particle electrostriction). Note that in the Eq. (1) there is no term responsible for the Dufour effect, due to its smallness. To close the initial-boundary-value problem, we formulate the initial and boundary conditions for Eqs. (1) and (2). The initial values of temperature and concentration will be considered constant: T ðx; 0Þ ¼ T0 ;

Cðx; 0Þ ¼ C0 ;

x 2 ½0; L:

ð3Þ

We also assume that at x ¼ 0 there is no heat transfer, and at x ¼ L we take into account heat transfer according to the Newton – Richmann law [16]: 
  @T  @T  rT  ¼ 0; t 2 ½0; T : k  ¼ 0; k ð4Þ @x x¼0 @x x¼L The considered physical processes are considered closed, i.e. assumed lack of interaction with the environment. Therefore, the sum of convection, thermal diffusion, and electrostrictive flows at the boundaries of the considered region should be equal to 0:
 @C @T @I  þ DT C ð1  CÞ  cC D ¼ 0; @x @x @x x¼0;L

t 2 ½0; T :

ð5Þ

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Hence, taking into account the boundary conditions (4) and assuming that @I=@xjx¼0 ¼ 0 we get:  @C  D  ¼ 0; @x x¼0




 @C @I   cC D ¼ 0; @x @x x¼L

t 2 ½0; T :

ð6Þ

As a result, we obtain the following initial-boundary-value problem for determining the distribution of the medium temperature T and particle concentration C:
 @T @ @T ¼ k þ aI; @t @x @x
 @C @ @C @T @I ¼ D þ DT Cð1  CÞ  cC ; @t @x @x @x @x V

x 2 ð0; LÞ;

t 2 ð0; T  ;

x 2 ð0; LÞ;

t 2 ð0; T  ;

T ðx; 0Þ ¼ T0 ; C ðx; 0Þ ¼ C0 ; 
  @T  @T  rT  ¼ 0; k  ¼ 0; k @x @x  x¼0
x¼L @C @C @I   cC D ¼ 0; D  ¼ 0; @x @x @x  x¼0

x¼L

x 2 ½0; L ; ;

ð7Þ

t 2 ½0; T  ; t 2 ½0; T  :

3 The Finite-Difference Algorithm of Numerical Simulation We introduce the following notation: U ðx; tÞ ¼ T ðx; tÞ  T0 ; g ¼

c @I D @x

ð8Þ

In the previously posed initial-boundary-value problem, we replace the temperature T with the expression U þ T0 . As a result, we obtain the initial-boundary value problem for determining U and C:
 @U @ @U ¼ k V þ aI; x 2 ð0; LÞ; @t @x @x 
  @U  @U  rðU þ T0 Þ  ¼ 0; ¼ 0; k k @x x¼0 @x x¼L U ðx; 0Þ ¼ 0;

t 2 ð0; T  ; t 2 ½0; T  ;

ð9Þ

x 2 ½0; L ;


 @C @ @C @U ¼ D þ DT Cð1  CÞ  gC ; x 2 ð0; LÞ; @t @x @x @x 
  @C  @C  gC  ¼ 0; D  ¼ 0; D @x x¼0 @x x¼L C ðx; 0Þ ¼ C0 ;

t 2 ð0; T  ; t 2 ½0; T  ; x 2 ½0; L :

ð10Þ

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Note that in the absence of a dependence of the coefficients of Eq. (1) on the concentration C, this system is significantly simplified. In this case, the problem of finding the temperature distribution can be solved independently of solving the equation for the concentration of particles. For the numerical solution of nonlinear evolution equations, finite difference methods are most widely used [17, 18]. The finite difference method physically means the transition from a continuous medium to some of its discrete models. The main requirement of such a transition is to preserve the properties of the physical process. In this case, difference schemes expressing conservation laws on the grid are called conservative. To obtain conservative difference schemes, it is natural to proceed from the balance equations written for the elementary volumes of the grid domain. The integrals and derivatives entering into these equations should be replaced by approximate difference expressions. The result of this replacement will be a uniform difference scheme. We set h ¼ L=N; s ¼ T=M - the grid steps xhs along x and t, respectively, where N, M are natural numbers, xhs ¼ fðxi ; tk Þ : i ¼ 0; 1; . . .; N; k ¼ 0; 1; . . .; M g is the set of nodal points of the grid region (grid), xi ¼ ih; tk ¼ ks. To simplify the further notation, we will use the following notation for grid functions and expressions containing finitedifference derivatives    k    k k k k k k Fi;x h ; Fi; h; Fi;x ð2hÞ ; ¼ Fik  Fi1 ¼ Fikþ 1  Fi1 x ¼ Fi þ 1  Fi 

 k k k k aki Fi;x ¼ akiþ 1 Fi; h ; x  ai Fi;x  x     Fi;kt ¼ 2 Fik  Fik0:5 s; Fi;kt ¼ 2 Fik þ 0:5  Fik s ;

ð11Þ

where Fik ¼ F ðxi ; tk Þ, tk0:5 ¼ tk  0:5s. The nonlinearity of Eqs. (9) and (10) imposes serious restrictions on the difference methods used. The problem of constructing efficient and economical algorithms for numerical solutions for such equations remains relevant today. The use of explicit difference schemes for the numerical solution of the problem here is inefficient due to the strict restrictions on their stability, therefore, when solving the problem, it is preferable to use implicit schemes with relatively weak stability restrictions. To simplify further writing, we put vki

1 ¼ sh

Ztk þ 1 xZi þ 0:5

fik

V ðx; tÞdxdt; tk

xi0:5

 k 1 1 ai ¼ sh

Ztk þ 1 Zxi tk

xi1

  k 1 k ski ¼ DkT;i 1  Cik Ui; x  gi ¼ s

dxdt ; kðx; tÞ

Ztk þ 1 tk

1 ¼ sh

Ztk þ 1 xZi þ 0:5 aðx; tÞI ðx; tÞdxdt ; tk

xi0:5

 k 1 1 di ¼ sh

Ztk þ 1 Zxi tk

xi1

dxdt ; Dðx; tÞ

  k DT ðt; xi Þ 1  Cik Ui; x þ gðt; xi Þ dt ;

ð12Þ

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where all integrands are complemented by zero outside the interval ½0; L. Equations (9) and (10) will be approximated by the following difference equations: 2vki Ui;kt ¼



 kþ1 k aki Ui;x þ Ui;x þ 2fik ; i ¼ 1; . . .; N  1; x





kþ1 k k k k ak0 U0; x þ U0;x ¼ h v0 U0;t þ f0 ;


  k kþ1 k k k aN UN;x þ UN;x  rkN UNk þ 1 þ UNk þ 2T0 ¼ h vkN UN; t þ fN ;

ð13Þ

Ui0 ¼ 0; i ¼ 0; . . .; N; k ¼ 0; . . .; M  1; 2Ci;kt 2d0k

1 X l¼0



    k kþ1 k ¼ di Ci;x þ Ci;x þ Ski Cik þ 1 þ Cik i;x ; i ¼ 1; . . .; N  1; x

1 1

X X kþl k k k kþl k k k C0; þ 0:5 D U  g C  D U Cik þ 1 Cik ¼ hC0; t ; x T;0:5 0;x 0:5 i T;0:5 0;x i¼0

i;l¼0

ð14Þ 2dNk

1 X l¼0

kþl CN;x 

k ¼ hCN; t


X 1 1 X k k k kþl k kþ1 k þ 0:5 DT;N0:5 UN;x  gN0:5 CNi  DkT;N0:5 UN;x CNi CNi  i;l¼0

i¼0

Ci0 ¼ C0 ; i ¼ 0; . . .; N; k ¼ 0; . . .; M  1: The finite-difference scheme under consideration is implicit.

4 Results of a Numerical Experiment We will study the dynamics of nanoparticles, assuming that mass transfer processes occur against the background of a steady temperature. In this case, we proceed from the inequality sT \\sD , which gives this opportunity. Thus, in the heat equation we put @T=@t ¼ 0. Let us imagine the concentration dependence of the thermal conductivity in the form kðCÞ ¼ k0 ð1 þ pCÞ ; where p ¼ b=k0 and pC\1, k0 is the coefficient of thermal conductivity of the liquid. This kind of dependence was theoretically found in [12] and experimentally confirmed in publications [10–12]. Note that in [12], a model problem was studied on the conditions of bistable behavior of such a nanofluid. In the process of solving the

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problem, it was reduced to a parabolic equation with constant coefficients with cubic nonlinearity (Fig. 3). The luminous flow affecting the liquid-phase medium has a Gaussian intensity profile, i.e.    I ¼ I0 exp x2 r02 ; where r0 is the minimum value of the radius of curvature of the wave front. Note that the dynamics of particles in nanosuspension was also studied in [13], a linearized equation was considered with a dominant contribution from particle convection, but unlike the described approach, the authors of [13] solved the problem analytically. The results of the solutions indicated the existence of concentration waves under these conditions. As the base fluid, we will consider distilled water with latex particles. The particle size of latex is 10 – 100 nm. The choice of starting materials was determined by a number of factors: practical interest; the presence of experimental data obtained during the full-scale experiment, performing the role of test data to verify the correctness of the constructed mathematical model. For the selected medium and particles, we assume the following values of the input parameters: T ¼ 10 s; L ¼ 100 mm; p  1:5; k0  0:62 W=m
k; a0  0:06; eP ¼ 2:52 F=m; el ¼ 1:77 F=m ; nef ¼ 0:2 ; D  1:64: In order to conduct a numerical experiment, a numerical simulation algorithm was developed and programmed, and the results obtained using the supercomputer of the Computing Center of the Far Eastern Branch of the Russian Academy of Sciences [19] are presented below. Figure 1 shows the time dependences of the solution for fixed spatial coordinates, for x ¼ 30 the solid line, and for x ¼ 70 the dashed line. The shape of the curves shows that the nature of the transition processes strongly depends on the origin: near the origin, the corresponding curve has a pronounced maximum. Over time, both curves almost correspond to the same concentration values. Figure 2 shows that over time, the “spreading” of the initial Gaussian distribution of particle concentration occurs. At the same time, portions of space far from the origin are involved in the transfer processes. It can be seen from the numerical experiment that the use of nanofluids makes it possible to intensify the local heat transfer over the entire length of the cell by more than 10%. Similar dependences are obtained for other nanofluids (the experimental results are not shown in view of the limited publication volume), but the effect decreases with decreasing nanoparticle concentration. This is due to a change in the density and viscosity coefficient of the nanofluid compared with the corresponding values for water.

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Fig. 1. Time dependences of the solution of problem (9) – (10). The curves are presented with the following values of the spatial variable: x1 ¼ 30; x2 ¼ 70

Fig. 2. Coordinate dependences of the solution to problem (9) – (10). Curves are presented for time points: t1 ¼ 0:02; t2 ¼ 3; t3 ¼ 7.

Fig. 3. Graphic dependence of the numerical solution of problem (9) – (10) on the spatial coordinate and time.

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The graphical dependence of the numerical solution of problem (9) – (10) on the spatial coordinate and time is shown in Fig. 3.

5 Conclusion The paper was formulated and solved the problem numerically study the evolution of the concentration of nanoparticles in the liquid, the exposed light field. It was taken into account dependence of the thermal conductivity of the concentration. The studied problem was considered against a background of stationary temperature. The problem was solved numerically using the predictor-corrector method with second-order accuracy. An analysis of the solution describing the unsteady concentration dynamics indicates the existence of a point in time from which the solution (particle concentration) is stabilized. In contrast to the above-mentioned work [13], in the nonlinear approach used by the authors of this article, oscillatory solutions were not revealed. The analysis showed that the determining parameter of heat transfer processes in nanofluids is dimensionless, a parameter that includes the particle size, thermal conductivity of the base fluid, and the contact resistance between the fluid and the particle material. The study showed that it is too early to talk about the use of colloidal nanosuspensions (nanofluids) as a thermal interface for protecting components of electronic devices from overheating. This can be justified by the lack of systematic experimental data on the heat transfer coefficient of nanofluids, which requires an appeal to mathematical modeling methods in this area, since it is this methodology that eliminates the need for complex field experiments and the purchase of expensive laboratory equipment for these experiments. The difficulty lies in the fact that the study of the heat transfer coefficient is a complex task, taking into account the viscosity of the nanofluid. All this suggests that for a more complete understanding of the processes of heat and mass transfer in nanofluids taking into account various physical phenomena that can occur when using the developed nanofluid as a thermal interface to protect the components of electronic devices from overheating, it is necessary to complicate the mathematical model described in the work. Unfortunately, the limited volume of the publication did not allow us to present in more detail the results describing the effect of the magnitude of the electrostrictive flow in the problem (9) - (10) on its solution. We did not consider cases of large Peclet numbers at which the influence of the boundary layer phenomenon begins. We plan to present all these unresolved issues in the following publications. Acknowledgment. The reported study was funded by RFBR, project number 19-31-90070. This research was supported in through computational resources provided by the Shared Services Center « Data Center of FEB RAS » (Khabarovsk) [19].

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References 1. El-Ganainy, E., Christodoulides, D.N., Rotschild, C., Segev, M.: Soliton dynamics and selnduced transparency in nonlinear nanosuspensions. Opt. Express 15, 12207–12218 (2007) 2. Livashvili, A.I., Krishtop, V.V., Karpets, Y.M., Bryuhanova, T.N., Kireeva, N.M.: Laser beam-induced bistability of concentration in nanouids. J. Phys: Conf. Ser. 737(1), 012011 (2016) 3. Chol, S.U.S., Estman, J.A.: Enhancing thermal conductivity of fluids with nanoparticles. ASME-Publications-Fed, vol. 231, pp. 99–106 (1995) 4. Hasenöhrl, T.: An Introduction to Phase Change Materials as Heat Storage Mediums. Project Report, vol. 160 (2009) 5. Ghosh Chaudhuri, R., Paria, S.: Core/shell nanoparticles: classes, properties, synthesis mechanisms, characterization, and applications. Chem. Rev. 112(4), 2373–2433 (2011) 6. McGill, S.L., et al.: Magnetically responsive nanoparticles for drug delivery applications using low magnetic field strengths. IEEE Trans. Nano Biosci. 8(1), 33–42 (2009) 7. Yu, W., Xie, H.: A review on nanofluids: preparation, stability mechanisms, and applications. J. Nanomater. 2012, 1 (2012) 8. Ramesh, G., Prabhu, N.K.: Review of thermo-physical properties, wetting and heat transfer characteristics of nanofluids and their applicability in industrial quench heat treatment. Nanoscale Res. Lett. 6(1), 334 (2011) 9. Maxwell, J.C.: A Treatise on Electricity and Magnetism, 1st edn. Clarendon Press, United Kingdom (1881) 10. Livashvili, A.I., Krishtop, V.V., Karpets, Y.M., Bryuhanova, T.N., Kireeva, N.M.: Laser beam-induced bistability of concentration in nanouids. J. Phys: Conf. Ser. 737(1), 012011 (2016) 11. Krishtop, V.V., Livashvili, A.I., Vinogradova, P.V., Kostina, G.V., Bryukhanova, T.N.: Dynamics of nanoparticle concentration in nanouids under laser light eld. IOP Conf. Ser. J. Phys. Conf. Ser. 936, 012079 (2017) 12. Ivanov, V.I., Livashvili, A.I.: Self-action of a Gaussian radiation beam in a layer of a liquidphase microheterogeneous medium. Atmos. Oceanic Opt. 23(1), 7–8 (2010) 13. Livashvili, A.I., Kostina, G.V., Yakunina, M.Y.: Temperature dynamics of a transparent nanoliquid acted on by a periodic light field. J. Opt. Technol. 80(2), 124–126 (2013) 14. de Groot, S.R., Mazur, P.: Non-Equilibrium Thermodynamics, p. 510. Courier Corporation, United States (1984) 15. Minakov, A.V., Rudyak, V.Y., Guzey, D.V., Lobasov, A.S.: Measurement of the heat transfer coefficient of a nanofluid based on water and particles of copper oxide. TVT 53(2), 256–263 (2015). https://doi.org/10.7868/S0040364415020167 16. Incropera, F., Bergman, T.L., DeWitt, D., Lavine, A.S.: Fundamentals of Heat and Mass Transfer, 6th edn., pp. 260–261. John Wiley & Sons, Hoboken (2007) 17. Matus, P.P., Le Minh, H., Vulkov, L.G.: Analysis of second order dierence schemes on nonuniform grids for quasilinear parabolic equations. J. Comput. Appl. Math. 310, 186–199 (2017) 18. Shi, D.: Numerical methods in problems of heat exchange. Moscow, 544 (1988) 19. Sorokin, A.A., Makogonov, S.V., Korolev, S.P.: The information infrastructure for collective scientific work in the far East of Russia. Sci. Tech. Inf. Process. 44(4), 302– 304 (2017)

Review of Current Data Mining Techniques Used in the Software Effort Estimation Julius Olufemi Ogunleye(&) Tomas Bata University in Zlin, Nad Stranemi 4511, 760 05 Zlín, Czech Republic [email protected]

Abstract. Data Mining is a method of finding patterns from vast quantities of data and information. The data sources include databases, data centers, the internet, and other data storage forms; or data that is dynamically streaming into the network. Estimation of effort is very important in the cost estimation of a software development project, and very critical in the software life development cycle planning process. This paper offers a description of the latest data mining techniques used in estimating software effort, and these techniques are divided into two, namely: Classical and Modern, based on when they were developed and when they started to be used in business administration. The Classical techniques are the ones that have been in use for decades and are still relevant until today, while the Modern ones are the ones that have been introduced recently and have gained wide acceptance in the system. The Classical techniques are Statistical methods, Nearest Neighbours, Clustering and Regression Analysis, while Neural Networks, Rule Induction Systems and Decision Trees are included in the Modern techniques. This paper offers an overview of these strategies in terms of their features, benefits, drawbacks and use areas. Keywords: Software effort estimation
Data mining techniques
Regression analysis
Classification techniques
Clustering techniques
Neural networks
Nearest neighbours
Decision trees
Rule induction systems

1 Introduction Today’s advances paved the way for an automated extraction of hidden predictive information from databases, along with many other fields of knowledge such as analytics, artificial intelligence, machine learning, database management, data visualization and recognition patterns. Through data mining, a person can use different analytical methods, data analysis and machine learning to explore and analyze large data sets, thereby extracting new and useful knowledge that will improve decision-making processes [1]. Companies in information technology are currently using data mining techniques in different fields with the goal of increasing decision-making efficiency and enhancing business results. The amount of data produced and processed is rising exponentially, owing in large part to the continuing developments in computer technology. This provides immense opportunities for those who are able to access the knowledge hidden in this data but also poses new challenges. This paper is a review on the latest © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 393–408, 2020. https://doi.org/10.1007/978-3-030-63322-6_32

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methods/techniques for data mining used in estimating the program effort. The methods/techniques use simple cognitive metrics which include all of the software’s important parameters. This will serve as a reference for the professionals involved in the software development process in cost estimation, time schedule, and manpower requirement. It would be prudent, therefore, to use these estimates as additional feedback in the decision-making process. Since this paper is a review of current data mining techniques, the research methodology is based on secondary data. Techniques of data extraction include data research, data reformatting and data restructuring. The structure of the necessary information is dependent on the methodology and the research to be carried out. Finally, all the techniques, approaches and frameworks of data mining help to explore new innovative technologies. 1.1

Data Mining

The process of finding trends in large data sets involving approaches at the intersection of machine learning, statistics, and database systems [2] is data mining, also referred to as data or information discovery. It could also be defined as a computer science and statistics interdisciplinary subfield with an overall objective of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use [3]. The functionalities of data mining are used to define the patterns to be found in data mining tasks. Data mining activities can usually be divided into two categories: descriptive, and predictive. Descriptive mining activities are characteristic of the general data resources in the database. Predictive mining tasks conclude on the current data to make predictions [4]. Data mining benefits cover nearly every facet of life that includes; gaming, police, industry, research, engineering, human rights organizations, and surveillance. The best way to gain an understanding of data mining is to consider the types of tasks, or issues, it can solve. The advantages of data mining techniques emphasize their importance in software effort estimation (Table 1).

Table 1. Advantages and disadvantages of Data Mining Techniques Advantages • Helps to predict future trends • Signifies customer habits • Helps in decision making • Increases company revenue • Depends on marketbased analysis • Quickly detects fraud

Disadvantages • Violates user privacy (Information collected through data mining and intended for ethical purposes can be misused) • Uses additional irrelevant information • Information can be misused • Accuracy of data is within its own limits

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1.2

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Software Development Effort Estimation

Cost estimation of software development is very important for IT professionals and it’s an important task that affects an organization’s software investment decisions. It is a job that must be completed before any contract is entered into, or the dedication of the resources committed to any project. Both developers and customers need precise estimates of the cost of software to make far-reaching project decisions. Precise estimates of the cost of software can be used to make proposals and schedule, monitor and control requests. Although several software projects have been developed for accurate cost estimation purposes, it is still difficult to claim that there is a specific model that can offer estimation close to the actual cost. In reality, over-estimated or underestimated costs can result in the production delay of the final software product, inefficient resource use, poor software project quality or unexpected budget increase. Therefore, it is difficult to make correct decisions [5].

2 Related Works 2.1

Analysis of Data Mining Techniques for Software Effort Estimation

Under Sehra, S. K. et al. (2014), Software effort estimation requires a high degree of precision but, sadly, accurate estimates cannot be easily obtained. The use of data mining to enhance software process efficiency for an enterprise is on the rise. There are several different method combinations available when conducting software effort estimation, but it had become difficult to pick the most appropriate combination. The analysis offered opportunities for how data pre-processing was applied and effort estimation using the COCOMO Model. OLS Regression and K-Means Clustering data mining techniques were subsequently applied on preprocessed data and were correlated with results obtained. Implementing the data mining techniques on pre-processed data was more effective than OLS Regression Technique [6]. 2.2

Data Mining Techniques for Software Effort Estimation: A Comparative Study

Dejaeger K. et al. (2012) considered that a predictive model had to be reliable and easily understandable in order to inspire trust in a business environment. Although both aspects (accuracy and understanding) were evaluated by previous studies in a software effort estimate environment, no definitive conclusion was drawn as to which technique was the most suitable. This issue was dealt with as a benchmark through reports of the results of a large-scale study. There were various types of techniques under consideration, including tree or rule-based models such as M5 and CART, linear models (linear regression), nonlinear models (MARS, multilayered perceptron neural networks, radial base function networks, and least squares support vector machines), and several other inference techniques that do not directly trigger a model (e.g. case-based reasoning). Additionally, the function subset selection aspect was investigated using a generic backward input selection wrapper. The results were subjected to rigorous statistical testing which suggested that the best results were actually obtained by

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ordinary least square regression in combination with a logarithmic transformation. In addition, another important finding was that a substantial increase in estimation accuracy can be achieved when selecting a subset of highly predictive attributes such as project size, growth, and environment-related attributes [7]. 2.3

Software Test Effort Estimation

D. S. Kushwaha and Misra A.K. (2008) demonstrated that software testing is an important software development process and is carried out to help and enhance the reliability and consistency of the program. The method involves estimating the test effort, choosing the correct test team, planning test cases, executing the program with the test cases and reviewing the results provided by those executions. Statistics indicate that more than fifty per cent of the software development cost is spent on testing, with the figure being higher for essential software testing. Unless we can predict the testing effort and find effective ways to perform effective testing, there will be a significant increase in the percentage of development costs spent on testing, coupled with the project costing and development schedule mismatch. This paper sought to establish the Cognitive Information Complexity Measurement (CICM) as an appropriate estimation tool for estimating the test effort [8].

3 Methods Data scientists currently use multiple data mining techniques, and these techniques vary from each other based on their accuracy, performance, and the type and/or volume of data available for analysis [9]. These techniques can be classified into the Classical and Modern techniques of data mining. 3.1

Classical Techniques

Statistical Techniques Statistics is a branch of mathematics related to data collection and explanation. Software mining is not statistics or mathematical techniques. They were being used to apply to business applications long before the term data mining was coined. Nevertheless, the data drive statistical techniques and are used to discover trends and to construct predictive models. And from the user viewpoint, when solving a data mining problem, you’ll face a conscious decision as to whether you want to solve it using statistical methods or other data mining techniques (Fig. 1). Statistics can be of great help in answering various important data questions, namely: 1. 2. 3. 4.

What trends to my database are there? What is the probability there will be an event? Which patterns are meaningful? What is the high-level overview of the data that gives me some insight into what my database contains?

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There are several different aspects of statistics but sometimes the principle of gathering and recording data is at the core of all these more complex techniques. The first step then in understanding statistics is to consider how the data is obtained in a higher-level context-with the histogram one of the most prominent ways to do so. A histogram provides an alternative way to show a quantitative variable distribution. Histograms are of particular interest to vast sets of data. A histogram divides the values of the variable into intervals of equal dimensions. Within each interval we can see the number of individuals [10].

Fig. 1. A histogram showing the relative size of software projects and their frequencies [11].

This histogram shows clearly that the majority of the software projects in selected dataset are projects known as M1 i.e. their sizes are from interval [11]. Some of the overview statistics most widely used include: • • • •

Max - maximum value for a predictor given. Min - the minimum of a given predictor value. Mean - average of a given predictor value. Median - the value for a given statistic that splits the database into two sets with equivalent numbers with records as close as possible. • Mode - most common indicator value. • Variance - a calculation of how the average value is spread out. Numbers can also be used for study of Predictions and Linear Regression.

Nearest Neighbours This is one of the oldest data-processing techniques used. This is a supervised learning technique that is commonly used for predictions, and during a Euclidean space, instances are typically represented as points. The Nearest neighbor is defined in terms of Euclidean distance, e.g. distance(x1, x2), and the target function may be evaluated in

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a discrete or actual way. Distance-weighted nearest neighbour algorithm weight the contribution of every of the k neighbours consistent with their distance to the query and give greater weight to closer neighbours. Nearest neighboring technique by averaging k-nearest neighbors is robust to noisy results, and could be a predictive technique that is somewhat like clustering. Objects close to each other at least would have similar predictive values, so one object’s predictive value can be used to predict that of its nearest neighbor. The nearest neighbor was commonly used in text retrieval for prediction. Space is described by the problem to be solved (supervised learning), and it uses distance metrics generally to evaluate closeness. Clustering Clustering is one of the oldest methods used in data mining. It is an example of unsupervised learning, that is, the class labels are not present in the training because it is not understood to start with. It is the grouping of similar/related data points or records from raw, unlabeled data, based on the concept of maximizing object homogeneity in the same group or class and minimizing object heterogeneity in the different groups or classes. Often clustering is used instead of segmentation which gives a general overview of the data set. The cluster analysis output is a collection of groups (clusters) that form a partition or partition structure of the data set, and it is a simplified definition of each cluster that is particularly important for a deeper analysis of the data set’s characteristics (Fig. 2). Cluster hierarchy is typically seen as a tree where the smallest clusters merge to create the next higher level of clusters, and those at that level merge to create the next higher level of clusters. There are two major types of strategies for clustering, namely: hierarchical and nonhierarchical. • The hierarchical clustering techniques establish cluster hierarchy from the smallest to the largest. Hierarchical clusters are specified purely by data (not by the users predetermining the number of clusters), and by simply going up and down the hierarchy, the number of clusters may be increased or decreased. • There are two major non-hierarchical clustering methods, both of which can be measured very easily on the database but have some disadvantages. The first is the single pass method which derives its name from the fact that in order to construct the clusters, the database must only be passed through once (i.e. each record is only read once from the database). The other class of techniques is called methods of relocation that derive their name from the movement or “reallocation” of records from one cluster to another to establish better clusters. The reallocation technique is faster than the hierarchical technique and uses multiple passes through the database [12]. • In clustering, space is either defined as the default n-dimensional space, or is defined by the user, or a predefined space driven by previous experience (unsupervised learning) and it can use other metrics besides distance to evaluate the closeness of two records, e.g. to link two points together.

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Fig. 2. Diagrammatic representation of the hierarchy

Regression Analysis This form of analysis is supervised and determines which item sets are linked to or separate from each other among the different relationships. It can predict human actions, sales, income, temperature, etc. It has an already known data set value. When an input is given, the input and expected value will be compared with the regression algorithm, and the error will be determined to get to the exact result. Regression is generally synonymous with regression of some sort in the statistics. The purpose of the regression analysis is to find the best model which can relate the output variable to different input variables. This analysis is the method of evaluating the relationship between a variable Y and one or more other variables: X1, X2, to Xn. The dependent variable (or response output) is Y, while X1 to Xn are the independent variables or inputs. This technique is used to establish the dependence between the two variables so that causal relationship can be used to predict the outcome, and it helps to know the dependent variable’s characteristic value. Regression is commonly used for forecasting and prediction. A model is generated in regression which maps values from predictors in such a way that the lowest error occurs when making a prediction. Simple linear regression is the simplest type of regression which contains just one predictor and one prediction [12].

Fig. 3. Illustration example of linear Regression on a set of data

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Modern Techniques

Neural Network This is an effective predictive modeling technique but some of the power comes at the cost of user-friendliness and ease of use. It is an unsupervised learning method and during the formative stages of data mining technology has possibly been of greater interest than Decision trees. It produces very complex structures that are almost always difficult for even experts to completely understand. In a complex calculation, the model itself is defined by numeric values that allow all of the predictor values to be in the form of a number. Neural network performance is always numerical and must be translated if the real predictive value is categorical [13].

Fig. 4. Example of a neural network

Rule Induction Rule induction is a field of machine learning, in which a series of observations extract formal laws. The rules extracted can represent a complete data science model, or merely reflect local trends in the data. Rule induction generates rules that are not mutually exclusive and may be necessarily exhaustive, and produces a model based on If – Then – Else style rules. It can function with numerical values as well as categorical values and the models have a number of input variables and one or more output variables, but are different from the neural networks in that we can actually see within the model and how it generates the output or outcome. The models and laws are typically built from decision trees in the rule induction data models. Rule induction is the most common form of discovery of knowledge in highly automated unsupervised learning systems, and is possibly the best form of data mining techniques for discovering all possible predictive patterns in a database. This can be modified for use in problems of prediction but the algorithms for integrating evidence from a variety of rules come more from thumb rules and practical experience (Fig. 3). Decision Trees This technique is used for categorizing or predicting data, and it produces rules that are mutually exclusive and collectively exhaustive with respect to the training database. The root of a decision tree is a simple question but have multiple answers. A decision tree is a predictive model that, as its name implies, can be viewed as a tree and each branch of the tree is a classification question and the leaves of the tree are partitions of the dataset with their classification. Decision tree algorithms tend to automate the entire

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process of hypothesis generation and validation much more completely, and in a much more integrated way than any other data mining technique. They are also particularly adept at handling raw data with little or no pre-processing. Perhaps also because they were originally developed to mimic the way an analyst interactively performs data mining, they provide a simple to understand predictive model based on rules. They can be used in a wide variety of business problems for both exploration and prediction. Due to their tree structure and ability to easily generate rules, decision trees are the favored technique for building understandable models. As a result of this clarity Decision trees also allow for more complex profit and ROI (Return-On-Investment) models to be added easily on top of the predictive model. Because of their high level of automation and the ease of translating decision tree models into SQL for deployment in relational databases, the technology has also proven to be easy to integrate with existing IT processes, requiring little preprocessing and cleansing of the data, or extraction of a special purpose file specifically for data mining [13] (Figs. 4 and 5).

Fig. 5. Example of a decision tree

4 Discussion With an immense amount of data being collected every day, the businesses are now involved in figuring out the patterns from them. The methods for extracting data help turn the raw data into usable information. Computer software is needed to mine massive volumes of data, because it is difficult for a person to go through the vast volume of data manually. Below are illustrations on the areas of strengths and weaknesses of the traditional and modern data mining techniques: 4.1

Statistical Techniques

Advantages • Because secondary data is usually cheap and it takes less time because someone else has compiled it. • The trends and similarities are evident and consistent. • Taken from large samples, to ensure high generalizability. • Can be used many times to check different variables. • Changes which improve reliability and representativeness can be imitated to test.

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Disadvantages • The researcher cannot test the validity and cannot consider a causation theory mechanism only to draw trends and associations from the data • Statistical data are often secondary data, meaning that misinterpretation is simple. • Statistical data is subject to manipulation and can be distorted and phrased to make the argument the researcher wishes to prove (affects objectivity). • Since these are often secondary data, it is difficult to access and verify. 4.2

Nearest Neighbours

Advantages • • • • • • •

Pretty easy and intuitive Does not have any assumptions No Education Move It grows continuously Multi-class problem is very easy to implement. Can be used for Regression and Classification. It might take some time to select the first hyper parameter but the rest of the parameters are aligned with it after that. • Has different distance parameters (Euclidean distance, Hamming distance, Manhattan distance, Minkowski distance) to choose from.

Disadvantages • Irrelevant attributes could dominate the distance between neighbors. • Implementation might be very easy but efficiency (or speed of algorithm) declines very fast with the growth of the dataset. • Can accommodate small number of input variables but as the number of variables grows, the algorithm finds it difficult to predict the output of new data point. • Features need to be homogeneous • In the classification of new data entry, there is usually the problem of choosing an optimal number of neighbours to be considered. • Problems with the use of imbalanced data. • Because neighbours are simply chosen based on distance criteria, it is sensitive to outliers. • Cannot deal with missing value problem because it inherently has no capability to do so. 4.3

Clustering

Advantages • Hierarchical approaches allow the end user to choose between multiple clusters or only a few. • Suitable for arbitrarily-shaped data set and arbitrary type attribute. • The hierarchical relationship between clusters is easy to identify, with fairly high scalability.

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• Various and well-developed models provide a means to adequately describe the data, and each model has its own special characters that may bring significant advantages in certain specific areas. Disadvantages • Usually has a fairly high length of time. • Cluster numbers must be preset. • The assumption is not entirely right and the clustering outcome is sensitive to the parameters of the chosen models. 4.4

Regression Analysis (MARS- Multivariate Analysis for Regression Splines, OLS - Ordinary Least Square Regression, SVR-Support Vector Regression, Radial Basis Function Networks)

Advantages • Some very easy problems can be solved much faster and simpler by linear regression, where prediction is just an easy multiple of the predictors. • Linear regression: the speed of modeling is high, does not require very complicated calculations and runs quickly when the data is big. • Linear regression: the understanding and interpretation of each variable may be given by the factor. • Linear Regression works well in linearly separable datasets. • Linear regression is simpler to implement, easier to analyze and more efficient to practice. • Dimensionality reduction, regularization (L1 and L2) and cross-validation techniques can easily be used to avoid over-fitting in linear regression. • Multiple regression is capable of evaluating the relative contribution of one or more predictor variables to the value of the criterion. • Multiple regression is capable of finding outliers, or anomalies. Disadvantages • Linear regression: Linear relationship is minimal, and the outliers are quickly influenced. • Regression solution is likely to be dense (because there is no regularization) • Linear regression is noise- and overfitting-prone. • Regression solutions achieved using various methods (e.g. optimization, lesssquare, decomposition of QR, etc.) are not inherently special. • Vulnerable to multicollinearity: Multicollinearity (using dimensionality reduction techniques) should be eliminated before implementing linear regression since it implies there is no relationship between independent variables. • Any drawback of using a multiple regression model is typically due to the data being used, either by using insufficient data or by misleadingly assuming that a correlation is the cause.

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4.5

Neural Networks

Advantages • Artificial Neural Networks(ANN) are capable of studying and modeling nonlinear, complex relationships • Has predictive models that are highly accurate and can be applied across a large number of different types of problems. • Stores information on the network as a whole, not on a database, and the absence of a few pieces of information at one location does not prevent the network from functioning. • Ability to work with inadequate information. • Has fault tolerance (i.e. the contamination of one or more ANN cells does not prevent production generation). • Has a given memory. • Gradual corruption: A network slows over time and becomes increasingly compromised. The problem with the network doesn’t corrode right away. • Ability to train machines: Artificial neural networks learn events by commenting on similar events and making decisions. • Parallel processing capability: Artificial neural networks have computational power capable of doing more than one job simultaneously. Disadvantages • Limiting usability and ease of deployment. • Extraction of features: question of deciding which predictors are the most appropriate and the most important in building models that are predictably accurate. The predictors may be used on their own, or they may be used to shape function in conjunction with other predictors. • Dependence on hardware: Artificial neural networks need parallel processing processors, by their nature. It is for this reason that the equipment realization is based. • Assurance of proper network structure: There is no clear law for artificial neural network construction. The proper configuration of the network is accomplished by practice, and trial and error. • Unexplained network functioning: If ANN offers a sampling solution, it does not provide any hint as to why and how. • Difficulty in showing the network problem: ANNs can work with the numerical details. Problems need to be converted into numerical values before integration into ANN. • The duration of the network is unknown: Reducing the network to a certain value of the sample error means the training was completed. This interest does not produce optimal performance.

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Rule Induction

Advantages • IF-THEN rules are simple to understand and are supposed to be the most interpretable model, especially when dealing with a small amount of rules. • The decision rules are as descriptive as the decision trees, while being more compact. • IF-THEN rules are easy to predict, as only certain conditional statements have to be tested to decide which rules apply. • Decision rules are resilient to monotonous input function transformations, as conditions change only at the threshold. • IF-THEN rules create models that are typically sparse (i.e. there are not many features). They select only the features that are pertinent to the model. • Simple rules such as OneR can be used as benchmarks for more complicated algorithms. Disadvantages • IF-THEN rules focus on grouping, and ignore regression almost entirely. • Functions must be categorical, too. This means that if you want to use them, numerical features must be categorized. • Most of the older algorithms for rule-learning are susceptible to overfitting. • Decision rules are poor in the analysis of linear feature-output relations. 4.7

Decision Trees (CART – Classification and Regression Trees)

Advantages • Ideal to catch interactions in the data between apps. • Data ends up in distinct groups which are often easier to understand than points on a multidimensional hyperplane as in linear regression. • The tree structure, with its nodes and edges, also has a natural visualization. • Usually the models to be constructed and the interactions to be detected are much more complex in real-world problems. • CART immediately validates the Tree, i.e. the algorithm has the validation of the model and the discovery of the general model developed inside it. • The CART algorithm is relatively sturdy in relation to missing data. • Decision trees label so strongly on so many important data mining features. Disadvantages • Is not going to do well with some very simple problems where prediction is just a simple multiple of predictors. • Trees do not handle the linear relationships. Any linear input-output relationship must be approximated by splits, creating a step function. • Felt smooth. Slight changes in the input feature can have a major impact on the forecast result, which is usually not desirable.

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• The trees are pretty unstable too. A few changes in the training dataset can build a different tree altogether. This is because any split is based on splitting the parent. These techniques have specific tasks where they can be best applied in order to produce optimal results. The following table shows the data mining tasks with the appropriate data mining techniques to accomplish them (Table 2):

Table 2. Data mining tasks with the appropriate data mining techniques No 1

Data mining task Classification

2 3

Prediction Dependency Analysis

4

Data description and summarization Segmentation or clustering Consolidation

5 6

Data mining techniques Decision trees, Neural networks, K-nearest neighbors, Rule induction methods, SVM-Support vector machine, CBRCase based reasoning Neural networks, K-nearest neighbors, Regression Analysis Correlation analysis, Regression Analysis, Association rules, Bayesian networks, Rule Induction Statistical techniques, OLAP (Online Analytical Processing) Clustering techniques, Neural Networks Nearest neighbours, Clustering

The table below shows the data mining techniques and their areas of use (Table 3):

Table 3. Data mining techniques and their areas use. Data mining techniques Association Analysis Classification (K-nearest neighbor, etc.) Decision Trees

Clustering Analysis Outlier Detection Regression Analysis (Knearest neighbor, …)

Areas of use Designing store shelves, marketing, cross-selling of products Banks, marketing campaign designs by organizations Medicine discovery and prediction, engineering, manufacturing, astronomy etc. They were used for problems ranging from estimation of credit card depletion to estimation of time series of the exchange rate of various international currencies Image recognition, web search, and security Detection of credit card fraud risks, novelty detection, etc. Marketing and Product Development Efforts comparison (continued)

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Table 3. (continued) Data mining techniques Artificial Neural networks

Support vector machines regression Multivariate Regression algorithm Linear Regression

Areas of use Data compression, feature extraction, clustering, prototype formation, function approximation or regression analysis (including prediction time series, fitness approximation and modeling), classification (including pattern and sequence recognition, novelty detection and sequential decision making), data processing (including filtering, clustering, blind source separation and compression), and robotic compression Oil and gas industry, classification of images and text and hypertext categorization Retail sector Financial portfolio prediction, salary forecasting, real estate predictions and in traffic estimated time of arrivals (ETAs)

5 Conclusion Defining which methodology to use, and when, is obviously one of the hardest things to do when deciding to implement a data mining method. How are neural networks acceptable and when are the decision trees suitable? How is data mining appropriate to work with relational databases and reporting? How would it be acceptable to use only OLAP and a multidimensional database? Trial and error determine some of the criteria which are important in determining the technique to be used. There are clear variations in the types of problems that are most conducive to each approach, but the nature of data from the real world and the complex way in which markets, consumers and thus the data representing them are created show that the data is constantly evolving. Therefore, there is no clear law which recommends a particular technique over another. Sometimes, decisions are made based on the availability of experienced data mining analysts in one technique or the other. The preference of some classical techniques over the newer techniques depends more on getting good resources and good analysts. Acknowledgment. This work was supported by the Faculty of Applied Informatics, Tomas Bata University in Zlín, under Projects IGA/CebiaTech/2020/001 and RVO/FAI/2020/002.

References 1. Kamber, H., et al.: Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann (2011). ISBN 978-0-12-381479-1 2. ACM SIGKDD, 30 April 2006. Data Mining Curriculum. Accessed 27 Jan 2014 3. Clifton, C.: Encyclopædia Britannica: Definition of Data Mining (2010). Accessed 09 Dec 2010

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4. Trevor, H., et al.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Archived from the original on 2009–11-10. Accessed 07 Aug 2012 5. Jiawei, H., Micheline, K.: Data Mining: Concepts and Techniques (2000) 6. Sehra, S.K., et al.: Analysis of data mining techniques for software effort estimation (2014) 7. Dejaeger, K., et al.: Data mining techniques for software effort estimation: a comparative study (2012) 8. Weiss, G.M., Davison, B.D.: Data Mining (Handbook of Technology Management, H. Bidgoli (Ed.). John Wiley and Sons, 2010) (2010) 9. Berson, A., et.al.: An Overview of Data Mining Techniques (Excerpts from the book by Alex Berson, Stephen Smith, and Kurt Thearling) (2005) 10. Mehmed, K.: Data Mining Concepts, Models, Methods, and Algorithms (Second Edition) (2011) 11. Silhavy, P., Silhavy, R., Prokopova, Z.: Categorical variable segmentation model for software development effort estimation. IEEE Access 7, 9618–9626 (2019) 12. Software Testing Help.: Data Mining Techniques: Algorithm, Methods & Top Data Mining Tools, 16 April 2020 13. Kushwaha, D.S., Misra, A.K.: Sofware test effort estimation (2008)

Dispatching GPU Distributed Computing When Modeling Large Network Communities of Agents Donat Ivanov1(&) 1

and Eduard Melnik2

Southern Federal University, 2 Chehova st., 3479328 Taganrog, Russia [email protected] 2 Southern Scientific Center, Russian Academy of Sciences, 41 Chehova st., 344006 Rostov-on-Don, Russia

Abstract. This article discusses the task of modeling the behavior of a large number of individuals (groups of robots, participants in network communication, etc.) included in some network communities. In the simulation, it is proposed to use a set of workstations with GPUs connected by an Ethernet network. Particular attention is paid to the distribution of simulated agents between computing units, taking into account the nature of the information exchange between them. The algorithm of the distributed dispatcher is proposed. Keywords: Multi-agent interaction
Agent based modeling and simulation tool
Network community
Distributed computing
Group of robots

1 Introduction At present, in various fields of science, the problem arises of studying the behavior of a large number of individuals (groups of robots, participants in network communication, etc.), taking into account the complex and uneven structure of information exchange between them. For example, when studying the behavior of numerous large groups of mobile robots that perform the task of monitoring spaced apart areas of the territory [1], the group of robots is divided into subgroups (or clusters), each of which operates in a separate area. At the same time, information exchange between robots operating in the same area is usually active, while information exchange between robots operating in different areas is small or absent. When studying the behavior of a large number of individuals included in certain sets of network communities, it is also necessary to analyze the behavior taking into account the presence of some subsets with active information exchange between participants, as well as taking into account some informational connections between participants in various subsets. Even simple patterns of behavior of individuals in the community can lead to complex patterns of behavior of the community as a whole. For example, the study of the behavior of ants, bees, schools of fish and a number of other living organisms made it possible to develop optimization methods and algorithms [2, 3], which have found application in many areas of human activity [4–7]. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 409–418, 2020. https://doi.org/10.1007/978-3-030-63322-6_33

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Studying the possibilities and characteristics of communities requires modeling. The most promising analysis tool in this area is simulation based on an agent-based approach [8]. Agent - a program that simulates the behavior of a simulated community member. A number of works [9, 10] present the results of applying this method for the analysis of significant social groups. Including, in the tasks of the distribution of roles in distributed groups with limited communications [11]. The successful use of agentbased modeling in the field of network communities is largely due to the fact that the method is well suited to the subject area. When modeling sets of agents with dozens of participants, the personal computer is usually sufficient. But when modeling sets of agents, numbering many thousands of agents, high-performance computing systems are required. One of the promising approaches to modeling numerous network communities of agents is the use of a certain set of workstations with GPUs connected by an Ethernet network. This solution is simply scalable. If necessary, you can add or remove workstations. However, using Ethernet to exchange information between stations becomes the bottleneck of the system. To reduce the negative impact of the relatively low bandwidth of data transfer channels between workstations, it is necessary to place simulated agents at workstations, taking into account the nature and volume of information exchange between agents. Thus, this raises the problem of distributing simulated agents between workstations, taking into account the nature of information exchange within the simulated system.

2 Statement of the Task Consider a set R consisting of N agents ri, where i 2 [1, N]. Between the agents of the set R there is a messaging. Let si;j be the intensity of messaging between agents ri, and rj (i 6¼ j, i 2 [1, N], j 2 [1, N]). Suppose that the intensity of messaging between some pairs of agents is relatively high, while between some others it is relatively low or completely absent. We assume that the set R can be arbitrarily divided into M subsets of Ri (i 2 [1, M]) in such a way that each subset of Ri (i 2 [1, M]) contains agents between which a high message intensity is observed, while between pairs of agents from different subsets the message exchange rate is relatively low or absent. Let Ni (i 2 [1, M]) be the number of agents in the subset Ri (i 2 [1, M]). Given the characteristics of information exchange, such subsets can be called a network community of agents. It is possible to divide all messages si;j ði 6¼ j; i 2 ½1; N ; j 2 ½1; N Þ into two types: – sin – messages between agents ri and rj belonging to the same community ri 2 R k ; rj 2 R k – sout - messages between agents ri and rj belonging to different communities ri 2 Rk ; rj 2 Rn ; k 6¼ n. out In this case, for any community of Ri agents, the inequality sin Ri  sRi holds, where is the intensity of all messages of the agents of the community Ri addressed to agents in the same community, and sout Ri is the intensity of all messages of the agents of

sin Ri

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the Ri community addressed to agents of other communities. Figure 1 shows an example of information exchange in a fragment of a group of agents divided into several network communities.

R1 in 1,2

s

r1

in s2,1

out

s 2,4

r2

R

out

s 4,2

... r5

r3

R2

r4

rN-3

rN-2

rN-1

rN

RM

in s6,5 in s5,6

r6

...

Fig. 1. An example of information exchange in a fragment of a group of agents divided into several network communities

We assume that we are able to evaluate the computational complexity of modeling qi for each agent ri. Then Qj is an estimate of the computational complexity of modP eling agents of the community Rj. In the general case, Qj ¼ Nj 1 qj . Then Q is the total estimated computational complexity of modeling all the agents of the communities in P PN question, that is, Q ¼ M j¼1 Qj ¼ i¼1 qi . To simulate the behavior of network communities of agents, K computing nodes are used. Each computing node has some estimated computing power Ph, where h 2 [1, K]. The throughput of communication channels between two computing nodes Pi and Pj is estimated as Li,j. The problem arises of distributing agent modeling between computing nodes taking into account the peculiarities of telecommunications within communities in such a way that the intensity of message transmission between computing nodes does not exceed the throughput of communication channels between the same computing nodes.

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3 Brief Overview of Agent Based Simulation Tools The use of multi-agent modeling in practical application encounters some difficulties. Firstly, this is the inadequacy of the used agent model, in particular, due to insufficient data on the simulated area [12]. Secondly, the imperfection of the tools used [13–16], which require specific knowledge from the user that is not related to the subject area of modeling. An extensive review of modern systems of agent-based modeling is given in [15]. Table 1 shows the results of comparing agent based modeling and simulation (ABMS) tools according to the criteria “complexity of creating a model - scalability”. Table 1. Comparison of the agent based modeling and simulation (ABMS) tools according to the criteria “complexity of creating a model - scalability” (according to [15]). Extreme scale Large scale

Easy

Moderately

Altreva Adap Modeler, SeSAM

AnyLogic (2D/3D), AOR Simulation, FLAME, LSD (2D/3D), MASS, Pandora, UrbanSim

Hard Repast HPC, MATSIM, PDES-MAS, Swarm Agent Cell (2d/3D), Brahms, BSim(2d/3D), CloudSim, CybelePro, D-OMAR, Echo, Ecolab, FLAME GPU (3D), GridABM, HLA_Agent, HLA_Repast, Repast-J, Repast Symphony (2d/3D)

It should be noted that the majority of ABMS tools oriented towards the implementation of large-scale models support the work on clusters and achieve the corresponding optimized implementations for achieving the required performance (for example, Flame-HPC [17], Repast-HPC [18]). The behavior of a large number of agents is usually modeled on a single computing node, which requires parallel computing. In this direction, the GPU outperforms the CPU. This led to the need to implement ABMS tools on the GPU [19]. On the basis of the FLAME project, ABMS FLAME-GPU [20] was developed, focused on the use of Nvidia’s GPU accelerators. This ABMS system allows modeling large-scale systems without clusters. But GPU performance with a population growth begins to decline much faster than in the case of clusters. Nevertheless, as a “boxed solution”, with non-extreme population sizes and with appropriate GPU performance, the FLAME-GPU may be the only one acceptable alternative to cluster solutions. Summing up, we can say that when modeling communities in the context of using large models with complex agent behavior, the best choices from open Sagent-based modeling system would be: FLAME, FLAME-HPC, Repast-HPC, FLAM-GPU.

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4 Analysis of Platforms for the Agent-Based Modeling and Simutation Systems Based on the analysis of ABMS tools, we can conclude that the performance of ABMS largely depends on the chosen agent model. Therefore, it is not obvious which solution is preferable: clustered or based on GPU (especially in the initial stages). The Table 2 shows a comparison of ABMS tools in an implementation platform. Table 2. Implementation platforms for ABMS tools Implementation platforms for ABMS – cluster Advantages Disadvantages No scalability restrictions High cost of the cluster High performance The need for high-speed communication channels The need to solve the configuration problem of placing the model’s subtasks in a cluster High complexity of deployment and use Implementation platforms for ABMS – GPU Advantages Disadvantages High performance Scaling limit Relatively low cost Restrictions on the agent model imposed by the size of the GPU memory Results visualization support The deployment and usage are ease

The Table 2 shows that GPU solutions benefit in terms of productivity and cost, however, restrictions on model size do not allow the use of GPU solutions for extremely large models. To some extent, this can be offset by using multiple GPUs in the same workstation. However, this requires both the purchase of specialized GPUs and a specialized motherboard. However, this approach does not solve the problem of scaling, but only pushes the limit after which the speed of simulation of ABMS on the GPU begins to fall. Thus, a combined solution with a small cluster of workstations with a GPUs would be optimal. In this case, the number of workstations can be increased as the requirements for scaling increase. However, with such organization of the execution platform, it is necessary to solve the problem of ensuring communication between the GPU, both within the same workstation and the GPU located on different nodes of the cluster. To solve the problem of communication between GPUs, NVIDIA developed NCCL - the NVIDIA collective communications library, which allows organizing communication between several nodes with a GPU [21]. On the one hand, using cluster solutions implies the use of high-performance data transfer interfaces, both between cluster nodes and for interconnect, for example, the

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above-mentioned NCCL library supports data transfer between nodes using InfiniBand verbs, libfabric, RoCE and IP Socket. On the other hand another approach to solving the scaling problem may be to use middleware that translates model code written in some high-level subject-oriented language into the target agent-based modeling system’s code. This approach allows the use of several ABMS of various types with different execution platforms. For example in [22], a similar approach is described using the OpenABL language. This approach allows solving another problem – the high complexity of creating a model. High-performance ABMS systems are characterized by the high complexity of the model building process (see Table 1 and [15]). At the same time, general-purpose programming languages are used to create a model in high-performance ABMS systems. In case of frequently change the model during the modeling process, this will require the constant involvement of programmers, which can lead to the fact that the modeling bottleneck is not the speed of the model, but the speed of the model changing by the programmers for the needs of applied specialists. In [22], the “cost” of creating a model in eLOC (in lines of source code, excluding blank lines and comments) for different ABMS systems is presented. These data show that the “cost” of creation, comparable to ABL, is demonstrated only by the FLAME GPU, which is explained by the use of the template system when creating the model [19, 23]. Another way to simplify model’s development is using middleware that provides a graphical interface for creating the model. In this case, the source code of the model is automatically generated based on the data entered by the user. This approach is most simple to implement for ABMS systems, which use some configuration files (for example, FLAME, FLAMEGPU) to describe the model. The code generation should be taken out in a separate module, as in [22], so that its functionality (for example, support of various SAOMs) can be increased independently of the middleware that provides a graphical interface for creating the model. Also it is necessary to take into account the factors affecting the speed of the model described above i.e. In a system running on a cluster, you should introduce a component that would perform the relocation of agents (agent communities) - the dispatcher. The dispatcher is responsible for monitoring the state of the cluster and performs the redeployment of agents (case agent communities) in case of detection of exceeding threshold load values of computing nodes or data transmission interfaces. To ensure system performance and scalability, such a dispatcher must be distributed.

5 Proposed Approach The use of middleware that translates the code of a model written in some high-level, subject-oriented language into the code of the target system of agent-based modeling is proposed. It is also proposed to use a distributed dispatcher to place agents on the computing nodes of the system. On each computing node, one dispatcher is launched, which is responsible for the distribution of agents on this node. In order to organize the work of a distributed dispatcher, it is proposed to use a distributed bulletin board, which

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displays information about agents that are not yet distributed and already distributed. The operation of a distributed bulletin board is based on a distributed registry. In order to reduce the communication load on communication channels between computational nodes during modeling, the distribution and redistribution of simulated agents between nodes is carried out in such a way that as much as possible information links between agents are located on the same computational node. For this, preference is given to placing the community on a single site as a whole. And if it is impossible to place the community as a whole on one computing node, the community is divided into subgroups so that the most intensive information exchange occurs within one subgroup. Each subgroup is located on one calculator. The procedure for placing agent communities on computing nodes is also important. The first is the community with the maximum intensity of information exchange within the community itself. For its placement, the computing node with the largest reserve of performance is selected. Then, those communities are located on the same node, in which the most active information exchange with the already posted community is predicted. The number of communities that can be hosted on one node depends on the estimated computational complexity of these communities and the available reserve of node performance. When the first node no longer allows you to host at least one whole community on it, the next community with the most active internal information exchange (from among those not yet hosted) is located on the computing node, which has the maximum reserve of productivity. And at the same site, as many communities as possible have the most voluminous information exchange with the hosted community. By analogy, the remaining communities are hosted until all communities are hosted, or until it turns out that no node has sufficient performance reserves to host at least one more community as a whole. In this case, the remaining communities are divided into subgroups, and the process of relocation continues until all subgroups (or even individual agents) are located on computing nodes.

6 Distributed Dispatcher Algorithm for Modeling Large Network Communities of Agents Based on the approach proposed above for the distribution of modeled agents between computing nodes, a distributed dispatcher operation algorithm has been developed: 1. Download initial data. 2. Synchronizing a local copy of a distributed bulletin board. If there are no unplaced communities, subgroups or agents on the bulletin board, go to step 13; otherwise, go to step 3. 3. If there are unplaced communities on the bulletin board, then go to step 4, otherwise go to step 10. 4. Evaluate and transfer to the communication channel of distributed dispatchers data on the free computing power (performance reserve) of their node. 5. Receive data from other dispatchers on the amount of free computing power available on their computing nodes.

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6. If on this computing node the performance reserve is higher than on all other computing nodes of the system, then go to step 7, otherwise go to step 2. 7. If there is at least one unplaced community on the bulletin board, go to step 8, otherwise go to step 10. 8. The dispatcher selects an unplaced community with the maximum intensity of internal information exchange from among those whose computational complexity allows placing them on the resources available on the computing node. If such a community is located, then the dispatcher places this community on his computing node, after which he updates the data on the distributed bulletin board and goes to step 2, otherwise go to step 9. 9. The dispatcher selects among unplaced communities one that has the maximum computational complexity and breaks it into two subgroups so that at least one subgroup can be placed on the available computing resources. Places it on its computing node, after which it updates the data on the distributed bulletin board and proceeds to step 2. 10. The dispatcher selects an unplaced subgroup (community fragment) with maximum computational complexity from among those that can be placed on existing computing resources. If such a subgroup is located, the dispatcher places it on his computing node, after which he updates the data on the distributed bulletin board and goes to step 2, otherwise go to step 11. 11. If the computational complexity of all unplaced subgroups exceeds the available computational resource, then the dispatcher selects a subgroup with the maximum computational complexity, splits it into two subgroups so that at least one subgroup can be placed on the available computing resources. Places it on its computing node, after which it updates the data on the distributed bulletin board and proceeds to step 2. If such a partition is not possible, go to step 12. 12. The dispatcher generates an error message: “The current configuration cannot be placed on existing computing resources. Scaling up the system is required.” 13. If the computing node of this dispatcher is not involved in the simulation, the dispatcher generates a message to the user that the given node can be disconnected from the system, or reprofiled to solve other problems. 14. The end of the program. Such a distributed dispatcher operation algorithm leads to one of three possible results: – All simulated communities and agents will be successfully placed on available computing resources, while all available calculators will be used. – All simulated communities and agents will be successfully placed on available computing resources, while some calculators can be excluded from the system. – Available computing power is not enough to model this configuration. In this case, it is necessary to increase the computing power of the system used by adding additional computing nodes, or by replacing some weak computing nodes with more powerful ones. The disadvantage of the proposed algorithm is that it does not provide equalization of the computational load between the available computing nodes.

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7 Conclusions This article discusses the problem of modeling a large number of individuals (for example, robots, members of social groups, etc.), united in network communities, provided that the behavior of each individual is modeled by some agent. Agents are united in communities. The intensity of information exchange within a community is higher than the intensity of information exchange between agents of different communities. It was shown that the main difficulties of agent-based modeling in this area are associated with the large dimensionality of the models and the complexity of creating models associated with the shortcomings of existing modeling tools. The article performed an analysis of existing ABMS systems, and also examined the features of the platforms on which the agent-based modeling system is launched (cluster, GPU). The approaches to ensure the scaling of models and simplify their creation are considered. The authors of the article proposed using a cluster with a GPU as a platform for performing agent modeling. The assessment of factors affecting the speed of modeling is carried out, as a result of which estimates of the influence of clustering of the simulation problem on the speed of modeling were obtained. The authors proposed an approach to organizing the distribution of modeled agents and their communities on existing computing nodes, taking into account the features of information exchange. An algorithm for the operation of a distributed dispatcher using a distributed bulletin board is also proposed. The disadvantage of the proposed algorithm is that it does not provide equalization of the computational load between the available computing nodes. Acknowledgement. The reported study was funded by RFBR according to the research projects №17-29-07054 and №18-29-22046.

References 1. Kalyaev, I., Kapustyan, S., Ivanov, D., Korovin, I., Usachev, L., Schaefer, G.: A novel method for distribution of goals among UAVs for oil field monitoring. In: Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT), 2017 6th International Conference on, pp. 1–4 (2017) 2. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the European Conference on Artificial Life, pp. 134–142 (1991) 3. Karaboga, D.: An idea based on Honey Bee Swarm for Numerical Optimization. Technical report TR06, Erciyes Univ. 10 (2005). https://doi.org/citeulike-article-id:6592152 4. Di Caro, G., Dorigo, M.: AntNet: distributed stigmergetic control for communications networks. J. Artif. Intell. Res. 9, 317–365 (1998). https://doi.org/10.1613/jair.530 5. Di Caro, G., Ducatelle, F., Gambardella, L.M.: AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Eur. Trans. Telecommun. 16, 443–455 (2005). https://doi.org/10.1002/ett.1062 6. Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. John Wiley & Sons, Hoboken (2005). https://doi.org/10.1002/0471739383

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7. Civicioglu, P., Besdok, E.: A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif. Intell. Rev. 39, 315–346 (2013). https://doi.org/10.1007/s10462-011-9276-0 8. Gilbert, N., Troitzsch, K.: Simulation for the Social Scientist. Open University Press, United Kingdom (2005) 9. Deissenberg, C., Van Der Hoog, S., Dawid, H.: EURACE: a massively parallel agent-based model of the European economy. Appl. Math. Comput. 204, 541–552 (2008) 10. Okrepilov, V.V., Makarov, V.L., Bakhtizin, A.R., Kuzmina, S.N.: Application of supercomputer technologies for simulation of socio-economic systems. R-Economy. 2015. Vol. 1. Iss. 2 1, 340–350 (2015) 11. Ivanov, D., Melnik, E.: Multiagent distribution of roles in communities with limited communications. In: Silhavy, R. (ed.) Software Engineering Methods in Intelligent Algorithms. (CSOC 2019). Advances in Intelligent Systems and Computing, pp. 77–82. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19807-7_8 12. Makoveev, V.N.: Using agent-based models in the analysis and forecast of socio-economic development of territories. Econ. Soc. Chang. Facts, Trends, Forecast, 272–289 (2016) 13. Pavón, J., Sansores, C., Gómez-Sanz, J.: Modeling of social systems with Ingenias. In: Proceeding of 1st Workshop on Multi-Agent Systems and Simulation (MAS&S’2006), pp. 1–8 (2006) 14. Sansores, C., Pavón, J.: Agent-based modeling of social complex systems. In: Conference of the Spanish Association for Artificial Intelligence, pp. 99–102 (2005) 15. Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.P.: Agent based modelling and simulation tools: a review of the state-of-art software. Comput. Sci. Rev. 24, 13–33 (2017) 16. Railsback, S.F., Lytinen, S.L., Jackson, S.K.: Agent-based simulation platforms: review and development recommendations. Simulation 82, 609–623 (2006) 17. Petreska, I., Stamatopoulou, I.: A comparative study of tools for visualisation of state-based spatial multi-agent models. In: Proceedings of the 6th Balkan Conference in Informatics, pp. 53–60 (2013) 18. Collier, N., North, M.: Repast HPC: a platform for large-scale agent-based modeling. LargeScale Comput. 10, 81–109 (2012) 19. Richmond, P., Walker, D., Coakley, S., Romano, D.: High performance cellular level agentbased simulation with FLAME for the GPU. Brief. Bioinform. 11, 334–347 (2010) 20. Flexible Large Scale Agent Modelling Environment for the GPU (FLAMEGPU). http:// www.flamegpu.com. Accessed 10 May 2020 21. NVIDIA Collective Communications Library (NCCL). https://developer.nvidia.com/nccl. Accessed 10 May 2020 22. Cosenza, B., Popov, N., Juurlink, B., Richmond, P., Chimeh, M.K., Spagnuolo, C., Cordasco, G., Scarano, V.: OpenABL: a domain-specific language for parallel and distributed agent-based simulations. In: European Conference on Parallel Processing, pp. 505–518 (2018) 23. Richmond, P., Chimeh, M.K.: Flame gpu: complex system simulation framework. In: 2017 International Conference on High Performance Computing & Simulation (HPCS), pp. 11–17 (2017)

OIDC Authentication for Educational Purposes and Solving Problems for Localization of Faults in Combinational Circuits Barish Yumerov(&)

and Galina Ivanova

University of Ruse, Ruse, Bulgaria [email protected], [email protected]

Abstract. Web system for detecting malfunctions in combinational logic circuits using OIDC authentication is designed. This paper discusses some problems which can occur in combinational logic circuits, user authentication, and how we can cope with them. Online system using OIDC for solving such problems using the method “truth table” is described. Creating and using multiple login accounts for different educational systems can be problematic and hard to track for students. The web application for educational purposes will track student’s performance and will help teachers in evaluating their work and efforts. Keywords: Combinational circuits
Faults
Logic gates
Testing Automation
Web application
Authentication
JWT
OIDC




1 Introduction With each passing day, it is noticeable that we are entering a more automated world. Computer technology today is crucial to our evolution. Computer technology in modern society has responsible functions and low reliability can lead to large material and moral losses. The growing complexity of modern technical systems and products requires the use of a large number and relatively small size elements. Local defects can lead to disruption or complete cessation of operation of the system. Most of these constituents are logical elements. Although they become smaller and more reliable over time, defects can occur that could cause calculation errors. These faults can be detected and even localized by testing the combinational circuits with all possible combinations of input data and analyzing the output (method using truth tables). After all the calculations are made, a table is created, on which a matrix G is built. Matrix G is reduced according to certain rules and from the obtained smaller matrix (GL) the list of input vectors required for the diagnostic test is generated. Nowadays web systems require their users to be registered in order to be used after singing in. Keeping track of all our accounts for different applications can be hard and time consuming. This problem can be solved via centralized authentication servers which register and authenticate users for many applications. Using an authentication service helps users to use multiple applications and services by configuring and using single account (username and password). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 419–429, 2020. https://doi.org/10.1007/978-3-030-63322-6_34

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2 Combinational Logic Circuits 2.1

Introduction

A logic circuit is combinational if its output signals z1, z2… zm can be described as a Boolean function of its input signals x1, x2… xn. They are a type of electronic circuit. The combinational [1] logic circuit is a set of inputs in which binary signals are received and interconnected logic elements that transform the input signals into outputs. Combinational logic circuits are characterized by the following characteristic: • They do not contain memory, so the output signals depend only on the input and do not depend on a previous state of the circuit; • There cannot be closed circuit through which a signal from the n-th element, passing through several other logic elements, enters the input of the n-th element; • They perform sequential conversion of the input signals into intermediate, and the intermediate - into output (sequential multistage transformation). The combinational schemes can be: Encoders, Decoders, Multiplexers, Code Converters, Digital Comparators, Adders and others. 2.2

Troubleshooting in Combinational Logic Schemes

The method using truth tables [4] allows to build tests, detecting and locating faults for small combinational circuits. Let the combination scheme be given with n inputs x1, x2, … xn and m outputs z1, z2,… zm. For this scheme, p faults are defined for which tests must be generated. For the non-faulty circuit and for the faulty circuits, the truth tables are built. They display the values of the outputs of the circuit for each binary set of the input variables. This table contains 2n rows and p + 1 ladders and has the following form displayed in Fig. 1.

Fig. 1. Example of “Truth table”

Zik (i = 1  p, k = 0  2n − 1) is an m-dimensional binary vector that gives the values of the outputs of the circuit containing the i-th failure when applying the k-th binary set of system inputs.

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Column f0 contains the output vectors of the non-faulty circuit. To detect the fault, all vectors on column f0 must be different from all vectors on each of the other columns. Therefore, all columns that coincide with the vectors of f0 are removed from the table. Also, if the vectors of two columns coincide, one of them is removed. In this way the fault matrix F is obtained. To obtain fault tests, a matrix G is built, which has m columns. Each of these columns corresponds to a pair of columns [f0, fj] (j = 1,… m  p) of the output matrix F. An element of column 0j of the matrix G is equal to 0 if the corresponding vectors of columns f0 and fj of the matrix F are the same. The element is equal to 1 if the vectors are different. If there is 1 on the k-th row of column 0j, the k-th set of input variables detects the j-th fault. To generate diagnostic tests, based on the matrix F according to the above rules, the matrix GL is built. It is necessary to select a minimum subset of rows from the matrices G and GL, in which to preserve the distinctiveness of the individual faults. Let the set E = {ek} of binary sets constituting G be given; ek = {ek1, ek2,… ekm}, ekj Є {0,1}; The sets ek and ev are comparable (ek  ev) if for each j (j = 1, 2,… m) ekj  evj is satisfied. The rules for reduction are as follows: • If there is such a pair of rows ai and aj in the matrix for which ai  aj is satisfied, the row ai is shortened. • If there is such a pair of columns bi and bj in the matrix for which the condition bi  bj is fulfilled, the column bj is shortened. As a result of the reductions, a G* matrix (respectively GL*) is obtained. Each row of the matrix G* is denoted by a boolean variable (a, b, c…), which is a corresponding input vector. Each column is then represented by a disjunction (OR) of these variables. If a variable in the column is zero, it does not enter the disjunction. The Boolean function L(G*) is compiled as a conjunction (AND) of these sums. By opening the brackets, a disjunction of conjunctions is obtained. When selecting an arbitrary conjunction, a corresponding list of input test vectors is obtained.

3 OIDC Authentication Server The authentication server manages the whole user registration, authentication and configuration [6]. It is responsible for storing user data like credentials, access level and other sensitive information. The authentication server provides access tokens to its clients. Those tokens are passed to so-called Service Providers (SP) to access the requested services and data. In this way the user is entering credentials only to the authentication servicer. Authentication tokens provided by the Identity Provider are JSON Web Tokens (JWT). They are JSON based divided into three parts. The first part describes the algorithm used for the token signature. The second part is the body of the token. Here we have the data which reveals the identity, level access, issuer, expire date and other information for the holder of the token. The third part is the token signature which is used to verify the validity of the token. The most used algorithm is RS256 (RSA using SHA-256) [9]. The authentication server holds private key used to sign the generated

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access tokens. Consumers of the JWT can access and use the public key to validate the signature.

4 OIDC Code Flow The most used authentication flow in the OIDC standard is the code flow [5]. The authentication is initiated by the client via passing the following parameters: • • • • •

response_type: code – indicates the code flow; scope – opened – indicates requestion for OpenID authentication ID token; client_id – the identifier of the resource provider; state – value set by the RP to maintain state between requests and calbacks; redirect_uri – the callback URI for the authentication response.

After the authentication on the identity provider, the client is redirected to the redirect_uri with the code token. The code token is exchanged for access token at the token endpoint by passing the following parameters: • grant_type - authorization_code – indicates that code is passed; • code – the code returned from the authentication server. The server returns ID token, access token and if needed refresh token. The access token is passed to the resource server in the request headers to authorize the user [10]. The code flow is shown on next figure.

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Fig. 2. OIDC code flow

5 Web System for Solving Problems of Combinational Logic Circuits Web applications are being used for educational purposes more each passing day because they are flexible and multiplatform [2, 7, 11]. Based on the advantages of the web applications the web system for solving problems of combinational logic circuits was planned and implemented. When starting the application, initially, a task is generated, which will be solved by the student. The task can contain one or two functions, generated randomly. Each function has a certain number of disjunctions, in which there are up to three Boolean conjunctions. Boolean variables can be inversed or straight. On top of the created Boolean function, 4 errors are generated, which will have to be localized. One of the faults is for the straight version of x and the other for the inverse. The other two are for whole disjunctions. The generated faults mimic defects in logic elements by always returning a logical zero or one. After successfully generating the task, the system draws a truth table that will be filled by the student. Given that f0 [3] is filled in for a nonfaulty combinational circuit, and f1, f2, f3, f4 for the respective fault. Figure 3 shows how it would look.

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Fig. 3. A sample view of a generated task

After successfully completing the truth table, the student proceed to the G-Matrix, which will detect errors, Fig. 4. It is filled in as each column (f0, f1, f2, f3 and f4) is compared with each as explained above. After successful completion of both tables, the application would look like this:

Fig. 4. An example for G-matrix

The next step after filling G-matrix (Fig. 4) is to reduce the rows and columns to obtain a GL* matrix as described above and then to obtain the necessary tests to diagnose the combinational logic circuit.

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6 An Example for Authentication and Task Solving Flow In order to use the web application students and teachers first need to register and sign in. The user is redirected to the identity provider environment to do so. After successful authentication using OIDC code flow as explained in Fig. 2, the user is redirected back and can continue using the web application. To solve a task the student needs to request a new one, and follow the instructions shown in the interface. The flow of authentication and solving new task is shown in Fig. 5.

Fig. 5. Authentication and task solving flow

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7 Description of an Example Flow of Actions (Flow Chart) In Fig. 6 is shown the steps that a student needs to take to solve a generated task by the environment. There are 4 stages to be completed in the solution. In training mode the web application will not allow the user to continue unless the current one is finished correctly. After completed all steps then the solution can be accepted and stored in the database.

Fig. 6. Description of an example flow of actions (flow chart)

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8 A Survey Measuring the Satisfaction of Students Feedback with students is of great importance for the design process of the system. To determine the degree of student’s satisfaction with the web system, an anonymous online survey was developed and posted in the social media group, created for the students. Students have to answer to several questions related to some characteristics of the web system and its functional capabilities. The survey was conducted in May 2020 in the end of the semester, after students had the opportunity to get acquainted with the web system and to express their opinion. It is planned such survey to be held annually. The survey is formulated to require a degree of satisfaction, assessed on five-degree Likert scale from one (very dissatisfied) to five (very satisfied), Fig. 7.

Fig. 7. Bar chart displaying survey answers

According to the analysis of survey results, the following summaries can be made: • A large number of students believe that the practicing and consolidating problemsolving skills is very satisfying; • The majority of the students are satisfied with the description of the mathematical algorithm; • Optimizations on the application speed and task solving guides can be made At the end of the survey, some recommendations were given by the students about the user interface and responsiveness of the application. Recommendations will be taken into consideration and will be implemented in future releases.

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9 Conclusion The presented system for solving problems for localization of faults in combinational logic circuits will be used in classes for “Reliability and diagnostics of computer systems” in University of Ruse. The aim is to ease teachers and students, as the tasks are generated by the system and the results of their solution will be visualized immediately. Each student will have their own user account, which is accessible to teachers. All solved tasks and mistakes made during solutions will be reflected in the database. Teachers will be able to see the performance of their students so they can evaluate them better. The presented identity provider service is going to be deployed and used by students and teachers for educational purpose. It will be used for authenticating multiple services providing SSO (single sign in). Therefore students will have central profile containing progress and status information for students and teachers. APIs of the authorization provider are open and other web solutions are going to be able to use easily the provided services. Acknowledgement. This paper is supported by project 20-RU-01 “Design and construction of a smart educational and research laboratory for doctoral students”, funded by the Research Fund of the “Angel Kanchev” University of Ruse.

References 1. Rabaey, J.: Digital Integrated Circuits: A Design Perspective. Prentice-Hall, Inc. Division of Simon and Schuster One Lake Street Upper Saddle River, United States (1996). ISBN:9780-13-178609-7 2. Antonova, D., Kunev, S., Hristov, T.: Concept of online distance learning system on sustainable development in the cross-border region. TEM J. 7(4), 915 (2018) 3. Coello, C.A.C., Christiansen, A.D., Aguirre, A.H.: Use of evolutionary techniques to automate the design of combinational circuits. Int. J. Smart Eng. Syst. Des. 2, 299–314 (2000) 4. Wójcicki, R.: Theory of Logical Calculi: Basic Theory of Consequence Operations. Springer Science & Business Media, vol. 199 (2013). ISBN 978-94-015-6942-2 5. Sakimura, N., Bradley, D., de Mederiso, B., Jones, M., Jay, E.: OpenID connect standard 1.0-draft 07 (2011) 6. Christie, M.A., Bhandar, A., Nakandala, S., Marru, S., Abeysinghe, E., Pamidighantam, S., Pierce, M.E.: Using keycloak for gateway authentication and authorization (2017) 7. Kalushkov, T., Valcheva, D., Markova, G.: A model for pseudo-cloud hosted e-learning module for collaborative learning. In: 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–5. IEEE (2018) 8. Jones, M., Campbell, B., Mortimore, C.: JSON Web Token (JWT) profile for OAuth 2.0 client authentication and authorization Grants (2015)

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9. Visočnik, V.: Comparison of JWT and OAuth 2.0 for authorisation and authentication in rest services, Doctoral dissertation, Univerza v Mariboru, Fakulteta za elektrotehniko, računalništvo in informatiko (2018) 10. Mortimore, C.: JSON Web Token (JWT) Profile for OAuth 2.0 Client Authentication and Authorization Grants draft-ietf-oauth-jwt-bearer-06. Ident. Interface Inf. Virtual Data Integr. (2013) 11. Evstatiev, B., Evstatieva, K., Doychinov, Y., Stoyanov, I., Iliev, T.: Design and implementation of a virtual multimeter in the EVEEE environment. In: 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 1–4. IEEE (2019)

Users Activity Time Series Features on Social Media Andrey M. Fedorov, Igor O. Datyev(&), and Andrey L. Shchur Institute for Informatics and Mathematical Modeling, Kola Science Centre of the Russian Academy of Sciences, 14, Fersman St., Apatity 184209, Russia {fedorov,datyev,shchur}@iimm.ru

Abstract. Studying the features of users’ activity time series on social media is relevant for solving the problem of identifying user interest bursts for various publications, as well as modeling and forecasting user activity. The article examines general issues related to the detection of time series anomalies, and discusses the differences in the values of user activity indicators of different social network communities. In particular, special attention is paid to determining the mean values for indicators of user activity: one-dimensional time series, the values of which are the number of views, likes, reposts, comments. To assess the potential for determining stable mean values, the stationarity properties are investigated using the ADF and KPSS tests. The statistical characteristics of user activity at different time intervals are compared. Conclusions are drawn about the possibility and advisability of converting such series to a stationary form using differencing in solving problems of monitoring, modeling and predicting user activity. Keywords: Online social networks features analysis


Users activity anomaly
Time series

1 Introduction World digitalization has brought new methods of solving problems in the vast majority of areas of our life. Social media is one of the fruits of ubiquitous digitalization and the most responsive and sincere mirror of everyday actions and opinions of people. The speed of disseminating information through social media has already exceeded the traditional media thanks to the relative ease of preparation and publication of multisubject content. Today no one denies the mutual influence of human life in the real world and its virtual presentation in social media. The concept of e-government, among other things, implies the involvement of ordinary citizens in decision-making processes. In this area the potential of social media cannot be overemphasized as a source of “uncut” opinions as well as an instrument of social interaction and influence. Social media analysis is used to solve a wide range of problems: studying the dissemination of information, people movement – including migration and transport systems optimization; exploring the popularity of users or real people, events, geosites, tourist routes; political and commercial advertising; organizing various virtual and real world events; identifying terrorist and corruption groups; countering natural and © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 430–441, 2020. https://doi.org/10.1007/978-3-030-63322-6_35

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man-made disasters, etc. Among the above, a class of problems can be distinguished related to the need of searching for deviations (or so-called anomalies) arising in social media at certain time intervals. A striking example of this is the problem of detecting bursts of user activity in social networks caused by various events. Solving this type of problems often brings out the following questions: What exactly is a user activity burst? How to detect it? Do its characteristics depend on a particular community (public, group) of a social network? It should be noted that there are different layers of these anomalies: a single publication (post), a community, a specific online social network, a set of social networks, etc. In addition, various combinations of these layers can be studied. For example, detect all burst posts that happened in a community and then received responses only from the members of this community, or get a superposition (overlap) of the same indicators for all posts of a given community. It’s also possible to conduct a priori research with a reference to a specific event, etc. On the way to solving the aforementioned issues, the object of this study is the time series of indicators of user activity in relation to a publication posted in a Social Networking Service (SNS) community. Subject of research: the possibility of finding stable mean values of these indicators for a specific SNS community.

2 Time Series Anomalies Survey In a general sense, the problem of finding bursts (sometimes also called spikes) in social media refers to the problem of finding anomalies in the time series. Formally, finding bursts can be attributed to the mathematical problem of finding outliers. An outlier can be defined as “is one that appears to deviate markedly from other members of the sample in which it occurs” [1] or “an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism” [2]. In other words, an outlier is an observation that is abnormal in one sense or another. In some works, scholars divide the meanings of the terms ‘outlier’ and ‘anomaly’, in others they do not [3]. In this study, including the overview part, we will use the terms ‘outlier’ and ‘anomaly’ as synonyms. There are various classifications of anomalies. According to the form of occurrence they are most often distinguished as point, context (conditional) and collective [4, 5]. Another classification, based on the form of influence (a qualitative description of the consequences of changes caused by anomalies), discerns the following main types of anomalies: unexpected growth, drop, trend change and level shift [6]. In this paper, we focus on the problem of detecting an abnormal subsequence based on a point anomaly of the type ‘unexpected growth’ (burst, spike) of users activity indicators (quantity of likes, reposts, views, comments) in a short period of time. These types of anomalies are usually called ‘additive outliers’. The features of revealing collective anomalies, including those based on methods for identifying point anomalies, are discussed in [7, 8]. Survey [9] provides an overview of the methods used to identify the point and group type of anomalies in social media, which stand for “the abnormal behaviors of

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individual users” and “the unusual patterns of groups of people” respectively. The construction of the “reference model, i.e., a statistical model that captures the generation process of the observed (or normal) data” [9] and, based on this model, a further assessment of the anomaly’s probability for a new observation [4, 10]. The temporal perspective is one of the important features of social media. That is, the users (actions and content) are usually time-sensitive and the networks themselves evolve over time [9, 11, 12]. But, on the other hand, “very few models are available to capture the temporal aspects of the problem” [9]. The authors [9] also note that “Existing work on traditional anomaly detection… have identified two types of anomalies: one is “univariate anomaly” which refers to the anomaly that occurs only within individual variable, the other is “dependency anomaly” that occurs due to the changes of temporal dependencies between time series.” At this stage of the study, we will cover anomalies of the first type. The main statistical methods for studying time series are: regressive, autoregressive [13, 14], moving average [15], ARIMA [16], autocorrelation [17], the method of harmonic analysis [18], singular spectral analysis [19], bootstrap (numerical reproduction of samples) [20] and other similar ones. Furthermore, one should also note the methods of machine learning that are popular today, most of which are also based on statistical methods and mathematical classifiers [21, 22]. The listed statistical methods can be correctly applied only to stationary processes in which the average values (expected value, variance, covariance, etc.) are constant in time [23]. At the same time, social networks are constantly changing over time. The refinement of these models in case of the dependence of these quantities on time can be done, including the transition to the first, second, etc. differences. In this study, we will try to highlight the features of the time series of user activity and convert them to stationary using the operation of taking sequential differences.

3 In Search of Mean Value and Stationarity The focus of this study are the time series of user activity indicators (likes, reposts, views, comments), used for the task of finding stable mean values of these indicators for a specific community of SNS users. Is it possible to convert these time series to a stationary form to obtain stable averages? In what order of difference stationarity is achieved? For the study, the most popular communities with the general theme of one of the Arctic regions were selected. These communities are created in the popular SNS “VKontakte”. For each publication (post) of the community, as an object of study, we formed four time series, the elements of which are, respectively, the number of likes, reposts, views, comments at a certain point in time. By doing so, we faced the following problem: when does a time series end? Finding the beginning of a time series is quite simple: it corresponds to the time of publication creation and the publication itself should be involved in the monitoring process. Finding the end of a time series is proved to be somewhat more complicated. As a rule, the so-called “lifetime” of a post, i.e. the time

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that passes until the post is lost (“drowns”) among others and its characteristics (views, likes, reposts, comments) either cease to change, or will change very slightly, - is quite brief. Such features are related to the properties of the news feed settings that are specific to SNS VKontakte - and may not be present in, say, Facebook. Such small changes (or their absence) are usually characterized by the appearance in the time series of values equal to zero and further replication of these values, which at the limit can lead to a change in the statistical characteristics of the series. For example, the mean value will also tend towards zero. According to our observations, in some rare cases, a slight change in the values of the series is possible, i.e. single renewal of interest in a post (random view or ‘like’). In this study, we decided to break off the time series if there were no changes in it, i.e. values corresponding to timestamps were equal to zero for a predetermined time interval. Moreover, we deleted the last zero values (“zero tail”). Note that the posts lifetime problem can be the subject of a separate detailed study. In our case, the simplification we used can be considered advisable on the way to solving the question of the possibility of obtaining stationary time series with stable probabilistic characteristics. At the preliminary stage of time series analysis, we plotted the graphs of the autocorrelation function (ACF) for 100 randomly selected posts. A typical graph for the autocorrelation function of the time series containing the number of likes is shown in Fig. 1. ACF charts for time series containing the number of views, comments and reposts have a similar form. Based on such ACF graphs, we can conclude that the series are not stationary.

Fig. 1. ACF for the time series containing the number of likes

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Thus, the use of classical statistics to study the initial time series will not have a probability basis. At the same time, the conversion of the original time series to a stationary form would allow obtaining reasonable, relatively stable probabilistic characteristics for established communities and further use of these characteristics in identifying anomalies, as well as solving problems of modeling and predicting the values of these time series. As part of our planned future work to identify anomalies in the form of user activity bursts in relation to publications (posts), the mean value is of particular importance. Differencing stabilizes the mean value and eliminates the trend and seasonality. For each time series obtained at the preliminary stage of the analysis (likes, reposts, views, comments), we formed the corresponding time series of the first, second, third and fourth order differences. Then, we plotted the autocorrelation function graphs for them as well. A typical view of most of the graphs we constructed for the autocorrelation function of the first and subsequent differences is presented in Fig. 2.

Fig. 2. ACF for the first differences (initial time series - number of likes)

Since the autocorrelation function graphs do not allow us to adequately automate the process of checking the time series for stationarity and, in addition, based on visual analysis only for some of the constructed graphs of the first and further differences it is rather difficult to conclude that the time series is stationary, we used KPSS and Augmented Dickey-Fuller (ADF) tests. According to the results of our experiments, the ADF test is “gentler” and often does not detect the presence of a unit root (i.e. identifies the time series as stationary). Therefore, we provide the results of the KPSS test calculations below. It should be noted that in addition to the ACF graphs, the nonstationary nature of the initial time series of the preliminary analysis stage (the number of likes, reposts, views, comments) is also confirmed by the KPSS and ADF tests.

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The further research plan was as follows. 1. During a certain time interval, collect user activity data for publications in selected VKontakte SNS communities, using the following indicators of user activity: likes, reposts, comments, views. Present the result in the form of time series for each indicator of each community post. 2. For each initial time series, construct series of the first, second, and third order differences. 3. Find statistical characteristics and evaluate the stationarity of each obtained series using KPSS and ADF tests. 4. For each surveyed community, find averaged statistical characteristics calculated for its publications. 5. Perform steps 1–4 for the data of the same communities at a different time interval. 6. Compare the corresponding averaged statistical characteristics of user activity in communities at different time intervals. The main experiment results are presented in the next section.

4 Results and Discussion To get the source data, 21 communities were selected. The total number of publications studied was 34816. The initial data was obtained by monitoring the selected communities at intervals equal to 15 min. A separate time series was obtained for each indicator (likes, reposts, comments, views) of each community publication. As a result, a table was formed for the database where each publication was represented by four time series (likes, reposts, comments, views). A descriptive part was added to each series, holding the identification data of the post, the date of post publication, the dates of the beginning and the end of monitoring, as well as the number of elements in the time series. At the next stage, four more series were added to each time series, obtained by differencing the original series and containing differences of the corresponding order (first-, second-, third-, and fourth). Then the statistical characteristics were calculated for each series: minimum, maximum, and mean value. As user activity regarding the publications faded over time, during long monitoring intervals, the indicators stopped changing and their differences became equal to zero. This so-called “zero tail” strongly influenced statistical characteristics. To reduce its effect, two subsequences were selected from each time series. The first one was the time series of the maximum length (from the beginning of the active users reaction till its end). The beginning and end of this series are determined by zero changes in indicators taken with the initial monitoring frequency (15 min). However, in practice, it turned out that zero changes in indicators were sometimes temporary and, after some time, user activity resumed. The second subsequence was formed with this peculiarity in mind. The beginning and end of this subsequence were determined by zero changes in indicators for more than 4 h. This value was obtained empirically and corresponded with the stoppage of user activity in relation to a particular publication in 99% of cases in a monthly time interval. All previously indicated statistical characteristics were calculated both for the initial series and their differences, and separately for the

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subsequences described above. Further calculation results are given for the second subsequence. Note that during the monitoring process, the database got information about posts that were published long before the start of monitoring, and posts, which activity had not “died out” by the end of the monitoring. Such posts were excluded from the sample under examination. Also, posts were excluded, which activity was at a very low level from the moment of their publication until the end of the monitoring. Those were probably advertising posts or the ones that had been published and then deleted. The series obtained at the previous stages (initial ones and their differences) were checked for stationarity. To determine stationarity, KPSS and ADF tests were used. At the next stage, for each community, averaged values were obtained for all statistical characteristics calculated earlier for its publications (including the results of the stationarity test). The study conducted pairwise comparisons of statistical characteristics of the activity indicators time series in the same communities at two different time intervals (with one-month difference). Figure 3 shows an example of such pairwise comparison. The histogram block lines represent activity indicators in accordance with the order of

Fig. 3. The result of comparing the values of activity indicators for community publications at two different monitoring time intervals

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the difference (in this case, dx1 is the first difference). The columns show the compared statistics of the investigated subsequence (test_burst): length (len), minimum (min), maximum value (max) and mean value. On the histograms themselves, the statistic value is plotted on the X axis, and the number of relevant publications in the community is plotted on the Y axis. The first monitoring data of the time interval is displayed in blue, the second data in pink, and the intersection in brown. The above comparison allows evaluating only the changes that have occurred with one community. To analyze the overall picture of changes across all communities, for each of them, averaged values of publication statistics were obtained. Based on the averaged values, a study of changes in the stationarity characteristics of the considered time series have been performed. Figure 4 represents a comparison of the averaged ‘stationarity’ indicator for public community posts at two different time intervals. Histogram block lines show activity indicators: likes, reposts, comments, views. The columns of the histogram block determine the statistics under study (in this case, kpss_stat_res), taken in accordance with the difference order (dx0..dx3) for the appropriate subsequence (burst). In each histogram, X axis represents the value of the averaged community statistics, and Y axis represents the number of corresponding communities. In this case, the average community statistic — stationarity — is represented by the fraction of its publications whose time series passed the stationarity test. The stationarity of the time series of each publication is determined by the KPSS test. The data of the first stage of monitoring is displayed in blue, pink - the second, brown the intersection. It can be seen that the increase in the difference order is followed by the increase of the publications number in the community for which the stationarity test is performed. Even with a second-order difference, the time series of user activity indicators in relation to most of their publications become stationary in almost all of the communities. Similar comparisons were made on all selected subsequences for all previously calculated averaged statistical characteristics of the studied communities: length (len), minimum (min), maximum value (max) and mean value. Table 1 estimates the level of changes in community activity indicators (the values of indicators, obtained at the first and second monitoring time intervals are compared). Lines of the table are the indicators of activity, and columns are the criteria for assessing the level of changes. The table cells show the corresponding number of communities that meet the specified criteria. As a result, general trends were identified according to which community activity changed during monitoring. To assess the changes reflected in the table, the authors used the following assumptions. The most significant are the first-order differences, which correspond to the changes in indicator values (likes, views, reposts, comments) per time interval. Temporal subsequences without “zero tails” were used as the main ones in the calculations. Assessment of changes was carried out for averaged statistical characteristics: length (len), maximum (max) and mean value. The ratios of these statistical characteristics obtained in the second time interval to the characteristics obtained in the first monitoring time interval were calculated. In accordance with the results, the communities were divided into groups where activity indicators changed by 75%, 50%,

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Fig. 4. The result of comparing the averaged stationarity value for publications in communities at two different time intervals

25% both upward and downward. Communities with activity indicators that did not allow them to be assigned to any of the above groups (n/a) were also identified. Based on the table data, the following conclusions can be drawn. For the “likes” indicator, the lengths of temporary subsequences slightly increased, the maximum values remained almost at the same level, and the mean values decreased. This indicates an increase in time length during which the interest of users remained, but also indicates the decrease in volume of this interest expressed in the number of likes. For the repost indicator, the lengths of time sequences decreased on average, their maximum values also decreased and the mean has retained its level. Overall, this indicator shows lower activity compared to other indicators. This is due to the fact that users usually preferred to “like” publications rather than repost them. Thus, the interest of users in reposting community messages decreased. For the “comments” indicator, the average length of time series increased and the levels of maximum values increased, while the mean values decreased slightly. The data obtained indicate that user activity in commenting community publications increased, and the nature of these processes gained more “burst”. The “views” indicator, on average, retained the lengths of the corresponding time sequences; mean values remained at the same level. The maximum view rate has decreased. Of all the indicators of user activity, “views” can be attributed to the most indirect, since this type of activity does not require any special additional actions from the user and can be triggered by a simple scrolling of the news feed. With this in mind,

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Table 1. Assessment of the level of changes in user activity indicators in communities (number of communities) Users activity indicator

Averaged statistical characteristic

Likes

len max mean

Reposts

Views

Comments

No activity (n/a)

x < 25%

25% N P N P > > > r ¼ Xi Xj covij ! min; > < p i¼1 j¼1 rp  rmin ; > > > N P > > : X ¼ 1; X  0: i

ð5Þ

i

i¼1

The models use the following variables: rp – the return on the investment portfolio, rmin – the given minimum rate of return, rp – the risk of the investment portfolio, rmax – the given maximum risk index, Xi – the share of the investments in the i-th asset in the aggregate portfolio investments. W. Sharpe, H. Markowitz’s successor, proposed an index model that significantly reduced the complexity of the task of constructing a portfolio. After analyzing the securities market in the 1960s, W. Sharpe concluded that there was no need to determine the correlation between individual stocks. In this case, it is sufficient to determine how all stocks are interconnected with the securities market. The rate of return for a portfolio based on the model of W. Sharpe is obtained using the formula (6): RP ¼

n X

x i ð a i þ bi R m þ e i Þ

ð6Þ

i1

where xi – the share of the i-th security in the portfolio; Rm – market index return; ai bi , ei – alpha coefficient, beta coefficient and random deviation of each security, respectively.

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Statistically, the beta indicator can be determined by the formula (7): b¼

covðgRM Þ r2M

ð7Þ

where n – the industry-average return; r2M – the variance of the market-average return. The rate of return for the portfolio based on the model of W. Sharpe is equal to: rp ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b2q r2m þ r2r

ð8Þ

where rp – the standard deviation of the market index; b2q r2m – the market risk of the portfolio; r2r – the portfolio’s own risk. Creating an optimal investment portfolio involves identifying the investor’s most preferred portfolio in the fields of the efficient set. Such a portfolio is selected using the investor’s indifference curve, where the most preferred portfolio is the one that is located on the frontier of the efficient set at the point of contact with the indifference curve. The investor’s indifference curve represents the investor’s particular preference for expected returns, i.e. the risk that the investor is willing to take in order to achieve the desired return.

3 Algorithm for Constructing an Efficient Investment Portfolio Let us consider an algorithm for generating an efficient investment portfolio using Python based on real stock market data. To implement an algorithm for constructing an efficient investment portfolio in Python, we use the following libraries: import pandas as pd import numpy as np import matplotlib.pyplot as plt import datetime from pandas_datareader.moex import MoexReader import scipy.optimize as sco

We will build an efficient portfolio consisting of three stocks, issued by: PJSC Severstal, PJSC Sberbank, and PJSC Surgutneftegas. From the website of the Moscow exchange, we download the stock quotes of companies: PJSC Severstal, PJSC Sberbank, and PJSC Surgutneftegas. Based on the matplotlib library, we plot daily return charts obtained using the pct_change () method. Figure 1 shows the daily return charts for stocks.

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Fig. 1. Daily return on stocks of PJSC “Severstal”, PJSC “Sberbank”, PJSC “Surgutneftegas” plotted in a chart.

We will model random weights for each stock in the portfolio, and then calculate the total annual return of the portfolio and the annual volatility. The “random_portfolios” function generates portfolios with random weights assigned for each stock. The value assigned to the num_portfolios variable indicates the number of portfolios generated.

Figure 2 shows the portfolio with the highest Sharpe ratio and the minimum risk portfolio. Table 2–3 provides information about the shares of assets in the portfolio. Hence, depending on the investor’s preference, a particular portfolio can be selected. If the investor is risk-averse, then a minimum risk portfolio is preferred. If the investor wants to increase returns, the preferred portfolio would be the one with the highest Sharpe ratio.

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Fig. 2. Optimization results for portfolios with different stock weights. Red Star: the maximum Sharpe ratio portfolio. Green star: the minimum risk portfolio. Table 2. The maximum Sharpe ratio portfolio. Assets Shares Annual portfolio return Annual portfolio risk

CHMF SBER SNGS 52,43 47,54 0,03 0,24 0,2

Table 3. The minimum risk portfolio Assets Shares Annual portfolio return Annual portfolio risk

CHMF SBER SNGS 27,0 19,01 53,99 0,09 0,15

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4 Conclusion Thus, the characteristics of an efficient investment portfolio were examined, models and methods for constructing an investment portfolio were analyzed, and algorithms for generating an efficient investment portfolio in Python were considered. Using the example of specific assets, the task of developing an efficient investment portfolio was solved.

References 1. Ahmadi-Javid, A., Fallah-Tafti, M.: Portfolio optimization with entropic value-at-risk. Eur. J. Oper. Res. 279(1), 225–241 (2019) 2. Black, F., Litterman, R.: Asset allocation: combining investor views with market equilibrium. Goldman Sachs Fixed Income Res., 115 (1990) 3. Black, F., Litterman, R.: Global portfolio optimization. Financ. Anal. J. 48(5), 28–43 (1992) 4. Chu, G., et al.: A new online portfolio selection algorithm based on Kalman Filter and anticorrelation. Phys. A: Stat. Mech. Appl., 536, 120949 (2019) 5. Dai, Z., Wang, F.: Sparse and robust mean–variance portfolio optimization problems. Phys. A 523, 1371–1378 (2019) 6. Halkos, G.E., Tsirivis, A.S.: Value-at-risk methodologies for ef-fective energy portfolio risk management. Econ. Anal. Policy 62, 197–212 (2019) 7. Ivanyuk, V.: Econometric forecasting models based on forecast combination methods. In: 2018 Eleventh International Conference Management of Large-Scale System Development (MLSD). IEEE (2018) 8. Ivanyuk, V., Vladimir, S.: Efficiency of neural networks in forecasting problems. In: 2019 Twelfth International Conference Management of Large-Scale System Development (MLSD). IEEE (2019) 9. Koroteev, M.V., Terelyanskii, P.V., Ivanyuk, V.A.: Approximation of series of expert preferences by dynamical fuzzy numbers. J. Math. Sci. 216(5), 692–695 (2016) 10. Lintner, J.: The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. In: Stochastic Optimization Models in Finance. Academic Press, Cambridge, pp. 131–155 (1975) 11. Markowitz, H.: Portfolio Selection. J. Financ., 77–91 Markowitz HM—1952 (1952) 12. Mikhaylov, A., Sokolinskaya, N., Nyangarika, A.: Optimal carry trade strategy based on currencies of energy and developed economies. J. Rev. Glob. Econ. 7, 582–592 (2018). https://doi.org/10.6000/1929-7092.2018.07.54 13. Mikhaylov, A.: Oil and gas budget revenues in russia after crisis in 2015. Int. J. Energy Econ. Policy, 9(2), pp. 375–380 (2019) 14. Nyangarika, A., Mikhaylov, A., Tang, B.-J.: Correlation of oil prices and gross domestic product in oil producing countries. Int. J. Energy Econ. Policy 8(5), 42–48 (2018) 15. Platanakis, E., Urquhart, A.: Portfolio management with crypto-currencies: the role of estimation risk. Econ. Lett. 177, 76–80 (2019) 16. Sharpe, W.F.: Adaptive asset allocation policies. Financ. Anal. J. 66(3), 45–59 (2010) 17. Sunchalin, A.M., et al.: Methods of risk management in portfolio theory. Revista Espacios, vol. 40, p. 16 (2019) 18. Tobin, J.: Liquidity preference as behavior towards risk. Rev. Econ. Stud. 25(2), 65–86 (1958)

Development of an Intelligent Ensemble Forecasting System Vera Ivanyuk1,2(&) 1

, Andrey Sunchalin1,2, and Anna Sunchalina2

Financial University under the Government of the Russian Federation, Moscow, Russia [email protected] 2 Bauman Moscow State Technical University, Moscow, Russia

Abstract. The objective of this work is to develop an intelligent ensemble forecasting system. The task of forecasting is to assess the trend of a particular indicator. Using various forecasting methods, we can obtain predictions of the future values of a selected indicator. Scholars and practitioners often face the problem of choosing the best forecasting method. A combination of independent individual forecasts offers a solution to this problem. In this article, the models and methods for financial time series forecasting and periods of speculative growth in the stock markets identifying are considered. The article proposes a concept of an intelligent forecasting system and presents a software product called MultFinance, which implements both artificial intelligence methods and statistical methods. Multifinance software complex is used for forecasting of micro - and macro-economic indicators as well as in forecasting of the securities, currency exchange and commodity market parameters. Due to the fact that the neural analysis unit is implemented in the system, the system is able to generate a precise forecast in time of crisis. Multiple factor analysis is also represented in the MultFinance system, which has a positive impact on the forecast efficiency and performance. The MultFinance system includes a unit for identifying periods of speculative growth, which is implemented on the basis of a perceptron. It can be used to assess the risk of assets and determine the number of financial bubbles in the financial market. #COMESYSO1120 Keywords: Forecasting network


Identification
Artificial intelligence
Neural

1 Introduction The interest in ensemble forecasting has been growing year after year. The study by J. M. Bates and C. W. J. Granger [1] was one of the first scientific papers written on combining forecasts. Since then, more than a dozen scientific works on the problems of combining forecasts have been published. The most prominent among them are R. T. Clemen [2], S. Makridakis, E. Spiliotis, V. Assimakopoulos [8], F. Prado, M.C. Minutolo, W. Kristjanpoller [13], L. Yu, S. Liang, R. Chen & K. K. Lai [15]. The authors J.M. Bates and C.W.J. Granger [1] were among the first to consider the advantages of the combined forecast and showed that the use of the method based on the arithmetic mean of individual forecasts can enhance the forecasting accuracy. The © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 491–500, 2020. https://doi.org/10.1007/978-3-030-63322-6_40

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forecast was constructed using the airline passenger data. To build the forecast, exponential smoothing and Box-Jenkins method were used. In the article by F. Prado, M. C. Minutolo, W. Kristjanpoller [13], a new method for constructing an ensemble forecast is considered. The authors proposed an aggregate ensemble forecast comprising an autoregressive integrated moving average (ARIMA), artificial neural network, fuzzy inference system model, adaptive neuro-fuzzy inference system, support vector regression, extreme machine learning, and genetic algorithm to forecast aggregated long-term energy demand. The paper by L. Yu, S. Liang, R. Chen & K. K.Lai [15] suggests the forecasting methodology based on the hybrid ensemble comprising the EMD-LSTM-ELM model originated from the decomposition-reconstruction-ensemble framework. To investigate ensemble forecasts, Makridakis and Winkler [7] held the “M1competition”. It aimed to study 1001 time series in various fields of knowledge. The competition was focused on examining how the forecast accuracy would change when combining individual forecasts. To evaluate the performance of the criterion, the mean absolute percentage error (MAPE) was taken. It was calculated for each individual forecast and any possible combination of them. In the course of the study, the maximum, average and minimum MAPEs were compared as well. As one of the findings of the study, it was concluded that the optimal number of individual forecasts participating in the combination should not exceed six. Otherwise, adding more individual forecasts had a negative effect on the combined forecast. In their work, S. Makridakis and R. L. Winkler [7] also noted that the combined forecast reduced the risk of choosing an insufficiently accurate forecast method. There are many branches of economics requiring qualitative analysis and forecasting [9, 10]. The major issue lies in the economic health of an object influenced by a great variety of factors [11, 14]. Since analyzing only economic factors generate forecasts of poor quality, for obtaining more reliable results it is necessary to use data, which directly or indirectly influence the object of study. The analysis of these data could result in better displaying of the economic situation and make the forecast more reliable [12]. The forecasting is mainly targeted at defining leading processes, significance and the way these influence the forecasted index [6]. There is an approach to the synthesis of the forecasting system proposed, with which the economic index is represented as a superposition of leading and external influencing factors. The main concept of the MultFinance intelligent forecasting system developed by the author V. A. Ivanyuk is depicted in Fig. 1 [2–5]. Figure 1 shows the major units of the system: the statistical analysis and forecasting unit, the neural analysis and forecasting unit, and the genetic algorithm unit. Also, there is a separate unit for identifying periods of speculative growth, which is necessary to determine the risk of a financial asset. In the following, we will consider the components of the system (Fig. 1).

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Fig. 1. Structural elements of the system.

2 Intelligent Forecasting System Conceptual Design Let us consider the elements of the statistical analysis and forecasting unit: The statistical analysis unit includes several methods: – a linear forecasting based method that perfectly reflects current trends in changing the economic situation; – a historical forecasting based method that searches for a similar sector in historical data; – a method of searching for cryptic correlation of ordered samples using the time lead technique, which is based on an assumption that the series being analyzed invisible or obviously interdepend with a certain time delay. An artificial neural network (ANN) is a system of connected and interacting neurons. Artificial neural networks are mathematical models based on the principle of construction and operation of biological neural networks. Mathematically, an artificial neuron is represented as a nonlinear function of a single argument – a linear combination of all input signals.

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The neuron performs a nonlinear transformation of the sum of products of input signals by weight coefficients. Depending on the functions performed by neurons in the network, three types of neurons can be distinguished: 1. input neurons that are fed a vector encoding the input effect or the image of the external environment; they usually do not perform any computational procedures; 2. intermediate neurons that form the basis of neural networks; 3. output neurons whose output values represent the outputs of the neural network. The neuron model is described by the following equation: y¼F

Xk

 w x ¼ F ðW; X Þ i i i¼1

ð1Þ

where X ¼ ðx1 ; x2 ; . . .; xn ÞT – input signal vector; W ¼ ðw1 ; w2 ; . . .; wn Þ – weight vector; F – nonlinear transformation operator. The current state of a neuron is defined as the weighted sum of its inputs: S¼

n X

xi w i

ð2Þ

i¼1

The output of a neuron is a function of its state: y ¼ f ð SÞ

ð3Þ

Any neural network consists of three main elements: 1) synapses and connections; 2) adder; 3) activation function. Of particular interest are the activation functions used to calculate values at the outputs of neurons. They can be of different forms. One of the most common is a nonlinear function with saturation, the so-called logistic function or sigmoid. When decreasing, the sigmoid becomes flatter, in the limit with a = 0 degenerating into a horizontal line at the level of 0.5. From the expression for the sigmoid, it is obvious that the output value of the neuron lies in the range [0,1]. f ð xÞ ¼

1 1 þ eax

ð4Þ

The sigmoid function is differentiable on the entire x-axis, which is taken advantage of in some learning algorithms.

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In ANN-based predictive systems, the best performance is shown by heterogeneous networks consisting of hidden layers with a nonlinear activation function of neural elements and an output linear neuron. Several distinctive features of ANNs make them valuable and attractive in forecasting. First, ANNs operate with nonlinear data. They are able to perform nonlinear modelling without prior knowledge of the relationships between input and output variables. Thus, they appear to be more general and flexible modelling tools for forecasting. Second, ANNs are universal functional approximators. The network can approximate any continuous function with any required accuracy. Third, ANNs can generalize information. Since prediction is performed by determining future behaviour based on the past, it is an ideal application area for neural networks. Despite the wide array of features, artificial neural networks have several shortcomings in solving problems: 1. to build a neural network, one needs to configure a lot of settings for internal network elements and connections between them; 2. the selection of a neural network training algorithm is rather difficult; 3. there are strict requirements for the training sample; 4. the time-consuming procedure of training the network does not allow the real-time execution of neural networks; 5. designing neural network architecture is a challenging task as well. The use of artificial neural networks allows for more accurate forecasting; moreover, the network can learn, generalize the knowledge it obtained, and adapt: that is, it shows high resistance to interference. However, when solving forecasting problems using neural networks, it is difficult to prepare and process input values and choose the appropriate network architecture. It is reasonable to use a combined system composed of statistical analysis methods and neural network model for forecasting and analysis. Combining these approaches, we obtain an ensemble forecast that has better quality characteristics. The next element of the forecasting intelligent system is a genetic algorithm. GA bases on the biological principles of evolution and contains operators for the formation of initial population, crossing, mutation and selection. The software complex MultFinance embodies a genetic algorithm which is based on the method of optimal polynomial coefficients selection using stochastic laws. Main advantages of the genetic algorithm are as follows: good convergence on indeterminate tasks, impossibility of falling in local optimum, and model simplicity. The disadvantage of the genetic algorithm is as follows: long duration of the calculation process.

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The genetic algorithm in the MultFinance intelligent forecasting system is aimed at identifying the most viable individuals among data, functions, and analytical methods. Depending on the viability (probability of a reliable forecast) of an individual, a larger number of resources (processor time) shall be allocated for its processing. This will allow optimizing and accelerating the calculation in the system, and, thus, will result in the most reliable forecast and more accurate analytical results. In the designed software complex, a genetic algorithm is represented as a forecasting function superposition with a final forecast as an aggregate result. For each of the functions in the superposition, it is necessary to select a reliability coefficient with the optimization expressed as the least error when forecasting via superposition on historical data. The MultFinance forecasting system is written in C++. The following is a code snippet of the genetic algorithm in the MultFinance system. Result - >AppendText(wxT); float gk[5]={0,0,0,0,0}; float gp=0; gastart: // Result->AppendText(wxString::Format(wxT("\r %i "),(int)zhivih)); sv++; de++; mu++; if (sv==svadba && zhivih>3){svadba_p(zhivih);sv=0;zhivih++;} if (de==death && zhivih>1){death_p(zhivih,rmax,columns,n_maxoper);de=0;zhivih--;} if (mu==mut && zhivih > 3){mutes_p(zhivih);mu = 0;} if (zhivih > 2){goto gastart;} if (zhivih == 2){zhivih--;} i=0; cp = rmax; progn = kp[0][i]*ln_pr(cp)+kp[1][i]*sl_pr(cp)+kp[2][i]*ko_pr(cp)+kp[3][i ]*is_pr(cp)+kp[4][i]*nr_pr(columns,rmax); gp = gp + progn /10; for (long n = 0; n < 5; n++) { gk[gac] = gk[gac] + kp[n][0] /10; } } //Result->AppendText(wxString::Format(wxT(" %3.3f "),(float)(progn))); Result->AppendText(wxString::Format(wxT(" %3.3f "),gp))

Figure 2 shows an example of a financial asset forecast. The forecast presented is formed for one period ahead.

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Fig. 2. MultFinance intelligent forecasting system for financial time series.

3 Intelligent Forecasting System Conceptual Design The last element of MultFinance intelligent forecasting system is a unit for identification of speculative growth periods. This module allows identifying financial bubbles. The continuous speculative asset price growth in financial markets give rise to an increasingly higher concern among researchers, experts, and financial regulators. As a result, an increasingly higher number of scientists are now working towards the development of efficient bubble forecasting, identification, measurement, and age dating methods. Researched made by Robert Schiller, Paul Krugman, Josef Stieglitz, Didier Sornette shall be emphasized among the most recent ones. This research offers an author’s concept of identifying speculative growth periods in a securities market. This analytical module is implemented on the basis of the conventional perceptron, which allows identifying specific areas in time series. Let us consider the methodology of time series forecasting based on the perceptron. To determine the characteristic area (shape) of the time series it is necessary and sufficient to obtain the first-order difference Dyðxn Þ ¼ yðxn Þ  yðxn1 Þ and the secondorder difference D2 yðxn Þ ¼ Dyðxn Þ  Dyðxn1 Þ for all xn from the area of analysis. Let us consider the methodology of time series forecasting based on the perceptron. To determine the characteristic area (shape) of the time series it is necessary and sufficient to obtain the first-order difference Dyðxn Þ ¼ yðxn Þ  yðxn1 Þ and the secondorder difference D2 yðxn Þ ¼ Dyðxn Þ  Dyðxn1 Þ for all xn from the area of analysis. When we convert data into a binary form, the information about the signs of both differences Dy; D2 of y(x) function remains unchanged:

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8 8 < 00; D2 y ¼ 0 < 00; Dy ¼ 0 2 dy ¼ 01; Dy\0 ; d y ¼ 01; D2 y\0 : : : 10; Dy [ 0 10; D2 y [ 0 Thus, the characteristics of time series behaviour on dx could be expressed as a consequent four-digit binary figure of dyd2 y type. This figure includes all possible lines of behaviour (for example 0010 or 0100). We will use the classic perceptron to search for such figures. The function realized by perceptron can be recorded as: f ðSÞ ¼ sign

X n

Vn sign

X

! Wi Si  hAn

!  hR

i

where Wi ,Vn – coefficients hAn , hR – thresholds of activation; Si – elements of an ordered set; Wi ; Vn , hAn , hR – are tried till we get f ðSÞ ¼ 1: Identification of speculative growth periods» module of MultiFinance system enabling to identify bubbles along the entire time series is shown on Fig. 3, 4 and 5. Based on the example of a financial asset owned by Gazprom JSC, periods of speculative growth and risks are determined.

Fig. 3. Detecting the number of financial bubbles at the given parameters as in the case of Gazprom JSC’s stocks. 2 bubbles have been detected.

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Fig. 4. Detecting the number of financial bubbles at the given parameters as in the case of Gazprom JSC’s stocks. 4 bubbles have been detected.

Fig. 5. Detecting the number of financial bubbles at the given parameters as in the case of Gazprom JSC’s stocks. 10 bubbles have been detected.

4 Conclusion Thus, the above methods and algorithms provide a basis for designing a computational system for the multifactorial analysis, forecasting and identification of financial time series. This system has advantages over other systems such as: – the advanced unit of economic analysis is able to find the explicit and implicit relationship between economic data series is implemented; – since the neural analysis unit is implemented in the system, the system is able to generate a precise forecast in time of crisis.

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MultFinance software complex is used for forecasting of micro- and macroeconomic parameters as well as in forecasting of the securities, currency exchange and commodity market parameters.

References 1. Bates, J.M., Granger, C.W.J.: The combination of forecasts. J. Oper. Res. Soc. 20(4), 451– 468 (1969) 2. Clemen, R.T.: Combining forecasts: a review and annotated bibliography. Int. J. Forecast. 5 (4), 559–583 (1989) 3. Ivanyuk, V.: Econometric forecasting models based on forecast combination methods. In: 2018 Eleventh International Conference Management of Large-Scale System Development (MLSD). IEEE (2018) 4. Ivanyuk, V., Tsvirkun, A.: Intelligent system for financial time series prediction and identification of periods of speculative growth on the financial market. IFAC Proc. Vol. 46 (9), 1128–1133 (2013) 5. Ivanyuk, V., Soloviev, V.: Efficiency of neural networks in forecasting problems. In: 2019 Twelfth International Conference Management of Large-Scale System Development (MLSD). IEEE (2019) 6. Koroteev, M.V., Terelyanskii, P.V., Ivanyuk, V.A.: Approximation of series of expert preferences by dynamical fuzzy numbers. J. Math. Sci. 216(5), 692–695 (2016) 7. Makridakis, S., Winkler, R.L.: Averages of forecasts: Some empirical results. Manag. Sci. 29 (9), 987–996 (1983) 8. Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The M4 Competition: 100,000 time series and 61 forecasting methods. Int. J. Forecast. 36(1), 54–74 (2020) 9. Mikhaylov, A., Sokolinskaya, N., Lopatin, E.: Asset allocation in equity, fixed-income and cryptocurrency on the base of individual risk sentiment. Invest. Manag. Financ. Innov. 16(2), 171 (2019) 10. Mikhaylov, A.Y.: Pricing in oil market and using probit model for analysis of stock market effects (2018) 11. Nyangarika, A., Mikhaylov, A., Richter, U.: Influence oil price towards economic indicators in Russia. Int. J. Energy Econ. Policy 1(6), 123–130 (2019) 12. Nyangarika, A., Mikhaylov, A., Richter, U.: Oil price factors: forecasting on the base of modified auto-regressive integrated moving average model. Int. J. Energy Econ. Policy 9(1), 149–159 (2019) 13. Prado, F., Minutolo, M.C., Kristjanpoller, W.: Forecasting based on an ensemble autoregressive moving average-adaptive neuro-fuzzy inference system–neural networkgenetic algorithm framework. Energy 197, 117159 (2020) 14. Soloviev, V.: Fintech ecosystem in Russia. In: 2018 Eleventh International Conference Management of Large-Scale System Development MLSD. IEEE (2018) 15. Yu, L., Liang, S., Chen, R., Lai, K.K.: Predicting monthly biofuel production using a hybrid ensemble forecasting methodology. Int. J. Forecast., 1–18 (2019). ISSN 0169-2070. https:// doi.org/10.1016/j.ijforecast.2019.08.014

Intelligent Methods for Predicting Financial Time Series Vera Ivanyuk1,2(&) 1

and Kirill Levchenko1

Financial University under the Government of the Russian Federation, Moscow, Russia [email protected] 2 Bauman Moscow State Technical University, Moscow, Russia

Abstract. In modern conditions, to ensure the successful implementation of any activity, it is necessary to perform high-quality and efficient forecasting of current processes. The scope of application is expanding, making the task of forecasting even more important and complex. The increasing role of forecasting in the modern world has given rise to over a hundred models and methods of forecasting. For this reason, the challenge becomes to select the optimal variant of forecasting the process or system under study. In the present article, the main mathematical methods of forecasting time series are analyzed and their advantages and disadvantages described. Criteria for the accuracy of forecasting models are defined. Practical application of various models is considered. The possibilities for implementing forecasting models are investigated. The capabilities of the Python programming language for developing forecasting models are evaluated. The paper solves the problem of constructing a weighted-average forecast, which consists of several individual forecasts. Original forecasting models that were used in the combination included Arima, gradient boosting and a fully connected feed-forward neural network. Neural networks are growing more relevant today, as they enable forecasting in the event of a crisis and uncertainty. During the implementation of the programming solution, the mean absolute errors were computed for each forecasting method as well as for the weighted-average forecast. #COMESYSO1120. Keywords: Algorithmization


Programming
Ensemble forecast

1 Introduction A forecast is a scientifically based judgment about the possible states of an object in the future, alternative paths and the duration of its existence. There are a large number of methods for making a forecast: 1. Regression models and methods. Regression has been used in a wide variety of applications including forecasting and management tasks. The purpose of regression analysis is to determine the relationship between the source variable and a set of external factors. In the process, the regression coefficients can be determined using the least-squares method or the maximum likelihood method.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 501–509, 2020. https://doi.org/10.1007/978-3-030-63322-6_41

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2. Autoregressive models and methods. Autoregressive models are based on the assumption that the value of the process depends linearly on a certain number of previous values of the same process. 3. Neural network models and methods. Using neural networks, it is possible to model the nonlinear dependence of the time series’ future value on its actual values and on the values of external factors by training it over available data [1, 7, 12]. 4. Exponential smoothing is used to model financial and economic processes. The underlying idea of exponential smoothing is the constant revision of the forecast values as actual values are received. 5. Probabilistic modelling. It is based on the exponential smoothing method, but it is probabilities that are estimated, not coefficients. It is intended for making specific forecasts. 6. Models based on Markov chains. It is assumed that the process state is affected only by the current state, while the previous state has no effect.

2 Forecast Accuracy Assessment Table 1 shows the advantages and disadvantages of each forecasting method. Table 1. Comparison of forecasting models and methods. Forecasting models and methods Regression models and methods Autoregressive models and methods Neural network models and methods Exponential smoothing models Probabilistic modelling Models based on Markov chains

Advantages

Disadvantages

Simplicity, flexibility, transparency of modelling;

Difficulty of determining the functional dependence;

Consistency of analysis and design; quick generation of results Non-linearity of models; scalability, high adaptability; applicability for a wide range of problems Quick generation of results; ability to solve long-term tasks

Complexity of modelling nonlinearities; low adaptability

Provides a reliable forecast given the sufficient amount of input data Simplicity of models; consistency of analysis and design

Necessity of a large number of observations Narrow applicability of models

Complexity of choosing the architecture and the software implementation; resource-intensive learning process Narrow applicability of models

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The most frequent choice is a forecasting method that gives a more accurate forecast [4, 8, 11]. Considerable attention in research is paid to the combination of forecasts as a means of improving the quality of forecasting [2, 3, 5, 9, 14]. The quality of the forecast is determined by the following main requirements: accuracy, verifiability, and relevance. The accuracy of the forecast depends on how close the forecast values and the forecast model itself are to the actual process or object of forecasting. Verifiability is a quantitative measure of a forecast’s reliability. Relevance is associated with the ability to adapt and adjust the model in connection with changes in the external environment. The model used to solve forecasting problems must meet several criteria. These criteria are: • completeness of the model, flexibility to changes, ability to adapt and evolve; • abstractness of the model, i.e. the model must include some variables; • ability to check the accuracy and reliability of results. Predictive estimates may differ from actual estimates for the following reasons: • reinsurance (for example, excessive caution when estimating). Any change in the initial values will obviously affect the accuracy and reliability of forecast indicators; • unjustified overestimation associated with the analyst’s desire to pass off their assumptions as fact; • absence or shortage of information impeding one’s ability to foresee the future development of the process; • errors in the forecast model. The accuracy indices of the forecasting model determine the value of the resulting error. To confirm the quality and suitability of the model used, one needs to analyze the system of indicators that reflect both the adequacy of the model and its accuracy. The accuracy of the forecast is determined by the amount of error in the forecast. As the error value decreases, the accuracy of the forecast increases accordingly. There are different margins of error that can be used to evaluate forecast indicators, including the mean squared error (MSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) [6, 10]. In contrast to measures based on absolute error, the main advantage of the mean squared error MSE is that it can be decomposed into a number of basic components that allow us to evaluate the accuracy of the forecast [13, 15, 16]. The square root of the MSE error value is denoted as RMSE – the rootmean-square error of the forecast. MAD, the mean absolute deviation, measures the accuracy of the forecast by averaging the values of forecast errors. Most often, MAD is used when the forecast error must be measured in the same units as the original values of the time series. Validation of the model for adequacy is made in terms of formal statistical criteria. A prerequisite for such verification is the availability of valid and reliable statistical parameters of the forecast object and the model itself. There are several methods for evaluating the adequacy of the forecast: – If the model adequately represents the data, then the residuals must be independent and have zero mean.

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– The second method is based on direct comparison of theoretical characteristics of the model with relevant sample characteristics. As such, it is possible to use theoretical and sample autocorrelations. Model verification implies an analysis of the functional completeness, accuracy and reliability of the model, carried out on the basis of all available information in situations where the adequacy test cannot be performed for some reason. The verification method is very often used in forecasting in the absence of a real object, as well as in the development of new functions for some forecasting object. There are several verification methods. – Direct verification is the development of a model of the same object using a different forecasting method. – Indirect verification is the comparison of results obtained using this model with information received from other sources. – Sequential verification is the verification of the modelling results performed using analytical or logical forecast generation based on previously received forecasts. The task of verifying the forecast model remains very relevant since the absolute agreement of the forecast and the real process is extremely rare.

3 Forecasting Method Based on Combining Individual Forecasts With proper analysis of individual forecasts, one can obtain a more accurate combined forecast that exceeds the accuracy of either of the original forecasts. The aggregate forecast is given by: F¼

Xn i¼1

wi fi

ð1Þ

where F – the aggregate forecast, fi – the i-th individual forecast, wi – the weight coefficient of the i-th individual forecast with which it was included in the aggregate forecast. Finding the right weight coefficients to attribute to each forecast that is included in the combination is the main task when choosing a method for combining forecasts. There are several restrictions on weight coefficients: • The sum of the coefficients must be equal to 1; • The weight coefficients must belong to the interval [0,1]. There are several methods for selecting weights, all of which are aimed at assigning a larger weight to the forecast that has a smaller error. These methods take into account the correlation of individual forecasts with their accuracy and must meet the following requirements: • The accuracy of individual forecasts is constant; • Forecasts do not contain systematic errors.

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Let us consider a practical implementation of the weighted-average forecast based on oil prices from January 1, 2015 to March 1, 2020. The ensemble forecast includes three forecasting methods: Arima, gradient boosting, and a fully connected feedforward neural network. The following is the programming implementation of the Arima method. def evaluate_arima_model(train,test, arima_order): # prepare training dataset # train_size = int(len(X) * 0.70) # train, test = X[0:train_size], X[train_size:] history = [x for x in train] # make predictions predictions = list() for t in range(len(test)): model = ARIMA(history, order=arima_order) model_fit = model.fit(disp=0) yhat = model_fit.forecast()[0] predictions.append(yhat) history.append(test[t]) # calculate out of sample error error = MAPE(test, predictions) return predictions pred1=evaluate_arima_model(np.array(train),np.array(test),(0,1,1 )) mape1=MAPE(test,pred1) mse1 = mean_squared_error(test, pred1)

Figure 1 shows the 10-day Arima forecast and the confidence interval.

Fig. 1. Arima forecast for 10 days ahead.

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Table 2 shows basic quality metrics for the Arima forecast model.

Table 2. Basic statistics for the Arima forecast. Number of values 476 MAPE 11.769 MSE 1.421

Let us build a gradient boosting model and estimate its quality using a training sample: Figure 2 shows a 10-days forecast made using the Gradient boosting method.

Fig. 2. Gradient boosting forecast for 10 days ahead.

Table 3 shows basic quality metrics for the Gradient boosting model. Table 3. Basic statistics for the Gradient boosting forecast. Number of values 476 MAPE 11.386 MSE 2.559

Let us build a neural network and estimate the quality of this model based on the training sample. The neural network has three hidden layers and one output layer.

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Figure 3 shows an NN-based forecast for ten days ahead.

Fig. 3. 10-day forecast based on a neural network.

Table 4 shows basic quality metrics for the neural network model. Table 4. Basic statistics for the neural forecast. Number of values 476 MAPE 1.374 MSE 1.575

Using the above forecasts that we built earlier, we will now generate a weighted average forecast and assess its performance (Fig. 4).

Fig. 4. Aggregate forecast for 10 days ahead.

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vesa1=1-mape1/(mape1+mape2+mape3) vesa2=1-mape2/(mape1+mape2+mape3) vesa3=1-mape3/(mape1+mape2+mape3) k1=vesa1/(vesa1+vesa2+vesa3) k2=vesa2/(vesa1+vesa2+vesa3) k3=vesa3/(vesa1+vesa2+vesa3) kl["4"]=k1*kl["1"]+k2*kl["2"]+k3*kl["3"] x = np.linspace(0, 476, 476) fig, ax = plt.subplots(figsize=(18, 10)) plt.plot(x,kl["4"],color="r",label = 'нейронная сеть') plt.plot(x,y_holdout,color="g",label = '') ax.set_title plt.xlabel plt.ylabel ax.legend()

Table 5 shows basic quality metrics for the ensemble forecast.

Table 5. Basic statistics for the ensemble forecast. Number of values Average Standard deviation Minimum value 25 percent quantile MAPE MSE

476 65.199 6.887 49.192 60.683 11.650 1.515

4 Conclusions In the course of the work, the following tasks have been solved: the main mathematical methods for time series forecasting have been analyzed; the criteria for the accuracy and reliability of forecasting models have been defined; the practical application of an aggregate forecast has been considered; the capabilities of the Python programming language to generate an aggregate forecast have been assessed. We have constructed individual forecasting models such as Arima, gradient boosting, and a fully connected feedforward neural network and formed a weightedaverage forecast.

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References 1. An, J., Mikhaylov, A., Kim, K.: Machine learning approach in heterogeneous group of algorithms for transport safety-critical system. Appl. Sci. 10(8), 2670 (2020) 2. Armstrong, J.S. (ed.): Principles of Forecasting: A Handbook for Researchers and Practitioners, vol. 30. Springer, Heidelberg (2001) 3. De Menezes, L.M., Bunn, D.W., Taylor, J.W.: Review of guidelines for the use of combined forecasts. Eur. J. Oper. Res. 120(1), 190–204 (2000) 4. Elizarov, M., et al.: Identification of high-frequency traders using fuzzy logic methods. In: 2017 Tenth International Conference Management of Large-Scale System Development (MLSD). IEEE (2017) 5. Fischer, I., Harvey, N.: Combining forecasts: What information do judges need to outperform the simple average? Int. J. Forecast. 15(3), 227–246 (1999) 6. Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006) 7. Ivanyuk, V., Soloviev, V.: Efficiency of neural networks in forecasting problems. In: 2019 Twelfth International Conference Management of Large-Scale System Development (MLSD). IEEE (2019) 8. Koroteev, M.V., Terelyanskii, P.V., Ivanyuk, V.A.: Fuzzy inference as a generalization of the Bayesian inference. J. Math. Sci. 216(5), 685–691 (2016) 9. Leitner, J., Leopold-Wildburger, U.: Experiments on forecasting behavior with several sources of information–a review of the literature. Eur. J. Oper. Res. 213(3), 459–469 (2011) 10. Makridakis, S., Hibon, M.: The M3-competition: results, conclusions and implications. Int. J. Forecast. 16(4), 451–476 (2000) 11. Mikhaylov, A., Tarakanov, S.: Development of Levenberg-Marquardt theoretical approach for electric networks. In: Journal of Physics: Conference Series, vol. 1515 (2020) 12. Radosteva, M., et al.: Use of neural network models in the market risk management. Adv. Syst. Sci. Appl. 18(2), 53–58 (2018) 13. Stewart, T.R., Lusk, C.M.: Seven components of judgmental forecasting skill: implications for research and the improvement of forecasts. J. Forecast. 13(7), 579–599 (1994) 14. Stock, J.H., Watson, M.W.: Combination forecasts of output growth in a seven-country data set. J. Forecast. 23(6), 405–430 (2004) 15. Thomson, M.E., et al.: Combining forecasts: performance and coherence. Int. J. Forecast. 35 (2), 474–484 (2019) 16. Wilkie, M.E., Pollock, A.C.: An application of probability judgement accuracy measures to currency forecasting. Int. J. Forecast. 12(1), 25–40 (1996)

ASC-Analysis of the Dependence of Volume and Structure of Highly Productive Dairy Cattle Incidence in Krasnodar Region E. V. Lutsenko1, V. A. Grin2, K. A. Semenenko2, M. P. Semenenko2(&), E. V. Kuzminova2, and N. D. Kuzminov2 1

2

Kuban State Agrarian University named after I.T. Trubilin, 350004 Krasnodar, Russia [email protected] Krasnodar Research Center for Animal Husbandry and Veterinary Medicine, 350055 Krasnodar, Russia [email protected], [email protected], [email protected], [email protected], [email protected]

Abstract. The aim of the research was to make a quantitative study of the dependence of volume and structure of the incidence of the imported selection cattle on the importing country. To identify and to study these dependencies on the basis of directly empirical data, a new innovative method of artificial intelligence was used - Automated system-cognitive analysis (ASC-analysis) and its software tool - the intellectual system “Eidos”. As a result of the study, statistical and system-cognitive models were created, which have a very high reliability according to the van Rijsbergen F-measure. Based on these models, graphic and tabular output forms were obtained that show the determination system, which reflects the high, medium, and low incidence of dairy cows of imported selection of various diseases. The studies will be useful to specialists whose area of competence includes the procurement of cattle outside the region and from importing countries. #COMESYSO1120.

1 Introduction In modern conditions of the Russian economy the problem of intensification of dairy cattle breeding is becoming particularly relevant in connection with the need to ensure food security of the country [1]. Moreover, the main way to achieve this goal is to increase the genetic potential of animals of dairy breeds due to the import cattle [2]. Animals with the high breeding value of the best foreign genotypes are imported into our country with the aim of using them in domestic cattle breeding and completing modern milk production complexes. Animals imported into our country have a characteristic genetically based ability to convert the feed nutrients of the diet into milk at a low cost per unit of output. However, as a result of targeted selection only for milk productivity, highly productive cows often show low resistance, gentleness, increased sensitivity to stress, and a pathological response to changing conditions of maintenance and feeding [3–6]. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 510–526, 2020. https://doi.org/10.1007/978-3-030-63322-6_42

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Therefore, the majority of farms in the Krasnodar Region that acquired expensive breeding animals faced the problem of difficult adaptation of imported cows to the conditions of our region, accompanied by not only a decrease in milk productivity, but also significant mortality. The increased incidence and disposal of dairy cows necessitates the annual replacement of some imported animals with the new livestock [7, 8]. In this case, measures aimed at reducing the incidence and deaths of imported livestock are relevant not only in terms of increasing its safety, but also restoring the productive health of the animal [9]. It should be noted that there are factors that reduce the risk of developing metabolic pathology in dairy cows, which is the trigger for many diseases of noninfectious pathology. Such factors include pronounced causal relationships between the volume and structure of the incidence of cattle from the importing country. However, there are practically no studies in Russia or in the world devoted to the study of such dependencies, which makes them relevant for a wide range of veterinary specialists. With the development of intelligent technologies, the field of their successful application is constantly and rapidly expanding. Scientific research in this area is traditionally devoted to the development of conceptual approaches and mathematical models, while for specific researchers it is not so important to have a detailed description of the mathematical model, but rather implement software tools that are effective for solving specific problems. However, according to the studied literature, intelligent technologies were practically not used to analyze the consequences of the purchase of cattle from various nonregional and foreign suppliers. At least we were not able to find works devoted to this topic in the English language search engines. The fact is that the path from a mathematical model to an implementation software system is quite long. This path includes the development of a numerical calculation technique (i.e., data structures and algorithms for their processing), as well as software implementation. All this involves significant costs of various types of resources, especially intellectual, long time and financing. Apparently, at the present time, intelligent technologies are still too expensive and difficult to use for veterinary medicine. Therefore, the authors tried to solve this problem, or at least set the stage for its solution by proposing a new mathematical model based on the theory of information, which provides the identification of the strength and direction of the places of cattle purchase on the volume and structure of its incidence. In addition, an appropriate numerical calculation technique and software tools that implement them have been developed [10, 11]. The aim of the work was to make a quantitative study of the dependence of the volume and structure of the incidence of cattle on their places and countries of purchase.

2 Materials and Methods 2.1

Justification of the Choice of Method and Tools for Solving the Problem

As a method of the research we used automated system-cognitive analysis (ASCanalysis), which is a new innovative method of artificial intelligence: it also has its own software tool – an intelligent system “Eidos” (open source software) [10, 11].

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Stages of the ASC-analysis are following: 1. Cognitive-targeted structuring of the subject area: – Development of classification scales; – Development of descriptive scales. 2. Formalization of the subject area: – Development of gradations of classification scales; – Development of gradations of descriptive scales. – Coding of source data using classification and descriptive scales and gradations and the formation of a training sample. 3. Synthesis, quality improvement and verification of statistical and system-cognitive models. 4. Solution in the most reliable model: – Identification (recognition, diagnosis, classification); – Decision support; – Research of the simulated domain by examining its model. Consider the essence of the mathematical model of ASC-analysis and its application to solve the tasks. 2.2

The Essence of the Method and Mathematical Model of ASCAnalysis

The essence of the ASC-analysis method is to consistently increase the degree of formalization of the model, transform the data into information, and information into knowledge and solve identification problems based on this knowledge, support for decision-making and research of the simulated subject area. In the ASC-analysis, all the values of the factors are considered from one single point of view: how much information is contained in their values about the transition of the modelling and control object on which they influence, in a certain future state described by the class (gradation of the classification scale), and at the same time, the strength and direction of the influence of all the values of factors on the object are measured in the same units common to all factors: units of the amount of information. 2.3

Synthesis of System-Cognitive Models and Particular Criteria of Knowledge, Multi-parameter Typing

The mathematical model of the ASC-analysis and the “Eidos” system is based on systemic fuzzy interval mathematics and provides comparable processing of large volumes of fragmented and noisy interdependent data presented in various types of scales (nominal, ordinal and numerical) and various units of measurement. The essence of the mathematical model of ASC-analysis is following. The matrix of absolute frequencies is calculated directly on the basis of empirical data. Using the

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“programming style”, the calculation of this matrix is written using formula (1). If the i-th word is found in the text belonging to the j-th class, then: Nij ¼ Nij þ 1; NiR ¼ NiR þ 1; NRj ¼ NRj þ 1; NRR ¼ NRR þ 1

ð1Þ

Based on expression (1), formulas (2) calculate the matrices of conditional and unconditional percentage distributions: Pij ¼

Nij NiR ; PiR ¼ NRj NRR

ð2Þ

In the ASC-analysis and its software tool, the “Eidos” intellectual system, two methods for calculating matrices of conditional and unconditional percentage distributions are used: 1st method: as NRj the total number of features in the class is used; 2nd method: as NRj the total number of objects of the training sample in the class is used. Then, based on expressions (2) using particular criteria given by formulas (3), matrices of system-cognitive models are calculated: P

N N

Iij ¼ W  Log2 Piji ; Iij ¼ W  Log2 Niji Nj ; Iij ¼ Nij  N N

Ni Nj N ; Iij Nij Ni Nj  N

¼

Pij Pi

1¼

Iij ¼ NiijNj  1; Iij ¼ Pij  Pi ; Iij ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 W  W P M  2 P P 2 2 1 1   riR ¼ W1 Iij  Ii ; H ¼ ðWM1Þ Iij  I j¼1

Pij Pi Pi

ð3Þ

j¼1 i¼1

Legend for formulas (1)–(3): i - is the value of the factor; j - is the class; Nij - is the number of meetings of the i-th value of the factor in the observations of objects of the training sample of the j-th class; Ni – is the number of meetings of the i-th factor value over the entire training sample; Nj - is the number of factor values in observations of objects of the j-th class; N - is the number of factor values for all objects of the training sample; Iij - is a private criterion of knowledge: the amount of knowledge in the fact of observing the i-th value of a factor that the object, on which this value of the factor influences, belongs to the j-th class; W - normalization coefficient (E.V. Lutsenko, 2002), which converts the amount of information in A. Kharkevich’s formula into bits and ensures that the principle of compliance with R. Hartley’s formula is observed; Pi - is the unconditional relative frequency of the meeting of the i-th value of the factor in the training sample; Pij - is the conditional relative frequency of the meeting of the i-th factor value for objects of the j-th class; W - is the number of classes; M - is the number of different factor values in all objects of the training sample; ri - is the valuation of the i-th factor value for the classification of observations; H - is the quality of the model. Based on the system-cognitive models, which are distinguished by frequent criteria (3), identification problems, decision support and the task of researching a simulated subject area by studying its system-cognitive model are solved. To solve these

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problems, two additive integral criteria are currently used in the ASC-analysis and the “Eidos” system. 2.4

Integral Criteria and Solving System Identification and DecisionMaking Tasks

Determining the degree of similarity (and difference) of the particular text with generalized images of classes corresponding to future states of the modeling object is the task of system identification. The models presented in expressions (4) reflect how much information about the belonging of the modeling object to each class is contained in each value of the factor. The function of particular criteria, which has a specific numerical value for each class and reflects the degree of belonging of the modeling object to this class, is called an integral criterion. As a result a certain specific concrete object belongs to different degrees and does not belong to different classes. Integral criteria are used in solving both identification or forecasting problems and decision-making tasks. Currently, two additive integral criteria – the sum of knowledge and the resonance of knowledge – are applied in the “Eidos” system. The first integral criterion the “Sum of knowledge” is the total amount of knowledge contained in the features of the object about its similarity/difference with each of the classes. The integral criterion is an additive function of particular knowledge criteria (4): Ij ¼ ð~ Iij ; ~ Li Þ:

ð4Þ

In the expression, parentheses denote the scalar product. In coordinate form, this expression has the following form (5): Ij ¼

M X

Iij Li ;

ð5Þ

i¼1

where: ~ Li ¼ fLi g – is the vector of the text Iij ¼ fIij g – is the vector of the j-th class; ~ recognition, i.e.: 8 < 1; if i  th factor acts; ~ Li ¼ n; where : n [ 0; if i  th factor acts with truth n; : 0; if i  th factor doesn0 t act: The second integral criterion the “Semantic resonance of knowledge” is the normalized total amount of knowledge contained in the attributes of an object about its similarity/difference with each of the classes. The integral criterion is an additive function of particular knowledge criteria and has the following form (6):

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Ij ¼

M   1 X Þ; Iij  Ij ðLi  L rj rl M i¼1

515

ð6Þ

where: rj – is the standard deviation of particular class vector knowledge criteria; rl – is the standard deviation of the vector of the text recognition.

3 Results 3.1

Cognitive-Targeted Structuring of the Subject Area

Cognitive-targeted structuring of the subject area includes the development of classification and descriptive scales (Tables 1 and 2). Cognitive target structuring of the subject area is the only non-automated stage of the ASC-analysis. Classification scales describe the future conditions of the modeling Table 1. Classification scales. Code Name 1 2 3 4 5 6 7 8 9 10 11 12 13 14

TOTAL NUMBER OF CASES: - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS) TOTAL NUMBER OF CASES: - DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS) TOTAL NUMBER OF CASES: - DIED CATTLE, INCL. YOUNG CATTLE (%) TOTAL NUMBER OF CASES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS) TOTAL NUMBER OF CASES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%) INCLUDING: - DISEASES OF THE DIGESTIVE SYSTEM; - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS) INCLUDING: - DISEASES OF THE DIGESTIVE SYSTEM; - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (%) INCLUDING: - DISEASES OF THE DIGESTIVE SYSTEM; -DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS) INCLUDING: - DISEASES OF THE DIGESTIVE SYSTEM; - DIED CATTLE, INCL. YOUNG CATTLE (%) INCLUDING: - DISEASES OF THE DIGESTIVE SYSTEM; COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS) INCLUDING: - DISEASES OF THE DIGESTIVE SYSTEM; COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%) - DISEASES OF THE RESPIRATORY SYSTEM; - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS) - DISEASES OF THE RESPIRATORY SYSTEM; -REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (%) - DISEASES OF THE RESPIRATORY SYSTEM; - DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS)

(continued)

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E. V. Lutsenko et al. Table 1. (continued)

Code Name 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

- DISEASES OF THE RESPIRATORY SYSTEM; - DIED CATTLE, INCL. YOUNG CATTLE (%) - DISEASES OF THE RESPIRATORY SYSTEM; - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS) - DISEASES OF THE RESPIRATORY SYSTEM; - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%) - METABOLIC DISEASES; - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS) - METABOLIC DISEASES; - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (%) - METABOLIC DISEASES; - DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS) - METABOLIC DISEASES; - DIED CATTLE, INCL. YOUNG CATTLE (%) - METABOLIC DISEASES; - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS) - METABOLIC DISEASES; - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%) - DISEASES OF THE REPRODUCTIVE ORGANS: - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS) - DISEASES OF THE REPRODUCTIVE ORGANS: - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (%) - DISEASES OF THE REPRODUCTIVE ORGANS: - DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS) - DISEASES OF THE REPRODUCTIVE ORGANS: - DIED CATTLE, INCL. YOUNG CATTLE (%) - DISEASES OF THE REPRODUCTIVE ORGANS: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS) - DISEASES OF THE REPRODUCTIVE ORGANS: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%) - INCLUDING MASTITIS; - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS) - INCLUDING MASTITIS; - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (%) - INCLUDING MASTITIS; - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS) - INCLUDING MASTITIS; - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%) - POISONING; - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS) - POISONING; - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (%) - POISONING; - DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS) - POISONING; - DIED CATTLE, INCL. YOUNG CATTLE (%) - INJURIES: - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS) - INJURIES: - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (%) - INJURIES: - DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS) - INJURIES: - DIED CATTLE, INCL. YOUNG CATTLE (%) - INJURIES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS) - INJURIES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%)

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object, in our case these are the volumes and structure of the incidence. Descriptive scales describe factors affecting the modeling object; in this paper, these are the locations and volumes of cattle purchasing. Table 2. Descriptive scales Code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

3.2

Name HUNGARY AUSTRIA USA NETHERLANDS DENMARK GERMANY TOTAL FROM ALL COUNTRIES CATTLE, PURCHASED BY ORGANIZATIONS, TOTAL, ANIMALS CATTLE, PURCHASED FROM OUTSIDE THE REGION, ANIMALS CATTLE, PURCHASED FROM OUTSIDE THE REGION, % PURCHASED CATTLE, INCL. IMPORT, ANIMALS PURCHASED CATTLE, INCL. IMPORT, % DAIRY CATTLE, PURCHASED BY ORGANIZATIONS, TOTAL, ANIMALS DAIRY CATTLE, PURCHASED FROM OUTSIDE THE REGION, ANIMALS DAIRY CATTLE, PURCHASED FROM OUTSIDE THE REGION, % PURCHASED DAIRY CATTLE, INCL. IMPORT, ANIMALS PURCHASED DAIRY CATTLE, INCL. IMPORT, %

Preparation of Initial Data, Formalization of the Subject Area

The initial data for the development of the “Eidos” intelligent cloud application is taken from the official statistics of regional veterinary services and performed in Table 3. The full data file has a significantly larger dimension and cannot be given in this paper due to the limitations on its size. But it can be downloaded from the link from the “Eidos” intelligent cloud application: http://aidos.byethost5.com/Source_data_applications/ Applications000202/Inp_data.xls The classification scales are highlighted in yellow. The source data contains 3364 facts. Each fact is an observation of a certain value of a factor during the transition of a modeling object to some future state.

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E. V. Lutsenko et al. Table 3. Initial data (fragment).

3.3

Formalization of the Subject Area

An automated program interface (API-2.3.2.2) was used to enter these initial data into the “Eidos” system and formalize the subject area (Fig. 1). Since numerical scales are used in this application, the system asks for the number of numerical ranges into which they should be divided, and we set 3 ranges in classification scales and 5 in descriptive scales. As a result, in this mode, gradations of classification and descriptive scales (Tables 4 and 5) are automatically developed, and then the source data are encoded using them, as a result of which a training sample is formed, which is a database of initial data normalized with classification and descriptive scales and gradations and having the same form, where instead of numbers and texts their codes are given. Full catalogues of classification and descriptive scales and gradations are not given, because in the first of them there are 129 lines and in the second there are 85 lines.

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Fig. 1. Program interface (API-2.3.2.2).

Table 4. Classification scales and gradations (fragment). Code 1 2 3 4 5 6 7

Name TOTAL NUMBER OF CASES: - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS)-1/3-{210801.0000000, 242767.3333333} TOTAL NUMBER OF CASES: - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS)-2/3-{242767.3333333, 274733.6666667} TOTAL NUMBER OF CASES: - REGISTERED SICK CATTLE, INCL. YOUNG CATTLE (ANIMALS)-3/3-{274733.6666667, 306700.0000000} TOTAL NUMBER OF CASES: - DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS)-1/3-{6248.0000000, 6636.6666667} TOTAL NUMBER OF CASES: - DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS)-2/3-{6636.6666667, 7025.3333333} TOTAL NUMBER OF CASES: - DIED CATTLE, INCL. YOUNG CATTLE (ANIMALS)-3/3-{7025.3333333, 7414.0000000} TOTAL NUMBER OF CASES: - DIED CATTLE, INCL. YOUNG CATTLE (%)1/3-{1.6000000, 1.9333333} (continued)

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E. V. Lutsenko et al. Table 4. (continued)

Code 8 9 10 11 12 13 14 15

Name TOTAL NUMBER OF CASES: - DIED CATTLE, INCL. YOUNG CATTLE (%)2/3-{1.9333333, 2.2666667} TOTAL NUMBER OF CASES: - DIED CATTLE, INCL. YOUNG CATTLE (%)3/3-{2.2666667, 2.6000000} TOTAL NUMBER OF CASES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS)-1/3-{2196.0000000, 2797.3333333} TOTAL NUMBER OF CASES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS)-2/3-{2797.3333333, 3398.6666667} TOTAL NUMBER OF CASES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (ANIMALS)-3/3-{3398.6666667, 4000.0000000} TOTAL NUMBER OF CASES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%)-1/3-{0.7200000, 0.9466667} TOTAL NUMBER OF CASES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%)-2/3-{0.9466667, 1.1733333} TOTAL NUMBER OF CASES: - COMPULSORY SLAUGHTERED CATTLE, INCL. YOUNG CATTLE (%)-3/3-{1.1733333, 1.4000000}

Table 5. Descriptive scales and gradations (fragment). Code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Name HUNGARY-1/5-{136.0000000, 158.0000000} HUNGARY-2/5-{158.0000000, 180.0000000} HUNGARY-3/5-{180.0000000, 202.0000000} HUNGARY-4/5-{202.0000000, 224.0000000} HUNGARY-5/5-{224.0000000, 246.0000000} AUSTRIA-1/5-{55.0000000, 155.4000000} AUSTRIA-2/5-{155.4000000, 255.8000000} AUSTRIA-3/5-{255.8000000, 356.2000000} AUSTRIA-4/5-{356.2000000, 456.6000000} AUSTRIA-5/5-{456.6000000, 557.0000000} USA-1/5-{1409.0000000, 1561.2000000} USA-2/5-{1561.2000000, 1713.4000000} USA-3/5-{1713.4000000, 1865.6000000} USA-4/5-{1865.6000000, 2017.8000000} USA-5/5-{2017.8000000, 2170.0000000} NETHERLANDS-1/5-{9.0000000, 139.2000000} NETHERLANDS-2/5-{139.2000000, 269.4000000} NETHERLANDS-3/5-{269.4000000, 399.6000000} NETHERLANDS-4/5-{399.6000000, 529.8000000} NETHERLANDS-5/5-{529.8000000, 660.0000000}

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Synthesis and Verification of Models

Next, the mode of synthesis and verification of models 3.5 is launched. The created model is about 1.2% of the theoretically maximum model that can be created and processed in the “Eidos” system. To evaluate the reliability of the models in the “Eidos” system, the van Rijsbergen F-measure and its two improved modifications proposed by prof. E.V. Lutsenko are used. According to the L2 criterion, the best-validated system-cognitive model is the INF3 model with the integral criterion “Sum of knowledge”: L2 = 0.926 with a maximum value of 1 (Figs. 2 and 3).

Fig. 2. Verification of models in the “Eidos” system.

Fig. 3. Frequency of distribution of decision similarity levels in the INF3 model.

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This is a very good result, which means that the created models can be successfully applied to solve the tasks. Therefore, this model was chosen as the current one for solving the tasks set in the work at the subsequent stages of the ASC-analysis. 3.5

Giving the Status of the Current Most Reliable INF3 Model

Then, in the mode 5.6, the status of the current model is assigned to the most reliable INF3 model and all previously set tasks are solved in it. 3.6

Solution the Problem of System Identification

When solving the identification problem for each object of the recognized sample in the most reliable INF3 model, the values of the integral criterion for each class are calculated. In this case, the degree of similarity of each object with the generalized images of all classes is determined, and then for each object all classes are ranked in decreasing order of similarity with the object and, thus, the volume and structure of the incidence of cattle are predicted. The “Eidos” system has an output form (there are hundreds of them in general), in which classes are ranked in descending order of similarity with the selected text according to two integral criteria. There is also a form in which we, on the contrary, see texts ranked by the degree of similarity with the given classes. 3.7

Solution of the Decision-Making Problem (Displaying Information on the Results of Multi-parameter Typing)

At the stage of synthesis of models by summarizing examples of the training sample, generalized images of classes were created. For scientific research and decision making it is interesting to study the images of classes and their content. Such information can be presented in the form of SWOT diagrams, from which it is seen which values of the factors are most characteristic (left) and most uncharacteristic (right) for this class, i.e. volume and type of disease (Fig. 4). 3.8

Solving the Problem of Researching Subject Area by Examining Its Model

If the model correctly reflects the simulated subject area, then the study of the model can reasonably be considered the study of the simulated subject area itself. In the “Eidos” system, the study of a simulated subject area includes: inverted SWOT diagrams of factor values (semantic word potentials); cluster-constructive analysis of classes; cluster-structural analysis of the meanings of factors (words); nonlocal neurons; non-local neural network; 3D-integrated cognitive maps; 2D-integrated cognitive maps of meaningful comparison of classes; 2D-integrated cognitive maps of meaningful comparison of the values of factors (words); cognitive function; the significance of factors and their meanings; the degree of determinism of classes and classification scales.

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Fig. 4. SWOT diagrams in the INF3 model.

In this paper, due to limitations on its volume, we will not consider the tasks of studying a simulated subject area by examining its model (Figs. 5 and 6).

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Fig. 5. Pareto-fragment of a non-local neural network in the INF3 model.

4 Discussion According to the authors, new and scientifically interesting results have been obtained that have practical importance for making decisions on the places of cattle purchase. These results were obtained through the use of modern intelligent technologies, namely, automated system-cognitive analysis and its software tool - the intellectual system “Eidos”. Apparently, the results should be recognized as preliminary, since the initial data had a relatively small longitude (9 years) and were significantly fragmented. In the future, while continuing this research, it is recommended to pay special attention to obtaining more complete data. Nevertheless, the obtained results are quite sufficient to assess the general picture of patterns in this subject area, i.e. they adequately quantitatively reflect the veterinary consequences of decisions to purchase a certain amount of cattle from suppliers from other regions and countries. It is not possible to compare our results with the work of other scientists, because there are no studies in this area, especially based on Russian data for the Krasnodar region. Automated system-cognitive analysis and its tool allow correctly processing large volumes of fragmented and noisy data presented in various types of scales (nominal, ordinal and numerical) and in various units of measurement. However, the time period that these data cover must be increased. To increase the scientific and practical significance of the study, it is advisable to conduct a similar study in other regions of Russia. It is good to take into account regional specifications in the created models, because they will work better in these regions than the models created on the data of the Krasnodar region. However, in the Krasnodar region the obtained results can be already used.

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Fig. 6. Results of the cognitive clustering of classes.

When writing this article, numerous works of various authors in the field of veterinary medicine were studied. However, none of them provides the mathematical model proposed in this article and does not use the particular and integral criteria proposed in it (1)–(6). Therefore, this article is relevant and can be interesting for researchers in this subject area.

5 Conclusion Thus, the article provides a new mathematical model, which was not previously described in the world literature, based on the theory of information, as well as implementing its software tool and describes their application for solving the tasks - a quantitative study of the dependence of the volume and structure of the incidence of imported cattle from the importing country in the conditions of the Krasnodar region.

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The main conclusion of the work is that in order to reduce the incidence of cattle in the region, it is necessary, first of all, to pay attention to the country from which the purchase of imported animals is carried out, since cows imported from these countries have limitations of adaptive potential, contributing to a decrease in the body’s resistance to adverse environmental factors, and as a result, increased susceptibility to various diseases. This work demonstrates that mathematical models (particular and integral criteria), methods of numerical calculations (data structures and algorithms for their processing), screen forms of process control, the program interface for inputting initial data into the “Eidos” system (API) and increasing the degree of formalization of initial data to normalized databases, screen forms of text and graphic output forms based on the results of solving forecasting problems, decision making and research, software implementation of mathematical models, numerical methods calculations, interface and cognitive graphics in the “Eidos” system are an adequate means of solving the problems and tasks presented in the work.

References 1. Amerkhanov, Kh.A.: Status and development of dairy cattle farming in the Russian Federation. Dairy Beef Cattle Farming 1, 2–5 (2017) 2. Sidorenko, V., Mikhaylushkin, P.: Food security in the modern world. Int. Agric. J. 2, 40–45 (2012) 3. Sharkaeva, G.: Monitoring of cattle imported to the Russian Federation. Dairy Beef Cattle Farming 1, 14–16 (2013) 4. Seibotalov, M.: Problems of livestock import to Russia. Dairy Beef Cattle Farming 1, 5–8 (2013) 5. Kanareikina, N.N., Tamarova, R.V.: Morbidity and safekeeping of animals of various genetic groups under conditions of intensive technology of milk production. Veterinarny Vrach 1, 65 (2011) 6. Belova, YuN, Rostovtseva, N.M.: Dairy efficiency of import cows in the conditions of Krasnoyarsk krai. Agric. Bull. Stavropol Reg. 1(17), 138–140 (2015) 7. Antipov, V.A., Semenenko, M.P., Basova, N.Yu., Turchenko, A.N., Sapunov, A.Ya., Kuzminova, E.V.: Increase of Safety and Productivity of Health of Imported Dairy Cattle (methodical recommendations), Krasnodar, Russia (2009) 8. Dunin, I., Dankvert, A., Kochetkov, A.: Prospects for the development of dairy cattle breeding and the competitiveness of dairy cattle bred in the Russian Federation. Dairy Beef Cattle Farming 3, 1–5 (2013) 9. Markova, D.S., Kalyuzniy, I.I., Bayzuldinov, S.Z.: Analysis of the diseases of cows and the terms of their use in economics with different economic indicators. Agrarian Sci. J. 1, 53–57 (2019) 10. Lutsenko, E.V.: Automated system-cognitive analysis in veterinary science (on the example of diagnostic tests development). Sci. J. KubSAU 3(137), 143–196 (2018) 11. Lutsenko, E.V., Pechurina, E.K.: Automated system-cognitive analysis and classification of cattle breeds. Sci. J. KubSAU 8(142), 68–95 (2018)

Detecting the Abrupt Change in the Bandwidth of a Fast-Fluctuating Gaussian Random Process Oleg Chernoyarov1,2,3(&) , Serguei Dachian4, Tatiana Demina3, Alexander Makarov1,2,3, and Alexandra Salnikova3 National Research University “MPEI”, Krasnokazarmennaya Str. 14, 111250 Moscow, Russia [email protected] 2 National Research Tomsk State University, Lenin Avenue 36, 634050 Tomsk, Russia 3 Maikop State Technological University, Pervomaiskaya Str. 191, 385000 Maikop, Russia University of Lille, 42 Rue Paul Duez, 59000 Lille, France 1

4

Abstract. In the present paper, there are introduced the synthesis, analysis and simulation of the generalized maximum likelihood algorithm for detecting the abrupt change in the bandwidth of a fast-fluctuating high-frequency Gaussian random process with the uniform spectral density. For this purpose, new approximations of decision statistics for the two possible hypotheses are calculated, with their subsequent maximization by the unknown parameter. As result, the detector is designed and developed that can be technically implemented in a much simpler way than the detectors that have a similar functionality and can be obtained by means of the more common approaches. As the accepted model of the information random process is the discontinuous one, in order to determine the performance of the synthesized algorithm, there is used the method of the multiplicative and additive local Markov approximation of the decision statistics and its increments. According to it, the asymptotically exact analytical expressions for the detection characteristics, such as the probabilities of the Type I and Type II errors, can be obtained. The presented algorithm is tested by means of statistical computer simulation, and it is established then that it is operable, and the theoretical formulas for the probabilities of the Type I and Type II errors well conform with the corresponding experimental data in a wide range of the analyzed process parameter values. These results can be evidently used for the synthesis and analysis of the modern and technically simply implemented algorithms for processing fast fluctuating random processes with the abruptly changing characteristics in the conditions of the varying parametrical prior uncertainty. #COMESYSO1120. Keywords: Fast-fluctuating random process
Abrupt change
Maximum likelihood method
Discontinuous parameter
Unknown bandwidth
Local Markov approximation method
False alarm probability
Missing probability
Statistical simulation

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 527–541, 2020. https://doi.org/10.1007/978-3-030-63322-6_43

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1 Introduction The problem of statistical analysis of the abrupt change in the random process parameter values at some unknown point in time is considered in a number of studies [1–3]. As a rule, in common papers, it is presupposed that the observable realization can be described by the Gaussian distribution, but other than that, some additional restrictions are imposed, for example, the ones related to the uncorrelated (and, therefore, independent) samples being processed [1], the narrower class of stochastic signal models being analyzed [2, 3], etc. In this paper, a simple (in terms of practical implementation) technique is proposed for detecting the moment of the unknown abrupt change in the bandwidth of a Gaussian random process, provided that its fluctuations are fast and its spectral density within the working frequency band is uniform. The characteristics of the designed detector are determined both theoretically and experimentally.

2 The Problem Statement Let us determine analytically the fast-fluctuating random band-pass process with the abrupt change in the bandwidth at the point in time k0 as follows nðtÞ ¼ ½1  hðt  k0 Þm1 ðtÞ þ hðt  k0 Þm2 ðtÞ:

ð1Þ

Here hðtÞ is the Heaviside function: hðtÞ ¼ 0, if t\0, and hðtÞ ¼ 1, if t  0, while mi ðtÞ, i ¼ 1; 2 are statistically independent centered stationary Gaussian random processes with the uniform spectral densities within the specified bandwidths X0i : 

 d 0x 0þx I Gi ðxÞ ¼ þI ; 2 X0i X0i

( I ð xÞ ¼

1 ; j xj  1=2 ; 0; j xj [ 1=2 :

ð2Þ

In (2), the notations are: 0 is the band center, d is the intensity (magnitude of the spectral density) of the process mi ðtÞ determining its mean power (dispersion) Di ¼ dX0i =2p, and, in general case, X01 6¼ X02 . The interferences and the registration errors of the process (1) are approximated by Gaussian white noise nðtÞ with the one-sided spectral density N0 . Thus, the additive mix of the form xðtÞ ¼ nðtÞ þ nðtÞ;

t 2 ½0; T 

ð3Þ

is observed over the time interval T. The parameters k0 and X02 are unknown and take the values from the prior intervals ½K1 ; K2 , ½Y1 ; Y2 . It is presupposed that Y1 \X01 \Y2 , while the condition of “fast” process fluctuations can be written in the following way:

Detecting the Abrupt Change in the Bandwidth

lmin ¼ Tmin Y1 =4p [ [ 1;

529

ð4Þ

where Tmin ¼ minðk0 ; T  k0 Þ. According to the observable realization (3) and a priori information available, it is necessary to make a decision on the presence or absence of the abrupt change in the process nðtÞ bandwidth.

3 The Synthesis of the Detection Algorithm In order to carry out the synthesis of the algorithm for detecting the abrupt change in the bandwidth of the process nðtÞ, let us singled out two possible hypotheses: 1) hypothesis H0 stating that X01 ¼ X02 , i.e. the abrupt change is absent; 2) hypothesis H1 stating that X01 6¼ X02 . In this case, the maximum likelihood method is used [4–6], according to which, there should be found the analytical expressions for the logarithms of the functionals of the likelihood ratio (FLR) to test the formulated alternatives. By applying the results obtained in [4, 7], for the logarithms of FLR under the hypotheses H0 and H1 against the alternative H: xðtÞ ¼ nðtÞ one gets d H0 : L0 ¼ N0 ðN0 þ d Þ " þ

d N0 ðN0 þ d Þ

RT k

ZT y2 ðt; X01 Þ dt  0


 X01 T d ln 1 þ ; 2p N0

H1 : L1 ðk; XÞ ¼#L0  RT y2 ðt; XÞ dt  y2 ðt; X01 Þ dt  ðTkÞð2pXX01 Þ ln 1 þ k

ð5Þ

d N0



:

R1 Here yðt; HÞ ¼ 1 xðt0 Þ hðt  t0 ; HÞ dt0 is the response of the filter to the observable realization (3) while the transfer function H ðx; HÞ of this filter satisfies the condition jH ðx; HÞj2 ¼ I ½ð0  xÞ=H þ I ½ð0 þ xÞ=H, and k, X are the current values of the unknown parameters k0 and X02 , respectively. Then the generalized maximum likelihood algorithm for detecting the abrupt change takes the form [4–6] max

k2½K1 ;K2  ;X2½Y1 ;Y2 

L1 ðk; XÞ  L0 [ c;

ð6Þ

where c is the threshold calculated according to the chosen optimality criterion. Taking into account (5), the decision rule (6) can be presented as follows:

max

8 T

> <   0  0  1  0 0 V v1 V v2 ¼ b min v1 ; v2 ; v01 [ 0; v02 [ 0 ; 2 l1 > > : 0; v01 v02  0 ;

ð21Þ

where b1 ¼ ð1 þ qÞ2 , b2 ¼ 1. From (21), it follows that 1) the realizations of the   process V ðv0 Þ at the intervals Y~ 1  1; 0 and 0; Y~ 2  1 are not correlated and, therefore, they are statistically independent, as being Gaussian; 2) within each of the   ~ 1  1; 0 and 0; Y ~ 2  1 , the correlation function of the process V ðv0 Þ intervals Y satisfies the conditions of the Doob’s theorem [10], so the process V ðv0 Þ is the Markov random process of the diffusion type, and its the drift KV1 and diffusion KV2 coefficients can be determined as ( KV1 ¼

a1 ; v0 \0 ; a2 ; v0 [ 0 ;

;

KV2

1 ¼ l1

(

b1 ; v0 \0 ; b2 ; v 0 [ 0 :

Here a1 ¼ ð1 þ qÞ ½ 1  lnð1 þ qÞ=q , a2 ¼ ð1 þ qÞ lnð1 þ qÞ=q  1, while b1 , b2 are defined as the same as in (21). A general expression for the distribution function of the greatest maximum of the Gaussian Markov random process with the piecewise constant drift and diffusion coefficients has been obtained, for example, in [11]. Using these results, for the function (18) one gets

Detecting the Abrupt Change in the Bandwidth

 
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi  qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R1 pffiffiffiffiffi l1 n ~ p ffiffiffiffiffiffiffiffi FV ð yÞ ¼ 4p b 1Y~ U l1 a2 Y 2  1 þ 1ð 1Þ Y~ 1 0 
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 pffiffiffiffiffi n expð2l1 a2 nÞ U l1 a2 Y~ 2  1  pffiffiffiffiffiffiffiffi Y~ 2 1 (  #  " pffiffiffiffiffi 2a1 ð1Y~ 1 Þ þ y þ n l1 ðynÞ2  exp  4b 1Y~ U l1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1ð 1Þ 2b1 ð1Y~ 1 Þ #)   " pffiffiffiffiffi 2a1 ð1Y~ 1 Þ þ yn l1 ðy þ nÞ2 2l1 a1 n exp  b1  4b 1Y~ U l1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi dn : 1ð 1Þ 2b1 ð1Y~ 1 Þ

533

ð22Þ

Under sufficiently great values of the parameter l (l  100) and not too small values of the parameter q (q  0:25), the simpler approximation of the form " #  qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi    pffiffiffiffiffi y4a2 b1 ð1Y~ 1 Þ l1 ~ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi p FV ðyÞ ¼ U y 2b 1Y~  exp 2l1 a2 2a2 b1 1  Y 1  y U l1 1ð 1Þ 2b1 ð1Y~ 1 Þ ( " #) h   i pffiffiffiffiffi y þ 4a1 ð1Y~ 1 Þ 1 ~  exp 2l 1  U l1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b1 a1 2a1 1  Y 1 þ y 2b1 ð1Y~ 1 Þ ( " #) h i   pffiffiffiffiffi y þ 4ða1 þ a2 b1 Þð1Y~ 1 Þ 2l1 ~ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þ exp b1 ða1 þ a2 b1 Þ 2ða1 þ a2 b1 Þ 1  Y 1 þ y 1  U l1 2b1 ð1Y~ 1 Þ 

can be used instead of (22) without significant loss in accuracy. The error in calculating the false alarm probability values (12) by the formulas (19), (20), (22) decreases with lmin (4) increasing. Let us consider now the case when X01 6¼ X02 and represent the missing probability in the following way: b ¼ P½

sup

M ðl; vÞ\~cjX01 6¼ X02 :

ð23Þ

~ 1 ;K ~ 2 ; v2½Y~ 1 ;Y~ 2  l2½K

Then one moves to the new variables l0 , v0 (15) and, taking into account (10), (11), the regular component Sðl; vÞ ¼ hM ðl; vÞi and correlation function of the fluctuation component N ðl; vÞ ¼ M ðl; vÞ  hM ðl; vÞi of the decisive statistics (8) can be represented in the form  Sðl0 ; v0 Þ ¼ ½ 1  ð1 þ qÞ lnð1 þ qÞ=q l0 v0  qmin 0; l00  l0     minð0; v0 Þ þ qmin l00 ; l0 min v002 ; v0  min 0; m002 ;

ð24Þ

  0 0  0 0 

     N l1 ; v1 N l2 ; v2 ¼ ð1=l1 Þ min l01 ; l02 min v01 ; v02  min 0; v01  min 0; v02    0 0 0   0 0  0  0 þ qð2 þ qÞ max 0; min l1 ; l2  l0 min 0; v1 ; v2  min 0; v1  min 0; v2  0 0 0       : þ min l0 ; l1 ; l2 min v002 ; v01 ; v02 þ min 0; v002  min 0; v002 ; v01  min 0; v002 ; v02

Here l00 ¼ 1  l0 , v002 ¼ v02  1.

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0 0 As it can be seen from  0 (24), the regular component Sðl ; v Þ reaches the greatest 0 maximum at the point l0 ; v02 , while the realizations of the fluctuation component N ðl0 ; v0 Þ are continuous with the probability 1. Then the output signal-to-noise ratio for the algorithm (8) is determined as [4–6]

z2 ¼ 

  2   S2 l00 ; v00 1 þ cq  0 0  ¼ l1 l00 v002  1  ln ð 1 þ q Þ : q N 2 l 0 ; v0

ð25Þ

where cq ¼ 0, if v002 [ 0, and cq ¼ q, if v002 \0. When the value of q is not too small and the condition  (4) is satisfied, it follows from (25) that z2 [[ 1 and, therefore, the coordinates l0m ; v0m of the position of the greatest maximum M ðl0 ; v0 Þ are located within a small d-neighborhood  0 0of the functional 2 of the point l0 ; v02 . With z increasing (z2 ! 1), the size of this neighborhood     converges to zero, that is, d ¼ max l0  l00 ; v0  v002  ! 0 [4, 5] and for the regular component and the correlation function of the fluctuation component (24), the following asymptotic representations can be applied:   Sðl0 ; v0 Þ ¼ S0 þ S1 l~0 þ S2 v~0 þ oðdÞ ;   0 0  0 0    N l1 ; v1 N l2 ; v2 ¼ r2 þ R1 l~0 1 ; l~0 2 þ R2 v~0 1 ; v~0 2 þ oðdÞ ;

ð26Þ

where l~0 ¼ l0  l00 , v~0 ¼ v0  v002 , 

ð1 þ qÞ ½1  lnð1 þ qÞ=q ; v002 [ 0 ; 1 (ð1 þ qÞ lnð1 þ qÞ=q ; v002 \0 ;   l00 v002  ð1 þ qÞ2 ; v002 [ 0 ; 2 r ¼ l1 1; v002 \0 ;

S0 ¼ l00 v002

 S1 l~0 ¼ v002

(

 l~0 ½1  ð1 þ qÞ lnð1 þ qÞ=q þ qmin 0; l~0 ; v002 [ 0 ;  0 l~0 ½1  ð1 þ qÞ lnð1 þ qÞ=q þ qmax 0; ~ l0 ; v \0 ; 02

   S2 v~0 ¼ l00 v~0 ð1  ð1 þ qÞ lnð1 þ qÞ=qÞ þ qmin 0; v~0 ; 

R1 l~0 1 ; ~l0 2



   0 ( v  min l~0 1 ; l~0 2 þ qð2 þ qÞmin 0; l~0 1 ; l~0 2 ; v002 [ 0 ; 02 ¼    l1 min l~0 1 ; l~0 2 þ qð2 þ qÞmax 0; min l~0 1 ; l~0 2 ; v0 \0 ; 02

 l0 R2 v~0 1 ; v~0 2 ¼ 0 l1

(

  qð2 þ qÞmin 0; v~0 1 ; v~0 2 þ min v~0 1 ; v~0 2 ; v002 [ 0 ;      0 0 0 0 ~ ~ ~ ~ qð2 þ qÞ min 0; v 1 ; v 2  min 0; v 1  min 0; v 2  max v~0 1 ; v~0 2 ; v002 \0 :

Following the previous studies [5, 12], one now introduces the difference functional

Detecting the Abrupt Change in the Bandwidth

 D l~0 ; v~0 ¼ M ðl0 ; v0 Þ=r  M0 ;

535

ð27Þ



where M0 ¼ M l00 ; v002 r is the asymptotically (under lmin (4) increasing) Gaussian D E random variable with the characteristics hM0 i ¼ z, ½M0  hM0 i2 ¼ 1 (26). With (27) applied, one can rewrite the missing probability (23) in the following way: b¼P

h sup ~ 2 ; l0  K ~ 1 ; l~0 2 ½l0  K 0 v~ 2 ½Y~ 1  v02 ; Y~ 2  v02

   ~c  M0  0 0  ~ ~ X01 6¼ X02 : D l ;v \ r 

ð28Þ

From (26), (27), it follows that under d ! 0 the first two moments of the functional  0 ~ D l ; v~0 are determined as        D l~0 ; v~0 ¼ A1 l~0   A2 v~0  þ oðdÞ ;              D l~0 1 ; v~0 1  D l~0 1 ; v~0 1 D l~0 2 ; v~0 2  D ~ l0 2 ; v~0 2 ¼ BD1 l~0 1 ; l~0 2 þ BD2 v~0 1 ; v~0 2 þ oðdÞ ;

ð29Þ

where 8     > l0 2  ; l~0 1  0 ; l~0 2  0 ; r2l1 min l~0 1 ; ~ > > < Al1 ; l~0  0 ;   A1 ¼ BD1 l~0 1 ; l~0 2 ¼ r2l2 min l~0 1 ; l~0 2 ; l~0 1 [ 0 ; l~0 2 [ 0 ; > 0 > Al2 ; l~ [ 0 ; > : 0 ; l~0 \0 ; l~0 [ 0 or l~0 [ 0 ; l~0 \0 ; 1 2 1 2 8     2 0 0 0     > rv1 min v~ 1 ; v~ 2 ; v~ 1  0 ; v~0 2  0 ; > ( > 0 ~ < Av1 ; v  0 ;   A2 ¼ BD2 v~0 1 ; v~0 2 ¼ r2v2 min v~0 1 ; v~0 2 ; v~0 1 [ 0 ; v~0 2 [ 0 ; > > Av2 ; v~0 [ 0 ; > : 0 ; v~0 \0 ; v~0 [ 0 or v~0 [ 0 ; v~0 \0 ; (

1

2

1

2

ð30Þ and rffiffiffiffiffiffiffiffiffiffiffi8 rffiffiffiffiffiffiffiffiffiffiffi 8 < S1 ; v0 [ 0 ; < S2 ; v0 [ 0 ;

l1 jv002 j l1 jv002 j 02 1 þ q 1 þ q 02 Al1 ¼ ¼ A r2l1 ¼ 1 l00 0 0 l2 l0 : l0 : S2 ; v002 \0 ; S1 ; v002 \0 ; 8 8 ( rffiffiffiffiffiffiffi< S1 rffiffiffiffiffiffiffi< S2 1 ; v002 [ 0 ; ; v002 [ 0 ; ; v002 [ 0 ; 2 l1 l00 l1 l00 1 1 þ q 1 þ q Av1 ¼ ¼ ¼ A r v2 v1 jv002 j: jv002 j: jv002 j ð1 þ qÞ2 ; v0 \0 ; 0 02 S1 ; v8 S2 ; v002 \08 ; ; 02 \0 . . < 1 ð1 þ qÞ2 ; v0 [ 0 ; < 1 ð1 þ qÞ2 ; v0 [ 0 ; 02 02 r2v2 ¼ v10 r2l2 ¼ l10 j 02 j : 0 : 2 0 0 1 ; v \0 ; ð1 þ qÞ ; v \0 ; 02

S1 ¼ ð1 þ qÞ½1  lnð1 þ qÞ=q;

02

S2 ¼ ð1 þ qÞ lnð1 þ qÞ=q  1:

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One should take into consideration the statistically independent Gaussian random     processes r1 l~0 , r2 v~0 with the mathematical expectations hr1 ðl0 Þi ¼ A1 l~0 ,      r2 v~0 ¼ A2 v~0  (30) and the correlation functions (30)             r1 l~0 1  r1 l~0 1 l0 1 ; l~0 2 ; r1 l~0 2  r1 l~0 2 ¼ BD1 ~              r2 v~0 1  r2 v~0 1 r1 v~0 2  r1 v~0 2 ¼ BD2 v~0 1 ; v~0 2 :  If d ! 0, then the characteristics (30) of the functional D ~ l0 ; v~0 coincide with the   corresponding characteristics of the sum r1 l~0 þ r2 v~0 . Then, while z2 [ [ 1 (25), the probability (28) can be approximately represented as follows (

)   ~cM0 0 0 ~ ~ bP sup r1 l þ sup r2 v \ r l~0 2½d; d  v~0 2½d; d  " # ~cR=r ~c=ru R ¼ w0 ðuÞ F1 ð~c=r  u  xÞ w2 ð xÞ dx du : 1

ð31Þ

0

  Here F1 ð xÞ ¼ P ½ sup r1 l~0 \x, F2 ð xÞ ¼ P ½ sup r2 v~0 \x are the distribul~0 2½d; d

v0 2½d; d

tion functions of the greatest maximum values of the random processes r1 ðl0 Þ and r2 ðv0 Þ, w2 ð xÞ ¼ dF2 ð xÞ=dx, while h . i.pffiffiffiffiffiffi w0 ðuÞ ¼ exp ðu  zÞ2 2 2p

ð32Þ

is the probability density of the random variable M0 =r, with z and r determined from (25), (26), respectively. In (31), it is taken into account that if x\0, then w2 ð xÞ ¼ 0 and F1 ð xÞ ¼ 0 as r1 ð0Þ ¼ 0 and r2 ð0Þ ¼ 0.   According to (29), (30), the values of the Gaussian process r1 l~0 (r2 v~0 ) at the intervals ½d; 0 and ð0; d are not correlated and, therefore, they are statistically independent. Then F1 ð xÞ ¼ F11 ð xÞ F12 ð xÞ;

F2 ð xÞ ¼ F21 ð xÞ F22 ð xÞ;

where  F11 ð xÞ ¼ P ½ sup r1 l~0 \x ; l0 2½d; 0

 F12 ð xÞ ¼ P ½ sup r1 l~0 \x ; l~0 2ð0; d

 F21 ð xÞ ¼ P ½ sup r2 v~0 \x ;

 F22 ð xÞ ¼ P ½ sup r2 v~0 \x :

v~0 2½d; 0

v0 2ð0; d

ð33Þ

Detecting the Abrupt Change in the Bandwidth

537

Now it is time to introduce the random processes        D11 l~0 ¼ x  r1 l~0 ; D21 v~0 ¼ x  r2 v~0 ; D12 l~0 ¼ x  r1 l~0 ; D22 v~0  ¼ x  r2 v~0 :   From (29), (30), it follows that the processes D1i l~0 , D2i v~0 , i ¼ 1; 2, while l~0  0, v~0  0, satisfy the conditions of the Doob’s theorem [10] and they are Gaussian Markov processes with the drift coefficients ali ¼ Ali , avi ¼ Avi and the diffusion coefficients bli ¼ r2li , bvi ¼ r2vi . Moreover, according to [4, 12], the desired distribution functions (33) can be represented in the form  F1i ð xÞ ¼ P ½ sup D1i l~0 [ 0 ¼ l~0 2½0;d

Z1

Z1 0

 w1i ðy; dÞ dy; F2i ð xÞ ¼ P ½ sup D2i v~0 [ 0 v~0 2½0;d

w2i ðy; dÞ dy;

¼ 0

  where the probability densities w1i y; l~0 , w2i y; v~0 are determined from the solution of the direct Fokker-Planck-Kolmogorov equation [8] with the drift coefficients ali , avi and the diffusion coefficients bli , bvi , while the starting conditions are w1i ðy; 0Þ ¼ w2i ðy; 0Þ ¼ dðx  yÞ and the boundary conditions are     w1i 0; l~0 ¼ w1i 1; l~0 ¼ w2i 0; v~0 ¼ w2i 1; v~0 ¼ 0. After solving these equations and integrating the found solutions as described, for example, in [12], taking into account (33), one obtains     F2 ð xÞ ¼ f x; Av1 ; r2v1 f x; Av2 ; r2v2 ; F1 ð xÞ ¼ f x; Al1 ; r2l1 f x; Al2 ; r22 ;     ð34Þ w2 ð xÞ ¼ g x; Av1 ; r2v1 f x; Av2 ; r2v2 þ g x; Av2 ; r2v2 f x; Av1 ; r2v1 ; if x  0, and F1 ð xÞ ¼ F2 ð xÞ ¼ 0, w2 ð xÞ ¼ 0, if x\0. Here the values Ali , Avi , r2li , r2vi , i ¼ 1; 2 are determined from (30), while    adx ffiffiffiffix  exp  2ax ffi ; U pffiffiffi f ðx; a; bÞ ¼ U adpþ b bd bd q ffiffiffiffiffi ffi   h i  2ax adx ðad þ xÞ2 2 pffiffiffiffi þ U exp  exp  : gðx; a; bÞ ¼ 2a b b pbd 2bd bd

ð35Þ

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O. Chernoyarov et al.

In practice, the calculation of probability (23) by the formula (31) using (34), (35) is very difficult (as the value of d cannot be exactly determined). However, if condition z2 [ [ 1 (25) holds, then in (34) one can use the asymptotic approximations of the functions (35) of the form [12] f ðx; a; bÞ ¼ 1  expð2ax=bÞ;

gðx; a; bÞ ¼ ð2a=bÞ expð2ax=bÞ:

ð36Þ

By substituting (36) into (34) and then (32), (34) into (31) with subsequent integration by the variables x, u, after all the corresponding transformations, one gets
 ~c  S0 2v2 þ v1 v2 þ 2v22 2v2  v1 v2 þ v22 X1 ðv1 Þ bU X1 ðv1 þ v2 Þ  1  1 r v1 v2 v2 ðv2  v1 Þ 2v2  v1 v2 þ v21 X1 ðv2 Þ  X2 ðv1 þ v2 Þ  X2 ðv1 Þ  X2 ðv2 Þ :  2 v1 ðv1  v2 Þ

ð37Þ

Here   

 ~c  S0 2l1 lnð1 þ qÞ r2 x ; X1 ð xÞ ¼ exp x ~c  S0  U 1  rx ; q 2 1þq r ( ) " #    1þq r ð~c  S0 Þ2 2 v2 ¼ 2l1 lnð1 þ qÞ  1 ; X2 ð xÞ ¼ x pffiffiffiffiffiffi exp  þ ~c  S0  r x X1 ð xÞ : q 2r2 2p v1 ¼

The accuracy of the formula (37) increases with lmin (4) и z (25).

5 The Simulation Results In order to determine the errors in the approximate formulas found for the characteristics of the synthesized detection algorithm, the statistical computer simulation of the detector (7) operation is carried out. In the simulation process, over the interval ~t 2 pffiffiffiffiffiffiffiffiffiffiffi ½0; 1 (9) the samples ~ykm ¼ yð~tk ; X01 vm Þ T=N0 (9) are formed at the discrete points in ~ 1 þ mDv, m ¼ time ~tk ¼ kD~t, k ¼ 0; intf 1=D~tg and for each of the values of vm ¼ Y

  ~2  Y ~ 1 Dv belonging to the normalized frequency bandwidth v (9), as 0; int Y described in [12]. Further, using the generated samples ~ykm , within the intervals   ~2 , Y ~ 1; K ~ 2 the samples M1nm ¼ M1 ðnDl; mDvÞ, M2m ¼ M1 ðnDl; m0 DvÞ and ~ 1; Y K S3nm ¼ S3 ðnDl; mDvÞ are calculated of the random field M1 ðl; vÞ, the random process M2 ðlÞ and the deterministic function S3nm ¼ S3 ðnDl; mDvÞ (9). Here the value m0 ~ 1 þ m0 Dv ¼ 1. The discretization steps are corresponds to the bandwidth X01 , so Y chosen to be equal to D~t ¼ 0:05=lmin by the variable ~t and to be equal to Dl ¼ Dv ¼ 0:01 by the variables l and v. As a result, the relative standard error of the stepwise approximation of the functional M ðl; vÞ (8) based on the generated samples, calculated by the technique proposed in [13], does not exceed 10%.

Detecting the Abrupt Change in the Bandwidth

539

As an empirical estimate of the false alarm probability a (12), there is used the relative frequency of exceeding the threshold ~c (8) by the greatest samples out of the decision statistic samples Mnm ¼ M ðnDl; mDvÞ in the absence of the abrupt change in the random process (1) bandwidth. As an empirical estimate of the missing probability b (23), there is taken the relative frequency of not exceeding the threshold ~c (8) by the greatest sample out of the decision statistics samples Mnm in the presence of the abrupt change in the random process bandwidth. In Figs. 1 and 2, there are presented some simulation results and the corresponding theoretical curves. In order to obtain each experimental value, at least 104 realizations ~ 1 ¼ 0:1, K ~ 2 ¼ 0:9, Y ~ 1 ¼ 0:1, of the decision statistics M ðl; vÞ (8) are processed under K ~ Y2 ¼ 3. All of this allows us to provide such deviation of the confidence interval boundaries from the obtained experimental data that is not greater than 15% with the probability 0.9.

Fig. 1. The false alarm probability while detecting the abrupt change in the random process bandwidth.

In Fig. 1, by solid lines the theoretical dependences are presented of the false alarm probability a upon the threshold ~c calculated by the formulas (19), (20), (22). The curve 1 is plotted for l1 ¼ 500, q ¼ 1, 2 – l1 ¼ 1000, q ¼ 1, 3 – l1 ¼ 1000, q ¼ 0:25, 4 – l1 ¼ 3000, q ¼ 0:25. The corresponding experimental values of the false alarm probability are drawn by squares, crosses, rhombuses and circles. In Fig. 2, by solid lines, the theoretical dependences are presented of the missing probability b upon the value of the parameter l1 (9) calculated by the formula (37). The threshold value ~c is calculated by means of (19), (20), (22) according to the Neumann-Pearson criterion for the chosen level of false alarm probability that is 0.01 in

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Fig. 2. The missing probability while detecting the abrupt change in the random process bandwidth.

this case. The curve 1 is plotted for q ¼ 0:5, l0 ¼ 0:5, v002 ¼ 0:75 (X02 ¼ 1:75X01 ), 2 – q ¼ 0:5, l0 ¼ 0:25, v002 ¼ 0:75, 3 – q ¼ 1, l0 ¼ 0:25, v002 ¼ 0:75 (X02 ¼ 0:25X01 ), 4 – q ¼ 1, l0 ¼ 0:25, v002 ¼ 0:75. The corresponding experimental values of the missing probability are drawn by squares, crosses, rhombs and circles. From Fig. 1, Fig. 2 and the additional analysis performed, it follows that the theoretical dependences obtained for the probability a that are (19), (20), (22) and for the probability b that is (37) are in good agreement with the experimental data, at least in ~ 1  0:1, K ~ 2  0:9, jX02  X01 j=X01  0:1. case when lmin  25, q  0:1, K

6 Conclusion Based on the results obtained, the following conclusions can be made. 1. The developed algorithm applying the maximum likelihood method for detecting the unknown abrupt change in the random process bandwidth in the conditions of the fast fluctuations of the observable data realization allows such hardware implementation that is significantly simpler than the ones that are required by the algorithms obtained by means of the common approaches. 2. The characteristics of the algorithm for detecting the unknown abrupt change in the fast-fluctuating random process bandwidth can be analytically found with the help of the multiplicative and additive generalizations of the local Markov approximation method adapted for the case of several unknown discontinuous parameters. And it should be noted that the abrupt change detection algorithm provides the best performance in the conditions of abruptly increasing process bandwidth analyzed.

Detecting the Abrupt Change in the Bandwidth

541

3. The presented theoretical results deal with a wide range of the random process parameter values and as such are in good agreement with the corresponding experimental data obtained by means of statistical computer simulation. 4. As it can be seen from the additional analysis, the detectors synthesized on the basis of the proposed approach can also be used, and without any significant loss in performance, in case when receiving high-frequency fast-fluctuating non-Gaussian random processes with the unknown piecewise-constant frequency parameters. Acknowledgements. The reported study was funded by RFBR and CNRS, project number 2051-15001.

References 1. Zhigljavsky, A.A., Krasnovsky, A.E.: Detection of the abrupt change of random processes in radio engineering problems. Leningrad State University, Leningrad (1988). (in Russian) 2. Kligene, N., Tel’ksnis, L.: Methods to determine the times when the properties of random processes change. Autom. Remote Control 41(10), 1241–1283 (1983) 3. Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Application. Prentice-Hall, New Jersey (1993) 4. Trifonov, A.P., Nechaev, E.P., Parfenov, V.I.: Detection of Stochastic Signals with Unknown Parameters. Voronezh State University, Voronezh (1991). (in Russian) 5. Trifonov, A.P., Shinakov, Y.S.: Joint Discrimination of Signals and Estimation of Their Parameters Against Background. Radio i Svyaz’, Moscow (1986). (in Russian) 6. Van Trees, H.L., Bell, K.L., Tian, Z.: Detection, estimation, and modulation theory, Part I. Detection, estimation, and filtering theory, 2nd edn. Wiley, New York (2013) 7. Chernoyarov, O.V., Shahmoradian, M.M., Kalashnikov, K.S.: The decision statistics of the Gaussian signal against correlated Gaussian interferences. In: 2016 International Conference on Mathematical, Computational and Statistical Sciences and Engineering, MCSEE2016, pp. 426–431. DEStech Publications, China (2016) 8. Dynkin, E.B.: Theory of Markov Processes. Dover Publications, New York (2006) 9. Chernoyarov, O.V., Salnikova, A.V., Golpaiegani, L.A.: On probability of the Gaussian random processes crossing the barriers. In: 2017 3rd International Conference on Frontiers of Signal Processing, ICFSP, France, pp. 1–7. IEEE (2017) 10. Kailath, T.: Some integral equations with nonrational kernals. IEEE Trans. Inf. Theory 12 (4), 442–447 (1966) 11. Chernoyarov, O.V., Salnikova, A.V., Rozanov, A.E., Marcokova, M.: Statistical characteristics of the magnitude and location of the greatest maximum of Markov random process with piecewise constant drift and diffusion coefficients. Appl. Math. Sci. 8(147), 7341–7357 (2014) 12. Chernoyarov, O.V., Min, S.S.T., Salnikova, A.V., Shakhtarin, B.I., Artemenko, A.A.: Application of the local Markov approximation method for the analysis of information processes processing algorithms with unknown discontinuous parameters. Appl. Math. Sci. 8 (90), 4469–4496 (2014) 13. Zakharov, A.V., Pronyaev, E.V., Trifonov, A.P.: Detection of step random disturbance. J. Comput. Syst. Sci. Int. 40(6), 869–877 (2001)

Cognitive Interaction of Robot Communities, Simulation Modeling G. V. Gorelova1, E. V. Melnik2, A. B. Klimenko3, and I. B. Safronenkova2(&) 1

Engineering and Technology Academy of the Southern Federal University, 44, Nekrasovsky Lane, GSP-17, 347922 Taganrog, Russian Federation 2 Federal Research Centre the Southern Scientific Centre of the Russian Academy of Sciences, 41, Chekhov Street, 344006 Rostov-on-Don, Russian Federation [email protected], [email protected] 3 Scientific Research Institute of Multiprocessor Computer Systems of Southern Federal University, 2, Chekhova Street, 347928 Taganrog, Russian Federation

Abstract. The aim of the article is to present some features of cognitive simulation of complex systems, which are proposed to use at the stage of pre-design studies of robot groups (communities) interaction. The structure of interacting robot communities with a leader can be represented in a hierarchical cognitive map. To develop such a map, it is necessary to use theoretical knowledge, expert and domain statistical data. Minimal theoretical information of complex systems cognitive modeling is presented. It is necessary to explain the possibility of cognitive modeling using in the context of robot groups interaction. An example which illustrates the development of a hierarchical cognitive map of three robot groups interaction is given in this paper. A study of the structural properties of the model was made. Its structural stability and resistance to disturbances have made it possible to conclude that the model does not contradict possible real situations. Scenario modeling results of situations development for various internal and external environment variations are presented on the model. The possibility of adjusting the structure of a cognitive model to provide the desirable qualities of situations development processes on the model is also shown. Keywords: Community of robots Interaction scenario


Simulation
Cognitive modeling


1 Introduction The design of robotic systems with elements of artificial intelligence is currently a global trend. Such complexes are able to analyze the external environment and make informed decisions to achieve their goals. Robotic systems are complex systems consisting of many components (elements, objects).

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 542–554, 2020. https://doi.org/10.1007/978-3-030-63322-6_44

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During the process of systems development, whose functioning is based on the interaction of many components, an important task is to study the behavior of the system and its elements in certain situations before the system is physically implemented. It is necessary to provide the desired level of efficiency and safety of robotic systems during their further operation [1–4]. The specified task is complex, its solution requires a lot of time, and it is also a great expense item in the project budget, therefore, there is a need for serious preproject studies. A specific task in pre-design studies of robotic systems is also the task of analyzing the results of objects interaction aimed at fulfilling a common goal. By the objects of robotic systems, we understand robots, collectives of robots, objects that make decisions. The widespread use of robots and robot teams (communities) to solve a wide range of problems is perspectively nowadays. For example, monitoring arrangement and delivery problem solving. As you know, to solve the problem of studying the behavior of complex systems, there are various methods that differ in the degree of accuracy of the obtained result and the costs for not obtaining it. The most accurate result can be obtained if you study the structure and behavior of the disposed system. However, it is the most expensive method. In addition, it is not applicable at the initial design stages, since there is no system yet. But it can be realized with the help of appropriate simulation modeling of the designed structures and the behavior of a complex system. Currently, there are a large number of simulation methods for a complex system both “from above” (starting with the general properties of the system that determine the behavior of the entire system and its components) and “from below” (agent-based modeling - the behavior and structure of a system is formed by properties, behavior, interaction of components) [5–9]. Each of these approaches has its own advantages and disadvantages. We believe that a combination of these approaches in the sequence of stages of modeling will be useful, since in the early stages of design it is necessary to consider many different options for system development and its behavior before the system is practically implemented. In this work, at the stage of pre-design studies of various robotic complexes, it is proposed to use models and methods of cognitive simulation of complex systems in which agent-based modeling can be applied. Currently, cognitive modeling is widely used in different applications in the field of various socio-economic systems [10–18]. This article discusses the possibilities of cognitive modeling using to study the structure and behavior of robotic systems, an essential feature of which is the interaction of parts and the whole. The concept of “cognitive” has been recently used not only in relation to processes of cognition in humans, but also in complex systems of various nature, including sociotechnical, cyberphysical systems, especially when it comes to intelligent support systems decision making. In such systems, communication processes, information exchange processes between objects have the meaning of “cognitive interaction”. This thereby emphasizes the presence of properties of intelligence in the system, the possibility to make “independent” decisions, which significantly increases the efficiency of achieving the goal.

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2 On Cognitive Modeling of Complex Systems The basis of cognitive modeling, which includes a number of stages, is the development of a cognitive model, starting with its simplest form - a cognitive map. G ¼ \V; E [

ð1Þ

where V ¼ fvi jv
i 2 V; i ¼ 1; 2; . . .; kg – vertices of the cognitive map; E ¼ feij
eej 2 E; i; j ¼ 1; 2; . . .; kg – arcs representing relationships (causal relationships) between vertices. Graph G can be specified in the form of a relational matrix (2), which is further necessary for the mathematical analysis of the properties of the model (1):   A ¼ aij ; aij ¼



1; if Vi connected with Vj 0; otherwise

ð2Þ

The ratio aij = +1, if an increase (decrease) in the signal in Vi leads to an increase (decrease) in the signal in Vj; aij = −1, if an increase (decrease) in the signal in Vi leads to a decrease (increase) in the signal in Vj; aij = 0 if the vertex Vi does not affect the vertex Vj in the situation under consideration. Certain transformations of the AG matrix allow conducting a mathematical analysis of various properties (connectivity, complexity, stability, paths and cycles, scenario modeling, etc.) of the studied object on its cognitive model. n o ðvik Þ

If the vertices Vi are specified by the vector of its parameters X ¼ xl

;i ¼

1; . . .; n; k ¼ 1; . . .; K; l ¼ 1; . . .; L, then the cognitive model is a vector parametric digraph. If the relationships between the vertices are defined as functions, there is a cognitive model in the form of a vector parametric functional digraph: UP ¼ hG; X; F; hi

ð3Þ

where Фп is a tuple in which G is a cognitive map; X : V ! h, X – is the set of vertex parameters; h- is the space of vertex parameters; F = (X, E) = f(xi, xj, eij) - functional conversion of arcs; F - the transformation may take the form of a function fij, as well as the weight coefficient wij determined by expert or statistical data.

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In the case of studying the hierarchy of control levels, cognitive maps of individual levels can be combined into a hierarchical system [4, 7] IG0 ¼ hGk ; Gk þ 1 ; Ek i; k  1

ð4Þ

where Gk and Gk+1 are cognitive maps of k and k + 1 levels, whose vertices are respectively connected by arcs Ek. The cognitive model is a simulation model that makes it possible not to conduct an experiment on a “living” system, but to simulate its behavior and possible future development under the influence of various factors, generating new knowledge about the system. This allows you to justify management decisions in a given situation. Before using the cognitive model to determine its possible behavior, the second stage of modeling analyzes the various properties of the model. Let us give an illustrative example of cognitive modeling of the interaction of complex systems in the form of some communities consisting of cognitively interacting objects (information and communication related). Let’s look at the communities (collectives) of robots in which there is a “leader”. Cognitive interaction between robots is an information interaction. By informational, cognitive interaction is meant such an exchange of signals between system objects in which the state of at least one of them changes, while one of the objects acts as a receiver and the other as a source of “information” necessary for decision-making. Suppose the existence of several communities of objects (robots) that are in a state of “commonwealth” and “confrontation”. It is desirable to determine the possible behavior of such a complex system of interacting communities with various changes in the internal and external environment. For this purpose, we use the methodology of cognitive modeling of complex systems [16] and the CMLS [17] software system to simulate the possible structure and behavior of interacting communities.

3 An Illustrative Example of Cognitive Modeling of the Interaction of Three Robot Communities Figure 1 shows three structures unconnected by cognitive interaction, the interaction exists only within each collective, and Fig. 2, on which the interaction arose. In the process of cognitive modeling, at its second stage, an analysis was made of the structural properties of the models of Fig. 1 and Fig. 2 and their resistance to perturbations and structural stability. We present the results of the analysis of the model properties (Fig. 2) obtained using the CMLS software system [17]. The cognitive IG model was not resistant to perturbations according to the accepted criterion [15]: the maximum modulo M root of the characteristic equation of the matrix of relations of the graph IG is |M| = 2.64 > 1 (must be less than 1). An analysis of the ratio of the number of stabilizing cycles (13 negative feedbacks) and process accelerator cycles (15 positive feedbacks) indicates the structural stability of such a system [10]. Figure 3 shows an example of determining the cycles of the cognitive model IG. The figure shows one of the positive feedback cycles, a sign of which is an even number of negative arcs in it.

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Fig. 1. Hierarchical cognitive maps of non-interacting communities of objects.

Fig. 2. Hierarchical cognitive map IG of interaction communities of objects.

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Fig. 3. Isolation of cognitive model cycles.

The third stage of cognitive modeling allows us to judge scenarios of the development of situations when disturbances are introduced at various vertices of the cognitive model, thereby predicting the possibility of the appearance of desirable and undesirable, dangerous situations in the system. Before scenario modeling, carried out by means of pulsed modeling, an expert plan for a computational experiment was developed. The vertices were determined, in which it was envisaged to introduce disturbances. Let us cite, as an example, the simulation results in two scenarios. Scenario №1. Suppose that all three robot communities begin to act, perturbations q1 = +1 q14 = +1, q20 = +1 are introduced at the vertices V1, V14, V20. Figure 4 shows the results of calculating impulses at all vertices of the cognitive model for 10 modeling steps. Graphs of pulsed processes corresponding to this scenario are shown in Fig. 5 and Fig. 6. The graphs are constructed according to the data in Fig. 4. For the sake of clarity, they display the first steps of modeling.

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Fig. 4. The results of the calculation of impulses at all vertices of the cognitive model. Scenario №1.

As can be seen from Fig. 5, from the first steps of modeling, trends in the development of situations in the upper peaks of all three communities are determined. In the tops of the 1-Leader and 2-Leader, the trends in the development of situations are growing, which “suppress” the processes in the top of the 2-Leader. Figure 6 shows graphs of pulsed processes at some peaks of the lower level of three robot communities from 3 to 7 simulation cycles. The tendencies of changes in processes at the lower levels repeat the tendencies of the development of situations in the “head” vertices: the joint “positive” actions of the robots of the first and second communities actively “suppress” the activity of the third group.

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Fig. 5. Graphs of impulse processes at the vertices V1, V14, V20. Scenario № 1.

Fig. 6. Graphs of impulse processes at the vertices of the lower level. Scenario № 1.

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Consider the option of mismatched actions at the vertices V1 (1-Leader) and V14 (2-Leader) at the start of actions V20 (3-Leader). Scenario № 2. Let perturbations be introduced at the vertices V1, V14, V20; q1 = +1, q14 = +1, q20 = +1. Figure 7 shows the results of calculating impulses at all vertices of the cognitive model for 10 modeling steps. Graphs of pulsed processes corresponding to this Scenario №2 are shown in Fig. 8.

Fig. 7. The results of the calculation of impulses at all vertices of the cognitive model. Scenario №2.

As can be seen from Fig. 8, Scenario No. 2 generates increasing oscillatory processes in the system that cannot lead to the achievement of the goals 1-Leader, 2-Leader, 3-Leader. If it is desirable to stabilize the development trends of processes in the system, it is necessary either to change the system of disturbing effects on objects, or to change the structure of the model.

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Fig. 8. Graphs of impulse processes at the vertices 1-Leader, 2-Leader, 3-Leader. Scenario № 2.

Let us slightly modify the structure of the cognitive model by changing the interaction between the vertices, Fig. 9. The system will remain structurally stable, as in it among the 28 cycles an odd number 13 of negative cycles is observed. But the model will not be resistant to disturbances, as |M| = 2,64 > 1 and as can be seen from Fig. 10 graphs of the impulse processes of scenario №3. Scenario № 3. Let the vertices V1, V14, V20 be initiated and a disturbing effect be added to the vertex of the second level V13, perturbations q1 = +1, q13 = +1, q14 = +1, q20 = +1 are introduced at the vertices. The structure of the cognitive map in Fig. 9 can be considered more successful, since it significantly weakens the oscillatory processes in the system.

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Fig. 9. Redesigned hierarchical cognitive map.

Fig. 10. Graphs of impulse processes, Scenario № 3

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4 Conclusion Cognitive simulation modeling of complex systems, which is carried out on cognitive maps - sign oriented graphs, allows us to predict possible options for the situations development of robot groups interaction in complex systems in a general way. It was illustrated by an abstract simplified example in this article. To analyze real situations, it is necessary to proceed to the simulation of complex systems using functional parametric graphs, in which the interaction between objects at the same and different hierarchical levels is represented by functions defined by quantitative parameters. But this new data does not affect the cognitive model structure and the general nature of the impulse processes in the system, therefore, at the pre-design stage, we consider it is necessary to conduct a research on the possible robot groups interactions belonging to different conflicting or friendly parts on cognitive maps. Acknowledgements. The reported study was funded by the RFBR project 18-29-03229 and the GZ SSC RAS N GR project AAAA-A19-119011190173-6.

References 1. Kalyaev, I., Kapustyan, S., Ivanov, D., Korovin, I., Usachev, L., Schaefer, G.: A novel method for distribution of goals among UAVs for oil field monitoring. In: 2017 6th International Conference on Proceedings of Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEVISCMHT), Danvers, pp. 1–4. IEEE (2017) 2. Casbeer, D.W., Beard, R.W., Mehra, R.K., McLain, T.W.: Forest fire monitoring with multiple small UAVs. In: Proceedings of the 2005, American Control Conference, 2005, Danvers, pp. 3530–3535. IEEE (2005) 3. Merino, L., Caballero, F., Martinez-de Dios, J.R., Ferruz, J., Ollero, A.: A cooperative perception system for multiple UAVs: application to automatic detection of forest fires. J. Field Robot. 23(3–4), 165–184 (2006) 4. Sujit, P.B., Kingston, D., Beard, R.: Cooperative forest fire monitoring using multiple UAVs. In: 46th IEEE Conference on Decision and Control Proceedings, Danvers, pp. 4875– 4880. IEEE (2007) 5. Ondráček, J.: Intelligent Algorithms for Monitoring of the Environment Around Oil Pipe Systems Using Unmanned Aerial Systems (2014) 6. Ivanov, D., Korovin, I., Shabanov, V.: Oil fields monitoring by groups of mobile microrobots using distributed neural networks. In: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR) Proceedings, Danvers, pp. 588–593. IEEE (2018) 7. Sansores, C., Pavón, J.: Agent based simulation for social systems: from modeling to implementation. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds.) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science, vol. 4177, pp. 79–88. Springer, Heidelberg (2006) 8. Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.P.: Agent based modelling and simulation tools: a review of the state-of-art software. Comput. Sci. Rev. 24, 13–33 (2017)

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9. Railsback, S.F., Lytinen, S.L., Jackson, S.K.: Agent-based simulation platforms: review and development recommendations. Simulation 82(9), 609–623 (2006) 10. Roberts, F.: Graph Theory and its Applications to Problems of Society. Society for Industrial & Applied Mathematics (SIAM) (1978) 11. Langley, P., Laird, J.E., Rogers, S.: Cognitive architectures: research issues and challenges. Cogn. Syst. Res. 10(2), 141–160 (2009) 12. Avdeeva, Z.K., Kovriga, S.V.: On governance decision support in the area of political stability using cognitive maps. In: Kopacek, P., Ibrahimov, B. (eds.) 18th IFAC Conference on Technology, Culture and International Stability (TECIS 2018), Amsterdam, vol. 51, no. 30, pp. 498–503. Elsevier (2018) 13. Gorelova, G.V., Pankratova, N.D.: Scientific foresight and cognitive modeling of socioeconomic systems. In: Kopacek, P., Ibrahimov, B. (eds.) 18th IFAC Conference on Technology, Culture and International Stability (TECIS 2018), Amsterdam, vol. 51, no. 30, pp. 145–149. Elsevier (2018) 14. Gorelova, G., Pankratova, N.: Strategy of complex systems development based on the synthesis of foresight and cognitive modelling methodologies. In: 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC), Danvers, pp. 1–6. IEEE (2018) 15. Gorelova, G.V., Pankratova, N.D., et al.: Innovative development of socio-economic systems based on foresight and cognitive modelling methodologies. In: Gorelova, G.V., Pankratova, N.D. (eds.) Naukova Dumka, Kiev (2015) 16. Ginis, L.A., Gorelova, G.V., Kolodenkova, A.E.: Cognitive and simulation modeling of development of regional economy system. Int. J. Econ. Financ. Issues 6(5S), 97–103 (2016) 17. Program for cognitive modeling and analysis of socio-economic systems at the regional level. Certificate of state registration of computer programs # 2018661506 (2018) 18. Klimenko, A., Gorelova, G., Korobkin, V., Bibilo, P.: The cognitive approach to the coverage-directed test generation. Adv. Intell. Syst. Comput. 662, 372–380 (2017)

Construction of an Automated Process Control System for the Exploitation of Oil and Gas Fields in a Heterogeneous Information Environment Gritsenko Yury(&)

, Senchenko Pavel

, and Sidorov Anatoly

Tomsk State University of Control Systems and Radio-Electronics, Tomsk, Russia [email protected]

Abstract. The article presents a solution ensuring the automated process control system (APCS) of underwater oil and gas production to function in a heterogeneous information environment. The authors consider the necessity to apply hazard identification and risk assessment tools to further control of the process hazards. The modules of expert and simulation model design may serve as such tools. Furthermore, the review of APCS control schemes is given, and a comprehensive scheme of APCS block integration and the composition of the unified APCS software package are proposed. The most suitable scheme for controlling the heterogeneous information environment is the scheme of direct digital control. However, it has a distinct disadvantage which appears in case of a computer failure. One way to avoid this situation is to arrange a computer backup or replace one computer with a system of machines (a cluster). The unified cluster uses the XML-format for the data exchange. This allows for cluster functioning in a heterogeneous information environment. A centralised data repository consisting of a database and file storage is used for storing the incoming data #COMESYSO1120. Keywords: Automated process control systems
Submarine mining complex
Heterogeneous information environment

1 Introduction The development of automated process control systems is related to a set of emerging trends. First of all, it is the arrangement of safe control of comprehensive electronic integration between system components. The second trend is the high number of heterogeneous industrial facilities. And, the third trend is manifested in the stochastic character of information flows, which shall be often processed on a real-time basis. The ACPSs applied during the development and operation of subsea oil and gas fields are not an exception. Moreover, in this sphere, this work is related to different risks which may lead to serious accidents in case of inadequate control. The risk is a potential hazard leading to an occurrence of unintended consequences of a certain event [1].

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 555–570, 2020. https://doi.org/10.1007/978-3-030-63322-6_45

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The risks may be measured as a result of the probability analysis of an event or the consequences of this event. The risk control is a systematised process of determination, analysis and measures taken in accordance with the identified project risks. This process includes minimising the probability of an adverse event and its consequences as well as increasing the likelihood of a desirable effect and its consequences within the project. The risk control may be considered as a continuous assessment process realised during the design, construction, operation, maintenance and implementation of the project. The life cycle of the integrated risk management system is shown in Fig. 1.

Fig. 1. Life cycle of an integrated risk management system.

During field development and operation, the reliability and robustness of the systems are among the key factors influencing the safety, product quality and service cost. A program of systematisation and reliability control enables setting the target levels of reliability and product quality at the early stages of the project development [2]. Submarine mining projects have a complex architecture and are subject to the occurrence of undefined states for a wide range of reasons. The control of such states and, especially, critical ones at the system level improves the system efficiency, which is the aim of successful risk management plan implementation. Special attention shall be paid to APCS control schemes, which may be constructed in several ways [3]: • • • •

process control in the data collection mode; operator advisor control mode; supervisory control; direct digital control.

The analysis of the risk control theory and APCS control scheme applicability allows setting the requirements, which may be used for the APCS information and control functions at a submarine oil and gas mining complex. The requirements may further be used as a basis for a comprehensive APCS block integration scheme.

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The comprehensive scheme arises a problem of building an information system with characteristics and opportunities that have not yet been implemented within one programming system or a database control system. However, there are approaches to form a shared information space to combine the available heterogeneous data sources to use them in a shared manner in control of complex processes [4, 5]. The unification and integration of the applied sector-specific IT solutions ensure significantly decreased terms, cost and risks of integration and ownership of the information systems at sector enterprises, thus, increasing the enterprise competitiveness.

2 Hazard Analysis 2.1

Hazard Identification and Risk Assessment

A Process Hazard Analysis (PHA) is an elaborate, well-structured and systematic approach for identification, assessment and control of process hazards [6]. The Process Hazard Control may be divided into hazard source identification and the analysis of failure types and consequences. Risk assessment is a process of evaluation of possible risk situations and factors influencing project safety. It includes the investigation of possibilities of hazardous situations or system states and their development before an accident. A risk assessment system should commensurate with the level of technical risk and its source, the type of the project and the stage of its development. Technical risk analysis also varies at different project stages, for example: • a simple criterion of selecting the top-class equipment will eliminate unreliable equipment; • an analysis of failure consequence severity may be applied to identify the most sensitive components in terms of operation, safety and environment; • it is possible to use various emergency operation modes to determine the probable fault location. Table 1 shows the parameters that can be used for risk identification and assessment.

Table 1. Parameters for risk identification and assessment. Evaluation options

Key indicators

Staff

Qualification and level of experience of staff Organisation Required presence Shift availability Availability of duty repair crews

(continued)

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Evaluation options

Key indicators

Features of the organisation of the project as a whole Delay Cost/Replacement time Repair options Number of interacting contractors/subcontractors Project development time Field existing infrastructure Infrastructure on the surface and underwater Object features Cost, structural strength and reliability Type of maritime management Novelty/Appropriateness Reliability Type of control Previous experience Ease of installation Equipment used Mean time between failures/reliability Condition/Serviceability Previous experience Conformity Experience with operators/contractors Operating conditions Cost of equipment involved and running costs Presence of language or other barriers Seasonal/environmental conditions Availability of shipping in the area Distance from the coast

As not all the risks lead to a certain event at the facility, a probabilistic assessment is used to assess the risks, as it gives a common value of a risk likelihood of occurrence. During the assessment, risks are removed to get the overall picture. This method is based on functional expertise, and the utilised values of probabilistic indicators provide a balanced result. For example, if a risk is assessed as one with a probability from 1% to 20%, this means that the mean value of 10% will be taken into account. Table 2 shows the values used for calculating various risk levels for the risk assessment. Table 2. Probabilistic indicators in risk assessment. Risk Incredible Unlikely Maybe Probably Very likely

Probability 80%

Estimated value 10% 30% 50% 70% 90%

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100% probability is not shown in the table, as it is impractical to handle such probability. The risk assessment is only performed for the scenarios which could occur. As soon as the probability and the risk level are determined, it is necessary to set the priorities for the measures to be taken. The determination of risk criteria comes down to assuming the risk level as acceptable/unacceptable. The decision-making process employs the criteria describing the risk level as acceptable, unacceptable or to be reasonably minimised. Numerical criteria of the risk assessment are used in a qualitative risk assessment. As described earlier, the risk assessment is only deals with uncertainties and cannot be used to assess tough scenarios. Sometimes, due to the uncertainty of some particular events, it may be impossible to use the numerical criteria. The risk assessment criteria may differ for different projects and vary in different communities and over time. In addition, they can change with the experience in overcoming critical situations and when priorities shift. For a hazard analysis, the acceptance criteria should be a subject for discussion and be the first to be determined. In [7], there are three categories of potential risk proposed: Low, Medium, High. Such division is based on the assessment of both consequences and probability applied to quantitative indicators. These categories shall be determined in relation to the following areas: personnel safety, environment, property and reputation. It is recommended to use a matrix to determine the risk acceptance criteria; an example of such a matrix is given in Fig. 2.

Fig. 2. Risk acceptance matrix.

High-Risk Level. If the measures on risk mitigation did not change the level of an undesired event probability below the unacceptable level, the initiating operations should not be performed. If the operations are already being performed, the process of their termination shall be initiated as per the established scenarios.

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Medium-Risk Level. The operations may be performed after taking the measures determined by the risk assessment and as per a developed risk mitigation scenario. Low-Risk Level. An acceptable level of risk which fits into the principle of a minimal reasonably practicable risk. 2.2

Process Hazard Control

Identification of the Hazard Source. The HAZID (hazard identification) method may be used to identify hazard sources [8]. The method is used to determine all sorts of hazard sources having the potential of becoming the reason for an emergency occurrence. The identification of hazard sources should be performed at the early stage of the project, and the result is to be embodied in a conceptual engineering solution for an end operator. The HAZID method should be used by qualified and experienced personnel for the determination of hazard sources at a certain facility. The HAZID method helps to select serious risks in all sorts of described risks. The given method is also used for the assessment of potential risks at the early stages of project development. HAZID advantages: • applicable at the early stages of a project with a possibility to select alternatives; • determines particular types of hazards and threats; • forms a list of typical hazards and consequences for detailed analysis at the later stages of the life cycle. Analysis of Failure Types and Consequences. The analysis of failure types, consequences and criticality may be performed with the use of the FMEA (Failure modes and effects analysis) method or its modification FMECA (Failure Mode, Effects and Criticality Analysis) [9]. The given method is used for determination, identification and, if possible, simulation of potential failures. The use of the FMECA process for determination of potential failures, which may occur at each stage and should invoke risk searching and mitigation procedures in future, as well as the search of the ways of how to complete the task successfully (for high-risk operations only). All operations related to the FMECA procedure and expert evaluation shall be included in the project. FMECA advantages: • • • •

applicable at all stages of a project; universally applicable to high-level systems, components and processes; able to determine particularly vulnerable places; systematised determination of all sorts of failure modes.

FMECA disadvantages: • do not determine the actual cause of failure; • labour-intensive. A module of the expert system design is required to train and provide operators with real-time advice on further actions for the case of any abnormal operations, with regard to probable development of the situation at the control object. In order to avoid

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abnormal situations, a simulation model design module is required. It should have the means of simulation model creation, adjustment and running.

3 APCS Control Schemes The simplest and pioneer scheme is the process control scheme in data collection mode (Fig. 3). It requires developing a complex of computer connection with the objects to be controlled.

Fig. 3. Control scheme in data collection mode.

The variables of the operator’s interest are transformed into a digital form perceived by the input system and are placed into the computer data storage medium. At this stage, the values are digital representations of the voltage generated by the sensors. These values are transformed into technical units according to the corresponding formulas. The calculation results are registered by the APCS output devices for further use by operators and process engineers. The data are primely collection to study the process in various conditions. As a result, the process engineer receives an opportunity to build and (or) specify the mathematical model of the process, which is to be controlled. The data collection does not directly impact the process; it is a precautionary approach to implement computer-aided control methods. However, even in the most complicated process control schemes, the data collection system is used as one of the obligatory control subschemes for the purposes of analysis and specification of the process model.

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Operator advisor control (Fig. 4) implies that the computer as a part of an APCS operates with a process in an open circuit, i.e. the APCS outputs are not connected to the process managing bodies. The control is practically performed by the process operator receiving the advice from the computer.

Fig. 4. Operator advisor control scheme.

All necessary control actions are calculated by the computer in accordance with the process model, and the calculation results are sent to the operator in a printed form (or in the form of messages on display). The operator controls the process by changing the setting of the regulators. The regulators are the means of keeping an optimal process control where an operator plays the role of the follow-up and control link. The APCS plays the role of a device which faultlessly and continuously guides an operator in his/her efforts to optimise the process. The main disadvantage of this control scheme is the constant presence of an operator in the control circuit. If there are a lot of input and output variables, such a control scheme cannot be used because of the limited human psychophysical abilities. However, the control of this type also has some advantages. It meets fits the precautionary approach to new control methods. The operator advisor mode provides good opportunities for inspecting new process models. A process engineer with good knowledge of the technology may be an operator. He/she is likely to determine the wrong combination of set points which may be generated by the APCS program, which has not been completely adjusted.

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The APCS may also monitor the occurrence of emergencies for the operator to have an opportunity to pay more attention to work with setpoints; besides, the APCS monitors more types of emergencies than an operator can do. In the supervisory control scheme (Fig. 5), the APCS is used in a closed circuit, i.e. the setpoints are sent to the regulators directly by the system. The task of the supervisory control is to keep the process close to the optimal working point by means of prompt action on it. This is one of the main advantages of the given mode.

Fig. 5. Supervisor control scheme.

The operation of the input part of the system and calculation of control actions are little different from the control system operation in the operator advisor mode. However, after the setpoint values have been calculated, they are transformed into the values which can be used to adjust the regulator settings. If the regulators perceive analogue signals, the values processed by the computer should be transformed into binary codes which are converted into analogue signals of the corresponding level and sign, with the use of a digital-to-analogue converter. Process optimisation in this mode is performed regularly, for example, once a day. The new parameter values for the control algorithms are to be entered. Either an operator enters them via a keyboard, or the new calculation results are read by a computer of a higher level. After that, the APCS is capable of operating without outside interference for a long time. In the direct digital control (DDC) mode (Fig. 6), the signals used for actuation of the control bodies come directly from the APCS, and the regulators are eliminated from the system. Essentially, the regulators are small analogue computers which solve an only equation.

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The setpoints are entered into the APCS by an operator or the computer performing the process optimisation calculations. If the direct digital control system is available, an operator has an opportunity to change the control parameters, control some selected variables, modify the ranges of permissible variation of measured variables, change setting parameters and, in general, should have access to the control program. One of the main advantages of the DDC mode is the possibility to change thy control algorithms for circuits via a simple introduction of the changes into the stored programme.

Fig. 6. Direct digital control circuit.

This control principle is used in numerical control machines. An operator should have an opportunity to change the setpoints, control output parameters of the process, modify the ranges of permissible variable variations, change setting parameters and have access to the control programme. The most obvious disadvantage of the DDC appears in case of a computer failure. One way to avoid this situation is to arrange a computer backup or replace one computer with a system of machines (a cluster).

4 General Requirements and a Comprehensive Scheme of the APCS Block Integration Generally, for implementation of the information and control functions, the APCS should include the following technical means: a computing complex, process interface units (a set of devices for receipt and conversion of command and control signals, signalling channel switching), traditional automation devices (sensors, converters, secondary instruments, automatic regulators). The universal automated workstation of the production unit ensures: • visualisation of information on the state and operation of the technological complexes, processes and separate systems within the APCS;

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• control of all technical means of the production unit; • monitoring of emergency events and taking emergency measures. Based on the universal workstation, it is possible to create separate workstations for the operators of production unit APCS subsystems of various processes and complexes, surface and underwater mining equipment, depending on the type of the extracted product and peculiarities of the field, transportation and location. On the grounds of the operator’s role, the workstation may be designated: • • • •

for for for for

an APCS operator; a process safety operator; an operator of the leakage detection system; an operator specialised in energetics, etc.

Fig. 7. The scheme of integration of automatic process control units.

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The universal character of the automated workstation of the production unit is ensured by software setting and the possibility to upload different configurations of the graphical user interface depending on the authorised user profile. The automated operator workstations are combined into a top-level network and receive data from SCADA redundant servers [10]. The source of data for SCADA servers is a set of main controller devices of packaged equipment and subsystems combined into a low-level network. Figure 7 shows a scheme of the APCS blocks integration. In terms of structure and functionality, the systems may be divided into: • • • •

an underwater part of the APCS; a surface part of the APCS; emergency systems; service systems.

To adjust the developed APCS project, we will use a controller device emulator (the emulator is developed as a part of State Contract of the Ministry of Industry and Trade of the Russian Federation No. 16411.1810190019.09.015). Depending on the task and the development stage, it may simulate both a separate controller and a set of subsystems.

5 Formation of the Unified APCS Software Package As we see on the scheme in Fig. 7, the system has a wide range of heterogeneous information flows. To ensure the necessary functionality of the application software, different enterprises implement different software systems. The heterogeneousness of the software systems applied at enterprises will not allow ensuring transparent interrelation of both separate subdivisions and enterprises involved in the general process chain, which, in its turn, impacts the terms of product release and its quality. The lack of standards for the project data storage format in the object representation does not allow the efficient transfer of these data not only in case of interdisciplinary interaction of project groups but also in case of data exchange within one discipline among different information systems without the development and implementation of necessary converters. The lack of unified classifiers, lists and guides prevent the efficient use and update of the data. The lack of integration among CAD [11], SCADA and simulation modelling systems leads to extra costs during project data transfer, often in the manual mode, and getting inadvertent errors related to the human factor. In order to eliminate the disadvantages mentioned above, it is necessary to use the unified complex of software systems which enables designing new automated control systems of various purpose, local and integrated process control systems, in particular during the use of new equipment with a possibility to enrich and edit the library of project elements. The complex should adjust and tune up the systems by using

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mathematical simulation models, particularly during partial replacement of the equipment. The complex is built on a modular basis. Separate modules are in charge of specialised operations and functions. The data are exchanged between the modules in an open format by an XML-file transfer [12]. The central unit of the complex houses the data storage and a web-server ensuring the clients’ access to the storage. The following applications are installed on the client workstations: • Web-client—for access to project data and project documents, for the launch of local applications, acquisition of project data in the form of an XML-file from the web-server. • CAD—for development and release of design and engineering documentation, and formation of initial data for SCADA. The CAD application provides descriptions of the remote control object including process equipment, actuators, process pipelines and their characteristics. Using CAD library elements, the designer locates them on the sketch of the control object process scheme. During connection of the located elements with pipelines, bonds are formed which determine process flows at the control object. • SCADA—for development of a configuration of the automated control system project. The information of the graphical user interface of operator stations is displayed with the use of standard icons of hydraulic diagrams accepted in the design documents or pseudo-three-dimensional sections of the real equipment depending on the libraries of graphical elements used for the screen form construction. • Simulation model design module—for the development of simulation models, including the means of creation, adjustment and implementation of simulation models. The design module is intended for the bench test of the automated process control system operation. Also, it trains the operators submarine mining complexes. It ensures dynamical simulation of the offshore oil deposit development, flow of liquid-gas mixture in the production wells equipped with electrical centrifugal pump units; controls the operation modes of the production wells; adjusts water injection levels in the formation; controls pressure, temperature and flow-rate down the hole, in the wellhead, in the annulus and at the downhole equipment suction; simulates measurements of oil, water and gas rates of the production wells. Calculation results may be read by the external program or the SCADA system. • Expert model design module—to train and provide operators with real-time advice on further actions for the case of any abnormal operations, with regard to probable development of the situation at the control object. The advice is given based on the input data incoming from external systems and the set of rules determined by experts. The set of rules is a knowledge base. The rules may take into account current values of variables, variables change history, assessment of variables change in the future by means of extrapolation of the incoming data to the set time interval. The expert system may also take into account the operator’s response to the advice provided by the expert system. Thus, the expert system which has been properly designed in the ES design module is capable of helping specialist experts and operators to solve problem situations.

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The components of client applications may vary depending on the role of the user in the design process. The data storage consists of the database and file storage. The database of the unified software package stores: • • • •

object-attribute data of projects; object-attribute descriptions of the library of CAD elements; object-attribute descriptions of the library of SCADA elements; classifiers. Project data represent:

• description of the control object including its territorial division, process equipment and pipelines with their characteristics, as well as the interconnection of these components; • description of the control system including its division into subsystems, description of control system components, such as instrumentation, actuators, programmable logical controllers, as well as the interconnection of these components; • description of project documentation; • description of mathematical simulation models within the project; • description of the SCADA configuration within the project; • description of the expert system within the project. The CAD library is designated for the formation of the object-attribute description of components and their further use during the document creation. When creating library elements, their graphical representation is also formed for various documents and stored in the file storage. Besides, the connection is established with the corresponding elements of the SCADA library for further generation of the SCADA project configuration. The description of SCADA libraries contains the data on the name and the version of the library, its elements (for elements related to the CAD library elements), and the data on the library file connected in the SCADA editor. For integration with external systems, each element is provided with the reference field to an external guide element. The data may be exchanged via an XML-file corresponding to the XSD format of the scheme and the software development converter. The classifiers are used for filling CAD library element cards necessary to form the control object description, configuration of the control system, and the description of the control object mathematical simulation models. The classifiers can be filled with the elements in several ways: • directly in the CAD application launched in the library edit mode; • with the administrator tools by downloading the XML-file corresponding to the XSD [13] scheme; • using an external converter module of an external system.

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A file system of the server operating system is used as the file storage. The file storage houses: • DWG files containing a graphical representation of CAD library elements for different documents; • DWG files containing CAD project documentation; • ZIP archives containing SCADA libraries; • a ZIP archive containing SCADA project configuration; • XML files containing a configuration of mathematical simulation models of the project; • an XML-file containing a configuration of the expert system; • files of other formats containing project documents developed in an external application and downloaded into the file storage.

6 Conclusion This paper considers several key aspects of constructing an automated process control system (APCS) for oil and gas field operation in the heterogeneous information environment. The mechanisms of process risk analysis and possible control schemes of automated process control systems described in the paper allow formulating some requirements for constructing a comprehensive scheme of the APCS block integration and the principles of a unified APCS software package formation in a heterogeneous information environment. The proposed solutions are based on the authors’ experience obtained in the course of earlier implemented large projects on automation of process and production control systems. Acknowledgements. The article is written within the framework of the state assignment of the Ministry of Education and Science of the Russian Federation, project FEWM-2020-0036.

References 1. Broder, J., Tucker, E.: Risk Analysis and the Security Survey. Butterworth-Heinemann is an imprint of Elsevier 225 Wyman Street, Waltham, MA 02451, USA The Boulevard, Langford Lane, Kidlington, Oxford, OX5 1 GB, UK © Elsevier Inc. All rights reserved – 4th ed (2012) 2. Beyer, B., Jones, C., Petoff, J., Murphy, N.: Site Reliability Engineering: How Google Runs Production Systems. O’Reilly Media, Sebastopol (2016) 3. Skhemy upravleniya v ASUTP. https://automation-system.ru/main/11-asutp/asu-tp/47-42sxemy-upravleniya-v-asutp.html. Accessed 29 June 2020 4. Meshcheryakov, R.: Osobennosti arkhitektury yedinogo informatsionnogo prostranstva pri upravlenii slozhnymi tekhnologicheskimi protsessami. Doklady TUSUR, T. 20, № 4, pp. 75–81 (2017)

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5. Senchenko, P.V., Gritsenko, Y.B., Zhukovsky, O.I., Mesheryakov, R.V.: Architectural principles of common information space development for control of complex production processes. In: The collection: 11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings 11 (2019). C. 8687007. https://doi.org/10.1109/icaict.2017.8687007. ISBN 978-153860501-1 6. Process Hazard Analysis (PHA) is a thorough, orderly, and systematic approach for identifying, evaluating, and controlling the hazards of processes. http://www.cholarisk.com/ services/process-safety/qra-hazop/ 7. DNVGL-RP-N101 Risk management in marine and subsea operations. Recommended practice 8. ISO 17776:2000 “Petroleum and natural gas industries - Offshore production installations Guidelines on tools and techniques for hazard identification and risk assessment” 9. What is a FMEA? https://www.fmea-fmeca.com/what-is-fmea-fmeca.html 10. Boyer, S.A.: SCADA Supervisory Control and Data Acquisition. USA: ISA - International Society of Automation, p. 179 (2010). ISBN 978-1-936007-09-7 11. What is CAD/CAM software? https://www.autodesk.com/solutions/cad-cam 12. XML File Format. https://whatis.techtarget.com/fileformat/XML-eXtensible-markuplanguage 13. XSD (XML Schema Definition). https://whatis.techtarget.com/definition/XSD-XMLSchema-Definition

Mobile Robots Groups Use for Monitoring and Data Collection in Continuous Missions with Limited Communications Donat Ivanov(&) Southern Federal University, 2 Chehova St., 3479328 Taganrog, Russia [email protected]

Abstract. This article describes the scenario of the use of numerous groups of mobile robots in the tasks of monitoring and collecting environmental data for a long time. The situation is considered when the task execution time significantly exceeds the battery life of individual robots of the group. The communication range of robots is limited. The group forms a reserve and several working subgroups; some of the robots are also involved in providing signal relaying. Keywords: Multi-robot technology robots
Communication restrictions


Multi-agent interaction
Clustering

1 Introduction Land-based and air-based mobile robots can be used for monitoring remote and inaccessible objects [1], agricultural [2], oil fields [3], oil and gas pipelines [4], coastal or forest zones [5], observation of moving targets [6]. When using remotely controlled robots, a dedicated control panel and a human operator are required to control each robot. The battery life of each robot is limited by the on-board supply of energy resources (some robots can autonomously recharge from solar panels) and the resource of functioning devices. After exhausting the available resources, the robot, as a rule, needs to return to some “base” to replenish the resources, recharge the batteries and, possibly, minor repairs. Thus, the time of missions, as a rule, is limited due to the available resources on board, and the number of robots involved in the work is limited by the number of personnel of control operators, as well as the number of control panels. If it is necessary to monitor vast or extended territories, there is a need for the simultaneous use of a large number of robots. For this, robots must have a sufficient level of autonomy and the ability to interact with each other so that the entire group of robots can be controlled by one human operator. For example, when studying the behavior of numerous groups of mobile robots that perform the task of collecting information about spaced apart areas of the territory [7], group robots are divided into subgroups (or clusters), each of which operates in a separate area. At the same time, information exchange between robots operating in the same area is usually quite active, while information exchange between robots operating in different areas is small or absent. But even in this case, as a rule, the duration of the © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 571–581, 2020. https://doi.org/10.1007/978-3-030-63322-6_46

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mission by a group of robots is comparable with the battery life of individual robots of the group. Although in some projects (for example, Intel Shooting Star [8–10]), robots that have consumed the on-board supply of energy can be replaced by backup robots, which can somewhat extend the working time of the group as a whole. This article discusses the scenario of using a large group to perform long (or even continuous) monitoring missions and collect data on specified areas of the territory.

2 Starting Points We will consider such a concept as a “base”. Under the “base” in this work we will understand the place from which the robots of the group begin work. We will also assume that after the completion of the mission, the robots of the group should return to the base. We will also assume that the operator’s control panel of the group, a highperformance cloud server and a recharging and minor repair station are also located at the base. In practice, the cloud server can be located at a considerable distance from the base, but in this case it is not important if high-speed data exchange between the base and the cloud server is provided. There are several workspaces some distance from the base. By working area we mean a continuous area of space in which it is necessary to monitor and collect data on the state of the environment. We assume that between the base and each of the workspaces it is possible to build a movement route available for all robots of the group. The robots of the group are able to autonomously move in space, overcome or avoid obstacles and carry out video monitoring of the territory, as well as collecting data on the state of the environment using on-board sensory devices. Also, all robots of the group are equipped with on-board telecommunication devices that allow them to exchange data with each other and with the base at some distance from each other. In this case, the distance between the work areas and the base can exceed the direct communication range using the on-board telecommunication devices of the robots of the group. We distinguish six types of state of the robot: – – – – – –

robot-worker; robot-repeater; robot in a reserve (backup robot); robot returns to base; robot is being recharged or under repair; robot is irretrievably lost.

Work robots must move (on the ground or in the air, depending on the capabilities of the robot) along a given path in order to collect data from a given part of the work area. The trajectories of the movement of working robots should be selected in such a way as to collect data at a given frequency. Depending on the nature of the problem being solved, this frequency may be different - 10 min, an hour or longer.

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Relay robots are positioned in such a way as to receive and transmit data between the working robots of the group and the base. Ground robots, as well as some aerial robots (quadrocopters, helicopters) can be in one place. Aircraft-type relay robots are forced to circle around a given area so that when they are at any point on the path, they can confidently receive and transmit data. Reserve robots are at the base. Each of them can replace a repeater robot and/ or a working robot. At reserve robots, the on-board supply of energy resources has been replenished. Robots that have consumed on-board energy supplies to critically low values, as well as robots that have suffered damage but have not lost the ability to move independently after replacement, must wait for the replacement and move to the base for recharging or repair. Thus, some time after the start of the mission by a group of robots, there will be a number of robots on the base for recharging or repair. After recovery, these robots replenish the number of backup robots. It should also be noted that in the process of performing work in an unprepared environment, some robots may receive damage that prevents them from continuing to work or returning to the base on their own. In practice, failed robots can be returned to the base and repaired. But, in the framework of this work, failed robots will be considered irretrievably lost.

3 Description of Scenario At the initial moment of time, all the robots of the group are on some “base”. Group robots distribute tasks to be solved among themselves. Work robots are advanced to work areas and monitors and collects environmental data in these areas. Robotsrepeaters move in the relay area to maintain a stable connection between working robots (robot-workers) and the base. The remaining robots are in reserve. When one of the working robots or robot-repeaters fails, or requires recharging, one of the backup robots is put forward to replace it. There are two possible replacement options. If the group is heterogeneous (the design and functionality of the robots are different), then the backup robot replaces the failed robot. If the group is homogeneous and all robots are the same in design and functionality, it is possible to redistribute roles between robots in such a way as to minimize the negative consequences of replacing the robot. An example of visualization of the scenario for using a group of robots is shown in Fig. 1.

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Fig. 1. Visual representation of the considered scenario of using a group of robots in a monitoring task

4 Proposed Approach It is proposed to use multi-agent interaction. Multi-agent interaction is used in many projects using group robotics technology [11–13]. Agent - a program representing the interests of the object. An agent of this robot is launched on the on-board computing device of each robot. Also, agents on each workspace are running on the cloud server. Robot agents can interact directly with each other, or through a virtual bulletin board. Workspace agents only interact with robot agents through a virtual bulletin board. The scheme of information interaction of agents is presented in Fig. 2.

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Fig. 2. Scheme of information interaction between agents and a virtual bulletin board

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Initially, workspace agents publish task data on a virtual bulletin board. This includes the area of each work area, the coordinates of the entry point for this area, the period of repeated passage of the given points, the routes from the base to this work area, the location coordinates of the robot-repeaters and other characteristics of the work area. Robot agents access this data. Robot agents distribute roles among themselves. Some agents choose the role of a working robot for their robots. Others play the role of a robot-repeater. The rest are still in reserve. Workers robots and repeater robots begin to move into predetermined areas of space. When several robots move towards the work area, robots must form and maintain a system. The task of forming a system in a group of robots is considered in a number of works [14–18]. The agent of each robot periodically transmits a message about the status of its robot to the virtual bulletin board: coordinates, task being performed. Also, robot agents question the agents of neighboring robots about their condition. If the agent of some robot does not respond to the status request for a long time and does not send this data to the virtual bulletin board, this robot is considered to be out of order. Then the agents of neighboring robots send a message to the virtual bulletin board that a robot replacement is required. A message about the required replacement is also sent if the robot agent detects a decrease in the energy level or a malfunction of the robot. After the message about the need to replace the robot is posted on the virtual board, the replacement process begins. In the simplest case, one of the backup robots selects this task and goes to replace it. But this can take an unacceptably long time. Therefore, it is preferable that the agents of neighboring robots redistribute among themselves the tasks of the failed robot in anticipation of the robot arriving from the reserve. In the event that neighboring working robots can cover the entire necessary area of the working area without exceeding the period of re-inspection of control points, the reallocation of roles occurs only among working robots of one working area. Otherwise, the robot-repeater closest to the working area begins to move to this working area and upon reaching it becomes a working robot. Other repeater robots along this route also begin to move towards the working area, occupying alternately abandoned positions. Then the robot that has left the reserve will take the last vacant position of the repeater robot. This allows you to reduce the time of replacing the robot by k times, where k is the number of robot-repeaters on the route to this work area. Work robots collect environmental data and monitor workspaces. The primary processing of the collected data is carried out on-board computing devices of these robots. Then the pre-processed data is transferred to the cloud server. To do this, routing is performed using robot-repeaters. In the event that data transfer to the cloud server is temporarily impossible, the robots accumulate data in the on-board storage device. After the connection with the cloud server is restored, the robots transfer the accumulated data. Moreover, the transmission of accumulated data is less priority than the transmission of fresh data.

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5 Proposed Methods and Algorithms Based on the approach proposed above, the basic algorithms are developed that allow implementing the described scenario for using a group of robots in the task of data collection and monitoring. 5.1

Graph of the Change State of Robot’s Agents

The scenario described above for the use of groups of mobile robots implies that each robot in the group is in one of six types of state. At the initial time, all the robots in the group are in the “robot in a reserve” state. After the distribution of roles [19], part of the reserve-robots enters the “robot-worker” state, part of the robots enters the “robot-repeater” state. Also, the status of the “robot in a reserve” can change to “robot-worker” or “robot-repeater” during the mission if a message appears on the virtual bulletin board about the need to replace the robot. The “robot-worker” state can change to one of three possible states: “robot in a reserve”, “returns to base”, “irretrievably lost”. The robot-worker is returned to the reserve only if the entire mission is successfully completed. In the event of a low battery or minor damage, the robot worker enters the “returns to base” state. In the event of a critical failure, it acquires the status of “irretrievably lost”. The “robot-repeater” status can change to one of four possible states: “robot in a reserve”, “robot-worker”, “returns to base”, “irretrievably lost”. The robot-repeater is returned to the reserve only if the entire mission is successfully completed and all the robot-workers have already returned to the base. The robot-repeater can change its status to “robot-worker” if one of the working robots needs to be replaced in a nearby work area, the replacement request has already been placed on the virtual bulletin board. In the event of a low battery or some damage, the robot-repeater enters the “returns to base” state. In the event of a critical failure, it acquires the status of “irretrievably lost.” The “robot returns to base” condition is temporary. The robot gains this status from the moment it sent a message to the virtual bulletin board about the required replacement due to low battery or malfunction. After receiving this status, the robot starts moving towards the base. If the robot failed to reach the base, then it receives the status of “irretrievably lost”, and if it succeeds, this robot acquires the status of “robot is being recharged or under repair”. Robots that are being recharged or under repair at the base can change their status to “robot in reserve” in the event of a successful recovery, or change their status to “irretrievably lost” if repair is impossible or not economically feasible. The graph of the change of state of the robot’s agents is shown in Fig. 3. 5.2

General Algorithm for Agent of a Work Area

The work area agent algorithm is presented below: 1. Download the source data (the area of the work area, its coordinates, the coordinates of the base).

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2. Define the “entry point” in such a way as to ensure the approach of robots from the base and their distribution within the workspace. 3. Identify preliminary robot movement routes between the base and entry point. 4. Determine the number and coordinates of the locations of the robot-repeaters. 5. Publish the received data on a virtual bulletin board. 6. Receive messages from robotic agents from a virtual bulletin board who want to perform tasks of workers or repeaters for servicing this work area. 1

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Fig. 3. Robot agent state transition graph

7. Choose the most suitable set of robots to service your work area (in the case of a homogeneous group - the first to submit a request). 8. Post on the virtual bulletin board a message about which robots are involved in servicing this work area and with what roles. 9. In the event of a message appearing on the virtual bulletin board about the required replacement of one of the robots serving this work area, ensure that this robot is replaced by a nearby robot-repeaters, and also ensure that the robot-repeaters is replaced by other robot-repeaters or a robot in a reserve, depending from the topology of the location of these robots in space. 10. If the operator receives a message about the completion of the mission, place a message on the virtual bulletin board and ensure the return of robots serving this workspace to the base. 5.3

General Algorithm for Agent of a Mobile Robot

The algorithm of the robot agent is presented below. 1. Download input data. 2. Downloading data on tasks placed on the virtual bulletin board.

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3. If a message is posted on the bulletin board that the mission is completed, then the end of the work. 4. Determination of the most suitable for the task. 5. Placement on a virtual bulletin board of a request to perform the selected task. 6. If the request to complete the selected task is not confirmed (another robot of the group has been assigned to perform this task), then go to step 3. Otherwise, go to step 7. 7. Determining the route of movement to the place of work in accordance with the preliminary route of movement (from the virtual bulletin board) and data on the presence of obstacles to the movement received from on-board sensor devices and neighboring robots. Moving to the required area of space with avoiding obstacles and overcoming collisions. 8. The agent polls neighboring robots. If one of them does not answer the request for a long time, information on the possible malfunction of this robot is placed on the virtual bulletin board. 9. If the selected task assumes the status of “robot-worker”, then the agent ensures the collection of environmental data, monitoring of its area of space and movement along a given trajectory with a given frequency. 10. If a message on the successful completion of the mission is posted on the virtual bulletin board, then return to the base. 11. If the charge level is low or a malfunction is detected, the agent places a message on the virtual bulletin board about the necessary replacement of the “robot-worker” and the robot returns to the base. 12. If the selected task assumes the status of a “robot-repeater”, the agent provides reception and transmission of data between robots and the base. 13. If a message about the failure of a nearby robot-worker is posted on the virtual bulletin board, the agent places a request on the virtual bulletin board to replace this robot. 14. If the request is confirmed, the agent puts the robot in the “robot-worker” state. Go to step 7. 15. If the charge level is low or a malfunction is detected, the agent places a message on the virtual bulletin board about the necessary replacement of the “robotrepeater” and the robot returns to the base. 5.4

Robot Replacement

Let us consider in more detail the process of replacing a failed working robot (an example is shown in Fig. 4). Robot r11 has exhausted the supply of on-board battery. In this case, the r11 robot agent places a message on the virtual bulletin board about the need to replace its robot. The r7 robot relay repeater closest to this work area can replace it. However, a replacement is required for the r7 repeater robot. Replace the robot repeater with another r6 robot repeater. As the two relay robots move to new target positions, the backup robot r17 is pulled out of the reserve in order to take the initial position of the robot r6. At the same time, the r11 robot continues to fulfill its mission until it finds a chain of messages about all necessary replacements on the virtual bulletin board. After that, the r11 robot starts returning to the base for recharging (see Fig. 4, a).

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Fig. 4. Example of replacing a robot-worker due to a low battery level

The target positions of the relay robots are temporarily changing due to the fact that one of these robots r7 plans to replace the robot-worker r11. Now, instead of two relay robots, three robot-repeaters r6, r7 and r17 are used. This will allow you to maintain a stable connection between the working robots and the cloud-based server. The r11 robot continues to return to the base for recharging (see Fig. 4, b). When the r7 robot reaches the workspace, it acquires the status of a “robot-worker”, makes a message about it on the virtual bulletin board, securing the monitoring route. Robots r6 and r17 have the status of a “robot-repeaters”. And the robot r11 gains the status of “Robot recharged/ repair” until the full recovery. After that, the robot r11 will replenish the reserve (see Fig. 4, c).

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The proposed scheme for replacing a working robot can reduce the time required to replace a working robot, compared to when a robot from the reserve is sent to replace the working robot.

6 Conclusions This article discusses the scenario of using a group of mobile robots in the task of monitoring and collecting data on the state of the environment during long missions. An approach to solving the problem based on the use of multi-agent interaction, a virtual bulletin board and decentralized management is proposed. Algorithms for agent actions are developed. The scheme of replacing a robot-worker with a low battery level or when faults are detected is considered in detail. In the future, it is planned to conduct computer simulation and field experiments. Acknowledgement. The reported study was funded by RFBR according to the research projects № 19-07-00907 and № 18-05-80092.

References 1. Jahanshahi, M.R., Shen, W.-M., Mondal, T.G., Abdelbarr, M., Masri, S.F., Qidwai, U.A.: Reconfigurable swarm robots for structural health monitoring: a brief review. Int. J. Intell. Robot. Appl. 1(3), 287–305 (2017). https://doi.org/10.1007/s41315-017-0024-8 2. Lysenko, V., Opryshko, O., Komarchuk, D., Pasichnyk, N., Zaets, N., Dudnyk, A.: Usage of flying robots for monitoring nitrogen in wheat crops. In: 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). pp. 30–34 (2017) 3. Ivanov, D., Korovin, I., Shabanov, V.: Oil fields monitoring by groups of mobile microrobots using distributed neural networks. In: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR). pp. 588–593 (2018) 4. Ogai, H., Bhattacharya, B.: Pipe inspection robots for gas and oil pipelines. Pipe Inspection Robots for Structural Health and Condition Monitoring. ISCASE, vol. 89, pp. 13–43. Springer, New Delhi (2018). https://doi.org/10.1007/978-81-322-3751-8_2 5. Ghamry, K.A., Kamel, M.A., Zhang, Y.: Cooperative forest monitoring and fire detection using a team of UAVs-UGVs. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS). pp. 1206–1211 (2016) 6. Khan, A., Rinner, B., Cavallaro, A.: Cooperative robots to observe moving targets. IEEE Trans. Cybern. 48, 187–198 (2016) 7. Kalyaev, I., Kapustyan, S., Ivanov, D., Korovin, I., Usachev, L., Schaefer, G.: A novel method for distribution of goals among UAVs for oil field monitoring. In: 6th International Conference on Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT), pp. 1–4 (2017) 8. Intel Lights Up the Night with 500 “Shooting Star” Drones, http://www.intel.com/content/ www/us/en/technology-innovation/videos/drone-shooting-star-video.html 9. Kesteloo, H.: The 2018 Winter Olympics close with another spectacular “Shooting Star” drone show from Intel. Drone DJ, blog. (2018)

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10. Kshetri, N., Rojas-Torres, D.: The 2018 winter olympics: a showcase of technological advancement. IT Prof. 20, 19–25 (2018) 11. Mercier, M., Phillips, S., Shubert, M., Dong, W.: Terrestrial testing of multi-agent, relative guidance, navigation, and control algorithms. In: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). pp. 1488–1497 (2020) 12. Vorotnikov, S., Ermishin, K., Nazarova, A., Yuschenko, A.: Multi-agent robotic systems in collaborative robotics. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2018. LNCS (LNAI), vol. 11097, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/ 978-3-319-99582-3_28 13. Jiménez, A.C., Garc\’\ia-D\’\iaz, V., Bolaños, S.: A decentralized framework for multi-agent robotic systems. Sensors. 18, 417 (2018) 14. Ivanov, D., Kalyaev, I., Kapustyan, S.: Formation task in a group of quadrotors. In: Kim, J.H., Yang, W., Jo, J., Sincak, P., Myung, H. (eds.) Robot Intelligence Technology and Applications 3. AISC, vol. 345, pp. 183–191. Springer, Cham (2015). https://doi.org/10. 1007/978-3-319-16841-8_18 15. Ivanov, D., Kapustyan, S., Kalyaev, I.: Method of spheres for solving 3d formation task in a group of quadrotors. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2016. LNCS (LNAI), vol. 9812, pp. 124–132. Springer, Cham (2016). https://doi.org/10.1007/978-3-31943955-6_16 16. Tahir, A., Böling, J.M., Haghbayan, M.-H., Plosila, J.: Comparison of linear and nonlinear methods for distributed control of a hierarchical formation of UAVs. IEEE Access. 8, 95667–95680 (2020) 17. Ge, X., Han, Q.-L.: Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism. IEEE Trans. Ind. Electron. 64, 8118– 8127 (2017) 18. Yu, B., Dong, X., Shi, Z., Zhong, Y.: Formation control for quadrotor swarm systems: algorithms and experiments. In: Proceedings of the 32nd Chinese control conference. pp. 7099–7104 (2013) 19. Ivanov, D., Kapustyan, S., Petruchuk, E.: Distribution of roles in a dynamic swarm of robots in conditions of limited communications. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2019. LNCS (LNAI), vol. 11659, pp. 99–108. Springer, Cham (2019). https://doi. org/10.1007/978-3-030-26118-4_10

The Analysis of EEG Signal and Comparison of Classification Algorithms Using Machine Learning Methods Andrea Nemethova(&), Dmitrii Borkin, and Martin Nemeth Institute of Applied Informatics, Automation and Mechatronics, Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology in Trnava, Bratislava, Slovakia {andrea.peterkova,dmitrii.borkin, martin.nemeth}@stuba.sk

Abstract. In this paper we present comparison of classification models on EEG dataset to recognize the possibility of difference between right-handed and lefthanded subjects. The research come out from the hypothesis, that it is possible to differentiate between different states of mind for left and right-handed people based on their EEG signal. In our research, there were used several machine learning methods like K-neighbors, support vector machines and decision tree classifier. These methods were explored to seek the difference between EEG signal of subjects using signal processing. The EEG data were obtained during reading various particular text samples with different meanings and from different areas. First chapters of this paper are devoted to the description of the experiment and data analysis with EEG signal processing. The final parts of the paper are evaluating our hypothesis from the previous parts of this paper with the use of various machine learning methods. We found correlations between left-handed people during reading the text samples. The correlations were significant while reading horror text sample. Keywords: Machine learning interface
Classification


Signal processing
EEG signal
Neural

1 Introduction Multichannel EEG digital signal processing have their origin in distinct application areas such as speech and music signal processing and of course electrocardiograms. [1] To find pattern and relationships in such a multichannel digital signal can be challenging. However, there are several methods that can be used to aim the goal. A huge category of methods suitable for analyzing EEG data are machine learning methods. These methods have various range of use. Even in the field of signal processing and classification. There are two main tasks that can be solved by using machine learning methods. First there is a classification problem and then regression type of task. In this paper we aim to analyze the EEG signal to be able to differentiate between left-handed and right-handed people. This means that we are solving a classification problem. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 582–590, 2020. https://doi.org/10.1007/978-3-030-63322-6_47

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2 Background and Methods In this research, there were used several methods. This chapter is devoted to the description of experiment and on the obtained dataset. To be able to correctly work with the obtained data, there were also used signal processing methods explained below. 2.1

Experiment

To obtain raw EEG data, it is necessary to make experiments with subjects. In our experiment, there were people randomly chosen, but 50% were right-handed and 50% left-handed people. All subjects were reading samples of 5 texts from various areas. During experiment subjects wore EMOTIVE EPOC + Headset to obtained raw EEG data. There are 14 channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4), also see Fig. 1, 2. To initiate different mental response of subjects by reading texts, they were chosen under specific conditions. It was chosen funny fiction story, a horror fiction story, a chosen neutral piece of newspaper, an instruction manual and a sad story. The reading time of each text was approximately 3 min depending on the subjects. The experiment was held in the quiet room with normal temperature. Subjects were also not disturbed during the reading session. Each subject was the only person in the room during reading. Between reading each text sample, there were 1-minute pauses so the subject could calm down. 2.2

Dataset

In our dataset we used multiple sets. One of the sets was for subject and another sets for samples. We have used openViBE software to obtain EEG signal while subject was reading the text samples.

Fig. 1. The example of EEG data records from randomly chosen right-handed subject

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Fig. 2. The example of EEG data records from randomly chosen left-handed subject

In the figures below there are examples of recorded EEG data for 2 randomly chosen subjects, one for lefthanded and one for righthanded subject. These data were recorded while subject was reading text sample: story with sad emotions. The x axis is representing time of the EEG scanning and the y axis represents the actual EEG values in µV. Each channel of the headset is displayed with a different color.

3 Comparison of the Classification Models The initial analysis of the EEG signal of each subject and each case showed the possibility of a clear difference between right-handed and left-handed subjects. We have used several classification models on our data to evaluate this hypothesis, so the goal was to evaluate if it is possible to classify subjects to 2 main classes (left-handed and right-handed). Following examples and tables show cases when subjects were reading an instructional text sample. Several indicators were computed for each model. The main indicators are precision, recall and f1-score. In pattern recognition and classification areas, precision (which is often called positive predictive value) is the fraction of relevant instances among the retrieved instances. Recall (often referred to as sensitivity) can be described as the fraction of the total number of relevant instances that were retrieved. Both measures, precision and recall, are based on an understanding and measure of relevance.

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Precision is defined as follows: Precision ¼

tp tp þ fp

ð1Þ

Where, tp is true positive and fp is false positive. Recall can be described by following formula: Recall ¼

tp tp þ fn

ð2Þ

Where fn is false negative. Next indicator is a measure that combines both precision and recall and is the harmonic mean of the previous two indicators. It is traditionally called F-measure or balanced F-score. This measure is approximately the average of the two the precision and recall indicators, when they are close, and more generally it is the harmonic mean, which, for the case of two numbers, coincides with the square of the geometric mean divided by the arithmetic mean. This measure is defined as follows: F¼2

3.1

precision:recall precision þ recall

ð3Þ

K-Neighbors Method (5 Neighbors)

K-neighbors model was the first, we have tested. Following table shows the results for the k-neighbors model for all subjects and non-mixed data set (Table 1).

Table 1. The results for the k-neighbors model for all subjects and non-mixed data set Class 0 1 Accuracy Macro avg Weighted avg

Precision Recall F1-score 0.96 0.99 0.98 0.99 0.97 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98

Support 8829 12023 20852 20852 20852

The Following table shows the results for the k-neighbors model for all subjects and mixed data set. That means that the records were randomly mixed within the data set (Table 2).

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Precision Recall F1-score 0.96 0.99 0.98 1.00 0.97 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98

Support 8804 12048 20852 20852 20852

Following table shows the results for the same method but in this case, we have decided to remove all correlating features from the data set (Table 3)

Table 3. The results for the same method with removing all correlating features from the data set Class 0 1 Accuracy Macro avg Weighted avg

3.2

Precision Recall F1-score 0.89 0.98 0.94 1.00 0.97 0.96 0.95 0.94 0.95 0.95 0.95 0.95 0.95

Support 8227 12625 20852 20852 20852

SVM – Support Vector Machines

Method Support Vector Machines (SVM) performs regression and classification types of tasks by constructing nonlinear decision boundaries. Taking into account the nature of the feature space where the boundaries are found, this method can exhibit a large degree of flexibility in solving many classification and regression tasks of varied complexities according to [2, 3]. However, it is mostly used in classification problems [4]. Following table shows the results for the SVM model with for all subjects and nonmixed data set. The SVM algorithm also offers several options for dividing a data file into sets. The first option is to divide the data randomly by choosing a percentage that will correspond to the training set of data. Another option for dividing a dataset is the use of the so-sample variable [4] (Table 4). Table 4. The results for the SVM model for all subjects and non-mixed data set Class 0 1 Accuracy Macro avg Weighted avg

Precision Recall F1-score 0.54 0.90 0.68 0.95 0.73 0.83 0.78 0.75 0.82 0.75 0.85 0.78 0.79

Support 5458 15394 20852 20852 20852

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Following table shows the results for the SVM model for all subjects and mixed data set. That means that the records were randomly mixed within the data set (Table 5).

Table 5. The results for the SVM model for all subjects and mixed data set Class 0 1 Accuracy Macro avg Weighted avg

Precision Recall F1-score 0.54 0.90 0.67 0.95 0.73 0.83 0.77 0.75 0.81 0.75 0.85 0.77 0.79

Support 5383 15469 20852 20852 20852

Following table shows the results for the same method but in this case, we have decided to remove all correlating features from the data set in the same way as for the first model (Table 6).

Table 6. The results for the same method with removing all correlating features from the dataset Class 0 1 Accuracy Macro avg

3.3

Precision Recall F1-score 0.59 0.85 0.69 0.95 0.74 0.82 0.78 0.75 0.80 0.76

Support 6214 14638 20852 20852

Decision Tree Classifier

The classification and regression trees are supposed to be a machine-learning method for building a prediction models from datasets. This recursive partitioning methods build classification and regression trees for predicting continuous dependent variables for regression problems; and categorical predictor variables - for classification problems [5]. In most cases, the interpretation of the result summarized in a tree is very clear and simple. This simplicity is useful not only for purposes of rapid classification of new observations, but can also often yield a much simpler “model” for explaining why observations are classified or predicted in a particular manner [5] (Tables 7, 8, 9).

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Precision Recall F1-score 0.94 0.95 0.95 0.96 0.96 0.96 0.95 0.95 0.95 0.95

Support 9129 11723 20852 20852

Table 8. Mixed data results Class 0 1 Accuracy Macro avg Weighted avg

Precision Recall F1-score 0.94 0.94 0.94 0.96 0.96 0.96 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Support 9065 11787 20852 20852 20852

Table 9. Results without correlating features Class 0 1 Accuracy Macro avg Weighted avg

Precision Recall F1-score 0.92 0.92 0.92 0.94 0.94 0.94 0.93 0.93 0.93 0.93 0.93 0.93 0.93

Support 9103 11749 20852 20852 20852

4 Results The main hypothesis of our research is, that it is possible to differentiate between different states of mind based on the EEG signal. Such ability will lead into designing a neural interface, which can be used even as a safety feature for operators in safetycritical processes. In this stage of our research we have decided to assess simpler hypothesis, which could serve as a proof of concept for continuing with our research. For this hypothesis we wanted to assess if it is possible to differentiate between lefthanded and right-handed subjects based on the EEG signal from described controlled experiment. After analyzing the acquired data, we have discovered significant correlations between features (EEG channels) of right-handed subjects and no correlations for left-handed subjects. This result predicted the possibility of classifying subjects to left-handed and righthanded classes. To assess this result, we have decided to build multiple classification models based on machine learning methods and to evaluate the performance of each model. Following table shows the example of performance of each machine-learning

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based classification model. From tables in previous parts of this paper and also from the results shown in following table it is clear that the classification models performed reliably and that they were able to classify subjects into two defined classes (Table 10).

Table 10. Summary results from all subjects, example from one reading for all methods Reading – Horror text sample KNN Precision Recall F1-score 0 1 1 1 1 1 1 1 Accuracy 1 Without correlation’s features Precision Recall F1-score 0 0.98 0.99 0.99 1 0.99 0.99 0.99 Accuracy 0.99 Decision tree Precision Recall F1-score 0 0.99 0.99 0.99 1 0.99 1 0.99 Accuracy 0.99 Without correlation’s features Precision Recall F1-score 0 0.97 0.96 0.96 1 0.97 0.98 0.98 Accuracy 0.97 Logistic Regression Precision Recall F1-score 0 0.69 0.94 0.79 1 0.97 0.84 0.9 Accuracy 0.87 Without Correlation’s Features precision recall f1-score 0 0.02 0.57 0.04 1 0.99 0.63 0.77 Accuracy 0.63 (continued)

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5 Conclusion In this paper we have analyzed the EEG data of left-handed and right-handed subjects. The EEG data were recorded while the subjects were reading particular text samples with different meanings and from different areas. These texts were chosen to invoke significantly different mental response. In our future work we would like to focus on classification of the red text sample (whether it was an instructional text, horror story or other from the set of cases) and also identifying the mental or emotional state of the subject. Acknowledgments. This publication is the result of implementation of the project VEGA 20/1769, 005STU-4/2020: “Innovation and new learning opportunities in industrial process management with PLC” supported by the KEGA.

References 1. Sanei, S.; Chambers, J.A.: EEG signal processing. John Wiley & Sons, New Jersey (2013) 2. Meyer, D., Wien, F.H.T.: Support vector machines. The Interface to libsvm in package. p. e1071 (2015) 3. Shmilovici, A.: Support Vector Machines Data Mining and Knowledge Discovery Handbook., pp. 257–276. Springer, US (2005) 4. Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: A review of classification techniques (2007) 5. Strobl, C., Malley, J., Tutz, G.: An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods. 14(4), 323, (2009)

The Analysis of EEG Signal and Finding Correlations Between Right-Handed and LeftHanded People Martin Nemeth1,2(&), Andrea Nemethova1,2, and Dmitrii Borkin1,2 1 Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, Trnava, Slovakia {martin.nemeth,andrea.peterkova, dmitrii.borkin}@stuba.sk 2 Institute of Applied Informatics, Automation and Mechatronics, Trnava, Slovakia

Abstract. This paper covers the initial research and analysis of the EEG signal for the purpose of designing a neural interface for identification of the mental state. Such neural interface can be beneficial in various fields of automation and industry and can also potentially serve as a safety feature for safety critical processes. In the first section of this paper we discuss the performed experiment and also the technical means for the EEG data acquisition. In following chapter, we are describing the data itself and we are also performing the basic data analysis as well as the correlation identification. Final part of this paper we are evaluating our hypothesis to finding correlations in the dataset. Keywords: Signal processing


EEG signal
Neural interface

1 Introduction Machine learning methods are used in wide range of areas and cover many applications. However, there still are new possibilities of utilizing machine learning methods in new applications [4]. Even in the field of automation and control, there are numerous possibilities of applying these methods for the purpose of process control optimization, predictive maintenance purposes, building autonomous systems and many other applications. Machine learning can be described as algorithms and statistical models that computer systems use to perform a specific task with assumption of non-existing explicit instructions, relying on patterns and inference instead [5]. Machine learningmethods can be seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data. These data are often referred to as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. In our research we aim at using brain-computer interface (BCI) in the field of process automation and process control. In the conclusion part of this paper we present future works and goals of our research. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 591–597, 2020. https://doi.org/10.1007/978-3-030-63322-6_48

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This paper is aimed at analyzing the EEG signal and finding patterns, relationships and valid correlations in our data, which could lead to design of a brain computer interface dedicated to the field of automation.

2 Background and Methods 2.1

Experiment

In our research we aim at analyzing the raw EEG data. The EEG data in our experiment was obtained by using the EMOTIVE EPOC + Headset. The Emotiv EPOC + is a high resolution, multi-channel, portable system which has been designed for practical research applications. Following table shows the basic technical specifications of the used headset Table 1.

Table 1. The basic technical specifications of the used headset in our research Number of channels Channel names Sampling method Sampling rate Bandwidth Dynamic range

14 (plus CMS/DRL references, P3/P4 locations AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4 Sequential sampling. Single ADC 128 SPS (2048 Hz) 0.2–45 Hz, digital notch filters at 50 Hz and 60 Hz 8400 mV

Fig. 1. Main 14 channels of EEG signal and P3–P4 locations

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In our experiment we have worked with group of left-handed and right-handed people (subjects), who underwent the EEG scanning under specific conditions. Each subject had to read 5 text-based samples. Each text, out of these 5 samples, was obtained from different areas and genres to ensure that they will initiate different mental response. For the purpose of the experiment, we have chosen a funny fiction story, a horror fiction story, a chosen neutral piece of newspaper, an instruction manual and a sad story. We have modified the original texts to be approximately same length, so our scanned subjects will read these samples in approximately same time. The scanned readings were held in controlled quiet environment with stable and comfortable temperature. We have also incorporated a one-minute time period between reading of each text sample for subjects to calm down after each sample. 2.2

Dataset

The dataset was divided into multiple sets. One set per subject and sample. The EEG data was obtained and recorded with the use of openViBE software. This software platform is dedicated for testing and using brain-computer interfaces. This software is capable of real-time neurosciences which means, real-time processing of brain signals. It can serve to acquire, filter, process, classify and visualize brain signals in real time. Application fields of the OpenViBE software platform are medical, multimedia, robotics and other application fields which are related to using a brain-computer interfaces and real-time neurosciences. In the Fig. 1 there is an example of recorded EEG data for choosen subjects. These data were recorded for the case of sad story text example. The x axis shows the time of the EEG scanning and the y axis shows the actual EEG values in µV. Each channel of the headset is displayed with different color.

Fig. 2. The example of EEG data records from randomly chosen left-handed subjects

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Fig. 3. The example of EEG data records from randomly chosen right-handed subjects

3 Methods In our research, we used various methods for signal analysis. We performed several basic data analyses and also the correlation identification. As it is clear from Fig. 3, we are starting with raw EEG data which have to be filtered. We have tried several noise filtering methods, but it is not a topic of this article. In this article, we are devoting specially to the finding correlations in EEG data. The advantage of such approach is that it is possible to move back and restart the process at any step (Fig. 4).

Raw EEG signal

Noise filtering

Probability distribution

Finding correlations

Fig. 4. The process of finding correlation has few steps

3.1

Distribution

First step in the process of the analysis of obtained EEG data was to evaluate the fitting of the normal distribution on our data. First of all, it was necessary to filter out the noise out of our data. We have estimated the range of values of the EEG signal according to the Fig. 1. After filtering out all of the noise we have graphically evaluated the probability distribution. Figure 2 shows the histogram plot of the EEG signal for one case as an example. From this figure it is clear, that the signal has shape and properties of the normal probability distribution. This also applied to other subjects and all other cases (Fig. 5).

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Fig. 5. The probability of normal distribution of the signal

3.2

Signal Analysis

After evaluating the probability distribution of the EEG data, we have decided to evaluate potential correlations between used channels. As mentioned in previous section our headset provides 14 channels, which means each dataset consisted of 14 features for one measurement. We have computed correlations between each channel for all subjects and all cases as a Pearson correlation coefficient. The linear relationship between two data sets is measured by the Pearson correlation coefficient [1, 6]. The calculation of the p-value relies on the assumption that each dataset is normally distributed. The correlation coefficient value can vary between −1 and +1. If the value of the Pearson correlation coefficient is equal to 0, it represents that there is no correlation. However, if the value of the coefficient is −1 or +1, it represents that there is the exact linear relationship. Positive values of correlation coefficient imply that as x increases, so does y. Negative correlation values imply that as x increases, y has decreasing character as well. The correlation coefficient is calculated as follows [1–3]   P ðx  m x Þ y  m y r ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 P P ðx  mx Þ2 y  my where mx is the mean of the vector x and my is the mean of the vector y.

ð1Þ

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We assume, that x and y are picked from independent normal distributions and we can also say, that the population correlation coefficient is equal to 0, and the probability density function of the sample correlation coefficient r is defined as follows [1, 2]: n 2 ð1  r 2 Þð2 Þ   f ðr Þ ¼ B 12 ; n2  1

4 Results In this paper, we were focusing on finding relationships between EEG data. We used correlation analysis to obtain the following results. It reveals which hemisphere were connected stronger by subjects while reading particular texts. First of all, it was necessary to obtain raw data, make a noise filtering and probability distribution. After these steps we were able to find the clear correlations between EEG data. The results are clearly described in the tables.

Table 2. The examples of the discovered correlations for 2 tested right-handed subjects: Results for subject 1 Text sample Instruction News Fun Sad Science Horror

AF4 - AF3 0.920521402 0.923584435 0.944910701 0.94035507 0.960273627

FC6 - F8 0.962060207 0.950195701 0.98547055 0.97811856

O2 - FC6 0.927419161 0.906420511 0.93151044 0.948410854 0.941103178

0.946397236

Following tables shows the examples of the discovered correlations for 2 tested right-handed subjects. Table 2 shows the results for subject 1 and Table 3 shows the results for subject 2. In each table, there are picked the strongest channel correlations.

Table 3. The examples of the discovered correlations for 2 tested right-handed subjects: Results for subject 2 Text sample Instruction News Fun Sad Science Horror

O2 - FC6 0.992458877 0.906420511 0.93151044 0.977405529 0.978606431 0.988578463

P8 - O2 AF4 - AF3 0.986609277 0.963625422 0.923584435 0.910970718 0.944910701 0.973754301 0.935235049 0.990902417 0.983293728 0.960355581

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The correlation analysis showed that the right-handed subjects had strong channel correlation on the right hemisphere. This result is presented in the Fig. 3, which shows the results from the Tables 2 and 3. Figure 3 a corresponds to the Table 2 and Fig. 3 b corresponds to the Table 3. The correlation analysis also showed that there are no significant channel correlations for all left-handed subjects in our research. This result proves that the left-handed subjects as well as right-handed subjects have obvious in-class similarities. Results for the right-handed subjects from the Table 3 and 4 shows that there are strong correlations between channels for the frontal lobe section, which is responsible for behavioral patterns and memory. There can also be seen a correlation of channels for the rear part, which is responsible for visual perception, which includes the reading process which is the foundation of our experiment.

5 Conclusion The aim of this paper was to analyze raw EEG data obtained with the Emotiv Epoc + headset. The aim was also to evaluate the hypothesis, that there are significant correlations and similarities for left-handed and right-handed people. We used experiment where subjects were reading different text samples with various thematic. All interested subjects were agreed with the experiment and they underwent it voluntarily with the intention of processing their EEG data. Results show that for right-handed people there are significant similar correlations between certain channels. For this part of the research we have used traditional methods to compute the correlations. After noise filtering, the probability distribution was used. Our next work is dealing with new hypothesis which was derived from the results of this paper. For the new hypothesis we were evaluating the possibility of classification of left-handed and right-handed people based only on the EEG data with the use of machine learning methods. Acknowledgments. This publication is the result of implementation of the project VEGA 20/1769, 005STU-4/2020: “Innovation and new learning opportunities in industrial process management with PLC” supported by the KEGA.

References 1. Benesty, J., et al.: Pearson correlation coefficient. Noise Reduction in Speech Processing, pp. 1–4. Springer, Heidelberg (2009) 2. Lawrence, I., Lin, K.: A concordance correlation coefficient to evaluate reproducibility. Biometrics, 255–268 (1989) 3. Hauke, J., Kossowski, T.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) 4. Alpaydin, E.: Introduction to Machine Learning. MIT press, Cambridge (2020) 5. Marsland, S.: Machine Learning: An Algorithmic Perspective. CRC press, Boca Raton (2015) 6. Benesty, J., Chen, J., Huang, Y.: On the importance of the Pearson correlation coefficient in noise reduction. IEEE Trans. Audio Speech Lang. Process. 16(4), 757–765 (2008)

Research of Data Analysis Techniques for Vibration Monitoring of Technological Equipment Vladimir Bukhtoyarov1,2(&) , Danil Zyryanov2 , Vadim Tynchenko2 , Kirill Bashmur2 , and Eduard Petrovsky1 1

Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia [email protected] 2 Siberian Federal University, Krasnoyarsk, Russia

Abstract. The article considers the problem of choosing a data analysis technology for designing a system for identification and predicting failures of technological equipment based on vibration monitoring data. The task of analyzing vibration monitoring data is solved in relation to the technological equipment of a fuel-oriented oil refinery. The article presents the results of the sensitivity analysis of the models for determining the type and failures with respect to various vibration parameters recorded by a system of vibration sensors. The results of the analysis based on data on failures show a difference in determining the most significant factors for different methods of data analysis. In the article for designing models for determining failure types, methods of discriminant analysis, decision trees, multidimensional regression splines, and a neural network approach are considered. As a result of applying the methods to the data set on failures of technological pumping equipment, it was determined that the method based on artificial neural networks is the most effective. Taking into account the use of tools for the automatic construction of neural network classifiers, such models can be further used in an automatically deployed global system for ensuring the reliability of technological equipment in oil and gas production. #COMESYSO1120 Keywords: Data analysis
Classification of failures networks
Technical diagnostics


Artificial neural

1 Introduction The current state of oil and gas production is characterized by an increasing integration of information technology in various production and auxiliary processes. Such processes include the process of technological equipment maintenance and the operational reliability ensuring [1–3]. Modern requirements for the efficiency and safety of production systems place high demands on the reliability of technological complexes, and, therefore, on the accuracy of diagnostic and monitoring procedures [4–6]. In this regard, the study of methods and technologies for constructing highly efficient systems for determining emergency situations, equipment failures and their prediction is an © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 598–605, 2020. https://doi.org/10.1007/978-3-030-63322-6_49

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actual area of research. Automation of such systems will allow them to smoothly integrate into the created cyberphysical production systems, which are being developed, including for enterprises in the oil and gas industry [6–8]. Large-scale distributed technological facilities consist of diverse types technological equipment which integrated into technological complexes. An example of a distributed technological facility is the infrastructure of a modern oil refinery, which operates on a large territory and consists of several thousand elements of equipment and various technological complexes that service the processes of oil refining. Even one element failure may lead to the disturbance of the entire process and the transition of many related elements of equipment to the pre-emergency state. One of the possible problem solution is end-to-end monitoring, taking into account the entire structure of the complex and the interdependence of processes. The absence of a system including end-to-end monitoring increases the economic risks of the enterprise, as well as the risks of safety breaches, including environmental and energy breaches. Its presence makes it possible to automate ongoing processes, to conduct timely monitoring of changes in performance indicators. The implementation of a monitoring system for the state of production facilities will allow achieving maximum productivity due to smooth operation [9]. Also, at the present stage of oil and gas production, the main innovations in oil refining are associated with the development of digital technologies that ensure higher efficiency and safety of production processes. The use of modern systems for monitoring the state of technological objects that is one of the key tasks in implementing the concept of a “digital plant”, which the vast majority of world oil refineries are oriented towards [10–13]. However, in many types of equipment of oil refineries such as pumps or compressors, there are defects which can cause an increase in the vibration level [14, 15]. As for compressors vibration measurements are usually used to predict such defects as poorly installed and loose base frame; defects in the plain bearings or in gearing of the gearbox and the coupling; problems associated with lubrication; defects of rotor blades; malfunctions of the drive motor; rotor imbalance and poor shaft alignment [16]. For pumps vibration analysis make it possible to detect and forecast the development of such defects as incorrect alignment with the driven mechanism (pump); poor condition (or poor manufacturing) of the coupler, finger wear, misalignment of the holes for the fingers or misalignment of the coupling halves; imbalance of the impeller (rotor) of the driven pump, which is especially common in high speed pumps or pumps with dynamically unbalanced impellers; imbalance of the rotor of the electric motor; defect in the bearings of the pump or electric motor; defects in the foundation and foundation frame of the unit; bending of the shaft [17, 18]. Equipment vibrations caused by the above problems pose a serious threat to the safety and operational reliability of the equipment so it is very important to recognize such defects in the early stages of their formation. For this purpose, the problem of designing a system of continuous vibration monitoring of the technical condition for the pump unit is developed and presented in next sections. Taking into account the developed scheme, several data mining approaches for vibration monitoring data analysis were evaluated using real-world and well-known data sets.

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2 Vibration Parameters and Monitoring Vibration level and vibration parameters are main data receiving by vibration monitoring system. It allows to identify changes in the equipment elements state and in some cases predict possible failures. Vibration monitoring of compressor and pumping equipment is based on the principle of data collection and data analysis based on the analysis of vibration transducers. For example, to calculate and compare the root-meansquare (RMS) values of vibrations with the limit settings for 16 spectral bands, a signal is continuously collected and processed. In case of exceeding the warning level about alarms, informing staff about the need for action. If vibrations exceed the emergency level, the unit can be stopped automatically. Existing vibration monitoring methods for the most part implement the so-called principle of “information completeness”. Under conditions of uncertainty about the type of failure, for diagnostics, in addition to previously known signs of failure, unknown ones are also used that remained in the vibro-acoustic signal after recursive selection of known ones. The selection of known features occurs in such a way that events leading to a change in unknown features constitute a “complete group” in a statistical sense. Based on this approach, the root-mean-square values of vibration acceleration, vibration velocity and vibration displacement were selected as diagnostic attributes of the first level that make up the “full group”. These parameters are standard for vibration diagnostics and emphasize respectively high-frequency, mid-frequency and low-frequency vibration bands. The high-frequency component best describes the problems of the mechanisms (defects and malfunctions in bearings, blades, screws and similar elements). The mid-frequency and low-frequency components are correlated, respectively, with the problems of the state of aggregation (balancing, alignment, alignment) and the problems of fastening the units and connecting structures (foundations, bases, pipelines). Accordingly, the data acquisition and analysis module in the vibration monitoring system, in addition to having some type of classification algorithm initially, must additionally self-learn through the accumulation and storage of the much unknown signs of failures mentioned above. This is necessary in order to increase the accuracy of defects detecting. Vibration parameters sensitivity analysis was preformed using original data obtained from petroleum-oriented oil refinery data base. It consists of about 300 diagnostic and failure analysis reports with the information of periodical maintenance procedures and vibration monitoring data. The sensitivity analysis was performed to find patterns between the values of vibration characteristics and failures that occur in equipment. It was proposed to analyze the sensitivity of the available variable values to further develop a mathematical apparatus for studying the reliability of pumping equipment. Sensitivity analysis was made using preliminary application of several classification methods: linear discriminant analysis, interactive trees, stochastic gradient boosting trees, multivariate adaptive regression splines (MAR Splines) and artificial neural networks [19–23]. The results of determination of values of the variables influence on the determination of a particular type of failure are presented in Table 1.

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Table 1. Predictors sensitivity analysis (1 means most important). Predictor

Amplitude, axial direction Amplitude, horizontal direction Amplitude, vertical direction Velocity, axial direction Velocity, horizontal direction Velocity, vertical direction

Linear discriminant analysis 1

Interactive trees

MAR splines

1

Stochastic gradient boosting trees 2

3

Artificial neural network 3

2

2

1

3

3

3

3

3

2

3

3

3

3

3

2

3

3

3

3

2

3

3

3

2

2

As seen, for different methods of data classification, the most important variables for determining the target value are different. For neural network classification, the key predictors are vibration velocity indicators. An analysis of a number of articles and studies on vibration diagnostics shows, it is precisely the vibration velocity indicators that are the main mid-frequency markers of vibration bands and allow to determine with equal accuracy both problems in the mechanisms and components of technological equipment and problems in the state of the unit as a whole (foundation, balancing, fastening and etc.). For discriminant analysis and two types of decision trees, the most important of the variables for classification are vibration amplitude indicators. Based on existing studies, this type of measurement is not fully capable of reflecting failures arising in the equipment, because vibration amplitude is often used to detect failures at low vibration frequencies.

3 Numerical Experiments The data set obtained from machine learning repository was used for numerical experiments during experimental study [24]. This data set consists of a sample with measurements of vibrational characteristics (vibration velocity and vibration displacement) for 221 objects. The objects are pumping units. In total, up to 9 types of measurements were observed for each object:

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sensor sensor sensor sensor sensor sensor sensor sensor sensor

without filter, vibration amplitude, axial direction; without filter, vibration amplitude, horizontal direction; without filter, vibration amplitude, vertical direction; with filter, vibration velocity, axial direction; with filter, vibration velocity, horizontal direction; with filter, vibration velocity, vertical direction; without filter, vibration velocity, axial direction; without filter, vibration velocity, horizontal direction; without filter, vibration velocity, vertical direction.

Classes description and distribution of objects from data set used in numerical study are presented in Table 2. Table 2. Description of data set for numerical experiments. Class 1 2 3 4 5 6 7 8 9 10

Description Problems in the joint Faulty bearings Mechanical loosening Basement distortion Unbalance Normal operating conditions Shaft misalignment Problems in the pump Problems in the motor Problems in the machine (includes first 5 classes,)

Number of objects 13 23 6 5 12 27 42 26 8 59

In the data set under consideration, each object corresponds to only one type of failure - either basic (failure class 1–6), or composite (failure class 7–10), which is a combination of basic types. The purpose of the numerical experiments was to evaluate the effectiveness of the data analysis methods involved for their subsequent use as part of a high-performance system (platform) for managing the reliability of technological pumping equipment. An assessment of the reliability of classification was used as an assessment of the effectiveness of methods for constructing classifiers. The conditions for performing numerical experiments and obtaining estimates of the reliability of the classification of failure types are described in more detail below. To carry out numerical experiments, we used the implementation of the algorithms in the package of applied statistical analysis Statistics, as well as the author’s implementation of the data processing methods under consideration. To ensure the uniformity of the conditions for conducting experiments, the settings of the methods were chosen from the condition as close as possible to the execution time of the procedure for constructing the corresponding classifier. To ensure the correctness of the estimates, the methods were evaluated on a test sample extracted from the general sample. Partitioning of the total sample was carried out in a proportion of 80 by 20. Such

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repartitioning was carried out 10 times in accordance with the numerical experiment scheme. Statistical differences in the results were evaluated using the ANOVA method at a significance level of 0.05. The processed results of the numerical study for the sample used to construct the classifiers (training sample) and the test sample of methods are shown in Table 3. Table 3. Result of numerical experiment. Method of data analysis Neural network Linear discriminant analysis Interactive tree Stochastic gradient boosting tree MARSplines

Reliability of failure classification (training sample/test sample), % 95.7/87.3 51.6/43.4 62.5/59.3 70.1/64.7 47.2/41.6

The obtained results demonstrate the effectiveness neural network approach to solve the problem of vibration data analysis. Other methods of data analysis showed lower value of accuracy for the failure classification. The accuracy of the resulting neural network model with multilayer perceptron architecture is characterized by 5–6% classification error on the training data and 12–13% on the test data. To decreases the value of testing data error, obtained neural network can be retrained with new input predictors. The resulting neural network as an analyzer of data from vibration sensors, when used in production, will work as follows. Neural network installed as a program in the data analysis module for the process equipment monitoring system will convert the values of the vibration characteristics received from the sensors and based on them predict a possible type of failure or inform about normal operation. In the future, in order for the neural network to be able to independently convert the input data to average values and RMS, it is necessary to improve the program, which we will further load into the data analysis module of complex reliability assurance and control system.

4 Conclusion The article presents the results of testing several classification methods to solve the problem of recognition of defects in technological pumping equipment. A preliminary analysis and preparation of data based on the results of assessing the sensitivity of the approaches under consideration when processing vibration monitoring data from an oil refinery was performed. The most significant vibration parameters for the considered methods are determined, classification for their use as methods for determining the types of failures of technological pumping equipment. It is shown that for the data

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considered in the article, the method based on artificial neural networks allows the greatest efficiency. This method allows to ensure the reliability of defect determination according to vibration monitoring data at the level of 87–95%. Such recognition reliability values are acceptable for the implementation of the fault recognition module as part of an integrated reliability system. Given the complexity of the task, the resulting neural network model with the architecture of a multilayer perceptron has an acceptable level of complexity. The deployment of such a model can be provided with modern automation tools as part of integrated computer-aided design systems for intelligent data analysis technologies. In cases where the reliability of a single neural network model seems limited and insufficient to solve a specific practical problem, it is proposed to further implement an ensemble neural network approach. In the future, it is planned to test the method using larger data on various types of technological equipment of oil and gas industries. The result of the research to be the development of a highly effective decision support platform while ensuring the reliability of production equipment in the framework of the concept of cyber physical production systems. Acknowledgments. The reported study was partially funded Scholarship of the President of the Russian Federation for young scientists and graduate students SP.869.2019.5.

References 1. Gupta, G., Mishra, R.P.: A SWOT analysis of reliability centered maintenance framework. J. Qual. Maintenance Eng. 22(2), 130–145 (2016) 2. Bousdekis, A., Magoutas, B., Apostolou, D., Mentzas, G.: Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance. J. Intell. Manufact. 29(6), 1303–1316 (2018) 3. Bukhtoyarov, V., Tynchenko, V., Petrovskiy, E., Bukhtoyarova, N., Zhukov, V.: Investigation of methods for modeling petroleum refining facilities to improve the reliability of predictive decision models. J. Appl. Eng. Sci. 16(2), 246–253 (2018) 4. Ahadi, A., Ghadimi, N., Mirabbasi, D.: An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability. Complexity 21(1), 99–113 (2015) 5. Qingfeng, W., Wenbin, L., Xin, Z., Jianfeng, Y., Qingbin, Y.: Development and application of equipment maintenance and safety integrity management system. J. Loss Prevent. Process Ind. 24(4), 321–332 (2011) 6. Subramanian, N., Zalewski, J.: Quantitative assessment of safety and security of system architectures for cyberphysical systems using the NFR approach. IEEE Syst. J. 10(2), 397– 409 (2014) 7. Irisarri, E., García, M.V., Pérez, F., Estévez, E., Marcos, M.: A model-based approach for process monitoring in oil production industry. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, Germany, pp. 1–4. IEEE (2016) 8. Bukhtoyarov, V.V., Tynchenko, V.S., Petrovskiy, E.A., Tynchenko, V.V., Zhukov, V.G.: Improvement of the methodology for determining reliability indicators of oil and gas equipment. Int. Rev. Mod. Simul. 11(1), 37–50 (2018)

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9. Abramkin, S.E., Dushin, S.E.: Prospects for the development of control systems for gas producing complexes. In: 2017 IEEE II International Conference on Control in Technical Systems, St. Petersburg, Russia, pp. 150–153. IEEE (2017) 10. Anderson, R.N.: Petroleum analytics learning machine for optimizing the internet of things of today’s digital oil field-to-refinery petroleum system. In: 2017 IEEE International Conference on Big Data, Boston, MA, USA, pp. 4542–4545. IEEE (2017) 11. Lu, H., Guo, L., Azimi, M., Huang, K.: Oil and gas 4.0 era: a systematic review and outlook. Comput. Ind. 111, 68–90 (2019) 12. Shoja, S., Jalali, A.: A study of the internet of things in the oil and gas industry. In: 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran, pp. 0230–0236. IEEE (2017) 13. Khadersab, A., Shivakumar, S.: Vibration analysis techniques for rotating machinery and its effect on bearing faults. Procedia Manufact. 20, 247–252 (2018) 14. Telford, S., Mazhar, M.I., Howard, I.: Condition based maintenance (CBM) in the oil and gas industry: an overview of methods and techniques. In: Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management (2011) 15. Al-Badour, F., Sunar, M., Cheded, L.: Vibration analysis of rotating machinery using time– frequency analysis and wavelet techniques. Mech. Syst. Sign. Process. 25(6), 2083–2101 (2011) 16. Bukhtoyarov, V.V., Zyryanov, D.K., Bukhtoyarova, N.A., Tynchenko, V.S., Kukartsev, V.V., Bashmur, K.A.: Expert analysis of elements of the diagnostic system for compressor technological equipment. J. Phys: Conf. Ser. 1399(4), 044113 (2019) 17. Srinivas, H.K., Srinivasan, K.S., Umesh, K.N.: Application of artificial neural network and wavelet transform for vibration analysis of combined faults of unbalances and shaft bow. Adv. Theor. Appl. Mech. 3(4), 159–176 (2010) 18. McKee, K.K., Forbes, G.L., Mazhar, I., Entwistle, R., Howard, I.: A review of machinery diagnostics and prognostics implemented on a centrifugal pump. In: Engineering asset management), pp. 593–614. Springer, London, UK (2011) 19. Izenman, A.J.: Linear discriminant analysis. In: Modern multivariate statistical techniques, pp. 237–280. Springer, New York, NY, US (2013) 20. Van Den Elzen, S., Van Wijk, J.J.: Baobabview: interactive construction and analysis of decision trees. In: 2011 IEEE conference on visual analytics science and technology, Providence, RI, USA, pp. 151–160. IEEE (2011) 21. Chopra, T., Vajpai, J.: Fault diagnosis in benchmark process control system using stochastic gradient boosted decision trees. Int. J. Soft Comput. Eng. 1, 98–101 (2011) 22. Sikka, G., Kaur, A., Uddin, M.: Estimating function points: using machine learning and regression models. In: 2010 2nd International Conference on Education Technology and Computer, Shanghai, China, vol. 3, pp. 43–52. IEEE (2010) 23. Haykin, S.: Kalman Filtering and Neural Networks. John Wiley & Sons, Hoboken, NJ, US (2004) 24. Asuncion, A., Newman, D.: UCI Machine Learning Repository. University of California, Irvine, CA, US (2007)

Using the Mathematical Modeling Method for Forecasting Severe Bronchial Obstruction Syndrome with ARVI in Children L. V. Kramar(&) and T. Yu. Larina Volgograd State Medical University, 1, Pl. Pavshikh Bortsov Square, Volgograd 400131, Russia [email protected], [email protected]

Abstract. The objective of the simple open comparative clinical research is the development of a mathematical model for forecasting severe bronchial obstruction syndrome (BOS) in children with acute respiratory viral infections at the pre-hospital stage. The sample range with the confidence interval of 95% and a possible error of 5% constituted 384 people; the total number of examined children was 386. The criteria of patient selection: age ranging from one month to five years; acute respiratory viral infection complicated with the bronchial obstruction syndrome; absence of any chronic concurrent diseases; absence of the confirmed pre-existing bronchial asthma diagnosis; negative results of Mycoplasmae spp., Chlamidia spp. tests; informed consent to participation in the survey. The first main group (I) consisted of 94 children with severe BOS; the comparison group (II) was made up of 292 patients of the same age with light and medium syndrome. For every patient, 34 features characterizing the family history, specificity of the pregnancy and birth, feeding specificity, presence of allergies, and environmental conditions of the residence area. The collected data were converted to the scoring system and statistically processed with the Statistica 10.0 (StatSoft Inc., USA) and IBM SPSS Statistics software. The impact made by the independent variables of the index in question was assessed with the multifactor logistic regression model, which included the variables that reached the value of p
0,05 in the two-dimensional analysis. After that, all the indexes were analyzed with the Kullback information measure and Wald test to produce a diagnostic table for forecasting severe disease development. The quality of the produced model was qualified as excellent (area under the ROC curve AUC = 0.912, p < 0.001) with high sensitivity (Se = 88.2%) and specificity (Sp = 94.1%) values. Wald consistency test and diagnostic table development can be qualified as a high precision method for forecasting the BOS progress in children at the pre-hospital stage. #COMESYSO1120. Keywords: Bronchial obstruction syndrome  Children modeling of the disease severity development

 Mathematical

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Silhavy et al. (Eds.): CoMeSySo 2020, AISC 1294, pp. 606–614, 2020. https://doi.org/10.1007/978-3-030-63322-6_50

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1 Introduction Bronchial obstruction syndrome (BOS) is an urgent problem of global healthcare. Timely diagnostics of the bronchial obstruction syndrome (BOS) in children is one of the most acute problems of today’s pediatrics and infectiology which, regardless of the numerous studies carried out, still cannot be deemed completely solved [1, 2]. The urgency and rationality of mathematic methods application in the medical domain are doubtless. The main problem of mathematical modeling in medicine is a big number of interconnected biological factors, i.e. the biological systems are sophisticated scholastic systems with a great number of elements and interconnections between them. The specificity of studying such matters is the need for processing large volumes of input data subject to preliminary systematic analysis intended to study the tendencies of the human body function as a biological system. In this situation, particular relevance is gained by the multi-dimensional statistical analysis methods, which can be used not only to systematize and process the medical survey data but also to detect the nature and the structure of the complex interconnections between the studied sign components for further predictive model development [3, 4].

2 The Objective of the Study The objective of the study is the search for the informative anamnestic signs relevant for the severity of the bronchial obstruction syndrome affected by acute respiratory viral infection (ARVI) in children; the development of a mathematical model for severe BOS development forecasting at the pre-hospital stage.

3 Tasks of the Research Tasks of the research include the collection of proofs on the influence made by the premorbid background and environment factors on the development of acute bronchial obstruction syndrome; production of a mathematical model for forecasting the severity of the bronchial obstruction syndrome affected by acute respiratory viral infections in children, applicable in the outpatient setting.

4 Materials and Methods For the solution of the tasks set above, a simple, open, prospective comparative clinical survey was carried out. The subjects of the research: from the methodological point of view, timely diagnostics of severe BOS development in children; from the empirical point of view, children with BOS of different severity degrees and parents of the patients.

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Before the commencement of the study, a permission of the Ethics Committee for Clinical Research of Volgograd State Medical University was obtained. With reference to the confidence interval of 95% and a possible error of 5%, the number of the sample range of 384 people was determined. In fact, 386 children were examined. The study was carried out at Regional Children’s Clinical Infection Hospital (Volgograd) in the years 2013–2018. The criteria of patient selection: age ranging from one month to five years; acute respiratory viral infection complicated with the bronchial obstruction syndrome; absence of any chronic concurrent diseases; absence of the confirmed pre-existing bronchial asthma diagnosis; negative results of Mycoplasmae spp, Chlamidia spp. tests; informed consent to participation in the survey. Exclusion criteria: ARVI of different severity without bronchial obstruction syndrome symptoms; presence of any chronic somatic diseases; confirmed bronchial asthma diagnosis; positive Mycoplasmae spp, Chlamidia spp tests; absence of the signed informed consent form. Depending on the severity degree of the bronchial obstruction, the children were divided into two groups. The first main group (I) consisted of 94 children with severe BOS, receiving therapy at the intensive care unit of the children’s infection hospital. The comparison group (II) consisted of 292 patients of the same age, suffering from light and medium syndrome, treated at the general respiratory unit of the hospital. To identify the most significant risk factors relevant to the BOS severity level, parents of all the children were interviewed to collect their medical history. The total of 34 anamnestic signs including the family history (age of the mother under 18 or over 35 years old, somatic diseases of the mother, allergic diseases of the parents, smoking of the mother during pregnancy, smoking of the father) were studied, along with specificity of pregnancy and delivery (threatened miscarriage, toxicosis, fetal hypoxia, hydramnios, ARVI during pregnancy, preterm birth, prolonged, accelerated labor, weakness of labor, cesarean section, type of delivery (cesarean section or natural birth), weight of the baby at birth under 3 kg or over 4 kg); specificity of feeding during the first year of life (breastfeeding, bottle feeding or mixed); allergic diseases in children (atopic dermatitis, medicine allergies, food allergies); environmental factors (presence or absence of major industrial enterprises or motorways in the close proximity to the place of residence). All information was recorded in an electronic database. The collected interviews and objective examination results were statistically processed with the Statistica 10.0 (StatSoft Inc., USA) and IBM SPSS Statistics software. For the assessment of the quantitative indicators, the average value of the attribute, the standard deviation, the median value, and Student’s t-test were used within the confidence interval of 95%. For the assessment of non-parametric parameters, Student’s t-test was used for independent samplings and Pearson’s chi-squared test was used at p < 0.05. The influence of the independent variables on the value studied (severity of BOS) was assessed with a multifactor logistic regression. For this purpose, the variables reaching the value of p
0.05 in two-dimensional analysis were introduced into the direct logistic regression model. At that, all the assessed non-parametric values were converted into a score. The logistic regression model output was presented as adjusted odds ration coefficients (OR) with regard to the confidence intervals (CI) of 95%. The statistically significant confidence level was p < 0.05.

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Moreover, all analyzed values were studied with the Kullback information measure and Wald consistency analysis [5–8]. For that, the diagnostic criteria (DC) for every confidently relevant listed anamnestic risk factor were calculated with the following formula: DC ¼ 10  lg

P1 P2

ð1Þ

where DC is the diagnostic coefficient; P1 is a relative frequency of the attribute in the main group expressed as decimal quantity; P2 is a relative frequency of the attribute in the comparison group expressed as a decimal quantity. The explanatory value of each of the diagnostic criteria was calculated with Kullback’s formula: J ¼ 0; 5  DC  ðP1  P2Þ

ð2Þ

were J is the explanatory value of a diagnostic coefficient; DC is the diagnostic coefficient; P1 is a relative frequency of the attribute in the main group expressed as decimal quantity; P2 is a relative frequency of the attribute in the comparison group expressed as a decimal quantity. At DC < 0.25, the associated anamnestic risk factors were rejected, and those with sufficient forecasting capacity were included in the mathematical model of forecasting the severity of BOS in children. However, every calculated DC was to be converted into a score (under the method proposed by Z.K. Trushinsky) [9, 10]. For this purpose, the following rule was used: the values within the diapason of 0.25
DC
0.75 were rounded to 0.5, and the values of DC > 0.75 were approximated to one (to a round number, large than the last number before the dot by 1). For the assessment of the produced forecasting model, the ROC analysis (Receiver Operator Characteristic) was used together with the AUC (Area Under Curve) index, the numeric value of the area underneath the ROC curve. The greater the AUC value is, the better is the forecasting capacity of the given model [11, 12].

5 Discussion of the Results The results of the multidimensional logistic regression analysis have shown that only 17 factors could be identified as relevant to the severity of the disease: allergies in the medical history of the mother (OR = 4.37, p < 0.001), allergic diseases in the nearest relatives (OR = 4.21, p < 0.001), absence of allergy family history (OR = 0.24, p < 0.001), mother’s smoking during pregnancy (OR = 4.53, p < 0.001), threatened miscarriage (OR = 3.34, p < 0.001), chronic fetal hypoxia (OR = 2.84, p < 0.001), ARVI during pregnancy (OR = 4.42, p < 0.001), preterm delivery (OR = 3.16,

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p < 0.001), birth through cesarean section (OR−2.83, p < 0.001), physiological birth (OR = 0.35, p < 0.001), weight under 3 kg at birth (OR = 2.29, p < 0.001), pregnancy and birth pathologies (OR = 3.56, p < 0.001), bottle feeding during the first year of life (OR = 5.53, p < 0.001), breastfeeding (OR = 0.22, p < 0.001), residence in an area with adverse environmental conditions (OR = 4.37, p < 0.001), absence of adverse environmental factors (OR = 0.24, p < 0.001). At the same time, the rest of the factors were not among the potential determinants influencing the severity of BOS. During the anamnestic risk factor studies, the most significant factors making a confident impact on the disease severity were found (Table 1).

Table 1. Factors relevantly related to the development of severe bronchial obstruction syndrome in children with ARVI Attribute

IGroup I n = 94

Children with allergies in mother’s medical history Total number of children with allergic disorders in the family medical history Absence of allergic diseases in the parents Mother’s smoking during pregnancy Threatened miscarriage Fetal hypoxia ARVI during pregnancy Preterm delivery Cesarean section Natural birth Body weight under 3000 g at birth Total pregnancy and birth pathologies Absence of pregnancy and birth complications Breastfeeding Bottle feeding during the first year of life Atopic dermatitis in the child Food allergy in the child Allergic diseases in the child Absence of allergic diseases in the child Residence in the areas with adverse environmental conditions Absence of adverse environmental conditions

Confidence, (p-value)

Group Confidence IOdds interval ratio (OR) (95% CI)

35 (37.2%) 41 (14.0%)