Innovation in Information Systems and Technologies to Support Learning Research: Proceedings of EMENA-ISTL 2019 [1st ed. 2020] 978-3-030-36777-0, 978-3-030-36778-7

Table of contents :
Front Matter ....Pages i-xvii
Mobile Learning Systems’ Functionalities in Higher Education Institutions in Tanzania: Teachers and Students’ Readiness at the College of Business Education (Godfrey Isaac Mwandosya, Calkin Suero Montero, Esther Rosinner Mbise, Solomon Sunday Oyelere)....Pages 1-13
TIMONEL: Recommendation System Applied to the Educational Orientation of Higher Education Students (Antonio Pantoja-Vallejo, Beatriz Berrios-Aguayo)....Pages 14-26
Modeling the Acceptance of the E-Orientation Systems by Using the Predictions Algorithms (Rachida Ihya, Abdelwahed Namir, Sanaa Elfilali, Fatima Zahra Guerss, Mohammed Ait Daoud)....Pages 27-31
Virtual Teacher Based Tool for Teaching Context-Free Grammars by Active Pedagogy and eMathTeacher Philosophy (Mohammed Serrhini, Abdelmajid Dargham)....Pages 32-39
MOOC of Algorithmic: Elaboration of Content and Difficulties Encountered by Students (Abdelghani Babori)....Pages 40-46
Content Analysis and Learning Analytics on Interactions of Unsupervised Learners in an Online Learning Environment (Shireen Panchoo, Alain Jaillet)....Pages 47-54
Multimedia System for Self-learning C/C++ Programming Language (José Galindo, Patricia Galindo, José María Rodríguez Corral)....Pages 55-64
A Recommendation Approach Based on Community Detection and Event Correlation Within Social Learning Network (Sonia Souabi, Asmaâ Retbi, Mohammed Khalidi Idrissi, Samir Bennani)....Pages 65-74
A New Approach to Detect At-Risk Learning Communities in Social Networks (Meriem Adraoui, Asmaâ Retbi, Mohammed Khalidi Idrissi, Samir Bennani)....Pages 75-84
A New Approach of Integrating Serious Games in Intelligent Tutoring Systems (Mohammed Beyyoudh, Mohammed Khalidi Idrissi, Samir Bennani)....Pages 85-91
A Case Study on Teaching a Software Estimation Course (Marcelo Jenkins, Cristian Quesada-Lopez)....Pages 92-101
Simulation and Analyze of Global MPPT Based on Hybrid Classical-ANN with PSO Learning Approach for PV System (Ihssane Chtouki, Patrice Wira, Malika Zazi, Houssam Eddine Chakir, Bruno Collicchio)....Pages 102-115
The Contribution of Big Data to Achieving a Competitive Advantage: Proposal of a Conceptual Model Based on the VRIN Model (Abdelhak Ait Touil, Siham Jabraoui)....Pages 116-123
Robust Domain Adaptation Approach for Tweet Classification for Crisis Response (Reem ALRashdi, Simon O’Keefe)....Pages 124-134
Classification of Learners During an Educational Simulation: Case Study on a Stock Management Simulator (Denis Guibert, Thibaud Serieye, Nicolas Pech-Gourg)....Pages 135-144
A Machine Learning Model to Early Detect Low Performing Students from LMS Logged Interactions (Bruno Cabral, Álvaro Figueira)....Pages 145-154
Energy Consumption Forecasting in Industrial Sector Using Machine Learning Approaches (Mouad Bahij, Mohamed Cherkaoui, Moussa Labbadi)....Pages 155-164
Analysis of ESP32 SoC for Feed-Forward Neural Network Applications (Kristian Dokic, Dubravka Mandusic, Bojan Radisic)....Pages 165-175
Graph Schema Storage in SQL Object-Relational Database and NoSQL Document-Oriented Database: A Comparative Study (Zakariyaa Ait El Mouden, Abdeslam Jakimi, Moha Hajar, Mohamed Boutahar)....Pages 176-183
Sentiment Analysis on Twitter to Measure the Perception of Taxation in Colombia (Mónica Katherine Durán-Vaca, Javier Antonio Ballesteros-Ricaurte)....Pages 184-193
Big Five Personality Traits and Ensemble Machine Learning to Detect Cyber-Violence in Social Media (Randa Zarnoufi, Mounia Abik)....Pages 194-202
A Comparative Study of Feature Selection Methods for Informal Arabic (Soukaina Mihi, Brahim Ait Ben Ali, Ismail El Bazi, Sara Arezki, Nabil Laachfoubi)....Pages 203-213
A Review of Engines for Graph Storage and Mutations (Soukaina Firmli, Dalila Chiadmi)....Pages 214-223
Towards an Improved CNN Architecture for Brain Tumor Classification (Hajji Tarik, Masrour Tawfik, Douzi Youssef, Serrhini Simohammed, Ouazzani Jamil Mohammed, Jaara El Miloud)....Pages 224-234
Machine Learning for Forecasting Building System Energy Consumption (Mountassir Fouad, Reda Mali, Mohamed Pr.Bousmah)....Pages 235-242
Methodologies for Large SAP ERP Projects Implementation (Fabiane Ayres, Franklin Ayres, Alexandre Barão)....Pages 243-247
A Software Testing Strategy Based on a Software Product Quality Model (Narayan Debnath, Carlos Salgado, Mario Peralta, Daniel Riesco, Luis Roqué, Germán Montejano et al.)....Pages 248-259
A DSL-Based Framework for Performance Assessment (Hamid El Maazouz, Guido Wachsmuth, Martin Sevenich, Dalila Chiadmi, Sungpack Hong, Hassan Chafi)....Pages 260-270
Towards a Framework Air Pollution Monitoring System Based on IoT Technology (Anass Souilkat, Khalid Mousaid, Nourdinne Abghour, Mohamed Rida, Amina Elomri)....Pages 271-280
Connected Objects in Information Systems (Onyonkiton Théophile Aballo, Roland Déguénonvo, Antoine Vianou)....Pages 281-285
The Efficient Network Interoperability in IoT Through Distributed Software-Defined Network with MQTT (Rajae Tamri, Said Rakrak)....Pages 286-291
5G Network Architecture in Marrakech City Center (Fatima Zahra Hassani-Alaoui, Jamal El Abbadi)....Pages 292-301
A Distance Integrated Triage System for Crowded Health Centers (Kambombo Mtonga, Willie Kasakula, Santhi Kumaran, Kayalvizhi Jayavel, Jimmy Nsenga, Chomora Mikeka)....Pages 302-311
Design of RC-Based Low Diameter Two-Level Hierarchical Structured P2P Network Architecture (Swathi Kaluvakuri, Bidyut Gupta, Banafsheh Rekabdar, Koushik Maddali, Narayan Debnath)....Pages 312-320
Grey Wolf Optimizer for Virtual Network Embedding in SDN-Enabled Cloud Environment (Abderrahim Bouchair, Sid Ahmed Makhlouf, Yagoubi Belabbas)....Pages 321-330
A Small Robotic Step for the Therapeutic Treatment of Mental Illnesses: First Round (Carlos Martinez, David Castillo, Ruth Maldonado Rivera, Hector F. Gomez A)....Pages 331-336
Accuracy of Classification Algorithms Applied to EEG Records from Emotiv EPOC+ Using Their Spectral and Asymmetry Features (Kevin Martín-Chinea, Jordan Ortega, José Francisco Gómez-González, Jonay Toledo, Ernesto Pereda, Leopoldo Acosta)....Pages 337-342
Organizational Model for Collaborative Use of Free and Open Source Software: The Case of IT Departments in the Philippine Public and Private Sectors (Ferddie Quiroz Canlas)....Pages 343-351
A Framework Supporting Supply Chain Complexity and Confidentiality Using Process Mining and Auto Identification Technology (Zineb Lamghari, Maryam Radgui, Rajaa Saidi, Moulay Driss Rahmani)....Pages 352-361
3D Recording and Point Cloud Analysis for Detecting and Tracking Morphological Deterioration in Archaeological Metals (Alba Fuentes-Porto, Drago Díaz-Aleman, Elisa Díaz-González)....Pages 362-367
Convolutional Neural Network Architecture for Offline Handwritten Characters Recognition (Soufiane Hamida, Bouchaib Cherradi, Hassan Ouajji, Abdelhadi Raihani)....Pages 368-377
Comparative Study of Methods Measuring Lexicographic Similarity Among Tamazight Language Variants (Ikan Mohamed, Abdessamad Jaddar, Aissa Kerkour Elmiad, Ghizlane Kouaiba)....Pages 378-389
Comparative Study of DICOM Files Handling Software’s: Study Based on the Anatomage Table (Zineb Farahat, Mouad Hasni, Kawtar Megdiche, Nissrine Souissi, Nabil Ngote)....Pages 390-399
Fake News Identification Based on Sentiment and Frequency Analysis (Jozef Kapusta, Ľubomír Benko, Michal Munk)....Pages 400-409
Arab Handwriting Character Recognition Using Deep Learning (Aissa Kerkour Elmiad)....Pages 410-415
Automatic Evaluation of MT Output and Post-edited MT Output for Genealogically Related Languages (Daša Munková, Michal Munk, Ján Skalka, Karol Kasaš)....Pages 416-425
3D Objects Learning and Recognition Using Boosted-SVM Algorithm (Youness Abouqora, Omar Herouane, Lahcen Moumoun, Taoufiq Gadi)....Pages 426-435
Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQL (Youssef Mellah, El Hassane Ettifouri, Toumi Bouchentouf, Mohammed Ghaouth Belkasmi)....Pages 436-441
Privacy by Design and Cybersecurity for Safe, Effective and Reliable Home Health Care for Aging in Place (Helene Fournier, Heather Molyneaux, Irina Kondratova, Noor Ali)....Pages 442-450
Feature Reduction Algorithm for Universal Steganalysis (François Kasséné Gomis, Mamadou Samba Camara, Idy Diop)....Pages 451-457
A Composite Framework to Promote Information Security Policy Compliance in Organizations (Eric Amankwa, Marianne Loock, Elmarie Kritzinger)....Pages 458-468
Authentication Model Based on JWT and Local PKI for Communication Security in Multi-agent Systems (Badr Eddine Sabir, Mohamed Youssfi, Omar Bouattane, Hakim Allali)....Pages 469-479
A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease (Zahia Aouabed, Moloud Abdar, Nadia Tahiri, Jaël Champagne Gareau, Vladimir Makarenkov)....Pages 480-489
Optimized Management of the Health Emergency Services Regional Network of Rabat Region (Ibtissam Khalfaoui, Amar Hammouche)....Pages 490-499
Evolution of Cooperation in E-commerce Based on Prisoner’s Dilemma Game (Jalal Eddine Bahbouhi, Najem Moussa)....Pages 500-505
Quality Measurement Systems in Public Services and E-Government (Hajar Hadi, Ibtissam Elhassani, Souhail Sekkat)....Pages 506-514
Using Ontology and Context-Awareness for Business Process Modelling: An Overview (Jamal El Bouroumi, Hatim Guermah, Mahmoud Nassar, Abdelaziz Kriouile)....Pages 515-522
River Flow Forecasting: A Comparison Between Feedforward and Layered Recurrent Neural Network (Sultan Aljahdali, Alaa Sheta, Hamza Turabieh)....Pages 523-532
An Experimental Artificial Neural Network Based MPP Tracking for Solar Photovoltaic Systems (Yassine Chouay, Mohammed Ouassaid)....Pages 533-542
Mobile Data Collection Using Open Data Kit (Patrick Loola Bokonda, Khadija Ouazzani-Touhami, Nissrine Souissi)....Pages 543-550
Two Quantum Attack Algorithms Against NTRU When the Private Key and Plaintext Are Codified in Ternary Polynomials (El Hassane Laaji, Abdelmalek Azizi, Siham Ezzouak)....Pages 551-562
Maximum Power Point Tracking of Photovoltaic System Based on Fuzzy Control to Increase There Solar Energy Efficiency (Ahmed Hafaifa, Kaid Imed, Mouloud Guemana, Abudura Salam)....Pages 563-571
A New Technique of Harmonic Currents Extraction Based on a Fuzzy Logic Controller Applied to the PV-SAPF System (Asmae Azzam-Jai, Mohammed Ouassaid)....Pages 572-582
A New TSA-Fuzzy Logic Based Diagnosis of Rotor Winding Inter-turn Short Circuit Fault in Wind Turbine Based on DFIG Under Different Operating Wind Speeds (Hamza Sabir, Mohammed Ouassaid, Nabil Ngote)....Pages 583-592
A New Fuzzy Clustering Method Based on FN-DBSCAN to Determine the Optimal Input Parameters (Sihem Jebari, Abir Smiti, Aymen Louati)....Pages 593-602
Diagnosis of Brain Tumors in MR Images Using Metaheuristic Optimization Algorithms (Malik Braik, Alaa Sheta, Sultan Aljahdali)....Pages 603-614
Biometric Person Authentication Using a Wireless EEG Device (Jordan Ortega, Kevin Martín-Chinea, José Francisco Gómez-González, Ernesto Pereda)....Pages 615-620
Quadcopter Attitude Stabilization in a Gyroscopic Testbench (Yassine El Houm, Ahmed Abbou, Ali Mousmi, Moussa Labbadi)....Pages 621-630
A Novel Multicore Multicasting Scheme for PIM-SM (Indranil Roy, Banafshah Rekabdar, Swathi Kaluvakuri, Koushik Maddali, Bidyut Gupta, Narayan Debnath)....Pages 631-638
Back Matter ....Pages 639-641

Citation preview

Learning and Analytics in Intelligent Systems 7

Mohammed Serrhini Carla Silva Sultan Aljahdali Editors

Innovation in Information Systems and Technologies to Support Learning Research Proceedings of EMENA-ISTL 2019

Learning and Analytics in Intelligent Systems Volume 7

Series Editors George A. Tsihrintzis, University of Piraeus, Piraeus, Greece Maria Virvou, University of Piraeus, Piraeus, Greece Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, NSW, Australia; University of Canberra, Canberra, ACT, Australia; KES International, Shoreham-by-Sea, UK; Liverpool Hope University, Liverpool, UK

The main aim of the series is to make available a publication of books in hard copy form and soft copy form on all aspects of learning, analytics and advanced intelligent systems and related technologies. The mentioned disciplines are strongly related and complement one another significantly. Thus, the series encourages cross-fertilization highlighting research and knowledge of common interest. The series allows a unified/integrated approach to themes and topics in these scientific disciplines which will result in significant cross-fertilization and research dissemination. To maximize dissemination of research results and knowledge in these disciplines, the series publishes edited books, monographs, handbooks, textbooks and conference proceedings.

More information about this series at http://www.springer.com/series/16172

Mohammed Serrhini Carla Silva Sultan Aljahdali •

•

Editors

Innovation in Information Systems and Technologies to Support Learning Research Proceedings of EMENA-ISTL 2019

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Editors Mohammed Serrhini Department of Computer Science, Faculty of Sciences Mohammed Premier University Oujda, Morocco

Carla Silva Lusophone University of Humanities and Technologies Lisbon, Portugal

Sultan Aljahdali Department of Computer Science Taif University, College of Computers and Information Technology Al Huwaya, Saudi Arabia

ISSN 2662-3447 ISSN 2662-3455 (electronic) Learning and Analytics in Intelligent Systems ISBN 978-3-030-36777-0 ISBN 978-3-030-36778-7 (eBook) https://doi.org/10.1007/978-3-030-36778-7 © 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 contains a selection of papers accepted for presentation and discussion at the third edition of International Conference Europe, Middle East and North Africa on Information System Technology and Learning Researches 2019 (EMENA-ISTL’19). This conference had the support of the University Mohamed First Oujda, Morocco; College of Computers and Information Technology, Taif University, Taif, Saudi Arabia; Institute of Artificial Intelligent Systems and Behavioural Science (ISCIAC), Atlantica; and School of Management Sciences, Health, IT & Engineering Lisbon, Portugal. EMENA-ISTL’19 conference has two aims; first, it provides the ideal opportunity to bring together professors, researchers, and high education students of different disciplines, discusses new issues, and discovers the most recent developments, researches, and trends in information and communication technologies, emerging technologies, and security to support learning. Second goal is to focus on to boost future collaboration and cooperation between researchers and academicians from Europe, Middle East, and North Africa, and universities from all over the world. The international program committee are from more than 60 countries around the world; EMENA-ISTL’19 was composed of a multidisciplinary group of experts and those who are intimately concerned with information systems and communication technologies, artificial intelligence, big data analytics and applications, intelligent data systems, machine learning, and security. They have had the responsibility for evaluating, in a ‘blind-review’ process, the papers received for each of the main themes proposed for the conference: (A) information systems technologies to support education; (B) education in science, technology, engineering and mathematics; (C) emerging technologies in education learning innovation in digital age; (D) software systems, architectures, applications and tools; (E) multimedia systems and applications; (F) computer communications and networks; (G) IoT, smart cities and people, wireless sensor and ad hoc networks; (H) organizational models and information systems and technologies; (I) human– computer interaction; (J) computers and security, ethics, and data forensic; (K) health informatics and medical informatics security; (L) information and v

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Preface

knowledge management; (M) big data analytics and applications, intelligent data systems, and machine learning; (N) artificial intelligence, high-performance computing; (O) mobile, embedded, and ubiquitous systems; (P) language and image processing, computer graphics and vision; (Q) interdisciplinary field of fuzzy logic and data mining; (R) bioinformatics and computational biology, (S) intelligent robotics and multi-agent systems; and (T) information storage, indexing, and retrieval. EMENA-ISTL’19 received contributions from 48 countries around the world. The papers accepted for presentation and discussion at the conference are published by Springer (this book) and by EMENA-ISTL’19 (another e-book) and will be submitted for indexing by ISI, Ei Compendex, SCOPUS, DBLP and/or Google Scholar, among others. Extended versions of selected best papers will be published in relevant journals, including SCI/SSCI and Scopus indexed journals. We acknowledge all those who contributed to the staging of EMENA-ISTL’19 (authors, committees, and sponsors); their involvement and support are very much appreciated. November 2019

Mohammed Serrhini Sultan Aljahdali Carla Silva

Organization

Conference General Chair Mohammed Serrhini

University Mohamed First Oujda, Morocco

Conference Co-chairs Sultan Aljahdali Carla Silva

Taif University, Taif, Saudi Arabia School of Management Sciences, Health, IT & Engineering Lisbon, Portugal

Local Chairs El-Mostafa Daoudi Tarik Hajji Ahmed Tahiri El Miloud Jaara Kerkour el Miad Mohamed Moussi

University Mohamed First Oujda, Morocco Private University of Fez, Morocco University Mohamed First Oujda, Morocco University Mohamed First Oujda, Morocco University Mohamed First Oujda, Morocco University Mohamed First Oujda, Morocco

Advisory Committee Antonio J. Jara Alaa Sheta Hisham Al-Mubaid

University of Applied Sciences Western, Switzerland Texas A&M University-Corpus Christi, TX, USA University of Houston-Clear Lake, TX, USA

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Organization

Program Committee Izolda Fotiyeva Olaf Maennel Mohsine Eleuldj Houda Hakim Guermaz Ernest Cachia (Dean) Raúl Cordeiro Correia Hugo Romero B. Ronan Champagnat Marko Hölbl Rita Francese Naceur Ben Hadj Braiek Fernando Moreira Maria José Angélico Gonçalves Maria José Sousa Maytham Hassan Safar James Uhomoibhi Jarno Limnéll Esteban Vázquez Cano Juarez Bento Silva Anouar Belahcen Peter Mikulecky Katherine Maillet Manik Sharma Rafael Valencia-Garcia Nissrine Souissi Luis Anido Rifon Mraoui Hamid Carla Silva Rolou Lyn Rodriguez Maata Ali Shaqour Ahmed Tahiri Abdullah Al-Hamdani Muzafer Saracevic Manuel Caeiro Rodríguez Rafik Zitouni Utku ZKose

Howard University, Washington D.C., USA Tallinn University of Technology, Estonia Mohammadia School of Engineering, Morocco Manouba University, Tunisia Faculty of ICT, University of Malta, Malta Instituto Politécnico de Setúbal, Portugal Technical University of Machala, Ecuador Universite de La Rochelle, France Faculty of Electrical Engineering and Computer Science, Koroška cesta, Slovenia University of Salerno, Italy Polytechnic School of Tunis, Tunisia Oporto Global University, Portugal ISCAP/Polytechnic Institute of Porto, Portugal Universidade Europeia de Lisboa, Portugal Kuwait University, Kuwait University of Ulster, UK Aalto University, Finland Universidad Nacional de Educación a Distancia, Spain Universidade Federal de Santa Catarina, Brasil Aalto University, Finland University of Hradec Kralove, Czech Institut Mines-Télécom Paris, France DAV University, Jalandhar, India Universidad de Murcia, Spain ENIM, Ecole Nationale de l’Industrie Minérale Rabat, Morocco Universidade de Vigo, Spain Faculty of Sciences Oujda, Morocco University Lusófona de Humanidades e Tecnologias Lisbone, Portugal Faculty of Computing Sciences, Gulf College Oman, Oman Najah National University, Palestine University Mohamed First Oujda, Morocco Sultan Qaboos University, Muscat, Oman International University of Novi Pazar, Serbia Universidade de Vigo, Spain Ecole d’ingénieur généraliste et high-tech à Paris, France Usak University, Turkey

Organization

Tajullah Sky-Lark Otmane Ait Mohamed Mohammad Hamdan Wail Mardini Francesca Pozzi Filipe Cardoso Abdel-Badeeh Salem Mohammad Al-Smadi Mohamad Badra Amal Zouaq Pedro Guerreiro El Bekkay Mermri Martin Llamas-Nistal Camille Salinesi Jorge Pires Ali Jaoua Osama Shata Abdelkarim Erradi Mohammed Gabli Osama Halabi Rachid Nourine Abdelhafid Bessaid Lehsaini Mohamed Carla Silva John Sahalos Lebbah Yahia Kashif Saleem Amjad Gawanmeh Abdulmalik Al-Salman Olivier Markowitch Tolga Ensari David Baneres Yahya Tashtoush Stephanie Teufel Majida Ali Abed Alasady (Associate Dean) Pierre Manneback Mohammed Benjelloun

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Sustainable Knowledge Global Solutions, USA Concordia University, Canada Yarmouk University, Jordan Jordan University of Science and Technology, Jordan Istituto Tecnologie Didattiche-CNR, Italy Polytechnic Institute of Setubal, Portugal Ain Shams University, Egypt Jordan University of Science and Technology, Jordan Zayed University, United Arab Emirates Royal Military College of Canada, Canada Universidade do Algarve, Portugal University Mohamed First Oujda, Morocco University of Vigo, Spain CRI, Université de Paris1 Panthéon-Sorbonne, France Polytechnic Institute of Castelo Branco, Portugal Qatar University, Qatar Qatar University, Qatar Qatar University, Qatar University Mohammed Premier, Oujda, Morocco Qatar University, Qatar Oran 1 University, Algeria University of Tlemcen, Algeria University of Tlemcen, Algeria University Lusófona de Lisbone, Portugal University of Nicosia, Cyprus Oran 1 University, Algeria King Saud University, Saudi Arabia Khalifa University, United Arab Emirates King Saud University, Saudi Arabia Université Libre de Bruxelles, Belgium Istanbul University, Turkey Universitat Oberta de Catalunya, Spain Jordan University of Science and Technology, Jordan University of Fribourg, Switzerland Tikrit University, Iraq Faculté Polytechnique de Mons, Belgium Faculté Polytechnique de Mons, Belgium

Contents

Mobile Learning Systems’ Functionalities in Higher Education Institutions in Tanzania: Teachers and Students’ Readiness at the College of Business Education . . . . . . . . . . . . . . . . . . . . . . . . . . . Godfrey Isaac Mwandosya, Calkin Suero Montero, Esther Rosinner Mbise, and Solomon Sunday Oyelere

1

TIMONEL: Recommendation System Applied to the Educational Orientation of Higher Education Students . . . . . . . . . . . . . . . . . . . . . . . Antonio Pantoja-Vallejo and Beatriz Berrios-Aguayo

14

Modeling the Acceptance of the E-Orientation Systems by Using the Predictions Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . Rachida Ihya, Abdelwahed Namir, Sanaa Elfilali, Fatima Zahra Guerss, and Mohammed Ait Daoud

27

Virtual Teacher Based Tool for Teaching Context-Free Grammars by Active Pedagogy and eMathTeacher Philosophy . . . . . . . Mohammed Serrhini and Abdelmajid Dargham

32

MOOC of Algorithmic: Elaboration of Content and Difficulties Encountered by Students . . . . . . . . . . . . . . . . . . . . . . . . Abdelghani Babori

40

Content Analysis and Learning Analytics on Interactions of Unsupervised Learners in an Online Learning Environment . . . . . . . Shireen Panchoo and Alain Jaillet

47

Multimedia System for Self-learning C/C++ Programming Language . . . José Galindo, Patricia Galindo, and José María Rodríguez Corral A Recommendation Approach Based on Community Detection and Event Correlation Within Social Learning Network . . . . . . . . . . . . Sonia Souabi, Asmaâ Retbi, Mohammed Khalidi Idrissi, and Samir Bennani

55

65

xi

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Contents

A New Approach to Detect At-Risk Learning Communities in Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meriem Adraoui, Asmaâ Retbi, Mohammed Khalidi Idrissi, and Samir Bennani A New Approach of Integrating Serious Games in Intelligent Tutoring Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Beyyoudh, Mohammed Khalidi Idrissi, and Samir Bennani A Case Study on Teaching a Software Estimation Course . . . . . . . . . . . Marcelo Jenkins and Cristian Quesada-Lopez

75

85 92

Simulation and Analyze of Global MPPT Based on Hybrid Classical-ANN with PSO Learning Approach for PV System . . . . . . . . 102 Ihssane Chtouki, Patrice Wira, Malika Zazi, Houssam Eddine Chakir, and Bruno Collicchio The Contribution of Big Data to Achieving a Competitive Advantage: Proposal of a Conceptual Model Based on the VRIN Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Abdelhak Ait Touil and Siham Jabraoui Robust Domain Adaptation Approach for Tweet Classification for Crisis Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Reem ALRashdi and Simon O’Keefe Classification of Learners During an Educational Simulation: Case Study on a Stock Management Simulator . . . . . . . . . . . . . . . . . . . 135 Denis Guibert, Thibaud Serieye, and Nicolas Pech-Gourg A Machine Learning Model to Early Detect Low Performing Students from LMS Logged Interactions . . . . . . . . . . . . . . . . . . . . . . . . 145 Bruno Cabral and Álvaro Figueira Energy Consumption Forecasting in Industrial Sector Using Machine Learning Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Mouad Bahij, Mohamed Cherkaoui, and Moussa Labbadi Analysis of ESP32 SoC for Feed-Forward Neural Network Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Kristian Dokic, Dubravka Mandusic, and Bojan Radisic Graph Schema Storage in SQL Object-Relational Database and NoSQL Document-Oriented Database: A Comparative Study . . . . . 176 Zakariyaa Ait El Mouden, Abdeslam Jakimi, Moha Hajar, and Mohamed Boutahar Sentiment Analysis on Twitter to Measure the Perception of Taxation in Colombia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Mónica Katherine Durán-Vaca and Javier Antonio Ballesteros-Ricaurte

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Big Five Personality Traits and Ensemble Machine Learning to Detect Cyber-Violence in Social Media . . . . . . . . . . . . . . . . . . . . . . . 194 Randa Zarnoufi and Mounia Abik A Comparative Study of Feature Selection Methods for Informal Arabic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Soukaina Mihi, Brahim Ait Ben Ali, Ismail El Bazi, Sara Arezki, and Nabil Laachfoubi A Review of Engines for Graph Storage and Mutations . . . . . . . . . . . . 214 Soukaina Firmli and Dalila Chiadmi Towards an Improved CNN Architecture for Brain Tumor Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 Hajji Tarik, Masrour Tawfik, Douzi Youssef, Serrhini Simohammed, Ouazzani Jamil Mohammed, and Jaara El Miloud Machine Learning for Forecasting Building System Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Mountassir Fouad, Reda Mali, and Mohamed Pr.Bousmah Methodologies for Large SAP ERP Projects Implementation . . . . . . . . . 243 Fabiane Ayres, Franklin Ayres, and Alexandre Barão A Software Testing Strategy Based on a Software Product Quality Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 Narayan Debnath, Carlos Salgado, Mario Peralta, Daniel Riesco, Luis Roqué, Germán Montejano, and Mouna Mazzi A DSL-Based Framework for Performance Assessment . . . . . . . . . . . . . 260 Hamid El Maazouz, Guido Wachsmuth, Martin Sevenich, Dalila Chiadmi, Sungpack Hong, and Hassan Chafi Towards a Framework Air Pollution Monitoring System Based on IoT Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Anass Souilkat, Khalid Mousaid, Nourdinne Abghour, Mohamed Rida, and Amina Elomri Connected Objects in Information Systems . . . . . . . . . . . . . . . . . . . . . . 281 Onyonkiton Théophile Aballo, Roland Déguénonvo, and Antoine Vianou The Efficient Network Interoperability in IoT Through Distributed Software-Defined Network with MQTT . . . . . . . . . . . . . . . . . . . . . . . . . 286 Rajae Tamri and Said Rakrak 5G Network Architecture in Marrakech City Center . . . . . . . . . . . . . . . 292 Fatima Zahra Hassani-Alaoui and Jamal El Abbadi

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A Distance Integrated Triage System for Crowded Health Centers . . . . 302 Kambombo Mtonga, Willie Kasakula, Santhi Kumaran, Kayalvizhi Jayavel, Jimmy Nsenga, and Chomora Mikeka Design of RC-Based Low Diameter Two-Level Hierarchical Structured P2P Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Swathi Kaluvakuri, Bidyut Gupta, Banafsheh Rekabdar, Koushik Maddali, and Narayan Debnath Grey Wolf Optimizer for Virtual Network Embedding in SDN-Enabled Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Abderrahim Bouchair, Sid Ahmed Makhlouf, and Yagoubi Belabbas A Small Robotic Step for the Therapeutic Treatment of Mental Illnesses: First Round . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Carlos Martinez, David Castillo, Ruth Maldonado Rivera, and Hector F. Gomez A Accuracy of Classification Algorithms Applied to EEG Records from Emotiv EPOC+ Using Their Spectral and Asymmetry Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Kevin Martín-Chinea, Jordan Ortega, José Francisco Gómez-González, Jonay Toledo, Ernesto Pereda, and Leopoldo Acosta Organizational Model for Collaborative Use of Free and Open Source Software: The Case of IT Departments in the Philippine Public and Private Sectors . . . . . . . . . . . . . . . . . . . . . 343 Ferddie Quiroz Canlas A Framework Supporting Supply Chain Complexity and Confidentiality Using Process Mining and Auto Identification Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Zineb Lamghari, Maryam Radgui, Rajaa Saidi, and Moulay Driss Rahmani 3D Recording and Point Cloud Analysis for Detecting and Tracking Morphological Deterioration in Archaeological Metals . . . . . . . . . . . . . . 362 Alba Fuentes-Porto, Drago Díaz-Aleman, and Elisa Díaz-González Convolutional Neural Network Architecture for Offline Handwritten Characters Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . 368 Soufiane Hamida, Bouchaib Cherradi, Hassan Ouajji, and Abdelhadi Raihani Comparative Study of Methods Measuring Lexicographic Similarity Among Tamazight Language Variants . . . . . . . . . . . . . . . . . . 378 Ikan Mohamed, Abdessamad Jaddar, Aissa Kerkour Elmiad, and Ghizlane Kouaiba

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Comparative Study of DICOM Files Handling Software’s: Study Based on the Anatomage Table . . . . . . . . . . . . . . . . . . . . . . . . . . 390 Zineb Farahat, Mouad Hasni, Kawtar Megdiche, Nissrine Souissi, and Nabil Ngote Fake News Identification Based on Sentiment and Frequency Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Jozef Kapusta, Ľubomír Benko, and Michal Munk Arab Handwriting Character Recognition Using Deep Learning . . . . . . 410 Aissa Kerkour Elmiad Automatic Evaluation of MT Output and Post-edited MT Output for Genealogically Related Languages . . . . . . . . . . . . . . . . . . . . . . . . . . 416 Daša Munková, Michal Munk, Ján Skalka, and Karol Kasaš 3D Objects Learning and Recognition Using Boosted-SVM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 Youness Abouqora, Omar Herouane, Lahcen Moumoun, and Taoufiq Gadi Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQL . . . 436 Youssef Mellah, El Hassane Ettifouri, Toumi Bouchentouf, and Mohammed Ghaouth Belkasmi Privacy by Design and Cybersecurity for Safe, Effective and Reliable Home Health Care for Aging in Place . . . . . . . . . . . . . . . . 442 Helene Fournier, Heather Molyneaux, Irina Kondratova, and Noor Ali Feature Reduction Algorithm for Universal Steganalysis . . . . . . . . . . . . 451 François Kasséné Gomis, Mamadou Samba Camara, and Idy Diop A Composite Framework to Promote Information Security Policy Compliance in Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458 Eric Amankwa, Marianne Loock, and Elmarie Kritzinger Authentication Model Based on JWT and Local PKI for Communication Security in Multi-agent Systems . . . . . . . . . . . . . . . 469 Badr Eddine Sabir, Mohamed Youssfi, Omar Bouattane, and Hakim Allali A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 Zahia Aouabed, Moloud Abdar, Nadia Tahiri, Jaël Champagne Gareau, and Vladimir Makarenkov Optimized Management of the Health Emergency Services Regional Network of Rabat Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . 490 Ibtissam Khalfaoui and Amar Hammouche

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Evolution of Cooperation in E-commerce Based on Prisoner’s Dilemma Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500 Jalal Eddine Bahbouhi and Najem Moussa Quality Measurement Systems in Public Services and E-Government . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506 Hajar Hadi, Ibtissam Elhassani, and Souhail Sekkat Using Ontology and Context-Awareness for Business Process Modelling: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 Jamal El Bouroumi, Hatim Guermah, Mahmoud Nassar, and Abdelaziz Kriouile River Flow Forecasting: A Comparison Between Feedforward and Layered Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 523 Sultan Aljahdali, Alaa Sheta, and Hamza Turabieh An Experimental Artificial Neural Network Based MPP Tracking for Solar Photovoltaic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 Yassine Chouay and Mohammed Ouassaid Mobile Data Collection Using Open Data Kit . . . . . . . . . . . . . . . . . . . . . 543 Patrick Loola Bokonda, Khadija Ouazzani-Touhami, and Nissrine Souissi Two Quantum Attack Algorithms Against NTRU When the Private Key and Plaintext Are Codified in Ternary Polynomials . . . . . . . . . . . . 551 El Hassane Laaji, Abdelmalek Azizi, and Siham Ezzouak Maximum Power Point Tracking of Photovoltaic System Based on Fuzzy Control to Increase There Solar Energy Efficiency . . . . 563 Ahmed Hafaifa, Kaid Imed, Mouloud Guemana, and Abudura Salam A New Technique of Harmonic Currents Extraction Based on a Fuzzy Logic Controller Applied to the PV-SAPF System . . . . . . . . 572 Asmae Azzam-Jai and Mohammed Ouassaid A New TSA-Fuzzy Logic Based Diagnosis of Rotor Winding Inter-turn Short Circuit Fault in Wind Turbine Based on DFIG Under Different Operating Wind Speeds . . . . . . . . . . . . . . . . . . . . . . . . 583 Hamza Sabir, Mohammed Ouassaid, and Nabil Ngote A New Fuzzy Clustering Method Based on FN-DBSCAN to Determine the Optimal Input Parameters . . . . . . . . . . . . . . . . . . . . . 593 Sihem Jebari, Abir Smiti, and Aymen Louati Diagnosis of Brain Tumors in MR Images Using Metaheuristic Optimization Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Malik Braik, Alaa Sheta, and Sultan Aljahdali

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Biometric Person Authentication Using a Wireless EEG Device . . . . . . . 615 Jordan Ortega, Kevin Martín-Chinea, José Francisco Gómez-González, and Ernesto Pereda Quadcopter Attitude Stabilization in a Gyroscopic Testbench . . . . . . . . 621 Yassine El Houm, Ahmed Abbou, Ali Mousmi, and Moussa Labbadi A Novel Multicore Multicasting Scheme for PIM-SM . . . . . . . . . . . . . . 631 Indranil Roy, Banafshah Rekabdar, Swathi Kaluvakuri, Koushik Maddali, Bidyut Gupta, and Narayan Debnath Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639

Mobile Learning Systems’ Functionalities in Higher Education Institutions in Tanzania: Teachers and Students’ Readiness at the College of Business Education Godfrey Isaac Mwandosya1(&), Calkin Suero Montero2, Esther Rosinner Mbise1, and Solomon Sunday Oyelere2 1

2

College of Business Education, Dar es Salaam, Tanzania [email protected], [email protected] School of Computing, The University of Eastern Finland, Joensuu, Finland {calkin.montero,solomon.oyelere}@uef.fi

Abstract. Mobile learning (m-learning) through mobile devices has been playing a significant role in enhancing teaching and learning in higher education institutions (HEIs). However, little is known about the readiness of both teachers and students to extend the traditional face-to-face pedagogy into using their mobile devices for mobile learning. This study, therefore, aimed at investigating the readiness of both teachers and students at the College of Business Education (CBE) for m-learning and the suitable m-learning systems’ functionalities to enhance the teaching and learning. The study applied a survey strategy to obtain the views of both the teachers and students. Using the random sampling technique, 35 teachers and 141 third year bachelor students were selected for the study. The analysis of data was done using statistical package for social sciences (SPSS) descriptive statistics. Results showed the majority of teachers (Mean = 4.5143) and students (Mean = 4.3551) are ready for mobile learning. Keywords: Mobile learning systems
Higher education institutions
Teachers and student’s mobile learning readiness
CBE
Tanzania

1 Introduction The advancement of mobile technologies has sparked mobile learning to be used as an alternative and innovative way to deliver education in higher education institutions [1, 2]. Higher education institutions in emerging countries like Tanzania have in recent years been encouraged as a result to invest in information communication technologies (ICTs) and mobile technology infrastructures to support mobile learning in the teaching and learning environments. The traditional classroom face-to-face teaching and learning have been enhanced by the use of computers and network (electronic learning) to access learning materials and now are being further enhanced by the mobile learning (using mobile devices) decreasing the limitation of the traditional education [3]. This is known as the teaching and learning on the go [4, 5] where teachers and students can be involved in teaching and learning respectively online at any time. The flexibility in © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 1–13, 2020. https://doi.org/10.1007/978-3-030-36778-7_1

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learning by students and the teachers being able to communicate with students in matters of education without barriers of time and place is a huge benefit in the education sector. M-learning has potential benefits including ubiquitous communications, anytimeanywhere learning, cost savings, self-learning and location-based services [6]. Several studies have reported mobile learning in higher education, whereby students and teachers are in a constant exchange of learning materials regardless of time and location [1]. In Tanzania, to capture the benefits of m-learning for example, there are reported a good number of studies linking higher education institutions using mobile learning systems through mobile devices in their teaching and learning environments, one example is a study by Mtega et al. [1]. The m-learning success implementation in higher education in Tanzania is crucial for innovative teaching and learning for nations’ dream of an innovative society, industrialization policy and the Tanzania development vision 2025 [7]. Mobile learning success implementation in higher education institutions include factors, for example, the mobile learning infrastructure, willingness of the management teams in higher education institutions (HEIs) to invest in mobile technologies and another factor important factor is the readiness of teachers and students for mobile learning also getting views of teachers and students of how best they can use their mobile devices in the teaching and learning environments. A study by Tedre et al. [8], identified a number of pitfalls including the infrastructure in Tanzanian higher education institution when attempting to develop e-learning in a Tanzanian higher education institute in Iringa. They identified a number of measures for the successful implementation of e-learning at the Tumaini University, one of the identified measures were the human capacity development and being ready for e-learning with enough Information Technology (IT) knowledge. A study by Lwoga, Tandi [9] had indicated among other things the success factors for the implementation of web-based learning management systems in higher education institutions in Tanzania as being the quality systems and proper infrastructure. Despite that mobile learning systems are in use in higher education institutions in Tanzania to-date, still little is known in Tanzanian higher education environment relating to the readiness of the teachers and students for mobile learning and on the best possible ways to effect mobile learning systems as a pedagogical alternative way of teaching and learning. Furthermore, most of the studies reporting on mobile learning does not elaborate on what are the users’ opinions on the suitable functionalities expected of the mobile learning systems in the contextual requirements of the users before even implemented. The majority of studies report on technology acceptance, perceived usefulness, and intention to use the technology of either the students or the teachers when mobile learning systems or technologies have already been implemented for example, [10, 11]. This study takes a further step of investigating the readiness of both the teachers and students to use their mobile devices for mobile learning and their expectation of the suitable functionalities of a mobile learning system. The aim of this study, therefore, was to investigate the teachers and students’ mobile learning readiness at CBE preceded by investigating their current mobile device usage patterns and finally, to find suitable functionalities of the mobile learning system expected in the mobile learning platform.

Mobile Learning Systems’ Functionalities in HEIs in Tanzania

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In order to meet the objectives of this study, and m-learning to succeed at the CBE, it is necessary to understand the readiness of teachers and students for the mobile learning pedagogy. To this end, the study was set to address three research questions (RQs): RQ1: What are the mobile device usage patterns for the CBE teachers and students in accessing learning contents? RQ2: To what extent are the teachers and students at CBE ready in using their mobile devices for teaching and learning purposes? RQ3: What are the suitable functionalities of a mobile learning system for CBE?

2 Literature Review 2.1

Defining Mobile Learning

Mobile learning systems have been on the increase in higher education institutions accelerating new pedagogical change in the way learning is done [12]. In this aspect, learning is done anywhere anytime through mobile devices connected to the internet through wireless technologies [6]. M-learning is defined and explained in different ways by scholars, for example, Sharples et al. [13] defines m-learning as any learning contexts are created through interaction, and how portable and ubiquitous technologies that can support effective conversations for learning. A definition by El-Hussein and Cronje [14] is that m-learning is a learning environment based on the mobility of technology, the mobility of learners, and mobility of learning that augments the higher education landscape. Both definitions offer a hint that mobile devices and learners are potential for this phenomenon of mobile learning supported by mobile technology. 2.2

Mobile Learning in Higher Education

Mobile learning has proved to be a potential weapon in improving the delivery of education in higher education where students and teachers are able to share educationally related contents through mobile devices [14]. The advancement of mobile technologies coupled with ever-increasing in the manufacturing of the mobile devices like the portable digital assistants (PDAs), mobile phones, tablet PCs, pocket PCs, palmtop computers and personal media players have simplified the possibility for managers in education to integrate mobile learning in education [10]. These mobile devices play a vital role in higher education’s collaboration between teachers and students in the exchange of learning materials, assignments, aiding them to engage online and therefore enhancing their interest [3]. According to Oyelere et al. [15] mobile learning is a strong pedagogical domain and in fact, is a relatively new one. Recent studies have identified the importance of mobile learning for example, in aiding students to learn through podcasts [16], and to assist in student retention in higher education [2]. A study by Foti and Mendez [12] explained mobile learning ability to enhance or support graduate-level occupational therapy program to facilitate students’ achievement.

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Mobile learning has been receiving much attention in higher education institutions in Tanzania recently, a good number of institutions have already implemented mobile learning applications, for example, the Sokoine University of Agriculture (SUA) [1] and the University of Dar es Salaam (UDSM) [17, 18]. The mobile learning applications are geared towards enhancing teaching and learning, especially learning flexibly regardless of the time of day and geographical location. 2.3

Mobile Learning Challenges

The challenges in implementing mobile learning systems in higher education institutions resurface still. In the meantime, while mobile learning offers a variety of options in the education sector, challenges in the acquisition and usage of mobile learning in HEIs in Tanzania and other emerging economies exist for example [2]. A study by Aljuaid et al. [19] narrates that the barriers to mobile learning implementing as cost, technology, access, usability, course design, and lack of acceptance. One of the many challenges facing the mobile learning implementation in HEIs is the readiness of both the teachers and the students to accept and use mobile learning facilities [20]. Cheon [21], stated that to ensure success in learning, the technological readiness of the end-users is an important factor and not only the use of technology. Supported by Mahat [20] who used SMS as an m-learning method accessed by 120 students as respondents to obtain announcements, course information, and quizzes found that these students highly rated the readiness of using m-learning as a part of teaching and learning. Basing on the theory of planned behavior to 177 college students, a study by Osang [22] using structural equation modeling to analyze self-report data revealed that students reasonably accepted the mobile learning readiness. 2.4

Teachers and Students’ Readiness for Mobile Learning

A study by Abas et al. [5] sought to first determine the readiness of learners at Open University of Malaysia before introducing mobile learning. The learners were given a questionnaire and the results showed the willingness to use mobile learning. Another study by Mahat et al. [20], involved trainee teachers to examine the relationship between teachers’ self-efficacy and readiness towards m-learning with the learners’ perception of m-learning. They identified the m-learning as the short messaging service (SMS) from which the questionnaire was sent to the respondents to gather the necessary data for the study. Through a theory of planned behavior (TPB), a study by Cheon [21] investigated the readiness of college students to use m-learning in their courses in the United States. Most of the past studies on mobile learning systems concentrate on finding out the readiness of either teachers or students or both for mobile learning after it has been employed. However, few studies in developing countries like Tanzania have considered obtaining in the first place a combination of both the teachers and students’ readiness on adopting m-learning to explore maximum benefits. The technology acceptance models used in previous studies focused on the readiness of either teachers or students after they have been in use and also, they only looked at users’ perception toward a specific technology’s functionality and characteristics [23]. It is with this in

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mind that an m-learning readiness views survey was conducted among teachers and students of CBE to determine first, their mobile device usage patterns and secondly, their readiness for mobile learning and lastly to investigate their opinions on the suitable mobile learning system to be used at the College of Business Education.

3 Research Methodology 3.1

Study Design

This study used a survey strategy in getting the opinions from both the teachers and students at the College of Business Education, in Tanzania. According to Denscombe [24], survey strategies are used to best effect when the researcher wants factual information relating to groups of people: what they do, what they think, who they are. The questionnaire was the data collection technique. The analysis of data was done using the statistical package for social sciences (SPSS). A survey questionnaire was administered through random sampling, as the instrument for data collection. The questionnaire was administered to solicit responses from the teachers and students. The questionnaire was sent by e-mail to all teachers through CBE emailing system. For students, apart from sending the questionnaires through their emails, a team of 2 research assistants was employed to distribute and collect the questionnaire from the students to ensure that all participate. 3.2

Research Area

This study was carried out in Tanzania at the College of Business Education, it is one of the non-university higher education institutions under the Ministry of Trade and Industries. The CBE has four campuses distributed strategically in four regions namely; Dar es Salaam, Dodoma, Mwanza, and Mbeya. Each of these campuses has own network supported by local wireless and internet services providers (ISPs). Efforts are underway to implement a virtual private network at CBE to join the campuses with quality internet services, the good news for the mobile learning systems’ implementation. 3.3

Population and Sample Size

For this study, a sample of 176 participants was randomly selected including 35 teachers and 141 students from a population of 150 teachers and 300 bachelor III students. Bachelor III students were available during the undertaking of the research at the start of the 2018/2019 academic year the second semester in January 2019. 3.4

Research Instrument and Data Collection

In this study, three external variables which related to mobile learning were identified, namely mobile usage patterns of teachers and students, mobile learning readiness for both teachers and students, and expected mobile learning systems’ functionalities. Two types of questionnaires containing the three mentioned items were prepared. The items related to mobile usage and functionality of the mobile learning system was based on

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the researcher experience and the items for the readiness of mobile learning for teachers and students which were adapted and modified from an instrument by Hussin et al. [25]. A five-point Likert scale that demonstrated the degree of agreement from strongly disagree to strongly agree was applied to capture teachers and students’ perceptions about their readiness for mobile learning. Teachers’ questionnaire had 43 items divided into 6 sections while the students’ questionnaire had 39 items divided into 5 sections. A Likert scale ranging from “1 = strongly disagree” to “5 = strongly agree” was used for the questions that needed the rating from teachers and students on constructs. The collected two types of questionnaires were both entered and analyzed by an IBM SPSS version 23 descriptive statistics. The alpha Cronbach reliability for teachers and students’ readiness towards m-learning were as follows: (.780) for teachers and (.574) for students indicating that the items in the readiness construct were reliable.

4 Findings The results of our study are presented according to the research questions to find out the views of the teachers and students. This was purposely done because they are the main stakeholders in the teaching and learning environment. furthermore, the successful application of m-learning as a pedagogical method largely concerns them to a greater extent. 4.1

Mobile Device Usage Patterns

Teachers and Student’s Usage Patterns of Their Mobile Devices A questionnaire administered to 35 teachers and 141 students of CBE on the usage patterns of their mobile devices revealed the following results: The usage patterns for both teachers and students of CBE shows majority use their mobile devices for sending and receiving short text messages (SMS) and also, sending and receiving calls followed by the use of e-mails and social media. In connection to using for educational purposes, there is a potential since the downloading and reading electronic books shows a significant percentage. Students’ ability to download and read electronic books (e-Books), indicates an opportunity available for the m-learning to be able to facilitate educational contents for the students to access (see Table 1).

Table 1. Usage patterns of the teachers and students’ mobile devices

Send and receive short text messages (SMS) Play educational games Listen/watch the news (BBC, CNN, Sky News, etc.) Download and read e-Books

Teachers (n = 35) 33

Percentage (%) 94%

Students (n = 141) 128

Percentage (%) 91%

6 16

17% 46%

64 73

45% 52%

19

54%

99

70% (continued)

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Table 1. (continued) Teachers (n = 35) Send and receive e-mails 28 Chatting/social media (Facebook, Twitter, 26 WhatsApp, Instagram, etc.) Making and receiving calls 34 Take videos and photos 24 Download and listen to music 10 Browsing the Internet 23 Other usages 7

4.2

Percentage (%) 80% 74%

Students (n = 141) 96 100

Percentage (%) 68% 71%

97% 67% 27% 66% 20%

106 98 96 104 0

75% 70% 68% 74% 0%

Teachers and Students’ Readiness for M-Learning Systems at CBE

The overall mean for the readiness factor for the teachers and students is shown in Table 2. The highest mean obtained was for students’ mobile learning readiness (M = 4.11752, Std. Deviation = 0.939022) followed by the scores for teachers mobile learning readiness (Mean = 3.95428, Std. Deviation = 1.081374). These results show that both teachers and students as respondents were highly rated in the mobile learning readiness as part of the teaching and learning process at CBE. Table 2. Overall mean for teachers and students Number of items Mean Std. Deviation Level Teachers mobile readiness 5 3.95428 1.081374 High Students’ mobile readiness 5 4.11752 0.939022 High

The readiness of teachers and students to use mobile devices in m-learning was a crucial factor leading to the successful application of mobile learning in teaching and learning at CBE. Teachers’ Readiness The majority of teachers agreed to the construct of being willing and ready to use mobile devices for educational purposes and that mobile learning is appropriate for teaching and learning (Table 3). Overall, three items were highly rated by the teachers, for example, the item “I am ready to use mobile devices for educational purposes” (Mean = 4.5143, Std. Deviation = 0.74247) showing that teachers at CBE are ready to use their mobile devices for teaching apart from other usages like social media and others. The mobile devices showed to have necessary features for mobile learning (Mean = 4.0286, Std. Deviation = 1.36092). However, two items, first the mobile communication infrastructure and facilities construct was found to have scored a moderate level response of (Mean = 3.3714, Std. Deviation = 1.13981) and second, the affordability to buy internet data bundles (Mean = 3.4857, Std. Deviation = 1.22165) had moderate level score indicating that the mobile technology infrastructure at CBE should be improved more to suit the mobile learning and success.

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G. I. Mwandosya et al. Table 3. Mobile learning readiness for teachers at CBE

Mobile learning N = Teachers N = Students Mean Mean Std. readiness constructs Students Teachers Deviation Teachers I am ready to use 35 138 4.3551 4.5143 .74247 mobile devices for educational purposes Mobile learning is 35 138 4.3261 4.3714 .94202 appropriate for teaching and studying My mobile devices 35 139 4.2086 4.0286 1.36092 have several features to support mobile learning Mobile 35 139 3.9784 3.3714 1.13981 communication infrastructure and facilities at CBE is adequate to support mobile learning I can afford to buy 35 139 3.7194 3.4857 1.22165 data bundles for mobile learning

Std. Deviation Students .82657

.76572

.89665

.99613

1.21004

Students’ Readiness For the students, the idea of embracing mobile learning is essential so that learning is not restricted to classrooms only. Five significant items were considered for this study (Table 3). Overall, three items were highly rated by the students. The most rated item was the one “I am ready to use a mobile device for educational purposes” (Mean = 4.3551, Std. Deviation = 0.82657), followed by the item “Mobile learning is appropriate for studying anywhere at any time” (Mean = 4.3261, Std. Deviation = 0.76572), and item “My mobile device has several features to support mobile learning” (Mean = 4.2086, Std. Deviation = 0.89665). For students, it shows that they are eager to collaborate with their teachers to use their mobile devices for mobile learning. Just as was for the teachers, the constructs leading to a willingness to shift from constant access of social media to using mobile devices for educational purposes had a high level of score. In general, both the teachers and students are ready and willing for m-learning as one of the pedagogical means for enhancing their teaching and learning at CBE. However, the students had a low mean score (3.9784) on the mobile communication infrastructure and facilities at CBE to support to adequately support mobile learning. The same was observed by teachers as well, meaning that for successful implementation of mobile learning at CBE there is a need for improvement

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of the mobile communication infrastructure in all four campuses of CBE. The affordability to buy internet bundles by the students also had a low mean score (3.7194) (see Table 3 above). 4.3

The Suitable M-Learning Functionalities to Enable Mobile Learning at CBE

The failure to successfully implement and use mobile learning systems in some of the higher education institutions is attributed to lack of capturing users’ needs. One of the research questions were about getting the views of the teachers and students of CBE on the functionalities of the expected m-learning system. Teachers and Students’ Responses According to the responses of the teachers at CBE four items scored high level of the mean, for example, the item “Shared educational resources” (Mean = 4.4857, Std. Deviation = 0.56211), the item “Students and teachers’ forum” (Mean = 4.2059, Std. Deviation = 0.80827, the item “Announcements/information/news” (Mean = 4.5, Std. Deviation = 0.50752) and the item “Help facilities” (Mean = 4.4, Std. Deviation = 0.60391). For the students, the items that scored high levels of the means were “Announcements/information/news” (Mean = 4.1815, Std. Deviation = 0.94543), the item “Posted lessons” (Mean = 4.1145, Std. Deviation = 0.93349), “Help facilities” (Mean = The item “Educational games” (Mean = 3.6563, Std. Deviation = 0.82244). The higher scores for students have obtained in the items “Students’ teacher forum”, “Shared educational resources”, “Monitoring teaching and learning”, “Assessments and grades”, “Assignments, quizzes and exams” and “User authentication/identification/ security”. On the other hand, for both the teachers and students seemingly they did not prefer educational games to be included in the expected mobile learning system due to the low score of the means (Mean = 3.6563 and 3.7895 respectively) (see Table 4 below). Table 4. Teachers’ and Students’ responses to the expected functionalities of the expected mobile learning system Expected functionalities of a mobile learning system Help facilities Announcement/information/news Posted lessons Educational games Students and teachers’ forums Shared educational resources Monitoring teaching and learning Assessments and grades Assignments, quizzes, and exams Social media User authentication/identification/security

Stud. N 138 138 131 133 134 136 135 133 135 136 135

Teach. N. 35 34 32 32 34 35 33 34 34 34 33

Stud. mean 3.8623 4.1815 4.1145 3.7895 4.1791 4.4559 4.3778 4.2632 4.2444 3.7353 4.0815

Teach. mean 4.4 4.5 3.9688 3.6563 4.2059 4.4857 3.9091 3.8824 3.9706 3.7941 3.8485

Stud. Std. Dev. 1.31356 0.94543 0.93349 1.20008 0.94869 0.73904 0.83636 0.93659 0.85052 1.29521 0.99290

Teach. Std. Dev. 0.60391 0.50752 0.82244 1.03517 0.80827 0.56211 1.04174 1.12181 1.11424 1.20049 0.97215

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5 Discussions M-learning in Tanzanian higher education context even though it is a new concept, is promising to improve the teaching and learning. The access to a variety of educational materials without restrictions of time and place is a motivation for students as well as for the teachers. Through this study, we have found that at the College of Business Education in Tanzania both the teachers and students are ready for mobile learning according to their responses. The study by Mtega et al. [1] in Tanzanian Sokoine University, had involved teachers and students to exchange learning materials through SMS, but this study, through the identified preferences of a suitable mobile learning functionalities, the exchange of learning contents among teachers and students could provide an application that is more flexible. This study has found out both teachers and students are good users in communicating through SMS. The research questions to fulfill the objectives of the study were related to, 1. The mobile device usage patterns for the CBE teachers and students in accessing learning contents, 2. The readiness of both the teachers and students at CBE in using their mobile devices for teaching and learning and 3. The suitable functionalities of expected mobile learning system at CBE. In that order, the pattern of mobile devices usage by teachers and students showed that playing educational games were found to be less popular for both the teachers 17% and for students 45% meaning that the development of an m-learning system at CBE may avoid the use of educational games or else special attention should be taken to ensure the successfulness of educational games in the mlearning system. M-learning is still at its infancy stages in the higher education institutions in Tanzania. Several universities and other HEIs including the College of Business Education are slowly embarking on integrating ICTs and m-learning to enable the access and communication of learning contents or educational resources be available anywhere anytime through mobile devices [1, 17, 18]. There have been some challenges though, on the acquired m-learning systems and learning management systems (LMS) reported in several HEIs in Tanzania [10, 11]. The challenges in implementing m-learning systems and other related mobile educational tools have at large been attributed to being acquired at the time when teachers and students are not ready for such technologies [26]. Another challenge, for example, relates to the ability to constantly afford to have enough bundles internet bundles in mobile devices due to economic hardships. The challenges of affordability of buying internet bundles at the time both the teachers and students are at the campuses can be solved for example, by asking the mobile network operators to consider having special affordable rates specifically for the learning institutions to enable teachers and students to use in educational purposes smoothly. Our study has therefore highlighted on investigating to both the teachers and students’ views on their willingness and readiness for m-learning and the suitable functionalities of the expected mobile learning system. The results have shown that both teachers and students at CBE are not only ready for m-learning at CBE but also eager to share their educational contents among themselves. On the other hand, the views of teachers and students on the functionalities of the expected m-learning are basically the

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requirements from them for any m-learning system at CBE to be successful [27] therefore reducing some of the challenges of improper functionalities of m-learning systems [9]. The investigation of the mobile devices usage patterns of the teachers and students had revealed some interesting hints on the type of m-learning system suitable for teachers and students at CBE. For example, both teachers and students have shown they frequently use SMS for communication (94%) and (91%) respectively and this hints that the expected m-learning system can have the provision of SMS as one its features for the functionalities in teaching and learning [1]. Furthermore, the usage patterns on browsing the internet, e-mails, and social media are crucial issue to be considered by developers such that similar functionalities in m-learning system could provide a type of the system which is easy to use, and perceived ease of use as described by requirements of the users [27] and therefore to be easily accepted and utilized well.

6 Conclusion This research work is one of the contributions to the ongoing discussions on using technologies in enhancing teaching and learning in HEIs through the use of mobile devices for m-learning in HEIs in Tanzania. By involving more than one stakeholder in finding out the views enabled the discovery of a wider spectrum of the requirements for the future development of suitable m-learning systems. For example, both the teachers and students expressed their desire for shared educational resources but differed in the item relating monitoring of teaching and learning where teachers gave low positive response compared to the students who gave this construct high response, this was a bit strange as it was expected teachers will be more positive with the construct than the students. M-learning has a huge potential to transform and revolutionize the learning process. The abundance availability and affordable mobile gadgets such as the handphones, Personal Digital Assistants, Smartphones, and iPod not only allow users to communicate anytime anywhere, or in entertainment (music, movies, etc.) but they support m-learning [28]. This study has some limitations. First, it was undertaken in one higher education college out of about more than 50 higher education institutions in Tanzania. The results in this study do not necessarily apply to other colleges in Tanzania with different learning environment and policies. Furthermore, during the study the available students at the College were only third-year bachelor students, other students were in fields and on leave something that was not anticipated before. Other students would have provided extra different views on the suitable functionalities of mobile learning to cover a wider spectrum of the views. Also, since the study was done at CBE, the situation would have been observed differently if more HEIs were involved. Furthermore, we recommend future works to be geared towards developing mobile learning systems according to the users’ preferences and then develop accordingly education tools in different disciplines to capitalize the features of mobile education (for example programming tools for programming students, accounting tools for accounts students, and marketing tools for students studying marketing, etc.).

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References 1. Mtega, W.P., Bernard, R., Msungu, A.C., Sanare, R.: Using mobile phones for teaching and learning purposes in higher education: a case of the Sokoine University. In: The 5th UbuntuNet Alliance Annual Conference, Dar es Salaam (2012) 2. Oyelere, S.S., Paliktzoglou, V., Suhonen, J.: M-learning in Nigerian higher education: an experimental study with Edmodo. Int. J. Soc. Media Interact. Learn. Environ. 4(1), 43–62 (2016) 3. Gikas, J., Grant, M.M.: Mobile computing devices in higher education: student perspective on learning with cellphones, smartphones & social media. Internet High. Educ. 19, 18–26 (2013) 4. Shonola, S.A., Joy, M., Oyelere, S.S., Suhonen, J.: The impact of mobile devices for learning in higher education institutions: Nigerian University case study. Int. J. Mod. Educ. Comput. Sci. 8, 43–50 (2016) 5. Abas, Z.W., Peng, C.L., Mansor, N.: A study on learner readiness for mobile learning at Open University Malaysia. In: IADIS International Conference Mobile Learning, Barcelona, Spain (2009) 6. Young, J.R.: Smartphones on campus: the search for “killer” apps. Chron. High. Educ. 86– 88 (2011) 7. The United Republic of Tanzania: Tanzania Development Vision 2025. Ministry of Finance and Planning, Dar es Salaam (2000) 8. Tedre, M., Ngumbuke, F., Kemppainen, J.: Infrastructure, human capacity, and high hopes: a decade of development of e_learning in a Tanzanian HEI. Revista de Universidad y Sociedad del Conocimiento (RUSC) 7(1), 1–14 (2010) 9. Lwoga, E.T.: Critical success factors for adoption of web-based learning management systems in Tanzania. Int. J. Educ. Dev. Using Inf. Commun. Technol. 10(1), 4–21 (2014) 10. Mtebe, J.S., Raisamo, R.: Challenges and instructors’ intention to adopt and use open educational resources in higher education in Tanzania. Int. Rev. Res. Open Distance Learn. 15(1), 249–271 (2014) 11. Mtebe, J.S., Raisamo, R.: Investigating students’ behavioral intention to adopt and use mobile learning in higher education in East Africa. Int. J. Educ. Dev. Using Inf. Commun. Technol. 10(3), 4–20 (2014) 12. Foti, M.K., Mendez, J.: Mobile learning: how students use mobile devices to support learning. J. Lit. Technol. 15(3), 58–78 (2014) 13. Sharples, M., Arnedillo-Sánchez, I., Milrad, M., Vavoula, G.: Mobile learning: small devices, big issues. In: Technology-Enhanced Learning: Principles and Products, pp. 1–20. Springer, Dordrecht (2009) 14. El-Hussein, M.O., Cronje, J.C.: Defining mobile learning in the higher education landscape. Educ. Technol. Soc. 13(3), 12–21 (2010) 15. Oyelere, S.S., Suhonen, J., Shonola, S.A., Joy, M.S.: Discovering students mobile learning experience in higher education in Nigeria. In: Frontiers in Education 2016 Conference, Erie, PA (2016) 16. Parsons, G.: Information provision for Higher Education distance learners using mobile devices. Electron. Libr. 28(1), 231–244 (2010) 17. Mtebe, J.S., Dachi, H., Raphael, C.: Integrating ICT into teaching and learning at the University of Dar es Salaam. Distance Educ. 32(2), 289–294 (2011) 18. Mtebe, J.S., Kondoro, A.W.: Using mobile Moodle to enhance Moodle LMS accessibility and usage at the University of Dar es Salaam. In: IST-Africa 2016 Conference Proceedings, Dar es Salaam (2016)

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19. Aljuaid, N.M.F., Alzahrani, M.A.R., Islam, A.Y.M.A.: Assessing mobile readiness in Saudi Arabia higher education; an empirical study. Malays. Online J. Educ. Technol. 2(2), 1–14 (2014) 20. Mahat, J., Ayub, A.F.M., Wong, S.L.: An assessment of students’ mobile efficacy, readiness, and personal innovativeness towards mobile learning in higher education in Malaysia. Soc. Behav. Sci. 64(9), 284–290 (2012) 21. Cheon, J., Lee, S.C.S.M., Song, J.: An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Comput. Educ. 59, 1054–1064 (2012) 22. Osang, F.B., Ngole, J., Tsuma, C.: Prospects and challenges of mobile implementation in Nigeria: case study National Open University. Harare, Zimbabwe (2013) 23. Benbasat, I., Barki, H.: Quo Vadis, TAM? J. Assoc. Inf. Syst. 8(4), 211–218 (2007) 24. Denscombe, M.: The Good Research Guide: For Small-Scale Social Research Projects, p. 25. McGraw-Hill, Leicester (2014) 25. Hussin, S., Manap, M.R., Amir, Z., Krish, P.: Mobile learning. In: APAC M-LEARNING Conference, Bandung (2011) 26. Lwoga, E.T.: Making learning and web 2.0 technologies work for higher learning institutions in Africa. Campus-Wide Inf. Syst. 29(2), 90–107 (2012) 27. Mwandosya, G.I., Montero, C.S.: Towards a mobile education tool for higher education teachers: a user requirements definition. In: Proceedings of the 2017 IEEE Science Technology & Innovation Africa Conference, Cape Town (2017) 28. Oyelere, S., Suhonen, J., Sutinen, E.: M-learning: a new paradigm of learning ICT in Nigeria. Int. J. Interact. Mob. Technol. 10, 35–44 (2016)

TIMONEL: Recommendation System Applied to the Educational Orientation of Higher Education Students Antonio Pantoja-Vallejo(&)

and Beatriz Berrios-Aguayo

University of Jaen, Jaen, Spain {apantoja,bberrios}@ujaen.es

Abstract. The needs derived from the educational orientation have increased to the extent that the number of students enrolled in the schools of Higher Education has been higher. For this reason, numerous digital tools are emerging that seek to respond to these needs, especially in the form of Web pages and platforms with diverse content, which are still traditional adaptations to the digital world. The TIMONEL Recommendation System (RS) is designed with an innovative and differentperspective, the result of research in several European universities (University of Jaen, University of Granada, The Polytechnic Institute of Coimbra and Queen Mary University of London)., Recommendation System (SR) is designed, the result of research in several European universities, which was created with the aim of becoming an advisory tool in the area of educational guidance and as support to the tutorial action carried out by faculties and teachers within the Tutorial Action Plans that are being developed in all the universities as part of the process of the European Higher Education Area. This communication presents the system and its future applicability that will make it possible to offer collaborative answers to the questions of university students, as well as graduates, in matters related to academic, personal and professional guidance. Keywords: Recommendation system
Orientation
Needs
Higher education

1 Introduction The growth of orientation needs in recent years when the number of university students with different profiles has increased exponentially is a reality. This growth, together with the appearance of new forms of approach to the student collective, through Information and Communication Technologies (ICT), is assuming the creation of recommendation systems focused on the educational context (Casali et al. 2011; López et al. 2016). The search to find those reasons why different needs in learning, training, job search, aspects of personal life, etc., have been presented at the university level, has been the main objective of numerous investigations (Al-Qirim et al. 2018; ÁlvarezRojo et al. 2011; Guilbault 2018; Hablich Sánchez et al. 2018). The absence of a quality educational guidance service that offers students advice on various academic, © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 14–26, 2020. https://doi.org/10.1007/978-3-030-36778-7_2

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personal and professional aspects is the main need detected among students and teachers of Higher Education (Gairín Sallán et al. 2004). The support and tutoring to the students by the teachers, or in this particular case of the advisers or tutors, through ICT is still a challenge to be achieved. Whether due to the ignorance on the part of the tutors of the handling of the new digital systems created to have a greater approach with the students, the lack of training in subjects that derive from their use, the apathy on the part of the same ones to investigate in new digital pedagogies, or simply the lack of institutional strategies for their development, the truth is that there has been a stagnation in terms of diversification in the use of new generation applications and tools to support orientation (García Torres et al. 2018; McGarr and Gavaldon, 2018; van der Wende and Beerkens 2010). Vajargah et al. (2010) for their part, observed four risks or obstacles encountered when the student or teacher uses ICT in the university context: (a) The inadequate use of ICT in curricular decisionmaking in education higher on the part of the teaching staff. (b) The lack of training and active management of ICT for curricular development by university-level teachers (c) Difficulty understanding ICT as a necessary factor or facilitator for the curricular development of higher education. (d) Difficulty in the development of curricular activities using ICT as the main tool. Another reason why there are needs among students mainly international in nature, is the poor collaboration between universities perceived by students who travel to study at foreign universities (Aubert Bonn et al. 2017) These students determine that the main problem lies in the absence of a common content between universities, which would facilitate the transition from one to the other. While the proper use of ICT in issues related to internationalization, it can exponentially improve the quality of the services received by foreign students at the reception universities, as well as improving digital skills in students (Aguaded Gómez and Pozo Vicente 2009). On the other hand, the use of ICT in university orientation is supported by the large amount of information and resources in the network, commonly used by students, regardless of the greater or lesser application of the same to their subjects by the teachers. This is a usual practice that reinforces the best knowledge of the subjects, through the information provided by the Web portals, being unusual forums and chat (Michavila et al. 2003) even though these are more collaborative and interactive. Another interesting option is e-learning applied to tutoring and learning (FernándezJiménez et al. 2017). In any case, the use of ICT implies greater dedication for the teachers and in the students a training in collaborative work that they lack. These basic questions lead us to conclude that more attention is needed from the universities in relation to these issues, as an intermediate step necessary for the main ICT applications and tools to take hold as part of the teaching and tutorial work (Pantoja-Vallejo and Campoy-Aranda 2009). The research on which this communication is based is part of the TIMONEL R & D Excellence Project approved in the 2016 call of the Ministry of Economy and Competitiveness of Spain (Ref. EDU2016-75892-P). The TIMONEL project includes three phases, among which phase 3 of the SR is created. Prior to its creation, an extensive

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detection of the needs of the group to which the system has been assigned has been carried out for a period of two years. The aim of this article is to present the presentation of the TIMONEL recommendation system and its practical application in the European university context, serving as a showcase for future usability.

2 Recommendation Sistems Within the ICT landscape to which we referred earlier, SRs constitute at the arrowhead of applications that entail a double component: advanced computer systems and shared information management. In this sense, SR can be defined as a digital tool that offers users results after a specific search for information close to their needs (Niinivaara 2004). There are numerous techniques to be used depending on the results that their creators expect the users to reach (Montaner et al. 2003). You can talk about collaborative filtering systems, content-based filtering, knowledge-based filtering and hybrid systems. Currently, we can find multi-agent systems that allow the development of others with more complex designs that are used essentially for SR creation. This type of technology is relevant when the user creates their profile, which will self-feed the system; provide it with information that can be collected from various sources; and generate support for users in the least number of steps, through scalable, open and secure systems. At present, these types of computer systems have become evident in the educational context, given the diverse needs that have appeared in it. For example, Bustos-Lopez et al. (2015) presented a type of system architecture that would serve for any educational SR regardless of the recommendation it offered. Some of the new SRs that have appeared, offer besides an academic support, a learning. This would be the case of the proposal of Hsu et al. (2013), who presented a system of mobile language learning based on personalized recommendations. It offered a mechanism of recommendation of how to perform a type of reading of the foreign language (English) to guide the students to read the texts based on their preferences and levels of knowledge, taking into account the tastes of the rest of the student user of the system. For their part, Pera et al. (2011) presented the SR called PBRecS, which was based on the recommendation of books through the social interactions and personal interests of all users of that system. Poy and Gonzales-Aguilar (2014) presented a type of SR based on MOOCs (open and massive online courses), tools that would unite neologism e-learning and social network models, as the main responsible for interaction and learning at distance. The aforementioned study highlights the analysis carried out on the educational SRs, which, according to the authors, should be based on four critical factors: the design of the software following certain pedagogical principles, the needs from which it is based and what is generated by the system, the scope that the system can achieve, and the business that can be created once the system is developed based on the quality of the answers offered. Finally, it is relevant to mention Arroyave et al. (2016), Castellano et al. (2007), Hernández Calle and Pernet González (2013), Vialardi et al. (2009), who

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developed SR to respond to certain profiles of students, who present needs and who require guidance in the academic, personal or professional dimensions (Arroyave et al. 2016).

3 Architecture of the Recommendation System TIMONEL (SR based on needs for guidance and tutoring of students and university graduates) is part of the collaborative filtering of the needs of users. Its objective is to carry out an academic, personal and professional advice. Through the system it is intended that users save time in the search for information, achieving an effective tool for students and university graduates. It is available at http://www.timonel.net. We have opted for a client + server architecture, so that the server acts as a REST API to which the rest of the systems connect (Fig. 1).

Fig. 1. Architecture of the TIMONEL recommendation system

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This architecture has several advantages, among which are: • It is easier to perform unit tests, since the API tests are easily automated. This results in more stable and error-free applications. • The server is less heavy, so it can serve more users simultaneously. • Scalability of the system is easier by adding new nodes • The response time of the interface is reduced, resulting in applications similar to desktops • The integration of other platforms such as mobile applications or the interconnection with other applications is simply reduced to the implementation of the communication protocol (API) to be defined. Interconnection. 3.1

Technologies Used

It requires a technological system based on the Client - Server paradigm, already mentioned. For this, it has been chosen to create a technological ecosystem that can provide maximum flexibility for later use, so that the architecture is differentiated into two large layers: • Backend: Server with MVC methodology (Vista Controller Model) based on Python & Django technology, and using Django Rest Framework to export a complete RESTFUL API that can be used by any external system. • Frontend: Frontal web under the SPA paradigm (Single Page App) based on Angular8 technology, it connects to the backend API to obtain all its functionality. Django Framework (Backend). Django is the most used framework for the development of web applications for Python. It is based on the model-view-controller model, which advocates separating the domain objects from the visualization software and the business logic required by the application. Django has a series of tools for conducting unit tests that allow you to test applications easily and quickly. In this way, both at the application level and at the component level, tests can be performed to ensure the correct functioning of the software. Both Django and Python have a vibrant community of developers, which makes the development of specific utilities much easier than in other languages or platforms since there is a much wider ecosystem available than in these others. Python & Django together with Django Rest Framework creates a development environment under the MVC methodology: • Model: Represents the database and the relationships between the different entities. It will store all the information of the Recommendation System, and allow access to related information. This layer will be responsible for creating the data structure, storing, consulting, modifying or deleting the information. • View: This layer is responsible for publishing the RESTFUL API, and therefore will be responsible for communicating with the outside of the server following the following flow:

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• Receive request: Categorizing what type of request is and what data is received to make such a request. • Process request: Executing the controller processes with the necessary data and receiving the response from these processes. • Send response: Transforming the output of the controller processes to the necessary formats in the API, usually in JSON format (JavaScript Object Notation). • Controller: This layer is responsible for carrying out all the business logic. It will create all the necessary processes for the complete functioning of the system and will be related to the View and to the Model to perform its functions. The Backend structures all the recommendations and problems through decision trees that help to structure the information, and also facilitate the user to navigate through it. A profile of each user is created in which recommendation information is stored, and statistical information of interest is also stored in each node of the decision tree. By means of this statistical information it will be possible to make a decision as to which branches of the tree there is more demand, and therefore, they require more depth of branching. In each recommendation, the Backend will generate a document in PDF format to facilitate the download by the user, where you can access the different recommendations of the Recommendation System and the experiences of other users, creating a history of recommendations provided to users. Angular Framework (Frontend). Angular is a framework developed by Google based on JavaScript that allows the development of complex dynamic web applications. The source code of the applications developed in Angular is executed in browsers and allows to make web applications based on the REST paradigm. Therefore, the presentation layer is completely decoupled from the server, which translates into faster interfaces, cleaner code and easier maintenance. Thanks to Angular it is possible to program web interfaces with response times similar to those of desktop applications resulting in much more usable applications that significantly improve the user experience. Angular 8 allows us to create a front SPA that connects in real time to the API published by the Backend. The front application is based on components, and its main mission is to create a working environment for users where the usability of the system is prioritized. It uses the programming language TypeScript that through the compilers of Angular is converted into an application formed by Javascript + HTML + CSS, which can be executed in any modern browser, both in personal computers and mobile devices. Interconnection. To connect the Backend layer with the Frontend layer, an automatic Angular generator is used to create services connected to each endpoint of the backend API. The automated generator is called YASAG and supports as input a document in OpenAPI 2.0 format, and as an output generates Angular services for each endpoint. To generate the document in OpenAPI 2.0 format, the backend uses the YASG library, which allows you to create a standard OpenAPI 2.0 document and also uses a Swagger front user interface to facilitate the development task.

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4 Applicability of the Recommendation System Based on the design of the system, the following phases were established: • • • •

Phase Phase Phase Phase

1: 2: 3: 4:

Development and layout of the software’s graphic aspect. Development of system functionalities. Testing phase in the servers. Pilot test with the students of the University of Jaén (Spain).

The interface of the system was designed so that the user could navigate through it easily, looking like an intuitive system at a glance. It was crucial in the design phase that the first screens collected the user’s personal information (Fig. 2).

Fig. 2. Introduction of personal data to the system

Next, the system asks the user to determine in which area his problem is located (academic, personal or professional). Next, the user will inform about the academic stage in which the problem is located and within the stage, it will try to specify at what exact moment (Fig. 3).

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Fig. 3. Example of contextualization of the problem

Once the situation is contextualized, the user is presented with a series of problems based on the information provided. This should select which is more appropriate or the one that best defines your need, within the list offered or select which is not any of them and continue with the search (Fig. 4).

Fig. 4. Example of problem selection.

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Finally, TIMONEL, based on the selected problem, processes a series of recommendations, selecting the user that best suits their requirements. The recommendations have been created carefully by a team of researchers specialized in each of the topics addressed by the SR. The system has the ability to provide the most user-friendly options, according to the information provided by it, functioning as a multi-agent intelligent system. In addition, you will be able to observe the recommendations of other users of the system who have previously completed the information search steps, thus giving a continuous feedback of information (Fig. 5).

Fig. 5. Example of recommendations given by the system and selection of recommendations from other users.

Finally, the user will have the possibility to leave their opinions to help other people who access to TIMONEL and, at the same time, will receive communication from the SR so that they can reinforce, change or improve them in order to achieve a true collaborative environment. For the latter option, a maximum period of two weeks is foreseen. If the user does not leave such opinion and recommendation, will be informed via email asking him to do so since the system feeds back the recommendations that users leave in it (Fig. 6).

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Fig. 6. Evaluation of the recommendation received by the system

Nowadays, the research team works on the design of the website and how it handles the main features: • Marine metaphor: In TIMONEL the user is the protagonist and all the pages that compose the Web have the present process of guiding the functioning of the metaphor that accompanies the seafarer, knowing that the user feels part of my mind and the motivation. • Card-based layout design, which makes the page look bad and focus on those concepts that are considered to be of greater interest and make it more responsive. • Speed of operation: For it there is no need for superfluous elements that can slow down the load of the pages. • Baseline images, based on interlaced scanning, which makes it easier for the user to have a previous view of the same, since it is of less quality. If so the interest and attention. • User experience (UX), known as microinteractions or effective animation by touch mode and support to the user experience. • Infinite scrolling: It is present on my web pages and facilitates a better visualization and reading for the user, as it only has to slide through the page without the need to click giving continuity to the information. • Updated gif design as a way of presenting certain information and information automatically.

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The system administration pages on the Web prototype are all translated into English, and are in a previous phase, so they do not include images in this language. Also, it is preparing an App for the mobile.

5 Conclusions and Future Work In this article we have presented the architecture and the implementation of SR TIMONEL. As for the architecture, it is based on a multi-agent system allowing flexibility in the work. In addition, it favors the incorporation of heterogeneous information from different repositories. The digital tool presented makes it possible to respond to the diverse academic, personal and professional needs of Higher Education students contextualized in European universities. As future work, we are already working on the analysis of generated texts, through what is known as opinion mining and feelings analysis (Opinion Mining and Sentiment Analysis - Pang and Lee 2008). In a special way, it is especially interesting to filter the information and eliminate what is irrelevant to the user’s feelings. For this it is necessary to process the natural language reflected in their opinions through the analysis of feelings (sentiment analysis), aspects such as the classification of the polarity of feelings or subjectivity expressed in a text. The research team already has previous experience in this subject (Blanco et al. 2011).

References Aguaded Gómez, J.I., Pozo Vicente, C.: Erasmus students in the tower of babel. Foreign language learning based on communicative competencies and the use of ICT. TESI 10(2), 377–411 (2009). http://rabida.uhu.es/dspace/bitstream/handle/10272/6315/Los_alumnos_Eras mus.pdf?sequence=2 Al-Qirim, N., Tarhini, A., Rouibah, K., Mohamd, S., Yammahi, A.R., Yammahi, M.A.: Learning orientations of IT higher education students in UAE University. Educ. Inf. Technol. 23(1), 129–142 (2018). https://doi.org/10.1007/s10639-017-9589-y Álvarez-Rojo, V., Romero, S., Gil-Flores, J., Rodríguez-Santero, J., Clares, J., Asensio, I., Salmeron-Vilchez, P.: Necesidades de formación del profesorado universitario para la adaptación de su docencia al Espacio Europeo de Educación Superior (EEES). Relieve 17(1) (2011). https://www.uv.es/RELIEVE/v17n1/RELIEVEv17n1_1.htm Arroyave, M.R.M., Estrada, A.F., González, R.C.: Modelo de recomendación para la orientación vocacional basado en la computación con palabras. Int. J. Innov. Appl. Stud. 15(1), 80 (2016). https://search.proquest.com/openview/bb6f54405ab1de512ee739db34aba36d/1?pq-origsite= gscholar&cbl=2031961 Aubert Bonn, N., Godecharle, S., Dierickx, K.: European universities’ guidance on research integrity and misconduct. J. Empir. Res. Hum. Res. Ethics 12(1), 33–44 (2017). https://doi. org/10.1177/1556264616688980 Blanco, E., Martínez-Santiago, F., Pantoja-Vallejo, A.: Análisis automático de emociones en la Red Internacional e-Culturas. Revista Electronica de Investigacion Y Docencia 5, 53–68 (2011) Bustos-Lopez, M., Vázquez-Ramírez, R., Alor-Hermández, G.: An architecture for developing educational recommender systems. Res. Comput. Sci. 106, 17–26 (2015)

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Casali, A., Gerling, V., Deco, C., Bender, C.: Sistema inteligente para la recomendación de objetos de aprendizaje. Revista Generación Digital (9) (2011). http://www.ariadne-eu.org/ Castellano, E.J., Martínez, L., Barranco, M., Pérez Cordón, L.G.: Recomendación de perfiles académicos mediante algoritmos colaborativos basados en el expediente. In: Conferência IADIS Ibero-Americana (2007). https://sinbad2.ujaen.es/sites/default/files/publications/Cas tellano2007_IADIS.pdf Fernández-Jiménez, M., Mena-Rodríguez, E., Tójar-Hurtado, J.: Funciones de la tutoría en e-learning: Estudio mixto de los roles del tutor online. Revista de Investigación Educativa 35 (2), 409–426 (2017). https://doi.org/10.6018/rie.35.2.273271 Gairín Sallán, J., Feixas i Condom, M., Guillamón Ramos, C., Quinquer Vilamitjana, D.: La tutoría académica en el escenario europeo de la Educación Superior. Revista Interuniversitaria de Formación Del Profesorado (49), 61–78. (2004). https://dialnet.unirioja.es/servlet/articulo? codigo=1057097 García Torres, I., Plaza Vargas, A., Zurita Hurtado, H., Hurtado, H.Z.: ICT in structured programming learning through constructivist techniques for education. J. Sci. Res.: Revista Ciencia E Investigación, 3(CITT2017), 69–71 (2018). http://doi.org/10.26910/issn.25288083vol3issCITT2017.2018pp69-71 Guilbault, M.: Students as customers in higher education: the (controversial) debate needs to end. J. Retail. Consum. Serv. 40, 295–298 (2018). https://doi.org/10.1016/J.JRETCONSER.2017. 03.006 Hablich Sánchez, F.C., Navarro Sangurima, D.D., Toala Rocuano, I.I.: Impacto positivo en la transformación de la Educación Superior. Dominio de Las Ciencias 4(1), 673–708. (2018). https://dialnet.unirioja.es/servlet/articulo?codigo=6657429 Hernández Calle, A., Pernet González, R.: Prototipo de un sistema experto de orientación vocacional (SEORIV). ARTSEDUCA (5) (2013). https://dialnet.unirioja.es/servlet/articulo? codigo=4339757 Hsu, C.-K., Hwang, G.-J., Chang, C.-K.: A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Comput. Educ. 63, 327–336 (2013). https://doi.org/10.1016/J.COMPEDU.2012.12.004 López, M.B., Montes, A.J.H., Ramírez, R.V., Hernández, G.A., Cabada, R.Z., Estrada, M.L.B.: EmoRemSys: Sistema de recomendación de recursos educativos basado en detección de emociones. RISTI - Revista Ibérica de Sistemas E Tecnologias de Informação (17), 80–95 (2016). http://doi.org/10.17013/risti.17.80-95 McGarr, O., Gavaldon, G.: Exploring Spanish pre-service teachers’ talk in relation to ICT: balancing different expectations between the university and practicum school. Technol. Pedag. Educ. 27(2), 199–209 (2018). https://doi.org/10.1080/1475939X.2018.1429950 Michavila, F., García Delgado, J., Alcón Soler, E.: La tutoría y los nuevos modos de aprendizaje en la universidad. Conserjería de Educación, Madrid (2003). https://dialnet.unirioja.es/servlet/ libro?codigo=5538 Montaner, M., López, B., De la Rosa, J.L.: A taxonomy of recommender agents on the Internet. Artif. Intell. Rev. 19, 285–330 (2003). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10. 1.1.9.2519&rep=rep1&type=pdf Niinivaara, O.: Agent-Based Recommender Systems. https://s3.amazonaws.com/academia.edu. documents/12439/niinivaara04agent.pdf?response-contentdisposition=inline%3B% 20filename%3DAgent-Based_Recommender_Systems.pdf&X-Amz-Algorithm=AWS4HMAC-SHA256&X-Amz-Credential=AKIAIWOWYYGZ2Y53UL3A%2F20191113% 2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20191113T105450Z&X-Amz-Expires= 3600&X-Amz-SignedHeaders=host&X-Amz-Signature= b54ea2cec83e47cffed3ba7fcede20e3005477a5d45de3756331c7dde0621c8e

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Modeling the Acceptance of the E-Orientation Systems by Using the Predictions Algorithms Rachida Ihya1(&), Abdelwahed Namir1, Sanaa Elfilali1, Fatima Zahra Guerss2, and Mohammed Ait Daoud1 1

Laboratory of Information Technologies and Modeling, Department of Mathematics and Computer Science, Faculty of Sciences Ben M’Sik, University Hassan II of Casablanca, Casablanca, Morocco [email protected], [email protected], [email protected], [email protected] 2 Computer Laboratory of Mohammedia, Computer Sciences Department, Faculty of Sciences and Technicals Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco [email protected]

Abstract. Our research work addresses the problem of educational and vocational orientation. Thus, the aims of this study is to set up a prediction model of the acceptance of the E-orientation system “Orientation-chabab.com” by users. We will determinate the factors related to the leads educational and professional orientation of the students. We established a qualitative questionnaire based in TAM theoretical model, sharing with social networks, SMS sending, individual interviews in collaborations with the experts in the field of orientation. After we collected the feedback from our study sample, we use the information gain attribute to reduce our data and we apply applied and compare several predictive Machine Learning algorithms to select the best one with the highest accuracy. Keywords: E-orientation


TAM
Machine learning
Accuracy

1 Introduction The orientation of the post-baccalaureate students in Morocco is a very important step of their curriculum. Making a decision about higher education should be carefully considered and this can only be achieved if the student will have access to any orientation information he needed anytime and anywhere. For all this reason and with the emergence of the Information and Communication Technologies (ICT) [1] we have seen the birth of the E-orientation systems. The Platforms dedicated to orientation, are put online which can help the students in their choice of orientation by leading them to reflect on their skills and abilities. This study aims to predict the acceptance of the E-orientation systems by users that will help us to achieve an acceptance model of E-orientation platform “Orientation-chabab.com” [2]. Since our data is tremendously increasing, it becomes difficult for us to establish a relationship between multiple features, which makes it difficult for us to manually analyzing the data for strategic decision making. Machine learning is a method of data analysis that automates analytical model building. © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 27–31, 2020. https://doi.org/10.1007/978-3-030-36778-7_3

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It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention [3]. Our database includes 256 samples. Using various Machine learning classifier algorithms, the best results were obtained by a J48 with accuracy rates of “80, 46%”. In this paper a review of literature regarding Technology Acceptance Model (TAM) and E-orientation is carried out before the methods used and the results of this study are presented. Finally, we discuss the findings of the analysis and the conclusions.

2 Theoretical Background TAM, proposed by Davis in 1985 [4], explains and predicts the usage of information technologies based on the Theory of Reasoned Action (TRA). The TAM includes perceived usefulness “the degree to which a person believes that a particular technology would enhance his or her performance”, and perceived ease of use «the degree to which a person believes that using a particular technology would be effortless» as the main influencing variables of an individual’s acceptance of information technologies. The acceptance and the usage of the platform “orientation-chabab.com” [2] have been examined using TAM. To understand the behavior of the individual towards the orientation systems, it is essential to research for the factors which explain the users’ acceptance of E-orientation systems. The next section determines the factors affecting users to accept E-orientation systems according to the TAM.

3 Methodology and Algorithms 3.1

Dataset and Machine Learning

This study was conducted in MOROCCO and our field of study is predicting the utilization acceptance of the platform “orientation-chabab.com” [2]. We first distribute the questionnaire for the interviewers and then asked them to use the E-orientation platform during a period of one month. The approach TAM is used to construct a qualitative survey that was developed for the target population of women and men aged 18 to 60, and who are mainly (students, employers, and traders, farmers, unemployed and inactive…). We received 356 returns, and to analyses our data we applied the machine learning algorithms [3]. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. In our data analysis, we have downloaded and installed the software “WEKA” version 3.9 which is available from WEKA University of Waikato website [5]. 3.2

Feature Selection

Feature Selection (FS) is a crucial part of the preprocessing step in the process of our data analysis [6]. They are predominantly used for data reduction by removing

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irrelevant and redundant data. Information Gain Attribute Ranking is one of the simplest (and fastest) attribute ranking methods and is often used in text categorization applications, where the sheer dimensionality of the data precludes more sophisticated attribute selection techniques [7]. The Information Gain Attribute evaluates the value of an attribute in the correlation between it and the OUTPUT class: “make the decision to use an electronic guidance system” and we got 26 attributes (see Fig. 1).

Fig. 1. The select attribute of our data training

3.3

Classification Algorithms

Data classification algorithm is a procedure for selecting a hypothesis from a set of alternatives that best fits a set of observations [8]. Data Classification process includes building the classifier model by learning the training set and their associated class labels. In our research we used five classification algorithms: J48 [9], LMT [10], NaïveBayes [11], SMO [12] and SimpleLogistic [14] applied to our data. In the next section we compare the classification accuracy results of the five algorithms in order to choose the best between them at the top shows the accuracy of each algorithm.

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4 Results and Discussion In our research we used five classification algorithms. J48, LMT, NaïveBayes, SMO and SimpleLogistic applied to our data. In this section we compare the classification accuracy results of the five algorithms in order to choose the best between them (Table 1) at the top shows the accuracy of each algorithm. Table 1. A comparative study of classification algorithms Classification algorithms J48 LMT NaïveBayes SMO SimpleLogistic

Accuracy 80,46% 75,79% 71,87% 48,27% 80,12%

It is evident from the (Table 1) that J48 has the highest classification accuracy (80.46%) where 206 instances have been classified correctly and 50 instances have been classified incorrectly. The Second highest classification accuracy for SimpleLogistic algorithm is (80.12%) Moreover the LMT and NaïveBayes showed a classification accuracy of (75.7813%) and (71.87%). SMO results in lowest classification accuracy which is (48.27%) among the five algorithms. So the J48 outperforms the LMT, NaïveBayes, SMO and SimpleLogistic in terms of classification accuracy. In addition to obtaining the best results, it offers very low computing times ( > > < 0;2 Functional Completeness ðBFÞ ¼ 0;4 > > 0;75 > > : 1 AF ¼

BF ¼ 0 0 \ BF \¼ 0;4 0;4 \ BF\ 0;8 0;8 \¼ BF \1 BF ¼ 1

Number of Alternative Functions Number of Functions

Elemental Indicator: 8 0 > > > > < 0;2 Functional Completeness ðAFÞ ¼ 0;4 > > 0;75 > > : 1

AF ¼ 0 0 \ AF \¼ 0;4 0;4 \ AF\ 0;8 0;8 \¼ AF \1 AF ¼ 1

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Functional Correction: For this characteristic/attribute, some metrics are used, such as those detailed below: – – – –

Number Number Number Number

of of of of

Stimulus-Response Rules (NSRR) Objects Restriction Rules (NORR) Application Restriction Rules (NARR) Inference Rules (NIR)

As an example, the elementary indicator associated with the NSRR metric is shown. It should be noted that the rest of the indicators are defined in a similar way. Elemental Indicator: 8 < 0;1 0 \¼ NSRR \¼ 1 Functional Correction ðNSRRÞ ¼ 0;6 1\ NSRR \¼ 3 : 1 NSRR [ 3

2.3

Tests Directed by Use Cases

The UC have been characterized as a “behavior contract” because they define the way in which an Actor uses an application to achieve some goal. Its usefulness and influence extend to numerous topics of development, both in analysis and design and construction actions, making them a key foundation on which to base any strategy of quality assurance. The analysis action, based on information obtained during the execution of the communication activity, examines the requirements of the interested parties with the focus on aspects of content, interaction modes (including navigation), functionality and technical configuration. The analysis action includes tasks to: (1) Evaluate the complexity of the requirements in the increments. (2) Identify the content, classify it, relate it and establish its dynamism and links with the interested parties. (3) Review usage scenarios, refine them and extend them when necessary. (4) Identify non-functional requirements involved in the increments. The design aims to produce a representation from which it is possible to build a SP that meets the identified requirements, expressed as a set of UC, grouped in increments and with all the properties identified during the analysis. From this set of requirements, this design representation should exhibit, according to Kapor [11] and Pressman [12]: (1) Firmness: it should not have errors that inhibit its function; (2) Product Condition: should be adequate for the purposes for which it was intended and (3) Satisfaction: the using experience the Web Application should be pleasant. Based on the collected information and data in the analysis and design phases, the batches and test cases based on the UC are used. For this, the situation of the correctness and

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completeness of the UC specification must be considered, that is, the detail and accuracy with which they were defined. The scheme used for the UC specification is as follows: -

A basic Flow: Possible scenarios. Success scenarios.

-

Various Alternative Flows: Regular variants. Particular cases. Exceptional Flows.

To ensure the quality of the SP, the system testing must be performed. In the testing process there are, minimally, three different actors or roles of the people who interact in the Quality Assurance team that is assigned to the testing: (1) Leader; (2) Analyst; (3) Tester. The tester in the company does the work of a test robot, should not think, because otherwise it would modify the batches of tests or, worse, the test cases. Quality assurance is a fundamental part of the proposed strategy, that is, it is an umbrella activity for Web Engineering. Quality assurance focuses on each product that is produced as an increment is built. The focus is to ensure that firmness, product condition and satisfaction [14, 15] will be the output when the increase is deployed.

3 Study Case In order to validate the proposed strategy, and in a framework of collaboration with the local industry, we contacted the management of a local company that is in the process of migrating its business to the cloud and adapting it to the new trends of the business globalization and its adaptation to new technologies. This company is dedicated to centralize and coordinate the orders of services of different marks of electronic devices of the Cuyo Region of Argentina, and transfer the work order to the closest branch to the request for assistance that is available to respond and/or meet the customer’s need. The company management decided to carry out a control over the software involved in the transition of the company in the new world panorama with regard to the Business Processes (BP) in the new paradigms. From this viewpoint, the decision was made to restructure the BPs, adapting them to the new technological requirements. As a first step, an internet provider was hired in the cloud. Despite the importance of making this transition, this process brought with it a problem of adaptation for the work teams of the company. The migration represented a challenge because different requirements were required to be met by the company’s software and, in addition, to adjust the validation and verification processes to achieve quality assurance. In addition, it was necessary to control what refers to the external part: the different applications, repositories, etc. that emerged in the new paradigm of the cloud. For this purpose, the work of the company’s human resources was reorganized with the use of a strategy. For this work, the functionalities of the system were obtained from the CU, while the non-functional requirements were specified from restrictions and Business Rules

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(BR) that restrict and condition the functionalities and, therefore, so important to check or control as the same functionalities. For this, a SP QM based on Functional Suitability was proposed. The proposed QM is composed of a set of metrics associated with attributes which are obtained from UCs well formed and prepared to evaluate the quality of a SP. These attributes are detailed as follows: Expected function, preconditions, Trigger and Postconditions as expected result, Alternatives, Variations, Inclusions, Extensions and debug pre and post conditions. What things can be anticipated: Alternative functions, New denials of preconditions. The tests dependent on the interface and New postconditions are not established as expected result. An analysis was made of the UCs that the company had, associated with the functionalities that their applications provided, comparing them with the proposed model. The analyzed UCs were poor, with insufficient or incomplete information. From them it was only possible to determine topics such as expected function, pre-conditions negation, trigger validation, and post-conditions as expected result. Regarding characteristics of Functional Completeness, it was determined that 90% of the total of UC presented basic or success flow, only 40% included descriptions of alternative or exception flows and, in some cases, the pre- and post-conditions are very vaguely defined. When working on the improvement of the BP, it was necessary to bring to the cloud certain processes and tasks gradually. To do this, a strategy had to be adopted regarding the product and services quality that were wanted or should be offered to customers. For this reason, it was necessary to define a work strategy consisting of a QM and some indicators that would allow measuring it for subsequent decisionmaking. For this purpose, the definition of UCs is proposed to carry out the testing of the different SPs that have to interact, both in the cloud and in the company’s own servers. For this, it was necessary to take into account the context of the reality where the company is inserted, since it conditions the testing work. It is very important that, in addition to the technical and methodological part of the testing teams (in their highest roles), the human resource is well chosen. For example, choosing personnel of a certain nationality, at this stage of software development, is important because of the vision and idiosyncrasies that characterize them in terms of the way of seeing and thinking about the different possible scenarios of a reality. Also, as part of the proposed strategy, a model/schema/template was used to define the UC. Next, a brief explanation of the usefulness of the flow structure is given. It facilitates following the different scenarios and understanding what happens when the alternatives occur and where they take place. The basic flow shows the steps necessary to achieve the main objective of the UC. Then, for the basic flows, the following scenarios are considered: (1) The desired scenario, (2) The successful scenario from the beginning to the end. The Structure of a Basic flow of event is defined with the following scheme: – – – –

Structure the flows in steps. Number each step. If necessary, describe the step. Describe each step taking into account that the events “travel” back and forth.

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Alternative flows are those that “deviate” from the basic flow, being able, in some cases, to return to the basic flow or, in other cases, terminating the execution of the UC. Alternative flows can be classified or categorized into: (1) Regular variants, (2) Particular cases and (3) Exceptional flows (error). The Structure of an alternative event flow describes what happens taking into account the following points: (i) When it starts? (ii) What conditions must be met? (iii) What actions will be carried out? (iv) The point where the basic flow is resumed. The web application migrated to the cloud provides functionalities regarding the reservation of a service shift (repair/maintenance) of an electronic product. For this functionality, one possibility is to complete a web form to make a new reservation request. For the UC: reserve a shift. The following precondition was defined: For the correct functioning of this UC, the people or clients and the agendas of the services must be loaded into the system. For the tests, it is necessary to take into account certain BR that influence the different tasks that make up the BP: Reserve a shift by the customer to control the electronic product. The BR state the characteristics of the domain that must be considered when proposing uses of the system. They arise as a result of the surveying activities: they are not inferred, they are captured and if there is not enough information to enunciate them in full, a reference to the business must be made. The BRs are strongly linked to the UC and the objects of the application domain, indicating restrictions on their values, relationships and behavior. Thus, for the present work there were rules to be considered as those described below: Stimulus-response rules are those that indicate when and in what conditions a transaction must be made in the domain. e.g., when a customer requests a service shift, it must be verified that their payments are up to date. Objects restriction rules, define conditions and policies that affect the entities of the domain and that cannot be violated. e.g., each client is entitled to N free annual benefits. Application restriction rules, which define conditions and policies that restrict services and that should be considered part of the logic of the application. For example, the search criteria for the reservation shifts are by customer number and product brand or by reservation period of the shift for the service and are mutually exclusive. Inference rules should be considered, which indicate associative and transitive relations between the objects. e.g., if a client did not use the guarantee services during the validity period, then they will benefit from it with a 10% discount during the first reservation of shift. As a post-condition, it was defined that, after the execution of this UC, a new shift is registered and/or an existing shift for the selected client is canceled. Thus, the following is the result of the application of the sub-characteristics: functional completeness and functional correction.

A Software Testing Strategy Based on a Software Product Quality Model Subcharacteristic Attributes Results Calculation mode of global indicator Recommendation Subcharacteristic Attributes

Results

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Functional completeness – Basic Flows – Alternative Flows The 90% the basic flows were defined. The 40% the alternative flows were not defined or their specification was very weak GIFC = BF * P1 + AF * P2; GIFC = 0,65 Where: P1 = P2 = 50%; BF = 0,90; AF = 0,40 Define alternative flows with more details to facilitate subsequent tasks Functional correction – Stimulus-Response Rules (NSRR) – Objects Restriction Rules (NORR) – Application Restriction Rules (NARR) – Inference Rules (NIR) The 10% Stimulus-Response Rules were found. The 12% Rules of Restriction of Objects. The Restriction Rules of the Application The 15%. Only 7% refer to Rules of Inference GIFC = NSRR * P1 + NORR * P2 + NARR * P3 + NIR * P4; GIFC = 0,1175 Where: P1 = 30%; P2 = 30%; P3 = 25%; P4 = 20% GIFC = 0,1 * 0,3 + 0,12 * 0,3 + 0,15 * 0,25 + 0,07 * 0,2 Training to define a data and information collection scheme

4 Conclusions Given the globalization of communications and electronic commerce, and the need for organizations to keep their business highly competitive, companies are migrating their BP to the cloud. This is because being in the cloud means mobility, security, scalability and elasticity. That is, to be able to size the services to what is required. Despite its advantages, not always the organizations are willing to upload all their processes to the cloud, since they do not trust in the security of the cloud or because they consider that it will not be profitable. Therefore, the owners of the processes need to have a means to evaluate the convenience of uploading all or part of their processes to the cloud and, in case of uploading any of them, decide which of them to upload. In the context of our research, we defined a software testing strategy based on a SP QM to measure and evaluate the quality of those products through software testing. For this, a QM based on the ISO 25010 standard was defined together with a set of metrics and indicators, complemented with the use of testing driven by UC. In order to perform the test through UC, it was necessary to use well-defined and complete UC, with a predefined scheme for its standardization. In this scheme, the different execution flows of SP are detailed. In order to validate the proposed strategy, it was applied to BP of a local company. As a result, it was observed that basic flows, mostly were defined, but for alternative flows, there was some weakness in their definitions or they were not represented. Regarding the BR, there was a great void. This, many times, caused that

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the human resources of the company were over demanded by lack of information or data, and in another moment not taken into account. This strategy allowed organizing the working groups: development, SQA, analysts, designers and adapt the different ways of working. An enriching point was the harmonization, facilitation, and interaction of the tasks and processes of each one. Many times, they found errors that later made the task of reproduction difficult because of the little information that the analysts added of their survey in the UC. That is, they took for granted a lot of things that were necessary for a better understanding of the reality or domain of the problem. Being able to describe each of the event flows, based on the proposed strategy, as if it were a template to be completed for the analysts, allows the testing groups, for example, to discover own business constraints that must be tested. In addition, those who carry out the various tests, structural or functional, began to have the necessary data and information and with certain quality for the assembly of the test cases. Knowing the company’s BR and the market where it is inserted, allowed a better planning of the test, which results in the best quality of the SP and the human resource task. Nowadays, we are focused to the analysis of the models that correspond to the workflow: Sales and Collections and studying the degree of coupling or cohesion that this BP in the cloud should have. On the other hand, in the continuity of the work, the QM, the metrics and indicators defined will be applied to a new study case. In addition, the need to automate, through scripts or own tools, new metrics to evaluate other aspects of the workflow process will be analyzed.

References 1. Pressman, R.: Isn’t web engineering all about and technology. In: Web Engineering: A Practitioner’s Approach, 1st edn., pp. 19–20 (1991) 2. Bermeo Conto, J., Sánchez, M., Maldonado, J.J., Carvallo, J.P.: Modelos de calidad de software en la práctica: Mejorando su construcción con el soporte de modelos conceptuales. CEDIA (2016) 3. Carvallo, J.P., Franch, X., Quer, C.: Calidad de componentes software. In: Calero, C., Moraga, Mª.A., Piattini Velthuis, M.G. Calidad del producto y proceso software - capítulo 10, pp. 287–316. R.-M. Editorial (2010) 4. McCall, J.A., Richards, P.K., Walters, G.F.: Factors in software quality. RADC TR-77-369, vols. I, II, III, US Rome Air Development Center Reports NTIS AD/A-049 (1977) 5. Boehm, B.W., Brown, J.R., Kaspar, H., Lipow, M., Macleod, G.J., Merrit, M.J.: Characteristics of Software Quality. North Holland Publishing Company, Amsterdam (1978) 6. IEEE, “IEEE std 1061-1998”: Institute of Electrical Electronic Engineering (1998) 7. Horgan, G., Khaddaj, S., Forte, P.: An essential views model for software quality assurance. In: Project Control for Software Quality. Shaker Publishing (1999) 8. Gilb, T.: Principles of Software Engineering Management. Addison Wesley, Boston (1988) 9. ISO, “ISO/IEC 9126-1:2001”: Software engineering - software product quality -part 1: Quality model, int’l org. For standardization, Geneva (2001) 10. ISO “ISO/IEC 25010:2011”: Systems and software engineering – Systems and software Quality Requirements and Evaluation - System and software quality models (2011) 11. Granda, M.F.: An experiment design for validating a test case generation strategy from requirements models. In: EmpiRE 2014, Karlskrona, Sweden (2014)

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12. Bystrický, M., Vranić, V.: Use case driven modularization as a basis for test driven modularization. In: Federated Conference on Computer Science and Information Systems (2017) 13. Wang, C., Pastore, F., Briand, L.: System testing of timing requirements based on use cases and timed automata. In: 10th IEEE International Conference on Software Testing, Verification and Validation (2017) 14. Kapor, M.: A software design manifesto. Dr. Dobbs’ J. 172, 62–68 (1991) 15. Pressman, R.: What are the elements of a design model? In: Web Engineering: A Practitioner’s Approach, 1st edn., p. 37 (1991)

A DSL-Based Framework for Performance Assessment Hamid El Maazouz1,2(B) , Guido Wachsmuth2 , Martin Sevenich2 , Dalila Chiadmi1 , Sungpack Hong2 , and Hassan Chafi2

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1 Ecole Mohammadia d’ing´enieurs, Avenue Ibnsina B.P. 765 Agdal, Rabat, Morocco [email protected] Oracle Labs, 500 Oracle Parkway, Redwood Shores, CA 94065, USA https://www.emi.ac.ma, https://labs.oracle.com

Abstract. Performance assessment is an essential verification practice in both research and industry for software quality assurance. Experiment setups for performance assessment tend to be complex. A typical experiment needs to be run for a variety of involved hardware, software versions, system settings and input parameters. Typical approaches for performance assessment are based on scripts. They do not document all variants explicitly, which makes it hard to analyze and reproduce experiment results correctly. In general they tend to be monolithic which makes it hard to extend experiment setups systematically and to reuse features such as result storage and analysis consistently across experiments. In this paper, we present a generic approach and a DSL-based framework for performance assessment. The DSL helps the user to set and organize the variants in an experiment setup explicitly. The Runtime module in our framework executes experiments after which results are stored together with the corresponding setups in a database. Database queries provide easy access to the results of previous experiments and the correct analysis of experiment results in context of the experiment setup. Furthermore, we describe operations for common problems in performance assessment such as outlier detection. At Oracle, we successfully instantiate the framework and use it to nightly assess the performance of PGX [6, 12], a toolkit for parallel graph analytics. Keywords: MDE · DSL · Syntax · Compiler · Performance Experiment · Design · Assessment · Automation

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Introduction

With our increasing reliance on software systems in various domains such as defense and health care, verification and quality assurance have grew fundamental in insuring that functional and non-functional requirements are met. Companies needed to validate features and quality of their products and services, they needed to assure customers and gain their confidence. Researchers needed c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 260–270, 2020. https://doi.org/10.1007/978-3-030-36778-7_28

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to provide credible contributions, they needed to build on existing experiments, and assess the progress of their endeavors. This overwhelmed 3rd generation technologies and made development and testing in today’s platforms tedious and complex [13]. Performance assessment, for example, involves measurement of metrics such as response time, bandwidth utilization, and memory consumption with regard to system settings, execution environment settings, and workload definitions. System settings are both domain and system specific. Execution environment settings may include different processor architectures such as Intel 64 and Sparc and different types of software such as the Linux Kernel and the Java Runtime Environment. Workload definitions may include datasets, sessions, and requests. This process also involves activities such as result storage, analysis, and visualization. Setting up this process for a software system is challenging because of the big dimensionality present in these settings and the nowadays’ complex experimentation requirements [2,4,17]. Typical approaches to performance assessment rely on scripting languages as they are weakly typed and allow developers to easily hook different components of the software system. However, this encourages uniformity of code and data, and renders different concepts interchangeable [11]. Consequently, these approaches tend to flatten experiment setups with commands, which often become mixed and duplicate mainly due to different levels of expertise and objectives of involved human resources [3], and their lack of clean code guidelines [10]. Developers, for example, have more technical knowledge about the system than operations engineers which in their turn are more attached to the domain compared to quality assurance engineers. This results in a complex system that makes it hard to extend and maintain experiment setups or reuse features such as result storage and analysis consistently. These scripts are also considered a valuable investment that is unfortunately often thrown away [7]. All of these factors make it challenging to systematically express, extend, reuse, or link experiment setups to results for analysis and visualization [17]. To address these issues, this paper presents a generic approach to performance assessment by introducing a powerful representation of experiment setups. Based on this approach, a DSL-based framework defines a language module for expressing experiment setups explicitly in addition to other modules that help organize and automate performance assessment activities such as result storage and analysis. Our specific contributions in this paper can be summarized in the following: – A generic DSL-based framework for performance assessment (cf. Sect. 3). – A practical approach to implementing DSLs using the Gradle build language (cf. Sect. 4). – A successful instantiation of the framework and its evaluation in the context of PGX, a toolkit for parallel graph analytics (cf. Sect. 5). Using this approach to performance assessment considerably reduces the complexity of experiment setups, their organization, and association to results as well as their storage for further purposes such as analysis and visualization.

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This is prominent because it saves developers and experimenters from many repetitive tasks enabling them to focus more on actual development and performance tuning without worrying as much about managing performance related activities.

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Related Works

An experiment in general is an empirical procedure carried out to investigate the validity of a hypothesis. It mainly aims at studying correlations between dependent and independent variables. This is done by manipulating the independent variable and measuring the effect on the dependent variable. Whether the hypothesis is refuted or supported, an experiment brings new knowledge about the variables and their variations. Performance assessment, for example, is an experimental process that involves execution of experiments to assess how different parts of a system perform. Setting up these experiments, expressing them, and managing activities related to performance has been impeding systematic integration of performance assessment as part of the project’s regression pipeline [3,7]. Model-driven engineering (MDE) is a paradigm that came to raise abstraction level at which computer programs are written. In this paradigm, the complexity of platforms and applications is alleviated by considering models as firstclass entities in the software development process [13]. For this purpose, MDE technologies aim at building models to capture domain concepts, their relationships, and the constraints associated to these concepts. These models are then used to automatically produce artifacts such as application source code, configuration, and documentation. This automated transformation helps reduce implementation effort and time and ensures consistency and exchange between implementations. The use of MDE techniques is ubiquitous for many purposes such as augmenting developer productivity, reducing domain complexity [5,8,16], and facilitating integration of functional and non-functional requirement testing [3]. For performance assessment, da Silveira et al. [14] adopted a model-based testing approach to automate activities of performance testing in web applications. They proposed a domain specific language (DSL) for modeling performance tests and generating test scenarios and scripts automatically. Barve et al. [1] created a framework for assessing cloud application performance. The proposed framework is derived from a set of meta-models that describe an aspect of the performance assessment pipeline. It also comprises a graphical domain specific modeling language (DSML) for specifying performance experiments which are transformed into low-level scripts for configuring, deploying and measuring metrics of cloud applications on a given platform. The drag-and-drop nature of the language in addition to high level abstractions make it easy for performance engineers to monitor cloud application performance. Bianculli et al. [2] proposed a framework for defining testbeds for service-oriented architecture. This framework consists of a modeling environment that features a DSL (based on a metamodel) for

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defining a testbed model of a service-oriented architecture (SOA), a set of generators that process an input testbed (using scripts) to produce the actual testbed components (i.e. the mock-ups of the services, the testing clients, and the service compositions to execute) in addition to other artifacts such as deployment descriptors and helper scripts, and a compiler that transforms the generated artifacts into a format supported by the underlying platform that will execute experiments. Ferme et al. [3] introduced and implemented a global approach to integrating performance engineering activities into continuous software integration pipeline (CSI). Their approach features templates for users to set test requirements using a YAML-based DSL to declaratively express performance test configurations (e.g. load functions and workload definitions). Our approach to performance assessment is domain agnostic and presents a more global framework in which the DSL and Runtime modules are inspired from MDE techniques. The DSL is based on a strong formalism and supports explicit and organized experiment models. Moreover, our unique use of the Gradle build language to implement the DSL is practical for any Gradle project.

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DSL-Based Framework

Experimental design generally consists of choosing participants, partitioning them into groups, and assigning the groups to different environments. Both the participants and the environments can have properties where various combinations are possible within an experiment setup. In the context of performance assessment, the experiment setup space can be similarly organized by deriving participant and environment types from the experimentation requirements. This is possible because they necessarily describe relationships between system settings, execution environment settings, and workload definitions. These relationships help identify similarities in the settings which makes it possible to group them into participant and environment types. This grouping also reduces the dimension of the experiment setup space and leverages good understanding of these relationships. Performance assessment is additionally responsible for concerns such as experiment setup processing, experiment execution, result storage, analysis, and visualization. Scripting languages were mainly meant for wiring parts of a modular system instead of implementing the system itself [11]. Therefore they tend to render these parts equipotent and do not encourage separation of concerns. Although they can still provide this organization of the experiment setup space for the software system, they cannot protect or maintain it due to their weak typedness and their unrestrictive coding style. In order to address these shortcomings, we extend the DSL design methodology in [15] by further deriving a framework from the DSL to address the concerns of performance assessment. 3.1

Performance Assessment DSL

Organization of the experiment setup space requires addressing challenges raised in earlier 3rd generation approaches [13,15] such as we gathered in the following:

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– Explicitness: The DSL syntax needs to allow clear and declarative expression of an experiment’s intent. – Completeness: The DSL should be capable of expressing any experiment setup. – Flexibility: Requirements evolve and can become complex. The DSL needs to provide flexible control on the granularity of the settings. This means the DSL needs to be resilient, extensible, and maintainable. – Reusability: Settings in the experiment setup space should be reusable. This will allow users to briefly express experiment setups. Organization of the experiment setup space based on experimental design establishes relationships between participant and environment types. These relationships are hierarchical and can be best formalized by a tree structure such as in Fig. 1 where every node (e.g. R) represents a participant type and is fully described with its properties in the form of key-value pairs (e.g. ∀i : R.propi = ui ). {R.propi = ui } {A.propi = vi } {B.propi = wi }

R A

D

...

B ... E F ...

Fig. 1. Example of an annotated tree structure.

We based the DSL on the annotated tree structure because it adequately and completely models the hierarchical relationships between the concepts and allows for explicit expression of these concepts and their properties. Additional syntactic and semantic rules could be enforced on the DSL to allow flexibility and reusability of concepts and/or their properties. 3.2

Performance Assessment Framework

The framework presented in Fig. 2 is composed of 4 main modules. The DSL module allows expressing performance assessment experiment setups systematically. Experiment setups are written in the DSL and are compiled into concrete experiments. The Runtime module runs on the system under test and contains interpreter objects for executing these experiments in addition to APIs and helper utilities for performance metric measurement operations. When an experiment finishes running, both the concrete experiment and results are sent by the Runtime module to the Persistence module. This module is accessed through interfaces and exposes operations such as storage, retrieval, and basic statistical aggregation. The Analysis and Visualization module assess experiment results.

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It serves for extracting insights about the evolution of the project’s performance. A common problem in this assessment is outlier detection. This is addressed by collecting statistics on performance metrics during a period of time in the past and comparing them to current results. Tolerance intervals are defined to detect outliers which are included, among other statistics, in the performance reports which are then easily interpreted by the performance task force.

Fig. 2. Architecture of the performance assessment framework.

The DSL and Runtime modules are inspired from MDE techniques and provide a higher abstraction that is capable of closing the gap between the intent of an experiment setup and the expression of the intent [11]. We describe, in the next section, a practical implementation of the DSL module.

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Gradle Based Implementation of the DSL Module

The annotated tree structure formalism presented in Sect. 3 supports explicit and well organized experiment models. However, implementing a DSL for each experimentation domain from scratch is costly in terms of additional complexity incurred by the actual project as it requires compiler expertise and considerable man-hours to design, develop, and maintain. Embedded DSLs is an implementation approach based on extending a given base language and completely uses its compilation mechanisms [15]. This means the costs of building a DSL compiler or interpreter and maintaining them are completely eliminated. The main bottleneck of this approach is expressiveness of the base language, thus its choice matters most to fit notation requirements of the domain-specific constructs. An example of such a base language is the Gradle build language. Gradle provides this language itself as an embedded DSL in Groovy or Kotlin for setting up project configuration. We chose to embed our performance assessment DSL in the Gradle build language and provide it as a Gradle plugin. These choices are motivated by the following rationales: – The Gradle build language provides an explicit and declarative syntax and is also already available, thus we benefit from compilation and editor services for free. – Gradle supports plugin development. Implementation as a plugin allows for modularity, free and seamless integration into the project’s build system as well as into its continuous regression pipeline.

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– Gradle is widely adopted by many projects for its ease-of-use and its high degree of automation. This means this DSL implementation is far-reaching and very practical. Our implementation of the DSL module defines two types of data structures as Plain Old Java Objects (POJOs). The frontend POJOs describe the DSL syntax and are exposed to the project through Gradle extensions to hold experiment setups. The backend POJOs describe the concrete experiments that are generated from compiling the frontend POJOs. This compilation is achieved by methods defined in the frontend POJOs to analyze, optimize, and transform experiment setups into experiment objects. The plugin defines three main tasks to manage invocation of the performance assessment process. The first task performs analysis on the experiment setups, the second task transforms the analyzed experiment setups into concrete experiments, and the third task invokes the Runtime module and handles parallel execution of the generated concrete experiments. Both extensions and tasks are injected into the main project’s build system by simply applying the plugin in its build script. This means the performance assessment process can be made available for on-demand and continuous invocation for free. With the embedded languages approach, DSL implementation is reduced to only adding syntactic domain-specific constructs and implementing custom transformation rules. It also does not require much effort from a single developer. In fact, with prior comprehension of the domain idiom [15] and moderate experience in Gradle development, we estimated this effort to be maximum 40 man-hours or equivalent to implementing a fully functional simple Java project. Although we have designed generic interfaces for the other modules, we chose to omit them in this publication for simplicity and lack of space. Usage of the DSL consists of writing systematic experiment setups and using the produced concrete experiments in the context of a given project. This means implementation of a Runtime module is due to host and execute the produced artifacts. The cost of this implementation depends on the execution semantics of the concrete experiments, the project’s own functioning, and the target performance metrics to collect. In the following section, we evaluate the framework on a real academic and industrial project.

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Evaluation

The research and development team in PGX [6,12] is responsible for identifying and exploring new technologies that can improve graph analysis in Oracle products. For this purpose, it needs to comprehend nowadays’s industrial requirements as well as actively research on how to best address these requirements. In this section, we chose PGX to evaluate our approach to performance assessment. PGX is a high performance toolkit for graph analysis that supports running algorithms such as PageRank and performing SQL-like pattern matching on

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graphs [5,16]. The PGX toolkit includes both single-node in-memory engine and distributed engine for extremely large graphs. Graphs can be loaded from a variety of sources such as the filesystem, a database, and HDFS and in various formats such as adjacency list and edge list. Performance assessment of PGX mainly requires measurement of execution time and memory consumption of graph workloads such as graph loading, graph algorithms [5], and graph queries [16]. These operations can run in single-node in-memory or distributed engines and on many graph datasets. The single-node in-memory engine has many configurations such as number of threads and thread scheduling strategies. The distributed engine runs on many machines and has many configurations such as buffer size and graph partitioning strategies. Hardware resources are allocated in a cluster and need to be managed properly. Measurements are taken many times and stop conditions are needed to limit resource utilization in the case of lengthy or indeterministic experiments. Using the DSL module in Sect. 3, we systematically represented these requirements in the form of the annotated tree structure. Algorithms and graphs illustrated in Listing 1.1 are nodes of the tree, they both have properties and are organized in such a way to fulfill performance assessment idiom. All algorithms in the experiment setup space, for example, run on PGX single-node in-memory engine shared-memory and are limited to run under a timeout. They have mandatory properties such as source code and arguments, and optional properties such as the engine and timeout. "Pagerank" algorithm runs on "San Francisco street graph" and "Topological Schedule" runs on "LiveJournal social network" and "Twitter" graphs. Graph datasets also have mandatory properties such as the config file, and optional properties such as engine and timeout. To enable briefness, we provided support for default comprehensive configuration globally for the experiment setup space and locally for graph and operation nodes. We also enforced intuitive rules on the tree such as that all properties of a node apply to all of its children. This means users do not have to repeat setting the same settings thus allowing more reusability. Conflicts between the same properties are resolved either by giving priority to the innermost or by merging in the case of operation and graph arguments. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

algorithms { engine " shared - memory " timeout "3600 SECONDS " " Pagerank " { source "./ algorithms / pagerank . gm " arguments [[" tol ": 0.001 , " damp ": 0.85 , " norm ": false ]] graphs { " San Francisco street graph " { timeout "1200 SECONDS " } // Other graphs ... } } " Topological Schedule " { source "./ algorithms / t o p o l o g i c a l _ s c h e d u l e . gm " arguments [[" source ": [1 , 2 , 3]]]

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Listing 1.1. Experiment setup examples for performance assessment of Green-Marl algorithms.

Invocation of the performance assessment framework is done through Gradle commands. With no parameters, it will run the entire experiment setup space. To allow selection of targeted experiment setups, we devised filters with the help of Gradle project properties illustrated in Listing 1.2. These commands compile experiment setups and invoke the Runtime module on the generated experiments. Listing 1.3 shows a fragment of printed logs showing progress of execution. Results along with concrete experiments are optionally saved through REST calls to a database or simply saved on the local filesystem. 1 2 3

./ gradlew : qa_framework : gmBenchmark - Poperations = " Pagerank " - Pgraphs = " San Francisco street graph " - Pruntimes = " sm "

Listing 1.2. Gradle command to only run PageRank algorithm on San Francisco street graph using PGX SM engine. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Benchmarking GM algorithms for PGX SM Starting engine with 72 threads and ’ ENTERPRISE_SCHEDULER ’ Loading graph ’ San Francisco street graph ’ ... Graph ’ San Francisco street graph ’ loaded in 0 MINUTES Preprocessing graph for GM algorithm ’ Pagerank ’ Resizing thread pool to 1 threads for ’ ENTERPRISE_SCHEDULER ’ Running algorithm ’ Pagerank ’ 5 times or within 1200 SECONDS Starting BASIC memory listener Measurement 1/5 took 196 ms , time left : 1199803 ms Measurement 2/5 took 45 ms , time left : 1199757 ms Measurement 3/5 took 42 ms , time left : 1199714 ms Measurement 4/5 took 40 ms , time left : 1199674 ms Measurement 5/5 took 33 ms , time left : 1199640 ms Stopping BASIC memory listener Resizing thread pool to 4 threads for ’ ENTERPRISE_SCHEDULER ’ Running algorithm ’ Pagerank ’ 5 times or within 1200 SECONDS Starting BASIC memory listener

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Measurement 1/5 took 218 ms , time left : 1199781 ms Measurement 2/5 took 110 ms , time left : 1199671 ms Measurement 3/5 took 30 ms , time left : 1199640 ms Measurement 4/5 took 16 ms , time left : 1199624 ms Measurement 5/5 took 16 ms , time left : 1199608 ms Stopping BASIC memory listener

Listing 1.3. Log excerpt from the framework invocation by the command in Listing 1.2.

6

Conclusion

Performance assessment is ubiquitous in many industrial and academia projects. Moreover, evolution of projects necessitates a high degree of automation and ease-of-use in the performance assessment process. In this paper, we described our approach to performance assessment by introducing a DSL to express experiment setups systematically from which we also derived a framework to automate performance assessment activities such as result storage, analysis, and visualization. Experimental design inspired us to group settings into participant and environment types, which greatly helped in reducing the complexity and improved understanding and organization of the experiment setup space. Moreover, our choice of the Gradle build language as the base language for the DSL made it seamless to automate activities in the performance assessment process. We believe the presented DSL is powerful, inexpensive to implement, practical, and could be instantiated to address experimentation requirements of other projects. We envision as future work to support more advanced result analysis and visualization. Response time and memory consumption were the main performance metrics addressed in this work, and we plan to support detailed memory profiling as well as processor and cache performance. Resources for executing experiments such as individual machines or clusters have to be closely monitored and used optimally by the performance assessment framework. This relates to the number of measurements of a given experiment setup as it determines warmup and effective measurement phases. These two settings make sure no experiment runs indefinitely or no idle resource is held. Moreover, a fixed number of measurements for some experiments may not be enough for the numbers to eventually stabilize or may be excessive thus resulting in unnecessary resource utilization. Although we have flexibility in expressing these settings, and that we have useful estimations for PGX operations, we believe that there needs to be proper online support in the Runtime module for when measurements need to stop. We also consider to grow the framework and include stress testing [9].

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References 1. Barve, Y., Shekhar, S., Khare, S., Bhattacharjee, A., Gokhale, A.: UPSARA: a model-driven approach for performance analysis of cloud-hosted applications. In: 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC), pp. 1–10. IEEE (2018) 2. Bianculli, D., Binder, W., Drago, M.L.: SOABench: performance evaluation of service-oriented middleware made easy. In: 2010 ACM/IEEE 32nd International Conference on Software Engineering, vol. 2, pp. 301–302. IEEE (2010) 3. Ferme, V., Pautasso, C.: Towards holistic continuous software performance assessment. In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, pp. 159–164. ACM (2017) 4. Grønli, T.-M., Ghinea, G.: Meeting quality standards for mobile application development in businesses: a framework for cross-platform testing. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 5711–5720. IEEE (2016) 5. Hong, S., Chafi, H., Sedlar, E., Olukotun, K.: Green-Marl: a DSL for easy and efficient graph analysis. ACM SIGARCH Comput. Archit. News 40(1), 349–362 (2012) 6. Hong, S., Depner, S., Manhardt, T., Van Der Lugt, J., Verstraaten, M., Chafi, H.: PGX.D: a fast distributed graph processing engine. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, p. 58. ACM (2015) 7. Kats, L.C., Vermaas, R., Visser, E.: Integrated language definition testing: enabling test-driven language development. ACM SIGPLAN Not. 46(10), 139–154 (2011) 8. Kats, L.C.L., Visser, E.: The Spoofax language workbench: rules for declarative specification of languages and IDEs. ACM SIGPLAN Not. 45, 444–463 (2010) 9. Kersten, M.L., Kemper, A., Markl, V., Nica, A., Poess, M., Sattler, K.-U.: Tractor pulling on data warehouses. In: Proceedings of the Fourth International Workshop on Testing Database Systems, p. 7. ACM (2011) 10. Martin, R.C.: Clean Code: a Handbook of Agile Software Craftsmanship. Pearson Education, London (2009) 11. Ousterhout, J.K.: Scripting: higher level programming for the 21st century. Computer 31(3), 23–30 (1998) 12. Raman, R., van Rest, O., Hong, S., Wu, Z., Chafi, H., Banerjee, J.: PGX.ISO: parallel and efficient in-memory engine for subgraph isomorphism. In: Proceedings of Workshop on GRAph Data management Experiences and Systems, pp. 1–6. ACM (2014) 13. Schmidt, D.C.: Model-driven engineering. Comput. IEEE Comput. Soc. 39(2), 25 (2006) 14. da Silveira, M.B., et al.: Canopus: a domain-specific language for modeling performance testing (2016) 15. Van Deursen, A., Klint, P., Visser, J.: Domain-specific languages: an annotated bibliography. ACM SIGPLAN Not. 35(6), 26–36 (2000) 16. van Rest, O., Hong, S., Kim, J., Meng, X., Chafi, H.: PGQL: a property graph query language. In: Proceedings of the Fourth International Workshop on Graph Data Management Experiences and Systems, p. 7. ACM (2016) 17. Wienke, J., Wigand, D., Koster, N., Wrede, S.: Model-based performance testing for robotics software components. In: 2018 Second IEEE International Conference on Robotic Computing (IRC), pp. 25–32. IEEE (2018)

Towards a Framework Air Pollution Monitoring System Based on IoT Technology Anass Souilkat(&), Khalid Mousaid, Nourdinne Abghour, Mohamed Rida, and Amina Elomri LIMSAD Labs, Faculty of Sciences Ain Chock, Hassan II University of Casablanca, Km 8 Route d’El Jadida, Casablanca, Morocco [email protected], {khalid.moussaid, noreddine.abghour,mohamed.rida, amina.elomri}@univh2c.ma

Abstract. One of the most discussed and concerning environmental issues nowadays is air pollution. Fast-growing population and urbanization have resulted in deteriorated air quality in urban areas. Furthermore, heavy transportation help contributes to poor air quality, which can cause damages to human health due to prolonged exposure and inhalation of pollutants. Therefore, there has been a growing interest in developing a system for monitoring air quality using big sensor data analytics. The systems for inferring air quality are proposed to help inform the public with real time air pollution data and guide them in making daily decisions affecting their respiratory health. This paper presents an IoT-Based framework for environmental pollution monitoring and control system that can detect and monitor the existence of harmful gases in the environment using Big Data analytics. Integration of IoT technology with big data analytics creates an autonomic air pollution monitoring system that has great potential to assist in controlling air quality. Keywords: Pollution
Health risks
Environmental factors
Real-time monitoring
Internet of Thing (IoT)
Air quality
City dynamics
Human mobility

1 Introduction In the last decade, big data has become a phenomenon with the potential to alter and improve the standards of products and services in industry and business. Big data model can be applied to a variety of networked sensor system which allows the collection of large volume of data in structured or unstructured format using innovative technologies such as social media, Internet of Things (IoT), multimedia, and large-scale wireless sensor systems. Big data can be defined as processing information using cost effective and innovative methods from a large volume of data that is growing and is in a variety of forms with the purpose to improve insights and decision making [1]. To elaborate, big data systems is consisting of collection, preprocessing, transmission, storage, and extraction of data using methods such as machine learning and other statistical, analytical, and decision-making techniques. The five Vs in big data are as © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 271–280, 2020. https://doi.org/10.1007/978-3-030-36778-7_29

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follows: big volume of data (terabytes sets of data), variety of data types, high velocity of data generation and updating, veracity of acquired data (uncertainty and noise in data), and lastly, big value [2]. The emergence of big data techniques replaces traditional methods and technologies to solve system level problems where applications of big data have helped addressed problems in health science including recommendation system in healthcare [3–5], epidemic surveillance using internet [6–9], food safety monitoring [10], inferring air quality [11–15], and monitoring health using sensors [16–18]. Big data technologies have also help transformed problem solving process in engineering, finance, and business [19]. The first step in a big data analysis pipeline involves data acquisition which can be generated via multiple sensors and other sources such as public databases, historical records, and social media. Traditional sensors which measure physical quantities such as temperature, pressure, and light detectors, can help generate data. Data can also be derived from new devices such as smartphones, cameras, GPS, microphones, gyroscopes, and accelerometers which provides signal, image, and location-based information. Data from the different domains can be fused and utilized to solve the problem by making joint decision as illustrated by Zheng and coworkers where they developed a method to infer air quality information via multiple sensors for a city in Beijing [11]. Big data sensor-based systems face several challenges due to the alarming rate at which new data are being generated. Thus, advancements in data storage and mining technologies are aimed to provide enough power to process and store the data using techniques such as Hadoop, MapReduce, or cloud computing technologies [1]. Apart from volume of data, variety, velocity, and veracity can also produce challenges where sparse sensors can lead to missing data points, inconsistent sampling rates causes delay in data collection, and uncertainties in measurement values can arise from synchronization and localization difficulties. Amongst the various applications using big data sensor-based systems, inferring air quality has gained increasing attention by researchers as air pollution becomes a significant issue in developing countries [2]. Air pollution is an emerging public health concern as there is increasing amount of evidence that the quality of air substantially affects our health due to the presence of various toxic pollutants leading to higher risk of lung cancer, cardiovascular diseases, and birth defects [20–24]. Monitoring air quality can help to protect human health and control population by providing reliable information that could be used by the public in making daily decisions (wearing protective masks, or avoiding polluted areas, etc.). This article will review past work using big data methods to predict air pollution for control measures. Big data framework based on IoT technology can be suitable tools for self-detection and selfmonitoring air pollution systems. Therefore, we propose a framework for air pollution monitoring system based on IoT to make monitoring and control of air pollution more flexible and autonomic. The rest of this paper is organized as follows: Sect. 2 discusses the state of the art of big data and related work. Section 3 presents the proposed framework for air pollution monitoring system based on IoT technology. Finally, Sect. 4 provides concluding remarks on the paper and future lines of work on the topic.

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2 State of the Art of Big Data and Related Work In this section, we briefly discussed state of the art of big data and review some literatures regarding existing wide range air quality monitoring systems and the variety of pollutants. 2.1

Big Data and IoT Based System

Nowadays, in the era of Internet of Things (IoT), there are increasing number of devices that can communicate and connect with each other over the internet such as smartphones, sensors with wireless networks, wearable sensors, actuators, etc. In the world with many interconnecting systems, massive volume of data also known as Big Data is generated. Big data analysis needs to go through a process as follow: (1) data generation and collection, (2) communicating data via technologies such as Wi-Fi, Ethernet, 3G/LTE, (3) data management and processing using Hadoop system, and finally (4) interpretation of data. The Hadoop system is able to deal with large set of datasets because it contains HDFS that divides data into equal portions and stores them into various nodes. MapReduce system then performs parallel processing of stored data and results are generated. Lastly, results generated are analyzed, interpreted, predicted and visualized to assist in decision making using machine learning, pattern recognition, soft computing, and decision models [25]. Monitoring air pollution has become a recent success in big sensor data systems. In areas with high population, various factors contribute to the air quality predictions such as traffic volume, land use, meteorology, and urban structures. From literature, gas sensors equipped with wireless sensor networks (WSNs) are used to monitor concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3). Other pollutants with micro-sized particles from aerosol such as PM2.5 and PM10 which are responsible for some respiratory and cardiovascular diseases has also been monitored and detected [11, 13–15].

Fig. 1. Overview of the proposed approach.

Figure 1 shows the overview of proposed approach where the data will initially be collected from historical measurements using various sensors or public databases which then is processed and stored in the history database. The model developed then takes selected data from history database and use real-time measurements to generate new

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information or knowledge through statistical and analytical methods which are then presented in the form of graphs, charts, or mapped for easier interpretation and help guide human in decision making.

3 Related Works In many developing countries such as China, Brazil, and India, the air is susceptible to pollution. Therefore, governments have responded to this by building air quality monitoring stations throughout the cities to help control pollution and inform people of real-time pollutants concentration. However, these stations are expensive to build and maintain thus their locations are scares in the cities leading to insufficient and deviated data between each station. In a work by Zheng and coworkers, a system that allows user to monitor air quality information from a phone application within cities in China (such as Beijing, Shanghai, Guangzhou, and Shenzhen, etc.) was developed based on historical and real-time air quality data obtained from monitor stations as well as other sources of data from meteorology, traffic flow, human mobility, road network structures, and point of interests (POIs) around each station [12]. From the monitoring stations air quality index (AQI) levels for cells were collected. AQI is a number used as indicator of how polluted the air is where different colours are assigned to each values and descriptors as shown in Table 1 which is a standard issued by the United states environmental protection agency [12]. The computation of AQI values is taken from calculations using pollutants concentration levels which varies according to the country and the type of pollutant. Table 1. AQI values, descriptors, and colour codes [12].

5 types of indirect data were also collected around each station namely: (1) Meteorological features such as temperature, humidity, barometer, pressure, wind speed, and the weather condition (sunny, snowy, cloudy, foggy, rainy), (2) Traffic features, (3) Human mobility features such as the number of people arriving and departing, (4) POIs features such as their distribution, number of vacant places and any changes in their number, (5) Road network features such as the total length of highways and other road segments, and the number of intersections. The data collected were then processed using a semi-supervised approach using 2 classifiers: spatial classifier and temporal

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classifier. In this approach, the accuracy of air quality inferences was improved by using un-labelled data. The spatial classifier used road network and POIs data to model spatial correlation of air quality between different cells, while the temporal classifier used meteorological, traffic, and human mobility data to model the temporal dependency of air quality for each cell. Results showed that a high accuracy of PM10 concentration could be detected from which the AQIs at each station was calculated successfully in a short amount of time (5 min). In another research work, the air quality in the city of Taipei were monitored using a developed automatic micro-scaled air quality monitoring system based on wireless sensor networks integrated with global system (GSM) to allow mobile communication. In this instance the sensor detects meteorological data and concentration of CO in areas with high population density and vehicles [13]. The data collected are then stored and integrated in a database using LabVIEW programming and MySQL database. Their system enables users to inquire historical data and also latest updated data through a web page. The small-sized system enables a large amount of real-time data to be collected throughout many points in the city. CO is a toxic gas emitted by vehicles during incomplete combustion that could cause harm to human when its concentration reaches 9 ppm [26]. Researchers in Japan have proposed a pre-training method to improve accuracy in predicting PM2.5 concentration levels to infer air quality [14]. Their method uses data from openly available sensors and other features such as rain precipitations, and wind speed, in 52 cities all around Japan and introduces deep recurrent neural network (RNN) and a pre-training method using deep learning that significantly improves the predicted PM2.5 levels compared to conventional predicting system (VENUS) employed by the Japanese government. Results showed that the proposed method outperforms the traditional prediction system in accuracy. Hasenfratz et al. conducted a study to monitor the concentration of ultrafine particles PM10 and PM2.5 which are pollutants that have severe impact on human health in the city of Zurich, Switzerland using a mobile sensor system installed on public transport vehicles. Data were collected for a period of more than 2 years and processed using a novel modelling approach that reduces root mean square error by 26% [15]. Thus, improving the accuracy of air pollution maps produced by the proposed land used regression (LUR) model with high spatio-temporal at hourly time scale. Apart from measurements of PM10 and PM2.5 concentrations, the sensor nodes installed can also detect O3, CO, and NO2 concentrations. However only data for the ultrafine particles were focused on and processed due to the high volume of data collected. Historical measurements were used to enhance the original real-time dataset in order to derive pollution maps with high temporal resolution. Their method utilizes public transport vehicles, which cover a large urban area (100 m  100 m) on a regular schedule. Recently, IoT platform has gained popularity in many applications such as in smart cities designs, agriculture, industry, security, and health [27]. An example of recent application of IoT in air quality sensing systems is shown by Hu and co-workers where they did further improvements to the system architecture to obtain more reliable data [28]. They proposed an air quality sensing system that has 4 layers framework that perform ground and aerial sensing. The 4 layers are as follows: (1) sensing layer, (2) transmission layer, (3) processing layer, and (4) presentation layer. The sensing

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system which is responsible for detecting the air pollutants are improved by collecting real time data where the sensing devices are installed near the ground or mounted on a mobile unmanned aerial vehicle such as an air drone. This method gives air quality distribution maps with high spatial resolution and low latency.

4 Proposed Framework: Air Pollution Monitoring System Based on IoT Technology Our proposed framework will combine IoT and big data analytics for a smart solution to measure air pollution at traffic lanes through an embedded system on a geo-localized bus in real time. We propose a platform consisting of two layers namely: data monitoring, and data processing and visualization. To monitor air pollution, we propose the use of mobile sensors on buses that can measure a wide range of pollutant gases such as O3, NO2, CO, SO2, and ultrafine particles for example PM2.5 and PM10. This sensor method compared to immobile stations enables collection of big volume of data and in real-time able to cover more areas. The cost is also less expensive compared to building and maintaining fixed stations for measuring air pollution. Air quality measurements are also normally measured at ground level (between 0–2 m at a man’s height). Due to these reasons, we propose a technique that detect localized pollution from factory, or congested street levels using sensors that are equipped with microcontroller embedded module, Wi-Fi or GSM card, to allow transfer of measured data to our big data cluster over the internet connection via WiFi access point. After the data has been transferred, it is then processed and presented in a visually appealing map. The data processing and visualization layer is composed of three sub-modules namely: Data acquisition, Data processing and storage, Data visualization (Fig. 2).

Fig. 2. Proposed framework for air pollution monitoring system based on IoT technology.

Currently, we are working on evaluating our system by implementing the various steps starting from data generation and collecting, aggregating, filtration, classification, preprocessing, computing and decision making. we proposed to implement the system using Hadoop with Spark, voltDB, Storm or S4 for real time processing of the IoT data to generate results and thus establishing a smart city. For urban planning or city future development, the offline historical data is analyzed on Hadoop using MapReduce

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programming. IoT datasets generated by smart homes, smart parking weather, pollution, and vehicle data sets can be used for analysis and evaluation. 4.1

Proposed Sensor Design

To implement and test our proposed method, it is important to select the suitable sensing system which can provide high quality data that is accurate and give high spatial resolution for air pollution maps. It is also vital to keep the cost of such sensor low and the power to maintain the sensor low. Materials to be used to build the sensor is also preferably easily available and the portability of the measurement system is another important factor to take into consideration to ensure ease of use. Therefore, we proposed to implement the use of sensor which is based on mobile sensing system as presented by Hasenfratz and co-workers, where they utilized a smartphone connected to a low cost ozone gas sensors that can provide time and locations stamped measurements as most phones are equipped with GPS system and can send information to servers [29]. Hasenfratz et al. named their system GasMobile which uses a MiCS-OZ-47 sensor that can detect ozone concentration, and an HTC Hero smartphone that allows digital communication due to its RS232-TTL interface. The hardware is all available for a low cost making this sensing system cheap to build. A battery powers both components separately, thus allowing extended battery lifetime of approximately 50 h. The sensor is automatized to collect measurements every two seconds making frequent updates and give its ability to generate big data. 4.2

Work Flow of Proposed Method

To implement our proposed mechanism, it is necessary to define the tasks and the flows of works being used to execute the processes. We need to ensure a coherent and precise visibility on the relationship between them. As illustrated in Fig. 3, the framework for our proposed system has two major parts: offline learning and online inference. From this, we generate three kinds of data flows: preprocessing, inference, and learning.

Fig. 3. Mechanism of functioning IoT based on deep learning.

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The model is built using co-training in a semi-supervised approach. CRF is used for temporal characteristics while ANN is used for spatial characteristics. To estimate the temporal transformation of air quality in a location a time domain condition random (CRF) time-based classifier (TC) is utilized. While to modelling spatial correlations between air quality in different locations, artificial neural network (ANN) based spatial classifier (SC) is utilized.

5 Expected Results Figure 4 shows expected results obtained from proposed framework that will be tested and implemented to infer air quality in parts of the city of Casablanca, Morocco. The expected result of our framework allows citizens to find out real-time information about the weather, air pollution, public transport delays, public bike availability, river level, electricity demand, the stock market, twitter trends in the city, look at traffic camera feeds, and the happiness level in inquired areas. These data can also be mapped for easier visualization. This will be complemented by the Casablanca Dashboard, a data visualization site that tracks the performance of the city with respect to twelve key areas, namely: jobs and economy, transport, environment, policing and crime, fire and rescue, communities, housing, health, and tourism. Although these data are more administrative in nature and not in real-time.

Fig. 4. The expected result of our framework in Casablanca morocco showing AQI levels inferring air quality in the areas concerned.

6 Conclusion and Future Work This paper brings preliminary thoughts-opening, and challenging perspectives. literatures regarding existing wide-range air quality monitoring systems were reviewed and illustrated the importance of air monitoring systems as air pollutants can bring serious damages to human body. The architecture of the proposed framework as presented here provides an autonomic and flexible framework for inferring air quality using IoT technology. It shows potential to be a suitable tool for self-detection and self-

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monitoring of air pollution. It is expected to be able to monitor the quality of air pollution and help the process associated to regulate pollution such as prevention, correction, and control. The system will be equipped with an advanced autonomic engine that guarantees the performance and the reliability of the air quality measurements. Currently, we are working on evaluating our proposition on a critical system to produce a prototype which conforms to the proposed approach. Indeed, integrating IoT Technology with Big Data analytics is a promising method to help solve the issue of controlling pollutants in a more flexible and autonomic manner.

References 1. Ang, L.M., Seng, K.P.: Big sensor data applications in urban environments. Big Data Res. 4, 1–12 (2016) 2. Huang, T., Lan, L., Fang, X., An, P., Min, J., Wang, F.: Promises and challenges of big data computing in health sciences. Big Data Res. 2(1), 2–11 (2015) 3. Wiesner, M., Pfeifer, D.: Health recommender systems: concepts, requirements, technical basics and challenges. Int. J. Environ. Res. Public Health 11(3), 2580–2607 (2014) 4. Duan, L., Street, W.N., Xu, E.: Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterp. Inf. Syst. 5(2), 169–181 (2011) 5. Hoens, T.R., Blanton, M., Steele, A., Chawla, N.V.: Reliable medical recommendation systems with patient privacy. ACM Trans. Intell. Syst. Technol. (TIST) 4(4), 67 (2013) 6. Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012 (2009) 7. Carneiro, H.A., Mylonakis, E.: Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin. Infect. Dis. 49(10), 1557–1564 (2009) 8. Dugas, A.F., Jalalpour, M., Gel, Y., Levin, S., Torcaso, F., Igusa, T., Rothman, R.E.: Influenza forecasting with Google flu trends. PLoS ONE 8(2), e56176 (2013) 9. Signorini, A., Segre, A.M., Polgreen, P.M.: The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PLoS ONE 6(5), e19467 (2011) 10. Jie, Y.: Is your food safe. New ‘Smart Chopsticks’ can tell in: China real time. Wall Street J. (2014) 11. Zheng, Y., Liu, F., Hsieh, H.-P.: U-air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1436–1444. ACM (2013) 12. Zheng, Y., Chen, X., Jin, Q., Chen, Y., Qu, X., Liu, X., Chang, E., Ma, W.-Y., Rui, Y., Sun, W.: A cloud-based knowledge discovery system for monitoring fine-grained air quality. MSR-TR-2014–40, Technical report (2014) 13. Liu, H., Chen, Y.-F., Lin, T.-S., Lai, D.-W., Wen, T.-H., Sun, C.-H., Juang, J.-Y., Jiang, J.-A.: Developed urban air quality monitoring system based on wireless sensor networks. In: 2011 Fifth International Conference on Sensing Technology (ICST), pp. 549–554. IEEE (2011) 14. Ong, B.T., Sugiura, K., Zettsu, K.: Dynamic pre-training of deep recurrent neural networks for predicting environmental monitoring data. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 760–765. IEEE (2014)

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15. Hasenfratz, D., Saukh, O., Walser, C., Hueglin, C., Fierz, M., Beutel, T., Arn, J., Thiele, L.: Deriving high-resolution urban air pollution maps using mobile sensor nodes. Pervasive Mob. Comput. 16, 268–285 (2015) 16. Jiang, P., Winkley, J., Zhao, C., Munnoch, R., Min, G., Yang, L.T.: An intelligent information forwarder for healthcare big data systems with distributed wearable sensors. IEEE Syst. J. 10(3), 1147–1159 (2016) 17. Sendra, S., Granell, E., Lloret, J., Rodrigues, J.J.: Smart collaborative mobile system for taking care of disabled and elderly people. Mob. Netw. Appl. 19(3), 287–302 (2014) 18. González-Valenzuela, S., Chen, M., Leung, V.C.: Mobility support for health monitoring at home using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 15(4), 539–549 (2011) 19. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J., Barton, D.: Big data: the management revolution. Harvard Bus. Rev. 90(10), 60–68 (2012) 20. Raaschou-Nielsen, O., Andersen, Z.J., Beelen, R., Samoli, E., Stafoggia, M., Weinmayr, G., Hoffmann, B., Fischer, P., Nieuwenhuijsen, M.J., Brunekreef, B., Xun, W.W.: Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE). The lancet oncology 14(9), 813–822 (2013) 21. Lee, B.J., Kim, B., Lee, K.: Air pollution exposure and cardiovascular disease. Toxicol. Res. 30(2), 71 (2014) 22. Urban air pollution linked to birth defects. J. Environ. Health 65, 47–48 (2002) 23. Hansen, C.A., Barnett, A.G., Jalaludin, B.B., Morgan, G.G.: Ambient air pollution and birth defects in Brisbane, Australia. PLoS ONE 4(4), e5408 (2009) 24. Vinikoor-Imler, L.C., Davis, J.A., Meyer, R.E., Luben, T.J.: Early prenatal exposure to air pollution and its associations with birth defects in a state-wide birth cohort from North Carolina. Birth Defects Res. A: Clin. Mol. Teratol. 97(10), 696–701 (2013) 25. Rathore, M.M., Ahmad, A., Paul, A., Rho, S.: Urban planning and building smart cities based on the internet of things using big data analytics. Comput. Netw. 101, 63–80 (2016) 26. Environmental Protection Administration Executive Yuan R.O.C. (Taiwan) official website. http://210.69.101.63/taqm/en/PsiMap.aspx. Accessed 26 July 2019 27. Balaji, S., Nathani, K., Santhakumar, R.: IoT technology, applications and challenges: a contemporary survey. Wirel. Pers. Commun. 1–26 (2019) 28. Hu, Z., Bai, Z., Yang, Y., Zheng, Z., Bian, K., Song, L.: UAV aided aerial-ground IoT for air quality sensing in smart city: architecture, technologies, and implementation. IEEE Netw. 33 (2), 14–22 (2019) 29. Hasenfratz, D., Saukh, O., Sturzenegger, S., Thiele, L.: Participatory air pollution monitoring using smartphones. Mob. Sens. 1, 1–5 (2012)

Connected Objects in Information Systems Onyonkiton Th´eophile Aballo1(B) , Roland D´egu´enonvo2(B) , and Antoine Vianou1,2(B) 1

Doctoral School of Engineering Sciences, 01 BP 2009, Abomey-Calavi, Benin [email protected], [email protected] 2 Abomey-Calavi University, LETIA-UAC, Cotonou, Benin a [email protected]

Abstract. The Internet of Things connects technical systems and sometimes also mechanical parts, electronic or even raw materials, with the web, via standardized communication interfaces. This opens up the ability to monitor and control devices and feeds for authorized users, as well as providing access to malicious people. Connected objects are also increasingly used for industrial applications. We are now talking about Industry with connected and intelligent factories to gain competitiveness. But these devices present intrinsic vulnerabilities and risks related to the new uses made possible thanks to an almost continuous connectivity to the Internet. There are actually very few areas of empirical testing currently. While waiting for a greater technological maturity of the Internet of Things, using supervision provides complete visibility of the entire network, which can greatly contribute to its security. The purpose of this article is to study objects connected to information systems.. . .

Keywords: Flow

1

· Internet of Things · Risks · Vulnerabilities

Introduction

Nowadays, the internet has evolved to such an extent that it has become indispensable to life. At the very beginning, progress in the field of information and communication technologies plus, particularly the internet network and the services it offers, took place slowly but today innovation is occurring at a fast pace to the point of upsetting everyday life. In the same context, we can mention the appearance of connected objects, a new technology based on a combination of electronic equipment and smart software connected on the internet and offering services and applications ranging from the management of road traffic as a function of time and of the time in the field of transport, in the field of health. The development of communicating objects raises many issues related to the protection of information and communication systems that must guard against attacks and the diversion of their systems. c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 281–285, 2020. https://doi.org/10.1007/978-3-030-36778-7_30

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Connected Objects

A significant part of the billions of objects envisaged in the future will be measuring instruments, sensors, and remote actuators or communicating presence detectors. The table below compares the main technical characteristics of a connected object with those of office equipment (workstation and mobile, now well managed by the security of information systems). As shown in Table 1, above, the technical characteristics of connected objects are up to 1 million times lower than those of office equipment. Table 1. Technical comparison of an internal connected object Object connected to networks Operator Workstation or mobile RAM

3

100 ko

x20000

2 Go

Storage

256 ko

x1000000 256 Go

Frequency

32 MHz

x100

3 GHz

Consumption 10 Micronw

x1000000 10 W

Bandwidth

x10000

1 kbit/s

10 Mbit/s

Internet of Things

The Internet is a global network composed of several identifiable networks (public IP addresses) that can be reached through a standard communication protocol (TCP/IP), and an object is an element that cannot be precisely identified. Thus we can define the Internet of Things as a global element network communicating through a standard protocol. This approach shows two aspects of the Internet of Things (temporal and spatial) that allow people to connect from anywhere at any time through connected objects (Smartphone, tablets, sensors, CCTV cameras). The Internet of Things must be designed for easy use and secure manipulation to avoid potential threats and risks, while masking the underlying technological complexity. Communication between objects is a model based on wireless communication between two objects. The information is transmitted through the integration of a wireless communication technology like ZigBee or Bluetooth (Fig. 1).

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Fig. 1. Overview of a connected object environment

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Areas of Implementation

Internet of Things applications touch virtually every area of life: – – – – – – – – – –

Health and tele-monitoring systems; Connected agriculture to optimize the use of water; Connected vehicles to optimize urban traffic management; Appliances connected to optimize the consumption and distribution of electrical energy; Connected watches for well being and sport; Monitoring the temperature of a block within the laboratory Intrusion alert system in a server room- Automatic collection of symptoms in a patient Failure detection Smoke detection

To connect objects to information systems there are certain things to be aware of. It is: -Traffic model where the probability of observing k arrivals during a time interval t is given by

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P k(t) = [(βt)t/k!]E − βt

(1)

where β is the arrival rate – Access rate – Debit generated by the signaling.

5

Risk in Terms of Security

Two major aspects are critical this is the security of data and drifts related to the use of these data. These risks can be translated in several ways: – – – –

Theft of sensitive data for the benefit of a competitor or for blackmail, Loss of data on a hacked server, endangering some of the activities Loss of confidence following disclosure or loss of sensitive or private data, Involvement of industry materials in malicious acts with another of a third party. – Like a computer or smartphone, a connected object has a network address that allows it to communicate. And even if its operating system is more minimalist, it has the same flaws as any other connected media. In other words, all connected objects can be the target of an attack.

6

Conclusion

The Internet of Things has enabled the development of a large number of applications endowing intelligence in a number of areas: health, home, city, television, automotive, industrial processes. The number of connected objects grows exponentially. Technical solutions have been developed to allow interoperability between different levels such as applications, cloud services, communication networks and smart sensor components to the computer system. Security issues are a critical point. In addition, the rise of the Internet of Things does not depend solely on the possibility of cooperating common objects equipped with microelectronics. It is essential that there are simultaneously reliable and secure infrastructures, economic and legal conditions of use and a social consensus on how new technical opportunities should be used.

References 1. Forsberg, D., Ohba, Y., Patil, B., Tschofenig, H., Yegin, A.: Protocol for Carrying Authentication for Network Access (PANA), RFC 5191, IETF, May 2008 2. Aboba, B., Blunk, L., Vollbrecht, J., Carlson, J., Levkowetz, H.: Extensible Authentication Protocol (EAP), RFC 3748, IETF, June 2004 3. Pack, S., Choi, Y.: Pre-authenticated fast handoff in a public wireless LAN based on IEEE 802.1 x Model. Springer (2003) 4. Yeong, W., Howes, T., Kille, S.: Lightweight Directory Access Protocol, RFC 1777, IETF, March 1995

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5. Sun, S., Reilly, S., Lannom, L., Petrone, J.: Handle System Protocol (ver 2.1) Specification, RFC 3652, November 2003 6. Hern´ andez-Ramos, J.L., Jara, A.J., Mar´ın, L.: DCapBAC: embedding authorization logic into smart things through ECC optimizations, Int. J. Comput. Math. (2016) 7. Shelby, Z., Hartke, K., Bormann, C.: The Constrained Application Protocol (CoAP), RFC 7252, IETF, June 2014 8. Internet of things research study 2015 report, Hewlett Packard (2015)

The Efficient Network Interoperability in IoT Through Distributed Software-Defined Network with MQTT Rajae Tamri(&) and Said Rakrak Applied Mathematics and Computer Science Laboratory (LAMAI), Cadi Ayyad University, Marrakech, Morocco [email protected], [email protected]

Abstract. The IoT is an infrastructure of heterogeneous connected Things. The increase of different technologies used in IoT creates a high degree of heterogeneity at any level (device, network, middleware, application service, data, and semantics), thus bringing many challenges for ensuring interoperability. In this sense, this paper presented network interoperability, which is the ability to exchange information between heterogeneous smart Things/devices used heterogeneous communication protocols, also to integrate new devices into any IoT network. That requires efficient and dynamic solutions to ensure interoperability of this level. To deal with the IoT network heterogeneity challenge we propose an architecture solution that ensures networking interoperability using Distributed Software-defined networking (SDN) incorporate with MQTT (Message Queuing Telemetry Transport). In this architecture the control plan managed by several controllers and each of these controllers is responsible for the management of a particular domain of the IoT network and the exchange of information with the controllers of another domain of the same level. In addition, a powerful, high-level controller. That has a global view to manage and control entire architecture. It also equipped with the MQTT protocol, which offers a lot of advantages. Keywords: Interoperability


IoT
MQTT
Network interoperability
SDN

1 Introduction Interoperability in IoT is the ability to interoperate multiple smarts things and systems regardless of the deployed hardware and software. The diversity of heterogeneous technologies used in each layer of the IoT architecture raises the problem of interoperability on each of these layers. However, there is no universal standard or single architecture for IoT. In the literature [1–4], the IoT architecture is presented in different layered architectures of three, four, five layers and more. But, most of the proposed IoT architectures are presented in five layers. Each of them has a particular level of interoperability, as shown in Fig. 1.

© Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 286–291, 2020. https://doi.org/10.1007/978-3-030-36778-7_31

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The five layers of IoT architecture are: Sensing layer, Network layer, Middleware layer, Application layer, and Business layer. • The Sensing layer: is the first layer of the IoT architecture stack. This layer contains intelligent things (Tag (RFID), sensor (WSN)) whose main task is to automatically detect the environment. • The Network layer: The main goal of this layer is to ensure effective permanent connection between different smart things, gateways, network devices, and servers, despite the heterogeneous technologies used such as ZigBee, WPAN, WiFi, WiMAX, 5G, and LTE. • The Middleware layer: provides a software layer between applications, and network communication layers. The work of this layer is to process huge raw data from the network layer. The IoT middleware should integrate architectural features allowing programmatic abstraction, interoperability, adaptability, contextual knowledge, autonomy, and distribution, as well as the requirements mentioned in [5, 6]. • The application layer: is responsible for providing application-specific services to the end-user, also to deliver the required tools and services to monitor the environment. • The Business Layer: the main goal for this layer manages the overall IoT system activities and services to build a business model, graphs, flowcharts, etc. based on the received data from the Application layer.

Fig. 1. Levels IoT architecture and levels of interoperability in IoT

In this article, we focus on the second layer of IoT architecture, which must provide device connectivity, heterogeneous networks and a variety of communication protocols. Supporting the full connectivity of these heterogeneous devices is always a

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difficult task. Especially since existing architectures do not support it. Then the integration of other technologies to solve this interoperability problem is a necessity. One of these technologies [11] is to implement SDN, which provides a centralized control plane to all intelligent objects that only transmit data to each other with the help of heterogeneous technologies in the perception layer of the IoT architecture. Consequently, any new underlying switch/thing can be integrated without any incompatibility issues. Moreover, the SDN offers a lot of advantages for the management and monitoring of a mobile and heterogeneous IoT network [12]. In this paper, we propose a new distributed SDN architecture that separates the control plane and the data plane, with the combination of the Message Queuing Telemetry Transport (MQTT) protocol to solve the problem of interoperability in the network layer. This article is organized as follows: first, we discuss IoT interoperability in each layer, then SDN architecture, and finally our proposed architecture with some conclusions.

2 Interoperability of IoT IoT interoperability can be viewed from different perspectives [7], Noura et al. [8, 9] classified IOT interoperability: Device interoperability, Network interoperability, Syntactic interoperability, Semantic interoperability, and Platform interoperability. • Device interoperability: This interoperability is to enable exchanging information between heterogeneous devices that use heterogeneous communication technologies and the ability to integrate new smart things into any IoT platform. • Network interoperability: The IoT network environment is characterized by its heterogeneous and dynamic nature, the interoperability in this level should solve many problems such as addressing, routing, mobility, and quality of service, etc., to ensure efficient exchange of messages between different IoT systems through different heterogeneous networks. • Syntactical interoperability: the goal of semantic interoperability is ensuring data consistency also the Common understanding of the exchanged content meaningfully. • Semantic interoperability: usually refers to the data formats and structure used encodings of exchanged information in IoT heterogeneous environment, e.g., XML, JSON and RDF. • Platform interoperability: associated with the ability of different platforms to effectively communicate and transfer information even across the different architecture of information systems, domains, infrastructures or geographic regions, and cultures.

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3 SDN Architecture The SDN decouples the control plane from the data plane or forwarding devices (infrastructure layer) in IoT Network. This separation provides a network optimization architecture and several benefits that eliminate the rigidity present in traditional networks [10], also including the simplification of network management and control [11, 12]. The SDN architecture consists of three-layer [1, 13, 14] (i.e., Infrastructure layer, control layer, and application layer) and The Programming Interface APIs (i.e., southbound interface and northbound interface) allows communication between layers, while the east-west interface for the communication among multiple controllers, as shown in following Fig. 2. • Infrastructure layer: or The data plan includes the physical elements of the network, the function for these devices is forwarding the traffic according to the routing policy defined by the controller. The data plane layer are accessible and managed through Southbound APIs interface in SDN controller(s). The OpenFlow protocol is seen as the most popular southbound API for SDN [13]. • Control layer: maintains the global view of the entire network topology to configure the forwarding devices through a secure channel, It’s the core component of SDN. • The application layer: contains SDN applications, software services, and tools that are designed to meet network administrator requirements to monitor and configure the functionality remotely. It communicates with the control layer through the northbound APIs.

Fig. 2. SDN architecture

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4 The Proposed Architecture Our architecture is composed of two levels of controllers as shown in Fig. 3. The first level presents a more powerful main controller, called “Root controller” which treats the needs of the applications of the controllers requiring a global view of the network and rare events on all the different domains IoT. The root controller uses Publish/subscribe MQTT (Message Queuing Telemetry Transport) protocol, which is a widely adopted data distribution protocol in many international IoT standards, suitable for SDN because it uses a centralized broker to collect and transmit efficiently data. The information received from the broker allows this root controller to define the proper path for the transmission of data between the different lower-level controllers. The second level presents lower-level controllers, each controller manages a subnet/domain of the entire IoT network having a view on a particular IoT domain such as smart healthcare, smart city, smart house, smart grid, smart cars, etc. these controllers have their own view of their local network and each of its neighboring local networks is summarized as a logical node. They need to communicate through controller-to-controller channels to exchange needed state information (e.g. reachability information) regarding their domains. lower-level controllers (which are more localized than higher-level controllers) do not maintain a global view of the network IoT. This design is different concerning the network views of the controllers.

Fig. 3. MQTT SDN distributed architecture

5 Conclusions The paper presented a Distributed SDN architecture by incorporating the MQTT protocol, which offers multiple benefits in the IoT systems. this proposed architecture is an effective way to improve controller scalability and flexible interoperability in IoT

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networks. In such architecture, the control plane can consist of multiple controllers rather than a single one. These multiple controllers are responsible for managing different IoT domains in the network and exchanging information with controllers in other domains at the same level to improve the implementation of global policies. Our architecture is still in the simulation phase where we compare it with the works already proposed in the literature.

References 1. Bouchemal, N., Kallel, S., Bouchemal, N.: A survey : WSN heterogeneous architecture platform for IoT. Springer. https://doi.org/10.1007/978-3-030-19945-6 2. Burhan, M., Rehman, R.A.: IoT elements, layered architectures and security issues : a comprehensive survey 1–37 (2018). https://doi.org/10.3390/s18092796 3. Sciencedirect, P., Manoj, N., Kumar, P.: The Internet of Things: insights into the building blocks, component interactions, and architecture layers. Procedia Comput. Sci. 132, 109– 117 (2018). https://doi.org/10.1016/j.procs.2018.05.170 4. Dhivya, V., Singh, A.K.: Internet of Things : a survey of the advancements. Int. J. Eng. Technol. 7, 255–259 (2018) 5. Palade, A., Cabrera, C., Li, F., White, G., Siobhán, M.A.R.: Middleware for Internet of Things: an evaluation in a small-scale IoT environment. J. Reliab. Intell. Environ. (2018). https://doi.org/10.1007/s40860-018-0055-4 6. Rathod, D.: Survey of Middlewares for Internet of Things. In: International Conference on Recent Trends in Advance Computing, pp. 129–135 (2018) 7. Hatzivasilis, C., The, G.O., Hatzivasilis, G., Askoxylakis, I.: City Research Online City. University of London Institutional Repository the Interoperability of Things : 2600, pp. 0–2 (2018). https://doi.org/10.1109/CAMAD.2018.8514952 8. Noura, M.: Interoperability in Internet of Things : taxonomies and open challenges. Mob. Netw. Appl. 24(3), 796–809 (2019) 9. Noura, M., Gaedke, M., Atiquzzaman, M., Chemnitz, T.U.: Interoperability in Internet of Things infrastructure : classification, challenges, and future work interoperability classification in IoT (2018) 10. Son, J., Buyya, R.: A Taxonomy of software-defined networking (SDN) - enabled cloud computing. ACM Comput. Surv. (CSUR) 51(3), 59 (2018) 11. Valdivieso, L., Torres, R.V.: SDN/NFV architecture for IoT networks SDN/NFV architecture for IoT networks (2018). https://doi.org/10.5220/0007234804250429 12. Priya, I.D., Silas, S.: A survey on research challenges and applications in empowering the SDN-Based Internet of Things. Springer, Singapore. https://doi.org/10.1007/978-981-131882-5 13. Zhang, Y., Cui, L., Wang, W., Zhang, Y.: A survey on software defined networking with multiple controllers. J. Netw. Comput. Appl. 103, 101–118 (2018). https://doi.org/10.1016/j. jnca.2017.11.015 14. Karakus, M., Durresi, A.: Review article a survey: control plane scalability issues and approaches in software-defined networking (SDN). Comput. Netw. 112, 279–293 (2017). https://doi.org/10.1016/j.comnet.2016.11.017

5G Network Architecture in Marrakech City Center Fatima Zahra Hassani-Alaoui(&) and Jamal El Abbadi Smart Communications Research Team (ERSC), E3S Research Center, EMI, Mohammed V University Rabat, Rabat, Morocco [email protected], [email protected]

Abstract. The 5G architecture has been defined few months before launching the future generation of cellular networks. This revolutionary milestone encourages academics and researchers to improve this technology. The main goal of this perceivable work is to construct a 5G network in the center of Marrakech City, a convenable area to apply the Dense Urban Enhanced Mobile Broadband (eMBB) scenario. The non-standalone deployment was the best option for this work, due to its advantages for the signal transmission. Our construction study is based on the visualization of the Signal-to-Interferenceplus-Noise Ratio (SINR) in the simulation part, in order to test the transmissions effectiveness of the studied and the proposed scheme. This paper highlights the necessary elements that will constitute our 5G network. Using this framework, the 5G network planning in the center of Marrakech City will be evaluated. Keywords: 5G
5G New Radio (5G NR)
MIMO beamforming
mmWave
SINR
Marrakech City

1 Introduction Currently, the technology is developing day by day, due to the big concurrence between companies; as a result, consumers and businesses predict advanced opportunities in the coming technologies. The 4G mobile network provides the intended capacity, enhances throughput rate, reduces latency and makes new applications that never exist before with wireless networks. However, this situation will not last for a long period. As the number of connected devices increases, the actual networks will not respond to the consumer demands in the near future. For this reason, 5G has been emerged as a key to ameliorate the future wireless network. This generation will enhance the network capacity and throughput with peak data speeds up to 20 Gbps downlinks and up to 10 Gbps uplinks. The latency will be decreased to one millisecond. The energy efficiency will be over 100 times over the current network. On the other hand, the 5G mobile network takes the security priority to higher level, to protect both the networks and customers. As the world of technology is developing, Morocco is also concerned to improve its mobile network, because this country is one of the best investment destinations in Africa. © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 292–301, 2020. https://doi.org/10.1007/978-3-030-36778-7_32

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Marrakech City, which is one of the best touristic destinations in the world, is also an important geographic location which deserves to be technologically developed. The 5G mobile network is supposed to be part of the major important industrial domains, namely: education, healthcare, smart transportation…Researchers and Industrials have started to clarify the 5G architecture, and also the technologies that will be included into the network. In the first phase of 5G deployment, there will be improvements in LTE-Advanced and LTE Pro technologies. Those ameliorations will make the birth of 5G. Release 15, which is the first set of 5G standards, was delivered in December 2017, making a revolutionary milestone [1]. Rapidly than expected, the 5G planning has been developed. According to the 3rd Generation Partnership Project (3GPP), Release 17 will be defined at the end of 2019. In this paper, we invent a construction scenario of a 5G network in Marrakech City Center. The remainder of this detailed work is organized as follow. First, we present an overview of the fundamental technologies that make our 5G network. Then, we highlight the antennas conception as well as the SINR mathematical model used in this simulation-based work. From that point, we describe and discuss the simulation scenario and the results. Finally, we conclude by mentioning our future work.

2 Background The creation of a new mobile network generation begins by figuring out the requirements, the challenges and opportunities offered by this new technology. For this reason, it is compulsory to introduce our intervention by highlighting the indispensable elements and technologies that will constitute the 5G network. 2.1

The mmWave Spectrum Bands

Currently, the number of technological devices grows exponentially, the existing frequency bands [3 kHz–6 GHz] is starting to get overcrowded. Therefore, and in the future years, the communications will suffer from many issues, such as: dropped connections and overdue services. To resolve this problem, it is required to transmit signals on new frequency bands which called centimeter waves (cmWave) and millimeter waves (mmWave) (respectively [3 GHz–30 GHz] and [30 GHz–300 GHz]). The most interesting appeals for using this solution are: high capacity, high data rates and ultra-high reliability [2]. Contrariwise, using high frequency bands, present many challenges to surmount. The most important projects [3–8] proved that those new frequency bands have some shortcomings namely: higher sensitivity to obstacles, high pathloss and the decreased diffraction. Fortunately, this issue is solved by other technologies that will be implemented in the 5G infrastructure.

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MIMO Beamforming

Massive multiple input multiple output (MIMO) makes a big advancement in wireless systems. In the mmWave bands, a base station supports hundreds of antennas [9, 10]. The MIMO has many advantages, namely: limiting pathloss by using high antenna gain, improving the coverage. On the other hand, the MIMO system takes an important part in restricting interference. The MIMO realization can be configured in various options, the involvement of beamforming proceeding in those systems, is a particular method used to join multiple antenna elements to condense the power in a certain direction. The beamforming technology removes the interference, accordingly, upgrade the SNIR value [11]. In this stage, it is not imaginable a 5G network without this tech. 2.3

Deployment Cells

The criterion of using new spectrum preconditions the picking of the 5G deployment environment. The disadvantage of the cmWave bands and mmWave bands conduct us to think about the cell characteristics of the 5G network. As a solution, it is mandatory to deploy small cells and macro cells and ensuring the cooperation between those two kinds of cells. In this scenario, while small base stations provide high data rates, macrocells provide wide area coverage. All the previous elements will change the current mobile network infrastructure and bring the existence of the new generation 5G. As a result, a major step-up with the introduction of a new air interface 5G New Radio (5G NR) will take place [12]: • In Phase 1, the use of the Non-Stand Alone (NSA) option takes place. For this modality, devices will use the existing LTE radio and the core network. • In Phase 2, the use of the Stand Alone (SA) option is occurred. This deployment type involves, both, user and control plane using the new 5G core network architecture.

3 Methods 3.1

Antennas Conception

In this sub-section, we will point out the antenna characteristics for BS antennas and UE antennas, which will be used in this work. Base Station Antenna. BS antennas have one or multiple antenna panels (pending on the situation and condition) placed vertically, horizontally or in a two-dimensional array within each panel. Mathematically, each antenna panel possesses M  N antenna elements, namely N is the number of columns and M is the number of antenna elements with the same polarization in every column. The M  N elements may be single polarized or dual polarized. A uniform rectangular panel array is patterned, in the case where the BS has multiple antenna panels. This one includes Mi Ni antenna panels whither Mi is number of panels in a column and Nx is number of panels in a row. The

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antenna panels are evenly spaced with a center-to-center with a distance of di;H in the horizontal direction and di;V in the vertical direction. On the other hand, the orientation of the antenna is itemized as the angle among the principal antenna lobe center and an axis oriented toward east. The bearing angle increases in a clockwise orientation (see Fig. 1).

Fig. 1. The hexagonal cell is composed with three sites, and each site contains an antenna oriented in its direction.

User Equipment Antenna. By examining the UE antenna circumstance, there are two options for the antenna elements model, according to the frequency bands: • The Omnidirectional antenna element is presumed to be used in 700 MHz and 4 GHz cases. • The directional antenna panel is presumed to be used in 30 GHz and 70 GHz cases, for that the Mi Ni antenna panels can have various orientations. A general vision about BS and UE antennas is presented in Fig. 2.

Fig. 2. [13] position of BS and UE antennas during a transmission

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SINR Model

In this part, we will pinpoint the SINR mathematical model, with all its essential elements, that will be employed in the conception test of the 5G network, in order to put to test the robustness of radio links. The Signal-to-Interference-plus-Noise Ratio (SINR) is outlined as the amount used to give the upper theoretical limits on channel capacity in a wireless communication network. In other word, the SINR interprets the power of a signal divided by the sum of the interference power and the power of some noise (background noise) [14]. SINR is used to estimate the quality of the network connections (more details are presented in Table 1). Table 1. SINR values descriptions. SINR values  20 dB 13 dB to 20 dB 0 dB to 13 dB  0 dB

Signal description Excellent Good Fair to poor No signal

The general SINR formula is given by the following expression: SINR ¼

Signal power Interference power þ Noise

ð1Þ

Being more specific, the SINR mathematical formula is detailed as the following expression: SINRðlx Þ ¼

Prx ðtx Þ Pðtx ÞGðtx ; rx Þ    ¼ P ¼ N þ Irx ðLÞ N þ x6¼y P ty G ty ; rx

Pðtx Þ Dðtx ;rx Þr

Nþ

P

Pðty Þ

ð2Þ

x6¼y Dðty ;rx Þr

Whither: lx = (tx, rx): the pair of receiver and transmitter; Irx ðLÞ: the interference sum obtained by the set “L nodes” emitting simultaneously, excluding the chosen transmitter tx ; Prx ðtx Þ: the received power of a transmitted signal, which is sent by tx for an intended receiver rx ; Gðtx ; rx Þ: the propagation attenuation; r: the path-loss exponent, it depends on the environment conditions.

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4 Simulation Scenario Marrakech City Center is characterized by the high user density and traffic loads. Taking into account this condition, we will apply a convenable scenario for the Dense Urban Enhanced Mobile Broadband (eMBB) environment, which is one of the three principal 5G NR use cases environment. The 5G architectural construction network appropriate for this area, is based on a regular scheme, taking the form of a hexagon [13]. The selected study environment consists of two layers: • A macro layer: the base stations are placed in regular position (Fig. 3). • A micro layer: one cell contains three installed sites (Fig. 4).

Fig. 3. Structure of macro layer

Fig. 4. Structure of micro layer

The study area of this work is covered by 25 sites (Fig. 5), where three cells held in each site. Depending to the urban dense environment, the distance between two adjacent sites is determined to 400 m, in order to furnish the ubiquitous coverage, which will be used for the Urban Connected Car. On the other hand, we have well-respected that the UEs are distributed randomly and uniformly over the whole test area. In the first stage, we have created the locations corresponding to each cell site in the network plan, using the Guéliz region as the center location (Fig. 5).

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Fig. 5. Cell site emplacement in the center of Marrakech City

In the second stage, we have set the frequency used for the transmissions. As we have chosen the 5G Non-Standalone deployment option for this work, the determined frequency is 4 GHz. In the third stage, we have determined the antenna characteristics for both base station antennas, and UE antennas (Table 2). We have established a uniform rectangular phased array System MIMO 8  8. We have chosen the parameters in a way to increase the directional gain and the peak SINR values, in order to ensure the strongest transmissions. The International Mobile Telecommunications-2020 (IMT-2020) has defined three principal scenarios for 5G deployment. In this paper, we have inspired the Antennas configuration parameters from some IMT-2020 propositions presented in [13]. For the other parameters, we have set our optimum choice, based on a deep study, taking into account the infrastructure of Marrakech City, namely: the density, the buildings and the vehicles. More details about the configuration parameters are presented in Table 2.

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Table 2. Simulation parameters Parameters Carrier frequency Bandwidth Total transmit power TX antenna array TX antenna height TX antenna element gain TX azimuth beam width TX elevation beam width Tilt angle Down tilt RX antenna height RX antenna element gain RX noise figure TX noise figure Maximum backward attenuation Thermal noise level Simulation bandwidth for TDD

Value 4 GHz 20 MHz 44 dBm 88 25 m 8 dBi 65° 65° 0° 15° 1.5 m 0 dBi 7 dB 5 dB 30 dB −174 dBm/Hz 20 MHz

As mentioned before, this simulation-based work is founded on the visualization of the SINR on the map, in order to test the power and the efficiency of the transmissions and the signal.

5 Simulation Results The chosen configuration of rectangular antenna array, used in this work, has proved its efficiency; the SINR values are peaked, and the ubiquitous coverage has been guaranteed (Fig. 6). The configuration, that we have picked, belongs to the Non-standalone deployment mode; contrary to the Stand-alone deployment mode, this type uses low frequencies. In the non-standalone scenarios, there will be a co-deployment between nodes operated in high frequencies and low frequencies. This conception option affords more optimization opportunities for the system transmission, notably when low frequency warrant initial access. While in the standalone deployments, it is absolutely necessary to optimize the coordination of the Access Point. This considerable advantage makes the non-standalone deployment the winner option for this work. On the other hand, the proposed scenario will be the most economic deployment scenario in the near future, because it will be co-deployed with the current 4G generation. However, and in the long-term, it will be optimized towards the 5G Standalone option, because the need for capacity and throughput is increasing exponentially with the technology advancement.

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Fig. 6. 5G architecture simulation result in the center of Marrakech City

6 Conclusion and Future Trends 5G network characteristics are determined, more precisely, by the locations, carriers and devices. This paper aims to propose a scenario of 5G construction in the center of Marrakech City, one of the best touristic destinations in the world. Due to the importance of this region, we have chosen this location to propose a solution for the high user density and traffic loads. For that, we have applied the Enhanced Mobile Broadband (eMBB) scenario.

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While everyone looks impatience forward launching the 5G wireless networks, it is mandatory to think about Green Communications, which guarantee energy efficient, low cost and especially safe for the human body. Planning a 5G network in different important cities, while respecting Green Communications, presents the objective of a future work.

References 1. RAN Rel-16 progress and Rel-17 potential work areas (n.d.). https://www.3gpp.org/newsevents/2058-ran-rel-16-progress-and-rel-17-potential-work-areas 2. Hassani-Alaoui, F.Z., El Abbadi, J.: What Will millimeter wave communication (mmWave) Be?, pp. 119–131. Springer, Cham (2019) 3. Document Number: H2020-ICT-671650-mmMAGIC/D2.2, Project Name: Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications: Measurement Results and Final mmMAGIC Channel Models Measurement Results and Final mmMAGIC Channel Models (2017) 4. mmMAGIC – mm-Wave based Mobile Radio Access Network for 5G Integrated Communications (n.d.). https://5g-mmmagic.eu/ 5. Rappaport, T.S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., Wong, G.N., Schulz, J. K., Samimi, M., Gutierrez, F.: Millimeter wave mobile communications for 5G Cellular: it will work! IEEE Access 1, 335–349 (2013) 6. Maccartney, G.R., Rappaport, T.S., Samimi, M.K., Sun, S.: Millimeter-wave omnidirectional path loss data for Small cell 5G channel modeling. IEEE Access 3, 1573–1580 (2015) 7. Al-Dabbagh, R.K., Al-Raweshidy, H.S., Al-Aboody, N.A.: Performance comparison of exploiting different millimetre-wave bands in 5G cellular networks. In: 2017 International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), pp. 1–6 (2017) 8. MacCartney, G.R., Rappaport, T.S., Ghosh, A.: Base station diversity propagation measurements at 73 GHz millimeter-wave for 5G coordinated multipoint (CoMP) analysis. In: 2017 IEEE Globecom Workshops (GC Wkshps), pp. 1–7. IEEE (2017) 9. Abidin, I.S.Z., Brown, T.W.C.: Improving MIMO multiplexing for mmWave static links. In: 2018 Australian Microwave Symposium (AMS), pp. 41–42 (2018) 10. Kong, H., Wen, Z., Jing, Y., Yau, M.: A compact millimeter wave (mmWave) mid-field over the air (OTA) RF performance test system for 5G massive MIMO devices. In: 2018 IEEE MTT-S International Wireless Symposium (IWS), pp. 1–4 (2018) 11. Karabulut, U., Awada, A., Lobinger, A., Viering, I., Simsek, M., Fettweis, G.P.: Average downlink SINR model for 5G mmWave networks with analog beamforming. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2018) 12. 3gpp Release 15 Overview - IEEE Spectrum (n.d.). https://spectrum.ieee.org/telecom/ wireless/3gpp-release-15-overview 13. Draft new Report ITU-R M.[IMT-2020.EVAL] – Guidelines for evaluation of radio interface technologies for IMT-2020 (n.d.) 14. Dolev, S.,(ed.): Algorithmic aspects of wireless sensor networks, vol. 5804. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-05434-1

A Distance Integrated Triage System for Crowded Health Centers Kambombo Mtonga1(B) , Willie Kasakula1 , Santhi Kumaran1 , Kayalvizhi Jayavel2 , Jimmy Nsenga1 , and Chomora Mikeka3 1

2

African Center of Excellence in Internet of Things, University of Rwanda, P.O Box 3900, Kigali, Rwanda [email protected] Department of Information Technoloy, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India 3 Physics Department, University of Malawi, P.O Box 280, Zomba, Malawi

Abstract. Due to overcrowding in hospital waiting rooms, queue abandonment by frustrated patients remains a great problem. In the outpatient department, patients are normally served on a first-come-firstserve policy. Since there exists a distance decay association, whereby patients living further away from healthcare facilities experience worse health outcomes, it is these patients that are likely to return home without medical assistance. In the developing world, health facilities are few and scattered such that patients walk long distance to reach to the nearest health center. Triage can play an important role to ensure that such patients have a better chance to access medical care. Unfortunately, all the existing triage systems do not consider patient distance. In this paper, we propose a distance integrated triage system. We propose using patient distance as a queue shuffling variable. The patient’s vitals are captured by a kit of bio-sensors. This is unlike the existing triage systems that are associated with mis-triage due to lack of discriminator use or numerical miscalculations. Our work is based on the Charlotte Maxeke Johannesburg Academic Hospital triage system which is based on the Cape Triage System. Keywords: Patient triage · Overcrowded waiting room · Queue abandonment · Patient distance · Internet of things · Vital signs

1

Introduction

Overcrowding of patients in hospital waiting rooms is among the many challenges affecting the quality of health service. Other problems include; lack of proper medical equipment, low-skilled staffs, limited healthcare professionals, delay response in emergency services, and unreliable and error-prone diagnostics resulting from manual clinical data capture processes. The situation is dire in the developing world. Overcrowding of patients in waiting rooms implies long c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 302–311, 2020. https://doi.org/10.1007/978-3-030-36778-7_33

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queues and hence patients have to endure a lengthy and painful wait for treatment. Patients are likely to react with dissatisfaction with the clinical encounter to the point where they may leave the clinic before the exam is complete [1]. Queue abandonment can expose patients to the risk of adverse medical events. Triage (a word derived from the French word trier, meaning “to sort”), is an important process in establishing order in which patients receive care at the hospital [2,3]. The outpatient department (OPD) operates with both appointment and nonappointment patients. Non-appointment patients include new patients who first arrive at the hospital, walk-in patients and no-show patients who missed their appointments. In the sub-Saharan Africa, the challenge is mainly due to the fact that the OPD is flooded with non-appointment patients. Unfortunately, this high demand of health services usually exceeds the capacity of the health care facilities, that are seldom appropriately staffed. Worse, in the developing world, health facilities are few and scattered such that patients walk long distance to reach to the nearest health center. Since in the OPD patients are normally served on first-come-first-serve basis, patients that cover long distances are likely to experience worse health outcome than those that live nearer [4,5]. Considering that some patients may deteriorate while waiting to be attended to, it is important that patients in the OPD should be triaged so that treatment is administered in order of urgency, then in order of arrival [6]. 1.1

Patient Queue Abandonment and Information and Communication Technology Integration

Queue abandonment from an OPD is one problem that undermines patient access to health care services. Queue length and other observable queue flows during the waiting exposure have direct influence on queue abandonment. Literature on psychological responses to waiting shows that people are happier and that waiting seems less onerous when people are kept informed of why they are waiting and how long the wait will last [7,8]. With regard to these findings, it is clear that providing waiting patients with as much information as possible about the wait is beneficial. Hence, the integration of appropriate technology for information dissemination in hospital waiting rooms can help to minimize queue abandonment by patients.

Fig. 1. Considered hospital environment

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In this paper we consider a setting (See Fig. 1) in which waiting patients can observe the waiting room but they cannot observe the service-delivery portion of the system (the treatment area). We assume that the patients in the waiting room observe and consider two types of variables, namely; stock and flow variables. Stock variables describe the number of other patients in the waiting room, such as the total number of patients, the total number of patients with a higher priority, or the total number of patients with a later arrival time. Flow variables describe the rate with which the queue is depleted as well as the rate with which new patients arrive, such as the number of arrivals in the last hour, the number of departures in the last hour, or the number of patients who have been served in the last hour before patients who had an earlier arrival time. Some of these variables can be directly observed by the patient whereas others can be inferred [9]. We further assume that the priority data (i.e. the priority score) of patients are shared with all the patients in the waiting room through a TV screen, such that patients have an idea of patients with a high priority score [10].

2

Related Work

Several triage systems are in use in various hospitals today [11,12]. Basically, these triage systems aid the evaluation and classification of patients based on their urgency level based on the vital signs measures and clinical data of the patient. The goal is making the classification in the shortest possible time and with a minimal error percentage. The typical vital signs considered in the triage are Heart Rate, Respiratory Frequency, Oxygen Saturation, Corporal Temperature, and Blood Pressure. Each of these triage tools require extensive training to implement, making their widespread adoption in the Sub-Saharan Africa problematic. Furthermore, the time taken to triage each patient is too long for most health facilities in the Sub-Saharan African setting, where the caseload presenting to many of these health facilities is so large that a rapid system is required [13]. Table 1. Abridged CTS triage score table Triage score 2

1

0

SBP

71 − 80 81 − 100 101 − 199

HR

Group i other, resulting in the Gi Pi => group head of network diameter of 1. Group i Each Gi is connected to the transit ring network Fig. 1. A two-level structured architecture with distinct via its group-head Pi. resource types 3. Each peer on the transit ring network maintains a global resource table (GRT) that consists of n number of tuples. GRT contains one tuple per group and each tuple is of the form , where Group Head Logical Address refers to the logical address assigned to a node by our proposed overlay P2P architecture. Also, Resource Code is the same as the group-head logical address. 4. Any communication between a peer pi
Gi and pj
Gj takes place only via the respective group-heads Pi and Pj. The proposed architecture is shown in Fig. 1. 2.2

Relevant Properties of Modular Arithmetic

Consider the set Sn of nonnegative integers less than n, given as Sn = {0, 1, 2, .… (n – 1)}. This is referred to as the set of residues, or residue classes (mod n). That is, each integer in Sn represents a residue class (RC). These residue classes can be labelled as [0], [1], [2], …, [n – 1], where [r] = {a: a is an integer, a  r (mod n). For example, for n = 3, the classes are: [0] = {…., −6, −3, 0, 3, 6, …} [1] = {…., −5, −2, 1, 4, 7, …} [2] = {…., −4, −1, 2, 5, 8, …}

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Thus, any class r (mod n) of Sn can be written as follows: [r] = {.…, (r − 2n), (r − n), r, (r + n), (r + 2 n), …, (r + (j − 1). n), (r + j.n), (r + (j + 1). n), …..} In the proposed P2P architecture, we shall use the numbers belonging to different classes as the logical addresses of the peers; therefore, for the sake of simplicity we shall use only the positive integer values. Before we propose the mechanism of logical address assignments, we state the following relevant property of residue class. Lemma 1. Any two numbers of any class r of Sn are mutually congruent. Proof. Let us consider any two numbers N′ and N″ of class r. these numbers can be written as N0  r ðmod nÞ; therefore; ðN0 rÞ=n ¼ an integer; say I0

ð1Þ

and N00  r ðmod nÞ; therefore; ðN00 rÞ=n ¼ an integer; say I00

ð2Þ

Using (1) and (2) we get the following, (N′ – N″)/n = ((N′ – r) – (N″ – r)) /n = I′ – I = an integer. Therefore, N′ is congruent to N″; that is, N′ N″ (mod n); also, N″  N′ (mod n) because congruence relation () is symmetric. Hence, the proof. □ ″

Congruency Definition. *It may be noted that a  b (mod c) with c 6¼ 0, means that a is congruent to b provided (a − b)/c is an integer. 2.3

Assignments of Overlay Addresses

Assume that in an interest-based P2P system there are n distinct resource types. Note that n can be set to an extremely large value a priori to accommodate large number of distinct resource types. Consider the set of all peers in the system given as S = {PRi}, 0  i  n − 1. Also, as mentioned earlier, for each subset PRi (i.e. group Gi) peer Pi is the first peer with resource type Ri to join the system. In the proposed overlay architecture, the positive numbers belonging to different classes are used to define the following parameters: 1. Logical addresses of peers in a subnet PRi (i.e. group Gi). Use of these addresses will be shown to justify that all peers in Gi are directly connected to each other (logically) forming an overlay network of diameter 1. In graph theoretic term, each Gi is a complete graph. 2. Identifying peers that are neighbors to each other on the transit ring network. 3. Identifying each distinct resource type with unique code. The assignment of logical addresses to the peers at the two levels and the resources happen as follows: 1. At level-1, each group-head Pr of group Gr is assigned with the minimum nonnegative number (r) of residue class r (mod n) of the residue system Sn.

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2. At level-2, all peers having the same resource type Rr will form the group Gr (i.e. the subset PRr) with the group-head Pr connected to the transit ring network. Each new peer joining group Gr is given the group membership address (r + j.n), for j = 0, 1, 2, … 3. Resource type Rr possessed by peers in Gr is assigned the code r which is also the logical address of the group-head Pr of group Gr. 4. Each time a new group-head joins, a corresponding tuple is entered in the global resource table (GRT). Remark 1. GRT remains sorted with respect to the logical addresses of the groupheads. Definition 3. Two peers Pi and Pj on the ring network are logically linked together if (i + 1) mod n = j. Remark 2. The last group-head Pn−1 and the first group-head P0 are neighbors based on Definition 3. It justifies that the transit network is a ring. Definition 4. Two peers of a group Gr are logically linked together if their assigned logical addresses are mutually congruent. Lemma 2. Diameter of the transit ring network is n/2. Lemma 3. Each group Gr forms a complete graph. Proof. According to Definition 4, two peers of a group Gr are logically linked together if their assigned logical addresses are mutually congruent. Also from Lemma 1, we note that any two numbers of any class r of Sn are mutually congruent. Therefore, every peer has direct logical connection with every other peer in the same group Gr. Hence, the proof. □ 2.4

Salient Features of the Overlay Architecture

We summarize the salient features of the proposed architecture. 1. It is a hierarchical overlay network architecture consisting of two levels; at each level the network is a structured one. 2. Use of modular arithmetic allows a group-head address to be identical to the resource type owned by the group. We will show in the following section the benefit of this idea from the viewpoint of achieving reasonably very low search latency. 3. Number of peers on the ring is equal to the number of distinct resource types, unlike in existing distributed hash table-based works some of which use a ring network at the heart of their proposed architecture [5, 6]. 4. The transit ring network has the diameter of n/2. Note that in general in any P2P network, the total number of peers N  n. 5. Each overlay network at level 2 is completely connected. That is, in graph theoretic term it is a complete graph consisting of the peers in the group. So its diameter is just 1. Because of this smallest possible diameter (in terms of number of overlay hops) the architecture offers minimum search latency inside a group.

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3 Data Lookup Data lookup can be either intra-group or inter-group. The former one means that a peer p0i (
Gi) requests for some resource ˂Ri, V″˃ which it does not possess. Note that only some peer(s) p00i (
Gi) can possess ˂Ri, V″˃ if at all; no other peer in any other group Gk can possess it since it is an interest based architecture. The inter-group data lookup is invoked when a peer p0i (
Gi) requests for resource ˂Rj, V*˃, that can only be possessed, if at all, by some peer p0j in group Gj. The following data structure will be used for efficient data lookup. As mentioned earlier every group-head Pi will maintain a global resource table (GRT) with identical contents. We have earlier mentioned that the code of a resource type Ri is the same as the logical address of the corresponding group-head Pi. Apart from maintaining a GRT, each Pi maintains the following: each Pi has pointers to its two neighbors on the transit ring network. That is, each Pi knows the IP address of each of its two-neighboring group-heads Pi-1 and Pi+1. The pointer information of Pi is also saved in the peer (
Gi) with the next logical address. This saved information can be used to achieve fault tolerance in the event that Pi crashes or leaves. Each member peer in a group maintains a list of all its neighbors present in the group. It has been mentioned earlier in Sect. 2.3 that ‘n’ (number of distinct resource types) can be set to a very large value. However, it will not reduce the efficiency of the data look up because it may be noted that due to cluster formation even if number of peers is large, the number of clusters is expected to be much less than the total number of peers in the system. Hence it can justify the efficiency of the data lookup. 3.1

Intra-group Data Lookup

Without any loss of generality, let us consider a data lookup in group Gi by a peer p’ possessing ˂Ri, V′˃ and requesting for ˂Ri, V″˃. The algorithm for intra-group data lookup is presented in Algorithm-Intra. 3.2

Inter-group Data Lookup

In our proposed architecture, any communication between a node pi
Gi and pj
Gj takes place only via the respective group-heads Pi and Pj. Without any loss of generality let a peer p0i (
Gi) request for ˂Rj, V*˃. The following steps are executed to answer the query. Peer p0i knows that Rj 62 Gi. Assume that there are n distinct resource types. In order to locate resource Rj, a search along the transit ring network is required. The algorithm for intra-group data lookup is presented in Algorithm-Inter. 3.3

Data Lookup Complexity

In Chord [5] search along the chord is not followed, because it is very inefficient in a large peer to peer system since the mean number of hops required per search is N/2, where N is the total number of peers in the system. In our work, the mean number of hops required (on the ring network) per search is n/2, where n is the number of distinct

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resources. Fact is, in general, total number of peers N is much larger than the number of distinct resource types n; hence search along the transit ring network in our work can be quite efficient. It is also apparent from the fact that in Chord [5] and in other structured P-2-P systems [3, 4] the complexity involved in data lookup is a function of the no. of peers N in the system; where as in the proposed architecture it is a function of the number of distinct resource types n. The point to mention is that use of the same code to denote a resource type Ri and the corresponding group-head Pi has made the search process simple and efficient. Thus, the time
complexity for data lookup in our presented architecture is bounded by 1 þ n2 . In Table 1, we have presented data lookup complexity of our approach as well as those of some important existing DHT based systems. Observe that from the viewpoint of data lookup complexity, our proposed architecture offers better performance. The Data Lookup Complexity Comparison is in Table 1 (Figs. 2 and 3).

Algorithm-Inter 1.

pi' sends a data lookup request for ˂Rj,V*˃ to its group-head Pi

2.

Pi determines the group-head Pj's address code from GRT

/ one hop communication

/ address code of Pj = resource code of Rj = j

3. 4.

/ Looking for minimum no. of hops along the transit ring network

Algorithm-Intra 1.

p' broadcasts its request in Gi for ˂Ri,V"˃ / one hop communication since diameter of Gi is one

2.

if p" with ˂Ri,V"˃ then p" unicasts ˂Ri,V"˃ to p' else search for ˂Ri,V"˃ fails /search latency is minimum, i.e. only two hops

Fig. 2. Algorithm-Intra

Pi computes │i - j│= h if h ˃ n / 2 then Pi forwards the request along with the IP address of pi' to its immediate predecessor Pi-1 else Pi forwards the request along with the IP address of pi' to its immediate successor Pi+1

5.

Each intermediate group-head Pk forwards the request until Pk = Pj / no. of hops along the ring in worst case is n / 2

6.

7.

if Pj possesses ˂Rj,V*˃ then Pj unicasts ˂Rj,V*˃ to pi' else Pj broadcasts the request for ˂Rj,V*˃ in Gj / one hop communication if pj" (ϵ Gj) with ˂Rj,V*˃ then pj" unicasts ˂Rj,V*˃ to pi' else Pj unicasts ‘search fails’ to pi'

Fig. 3. Algorithm- Inter

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Table 1. Data Lookup Complexity Comparison CAN Architecture DHT-based

Pastry DHT-based

Lookup Protocol

Matching key and prefix in NodeID

Parameters

Chord DHTbased {Key, value} pairs to Matching map a point P in the key and coordinate space using NodeID uniform hash function N-number of peers in network, d-number of dimensions

Lookup O(d N1/d) performance

Our work RC-based

Inter-Group: Routing through group-heads Intra-group: Complete graph N-number N-number of peers in n - Number of network, b-number of distinct resource of peers bits (B = 2b) used for types in network the base of the chosen N-number of peers in network identifier nN O(log N) O(log BN) Inter-Group: O(n) Intra-group: O(1)

4 Conclusion In this paper, we have presented a new non-DHT based structured P2P architecture. We have applied some property of modular arithmetic, specifically residue class (RC), to design a scalable, hierarchical structured overlay P2P system, which provides highly efficient data lookup algorithms. One noteworthy point is that complexity involved in data lookup is a function of the number of distinct resource types n unlike in DHTbased systems. Another point to mention is that use of the same code to denote a resource type Ri and the corresponding group-head Pi has made the search process simple and efficient. No peer can have more than one resource type is not a restriction because it can be generalized. So that a peer can possess more than one distinct resource type (work under progress).

References 1. Ganesan, P., Sun, Q., Garcia-Molina, H.: YAPPERS: a Peer-to-Peer lookup service over arbitrary topology. In: Proceedings IEEE Infocom 2003, San Francisco, USA, 30 March–1 April 2003 2. Chawathe, Y., Ratnasamy, S., Breslau, L., Lanham, N., Shenker, S.: Making gnutella-like P2P systems scalable. In: Proceedings ACM SIGCOMM, Karlsruhe, Germany, 25–29 August 2003 3. Zhao, B.Y., Huang, L., Rhea, S.C., Stribling, J., Zoseph, A., Kubiatowicz, J.D.: Tapestry: a global-scale overlay for rapid service deployment. IEEE J-SAC 22(1), 41–53 (2004)

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4. Rowstron, A., Druschel, P.: Pastry: scalable, distributed object location and routing for large scale Peer-to-Peer systems. In: Proceedings FIP/ACM International Conference Distributed Systems Platforms (Middleware), pp. 329–350 (2001) 5. Stocia, I., Morris, R., Liben-Nowell, D., Karger, D.R., Kaashoek, M., Dabek, F., Balakrishnan, H.: Chord: A scalable Peer-to-Peer lookup protocol for internet applications. IEEE/ACM Tran. Netw. 11(1), 17–32 (2003) 6. Yang, M., Yang, Y.: An efficient hybrid Peer-to-Peer system for distributed data sharing. IEEE Trans. Comput. 59(9), 1158–1171 (2010) 7. Xu, M., Zhou, S., Guan, J.: A new and effective hierarchical overlay structure for Peer-toPeer networks. Comput. Commun. 34, 862–874 (2011) 8. Korzun, D., Gurtov, A.: Hierarchical architectures in structured Peer-to-Peer overlay networks. Peer-to-Peer Netw. Appl. 7, 1–37 (2013) 9. Peng, Z., Duan, Z., Qi, J.J., Cao, Y., Lv, E.: HP2P: a hybrid hierarchical P2P network. In: Proceedings International Conference Digital Society (2007) 10. Shuang, K., Zhang, P., Su, S.: Comb: a resilient and efficient two-hop lookup service for distributed communication system. Secur. Commun. Netw. 8(10), 1890–1903 (2015) 11. Kleis, M., Lua, E.K., Zhou, X.: Hierarchical Peer-to-Peer networks using lightweight SuperPeer topologies. In: Proceedings IEEE Symposium Computers and Communications (2005) 12. Zhang, R., Hu, Y.C.: Assisted Peer-to-Peer search with partial indexing. IEEE Trans. Parallel Distrib. Syst. 18(8), 1146–1158 (2007) 13. Cohen, E., Fiat, A., Kaplan, H.: Associative search in Peer-to-Peer networks: harnessing latent semantics. Comput. Netw. 2, 1261–1271 (2003) 14. Passarella, A.: A survey on content-centric technologies for the current internet: CDN and P2P solutions. Comput. Commun. 35, 1–32 (2012)

Grey Wolf Optimizer for Virtual Network Embedding in SDN-Enabled Cloud Environment Abderrahim Bouchair(&), Sid Ahmed Makhlouf, and Yagoubi Belabbas LIO Laboratory, University of Oran1, Oran, Algeria [email protected], [email protected], [email protected]

Abstract. Network technologies are dealing with a massive urge to breakthrough the fundamental endorsements of networks. Software-Defined Networking (SDN) is taking the lead in cloud Data Centers (DCs) to ensure the resource management of many policy adaptations, regarding the performance of Network Virtualization (NV) that must find the appropriate hardware components to map either a Virtual Machine (VM) or a virtual link, which resume the general concept of Virtual Network Embedding (VNE). In this paper, a Grey Wolf Optimizer (GWO) is represented as an intelligent approach for solving the VNE problem in the cloud with SDN consolidation. It is a recent meta-heuristic with low complex processing. Our implementation is based on CloudSimSDN that is an extension from the CloudSim simulation tool. The results indicate that maximizing the utilization of localhost resources maintain a considerable amount of energy consumption and consequently will provide better policy management for physical DCs. Keywords: Cloud computing
Software-Defined Networking
Virtual Network Embedding
Swarm Intelligence
Grey Wolf Optimizer
Resource utilization
CloudSimSDN

1 Introduction Cloud computing is having a significant matter related to its ability to control and monitor large resource quantities, in terms of data sharing, high availability of services with free open access to clients from anywhere. This will force to incorporate a particularly powerful computation requirement, distributed extensively in different spots from the cloud. That being said, a promising innovation presented as Software Defined Networking (SDN) has been rising over the past few years, to overrule these currently standing issues, by providing the possibility to have a global view to the system and centralized resource management. Accord- ing to the open networking foundation [1], SDN is an accomplished process of decoupling the control plane from the data plane through the abstraction of the underlying network infrastructure from the application, and logically centralize the network intelligence relying on the system condition. © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 321–330, 2020. https://doi.org/10.1007/978-3-030-36778-7_35

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Following the cloud development, the present cloud network services find many opening chances to be combined with the SDN architecture, in order to provide an efficient resource provisioning and a dynamic management-access inside DCs, and eventually led to deliver SDN-enabled cloud as a modern concept. NV allow the users to create Virtual Networks (VNs) that can be considered as a Substrate Networks (SNs, a physical networks) from a cloud customer perspective. It focuses on mapping the VN elements, which can be a virtual node or virtual link in an SN, by choosing the suitable substrate node or a substrate link. Virtual Network Embedding (VNE) is an NP-hard problem that has the same process defined as building a network on-demand, known generally as virtual machines deployment. Metaheuristicbased solutions like Practical Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Genetic algorithm (GA) have proved its efficiency upon many given scenarios, to find a close optimal solution for VNE problems in regards to specific quality measures [2]. We employed a novel Swarm Intelligence (SI) approach introduced as GWO [13] that depends on the host link capacity allocation within VNE, and the use of SDN in a cloud-based model. This paper is arranged in the following sequent: Sect. 2, presents the most associated works to our contribution. Next, Sect. 3 discusses the role of SDN in the cloud. Section 4 shows a brief summary of VNE and GWO. Our contribution, including an implementation description and the experimentation outcomes, are outlined in Sect. 5. Finally, a conclusion statement is featured along with recommended future works.

2 Related Works A major aspect of VNE studies was conducted into the field of cloud computing, where different challenging case scenarios can be found. Thus, many methods were applied to attempt to solve these problems by giving a near-optimal solution toward the diversity of cloud features. Mijumbi et al. [3], has presented effective resource management based on SDN controller by adjusting the map- ping status of virtual networks to substrate networks, the simulations performed on the Mininet simulator proved genuine improvements compared with other approaches in case of link and switch utilization and acceptance ratio value. Dehury et al. [4], has proposed a VNE algorithm that initially selects a set of physical hosts to reduce the embedding time of each virtual network request and maximize the resource utilization dynamically. The experiments were done on a physical cloud environment and its efficiency is based on fitness values. An optimal Virtual Machine Placement (VMP) approach based on GWO was employed by [5] to reduce the number of active servers, which lead to minimizing power consumption. The excremental results using the toolkit CloudSim [6] were extremely related to VMs number. In [7], the authors have proposed an intelligent VM deployment method to reduce power consumption using GWO, formulated as a binpacking optimization problem and simulated with the Claudio software. The results show a significant minimization of the server’s runtime and lowest energy consumption compared to other metaheuristics. A contribution was proposed by Nasiri and

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Derakhshan [8], addressing the VNE problem for SDN architectures to assign VNs on SNs through minimizing the physical resources, using Breadth-First Search (BFS) algorithm based on NetworkX software. The results show that each VN has a balanced CPU and bandwidth utilization. In [9], Yao et al. proposed a PSO approach based on Maximum Flow (PSO-MF) to expand the network resources utilization in SDN systems by adopting a set of forwarding rules. The evaluation results pointed out an advanced performance of the proposed approach compared with several greedy algorithms. We noted that the previously mentioned works were interested in optimizing the use of cloud resources in many cases, such as VNE and resource allocation regardless to SDN-based environment. The absence of flexible capacity and practical accessibility in these studies has taken our attention, knowing that they are beneficially available in an enabled cloud system and likely provides enhanced global solutions. From this perspective, this work is devoted to improving the collaboration of SDN architecture with cloud properties focusing on VNE.

3 SDN-Enabled Cloud According to [1], SDN is logically divided into three primary layers (planes) as shown in Fig. 1: (i) Application plane that essentially separates the actual software functionalities like the firewall software from the hardware firewall. It also represents abstract visibility on the decision process depending on the nature of the request; (ii) Control plane, which is a set of management servers such as SDN controller, that communicate with all types of data plane networking equipment, using the OpenFlow [10] protocol to emphasize the packets in traffic. (iii) The data plane (forwarding plane) exemplifies all the physical/virtual components and basic mechanisms to forward data to all the switches and routers that allows packets to go from the source to destination.

Fig. 1. Integration of SDN main layers onto the paradigm of cloud computing.

Cloud computing established SDN implementation to obtain advanced controllability with a dynamic network architecture, by having a sufficient programmable

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infrastructure, which will convert the classic cloud network basis into a full-service delivery [11]. This cloud support is presented in Fig. 1, as we can see the merging of every plane to its convenient model level; application plane to Software as a Service (SaaS) that contains the dematerialized frameworks, usually used in Pay-Per-Use tenet. Platform as a Service (PaaS) allows the users to build their applications to meet their specific needs; also, the cloud providers will have logically centralized supervision extended from the SDN control plane. The OpenFlow protocol engaged between the data plane and the control plane will adjust the resource arrangement in the Infrastructure as a Service (IaaS) model by having more efficient data-awareness. SDNenabled cloud is introduced as a new type cloud that expends the SDN employment not only in DC but in the intercloud networking [12], by adding a Network as a Service (NaaS) as a new sub-feature in the PaaS model to boost-up the cloud improvement interactions in Wide Area Networks (WANs).

4 Virtual Network Embedding and Grey Wolf Optimizer Recently, metaheuristic-based solutions are drawing much attention to optimize the VNE problems, allowing to make a rational analysis within a large scale environment. In this section, we described the fundamental aspects of the VNE problem then the novel metaheuristic GWO is presented. 4.1

Virtual Network Embedding

As previously clarified, VNE is a process of mapping every virtual Node/link from VN to the most adequate substrate Node/link, such as mapping a VM with 10 GB size which require to execute a method along with the objective to find a host with 10 GB minimum storage or above, same thing goes with a link mapping in case of bandwidth allocation. In conformity with [2], VNE Optimization (VNEO) problem has two main functions, Node Mapping Function (NMF) where SN = (N, L) consists of N nodes and L links, and Link Mapping Function (LMF) according to a given SN and a Virtual Network Request (VNR) where VNRi = (Ni, Li) designate the ith VNR that contain Ni nodes and Li links.
VNEO :

NMF : Ni ! N LMF : Li ! SN i  SN

ð1Þ

The NMF should map every virtual node Ni from a given VNRi to a specific substrate node N, likewise with LMF that will map each virtual link Li to a subsubstrate network that can contain multiple nodes and links. Based on [2] VNE approaches classification, our VNE approach has four preferences: (i) static (i.e. fixed physical infrastructure); (ii) redundant (i.e. execute the same process for every element); (iii) centralized (i.e. NOS takes control of VNE); offline (i.e. takes a set of VNs to map in FIFO).

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Fig. 2. Mapping a pair of virtual networks onto a single substrate network.

Figure 2 illustrates a mapping process of two VNs (i.e., three virtual nodes for the first VN and four virtual nodes for the second VN) on to a one SN. We note that every mapping success depends on physical cloud architecture capacities. Therefore, any VNR’s node or link is associated with a specific capacity to determine if it can be mapped or not. It can be seen that every substrate node can host multiple virtual nodes. However, every single virtual node can be assigned only on one substrate node. Thus, a VN is mapped onto just one merely SN. In case of a virtual link, any map method can make it possible for a virtual link to be mapped onto many substrate links crossing different substrate nodes from source to destination as shown in Fig. 2 (e.g., a to b and d to e). 4.2

Grey Wolf Optimizer

GWO is an SI method that mimics the social behaviors of grey wolves (Canis lupus) pack. This pack is formed in four classes; the top class is limited for the alpha (a) male or female wolf, which will lead and command the other wolves in almost every activity like hunting or migration. The Second class (beta wolves b) assist the alpha wolf in the decision-making in general. The third class named the delta (d) wolves, it includes the elders and scouts, and those are subordinates of the alpha wolf and the beta wolves. The bottom class is pawned for the omega (x) wolves, these wolves have the lowest dominance power and must always submit to the top ranking wolves. The grey wolves search process for hunting (optimization process) has three main phases; tracking, surrounding, and attacking the quarry. The search phase starts with generating a population of candidate solutions; then the second phase consists of pursuing and stopping the prey by harassing; this encircling operation is formulated in the following equations: * *  * *   D ¼ C : Xp ðtÞ  X ðtÞ

ð2Þ

*  * * *  X ð t þ 1Þ ¼  X ð t Þ  A : D 

ð3Þ

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*

*

*

A ¼ 2a : r1  a *

ð4Þ

*

C ¼ 2r2 *

ð5Þ *

D is a vector refers to the position of the grey wolf X relying on the vector position of *

*

the prey Xp , the wolf current location X will be updated in every iteration t using Eq. (4). *

*

*

The vectors A and C are calculated using Eqs. (5) and (6), where a is linearly lessened *

*

from 2 to 0 throughout the optimization process. r1 and r2 are randomly generated vectors in [0, 1]. Hypothetically, the location of the prey (optimum solution) is uncertain, hence GWO supposed that the alpha, beta, delta wolves have better expertise to locate the prey, so the other wolves are forced to update their positions as follows: * X3 X i X ð t þ 1Þ ¼ i¼1 3 *

*

ð6Þ

*

Where X i is calculated with Eq. (7) and Dw is calculated with Eq. (8). Every i is designated and fixed with only one single w in the whole process (i.e. respecting the order, thereby if i = 2 then w = b in each case). *

*

*

*

X i ¼ X w ðtÞ  Ai : DW ; i ¼ 1; 2; 3; w : a; b; d * *  * *  DW ¼ C i : X W  X ; i ¼ 1; 2; 3; w : a; b; d

ð7Þ ð8Þ

The GWO procedure starts by initiating random solutions and calculate their fitness. After that, choose alpha, beta, and delta as the best three search agents so far. Next, all the rest search agents update its position frequently using Eqs. (6) and (7) and then update the distance from three best search agents and the current solution in every iteration with Eq. (8). The parameters A, C, and a are also repeatedly updated before the position updating during the process which will return in the end the alpha position as the best solution.

5 Proposed Work Data Center Networks (DCNs) are the mainframe of almost every private facility in Cloud computing. Therefore, the enabling of SDN plane utilities will tend to guarantee the proper data modernization in DCNs. Since SDN is initially engaged in large scale environments, we implemented the Fat-tree [14] architecture as a hierarchical DCN on account of its structure scalability, and for its support to explore the data plane to host the VNRs coming from the cloud user. The virtual nodes/links from VNRs are processed and redirected to the optimized substrate nodes, using a specific SDN adaptation policy via Network Operating System (NOS) class, which represent the centralized SDN controller in the control plane as shown in Fig. 3:

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Fig. 3. Overview of the proposed approach based on CloudSimSDN

5.1

Implementation

In this contribution, we took in consideration the problem formulation mentioned in Subsect. 4.1 with the integration of NMF and LMF. Both of these functions must rely on a Virtual Node Capacity (Cap(VNo)) and Virtual Link Capacity (Cap(VLi)) where: CapðVNoÞt ¼

X

ðSize; Ram; Cpu; BwÞ; t ¼ 1. . .N

CapðVLiÞq ¼

X

ðBwÞ; q ¼ 1. . .L

ð9Þ ð10Þ

On the side of substrate network, an identical substrate node/link capacities (Cap(SNo)), (Cap(SLi)) are calculated uniformly to every VNR for pre-map checking. Find

H ¼ ðx1 ; . . .xn Þ; L ¼ ðy1 ; . . .ym Þ

So as to Min ½Hu ð xÞ; Lu ð yÞ Subject to CapðVNoÞ ðvt Þ  CapðSNoÞ ðxi Þ; v 2 RN ; x 2 H n

ð11Þ

    CapðVLiÞ sq  CapðSLiÞ yj ; s 2 K L ; y 2 Lm   CapðVLiÞ sq ; CapðVNoÞ ðvt Þ [ 0   CapðSNoÞ ðxi Þ; CapðSLiÞ yj  0 Where: H: a set of the available hosts in SN. | R: a set of the given virtual nodes in VN. L: a set of the available links in SN. | K: a set of the given virtual links in VN. Hu: host utilization function. | Lu: link utilization function. In a VNE problem, a VNR should be mapped entirely with no virtual node/link excluded. Thus, we defined a simple mathematical model that minimizes a substrate

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node/link selection, which will lead to increase its local re-sources (e.g., Storage, Ram, CPU, and Bandwidth) utilization while respecting the VNRs requirements. Proceeding on this track, a plain mathematical statement is formulated in (11) that adopt two objective functions. The main concern is to setup GWO as a VNE-oriented approach. For that, we developed a global VNE process in Algorithm 1 to solve our mathematical model. It takes in input 3 JSON pre-configured files, a physical DCN, a set of virtual topologies configurations and workloads that includes processing, transmissions and requests. The function Map verify in the first line store the accepted VNs in vector for the mapping. The node link mapping methods in line 7 and 11 have the same code steps, and they’re represented in Algorithm 2 with comments in case of link mapping method. Algorithm 2 starts with initiating wolf population size in line 1, calculating the fitness of every wolf in the population to determine the first Best Search Agent (BSA) as Xa, second BSA as Xb and third BSA as Xd in line 3. We added the same Evolutionary Population Dynamics (EPD) type of operator introduced in [15], but it’s employed with our predefined capacity variables presented in Eq. (12) to enhance the GWO exploration and fixing the search boundaries, where maxCap and minCap refers to the maximum and minimum value of substrate node link capacity respectively, r is a random value in [0, 1]. The search agents iterate through a node\link resources, where Xa position’s is the selected host\link to map in. X ðt þ 1Þ ¼ ðmaxCap  minCap:r þ minCapÞ

5.2

ð12Þ

Experimentation Results

We used an SDN-based Cloud DC modeling and simulation toolkit named CloudSimSDN [16] to validate our GWO for VNE (GWO-VNE), compared with basic PSOVNE and ACO-VNE due to their easy implementations and belongings to SI

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optimization field. The implemented fat-tree network has 36 Substrate nodes (16 servers, 20 switches) and 64 Substrate links, to map a 13 VNRs with various requirements. Energy consumption measurements for substrate nodes links is based on CloudSimSDN recommended model. The experiments were performed using an Intel i5-6200U CPU up to 2.8 GHz with 4 GB of Ram and 1000 iterations.

(b) Node/Link rate selection

(a) Energy consumption amount

Fig. 4. Resource utilization impact on power consumption.

From Fig. 4a, we note that GWO-VNE physical node link rate selection is getting lower by increasing the VNRs, due to its search agent that adapt effectively in a large population using Eq. (12). This will result in a considerable energy saving per VNR in terms of the active node link (e.i., creating and allocation of VMs and Switches active ports with links assignment), and rate selection as we can observe in Fig. 4b. On the contrary, we can deduce that ACO-VNE is limited due to its probability distribution; likewise, PSO-VNE suffers from a lack of global optimization with bigger data sizes.

6 Conclusion and Future Works Virtual employment techniques are widely emerging in cloud computing, thanks to their reduced cost and flexible performance. In summary, we have proposed a VNE approach based on a novel SI named GWO. The experimentations were simulated using CloudSimSDN that provide a clear vision of physical VN resources due to its orchestrator class (NOS), resulting in proving the GWO efficiency that reduces the aggregate energy consumption. Perspectively, we aim to merge GWO with a VNR scheduling algorithm as a first step to reduce running time, and then improve the convergence process of exploration located in NOS.

References 1. SDN-definition. https://www.opennetworking.org/sdn-definition. Accessed 23 June 2019 2. Fischer, A., Botero, J.F., Beck, M.T., De Meer, H., Hesselbach, X.: Virtual network embedding: a survey. IEEE Commun. Surv. Tutor. 15(4), 1888–1906 (2013)

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3. Mijumbi, R., Serrat, J., Rubio-Loyola, J., Bouten, N., De Turck, F., Latré, S.: Dynamic resource management in SDN-based virtualized networks. In: 10th International Conference on Network and Service Management (CNSM) and Workshop, pp. 412–417. IEEE, Brazil (2014) 4. Dehury, C.K., Sahoo, P.K.: DYVINE: fitness-based dynamic virtual network embedding in cloud computing. IEEE J. Sel. Areas Commun. 37, 1029–1045 (2019) 5. Al-Moalmi, A., Luo, J., Salah, A., Li, K.: Optimal virtual machine placement based on grey wolf optimization. Electronics 8(3), 283 (2019) 6. Calheiros, R., Ranjan, R., Beloglazov, A., De Rose, C., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.-Pract. Exp. 41, 23–50 (2011) 7. Shahbazi, H., Sepideh, J.N.: Smart deployment of virtual machines to reduce energy consumption of cloud computing based data centers using gray wolf optimizer. In: International Conference on Information and Software Technologies, pp. 164–177. Springer, Cham (2018) 8. Nasiri, A.A., Derakhshan, F.: Assignment of virtual networks to substrate network for software defined networks. Int. J. Cloud Appl. Comput. (IJCAC) 8(4), 29–48 (2018) 9. Yao, X., Wang, H., Gao, C., Yi, S.: Maximizing network utilization for SDN based on Particle Swarm Optimization. In: IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 921–925. IEEE, USA (2016) 10. McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., Shenker, S., Turner, J.: OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Comput. Commun. Rev. 38(2), 69–74 (2008) 11. Azodolmolky, S., Wieder, P., Yahyapour, R.: SDN-based cloud computing network- ing. In: 15th International Conference on Transparent Optical Networks (ICTON), pp. 1–4. IEEE, Spain (2013) 12. Son, J., Buyya, R.: A taxonomy of software-defined networking (SDN)-enabled cloud computing. ACM Comput. Surv. (CSUR) 51(3), 59 (2018) 13. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014) 14. Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. In: ACM SIGCOMM Computer Communication Review, vol. 38, no. 4, pp. 63–74, USA (2008) 15. Saremi, S., Mirjalili, S.Z., Mirjalili, S.M.: Evolutionary population dynamics and grey wolf optimizer. Neural Comput. Appl. 26(5), 1257–1263 (2015) 16. Son, J., Dastjerdi, A.V., Calheiros, R.N., Ji, X., Yoon, Y., Buyya, R.: CloudSimSDN: modeling and simulation of software-defined cloud data centers. In: 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 475–484. IEEE, China (2015)

A Small Robotic Step for the Therapeutic Treatment of Mental Illnesses: First Round Carlos Martinez1, David Castillo2, Ruth Maldonado Rivera3, and Hector F. Gomez A4(&) 1

Universidad Regional Autonoma de los Andes, Km 5 1/2 Vía, 180215 Ambato, Baños, Ecuador [email protected] 2 Facultad de Ingeniería y Tecnologías de la Información y Comunicación FITIC, Carrera de Ciencias de la Computación, Universidad Tecnológica Indoamérica, Quito, Ecuador [email protected] 3 Departamento de Psicologia, Universidad Técnica Particular de Loja, Loja, Ecuador [email protected] 4 Facultad de Ciencias de la Salud, Universidad Técnica de Ambato, Ambato, Ecuador [email protected]

Abstract. The advances in psychological therapies to treat mental illnesses have been of vital importance in recent times, especially by the combination with technology. Appealing to new mechanisms gives some hope of improvement in treatments and patients. In this work we show the ease in the development of routines that can help the quality of life of people suffering from Alzheimer’s and autism, diseases that lend themselves to the programming of routines related to the daily life of patients. The routines developed by students of psycho-pedagogy and documented by our work team are waiting for being applied in case studies in future works. The general conclusion of the assignment is the concentration that arose having to use a robot as part of therapy routines and the quest for information to combine new technologies with psychology. The assignment can be considered as impressive because working with a robot awakens positive emotions in those who are developing robotic therapies.

1 Introduction The development of applications that allow interacting with patients suffering from mental illnesses is very important to improve their quality of life and the development of routine activities of them. The main objective of this work is to show the scientific community the ease that computer programs have at present to develop support routines. Investigators are interested in finding out which robot was used, what the goals of the application were, how the robot was controlled, what kind of behaviors the robot exhibited, what kind of actuators the robot used and what kind of sensors the robot used [1]. This need is driving engineers to study human behavior toward other humans and toward robots, leading to greater understanding of how humans think, feel, and behave © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 331–336, 2020. https://doi.org/10.1007/978-3-030-36778-7_36

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in these contexts, including our tendencies for mindless social behaviors, anthropomorphism, uncanny feelings toward robots, and the formation of emotional attachments. Human robot interaction is a fascinating field and one in which psychologists have much to contribute, both to the development of robots and to the study of human behavior [2]. We focus specifically on two diseases: Alzheimer’s and Autism. Therapeutic pet robots designed to help humans with various medical conditions could play a vital role in physiological, psychological and social-interaction interventions for children with autism spectrum disorder [3]. The therapies with which one can face this type of diseases, find their support in computer components and in robotics [4]. Thus, Alzheimer’s is the most frequent type of dementia and its figures are increasing, its symptoms have a great impact on the patient’s life and society, so we would prepare routines for patients suffering from Alzheimer’s disease. When doing physical activity, the natural reduction of brain connections that occur as you get older is counteracted. Our goal is to combine robotic strategies with psychological therapies. The result shows a high level of attention in the developers of the routines and documentation of them to be applied in future studies cases. Finally, we present the conclusions and future work, focusing on case studies that allow us to observe the real interaction between the patient and the EVA robot.

2 State of the Art The process of social aging in the groups of first world countries increases arithmetically according to statistics reported from the World Health Organization, as well as the International Alzheimer Organization, which project that until 2050 the group of people with dementia will triple worldwide, affecting a group of approximately 115.4 million [5, 6]. In the article “Social robots in advanced dementia” it is mentioned that “Robots have less need of space, time or care. Its sensors can respond to environmental changes (movements, sounds, behavior) simulating the interaction with the patient. Monitoring the patient is achieved or to be used in therapy. Two experimentation phases were carried out (Phase 1) a humanoid robot (NAO), a robot in the shape of an animal (PARO), (CONTROL). (Phase 2) a trained dog (DOG); a robot in the shape of an animal (PARO), (CONTROL). The patient interacted with robots, animals and therapists to perform various therapeutic activities, including identifying numbers, words and colours using flash cards; practice the use of everyday objects such as combs; sensory stimulation exercises with different textured fabrics. “Use of a Robotic Seal as a Therapeutic Tool to Improve Dementia Symptoms: A Cluster-Randomized Controlled Trial” PARO is the most common therapeutic robot used in studies with people with dementia. The therapeutic version (version 9) is an autonomous robot that is similar in weight to a newborn baby and has 5 sensors that are processed by artificial intelligence software to allow PARO to respond to the user and the environment. Normally active during the day, PARO can move its tail and fins, open and close its eyes, and make sounds similar to a real baby harp seal. The benefits found partly support the effectiveness of PARO, but they also suggest that, when there are limited resources, a soft toy animal can be used effectively with a person with dementia. Taken

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from the book “Rehabilitation and Health Care Robotics” it is said that: The robot may be able to involve the patient in the types of therapy exercises that a therapist cannot do, such as therapy based on computer games or the increase in movement errors to cause adaptation. Adaptive therapy through robotics and feedback, the incorporation of therapy in computer games, music and sports, and the application of motor learning theories. The Romibo Robot Project for example, is an evolving robot for motivation, education and social therapy. Romibo is a social robot, able to convey emotions, communicate socially, and form relationships with individuals [7]. The therapy robots provide accurate and sensitive tools to evaluate and model human behaviour, far beyond the capacity of a human observer. This is very important to allow an adequate initial diagnosis, the early adoption of corrective clinical strategies and to identify verifiable milestones, as well as prognostic indicators of the patient’s recovery process. Nao, is a Spanish project where institutions such as the Carlos III University of Madrid, the University of Malaga, the University of Extremadura and the Virgen del Rocío Hospital in Sevilla work together. Its appearance seems more like a toy than a therapeutic support, but among its functions is to perceive the patient’s reactions and determine if they are correctly developing their exercises, contributing to the rehabilitation and proper physical performance of the patient. The baby seal called PARO designed in 1993 in the Intelligent System Research Institute of Japan, is a therapeutic robot that has been developed with the purpose of supporting cognitive functions, indirect work in basic activities, maintaining affective and emotional relationships, a sensory stimulation, as well as in some aspects an improvement in the psychosocial aspects such as interaction with users, relaxation, etc. Paro is the eighth generation that is being used both in Japan and in Europe since 2003 [8]. In this work we intend to show the first robotic routines that allow interacting with autistic and Alzheimer’s patients. The objective is to show that the robot can interact with patients and that it can help them in their daily tasks. The programming environment on which the tools will be developed in Choreography (Fig. 1):

Fig. 1. General choreography interface

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The simulator shown in Fig. 1 allows you to check the routines before they are executed in EVA [9] describe how using robotic platforms helps in teaching children with autism basic social abilities, imitation, communication and interaction; this encourages them to transfer the learned abilities to human interactions with both adults and peers, through human robot imitative modelling. [10] paraphrasing with this author we say that the first thing we must take into account is that children with autism abhor unforeseen changes. This causes them a lot of stress so it is frequent and in front of these situations they start to get altered, (it depends on each case in particular). Therefore we have created a routine with which you can assimilate and carry a daily routine such as greeting and saying goodbye to all people (this as a part of socializing with the child), we must also know routines for a correct verbal language or spoken by what is asked to repeat what the robot says nao (HI) or as we know it as EVA this will allow a correct pronunciation is created because autism prevents verbal learning, EVA also seeks the way to create learning Novelty in what is the motor parts for which EVA proceeds to perform basic movements such as putting on their feet (get up) thus generating expectation in the child by the movement that this will perform, recline backwards in a correct way without swaying (sit) thus producing in the individual a specific attention process and also proceeds to perform movements of the feet with a continuous sequence (walking) thus decreasing the stereotypy of walking on the tips of the feet something very common in children with Autism. We must take into account unforeseen changes and anticipate changes that can be made by children with autism, so we must ensure that the child puts his full attention on EVA in what we established short but effective routines, especially since the focus of attention the child is very limited so if he does not get his attention, he could run away at any moment, which would cause a problem that is too worrying for the therapist as well as the family, since it is known that an individual he does not focus his attention or that some activity is too stressful ends up leaving the place and leaving without direction.

3 Developed Routines Figure 1 shows the general interface of Choreography that allows you to test the routines in your simulator and then be transferred to EVA. In the following section we show the routines developed during the experiments. Routine 1: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Wake up To wake up and say “good morning Carlos” Walk towards the kitchen Eat and when you finish say “thank you” Walk to the bathroom Wash your teeth Wash your face Go back to the room to get dressed Walk to the door and say “Bye, see you in the afternoon”.

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Routine 2: The routine for people with Alzheimer’s is performed for a patient with Alzheimer’s at a mild level. This is characterized by small forgetfulness of recent events. The routine aims to remind the patient what to do during the morning. In order to continue each step in a chronological order and at the end of all these we have put a Tai-Chi activity that is very helpful for people with Alzheimer’s disease, since it does not ease, stop or calm down that there are people who cannot move or have to be helped to do the exercises, a certain amount of time passes and people begin to fend for themselves, without any problem, with this discipline they put to work both hemispheres of the brain, since coordination is done by moving the hands and the opposite foot, as well as being in constant movement. Routine 3: 1. Relaxation music throughout the routine so that the child sits in a pleasant and quiet environment to perform certain movements and the most effective way to get the child’s attention. 2. Lift the arms one by one. 3. Lower the arms as a whole. 4. Head movement Right-Left. 5. Flex the legs with the arms raised (this movement will be repeated 3 times). 6. Take a spin. 7. Sit down. 8. He will say thank you.

4 Conclusions and Future Works The Nao robot called Eva is very useful for the creation of various routines that allow a person with autism to develop in an appropriate environment and without disturbing their sensitivity. The detailed explanation of each movement belonging to the different parts of the routine causes the child to adequately understand all the movements that must be done to achieve a correct cleanliness and in this way begin to retain the cycle in his memory to determine it as his permanent routine. They experimentation with patients will be carried out and mechanisms will be established to identify their behaviour against the routines programmed in these works for the robot. We also intend to obtain that intelligent environment in our laboratory so that by means of ubiquitous computation we can obtain conclusions closer to the behaviour of our patients, this will be done in future work.

References 1. Erich, F., Hirokawa M., Suzuki, K.: A systematic literature review of experiments in socially assistive robotics using humanoid robots. Robotics (2017) 2. Broadbent, E.: Interactions with robots: the truths we reveal about ourselves. Annu. Rev. Psychol. 68, 627–652 (2017)

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3. Bharatharaj, J., Huang, L., AL-Jumaily, A., Elara Rajesh, M., Krageloh, C.: Investigating the effects of robot-assisted therapy among children with autism spectrum disorder using biomarkers. In: de Materials Science and Engineering (2017) 4. Taylor, H., Pettitt, J.: How robot therapists can fill a gap in health care, 21 July 2016. https:// www.cnbc.com/2016/07/21/how-robot-therapists-can-fill-a-gap-in-health-care.html. Accessed 20 Nov 2017 5. World Health Organization and Alzheimer’s Disease International, Dementia a public health priority, Geneva, Switzerland (2012) 6. Alzheimres Society of Canada: Rising tide: impact of dementia on Canadian Society, Toronto, Canada (2010) 7. Shick, A.: Romibo robot project: and open-source effort to develop a low-cost sensory adaptable robot for especial needs therapy and education. In: de SIGGRAPH, California (2013) 8. Paro Robots (2016). http://www.parorobots.com/ 9. Pennazio, V.: Social robotics to help children with autism in their interactions through imitation. Res. Educ. Media 9, 10–16 (2017) 10. Diario, A.: Rompamos juntos barreras por el autismo. Hagamos una sociedad accesible, 29 March 2017. https://autismodiario.org/2017/03/29/rompamos-juntos-barreras-por-el-autismo -hagamos-una-sociedad-accesible/

Accuracy of Classification Algorithms Applied to EEG Records from Emotiv EPOC+ Using Their Spectral and Asymmetry Features Kevin Martín-Chinea1(&), Jordan Ortega1, José Francisco Gómez-González1(&), Jonay Toledo2, Ernesto Pereda1, and Leopoldo Acosta2 1

2

Department of Industrial Engineering, University of La Laguna, 38071 La Laguna, Tenerife, Spain {kmartinc,jortegar,jfcgomez,eperdepa}@ull.edu.es Department of Computer and Systems Engineering, University of La Laguna, 38071 La Laguna, Tenerife, Spain {jttoledo,lacosta}@ull.edu.es

Abstract. To develop a good BCI, it is necessary to take into account what features can be extracted and what classification algorithm can be used. In this manuscript, a cross-validation method is used to compare different classification algorithms (SVM, KNN, discriminant analyses and decision trees) as applied to EEG records obtained by a non-invasive wireless electroencephalograph (Emotiv EPOC+). The features used in the classification algorithms are the power spectrum of the signal and the hemispheric asymmetry. The used experimental paradigms (e.g. motor imagery) are designed to be used with reduced mobility people, because the aim is to develop a BCI to control an external device such as a wheelchair or a prosthesis. Keywords: EEG


Machine learning
BCI
Virtual reality
Classification

1 Introduction In order to develop a brain-computer interface (BCI), it is first necessary to determine and understand how the brain behaves when it executes movements or imagines performing them. The imagination of movement is a very important factor for the control of prosthesis or a wheelchair and the rehabilitation of patients with neurodegenerative lesions or diseases. To start with development a BCI and to use it in this type of applications, choosing an appropriate algorithm to extract appropriate features from EEG signals is one of the most important steps to have a good one. Current researches focuses on information extraction algorithms and classifiers [1, 2]. The aims of this research are (a) see the behavior of the brain signals of a person with different motor actions and motor imageries using a low cost portable noninvasive electroencephalograph, and (b) compare the accuracy of the most used classification algorithms with the obtained features. All this to build a BCI to control a smart wheelchair for people with reduced mobility. © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 337–342, 2020. https://doi.org/10.1007/978-3-030-36778-7_37

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2 Materials and Methods The Emotiv EPOC+ wireless electroencephalograph was the device used. This lowcost device allows us to record the raw EEG signals with 16 sensors (14 channels and 2 reference electrodes). We used the Emotiv Xavier TestBench v3.1.21 to record the EEG and FieldTrip [3] is the tool used to analize this EEG records. Seven healthy volunteers (without any motor pathology) participated in this research. All of them were right-handed and aged between 18 and 50 years old. Each volunteer was asked to perform a task in two different ways, executing a motor action (MA) and a motor imagery (MI) with each hand. The motor task has defined squeezing an object. The experimental paradigm is defined in the Fig. 1, where the arrow means do the action with the left or the right hand. The tasks are repeated 4 times for each hand and each action (MA and MI).

Fig. 1. Experimental paradigm.

The objective is to design a BCI that works in real time and for this reason, the training of the classifier has been done with 2 s trials. Then, a bandpass FIR filter between 5 and 40 Hz is applied that allows this frequency range passes through and attenuate the rest, which corresponds to noise. Finally, the automatic elimination of artifacts is applied. For this, a threshold is calculated with respect to the average and the standard deviation on the z-score signal that Fieldtrip creates to calculate this signal. The features used to train the classifiers are the average power of the alpha band (8– 12 Hz) and beta (13–30 Hz) in each channel and their hemispheric asymmetries. The method used to perform the frequency analysis is mtmfft, which applies multitaper frequency transformation [4]. In this case, a set of orthogonal tapers, based on discrete prolate spheroidal sequences (DPSS) also known as the Slepian, are applied to the original signal generating new temporal signals and the power spectrum of original signal is estimated as the average of the Fast Fourier Transform of these tapered signals. The application of this frequency analysis in each band for each channel generates 28 characteristics. The brain asymmetry has been used to improve the classification, some researches have already focused on this topic [5–7].

Accuracy of Classification Algorithms Applied to EEG Records from Emotiv EPOC+

 channelleft AsymetryIndex ¼ ln : channelright

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ð1Þ

Consequently, applying the asymmetry on the channels in their different frequency bands 14 new features are obtained for training. As a result, the models of the classifiers have been generated with 42 characteristics and 1 label, which corresponds to the action to be classified (Fig. 2).

Fig. 2. Flow-chart indicating data-processing pipeline and training.

The Classification Learner application from Matlab is used to explore the data entered, select features, define validation schemes, perform a supervised training of models and evaluate results. A 5-fold cross validation is used to evaluate the algorithms.

Fig. 3. Examples of four subjects. Each topographic map represents the normalized power variation of the action with respect to its basal state in the two frequency bands used. Defining as action the motor action (MA) or the motor image (MI) of each hand (left or right).

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3 Results and Discussion The first step in this work has been to study the EEG patterns for different people when they carry out the proposed tasks. In this case, they are proposed to squeeze an object with their right or left hand or imagine that they squeeze an object after a visual stimulus. Figure 3 shows EEG topographic maps of four subjects built as the average EEG at time window of 2 s. The topographic maps show the percentage of activation of brain areas with respect to their basal state in the alpha (8–12 Hz) and the beta (13– 30 Hz) bands in their corresponding action. In general, these maps show an activation in the central area of the brain in the alpha and beta bands around the pre-motor cortex. However, although each subject has a predominant side over the other when performing a motor action or a motor image in both EEG bands, each one has a different EEG pattern, not only in distribution also in the power level. For example, in the case of the subject S1, there are a high activation in the left site (areas P7, T7, FC5 and FC6) versus the basal state when the left-hand squeezes. On the contrary, in the squeezing image there is more activation in the central region (FC5 and FC6) and less in the lateral (P7 and T7) in the alpha band. The situation is similar in the beta band. When S1 squeezes with the right hand, there is activation in the frontal region (F3, AF3 and AF4) and the left occipital and parietal region (O2 and P8). The same pattern but with lower power is shown with the squeezing image. However, this pattern is different from the pattern of other subjects. Therefore, from the topographic maps, we can conclude that each subject has a map of EEG pattern depending on the motor action developed, and that these patterns are similar to the motor image. Because this variability in the patterns, a machine-learning tool is needed to identify when the subject is done a particular action or task. Table 1. Classifier accuracy (%): Motor action. Accuracy (r) AUC (r) Basal Tree Fine/Medium 60.37 (9.16) 0.62 (0.05) Coarse 62.34 (10.01) 0.65 (0.07) Discriminant Linear 66.57 (11.08) 0.74 (0.1) SVM Linear 69.41 (8.26) 0.77 (0.08) Quadratic 71.9 (7.37) 0.8 (0.06) Cubic 71.19 (7.66) 0.79 (0.06) Medium Gaussian 71.54 (6.78) 0.79 (0.06) KNN Fine 65.24 (9.45) 0.67 (0.1) Medium 68.81 (8.99) 0.76 (0.07) Cosine 71.19 (9.21) 0.78 (0.05) Cubic 67.5 (7.49) 0.75 (0.07) Weighted 69.04 (7.94) 0.77 (0.07)

Left 0.77 (0.09) 0.8 (0.09) 0.84 (0.13) 0.91 (0.07) 0.91 (0.08) 0.91 (0.09) 0.92 (0.07) 0.79 (0.09) 0.9 (0.09) 0.91 (0.08) 0.89 (0.08) 0.91 (0.09)

Right 0.74 (0.07) 0.82 (0.1) 0.82 (0.11) 0.9 (0.07) 0.91 (0.05) 0.9 (0.05) 0.91 (0.04) 0.77 (0.08) 0.89 (0.08) 0.9 (0.07) 0.89 (0.09) 0.9 (0.07)

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Table 2. Classifier accuracy (%): Motor imagery. Accuracy (r) AUC (r) Basal Tree Fine/Medium 64.17 (6.15) 0.66 (0.04) Coarse 64.53 (5.86) 0.67 (0.04) Discriminant Linear 66.2 (9.25) 0.73 (0.09) SVM Linear 72.24 (6.63) 0.81 (0.09) Quadratic 75.94 (8.1) 0.82 (0.09) Cubic 75.21 (8.61) 0.8 (0.1) Medium Gaussian 73.7 (9.3) 0.81 (0.09) KNN Fine 67.37 (8.86) 0.67 (0.08) Medium 70.24 (8.37) 0.77 (0.11) Cosine 72.03 (8.06) 0.8 (0.09) Cubic 69.77 (7.15) 0.76 (0.1) Weighted 72.26 (7.81) 0.77 (0.11)

Left 0.78 0.82 0.85 0.93 0.94 0.93 0.93 0.79 0.91 0.91 0.91 0.92

(0.05) (0.04) (0.09) (0.03) (0.03) (0.04) (0.03) (0.06) (0.05) (0.04) (0.04) (0.04)

Right 0.81 (0.06) 0.83 (0.07) 0.83 (0.09) 0.92 (0.05) 0.94 (0.04) 0.93 (0.05) 0.92 (0.05) 0.8 (0.07) 0.9 (0.07) 0.9 (0.07) 0.89 (0.07) 0.9 (0.07)

Different classifiers have been applied to the data of each subject. The accuracy of each classifier is shown in Tables 1 and 2, belonging to motor action and motor imagery, respectively. Each value corresponds to the average accuracy of the results of all subjects. In general, the accuracy is between 60 and 70%, highlighting above all the SVM algorithm in both accuracy and the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC) for each state, especially in the motor imagery table (Table 2). The results show that the best option is to select an SVM classifier, since it is the one that has given the best results with the data used (between 5% and 10% more accurate). Besides you can see a similar difference in the AUC of the ROC curve, the SVMs tend to be 10% better than the rest of the algorithms. Also, it can be observed in this part of the table that all the algorithms present a greater difficulty to classify the basal state with respect to the motor activity of the left or right. Obtaining differences between one class and another higher than 12%.

4 Conclusions and Future Works The classification algorithms present the same accuracy in our system than other authors that used wired EEG device. Therefore, they are applicable to the EEG signals obtained through a commercial Emotiv EPOC+ wireless device that it has a low signalto-noise ratio. The best classifier to design a BCI for our application was the SVM. The next steps are to improve the accuracy of the classifier. On the one hand, to search for better metrics on brain functional connectivity in the tasks performed to use them as attributes, studying the correlation of the variables, etc. On the other hand, to optimize the variables applied to the algorithm to generate the model. Also, a future work would be the implementation of the BCI to control an external system, such as a software application, a prosthesis or an intelligent wheelchair.

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Acknowledgments. This work was conducted under the auspices of the Research Project ProID2017010100, supported by Consejería de Economía, Industria, Comercio y Conocimiento from Canary Government (Spain) and FEDER (European regional development fund (ERDF)), the Researches Projects TEC2016-80063-C3-2-R and DPI2017-90002-R, supported by Spanish Ministerio de Economía, Industria y Competitividad. J. Ortega has a fellowship by Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI) from Canary Government (Spain).

References 1. Martišius, I., Damaševičius, R.: A prototype SSVEP based real time BCI gaming system. Comput. Intell. Neurosci. 2016, 1–15 (2016). https://doi.org/10.1155/2016/3861425 2. Qin, Y., Zhan, Y., Wang, C., et al.: Classifying four-category visual objects using multiple ERP components in single-trial ERP. Cogn. Neurodyn. 10, 275–285 (2016). https://doi.org/ 10.1007/s11571-016-9378-0 3. Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.M.: FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. (2011). https://doi.org/10.1155/2011/156869 4. Based, P.: Multitaper spectrum estimation, pp. 1–7 (2004) 5. Lin, Y.P., Wang, C.H., Wu, T.L., et al.: Support vector machine for EEG signal classification during listening to emotional music. In: Proceedings of the 2008 IEEE 10th Work Multimedia Signal Processing, MMSP 2008, pp. 127–130 (2008). https://doi.org/10.1109/MMSP.2008. 4665061 6. Lin, Y.P., Wang, C.H., Wu, T.L., et al.: Multilayer perceptron for EEG signal classification during listening to emotional music. In: IEEE Region 10 Annual International Conference Proceedings/TENCON (2007). https://doi.org/10.1109/TENCON.2007.4428831 7. Bai, O., Mari, Z., Vorbach, S., Hallett, M.: Asymmetric spatiotemporal patterns of eventrelated desynchronization preceding voluntary sequential finger movements: a high-resolution EEG study. Clin. Neurophysiol. 116, 1213–1221 (2005). https://doi.org/10.1016/j.clinph. 2005.01.006

Organizational Model for Collaborative Use of Free and Open Source Software: The Case of IT Departments in the Philippine Public and Private Sectors Ferddie Quiroz Canlas(&) Department of Computing, Muscat College, Bowsher St., Muscat, Oman [email protected]

Abstract. Free and Open Source Software is a software development philosophy that gives organizations the freedom to use, study alter and redistribute the software to their own strategic and competitive advantage. This paper attempts to propose a model for the collaborative use of FOSS within IT departments of public and private sectors in the Philippines and to address the prevailing issues and challenges that hinder organizations in adopting FOSS to workplaces. The components of the model, alongside the applicable guidelines and best practices, are anchored to various literature. The study revealed that organizations involved in the study have high enthusiasm for FOSS collaboration and establishment of a FOSS community. Keywords: Free and Open Source Software Guidelines
FOSS adoption
FOSS collaboration




Organization model




1 Introduction Free/Open Source Software (FOSS) is a software development philosophy where the users are granted liberty to scrutinize, alter, customize or redistribute the source code of any software project under free software licenses [1]. Despite being a fundamentally new and revolutionary model in software development [2], many organizations are attracted and increasingly engaged with free and open source software [3]. The might of proprietary software industry is threatened by the large number of volunteers and communities of open source advocates [4]. The software market is undergoing a significant transformation as open source software continues to alter the competitive landscape of the industry [5] in fact, since 2000, it was reported that most web browsers, operating systems, servers, and databases are primarily open source [6] and FOSS influence continues to grow. FOSS is no longer becoming substitute to proprietary software, but it is increasingly integrated with corporate systems [7]. This claim is evidenced by Vaughan-Nichols [8] with the following remarkable findings: (1) with the perceived competitive advantages enterprises are beginning to integrate their open source applications with the cloud; (2) 78% of the respondents in his survey run some or all of their operations on open source software; (3) 66% said that their companies create software for customers built on open source; © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 343–351, 2020. https://doi.org/10.1007/978-3-030-36778-7_38

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(4) the same respondents prefer FOSS as their default development platform over their proprietary counterpart; and (5) cloud computing, big data, operating systems, and the Internet of Things will rely on FOSS in the next 2–3 years. Due to the scarcity of literatures tackling business models for the adoption of free and open source software in the Philippines, the study would like to introduce a collaborative framework that aims to provide insights to IT managers to reap the strategic benefits of FOSS thru collaborative strategy.

2 Literature Review 2.1

Perceived Benefits and Business Models of Free and Open Source Software

There is a wide array of studies enumerating the perceived benefits of FOSS to both public and private organizations. This study selected to discuss a few numbers of models that support the proposed organizational model for the adoption and collaborative use of free and open source software within the IT departments of the public and private sectors in the Philippines. Open Innovation Model. Ouchi and Bolton [9] define open innovation as systematically encouraging and exploring a wide range of internal and external sources for innovation opportunities, consciously integrating that exploration with firm capabilities and resources, and broadly exploiting those opportunities through multiple channels. Therefore, the open innovation paradigm therefore goes beyond simply the externalization of research and development. They [9] also added that a central concern to open innovation is how to best use the internal R&D capabilities of the firm to maximum advantage. Open innovation promises that the organization can achieve a greater return on innovative activities and results to intellectual property (IP). This can be achieved if the organization considers the creative use of broad range of resources, which include customers, rivals, academics, and firms in unrelated industries [10]. The benefits of open innovation are widely accepted in open source software development communities [11]. In its broadest sense, software innovation refers to research and development (R&D) activities that involve intellectual capital, physical products, and processes in software production [12]. Chesbrough [10] also observed that strategic innovations have typically been regarded as company’s most valuable competitive assets, which also serve as barriers to entry to competitors. This kind of proprietary development and competition is characteristic of closed innovation processes, where technological progress has generally been kept secret to capture the potential for extra ordinary returns [13]. Conversely, in an open environment, a company’s ability to remain competitive increasingly relies on utilizing accessible resources in the continuous development of new and superior products and services. In business environments characterized by growing instability, this approach enables them to remain competitive [12]. Reuse and interoperability are apparent in FOSS development [14]. FOSS adoption is recognized as a form of open innovation [1].

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Open Source Collaboration Model Within a Value Network. Due to increasing interest on the benefits of FOSS, new communities were created for the practice of collaborative innovations [15, 16]. These collaborations resulted to high – quality mainstream applications. Open innovation both enables and builds upon interorganizational collaboration. Such collaboration has been variously referred to as a network form of organization [17], value network [18] or an ecosystem [19]. FOSS code is generally of good quality [20] because it has many analytic models that suggest factors contributing to FOSS code quality, such as number of developers, mix of talent level, etc. [21]. 2.2

Issues and Challenges of Free and Open Source Software

Both public and private organizations are critical not only with FOSS’ perceived benefits but also with the issues and challenges, it introduces. This is very evident with the inundations of publications and technical articles, which proposed different solutions to specific FOSS challenge. Frej et al. [22] provide a summary of challenges posed by FOSS and were categorize into migration and technical. Under migration: qualification and selection of FOSS; procurement; human factors (also Zuliani and Succi [23]); and local development and language support were enumerated. On the technical challenges: usability; software development service and support; interoperability and integration (also Schweik and English [24]); security (also Ankerholz [25]); data migration; and FOSS code maintenance and management (also Zage and Zage [26]) were enlisted. The existence of the issues and challenges in the Philippines were validated through the study of Canlas [27]. In his baseline survey conducted to business, industry, education and government sectors in the Philippines revealed that: (1) Qualification and Selection of Free and Open Source Software; (2) Human Factors; (3) Legal/License Issues; (4) Usability; and (5) Software Development Service and Support were the top issues IT departments as shown in when facing in dealing with FOSS. The summary was shown in Table 1. Table 1. Summary of the issues and challenges and their level of difficulty Issues and challenges Qualification and selection of FOSS Human factors Legal issues Usability Software development and support

2.3

Rating 4.19 4.07 4.09 4.13 4.23

Verbal interpretation High High High High Very high

Gregory’s Generic Technology Management Model

The following are the processes involved in Gregory’s Model [28] which were used as baseline processes of the proposed model:

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Identification of technologies which are (or may be) of importance to the business; Selection of technologies that should be supported by the organization; Acquisition and assimilation of selected technologies; Exploitation of technologies to generated profit, or other benefits; and Protection of knowledge and expertise (i.e. embedded in products and manufacturing systems).

3 Thrusts of the Study Due to its several perceived benefits, several governments and private organizations around the globe are embracing Free and Open Source Software (FOSS) as an alternative to the proprietary ones (Lewis [29]). The FOSS development model seems to be working for many applications thus it is foreseen to be a game changer in the software realm for the coming years [30]. Despite the several strategic advantages, FOSS has to offer, there exist a lot of issues and challenges that discourage organizations from crossing the barriers from proprietary software to free and open source software [30]. CIOs and IT Managers attributed the reluctance of organizations in adopting FOSS to the lack of available guidelines and organizational model in managing FOSS and how to align it to the IT and Organizational Strategies in the Philippines. Most of the respondents of this study admitted that they do not have any guidelines or model being used in their organization as shown in Table 2. Table 2. Distribution of organizations regarding the use of guidelines and model for FOSS adoption (difference is not significant v2 (1, N = 117) = 0.584, p > .05) Implementing Not implementing Total Business 45 31 76 Educational 21 9 30 Government 7 4 11 73 44 117

Using collections of literatures on FOSS implementation frameworks and guidelines, the main thrust of the study is to propose an organizational model for collaborative use of free and open source software for IT departments of public and private sectors in the Philippines hence addressing the prevailing issues and challenges described by Canlas [27].

4 Research Methodology Research Design. The study utilized descriptive, inferential and exploratory research methods using literature reviews, survey and case study approaches.

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Samples and Sampling Techniques. There were 117 IT departments (represented by their IT Managers) from various organizations from the Business, Government and Education Sectors. Respondents were from the headquarters located in Regions III, IVA, and National Capital Region in the Philippines. Table 3 shows the distribution of organizations based on sector. Table 3. Distribution of organizations based on sector Sector Frequency % Business 76 64.96% Academic 30 25.64% Public/Government 11 9.40% 117

Sampling used were snowball and purposive techniques and criteria were set to qualify respondents in the study.

5 Discussion The proposed model shown in Fig. 1 is anchored to the generic technology management process of Gregory [28] and outlines the clear role of the three levels of management: Strategic (Senior Managers); Tactical (Chief Information Officer or IT Manager); and Operational Planning (Unit Heads). It also indicates multidirectional interactions between levels. 5.1

Structure of the Proposed Model

Using Gregory’s Model, the framework has three clusters namely: Change Management; FOSS Procurement; and FOSS Research and Development. These clusters were linked by generic, interrelated and interdependent entities and processes as outlined in Fig. 2: 1. Experienced user can spearhead collaborative learning of FOSS in the workplace using the FOSS Knowledge Base [31]: a. New employees should be trained with the currently used FOSS. Attain certification; and b. Senior employees should attend retooling for new FOSS. Attain certification. 2. Encourage IT personnel and Non-IT users to identify and select candidate FOSS that are meritorious of use in the workplace and submit them to the FOSS Review Board. The Free Software Foundation published the DrakkR framework providing detailed and generic guidelines on selecting and qualifying FOSS for organizations [32]. 3. The FOSS Review Board then accepts, reviews and decides based on pre specified criteria i.e. usability, security and others, on FOSS Proposals. FOSS Review Board submits candidate FOSS to FOSS Procurement.

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Fig. 1. The horizontal (bottom) portion of the model depicts Gregory’s generic technology management processes while the vertical (left most) portion outlines the three level of strategic management.

4. The FOSS Procurement prepares planning, tendering and specifications including licensing and legal issues. Submits these documents to the Research and Collaboration. 5. Using the Knowledgebase linked to the FOSS Community, the Research and Collaboration entity conducts market research, feasibility studies and other technical matters related to FOSS. Returns the results to FOSS Procurement and updates the Knowledge Base.

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6. FOSS Procurement entity performs: a. If the result is positive, selection and awarding. Provide feedback to various units including the proponent thru the FOSS Review Board; or b. If the result is negative, return application to FOSS Review Board with the corresponding recommendations, 7. The In House Application and Systems Development entity and the IT Labs exploit FOSS’s open innovation potentials to produce enterprise systems, protect them with Intellectual Property (IP) and use as competitive advantage against competitor [10]. The entity also helps to achieve IT goals and support organizational strategies. 8. The In House Application and Systems Development, IT Labs and Research and Collaboration update the Knowledge Base either: a. Internally; or b. Externally, i.e. FOSS Communities. Torres and Fernandez [33] cited that organization linked to external FOSS communities performs collective intelligence, gain access to shared knowledge; motivate its people to learn and to succeed in using FOSS for technological innovation. Peters and Ruff [34] published comprehensive guides and best practices on how organization can collaborate with various FOSS communities. The absence of a legal entity or an individual firm to act as the other party in the procurement process adds up to the difficulty of doing FOSS procurement. Unlike proprietary software, the vendor takes all the legal responsibilities like warranty and support. The public sector in the Philippines is highly impacted by this challenge due to strict regulations and compliance to auditing processes [27]. The study of Bouras et al. [35] gave clear understanding on how FOSS procurement could be done in the public sector. The sets of guidelines were not only beneficial to public organizations but to private entities as well.

Fig. 2. Mapping of each entity to Gregory’s Model and the proposed model’s element clusters

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6 Conclusion Organizations involved in the study signified to the collaborative use of FOSS through the establishment of a Philippine based FOSS community [F(2,111) = 0.349, p = 0.706]. Willingness to share the existing guidelines and best practice is also highly evident [F(2,111) = 0.731, p = 0.484] wherein the education sector is very willing to spearhead the conduct of research and development in FOSS field. Regarding the proposed model and suggested guidelines, both the public and private organizations will likely adopt it with provisos or alterations [v2 (3, N = 67) = 0.00059, p < .05]. Provisos and alterations are to be made to fit their respective organizational contexts.

References 1. West, J., Gallagher, S.: Patterns of open innovation in open source software. In: Open Innovation: Researching a New paradigm, Washington, pp. 2–46. Oxford University Press (2005) 2. Sharma, S., Vijayan, S., Balaji, R.: A framework for creating hybrid-open source software communities. Inf. Syst. J. 12, 7–25 (2002) 3. Link, G.J., Jeske, D.: Understanding organization and open source community relations through the attraction-selection-attrition model. In: OpenSym 2017, Galway (2017) 4. Fitzgerald, B.: The transformation of open source software. MIS Q. 30, 587–598 (2006) 5. The Economist. http://www.economist.com/node/13743278 6. Ghosh, R.A., Prakash, V.V.: The orbiten free software survey. First Monday 5 (2000) 7. GartnerGroup. https://www.gartner.com 8. Vaughan-Nichols, S.J.: It’s an Open-Source World: 78 Percent of Companies Run OpenSource Software. ZDNet UK Edition (2015) 9. Ouchi, W.G., Bolton, M.K.: The logic of joint research and development. Calif. Manag. Rev. 30, 9–33 (1988) 10. Chesbrough, H.W.: Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston (2003) 11. von Hippel, E., von Krogh, G.: Open source software and the private-collective innovation model: issues for organization science. Organ. Sci. 14, 208–223 (2003) 12. Vujovic, S., Ulhøi, P.J.: Online innovation: the case of open source software development. Eur. J. Innov. Manage. 11, 142–156 (2008) 13. Meyer, P.B.: Episodes of Collective Invention. U.S. Bureau of Labor Statistics, Washington DC (2003) 14. Sonderegger, C.: https://files.project21.ch › LinuxDays-Public › FOSS-Philo-FS15 15. Kogut, B., Metiu, A.: Open-source software development and distributed innovation. Oxf. Rev. Econ. Polic. 17, 248–264 (2001) 16. Dahlander, L., Magnusson, M.: How do firms make use of open source communities? Long Range Plan. 16, 629–649 (2008) 17. Powell, W.W.: Neither market nor hierarchy: network forms of organization. Res. Organ. Behav. 12, 295–336 (1990) 18. Christensen, C.M., Rosenbloom, R.S.: Explaining the attacker’s advantage: technological paradigms, organizational dynamics, and the value network. Res. Policy 24, 233–257 (1995)

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19. Iansiti, M., Levien, R.: The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability. Harvard Business Review Press, Boston (2004) 20. Stamelos, I., Angelis, L., Oikonomou, A., Bleris, G.: Code quality analysis in open source software development. Inf. Syst. J. 12, 43–60 (2002) 21. Raghunathan, S., Prasad, A., Mishra, B.K., Chang, H.: Open source versus closed source: software quality in monopoly and competitive markets. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 35, 903–918 (2005) 22. Frej, M.B.H., Bach, C., Shock, R., Desplaines, E.: Open-source software: adoption and challenges. In: 2015 ASEE Northeast Section Conference, Boston (2015) 23. Zuliani, P., Succi, G.: Migrating public administrations to open source software. In: IADIS e-Society 2008 Conference, Avila (2008) 24. Schweik, C.M., English, R.C.: Brooks’ versus Linus’ Law: an empirical test of open source projects. In: 9th Annual International Conference on Digital Government Research, Partnerships for Public Innovation, Montreal (2008) 25. Ankerholz, A.: 2016 Future of Open Source Survey Says Open Source is the Modern Architecture. https://www.linux.com/news/2016-future-open-source-survey-says-open-sourcemodern-architecture 26. Zage, D., Zage, W.: An analysis of the fault correction process in a large-scale SDL production Model. In: 25th International Conference on Software Engineering, Portland (2003) 27. Canlas, F.: Issues and challenges of free and open source software adoption in the Philippines: a baseline survey for information technology strategy formulation. In: Proceedings of the Joint Conference, The 4th International Conference on Organization and Management & The 6th Corporate Social Responsibility (CSR), Ethics, Governance, and Sustainability, Abu Dhabi (2019) 28. Gregory, M.J.: Technology management: a process approach. In: Proceedings of the Institution of Mechanical Engineers, London (1995) 29. Lewis, J.A.: Government Open Source Policies. Center for Strategic & International Studies, Washington D.C. (2010) 30. South African State Information Technology Agency: Flight Plan: Free Open Source Software (FOSS) deployment in the South African government. http://www.westerncape. gov.za/text/2008/12/foss_roadmap.pdf 31. Zhao, L., Deek, F.P.: User collaboration in open source software. Electron. Mark. 14, 89– 103 (2004) 32. Free Software Foundation: Qualification and Selection of Open Source Software (QSOS). http://dist.qsos.org/qsos-2.0_en.pdf 33. Torres, M.M., Fernandez, M.D.: Current Issues and Research Trends on Open-Source Software Communities. Technol. Anal. Strateg. Manag. 26, 55–68 (2014) 34. Peters, S., Ruff, N.: Participating in Open Source Communities. The Linux Foundation. https://www.linuxfoundation.org/resources/open-source-guides/participating-open-sourcecommunities/ 35. Bouras, C., Filopoulos, A., Kokkinos, V., Michalopoulos, S., Papadopoulos, D., Tseliou, G.: Guidelines for the procurement of free and open source software in public administrations. In: IADIS International Conference Information Systems Post-implementation and Change Management 2012, Lisbon (2012)

A Framework Supporting Supply Chain Complexity and Confidentiality Using Process Mining and Auto Identification Technology Zineb Lamghari1(&), Maryam Radgui1,2, Rajaa Saidi1,2, and Moulay Driss Rahmani1 1

2

LRIT Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat, Morocco [email protected], [email protected] SI2M Laboratory, National Institute of Statistics and Applied Economics, Rabat, Morocco {m.radgui,r.saidi}@insea.ac.ma

Abstract. Supply chain is a network between a company and its suppliers to produce and distribute a product to the final customer. This network includes different activities, people, entities, etc. The interaction between these elements provides a cross-organization Business Process. It has mainly treated with process mining techniques, to handle the resulted process instances as event logs. These events are obtained by implementing the auto identification technology within the supply chain related to materials or personal. By doing so, the provided events are not simply presented, they emerged more challenges like complexity and data confidentiality. Therefore, in this work we develop a descriptive framework, based on a literature study, to answer supply chain challenges related to the process mining field. This is done, by implementing process mining within the supply chain, using auto identification technology and taking into consideration recent challenges related to cross-organization Business Process. Keywords: Process mining
Cross-organizational
Supply chain
Complexity
Confidentiality
Auto Identification Technology
Alpha-T Encryption
Cross-organization Business Process




1 Introduction Recently, there have been several IT innovations that changes the way how supply chains [1] are managed. Most notably, the Auto Identification Technology (AIT) [2]. It can make supply chain data accessible for process mining, by providing precise data on product location and product characteristics as event logs. The collection of AIT data provides strategic value in discovery of new relationships and opportunities for process redesign. Therefore, in this context of supply chain, process mining constitutes a solution to construct the overall process model. Process mining [3] is a technique that automatically derives process models from a set of event logs. Its potential value for © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, 7, pp. 352–361, 2020. https://doi.org/10.1007/978-3-030-36778-7_39

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supply chain analysis relates to the discovery of interactions in distributed process [4], which is a prerequisite for process improvement, and helpful for logistics network design and supply chain planning [5]. For example, it can reveal undesirable behavior that is not compliant with the specification of a predefined process. Process mining has been successfully applied to various intra-organizational problems [6]. Although, the construction of supply chain wide processes poses a real challenge of complexity; because often the knowledge about the overall process is distributed over the involved parties and no single party has an overview on the complete process and all its details. Also, with the increasing number of participant in supply chain business process (crossorganization), it is important to secure data, where it will be only accessible to authorized persons. This is emerged a second challenge of event logs confidentiality. Although, there is consensus about the need for global supply chain analysis, there has none work for covering the supply chain and its related process mining challenges in term of cross-organization Business Process (BP): complexity and confidentiality. In this sense, this paper presents an overview, of how AIT and the response to related process mining challenges can be combined for better supply chain analysis. Indeed, we considered the AIT as event logs source of the supply chain or the crossorganization BP. First, we determine the recent suitable discovery algorithm for deriving simplicity from the provided complex events. Second, we present a proposed confidential solution according recent works in RDS (Responsible Data Science) project. This is combined with existed events for more limited access. This RDS [7] initiative focuses on four main questions: (1) How to avoid unfair conclusions even if they are true?, (2) How to answer questions with a guaranteed level of accuracy?, (3) How to answer questions without revealing secrets?, and (4) How to clarify answers such that they become indisputable?. Based on these two pillars (complexity and confidentiality), we can build our proposed framework, in order to describe the product workflow from the obtained events to the provided cross organizational process model. In this sense, we make process mining applicable. Through exploiting current knowledge, this framework can help managers in conceptualizing and addressing supply chain complexity and security. This paper is organized as follows: in Sect. 2 we present a literature review of the recent challenges related to process mining and supply chain. In Sect. 3, we show the suitable solutions to solve these challenges, relatively to process discovery algorithms and confidentiality approaches. In Sect. 4, we define our proposed framework. This framework presents a descriptive view of implementing process mining within the supply chain by using AIT and taking into consideration recent challenges related to cross-organization BP. Conclusion and further works are mentioned in Sect. 5.

2 Literature Review Actually, process mining techniques are implemented within supply chain by using AIT. Many researchers have explained and demonstrated the achievement and the implementation of this operation [8–10]. However, its objective is to make supply

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chain event logs accessible for process mining. Although, the accessibility to these events and its structure, two cross-organizational BP challenges are provided: complexity and data confidentiality. These two challenges are related to the main challenge cross-organizational, which is explicitly mentioned as research issue by the process mining community Manifesto [11]. Therefore, in this section we aim to provide a literature review of recent researches, to determine these challenges in deeper level, which can introduce adequate solutions. 2.1

Complexity

Process mining techniques have difficulties to handle complex event logs properly. Fortunately, it is generally agreed that decomposed process mining is the best solution to this question, as it decomposes the process mining problem into many smaller problems that can be solved in short time [12], and many ways to partition process mining problems exist. However, the approaches used for decomposition were not consistent among studies: some authors used the divide-and-conquer approach [13], some used the Single-Entry Single-Exit technique [14], others used the notion of process cubes [15, 16], the four conflicting quality dimensions [21], especially in conformance checking decomposition [17], some studies answer to issues related to computation and visualization [18] and others have based their decomposition on clustering [19]. Moreover, each study for process mining decomposition has some limitations and need to be investigated in further works. The most relevant problem is choosing the appropriate algorithm to mine the composed event logs provided from an original complex event logs. 2.2

Confidentiality

Several techniques have been applied in many papers to deal with the challenge of cross-organizational mining [20]. Most of them focused on commonality and collaboration between organizations, specifically on similarities between the process models and behavior of organizations. However, although these approaches provide the way to successful cooperation, organizations might refuse such collaborations to avoid leakage of private information [29]. Indeed, one common viewpoint on process mining in supply chain is that organizations have data that they want to remain private and other data that can be made public. Similarly, all researches distinguish between a private view on an organization’s part of the supply chain wide process, and a public view on the process. They consider an approach in which the public data is shared (with each other or with a trusted third party) to construct an overall process model and then each organization can link its private data or model to this public process model to complement it with the details of their internal business processes. In this sense, we find two main approaches: in [22], the authors have used a crossorganizational process discovery setting, where public process model fragments are shared as safe intermediates. [23] Proposed a possible approach for outsourcing process mining, which is capable of preventing the confidentiality of data, when operating

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cross-organizational mining by the encryption of strings and numerical attributes. Nonetheless, this approach was implemented in ProM [24] and most of the analysis plug-ins do not involve numerical data. So, each plug-in needs to be appropriately modified and adopt a full encryption of numerical values. These methods use cryptography methods to secure event logs, while it is a resource consuming activity. 2.3

Synthesis

In this section, we have presented two challenges, which are still encountered related to the provided supply chain event logs. Firstly, we find the complexity challenge, where each study for process mining decomposition/simplification has some limitations and need to be investigated. The most relevant problem is how choosing appropriate algorithm to mine these complex event logs. Secondly, for the security context and related to the cross-organizational approaches, the questionable point is how taking into consideration the confidentiality of event logs, i.e.; how to secure data, where it will be only accessible to authorized persons.

3 Framework In this section, we present our contribution for resolving the process mining challenges emerged during the supply chain BP. 3.1

The Recommended Complexity Solution

The main activity in mining is the discovery process, which examines the event log to produce a model that reflects the workflow of the business process. The workflow model should be accurate in describing the real operational activities, from the business transactions that generate the event log. Indeed, several algorithms have been developed to do the mining in the discovery process: a deterministic algorithm, Alpha* [25], a genetic algorithm [26], machine learning [27], discriminative patterns [28] and also based on association rule approaches [29], etc. From a performance comparison of the discovery algorithms, it was found that the best accuracy was achieved by the genetic algorithm and the best time complexity was achieved by the heuristic algorithm when compared with Alpha [30]. Although the heuristic algorithm produces an optimal solution with minimal side effects, it is unsuitable for complex mining containing invisible tasks [31]. Other than that, it produces a directed graph of a standard causality network (C-net) that has bias representational model and only supports structured relations, not in behavioral relations. Meanwhile, the computation complexity of the genetic algorithm is worse and unfavorable for big event logs. For these reasons, we propose to use the enhanced Alpha-T algorithm [32], which has successfully conducted discovery on a business process under certainty with complex concurrent transitions and places. It reduced the number of steps by localizing

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computation complexity in the pre-processing stage, and also minimizing complexity firing mapping of task and place gateway in post-processing. It produces more completeness and correctness model than other algorithms. Therefore, the alpha-T algorithm can be combined within the recent decomposition framework [33]. 3.2

The Recommended Confidentiality Solution

Generally and as explored by [23], using cryptography is a resource consuming activity, and decryption is even much more resource consuming than encryption. For these reasons, we recommend the ensure confidentiality event logs method [34]. Where, they keep some parts of a data as plain text even in the secure event log. With this method, authors defined three environment of application: - Forbidden Environment: a lot of valuable confidential information and except some authorized persons no one can access this data. -Internal Environment: Event logs in this environment are partially secure, selected results produced in this environment (e.g., a process model) are the same as the results produced in the forbidden environment, and data analyst is able to interpret the results without decryption. -External Environment: In this environment, unauthorized external persons can access the data. Event logs in this environment are entirely secure, and the results are encrypted. Whenever data analyst wants to interpret the results, these results have to be decrypted and converted to the internal version. Also, results from the external environment do not need to be exactly the same as the results from the internal environment.

4 Descriptive Framework In this section, we will detail our proposed framework. This framework can support practitioners in analyzing and addressing complexity and security, within the organization and across the supply chain. Our proposed framework is inspired by [8, 9], where three blocks are introduced: The first supply chain block presents the product passed from one contributor to another (Supplier, Factory, Distribution, Retail and Customer) forming a material flow. On this supply chain we have applied Management Business Process (BPM) [35] and Adaptive Case Management (ACM) [36] respectively to pilot business processes and interpret the resource knowledge within this chain. The material flow is expressed by an object flow link. The second middleware block describes how to collect event logs from each contributor. The third process mining block aims to resolve complexity and confidentiality challenges, related to the provided cross-organization BP, expressed in this framework as supply chain flow. This is done, by applying the two recommended solutions; we have investigated in Sect. 3. Therefore, if we follow our framework steps, we can answer to the complexity and the security challenges, which are mostly appeared within the supply chain/cross-organization BP (Cf. Fig. 1).

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Fig. 1. Standard framework used for responding complexity and confidentiality challenges

Figure 2 detailed how the middleware block has been collected event logs; from each part. This is done by implementing the AIT within the supply chain, and then converting the event logs AIT (material or product flow) format to the process mining event logs required format (using the transformer). Each contributor has produced specific logs, for instance from Supplier we have collected certain information etc. To get the total log file, which defined the whole cross-organization BP, we have applied an integration procedure in this order (from supplier events to the customer events) of the process flow. Finally, all these event logs have been recorded in the Repository. Figure 3 illustrates two recommended methods. The first method is answering the complexity challenge (we recommend applying the alpha-T algorithm in order to obtain a simple process model that represents simply the whole process instances of the supply chain). The Alpha-T algorithm is done by using GTPR (Generalized Tuple Pattern Recognition) based on the ALT (Adjacency List Tree) structure presents an approach that is able to accurately perform discovery from a structural and behavioral perspective while conforming to the SWF-net (Structured Workflow Net)and BPMN (Business Process Management Notation) model. Other than that, the step preprocessing in addition to the depth weighting process has benefit to generalize logic analysis that has not been applying to Alpha and other advanced algorithms.

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Fig. 2. Middleware block components

The second method is making event logs more confidential by using encryption algorithms. These algorithms include: 1- Symmetric Cryptosystem (the same secret key is used to encrypt and decrypt a message. Data manipulation in symmetric systems is faster than asymmetric systems as they generally use shorter key lengths. Advanced Encryption Standard (AES) is a symmetric encryption algorithm.). 2- Asymmetric Cryptosystem (systems use a public key to encrypt a message and a private key to decrypt it or vice versa like (RSA) Rivest-Shamir-Adleman). 3- Deterministic Cryptosystem (always produces the same ciphertext for a given plaintext and key, even over separate executions of the encryption algorithm.). 4- Probabilistic Cryptosystem (uses randomness in an encryption algorithm so that when encrypting the same plaintext several times it will produce different cipher texts.) and 5- Homomorphic Cryptosystem (allows computation on cipher text. E.g. Paillier is a partially homomorphic cryptosystem). First, the extracted events are clearly presented; we can read all information for each case ID. After the encryption method, all information is hidden. Only persons whom had the decryption key can read the resulted event logs. In other presentation, we can found some information in plain text format although they are in secure mode. It is important to choose the suitable information, which is still in plain text according to the business study.

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Fig. 3. Process mining block detail

5 Conclusion In this paper, we have developed a descriptive framework to answer supply chain challenges related to process mining. First, we present a literature review, which investigates research challenges related to supply chain as cross-organization BP and process mining field. Therefore, we conclude two challenges: - Complexity of the produced process model that describes the whole process flow, which is crossing each part of the supply chain (how to present the process model simply based on a suitable process discovery algorithm). - Confidentiality of the recorded event logs (how to appropriate access to data according to the personal role respectively to the public and the private parties). Second, we have defined two recommended solutions, in order to answer complexity and confidentiality challenges. Finally, we have implemented these two solutions in a descriptive framework (Alpha-T algorithm and confidential access). This framework describes the process flow from supply chain block to process mining block. This framework can help managers in conceptualizing and addressing supply chain complexity and security. As perspective, more supply chain challenges need to be defined to build an integrated framework for researches addressing supply chain challenges related to process mining field. We are working on a complete case study to validate the utility of our descriptive framework.

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Acknowledgement. This work was supported by the National Center for Scientific and Technical Research (CNRST) in Rabat, Morocco.

References 1. Hugos, M.H.: Essentials of Supply Chain Management. Wiley, Hoboken (2018) 2. Jamaludin, Z., Huong, C.Y., Abdullah, L., Nordin, M.H., Abdullah, M.F., Haron, R., Jalal, K.B.A.: Automated tracking system using RFID for sustainable management of material handling in an automobile parts manufacturer. J. Telecommun. Electron. Comput. Eng. (JTEC) 10(1–7), 35-40 (2018) 3. Van der Aalst, W.: Process Mining: Data Science in Action. 2nd edn. Springer (2016) 4. Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004) 5. Niederman, F., Mathieu, R.G., Morley, R., Kwon, I.W.: Examining RFID applications in supply chain management. Commun. ACM 50(7), 92–101 (2007) 6. Van der Aalst, W., Reijers, H.A., Weijters, A.J., Van Dongen, B.F., De Medeiros, A.A., Song, M., Verbeek, H.M.W.: Business process mining: An industrial application. Inf. Syst. 32(5), 713–732 (2007) 7. Van der Aalst, W., Bichler, M., Heinzl, A.: Responsible data science. Bus. Inf. Syst. Eng. 59 (5), 311–313 (2017) 8. Kang, Y.S., Lee, K., Lee, Y.H., Chung, K.Y.: RFID-based supply chain process mining for imported beef. Korean J. Food Sci. Anim. Resour. 33(4), 463–473 (2013) 9. Glaschke, C., Gronau, N., Bender, B.: Cross-system process mining using RFID technology, pp. 179–186. (2016). http://www.doi.org/10.5220/0006223501790186 10. Gerke, K., Claus, A., Mendling, J.: Process mining of RFID-based supply chains. In: 2009 IEEE Conference on Commerce and Enterprise Computing. IEEE, pp. 285–292 (2009) 11. Van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) Business Process Management Workshops. BPM 2011. Lecture Notes in Business Information Processing, vol 99. Springer, Heidelberg (2011) 12. Kalenkova, A., Lomazova, I.A., Van der Aalst, W.: Process model discovery: a method based on transition system decomposition. In: International Conference on Applications and Theory of Petri Nets and Concurrency, LNCS, pp. 71–90. Springer, Heidelberg (2014) 13. Van der Aalst, W.: A general divide and conquer approach for process mining, Ganzha. In: Federated Conference on Computer Science and Information Systems, pp. 1–10 (2013) 14. Munoz-Gama, J., Carmona, J., Van der Aalst, W.: Single-entry single-exit decomposed conformance checking. Inf. Syst. 46, 102–122 (2014) 15. Van der Aalst, W.: Process cubes: slicing, dicing, rolling up and drilling down event data for process mining. In: Asia Pacific Conference on Business Process Management, Lecture Notes in Business Information Processing, vol. 3159, pp. 1–22. Springer (2013) 16. Vogelgesang, T., Appelrath, H.J.: Multidimensional process mining with PMCube explore. In: Proceedings of the BPM Demo Session 2015 Co-located with the 13th International Conference on Business Process Management, Innsbruck, Austria, pp. 90–94 (2015) 17. Verbeek, H.M.W., Van der Aalst, W.: Merging alignments for decomposed replay. In: Application and Theory of Petri Nets and Concurrency, PETRI NETS, Lecture Notes in Computer Science, vol. 9698. Springer, Cham (2016) 18. Munoz-Gama, J., Carmona, J., Van der Aalst, W.: Conformance checking in the large: partitioning and topology. In: International Conference on Business Process Management. Lecture Notes in Computer Science, vol. 8094, pp. 130–145 (2013)

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19. Hompes, B., Verbeek, E., Van der Aalst, W.M.P.: Finding suitable activity clusters for decomposed process discovery. In: Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis, LN in Business Information Processing, pp. 32–57. Springer (2014) 20. Buijs, J.C.A.M., Dongen, B.F., Van der Aalst, W.: Towards cross-organisational process mining in collections of process models and their executions. In: Business Process Management Workshops, pp. 2–13. Springer, Heidelberg (2012) 21. Irshad, H., Shafiq, B., Vaidya, J., Bashir, M.A., Shamail, S., Adam, N.: Preserving privacy in collaborative business process composition. In: 2015 12th International Joint Conference on e-Business and Telecommunications, vol. 4, pp. 112–123. IEEE (2015) 22. Burattin, A., Conti, M., Turato, D.: Toward an anonymous process mining. In: Proceedings of the 3rd International Conference on Future Internet of Things and Cloud (FiCloud), pp. 58–63 (2015) 23. Liu, C., Duan, H., Zeng, Q., Zhou, M., Lu, F., Cheng, J.: Towards comprehensive support for privacy preservation cross-organization business process mining. IEEE Trans. Serv. Comput. 12, 639–653 (2016) 24. Van der Aalst, W., Van Dongen, B.F., Christian, G.W.: ProM: the process mining toolkit. In: BPM Demos, vol. 489, no. 31, p. 2 (2009) 25. Li, J., Liu, D., Yang, B.: Process mining: extending a-algorithm to mine duplicate tasks in process logs. In: Advances in Web and Network Technologies, and Information Management, vol. 4537, pp. 396–407 (2007) 26. Van der Aalst, W., Medeiros, A., Weijters, A.: Genetic process mining. In: Applications and Theory of Petri Nets, Lecture Notes in Computer Science, vol. 3536 (2005) 27. Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust process discovery with artificial negative events. J. Mach. Learn. Res. 10, 1305–1340 (2009) 28. He, Z., Gu, F., Zhao, C., Liu, X., Wu, J., Wang, J.: Conditional discriminative pattern mining: concepts and algorithms. Inf. Sci. 375, 1–15 (2017) 29. Sarno, R., Dewandono, R.D., Ahmad, T., Naufal, M.F., Sinaga, F.: Hybrid association rule learning and process mining for fraud detection. IAENG Int. J. Comput. Sci. 42(2) (2015) 30. Wang, J., Wong, R.K., Ding, J., Guo, Q., Wen, L.: Efficient selection of process mining algorithms. IEEE Trans. Serv. Comput. 6(4), 484–496 (2012) 31. Van der Aalst, W.: Responsible data science: using event data in a “people friendly” manner. In: International Conference on Enterprise Information Systems, pp. 3–28. Springer (2016) 32. Hermawan, S.R.: A more efficient deterministic algorithm in process model discovery. Int. J. Innov. Comput. Inf. Control 14(3), 971–995 (2018) 33. Yan, Z., Sun, B., Chen, Y., Wen, L., Hu, L., Wang, J., Wang, L.: Decomposed and parallel process discovery: a framework and application. Future Gener. Comput. Syst. 98, 392–405 (2019). https://doi.org/10.1016/j.future.2019.03.048 34. Rafiei, M., von Waldthausen, L., Aalst, W.: Ensuring confidentiality in process mining (2018) 35. Bae, H., Seo, Y.: BPM-based integration of supply chain process modeling, executing and monitoring. Int. J. Prod. Res. 45(11), 2545–2566 (2007) 36. Gold, S., Seuring, S., Beske, P.: Sustainable supply chain management and interorganizational resources: a literature review. Corp. Soc. Responsib. Environ. Manag. 17 (4), 230–245 (2010)

3D Recording and Point Cloud Analysis for Detecting and Tracking Morphological Deterioration in Archaeological Metals Alba Fuentes-Porto(&) , Drago Díaz-Aleman and Elisa Díaz-González

,

Universidad de La Laguna, San Cristóbal de La Laguna, Spain [email protected]

Abstract. Cultural Heritage documentation field is living an outstanding technological renovation, and techniques as image processing and computer vision are becoming a more and more valuable resource for its preservation, research and diffusion. Trying to contribute in this field, we present a line of research focused on determining how precision three-dimensional records can help us to detect and quantify formal changes in archaeological metals. This interest is marked by the fact that this heritage usually presents advanced stages of corrosion that imply material fragility and make them susceptible to suffer physical damage in a short period of time. Our experimental context was the temporary loan of a rusted iron helmet. Its state of preservation was recorded before and after it was being transferred, supporting us in two systems of great diffusion in the field of 3D digitalization of heritage: structured light 3D imaging and Structure from Motion (SfM) systems. Finally, the M3C2 technique was used to quantify and interpret the geometric differences between these records and assess the significance of their accuracy. Keywords: 3D recording
Point cloud analysis
Structured light imaging Structure from Motion (SfM)
Archaeological metal




1 Introduction An immense patrimony of metallic artifacts, used since the Bronze Age in different activities of daily life, is conserved in many museums around the world. We can find jewels, relics, coins, household goods and even sculptures and weapons among them. One of these unique and invaluable testimonies of the past offers us the experimental framework of this work: the remains of a Spanish iron helmet, a piece deposited in the Museo Histórico Militar de Canarias (Spain) whose origin is associated with the Conquest of the Canary Islands1 by the Crown of Castile at the end of the 15th century. As a war garment testimony of an outstanding historical and cultural context, it was requested on loan by the Deutsches Historisches Museum to be part of the temporary exhibition Europe and the Sea; which was an excellent opportunity for the diffusion 1

Archipelago located in the Atlantic Ocean, off the coast of the Sahara.

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and valorization of our cultural legacy. However, it presented a state of advanced corrosion and mineralization, a characteristic affection of ferrous objects from archaeological contexts that made it susceptible to fracture and suffer material losses. Because of this, extreme precautions had to be taken. This condition signaled the need to take extreme precautions during its packing and transportation and to develop a control system that could detect the appearance of possible structural changes during the loan (Fig. 1).

Fig. 1. Fragment of a 16th century Spanish morion helmet, military clothing associated with the Conquest of the Canary Islands by the Crown of Castile. Author: Alba Fuentes-Porto.

Currently, a wide range of analytical techniques allow deepen the knowledge of archaeological metals. Stereoscopic and optical microscopes, radiography, scanning electron microscopy (SEM), X-rays fluorescence and diffraction (XRF and XRD) or Fourier-transform infrared spectroscopy (FTIR) are used to understand their internal structure, composition and corrosion products [1]. However, the techniques responsible for achieving morphometric analyzes are still in experimentation and development. These methodologies are based in the computational study of high-precision threedimensional data. In recent years, a methodological proposal imported from the geomorphology field [2] have been used to quantify geometric differences over the time in estate heritage [3]. This could be also a resource to achieve our documentary objectives. Nevertheless, we should previously contrast the accuracy that a geometric comparison between two three-dimensional models could offer in the case of medium-sized objects, as well as its suitability as a methodology to deal with the detection and tracking of material alterations with millimeter dimensions.

2 Methodological Approach Since we intend to address an analysis focused on the detection and quantification of possible geometric changes in a medium scale object surface over a given period of time, when designing the methodological procedure, we consider it a priority to take

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care of two essential aspects: (a) the capture conditions were reproduced as accurately as possible throw all the registered and comparable moments, a step that seeks to minimize possible differences between 3D models due to acquisition process and data processing variations; (b) the quality needed to undertake analysis and comparisons as accurate as possible with the 3D models was obtained. According to technical recommendations of the processing software, that implied get high-density point clouds, devoid of noise and redundant data, and a correct orientation of normals. The formal characteristics of our object, smooth shapes with irregular edges of reduced thickness (around 2 mm) and very homogeneous optical qualities, often causes technical difficulties in 3D documentation. Since these are frequently encountered characteristics of metallic archaeological artifacts, as a starting point, we set out to test the quality that two different recording systems could provide us: structured light 3D imaging and the use of Structure from Motion (SfM) systems. The first one has been widely used for Heritage recording and documentation thanks to an outstanding geometric accuracy and reliability [4, 5]. The second one is a photogrammetric technique that has also proven to offer high-quality 3D models of small and medium scale pieces, besides economic accessibility and ease of implementation [6, 7]. These qualities have led it to consolidate as a solid alternative to scanning [3, 8]. Based on both structured light 3D imaging and SfM systems, we programed the recording of two different conservation status of the piece: one before leaving his usual location (outgoing), and another one immediately after its return (incoming). For the last step, the M3C2 (Multiscale Model to Model Cloud Comparison) technique was selected for studying the differences between the three-dimensional records [2]. Implemented as a CloudCompare plugin, an OS point clouds management and examination software, this tool allows to quantify geometric differences between point cloud pairs and generate a false color map with the intensity values obtained. 2.1

3D Data Acquisition

An Artec Eva Scanner was used as a structured light device. It is a medium price equipment that offers a resolution of 0.5 mm and a 0.1 mm accuracy; characteristics that may have led him to appear among one of the most extended scanners in the 3D documentation of cultural heritage [9]. Photogrammetric captures have been acquired using a Canon EOS 700D camera with a fixed focal distance of 47 mm. For outgoing and incoming photogrammetric mapping, 70 photographs of the obverse of the piece have been taken with spiral distribution, RAW format and maximum resolution. 2.2

Point Cloud Processing

Scanner datasets were processed by using the software provided with the device itself (Artec Studio Professional software V.10). Manufacturer recommendations sand default values for obtaining high quality 3D models were followed. At the time of testing SfM software, we wanted to contemplate two different alternatives widely used: an OS software, Visual SfM [10]; and a low cost one, Agisoft PhotoScan with a Standard Edition. In both cases, stipulated processing values for high

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quality acquisition were selected to generate our outgoing and incoming models. As a final step, outliers were eliminated and the orientation of the normals were corrected in MeshLab, another open source system which provides an interesting set of tools for editing, cleaning, healing point clouds. 2.3

Analysis of Differences by Cloud-to-Cloud Comparison

The M3C2 algorithm operation is based on point normal estimation and difference computation [2]. In our three pairs of 3D models, point clouds of the outgoings have been used as the reference cloud (Cloud#1). Also, in all cases a new cloud containing the differences between outgoing and incoming registrations was generated. At this point, it should be noted that, although real measurements in 1:1 scale were included in scanner dataset, photogrammetric ones must be previously scaled with a reference of a physical measurement.

3 Results A color deviation map of each recording system tested, supported from a scalar field, highlight the differences found in the geometry of our object throughout its departure and its return to the dependencies of the museum to which it belongs. In order to deepen in the compression of the geometric information obtained, the tails of each evaluated range have been cut off, set in the most significant measurement values and collected graphically in a histogram which was provided automatically by the analysis software itself (Table 1). All these data, interpreted jointly, allow us to estimate the degree of precision offered by the methodologies tested and assess their capacity to detect and quantify material losses on the surface of our object. The comparison made through scanning with structured light indicates that the maximum upper and lower deviations were at ±0.2 mm, and most of them were less than ±0.1 mm. These values, which match with the accuracy error margins of the equipment, discard reliability that mechanical alterations higher than its precision range (0.5 mm) have been generated. Visual SfM, which did not offer us stable results during the generation of dense point clouds, generated models with geometric differences that are perceptible to the naked eye and impossible to alienate each other. Consequently, the peaks of deviation reached 18 mm and the measures range were concentrated between ± 2.5 mm. These values not only show an insufficient precision to approach medium-scale geometric analysis, but also contradict the data provided by the structured light scanner, of certified accuracy. The second Stop for Motion software we tested, Agisoft Photo-Scan, generated maximum deviation values that hovered around 23 mm. However, these were caused by the presence of residual outliers on the back of the model; while most of the values were within a range of ±0.5 mm.

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Table 1. Results offered by the 3D analysis in the three capture methodologies evaluated.

Data origin

Differences distribution

Histogram of the main values

3D scanner

Visual SfM

PhotoScan

4 Conclusions, Open Issues and Future Developments As a study methodology focused on medium size objects, which are exposed to material losses and breaks of submillimeter dimensions, the accuracy of the measurements is crucially important. We consider that the resolution (0.5 mm) and the accuracy (0.1 mm) of the structured light device tested can be enough to detect and quantify mechanical damages. However, we estimate them insufficient for possible volume increases caused by corrosive processes, since their dimensions could be below our precision range. The suitability of the photogrammetric techniques to undertake the same study was strongly conditioned by the software selected for generating and processing the point clouds. In

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our case, Agisoft PhotoScan reached a resolution of less than 1 mm. Within this measurement range the appearance of cracks and fissures on the studied piece can be detected, but no superficial detachments that may derive from stratified forms of corrosion; and of course, no volume increases due to corrosion. For checking its proper functionality, a time tracking of each alternative must be also considered. 3D data acquisition of structured light imaging needed over 30 min, point cloud processing 3,5 h and point cloud analysis another 30 min; adding a total of 4,5 h. Meanwhile, time needed for photogrammetry data acquisition is a little bit higher, over 2 h, but processing times decrease to an hour; reaching a total of 3,5 h by adding the 30 min corresponding to the point cloud analysis. The similarity between both processing times discards this parameter as determining for choosing one or another alternative; but it does confirm that it implies acceptable times for any diagnosis of an archaeological good. The results offered by this first experimental work confirm the viability to detect and track mechanical deterioration in archaeological metals by using geomatic techniques and 3D imaging analysis. Nonetheless, it is necessary to emphasize that, as the resolution of the photogrammetric point cloud depends on the quality level achievable by both photographic record and the different software employed for the processing, approaching new experimentations is considered important in order to define and optimize the methodological proposal. Regarding the structured light scanning techniques, we highlight the interest of evaluating and contrasting the measurements other scanner devices used for heritage documentation could provide.

References 1. Ministerio de Educación, Cultura y Deporte, Gobierno de España: The COREMANS Project. Intervention criteria for metallic materials (2015) 2. Lague, D., Brodu, N., Leroux, J.: Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z). ISPRS J. Photogramm. Remote Sens. 82, 10–26 (2013) 3. Pereira-Uzal, J.M.: 3D modelling in cultural heritage using structure from motion techniques. PH Investig. 6, 49–59 (2016) 4. Díaz, F., Jiménez, J., Barreda, A., Asensi, B., Hervás, J.: Modelado 3D para la generación de patrimonio virtual. Virtual Archaeol. Rev. 6(12), 29 (2015) 5. Wachowiak, M.J., Karas, B.V.: 3D scanning and replication for museum and cultural heritage applications. J. Am. Inst. Conserv. J. Am. Inst. Conserv. 48(2), 140–158 (2009) 6. Gil-Melitón, M., Lerma, J.L.: Patrimonio histórico militar: digitalización 3D de la espada nazarí atribuida a Ali Atar. Virtual Archaeol. Rev. 10(20), 52–69 (2019) 7. Caro, J.L.: Fotogrametría digital para la difusión del patrimonio numismático. In: XV Congreso Nacional de Numismática, pp. 669–676, Madrid (2014) 8. Aicardi, I., Chiabrando, F., Maria, Lingua A., Noardo, F.: Recent trends in cultural heritage 3D survey: the photogrammetric computer vision approach. J. Cult. Herit. 32, 257–266 (2018) 9. Di Angelo, L., Di Stefano, P., Fratocchi, L., Marzola, A.: An AHP-based method for choosing the best 3D scanner for cultural heritage applications. J. Cult. Herit. 34, 109–115 (2018) 10. Changchang, W.: VisualSFM: a visual structure from motion system. http://ccwu.me/vsfm/

Convolutional Neural Network Architecture for Offline Handwritten Characters Recognition Soufiane Hamida1(&), Bouchaib Cherradi1,2, Hassan Ouajji1, and Abdelhadi Raihani1 1

SSDIA Laboratory, ENSET Mohammedia, UH2C, Mohammedia, Morocco [email protected], [email protected] 2 CRMEF Casablanca-Settat, S. P. El Jadida, Morocco

Abstract. Automatic handwriting recognition systems are a very wide-ranging research topic for many years. This type of intelligent systems is applied in various fields: Checks processing, forms processing, automatic processing of handwritten answers in an examination, etc. The purpose of this work is to propose a new Convolutional Neural Network (CNN) model and compare its performance with those of K-Nearest Neighbors (KNN) technique. Both used for the classification of complex multiclass problems. In this paper, we implement and evaluate the performance of this two-machine learning algorithms used to predict handwritten characters. The training and testing data have been extracted from the MNIST digits and letters database, which contains pre-processed images. The results obtained using different similarity measures such as accuracy, sensitivity and specificity confirm that the classification obtained by our proposed CNN architecture is the most accurate compared to the KNN studied in this work. Keywords: Machine learning
Convolutional neural network Neighbors
Prediction
Classification




K-Nearest

1 Introduction Today many activities can be done on digital media that facilitate the sharing of this content with other people [1]. However, handwriting is present in many fields of applications. The field of automatic check processing is an interesting scientific area. Researchers have largely invested in this topic through optimizing the performance of machine learning algorithms in the checks processing [2]. In education field, we continue to invite learners to write their answers on sheets. The diversity of questions makes the automatic correction very laborious. Automatic learning environments provide automated evaluation methods, such as automatic correction modes (Single/multiple choice questionnaires) [3]. However, these modalities remain very limited because the proposed questions are always closed. In this case, a learning system can only evaluate memory and comprehension abilities, ignoring others such as application, analysis, and synthesis abilities. This problem stems from the machine’s inability to handle written questions. Therefore, it is necessary to exploit the strength of machine learning by developing © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 368–377, 2020. https://doi.org/10.1007/978-3-030-36778-7_41

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intelligent algorithms able to process the answers and make a decision. Image classification algorithms involve taking an image as input and predicting the image content. The output is a label of image predicted. In fact, machine learning methods are widely used to predict handwritten figures and give satisfying results [4]. However, many other algorithms can be used to predict handwritten digits. An artificial neural network is a popular approach to machine learning that offers better performance especially for complex problems [5]. This paper proposes a new CNN architecture and compares its performances with those of one of most powerful machine learning tools, KNN. The two classifiers algorithms comparison are applied to MNIST datasets in the goal to recognize handwritten characters (digits/letters). The organization of this paper is as follows: Sect. 2 presents related work. Section 3 describes the used dataset, machine learning algorithms for prediction and some similarity metrics used for the algorithm’s performance comparison. Experimental results and discussion are given in Sect. 4. Finally, we conclude our study in Sect. 5 with perspectives.

2 Some Related Work The recognition of handwritten character has been the subject of much research and analysis. In the last years, several tools and methods have been proposed to try to realize recognition system of the manuscript character. Zhao and Liu [6] propose a framework based on CNN and the multi-level fusion of various classifiers. The proposed approach is implemented on the MNIST Dataset, and experimental results show that the classification accuracy exceeds 98%. A comparative study on the dataset MNIST published by Shamsuddin et al. [7], proves the importance of pretreatment methods before the machine learning modeling. Their CNN architecture implementation on normalized images achieves an accuracy of 99.4%. Moazam et al. [8] used two pretrained models GoogleNet and AlexNet. In this work they has utilized two datasets: The Chars74 K dataset that consists of 7705 characters categorized in 62 classes (0–9, A–Z, a–z). The second is a Local Dataset consists of 4320 characters. The experimental results prove that the proposed architecture of GoogleNet model gives a high accuracy of 88,89% and 77.77% for AlexNet. In other work, the authors in [9] analyzed the performance of three classifiers (Neural Network, Support Vector Machine and KNearest Neighbor) experimented and compared to find out the best technique. The authors experimented these models on a standard UCI database of handwritten digit. The highest recognition rates of 96.93% was obtained by SVM classifier. In [10], Shopon et al. propose a model based on the deep convolutional network in order to recognize handwritten Bangla digits. The datasets that are used in this work are CMATERDB with 6000 images and another dataset published by the Indian Statistical Institute (ISI) consisting of a total of 23299 images. They combined the two datasets to make two experiments on the own images of each dataset. The best approach for recognizing handwritten Bangla digits achieves 99.50% accuracy. In a study conducted by Niu and Suen [11], the authors proposed a new hybrid model CNN-SVM experimented on the MNIST digit database. CNN is considered an automatic feature extractor

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and allowed SVM to be the output predictor. This model achieved an error rate of 0.56% and 100% reliability. Another research conducted by Ashiquzzaman et al. [12]. In this work the authors tried to increase the accuracy of Arabic handwritten digit recognition using a convolutional neural network with the help of a multi-layer perceptron (MLP). The used dataset contains 3000 images scaled to the pixel size of 32  32 and converted to binary images. The proposed convolutional neural network method had attempted the accuracy of 97.4%. This record exceeds the accuracy of 93.8% achieved by Das et al. in [13]. In a study conducted by Rathi et al. [14], used the KNN algorithm as a classification technique, and achieved the accuracy of 96.14%. To compute the feature vector they used feature mining algorithms and tested the method on the “Devanagari vowels” database. Kim and Xie [15] built Hangul recognition systems HHR based on deep convolutional neural networks and they proposed several innovative techniques to improve network performance and training speed. They achieved 99.71% recognition rate on the MNIST database. Despite the advances realized in this works and in many others in the literatures, the handwritten characters recognition still active research area.

3 Materials and Methods 3.1

Handwritten Characters Dataset

The dataset used to train and test the models studied in this paper is the MNIST dataset digits and letters [16]. This dataset is considered a standard benchmark for learning and classification. The MNIST database is derived from a larger set available from NIST 19 Special Database. The MNIST database of handwritten digits has a training set of 240000 examples and a test set of 40000 examples. MNIST dataset of handwritten letters has a training set of 124800 examples and a test set of 20800 examples. The digits and letters have been size-normalized and centered in a fixed-size image 28  28 (Figs. 1 and 2).

Fig. 1. MNIST digits dataset samples.

Fig. 2. MNIST letters dataset samples.

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K-Nearest Neighbors Algorithm (KNN)

KNN is a well-known non-parametric and simple algorithm. It is an instance-based learning algorithm, which is suitable for large amounts of data. The algorithm has been used in different classification domains: statistics, image treatment, and writer identification systems. The operating principle of the KNN is to compare the input vector with training samples to compute the most similar k neighbors. Two factors are the keys to the effectiveness of the KNN algorithm: the value of the parameter k and a suitable distance function [17]. The parameter k is usually chosen as an odd number. For example, if parameter k = 3, the three closest neighbors are considered in order to determine the class for a particular input vector. The Euclidean distance is chosen as the function in order to calculate distance values from an input vector x to each training sample y. 3.3

Proposed Convolutional Neural Network (CNN)

A convolutional neural network is a multilayer neural network. This type of network use two operations called “convolution” and “pooling” to reduce an image to its essential features, and uses these features to understand and classify the image. Building a CNN requires having basic elements like the convolutional layer, activation layer, pooling layer and fully connected layer. Figure 3 represents the components of typical convolutional neural network.

Fig. 3. The components of convolutionnal neural network

The convolution layer extracts one block of pixels at a time, and then calculates the scalar product of the original pixel values with the weights defined in the filter. The results obtained are summarized in a number representing all the pixels observed by the filter. At the end of this process the convolution layer generates a matrix whose size is much smaller than that of the original image. This matrix is executed via an activation layer. The activation function is usually ReLu. Pooling layer reduces the size of the matrix. A filter passes on the results of the previous layer and selects a number in each group of values. This allows the network to train much faster, focusing on the most important information in each function of the image. Fully connected layer takes as input a one-dimensional vector representing the output of previous layers. Its output is a list of probabilities for different possible labels attached to the image. The label that receives the highest probability is the classification decision. There can be multiple layers of activation and grouping, depending on the CNN architecture [18]. Details of our proposed CNN architecture will be presented in sub-Sect. 4.1.

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Performance Evaluation Metrics

In the context of supervised machine learning, we usually use the confusion matrix to evaluate the performance of learning model. Since the handwritten character daset involves multiple classes, we used confusion Matrix for multi-class Prediction1. The construction of confusion matrix is based on the calculation of magnitudes: True Positive (TP), True Negative (TN), False Positive (FP) and Fuls Negative (FN). The confusion elements for each class are given by the following equations: TPClassX ¼ Ci;i FNClassX ¼ FNClassX ¼ TNClassX ¼

X10 X10 l¼1

k¼1

X10 l¼1

X10 l¼1

ð1Þ

Ci;l  TPClassX :

ð2Þ

Cl;i  TPClassX :

ð3Þ

Cl;k  ðFPClassX þ FNClassX þ TPClassX Þ:

ð4Þ

To calculate the criteria for the evaluation of classifiers, we will extract the values of True Positive, False Positive, False Negative and True Negative of the classes ‘0–9’ and ‘A–Z’ from the confusion matrix of each algorithm. The primary metric for evaluating a model classifier performance is classification Accuracy. The classification accuracy measures the percentage of test samples that the ability of a given classifier to correctly predict the label of new or previously unseen data. Sensitivity measures the fraction of positive cases that are classified as positive. Specificity measures the fraction of negative cases that are classified as negative [19]. The following equations define the used similarity metrics. Accuracy ¼

TP þ TN TP þ TN þ FP þ FN

ð5Þ

TP FN þ TP

ð6Þ

TN TN þ FP

ð7Þ

Sensivity ¼

Specificity ¼

4 Results and Discussion 4.1

Experimental Setup and Algorithms Hyperparameters

Machine learning models were implemented in MATLAB R2018b environment. The experimental setup computer had an Intel i7-8550U CPU @ 2.00 GHz turbo boost with 16 GB RAM. The whole training process is done for 20 epochs. 1

https://towardsdatascience.com/machine-learning-multiclass-classification-with-imbalanced-data-set29f6a177c1a.

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In this sub-section, we present the Machine Learning algorithms parameters that give the best results in terms of prediction accuracy. Knowing that in the absence of standard rules for configuring and choosing the best combination of parameters that makes a given algorithm give the best results, obtaining these parameters remains in most cases an empirical practice. For our case, this is obtained from the evaluation results of the models produced for each algorithm. For the proposed convolutionnal neural network algorithm, the best configuration that gives the best characters prediction from MNIST dataset is summarized in Table 1. Table 1. Proposed convolutionnal neural network architecture and hyperparameters. Proposed CNN Architecture and hyperparameters Dataset Training dataset: 240000 images digits Testing dataset: 40000 images digits Training dataset: 124800 images letters Testing dataset: 20800 images letters Size image Size: 28  28 - Channel: 1 The layers number 4 Convolutional layer Filter size: 5  5 Number of filters: 20 Max pooling layer Pool size: 4  4 Fully connected layer Input: Auto Output size: 10 or 26 (for letters) Softmax layer Softmax function Weights and bias Randomly initialized Learning rate 0.0001

In the first input layer, it is necessary to make dataset images in 4-D double type. Furthermore, labels must be categorical type. The convolution layer applied 20 filters with size of 5  5 pixels. The used activation function is “ReLu function”. Pooling layer takes the previous output of convolutional layer and applied a filter of 4  4 sizes. This operation of pooling reduces the number of pixels and gives in output the same number of input tensors. The fully connected layer takes as input the number of output tensors given by the pooling layer and convert all the pixels to vector. Softmax function takes the values obtained by neurons of output fully connected layer. For the hyperparameters of KNN algorithm, the best configuration of k parameters is 3. And the parameter of distance used by the model is Euclidian distance. 4.2

Experimental Output

The results obtained during the classification process were represented using a confusion matrix. This table is used to describe the performance of the classifier based on expected targets and actual results. The following figures represent the results obtained by the classifiers studied:

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Fig. 4. Confusion matrix representing the performance of the KNN classifier (Digits)

Fig. 5. Confusion matrix representing the performance of the CNN classifier (Digits)

From the confusion matrix representing in Figs. 4 and 5, we observe that both classifier have good accuracy. But we can also observe a slight difference in precision for each digit class. yet our proposed CNN provides a very low error rate compared to the result obtained by KNN classfier. For this classifier, we note that there is a data loss of 10.2% to predict the class ‘8’. This class is confused 23 times with class ‘3’ and 20 with class ‘5’. This is the largest loss of value recorded for the classification of this algorithm.

Fig. 6. Confusion matrix representing the performance of the CNN classifier (Letters)

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Fig. 7. Confusion matrix representing the performance of the KNN classifier (Letters)

In Fig. 6, the confusion matrix (Letters classes) of the KNN algorithm gives high loss values in relation to CNN algorithm. We also observe that the both algorithms has a high confusion in the prediction of the ‘j’, ‘i’ and ‘l’ classes. For example, the class ‘l’ is confused 220 times with the class ‘i’ by KNN algorithm and 271 times by CNN. In Fig. 7 CNN algorithm provides a classification that appears much more accurate than the classification of KNN. This latter provides predictions for particular classes (i, j and l classes) higher than CNN. After calculating the elements of the confusion matrix, the evaluation criteria of the classification models can be calculated using the equations that we presented in the subSect. 3.4. We obtained for each class the values of the calculated criteria. Then calculate the overall average of each evaluation criterion. We have grouped all these values into a table as shown in Table 2.

Table 2. CNN and KNN performance comparaison. KNN Digits Accuracy 0.9907 Sensitivity 0.9542 Specificity 0.9948

Letters 0.9873 0.8388 0.9934

CNN Digits 0.9974 0.9874 0.9986

Letters 0.9922 0.9017 0.9959

From the Table 2, we notice that the CNN classifier has reached the highest percentage on all the criteria. he obtained an accuracy of 0.9974 for classification digits and 0.9922 for classification letters.

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Discussion

The results obtained above are based on the classification of the data by the algorithms of machine learning. This data is extracted from the numbers and letters of the MNIST dataset, which is a standard point of reference for learning and classification. We have chosen two classifiers of multi-class problems and classification images: KNN and CNN. In order to evaluate the performance of each algorithm, we developed the confusion matrix and then calculated the statistical evaluation criteria (precision, sensitivity, specificity). After observing and analyzing the statistical results, we found that the architecture of the convolutional algorithm NN records very important results compared to the KNN algorithms. however, KNN outperforms the CNN in some classifications classes. From this study, we compare the performance of two powerful algorithms for classifying handwritten images. we experiment these algorithms on MNIST dataset. In Table 3 present the accuracy comparison of our proposed CNN and experimented KNN with some related work on handwritten digit recognition problem. Table 3. Accuracy comparison of our proposed CNN with other related work on MNIST dataset (digits). Method Accuracy Zhao, H., Liu, H. [6] 98.00% Shamsuddin, et al. [7] 99.40% Niu, X.-X., Suen C. Y. [11] 99.65% Kim I.-J., Xie X. [15] 99.71% This work 99.74% This work 99.07%

Method CNN CNN CNN–SVM CNN CNN KNN

5 Conclusion and Perspectives In summary, we compared two main types of multiclass problem classifiers. Extreme accuracy of 99.74% obtained by the CNN algorithm. This result proves the power of the convolutional neural networks in the classification and prediction of handwritten images. the KNN algorithm is a classifier very simple to implement that also record very good classification results. But, n summary, we compared two main types of multiclass problem classifiers. Extreme precision of 99.74% obtained by the CNN algorithm. This result proves the power of deep neural networks in the classification and prediction of handwritten images. the KNN algorithm is a classifier that also does not require to record very good classification results. But, he’s not suitable for high dimensional problems.

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References 1. Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell., 22 (2000) 2. Jayadevan, R., Kolh, E.S.R., Patil, P.M., Pal, U.: Automatic processing of handwritten bank cheque images: a survey. Int. J. Doc. Anal. Recogn. (IJDAR) 15(4), 267–296 (2011) 3. Karsenti, T.: Intelligence artificielle en éducation: L’urgence de préparer les futurs enseignants aujourd’hui pour l’école de demain ? Formation et profession, 112–119 (2018) 4. Mioulet, L.: Reconnaissance de l’écriture manuscrite avec des réseaux récurrents. HAL Id: tel-01301728 (2016) 5. Govindarajan, M.: Evaluation of ensemble classifiers for handwriting recognition. Mod. Educ. Comput. Sci. (2013) 6. Zhao H., Liu H.: Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition. Granular Comput. (2019) 7. Shamsuddin, M.R., Abdul-Rahman, S., Mohamed, A.: Exploratory analysis of MNIST handwritten digit for machine learning modelling. Soft Comput. Data Sci., 134–145 (2018) 8. Moazam, S., Muhammad, A., Rana, H.: Performance evaluation of advanced deep learning architectures for offline handwritten character recognition. In: The International IEEE Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan (2017) 9. Kaensar, C.: A comparative study on handwriting digit recognition classifier using neural network, support vector machine and k-nearest neighbor. Adv. in Intell. Syst. Comput. 155– 163 (2013) 10. Shopon, M., Mohammed, N., Abedin, M.A.: Bangla handwritten digit recognition using auto encoder and deep convolutional neural network. In: International Workshop on Computational Intelligence (2016) 11. Niu, X.-X., Suen, C.Y.: A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318–1325 (2012) 12. Ashiquzzaman, A., Tushar, A.K.: Handwritten Arabic numeral recognition using deep learning neural networks. In: IEEE International Conference on Imaging. Vision & Pattern Recognition (2017) 13. Das, N., Mollah, A.F., Saha, S., Haque, S.S.: Handwritten arabic numeral recognition using a multi-layer perceptron. CoRR. vol. abs/1003.1891 (2010) 14. Rathi, R., Ravi, V.C., Jangid, M.: Offline handwritten Devanagari vowels recognition using KNN classifier. Int. J. Comput. Appl. 49(23), 11–16 (2012) 15. Kim, I.-J., Xie, X.: Handwritten hangul recognition using deep convolutional neural networks. Int. J. Doc. Anal. Recogn. (IJDAR). 18(1), 1–13 (2014) 16. Cohen, G., Afshar, S., Tapson, J., Schaik, A.: EMNIST: an extension of MNIST to handwritten letters (2017) 17. Hu, L.-Y., Huang, M.-W., Ke, S.-W., Tsai, C.-F.: The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus, 5(1) (2016) 18. Zheng, S., Zeng, X., Lin, G., Zhao, C., Feng, Y., Tao, J., Xiong, L.: Sunspot drawings handwritten character recognition method based on deep learning. New Astron. 45, 54–59 (2016) 19. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

Comparative Study of Methods Measuring Lexicographic Similarity Among Tamazight Language Variants Ikan Mohamed1(&), Abdessamad Jaddar1, Aissa Kerkour Elmiad2, and Ghizlane Kouaiba3 1

3

Laboratory MAO, University Mohammed 1er, Oujda, Morocco [email protected], [email protected] 2 Laboratory RI, University Mohammed 1er, Oujda, Morocco [email protected] Laboratory AMGNPA, Faculty of Science, Ibn Tofail University, Kenitra, Morocco [email protected]

Abstract. In order to contribute to the standardization of the Tamazight language, we study in this article, the linguistic similarity between the different variants of the Tamazight language (Tamazight of Middle Atlas, Tachelhit, and Tarifit) using the most famous distances in the field of automatic natural language processing (NLP): the Jaro-Winkler distance and the Levenshtein distance. The first results from the application of these distances on our own corpus; based on equivalent words (from the lexicographic point of view), show that the similarity between the different variants of the Tamazight language is very obvious. This brings us to confirm the assumptions formulated in terms of linguistic or phonetic equivalence on certain characters or phone. Keywords: Tamazight
Lexicographic equivalence Jaro-Winkler
Distance from Levenshtein
NLP


Distance from

1 Introduction The integration of the Tamazight language in the field of new information and communication technologies has recently attracted considerable interest from all researchers defending the promotion of this language. Among the most interesting aspects, there is the standardization work carried out by IRCAM [1]. However, the work done so far in this area is more focused on the linguistic aspect and does not integrate the NITs to help or even accelerate the Tamazight language standardization process. Within this framework, and in order to contribute to the standardization process, we present in this paper a comparative study of the methods used to measure in an automatic way (i.e. using the computer tool) the lexicographic equivalence between three “different” variants of the Tamazight language namely: The Tachelhit spoken in southern Morocco, the Tamazight spoken in the Middle Atlas and Tarifit used in northern Morocco. In the field of automatic language processing, the notion of © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 378–389, 2020. https://doi.org/10.1007/978-3-030-36778-7_42

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“distance” is often used to assess the similarities or differences between the words of a language. Moreover, in the case of Tamazight language, the same word (lexicographically) is pronounced differently in the three variants (and therefore written differently). Through this article, we suggest to measure these differences by different methods in order to establish or confirm assertions of linguistic or phonetic equivalence between certain characters or phonemes of our language. This article is organized as following: Sect. 2 is devoted to a brief presentation on the methods of calculation used to justify its choice and use, to evaluate these measures whereas in Sect. 3, we apply these distances on our own corpus, to make some conclusions about equivalences. Section 4 draws a conclusion from our study and the perspectives envisioned especially in terms of expanding our work.

2 Employeed Distances In the field of automatic processing of natural languages, mathematical and algorithmic notions are often used to assess the “similarity”, “similarity” or “equivalence” between two or more words to try to meet the different needs in particular: orthographic correction of input errors [2], the classification of languages by dialectometry analyzes [3]. These achievements have contributed greatly in improving the level of mastery of languages both at the level of writing and at the level of oral use. The concept of distance; in the mathematical sense, has been revised and improved to cover natural languages and integrates the use of New Information Technologies. Among these techniques, the jaro-winkler distance and the Levenshtein distance is one of the most used methods in the field of automatic natural language processing (NLP) and the last was the subject of a proposal for the automatic correction of spelling errors by fellow researchers in our research team [4]. In mathematics and/or in computer science, a measure makes it possible to calculate the similarity between two chains of characters. In the field of NLP, these two character strings are just two words of a language or in our case, two words of two linguistic variants of a great mother tongue (Tamazight), as a result of our work, where precise details Phonetics are essential when applying our distances, we have noted exclusively our corpus in API (International Phonetic Alphabet). The measurement of linguistic similarity thus consists in comparing the corresponding phonemes. We describe in this section the two methods we propose to measure linguistic similarity; jaro-winkler distance and Levenshtein distance and we present the different steps carried out by the application. 2.1

Jaro-Winkler Distance

Jaro-Winkler distance, was proposed by William E. Winkler, in 1999 [5]. The measure is suitable for short character strings, such as those representing names and labels, and mainly used for detecting duplicates.

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The result is normalized so to obtain a measure varying between 0 and 1 such that 0 represents the absence of similarity and 1 the perfect similarity. The Jaro distance between two r and s chains is defined by: Jaroðc; d Þ ¼

1
com com com  tr  þ þ 3 m n com

ð1Þ

Such as: – com: is the number of corresponding characters; two identical characters of m and n, are considered to be corresponding if their distance (that is the difference between their position in their respective chains) does not exceed: max ðm; nÞ=2  1

ð2Þ

– tr: is the number of transpositions and is obtained by comparing the i th character of r with the i th corresponding character of s. The number of transpositions is equal to the number of times these characters are different, divided by two. Winkler added a prefix coefficient p to Jaro’s distance, favoring strings starting with the same prefix (of length l < 4). 2.2

Levenshtein Distance: Editing Distance

The editing distance is sometimes referenced as the Levenshtein distance in recognition of article [6] by Vladimir Levenshtein, the editing distance between two s and r chains is defined as the minimum number of editing operations (insertion, deletion, substitution) necessary to transform the first string into the second. Levenshtein proposed to associate the same cost to all transforms to calculate the editing distance between two strings. A generalization of the editing distance is to allow the association of a positive arbitrary weight, cost, or score to each editing operation. Levenshtein introduced the definition of edit distance but he never described the algorithm for finding the editing distance between two strings [7]. An algorithm for calculating the editing distance between the X and Y chains has been proposed by Wagner and Fischer [8]. It is a dynamic programming algorithm (bottom-up type solution), which uses a matrix D of dimension (n + 1)  (m + 1) where n and m are the lengths of the two character strings. 8   dist ci1 ; dj þ sup; > <     dist ci ; dj ¼ min dist ci ; dj1 þ sup; >     : dist ci1 ; dj1 þ sub Ci ; Dj ;

Or,

ð3Þ

  – dist ci ; dj is the editing distance between i first characters of the string c, of length m, and the first j characters of the string d, of length n;

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– Sub (c, d): the cost of the substitution of the letter “c” by the letter “d”; – sup: the cost of removing the letter; For data with a phonetic transcription, as is the case with our corpus, we can apply this distance in two different ways: the binary calculation and the calculation with the phonetic feature values. Binary Calculation In the first case (the binary calculation): the weight of each operation is identical, the value is 1 with each change in the chain whatever the type of the operation and the sounds that come into play. The binary method consists in assigning the number 1 when the opposite sounds are different and the number 0, when the latter are identical, the Table 1 above represents the table D of the distances on the same words of example 1 , corresponding the lexeme ‘man’. Table 1. Example of calculating the Levenshtein distance with binary values.

The distance of Levenshtein corresponds to the value contained in the last box of the matrix D and thus 1 in the preceding example. The complexity of this time and space algorithm is O (nm), where n and m represent the lengths of the two words. Multiple Calculation It is the method most used in the works of recent dialectometrics [9], and in particular those using the distance levenshtein. Indeed this measurement is based on the various phonetic features (phonetic feature bundles) of the sounds to calculate the distance between two units. Thus, the distance is not binary, but it is graduated according to the number of phonetic features which differentiate the two phonetic realizations compared. To compare two sounds, they must be transformed into phonetic feature vectors where each line is represented by a number. Once these phonetic features are cleared, they are recorded in the tables of values: one for consonants and one for vowels. Then these paintings will be considered as a kind of scale to form the vector of each sound. The values that make up the measurement in the tables that will be arbitrarily assigned [3, 9] then, we have assigned important values to the phonetic features of vowels, the purpose of which is to obtain distances between the smaller vowels relative to the distances between the consonants. For in the Tamazight language or the Berber

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languages in general, are more easily substituted than consonants, and most often without altering the meaning. Since the meaning of words is usually bound by consonants, they are the least stable element in passing from one to another. The consonants are distinguished on the bottom of four phonetic traits: The Place of Articulation: refers to the organ that participates in the production of sound, the values increase going from the most advanced position in the articulatory apparatus: the lips to the most remote position: the larynx (Table 2).

Table 2. Values of phonetic features of consonants (the place of articulation).

The Mode of Articulation: the values attributed to the different modes of articulation (Table 3).

Table 3. Values of phonetic traits of consonants (the mode of articulation).

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Voicing: The resonance of the vocal chords makes it possible to differentiate the sound consonants and the voiceless consonants, this feature serves to distinguish the sound consonants from the deaf consonants (Table 4). Table 4. Values of phonetic traits of consonants (voicing).

The Consonant Line: this feature serves only to distinguish consonants from semiconsonants (Table 5). Table 5. Values of phonetic features of consonants (the consonant line).

On the basis of the values we have attributed to the phonetic traits of consonants, here are the vectors of the different consonants and semi-consonants in tabular form (Table 6): Table 6. Vectors to calculate the distances between consonants and semi-consonants.

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As for the vowels, three phonetic features differentiate them: Aperture: the height of the vowels in reference to the tongue in the mouth, the value goes up a notch, from the closed vowel to the open vowel. We have dedicated use the positions: closed, neutral, opened. The Place of Articulation: This is the horizontal positioning of the tongue in the mouth, the value increases from the previous position to the posterior position, passing through the central position. Borough: When pronouncing a rounded vowel, the lips advance while rounding up. Two possible attributes, non-rounded (stretched) and rounded. Indeed, here are the values of phonetic feature vectors that we used for our vowels (Table 7):

Table 7. Vectors to calculate the distances between the vowels.

Calculating the Distance Among Sounds Once the vectors are formed, the distance between two sounds is the distance between the two corresponding vectors. The two main formulas used to calculate the distance between two n-dimensional vectors are the Manhattan distance and the Euclidean distance. Let us take the distance between two sounds a and b composed, respectively, by the X and Y strokes (Table 8): Manhattan Distance: d ð a; bÞ ¼

n X  j xi  yj 

ð4Þ

i¼0

d = the distance. n = the number of lines in each beam. i = the number of the beam during the operation, and it goes from 1 to n. The distances obtained between the consonants, by applying this formula are recorded in an Excel file, the latter will be used as a parameter file to calculate the distance between the words.

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Euclidean Distance: d ða; bÞ ¼

r
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ð x1  y1 Þ 2 þ ð x2  y2 Þ 2 þ . . . þ ð xn  yn Þ 2

ð5Þ

d = the distance n = the number of lines in each beam. Once the distance between the sounds is calculated with formulas just exposed, we go to the step of calculating the distance between two words and finally, the distance between our variants. It is during this step that the levenshtein algorithm intervenes. This is what we will see in the following application examples: We also use the words , of the lexeme ‘man’. With the Manhattan form: Table 8. Example of levenshtein distance calculation by using Manhattan formula.

The multivalued levenshtein distance calculated with the Manhattan form is therefore 2 (Table 9). With the Euclidean form: Table 9. Exemple de calcul de la distance de Levenshtein avec formulae d’euclidien.

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3 Lexicographical Application on Our Linguistic Variants The application of the distances that we have exposed above requires lexical resources, and as in our case, the Tamazight language to good the Berber language in general has experienced a great delay of linguistic research because of the reasons: socio-historical and politics. The lexicon remains the weakest link of the Berber studies, the lexicographical tools available seem limited because scattered and the existing works are partial and concern only one talk, it is the reason for which we elaborated our own body of data (the words) which are lexicographically equivalent in the three main Tamazight language variants spoken in Morocco: Tachelhit, Tamazight of Middle Atlas and Tarifit. Also, we implemented the algorithms associated with these distances to measure the distances between each pair of words in every bet of variants. 3.1

Presentation of Data Corpus

To be consistent with our goal of normalizing the Tamazight language, we have built our body of work from the dictionaries and dictionary books in use at the Royal Institute of Amazigh Culture (IRCAM) [10, 11]. Next, the words that make up our corpus are written in A.P.I. and in tifinagh, these words will be corrected using the speakers of each variant (Tachalhit, Tamazight, Tarifit). Currently we have to wait 3000 words horizon saved in Excel files, Fig. 1 below illustrates an example of some words of the corpus.

Fig. 1. Extract from the corpus

3.2

Outcome and Analyse

We then applied our distances on the entire body of work composed of 3000 words, the results resulting from this application gave deferential values solen the method used, for example the method of jaro-winkler, give us values in the meantime 0 and 1, unlike the levenshtein method, the values are between 0 and n, n the maximum word size of our corpus (in this case it is 9).

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Example: The lexeme: Heaven Tarifit: [aʒnna]; Tamazight of Middle Atlas: [iʒnna]; Tachalhit: [igna] The distance between these linguistic variants of this word by each distance is: D1 =; D2 =; D3 =; distance from jaro-winkler. D1 =; D2 =; D3 =; levenshtein distance with binary values. D1 =; D2 =; D3 =; levenshtein distance with multiple values (f. Manhattan). D1 =; D2 =; D3 =; distance of levenshtein with multiple values (f. Euclid). D1: the distance between the Tarifit variant and Tachalhit. D2: the distance between the Tarifit and Tamazight variant. D3: the distance between the Tamazight variant and Tachalhit. The analysis and the valuation of the results obtained by each method, requires in our case an essential step: it is the normalization of the values of tell sort to have values between 0 and 1. The following Table 10 shows an example of these results with standardized values: Table 10. Example of these results with standardized values

But to get a clear idea of the equivalence between the different variants of a word, we noticed that each word depends essentially on the distances D1, D2 and D3, for this, our approach consists in considering that the words are not that points in a threedimensional space where the coordinates are the distances between each pair of words of each language variant. After the interpretation of these points in a reference R of origin O (0, 0, 0), clouds of points are appearing for each method of calculation of the distance, let us not forget that our objective is to differentiate these methods to that which give us values of similarity very precise, in fact, we proposed to use another point G calculated based on the clouds of the preceding points, it is the need to define a central point which informs all the points Several ideas came to us during our work, in order to use the barycenter which will be well answered to our problem, the computation of the barycenter of each methods directed us to deduce the distance of each G point by contribution to the origin of the reference, the following Table 11 illustrates the four points (the barycenter) accompanied by their distance (the norm).

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The overall distance (the norm) computed from the isobarycenter of each point cloud will be played as the similarity scale for each method, so that the higher the value of the norm is the level of the similarity of the corresponding method is very precise that the other one, in our case the method of jaro-winkler having the maximum value 1.41 which allows it to be first. In the other side, we notice that the values of the norms obtained are very different, because we created our own corpus, we used words that are quite similar appear from a sub-variant, which leads to the hypotheses formulated in terms of equivalence on certain characters or phonemes of Tamazight language variants.

4 Conclusion and Perspectives In this paper, we presented a comparative study of methods measuring lexicographic equivalence between the three variants of the Tamazight language using the distant jaro-winkler and the distant Levenshtein. The results of this study, which is the result of this variation, we believe that we will be able to do this. and assumptions about the lexicographic and phonetic equivalences that will undoubtedly help to speed up the Tamazight language standardization process.

References 1. Ameur, M., Boumalk, A.: (coord) Standardisation de l’amazigh’, Rabat, publication du l’IRCAM (2004) 2. Guerrab, S.: Analyse dialectometrique des parles berbères de kabylie Thèse soutenu en (2014) 3. Kukich, K.: Techniques for automatically correcting words in text. ACM Comput. Surv. 24 (4), 377–439 (1992) 4. Gueddah, H., Yousfi, A., Belkasmi, M.: Introduction of the weight edition errors in the Levenshtein distance. Int. J. Adv. Res. Artif. Intell. 1(5) (2012) 5. Winkler, W.E.: The state of record linkage and current research problems. Technical report, Statistics of Income Division, Internal Revenue Service Publication R99/04 (1999) 6. Levenshtein, V.: Binary codes capable of correcting deletions, insertions and reversals. SOL Phys. Dokl. 10, 707–710 (1996)

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7. Pevzner, P.A.: Bio-informatique moléculaire une approche algorithmique collection IRIS, dirigé par Nicolas peuch, Tradiction: Delphine hachez, Springer Editions, October 2006 8. Wanger, R.A., Fisher, M.J.: The string to string correction problem. Commun. ACM 20(10), 168–173 (2014) 9. Heeringa, W.: Measure dialect pronunciation differences using levenshtein distance, These 2004, Université de Groningen (Holland) (2004) 10. Institut royal de la culture amazighe (IRCAM): ouvrage ‘Dictionnaire français tachalh’it et tamazir’t’.Auteur: S cid kaoui 11. Institut royal de la culture amazighe (IRCAM): ouvrage ‘Dictionnaire Tarifit-français. Mohamed serhoual, Auteur

Comparative Study of DICOM Files Handling Software’s: Study Based on the Anatomage Table Zineb Farahat1(&), Mouad Hasni1, Kawtar Megdiche2, Nissrine Souissi3, and Nabil Ngote1 1

2 3

Abulcasis International University of Health Sciences, Rabat, Morocco [email protected] Medical Simulation Center of the Cheikh Zaid Foundation, Rabat, Morocco Department of Computer Science, MINES-RABAT School, Rabat, Morocco

Abstract. The DICOM format is complex due to the number of information that these files must contain. Therefore, different software were developed in order to ensure the proper handling of DICOM files. In this paper, we first understood the structure of a DICOM file by using a proprietary raster graphics editor named Photoshop. Then we studied the existing software, which can handle the DICOM files. This study is based on the Anatomage table of virtual dissection used in the Cheikh Zaid Foundation Medical Center that we also tried to extend by the missing features. To add annotations to this table; we first used a VBA (Visual Basic for Applications) code and then we added these annotations directly to a copy of the CSV (Comma-separated values) file of the annotations, which is taken from the Anatomage table. The doctors can prepare their courses as well as some quizzes for their students by adding their own annotations to the 3D pathologic reconstructions done in the Anatomage table. Keywords: DICOM
Annotation Anatomage table
VBA


Benchmarking
Virtual dissection table


1 Introduction Nowadays, and specifically with large amounts of medical images acquired by modern modalities [1, 2], medical imaging has evolved to digital. In this context, the exchange of data between different equipment from different suppliers has become more and more numerous. For the implement of communication between the medical image equipment, the standard of Digital Imaging and Communications in Medicine (DICOM) is constituted, which embodies several major enhancements to previous versions of the ACR-NEMA Standard [3]. The DICOM does not present a simple image format, it is rather expanded, and it presents communication techniques and exchange between the different modalities used in medical imaging [4]. Moreover, the large amount of information stored in a DICOM file mainly explains the use of a complex platform to manage them [5]. Indeed, Anatomage has developed a virtual dissection table of the human body in real size [6] that can read and handle DICOM files. In addition to that, several hundred cases are implemented by the © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 390–399, 2020. https://doi.org/10.1007/978-3-030-36778-7_43

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manufacturer in the Anatomage Table. Virtual dissections on the Anatomage Table make its utilization simple because it did not need chemical treatment and it is fully reusable [7–9]. This study is based on the Anatomage table of virtual dissection [10] used in the Cheikh Zaid Foundation Medical Center that we tried to extend by the missing features by adding annotations to this table. In this paper, we propose a new approach based on the use of Anatomage Table as a diagnostic tool [7] and an innovative learning tool in medicine [9] to define different criteria. To conduct this study, two questions we tried to answer: how is a DICOM file organized? Moreover, how to manage this kind of files? In fact, we first tried to understand the structure of a DICOM file, then to create a new one using proprietary software. After that, a comparative study was carried out to retain two image processing software’s that are used in the handling of DICOM files, and that meet the most predefined criteria using a Benchmarking. Otherwise, we took the application TableEDU 4.0 of the Anatomage Table [7, 10] as a main example of a perfect tool of handling DICOM files, knowing that it is used in the Cheikh Zaid Foundation Medical Center for medical training not only in its pedagogical side [10], but also as a diagnostic tool [7]. The approach of the research is made using the same dataset, by opening computerized tomography (CT) images and magnetic resonance imaging (MRI) in different software. The rest of paper is organized as follows. Section 2 depicts an overview of DICOM. Section 3 is devoted to the materials and methods. Section 4 depicts results and discussion. Finally, conclusions are mentioned in Sect. 5.

2 An Overview of DICOM The DICOM is a standard that was created by the American College of Radiology (ACR) in association with the NEMA. It is standard norm for the informatics management of data issued from the medical imagery. The purpose of this norm is to improve the communication of the data regardless of device manufacturer, facilitate the development and expansion of the archiving system and the image communication as well [4]. The DICOM format differs from other image formats once it deals with its information. A DICOM file consists of a header and image data sets, all packed into a single file [Figure]. The first few packets of information in it constitute the “header.” It stores demographic information about the acquisition parameters, patient, for the imaging study, matrix size, image dimensions, color space, and a host of additional non-intensity information required by the computer to correctly display the image [11]. Each header begins with a preamble of 128 bytes usually set to zero followed by 4 bytes to write the characters ‘D’. ‘I’. ‘C’. ‘M’. As a result of the preamble, all kinds of information follow one another. They are organized into several groups of information. Each data element consists of three data fields if VR is implicit and four fields if VR is explicit. All of this data represents a DICOM data dictionary [11]. The header data information is encoded within the DICOM file so that it cannot be accidentally separated from the image data. If the header is separated from the image Data, the computer will not know which imaging study had been done or to whom it belongs and it will not be able to display the image correctly, leading to a potential medicolegal situation [11] (Fig. 1).

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Fig. 1. Structure of a DICOM image file [11].

The DICOM standard is an object-oriented language. Each DICOM object, usually an image, contains both information (patient’s name, image pixels, etc.) and functions (print, save, etc.) that this information must undergo. The DICOM processing of information therefore consists in matching an object DICOM (Information Object) to a specific function (Service Class). This combination is called (Service/Object Pair) or (SOP) (Fig. 2) [11].

Fig. 2. Combination (Service/Object Pair) of a DICOM file [13].

The software Adobe Photoshop permits to associate to the forms drawn and saved as DICOM format a list of attributes. These shown attributes contain the Data Tag Element, which is an ordered pair of 16-bit integers representing the group number, and followed by the element number. It indicates the type of information that will follow. It is broken down into 2 sets of 2 bytes, the first 2 bytes encode a group of information and the next 2 bytes specify the element of the group (Fig. 3).

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Fig. 3. Attributes of DICOM file

There is a complete list of possible values for the “Tag” field. The table below shows the most frequently used main groups of information (Table 1): Table 1. Group of Information [13]. Group of information 0008

0010 0018 0020 0028 4000 7FE0

Signification Meta information (Meta Information File, SOP Class UID…), Identification of the center (Examination date, Type of examination, Manufacturer of the machine, Hospital, Identification of the machine…) Information about the patient (Name, Date of birth, Sex…) Information on the acquisition of information (cutting thickness, kV parameters, time, patient position, etc.) Positioning and information relating to the acquisition (patient orientation, series, reference plane, number of images in the acquisition…) Presentation of the image (dimensions, grayscale, color tables, allocated bits, stored bits, most meaningful bit…) Text pixel data

In addition to that, the software Adobe Photoshop uses DCMTK (DICOM Toolkit); which is a set of libraries written in C++ permitting to examine, create and convert DICOM files.

3 Materials and Methods 3.1

Materials

Anatomage Table. The Anatomage Table is a table of virtual dissection [9, 12] realized to help the students to better understand their course of anatomy. It is run by windows 7 and uses the application TableEDU 4.0, which is pre-installed in the table by the constructor. This table permits to read and open the DICOM Files issued from

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different modalities of medical imagery. The Anatomage Table permits the 3D reconstruction from several complementary 2D images. Adobe Photoshop. The software Adobe Photoshop, which is a software of treatment, retouch and drawing, used in all systems of exploitation such as windows and MacOS… permits the image saving under several formats such as DCM, JPEG, PNG… TableEDU 4.0. In the medical field, several software is used to read and manipulate the DICOM Files. To keep the most reliable, a study of the existing is initialized. Based on the TableEDU 4.0 application, some choices criteria are defined, whose we can cite the fact that software must be Opensource to be able to modify it when necessary, the availability of documentation, the display of the attributes, the export and import of the DICM Files, the image modification, the 3D reconstruction. VBA Language. The VBA is a programming language that allows the use of Visual Basic code to run many features of the Excel application. A program written in VBA is often called a macro. 3.2

Methods

Comparative Study. To carry out this comparative study and Benchmark we followed the steps bellow. – Identify the Search Engines (Google Scholar, SpringerLink DL…). – Define Keywords. The key words we used are: Application/Software, DICOM files, create, edit, remove, open source, compare, and manage. – Define the Number of Applications to Compare. We have set the number to eight software. – Define the Comparison Criteria. Documentation, open source software, free software, operating system adapted to the one used at the medical simulation center of the Cheikh Zaid Foundation, programming language, export, import, registration in DICOM format, storage in the application, display information specific to DICOM files, reading DICOM files in 2D, 3D reconstruction from 2D images, simultaneous display of several sections. – Define the Rating System. We assigned a score (from 0 to 5) to each criterion with: 0–1: Unacceptable, 2: Weak, 3: Good, 4: Very Good, 5: Excellent. Adding of Annotations. To add annotations to the 3D reconstructions done by the embarked application TableEDU 4.0 of the Anatomage table, two tendencies were followed to modify the original file of annotations. The first one is the automatic adding of annotations lines by a VBA code (Fig. 4) by creating some command buttons as well as many boxes to fill in by the name of the wanted organ, besides its system.

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Fig. 4. The VBA code.

Fig. 5. The window generated by the code.

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By clicking on the command button “Add” (Fig. 5), a new line is added to the chart of annotations. However, the added annotation does not appear on the list of annotations of the Anatomage Table, which is explained by the difference between the format known by the Anatomage table (CSV) and the format of the file modified by the VBA Language (Excel). This led to a second tendency, which consists of recuperating a copy of the original file containing the annotations, which is in a CSV format, from the Anatomage Table and modifying it directly in our computers. For this reason, we had to open this file with Open office to keep the same CSV format by choosing as a character set Unicode “UTF-8” and as a means of separation “Tabulation”, then to add manually some lines of annotations to the chart. The last step consists of saving the modifications and transferring the new file to the Anatomage Table. The TableEDU 4.0 application permits to save the new spatial coordination’s of annotations (Fig. 6).

Fig. 6. Added three lines on the CSV file.

4 Results and Discussion 4.1

Results of Comparison Study

This study was realized using magnetic resonance imaging (MRI) images taken in the Cheikh Zaid hospital of Rabat of a breast of a patient and computerized tomography (CT) scans of a road polytrauma patient of the same health foundation. The study of the existing has been summarized as a cartography form containing 8 software to which some points on a scale of 0 (The software doesn’t respond to the defined criteria) to 5 (The software responds perfectly to the defined criteria) were attributed. This analysis was based on different criteria taken from the use of the Anatomage Table that provides a simple and interesting handling of DICOM file [7]. This permitted to create a Radar chart (Fig. 7) and to choose two software responding the most to the criteria.

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Fig. 7. Radar chart summarizing the Benchmark.

On this Radar chart (Fig. 7), both Weasis and 3D Slicer software occupy the majority of the surface of the circle, the reason why we can say that they are the software responding the most to the criteria. 3D Slicer. The 3D slicer is a software of reading and manipulation of medical images. It permits to display the information linked to DICOM files in addition to modifying the image parameters. This software had advantages such as; to do 3D reconstructions from 2D images issued from different modalities of medical energy, besides comparing simultaneously several views and to measure distances. The source code of this software is accessible so there will be a possibility to complete it by many missing functionalities if needed. A thing that was done by some searchers who added different extensions and modules (130 modules and more than 74 extensions). Weasis. Weasis is a software of visualization that permits to update the images DICOM, a simultaneous reading is comparison of many images by modifying the cutting plan. Moreover, this software permits to add some annotations, do some measures adjust the brightness and contrast scale of the image, add signs and forms geometric to design the wanted parts and to display the attributes and the information linked to the DICOM files. Comparison of the Two Software. Each of the two software has both advantages and limits as well. On one hand, Weasis permits to do measures and add annotations. However, this software does not permit to do 3D reconstructions. One the other hand, the 3D software slicer permits to do 3D reconstructions as well as measurement but without adding annotations.

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Adding of Annotations to 3D Reconstructions Done by the Anatomage

Thanks to the method explained previously, Annotations and explanations can therefore be added to 3D reconstructions done in the Anatomage table.

Fig. 8. Added annotations on the application TableEDU 4.0.

In the figure above (Fig. 8), the annotations added to the Anatomage table in CSV format have been attributed to the 3D reconstruction made using the images taken by a computerized tomography (CT) of the road polytrauma patient.

Fig. 9. Saving Added annotations on the 3D reconstruction.

As shown in the figure above of the 3D reconstruction done using images taken by a magnetic resonance imaging (MRI) (Fig. 9), by clicking on the annotations displacement tool in the Volume Visibility dialog box we choose their ideal location. The Anatomage table allows saving spatial coordination’s annotations automatically thanks to the TableEDU 4.0 application’s tools. Furthermore, the doctors of Cheikh Zaid foundation can prepare their courses as well as some quizzes for their students by adding their own annotations and explanations to 3D pathologic reconstructions realized by images taken by different modalities.

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5 Conclusion This study led us to understand the structure of a DICOM file by using a proprietary software called Photoshop, then to manage and manipulate DICOM files using a software too. A study of the existing DICOM Files Handling Software’s has been done, by defining the selection criteria that we drew from the TableEdu 4.0 application installed on the Anatomage table. We first choose eight software based on these criteria, and by Benchmarking, we have reduced this list to two software. In addition to that, many annotations were added to the 3D reconstructions done on the Anatomage table using images taken by different equipment. However, further investigations must be done, using these results, to both create a new DICOM file by a programming language and a new DICOM file manipulation software.

References 1. Rahmat, R.F., Andreas, T.S.M., Fahmi, F., Pasha, M.F., Alzahrani, M.Y., Budiarto, R.: Analysis of DICOM image compression alternative using huffman coding. J. Healthc. Eng. 2019, 11 (2019). Article id 5810540 2. Pujar, J.H., Kadlaskar, L.M.: A new lossless method of image compression and decompression using Huffman coding techniques. J. Theoret. Appl. Inf. Technol. (n.d.). Accessed 23 Apr 2016 3. Liu, B., Zhu, M., Zhang, Z., Yin, C., Liu, Z., Gu, J.: Medical image conversion with DICOM. In: Canadian Conference on Electrical and Computer Engineering, April 2007 4. Spilker, C.: The ACR-NEMA digital imaging and communications standard, August 1989 5. Patel, G.: DICOM medical image management the challenges and solutions: cloud as a service. In: Third International Conference on Computing, Communication and Networking Technologies (ICCCNT 2012) (2012) 6. Custer, T.M., Michael, K.: The utilization of the anatomage virtual dissection table in the education of imaging science students. Radiat. Sci. Technol. Educ. (2015) 7. Taoum, A., Sadqi, R., Zidi, M., d’Anglemont de Tassigny, A., Megdiche, K., Ngote, N.: On the use of anatomage table as diagnostic tool. Int. J. Biol. Biomed. Eng. 13 (2019) 8. Baratz, G., Wilson-Delfosse, A.L., Singelyn, B.M., Allan, K.C.: Evaluating the anatomage table compared to cadaveric dissection as a learning modality for gross anatomy, March 2019 9. Decorato, I.: Anatomage Table: dissection anatomique virtuelle pour l’enseignement de l’anatomie. Morphologie 100(330), 119 (2016) 10. Brucoli, M., Boccafoschi, F., Boffano, P., Broccardo, E., Benech, A.: The anatomage table and the placement of titanium mesh for the management of orbital floor fractures. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 126, 317–321 (2018) 11. Varma, R.: Managing DICOM images: tips and tricks for the radiologist. Indian J. Radiol. Imaging 22(1), 4–13 (2012) 12. Martín, J.G.: Possibilities for the use of anatomage (the anatomical real body-size table) for teaching and learning anatomy with the students, Department of Surgery and Social and Medical Sciences, Unit of Human Anatomy and Embryology, Professor of Human Anatomy and Embryology, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Madrid, Spain (2018) 13. Link consulted to understand the DICOM standard. https://www.creatis.insa-lyon.fr/*ds arrut/mybib/2004/rapportSeda2004.pdf

Fake News Identification Based on Sentiment and Frequency Analysis Jozef Kapusta1,2(&), Ľubomír Benko1, and Michal Munk1 1

Department of Informatics, Constantine the Philosopher University in Nitra, Nitra, Slovak Republic {jkapusta,lbenko,mmunk}@ukf.sk 2 Institute of Computer Science, Pedagogical University of Cracow, Cracow, Poland [email protected]

Abstract. The advent of social networks has changed how can be the thinking of the population influenced. Although the spreading of false information or false messages for personal or political benefit is certainly nothing new, current trends such as social media enable every individual to create false information easier than ever with the spread compared to the leading news portals. Fake news detection has recently attracted growing interest from the general public and researchers. The paper aims to compare basic text characteristics of fake and real news article types. We analysed two datasets that contained a total of 28 870 articles. The results were validated using the third data set consisting of 402 articles. The most important finding is the statistically significant difference in the news sentiment where it has been shown that fake news articles have a more negative sentiment. Also, an interesting result was the difference of average words per sentence. Finding statistically significant differences in individual text characteristics is a piece of important information for the future fake news classifier in terms of selecting the appropriate attributes for classification. Keywords: Fake news identification Frequency analysis




Text mining




Sentiment analysis




1 Introduction Fake News is currently the biggest bugbear of the developed world. Alongside increasing use of social networks, especially for communication, we observe the high increase in the distribution of false news, hoaxes and other half-truths in periodicals, as well as in society. People perceive social networks in particular as a space for expressing their opinions, but also as a space that brings together people with similar opinions. Although the spreading of false information or false messages for personal or political benefit is certainly nothing new, current trends such as social media enable every individual to create false information easier than ever with the spread compared to the leading news portals [1]. Importantly, while the spread of false information is simple, the correction of the record can be much more difficult. Most of use just scans social networks posts, so there is no time to confront the source of information, compared to the face-to-face dialogue. People simply register © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 400–409, 2020. https://doi.org/10.1007/978-3-030-36778-7_44

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the message. The general awareness that the media have the possibility of mutual crosschecking merely enhances this problem. It is convenient to slip into the belief that the number of people who have the opportunity to verify the validity of information has certainly done so. The paper analyses the available datasets of fake and real news. Basic analysis of these datasets was focused on the text content of the articles. We analysed the average number of words in sentences, number of stop words as well as basic classification of the sentiment of the examined news. The aim of our paper is to find out whether there are statistically significant differences in basic text characteristics between the fake news articles comparing with the real news articles. In the second chapter of the article, we summarize the current trends in fake news detection and fight against them. The third chapter is focused on the description of the examined dataset and the description of the data preparation method, which we applied to it. In the fourth chapter, we present the results of our analyses, and the discussion of the results is the content of the last chapter.

2 Related Work An experiment within the research of fake news studied, how people adapt their opinion if the information which formed the opinion, were incorrect. Research has shown that “correction” of opinion depends on the cognitive abilities of an individual. The results indicate that the correction of incorrect information does not always lead to a change of opinion [2]. In another research, 307,738 tweets with 30 fake and 30 real messages were analysed. The findings revealed that fake news tweets were mostly generated by regular users and often included a link to untrusted news sites. The results also showed that tweets about true news spread widely and rapidly, while tweets with fake news have been modified several times in the process of dissemination and therefore they spread slower [3]. There are several other similar studies on the negative impact of fake news as well as on the formation of opinions depending on the fake news [3, 4]. The seriousness of the situation illustrates the fact that the fight against the spread of fake news and development of effective strategy in this field is among the highest priorities of the EU. The European Commission has conducted a questionnaire survey on fake news. According to [5], more than 37% of respondents from EU countries are confronted with false reports on a daily basis. The key findings from the research are damaging society in areas such as political affairs or ethnic minorities. Research highlights the extent to which fake news is present in EU countries. As the topic of fake news is very current, many researchers try to overcome the issues of identifying fake news articles in the number of real news articles. Xu et al. [6] have characterized hundreds of popular fake and real news measured by shares, reactions, and comments on Facebook from two perspectives: Web sites and content. The presented analysis concludes that there are differences between fake and real news publisher’s web sites in the behavior of user registration. Also, the fake news tends to disappear from the web after a certain amount of time. The authors applied exploration of document similarity with the term and word vectors for predicting fake and real news. Braşoveanu and Andonie [7] introduced a novel approach to fake news detection combining machine learning, semantics and natural language processing. The authors

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used relational features like sentiment, entities or facts extracted directly from the text. The authors concluded that using the relational features together with syntactic features, it is possible to beat the baselines even without using advanced architectures. The experiment showed that consideration of relational features can lead to an increase in the accuracy of the most classifiers. Saikh et al. [8] correlated the Fake News Challenge Stage 1 (FNC-1) dataset that introduced the benchmark FNC stage-1: stance detection task with Textual Entailment. The stance detection task could be an effective first step towards building a robust fact-checking system. The proposed model outperformed the state-of-the-art system in FNC and F1 score, and F1 score of Agree class by the third Deep Learning model i.e. the model augmented with Textual Entailment features. The authors in [9] have focused on creating a model for fake news detection using the Python programming language. The authors used the Naive Bayes algorithm with two forms of tokenization- CountVectorizer, and TfidfVectorizer. The results showed that the CountVectorizer was more successful classification method since it achieved the accuracy of 89.3% of the news correctly classified.

3 Materials and Methods For our analysis we created a dataset that consists of merging many existing datasets: 1. Dataset of real news was created from articles analysed during three months1 that were validated using https://mediabiasfactcheck.com. The dataset contained 15 707 articles. 2. Dataset of fake news was created2 based on the text analysis of 244 web pages marked as “bullshit” from BS Detector Chrome Extension [10] by Daniel Sieradski. The important fact is that these articles were published in the same period (October – December 2016) as the articles in real news dataset. The fake news dataset contained 12 761 articles. 3. Dataset KaiDMML, the dataset of fake and real news was taken over3 and processed based on [11]. The dataset was relatively less extensive, including articles also from the fake news group (205 articles) as well as real news (197 articles). Despite the small number of articles in the dataset, we have taken it as the best-created one and in our analysis, we used it to verify the facts found in the first two datasets. The first two datasets were available on the server kaggle.com. We chose them because both datasets were already used in the analysis focused on fake news classifications and similar it was with the third dataset [12]. It were therefore datasets that were verified by other researchers. The following basic methods were applied to the created dataset: 1. For each article, the average number of words in the sentence was calculated. 2. So-called stop words were isolated from articles and the average number of words in the sentence without stop words was calculated. The stop words identification

1 2 3

https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016. https://www.kaggle.com/mrisdal/fake-news. https://github.com/KaiDMML/FakeNewsNet.

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was done using word comparison from the article with a list of stop words for the English language. 3. The sentiment rate for each article was calculated. This sentiment rate was done using the most basic method of difference between the number of positive and negative words in the article. It was a relative abundance so the difference was still divided by the number of words in the analysed article.

4 Results of Sentiment and Frequency Analysis The first analysis is focused on the basic overview of the average number of sentences in articles, number of words and the average number of words in the sentence. These were calculated using a separate algorithm where we used the NLTK library dedicated for analysis of natural language processing using programming language Python. Using the library’s functions we made the basic sentence tokenization and within sentences verbal tokenization. Quantities such as the average number of sentences, number of words in the article and the average number of words in the sentence were calculated for each article. Similarly, we calculated the listed variables after removing the stop words from the examined articles. The analysis of variance was used for testing the differences between independent samples (fake: 0/1) in count sentences, count words per sentence, count words without stop words per sentence and in the measure of sentiment. The null hypotheses state that there is no statistically significant difference in the number of sentences/words and in the measure of sentiment between fake and real news, i.e. classifying the news as fake do not depend on the number of sentences/words and the measure of sentiments. Based on ANOVA results (Table 1), we reject the null hypotheses at the 1% significant level, i.e. it was proven a statistically significant difference in the count sentences between fake and real news over all datasets (Table 1a) as well as in the KaiDMML validation dataset (Table 1b) (Fig. 1).

Table 1. Univariate results for count sentences (a) All Datasets (b) KaiDMML df SS MS F p All Datasets: count_sentences Intercept 1 38950472.0 38950472.0 15270.32 0.0000 Fake 1 2134151.0 2134151.0 836.68 0.0000 Error 28869 73637057.0 2551.0 Total 28870 75771208.0 KaiDMML: count_sentences Intercept 1 218778.7 218778.7 335.82 0.0000 Fake 1 5452.7 5452.7 8.37 0.0040 Error 401 261243.2 651.5 Total 402 266695.9 df - degrees of freedom, SS - sum of squares, MS - mean square, F- test statistic, p - probability value

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Fig. 1. Mean plot: point and interval estimates for count sentences (a) All Datasets (b) KaiDMML

The results show that there were fewer sentences in the fake news articles (approximately 28 sentences per one article) in comparison to the real news articles. If we take into account only the last third dataset, i.e. dataset that belongs to verified and recommended datasets, then the result is similar, although the amount of sentences per article is smaller (approximately 21 sentences per one fake news article).

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Another view on the examined data was the comparison of the average number of words in the sentence. We also examined the average number of words after removing so-called stop words. Similarly, in the case of count words, based on ANOVA results (Table 2), we reject the null hypotheses at the 0.1% significant level, i.e. a statistically significant difference in the count words per sentence (Table 2a) was proven as well as in the count words without stop words per sentence (Table 2b) between fake and real news. Similar results were also achieved in the KaiDMML validation dataset (Fig. 2).

Table 2. Univariate results for count words (a) count words per sentence (b) count words without stop words per sentence df SS MS F p All Datasets: words_per_sentence Intercept 1 20911584.0 20911584.0 185292.30 0.0000 Fake 1 29887.0 29887.0 264.80 0.0000 Error 28869 3258077.0 113.0 Total 28870 3287965.0 All Groups: words_wo_stopw_per_sentence Intercept 1 9222142.0 9222142.0 145087.10 0.0000 Fake 1 28400.0 28400.0 446.80 0.0000 Error 401 1834995.0 64.0 Total 402 1863395.0 df - degrees of freedom, SS - sum of squares, MS - mean square, F- test statistic, p - probability value

Fig. 2. Mean plot: point and interval estimates for count words

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It is obvious that the average number of words in sentences after the stop words removal was clearly smaller. It is interesting that by the experiment we found out that there is a statistically significant difference in the average number of words in sentence between the fake news and real news articles. This significant difference is observed whether after the removal of stop words or without this modification. Based on the results we can state that the fake news articles use more complex sentences, i.e. sentences that contain more words. A separate view was the research of positivity or negativity of the examined articles, the so-called sentiment of articles. The sentiment analysis was done only using the basic method of determining the sentiment since the sentiment analysis itself was not one of the main objectives of our research. However to verify general awareness of fake news, i.e. that fake news articles are negative oriented, was done this analysis. Similarly, in the case of measure of sentiment, based on ANOVA results (Table 3), we reject the null hypotheses at the 0.1% significant level, i.e. a statistically significant difference in sentiment between fake and real news was proven over all datasets (Table 3a) as well as in the KaiDMML validation dataset (Table 3b) (Fig. 3). Table 3. Univariate results for sentiment (a) All Datasets (b) KaiDMML df SS MS F p All Datasets: sentiment Intercept 1 0.478 0.478 1277.72 0.0000 Fake 1 0.013 0.013 34.13 0.0000 Error 28869 10.811 0.000 Total 28870 10.824 KaiDMML: sentiment Intercept 1 0.022 0.022 62.43 0.0000 Fake 1 0.004 0.004 11.68 0.0007 Error 401 0.139 0.000 Total 402 0.143 df - degrees of freedom, SS - sum of squares, MS mean square, F- test statistic, p - probability value

Based on the results it can be seen that the sentiment of all articles is rather negative. Also, the sentiment of each article is a relative small value. This is due to the simple calculation of sentiment that is calculated as the division of the difference between positive and negative words by the number of all words in the article. Based on the results was identified a statistically significant difference in the sentiment of fake news articles compared to the real news articles. This difference was demonstrated as for the whole dataset, as well as for the control dataset KaiDMML. Fake news articles are statistically significantly more negative.

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Fig. 3. Mean plot: point and interval estimates for sentiment (a) All Datasets (b) KaiDMML

5 Discussion and Conclusion In this paper, we analysed the fake and real news dataset that was created from three existing datasets. All three used datasets are currently freely available datasets. Their disadvantage is that they contain articles from the year 2016. It is possible that the style of fake news articles is changing and our results may no longer reflect the latest trends

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in writing fake news articles. However, they are verified and frequently analysed datasets and for this reason, they have been selected. The second problem of the datasets is their creation. Especially the first used dataset was created exclusively by the fake news classifier. It, therefore, contains articles that certainly have not been evaluated by a human. As a result of the fake news classifier, it contains articles that were clearly determined as fake news and probably the dataset does not contain borderline articles. Our analysis was focused on the basic characteristics of text in articles of the examined datasets. Among the most important findings was the confirmation of statistically significant difference in the article sentiment and it turned out that fake news articles had a more negative sentiment. The result is interesting mainly because of the relatively simple method of classification of the sentiment. If we were able to verify a statistically significant difference using the basic sentiment classification method, we can assume that using a more sophisticated classification method will confirm this result and even the differences in sentiment will be more pronounced. An interesting result was also the finding of a statistically significant difference in the average number of words per sentence. This was identified also for articles where stop words were removed. The result was a surprise for us, we assumed that the fake news articles would be simpler, more accurate, i.e. with a lower average number of words per sentence. The results have shown that fake news are probably trying to mislead the reader with their more complicated, more descriptive style. Finding statistically significant differences in individual textual characteristics is an important piece of information for the future fake news classifier in terms of selecting appropriate attributes for classification. Acknowledgment. This work was supported by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and of Slovak Academy of Sciences under the contract VEGA1/0776/18. This publication was supported by the Operational Program: Research and Innovation project “Fake news on the Internet - identification, content analysis, emotions”, co-funded by the European Regional Development Fund.

References 1. Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31, 211–236 (2017). https://doi.org/10.1257/jep.31.2.211 2. De Keersmaecker, J., Roets, A.: ‘Fake news’: incorrect, but hard to correct. The role of cognitive ability on the impact of false information on social impressions. Intelligence 65, 107–110 (2017). https://doi.org/10.1016/J.INTELL.2017.10.005 3. Jang, S.M., Geng, T., Queenie Li, J.-Y., Xia, R., Huang, C.-T., Kim, H., Tang, J.: A computational approach for examining the roots and spreading patterns of fake news: evolution tree analysis. Comput. Hum. Behav. 84, 103–113 (2018). https://doi.org/10.1016/ J.CHB.2018.02.032 4. Brigida, M., Pratt, W.R.: Fake news. North Am. J. Econ. Finance 42, 564–573 (2017). https://doi.org/10.1016/J.NAJEF.2017.08.012

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5. Eurobarometer 464 – Fake news and disinformation online. http://ec.europa.eu/ commfrontoffice/publicopinion/index.cfm/ResultDoc/download/DocumentKy/82798 6. Xu, K., Wang, F., Wang, H., Yang, B.: A first step towards combating fake news over online social media. Presented at the June (2018). https://doi.org/10.1007/978-3-319-94268-1_43 7. Braşoveanu, A.M.P., Andonie, R.: Semantic fake news detection: a machine learning perspective. Presented at the June (2019). https://doi.org/10.1007/978-3-030-20521-8_54 8. Saikh, T., Anand, A., Ekbal, A., Bhattacharyya, P.: A novel approach towards fake news detection: deep learning augmented with textual entailment features. Presented at the June (2019). https://doi.org/10.1007/978-3-030-23281-8_30 9. Agudelo, G.E.R., Parra, O.J.S., Velandia, J.B.: Raising a model for fake news detection using machine learning in Python. Presented at the October (2018). https://doi.org/10.1007/ 978-3-030-02131-3_52 10. Jane Wakefield: Fake news detector plug-in developed - BBC News. https://www.bbc.com/ news/technology-38181158 11. Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media (2018) 12. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media. ACM SIGKDD Explor. Newsl. 19, 22–36 (2017). https://doi.org/10.1145/3137597.3137600

Arab Handwriting Character Recognition Using Deep Learning Aissa Kerkour Elmiad(&) Computer Science Research Laboratory, Faculty of Sciences Oujda, University Mohammed 1er, Oujda, Morocco [email protected]

Abstract. Recent work has shown that neural networks have great potential in the field of handwriting recognition. The advantage of using this type of architecture, besides being robust, is that the network learns the characteristic vectors automatically thanks to the convolution layers. We can say that it creates intelligent filters. In this article we study deep learning in the field of Arab handwritten character in order to have a better understanding of its functioning. In this paper we present the work we have done on convolutional neural networks. First, we explain the theoretical aspects of neural networks, then we present our experimental protocols and we comment on the results obtained. Keywords: Convolutional neural network handwritten character recognition




Deep neural networks




Arabic

1 Introduction Automatic handwriting recognition has a diversity of applications. It is considered as one method of communication between man and machine. Many methods were provided to handle this problem with various accuracy [1]. The recognition method performance always depends on many attributes such as the size of the letters, the writing style, and the rate of recognition. One of the main challenges of handwritten charcter’s recognition is the inconsistency of the person handwriting style (i.e., width and shape), the type of device that collects the handwriting such as tablet or papers. Therefore, there is a need to have a system that can automatically recognize handwriting patterns with a high recognition rate. Many research results were reported for recognizing handwriting for Arabic and characters [2, 3]. The recognition of handwritten Arabic characters using CNN was recently reported. By using a deep learning technique we can reduce the number of classification errors in transcribing these handwritten characters and reduce the time taken to complete this task [4]. The advantages of using convolutional neural networks for handwritten Arabic characters. In addition to being robust, learning is guided by the goal of detecting an insertion algorithm. The classification and calculation of the characteristic vectors are done at the same time, which is not the case for conventional recognition methods with a classifier set where the two tasks are dissociated. During our study, we wanted to compare a Deep architecture with the classical method in other works [5] is the use of a simple neuron network to obtain better results on the recognition of Arabic manuscript © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 410–415, 2020. https://doi.org/10.1007/978-3-030-36778-7_45

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characters. In addition, we analyzed the results obtained using the convolutional neural network to better understand how it works. The rest of the paper is organized as follows: In Sect. 2 we will present the database of Arabic manuscript characters used. In Sect. 3 reminders about the concepts of convolutional neural networks and introduces deep learning with these different variants. In Sect. 4, we will see the architecture of our network. Then, in Sect. 5 we will first explain the methodology used to carry out our experiments, and in a second time we will present the results obtained. Finally, Sect. 6 concludes the paper and addresses the work in perspective.

2 Arabic Handwritten Characters Database Convolutional neural network need a big training data of handwritten characters images to get a good result. In this research, we utilized database of handwritten characters [5] available for free for researchers. These authors hi was collected and made a dataset of Arabic handwritten characters. The dataset is composed of 16,800 characters. The database of handwritten characters has 13,440 characters to 480 images per class assigned as training examples, and another 3,360 examples assigned as testing examples. The dataset was written by 60 participants, the age range is between 19 to 40 years, and 90% of participants are right-hand. Each participant wrote each character (from ‘alef’ to ‘yeh’) ten times on two forms as shown in Fig. X(a) & X(b). Figure 1 we show a sample of the training data set. The forms were scanned at the resolution of 300 dpi. The database has images of size 32  32 pixel square of 1024 pixels per image. This data set is used as a benchmark for various application models.

Fig. 1. Data collection for Arabic characters.

3 CNN Architecture Convolutional networks were introduced for the first time by Fukushima [6]. The results of Elleuch in 2017 [7] are very promising and demonstrate the superiority of DL methods for the recognition of manuscript Arabic characters. He continued his work on

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the DBN and Feature Extractor Stack, such as the Boltzmann Restricted Machine (RBM) and Auto-Encoder, and reported on the results achieved with character recognition, which was actually similar (97.8%) to the previous one. An optimization method to increase CNN’s performance was used in 2017 by El-Sawi et al. [5]. Their proposed CNN gave an average classification accuracy of 94.9% on the test data. 3.1

Convolutional Layers

The convolutional layers constitute the core of the convolutional network. These layers consist of a rectangular grid of neurons that have a small receptive field extended across the entire depth of the input volume. Thus, the convolutional layer is just an image convolution of the previous layer, where the weights specify the convolutional filter. 3.2

Pooling Layers

After each convolutional layer, there may be a pooling layer. The pooling layer subsamples their input. There are many ways to do this pooling, like averaging or maximizing, or a learned linear combination of neurons in the block. For example, Fig. 2 shows max pooling on a 2  2 window.

Fig. 2. Max pooling.

3.3

Layers Fully Connected

Finally, after several layers of convolution and pooling, the high-level reasoning in the neural network is via fully connected layers. In convolutional neural networks, each layer acts as a detection filter for the presence of specific characteristics or patterns present in the original data. The first layers of a convolutional detect features that can be recognized and interpreted relatively easily. Subsequent layers increasingly detect more abstract features. The last layer of the convolutional network is able to make an ultra-specific classification by combining all the specific characteristics detected by the previous layers in the input data. In the next section, the proposed architecture of the convolutional network for Arabic handwritten characters.

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4 CNN Architecture Our classification architecture is classic, it combines convolution and Max pooling. However, to obtain a fast classification allowing classification and localization in real time, we chose a light network. Figure 3 shows the seven layers of our convolutive network. An image successively passes through a convolutional operation with a 3  3 size core. A Max pooling 3  3 with step 2 follows two convolutional layers. The same structure is applied after the third layer. The three and four layers have 12 characteristic cards. Layer five and six are fully connected. The output of the last fully connected layer powers a 28-channel Softmax producing a 28-class distribution.

Fig. 3. Proposed CNN architecture.

We chose [8] for the convolutional layers and one fully connected with a Leaky ReLu activation function with coefficient a = 1/3 (see Fig. 4).

Fig. 4. Leaky ReLu.

5 CNN Simulation For a CNN the data are important for the learning phase; The highest convolutional layers thus learn sophisticated features. It provides the main indications for the conversion and construction of a precise CNN. In our case study, we created random

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sample training of 13,440 examples out of the 16,800 examples provided by the AHCD database as learning examples. And 3360 other examples to delicate tests. The proposed CNN will have grayscale images of size 32 * 32. We have formed the CNN at the 30 shown in Fig. 5. The prediction curves show the progress of the drive when the accuracy has increased near the iteration 19 and has become stable. It is clear that the overall recognition accuracy is greater than 98% (Table 1).

Fig. 5. Training and Loss using 13 440 examples.

Table 1. Comparison between proposed approach and other approaches on the same datasets. Authors

Database

Training data & Testing data

Torki et al. [9]

AIA9 k

El-Sawi et al. [5]

AHCD

Proposed approach

AIA9 k

Khaled [8]

AHCD

Our approach

AHCD

8738 images 85% training 15% testing (by gender) 16800 images 13440 Training images 3360 Testing images 8738 images 85% training 15% testing 16800 images 13440 Training images 3360 Testing images 16800 images 13440 Training images 3360 Testing images

Classification accuracy 94.28%

94.9%

94.8%

97.6%

98.0%

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6 Conclusion and Future Work In this work, we explored our initial ideas for using CNN to recognize different forms of handwritten Arabic characters. The system was implemented using TensorFlow and Keras framework. We used the AHCD database of handwritten characters. Different schemes have been adopted with a different number of character images. We have seen that developed CNNs have a steep learning curve. Although our network can achieve great accuracy, it is not perfect and there are ways to improve it. Finally, we would also like to explore the performance of our model by identifying not only handwritten Arabic characters, but also handwritten Arabic words.

References 1. Tsai, C.: Recognizing handwritten Japanese characters using deep convolutional neural networks. Technical report, Stanford University (2016) 2. Zhong, Z., Sun, L., Huo, Q.: Improved localization accuracy by LocNet for Faster R-CNN based text detection in natural scene images. Pattern Recognition 96, 106986 (2019) 3. Vibhute, P.M., Deshpande, M.S.: Performance analysis of deskewing techniques for offline OCR. In: Kumar, A., Mozar, S. (eds.) ICCCE 2019. Lecture Notes in Electrical Engineering, vol. 570. Springer, Singapore (2020) 4. ElAdel, A, Zaied, M., Amar, C.B.: Trained convolutional neural network based on selected beta filters for Arabic letter recognition, 05 March 2018. https://doi.org/10.1002/widm.1250 5. El-Sawy, A., Loey, M., El-Bakry, H.: Arabic handwritten characters recognition using convolutional neural network. WSEAS Trans. Comput. Res. 5, 11–19 (2017) 6. Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980) 7. Elleuch, M., et al.: Optimization of DBN using regularization methods applied for recognizing Arabic handwritten script. In: International Conference on Computational Science (ICCS 2017), Zurich, 12–14 June 2017, vol. 108, pp. 2292–2297 (2017) 8. Khaled, S.Y.: Arabic handwritten character recognition based on deep convolutional neural networks. Jordanian J. Comput. Inf. Technol. (JJCIT) 3(3) (2017). New Trends in Information Technology 9. Torki, M., et al.: Window-based descriptors for Arabic handwritten alphabet recognition: a comparative study on a novel dataset, arXiv preprint arXiv:1411.3519 (2014)

Automatic Evaluation of MT Output and Post-edited MT Output for Genealogically Related Languages Daša Munková1, Michal Munk1(&), Ján Skalka1, and Karol Kasaš2 1

2

Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 94973 Nitra, Slovakia {dmunkova,mmunk,jskalka}@ukf.sk University of Pardubice, Studentska 95, 53210 Pardubice, Czech Republic [email protected]

Abstract. The aim of the research is twofold: to evaluate the translation quality of the individual sentences of the MT output and also post-edited MT output on the basis of metrics of automatic MT evaluation from Slovak into the German language; and to compare the quality of MT output and post-edited MT output based on the same automatic metrics of MT evaluation. The icon graphs were used to visualize the results for individual sentences. A significant difference was found in sentence 36 in favor of the post-edited MT output and vice versa in sentence 5 in favor of MT output. Due to the error rate, a significant difference was in sentence 29 and 11 in favor of post-edited MT output and vice versa the sentence 26 in favor of MT output. Based on our results we can state that it is necessary to include into the evaluation of the quality of translation all automatic metrics for each sentence separately. Keywords: Language processing
Machine translation metrics
Genealogically related languages


Automatic MT

1 Introduction There are several reasons for this: growing international markets, increasing numbers of migrants, globalization, recognition of language minorities, their right to the development of cultures and languages; and use of the Internet, mass media, and technologies. In translation, there exist several technological innovations using computeraided translation tools and machine translation systems; devices that have accelerated the dissemination of information (via translations) and mediation in the globalized world. These innovations have significantly influenced and changed the way people communicate. In order to use them as functional tools in today’s dynamically developing multilingual society (for economization of the transfer and reducing the cost of human translation), their products (outputs) must be systematically evaluated [1]. Machine Translation (MT) represents the process in which a computer is used as a translation tool for transferring text between languages. Machine translation is not expected to be ‘perfect’, its aim is to be feasible in the form of the assimilation of © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 416–425, 2020. https://doi.org/10.1007/978-3-030-36778-7_46

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information or text; communication, discussion, fora; expanding internet markets; or with the specific intervention of a translator [2–4]. Although machine translation appears to be growing in its scope and research, most of the machine translations require human intervention: via post-editing – corrections in MT output to reach the required level of translation. DePalma [5] notes that similarly to translation memories in the 1990s, nowadays post-editing has become an important part not only of translation research but also of the world-wide translation industry. According to Bourquin [6], evaluation criteria for translation should differ according to the type of translation: human or machine. In the case of human translation, it requires fluency of translation - its openness to ethnocultural, language and the translator’s approaches: sensitivity, intuition and the sense of equivalence. When evaluating machine translation, regularity, accuracy, speed, reliability and encyclopedic carefulness is expected [6]. Although manual evaluation is considered to be the most variable, there are problems that he cannot cope with. Papineni et al. [7] noted that manual evaluation methods and metrics are too slow and expensive to develop MT systems for which rapid feedback on translation quality is important. Vilar et al. [8] state that also the subjectivity that is characteristic for manual evaluation causes a problem in terms of evaluators´ bias against machine translation as well as the unclear definition of numerical scale in manual evaluation. In an effort to make the evaluation more effective, several automated quality evaluation measures have been designed to reduce “time and labor” during the evaluation. Automatic evaluation metrics are used to complement human evaluation. They provide high efficiency and consistency and are relatively low cost. They are mostly based on the measurement of the similarity between the translation being considered (hypothesis) and human translation (reference translation). The aim of the research is twofold: (1) to evaluate the translation quality of the individual sentences of the MT output and also post-edited MT output on the basis of metrics of automatic MT evaluation from Slovak into the German language, and (2) based on the same automatic metrics of MT evaluation, to compare the quality between MT output and post-edited MT output. The rest of the paper is structured as follows: Sect. 2 describes automatic metrics of MT evaluation, Sect. 3 presents experiment and Sect. 4 its results. Subsequently, discussion and conclusion with future work are offered in the last section.

2 Automatic Metrics of MT Evaluation Precision and Recall are the simplest metrics of automatic MT evaluation and originate from natural language processing. They are based on lexical similarity (matching) between the words of the hypothesis (MT output) and the reference (human translation), regardless of the word position in the examined sentence. They have a mutual

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opposite relationship, i.e. as precision (P) score increases, recall (R) scores may decrease and vice versa. P¼

number of correct words in hypothesis : number of words in hypothesis

ð1Þ

R¼

number of correct words in hypothesis : number of words in reference

ð2Þ

The f-measure metric is actually the harmonic mean of both previous metrics (precision and recall). F1 ¼

2PR : PþR

ð3Þ

The metrics BLEU is currently the most widely used automatic metric in MT evaluation. It is based on the metric PER but takes into account the match between the hypothesis and reference based on n-grams. It calculates the geometric mean of precision of n-grams between the hypothesis and reference with an exponential factor of penalty as compensation for inadequate short translation [7]. BLEUðnÞ ¼ exp

XN n¼1

 BP ¼

wn log pn  BP; where wn are weights for different pn :

1; if r [ r r ; where r is a reference of a hypothesis h: e1h ; if h  r

ð4Þ ð5Þ

The metric WER is taken from speech recognition and is based on edit distance, taking into account word order. The edit distance is represented by the Levenshtein distance, which is defined as a number of minimum edits (insertions, deletions, and substitutions) needed to match two sequences (sentences) [9]. WERðh; r Þ ¼

mine2Eðh;rÞ ðI þ D þ SÞ : jr j

ð6Þ

While r is a reference of a hypothesis h, I - insert(e), D - delete(e), S - substitute(e), and mine2Eðh;rÞ is a number of minimum edits. The metrics PER is based on the metric WER but ignores the word order in both, hypothesis and reference [10]. PER ¼ 1 

correct  maxð0; hypothesis length  reference lengthÞ : reference length

ð7Þ

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The metric CDER requires both, hypothesis and reference to be covered completely and disjointly, while the reference must be covered only once. It originates from the principle that the number of blocks in a sentence is the same as the number of gaps between them plus one [11, 12]. The higher the scores of metrics PER, WER, and CDER (metrics of error rate), the lower the translation quality and vice versa. On the other hand, the higher the scores of the metrics of accuracy (Precision, Recall, F-measure, and BLEU-n), the better the translation quality.

3 Experiment Experiment and its procedure itself were inspired by studies focusing on MT evaluation from Slovak into English [12, 13]. In this experiment, we aim at MT evaluation, but in different translation direction and language pair, i.e. from the Slovak language into German. German and Slovak are genealogically related languages. Both come from the Indo-European language family. There are many similarities not only in the lexicon but also in the morphology and syntax. Both languages have common inflective features, e.g. synthetic formation of the diminutive or similar case system. They also agree in word order, because in both languages the position of the subject predominates (subject - predicate - object) and both have a loose word position in the sentence. The source popular-scientific text was written in Slovak, consisting of 360 sentences and subsequently translated into German by Google Translate API and postedited by eight master’s students of Translation Studies. The volume of the text (360 sentences) is limited because the presented data belongs to a part of large research obtained from the one-day workshop focusing on post-editing. We used Google Translate API (GT) as other researchers such as Seljan et al. [14], Dis Brandt [15], Babych et al. [16], Adly and Al Ansary [17]. GT is a free web translation service from/into low resources languages (supporting translation from/to Slovak and is equipped with the largest textual databases). The first objective of the research is to evaluate the translation quality of individual sentences of the MT output and also post-edited MT output based on the automatic metrics of accuracy and error rate from Slovak into German. The second objective consists of a comparison of MT output and post-edited MT output based on metrics of accuracy and error rate. To evaluate the quality of the translation, we used our own reliable tool [13]. Its output is a data file represented by 10 variables, where the automatic metrics of accuracy ([Precision, Recall, F-measure], [BLEU_1, BLEU_2, BLEU_3, BLEU_4]) and automatic metrics of error rate ([PER, WER, CDER]) are calculated for each sentence. Exploratory techniques will be used to present these data and to recognize the regularities and irregularities, structures, patterns and peculiarities within the examined

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text. To visualize the multidimensional data, we will use icon graphs, where each icon will correspond to one sentence characterizing the calculated score of the automatic metrics of accuracy or error rate. Icon graphs show the largest and smallest differences in accuracy and in error rate of hypothesis depending on the reference. The icons representing the translation are displayed from left to right one by one. By an icon graph, we can visually detect outliers- extreme cases- sentences with a significantly different score from the others.

4 Results The aim of the research is to evaluate the quality of the translation of the individual sentences of the examined MT text from Slovak to German, based on the metrics of automatic evaluation. And also to show the application of automatic metrics to the evaluation of the quality of post-edited MT output.

Fig. 1. Scores of Precision, Recall, f-measure for the first 37 MT sentences translated into German; clockwise direction.

MT sentences 5, 26, 29, and 37 achieved the highest scores based on the automated metrics of accuracy (Fig. 1). On the other hand, the smallest scores of metrics of accuracy were achieved for MT sentences 1, 3, and 34. The automatic metrics of the accuracy are symmetrical in almost all cases-sentences.

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Fig. 2. Scores of PER, WER, CDER for the first 37 MT sentences translated into German; clockwise direction.

MT sentences 5, 26, 29, and 37 achieved the highest scores of the automatic metrics of error rate (Fig. 2). On the other hand, the lowest scores were achieved for MT sentences 1, 3, and 34. The individual metrics of the error rate show slight asymmetry in some cases- sentences.

Fig. 3. Scores of BLEU-n for the first 37 MT sentences; clockwise direction.

MT sentences 29 and 37 were best rated on the basis of the automated metrics of accuracy when translated into German (Fig. 3). On the other hand, the lowest scores

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were achieved for MT sentences 1, 3, 4, 18, 20, 32, 34, and 36. The individual metrics of the accuracy show significant asymmetry in some cases- sentences.

Fig. 4. Scores of Precision, Recall, f-measure for first 37 post-edited MT sentences; clockwise direction.

Post-edited MT sentences 29, 36, and 37achieved the highest scores based on the automated metrics of accuracy (Fig. 4). On the other hand, the lowest scores of metrics of accuracy were achieved for post-edited MT sentences 3, 9, 34, and 35. The automatic metrics of the accuracy (Precision, Recall, and f-measure) are symmetrical in almost all cases-sentences.

Fig. 5. Scores of PER, WER, CDER for first 37 post-edited MT sentences; clockwise direction.

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Fig. 6. Scores of BLEU-n for first 37 post-edited MT sentences; clockwise direction.

Post-edited MT sentences 1, 5, 11, 25, 29, 36, and 37 achieved the highest scores of the automatic metrics of error rate (Fig. 5). On the other hand, the lowest scores were achieved for post-edited MT sentences 3, 9, and 34. The individual metrics of the error rate (PER, WER, CDER) show slight asymmetry in some cases- sentences. Post-edited MT sentences 29 and 37 were best rated on the basis of the automated metrics of accuracy when translated into German (Fig. 6). On the other hand, the lowest scores were achieved for post-edited MT sentences 3, 9, 18, 19, 22, 32, 34, and 35. The individual metrics of the accuracy (BLEU_1, BLEU_2, BLEU_3, and BLEU_4) show significant asymmetry in some cases- sentences.

5 Discussion and Conclusion We used icon graphs to display differences in the evaluation of the automatic metrics of MT evaluation in terms of accuracy (precision, recall, f-measure, and BLEU-n) and error rate (WER, PER, and CDER) depending on the reference. MT sentences 5, 26, 29, and 37 were the best translated in terms of accuracy (morphological and lexical) and also syntax structure, while the MT sentences 1, 3, 20, and 34 were translated the worst. For example: MT sentence 37: Beide haben einen entscheidenden Einfluss auf die Leistung der Maschinenübersetzungssysteme. [EN: Both have a decisive impact on the performance of machine translation systems]. Although it is not perfect (words are missing, synonyms are also used in comparison with the reference), it belongs to the best translated sentences. The same can be also applied to MT sentence 26, where the word is missing or is incorrect.

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MT sentence 26: Lets erkunden die Mechanismen und Theorie der Sprache Funktionieren mit Computer-Modellierung und Simulation (Hutchins und Somers 1992). [EN: It facilitates the discovery of language mechanisms and theories through computer modelling and simulation (Hutchins and Somers 1992)]. There were several reasons for the worst translated sentences: the use of Anglicism, omitting verbs, incorrect syntax and morphological forms, and the use of synonyms with respect to the reference. For example: MT sentence 3: Maschinelle Übersetzung hat sich ein Bereich von Interesse und weit verbreitet in unserer Gesellschaft verwendet. [EN: Machine translation has become an area of interest and is widely used in our society]. There is again a mistake in the lexicon (apart from wrong words, the verb is missing) and also in syntax. MT sentence 20 has the same problem as MT sentence 3. MT sentence 20: Wenn Sie alle noch nicht erwähnt grundlegende Zweck maschinelle Übersetzung kombinieren, können wir sagen, dass maschinelle Übersetzung ist “kommerzielles Produkt”, das in Reaktion wurde geschaffen, um für Übersetzungen zu verlangen. [EN: If we combine all the above noted basic purposes of machine translation, we can say that machine translation is a “commercial product “, which was created in response to demand for translations]. A significant difference was found in sentence 36 in favor of the post-edited MT output and vice versa in sentence 5 in favor of MT output (within the precision and recall of the translation). Due to the error rate, a significant difference was in sentence 29 and 11 in favor of post-edited MT output and vice versa the sentence 26 in favor of MT output. Based on our results we can state that it is necessary to include into the evaluation of the quality of translation all automatic metrics for each sentence separately. In future work, we would apply new designed automatic metrics for the evaluation of MT output. Acknowledgments. This work was supported by the SRD Agency under the contract No. APVV-18-0473 and Scientific Grant Agency of the ME SR and SAS under the contracts No. VEGA-1/0809/18. This publication was supported by the Operational Program: Research and Innovation project “Fake news on the Internet - identification, content analysis, emotions”, co-funded by the European Regional Development Fund.

References 1. Munkova, D., Munk, M., Benko, L., Absolon, J.: From old fashioned “one size fits all” to tailor made online training. In: Auer, M.E., Tsiatsos, T. (eds.) ICL 2018. Advances in Intelligent Systems and Computing, vol. 916, pp. 365–376. Springer, Cham (2019) 2. Chéragui, M.A.: Theoretical overview of machine translation. In: Proceedings of ICWIT 2012, pp. 160–169. CEUR-WS, Sidi Bel Abbes, Algeria (2012)

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3. Wallis, J.: Interactive translation vs. pre-translation in the context of translation memory systems: investigating the effects of translation method on productivity, quality and translator satisfaction. University of Ottawa, Ottawa (2006) 4. Munkova, D., Munk, M.: Evalvácia strojového prekladu. Univerzita Konštantína Filozofa v Nitre, Nitra (2016) 5. DePalma, D.A.: How to add post-edited MT to your service offerings. Common Sense Advisory, Cambridge, MA (2013) 6. Van Slype, G.: Critical study of methods for evaluating the quality of machine translation. Technical report, Bureau Marcel van Dijk/European Commission, Brussels (1979 7. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, Pennsylvania, pp. 311–318 (2002) 8. Vilar, D., Xu, J., D’haro, L.F., Ney, H.: Error analysis of statistical machine translation output. Language Resources and Evaluation, Genoa, Italy, pp. 697–702 (2006) 9. Nießen, S., Och, F.J., Leusch, G., Ney, H.: An evaluation tool for machine translation: fast evaluation for MT research. In: Proceedings of the Second International Conference on Language Resources and Evaluation, Athens, Greece, pp. 39–45 (2000) 10. Tillmann, C., Vogel, S., Ney, H., Zubiaga, A., Sawaf, H.: Accelerated DP based search for statistical translation. In: Fifth European Conference on Speech Communication and Technology, Rhodes, Greece, pp. 2667–2670 (1997) 11. Leusch, G., Ueffing, N., Ney, H.: CDER: Efficient MT evaluation using block movements. In: 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy, pp. 241–248 (2006) 12. Munk, M., Munkova, D., Benko, L.: Identification of relevant and redundant automatic metrics for MT evaluation. In: Sombattheera, C., Stolzenburg, F., Lin, L., Nayak, A. (eds.) MIWAI 2016. Lecture Notes in Artificial Intelligence, vol. 10053, pp. 141–152. Springer, Cham (2016) 13. Munk, M., Munkova, D.: Detecting errors in machine translation using residuals and metrics of automatic evaluation. J. Intell. Fuzzy Syst. 34(5), 3211–3223 (2018) 14. Seljan, S., Brkić, M., Kučiš, V.: Evaluation of free online machine translations for CroatianEnglish and English-Croatian language pairs. In: Proceedings of the 3rd International Conference on the Future of Information Sciences, Zagreb, pp. 331–345 (2011) 15. Brandt, D.: Developing an Icelandic to English shallow transfer machine translation system. Reykjavík University, Reykjavík (2011) 16. Babych, B., Hartley, A., Sharoff, S.: Translating from under-resourced languages: comparing direct transfer against pivot translation. In: Proceedings of the MT Summit XI. Citeseer, Copenhagen (2007) 17. Adly, N., Al Ansary, S.: Natural Language Processing and Information Systems. Springer, Heidelberg (2010)

3D Objects Learning and Recognition Using Boosted-SVM Algorithm Youness Abouqora(&), Omar Herouane, Lahcen Moumoun, and Taoufiq Gadi Laboratory of Informatics, Imaging, and Modeling of Complex Systems (LIIMSC), Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco [email protected]

Abstract. 3D object recognition is one of the most challenging tasks facing artificial systems. Thus, the ability to detect and localize the regions of interest is necessary to provide an enhanced searching and visualisation beyond a simple high-level categorisation. Recently, many approaches based on 3D objects learning have been proposed, they rely on learning objects characteristics from fully labelled 3D objects. However, such data training steps are difficult to be acquired at scale. In this paper we explore machine learning techniques to recognize objects, based on local parts, from a data base. The idea behind our approach is to compute and label the quantized local descriptor around 3D interest points using both intuitive geometric and spatial properties and an effective supervised classifier, named respectively Tri Spin Image and boostedSVM classifier. First, we extract the salient points using Harris3D then the significant and robust feature vectors representing the keypoints are computed then quantized using bag of features. Second, we use these vectors to train a boosted-SVM classifier. The performance of the proposed method is evaluated and proves encouraging results. Keywords: 3D object recognition
Computer vision
3D keypoint detector
3D local descriptor
Spin image
Bag of features
Boosted SVM
Confusion matrix
F1 metric

1 Introduction Designing local descriptors to describe 3D surface points is within common interests in both computer vision and computer graphics communities. Typically, a local descriptor refers to an informative representation stored in a multi-dimensional vector that describes the local geometry of the shape around the keypoints. It plays a crucial role in a variety of vision tasks, such as shape correspondence [1, 2], object recognition [3], shape matching [4, 5], shape retrieval [6, 7], and surface registration [8], to name a few. A large number of local descriptors have been actively investigated by the research community. However, designing discriminative and robust descriptors is still a nontrivial and challenging task. Early works focus on deriving shape descriptors based on hand-crafted features, including spin images [9], curvature features [10], heat kernel signatures [11], etc. © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 426–435, 2020. https://doi.org/10.1007/978-3-030-36778-7_47

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In this work we propose a new approach to classify 3D objects of a data base. The first contribution is the proposal of a new descriptor approach based on using the tri spin image [12] calculated around Harris3D key points [3] to describe the local surface and quantized by the bag of feature. The second contribution is related to a novel classifier approach of 3D objects. Indeed, we use a Boosted-SVM framework to train a bag of feature vectors, where the different 3D feature descriptors are automatically selected depending on the 3D mesh properties to be classified. To our knowledge, this is the first framework combining SVM with Adaboost algorithm to recognize the 3D objects based on keypoints features. Contributions of this paper are organized as follows. In the next section, we describe the selected Harris3D detector and the used 3D local descriptors are detailed. In Sect. 3, we describe the proposed algorithm combining SVM with Adaboost algorithm to obtain a powerful one called Boosted-SVM. Section 5, experiments are conducted to access the relevance of our approach and results are discussed. Finally, we conclude this paper in the last section.

2 3D Local Descriptors Recently, researchers have proposed many 3D local descriptors. Existing descriptors encode the geometrical information of a local surface [17], while others encode spatial information [12, 13, 16] or both [14, 15]. The Computational costs of extracting local 3D feature descriptors is relatively high. Thus, computing feature vectors for all points of a mesh is computationally expensive. That is why we choose to use these keypoints detectors [3, 19, 22, 23] to reduce the number of feature vectors. Based on the benchmark and the evaluation study proposed on [21], Harris3D introduced in [20] is selected as our keypoints detector that notoriously reduces the amount of irrelevant data with and speeds up the 3D processing. 2.1

Harris3D Detector

The 3D Harris detector is the 3D extension of the 2D corner detection method of Harris and Stephens [18]; it is based on the first order derivatives in two orthogonal directions on the 3D surface. For each vertex associated to its Harris operator, the value is calculated as follows: hðvÞ ¼ detðEÞ  kðtr ðEÞÞ

ð1Þ

where E represents the autocorrelation matrix, and k a parameter that needs to be tuned experimentally. Authors of [20] propose two ways of selecting the interest points of a given object. First, the vertices which are local maximum are extracted. To do so, each vertex v which satisfies the condition of Eq. (2) is selected: hðvÞ [ hðwÞ; 8w 2 ring1ðvÞ:

ð2Þ

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Second, the proposed approach to select the final set of interest points is based on clustering way that can be used when we want to get a good distribution of interest points in the object surface. This proposal consists of two steps. We sort the preselected interest points according to their Harris operator value in decreasing order. Then, we apply Algorithm 1 to cluster the sorted points and select the final set of interest points. Algorithm 1: Interest Points clustering Input: Set of pre-selected interest points in decreasing order of Harris Operator value: P Output: Candidates set of interest points: Q Let

be a set of points

for

do if

then

end if end for return

The value of q can be considered as a fraction of the diagonal of the object bounding rectangle and it has effect in the number of returned interest points. 2.2

Proposed Local Descriptor

Most of the existing local surface features suffer from either non-uniqueness, low descriptiveness, weak robustness to noise, or high sensitivity to varying mesh resolutions. To address these limitations, refer The Guo [17] we select a highly discriminative and robust local surface feature named Tri Spin Image (TriSI) [12], that is very descriptive in terms of precision and recall. TriSI uses a similar technique as in [13] to construct its local reference frame (LRF). Once the LRF is defined for a Harris3D keypoints, the local surface is aligned with the LRF. Next, a spin image is generated using the x axis as its Local Reference Axis (LRA) and the SI descriptor [11], procedure is then adopted. In addition, another two spin images are generated using the y and z-axes as the LRAs of these spin images, as shown in Fig. 1. The three spin images are then concatenated to form the TriSI descriptor. It significantly improves the descriptiveness and robustness compared to SI 2 or SHOT. The dimension of the TriSI descriptor is 3dtrisi , where dtrisi is the number of bins along each dimension. The TriSI_Harris descriptor is further compressed using the Principal Component Analysis (PCA) technique. Performance evaluation results in [17], show that the proposed descriptor is highly descriptive. It is very robust to both noise and varying mesh resolutions. The effectiveness of descriptor was also demonstrated by 3D modeling including pairwise and Multiview range image registration.

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Fig. 1. A schematic illustration of the Spin sheet axes

3 Learning and Recognition Approach A common way to classify an object based on a given set of local feature descriptions consists of two steps: The first step is inspired by text categorization approaches [24] namely bag-of-words representation that become an eligible method for categorizing visual content. In the second step these later are used as input vectors for our classifiers (Boosted-SVM). 3.1

Bag of Feature Representation and Matching

Once the salient points descriptors are computed for each shape, a dictionary is constructed by clustering all the descriptors with a k-means algorithm, and by considering their centroids as words of a dictionary C ¼ ðc0 ; . . .; ck Þ. A frequency histogram is then computed for each shape M, assigning each descriptor diM to the nearest word in the dictionary: bM ¼

X

e ; M i arg minj d ðdi ;cj Þ

ð3Þ

   P  Where ej is 1 at position j and 0 at all other indices, and d ci ; cj ¼ cti  ctj  is the t

cityblock distance. Finally, the distance between two shapes M and N is computed using the cosine similarity (Fig. 2): 0

dM;N

bM bN ¼ 1  pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; 0 0 ð bM bM Þ ð bN bN Þ

ð4Þ

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Fig. 2. A schema of our bag-of-feature approach.

3.2

Boosted-SVM Algorithm

Boosted-SVM is a machine learning technique for improving the performance of weak learners. It invokes iteratively a base learner and then, a single strong one is built by combining these weak learners generated by re-sampling the training set. The learned strong function is then used to recognize 3D objects. For this purpose, we use a base learner that is already strong (SVM) instead of a weak one. We choose SVM since it converges very fast and the execution time is not consuming when compared with other strong and stable classifiers like Artificial Neural Network. Also, little modification in extracted feature vectors does not affect its performance. SVM can as well perform in n-Dimensional space with a few training data [27]. The choice of the kernel function is also crucial for the success of the SVM algorithm, thus, for an ideal kernel, input patterns in the same class should have high kernel value while input patterns in different classes should have low kernel value. The RBF (Radial Basis Function) kernel [28] is chosen in this work, considering it can handle the case of the non-linearity between the attributes and the class labels.

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Algorithm 2: Boosted-SVM algorithm Input: Set of m observed feature vectors and their labels: SVM base-learner with fixed values of RBF kernel: {Gamma, C,} T: predefined number of iterations •

Let

•

for

be weights of all instances

do

end for Return

3.3

Our Recognition Pipelines

Processes of the proposed 3D object recognition approach, based on Boosted-SVM algorithm, consist of the two following steps: the feature learning process in which a classification function is learned and the recognition process where the learned function assigns a label to the appropriate class. Let TRISIMi be the observed feature vector associated with a 3D object. The objective is to assign the appropriate class Ci to TRISIMi , where C ¼ fC1 ; . . .; Cn g is the predefined classes for a 3D objects Dataset. The first step is to select the training samples and compute the feature information. We then use these feature vectors to train the recognition function using the Boosted-SVM algorithm. The second step is to select the testing samples and test them using the recognition function. This function will recognize and assign to each testing sample the appropriate class label. To do a comparison with the proposed approach based on [29], the Shape Spectrum Descriptor (SSD) will represent the shape of 3D object, it has been proposed by Zaharia [25]. SSD is based on the notion of shape index [26] that characterizes locally the shape of a surface. Each 3D object is represented by the distribution of its face’s areas with respect to their shape index values. The choice of the shape index is done as it allows the shape of a 3D object surface to be define.

4 Experiments and Results 4.1

Dataset

To evaluate the effectiveness of the proposed system for 3D local recognition, we use the “SHREC’07: Generic 3D Watertight Meshes”. It contains 19 each composed of 20 meshes. The categories are divided into homogeneous and heterogeneous classes, depending on the visual aspect of the shape of the 3D object, and the intra-class

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variations are so higher, for instance the FourLeg category contains different animals (horse, dog, cow,…). The optimization parameters of our system are a crucial step. We use the 10-cross-validation to fix values of C, c and T for Boosted-SVM. The suitable values of the optimal parameters C and Gamma of the RBF kernel for each classier are: C ¼ 5 and c ¼ 0:005 (these values make the SVM weak) for the Boosted-SVM. We fix the number of iterations T to 10. In order to train and test the proposed system, the size of each training subset was chosen to be 2/3 and the remaining 1/3 for testing. 4.2

Performance Evaluations

We execute the pipeline with an RBF kernel for optimal K, C and c, and overall accuracy is 92.06%. The average precision is 92%, average recall is 92% and average f1-score is 91%. The confusion matrix for the model is depicted in Fig. 3. We observe that executing the pipeline with SSD descriptor is providing an accuracy of 84.13%. The average precision is 84,53%, average recall is 85,13% and average f1-score is 84%.

Fig. 3. A confusion matrix using all available categories on the training set

We observe that by utilizing our local feature descriptor, our model accuracy is significantly improved. The relatively poor performance of models utilizing SSD descriptors can be attributed to its inability to deal with scale, noise and varying mesh resolutions. However, TriSI_Harris descriptor can be combined with a dense feature descriptor such as SSD which are capable of dealing with some limitations. Table 1 shows also that our global pipeline works perfectly with the accuracy of 100% for homogenous classes. The experimental results on SHREC 07 Watertight dataset clearly as certain that the proposed algorithms are able of categorizing objects. These results are encouraging, especially that our new TriSI_Harris descriptor performed the stateof-the-art methods in recognizing 3D objects under different views. Also, our approach improved the recognition rates thanks to the use of geometric and spatial information.

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Table 1. The performance of global pipeline using TriSI_Harris descriptor. Classes

SSD wrong class Human (1) (19) Cup (2) (8) Glasses (3) (2) Airplane (4) (13) Ant (5) (–) Chair (6) (7) Octopus (7) (2, 18) Table (8) (–) Teddy (9) (–) Hand (10) (13) Plier (11) (17) Fish (12) (–) Bird (13) (3, 17, 18) Armadillo (14) (–) Bust (15) (18) Mech (16) (–) Bearing (17) (–) Vase (18) (2) Fourleg (19) (1, 13) Average –

f1-score 75% 75% 80% 92% 100% 92% 55% 91% 100% 91% 93% 100% 40% 100% 93% 100% 80% 63% 74% 84%

TriSI_Harris wrong class (10) (–) (13) (–) (–) (–) (–) (–) (–) (–) (–) (–) (4, 10) (–) (–) (15) (19) (13, 15) (18) –

f1-score 91% 100% 93% 92% 100% 100% 100% 100% 100% 75% 100% 100% 67% 100% 80% 94% 91% 75% 75% 91%

5 Conclusion In this paper, we propose new approaches for 3D objects categorization and recognition. The TriSI_Harris descriptor that combines geometric features and spatial information based on spin image paradigm and harris3D is quantized by Bag of Words (BoW) that are discretized into a visual vocabulary. For the learning process we used the Boosted-SVM that combines the SVM classifier with Radial Basis Function kernel and Adaboost algorithm. We evaluated the performance of the proposed system in terms of recognition rate and training time using “SHREC 07 Watertight dataset”. The experimental results obtained demonstrate that the proposed Boosted-SVM approach and the utilized shape descriptor are discriminative and robust in terms of recognition accuracy rate. The obtained 92.06% accuracy rate for the Boosted-SVM classifier shows undoubtedly its powerful recognition ability with an effortless way and not time consuming. In a future work, we will attempt to improve the performance of our proposed descriptor by using other machine learning mechanisms in order for it to recognize and manipulate a large quantity of 3D objects. We will also develop a new approach using 3D local descriptors and other deep learning methods.

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References 1. Van Kaick, O., Zhang, H., Hamarneh, G., Cohen-Or, D.: A survey on shape correspondence. Comput. Graph. Forum 30(6), 1681–1707 (2011) 2. Ovsjanikov, M., Ben-Chen, M., Solomon, J., Butscher, A., Guibas, L.: Functional maps: a flexible representation of maps between shapes. ACM Trans. Graph. 31(4), 30 (2012). (Proc. SIGGRAPH) 3. Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J.: 3D object recognition in cluttered scenes with local surface features: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2270–2287 (2014) 4. Corman, É., Ovsjanikov, M., Chambolle, A.: Supervised descriptor learning for non-rigid shape matching. In: European Conference on Computer Vision (ECCV), pp. 283–298. Springer, Cham (2014) 5. Cosmo, L., Rodola, E., Masci, J., Torsello, A., Bronstein, M.M.: Matching deformable objects in clutter. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 1–10. IEEE (2016) 6. Bronstein, A.M., Bronstein, M.M., Guibas, L.J., Ovsjanikov, M.: Shape google: geometric words and expressions for invariant shape retrieval. ACM Trans. Graph. 30(1), 1 (2011) 7. Lian, Z., Godil, A., Bustos, B., Daoudi, M., Hermans, J., Kawamura, S., Kurita, Y., Lavoué, G., Van Nguyen, H., Ohbuchi, R., et al.: A comparison of methods for non-rigid 3D shape retrieval. Pattern Recogn. 46(1), 449–461 (2013) 8. Shah, S.A.A., Bennamoun, M., Boussaid, F.: A novel 3D vorticity based approach for automatic registration of low resolution range images. Pattern Recogn. 48(9), 2859–2871 (2015) 9. Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell 21(5), 433–449 (1999) 10. Gal, R., Cohen-Or, D.: Salient geometric features for partial shape matching and similarity. ACM Trans. Graph. 25(1), 130–150 (2006) 11. Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multiscale signature based on heat diffusion. Comput. Graph. Forum 28(5), 1383–1392 (2009). (Proc. SGP) 12. Guo, Y., Sohel, F.A., Bennamoun, M., Lu, M., Wan, J.: TriSI: a distinctive local surface descriptor for 3D modeling and object recognition. In: GRAPP/IVAPP, pp. 86–93 (2013) 13. Guo, Y., Sohel, F., Bennamoun, M., Lu, M., Wan, J.: Rotational projection statistics for 3D local surface description and object recognition. Int. J. Comput. Vis. 105(1), 63–86 (2013) 14. Yang, J., Cao, Z., Zhang, Q.: A fast and robust local descriptor for 3D point cloud registration. Inf. Sci. 346–347, 163–179 (2016) 15. Salti, S., Tombari, F., Di Stefano, L.: SHOT: unique signatures of histograms for surface and texture description. Comput. Vis. Image Underst. 125, 251–264 (2014) 16. Yang, J., Zhang, Q., Xiao, Y., Cao, Z.: TOLDI: an effective and robust approach for 3D local shape description. Pattern Recogn. 65, 175–187 (2017) 17. Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J., Kwok, N.M.: A comprehensive performance evaluation of 3D local feature descriptors. Int. J. Comput. Vis. 116(1), 66–89 (2016) 18. Harris, C.G., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, Manchester, Britain, pp. 10–5244 (1988) 19. Godil, A., Wagan, A.I.: Salient local 3D features for 3D shape retrieval. In: ThreeDimensional Imaging, Interaction, and Measurement. International Society for Optics and Photonics, vol. 7864, p. 78640S (2011)

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20. Sipiran, I., Bustos, B.: Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes. Vis. Comput. 11(27), 963 (2011) 21. Dutagaci, H., Cheung, C.P., Godil, A.: Evaluation of 3D interest point detection techniques via human-generated ground truth. Vis. Comput. 9(28), 901–917 (2012) 22. Pratikakis, I., Spagnuolo, M., Theoharis, T., Veltkamp, R.: A robust 3D interest points detector based on harris operator. In: Eurographics workshop on 3D object retrieval, vol. 5 (2010) 23. Mian, A., Bennamoun, M., Owens, R.: On the repeatability and quality of keypoints for local feature-based 3D object retrieval from cluttered scenes. Int. J. Comput. Vis. 89(2–3), 348– 361 (2010) 24. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: 10th European Conference on Machine Learning, ECML 1998, pp. 137–142 (1998) 25. Zaharia, T., Preteux, F.: Shape-based retrieval of 3D mesh models. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2002), Lausanne, Switzerland, pp. 437–440 (2002) 26. Koenderink, J.J., van Doorn, A.J.: Surface shape and curvature scales. Image Vis. Comput. 8 (10), 557–564 (1992) 27. Lodhi, R.S., Shrivastava, S.K.: Evaluation of support vector machines using kernels for object detection in images. Int. J. Eng. Res. Appl. (IJERA) 2, 269–273 (2012) 28. Scholkopf, B., Sung, K.K., Burges, C.J., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.: Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45(11), 2758–2765 (1997) 29. Herouane, O., Moumoun, L., Gadi, T., Chahhou, M.: A hybrid boosted-SVM classifier for recognizing parts of 3D objects. Int. J. Intell. Eng. Syst. 2(11), 102–110 (2018)

Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQL Youssef Mellah1(&), El Hassane Ettifouri1,2(&), Toumi Bouchentouf2(&), and Mohammed Ghaouth Belkasmi2(&) 1

Novelis, Oujda, Morocco {ymellah,eettifouri}@novelis.io 2 National School of Applied Sciences, Oujda, Morocco [email protected], [email protected]

Abstract. Building natural language interfaces to relational databases is an important and challenging problem in natural language processing (NLP), it requires a system that is able to understand natural language questions and generate corresponding SQL queries. In this paper, we present our idea of using type information and database content to better understand rare entities and numbers in natural language questions, in order to improve the model SyntaxSQLNet as the state of the art in Text-to-SQL task. We also present the global architecture and techniques that can be used in the implementation of our Neural Network (NN) model Text2SQLNet, with the integration of our idea that consists of using type information to better understand rare entities and numbers in natural language questions. We can also use the database content to better understand the user query if it is not well-formed. The implementation of this idea can further improve performance in the Text-to-SQL task. Keywords: NLP
SQL
NN
SyntaxSQLNet
Text-to-SQL
Text2SQLNet

1 Introduction Vast amount of today’s information is stored in relational database and provide the foundation of applications such as medical records Hillestad et al. [1, 2], financial markets Beck et al. [3], and customer relations management Ngai et al. [4]. However, accessing relational databases requires an understanding of query languages such as SQL, which, while powerful, is difficult to master. Natural language interfaces (NLI), a research area at the intersection of natural language processing and human-computer interactions, seeks to provide means for humans to interact with computers through the use of natural language Androutsopoulos et al. [5]. Natural language always contains ambiguities, each user can express himself in his own way. In our context (i.e. the interaction with the databases), the question of the user must be a bit specific in which the sentence must contain the information necessary to interact with the database (name of the database, entities, columns…), which is not the case for all users questions. So with this work, we like to put in place techniques to better understand what the user wants exactly. By doing that, questions that are poorly formulated by the users or that contain little information about the database will be © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 436–441, 2020. https://doi.org/10.1007/978-3-030-36778-7_48

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treated as well, which leads to improve the performances compared to the old models. In this article, we want essentially describe our idea of using type information to better understand rare entities and numbers in natural language questions. We can also use the database content to better understand the user query if it is not well-formed. This idea will be implemented with our model Text2SQLNet, which will be based on SyntaxSQLNet Yu, et al. [6] model.

2 Related Work There is a range of representations for semantic parsing or mapping natural language to formal meaning, such as logic forms and executable programs Zelle and Mooney [7]; Zettlemoyer and Collins [8]; Wong and Mooney [9]; Das et al. [10]; Liang et al. [11]; Banarescu et al. [12]; Artzi and Zettlemoyer [13]; Berant and Liang [14]; Pasupat and Liang [11]; Herzig and Berant [14]. As a sub-task of semantic parsing, the Text-to-SQL problem has been studied for decades Warren and Pereira [15]; Popescu et al. [16]; Li et al. [17]; Giordani and Moschitti [18]; Wang et al. [19]. Database community Li and Jagadish [20]; Yaghmazadeh et al. [21] proposed methods that tend to involve hand feature engineering and user interactions with the systems. In this work, we focus on recent neural network-based approaches Zhong et al. [22]; Xu et al. [23]; Gur et al. [24], and in this direction, Dong and Lapata [25] introduced a sequence-to-sequence (seq2seq) approach to convert texts to logical forms. Our work is related to recent work that exploits syntax information for code generation tasks Yin and Neubig [26]; Rabinovich et al. [27].

3 Problem Formulation Natural language questions often contain rare entities and numbers specific to the underlying database. Some previous work Agrawal and Srikant [28] already shows those words are crucial to many downstream tasks, such as inferring column names and condition values in the SQL query. However, it is unrealistic that users always formulate their questions with exact column names and string entries in the table.

4 Solution and Model Overview 4.1

Proposed Solution

To address the problem of poorly formulated user’s questions, or that contains little information about the exact names of columns and entities, Text2SQLNet assigns each word a type as an entity from either the knowledge graph, a column or a number. For example, for the question in Fig. 1, we label “mort drucker” as PERSON according to our knowledge graph; “spoofed title”, “artist” and “issue” as COLUMN since they are column names; and “88.5” as FLOAT. Furthermore when scalability and privacy are not of a concern, the system needs to search databases to better understand what the user is querying. Incorporating these techniques, Text2SQLNet can further improves the stateof-the-art performance in Text-to-SQL task, namely SyntaxSQLNet [5] model.

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Fig. 1. Question and type encoding

4.2

Model Overview

The global architecture of the model employs a tree-based SQL generator Fig. 2. The part that change compared to the model SyntaxSQLNet is the encoder. Instead of encode the words in questions only, we encode also the types of words to properly detect what the user is talking about and what exactly he wants. For the decoding part, it stays as a collection of recursive modules. The model decomposes the SQL decoding process into modules to handle the prediction of different SQL components such as keywords, operators, and columns. We describe only the part which will be changed compared to SyntaxSQLNet model, specifically, the input preprocessing as well as the encoder.

Fig. 2. To address the complex text-to-SQL generation task, Text2SQLNet employs a treebased SQL generator

Type Recognition for Input Preprocessing. We first, tokenize each question into ngrams of length 2 to 6, and use them to search over the table schema and label any column name appears in the question as COLUMN. Then, if the question contains

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numbers and dates, we assign them into four self-explanatory categories: INTEGER, FLOAT, DATE, and YEAR. We can identify named entities, by searching for types of entities: PERSON, PLACE, COUNTRY, ORGANIZATION, SPORT…, on Freebase1 using grams as keyword queries. And if the content of databases is available, we match words in the question with both the table schema and the content and labels of the columns as COLUMN and match the input values as the corresponding column names. Input Encoder. Our inputs of each module consist of three types of information: question (with type), table schema, and current SQL decoding history path. It consists of two bi-directional LSTMs, BI-LSTM-qt and BI-LSTM-col. To encode word and type pairs of the question, we concatenate embedding of words and their corresponding types and input them to BI-LSTM-qt. We encode history path and table schema (columns) in the manners described below. Table-Aware Column Representation. In order to generalize to new databases in testing, we propose to use both table and column names to construct column embedding. Specifically, given a database, for each column, we first get the list for words in its table name, words in its column name, and the type information of the column (string, or number, primary/foreign key), as an initial input of the column. Then, like SQLNet [23], for a given column, the table-aware representation is computed as the final hidden state of a BiLSTM running on top of this sequence. This way, the encoding scheme can capture both the global (table names) and local (column names and types) information in the database schema to understand a natural language question in the context of the given database. SQL Decoding History. We pass the SQL query’s current decoding history as an input to each module, in addition to question and column information. So in order to predict the next SQL token, we can use the information of previous decoding states. If we take the example, in Fig. 2, the COL module would be more likely to predict salary in the subquery by considering the path history which contains salary for HAVING, and SELECT in the main query. Attention for Input Encoding. For each module, like SQLNet Xu et al. [23], we apply the attention mechanism to encode question representation. This techniques can be employed also on SQL path history encoding.

5 Conclusion and Future Work In order to improve the performances of the SyntaxSQLNet model, we want to solve the problem of ambiguity in natural language. In this sense, user’s questions may not give sufficient information to interact with a database. So we present the idea of using type information and database content to better understand rare entities and numbers in natural language questions.

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In future work, we want to implement our novel model Text2SQLNet (on Pytorch or Keras using GPU) based on techniques described in this article, taking into consideration the idea of better understanding the user question. Our model will be formed on Spider Dataset, which contains databases with multiple tables and complex SQL queries. After the implementation and the training of the model, we will evaluate and compare it results of execution with the best model on the Text-to-SQL task, namely SQLNet and SyntaxSQLNet models.

References 1. Hillestad, R., Bigelow, J., Bower, A., Girosi, F., Meili, R., Scoville, R., Taylor, R.: Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff. 24(5), 1103–1117 (2005) 2. Maxwell, C.: A Treatise on Electricity and Magnetism, vol. 2, 3rd edn, pp. 68–73. Clarendon, Oxford (1892) 3. Beck, T., Demirgüç-Kunt, A., Levine, R.: A new database on the structure and development of the financial sector. World Bank Econ. Rev. 14(3), 597–605 (2000) 4. Ngai, E.W.T., Xiu, L., Chau, D.C.K.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36(2), 2592–2602 (2009) 5. Androutsopoulos, I., Ritchie, G.D., Thanisch, P.: Natural language interfaces to databases an introduction. Natural language engineering 1(1), 29–81 (1995) 6. Yu, T., Yasunaga, M., Yang, K., et al.: SyntaxSQLNet: syntax tree networks for complex and cross-domaintext-to-SQL task. arXiv preprint. arXiv:1810.05237 (2015) 7. Zelle, J.M., Mooney, R.J.: Learning to parse database queries using inductive logic programming. In: AAAI/IAAI, pp. 1050–1055, Portland, OR. AAAI Press/MIT Press, Cambridge (1996) 8. Zettlemoyer, L.S., Collins, M.: Learning to map sentences to logical form: structured classification with probabilistic categorial grammars. UAI (2005) 9. Wong, Y.W., Mooney, R.J.: Learning synchronous grammars for semantic parsing with lambda calculus. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007), Prague, Czech Republic (2007) 10. Das, D., Schneider, N., Chen, D., Smith, N.A.: Probabilistic frame-semantic parsing. In: NAACL (2010) 11. Liang, P., Jordan, M.I., Klein, D.: Learning dependency-based compositional semantics. In: Association for Computational Linguistics (ACL), pp. 590–599 (2011) 12. Banarescu, L., Bonial, C., Cai, S., et al.: Abstract meaning representation for sembanking. In: Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pp. 178–186 (2013) 13. Asian Federation of Natural Language Processing, ACL, July 26–31, 2015, Beijing, China, Long Papers, vol. 1, pp. 1470–1480 (2015) 14. Herzig, J., Berant, J.: Decoupling structure and lexicon for zero-shot semantic parsing. In: EMNLP (2018) 15. Warren, D.H., Pereira, F.C.: An efficient easily adaptable system for interpreting natural language queries. Comput. Linguist. 8(3–4), 110–122 (1982) 16. Popescu, A.-M., Etzioni, O., Kautz, H.: Towards a theory of natural language interfaces to databases. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 149–157. ACM (2003)

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17. Li, Y., Yang, H., Jagadish, H.V.: Constructing a generic natural language interface for an XML database. In: EDBT, vol. 3896, pp. 737–754. Springer, Berlin (2006) 18. Giordani, A., Moschitti, A.: Translating questions to SQL queries with generative parsers discriminatively reranked. In: COLING (Posters), pp. 401–410 (2012) 19. Wang, C., Cheung, A., Bodik, R.: Synthesizing highly expressive SQL queries from inputoutput examples. In: Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 452–466 (2017) 20. Li, F., Jagadish, H.V.: Constructing an interactive natural language interface for relational databases. VLDB 8(1), 73–84 (2014) 21. Yaghmazadeh, N., Wang, Y., Dillig, I., Dillig, T.: SQLizer: query synthesis from natural language. Proc. ACM Program. Lang. 1(OOPSLA), 63:1–63:26 (2017) 22. Zhong, V., Xiong, C., Socher, R.: Seq2SQL: generating structured queries from natural language using reinforcement learning. CoRR. arXiv:1709.00103 (2017) 23. Xu, X., Liu, C., Song, D.: SQLNet: generating structured queries from natural language without reinforcement learning. arXiv preprint. arXiv:1711.04436 (2017) 24. Gur, I., Yavuz, S., Su, Y., Yan, X.: DialSQL: dialogue based structured query generation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1 (Long Papers), pp. 1339–1349. Association for Computational Linguistics (2018) 25. Dong, L., Lapata, M.: Language to logical form with neural attention. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7–12, 2016, Berlin, Germany, vol. 1, Long Papers (2016) 26. Yin, P., Neubig, G.: A syntactic neural model for general-purpose code generation. In: ACL, no. 1, pp. 440–450. Association for Computational Linguistics (2017) 27. Rabinovich, M., Stern, M., Klein, D.: Abstract syntax networks for code generation and semantic parsing. In: ACL, no. 1, pp. 1139–1149. Association for Computational Linguistics (2017) 28. Agrawal, R., Srikant, R.: Searching with numbers. IEEE Trans. Knowl. Data Eng. 15(4), 855–870 (2003)

Privacy by Design and Cybersecurity for Safe, Effective and Reliable Home Health Care for Aging in Place Helene Fournier1(&) 1

2

, Heather Molyneaux2, Irina Kondratova2, and Noor Ali3

National Research Council Canada, Moncton, NB, Canada [email protected] National Research Council Canada, Fredericton, NB, Canada 3 University of New-Brunswick, Fredericton, NB, Canada

Abstract. This short paper presents findings from a research and development project on remote home health monitoring, specifically tools and technologies for Aging in Place to help seniors live independently at home for longer. A comprehensive literature review was completed to feed into an early concept design and prototype for a mobile video-conferencing application for Android, for use in remote health care services. Findings from the literature review will be presented as well as next phases of the research and development process. Keywords: Human-computer interaction
Home health care
Aging in place
Cybersecurity
IoT

1 Introduction The population of the developed world is aging and building digital technologies which meet the needs of an aging population is critical.This short paper presents findings from a research and development project focused on remote home health monitoring, specifically tools and technologies for ‘Aging in Place’, to assist seniors in living independently at home for longer. The project included a comprehensive literature review and market scan, as well as an early concept design and prototype for a mobile video-conferencing application for Android. HCI and human factors research provide a unique opportunity to bridge the gap in home health care services for seniors living at home in remote and rural locations. Specifically, in area of augmented reality applications for remote health and wellness tracking, with a focus on mobile VC applications for Android with QR code integration. There are important issues and challenges to address in these areas of research however. The literature review section will highlight some of them.

2 Literature Review A search for the literature on topics of seniors aging in place and home health monitoring resulted in 357 relevant articles (keyword search included seniors, aging in place, remote check-in, AI based video systems, visual or video, home health © Crown 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 442–450, 2020. https://doi.org/10.1007/978-3-030-36778-7_49

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monitoring, gerontechnology, community health). The most important themes arising from the articles search are presented as a text cloud in Fig. 1.

Fig. 1. Main concepts from a literature search on aging in place and home health monitoring

The literature review revealed important gaps in research on seniors, ‘Aging in Place’ and home health care services, including the need for more data on current practices, perceptions and attitudes around the use of health-related technologies and assistive devices in home health monitoring and remote check-ins. 2.1

Human-Computer Interaction and Human Factors

Literature in the area of Human Computer Interaction (HCI) and Human Factors highlighted important gaps in knowledge about designing useful feedback with appropriate levels of explanation for information from SMART and ASSISTIVE devices and technologies, so that feedback is useful and meaningful for a rapidly growing end-user demographic over 50+ years of age. There are also gaps and challenges around ethical issues and cybersecurity that need to be addressed. Trends around digital health services and assessment tools also bring up issues of bias in referrals to special care facilities for seniors who may do well at home. Data protection and privacy are areas in need of further research given the massive amounts of data now available and accessible, with Big Data sets, Internet of Things (IoT), and sensor technologies. The implications of Machine Learning (ML), Artificial Intelligence (AI) and recommendation engines that offer personalized suggestions, advice and referrals also requires further investigation. Fairness and competence of AI could have serious implications for individuals and society from a health and safety standpoint. Research and development of augmented reality applications in healthcare have the potential to transform the way home health care is delivered, while achieving important outcomes with societal impact, however, there are important issues and challenges to be addressed in terms of technology design. HCI and human factors research in areas such as augmented reality applications for remote health and wellness tracking,

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specifically the use of augmented reality on Smart Phones, represents an opportunity to conduct transformative research and contribute to the advancement of science and engineering. QR codes have been referred to as a gateway to the IoT [1]. QR coding systems can potential turn any material surface into information, knowledge, data or advertising of any kind. Studies have looked at the use video chat to combat depression and social isolation among seniors [2, 3]. Potential applications of this technology in the context of home health care for ‘Aging in Place’ will be investigated in future studies. 2.2

HCI Interfaces and Next Generation Technologies

The literature review highlighted trends in HCI interfaces and next generation technologies, including moving away from tangible interfaces to no-touch interfaces [4] such as Microsoft’s Kinect, Apple’s Siri and Google’s Project Glass, with the expectation that computers will adapt to us rather than the other way around [5]. Other trends include the extension of smart technologies such as the Smart Watch to textile and smart skin applications [6], the use of Internet of Things (IoT) in health care for remote monitoring, smart sensors and medical device integration [7], and context-aware sensors and assistive technologies for “Aging in Place” [8–10]. Ultimately, it is projected that the human body will be the next computer interface, enabled by wearables, living services, the IoT, and Smart Technologies landscape of the future [11]. More natural ways to interact, including touch, gesture, and voice, will become an integral part of the user interface of the future. However, the literature also highlights some important limitations in multimodal interfaces, such as accuracy and privacy issues [12]. Current developments in next generation networked environments include safeguards for data protection and privacy, and open and flexible standards that lead to a sustainable digital transformation strategy [13]. 2.3

Big Data Technology Landscape

The current digital technology landscape is changing fast and the amount of data generated today is several magnitudes larger than what was generated just a few years ago [14]. The expansion and availability of data in the new digital age has triggered research and discussion around data protection, privacy and ethics (i.e., Big Data, digital data, and privacy-sensitive data) with important cautions as to potential biases in data cleaning, selection and interpretation methodologies [15]. Some argue that the invasive potential of powerful data analytics can lead to potentially dehumanizing effects of automated machine-learning and algorithms that reduce complex human behaviors into few technical, knowable, measurable parameters that can be solved through technical calculation [16]. In the new digital age, “the complexities of ethics and values, ambiguities and tensions of culture and politics, and even the context in which data is collected, are not accounted for” [16, p. 70]. The dynamic pace of technological innovation also requires safeguarding of privacy in a proactive manner. In order to achieve this goal, researchers and system designers, including HCI specialists, are encouraged to practice responsible innovation

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that integrates privacy-enhancing technologies directly into their products and processes, based on principles of Privacy by Design [17]. To achieve this, scientists and specialists in HCI, computer science and social science are encouraged to work together at developing bias-free data-driven systems and devices to support end-users across the spectrum—whether to enhance learning in networked environments, using context-aware sensors in health applications, or IoT and assistive technologies for ‘Aging in Place’. Collaboration between specialists in different fields is desirable. Privacy by Design considerations include human values as well as privacy-enhancing aspects of technologies, and responsible practice for addressing factors such as accountability, research integrity, data protection, privacy, and consent [17, 18]. 2.4

Designing for an Aging Population

Recent HCI research addresses a major societal concern that is, the population of the developed world is aging. Currently most websites, apps, and digital devices are used by adults, younger adults and those aged 50+, thus they should not be design with a ‘one size fits all’ approach [19]. HCI researchers are looking into age-related factors that affect older adults’ ability to use digital technology, and work on technology design guidelines that reflect older adults’ varied capabilities, usage patterns, and preferences. The 50+ age group is pretty savvy with digital technology: it is the fastest growing demographic online, and, according to Home Care Magazine, 46% of baby boomers use a cell phone, 65% are active on social media, and a whopping 75% are digital buyers [20]. Research has shown that the ability and motivation to use new technologies are strongly determined by work experience and education, and the difficulties people experience around the use of technology are related to past performance rather than age-related factors [21]. Rapid development of digital proficiency in an aging population has even led to the development of a new research discipline. Gerontechnolgy is a scientific field that merges the study of aging with the study of technology, and includes the following five aspects: enhancement, prevention, compensation, care and research [22]. With over 10,000 people turning 65 every day in America, this is a market about to explode, and building technology for an aging population is at a critical tipping point [20]. However, the HCI literature points to important unresolved issues related to usability and AI in designing for an aging population [23]. The dynamic pace of technological innovation comes with challenges of integrating “intelligent” technologies into people’s daily lives. Described as “do it for me” or “do it myself” automation debate, designers and developers are faced with the challenge of determining when full automation is desirable, and when people need to exercise control over systems and devices [24]. As such, user experience (UX) design for human-AI interaction in the age of machine learning, including trust and skepticism in adopting technologies, is poorly understood or can be viewed as skewed (i.e., discriminatory algorithms) or prone to exploitation (e.g., Cambridge Analytica). The issue remains as to how humans can assess the validity of data and actions produced by powerful (but potentially invasive) Big Data and IoT systems and devices, over which

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humans have little or no control. The above mentioned issues are of concern across a wide demographic, including the most ‘tech savvy’ of end users. 2.5

Designing for Dynamic Diversity

Research and development of health-related applications, including mobile or mHealth applications, is crucial in managing the health and well-being of an aging population. There is a trend in new mHealth apps that help to manage health and well-being through smart systems, and Internet of Things (IoT) devices [23]. The success of smart systems is tightly linked to features such as ease of use and user-friendly graphical interfaces (GUI). Adaptive Interfaces represent a new way to improve the GUI usability, adapting and customizing automatically features of the UI to the user characteristics, to accommodate users with different skills and impairments [25]. There is an urgent need to address the issues of designing for dynamic diversity with an aging population in mind [26]. Interestingly, User Centered Design (UCD) principles have been described recently as inadequate for addressing the needs of an aging population, since the principles were developed for user groups with relatively homogeneous characteristics. “Older people” encompass an incredibly diverse group of users, and even small subsets of this group tend to have a greater diversity of functionality than is found in groups of younger people [27]. It was found that, among a sample of 65 year olds, a laborious interface was the main reason for terminating use of health-related applications within the first year [2]. It is no longer enough for UX designers to focus on improving user experiences by paying close attention to usability, utility, and interaction aesthetics. Instead, the best user experiences may come from services that automatically personalize their offerings to the user and context, and from systems that leverage more detailed understanding of people and their daily lives in order to provide new value [24]. New trends in HCI research focus on designing natural, social and safe user interfaces to reduce the digital marginalization of older adults. Despite growing technical literacy, for some marginalized adult user groups, interactions with emerging technologies (digital devices) may present insurmountable barriers that only widen the digital divide in a society that is information centric [3]. Currently, two major research questions have been identified in the literature on designing user interfaces for seniors [28]. What are the challenges that older users experience with user interfaces? Which solutions have been proposed by researchers and designers to address the identified challenges in user interfaces for seniors? Seniors have vastly different requirements than younger users, and solutions that meet their requirements require some level of participatory or human-centered design. The literature identifies the following domains of human-computer interaction as relevant in the design of user interfaces for an aging population: (1) ambient-assisted living, (2) conceptual user interfaces, (3) mobile user interfaces, (4) user input devices, and (5) website user interfaces [28]. Unfortunately, designing useful feedback mechanisms and appropriate levels of explanation for machine learning outputs to be meaningful to end users, especially for recommender systems, has yet to be explored in gerontechnology research. As the population steadily ages, so does the need for

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alternative health and wellness options, including home telehealth and virtual visits as part of comprehensive health care services. 2.6

Privacy by Design and Cybersecurity

Cybersecurity is an essential part of a safe, effective and reliable health care delivery system [29]. Researchers note that security and privacy challenges can be overcome by implementing best practices to safeguard the system [30]. The context of use is important: devices operating in the home are more exposed to unauthorized access than those in more controlled environments, such as nursing homes and hospitals [31]. However, there are clear benefits to homeware and remote monitoring. For example, in Sweden both cost effectiveness of remote monitoring, as well as consideration of human dignity is taken into account - the needs of individuals are considered in order to strive for a balance between independence and social contact [32]. So even though there are additional security challenges to consider with remote monitoring, this should not act as a barrier to accessibility. In order to protect privacy, privacy should be built into the system. The concept of Privacy by Design by Dr. Ann Cavoukian states that the principles of Fair information Practice (FIPs) need to be built into system. Compliance with regulatory frameworks alone does not assure privacy - privacy must become the default mode of operation. Privacy by design is a way in which to integrate FIPs into products and offers system designers 7 key guidelines for ensuring Privacy by Design [33]: 1. 2. 3. 4. 5. 6. 7.

Proactive not reactive; Preventative not remedial Privacy as the default Privacy embedded into design Functionality - Positive-sum, not zero-sum End-to-end lifecycle protection Visibility and transparency Respect for users’ privacy.

Cyber threats are not all digital, they also include other real life potential breeches [28]. It is also not enough to just ensure that security is built into the system; human needs and perceptions of security and privacy also need to be taken into consideration [34]. Research demonstrates that older populations are very aware of privacy issues [34, 35]. Studies show that elderly subjects see protection of personal data as only one important dimension of privacy: they also have other privacy concerns related to bodily privacy, privacy of personal behaviours and privacy of personal communications. It is important to consider all these privacy concerns when designing health technologies for in home use [35]. Additionally, systems perceived as intrusive can impact user acceptance – a fact that many researchers overlook [34]. Applications of technological solutions still suffer from sociocultural misunderstanding of group differences, and poor acceptability of technology for patients and caregivers [34]. Researchers emphasize that elderly users should be included in the design of remote home monitoring technologies and in gathering privacy requirements for such technologies [35, 36].

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3 Conclusion This short paper presents findings from a research and development project on remote home health monitoring, specifically tools and technologies for Aging in Place to help seniors live independently at home for longer. A survey of the literature points to important gaps in knowledge in designing assistive technologies for an aging population, specifically how to include useful feedback with appropriate levels of explanation for information from SMART technologies. The protection of personal data is an area of concerned expressed by seniors. Privacy extends into areas of bodily privacy, privacy of personal behaviours and privacy of personal communications. It is important to consider all these privacy concerns when designing health technologies for in home use. A survey with home health care professionals is currently underway to determine the level of technology use and integration in home health care services as well as senior’s level of technology adoption and acceptance for managing their health and wellness. There are currently gaps in our knowledge about the level of use and acceptance of technology in home health care delivery services. Survey data will help to inform future phases of the research on assistive technologies for Aging in Place.

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11. Goodman, A., Righetto, M.: Why the human body will be the next computer interface (2013). https://www.fastcodesign.com/1671960/why-the-human-body-will-be-the-next-com puter-interface. Accessed 20 Apr 2019 12. Lee, J., Kim, S., Fukumoto, M., Lee, B.: Reflector: distance-independent, private pointing on a reflective screen. UIST 2017, 22–25 October, Quebec City, Canada (2017) 13. IMS Global Learning Consortium (2018). https://www.imsglobal.org/aboutims.html. Accessed 5 Aug 2018 14. O’Reilly.com: Big data technology landscape (2018). https://www.oreilly.com/library/view/ big-data-analytics/9781484209646/9781484209653_Ch01.xhtml. Accessed 10 Sept 2018 15. Boyd, D., Crawford, K.: Critical questions for big data. Inform. Commun. Soc. 15(5), 662– 679 (2012). https://doi.org/10.1080/1369118X.2012.678878 16. Fenwick, T.: Professional responsibility in a future of data analytics. In: Williamson, B. (ed.) Coding/Learning, Software and Digital Data in Education. University of Stirling, Stirling, UK (2015) 17. Cavoukian, A., Jonas, J.: Privacy by design in the age of big data (2012). https://jeffjonas. typepad.com/Privacy-by-Design-in-the-Era-of-Big-Data.pdf. Accessed 10 Aug 2018 18. Cormack, A.: A data protection framework for learning analytics. Community.jisc.acuk (2015). http://bit.ly/1OdIIKZ. Accessed 5 Sept 2018 19. Johnson, J.A.: Designing technology for an aging population. In: CHI (2018). https://dl.acm. org/ft_gateway.cfm?id=3170641&type=pdf. Accessed 2 Sept 2018 20. Burkhardt, W.: The next hottest thing in silicon valley: gerontechnology (2016). https:// www.forbes.com/sites/vinettaproject/2016/09/20/the-next-hottest-thing-in-silicon-valleygerontechnology/#5b8b70763abe. Accessed 8 Aug 2018 21. Tacken, M., Marcellini, F., Mollenkopf, H., Ruoppila, I., Széman, Z.: Use and acceptance of new technology by older people. Findings of the international MOBILATE survey: enhancing mobility in later life. Gerontechnology, 3(3), 128–137 (2005). http://citeseerx.ist. psu.edu/viewdoc/download?doi=10.1.1.474.3979&rep=rep1&type=pdf. Accessed 30 May 2018 22. Jansson, T., Kupiainen, T.: Aged people’s experiences of gerontechnology used at home: a narrative literature review. Final Thesis. Helsinki Metropolia University of Applied Sciences (2017). https://www.theseus.fi/bitstream/handle/10024/129279/Jansson_Kupiainen_ONT_ 21.4.17.pdf?sequence=1. Accessed 3 Sept 2018 23. Pan, A., Zhao, F.: User acceptance factors for mHealth. In: Proceedings of the 20th International Conference, HCI International 2018, Part 1, pp. 173–184 (2018) 24. Dove, G., Halskov, K., Forlizzi, J., Zimmerman, J.: UX design innovation: challenges for working with maching learning as a design material. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 278–288 (2017). https://dl.acm. org/citation.cfm?id=3025739. Accessed 20 Aug 2018 25. Gullà, F., Papetti, A., Menghi, R., Germani, M.: A method to make an existing system adaptive. In: Proceedings of the 20th International Conference, HCI International 2018, Part 1, pp. 91– 101 (2018) 26. Vasilyeva, E., Pechenizkiy, M., Puuronen, S.: Towards the framework of adaptive user interfaces for eHealth. In: Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, pp. 139–144 (2005). http://www.win.tue.nl/*mpechen/publications/pubs/ VasilyevaCBMS05.pdf. Accessed 15 June 2018 27. Gregor, P., Newell, A.F., Zajicek, M.: Designing for dynamic diversity—interfaces for older people. In: Proceedings of the Fifth International ACM Conference on Assistive Technologies, pp. 151–156 (2002). https://dl.acm.org/citation.cfm?id=638277. Accessed 15 Sept 2018

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Feature Reduction Algorithm for Universal Steganalysis Fran¸cois Kass´en´e Gomis(B) , Mamadou Samba Camara, and Idy Diop Polytechnic School (ESP), Cheikh Anta Diop University, Dakar, Senegal [email protected]

Abstract. Steganography is the practice of communicating a secret message by hiding it in a cover medium like an image file. Steganalysis is the detection of steganography. There are two kinds of steganalysis approaches: specific steganalysis where the steganalyst has knowledge about the algorithm of steganography used to hide data and universal (blind) steganalysis where he has no idea about the algorithm used to hide data. In this paper, we use an image as support for steganalysis. In the case of blind steganalysis, a lot of features are extracted from the image to train a classifier. The great number of features makes computation very difficult and, by the way, prevents the implementation of certain supervised learning algorithms to build models for universal steganalysis. In this work, we propose a universal steganalysis method based on a new way of selecting the most relevant features to train a classifier. This algorithm consists of choosing the best set of features, generated by PFA (Principal Feature Analysis) for unsupervised learning [6], which makes it possible to obtain the best result for k-means with two classes (k ← 2). After finding the best set of features we use it to train a model for binary classification (cover vs stego). This approach gave good results in our experiments and open a new way of doing universal steganalysis without expensive computation resources. Keywords: Steganography · Steganalysis · Clustering · k-means Feature selection · MultiLayer Perceptron · Machine learning

1

·

Introduction

The effort concerned with developing methods for detecting the presence of secret messages is called steganalysis. In the case of universal steganalysis of images, a lot of features are extracted from the image. All these features are sensitive to steganographic algorithms. The longer the feature vector length, the more computational time is consumed in training and testing models. To build efficient models based on extracted features, algorithms like Forward selection, Backward selection, and Stepwise selection are often used to select the most relevant features for classification or regression purposes. c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 451–457, 2020. https://doi.org/10.1007/978-3-030-36778-7_50

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In this work we propose a feature reduction algorithm, based on PFA method for unsupervised learning, to select the best set of features for universal steganalysis. We called this algorithm S-SELECT. After a commentary about related works on universal steganalysis in Sect. 2, we detail our methodology and explain the algorithm behind it in Sect. 3. In Sect. 4, we present our experimental results. In Sect. 5 we discuss the results of our experiments and conclude in Sect. 6.

2

Related Works

An overview of the literature shows that universal steganalysis is an important task to enhance security in the transmission of messages through the communication channels and in the databases. To perform it we need to build strong models based on the most relevant features for classification or regression. Challenges in universal steganalysis turn around finding good features, which are very sensitive to any kind of steganographic algorithms, and strong classification methods to build classifiers. In many research papers, authors proposed features and classification algorithms for universal steganalysis. Most used features for universal image steganalysis are both intra-block and interblock correlations [4]. To reduce the number of features some available algorithms like Forward selection, Backward selection and Stepwise selection [4] have been used for universal steganalysis. We add to these algorithms a new one which can be used for universal steganalysis or any binary classification problem. From a given number of features n, the PFA method [6] selects the best set of features for unsupervised learning. We hypothesize that, for different values of n, the best set of features given by PFA algorithm (implementation of PFA method) which satisfies better k-means for two classes, is the best one for implementing a supervised learning algorithm to perform binary classification.

3

Methodology and Proposition

3.1

Methodology

In this section, we detail our methodology to perform universal steganalysis: – – – –

Database based on all extracted features for universal steganalysis Execution of S-SELECT to get the best set of features for supervised learning Implementation of supervised learning algorithm based on selected features Utilization of the model

3.2

S-SELECT: A Feature Reduction Algorithm

S-SELECT algorithm is based on PFA method. The PFA algorithm requires the number of features n and the Dataset as input to give the selected features for

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unsupervised learning. From all available features, PFA algorithm gives the n best features for unsupervised learning. Dataset, n ⇒ PFA algorithm ⇒ selected features To start the S-SELECT algorithm, we need the full dataset, the minimum number of features m and the maximum number of features p acceptable for good implementation of the chosen supervised learning algorithm. Here, in detail, the S-SELECT algorithm for feature reduction:

Algorithm Input: Dataset, m, p Ouput: S 1. 2. 3. 4. 5. 6.

7. 8. 9.

Initialize n, the number of selected features (n ← m) Initialize L, the measure to evaluate k-means, to its lowest value Initialize S, the vector of selected features, to empty. Execute the PFA algorithm to choose n features from all available features Execute k-means for two classes on selected features in step 4 and calculate the measure (average silhouette score [7]) If the measure is better than L assign to L this new value and store the corresponding features, that make it possible to obtain this new value of L, in S. Increment n: n ← n + 1 Repeat step 4, 5, 6, 7 until the stopping condition is met (n > p) Return S

Dataset is the full database (without the outcome column), a data frame of size l × k. m and p represent respectively the minimum and the maximum number of features required for a specific supervised machine learning algorithm to avoid under-fitting and over-fitting [3]. m and p must be given by the user of SSELECT according to his knowledge about data and his targeted supervised machine learning algorithm. Thus S-SELECT will give a number of features between m and p. From a given dataset, S-SELECT algorithm generates a set of features S. This set of features satisfies the best k-means for two classes. The subset including these features will be used to create models for universal steganalysis.

4 4.1

Experimental Image Database

We tested our algorithm on the image database BOSSbase 1.01 [1]. It contains 10, 000 grayscale images of size 256 × 256. After compressing them into JPEG

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format, we use the module Stegano of python [2] to embed messages into 5000 images of this base that we mixed with the cover images. After that process, we extracted 486 features based on both intra-blocks and inter-blocks correlations [4]. ⎞ ⎛ Y1 X11 X12 . . . X1k ⎜. . . ... . ⎟ ⎟ ⎜ ⎝. . . ... . ⎠ Yl Xl1 Xl2 . . . Xlk Mathematically, we got a data frame of size l × (k + 1) with l = 10, 000 and k = 486. The first column represents the outcome vector and the other columns correspond to the 486 extracted features. The outcome is a categorical variable (1 for stego images and 0 for cover images). For executing the feature reduction algorithm S-SELECT, we don’t use the outcome vector. 4.2

MultiLayer Perceptron

For the parameters settings of S-SELECT, we chose m = 10 and p = 30. SSELECT algorithm selects 15 features for p − m + 1 iterations. We implement an MLP [5] neural network with three hidden layers on these 15 features. In Fig. 1, the MLP is configured for binary classification (stego vs cover).

Fig. 1. MLP architecture

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5

455

Analysis of Results

In Fig. 2, we can see the result of our algorithm on this database:

Fig. 2. Silhouette score evolution

Some measures are required to get the best set of features which satisfies a k-means clustering for two classes (k ← 2). To implement S-SELECT algorithm, we chose silhouette score value [7], as a measure, to evaluate the performance of the k-means for various number of features in the subset. The silhouette score values are in range [−1, 1]. Due to correlated features their might not exist a unique optimal feature subset. Plotting the evolution of the measure values helps to visualize the number of iterations (number of features) which gives the best subset. To prevent the search from being exhaustive, a stopping criterion is added n > p (maximum number of iterations). The Fig. 2 shows the silhouette score evolution over the number of features (iterations) n. The 15th iteration corresponds to the maximum value of the silhouette score. The algorithm returns these 15 corresponding features that make it possible to have this best silhouette score. Those features will be used to implement a supervised learning algorithm for universal steganalysis. An MLP on these features gives an accuracy score value of 0.99 on the image database BOSSbase 1.01. In Fig. 3, we can see the accuracy score value of the MLP on the selected features by S-SELECT (subset) which is better than the accuracy score value when the model is built on all extracted features (dataset). This shows the interest of using a feature reduction algorithm. Table 1, shows the results of S-SELECT and Stepwise selection. On the same dataset, we got the same performances for an MLP with the same architecture. But S-SELECT selects only 15 features while stepwise selection selects 51 features. Some supervised learning algorithms require a limited number of features

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Fig. 3. Accuracy score of the MLP Table 1. Comparison between stepwise selection and S-SELECT Algorithms

Performance

S-SELECT

Number of selected features 15 MLP accuracy on selected features 0.99

Stepwise selection Number of selected features 51 MLP accuracy on selected features 0.99

to avoid under-fitting and over-fitting. In that case, S-SELECT is the best choice for selecting features in the perspective of doing binary classification. Another advantage is that it can be combined with any supervised learning algorithm to build models for binary classification.

6

Conclusion

Generally, in universal steganalysis, many features are extracted from the image. To build classification algorithms or regression algorithms, we don’t need to compute with all the features. Forward selection, Backward selection, and Stepwise selection are often used to select relevant features to build the model. In this work, we proposed a new way of choosing relevant features for classification between cover and stego images in universal steganalysis. This algorithm is based on Principal Feature Analysis for unsupervised learning. From a given feature vector, the algorithm reduces it and gives the best subset for universal steganalysis. The experiments show that, in the perspective of doing binary classification in universal steganalysis, S-SELECT is the best choice for feature reduction.

References 1. Image database. http://dde.binghamton.edu/download/. Accessed 07 Nov 2019 2. A pure python steganography module. https://pypi.org/project/Stegano/. Accessed 07 Nov 2019

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3. Allamy, H.: Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study) (2014) 4. Chen, C., Shi, Y.Q.: JPEG image steganalysis utilizing both intrablock and interblock correlations. In: 2008 IEEE International Symposium on Circuits and Systems, pp. 3029–3032 (2008). https://doi.org/10.1109/ISCAS.2008.4542096 5. Cortez, P.: Data Mining with Multilayer Perceptrons and Support Vector Machines, pp. 9–25. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-23241-1 2 6. Lu, Y., Cohen, I., Zhou, X.S., Tian, Q.: Feature selection using principal feature analysis. In: Proceedings of the 15th ACM International Conference on Multimedia, MM 2007, pp. 301–304. ACM, New York (2007). https://doi.org/10.1145/1291233. 1291297. 7. Thinsungnoen, T., Kaoungku, N., Durongdumronchai, P., Kerdprasop, K., Kerdprasop, N.: The clustering validity with silhouette and sum of squared errors, pp. 44–51 (2015). https://doi.org/10.12792/iciae2015.012

A Composite Framework to Promote Information Security Policy Compliance in Organizations Eric Amankwa1,2(&), Marianne Loock2, and Elmarie Kritzinger2 1

Department of ICT, Presbyterian University College Ghana, Abetifi, Ghana [email protected] 2 School of Computing, University of South Africa, Pretoria, South Africa {loockm,kritze}@unisa.ac.za, [email protected]

Abstract. Information security policy (ISP) noncompliance continue to impede information security in organizations. This paper consolidates the strength of previous studies into an effective single solution. The paper, first, synthesizes the existing literature and groups relevant ISP compliance factors into user involvement, personality types, security awareness and training, behavioral factors, and information security culture. Secondly, a generic framework that guides the development of frameworks for ISP compliance in organizations was developed based on the literature review. The generic framework categorized elements required for developing an ISP compliance framework into structure, content and outcome elements. Thirdly, the generic framework was applied to develop a composite ISP compliance framework that proposes the establishment of ISP compliance as a culture in organizations. Finally, the results of the expert review assessment showed that the proposed composite ISP framework was suitable, structurally sound and fit for purpose. Keywords: Information security
Policy
Security culture
Security compliance
Behavior intentions
ISPCC
Compliance framework

1 Introduction Over the years, organizations have introduced a number of interventions including information security education, training, and awareness (SETA) programs, and Information Security Policies (ISP) to shape security behaviors of employees [1, 2]. However, information security breaches that result from poor security behaviors continue to exist in organizations [3, 4]. The existing literature largely attributes this to employees’ disregard for information security policies [5, 6]. This, therefore, brings a sense of realization in Organizations that, information security efforts cannot end with the provision of SETA programs and security policies. Despite the realization, how to get employees to internalize information security knowledge acquired and translate it into ISP compliance remains a challenge that confronts organizations. Specifically, how to influence employees to follow information security policies remains a major challenge in organizations. Alotaibi et al. [7] paper summarize the challenges of © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 458–468, 2020. https://doi.org/10.1007/978-3-030-36778-7_51

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security policy implementation into security policy promotion; noncompliance with security policy; security policy management and updating; and shadow security. Among these challenges, noncompliance with information security policy presents the greatest threat to information security and remains a major challenge to information security practitioners [4, 5, 8]. Information security researchers continue to advance research to provide an effective approach to address the noncompliance challenge that imperils information assets in organizations. This paper investigates ISP compliance in organizations in an attempt to answer the following research questions: • Which factors have been recently applied in IS literature for promoting ISP compliance in organizations? • What are the requirements for developing a framework that promotes ISP compliance in organizations? The purpose of this research paper is to develop a framework that could guide practitioners on how to promote Information Security Policy (ISP) compliance in response to employees’ noncompliance behaviors in organizations. The framework will be developed by combining the strength of previous studies with elements extrapolated from previous frameworks available in the existing literature. The paper offers new insights on how to mitigate information security breaches caused by employees’ noncompliant behaviors in organizations.

2 Factors for Promoting ISP Compliance in Organizations Information security breaches that result from employee’s noncompliance with security policy lingers on in organizations. As a result, information security researchers continue to recommend various factors that could influence compliance with information security policy in organizations. These factors were categorized into five themes and discussed as follows: • End-Users Involvement: Involvement refers to the extent to which end users participate in the process of drafting or updating security policies. Previous studies have explained that involvement influences attitude and can manifest in different forms [8, 9]. When users are involved in the process of security policies development, they tend to feel that they are part of the ‘law-makers’ and will do everything possible to comply and also encourage compliance behaviors among each other. • Personality Types: It is argued in the existing literature that the personality of employees influences their attitudes and behavioral intentions to comply with ISP [3, 10–12]. In a study on personality traits, [3] found that an individual’s personality traits and risk-taking propensity explain the variances in personal information security awareness of policies. Personality type is therefore recommended in the literature as an important factor that requires close attention in the efforts to promote ISP compliance in organizations. • Security Awareness and Training: Information security awareness and training play important roles in organizations. Amankwa et al. [13] explained that the purpose of

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awareness is to draw employees’ attention to their roles and responsibilities towards information security whereas training provides skills and knowledge for their specific roles and responsibilities in the organization. Information security awareness and training have been recommended for promoting security policy towards compliance in studies including [14–16]. Palega and Knapinski [16] conclude that employees’ awareness of information security policies can reduce the risk of information security threats. • Information Security Culture: Information security culture (ISC) is another important factor recommended in the extant literature for ensuring compliance with organizational Information Security Policy. Studies that recommend ISC to improve information security and ISP compliance include [17–21]. Da Veiga [22] empirically investigated the influence of information security policy on information security culture by comparing the culture of employees who read the policy to those who do not read. The overall information security culture was found to have improved significantly over time in those organizations where employees read the information security policy. • Behavioral factors: Constructs of behavioral theories are often recommended in the extant information security literature for addressing employees’ noncompliance with Information Security Policy is the use of behavioral theories [23–27]. The most frequently used behavioral theories in information security are the theory of reasoned action or theory of planned behavior, general deterrence theory, protection motivation theory and technology acceptance model [23]. Constructs of these theories have been used in predicting information security policy compliance and behavioral intentions of employees.

3 Existing Frameworks for Promoting Information Security Policy Compliance in Organizations In this paper, frameworks for promoting Information Security Policy compliance in organizations have been grouped into (1) frameworks with no primary theory or practical validation, (2) frameworks with theory but no practical validation, and (3) frameworks with both theory and practical validation. Frameworks with no primary theory or practical validation: based on information security principles provided in industry guidelines, these frameworks offer suggestions aimed at promoting compliance with ISP. Concepts of information security principles and practices usually form the building blocks of proposed frameworks with no theoretical backing. The point of departure is often the offer of recommendations for empirical validation [27]. Frameworks with theory but no practical validation are theoretical frameworks grounded in existing theories to provide insights into how organizations can promote employees’ compliance with information security policy. Similar to conceptual frameworks in the existing literature, these frameworks lack empirical support. A typical example is a composite framework by Aurigemma and Panko [28]. These

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frameworks postulate that constructs of behavioral theories can be used to induce employees towards compliance with ISP. Empirically tested frameworks grounded in theory are frameworks that are both theory-based and empirically validated [29]. These frameworks combine conceptual principles and constructs of theories and validate propositions in organizational settings [2]. Siponen et al. [29] proposed a theoretical research model that combined constructs of Protection Motivation Theory, the Theory of Reasoned Action, and the Cognitive Evaluation Theory to explain employees’ adherence to security policies. The analysis of existing frameworks for ISP compliance indicates that existing frameworks are largely focused on shaping employees’ attitude and behavior intentions to comply with policies. These frameworks are defined in terms of structure, content, and outcomes. However, the existing frameworks are fragmented and elusive in nature, thus, lacking clear guidelines and procedures on how information security practitioners should carry out recommendations to achieve desired results. There is, therefore, a need for a framework that could integrate the strengths of previous frameworks and at the same time provide clear guidelines and procedures for security practitioners. In addition, a framework that could future researchers in the development of ISP compliance frameworks is also lacking.

4 Towards a Framework to Promote Information Security Policy Compliance in Organizations This paper proposes establishing an ISP Compliance Culture (ISPCC) in organizations in response to information security breaches caused by employees’ noncompliance with ISP. The protection of information assets needs a culture that is conducive to information security policy compliance at all levels of the organization. Information security policy compliance culture (ISPCC) therefore requires all employees to follow information security rules and procedures on a daily basis and demonstrate values, attitudes and behavior intentions that contribute to information protection. This culture is subsequently taught to new members as the way things are done in the organization. ISPCC creation is necessary to mitigate the debilitating effects of information security incidents in organizations. Based on the analysis of existing literature relevant elements that should be considered in the development of ISP compliance culture in organizations were identified. The elements defined in Table 1 should guide the development of frameworks for promoting ISP compliance as a culture in organizations. An ISP compliance framework should be defined by its structure, content and outcome elements. The structure elements define the logical relationship between building blocks (that is concepts, constructs, principles, and practices) of the framework. Content elements define the activities including standards, guidelines, and procedures for achieving the objective of the framework. Outcome elements describe the expectation from the framework or what would be achieved upon successful implementation of the framework. Table 1 presents the generic framework to guide the development of ISP compliance frameworks for organizations.

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Elements

Description

Logically-structured

Category S E CE OE ✓

Should indicate the logical relationship between constructs applied Allow complexities Should consider success or failure as the result of ✓ planning, design, implementation and evaluation processes that are integrated Incorporate constructs of Should accommodate constructs behavioral theories Behavioural theory that are either attitude-focused, behavior-focused or implied Incorporate relevant Should accommodate previously validated factors factors of previous for promoting ISP compliance studies Organizational relevance Should be able to address real and complex organizational problems Attitude and Behavior Should be focused on shaping employees’ attitudes intention focused and behavioral intentions to comply with ISP Defined standards Should define information security standards (e.g. ISO 27001) that can be used to regulate information security across the organization Provide guidelines and Should provide clear guidelines to support the procedures standards Should also provide a series of procedures to aid the ISP compliance process Goal focused Should guide and support a process of ISP compliance creation Culture creation Should create and nurture a culture of compliance with ISP in the medium to long term in the organization Note: SE – Structure Element, CE – Content Element, and OE – Outcome Element.

4.1

✓

✓

✓ ✓

✓

✓

✓

✓

✓ ✓

The Composite ISP Compliance Framework

The composite framework for ISP compliance was developed by applying the generic framework developed in this study. Based on elements presented in the generic framework, the composite ISP compliance framework, illustrated in Fig. 1 was developed. Figure 1 depicts the composite ISP compliance framework for establishing information security policy compliance as a culture in organizations. The framework has five components that are based on elements extrapolated from existing literature on information security policy compliance frameworks. It aims to achieve two significant effects, which are to (1) establish information security culture and (2) encourage information security policy compliance. The framework ultimately integrates the essential elements to establish an ISPCC in organizations. The framework is executed in five complex and multi-process steps as follows:

A Composite Framework to Promote ISP Compliance in Organizations

Behavioral constructs and/or ISP compliance factors

Employees’ attitude towards ISP compliance

Behavior Intentions towards ISP compliance

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Established ISP compliance culture

IMPLEMENTATION GUIDELINES AND PROCEDURES

Fig. 1. The composite ISP compliance framework

• Behavior constructs and ISP Compliance Factors: This component of the framework defines a list of constructs that are used to shape employees’ attitudes and behavioural intentions towards establishing an ISP compliance culture in organizations. • Attitude towards ISP compliance: this component describes the structural and logical effect of employees’ attitude on behavior intentions toward establishing an ISP compliance culture in organizations. The effect of this component on behavior intentions is largely influenced by the consolidated effect of multiple factors at play. • Behavior intentions towards ISP compliance: this component describes employees’ desire to prevent security breaches by complying with existing ISP on a daily basis. • ISP compliance culture: describes a situation where all employees’ accept and actually follow all security rules and regulations on a daily basis; becoming the way things are done over time and taught to new employees. • Implementation guidelines and procedures: Information security practitioners should follow the under-listed procedures to establish the proposed ISPCC in organizations: a. Initiate ISP compliance process through seminars or workshops to sensitize all stakeholders on the organization’s position on ISP compliance. b. Organize ISP awareness and compliance monitoring by using specialized software tools or appointing an officer in charge of ISP compliance monitoring. c. Check and assess ISP compliance frequency, extent and the time of occurrences. d. Assess the established ISP compliance culture by diagnosing the organization’s ISP compliance culture, planning interventions for improvement, organizing resources to implement the plan and assess the effects. Over time, a culture that is conducive to information security that encapsulates ISP compliance activities would emerge in the medium to the long term of implementing the composite ISP compliance framework.

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Assessment of the Composite ISP Compliance Framework

This study proposes a framework as an artifact that is in line with the objectives of the design science research strategy [30]. Accordingly, the design science research (DSR) strategy of [31] was applied to guide the design, development, and evaluation of the proposed framework. The proposed framework was evaluated using expert opinion. The opinions of five experts in two rounds of face-to-face interviews were used for verifying the framework’s validity and suitability. These experts were purposefully selected from academia, industry, and government, based on their contributions and experiences with information security policies in their respective fields of work. The experiences and opinions of experts strengthen the grounding of the proposed framework and increase the likelihood that the resulting framework will hold across multiple contexts and settings [32]. In the first round of interviews, the researcher read out the questions to each expert and their responses were recorded. The second round of interviews was conducted to present summaries of the captured responses to each of the experts for confirmation, making each expert aware of the responses from other experts in the study and asking if they want to alter their earlier responses. The Design Science artifact evaluation criteria proposed by Prat, Comyn-Wattiau, and Akoka [33] was applied as the baseline for the development of interview questions used in the expert review evaluation. These criteria were applied to generate five questions to guide the interview process. A draft of the proposed framework together with the interview guide was emailed to each of the experts two weeks before the scheduled face-to-face interview. This was done to allow the experts enough time to independently study and judge the framework. All experts answered questions on the goal, environment, structure, activity, and evolution of the proposed ISP compliance framework. Responses from the expert review assessment can be summarized as follows: In assessing the extent to which the framework supports the goal of promoting ISP compliance as a culture in small and medium-sized organizations, the opinions of experts selected from industry were closely related to the opinions of experts from academia. Both groups of experts believed that ISP compliance could be established as a culture since each component incorporates factors, which have already been validated statistically in various empirical studies. One of the experts from the academia group explained that security education, training, and awareness are essential parts of every information security effort. He cited studies such as [21, 34]. Another also cited [22] and explained that when employees are made aware of the existing ISP, it could have a positive effect on their behavior and subsequently the existing information security culture. In assessing the structure of the framework, the experts were requested to comment on the completeness of the framework components. The majority (three out of five) of experts responded that the components of the framework are enough for achieving its stated goal. However, two experts disagreed on this. They individually argued that culture is dynamic and goes beyond putting together statistically validated factors. They suggested that the framework should be extended to include some extrinsic factors such as national culture, legal factors and industry standards [35].

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The consistency of the framework with the environment to provide protection for people, processes, and technology was also assessed. The experts unilaterally argued that the establishment of a culture of compliance for ISP would ultimately provide some protection for employees who are responsible for ensuring the protection of information assets in their possession. Moreover, the experts agree that the framework is relevant to the protection of laid down procedures for ensuring information security in the organization. Finally, the majority (3/5) of experts agree that the framework is relevant to the protection of technologies for ensuring information security. Accordingly, the framework can be said to be relevant to the three main aspects of information security in organizations. In addition, the application of the framework for beneficial outcomes was assessed. The majority (3/5) of experts expressed that the content of the implementation guidelines and procedures component should be strengthened. They, however, agreed that the framework could produce beneficial outcomes. Finally, on the assessment of the framework’s ability to allow modifications, the experts described the framework as dynamic and flexible for any form of modification. They noted that constructs and factors for influencing attitudes and behavior intentions are not predetermined and could be altered at the pleasure of practitioners during implementation.

5 Conclusion and Future Work This paper reviewed prior information security literature and classified factors for promoting ISP compliance into five themes. In addition, existing ISP compliance frameworks were grouped into frameworks with no theory or practical validation, frameworks with theory but no practical validation and frameworks with both theory and practical validation. Further, a generic framework to guide the development of ISP compliance frameworks in organizations was developed based on the analysis of literature. The generic framework was subsequently applied to develop the composite ISP compliance framework for promoting information security policy compliance in organizations. The composite ISP compliance framework argues that organizations need to establish and nurture a culture of compliance for information security policies to address security breaches. Finally, the composite ISP compliance framework was assessed for its suitability to the goal of establishing an ISP compliance culture. The results of the evaluation indicate that the framework supports its stated goal, its structural components are comprehensive and adequate, provides support for people, processes and technology making it consistent with the environment, it provides beneficial results to organizations, and it is dynamic and flexible to allow any form of modification. In view of the assessment results, it is concluded that the composite ISP framework is suitable, structurally sound and fit for establishing ISP compliance as a culture. A limitation of the study is that the proposed framework was evaluated using a small group of experts and could require different levels of empirical validation. In addition, the framework is at the conceptual stage and requires empirical evidence to be conclusive. In view of the limitations, future researchers could examine the impact of

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the proposed framework in a quantitative study with a large sample. Future researchers could also assess the effects of information security culture and compliance critical factors, on the establishment of ISP compliance culture in organizations.

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17. Amankwa, E., Loock, M., Kritzinger, E.: Establishing information security policy compliance culture in organizations. Inf. Comput. Secur. 26(4), 420–436 (2018) 18. Tolah, A., Furnell, S.M., Papadaki, M.: A Comprehensive Framework for Cultivating and Assessing Information Security Culture, Haisa, pp. 52–64 (2017) 19. da Veiga, A., Martins, N.: Defining and identifying dominant information security cultures and subcultures. Comput. Secur. 70(2017), 72–94 (2017) 20. Alhogail, A.: Design and validation of information security culture framework. Comput. Hum. Behav. 49, 567–575 (2015) 21. Sherif, E., Furnell, S., Clarke, N.: An identification of variables influencing the establishment of information security culture. In: Tryfonas, T., Askoxylakis, I. (eds.) The HumanComputer Interaction (HCI) Conference – Human Aspects of Information Security, Security, Privacy and Trust (HAS), LNCS 9190, pp. 436–448. Springer, Heidelberg (2015) 22. Da Veiga, A.: Comparing the information security culture of employees who had read the information security policy and those who had not - illustrated through an empirical study. Inf. Comput. Secur. 24(2), 139–151 (2016) 23. Lebek, B., Uffen, J., Breitner, M.H., Neumann, M., Hohler, B.: Employees’ information security awareness and behavior: a literature review. In: Proceedings of Annual Hawaii International Conference System Science, pp. 2978–2987 (2013) 24. Sommestad, T., Karlzén, H., Hallberg, J.: The sufficiency of the theory of planned behavior for explaining information security policy compliance. Inf. Comput. Secur. 23(2), 200–217 (2015) 25. Hina, S., Dominic, D.D.: Information security policies : investigation of compliance in universities. In: 3rd International Conference on Computer and Information Sciences (ICCOINS) Information, pp. 1–6 (2016) 26. Safa, N.S., Maple, C., Watson, T., Furnell, S.: Information security collaboration formation in organizations. IET Inf. Secur. 12(3), 238–245 (2018) 27. Lembcke, T.-B., Masuch, K., Trang, S., Hengstler, S., Plics, P., Pamuk, M.: Fostering information security compliance : comparing the predictive power of social learning theory and deterrence theory. In: Twenty-Fifth Americas Conference on Information Systems, pp. 1–10, August 2019 28. Aurigemma, A., Panko, R.: A composite framework for behavioral compliance with information security policies. In: Proceedings of the 45th Hawaii International Conference on System Sciences (HICSS), pp. 3248–3257 (2012) 29. Siponen, M., Mahmood, M.A., Pahnila, S.: Employees’ adherence to information security policies: an exploratory field study. Inf. Manage. 51(2), 217–224 (2014) 30. Drechsler, A., Hevner, A.: A four-cycle model of is design science research : capturing the dynamic nature of IS artifact design. In: Parsons, J., Tuunanen, T., Venable, J.R., Helfert, M., Donnellan, B., Kenneally, J. (eds.) Breakthroughs and Emerging Insights from Ongoing Design Science Projects: Research-in-progress papers and poster presentations from the 11th International Co, pp. 1–8 (2016) 31. Peffers, K., Tuunanen, T., Niehaves, B.: Design science research genres: introduction to the special issue on exemplars and criteria for applicable design science research. Eur. J. Inf. Syst. 27(2), 129–139 (2018) 32. Cooper, D.R., Schindler, P.S.: Business Research Methods, 12th edn. McGraw-Hill/Irwin, New York (2014) 33. Prat, N., Comyn-Wattiau, I., Akoka, J.: Artefact evaluation in information systems designscience research—a holistic view. In: PACIS 2014 Proceedings (2014). http://aisel.aisnet. org/pacis2014/23. Accessed 15 Mar 2017

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Authentication Model Based on JWT and Local PKI for Communication Security in Multi-agent Systems Badr Eddine Sabir1(&), Mohamed Youssfi2, Omar Bouattane2, and Hakim Allali1 1

Laboratory LAVETE, FST Settat, Hassan I University of Settat, Settat, Morocco {b.sabir,hallali}@uhp.ac.ma 2 Laboratory SSDIA, ENSET Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco [email protected], [email protected]

Abstract. This paper aims to present a new model based on JSON Web Token (JWT) and Public Key Infrastructure (PKI) for communication security as part of a Multi-Agent System Middleware for massively distributed systems. The proposed model aims to provide secure communications between agents to ensure the integrity of the exchanged messages, the authentication of agents, and the no-repudiation, articulated on an approach based on a Registration Authority (RA) and a Certification Authority (CA) that are managed by a Public Key Infrastructure (PKI). This architecture is based on the Stateless JWT security technology based on the asymmetric cryptographic algorithm used for validation of subsequent client requests for making frequent remote calls to the target server resources. The proposed solution uses a digital signature claim using a KeyStore.p12 generated periodically by the local PKI, to ensure message integrity, transmitter authentication, and non-repudiation based on asymmetric cryptographic technology. The article presents an approach based on digital trust micro-agent for better security. Keywords: Authentication
Digital signature
Multi-Agent Systems Middleware
JSON Web Token
Public Key Infrastructure




1 Introduction In recent years, we have testified an impressive evolution of information technologies Data-intensive and compute-intensive applications require huge computing power and very large data storage resources. Traditionally, to speed up calculations, massively parallel machines are used, composed of a multitude of microprocessors interconnected with each other according to different topologies. This is the case of the GPU architectures used to handle an extensive amount of computation [1–5], which is widely used in recent years to accelerate graphics applications as well as other parallel applications with large mass of data on various scientific fields [6, 7]. © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 469–479, 2020. https://doi.org/10.1007/978-3-030-36778-7_52

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However, a single parallel machine, as powerful as it is, is not enough to cope with the explosion of masses of data that applications must analyze and process in a very short time. Distributed systems used by large-scale systems [8] are therefore an essential and a necessary solution for High-Performance Computing (HPC) [9]. In fact, with the evolution of computer networks performance and the development of new high-performance middleware distributed systems, it is possible to collaborate a multitude of heterogeneous computers to achieve high performance for computeintensive or data-intensive applications. The Multi-Agent Systems (MAS) present themselves as an attractive solution for modeling complex distributed systems. In the field of MAS, some of the MAS Middleware are widely used to implement models based on Multi-Agent approaches. However, in applications using a very large number of agents, these Middlewares have shown many limits in terms of communication security between the agents. Therefore, this fundamental question must be answered: • How to ensure the security of the communication between the agents? In this paper, we present a conceptual approach to the proposed architecture describing a flexing, lightweight, and secure system for better security in Multi-Agents System, answering the question above, based on a digital trust agent using the PKI that is recognized as a powerful technique to satisfy the security services including confidentiality, authentication, integrity, and non-repudiation, for the generation of PKCS #12 certificates that are requested for the electronic signature of JWT. After presenting the related works in the second section, the third section is devoted to the description of the JSON Web Token (JTW), the Public Key Infrastructure and the PKCS #12 certificates. In the fourth section, we will present the proposed model and its description. The last section gives some concluding remarks and perspectives for future related works.

2 Related Work 2.1

Multi-agent System Security

In [10], authors propose a lightweight security protocol for integrated agents and smart objects that facilitate cooperation and global intelligence, they have implemented and enhanced a secure mobile agent protocol, Broadcast based Secure Mobile Agent Protocol (BROSMAP), to enable interoperability and global intelligence with smart objects in the Internet of Things with consideration of overall performance, using Elliptic Curve Cryptography (ECC) which is one of the most secure protocols that provides confidentiality, authentication, authorization, accountability, integrity, and non-repudiation. The authors have conducted an experimental performance evaluation to compare and measure the performance of RSA-based BROSMAP, and ECC-based BROSMAP on a client side (Android Java) and server side (XAMPP). The proposed lightweight security protocol for Multi-Agent based IoT systems was verified using Scyther tool before the implementation process. They have measured the performance in terms of important metrics which include execution time and computational cost.

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They have also compared performance measures on varying key sizes. In terms of execution time, concluding that the ECC is almost twice as fast as RSA 2048 and 4 times faster than RSA 3072. The main reason behind the superior performance of ECCBROSMAP is the reliance on smaller key sizes and encrypting/decrypting using symmetric cryptography only. The authors recommend for systems with limited resources such as IoT devices and agent-based systems that require high security, the use of ECC-BROSMAP over RSABROSMAP. ECC-BROSMAP provides all the security requirements that RSA-BROSMAP provides but it is more efficient and lightweight because of the elimination of asymmetric encryption, the use of smaller key sizes and the combination of ECC keys with symmetric encryption only. In reference [11], authors propose to secure architecture of mobile cloud computing composed of a cluster of mobile devices suffering from some security problems such as user’s privacy when different node communicate with each other, by integrating a Multi-Agent System which provides the required intelligence level to overcome privacy, availability issues and to support the computing performance. This new architecture based on the concept of the MAS provides a high level of security during the data transfer on the mobile network and to optimize the computing. JADE platform has been used as Multi-Agent System platform, allowing to encrypt the data between nodes and to split and assign the processing to a different mobile device which allows to speed up the processing and therefore increase the performance and dwindle the energy cost of the mobile device while preserving privacy and integrity of user’s data. In [12], the authors offered a dynamic federated identity management model that allows rapidly decrease and increase the scope of the cloud. For this reason, they applied the MAS allowing to perform the collection of the necessary information about the user, which allows making dynamic decisions in the real-time system. For providing a user’s single access to cloud infrastructure the protocol SAML is used. The users’ dynamic federated identity management process is based on PKI technology and the calculation of trust value. The user’s access to the cloud infrastructure is provided based on their attribute certificates, also the privileges to each user are given by the role based on the access control mechanisms. Cloud environments are dynamic, because of that statically the determination of the users’ roles only by certificates has lost its actuality. In this case, the dynamic management of roles becomes necessary. The application of multiagent systems in architecture allows the dynamic determination of users’ roles. The content of these roles also contains behavioural characteristics of the users. Multi-Agent System provides the authentication, authorization and audit operations within the cloud environment. The trust degree of the MAS module is defined by the Public Key Infrastructure (PKI). In reference [13], the authors proposed a security policy responding to security properties (access control, authentication, confidentiality, integrity) based on a normative Multi-Agent System for securing complex applications. In the complex systems, the information flows are dense and confused consequently, risks and menaces rates increase more and more. This model considering the high heterogeneity of the agent execution environments in terms of resources and computing devices and the high dynamicity that causes frequent changes in the agent execution context. Also, they have focused on the notion of context which becomes more and more convincing the Multi-Agent’s System community by its importance to respond to novel challenges. In

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this perspective, they have used the notion of norms to specify the secured behaviour of agents in such environments (norms manage a variety of agents and regulate a uniform behaviour within a social group), adopting the ontology for the specification of contexts. In fact, it contributes in term of expressivity and subjectivity and averts ambiguity. The Model is highlighted by an architecture showing the distribution of the Multi-Agent System in the environment. 2.2

Deduction

After this first part, we concluded that the solutions proposed to secure communication between agents, namely integrity, authentication, and non-repudiation, adds a layer of complexity in the development of these solutions in addition to presenting a risk hinder the performance of this middleware.

3 Background 3.1

JWT

JWT is a compact, URL-safe means of representing claims to be transferred between two parties. The claims in a JWT are encoded as a JSON object that is used as the payload of a JSON Web Signature (JWS) structure or as the plaintext of a JSON Web (JWE) structure, enabling the claims to be digitally signed or integrity protected with a Message Authentication Code (MAC) and/or encrypted [14]. JWTs are based on Web JWS and JWE [15]. it consists of three structures separated by a full stop (.), namely: • Header. There are two parts in the header, one of them is token type, namely JWT, and another one is the hashing algorithm used, such as HMAC SHA256 or RSA. • Payload. It is the second part of the token which consists of a claim. The Claim is the statement about an entity (usually users) and supplementary metadata. • Signature. To create a signature, JWT must follow the JWS specification. JWS is the content secured by digital sign or MACs which uses JSON as the basic data structure [16]. JWT access tokens can be used for validation of subsequent client request without making frequent calls to the resource server or database. Access tokens can have limited validity periods via embedded expiration time. Access-related claims can also be embedded as part of its payload [17]. 3.2

Public Key Infrastructure

Public Key Infrastructure is a basic security infrastructure based on public encryption technology to provide users with digital signatures and authentication services to ensure that information is true and complete [18, 19]. The main functionalities for any PKI are: First, to be able to register users and issue their public key certificates as well as to build trust relations between the users in public

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key infrastructure. The idea is that either you have met the person before, or this person has been recommended by another person or entity that you trust. Second, a policy to implement certificates revocation shall be put in place and a facility to publish this information to all concerned parties. PKI is a framework that enables the integration of various services that are related to cryptography. PKI aims to provide confidentiality, integrity, access control, authentication, and non-repudiation. One important fact about PKI is that it offers the nonrepudiation (non-rejection) which prevents parties from denying involvement in the online transaction and supports the digital signature and message encryption that further enhance the security elements within any network [20]. Encryption and decryption, digital signature, and key exchange are the three primary functions of a PKI [21]. 3.3

PKCS#12

PKCS #12 describes a transfer syntax for personal identity information, including private keys, certificates, miscellaneous secrets, and extensions. Machines, applications, browsers, Internet kiosks, and so on, that support this standard will allow a user to import, export, and exercise a single set of personal identity information. This standard supports the direct transfer of personal information under several privacy and integrity modes. The most secure of the privacy and integrity modes require the source and destination platforms to have trusted public/private key pairs usable for digital signatures and encryption, respectively. The standard also supports lower security, password-based privacy and integrity modes for those cases where trusted public/private key pairs are not available [22]. PKCS#12 is the encryption standard of personal exchange information, its output form is pfx file that is convenient to store or transfer user information [23]. 3.4

X.509 Certificate

Public key certificates play an important role in binding the identity of an entity, either a person or an electronic resource to its public key. The most popular format for a public key certificate is the X.509 Certificate. A X.509 Certificate encapsulates values, such as the serial number of the certificate, certificate issuer’s name, the certificate holder’s name (subject), the public key associated with the certificate holder, the signature of the certificate authority (CA) and the validity period of the certificate [24].

4 Proposed Model 4.1

Components of the Proposed Model

The proposed model, using JWT, allows secure communication between agents by ensuring the integrity of the exchanged messages, sender authentication, and nonrepudiation, based on digital signature using a KeyStore.p12 delivered by a local PKI (Fig. 1).

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Fig. 1. Proposed model architecture.

• Agents 1…n: Agents belonging to the domain to which the Authentication Agent belongs (Machine, logical domain, desktop grid,…). • Authentication Agent: Responsible for the generation of JWT for the different agents wants to communicate with other agents. The JWTs generated by this Agent are used to ensure the integrity, authentication and non-repudiation of the exchanges between Agents. Several Authentication Agents can ensure the generation of JWT using the same private key to ensure fault tolerance and avoid the Single Point of Failure. • Agents Whitelist: Responsible for adding the newly created agents and belonging to the same domain of the Authentication Agent in the whitelist and checking the membership of the agents in this domain. Before generating a JWT for an Agent, the Authentication Agent must first ensure that the Agent requesting the JWT belongs to the Authentication Agent domain. The whitelist agent performs this function thanks to its whitelist. • JWT Revocation List (JRL): is a list of JWTs that has been revoked by an Authentication Agent before their scheduled expiration date and should no longer be trusted. JRLs are a type of blacklist and are used by all Authentication Agents to verify whether a JWT is valid and trustworthy. When an Authentication Agent must trust an Agent X, he checks that the JWT is not listed in a JRL. These checks are crucial steps in any transaction agents because they allow verifying that the JWT still trustworthy. • KeyStore.p12 (PKCS#12 File) – Private Key: Used by the Authentication Agents for signing generated JWTs. – Public Key: Used for the verification of all JWTs generated by the Authentication Agent. • Registration Authority: The main mission of the RA is to verify the identity of the agents by applying a Registration Policy. • Certification Authority: The CA is responsible for providing the X.509 Certificates management services contained in the KeyStore.P12 issued, during their life cycle (generation, issue, revocation, dissemination, etc.), by applying a Certification Policy, and different service in the PKI.

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Generation of a New JWT

The following diagram shows the steps for generating a new KeyStore.p12, generating a new JWT, checking the validity of JWT, and verifying authentication and nonrepudiation of agents (Fig. 2).

Fig. 2. JWT generation scenario.

Here are the steps of generating a new JWT in chronological order: • Agent X requests the Authentication Agent to generate a JWT for it to communicate with Agent Y; • The Authentication Agent verifies if Agent X belongs to the Whitelist by checking if his Agent ID (AID) is registered in this list; • The Whitelist Agent returns the response to the Authentication Agent; • If Agent X belongs to the Whitelist, then:

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– The Authentication Agent requests the RA to identify Agent X by passing the ID of the Registration Policy to be applied – If Agent X is identified by the RA, then: • Agent X verifies the validity of KeyStore.p12 • the KeyStore is valid, then: – The Authentication Agent generates a JWT for Agent X using the KeyStore.p12, based on the following equation: f ðbase64Encoded Þ ðheader:payload:signatureÞ

ð1Þ

Fig. 3. New JWT generation scenario.

• Else, If the KeyStore.12 is expired or revoked then: – The Authentication Agent sends a Certificate Signing Request to the CA by passing the ID of the Certification Policy to be applied – Signature of the CSR by the CA – Generation of KeyStore in PKCS # 12 format – The CA sends the KeyStore.p12 to the Authentication Agent – The Authentication Agent generates a JWT for Agent X using the received KeyStore.p12, based on the equation mentioned above (1) Else, the Authentication Agent returns a message to Agent X indicating that his request is rejected; • The Authentication Agent sends the generated JWT to Agent X. The generated JWT contains the URL to retrieve the X.509 Certificate that must be used for the verification of all JWTs generated by the Authentication Agent; • Agent X retrieves the JWT, then the X.509 Certificate via the URL indicated in this JWT to stores it in its own KeyStore;

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• Agent X tries to prove his identity to Agent Y by inserting the recovered JWT into the send message; • Agent Y checks the validity of the JWT contained in the Agent X request. The verification operation is performed by carrying the following operations: – Retrieving the X.509 Certificate of the Authentication Agent certificate that is in his KeyStore; – Comparison of the recovered X.509 Certificate with that deposited by the Authentication Agent: • If it finds that the X.509 Certificate has not changed, it uses the one stored on his KeyStore. • If not, it gets the new X.509 Certificate, puts it in his KeyStore, then carries out the verification. • Agent Y after checking the validity of the JWT, checks if the JWT is not registered in the JRL: – If the JWT is on this list, it rejects the request of Agent X. – Else it considers this request. • At this level, the Agent Y is sure that the message has not been tampered with and the JWT is valid. To ensure authentication and non-repudiation, Agent Y can retrieve the Agent AID contained in the JWT and then compare it with AID of Agent X, and if both AIDs are the same that means that the JWT belongs to Agent X.

5 Conclusion An architecture based on the JWT technology has been proposed in this document to ensure a secure channel for the exchanged messages between agents based on a digital trust agent, using a KeyStore.p12 delivered periodically by a Certification Authority managed by a local PKI. The model takes advantage of the electronic signature of JWTs to ensure the integrity of the messages exchanged between agents, the authentication of agents, and non-repudiation, based on the asymmetric cryptographic technology. The local PKI was well developed and implemented. The simulated tests of the proposed model will be a cause for future research to measure stability and performance in case of scalability and possible safety failures.

References 1. Lee, H., Al Faruque, M.A.: GPU architecture aware instruction scheduling for improving soft-error reliability. IEEE Trans. Multi-Scale Comput. Syst. 3(2), 86–99 (2017) 2. Lee, H., Faruque, A., Abdullah, M.: GPU-EvR: runtime event based real-time scheduling framework on GPGPU platform. In: Proceedings of the Design, Automation & Test Europe Conference Exhibition, pp. 1–6 (2014)

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A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease Zahia Aouabed, Moloud Abdar(&), Nadia Tahiri, Jaël Champagne Gareau, and Vladimir Makarenkov Department of Computer Science, University of Quebec in Montreal, 201, av. Président Kennedy, Montreal, QC H2X 3Y7, Canada [email protected], [email protected]

Abstract. One of the major types of cardiovascular diseases is Coronary Artery Disease (CAD). This study tackles the problem of CAD detection using a new accurate hybrid machine learning model. The proposed ensemble model combines several classical machine learning techniques. Our base algorithm is used with four different kernel functions (linear, polynomial, radial basis and sigmoid). The new model was applied to analyze the well-known Cleveland CAD dataset from the UCI repository. To improve the performance of the model, we first selected the most important features of this dataset using a genetic search algorithm. Second, we applied a multi-level filtering technique to balance the data using the ClassBalancer and Resample methods. Our model provided the average CAD prediction accuracy of 98.34% for the Cleveland data (the average was taken over the four kernel functions). Keywords: Machine learning
Data mining Ensemble learning
Nested Ensemble


Coronary Artery Disease


1 Introduction Recent advances in computer science, data mining and artificial intelligence (AI) led to the emergence of new intelligent automatic systems based on the use of machine learning approaches. These systems act as a bridge between different fields such as economics, management, physics, mathematics, chemistry, etc. [1–5]. They can be applied to solve a variety of challenges present in those fields, providing more accurate solutions than standard classification methods. Machine learning methods have been also widely used in the area of healthcare science. This field of science is characterized by large data volumes [6]. Furthermore, medical and bioinformatics data are generally very heterogeneous. This motivates the development of specialized machine learning-based methods dedicated to their effective processing [7]. Machine learning algorithms, including decision trees (DTs) [8], support vector machines (SVMs) [9] and artificial neural networks (ANNs) [10], applied in the framework of the deep learning approach [11], can be efficiently used to address different complex problems related to healthcare science and bioinformatics Z. Aouabed and M. Abdar—These authors have equal contribution to the study. © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 480–489, 2020. https://doi.org/10.1007/978-3-030-36778-7_53

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[12, 13]. Without these intelligent techniques, an accurate analysis of these problems is extremely difficult, and sometimes even impossible. Coronary Artery Disease (CAD) is one of the most dangerous heart diseases nowadays. In the United States of America, CAD is the major cause of heart attacks among both males and females [14]. CAD is also a major cause of death in the United Kingdom and Australia [15]. Its early and accurate identification remains a relevant and challenging problem for many researchers around the globe. In this paper, we propose to investigate this problem using a new efficient machine learning model. The main goal of this work is to describe a new CDSS (Clinical Decision Support System) intended for an accurate prediction of CAD. The proposed model is based on a nu-SVC algorithm (used with linear, polynomial, RBF, and sigmoid kernel functions), called the Nested Ensemble nu-SVC model (NE-nu-SVC). In order to achieve better results, four ensemble learning techniques were combined at three different levels in the framework of NE-nu-SVC. In order to confirm the effectiveness of the proposed methodology we considered the well-known Cleveland CAD dataset [14] in our analysis. To this end, we first applied both the nu-SVC and NE-nu-SVC models with four kernel functions to the dataset containing all the original features. Second, we carried out the feature selection using a genetic search algorithm. This algorithm helped us to get rid of redundant and noisy features. For a further improvement, the entities in the dataset were reweighted using the supervised ClassBalancer (CB) approach [16] and resampled using the Resample approach (supervised and unsupervised) [17]. Indeed, the applied multi-step balancing of data helped us improve the model’s accuracy for both the minority and majority classes. At the last stage, our new ensemble model (NE-nu-SVC) was applied on the selected features of the balanced Cleveland CAD data. As a result, we could greatly improve the performance of the traditional nu-SVC model with all four kernel functions. Overall, we obtained the average prediction accuracy of 98.34% for the Cleveland CAD data, which is the best result presented in the literature so far.

2 Literature Review Several recent works tackle the issue of effective diagnosis of CAD. In this section, we briefly describe those related to our study. Alizadehsani et al. [18] have applied a number of machine learning algorithms to investigate CAD and some of its cases. Specifically, these authors have obtained the accuracy of 83.50%, 86.14% and 83.17% during the identification of the right coronary artery, left anterior descending and left circumflex, respectively. Alkeshuosh et al. [19] generated different rules for the CAD detection using machine learning algorithms. For this purpose, the authors used the wellknown particle swarm optimization (PSO) evolutionary algorithm. Furthermore, the performance of PSO was compared to that of the C4.5 algorithm. The authors indicated that the average accuracy of their PSO algorithm, which outperformed the C4.5 algorithm, was around 87%. Abdar [20] applied four well-known decision tree algorithms, including C5.0, CART, CHAID and QUEST, to analyze the Cleveland CAD data. According to the results of that study, the C5.0 algorithm provided the greatest accuracy of 85.33% among the competing methods. According to [20], Cp, Old Peak, Slope, Thalach, Rest Ecg and Trestbps, were the most important features for predicting heart

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disease. Babič et al. [21] tackled the problem of CAD detection applying different machine learning methods to three well-known CAD datasets. According to this study, SVM is the machine learning method that can be recommended for CAD identification. Polat et al. [22] proposed to use the k-nearest neighbors algorithm at a pre-processing step of CAD detection. An Artificial Immune Recognition System method using fuzzy resource allocation was then applied to identify CAD cases. The average CAD detection accuracy achieved in this work was 87%. Acharya et al. [23] addressed the CAD prediction problem by means of electrocardiogram (ECG) signals. These signals were used as entry data for several machine learning algorithms and a new CAD diagnostic system, including Myocardial Infarction (MI) detection, was introduced. This system provided a good performance with the accuracy of 98.5%. Patidar et al. [24] described carried out the tunable-Q wavelet transform (TQWT) method to predict CAD cases. Then, the principal component analysis and least-squares support vector machine were used by Patidar et al. to get the CAD recognition accuracy of 99.72%.

3 Data Description To test our methodology, we considered the well-known Cleveland [14] heart disease dataset available in the University of California, Irvine (UCI) machine learning repository. This dataset contains 303 records (i.e., entities) and 14 features; 13 of them were chosen as the input of our model and the remaining one was selected as our target attribute. The Cleveland CAD data have been categorized into 5 major classes. The first of them represents the healthy patients (164 records), whereas the four other classes correspond to different types of CAD patients (139 records in total). Here, we combined the data of the four latter classes into a new class of CAD patients (Class 2 in our work; Class 1 represents healthy patients). More information about the Cleveland CAD dataset can be found in [14]. In this study, the 6 data records containing missing values were removed. Thus, we conducted our analysis using the complete 297 records.

4 Nested Ensemble (NE) Model The NE approach [25] is used to combine several ensemble learning techniques and machine learning algorithms. Using this approach one can carry out an ensemble learning technique inside of another ensemble learning technique. A good general description the NE approach is presented in [25]. In general, an NE model can include different numbers of ensemble learning techniques at different levels of the model. In this study, we applied a three-level NE model using four ensemble learning techniques. At the first level, we used the stacking technique as our first ensemble learning technique. The stacking technique has two main parts: “classifier” and “metaClassifier”. In the “classifier” part, we used the three following methods: nu-SVC, SGD (Stochastic Gradient Descent) and Random Forest (RF). In this part of the NE model, the RF classifier was embedded into our stacking ensemble learning technique. It should be noted that the loss function we used for training in SGD was the Hinge Loss (SVM) function. As to “metaClassifier”, we used the “Bagging” ensemble learning technique.

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We added one more ensemble learning technique to our model at the second level. Finally, we applied a voting ensemble technique (also called the Voting technique) as a classifier at the previous level (Bagging technique). This voting technique included the SMO and Naïve Bayes classifiers. We also used the K-fold cross validation technique, where the value of K was equal to 10. In the first step, we used four different kernel functions within our nu-SVC model. Then, we selected the most important features using a genetic search algorithm. To balance the data, we used five-level filtering approach based on a multi-step filtering technique. As mentioned earlier, the flexibility of the NE model allows one to use several ensemble learning techniques. We found that a three-level NE model can provide accurate results for an early detection of CAD. The definition of a Nested Ensemble (NE) model is presented below. Let D ¼ fD1 ; D2 ; D3 ; . . .; Dn g be a set of different datasets, A ¼ fA1 ; A2 ; A3 ; . . .; Am g a set of various machine learning algorithms, E ¼ fE1 ; E2 ; E3 ; . . .; Ek g a set of ensemble learning techniques and, finally, L the number of levels in the NE model. Assume that we have a set of ensemble learning methods and two sets of machine learning algorithms (see Eqs. 1–6 below): E ¼ fE1 ; E2 ; E3 ; . . .; Ek g;  A1 ¼ A1;1 ; A1;2 ; A1;3 ; . . .; A1;m ;

ð2Þ

 A2 ¼ A2;1 ; A2;2 ; A2;3 ; . . .; A2;m :

ð3Þ

ð1Þ

For example, one can apply the ensemble learning methods and the machine learning algorithms as follows:  E1 ¼ A1;1 ; A1;2 ; A1;3 ; . . .; E2 :

ð4Þ

This means that E1 is the main ensemble learning technique including different traditional machine learning algorithms and at least one ensemble learning technique. However, an EN model can combine more than one ensemble learning technique at one level of the model:  E2 ¼ A2;1 ; A2;2 ; A2;3 ; . . .; E3 ;

ð5Þ

...  Ek1 ¼ A1;1 ; A2;2 ; A1;2 ; . . .; Ek :

ð6Þ

5 Results To show the effectiveness of the proposed methodology, the NE-nu-SVC model was applied to the Cleveland CAD dataset [14, 26]. The NE-nu-SVC model was executed on a PC computer equipped with 3.40 GHz Intel Core i7 CPU. It is worth noting that

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since the Cleveland CAD dataset contains categorical variables, a specific data encoding was used to deal with such data. 5.1

First Experiment

Here, we discuss the results obtained with/without using the NE model when all original features of the Cleveland CAD dataset were considered. The nu-SVC and NEnu-SVC models were applied with four different kernel functions: linear, polynomial, RBF and sigmoid functions. The obtained results are presented in Table 1 and Fig. 1. In summary, using the NE model, we were able to improve the performance of the traditional nu-SVC model with all kernel functions under study, and in particular, with the RBF and sigmoid functions. 5.2

Second Experiment

To further improve the model’s performance, we carried out a genetic algorithm to select the most important features of the Cleveland CAD dataset. The genetic algorithm used is based on a correlation-based variable selection approach to eliminate redundant features [27]. As in [20], the values of population size, number of generations and report frequency during our feature selection were set to 20. The probability of the crossover and the probability of the mutation were set to 0.6 and 0.033, respectively. Using this procedure, we selected 7 most important features to be used in further investigation. The selected features were: CP, Restecg, Thalach, Ex-ang, Oldpeak, Ca and Thal.

Table 1. Experimental results provided by different kernels of the nu-SVC and NE-nu-SVC models for the original Cleveland CAD dataset data without balancing and feature selection. Model nu-SVC

Measure Linear Polynomial RBF Sigmoid Precision 0.830 0.837 0.626 0.502 Recall 0.828 0.835 0.559 0.492 F-measure 0.827 0.834 0.441 0.487 ROC area 0.823 0.831 0.524 0.499 Accuracy 82.82 83.50 55.89 49.15 NE-nu-SVC Precision 0.839 0.849 0.849 0.846 Recall 0.838 0.848 0.848 0.845 F-measure 0.838 0.848 0.848 0.845 ROC area 0.907 0.906 0.906 0.907 Accuracy 83.83 84.84 84.84 84.51

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Fig. 1. Comparison of the Kappa statistic, MAE and RMSE obtained using different kernels the nu-SVC and NE-nu-SVC models when all original features of the Cleveland CAD dataset were considered.

We then applied the five-level balancing to maximize the performance of our NEnu-SVC model. The balancing method was applied five times, including a 1-time CB technique (supervised), 3-time resample technique (supervised) and 1-time resample technique (unsupervised). It should be noted that the overfitting may happen when a 10-fold cross validation is used. To deal with this issue, we used the “Randomize filter” procedure from the WEKA package. Balancing plays a significant role in the prediction process when applying machine learning methods. More details about both the original weights and reweighted Cleveland data are presented in Table 2. The prediction results obtained by the NE-nu-SVC model after using feature selection and multi-step balancing are presented in Table 3 and Fig. 2. As shown in Table 3 the featured NE-nuSVC model applied with feature selection and five-level balancing outperforms the existing approaches on the Cleveland CAD dataset, providing the average accuracy of 98.34% (average over all kernels used in this study). The obtained results indicate that the proposed NE-nu-SVC model can effectively improve the performance of all individual kernel functions. NE-nu-SVC was able to improve the performance of the RBF and sigmoid kernel functions compared to linear and polynomial kernel functions. The NE-nu-SVC model with the linear and sigmoid kernel functions provided the highest accuracy of 98.60%, followed by the polynomial kernel function with the accuracy of 98.24%. Moreover, the RBF kernel function yielded the accuracy of 97.93%, which is the lowest accuracy among the considered kernel functions. Finally, we compared the results yielded by our NE-nu-SVC model to those reported in some existing studies (see Table 4). According to the results reported in Table 4, the proposed NE-nu-SVC model outperformed the existing machine learning approaches used to analyze the Cleveland CAD dataset, followed by Terrada et al. [33] and Burse et al. [32] with the accuracy of 96.01% and 94.53%, respectively. Table 2. Original weights and reweighted records using fivelevel balancing approach of the Cleveland CAD dataset. Label CAD class Normal class

Original weights 137 160

Reweighted records using five-level balancing approach 164.75 134.57

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Table 3. Experimental results provided by different kernels of the NE-nu-SVC model with both feature selection and data balancing for the Cleveland CAD dataset. Approach Feature selection

Measure Linear Polynomial RBF Sigmoid Precision 0.838 0.838 0.845 0.845 Recall 0.838 0.838 0.845 0.845 F-measure 0.838 0.838 0.845 0.845 ROC area 0.886 0.892 0.886 0.890 Accuracy 83.83 83.83 84.51 84.51 Feature selection + data balancing Precision 0.986 0.983 0.979 0.986 Recall 0.986 0.982 0.979 0.986 F-measure 0.986 0.982 0.979 0.986 ROC area 0.990 0.990 0.993 0.991 Accuracy 98.60 98.24 97.93 98.60

Fig. 2. Comparison of the Kappa statistic, MAE and RMSE obtained using different kernels of the NE-nu-SVC model with both feature selection and data balancing for the Cleveland CAD dataset.

Table 4. Comparison of our accuracy result with those found in the existing studies for the Cleveland CAD dataset. Study

Year

Method

Polat et al. [26] Kahramanli and Alahverdi [28] Das et al. [14] Kusy and Zajdel et al. [29] Abdar [20] Alizadehsani et al. [22]

2006 2008

Fuzzy-AIRS-KNN based system ANN and FNN

2009 2014

Neural networks ensembles Probabilistic neural network with SVM (PNNVC) C5.0 Neural network and genetic algorithm

2015 2017

Accuracy (%) 87.00 86.80 89.01 90.40 85.33 89.40 (continued)

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Table 4. (continued) Study

Year

Method

Paul et al. [30] Amin et al. [31]

2017 2018

Burse et al. [32]

2019

Terrada et al. [33] Ali et al. [34] Proposed method

2019 2019 2019

Weighted fuzzy system ensemble Vote with Naïve Bayes and logistic regression Multi-Layer Pi-Sigma neuron model (MLPSNM) and PCA ANN, KNN, K-means, and K-medoids L1 linear SVM, L2 linear, and RBF SVM NE-nu-SVC+GA+Multi-level balancing approach

Accuracy (%) 92.31 87.41 94.53 96.01 92.22 98.34

6 Conclusion Currently, Coronary Artery Disease (CAD) remains one of the main causes of death around the world, attracting valuable attention from many researchers from different countries. In this paper, we introduce a novel hybrid ensemble learning technique for diagnosis of CAD. Our new technique was tested on the well-known Cleveland CAD dataset available in the UCI repository. The proposed model is a part of the Nested Ensemble (NE) approach which allows one to apply together different traditional machine learning algorithms and ensemble learning techniques. In this study, the nuSVC algorithm was selected as the base algorithm of our NE model. The NE approach allowed us to combine several ensemble learning techniques at different levels. Here, we applied four ensemble learning techniques at three levels. At the first level, the nuSCV, SGD and random forest methods were combined using the stacking and bagging techniques (used as a metaclassifier). At the second level, the voting method, and at the third level the SMO and Naïve Bayes methods, were used. Furthermore, both data preprocessing and features selection procedures were carried out to enhance the performance of the proposed NE-nu-SVC model. We applied a genetic search algorithm for feature selection. Since the original Cleveland dataset was not well-balanced and the nu-SVC and NE-nu-SVC models (with different kernel functions) did not provide good results, we balanced the original data using a multi-step balancing approach. Thus, both ClassBalancer and Resample methods were used. Our final NE-nu-SVC model provided the accuracy of 98.34% (the average taken over the four kernel functions) for the Cleveland CAD dataset. This accuracy is among the best results that have been obtained in the literature for the Cleveland data. In the future, it would be interesting to compare the proposed NE-nu-SVC model with some novel and efficient machine learning techniques such as ensemble and evolutionary data mining techniques [35, 36].

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19. Alkeshuosh, A.H., Moghadam, M.Z., Al Mansoori, I., Abdar, M.: Using PSO algorithm for producing best rules in diagnosis of heart disease. In: 2017 International Conference on Computer and Applications (ICCA). pp. 306–311. IEEE (2017) 20. Abdar, M.: Using decision trees in data mining for predicting factors influencing of heart disease. Carpathian J. Electron. Comput. Eng. 8(2), 31–36 (2015) 21. Babič, F., Olejár, J., Vantová, Z., Paralič, J.: Predictive and descriptive analysis for heart disease diagnosis. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 155–163. IEEE (2017) 22. Polat, K., Şahan, S., Güneş, S.: Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing. Expert Syst. Appl. 32(2), 625–631 (2007) 23. Acharya, U.R., Fujita, H., Adam, M., Lih, O.S., Sudarshan, V.K., Hong, T.J., Koh, J.E., Hagiwara, Y., Chua, C.K., Poo, C.K., San, T.R.: Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: a comparative study. Inf. Sci. 377, 17–29 (2017) 24. Patidar, S., Pachori, R.B., Acharya, U.R.: Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowl.-Based Syst. 82, 1– 10 (2015) 25. Abdar, M., Zomorodi-Moghadam, M., Zhou, X., Gururajan, R., Tao, X., Barua, P.D., Gururajan, R.: A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recogn. Lett., 1–11 (2018) 26. Polat, K., Güneş, S., Tosun, S.: Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing. Pattern Recogn. 39(11), 2186–2193 (2006) 27. Hall, M.A.: Correlation-based feature selection for machine learning, Hamilton, New Zealand (1999) 28. Kahramanli, H., Allahverdi, N.: Design of a hybrid system for the diabetes and heart diseases. Expert Syst. Appl. 35(1–2), 82–89 (2008) 29. Kusy, M., Zajdel, R.: Probabilistic neural network training procedure based on Q (0)learning algorithm in medical data classification. Appl. Intell. 41(3), 837–854 (2014) 30. Paul, A.K., Shill, P.C., Rabin, M.R.I., Murase, K.: Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Appl. Intell. 48(7), 1739–1756 (2018) 31. Amin, M.S., Chiam, Y.K., Varathan, K.D.: Identification of significant features and data mining techniques in predicting heart disease. Telemat. Inform. 36, 82–93 (2018) 32. Burse, K., Kirar, V.P.S., Burse, A., Burse, R.: Various preprocessing methods for neural network based heart disease prediction. In: Smart Innovations in Communication and Computational Sciences, pp. 55–65. Springer, Singapore (2019) 33. Terrada, O., Cherradi, B., Raihani, A., Bouattane, O.: Classification and prediction of atherosclerosis diseases using machine learning algorithms. In: 2019 5th International Conference on Optimization and Applications (ICOA), pp. 1–5. IEEE (2019) 34. Ali, L., Niamat, A., Khan, J.A., Golilarz, N.A., Xingzhong, X., Noor, A., Nour, R., Bukhari, S.A.C.: An optimized stacked support vector machines based expert system for the effective prediction of heart failure. IEEE Access 7, 54007–54014 (2019) 35. Abdar, M., Makarenkov, V.: CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer. Measurement 146, 557–570 (2019) 36. Abdar, M., Wijayaningrum, V.N., Hussain, S., Alizadehsani, R., Plawiak, P., Acharya, U.R., Makarenkov, V.: IAPSO-AIRS: a novel improved machine learning-based system for wart disease treatment. J. Med. Syst. 43(7), 220 (2019)

Optimized Management of the Health Emergency Services Regional Network of Rabat Region Ibtissam Khalfaoui1(&) and Amar Hammouche2 1 Research Team IMOSYS, Department of Industry, Mohammadia School of Engineers, Mohamed V University, Rabat, Morocco [email protected] 2 PES, Research Team IMOSYS, Department of Industry, Mohammadia School of Engineers, Mohamed V University, Rabat, Morocco [email protected]

Abstract. Due to the many dysfunctions that are currently affecting the healthcare sector in Morocco, it is crucial to manage the Health Emergency Services Regional Network (HESs-RN) as optimally as possible in order to ensure the safety and the quality of patients care at the right time. Our objective is to improve the performance of the network at the regional level. To do that, we based our approach on modelling and simulation, using an Integrated Geographic Information System (GIS). In this paper, we propose both the structuring of HESs in the form of the HESs-RN modelled as a graph, and the modelling of the HESsRN by considering a medical emergency event as the Occurrence of events of Health Emergency (OHE). Moreover, a Decision Support Model (DSM) is proposed to manage and control at best the different HESs-RN’s OHE, and, in particular to determine, in real time, the fastest path for the patient’s transfer. Keywords: Health regional network management support model


GIS mapping
Decision

1 Introduction As in [1], the hospital is considered as an enterprise, and its conception as a production function. It describes hospitals as ‘job shops’ that must deal with any and all types of patients who arrive at their doors. The production of health is a complex process that is facing increasing challenges in Morocco. The dysfunctions of such production concern particularly its flow of information, its knowledge through time and space, and its lack of traceability etc., which prevent our country from having a good performant health system. In our work, we are interested in the pre-hospital emergency sector, and we consider care units as part of a Health Emergency Services Network (HES-RN). We conduct our work within the framework of the region of Rabat (Rabat-Sale-ZemmourZaer RSZZ), which is one of the twelve administrative regions of Morocco. Considering an Occurrence of an event of Health Emergency (OHE) in the global context of HES-RN of RSZZ and the local neighborhood of this OHE as mentioned © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 490–499, 2020. https://doi.org/10.1007/978-3-030-36778-7_54

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in [2], we construct a GIS based decision support system to manage it as optimally as possible.

2 State of the Art Many researches [3–5] consider the health information systems (HISs) as computing systems that capture, store, manage, or transmit a vast amount of information as it pertains to the health of individuals, clinical care, or the activities of health-related organizations. The HISs can be divided into 4 categories: (1) foundational systems, (2) financial systems, (3) departmental systems, and (4) electronic medical records (EMRs). As mentioned in [6], the principal aims of the HISs are as follow: enhancing personnel efficiency and patient care quality, eliminating iterates and unnecessary procedures, utilizing computers in a variety of operations, producing information more efficiently through statistical and data mining techniques, creating hospitals with modern work methods, improving systems and standards, maintaining data communication among hospitals and medical centers, and improving overall public health. Health information systems play a remarkable role in reducing medical errors, supporting health personnel, increasing patient care productivity, and enhancing patient care quality. As for [7], the IS is a very important instrument, which contributes to develop informational strategies for health. The software application is a most important support key, which benefits health facilities due to the increase services’ efficiency, quality, safety and efficiency, and access to users. To ensure the safety and quality of delivery of healthcare, it is essential that communication between the network systems, are timely and effective, to avoid situations of failures or short circuits, as these problems can cause errors or delays in the user treatments

3 Methods It is essential to model the HES-RN in order to have a complete understanding of the network’s functioning and to help us visualize where the dysfunctions are and what needs to be done to alleviate them. To this aim, we modelled the structure and the dynamics of the HES-RN-RSZZ. The dynamic modeling describes the functioning of the studied network, it represents the interaction, workflow and different states of the static constituents of the network [8]. While the static modeling is used to represent the static constituents of the network [8]. This later is represented by a graph that is implemented using a GIS platform. As to the network’s dynamic modelling, we propose, among others, a framework for optimizing decision making about OHE events and in particular an adapted algorithm that indicates the closest qualified care unit (CU) to the OHE, in terms of distance and road traffic.

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Static Modeling

Data Collection. To model the HES-RN structure, data were collected from several organizations (Hospitals and Ambulatory Care Direction, Regional Health office, Health Delegations of RSZZ region…) and from QGIS Open source database. Then, the HES-RN’s mapping was developed, from the digital map of the studied region, under a GIS platform. HES-RN Mapping. The network mapping we created was accomplished in several GIS layers, each one having its own data, necessary to manage and regulate well such a network, as shown in [2]. 3.2

HES-RN’s Dynamic Modelling

Studying the network’s functioning or dynamics consists of studying the different OHE events, using the GIS mapping database [2] (see Fig. 1). Register OHE data First aid Transport the patient

Identify qualified CUs Deal with an OHE

Determine the best path to take

Treat the patient Close and archive OHE Dossier Fig. 1. How to deal with an OHE.

The dynamic routing of the patient concerned by the OHE depends on the length of the considered road and on the traffic flow along this road. The road density varying in time and in space can characterize this flow. The GIS that we use has a tool, which allows us to determine, in a given radius, the CUs of destination and the shortest path, which lead there. Yet, in our decision support model, in order to transfer the patient as quickly as possible to the CUd, we are looking for the fastest path between the OHE and the CUd. State of the Patient. The patient goes through different states, from the moment the OHE is observed until he leaves the hospital emergency service (See Fig. 2). According to [9], different states exist to describe the patient’s condition: • State 5 “Coma”: none of the components of consciousness is present (awakening, self-awareness, and awareness of the outside world). • State 4 “Vegetative state”: the person is awake, but has no self-awareness or environment awareness. • State 3 “Minimal Awareness”: the person is awake and sometimes shows fleeting signs of conscious actions.

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• State 2 “Locked-in syndrome”: the person is awake, perfectly conscious, but paralyzed, and communicates only by winking eyes. To complete the states of the patient during the OHE, we add the following states: • State 0: State of the patient before the OHE. • End state: the patient is treated and his condition has improved. • State 1: the components of consciousness are present, and the patient is able to communicate perfectly with his surroundings (he can move and feel the pain).

State 0

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State 1 Ambulance arrival First Aid

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State 5 Leaving the Hospital Emergency Service End State

Fig. 2. The patient’s states during an OHE.

Unique Patient Dossier. The traceability is an obligation that aims to ensure the visibility, got by the access to continuous and measurable data. In the absence of an effective traceability system, it is not possible to monitor and trace patient flows, nor to objectively measure the effectiveness of the activities of medical staff. In this study, a patient Dossier was created using a GIS software. This Dossier contains all the important information related to the concerned patient [10]: • Personal details (Name, age, gender, address, phone number, date of birth, blood group, emergency contact). • Patient’s anamnesis and history. • Usual treatment. • Allergies. • Clinical entry examinations. • Structured laboratory results. • Radiology examinations.

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• List of current problems. • Letters of exit and medical prescriptions. • Assessments of various consultants. This Dossier allows the doctors and medical staff to access the data of the registered patients, which can be used during the patient’s diagnostic [11]. The dossier represents as well, a proof in case of a medical misinterpretation. OHE Dossier. We have also created, under the GIS platform, an OHE Dossier. This one allows us to codify each of the OHEs processed, to collect and archive all the information relating to it. This GIS platform allows then one to visualize, analyze, interpret and display the OHE medical and geo-location data whenever it is necessary. Patient’s Transfer. In the case of an OHE and in particular the one with state 2 or above, the transport time of the patient to a qualified health unit is paramount critical. The problem of finding the fastest path to ensure a transfer from point A to point B of a road network is well known. Its formulation remains simplified and its resolution is complex especially at the mathematical level [12, 13]. Even the exact solutions developed for the simplified models provide only local optimality. Furthermore, the stochastic nature of the road traffic only complicates things. In the absence of having an optimal global solution for this problem, we settle for a metaheuristic that allows us to provide a good approximate solution. For this, we built and added the layer “Real-Time Traffic Conditions” to our HES-RN’s cartography, to have quantification and meta-visualization of road traffic at quasi-real time in this HES-RN (see Fig. 3).

Fig. 3. HES-RN GIS mapping with near-real time meta-traffic.

There are four levels of traffic quantification adopted by ArcGIS Online. These levels are qualified as follow: [stop and go] for the densest (average minimum speed: vmin), [slow] with average speed v1, [medium] with average speed v2, and [fluid] with maximum speed vmax.

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Thus, based on this information and the GIS data of the modelled HES-RN-RSZZ, the following meta-algorithm is proposed, to obtain the fastest possible transfer of the patient between the OHE and the CU of destination: 1. Pinpoint the OHE location (geographical coordinates) and characterize it. 2. Determine which CUs of destination are qualified (feasible points) to process the OHE. 3. Classify these qualified CUs of destination (depending on OHE severity) according to their qualifications. 4. Determine all paths connecting OHE location to the qualified and classified CUs of destination, within a given radius (this latter is determined on the OHE severity). 5. Among these paths, determine the path that optimizes the transfer time of the patient according to its length and its traffic: a. Insert in a table, all the segments of the paths found. b. Characterize and classify these edges . All the algorithm steps are explained in detail in [2]. In the general case of the road network, modeled as a non-oriented graph with circuits, reaching the destination node (the CUd in our case) is not guaranteed. We therefore, assume that the nodes cannot be revisited. In this case, our problem is formulated as a Discrete Dynamic One to Some Shortest Paths (D2OS2P) problem. Dynamic, because in our modeling we consider system state change, particularly road travel times and availability change of the CUd in time. Discrete, because changes in the system states, will be considered only at the next node on the traveling path. 3.3

Decision Support Model

When an emergency event occurs, emergency decision-making (EDM) is an important process that mitigates the losses of properties and lives caused by the event, which is typically characterized by time pressure and lack of information, resulting in potentially serious consequences. An emergency event might evolve into different situations due to its dynamic nature, which is one of the distinctive features of emergency events [14]. The management of an emergency is characterized by complexity, urgency, and uncertainty. Therefore, it is crucial to have a fast though smooth and effective decisionmaking process [15]. In healthcare sector, the task is much more difficult, and the necessity of coordination, collaboration and information exchange among the network’s components is of utmost importance. In order to analyze and evaluate the activity of healthcare institutions and to respond to the needs of decision makers, within the network, a pilot dashboard is used. It is a tool of control, diagnostic, dialogue and communication. The objective of the dashboard is to pilot the health organization through Key Performance Indicators (KPI) [2]. Furthermore, to achieve optimal decision-making and to act quickly and efficiently in the emergency sector, when there is an OHE, we propose a decision support model, using:

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• A HES-RN GIS mapping able to provide real-time data and information needed to determine the most appropriate Care Unit (CU) of the HES-RN to receive the patient concerned with the OHE, and to select the fastest routing for the transfer of this latter, taking into account the possible routing times to the unit care of destination. • An algorithm allowing determining the fastest routing of the patient, in real time, according to the candidate routings, their lengths and their traffic flows. This decision support model assumes that the HES-RN nodes work autonomously with provided regulation by the Control Centre node, insuring the access to the most effective care in the shortest possible time, with a sustainable quality of service, level of service and the best possible and with the least possible costs. On the other hand, this decision support model consists of 3 main axes: (1) CU’s qualification, (2) CU beds’ availability, and (3) communication protocol. Qualification of CUs. The CUs qualification results in all of the following criteria: • CUs location. • CUs category: CHC (Communal Health Center), CHCC (Communal Health Center with Childbirth Module), UHC (Urban Health Center), UHCC (Urban Health Center with Childbirth module), DR (Rural Dispensary), PGH (Provincial General Hospital), GUH (General University Hospital), LH (Local Hospital), UUH (Specialized University Hospital). • Public/private CU. • Provision of equipment and necessary equipment. • CUs KPIs. • Beds capacity. • CUs clinic services (neurology, neurosurgery, ENT…). • CU’s administrative departments (human resource, supply…). • Medical and technical services (emergency, resuscitation, radiology…). • Medical staff qualification. • CUs Ergonomics. • Availability, type and number of ambulances. CU’s Beds Availability. Before the patient is transported to the qualified CU assumed to be adapted to his medical condition, it is crucial to check the bed availability, in order to avoid the ambulance diversion problem. The communication protocol that provides such information is described in the following. Communication Protocols. We know that the HES-RN is composed of several components: • Hospital Emergency Services (HES). • Urgent Medical Assistance Service (SAMU) with its Medical Call Control Center (CRAM) and Emergency Care Education Center (CESU) (this structure may be common to several regions). • Hospital Emergency and Resuscitation Mobile Services, known as SMUR

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• Basic Health Care Structures participating in urgent medical activities (BHCS). • Means of medical transport, as part of the activity of Emergency Medical Assistance (AMU). Among these components, the SAMU is considered as the pilot node of the network, in terms of data broadcasting. Its most important activities are [16]: • Provides permanent medical listening. • Determines and trigger the answer best suited to the nature of the calls. • Ensures the availability of public or private means of hospitalization, adapted to the patient’s condition. • Organizes, where appropriate, transport in a public or private establishment by means of a public service or a private transport company. • Ensures the admission of the patient. As mentioned above, the SAMU is responsible of data broadcasting whether to the ambulance carrying the patient or to the CU receiving the patient (See Fig. 4). It is considered as a server and a client in the same time, in the network’s communication protocol. In push protocol, the SAMU considered as a client, gets all necessary data from the CU. As a sever, the SAMU sends the information collected, to the ambulance in need, in pull protocol.

HES-RN Database Ambulances (Type A, B

CUs (Care offer data)

SAMU laws and regula-

or C) Update CUs data HES

Transfer patient

Update CUs data

Consult Database

Transfer patient to/from

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AMU

to/from

SMUR

OHE Dossier

Mobilize SMUR

Add new OHE

SMUR

tions

SAMU

Consult patient’s medical history Decision Support Platform

Fig. 4. HES-RN-RSZZ platform.

In Fig. 4, the CUs represent both HES and BHCS.

Unique Patient Dossier

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4 Conclusions and Perspectives In this work we presented the static and the dynamic modellings of the studied regional health network. The modelling was performed using the GIS technology in order to visualize, store and modify easily all network’s data in the Medical Emergency field. The aim of this study is to optimize the functioning of the HES-RN-RSZZ, in particular its OHE management, and the complex problems of its dynamic routing optimization and the delivering of the right health care to the right patient and just in time. And since the time factor is very important in the medical emergency field, we proposed an algorithmic decision procedure for the optimization of OHE Cycle Time in particular, and of the critical decision processes through the network in general. The algorithm proposed, deals with Discrete Dynamic One to Some Shortest Paths (D2OS2P) problem, in order to obtain the fastest possible transfer of the patient, between the OHE and the CU of destination, taking into account road congestions and the consequent flow perturbation that propagates over the network. We have also focused on the DSM and presented a schema, which summarize the decision making process, assuming the SAMU (Control Centre node) is the pilot node of the network and the HES-RN nodes work autonomously with provided regulation by the SAMU. We also described the three main DSM axes that contribute to the best use of the HES-RN-RSZZ map database. The methodology proposed can be applied and is generalizable to other health networks in Morocco. With the GIS approach, it is relatively easy to add data layers for different regions of Morocco. As to our future works and in order to validate the developed system, we are working on the completion of (1) the simulation of our adapted algorithm D2OS2P [10], to be deployed on a GIS platform in combination with VBA; and (2) the implementation of the decision support component of the HES-RN-RSZZ system using C# with integration with the GIS aforementioned.

References 1. Phelps, C.E.: Perspectives in health economics. Health Econ. 4, 335–353 (1995) 2. Khalfaoui, I., Hammouche, A.: GIS platform for the optimized management of the health emergency services regional network (HES-RN) in Morocco. In: The International Conference on Geoinformatics and Data Analysis ICGDA 2018, Prague, Czech Republic, pp. 117–121 (2018) 3. Sirintrapun, S.J., Artz, D.R.: Health information systems. Clin. Lab. Med. 36(1), 133–152 (2016) 4. Champion, C., Kuziemsky, C., Affleck, E., Alvarez, G.G.: A systems approach for modeling health information complexity. Int. J. Inf. Manag. 49, 343–354 (2019) 5. Novak, S., Djordjevic, N.: Information system for evaluation of healthcare expenditure and health monitoring. Phys. A 520, 72–80 (2019)

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6. Sebetci, O.: Enhancing end-user satisfaction through technology compatibility: an assessment on health information system. Health Policy Technol. 7(3), 265–274 (2018) 7. Rocha, R., Malta, P.: The perception of health professionals of the information system of continuous care. In: Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist (2018). Procedia Comput. Sci. 138, 286–293 (2018) 8. Kumar, S.: UML Dynamic and Static Modeling (2013). https://fr.slideshare.net/100arab/ dynamic-and-static-modeling. Accessed 25 Jul 2019 9. Schnakers, C., Majerus, S., Laureys, S.: Diagnosis and investigation of altered states of consciousness. Réanimation 13, 368–375 (2004) 10. Bastardot, F., Wasserfallen, J.-B., Regamey, P.-F., Bart, P.-A.: Dossier patient informatisé – belle opportunité de repenser l’information médicale et son utilisation. Rev. Med. Suisse 13(20), 27–30 (2017) 11. Hathaliya, J.J., Tanwar, S., Tyagi, S., Kumar, N.: Securing electronics healthcare records in Healthcare 4.0: a biometric-based approach. Comput. Electr. Eng. 76, 398–410 (2019) 12. Babicheva, T.S.: The use of queuing theory at research and optimization if traffic on the signal-controlled road intersections. Procedia Comput. Sci. 55, 469–478 (2015) 13. Bressan, A., Canic, S., Garavello, M., Herty, M., Piccoli, B.: Flows on networks: recent results and perspectives. EMS Surv. Math. Sci. 1, 47–111 (2014) 14. Zhang, Z.-X., Wang, L., Wang, Y.-M.: An emergency decision making method based on prospect theory for different emergency situation. Int. J. Disaster Risk Sci. 9(3), 407–420 (2018) 15. Levenson, S.A.: The health care decision-making process framework. Maryland State Med. Soc. 11(1), 13–17 (2010) 16. Ministry of Solidarity and Health. https://solidarites-sante.gouv.fr/systeme-de-sante-et-medicosocial/structures-de-soins/article/samu-smur. Accessed 28 Jul 2019

Evolution of Cooperation in E-commerce Based on Prisoner’s Dilemma Game Jalal Eddine Bahbouhi(B) and Najem Moussa LAROSERI, Department of Computer Science, Faculty of Sciences, University of Chouaib Doukkali, EL Jadida, Morocco [email protected], [email protected]

Abstract. Electronic commerce transactions often occur between strangers. Without any information about theirs counterparts, individuals feel puzzled to continue in transactions. Game theorists have formalized this problem as a prisoner’s dilemma and predict mutual noncooperation between sellers and vendors. This study investigates how failure in transactions between individuals affects electronic commerce exchanges and empower successful transactions between participants. For this purpose, we study the evolutionary prisoner’s dilemma game (PDG) on scale-free networks and analyze the effect of failure. Individuals are represented by agents located on a vertices of a graph, whose edges define the network of contacts between those players. With the aid of the analysis of the PDG on a graph, we are able to investigate intuitively how the failure affects the transformation of individuals’ strategies. We find that the failure makes important changes in the structure of the graph, which induces considerable variation in the level of cooperation. As a result, our results show that failure inhibits the emergence and sustainment of the cooperation. Keywords: Electronic commerce · Evolution of cooperation Prisoner’s Dilemma · Scale-free network

1

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Introduction

Internet become a public network used to support commercial transactions called electronic commerce. It is Unclear for the users of this open market, what the fate of transactions when this network used as platform for commerce exchanges. The fear from cheating by criminals is a principal factor for hesitation. This type of trade has generated a ambiguity status among its users, especially the transactions occur between strangers. Transactions seem to take place in the dark, traders do not know each other, and this gives confusion in these operations, but also generates uncertainty in e-commerce as a whole. It is important for traders to evaluate each other before starting transactions. On one hand, the buyer needs to be sure that the goods he wants to buy include the required quality, and the selected quantity, before sending money to the c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 500–505, 2020. https://doi.org/10.1007/978-3-030-36778-7_55

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seller by credit card or any other means. In addition to the problem of whether the seller will send the goods to the buyer and will not cheat after he receives the required amount. On the other hand, the seller needs to make sure that the buyer would actually purchase the goods and he is ready to pay for them. Furthermore, if he took the initiative and sent the goods to the buyer, would he be able to receive the price of the goods, which must be sent by the buyer after the arrival of the goods. To understand and analyze dishonest behavior and its consequences in ecommerce, it’s crucial to study cooperation among individuals involving in this interactions. This problem of cooperation in e-commerce is considered as social dilemma characterized by dispute of interest between individuals. Promotion of cooperation among unconnected individuals in e-commerce remains one of the most difficult problems. Evolutionary game theory provides a fundamental framework for the research of the evolution of cooperation, for dealing with this challenge. In this paper, we use iterated prisoner’s dilemma game for simulating interactions in e-commerce environment. We study the evolution of cooperation in prisoner’s dilemma game interactions on a graph. Indeed, placing the agents witch represent individuals on the vertices of a graph, and edges define the network of contacts between agents. Here we investigate what happens when the topology of network changes through failure. It is shown that the failure favor and inhibit cooperation in the network. In the next section, related works are presented, the model and rules of the game are explained in detail in Sect. 3. In Sect. 4, computer simulation results of our model. Conclusions are given in the last section

2

Related Works

Accordingly, several mechanisms have been proposed to investigate prisoner’s dilemma game in e-commerce environment. Some empirical studies used reputation as mechanism to success transaction [1–3]. In addition, several models based on trust were proposed to assist users in theirs interactions on e-commerce environment [4–6]. These researchers has found that trust plays an important role in the success of purchases. However, Game Theory is introduced to this field as well to explain the emergence of cooperation, such as coevolution of strategy and structure [7], direct and indirect reciprocity [8], group selection [9], and reward and punishment [10].

3

Model

We used the Barab`asi- Albert (BA) scale-free model [9,10], as a complex network to describe the population structure on which the evolution of cooperation is studied. The players located on the vertices of the graph, whose edges define the network of contacts between those players. Moreover, we assume that each individual interacts, only with its ki neighbours, whereby self-interactions

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are excluded. Each player can decide either to cooperate (C) or to defect (D). Depending on the choice of their strategies, each two players (i, j) can, at every interaction, receive payoffs summarized succinctly by the so-called payoff matrix (Table 1). Table 1. Table type styles Cooperate (C) Defect (D) Cooperate (C) R/R

S/T

Defect (D)

P/P

T/S

Where mutual cooperation yields the reward R, mutual defection leads to punishment P , and the mixed choice gives the cooperator the sucker’s payoff S and the defector the temptation T . Following common studies [11], we choose R = 1, P = S = 0, and T = b where b > 1 is temptation to defect. Starting from uniformly distributed cooperators and defectors, each player can adopt its strategy according to the performance of neighboring players, whereby the probability that a player i will adopt the strategy of one of its randomly chosen nearest neighbors j is determined by the cumulative payoffs Pi and Pj of both players according to Wi←j =

1 1+

1 e− K (Pj −Pi )

(1)

Where K characterizes the noise introduced to permit irrational choices, and according to [11] we take k = 0,1. Above was the description of a prisoner’s dilemma game called P DG − N . Now we introduce P DG with failure called P DG − F . In a P DG − F Game, any player i in the network, with ki neighbors, can fail at any moment. When a player i finish all his ki games with his neighbors, if his payoff is not enough to be bigger that a threshold called (PT h ), then he became in a failure state. The failure state deprive him from participating in the subsequent games for a period (called τ ). We assume that an internally failed player recovers from its last failure after the period τ .

4

Simulation Results and Discussion

In the following results, we set the size of the Scale Free graph equal to N = 1000. A Monte Carlo (MC) simulation is repeated for 2000 times. We start by exploring the effect of failure on the evolution of cooperation. Figure 1a shows the evolution of the level of cooperation in both the normal prisoner’s dilemma game (P DG − N ) and the prisoner’s dilemma game with failure (P DG − F ). Observe through the curve the superiority of P DG − F on P DG − N . We note that adding failure to the game, makes a radical changes

Cooperation in E-commerce

(a) cooperation

503

(b) payoff

Fig. 1. Evolution of cooperation and payoff as function of time for PDG-N and PDG-F

in the level of cooperation. It is observed through the figure that the higher the value of the PT h threshold, the higher the level of cooperation within P DG − F . The level of cooperation within the P DG reaches 40%, while when applying the rule of failure, the level of cooperation rises by a record exceeding normal P DG. This is caused by the collapse in the number of cooperators in the network. When players fail to get the expected profit from theirs games with theirs neighbors, they leave the games for a period τ . As the increment of PT h , the cooperation level monotonously increases, indicating that the cooperator frequency is highly promoted by the fail of others players.

(a) Variation of PT h for different values of τ

(b) Variation of τ for different values of PT h

Fig. 2. Evolution of cooperation as function of PT h and τ in PGG-F.

The dominant strategy in P DG, which represents Nash’s equilibrium, is known to be defection. This strategy gives greater profit compared to the strategy of cooperation. That is, players who make cooperation as a strategy are more likely to fail than those who make defection as a strategy. This is due to the fact that the cooperators receive less profit than the defectors, so the probability of falling in failure is great for the cooperators.

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When a number of cooperators fall in failure, the network will be full of high percentage of defectors. Thus, most games happen only between the defectors, which leads to a decrease in theirs profits, so their fate will be failure. As result, we get a mix of cooperators and defectors in failure situation. However, the failure occurs only for just a period of time τ , then the players return to their normal state and begin to participate in the P DG. But in this time the network will be free of a high percentage of the defectors, but it contains a significant proportion of cooperators, as well as cooperators who failed and then returned to play. This leads to an increase in the cooperation as shown in Fig. 1a, and as a result increase in the payoff as shown in Fig. 1b. The number of cooperators or defectors playing prisoner’s dilemma game in a given moment is related to two variables. The first variable is the threshold PT h , which has a fundamental effect in the presence of players either in failure or normal situation. This variable transforms the players from the normal situation to failure situation, not vice versa. The values that PT h take determine the number of players who are in a failed state. The higher the values of PT h , the greater the number of failures. Which has a direct impact on the level of cooperation within the network as illustrated in Fig. 2a. Which shows the evolution of the level of cooperation when we change PT h from 1 to 10, and so for different values of τ . The figure is divided into two sections, a section where the ratio of cooperation increases when PT h increases, until the PT h reaches a certain value called PT h c (PT h c differs according to τ ). In addition, another section is characterized by low level of cooperation when PT h exceeds the value of PT h c, the level starts to decline as PT h increases. For example, for τ = 3, the cooperation ratio reaches the maximum value of 65% when PT h c reaches 6. It is worth mentioning that in normal P DG, the proportion of cooperation does not exceed the value of 40%. The second variable is τ , which determines the length of time the player will remain in a failing state. It also has an important impact on the evolution of the cooperation rate, and its effect is mainly associated with PT h . Figure 2b shows the effect of τ on the evolution of the cooperation, for different values of PT h . As shown in the figure, the effect of τ varies according to PT h values. For example, for PT h = 3, the ratio of cooperation increases as the value of τ increases. The increase continues until the value τ exceeds 10, after that the cooperation ratio stabilizes at a constant value, so that τ has no more effect on the evolution of cooperation. For PT h = 5, 7 or any larger value, the curve takes another form. The rate of cooperation increases initially as τ increases, until cooperation reaches a maximum value, when τ reaches τc . After that, the cooperation rate begins to decline with the increase in the value of τ , until it stabilizes at a fixed value, when τ exceeds 40. Note that τc corresponding to the maximum value of the cooperation ratio, changes according to PT h .

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We used an agent-based model to simulate transaction on e-commerce environment, by using prisoner’s dilemma game. We showed the effectiveness of failure in transaction on the promotion of cooperation in electronic commerce environment. In such environment, establishing a relationship of trust between different parties evolved in virtual transaction is a major challenge for electronic commerce. In this paper we try to give a simple approach to help in resolving this problem. Our procedure is to use failure as a manner to limit the defectors behavior. Failure consist of to not participating in the subsequent transactions for a moment, if the estimated payoff is not reached. As result, the number of cooperators players will decrease from the network for a moment, and then return to participating in the transactions. Our Findings show that this process of failure has a great affect on the promotion and evolution of cooperation in the network.

References 1. Yamamoto, H., Ishida, K., Ohta, T.: Modeling reputation management system on online C2C market. Comput. Math. Organ. Theor. 10(2), 165–178 (2004) 2. Strader, T.J., Ramaswami, S.N.: The value of seller trustworthiness in C2C online markets. Commun. ACM 45(12), 45–49 (2002) 3. Huang, Y., Wang, M.: Credit rating system in C2C e-commerce: verification and improvement of existent systems with game theory. In: 2009 International Conference on Management of e-Commerce and e-Government, pp. 36–39. IEEE, September 2009 4. Bahbouhi, J.E., Moussa, N.: Prisoner’s dilemma game model for e-commerce. Appl. Math. Comput. 292, 128–144 (2017) 5. Kim, Y., Peterson, R.A.: A meta-analysis of online trust relationships in ecommerce. J. Interact. Mark. 38, 44–54 (2017) 6. Oliveira, T., Alhinho, M., Rita, P., Dhillon, G.: Modelling and testing consumer trust dimensions in e-commerce. Comput. Hum. Behav. 71, 153–164 (2017) 7. Nowak, M.A., Sigmund, K.: Evolution of indirect reciprocity. Nature 437(7063), 1291–1298 (2005) 8. Dugatkin, L.A., Mestertongibbons, M.: Cooperation among unrelated individuals: reciprocal altruism, by-product mutualism and group selection in fishes. Biosystems 37(1–2), 19–30 (1996) 9. Perc, M., Szolnoki, A.: Self-organization of punishment in structured populations. New J. Phys. 14(4), 43013–43025 (2012) 10. Szolnoki, A., Perc, M.: Effectiveness of conditional punishment for the evolution of public cooperation. J. Theoret. Biol. 325(10), 34–41 (2013) 11. Santos, F.C., Pacheco, J.M.: Phys. Rev. Lett. 95, 098104 (2005) 12. Szab´ o, G., T˝ oke, C.: Evolutionary prisoner’s dilemma game on a square lattice. Phys. Rev. E 58(1), 69 (1998)

Quality Measurement Systems in Public Services and E-Government Benchmarking Hajar Hadi(&), Ibtissam Elhassani(&), and Souhail Sekkat(&) Artificial Intelligence for the Sciences of the Engineer, National School of Arts and Crafts, Moulay Ismail University, 50500, 15290 Meknes, Morocco [email protected], {i.elhassani,s.sekkat}@ensam.umi.ac.ma

Abstract. The quality improvement initiatives of public services are partly a test of practices reproduction of companies operating in a competitive market. Public services are led to reconsider the status of the user and to take inspiration from the managerial methods that give a very important place to the expectations of the customer. For companies, customer satisfaction is a way to maximize profits. While the satisfaction of users, with respect to the general interest, is the aim of public services. This article presents in the first a literature review focusing on researches done on quality services than we broad picture about efforts and initiatives of countries to improve public service quality. Keywords: Quality measurement E-government
Study case


E-services
Digital transformation


1 Introduction In Morocco, more and more modernization movements are being paid to the quality of public services responding to a feeling of dissatisfaction of users. A survey of public service users [1] revealed the extent of the deficit in the quality of public services provided to citizens. This survey revealed that only 22% of those consulted felt unequivocally that the effectiveness of public services was assured. According to OCDE/SIGMA [2], “There is no holistic approach or central mechanism will provide a global and shared view of the performance of public services. No administrative structure or service with a transversal vocation is explicitly entrusted with this task”. However, in the last years, improving the quality of public services, has begun a priority Since 2011 the new Moroccan constitution reserved three articles 156, 157 et 158 concerning quality, consequently Many improvement projects have been put in place. Indeed, the National Plan of the reformation of the Administration 2018–2021 [3] proposes an integrated and participative approach which draws the bases of a new culture of the public service. This plan articulates around 4 main transformations: the organisational transformation, the management transformation, the digital transformation and the ethical transformation. © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 506–514, 2020. https://doi.org/10.1007/978-3-030-36778-7_56

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The main objective of this research is first of all developing a measurement tool of quality for traditional and electronic services to improve it and simplify relations with users. The present paper is organized as follow: in the beginning we present a literature review relating to traditional and electronic public services, e-government, then benchmarking where we collect the experiences of some countries renowned for the quality level of their services.

2 Literature Review Our literature research was done to have a clear vision on the concept of quality of public services and to have a broader picture of the work done. 2.1

Service Quality

The measurement of the quality of public services has evolved over the years since 1984. Gronroos [4] proposes a technical and functional quality model that includes: (1) Interaction with the service. (2) Functional quality is how it achieves the technical result. (3) Image, which is very important for service and can be expected to be built taking into account the technical and functional aspects of other factors (tradition, ideology, word of mouth, pricing and public relations). Parasuraman [5] developed a quality of service model Fig. 1. It is based on a set of gaps. This research was developed on the following scale, called SERVQUAL, to measure customer perceptions of service quality. Haywood-Farmer [6] suggests an Attribute service quality model that indicates that a service organization is “high quality” if it consistently meets customer preferences and expectations. After that Spreng and Mackoy [7] suggest another model. It measures service quality through set of ten attributes of advising (convenience in making an appointment, friendliness of the staff, advisor listened to my questions, the advisor provided accurate information, the knowledge of the advisor, the advice was consistent, advisor helped in long-range planning, the advisor helped in choosing the right courses for career, advisor was interested in personal life, and the offices were professional). In 1999 Oh [8] proposed an integrative model of service quality, customer value and customer satisfaction. It incorporates key variables such as perceptions, service quality, consumer satisfaction, customer value and intentions to repurchase.

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Fig. 1. Parasuraman’s service quality model.

After that, an internal service quality model based on the concept of GAP model was developed [9]. It evaluates the dimensions, and their relationships, that determine service quality among internal customers (front-line staff) and internal suppliers (support staff) within a large service organization. In other way there is the Conmen Assessment Framework (CAF) [10] is a total quality management tool based on the European Foundation for Quality Management (EFQM). The CAF assesses the organization from a variety of perspectives, thus taking a holistic approach to the analysis of the organization’s performance. Its structure contains 9-criteria which can be considered in any organizational analysis. Criteria from 1 to 5 relate to organizational factors. They determine the nature of the organization’s activities and the approach taken to achieve its objectives. While criteria from 6 to 9 presents the results obtained from citizens/clients related to key performance. they are estimated by measures of perception and internal indicators are assessed. Then Each criterion is then broken down into sub-criteria. 2.2

E-Service Quality

Various models of e-government implementation have been advanced in the literature. In 2009, Papadomichelaki and Mentzas promoted [11] e-GovQual as a model developed to measure the quality of e-government services. In 2010, Alanezi, Kamil and Basri [12] developed a proposal to measure the quality of e-government services. This proposal is based on seven dimensions, based on the SERVQUAL methodology of Parasuraman. In 2012, Zaidi and Qteishat [13] developed the e-GSQA framework based on the following diagram (Fig. 2):

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Fig. 2. e-GSQA framework.

Then, Hien (2014) [14] considers that the quality of electronic services is assumed from two points of view: the quality of the service and the quality of the information.

3 International Experiences on Public Services In order to identify strength and best practices related to public services, we present in this section some of international experiences and analyze them. 3.1

Measurement Quality on Public Services

In order to collect best practices from the international measurement quality systems, we present international experiences by adopting the following approach. Methodology First, we propose 4 criteria [15] to identify pertinent experiences. Second, we propose different experiences like: The Service standards and Citizen first in Canada, the Observatory of the quality of public services in Spain, the Survey of life events (userscitizens, companies, associations), the Service Standards of National Health Service of England. Then, for each experience, we describe the following aspects: Methodologies tools used to set quality standards (if applicable) and to measure quality, Publication of results (channels and elements of communication) and the governance and experience management framework. Finally, we identify the good practices as aim result of our benchmarking.

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Results and Discussion The Table 1 presents the experiments selected for this benchmarking exercise. It shows a wide range of practices for measuring the quality of public services, particularly with regard to information on services, orientation, reception, provision services and the immediate result of the process. According to the following criteria which are: The inclusion of standard of service, Measuring the quality of concrete services; The combination of perception indicators and factual indicators and the possibility of using different sources of information to measure the indicators (administrative surveys, documentary revision, field visits, mystery shopper); we choose the following project as models to create Moroccan quality barometer: The service charters of Madrid City Hall; The Qualipref 2.0 reference system of the French territorial administration and The service standards of the National Health Service of England. 3.2

E-Service and E-Government

E-Government is always perceived as a means to improve public administration performance and services, as well as a means for better governance and access to political and social rights. That is why we look up for successful international experiences to take good practices in this way. Methodology The United Nation (UN) developed a tool to evaluate e-government maturity, called egovernment development index (EGDI), composed of three sub-indices: Online services (also called web measure index (WMI) in UN 2008), telecommunications infrastructure index (TCII) and human capital index (HCI). According to UN Egovernment survey 2018, Denmark and Singapore are among the top ten countries. In these paper we present the experience of Singapore in e-government while we will present the Denmark’s one on the future works [25]. Results and Discussion Based on the work of Watson and Mundy [26], we have broken down the various stages of Singapore’s e-government evolution that are initiation, dissemination and customization, as well as the main actions taken at each stage. First, stage of initiation where the government began developing official websites at the end of the IT2000 project, which aimed to make Singapore a smart island. Then, stage of infusion: This step is focused on: (1) Action plan that has been developed to define the main lines of the deployment of information and communication technologies (ICT) (2000–2002); (2) Firm support by committing $ 932 million over the period 2000–2003 to implement the action plan; (3) Centralized financing and common infrastructure and (4) Bridging the digital backlog finally, Customization step: the goal was to maximize the value of egovernment for citizens by enabling them to obtain an electronic personal profile of their interactions with the government. To achieve this goal, the government has addressed the challenges of integrating its portal with the information systems of various agencies, reengineering the public service delivery process and implementing management techniques in customer relationship. The government of Singapore mads a Masterplans [27] from 1980 until 2015 look at the following Fig. 3:

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• Automa on of public service • Basic IT infrastructure and data hubs

e-Government Ac on Plan I and II (2000-2005) • Online service delivery • Integrated services

CSCP (1980-1999)

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eGov 2015 (2011-2015) • Integra on of data, process and systems for Government agencies • 300 mobile government services deployed iGov2010

• Focus on collabora on xithin and outside Government

(2006-2010)

Fig. 3. Masterplans of Singapore government. Table 1. International measurement systems in public services. Criteria Size (million) Tradition

Canada [16] 36 Anglo-Saxon

Tool

1. Service standards 2. Citizen first

Type of measurement system

1. Perception study on the quality of services 2. Service charters combined with Perception study on the quality of services 1. Treasury Board 1. Ministry of Secretariat of the Finance and Public Service Federal 2. Madrid Government and Federal Agencies 2. Institute for Citizen-Centered Services

Responsible institution

1. Service standards 2. Citizen first

Spain [17, 18] France [19–22] 47 67 Continental Continental European European Napoleonic Napoleonic 1. Observatory of 1. Survey of life events (usersthe quality of citizens, companies, public services 2. Observatory of associations) 2. The barometer of the City the quality of the reception in the services of the State

1. Perception study on the quality of services 2. Measurement of the results of the service commitments set in the Marianne repository 1. Interdepartmental Direction for the Support of the Transformation of Public Action 2. Central Administration of the State

UK [23, 24] 66 Anglo-Saxon

1. Service Standards – National Health Service of England 2. Service Standards – Intellectual Property Office 3. Local Authority School Places Scorecards 1. service charters 2. service charters 3. Performance indicator

1. National Health Service of England 2. Intellectual Property Office 3. Office for Standards in Education, Children’s Services and Skills (Ofsted)

(continued)

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Criteria

Canada [16]

Spain [17, 18]

France [19–22]

Administration 1. Federal state 1. Central 2. All public sector Administration of the State 2. Local administration

Service concerned

All services

All services

All services

UK [23, 24] 1. National Executive Agency 2. National Executive Agency 3. Central Administration of the State 1. Health 2. Intellectual property protection 3. Education

4 Conclusion and Future Work To conclude, improving quality of public services it became a priority of all countries especially morocco. We have presented the context of quality services in our country, then we done a broad picture about literature research relative to different instrument and tools to measure quality and user’s satisfaction. In order to benefit from the successful international experiences, we have presented a benchmark of Canada, Spain, France, UK and Singapore. We noticed that the measurement of quality is based on indicators; then the combination of perception indicators and factual indicators; after that the possibility of using different sources of information to measure the indicators (administrative surveys, documentary revision, field visits, mystery shopper); then the strong leadership with vision is crucial for e-government success. The government should clearly articulate its vision and motivate all stakeholders to share that vision. In the future Works will look up for the e-government in Denmark then make a comparison between this experiences and morocco one and finally propose the perfect model which can improve the e-services and consequently the e-government in morocco. Acknowledgement. This research topic is allied with a Department of Administration and Public Service Reform project on the design of a public service quality barometer.

References 1. Observatoire marocain de l’Administration publique (OMAP): Rapport national sur l’évaluation du système de gouvernance au Maroc (2006) 2. OCDE/SIGMA (2019): (Principes d’Administration Publique), Éditions OCDE, Paris. https://www.mmsp.gov.ma/uploads/documents/RevuePrestationServicesAdministratifs26Jui n2019.pdf

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3. Plan National de la Réforme de l’Administration 2018–2021, Rabat (2019) 4. Gronroos, C.: A service quality model and its marketing implications. Eur. J. Mark. 18(4), 36–44 (1984) 5. Parasuraman, A., Zeithaml, V.A., Berry, L.L.: A conceptual model of service quality and its implications for future research. J. Market. 49(3), 41–50 (1985) 6. Haywood-Farmer, J.: A conceptual model of service quality. Int. J. Oper. Prod. Manag. 8(6), 19–29 (1988) 7. Spreng, R.A., Mackoy, R.D.: An empirical examination of a model of perceived service quality and satisfaction. J. Retail. 722, 201–214 (1996) 8. Oh, H.: Service quality, customer satisfaction and customer value: a holistic perspective. Int. J. Hospitality Manag. 18, 67–82 (1999) 9. Frost, F.A., Kumar, M.: INTSERVQUAL: an internal adaptation of the GAP model in a large service organization. J. Serv. Mark. 14(5), 358–377 (2000) 10. Cadre d’auto-évaluation des fonctions publiques (CAF), Centre de ressources CAF, PaysBas (2006) 11. Papadomichelaki, X., Mentzas, G.: A multiple-item scale for assessing e-Government service quality. In: Wimmer, M.A. (ed.) EGOV 2009, vol. 5693, pp. 163–175. Springer, Heidelberg (2009) 12. Alanezi, M.A., Kamil, A., Basri, S.: A proposed instrument dimensions for measuring egovernment service quality. Int. J. u- e-Serv. Sci. Technol. 3(4), 1–18 (2010) 13. Zaidi, S.F.H., Qteishat, M.K.: Assessing e-Government service delivery (government to citizen). Int. J. eBussiness eGovernment Stud. 4(1), 45–54 (2012) 14. Hien, N.M.: A study on evaluation of e-Government service quality. Int. J. Soc. Manag. Econ. Bus. Eng. 8(1) (2014) 15. Lazarov, G.B.B.: Quality measure in Moroco, chez MRAFP, Rabat (2018) 16. Canadian Centre for Management Development: Citizens First. Summary Report (1998) 17. Cuellar, E., del Pino, E., Ruiz, J.: Guía para la evaluación de la calidad de los servicios públicos. Agencia Estatal de Evaluación de las Políticas Públicas y la Calidad de los Servicios, Madrid (2009) 18. Jaraiz, E., Pereira, M.: Guía para la realización de estudios de análisis de la demanda y de evaluación de la satisfacción de los usuarios. Agencia Estatal de Evaluación de las Políticas Públicas y la Calidad de los Servicios, Madrid (2014) 19. Secrétariat Général pour la Modernisation de l’Action Publique. Premier Ministre (2017). Enquête événements de vie 2016. Volet Particuliers. Présentation globale (PPT), Janvier 2017 20. Goudarzi, K., Guenoum, M.: Conceptualisation et mesure de la qualité des services publics dans une collectivité territoriale. Politiques et management public, vol. 27/3 (2010) 21. Cornut-Gentille, F.: Modernisation de l’Etat, qualité des services publics et indicateurs. Rapport parlamentaire en mission auprès le Ministre du Budget, des comptes publics, de la fonction publique et de la réforme de l’Etat, Mars 2010 22. Secrétariat Général pour la Modernisation de l’Action Publique: Premier Ministre. Référentiel Marianne. Le service public s’engage pour améliorer la qualité de service, Septembre 2016 23. Fiszbein, A., Ringold, D., Halsey, F.: Making Services Work. Indicators, Assessments, and Benchmarking of the Quality and Governance of Public Service Delivery in the Human Development Sectors. Policy Research Working Paper 5690. The World Bank Human Development Network (2011) 24. Thijs, N.: Measure to Improve. Improving public sector performance using citizen – user satisfaction information. European Public Administration Network/European Institut of Public Administration (2011)

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25. Mohamed, H., Hatem, E., Sherine, G.: E-Government in Arab countries: challenges and evaluation. IOSR J. Comput. Eng. (IOSR-JCE) 20(2), 01–11 (2018). Ver. IV (Mar–Apr 2018) 26. Watson, R.T., Mundy, B.: A strategic perspective of electronic democracy. Commun. ACM 44(1), 27–30 (2001) 27. eGov Masterplans, January 2016. https://www.tech.gov.sg/media/corporate-publications/ egov-masterplans

Using Ontology and Context-Awareness for Business Process Modelling: An Overview Jamal El Bouroumi(&), Hatim Guermah(&), Mahmoud Nassar(&), and Abdelaziz Kriouile(&) IMS Team, ADMIR Lab, ENSIAS Rabat, Rabat, Morocco [email protected], [email protected], [email protected], [email protected]

Abstract. In recent years, several approaches have been proposed for modeling business process, with the emergence of Context-Awareness and semantic web, especially Ontologies. Researchers and practitioners had attempted to integrate them into Business Process Modeling. In this paper, we present, on the one hand, some classical approaches for BPM, particularly approaches that integrate Context and others that integrate Ontologies into BPM. On the other hand, we analyze their main characteristics through a comparative analysis based on a series of criteria. The purpose of this survey is to provide a support for a new proposal. Keywords: Business Process Modeling


Ontology
Context-Awareness

1 Introduction Over the last decades, many companies have focused on modeling their business processes to better understand their business and increase productivity. The semantic web evolution, and the ubiquitous computing and the consideration of context in services, requires taking into account the context in Business Process Modeling. Currently, there are a variety of business process modeling approaches ranging from traditional approaches such as Petri, PSL… to ontology and context-based approaches. Each approach has its advantages and limitations and it is adapted to a type of process. In this article we are trying to compare these different approaches on the basis of criteria that correspond to the important elements of the modeling in general and BP modeling in particular. The rest of the paper is organized as follows: Sect. 2 presents a background of context-awareness, ontology and BPMN, Sect. 3 presents the classical approaches to Business Process Modeling. Sections 4 briefly introduces some approaches that take into account ontology and Sect. 5 introduce context-aware approaches for BPM. In Sect. 6 we present a comparative study of the different approaches mentioned in the previous by Sect. 7 with the conclusions.

© Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 515–522, 2020. https://doi.org/10.1007/978-3-030-36778-7_57

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2 Background 2.1

Context and Context Awareness

To be able to use the notion of context effectively, it is imperative to understand its meaning and how we can use it. Thus, several researchers have attempted to formalize a generic specification of the context. However, a universally accepted definition is still debate in the scientific community. The first definition was proposed by Schilit [1] which divides the context into three types considered relevant, namely the location, the identity of the people in the vicinity, the objects and the modifications of these objects. Then, the time dimension was proposed by Chen et al. [2] which represents parameters such as time, date and some changes related to these periods. But the most common definition in the field of context awareness is that proposed by Dey et al. [3]. They formally define the context as follows: Context is any information used to characterize the situation of an entity. An entity may be a person, place or object considered relevant for the interaction between a user and an application, including the user and the application itself. Regarding context Awareness, several visions are proposed to define a contextaware system. Dey in [4] considers that a system is context-aware if it uses context information to make information or/and services useful to the user available and considers that this utility is dependent of the user’s task. This definition is also limited since a context-aware system deals with other aspects other than the context acquisition component, such as the interpretation of the context and the necessary adaptations. To overcome these shortcomings, Xiaohang in [5] perceives that a system’s sensitivity to context is its ability to acquire, manage, interpret, and respond to changes in the context in order to provide the appropriate services. Thus we can consider that the two definitions in [4, 5] complement each another and can be considered as reference to define a context-aware system. 2.2

Ontologies

Ontologies are one of the main tools of artificial intelligence and the semantic web. Their function is to model resources based on conceptual representations of relevant domains and allow systems and applications to draw inferences from them. It refers, according to [6], to “an explicit specification of a conceptualization”, or to a more refined way. According to [7] it relates to “a partial and formal specification of a shared conceptualization”. They help to solve the problem of finding a tool that specifies, on the one hand, knowledge of the reasoning describing the heuristic rules for using this domain knowledge. 2.3

Business Process Model and Notation (BPMN)

BPMN (Business Process Model Notation) is an Object Management Group (OMG) notation standard for process modeling [9]. The latest version (2.0) was formalized in 2010. This version allows to better consider the complexity of the

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interactions between processes by standardizing the notation systems of the graphical description of business processes, without considering the tool used. This standard allows us to benefit from the emergence of a common language: Simplify the understanding of processes, facilitates the manipulation of the business process and improve the quality of the description of business process. BPMN is based on five graphical elements: Flow Objects (Events, Activities and Gateways), Data (Data Objects, Data Inputs, Data Outputs and Data Stores), Connecting Objects (Sequence Flows, Message Flows, Associations and Data Associations), Swim lanes (Pools and Lines) and Artifacts (Group and Text Annotation).

3 Business Process Modeling Business process modeling aims to represent organization’s processes and analyze them to improve their efficiency and quality. To meet these needs, several methods have been proposed for business process modeling. Bellow we present the most wellknown classic approaches to Business Process modeling. As we have seen previously, BPMN is the standard commonly used to model Business Processes. It provides a graphical representation, which makes it more understandable. However, there are other competing models and approaches to BPMN [9]. UML is a standardized modeling language used for business process modeling, it is also an OMG standard. It allows you to model the business process in an objectoriented way. It is composed of a set of diagrams classified into three categories: structuration, interaction oriented and behavioral. UML is not a standard for business process modeling, however it can be used to model any system [9]. Among the methods for Business Process modeling, there is the EPC (Event-driven Process Chains), a technique proposed in 1990. It is based on the main following elements: event, functions and logical connector (and/OR). The function represents the activities performed, the event represents the status of the function and the logical connector determines the derivation of the control flow. The EPC is a simple modeling method. However, it does not take into account the semantic aspect of the model. The IDEF method is a set of standard techniques (IDEF0, IDEF3) for modelling business processes in the form of a series of steps, called behavioral units. IDEF0 is designed to model an organization’s action, decision and activities. This allows the activities and relationships between activities to be represented in a non-technical way. This does not allow to model all the specificities of the process. IDEF3 captures the causal relationships between events and provides a mechanism to document processes [8]. PSL (PROCESS SPECIFICATION LANGUAGE) is business process modeling language [10]. The basic component of PSL is an ontology that describes the manufacturing business process. It allows processes to be interconnected throughout the manufacturing process life cycle. The ontology of PSL represents the mathematical characteristics of process information and provides a precise expression of this information in the PSL language.

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Another method used to model the business process is Petri Network. Petri is a graphical mathematical method that describes the system under study. It is composed of several notions: • Place: this is the end point of the process, representing (in many cases) the attainment of a milestone. • Token: represents the current situation of the process, the tokens changes according to the process flow. • Transition: represents an action or event, represented by a rectangle. • Arc: is the connecting line between place and transition or transition and place. Several models are based on this model, such as Finite State Machines, Process Networks and the Dataflow Network. Since it is a mathematical model, it allows mathematical annotations to be integrated into the model.

4 Ontology and Business Process Modeling In this section, we present some Business Process approaches that take into account Ontology. 4.1

BPMN Ontology

In order to reach specific objectives, the authors in [11] proposed BPMN ontology. It formalizes the OWL-DL for all elements, attributes and properties of the BPMN. Indeed, it represents all those process models as an instantiation of an ontology in such a way that it must code all the structural aspects of the given process. The BPMN ontology allows to: Check the conformity of a process with the BPMN specification: In order to check that a given process is correctly specified according to the BPMN guidelines, for example: the process has at least one start event and one end event. Verification of other application-specific design guidelines: In some applications, it may be mandatory to monitor some additional modeling guidelines for the BPMN process, for example: To ensure the readability of processes, it may be necessary for each diagram to contain no more than ten sub-processes. BPMN expresses constraints at the top of the ontology so that it can be automatically identified. Semantic description and retrieval of processes (or process elements): The BPMN ontology assigns to each process element a semantic description defining the semantic domain of these elements. Consequently, the processes can be queried using semantic annotations. 4.2

BPAL: An Ontological Framework6 for BP

BPAL is a business process ontology management framework as part of the BPMN method proposed in [12]. It is based on a structure of many modeling concepts defined in the business culture, also corresponding to BPMN constructions:

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BPAL Atoms: represents the predicate, its BPAL’s core. A specific Business process ontology is modeled by the instantiation of one or more Atoms. BPAL Diagram: is a set of BPAL atoms that represents an abstract diagram. BPAL Diagram: is an intermediate form. Therefore, it is not necessary for the diagram to respects all axioms. BPAL Axioms: represent all the constraints and role that a BPAL diagram must respect to be considered as a BPAL Process. BPAL Process: is a diagram validated by the BPAL AXIOMS. BPAL Application Ontology: is a collection of BPAL processes in an application.

5 Context and Business Process Modeling 5.1

Cm4BPM

The authors in [13] proposed a meta-model that considers contextual information explicitly related to the business process modeling and context-dependent situations in order to represent the context in the business process modeling. In general, the meta-model is based on the following notions: – Context entity: Based on the entity concept proposed by Wang et al. [15, 16]. This concept allows contextual information to be represented as Actors, Environment and process. In fact, it’s divided into two parts: contextual attributes and relationships. – Context attribute: A Measurable Atomic Characteristic which characterizes the contexts of entities. – Contextual relationship: Allows to represent the relationships between the different Context entity, as the relationship is-located-at allows to represent the relationship between ACTOR and location. – Context element: This concept allows to characterize a Context entity. The Authors have defined two types of context elements: static and dynamic. If the value of context elements is defined, it is a static context element and dynamic if it is dynamic. – Method of capture: The concept is responsible for the extracting and collecting of contextual information from its sources. The values of this contextual information can be derived using inference rules that are defined simultaneously with the definition of the context models. – Contextual situation: A contextual situation is defined by one or more context elements with associated values. 5.2

uBPMN

In order to generalize the use of BPMN in ubiquitous environments, the authors in [14] proposed uBPMN which is an extension of BPMN. uBPMN is based on the BPMN

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meta-model as well as the new Component. It also presents the constraints of these new components expressed in OCL. Sensor Task: This is a task that uses some different types of sensors to extract contextual dimensions in the business environment. An object sensor task shares the same shape as BPMN tasks. The sensor task inherits the attributes and associations of the BPMN activity models. Reader Task: This is a task that uses smart readers such as barcodes, RFID, etc. An object task like the reader has the same shape as BPMN tasks. Collector Task: Is a task to collecting the context using sensors or smart readers. The collection often comes from the database, files or another object. It is based on the persistence data in question. A connector task object shares the same shape as the BPMN task. Smart Object: Is a statement of some specific data collected by sensor task or reader task. Smart object shares the same shape as a BPMN data object with an additional stereotype at the bottom. 5.3

Context-Aware Business Processes

The authors in [17] proposed a layered approach to conceptualize the various aspects of context based on the principle of separation between the concepts of contextual elements and context. It’s composed of three layers: • The first layer is the Meta-model Context, which refers to the elements of the context, including the situation concept and its relationships with other classes, such as the contextual element and the focus. This model will be used for each instance of the process. It allows to determine the relationship between the layers. • The second layer is the Business Process Meta-model Layer, which is the basic element in the construction of the process model. A process model element has become a contextual element if these classes extend the context element classes. In addition, inheritance relationships can be found between the relationships between all elements of the process layer and the context element in the context meta-model layer. • The third and last layer is the Domain Meta-model layer. It defines the data structure, relationships and constraints of the knowledge domain. It also specifies the basic concepts for building a domain model. Domain models created on the basis of this meta-model aim to represent the vocabulary and concepts of a knowledge domain, by describing entities, attribute, roles, relationships…

6 Synthesis Business processes Modelling is very important, because it formalizes organizational processes and helps to increase productivity. However, several approaches have been proposed to model the BP. Each approach has its advantages and disadvantages.

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In order to make a comparative study between the different approaches mentioned above and based on the main elements of BP modelling. We have identified the following comparison criteria: Complexity which measures the complexity of setting up a model, adaptability, Flexibility, formalization qui measures if is formalize well or not, context-aware to know if it’s take into account the context or not, and ontology support context-aware to know if it’s take into account the ontology or not.

Table 1. Comparative analyze of business process modeling approaches Approach

Complexity Adaptability Flexibility Formalization Ontology support ++ +++ +++ +++ ++ +++ ++ +++ + ++ + + +++ ++ + + ++ +++ +++ +++ +++

BPMN UML IDEF PSL BPMN Ontology BPAL +++ ++ CM4BPM + + UBPMN ++ +++ Context-Aware +++ + Business Processes +++: good ++: medium +: little -: not

++ ++ +++ +

+ + +++ +

+ -

Context aware +++ +++ +++

Table 1 presents a comparison between the different approaches that we have mentioned before. In practice, we cannot determine which method or technology is the best, But Through this study, we do not find approaches that take into account all the criteria mentioned above. For example, for classical approaches, they take into consideration formalization, flexibility, adaptability. For the approach [13], it take into account the context, but not ontology. While for the approach [12] it takes into consideration all these elements except Context. As a result, this comparative study may be an opening to a new proposals.

7 Conclusion and Perspective In this paper, we present several approaches for Business Process Modeling: classical approaches, ontology based-approaches and context aware approaches. To evaluate those different approaches, we had selected many comparison criteria to measure their limitations in relation to each other. Based on this comparative analysis, we found out that all these approaches respect only a certain number of essential criteria in the BP modeling. Thus we could not find an approaches that adheres to all the identified comparison criteria. Therefore, for our

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next step, we will work on a proposal that will take into account the context and the ontologies in Business Process modeling.

References 1. Schilit, B.N., Theimer, M.M.: Disseminating active map information to mobile hosts. IEEE Netw. 8(5), 22–32 (1994) 2. Chen, G., Kotzet, D., et al.: A survey of context-aware mobile computing research. Rapport technique, Technical Report TR2000-381, Department of Computer Science, Dartmouth College (2000) 3. Dey, A., Abowd, G., Brown, P., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and contextawareness. In: Handheld and Ubiquitous Computing, pp. 304–307. Springer (1999) 4. Dey, A.K.: Supporting the construction of context-aware applications. In: Dagstuhl Seminar on Ubiquitous Computing, Dagsthul, Germany (2001) 5. Xiaohang, W.: The context gateway: a pervasive computing infrastructure for context aware services, Technical report, National University of Singapore (2003) 6. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquisition 5(2), 199–220 (1993) 7. Guarino, N.: Formal ontology, conceptual analysis and knowledge representation. Int. J. Hum Comput Stud. 43(5/6), 625–640 (1995) 8. Menzel, C., Mayer, R.J.: The IDEF family of languages (1998) 9. OMG: Business Process Modeling Notation. www.omg.org 10. Michael, G., Craig, S.: Process specification language (PSL): results of the first pilot implementation. In: Proceedings of IMECE: International Mechanical Engineering Congress and Exposition, pp 1–10 (1999) 11. Rospocher, M., Ghidini, C., Serafini, L.: An ontology for the Business Process Modelling Notation, FOIS (2014) 12. De Nicola, A., Lezoche, M., Missikoff, M.: An Ontological Approach to Business Process Modeling, pp. 1794–1813 (2007) 13. Saidani, O., Rolland, C., Nurcan, S.: Towards a Generic Context Model for BPM (2015). https://doi.org/10.1109/HICSS.2015.494 14. Yousfi, A., Bauer, C., Saidi, R., Dey, A.K.: uBPMN: a BPMN extension for modeling ubiquitous business processes. Inf. Softw. Technol. 74, 55–68 (2016) 15. Wang, X.H., Gu, T., Zhang, D.Q., Pung, H.K.: Ontology based context modeling and reasoning using OWL. In: Pervasive Computing and Communications Workshops, pp. 18– 22 (2004) 16. Wang, J., Jiang, J.: Research on Flexible Management of Business Process. Commun. Comput. Inf. Sci. 308, 804–811 (2012) 17. Santoro, F., Baião, F., Revoredo, K., Nunes, V.: Modeling and using context in business process management: a research agenda. Modélisation et utilisation du contexte 17 (2017). https://doi.org/10.21494/ISTE.OP.2017.0130

River Flow Forecasting: A Comparison Between Feedforward and Layered Recurrent Neural Network Sultan Aljahdali1 , Alaa Sheta2 , and Hamza Turabieh3(B) 1

3

Department of Computer Science, Taif University, Taif, Saudi Arabia [email protected] 2 Computer Science Department, Southern Connecticut State University, New Haven, CT 06515, USA [email protected] Department of Information Technology, Taif University, Taif, Saudi Arabia [email protected]

Abstract. Forecasting the daily flows of rivers is a challenging task that have a significant impact on the environment, agriculture, and people life. This paper investigates the river flow forecasting problem using two types of Deep Neural Networks (DNN) structures, Long Short-Term Memory (LSTM) and Layered Recurrent Neural Networks (L-RNN) for two rivers in the USA, Black and Gila rivers. The data sets collected for a period of seven years for Black river (six years for training and one year for testing) and four years for Gila river (three years for training and one year for testing) were used for our experiments. An order selection method based partial auto-correlation sequence was employed to determine the appropriate order for the proposed models in both cases. Mean square errors (MSE), Root mean square errors (RMSE) and Variance (VAF) were used to evaluate to developed models. The obtained results show that the proposed LSTM is able to produce an excellent model in each case study.

Keywords: Forecasting

1

· Long short-term memory · River Flow

Introduction

Forecasting river flow is a challenging problem for water resource planning and management due to its importance to enhance the economic performance by preventing water shortages and floods [1]. The river flow patterns are always non-linear, random, dynamic and chaotic in nature. Many river flows are changing season by season and not stable due to catchment changes that created by global warming and climate change [2]. As a result, there is a need to improve forecasting methods for modeling horological systems as a time series problem. Forecasting river flow is a frame of time-series concept, where a huge data is collected with an arbitrary time horizon. Moreover, river flow levels are not stable c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 523–532, 2020. https://doi.org/10.1007/978-3-030-36778-7_58

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and unpredictable, especially in a tropical climatic environment that is affected by monsoon rainfall, causing an exponential variation in the river levels [3]. Two approaches are commonly used to build a model for river flow forecasting: (1) physical-based model that is generated from catchment fields and, (2) data-based model that depends on historically collected data [4]. Recently, several algorithms based on machine learning methods such as Artificial Neural Networks (ANN) [5], Support Vector Machine (SVM), Genetic Programming (G.P.), Fuzzy Logic (F.L.) were introduced. ANN has been widely investigated to hydrological predictions in last decay [6]. Kerh and Lee [7] applied backpropagation and conventional Miskingum neural networks to predict the Kaoping River flow with lack of measurements. The authors found that backpropagation can outperform conventional Miskingum neural network. Can et al. [8] applied an ANN model using streamflow data collected from for nine stations to predict the river flow for Coruh River in Turkey. The authors evaluated their proposed models with ARMA, and the obtained results show that ANN outperforms ARMA model. Genetic Programming (G.P.) is an evolutionary computational algorithm proposed by Koza in 1992 [9], that have been successfully applied in hydrological research area [10]. Babovic and Abbott [11] applied G.P. to solve several hydrological problems. GP approach have been employed successfully for rainfall-runoff [12], sea-level fluctuation [13], and others. A fuzzy-rule method is a qualitative modeling approach that can handle effectively dynamic, noisy, and nonlinear data set [14]. F.L. method can produce a robust, less complex, and more comprehensible model that fit complex systems. Several researchers applied F.L. successfully to predict hydrology systems. Corani et al. [15] applied a fuzzy logic with a neural network to forecast the flood for Olona River, located in Lombardia, Northern Italy. Authors in [16] employed Takagi-Sugeno Fuzzy Model to predict the Nile river flow in Egypt. In this paper, we investigate river forecasting due to its importance to enhance the economic performance by preventing water shortages and floods [17] for urban areas. We investigate models of time series for river flow predicting using DNN. DNN was successfully used for flood forecasting [18]. In general, there are several parameters that affect the next runoff, such as groundwater, precipitation, temperature, initial moisture content of the soil, etc.). It is acceptable to build an intelligent model using several parameters, but it is economically preferred to use historical discharge records for such a model. As a result, in this paper, we used the old discharge records as a time series input for a DNN. We adopt two different DNN architecture, the layered recurrent neural network, and the long short-term memory to build models for river flow forecasting. The rest of this paper is organized as follows: Sect. 2 presents the model order selection used in this research. Section 3 explores the forecasting models used. Section 4 presents the experimental data sets used. Section 5 shows the experimental results and analysis of the proposed approach. Section 6 draws the conclusion and future works.

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Model Order Selection

The wavelet transform is employed in this paper to decomposed the raw data (time series) for river flow into a single component that is used as an input to a deep learning neural network. The auto-regressive model using the partial autocorrelation function (pacf), that determines the statistical difference from zero at the lag m = 1, 2, . . . , p and zero thereafter. Equation 1 shows auto-regressive model AR(p). xt = φ1 x(t−1) + φ2 x(t−2) + · · · + φp x(t−p) + t

(1)

where φ are the auto-regressive parameters, x is the observation at time t, and  is the weight noise at time t. The autoregressive explores the correlation between current and past values. According to the auto-correlated nature of daily flow time series as shown in Fig. 1, it is clear that the delay = 5 is the most appropriate delay structure for both cases used in this paper. Partial Autocorrelation Sequence

1

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Fig. 1. The correlations of the river flow data for different time lags.

3 3.1

Proposed Forecasting Models Layered Recurrent Neural Network

Layered Recurrent Neural Network (LRNN) is a well known deep learning structure that is able to learn from previous inputs [19]. This structure has the ability to build reliable models for forecasting. This concept enables LRNN model to be used successfully in several complex domains [18]. The training process for LRNN is quite similar to a standard neural network with a little twist. The output of a neural network depends on two factors: the calculations of the current input and the output from the previous time step. This process enables LRNN to memories of previous output [20].

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Figure 4 demonstrates a basic example of LRNN. The demonstrated structure is presented at time t, where the input is a sequence L = (L1 , . . . , Lt ). The hidden vector sequence P = (P1 , . . . , Pt ) and output vector sequence y = (y1 , . . . , yt ) are evaluated iteratively based on Eqs. 2 and 3. Pt = f (WhL Lt + WP P Pt−1 + bP ) yt = f (Wyh Pt + by )

(2) (3)

The sigmoid adopted function f () has three different weight matrices: WhL , WP P , and Wyh . WhL represents the weight matrix between input and hidden layer. WP P presents a weight matrix between itself and next layer. Wyh presents weight matrix between hidden layer and output layer. The bias vectors bP and by are used to simplify the learning process for L-RNN.

Fig. 2. An example of Layer Recurrent Neural Network (L-RNN).

3.2

Long Short-Term Memory

Long short-term memory (LSTM) is a type of recurrent neural network (RNN) architecture that is proposed in 1997 by Hochreiter and Schmidhuber [21]. LSTM that have been successfully used in different domains such as forecasting [22], image processing [23], a language translation [24], and so on. The basic structure of LSTM consists of three gates: input gate (it), forget gate (ft), and output gate (ot). Each gate is a copy of input “Block” of RNN that has gating signals equation as shown in Eqs. 4, 5 and 6, respectively. Equation 7 is the “Input Block” equation. Equation 8 presents the cell-memory, and Eq. 9 shows the hidden unit/activation equation.

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it = σin (Wi xt + Ui ht−1 + bi ) ft = σin (Wf xt + Uf ht−1 + bf ) ot = σin (Wo xt + Uo ht−1 + bo )

(4) (5) (6)

c˜t = σin (Wc xt + Uc ht−1 + bc ) ct = ft  ct−1 + it  c˜t

(7) (8)

ht = ot  σ(ct )

(9)

where xt is an input data vector with dimension m, ct presents the memory state with dimension n. W∗ and b∗ is the weight matrix and bias vector for each gate. More details about LSTM can be found in [25].

4

Case Studies

In this paper, two case studies are used for two different rivers in the USA: Black River and Gila River. The datasets are collected by the U.S. Geological Survey (USGS). The locations for both rivers are shown in Fig. 3. The training dataset of six water years (01 Oct 1990 to 30 Sept 1996) for Black River, while testing dataset of one water year (01 Oct 1996 to 30 Sept 1997). Gila river training data was three water years (01 Oct 1995 to 30 Sept 1998) and one water year for the testing dataset (01 Oct 1998 to 30 Sept 1999). The datasets used in this research is available at https://waterdata.usgs.gov/nwis.

Black river.

Gila river.

Fig. 3. The Locations of Black and Gila rivers in USA.

5

Results and Analysis

To examine the performance of employed models based on LRNN and LSTM, several experiments were performed using MATLAB environment. First, the

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auto-correlation approach is examined to determine the suitable lag for each case study. The best correlation of Black and Gila Rivers found to be five. Three evaluation criteria are used in this work: Mean square errors (MSE), Root mean square errors (RMSE) and Variance (VAF). Equations 10, 11, and 12 demonstrate the calculations for criteria, 1 (yi − yˆi )2 n i=1 n

M SE =

(10)

  n 1  RM SE =  (yi − yˆi )2 n i=1 V AF = [1 −

(11)

V AR(yi − yˆi ) ] × 100% var(yi )

(12)

Table 1. Forecasting results for Black river dataset. LRNN Train Test VAF

LSTM Train Test

96.327 95.2455 97.643 95.642

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0.037

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Table 1 shows the obtained results for Black River case study. It is clear that the performance of LSTM outperforms LRNN over training and testing datasets. Figures 4 and 5 show the variation between actual and estimated water flow for training and testing data using LRNN amd LSTM models. It is clear that the estimated values are close to the observed one. Table 2. Forecasting results for Gila river data set. LRNN Train Test

LSTM Train Test

MSE

0.189

0.195

0.81

0.166

RMSE

0.435

0.441

0.28

0.407

Vaf

81.065 80.741 97.643 95.642

Table 2 shows the obtained results for Gila river. The performance of LSTM is 97.643 and 95.642 for training and testing dataset based on VAf value. It is obvious that LSTM model performs much better than LRNN model. Figures 6 and 7 shows the variation between actual and estimated water flow for training and testing data using LRNN and LSTM models.

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6

Conclusion and Future Works

River flow forecasting is essential process to enhance the economic performance by preventing water shortages and floods. In this paper, we employed two different deep learning neural network to forecast river flow. LRNN and LSTM were used as a predictive models. Auto- correlation approach is employed as a

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reprocessing step to determine an appropriate delay. The performance of LSTM model outperforms LRNN model for both cases and able to generate an appropriate model. Our future work aim to combine LSTM with Convolutional Neural Network to enhance the learning process for LSTM.

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An Experimental Artificial Neural Network Based MPP Tracking for Solar Photovoltaic Systems Yassine Chouay(&) and Mohammed Ouassaid Engineering for Smart and Sustainable Systems Research Center, Mohammed V University, Mohammadia School of Engineers (EMI), Rabat, Morocco [email protected], [email protected]

Abstract. This paper proposes an improved maximum power point tracking (MPPT) strategy based on Artificial Neural Network (ANN) to improve the efficiency of PV system. The proposed ANN controller tracks the MPP by estimating and adjusting the duty cycle of the DC-DC converter according to the climatic conditions (irradiance and temperature). The MPPT was trained using the measurement of the mean value of duty cycle given by a perturb and observe (P&O) algorithm under a variation of climatic data. The performances of the proposed tracking method are tested via simulation and experimental verification. The simulation results, proved that the proposed method is more efficient and can provide more energy, with less oscillation and overshoot, in comparison to the conventional P&O MPPT method. The performance of the proposed algorithm was also confirmed experimentally using dSPACE platform under relatively steady-state conditions. Keywords: Photovoltaic (PV)
Maximum Power Point Tracking (MPPT) Artificial Neural Networks (ANN)
Perturb and Observe (P&O)




1 Introduction As it is well known, the use of photovoltaic systems (PV) is growing rapidly over the world due to their sustainability and their environmental and economic benefits as one of the cleanest renewable energy. However, the PV technology still suffers from efficiency limits. To tackle this drawback, control techniques such as Maximum Power Point Tracking (MPPT) have been proposed to exploit the maximum available power. MPPT algorithms usually force the PV system to operate at its maximum power point by adjusting the DC-DC converter duty cycle, according to the operating conditions and thus the system production. The PV modules are characterized by a non-linear curve that varies with the continuous variation in the climatic conditions such as irradiance and temperature. This causes a time-varying maximum power point (MPP) problem, and thus a continuous calculation of the duty cycle with maximum accuracy and rapid convergence speed. In this context, various MPPT algorithms are introduced in the literature; they can be classified into two main categories, namely the conventional and soft computing © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 533–542, 2020. https://doi.org/10.1007/978-3-030-36778-7_59

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methods. For instance, the most used conventional MPPT methods include Perturb and Observe (P&O) [1], Hill Climbing (HC) [2] and Incremental Conductance (InCond) [3]. In a constant environmental condition, (uniform irradiance), these techniques are capable of tracking the MPP with an acceptable efficiency and a good convergence speed. In addition, instead of tracking the exact value of the MMP, these methods can only oscillate continuously around it. The oscillations can result in an important power loss during steady state. Some studies were carried out to minimize this oscillatory behavior, but it can be only achieved by sacrificing the tracking speed [4]. In order to overcome some of the conventional algorithms problems, newer MPPT techniques based on soft computing (SC) are proposed. These algorithms include Artificial Neural Network (ANN) [5], Fuzzy logic Controller (FLC) [6], Differential Evolution algorithm (DE) [7], Genetic algorithm (GA) [8], Particle Swarm Optimization (PSO) [9] and Ant Colony Optimization (ACO) [10]. Despite their advantages, SC algorithms are generally more complex and slower than the conventional methods. Moreover, due to their use of intensive calculations, some of these algorithms need to be implemented using expensive microcontrollers. In this paper an ANN-based MPPT algorithm to estimate the duty cycle directly from the measurements of climatic conditions is designed. The neural network is trained using a conventional P&O response under a variation of climatic conditions with a small duty cycle step. This allows the proposed tracker to compute instantly and with high accuracy the DC-DC converter duty cycle value instead of oscillating around it. Furthermore, a simple ANN structure is used with minimal layers and neurons to reduce computational complexity and therefor the implementation hardware requirement. The rest of this paper is organized as follows. In Sect. 2, the general mathematical model of the PV module is presented. The proposed ANN-based MPPT algorithm is introduced in Sect. 3. Section 4, presents the simulation results based on comparison with the conventional P&O. Experimental verification is presented in Sect. 5. Finally, the conclusions are drawn in Sect. 6.

2 PV Module Model and MPPT Variation Many mathematical models have been developed in the literature to describe the PV behavior. The one-diode equivalent circuit-based model adopted in this work is mostly used for monitoring and assessing the PV performance and exploring different PV Maximum Power Point Tracking (MPPT) MPPT techniques. This model is based on five parameters represented by a photo-current source, a diode, a parallel-resistor and a series-resistor describing the internal resistance to the current flow. The PV systems as describe this model, exhibit a nonlinear I–V and P–V characteristics which vary with the variation of the operating climatic conditions. Figure 1a shows the effect of solar irradiance intensity on the characteristic of a PV module. The current varies proportionally with the variation of incident irradiance. On the contrary, the voltage decreases with the increase in operating temperature (Fig. 1b). Therefore, the MPP is also affected and varies with the variation of the operating climatic

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conditions. As a result, a good knowledge of these parameters may allow a better tracking of the exact location of the MPP. 4

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The aim of this work is to conceive a MPPT controller based on the measurements of irradiance and operating temperature to extract the PV system maximum power. An ANN algorithm was chosen to achieve this goal, and training set of data is extracted from a conventional P&O algorithm configured to maximum accuracy.

3 The Proposed ANN MPPT Controller 3.1

The Proposed System Scheme

The adopted system consists of a PV generator connected to a resistive load via a DCDC converter controlled by the proposed MPPT controller to extract and deliver the maximum power as illustrated in Fig. 2.

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The ANN controller calculates the DC-DC converter switching duty cycle from the measurements of the PV generator from the operating irradiance and temperature. The maximum extracted power is then transferred to the load. The adopted converter in this paper is a boost converter based on a metal–oxide– semiconductor field-effect transistor (MOSFET) switch. The output voltage of the converter and photovoltaic voltage are related to the duty cycle by mean of the following equation: VOUT 1 ¼ 1a VPV

ð1Þ

where a is the switching period duty cycle, VOUT is the average DC-DC converter output voltage and VPV is the PV system input voltage. 3.2

Artificial Neural Network

In this paper, an ANN is used as a MPPT controller to estimate the DC-DC converter duty cycle directly from the measurement of climatic conditions (solar irradiance and module temperature). The ANN controller has been formed through three essential steps: Input-output variable selection: The adopted ANN controller consists of two neurons in the input layer corresponding to the measured values of irradiance (G) and temperature (T). The output layer, is represented by one output neuron corresponding to the duty cycle (D) value. Network architecture selection: As mentioned before, the network used in this work is a three-layered-feed-forward neural network, consisting of two neurons in the input and one in the output layer. One hidden layer contains 8 nodes as described in Fig. 3. The network uses the tangent sigmoid as a transfer function for the hidden layer and the purelin function for the output layer. Network training and testing: The training phase is the most important step that adapts the network to its application. To acquire the training data set, the duty cycle of a boost converter connected to a testing PV panel was taken under a variety of climatic data. Both inputs (G and T) and outputs D were recorded using a conventional P&O algorithm. The P&O perturbation step was reduced to achieve smaller oscillation around the duty cycle value, and only its mean value was taken as outputs of the network. The training set was implemented in MATLAB/SIMULINK environment through different operating points under a variation of irradiance from 100 to 1300 W/m2 and temperature values from 0 to 70 °C. Therefore, a training set of 600 samples was generated. The network is trained with the Levenberg-Marquardt (LM) algorithm. 80% of the patterns have been used for the training, while 20% have been used for testing and validating the model.

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4 Simulation and Results 4.1

ANN Algorithm Training Performance

A well-trained ANN should perform a generalization of new data. In other words, the network must be able to perform with accuracy on data outside the training set. Figure 4 depicts the performance plot of the ANN model in terms the Mean Squared Error (MSE). As can be noticed the regression plot displays a high correlation coefficient during training, testing and validating the ANN model. Furthermore, the regression (R) values are close to 1 indicating a perfect relationship between outputs of the network and the desired targets.

Fig. 4. The trained ANN regression plot.

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Effectiveness of the Proposed Tracker

In order to test and validate the proposed technique, the schematic presented in Fig. 2 was simulated. The main idea is to compare the ANN algorithm estimated values of the duty cycle and thus the output power with those given by a conventional P&O. The PV module having the characteristics illustrated in Table 1, was used as the PV generator. The adopted DC-DC converter is the same boost converter used during the phase of data generation. Table 1. The adopted solar PV panel parameters at 25 °C, AM1.5, and 1000 W/m2. Parameter Immp Vmmp Pmax ISC VOC NS A

Value 2.39 A 21 V 50 W 2.7 A 24.8 V 42 1.3

Figure 5 represents a comparison between the variation of the duty cycle of the proposed technique and the P&O controller against a sudden irradiance variation. As expected, the proposed technique computes instantaneously a constant value of the duty cycle instead of oscillating around it. 45

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The maximum power extracted using each controller under STC is represented in Fig. 6. The graph shows that the maximum power extracted by both algorithms matches the PV panel maximum power specified by the manufacturer (Table 1). Moreover, the proposed ANN controller reaches the maximum power faster than the P&O with

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less oscillations. The convergence speed of the proposed technique is 0.010 s against 0.094 s for the P&O algorithm.

Fig. 6. Power tracking of each algorithm under STC.

Figure 7 illustrates the shape of the extracted PV power, under a sudden irradiance variation for both algorithms. The results show that the proposed technique follows the sudden changes in the irradiance while the P&O algorithm takes much more time to reach the MPP with more oscillations. 70 2

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5 Experimental Results In order to confirm the functioning and the performances of the simulated ANN tracker, the proposed technique was implemented using dSPACE CLP1104 interface. The adopted PV generator has the same characteristics as the one used in simulation phase (Table 1). The same configuration of the boost converter and the load was realized with

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an identical switching frequency. The bench test contains a PV generator, a boost converter connected to the load, an irradiance and temperature sensors and dSPACE controller as shown in Fig. 8.

Fig. 8. Bench test of the proposed controller.

Normally, for a proper test of the performance of the proposed technique with reference to the P&O controller, these controllers should be implemented into two identical generators and converters under the same conditions. In order to avoid the mismatch between the two testing systems, only one system with the possibility to switch between the two algorithms in real time is used. In this case, the controller contains both trackers (ANN-based and the P&O). The test results are shown in Fig. 9 in term of the extracted power, the duty cycle variation and climatic conditions under each algorithm. As expected, the ANN algorithm gives an instantaneous and more accurate estimations of the duty cycle under constant climatic conditions instead of oscillating around its value. It can also be noticed that ANN estimations are not affected by the noise in the irradiance and temperature measurements. This enhances also the extracted power in term of accuracy, oscillations and response time. The slow response time of

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Fig. 9. MPPT parameters while switching between ANN and P&O.

the P&O algorithm is also noticeable, as the duty cycle is recalculated, taking much more time than the ANN controller. Consequently, it can be concluded that the use of irradiance and temperature to directly estimate the duty cycle gives better accuracy and an improved response time.

6 Conclusion A neural network-based maximum power point tracker has been presented in this paper. A conventional P&O algorithm is used to generate the training data by taking in consideration irradiance and temperature and the mean value of the duty cycle. The simulation results have proven that the trained ANN tracker is able to estimate the DCDC converter duty cycle with high accuracy from the measurements of irradiance and operating temperature. This allowed a good tracking of the maximum power with instantaneous response time and less oscillation compared with the P&O algorithm. An experimental implementation of the proposed trackers was also carried out using dSPACE. The results have confirmed that the proposed controller shows a fast convergence speed and less oscillation around the MPP.

References 1. Femia, N., Petrone, G., Spagnuolo, G., Vitelli, M.: Optimization of perturb and observe maximum power point tracking method. Power Electron, IEEE Trans. 20, 963–973 (2005) 2. Zhu, W., Shang, L., Li, P., Guo, H.: Modified hill climbing MPPT algorithm with reduced steady-state oscillation and improved tracking efficiency. J. Eng. 2018(17), 1878–1883 (2018)

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3. Lin, C.-H., Huang, C.-H., Du, Y.-C., Chen, J.-L.: Maximum photovoltaic power tracking for the PV array using the fractional-order incremental conductance method. Appl. Energy 88, 4840–4847 (2011) 4. Al-Amoudi, A., Zhang, L.: Optimal control of a grid-connected PV system for maximum power point tracking and unity power factor. In: Power Electronics and Variable Speed Drives, 1998 Seventh International Conference on (Conf Publ No. 456), pp. 80–85 (1998) 5. Agha, H.S., Koreshi, Z., Khan, M.B.: Artificial neural network based maximum power point tracking for solar photovoltaics. In: 2017 International Conference on Information and Communication Technologies (ICICT), Karachi, pp. 150–155 (2017) 6. El Khateb, A., Rahim, N.A., Selvaraj, J., Uddin, M.N.: Fuzzy-logic-controller-based SEPIC converter for maximum power point tracking. In: IEEE Transactions on Industry Applications, vol. 50, no. 4, pp. 2349–2358, July 2014–August 2014 7. Tey, K.S., Mekhilef, S., Seyedmahmoudian, M., Horan, B., Oo, A.T., Stojcevski, A.: Improved differential evolution-based MPPT algorithm using SEPIC for PV systems under partial shading conditions and load variation. IEEE Trans. Ind. Inform. 14(10), 4322–4333 (2018) 8. Sundareswaran, K., Vigneshkumar, V., Palani, S.: Development of a hybrid genetic algorithm/perturb and observe algorithm for maximum power point tracking in photovoltaic systems under non-uniform insolation. In: IET Renewable Power Generation, vol. 9, no. 7, pp. 757–765 (2015) 9. Koad, R.B.A., Zobaa, A.F., El-Shahat, A.: A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems. IEEE Trans. Sustain. Energy 8(2), 468–476 (2017) 10. Kefayat, M., Lashkar Ara, A., Nabavi Niaki, S.A.: A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. In: Energy Conversion and Management, vol. 92, pp. 149–161 (2015)

Mobile Data Collection Using Open Data Kit Patrick Loola Bokonda1(B) , Khadija Ouazzani-Touhami1,2(B) , and Nissrine Souissi1,2(B) 1 2

EMI, Siweb Team, E3S, Mohammed V University in Rabat, Rabat, Morocco [email protected], {ouazzani,souissi}@enim.ac.ma Department of Computer Science, MINES-RABAT School, Rabat, Morocco

Abstract. In an era where mobile devices are spreading around the world and becoming cheaper, many organizations are turning to mobile data collection. Open Data Kit (ODK) is one of the most well known mobile data collection framework. ODK has two suites of software: ODK and ODK-X. The purpose of this paper is to propose two architectures, one for ODK and an other for ODK-X (formely ODK 2); these architectures show data trajectories from the creation of the forms until to collected data visualization, and also help the user to choose between these two suites, the one that best fits his needs and context. In addition, this paper present a comparative study of several mobile data collection systems. This comparative study concluded that, among plenty frameworks, ODK and ODK-X are the ones that offer a complete and fully open source mobile data collection solution. Keywords: Data collection · Mobile data · Mobile device Data Kit · ODK · ODK 2 · ODK-X · Tool · Architecture

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Introduction

Data collection, storage and analysis are essential tasks in any organization [1–3]. Since time immemorial, compagnies, non-governmental organization (NGO), as well as governments have expressed the desire to have tools adapted to perform these tasks for various needs. The use of paper forms was one of the first responses to this problem. But the difficulty of collecting data from geographically very distant backgrounds using paper forms, for example during a census in a country like the Democratic Republic of Congo (DRC) which has an area of 2,345,409 km2 [4], as well as the difficulty of keeping in paper format a large and ever increasing number of information, in addition to transcription errors have demonstrated the limitations of this solution. A relatively recent invention comes to save the day: Smartphones. Marketed for the first time around 2007 [5], smartphones have taken a very important c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 543–550, 2020. https://doi.org/10.1007/978-3-030-36778-7_60

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place in today’s society. This, whether in developed countries or in developing countries. This broad expansion of smartphones is mainly due to their portability, ease of use, the variety of applications they offer and their declining prices. This new tool has inspired researchers and engineers to replace paper forms with mobile solutions for collecting, storing and analyzing data. Open Data Kit (ODK) [6] is one of the best-known software suites in this field and many researchers have worked to make it evolve to be highly efficient to better meet the diverse needs of users. This paper has four goals: (1) Introduce Open Data Kit (ODK) and Open Data Kit X (ODK-X) suites tools to users interested in mobile data collection; (2) propose two new architectures, one for ODK and an other for ODK-X; (3) Guide the user in the use of these two suites; (4) Help the user choose between these two suites, the one that best fits his needs and context. In order to address the above three objectives, this article is subdivided as follows: Sect. 2 presents a comparison with other mobile data collection solutions and explains the choice of presenting ODK and ODK-X in this paper; Sect. 3 presents the architecture, tools and operation of ODK; Sect. 4 presents the architecture, tools and operation of ODK-X; Sect. 5 explains in which case it is better to use ODK or rather ODK-X while showing the advantages and disadvantages of both; Sect. 6 presents the conclusion of this article.

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When creating ODK [7], other mobile data collection systems already existed. The best known of these are CyberTracker [8] and Pendragon [9]. Despite their existence, the will to propose ODK was based on the following motivations: – The very limited use cases and interactions supported by CyberTracker. CyberTracker is a system created in the mid-1990s to collect large amounts of geo-referenced data for illiterate field observers and synchronizing those data on a local computer [7]. ODK, by contrast, has been designed to support multiple use cases while allowing synchronization with a remote server; – Unlike CyberTracker, Pendragon supports a wide range of use cases by offering forms with logical navigation, multimedia support, form designer, and data synchronization. But the big difference with ODK is that Pendragon is a commercial solution while ODK is an open source. Which makes a big difference for the users. For example, for the functionality ODK provides for free, Pendragon Forms requires $80 per user; – Another difference between ODK and Pendragon is the ease of extensibility. The term “open source software” refers to a software that people can use, modify and share because its design is publicly accessible [10]. This characteristic of ODK allows organizations to easily expand the system to add the features they need. In addition ODK runs on a variety of mobile devices and supports multiple methods of transferring data to other services.

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There are open source alternatives to Pendragon including Frontline Forms [11], EpiSurveyor [12], CommCare [13], and JavaRosa [14]. But all these systems are Java Micro Edition (J2ME) platforms. J2ME is an ecosystem that does not facilitate the development and use of software. One of the disadvantages of this platform is that J2ME developers are forced to test each software version on each of the physical devices they wish to support because each device implements the interfaces diferently. In addition to this, despite their open source nature, these applications nevertheless require obtaining a certificate and an electronic signature before any interaction with storage, hardware accessories or networking can be made [7]. More generally, what differentiates ODK from other solutions is its focus on providing a suite of inter-operable tools that (1) aims to be customizable to a deployment context by a non-programmer and (2) can operate in disconnected environments [15]. This goal makes ODK different from a project such as Uju [16] which is designed to facilitate the creation of database-driven SMS applications, but unlike ODK-X, it was not designed to interact with other tools to obtain data from different inputs (eg sensors, paper forms) [17].

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ODK is a mobile data collection tool suite. It has desktop and mobile tools for collection and management of data. This section presents it tools and proposes a new architecture with data trajectories. 3.1

Tools

The main tools of this suite are six, according to the official documentation [18]. In this study, for a better understanding of the ODK architecture, the six tools were grouped under three entities (A, B and C). 1. Entity A: Desktop Clients This entity has three tools: ODK Build and ODK XLSForm to design forms and ODK Briefcase that helps to import and export forms on servers [18]. 2. Entity B: Servers ODK offers two storage tools:ODK Aggregate and ODK Central [18]. 3. Entity C: Mobile Client It contains the ODK mobile tool for collecting data: ODK Collect [18].

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Architecture

The architecture, presented in Fig. 1, proposes to the user 4 possible trajectories ((a), (b), (c) and (d)), from the creation of the forms until to the data storage. – 1(a), (b), (c), (d): the form is created using ODK XLSForm or ODK Build and stored in the computer in .xml format for use by ODK Briefcase;

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Fig. 1. ODK architecture

– 2(a), (b): Using ODK Briefcase, the previously created and saved form is uploaded to the server via the internet; – 3(a), (b): Using ODK Collect (on a mobile device), you can download the form from the server; – 4(a): After data collection, the completed form is uploaded again to the server via the internet; – 5(a): The form is then imported from the server to the computer via ODK Briefcase, via the Internet; – 4(b): ODK Briefcase also gives the possibility to import the form directly from ODK Collect. The form can then follow the workflow (a), if necessary; – 2(c), (d): The form can be directly transferred from the local computer to the mobile device, by means of a cable or a wireless network (WiFi, Bluetooth ...). To do this you must connect the mobile device to the pc then place the form (the .xml file) in the odk folder of the mobile device. The odk folder can either be in the memory of the mobile device or on the storage card; – 3(c): After data collection, the mobile device can be connected again to the pc and the form containing the data transferred to pc. The form can then follow the path (a) or (b) according to the choice made; – 3(d): At this point, there is also the possibility of using the Internet again,to send filled forms to the server.

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ODK-X is the new version of ODK suite [18]. This section presents it tools and proposes a new architecture with data trajectories.

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The official documentation presents seven main tools [18]. In this study, for a better understanding of the ODK-X architecture, the seven tools were grouped under three entities (A, B and C). 1. Entity A: Desktop Clients This entity has two tools: ODK Application Designer to create the data management applications in ODK-X, ODK Suitcase to provide access to the data stored in the ODK-X Server [18]. 2. Entity B: Servers ODK Cloud Endpoints is the global name of servers that communicate with ODK-X applications [18]. There is two Cloud Endpoints for communication with ODK-X tools: ODK Sync Endpoint, which is the ODK-X server and ODK Aggregate Tables Extension [18], which refers to all v1.x.x of ODK Aggregate server with a special configuration to support ODK-X tools. 3. Entity C: Mobile Clients ODK-X has three mobile clients applications: ODK Survey and ODK Tables to collect visualise and update data [18], ODK Services to synchronize data between both ODK-X mobile applications and manage the database and file access service [18].

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Architecture

The architecture, presented in Fig. 2, proposes to the user 4 possible trajectories ((a), (b), (c) and (d)), from the creation of the forms until to the data storage.

Fig. 2. ODK-X architecture

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– 1(a), (b), (c), (d): The form is created using the ODK Application Designer and stored in the hard disk of the computer in .csv format to be sent to the server by ODK suitcase; – 2(a), (b): Using ODK Suitcase the previously created and saved form is uploaded to the server via the internet; – 3(a), (b): ODK Services allows both mobile components (Survey and Tables) to import the form from the server; – 4(a): After data collection, the completed form is uploaded again to the server via the Internet; – 5(a): The form is then imported from the server to the computer using ODK Suitcase via the internet; – 4(b): ODK Suitcase does not provide the ability to import the form directly from ODK Survey or ODK Tables without going through the server, the use of connected mode is necessary. But this can be done by connecting the mobile device to the pc (via a cable or wireless network) and then copy the form to the hard disk of the pc. The form can then follow the path (a); – 2(c), (d): Via a cable or wireless network, you can directly pass a form from the pc to the mobile device. To do this you must connect the mobile device to the pc then place the form (the .json file) in the odk folder of the mobile device. The odk folder can either be in the memory of the mobile device or on the memory card; – 3(c): After data collection, the mobile device can be connected to the computer again to transfer the filled form. The form can then follow the path (a) or (b) according to the choice made; – 3(d): At this point, there is also the possibility to use the internet again, to send the data (filled form) to the server; – A(1) and A(2): Data is collected using ODK Survey; – B(1) and B(2): Data is collected using ODK Tables; – *AB: Shows the possibility of communication between ODK Survey and ODK Tables. Data collected via ODK Survey can be viewed and even updated with ODK Tables.

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Analysis and Discussion

For a technology to be really useful to many people in different environments, it must be designed to be adapted to the environmental constraints in which it is deployed. The two architectures above show ODK’s ability to adapt to different environments. ODK has been designed to be deployed and used without much difficulty even in challenged network environments. For example, after installing the tools, the trajectory (c) composed of 1(c), 2(c) and 3(c) proposed in both architectures, gives the possibility of using ODK in offline mode. While the trajectory (a) suggests a fully connected use. The trajectories (b) and (d) are most suitable for environments where the Internet connection is not stable, because they combine within them the possibility of using the connected mode and/or the disconnected mode.

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To make the choice between ODK and ODK-X, first of all note that ODK-X is the new name of ODK-2.0 sometimes called ODK-2. This name change, which is very recent, was made to avoid any confusion among users. It was tempting to look at the version number and assume that ODK-2.0 is better than ODK, but this is not always the case. The ODK-X (ODK-2.0) tool suite was designed to co-exist with the ODK tool suite and does not replace any 1.x tools [18]. The ODK tools are more easy to use than ODK-X tools and have a large user community. Most of the time ODK tools are sufficient for ordinary studies. However, if the process of the study is very complex and ODK does not cover it sufficiently and that technical skills are not lacking, ODK-X is the best choice [18]. The additional features of ODK-X which are not in ODK are: One to many mapping between a mobile survey field and database fields; Dynamic input contraint checks; Fully customizable mobile survey using HTML/JavaScript; Bidirectional synchronization of collected data across devices; Visualizations of collected data available on the device; Link longitudinal data to collected data; User permissions for row filtering of data available on the device [18].

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Conclusion

This work is about mobile data collection. To do this, several platforms have been studied. Open Data Kit (ODK), a mobile data collection platform, differs from other systems because it offers a complete and fully open source solution and its main objective is to provide a suite of inter-operable tools that aims to be customizable to a deployment context by a non-programmer and can operate in disconnected environments. ODK offers two tool suites: ODK and ODK-X (formerly known as ODK2.0). The ODK-X Tool Suite is a new set of ODK tools. It is more complex and customizable than ODK. While ODK offers only one mobile application: ODK Collect; ODK-X offers three: ODK Survey, ODK Tables and ODK Services. This paper propose an architecture and data trajectories for ODK as well as for ODK-X. This architectures have only retained the tools that are part of the official ODK and ODK-X suites and are not in the experimental stage.

References 1. El Arass, M., Souissi, N.: Data lifecycle: from big data to SmartData. In: IEEE 5th International Congress on Information Science and Technology (CiSt), Marrakech, pp. 80–87 (2018) 2. Tikito, I., Souissi, N.: Data collect requirements model. In: Proceeding of the BDCA 2017, 28–30 March, Tetuan (2017) 3. El Arass, M., Tikito, I., Souissi, N.: Data lifecycles analysis: towards intelligent cycle. In: Proceeding of The second International Conference on Intelligent Systems and Computer Vision, ISCV 2017, F`es 17–19 April, Fez (2017) 4. Wikipedia contributors: Democratic Republic of the Congo. Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/wiki/Democratic Republic of the Congo

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5. Wikipedia contributors: Smartphone. Wikipedia, The Free Encyclopedia. https:// en.wikipedia.org/wiki/Smartphone 6. Anokwa, Y., Hartung, C., Brunette, W., Borriello, G., Lerer, A.: Open source data collection in the developing world. Computer 42(10), 97–99 (2009) 7. Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., Borriello, G.: Open data kit: tools to build information services for developing regions. In: Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development - ICTD (2010) 8. CyberTracker. http://cybertracker.co.za 9. Pendragon Forms. http://pendragonsoftware.com 10. Shiferaw, S., Workneh, A., Yirgu, R., Dinant, G.-J., Spigt, M.: Designing mHealth for maternity services in primary health facilities in a low-income setting - lessons from a partially successful implementation. BMC Med. Inform. Decis. Mak. 18, 1 (2018) 11. FrontlineSMS. http://frontlinesms.com 12. EpiSurveyor. http://datadyne.org 13. CommCare. http://dimagi.com/commcare 14. JavaRosa. http://bitbucket.org/javaros 15. Brunette, W.: Building mobile application frameworks for disconnected data management. In: Proceedings of the 2017 Workshop on MobiSys 2017 Ph.D. Forum Ph.D. Forum (2017) 16. Wei-Chih, L., Tierney, M., Chen, J., Kazi, F., Hubard, A., Pasquel, J.G., Subramanian, L., Rao, B.: UjU: SMS-based applications made easy. In: Proceedings of the First ACM Symposium on Computing for Development - ACM DEV (2010) 17. Brunette, W., Sudar, S., Sundt, M., Larson, C., Beorse, J., Anderson, R.: Open Data kit 2.0: a services based application framework for disconnected data management. In: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services - MobiSys (2017) 18. Open Data Kit Documentation. https://docs.opendatakit.org

Two Quantum Attack Algorithms Against NTRU When the Private Key and Plaintext Are Codified in Ternary Polynomials El Hassane Laaji1(B) , Abdelmalek Azizi1 , and Siham Ezzouak2 1

2

Mohammed First University, Oujda, Morocco [email protected], [email protected] Sidi Mahammed Ben Abdellah University Fes, Fes, Morocco [email protected]

Abstract. Our cryptanalysis is focused on the NTRU second round candidate submitted to National Institute of Standards and Technology (NIST) competition. The NTRU domain is the ring Rq = Zq [X]/(X n −1) with the private keys and the plaintext are codified in ternary polynomials, that means all their coefficients are in {−1, 0, 1}. Our two quantum attack algorithms namely KA NTRU and PA NTRU, inspired from Grover’s Algorithm, targeted respectively to find Private Keys and Plaintext. To test the proposed algorithms, we create a test release named NTRU Attacks that integrate the principal cryptographic functions and the two attacks functions. In the general case, the quantum algorithms can break a system of dimension n in 2n/2 times. Keywords: NTRU · NewHope · Lattice- based-cryptography quantum cryptography · Grover’s algorithm

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Introduction

The big concern of the cryptographic community now, is to build new cryptosystem Post-Quantum cryptosystems able to resist against the quantum computer attacks, because the near future the cryptosystems used right now like RSA, ECDH, El Gamal will be easily broken by the quantum computer using quantum algorithms like Grover’s algorithm, Shor’s algorithm or other algorithms, “Quantum computer using a quantum algorithm can solve a problem of dimension n in 2n/2 time” [1]. This is why the National Institute of Standards and Technology (NIST) launch a competition since 2016 and it is still in process for choosing one or more post-quantum cryptosystems [2]. The NTRU team submitted four variants with different parameters set introducing both approach the Public Key Encryption (PKE) and Key Exchange Mechanism (KEM) namely NTRUhps2048509, c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 551–562, 2020. https://doi.org/10.1007/978-3-030-36778-7_61

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NTRUhps2048677, NTRUhps4096821 and NTRUhrss701 [3]. It is amongst the most important Lattice-Based submissions to the NIST competition [3]. It uses the hardest problems on points lattices in Rn like SVP (Short Vector Problem) and CVP (closet Vector Problem). the NTRU assumption is defined by: “Having h = g/f it is hard to find f and g. The NTRU assumption can be reduced to the uSVP for the NTRU lattices” [4]. 1.1

Related Works

The most attacks used to check the robustness of Lattice-Based Cryptosystems are the reduction algorithm attack and Meet-In-the-Middle (MIM) attack developed by (Oldgz) [4], or the hybrid attacks that use the mixture of the reduction attack and MIM attack [5]. Others types attacks exist like Cold Boot attack developed by Albrecht et al. [6] and another aspect of security problems named Side-channel attack developed by Kocher [7]. 1.2

Our Work

In this work based and inspired from Grover’s algorithm and Scott Fluhrer work [8], we present two attacks algorithms on NTRU implementation level. The first attack named KA NTRU targeted to find the private keys. It is based on the NTRU assumption defined by “Having the public key h and h = g/f , it is hard to find the private keys f and g”. The second attack named PA NTRU targeted to find the plaintext and it is based on the assumption defined by “Having the ciphertext c and the public key h with c = r ∗ h + m, it is hard to find the plaintext m”. To demonstrate the proposed attacks, we re-implemented new release of NTRU named NTRU Attacks which include the principal cryptographic functions and the two attacks functions. We not that all our test is performed in computer PC Toshiba Core™i7, 2 GHz, RAM 8Go under Windows7 platform and DevC++ Development environment. The NTRU scheme chooses all the ternary polynomials according to a Uniform Distribution (UFD), indeed, an attacker can use the Quantum Computer and our attacks to break the system by sampling simultaneously the coefficients of the ternary polynomials to find respectively the private keys and plaintext as we are going to describe in Sect. 5. 1.3

Outline

The remainder of our work is organized as follows: Section 1: this introduction; Section 2: preliminaries: We start by noting and defining the terminology used in this paper. Then we recall the necessary knowledge of the Lattice-BasedCryptography, a brief description of sampling methods Uniform distribution (UFD) and we also give a brief description of quantum algorithms. Section 3: We recall the related work’s description of the principal attacks on NTRU cryptosystem.

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Section 4: We give an overview of the original NTRU schemes and the releases submitted to NIST competition; Section 5: We present our two attacks algorithms KA NTRU and PA NTRU against NTRU to recover respectively the private keys and the plaintext. Section 6: In this section, we present the principal specification of our attack implementations and our new release NTRU Attacks implementation. Section 7: We draw conclusions and some topics that may provide interesting research area.

2 2.1

Preliminaries Notations

In the remainder of this paper, we use the following notations: LBC for LatticeBased-Cryptography; L(B) Lattice of Rn with base B = (e1 , ..., en ) with the form L(B) ={ v ∈ Rn , (a1 , ..., an ) ∈ Zn and v = a1 e1 + ... + an en }; R = Z[X]/(X n − 1) the ring polynomial with coefficient in Zn ; and Rq the quotient ring polynomial with coefficients in [0 , q]; v the norm of vector v; vs = max|vi | the low-norm; (a, b, ..) uppercase the elements of R; (a, b, ...) lowercase the coefficients of elements of R; the inner product of v and u; we refer to sampUFD(seed) the polynomial sampled according to Uniform Distribution. We refer to ternary polynomials. T (d1 , d2 ) = a(x ) ∈ R with the polynomial a(x) has d1 coefficients equal to 1 ; d2 coefficients equal to −1 and the rest of coefficients equal to 0. 2.2

Lattices Problems

LBC is one of the most attractive areas of post-quantum cryptography. It is based on mathematical concepts and theories to encrypt and decrypt as well as to demonstrate the complexity and the difficulty of breaking those cryptographic systems by posing problems that are hard to solve. The principal Lattice problems are SVP and CVP and their approximate versions defined as follow: The Shortest Vector Problem (SVP) [9]: Finding (SVP) in Lattice L( B) is finding a non-zero vector that minimizes the Euclidean norm. Formally the problem SVP is to find a non-zero vector: v ∈ L(B)

∀x ∈ L(B)

we

have v ≤ x.

(1)

The Closest Vector Problem (CVP): Given the Lattice L(B) , and a vector w ∈ Rm to find a vector v ∈ L(B) “Closest” to w, is to find a vector v ∈ L(B) that minimizes the Euclidean norm w − v where: w − v = min{w − v/v ∈ L(B) }.

(2)

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LBC Cryptosystems

There are different cryptosystems based on LBC the first construction was created by AJTAI-DWORK [10] in 1996 but in 2001 Nguyen and Stern broke this cryptosystem by solving the SVP and CVP. Also, Goldreich Goldwasser and Halevi (GGH) in 1997 are created, their cryptosystem, but also in 2001 Nguyen and Stern broke this cryptosystem by solving the SVP and CVP [10] and others cryptosystems exist like Learning With Errors (LWE) created by Oded Regev in 2005 but it is still of research stage right now. Only NTRU is standardized by IEEE P1363.1 on April 2011) and judged able to resist against all attacks conjectured [11]. 2.4

Description of Uniform Distribution Law(UFD) [12]

Definition: Let X be a random variable on (ω, P (ω), P ) on image space: X (ω) = {x1 , x2 , ..., xn } of law p. We say that X is uniform variable or X follows a Uniform law : 1 (3) if ∀ i ∈ {1 , 2 , ..., n} p(xi ) = . n Property: X being a uniform variable of image space  n X (ω) = {x1 , x E (X ) = n1 i=1 xi and the variance 2 , ..., xn } we have n Esperance n 1 1 2 2 Var (X ) = n i=1 xi − n 2 ( i=1 xi ) . 2.5

Quantum Algorithm

Quantum Algorithm Definition: The information in a quantum computer is in a superposition of states, therefore, the quantum algorithm performs simultaneously multiple treatments. The majority of quantum algorithms (like Shor ’s and Grover’s algorithms) are defined by three steps consecutive: 1. The creation of a configuration in which the amplitude of the system is in any of the 2 n basic states of the system are equals; 2. The Walsh-Hadamard transformation operations (is an example of a generalized class of a Fourier transform); transform N qubits initialized with (|0 >) into a superposition of all 2 n orthogonal states expressed in the base (|0 > |1 >) with equal weighting [13]. 3. The selective rotation of different states. When a classical algorithm finds an element in a list of N randomly selected elements with a probability of N 2 , 1 the quantum algorithm of Grover does so with a probability of O(N 2 ). 2.6

Grover’s Algorithm Principle

1. Problem: Search some solutions in an unstructured database; 2. Classical: Essential problem N entries → in average N /2 tests; 3. Quantum Grover’ algorithm: O(N 1 /2 ) In general can speed up all classical algorithms using a search heuristic. 4. Formulation of the problem: N elements indexed from 0 to N − 1, N = 2 n |U >x search register elements repertories via their index; The search problem admits M solutions (For more detail the reader can see [13]).

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Related Works

Many cryptanalysis works are performed, their principal goal was to check the robustness of the LBC. Others works are made for cryptosystems analysis like cold boot attack and side-channel attack as we are going to describe them in this section. The best tools used to prove the security and the efficiency of an LBC is Hybrid Attack that combines the Lattice reduction attack and Meet-in-TheMiddle attack (MIM) developed by (Odlgzko) [4]. In the NIST-2018 benchmarking realized by Albrecht et al. the authors claim that NTRUencrypt [14] warrant 198-bit security level against quantum attack and more than 256 bits of classical security. 3.1

Lattice Reduction

The reduction of Lattice Basis is very important, and the SVP or CVP will be easier to solve with a reduced Basis. The most reduction algorithms used to solve those Lattices problems are the LLL algorithm, BKZ. LLL: Lestro-Lenstra-Lovasz Algorithm: The role of the LLL algorithm is to transform a Bad Basis B = (e1 , ..., en ) to a Good Basis G = (u1 , ..., un ) of a Lattice L(B) ⊂ Rn . It allows approximating the smallest vector in polynomial times. This can provide a partially satisfactory answer to the problems Lattices SVP and CVP on which the security of the Lattices cryptosystems is based on [15]. Definition: The basis of a Lattice is said to be reduced by the LLL algorithm if it is obtained by the Gram-Schmidt algorithm and satisfies the following two properties: 1 |ei .uj | |λi,j | = . (4) ∀1 ≤ i ≤ n, j < i |λi,j | ≤ 2 uj 2 1 (5) δ ∈ [ , 1]. 4 BKZ Algorithm: The main goal of BKZ is to output a BKZ-reduced basis Gbkz = (u1 , ..., un ) with block size B[j ,min(j +β−1 ,n)] , with β ≥ 2 and factor ε > 0 from reduced LLL-reduced basis. B[j ,min(j +β−1 ,n)] is obtained by iteration reduction for j = 1 to n, until finding the block with the SVP in the projected lattice. For more detail of this algorithm, see the Chen et al. work [16]. ∀1 ≤ i ≤ n δ.ui 2 ≤ λi+1j ui + ui+1 2

3.2

Meet-in-the-Middle Attack

The goal of a general Meet-In-the-Middle attack, is to find specific elements x , x  in a search space of which it is known that f1 (x ) = f2 (x  ) ; the unique solution is called the golden collision; Having the private key decomposed into: f = f + f2 with f1 (deg = n2 − 1 ) and f2 (deg ∈ [ n2 , n − 1 ]) h = (f1 + f2 )−1 ∗ g =⇒ f1 ∗ h = g − f2 ∗ h

(6)

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All rotations of the keys f and g will be a solution to this equation. Search with (almost) equal number of ones; For more description of this attack, the reader can see the paper “Choosing Parameters for NTRUencrypt” [5]. 3.3

Cold Boot Attack

This attack is firstly described by Halderman et al. [6]. This attack consists of the fact that bits in RAM save their value for some time after power is cut, to explore this and retain a p0 = 1% bit-flip, the memory must have a cold temperature at about (−50 C ◦ ) to retain bits for 10mn after computer power down. Halderman et al. also noted that “bit-flip rates as low as p0 = 0.17% are possible when liquid nitrogen is used for cooling”. 3.4

Side-Channel Attack

The work realized by Liu et al. [7] and the work realized by Nadia El Mrabet [17], explain the side-channel attack. It allows the attacker to find the private keys, with the measurement of the times performing and energy consumption, or current intensity by intercepting their variations. To prevent this attack it is possible to add fictive operations for redressing (correlation of power) consumption. The main idea is to find witch fictive operation to add in each cryptographic function.

4 4.1

NTRU Overview of NTRU

It was created in 1996 by the three mathematicians Jeffrey Hoffstein, Jill Pipher and Joseph Silverman and published in 1998 [9]. It is the first cryptosystem that is completely LBC. Its domain of computation is the ring of the polynomials Rq =Zq [X]/(X N − 1) where N is a prime number. The major advantages of using a ring structure are a relatively smaller key size and faster speed that can be achieved. NTRU consists of two protocols the NTRUEncrypt Protocol and the NTRUSign Signature Protocol. NTRU was considered reliable, by the IEEE P1363.1 standard and in April 2011 NTRUEncrypt was accepted in the X9.98 standard [9]. In terms of security, NTRU was resisted for 20 years of cryptanalysis. Its low memory consumption allows us to use it for applications such as mobile devices or smart cards. 4.2

NTRU Schemes Submitted to the NIST Competition

The NTRU team [3] submitted four schemes: NTRUhps2048509, NTRUhps 2048677, NTRUhps4096821, and NTRUhrss701,

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NTRU-HPS DPKE

Our work focused and concern only the NTRU-HPS DPKE schemes when the polynomial coefficients of the private keys f, g and the plaintext m are codified in {−1, 0, 1} To explain more clearly our purpose and presenting easily our work, we are going to describe the principal cryptographic functions of NTRU cryptosystem. For more detail, the reader can see the author’s original document [3] available at the NIST competition web site. Parameters: As described in [3] The domain of NTRU is the quotient Ring with: R = Z[X]/(X N − 1) and Rq = Zq [X]/(X N − 1). For the actual parameters the reader can see [5]. 4.4

NTRU-HPS DPKE Algorithms

Algorithm 1: Keys Generation. Input : the sequence parameters {n, q, p = 3} and seed. 1. 2. 3. 4. 5.

f ,g ← sample fg(seed); as ternary Polynomials; Fq ← (1/f ) mod(q, Φn ); h ← 3.g.Fq mod(q, Φ1 Φn ); Hq ← (1/h) mod(q, Φn ); Fp ← (1/f ) mod(p, Φn );

Output: the public key h, Hq , Fp and the private key f. with Φ1 is the polynomial (x − 1) and Φn is the polynomial (xn − 1)/(x − 1) = xn−1 + xn−2 + ... + 1. Algorithm 2: Encryption. Input: The public key h,m, seed. 1. r ← sample r(seed) ; 2. m ← Lift(m) ; 3. c ← r .h + m mod (q, Φ1 Φn ) ; Output: The ciphertext c. Algorithm 3: Decryption. Input: The private key f, Fp , hq and the ciphertext c. 1. 2. 3. 4. 5. 6. 7.

if c ≡ 0mod(q, Φ1 ) return (0, 0, 1); a ← c.f mod(q, Φ1 Φn ); m ← a.Fp mod(3, Φn ) ; m ← lift(m) ; r ← (c − m ).hq mod(q, Φn ); if (r, m) ∈ Lr × Lm return (r,m, 0); else return (0, 0, 1).

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Output: the message m,r. We note that the NTRU team present an other variant, it takes the private key F in the form F = p ∗ f + 1 [16]. This form allows us to avoid the inverse calculation of f mod p because F = p ∗ f + 1 (mod p) = 1 , in Key Generation algorithm, and the multiplication by a.Fp mod(3, Φn ) in Decryption algorithm (since Fp = 1 ), and no change is made in encryption algorithm. In our work we choose to use this ephemeral key form F = p ∗ f + 1 for the attack algorithms as we are going to explain in the next section. For more detail the reader can see the original document at the NIST website [3].

5

Our Contribution

As we described before in the abstract and the introduction sections, our quantum attacks algorithms KA NTRU and KA NTRU aims to break the NTRU cryptosystem by finding respectively, the private key and the plaintext. An attacker with a quantum computer can use our algorithms by sampling simultaneously the polynomial coefficients respectively of f and of r until breaking the system (It is possible to store all possibility of polynomial f and of r and try to check simultaneously the assumptions until finding the right values. It depends on the development technique and capability of the quantum computer). We describe both algorithms as follow: 5.1

NTRU Assumptions

In this subsection we recall the assumption which our attacks based on, and we define the private key in the form F = p ∗ f + 1 . 1. Given the public key h with h = 3 ∗ g/f ← It is hard to find f, g 2. Given the ciphertext c with c = r ∗ h + m ← It is hard to find m. 5.2

Our Attacks Algorithms Description

Algorithm 4: KA NTRU. Input: the public key h, the modulus q, and n 1. Repeat: 2. f  ← sampUD(seed); 3. F  ← p ∗ f  + 1); 4. g  ← F  ∗ h mod (q); 5. if gi = q − 1 → gi = −1 ; 6. Until: g  ∈ {−1 , 0 , 1 }; Output: f = f  and g = g  . Comment: For the Algorithm 4 KA NTRU the attacker samples simultaneously the polynomial coefficients of f  (line 2) and computes g  = F ∗ h (mod q) (line 4) until the coefficients of g  are in {−1, 0, 1} (line 6) and then he finds the

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private keys f = f  and g = g  . We note that if a coefficient polynomial equal to q − 1 we must replace it by (−1) because −1 (mod q) = q − 1 as in (line 5). Algorithm 5: PA NTRU . Input: the public key c, the modulus q, and n. 1. Repeat: 2. r ← sampUFD(seed) ; 3. c ← p ∗ r ∗ h mod (q); 4. M  ← c − c  mod (q); 5. if Mi = q − 1 → Mi = −1 ; 6. Until: M  ∈ {−1 , 0 , 1 } Output: The plaintext M = M  . Comment: The same for the Algorithm 5 PA NTRU, the attacker samples simultaneously the polynomial coefficients of r (line 2) and computes a polynomial c  = p.r ∗ h (mod q) as in line 4 and computes a M  = c − c  (mod q) (line 5) until the coefficients of polynomials M  are in {−1, 0, 1} as in line 6, then he finds the plaintext M = M  . We known that if a coefficient polynomial equal to q − 1 we must replace it by (−1) because −1 (mod q) = q − 1 as in (line 5).

6

Implementations

Our implementation of KA NTRU algorithm and PA NTRU algorithm and our NTRU Attacks were implemented on C++ and performed in PC-TOSHIBA – Satellite, Processor Intel, Core™i7 -2630QM CPU, 2 GHz, RAM 8 GO, under Windows 7-32 bits platform and Dev-C++ 4.9.9.2. development environment. For NTRU Attacks we keep all the reference implementation functions of NTRU, with few modifications. For the polynomials multiplication we not used the Karashuba algorithm as in NTRU pke release submitted to NIST, we used our own polynomials multiplication algorithm in the ring Rq = Zq [X]/(X N − 1) named XKwarizm as we describe below: Algorithm 6: Xkhwarizm. Input: Polynomials f and g , with their degrees less than (n), and modulus q; Output: the polynomial h. 1. Function Xkhawarism (f, g) : 2. . For: int i = 0 to n − 1 do : 3. ... If fi ! = 0 then 4. ...... For : int j = 0 to n − 1 do : 5. ......... If gj ! = 0 then : 6. ............ If ((i + j) < n) then : hi+j ← hi+j + fi .gj ; 7. ............ else : hi+j−n ← hi+j−n + fi .gj ; 8. ............endif (line6) 9. .........endif (line5) 10. ......endfor (line4)

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... endif (line3) .endfor (line2) For: integer i = 0 to n do: hi ← hi (mod q);

For more details the reader can see our paper under title “An improvement of NTRU-1024 performance by speeding-up polynomial multiplication” submitted to “SmartICT19 conference” [19]. For testing the KA NTRU and PA NTRU attack algorithms, we integrate their implementation functions in NTRU Attacks as follow: – The program performs the keys generation function and encryption function and checks the decryption function; – The KA NTRU(.) function receives the public key h and returns the private keys f, g; – The PA NTRU(.) function receives the ciphertext c and returns the plain text m. 6.1

Result

In this subsection, we present the result obtained by using our NTRU attacks release implementation. Unfortunately, we haven’t a quantum computer, then we are going to try our algorithms in a classical computer with small parameters just for having an idea of both attack algorithms performance. The algorithms generate ternary polynomials with parameters d the number of coefficients in ternary polynomial equal to (1) and equal to (−1) chosen respectively in {2, 3, 4, 5, 6}, the dimension n chosen respectively in {7, 11, 13, 17, 19} and we chose also small modulus q = 127 . In the Table 1 we give the cost of the KA NTRU attack to find the private keys and in the Table 2 we give the cost of PA NTRU attack to find the plaintext. Table 1. Cost of KA NTRU attack against NTRU Attacks Times(ms)

n:7 n:11 n:13 46

69

Operations 346 690

n:17

4,7.103 98.103 95.103

n:19 3. .106

2,3.106 5,6.106

Table 2. Cost of PA NTRU attack against NTRU Attacks

n:7 n:11 5

1,5.103

Operations 54

35.103

Times(ms)

n:13 3.103

n:17

n:19

29.103 51.103

65.103 662.103 106

Note: All source implementations of those attacks are available into the website at [21].

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Conclusion

The evolution in quantum computer science is very fast, whereas the choice of PQcryptosystem for standardization is in the NIST process, to prepare for a smooth migration of classical cryptosystems to post-quantum cryptosystems [2]. We are not sure that some cryptosystems submitted will be resistant against quantum attacks. It is possible to modelling our algorithm attacks and apply them to some others Lattice-based submissions to NIST inspired from original NTRU schemes like NTRUprime [20] or inspired R-LWE (Ring Learning With Error) schemes like NewHope [22]. For example, to apply KA NTRU Attack to NTRUprime, the attacker can generate simultaneously a polynomial f and computing 3 ∗ h ∗ f = g (mod q) until the coefficients of g are in {−1, 0, 1}. The same for New Hope implementation, the hardness assumption is defined by: “Having b = a ∗ s + e, it is hard to find s ”. Therefore an attacker can sample a polynomial s and computes b − as = e (mod q) until the polynomial coefficients of e are in {0, 1} or in {0, 1, 2, 3 } according to the choice of error area and then return the private key s. A lot of researchers in this cryptographic domain said that “Only Quantum Cryptography will resist against Quantum computer Attacks”, but we continue our work on the cryptanalysis and improvement of the post-quantum cryptosystems and we hope to contribute with all cryptographic community to build the strong cryptosystems to save the private life of the person by learning from the best actual practices and innovate new methods.

References 1. Christine van Vredendaal. https://www.physik.uni-hamburg.de/en/forschung/ institute/ilp/ 2. Chen, L., Jordan, S., Liu, Y.-K., Moody, D., Peralta, R., Perlner, R., Smith, D.: NISTIR 8105- Report on post-quantum cryptography. Tone – Avril (2016) 3. Chen, C., Danba, O., Hofstein, J., H¨ ulsing, A., Rijneveld, J., Schanck, J, Schwabe, P., Whyte, W., Zhang, Z.: Algorithm specifications and supporting documentation, 30 March 2019 4. Chen, C., Danba, O., Hoffstein, J., H¨ ulsing, A., Rijneveld, J., Schanck, J., Schwabe, P., Whyte, W., Zhang, Z.: NIST PQ submission: NTRUencrypt a lattice-based encryption algorithm. Brown University and Onboard security Wilmington USA (2017) 5. Hofstein, J., Pipher, J., Schanck, J.M., Silverman, J., Whyte, W., Zhang, Z.: Choosing Parameters for NTRUencrypt. Brouwn University USA, Security Innovation Wilmington USA 6. Albrecht, M., Deo, A., Paterson, K.: Cold boot attacks on ring and module LWE Keys under the NTT. Royal Holloway, University of London 7. Liu, Z.: FourQ2 on embedded devices with strong countermeasures against sidechannel attacks. University of Waterloo, Canada (2017) 8. Fluhrer, S.: Quantum cryptanalysis of NTRU- cisco systems, 5 July 2015 9. Hofstein, J., Pipher, J., Silverman, J.: Introduction Mathematics and Cryptography, NTRU (1998) 10. Hartmann, M.: Ajtai-Dwork cryptosystem and other cryptosystems based on lattices. Universite de Zurich, 29 October 2015

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11. Micciancio, D., Regev, O.: Lattice-based cryptography, 22 July 2008 12. Fleury, D.: Probabilit´es. Vibert pr´epa, pp. 44–45, March 1986 13. Wiliams, C.P.: Grover algorithm explorations in quantum computing. Springer (2011) 14. Albrecht, M., Curtis, B., Deo, A., Davidson, A., Player, R.: Estimate all the fLWE, NTRU schemes. Version, 2 May 2018 15. Peikert, C.: Lattice cryptography for the Internet, 16 July 2014 16. Chen, Y., Nguyen, P.: BKZ 2.0. Better lattice security estimates. ENS Paris (2017) 17. El Mrabet, N.: Attaques par canaux caches. Universit´e de Caen, France (2010) 18. Mamdikar, R., Kumar, V., Ghosh, D.: Enhancement of NTRU public key. National Institute of Technology, Durgapur (2013) 19. Laaji, H., Azizi, A., Ezzouak, S.: An improvement of NTRU-1024 performance by speeding-up polynomial multiplication. XKhwarizm, Mohammed First University, Morocco (2019) 20. Bernstein, D.J., Chuengstiansup, C., Lange, T., van Vredendaal, C.: NTRU Prime. Department of Computer Science- University of Illinois at Chicago, Chicago, USA (2016) 21. Laaji, H., Azizi, A., Ezzouak, S.: NTRU Attacks impelementation. https://drive. google.com/open?id=12sG3-KXnAoJ2fDA0fbPXlry66l99iifI 22. Alkim, E., Ducas, L., Poppelman, T., Schwabe, P.: Post-quantum key exchange,New Hope. Department of Mathematics, Ege University, Turkey (2016)

Maximum Power Point Tracking of Photovoltaic System Based on Fuzzy Control to Increase There Solar Energy Efficiency Ahmed Hafaifa1(&), Kaid Imed1, Mouloud Guemana2, and Abudura Salam2 1

Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, 17000 DZ Djelfa, Algeria [email protected], [email protected] 2 Faculty of Science and Technology, University of Médéa, Medea, Algeria {Guemana.mouloud,abudura.salam}@univ-medea.dz

Abstract. Recently, the growing need for energy as well as pollution from the use of fossil fuels are driving the general public to use renewable energies. In this context, solar photovoltaic energy is one of the most important sources of renewable energy, which represents a solution to our energy production problems. In addition, this energy seems the most promising, non-polluting and inexhaustible. Nevertheless, the production system of this energy is nonlinear and it varies according to the luminous intensity and the temperature. Therefore, the operating point of the photovoltaic panel does not always coincide with the point of maximum power. This work proposes a mechanism that allows the research and the pursuit of the maximum power point based on a fuzzy control of photovoltaic system. To develop an algorithm to extract the maximum energy converted by the examined photovoltaics panels. Keywords: Artificial intelligence
Fuzzy control Energy storage
Efficiency
Photovoltaic energy




MPPT




Photovoltaics




1 Introduction Several industrial applications require the use of modern control methods, allowing a quick response and better performance of the dynamics of these systems. Among these methods is the fuzzy logic adjustment, which is characterized by its robustness and its insensitivity to the variation of the parameters. Indeed, fuzzy logic is now an interesting alternative approach. It has several advantages such as the reasoning close to that of the man, his ability to control dynamic performance and its interesting qualities of robustness. Recently, applications of fuzzy inference systems are very numerous besides the command, they are widely used for the control, modeling, diagnosis and pattern recognition. For a better understanding, we present some basic notions of these systems as well as their types and characteristics. Fuzzy inference systems can be thought of as logical systems that use language rules to establish relationships between their input and output variables. © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 563–571, 2020. https://doi.org/10.1007/978-3-030-36778-7_62

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This work proposes a mechanism that allows the research and the pursuit of the maximum power point based on a fuzzy control of photovoltaic system. To develop an algorithm to extract the maximum energy converted by the panels examined. The analysis and the modeling of the electrical operation of a photovoltaic system adapted by a fuzzy numerical control makes it possible to ensure the continuation of the maximum power supplied by the photovoltaic generator. The objective of this work is control and pursues the maximum power of a photovoltaic system based on fuzzy logic and improve its output voltage in order to obtain a good source that can be used as a generator of electricity.

2 Photovoltaic System Modeling The mathematical model for the current-voltage characteristic of a photovoltaic cell is given in several works by [1, 3, 5, 9]:
Ipv ¼ Iph  Isat

 eðvpv þ ðIpv  Rser ÞÞ vpv þ ðIpv  Rser Þ Þ1  expð nkT Rshu

ð1Þ

Hence Isat is the saturation current, K is the Boltzmann constant (1.381.10−23J/K), T: is the effective cell temperature in Kelvin (K), e: is the charge of the electron (e ¼ 1:6  1019 C), n: is the ideality factor of the junction (1 < n < 3), Ipv is the current supplied by the cell when it operates as a generator, vpv is the voltage at the terminals of this same cell, Ipv is the photo-current of the cell depending on the illumination and the temperature or current of (short circuit), Rshu is the shunt resistance characterizing the leakage currents of the junction, Rser is the series resistance representing the various resistances of contacts and connections. The parameters can be determined from the current-voltage curves, or the characteristic equation. The most common are short-circuit currents (Icc). This is the current for which the voltage at the terminals of the cell or of the PV generator is zero, in the ideal case (Rser null and Rshu infinite), this current merges with the current photo Iph in the opposite case, by canceling the voltage V in Eq. (1), we obtain:
   eðIcc  Rser Þ ðIcc  Rser Þ Icc ¼ Iph  Isat exp 1  nkT Rshu

ð2Þ

series resistance is low), we can neglect the term hFor most cells  (whose i eðIpv  Rser Þ  1 before Iph . The approximate expression of the short-circuit Isat exp nkT current is then: Icc ffi 

Iph 1þ

Rser Rshu



ð3Þ

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Quantitatively, it has the greatest value of the current generated by the cell (practically Icc ¼ Iph ). The open-circuit voltage (Vco) is the voltage vco for which the current delivered by the photovoltaic generator is zero (the maximum voltage of a solar cell or a photovoltaic generator is given by: h ev  i vpv pv 0 ¼ Icc  Isat exp 1  nkT Rshu

ð4Þ

In the ideal case, its value is slightly lower than:
vco ¼ vT ln

Iph þ1 Isat

 ð5Þ

And finally the energetic efficiency, which is the ratio between the maximum electrical power provided by the cell and the incident solar power pmax Iopt ; vopt . It is given by: g ¼

Iopt ; vopt pmax ¼ pinc pinc

ð6Þ

With pinc is equal to the product of the illumination and the total surface of the photocells. This parameter reflects the conversion quality of solar energy into electrical energy. The form factor FF, also called curve factor or fill factor (fill factor), the ratio between the maximum power supplied by the cell pmax Iopt ; vopt and the product of the short-circuit current Icc by the open-circuit voltage vco (it is i.e. the maximum power of an ideal cell). The form factor indicates the quality of the cell; the closer it gets to unity, the better the cell is, it is around 0.7 for efficient cells; and decreases with temperature. It reflects the influence of the losses by the two parasitic resistances Rser etRshu it is defined by: FF ¼

2.1

Iopt : vopt pmax ¼ Icc vco Icc vco

ð7Þ

Characteristic of the Examined Photovoltaic System

It is difficult to give a source of current or voltage to a photovoltaic panel over the entire range of the current-voltage characteristic. The photovoltaic panel is therefore to be considered as a source of power. One then realizes the existence of a point Pm where the power is to be maximum. It is undoubtedly interesting to be on this point to get the most energy and thus make the most of the peak power installed. Some solar regulators thus realize an impedance adaptation so that at every moment one is close to this point of maximum power MPPT. The actual characteristic and the equivalent scheme of the cell are shown in Fig. 1. The respective values of the various elements of the equivalent scheme determine the performance of the actual cell. Figure 2 shows the characteristics

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of a typical solar cell for several intensities of solar radiation. Note that the electric current is directly proportional to the radiation at these levels of illumination. On the other hand, the voltage is degraded slightly with respect to the current when the intensity of the light drops. The Fig. 3 illustrates the variation of the power delivered by the generator as a function of the voltage for different illumination values, which allows us to deduce the influence of illumination on the characteristic P(V). The influence of temperature is very significant, which leads us to a careful consideration, when starting photovoltaic systems. In Fig. 4, the effect of temperature on the behavior of the solar cells is illustrated. It shows a considerable decrease of the electric voltage delivered with the increase of the temperature. While, the current gains meanwhile of the intensity. This can be explained by the decrease of the gap, which causes the increase of the concentration of the carriers of charge, since the transition between the levels becomes more probable.

Fig. 1. Characteristic I = f (V) of a photovoltaic module

Fig. 2. Influence of illumination on the characteristic I = f (V)

Fig. 3. Influence of illumination on the characteristic P = f (V)

Fig. 4. Influence of the temperature on the characteristic I = f (V)

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The Fig. 5 illustrates the variation of the power delivered by the generator as a function of the voltage for different values of the temperature, which allows us to deduce the influence of the temperature on the characteristic P = fct (V).

Fig. 5. Influence of the temperature on the characteristic P = f (V)

The next section of this work will be devoted to presenting the different results obtained from the photovoltaic system control using fuzzy logic. We introduce the fuzzy concept and its essential properties, for the examined photovoltaic system.

3 Maximum Power Point Tracking Based on Fuzzy Logic Fuzzy logic asserts itself as an operational technique. Used alongside other advanced control techniques, it makes a discrete but appreciated input into industrial control automation. Indeed, fuzzy logic is a very powerful technique derived from fuzzy set theory, to bridge the gap between the precision of classical logic and the inaccuracy of the real world. Its fundamental feature is the use of linguistic variables instead of numerical variables in fuzzy conditional situations. The membership functions chosen in this work is of the triangular form given by:  x  a c  x  lð xÞ ¼ max min ; ;0 ba cb

ð8Þ

Also, the model used is Takagi-Sugeno-Kang (TSK) type consists of a rule base of the form: If X1 is Ai11 and X2 is Ai22 and . . .Xr is Airr Then y ¼ fi ðx1 ; x2 ; . . .; xr Þ

ð9Þ

where x1, x2, …, xr are the input numerical variables of the fuzzy model and ƒi is a numeric function of the input universe X1 x X2 x .. x Xr in Y. Each rule represents a local model on a fuzzy input region, or an input subspace. In each region, the fuzzy model is defined by the function ƒi which connects the inputs to

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the digital output. The global model consists of interpolation of local models [21]. Hence fi are often linear functions, given by: fi ðx1 ; x2 ; . . .; xr ; Þ ¼ bi0 þ bi1 x1 þ bi1 x2 þ bir xr

ð10Þ

where bi0 , bi1 , bi2 ,…..,bir are constant coefficients. Each rule can then be seen as a local model, linear according to the input variables x1, x2, …, xr. These models allow to approach the behavior of a complex system by a collection of local models. They have an important representation capacity. Indeed, the number of rules necessary to approach a system to a certain degree of precision is generally reduced [22]. The fuzzy controller inputs dp represent the slope of the P-I characteristic curve: dP ¼ Pph ðk þ 1Þ  Pph ðkÞ dI ¼ Iph ðk þ 1Þ  Iph ðkÞ
E ¼

ð11Þ

dP ET dE = E(k)  E(K  1) dI

With pph and vph are respectively: the power and the voltage of the generator photovoltaic. The MPPT control method of P & O (Disturbance and Observation) is applied this widely used approach in MPPT research because it is simple and requires only voltage and current measurements of the photovoltaic panel, VPV and Ipv respectively, it can track the maximum point of power even during variations in illumination and temperature. If the output power has increased VPV is adjusted in the same direction as in the previous cycle. If the output power has decreased VPV is adjusted in the opposite direction than in the previous cycle and thus VPV disturbed at each MPPT cycle. When the maximum power point is reached, it oscillates around the optimal value. This causes a loss of power which increases with the step of the incrementation of the disturbance. If this Vop incremental step is wide, the MPPT algorithm responds quickly to sudden changes in operating conditions. On the other hand, if the step is small, the losses, under conditions of slow or stable atmospheric changes, will be lower but the system will not be able to respond rapidly to rapid changes in temperature or illumination. The ideal step is determined experimentally according to the needs.

4 Application Results In this part the MPPT command adapted for the photovoltaic system under examination is presented, this system consists of a photovoltaic generator, a P & O type MPPT control based on fuzzy logic, as shown in Fig. 6. The photovoltaic system is PV generator type YGE 60 Cell Serie (YL245P-29b) is made of monocrystalline silicon consists of 60 photovoltaic cells elementary. It can deliver under standard test conditions (CST) a power of 245 W, a current of 8.28 A under an optimal voltage of 29.6 V.

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The parameters of YGE 60 Cell Series (YL245P-29b). The photovoltaic generator model has been implemented, as shown in Fig. 7. For the controller of the maximum power point the voltage and current of the photovoltaic panel are the inputs and the power is the output with the output power of the voltage and current. The duty cycle is increased or decreased until the maximum power point of the photovoltaic is reached. The duty cycle pitch is constant, and it determines the efficiency and accuracy of the MPPT controller.

Fig. 6. Photovoltaic system control

Fig. 7. Examined Photovoltaic module

The obtained results from the control of the photovoltaic system adapted by the control MPPT “Disturbance and observation” represent in Figs. 8, 9, 10, 11, 12 and 13 on two parts; The first part shows the results of application of the fuzzy MPPT command with constant temperature and irradiation (T = 25 °C and E = 1000 w/m2). The second part shows the results of application of the fuzzy MPPT command with variable temperature and irradiation, using the examined actual photovoltaic panel data. The input parameters for this model is the photovoltaic current and the change in the current of the fuzzy MPPT varies the photovoltaic output voltage. Figures 14, 15 and 16, shows the results of the fuzzy MPPT control with constant temperature and irradiation (T = 25 °C and E = 1000 w/m2).

Fig. 8. Current Variation Ipv (A) and Ich (A)

Fig. 9. Voltage Variation of Vpv and Vch

Fig. 10. Power Variation Ppv (W) and Pch (W)

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Fig. 11. Current of Ipv (A) and Ich (A)

Fig. 14. Output current I (t) of the photovoltaic generator wit load

Fig. 12. Voltage of Vpv (A) and Vch (A)

Fig. 15. Output voltage (t) of the photovoltaic generator wit load

Fig. 13. MPPT Power using P&O of the photovoltaic generator with load

Fig. 16. Output Power P (t) of the photovoltaic generator wit load

5 Conclusion In this work, the modeling of the set composed of photovoltaic generator, booster chopper and MPPT control is presented. The models obtained are built for the different components of the photovoltaic system. According to the results obtained, the performances of the PV generator are degraded with the increase of the temperature, the decrease of the intensity of the illumination and the variations of the load. The performance of the PV generator is evaluated from the standard conditions (CST) with illumination 1000 W/m2 and temperature 25 °C. However, the MPPT command adapts the PV generator to the load: transfer of the maximum power supplied by the PV generator, the input used in the MPPT (P & O and fuzzy logic) commands is the Ipv current and the Vpv voltage, but the entered from the MPPT command based on the fuzzy logic principle is the irradiation G and the temperature T and the output is always the duty cycle D.

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References 1. Sedaghatizadeh, N., Arjomandi, M., Kelso, R., Cazzolato, B., Ghayes, M.H.: Modelling of wind turbine wake using large eddy simulation. Renew. Energy 115, 1166–1176 (2018) 2. Akour, S.N., Al-Heymari, M., Ahmed, T., Ali Khalil, K.: Experimental and theoretical investigation of micro wind turbine for low wind speed regions. Renew. Energy 116, 215– 223 (2018). part A 3. Menezes, E.J.N., Araújo, A.M., da Silva, N.S.B.: A review on wind turbine control and its associated methods. J. Clean. Prod. 174, 945–953 (2018) 4. Tian, W., Ozbay, A., Hui, H.: An experimental investigation on the aeromechanics and wake interferences of wind turbines sited over complex terrain. J. Wind Eng. Ind. Aerodyn. 172, 379–394 (2018) 5. Mellit, A., Kalogirou, S.A.: MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: review of current status and future perspectives. Energy 70, 1–21 (2014) 6. Tahri, A., Oozeki, T., Draou, A.: Monitoring and evaluation of photovoltaic system. Energy Proc. 42, 456–464 (2013) 7. Ahadi, A., Ghadimi, N., Mirabbasi, D.: Reliability assessment for components of large scale photovoltaic systems. J. Power Sources 264, 211–219 (2014) 8. Bouabdallah, A., Olivier, J.C., Bourguet, S., Machmoum, M., Schaeffer, E.: Safe sizing methodology applied to a standalone photovoltaic system. Renew. Energy 80, 266–274 (2015) 9. Geurts, C., Blackmore, P.: Wind loads on stand-off photovoltaic systems on pitched roofs. J. Wind Eng. Ind. Aerodyn. 123, 239–249 (2013). Part A 10. Tsuanyo, D., Azoumah, Y., Aussel, D., Neveu, P.: Modeling and optimization of batteryless hybrid PV (photovoltaic)/diesel systems for off-grid applications. Energy 86, 152–163 (2015) 11. Caselli, D., Ning, D.Z.: Monolithically-integrated laterally-arrayed multiple bandgap solar cells for spectrum-splitting photovoltaic systems. Prog. Quant. Electron. 39, 24–70 (2015) 12. Karakaya, E., Hidalgo, A., Nuur, C.: Motivators for adoption of photovoltaic systems at grid parity: a case study from Southern Germany. Renew. Sustain. Energy Rev. 43, 1090–1098 (2015) 13. Karakaya, E., Sriwannawit, P.: Barriers to the adoption of photovoltaic systems: the state of the art. Renew. Sustain. Energy Rev. 49, 60–66 (2015) 14. Shariff, F., Rahim, N.A., Hew, W.P.: Zigbee-based data acquisition system for online monitoring of grid-connected photovoltaic system. Expert Syst. Appl. 42(3), 1730–1742 (2015) 15. Baig, H., Sellami, N., Mallick, T.K.: Trapping light escaping from the edges of the optical element in a concentrating photovoltaic system. Energy Convers. Manag. 90, 238–246 (2015)

A New Technique of Harmonic Currents Extraction Based on a Fuzzy Logic Controller Applied to the PV-SAPF System Asmae Azzam-Jai(B) and Mohammed Ouassaid Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers (EMI), Mohammed V University in Rabat, Rabat, Morocco [email protected], [email protected]

Abstract. In this paper, an efficient new harmonic extraction technique based on a fuzzy logic controller is proposed and applied to a photovoltaic shunt active power filter system (PV-SAPF). So as to overcome the limitation of the conventional Synchronous Reference Frame (SRF) control method of the PVSAPF, using a Low Pass filter (LPF), this new and simple Fuzzy Synchronous Reference Frame technique (FSRF) is proposed. The simulation results, performed in a Matlab/Simulink environment under online variation of system parameters, show that the proposed FSRF technique combined with a Fuzzy Vdc controller gives a fast and an accurate harmonic-extraction results, reduces up to ten times the overshoot than the conventional SRF, minimizes the response time, and significantly decreases the value and the peaks of the total harmonic distortion THD as well. Keywords: The harmonic currents extraction · A Fuzzy Synchronous Reference Frame technique (FSRF) · The dynamic performance improvement · The total harmonic distortion THD

1

Introduction

Power quality has gained a great attention over the years [1]. Indeed, it has become a disturbing issue in the last decade due to the proliferation of power electronic equipments and the extensive application of nonlinear loads [2]. These latest generate harmonic currents which cause adverse effects on the power system such as excessive heating in rotating mechanism, motor torque reduction, power line losses, etc [3]. Nevertheless, several IEEE and IEC standards have been established in order to define a basis for harmonic limits. According to IEEE 519-1992, the total harmonic distortion (THD) of power systems 69 KV and below is limited to 5.0% and each individual harmonic is limited to 3% [4]. c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 572–582, 2020. https://doi.org/10.1007/978-3-030-36778-7_63

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Active power filters present the most powerful solution to reduce harmonics from the electrical network, compared to passive filters which have several disadvantages such as resonance, voluminous size of components, and fixed frequency based compensation [5,6]. The shunt active filter (SAPF) device compensates harmonics by removing the fundamental component from the measured current of the load and inject at the point of common coupling PCC, the same harmonics as produced by this nonlinear load, but in opposite phase. The compensation quality of the SAPF is highly related to the extraction technique of harmonics [7]. A variety of methods to achieve this process have been developed in the literature, such as Fast Fourier Transform (FFT), Kalman Filter (frequency-based), Instantaneous reactive power theory and Synchronous reference frame (time-based) [8,9]. In [10], the author evaluates the commonly used techniques for harmonics extraction, the simulations show that the computational complexity of the frequency based methods leads to a slow settling time and large overshoot compared to the time domain techniques which gives a good performance of filtering. Therefore, this paper deals with a new intelligent harmonics extraction strategy based on fuzzy logic controller for a photovoltaic shunt active power filter PV-SAPF which ensures the power quality improvement as well as the solar energy production (Fig. 1). In view of the aforementioned facts, the contribution of this paper lies on the salient features highlighted below: (1) To enhance harmonic currents extraction of the studied system in terms of accuracy and fast convergence by using Fuzzy synchronous reference frame technique (FSRF). (2) To ensure high dynamic performances in terms of overshoot and response time under solar and system parameter variations. (3) To obtain better quality filtering with low THD levels. The rest of this paper is organized as follows. Section 2 develops the proposed new Fuzzy Synchronous Reference Frame technique (FSRF) and the Fuzzy Vdc Controller. The simulation results compared to the conventional SRF controller using LPF are discussed in Sect. 3. Some conclusions are mentioned in Sect. 4.

α R

PCC

L

Grid PV

NLL Boost

SAPF

DC DC

DC AC

PV-SAPF Fig. 1. Schematic diagram of the studied system.

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Vboost V*dc

α

iL a,b,c PLL

Ɵ

PI / FLC

~ iLdH FLC

abc iLd (Harmonics

i*d

extraction)

i*q

dq iLq

vSa,b,c

dq i* 1,2,3 abc

Control Bloc

ginv

Fig. 2. Schematic diagram of the proposed Fuzzy-Synchronous Reference Frame strategy ‘FSRF’ for harmonic currents Identification.

Alpha

FLC

iLd bar

iLd H -

+

iLd

Fig. 3. The structure of the proposed Fuzzy controller for harmonic currents extraction.

2 2.1

The Proposed Fuzzy Controllers Applied to the Studied System The Proposed New Fuzzy Synchronous Reference Frame Technique (FSRF)

As detailed in Fig. 2, in order to obtain iLd and iLq currents of the rotating d-q reference frame, the Park’s transformation is applied to the measured load currents: ⎡ ⎤      iLa 4π 2 cos(θ) cos(θ − 2π iLd ) cos(θ − ) 3 3 ⎣ iLb ⎦ (1) = 4π iLq 3 sin(θ) sin(θ − 2π 3 ) sin(θ − 3 ) iLc Each current contains a fundamental component iLbar and a harmonic component iLH : (2) iLd = iLdbar + iLdH iLq = iLqbar + iLqH

(3)

The conventional Synchronous Reference Frame strategy SRF uses the low pass filter LPF to extract the harmonic component of the load current in order to inject, at the point of common coupling PCC, the same harmonics as produced by the nonlinear load but in opposite phase [11].

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Nevertheless, this harmonics extraction method based on LPF is characterized by a slow adaptation to abrupt changes in the system parameters and systematically affects the performance of the PV-SAPF power system which constantly subjected to the variation in the climatic conditions and to the load changes [12]. In order to overcome this type of problem, a novel harmonic current extraction strategy based on Fuzzy logic theory is proposed. This fuzzy approach takes its basic knowledge from human thinking, and gives an approximate relationship between the input and the desired output according to knowledge database or experience. Figure 3 details the structure of the proposed Fuzzy Harmonic Currents Extraction technique. The proposed fuzzy controller includes one input: the ignition angle of the nonlinear load (α) which varies from 0◦ to 90◦ , whereas the desired output is the fundamental current (iLdbar ). The input and the output are converted to ten linguistic variables: ZE (Zero); VS (Very-Small); S (Small); SB (Small- Big); MS (Medium-Small); M (Medium); MB (Medium-Big); LS (Large-Small); L (Large): LB (Large-Big). The triangular membership functions of the inputs and output of the proposed FSRF controller are shown in Fig. 4. The proposed fuzzy controller uses the following ten simplified rules: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

If If If If If If If If If If

α α α α α α α α α α

is is is is is is is is is is

(ZE), then iLdbar is (LB). (VS), then iLdbar is (L). (S), then iLdbar is (LS). (SB), then iLdbar is (MB). (MS), then iLdbar is (M). (M), then iLdbar is (MS). (MB), then iLdbar is (SB). (LS), then iLdbar is (S). (L), then iLdbar is (VS). (LB), then iLdbar is (ZE).

ZE VS S

SB MS M MB LS

L LB

ZE VS S

1

1

0.5

0.5

0

0

10

20

30

40

50

60

70

80

a. The membership function of the input variable (the ignition angle of the nonlinear load ’α’)

90

0

0 2

4

SB

6

MS

8

M

10

12

MB

LS

14

16

L LB

18 20

b.The membership function of the output variable (the fundamental current ’iLdbar ’)

Fig. 4. The triangular membership functions of the proposed FSRF controller

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Hence, this harmonic current component iLdH extracted by the proposed fuzzy controller is used to calculate the reference currents of i∗d :   ∗  id Imax − iLdH (4) = i∗q −iLq where I max is the output of the Vdc voltage regulator. Finally, the inverse Park’s transformation is used to obtain the (i∗1 , i∗2 , i∗3 ) reference currents: ⎡ ∗⎤ ⎡ ⎤  ∗ cos(θ) sin(θ) i1 ⎣i∗2 ⎦ = ⎣cos(θ − 2π ) sin(θ − 2π )⎦ id∗ (5) 3 3 iq 4π i∗3 ) sin(θ − ) cos(θ − 4π 3 3

2.2

DC Voltage Regulation Using the Fuzzy Controller

In order to regulate the PV-SAPF dc bus voltage Vdc at a fixed value V*dc, a fuzzy logic controller is used with the following characteristics: – – – –

A fuzzy model using Mamdani. Implication using ‘min’ operator. De-fuzzification using the ‘centroid’ method. Seven triangular membership functions are used for the fuzzy inputs and output as depicted in Fig. 5: NL (Negative Large), NM (Negative Medium), NS (Negative Small), ZE (zero), PS (Positive Small), PM (Positive Medium), and PL (Positive Large). The inputs and the output of the fuzzy controller are defined as follows: ⎧ ∗ ⎪ ⎨e(k) = Vdc − Vdc (6) e(k) = e(k) − e(k − 1) ⎪ ⎩ Imax (k) = Imax (k − 1) + G.δImax (k) + Gp .e(k) where:

– e(k) and Δ e(k) are the inputs signal of the fuzzy regulator which represent the error and the variation of the error at the k th sampling instant, respectively. – δImax (k) is the output of the fuzzy controller at the k th sampling instant. – G and Gp are optimization gains for better performance of control. The Fuzzy Vdc controller rules are presented in Table 1.

A Fuzzy Harmonic Currents Extraction NL

NM NS ZE PS PM

PL

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Table 1. The Fuzzy Vdc controller rule table.

1

0.5

0

-1 -0.8 -0.6

-0.4 -0.2

0

0.2

0.4

0.6 0.8 1

Fig. 5. The membership function for the inputs variable (e(k) and e(k)), and the output variable δImax of the fuzzy Vdc controller.

3

e\  e

NL

NM

NS

ZE

PS

PM

PL

NL

NL

NL

NL

NL

NM

NS

ZE

NM

NL

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NL

NM

NS

ZE

PS

NS

NL

NL

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NS

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PM

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Results and Discussion

In this section, the proposed FSRF strategy is compared to the conventional SRF in order to prove its supremacy in terms of performance tracking and harmonics filtering. The simulations are executed in a MATLAB/Simulink environment under variable nonlinear load as a first simulation case, and then under a variable solar irradiance as the second one. 3.1

PV-SAPF System Under a Variable Nonlinear Load

This first simulation case aims to examine the performance of the proposed fuzzy strategy of harmonics current extraction during the change of the nonlinear load. Therefore, the solar irradiance is fixed to 1000W/m2 and a variable profile of the ignition angle of the thyristor bridge rectifier is applied to that purpose, as shown in Fig. 6a. Figure 7a, b, and c present the variation of PV-SAPF active power (PF), grid active power (Ps), and Vdc bus voltage, respectively, of the both methods of control: the proposed FSRF and the traditional SRF using LPF. Table 2

Fig. 6. Simulation of the studied system under a variable nonlinear load

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Fig. 7. Dynamic performances of the proposed FRSF strategy under a variable NLL.

Fig. 8. THD of ‘C-S’ (the Conventional strategy) and of ‘FSRF-S’ (the proposed FSRF strategy) under a fixed solar irradiance E = 1000 W/m2 .

summarizes the response time and the overshoot values obtained from Fig. 7. It can be observed that the overshoot is reduced up to ten times by the proposed strategy compared to the conventional one (PF (A): From 9.91% to 0.9%). The response time is reduced significantly as well (PF (C): from 0.9 s to 0.8 s) (Fig. 8). Figure 6b displays the waveform of the fundamental current extracted by the proposed FSRF strategy and the fundamental current obtained by the classical SRF using LPF. It is clear that the proposed FSRF controller gives a fast and accurate harmonic-extraction results in contrast to the classical SRF method which gives slowly and barely the correct fundamental current value during a fast and a sudden variation of the load. In order to test the performance of the proposed FSRF controller in terms of THD reduction different simulation cases were executed and the obtained values of THD from the both methods are summarized in Table 3. It is clear that the proposed FSRF controller gives better THD levels compared to the classical SRF controller under the load variation.

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Table 2. Response time and overshoot of the proposed Fuzzy extraction strategy and the conventional strategy using LPF under nonlinear load variations. Overshoot (%) Response time (s) LPF extract◦ FLC extract◦ LPF extract◦ FLC extract◦

3.2

PS

A 8.67% B 5.36% C 3.05%

1.84% 0.77% 1.02%

0.35 s 0.606 s 0.852 s

0.3 s 0.57 s 0.815 s

PF

A 9.91% B 6.89% C 3.83%

0.9% 1.88% 1.47%

0.35 s 0.607 s 0.9s

0.3 s 0.544 s 0.8s

Vdc A 1.06% B 0.85% C 0.53%

0.1% 0.13% 0.05%

0.374 s 0.6 s 0.84 s

0.325 s 0.55 s 0.78 s

PV-SAPF System Under a Variable Solar Irradiance

This second simulation case aims to examine the robustness of the proposed fuzzy strategy of harmonics current extraction during the change of the solar irradiance. Hence, the ignition angle of thyristor bridge rectifier is fixed to 10 degrees and a variable profile of solar irradiance, given in Fig. 9a, is applied to that purpose. It should be noted that the DC voltage control method of the PV-SAPF system must be carefully chosen in the case of the solar irradiance variation. Therefore, the proposed FSRF controller combined with a Fuzzy Vdc controller has been studied in this simulation case. Figure 10a, b, and c present the variation of PV-SAPF active power (PF), grid active power (Ps), and Vdc bus voltage respectively, of the FSRF strategy combined with a PI DC voltage controller and the FSRF strategy combined with a Fuzzy DC voltage controller. The results show that the proposed FSRF strategy of control using Fuzzy voltage regulation gives better dynamic performance in terms of overshoot and response time. Figure 9b gives the variation of the obtained unit value of the THD during the solar irradiance variation (Fig. 9a). It can be noticed that THD reaches peak values at the time of abrupt change in irradiance (at 0.05 s, 0.26 s and 0.52 s) and the proposed FSRF controller combined with a Fuzzy Vdc controller remarkably reduces the peaks of THD (Fig. 11).

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Fig. 9. Simulation of the studied system under a fixed NLL (α = 10◦ ) and a variable irradiance profile.

Fig. 10. Dynamic performances analysis of the FRSF strategy under a variable solar profile (α = 10◦ ).

Fig. 11. THD of ‘C-S’ (the Conventional Strategy), of ‘FSRF-S + PI-Vdc’ (the proposed FSRF strategy using PI Vdc regulator), and of ‘FSRF-S + F-Vdc’ (the proposed FSRF strategy using Fuzzy Vdc controller) under: ‘α = 10◦ ’ and ‘E = 900 W/m2 ’.

In order to test the robustness of the proposed controller in terms of THD reduction, different simulation cases were executed and the obtained values of THD are given in Table 4. It is clear that FSRF controller combined with a Fuzzy Vdc controller gives better THD levels compared to the classical SRF controller and to the FSRF strategy combined with a PI DC voltage controller, even under large solar irradiance variation.

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Table 3. THD comparison under a variable ignition angle of thyristor bridge rectifier Alpha α = 50

THD (SRF) THD (FSRF) ◦

4.25 %

4.10%

3.09%

2.93%

4.93%

4.87%

α = 60◦ 3.74%

3.58%

α = 0◦ α = 40

◦

Table 4. THD comparison under solar variation (α = 10◦ ) LPF-extract◦ + PI-VDC

FLC-extract◦ + PI-VDC

FLC-extract◦ + FLC-VDC

5.45%

4.42%

5.41%

5.24%

4.81%

E = 900 W/m2 3.44%

3.42%

2.65%

E = 700 W/m2 5.57 % E = 150 W/m

4

2

Conclusion

This paper develops a new Fuzzy synchronous reference frame technique in order to extract harmonics current generated by the nonlinear load, by using Fuzzy sets theory and then to control a photovoltaic shunt active power filter system (PV-SAPF). The simulation result show that the proposed new FSRF controller: – Gives a fast and accurate extraction of the fundamental load current component compared to the classical SRF method using LPF. – Ensures a good dynamic performance by reducing remarkably the overshoot and response time. – Minimizes the THD levels and remarkably reduces the peaks of THD under system parameter variation.

References 1. Mahela, O.P., Shaik, A.G.: Topological aspects of power quality improvement techniques: a comprehensive overview. Renew. Sustain. Energy Rev. 58, 1129–1142 (2016). https://doi.org/10.1016/j.rser.2015.12.251 2. Rohouma, W., Balog, R.S.: D-STATCOM for harmonic mitigation in low voltage distribution network with high penetration of nonlinear loads. Renew. Energy, 1449–1464 (2019). https://doi.org/10.1016/j.renene.2019.05.134 3. Yazdani, D., Bakhshai, A., Jo´ os, G., Mojiri, M.: A real-time three-phase selectiveharmonic-extraction approach for grid-connected converters. IEEE Trans. Ind. Electron. 56(10) (2009). https://doi.org/10.1109/TIE.2009.2024658 4. Blooming, T.M., Carnovale, D.J.: Application of IEEE Std 519-1992 harmonic limits. IEEE (2006). https://doi.org/10.1109/PAPCON.2006.1673767, ISBN 1-42440282-4

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5. El-Mamlouk, W.M., Mostafa, H.E., El-Sharkawy, M.A.: Active power filter controller for harmonic suppression in industrial distribution system. Ain Shams Eng. J. 2, 161–172 (2012). https://doi.org/10.1016/j.asej.2011.09.004 6. Singh, G.K., Singh, A.K., Mitra, R.: A simple fuzzy logic based robust active power filter for harmonics minimization under random load variation. Electr. Power Syst. Res. 77, 1101–1111 (2007). https://doi.org/10.1016/j.epsr.2006.09.006 7. Merabet, L., Saad, S., Ould Abdeslam, D., Omeiri, A.: A comparative study of harmonic currents extraction by simulation and implementation. HAL Id: hal00985541 (2014). https://doi.org/10.1016/j.ijepes.2013.05.003 8. Massoud, A.M., Finney, S.J., Williams, B.W.: Review of harmonic current extraction techniques for an active power filter. IEEE (2004). ISBN 0-7803-8746-5 9. Pereira, H.A., da Mataa, G.L.E., Xavier, L.S., Cupertino, A.F.: Flexible harmonic current compensation strategy applied in single and three-phase photovoltaic inverters. Electr. Power Energy Syst. 104, 358–369 (2019) 10. Asiminoael, L., Blaabjerg, F., Hansen, S.: Harmonic detection methods for active power filter applications. IEEE Ind. Appl. Mag. 1077–2618 (2007) 11. Bhattacharjee, K.: Harmonic mitigation by SRF theory based active power filter using adaptive hysteresis control. IEEE (2014). 978-1-4799-3421-8114 12. Panda, A.K., Penthia, T.: Design and modeling of SMES based SAPF for pulsed power load demands. Electr. Power Energy Syst. 92, 114–124 (2017). https://doi. org/10.1016/j.ijepes.2017.04.011

A New TSA-Fuzzy Logic Based Diagnosis of Rotor Winding Inter-turn Short Circuit Fault in Wind Turbine Based on DFIG Under Different Operating Wind Speeds Hamza Sabir1(B) , Mohammed Ouassaid1 , and Nabil Ngote2 1 Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco [email protected], [email protected] 2 ENSMR Engineering School, Rabat, Morocco [email protected]

Abstract. Doubly-fed induction generators (DFIGs) are the most utilized generator types of wind turbine systems, thanks to their important efficiency, elevated power factor, quicker response and robust construction. However, they may be subject of many kinds of defect. Condition monitoring methods and early failure diagnosis algorithms for wind turbine installation have become a fundamental practice as they serve to enhance wind farm dependability, performance and overall productivity. In this context, an advanced diagnostic technique for the detection of rotor inter-turn short circuit residual faults, based on the combination of the Time Synchronous Averaging (TSA) technique and Fuzzy Logic (FL) is established. In fact, rotor residual failure cannot be detected directly by analyzing the stator current, especially in the low wind speed (WS) case. The RMS of residual current and the value of wind speed will be used as inputs for the fuzzy logic bloc in order to give the decision about the state of the rotor. The proposed strategy has been implemented and verified using simulations built in MatLab  SIMULINK environment. The simulation results prove the efficiency and the reliability of the proposed approach. Keywords: Doubly-fed induction generators · Fault detection Synchronous Averaging technique · Fuzzy logic

1

· Time

Introduction

Wind energy has become a source that plays an essential role in the electricity production sector compared to other types of power generation in the world [1]. A great percentage of currently installed wind turbines use induction generators. However, the doubly fed induction generator (DFIG) in particular takes an c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 583–592, 2020. https://doi.org/10.1007/978-3-030-36778-7_64

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important place in the world of production of electrical power, on account of, the robustness and its aptitude to exploit high power from a large range of wind speed. Undetected faults in WT can be internal or external defects [2,3]. For the external defects, they are generated by the load and the wind turbine use environment [4]. The intern defects are caused in the stator or rotor windings, the eccentricity of the rotor, interruption of a phase and magnetic circuit fault, and wear of the bearings or the gearbox [5]. These failures provoke a partial interruption of the wind turbine and afterward an improvised shutdown of the electrical production of a wind farm which means a serious issue concerning the reliability of wind turbines. Substantial research in advanced monitoring system for diagnose the rotor inter-turn short-circuit faults have been widely established. Among these techniques, there are statistical analysis, vibration analysis and current signature analysis (CSA). The advantage of these analyses over time domain analysis is its ability to identify certain frequency-components of defect, this method is presented in [6]. A review of other techniques is presented in [7] and [8]. The TSA, also called time-domain averaging, has been satisfactorily applied for monitoring the state of the rotor, but it is not suitable to identify the nature of the fault. A review of others TSA algorithms is given in [9]. The failure statistics published by the IEE/IET confirm that the generator defect at the level of the rotor and stator are frequent (8% to 50% in rotor, 3% to 36% in stator) [10]. Nevertheless, the majority of the electrical failure concerns the short-circuit defect between rotor turns of the (DFIG) [11]. The short circuit inter-turn fault of rotor windings of DFIG generally begin as a residual undetected insulation fault between two neighboring turns. Subsequently, it progressively increased to a short circuit isolating a great number of adjacent turns [12,13]. The key purpose is to ensure that these DFIG can be more reliable and secure in order to minimize unscheduled downtimes that leads to a loss of investment and lost production of electricity. For this purpose, an early detection of short circuit inter-turn fault of rotor windings have been a challenging subject for numerous electrical researchers [14]. In recent years, the novel diagnostic techniques exploiting stator current signals have used to reduce unexpected stator failures and downtimes. These techniques have been improved from traditional algorithms to artificial intelligence algorithms based on neural network system [15], fuzzy logic to increase the efficacy and the reliability of the diagnosis in the supervision domain and diagnosis of the DFIG [16]. In the scientific literature, the fuzzy logic was used with different aspects for the diagnosis of stator and rotor defects in induction machines such as stator fault detection [17], stator faults for inverter-fed induction motor under low speeds [18], BRB defect diagnosis of SCIG-based wind energy system [19] and multi phase stator short-circuit faults diagnosis & classification in DFIG [20,21]. The fuzzy logic is widely adopted to diagnose inter-turn short-circuit fault in the rotor winding, which exceeds 10%, but it is no longer valid for defects less than 10%. Consequently, there is a necessity to establish monitoring techniques to tackle this issue in order to allow earlier detection of rotor faults.

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To overcome this limit, this study proposes an approach combining TSA and fuzzy logic techniques and consisting of using the residual stator current generated by the DFIG machine. This paper is organized as follows. The second section presents a background of the TSA method and Fuzzy logic approach followed by the proposed approach. The third section is devoted to the simulation results and discussion. Some conclusions are given in the last section.

2 2.1

Architecture of the Proposed Hybrid Approach The Proposed “TSA-Fuzzy Logic” Approach

To overcome the disadvantages of fuzzy logic to detect the residual fault using the stator current as input, a hybrid TSA and fuzzy logic is designed. The flowchart of the monitoring algorithm is depicted in Fig. 1.

Start Load Stator Current Is Read Stator Current Signal Is

TSA method

Apply synchronization algorithm to Is Synchronized Is Apply TSA method to synchronized Is +

–

< Is > Residual current Ires

Use residual current ”Ires ” and ”W S” as input Fuzzy Logic approach

Evaluate the inputs with the knowledge base

Decide and diagnose DFIG

Stop Fig. 1. Flowchart of the combination of the FL and the TSA algorithms.

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For the diagnosis of rotor faults, a ratio k is calculated as follows: K=

Iresd − Ires Ires

(1)

where: • Iresd is the RMS value of residual current in defective case; • Ires is the RMS value of residual current in healthy case; Fuzzy inference system (FIS) intended for defect classification is shown in the Fig. 2. The value of k and wind speed are transformed into fuzzy value and consider as inputs. Thereafter, the defect of the rotor DFIG is evaluated by the fuzzy logic inference system, then obtained as output.

Fig. 2. Fuzzy inference system for defect classification

A membership function allows a value, such as a K ratio (input), to be linked with a linguistic variable with some level of rotor fault severity. In Fig. 3, the degrees of membership of the K ratio are categorized using five linguistic variables. These categories are “Zero” (Z) “Very Small” (VS) “small” (S) “means” (M), “large” (L) or “Very Large” (VL). In Fig. 4, the amplitudes of the wind speed (WS) are associated to tree linguistic variables denoted as “Low” (L), “means” (M) or “High” (H).

Fig. 3. Membership functions of the variable “K”

Fig. 4. Membership functions of the variable wind speed “WS”

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Table 1. Fuzzy logic rule base. WS\K L M H

Z LSC VSC –

VS MSC LSC VSC

S HSC MSC LSC

M CSC HSC MSC

L – CSC HSC

VL – – CSC

Fig. 5. Membership functions for the rotor condition monitoring (CM).

Figure 5, shows that condition monitoring interprets the state of the rotor phase winding as a linguistic term, which may be f (CM) =Very Low Shortcircuit (VLSC), Low Short-circuit (LSC), medium Short-circuit (MSC), high Short-circuit (HSC), Critical Short-circuit (CSC). From the optimization of various possible combinations between the input variables, the following set of rule base of fuzzy system is given in Table 1. This aggregate of rules includes the description and knowledge of the DFIG machine condition.

3

Simulation Results and Validation

The aim of the present section is to evaluate the performances of the proposed fuzzy logic and TSA method. The proposed approach was implemented in MatlabSimulink environment. The short-circuiting inter turns defects in DFIG is examined by considering a variable wind speed ranging from8 m/s to 16 m/s. The block diagram of the overall system depicted in Fig. 6, is established for a (9-MW) wind farm Parameters of DFIG system are given in Table 2.

Fig. 6. Block Diagram of the implementation of diagnosis DFIG system.

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Values

Rated stator frequency

50 Hz

Rated stator phase voltage 575 V

3.1

Stator resistance

0.023 Ω

Rotor resistance

0.0178 Ω

Moment of inertia

0.685 kg m2

Number of pole pairs

3

Results

The DFIG machine is tested in the absence of faults (healthy case) Afterwards, the same simulation is carried out, for different operating speedsranging from 8 m/s to 16 m/s, in the presence of faults for three different residual fault levels (defective cases) to extract the current generated by its stator. A simple comparison between the current curves in the healthy and defective modes of DFIG under different wind speed conditions shown in Figs. 7 and 9. According to the results represented in these figures, it can be seen that the stator currents are slightly identical. Subsequently, the stator current signal cannot be used to predict a convincing indication for rotor inter-turn short circuit residual fault detection (less than 10%). The previous results affirm that it is compulsory to accomplish conditioning for the generated stator-current signal. To surmount this limitation, the TSA method is carried out to the stator current signal to extract the stator current residual signal [22–24]. The residual current signal curves in healthy and defective cases are displayed in Figs. 8 and 10. In accordance with the results depicted in these figures, the residual current will allow an easy discrimination of the healthy and defective cases when the wind speed changes. The aim of this work is to design a new system combining the TSA method and fuzzy logic, as proposed by the algorithm of figure1, in order to monitor and diagnosis the rotor of DFIG machine. For this reason, the value of K, calculated with Eq. 1, and the wind speed will be used as inputs in the fuzzy system, and the results are obtained as fuzzy deduction diagrams such as depicted in the Figs. 11, 12, 13 and 14. 3.2

Discussion

In order to demonstrate the efficiency of the association of the TSA method with fuzzy logic inference, various simulations were achieved. Firstly, one obtains the value of K ratio for two different level faults under a fixed wind speed (12 m/s). Figures 11 and 12 present the results of fuzzy system for diagnosis the DFIG rotor in different case of defect. For the first simulation,

A New TSA-Fuzzy Logic Based Diagnosis Residual current

Stator current Healthy case Defective case 4% Defective case 6% Defective case 8%

8000 6000

Healthy case Defective case 4% Defective case 6% Defective case 8%

150 100

4000

50

2000

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−100

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9.92

9.93

9.94

9.95 9.96 Time (s)

9.97

9.98

9.99

Fig. 7. Comparison of the stator current waveforms for the healthy case and three cases of short-circuit inter-turns defects under 10 m/s. x 10

0.2

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Time (s)

0.3

0.35

0.4

0.45

Residual current Healthy case Defective case 4% Defective case 6% Defective case 8%

150

100

0.5

50 Current (A)

Current (A)

0.15

Fig. 8. Comparison of the residual current waveforms for the healthy case and three cases of short-circuit inter-turns defects under 10 m/s.

Healthy case Defective case 4% Defective case 6% Defective case 8%

1

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0.05

0.1

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0.2

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0.3

0.35

0.4

0.45

Fig. 9. Comparison of the stator current waveforms for the healthy case and three cases of short-circuit inter-turns defects under 14 m/s.

Fig. 10. Comparison of the residual current waveforms for the healthy case and three cases of short-circuit interturns defects under 14 m/s.

Fig. 11. Rule view for a very small value of K ratio when the speed is 12 m/s.

Fig. 12. Rule view for a means value of K ratio when the speed is 12 m/s.

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Fig. 13. Rule view for a small value of K ratio when the speed is 11 m/s.

Fig. 14. Rule view for a small value of K ratio when the speed is 12 m/s.

the values assigned were 0.56 for K, and 12 for wind speed, the condition monitoring value of DFIG rotor is 0.04, which corresponds exactly to the defect of 4%. In the second simulation the values assigned were 1.68 for K, and 12 for wind speed, the condition monitoring value of DFIG rotor is 0.08, which corresponds exactly to the defect of 8%. The condition monitoring value which is the percentage of the defect increase, especially when K becomes large. As it can be noticed, the results demonstrate the performances of the proposed approach when the wind speed is constant. Secondly, once again, different tests were accomplished, to obtain the fuzzy logic inference value for a fixed K under different operating speeds ranging from 10 m/s to 14 m/s. Figure 13 and 14 show the simulation results of the fuzzy logic system for evaluating the severity level of the rotor winding fault. According to the results represented in these figures, it can be easily seen that the percentage of the defect decreases particularly when the wind speed becomes large. It can thus deduce that the failures of short-circuit between turns are outlined by a variation in the values of our FIS output. Consequently, the TSA- Fuzzy logic technique is able to ensure extremely accurate diagnosis.

4

Conclusion

In this paper, a new offline process has been developed to detect and classify the different levels of the winding inter-turn short circuit fault existing in the rotor windings of the DFIG machine. The proposed approach consists of combining the Time Synchronous Averaging (TSA) and fuzzy logic inference in order to perform a reliable and efficient diagnosis. The efficiency of the proposed hybrid approach has been successfully validated through simulation results. This technique is founded on the extraction of the residual current signal by the application of TSA method. The value of wind speed and the K ratio between the RMS value of residual current in defective state and healthy state are used as input in the FIS block, the outcomes delivered from this FIS are accurate and eligible to

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detect rotor winding inter-turn short circuit based on wind turbines. This can be used as an effective indicator for monitoring the condition of a DFIG rotor.

References 1. Kaidis, C., Uzunoglu, B., Filippos, A.: Wind turbine reliability estimation for different assemblies and failure severity categories. IET Renew. Power Gener. 9, 892–899 (2015) 2. Jaiswal, S., Pahuja, G.L.: Effect of reliability of wind power converters in productivity of wind turbine. In: IEEE 6th India International Conference on Power Electronics (IICPE), pp. 1–6 (2014) 3. Albarbar, A., Teay, S., Batunlu, C.: Smart sensing system for enhancing the reliability of power electronic devices used in wind turbines. Int. J. Smart Sens. Intell. Syst. 10, 407–424 (2017) 4. Chauhan, U., Pahuja, G.L., Singh, V., Rani, A.: Reliability analysis of wind turbine system using importance measures. In: IEEE India Conference (INDICON), pp. 1–5 (2015) 5. Wenxian, Y., Tavner, P.J., Court, R.: An online technique for condition monitoring the induction generators used in wind and marine turbines. Mech. Syst. Signal Process. 38, 103–112 (2013) 6. Sellami, T., Berriri, H., Mimouni, M.F.: Impact of inter-turn short-circuit fault on wind turbine driven squirrel-cage induction generator systems. In: Conf´erence Internationale en Sciences et Technologies Electriques au Maghreb CISTEM Tunis, Tunisia (2014) 7. Tchakoua, P., Wamkeue, R., Ouhrouche, M.: Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies 7, 2595–2630 (2014) 8. Marquez, F., Tobias, A., Perez, J., Papaelias, M.: Condition monitoring of wind turbines: techniques and methods. Renew. Energy 46, 169–178 (2012) 9. Bechhoefer, E., Kingsley, M.: A review of time synchronous average algorithms. In: Conference of the Prognostics and Health Management Society, San Diego, pp. 24–33 (2009) 10. Tavner, P.J.: Review of condition monitoring of rotating electrical machines. IET Electr. Power Appl. 2(4), 215–247 (2008) 11. Kandukuri, S.T., Klausen, A., Karimi, H.R., Robbersmyr, K.G.: A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renew. Sustain. Energy Rev. 53, 697–708 (2016) 12. Balasubramanian, A., Muthu, R.: Model based fault detection and diagnosis of doubly fed induction generators. Energy Procedia 117, 935–942 (2017) 13. Ngote, N., Guedira, S., Cherkaoui, M.: Conditioning of a statistical indicator for the detection of an asynchronous machine rotor faults. Mech. Ind. 13(3), 197–203 (2012) 14. Abdelmaleka, S., Rezazib, S., Azar, A.T.: Sensor faults detection and estimation for a DFIG equipped wind turbine. Energy Procedia 139, 3–9 (2017) 15. Toma, S., Capocchi, L.: Wound rotor induction generator inter-turn short-circuits diagnosis using a new digital neural network. IEEE Trans. Ind. Electron. 6, 4043– 4052 (2013) 16. Merabet, H., Bahi, T., Halem, N.: Condition monitoring and fault detection in wind turbine based on DFIG by the fuzzy logic. Energy Procedia 74, 518–528 (2015)

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17. Azgomi, H.F., Poshtan, J., Poshtan, M.: Experimental validation on stator fault detection via fuzzy logic. In: 3rd International Conference on Electric Power and Energy Conversion Systems, pp. 1–6 (2013) 18. Aydin, I., Karakose, M., Akin, E.: A new real-time fuzzy logic based diagnosis of stator faults for inverter-fed induction motor under low speeds. In: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), pp. 446–451 (2016) 19. Noureddine, L., Hafaifa, A., Kouzou, A.: Fuzzy logic system for BRB defect diagnosis of SCIG-based wind energy system. In: International Conference on Applied Smart Systems (ICASS), pp. 1–6 (2018) 20. Bourdim, S., Hemsas, K.E., Harbouche, Y., Azib, T.: Multi phase stator shortcircuit faults diagnosis & classification in DFIG using wavelet & fuzzy based technique. In: 3rd International Conference on Control, Engineering & Information Technology (CEIT), pp. 1–6 (2015) 21. Qiao, W., Lu, D.: A survey on wind turbine condition monitoring and fault diagnosis–Part II: signals and signal processing methods. IEEE Trans. Ind. Electron. 62(10), 6546–6557 (2015) 22. Sabir, H., Ouassaid, M., Ngote, N.: Diagnosis of rotor winding inter-turn short circuit fault in wind turbine based on DFIG using hybrid TSA/DWT approach. In: 6th International Renewable and Sustainable Energy Conference (IRSEC), pp. 1–6 (2019) 23. Himani, Dahiya, R.: Condition monitoring of wind turbine for rotor fault detection under non stationary conditions. Ain Shams Eng. J. (2017) 24. Sabir, H., Ouassaid, M., Ngote, N.: Diagnosis of rotor winding inter-turn short circuit fault in wind turbine based on DFIG using the TSA-CSA method. In: International Symposium on Advanced Electrical and Communication Technologies (ISAECT), pp. 1–5 (2018)

A New Fuzzy Clustering Method Based on FN-DBSCAN to Determine the Optimal Input Parameters Sihem Jebari1(B) , Abir Smiti2 , and Aymen Louati1 1

2

Institut Superieur Informatique du Kef, El Kef, Tunisia [email protected], [email protected] LARODEC, Institut Superieur de Gestion de Tunis, tunis, Tunisia [email protected]

Abstract. In recent years, data analysis has become important with increasing data volume. Clustering which groups objects according to their similarity, has an important role in data analysis. FN-DBSCAN is one of the most effective fuzzy density-based clustering algorithms and has been successfully implemented in medical field. However, it is a challenging task to determine its user-given input parameter values 1 and 2, which represent respectively the minimal threshold of neighborhood membership degrees and the minimal set cardinality. Both parameters have a significant influence on the clustering results. In this paper, we propose AF-DBSCAN algorithm which includes a new method to avoid the manual intervention and so permits to determine the 1 and 2 values automatically. In such way, the whole clustering process can be fully automated. Simulative experiments, carried out on real medical data sets, highlighted the AF-DBSCAN effectiveness even for high-dimension data sets, and showed that the proposed method outperformed the classical method since it can determine the two parameters more reasonably.

Keywords: Density-based clustering clustering · FN-DBSCAN

1

· DBSCAN · Automatic fuzzy

Introduction

With the rapid growth in number and size of databases, and in dimensions and complexity of data, data mining has become an important area of research for many scholars. It uses a variety of data analysis tools to discover patterns and relationship in data. One of the primary data mining techniques is the Cluster Analysis (Data Clustering) permitting to examine the structure and patterns in data. It is the c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 593–602, 2020. https://doi.org/10.1007/978-3-030-36778-7_65

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process of dividing objects into separated groups with respect to a distance, or equivalency, a similarity measure. The Data Clustering is performed by various methods which vary in the manner of clustering. Density-based Clustering methods are based on the idea that objects which form a dense region should be grouped together into one cluster. Data clustering is used in many fields including the medical area. One of the main challenges for medical applications is dealing with the big amount of data and the imprecision characterizing most types of medical data. Fortunately, there is an effective solution for analyzing this kind of data: The fuzzy Clustering , a sub-field of data clustering. FN-DBSCAN (Fuzzy Neighborhood DBSCAN), a fuzzy density-based clustering algorithm, is able to classify imprecise data containing noise with arbitrary shapes, out-performing the well-known DBSCAN algorithm. However, using FNDBSCAN to cluster points is very challenging. This is due to the difficulty of determining its input parameters, especially for someone who has no experience on the data set. For these reasons, automatic techniques should be developed to determine the values of these parameters. This paper focuses on automatic fuzzy clustering problems and proposes a novel automatic fuzzy clustering method based on FN-DBSCAN method used to perform the clustering task, and exploiting benefits of the k-dist plot used to determine the input parameter values. The proposed method, the AF-DBSCAN (Automatic Fuzzy DBSCAN), was evaluated on real medical data sets and was compared to two other clustering algorithms. The comparison results demonstrate the superiority of the proposed method in terms of effectiveness. The rest of this paper is organized as follows: In the second section, we will introduce the clustering method that lead to our new clustering algorithm: FN-DBSCAN algorithm. Section 3 describes our new algorithm AF-DBSCAN in details. Experimental setup and results are explained in the fourth section. The conclusion is stated in the final section.

2

State of Art

In the literature, crisp and fuzzy clustering methods can be broadly categorized as partitional, hierarchical, density-based, and probabilistic algorithms. This study deals with density-based fuzzy clustering algorithms. Before explaining our novel algorithm, we will go over one of the core clustering algorithms that mixed between fuzzy set concept and density-based clustering algorithms, the FN-DBSCAN (Fuzzy Neighborhood DBSCAN) [10], and we will discuss its limits. The algorithm follows a similar approach to the well-known density-based algorithm DBSCAN (Density-Based Spatial Clustering of Application with Noise) [3], with one main difference: instead of using the distance-based function

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to find the neighbors of the point, it uses the fuzzy neighborhood function to find the fuzzy neighbors and the fuzzy core points. To understand the difference, let us compare the two functions. The neighborhood set of the point x ∈ D, composed of the points in its - radius, can be defined using DBSCAN with the crisp neighborhood function described in (1) as:  1 if d(x, y) ≤  (1) Nx (y) = 0 otherwise Where  is the maximal distance for an object to belong to a cluster. In the case of FN-DBSCAN, which uses the fuzzy neighborhood function, Nx function can be described in (2) as:  Nx (y) =

1−

d(x,y) dmax

if d(x, y) ≤  0 otherwise

(2)

Where dmax = max d(x, y). x,y∈D

That’s why, the FN-DBSCAN provides a more realistic clustering result and give us results which are more similar to the expert’s clustering results. The FN-DBSCAN algorithm is able to classify imprecise data containing noise and with arbitrary shapes, out-performing the well-known DBSCAN. However, in contrast to its popularity, FN-DBSCAN has one major limit: two input parameters 1, the minimal threshold of neighborhood membership degrees, and 2, representing the minimal set cardinality, are acquired to run the algorithm. As explained in (3), the parameter 1 has the following relationship with  representing the maximum distance for an object to belong to a cluster on DBSCAN method:  (3) 1 = 1 − max d The parameter 2 has the following relationship with the minP ts parameter representing the minimal number of objects that must exist in the cluster (see 4): minP ts (4) 2 = wmax where wmax = max wi and wi is the cardinality of the point xi . In general i=1,...,n

words, wi is the sum of the membership degrees of points with 1 parameter to neighborhood set. Thus, n  Nxi (xk ) (5) wi = k=1

Where Nxi (xk ) is the neighborhood degree of point xk to the point xi . As mentioned above, the determination of 1 and 2 depends respectively on  and M inP ts values. Such parameters should be set properly according

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to the dataset scale and the clusters density: a very hard task for a user who has no experience on the dataset, especially for real world and high-dimensional datasets. To overcome these limits, automatic techniques should be developed. In literature, several solutions were proposed. One research accomplished about  parameter estimation is the Automatic Eps Calculation algorithm (AEC) which was described in [5]. The AEC performs several iterations and in each iteration a set of points is selected randomly. Then next, three coefficients are calculated: the distance between the points, the number of points located in a cluster between the points and the density of the cluster. The best possible result, which is the minimal distance between clusters, is selected by the algorithm. Unfortunately, The AEC algorithm can just estimate  in simple data sets with small noise ratio, while noisy data sets are common in practice. Also, it needs some other parameters to estimate the  (e.g. the width of the cluster) that are not easier to estimate than the . In addition, its time complexity is much more than the DBSCAN algorithm. Smiti and Elouedi combine Gaussian-Means (GM) and DBSCAN algorithm to determine the input parameters in DBSCAN. However, GM provides circular cluster shape not density-based clusters, and it is not strong against noise, [12]. In [2] Esmaelnejad and Habibi and Yeganeh proposed to find the appropriate value of  which can be estimated based on ρ, the noise ratio of data set and minP ts. Using noise ratio ρ is much more simpler than the  because it is relative, more probable to be known in advance, and also easier to estimate. However, the proposed solution has one major limit which is its high dependence of the value of minP ts which is itself an input parameter. In [4] another method for determining different ’ values was proposed. The method is based on the idea that points corresponding to noise are expected to have larger k-distance values. The author propose to calculate the “knee” (i.e. a threshold where a sharp change of gradient occurs along the k-neighbors plot) for estimating the set of  parameters. The algorithm starts first by drawing a k − dist plot for all the points, for a given k which is entered by the user. And after determines the different  values. The algorithm estimates the value of the minP ts using (6): n 1 Pi (6) minP ts = n i=1 where Pi is the number of points in  neighborhood of point Xi . Many optimization and meta-heuristic search algorithms have been also hybridized with clustering algorithms to improve the results of clustering. These algorithms include the ant colony algorithm [6,15], the bee optimization [7,9], tabu search [8,14] and more recently the genetic algorithm was combined with DBSCAN [11,13], etc.

3

The Proposed Method AF-DBSCAN

The purpose of our work in this study is to improve the FN-DBSCAN algorithm. Unlike the classical FN-DBSCAN algorithm, the proposed algorithm AF-

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DBSCAN (Automatic Fuzzy DBSCAN), focuses in the auto-determination of the input parameters 1 and 2. The goal is to facilitate the clustering task and to make the result of clustering more accurate. The AF-DBSCAN algorithm, has three steps: (1) Normalizing the data points so that all the data belongs to the interval [0, 1], (2) Determining the optimal values of the input parameters, and then (3) clustering the data points. Figure 1 illustrates these three steps.

Fig. 1. Overview of the AF-DBSCAN approach.

3.1

Data Normalization

Data normalization is a pre-processing tool used before the clustering task. It means transforming all variables in the data set to a specific range. The aim is to remove the difference between the scales of the different variables and so provide an easy way to compare values that are measured using different scales or different measuring units. The normalization technique used for the AF-DBSCAN is the Min-Max normalization. Let D = x1 , x2 , ..., xN denotes the k-dimensions Data set.  The method maps a value xij of D to xij , , i = 1..N, j = 1..k in a specific range. In our case, Min-Max normalization maps values in the range [0, 1]. In such cases, the Min-Max normalization formula is described as in (7): 

xij =

xij − xmin j (xmax − xmin ) j j

(7)

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where: xmin = min xij , xmax = max xij and j = 1, ..., k. j j i=1,...,n

i=1,...,n

The normalization of data before clustering the data set leads to obtain a better quality and accurate clustering result. 3.2

Estimation of the Input Parameters

As mentioned before, the selection of the input parameter values is important. Even when we use the well clustering algorithm, because of inappropriate choice of algorithmic parameters, the generated partitions may not reflect the desired clustering of the data. From (3), it’s clear that the  value must be calculated to can next calculate the 1 value. The optimal value of  can be obtained using the k-dist plot, where k is equal to M inP ts value. The main idea is to calculate the k-dist list (i.e. a list containing the sorted values representing the k th nearest neighbor distances of each point in the data set), then the slope of each point to the next point, defined as the slope of the line segment ki , ki+1 , is calculated. The slopes are calculated to determine the sharp changes in the plot, representing candidate . The first slope which is above the threshold (mean + standard deviation) will be selected as the optimal value of the input parameter . The optimal value of 2 will be calculated using (4). In [3], Ester, Kriegel, Sander and Xu demonstrate that the optimal value of the minimal number of points in the cluster minP ts can be determined as mentioned in (8): minP ts = ln(N ) (8) Where N is the size of the data set. Finally, to get the wmax , we calculate the sum of the membership degrees of all points to neighborhood set with respect to 2 parameter, and next, we search for the maximal value. 3.3

The Clustering Task

The Clustering phase works similar to the FN-DBSCAN. The algorithm uses the 1 and 2 values to cluster the data. It starts with an arbitrary starting point p that has not been visited and retrieves all points density reachable from p, with respect to 1 parameter. If a point is found to be a core point (i.e. a point which its summation of neighborhood degrees is greater than 2), its FN-neighbors are also part of that cluster. If p is a border point, the algorithm visits the next point of the data set, leading to the discovery of a further cluster or noise. The different steps of the AF-DBSCAN algorithm are represented in Algorithm (1).

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Algorithm 1. AF-DBSCAN Algorithm Input: Matrix representing the set of objects X, number of data points N. Output: C clusters. 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23:

4

(Data Normalization.) for each point xi Determine xmin and xmax values j j  N ormalizedXij ← xij (see equation (7)) (Automatic Determination of 1 and 2 Values.) minP ts ← ln(N ) Calculate similarity matrix D Sort D k ← minP ts for each point pi Calculate Slopes of k-dist list T hreshold = mean + standard deviation Calculate 1 Calculate 2 (Data Clustering.) Mark all points unclassified Arbitrary select a point xi Retrieve all points density-reachable from p with respect to 1 If xi is a core point, a new cluster C is formed if xi is a border point, visit next point in the data set Repeat the steps 17-18-19-20 until all the points have been processed Mark all points which do not belong to any cluster as noise Output the discovered clusters

Experimental Results

In this section, we will try to prove the effectiveness of our approach as well as the performance of the algorithm compared to other well-known algorithms. Implementation and simulation were conducted on an Intel core i7 2.7 GHz with 8 GB RAM. The algorithms are programmed in Java 8 using NetBeans IDE V.8.2. Let us remind that the aim of our contribution is the optimization of the fuzzy clustering task by the auto-determination of the input parameters. Thus, the clustering results obtained by the AF-DBSCAN algorithm are compared to those obtained by the DBSCAN and FN-DBSCAN algorithms. 4.1

Dataset and Evaluation Criteria

To analyze our proposed method we experimented our algorithm on two real medical datasets obtained from UCI repository: Thyroid Disease Dataset (TD) which contains three main classes to describe patients: Hyperfunction, subnormal functioning and normal with 7200 instances, and Breast Cancer Wiscon-

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sin (BCW) dataset used to distinguish malignant cases from the benign. BCW dataset is constituting of 699 sample points. And to measure the performance of our approach and the accuracy of the clustering results, the following criteria were considered: Classification Accuracy PCC, Conditional Error Rate (eT ) and the Run Time. Different results carried out from these simulations are presented and analyzed. 4.2

Results

We will use the DBSCAN and FN-DBSCAN algorithms, explained in the second section, as reference for comparison with AF-DBSCAN. The DBSCAN is run with  = 0.06 and minP ts = 9. This choice is recommended in [1]. Table 1 shows results when comparing PCC and eT measures for the three algorithms, helping us to evaluate the classification accuracy. Table 1. Classification Accuracy of DBSCAN, FN-DBSCAN and AF-DBSCAN algorithms. Data sets DBSCAN PCC eT

FN-DBSCAN AF-DBSCAN PCC eT PCC eT

BCW

0.851 0.148 0.865 0.134

0.931 0.068

TD

0.951 0.049 0.95

0.984 0.016

0.05

From Table 1, It can be clearly noticed that our method provided for both datasets the best results in terms of Classification Accuracy (0.931 for BCW dataset and 0.984 for TD dataset) and Error Rate (0.068 for BCW dataset and 0.016 for TD dataset). This can be explained by the fact that the AF-DBSCAN algorithm, due to the automatic determination of its input parameter, has the ability to predict properly the true class of most data points. To test the adaptation of the proposed solution for large databases, we have timed the clustering process. The Table 2 shows the time required in seconds to compile the clustering process by the three algorithms. Table 2. Retrieval time’s results for DBSCAN, FN-DBSCAN and AF-DBSCAN algorithms Data sets DBSCAN FN-DBSCAN AF-DBSCAN BCW TD

2.45

2.76

15.02

15.96

3.019 19.15

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From the Table 2, it can be clearly seen that when the number of data points increase, the run time increase too. The most time-consuming algorithm is the AF-DBSCAN (3.019 s for BCW dataset and 19.15 s for TD dataset).

Fig. 2. Relationship between run speed and data set’s size.

The run speed performances represented by the algorithm AF-DBSCAN in Fig. 2 are inferior to those given by the DBSCAN and FN-DBSCAN algorithms since the algorithm proceed firstly to the calculation of the input parameters. The minimal threshold of neighborhood membership degree is calculated for each data point separately to can predict clusters correctly even when they have not the same density. These calculations negatively influence the run time of the clustering task for the AF-DBSCAN. Globally, the experimental results indicate that the proposed clustering algorithm shows superior performances comparing to DBSCAN and FN-DBSCAN algorithms. However, it is the most time-consuming algorithm.

5

Conclusion

We proposed a novel fuzzy clustering method, called AF-DBSCAN, in order to enhance the FN-DBSCAN in noisy and imprecise environment. Our method was experimented on two real medical datasets and in both experiments AF-DBSCAN behaved in a satisfying manner by providing better results than other well-known methods. Our approach can still be improved by including a technique with which further speed gain can be achieved. Moreover, it could be useful to implement techniques to make AF-DBSCAN incremental, since new data can be continuously generated in medical datasets.

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References 1. Smiti, A.: COID: clustering, outliers and internal detection approach for case base maintenance. Master’s thesis, Institut Sup´erieur de Gestion de Tunis (2010) 2. Esmaelnejad, J., Habibi, J., Yeganeh, S.H.: A novel method to find appropriate  for DBSCAN. In: Nguyen, N.T., Le, M.T., Swiatek, J. (eds.) ACIIDS 2010, pp. 93–102 (2010) 3. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial data sets with noise. In: Proceeding 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996) 4. Gaonkar, M.N., Sawant, K.: AutoEpsDBSCAN: DBSCAN with EPS automatic for large dataset. ISSN (Print) 2(2), 2319–2526 (2013) 5. Gorawski, M., Malczok, R.: AEC algorithm: a heuristic approach to calculating density-based clustering EPS parameter. In: Proceedings of the 4th international conference on Advances in Information Systems, ADVIS 2006, pp. 90–99 (2006) 6. Han, Y., Shi, P.: An improved ant colony algorithm for fuzzy clustering in image segmentation. Neurocomputing 70, 665–671 (2007) 7. Hancer, E., Ozturk, C., Karaboga, D.: Artificial bee colony based image clustering method. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–5 (2012) 8. Lin, L., Suhua, L.: Wheat cultivar classifications based on tabu search and fuzzy c-means clustering algorithm. In: 4th International Conference on Computational and Information Sciences (ICCIS), pp. 493–496 (2012) 9. Marinakis, Y., Marinaki, M., Matsatsinis, N.: A hybrid discrete artificial bee colony - grasp algorithm for clustering. In: International Conference on Computers Industrial Engineering (CIE), pp. 548–553 (2009) 10. Nasibov, E.N., Ulutagay, G.: Robustness of density-based clustering methods with various neighborhood relations. Fuzzy Sets Syst. 160(24), 3601–3615 (2009) 11. Sharma, L., Ramya, K.: An efficient DBSCAN using genetic algorithm based clustering. Int. J. Sci. Eng. Res. 5, 1820–1826 (2014) 12. Smiti, A., Elouedi, Z.: DBSCAN-GM: an improved clustering method based on Gaussian means and DBSCAN techniques. In: 16th International Conference on Intelligent Engineering Systems (INES), pp. 573 – 578 (2012) 13. Tiwari, K.K., Jain, A.: An genetic based fuzzy approach for density based clustering by using k-means. Int. J. Sci. Res. Eng. Trends 2, 114–120 (2016) 14. Xu, H.B., Wang, H.J., Li, C.G.: Fuzzy tabu search method for the clustering problem. In: Proceedings International Conference on Machine Learning and Cybernetics, pp. 876–880 (2002) 15. Zhao, B., Zhu, Z., Mao, E., Song, Z.: Image segmentation based on ant colony optimization and k-means clustering. In: IEEE International Conference on Automation and Logistics, pp. 459–463 (2007)

Diagnosis of Brain Tumors in MR Images Using Metaheuristic Optimization Algorithms Malik Braik1(B) , Alaa Sheta2 , and Sultan Aljahdali3 1

Department of Computer Science, Al-Balqa Applied University, Al-Salt, Jordan [email protected] 2 Computer Science Department, Southern Connecticut State University, 501 Crescent Street, New Haven, CT 06515, USA [email protected] 3 Department of Computer Science, Taif University, Taif, Saudi Arabia [email protected]

Abstract. Image clustering presents a hot topic that researchers have chased extensively. There is always a need to a promising clustering technique due to its vital role in further image processing steps. This paper presents a compelling clustering approach for brain tumors and breast cancer in Magnetic Resonance Imaging (MRI). Driven by the superiority of nature-inspired algorithms in providing computational tools to deal with optimization problems, we propose Flower Pollination Algorithm (FPA) and Crow Search Algorithm (CSA) to present a clustering method for brain tumors and breast cancer. Evaluation clustering results of CSA and FPA were judged using two apposite criteria and compared with results of K-means, fuzzy c-means and other metaheuristics when applied to cluster the same benchmark datasets. The clustering method-based CSA and FPA yielded encouraging results, significantly outperforming those obtained by K-means and fuzzy c-means and slightly surpassed those of other metaheuristic algorithms. Keywords: Clustering · Flower Pollination Algorithm Algorithm · K-means · Fuzzy C-mean

1

· Crow Search

Introduction

Healthcare poses one of the critical sectors of civilized societies. Every day, physicians address new health problems, unprecedented ailments, and many challenges that arise from the emergence of many uncontrolled health problems. They capture benefits from up-to-date technologies to get over diseases and medical difficulties. One of the most substantial facets of healthcare is the process of diagnosing medical images, which presents a challenging task for any physician. The use of powerful image processing technologies, such as Magnetic Resonance Imaging (MRI) and X-ray as well as computed tomography, enables the physician c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 603–614, 2020. https://doi.org/10.1007/978-3-030-36778-7_66

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to better analyze medical images. Such technologies can provide an opportune suggestion for treatment or surgery [1]. Within the field of image analysis, image segmentation is typically used as a fundamental preliminary pre-step in several medical image analysis applications [1]. In such a context, image segmentation may be set out as a process of partitioning a particular image into non-intersecting and non-connected areas. These regions are featured and consistent in accordance to some basic image characteristics, which customarily comprise analogous objects or some parts of objects of interest. Generally, supervised and unsupervised segmentation algorithms are the two main categories of segmentation methods. In the supervised methods [2], it is necessary to provide a prior information, which composes the ground of the segmentation process. These methods are subject to manual intervention to provide a presumptively information. Moreover, they assume a certain intensity distribution function of the pixels. However, this procedure may not constantly fit to the practical intensity distribution of the image. In contrast, in unsupervised methods, pre-information is commonly not required; they proceed without any priori information [3]. In such an approach, segmentation is processed rapidly using the information retrieved from the image itself. This approach is known as clustering. Two of the popular clustering algorithms that have been widely used in image segmentation are K-Mean (KM) [4] and Fuzzy C-Mean (FCM) [5] clustering approaches. In many practical situations, it may be unknown or impracticable to identify or even approximate the proper figure of clusters in a previously unprocessed dataset. Actually, the images captured by a robot camera demand a rapid and automatic clustering method, so the analogous objects in the images can be distinguished similarly. The identification of an optimum number of clusters in a large database has always been a very difficult task. While the leading clustering methods, which have applied clustering procedures for the analysis of MRI in medical applications go back to nearly two decades, are mature and very close to routine clinical applications, we believe that these methods are either sophisticated or presented using methods tailored for particular medical applications. A need remains to resolve such problems of user-specific as inputs, particularly if the clustering problem relies on the number of clusters and initial centroids. So, clustering with an automatic determination of parameters remains an open problem. Motivated by the favorable outcome of nature-inspired metaheuristic algorithms in solving and processing a variety of medical image analysis applications [6], we propose here two metaheuristic algorithms, specifically Crow Search Algorithm (CSA) [7] and Flower Pollination Algorithm (FPA) [8], to clustering and diagnosis of brain tumors and breast cancer in MRI. These algorithms were proposed because of their powerful features in arriving at optimal or exact solutions and were used to implement the same clustering problem. The clustering approach based-CSA and FPA was carried out to automatically locate brain tumors and suspicious breast areas in abnormal MRI. In Sect. 2 a description of the basic theory of image segmentation along with a concise description of the well-known segmentation categories are presented.

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The following Sect. 3 focuses on the underlying concept of the clustering problem along with the mathematical formula of the objective function. A short statement of the prominence of metaheuristics in data clustering problems is presented in Sect. 4; this section also provides a brief description of CSA and FPA with an explanation of the clustering solution. The assessment methods and results are presented in Sect. 7. Section 8 then presents statistical analysis tests. Section 9 then discusses the results with closing comments in Sect. 10.

2

Image Segmentation Methods

Image segmentation performs a pivotal role in image processing throughout splitting an image into homogenous areas that share similar characteristics [9]. The following is a brief description of some of the well-known segmentation methods: 1. Threshold-based Segmentation: Threshold-based segmentation method is simple and effective in segmenting gray-scale images that can accomplish the segmentation task by comparing the intensities of the image under study to one, two or more intensity thresholds. Threshold-based methods as described elsewhere [10] are graded into local and global thresholdings. 2. Region-based Segmentation: this method explores image pixels and forms separate areas by combining adjacent pixels with homogeneity characteristics on the basis of a predetermined similarity function. Region growing method is the simplest and most popular region-based segmentation method, which is used to retrieve a continuous area of similar pixels from an image [3]. 3. Edge-based Segmentation: the objects in this method are identified as pixels encompassed by closed borders. These methods offer a potential merit to isolate compound objects into a single area. Pixels within closed borders may have large variations in their properties, as regions may be heterogeneous in their features. 2.1

Clustering-Based Segmentation

Clustering is the organization of data with high similarity within the cluster itself and low similarity between clusters. The most popular used clustering methods are briefly described below. 1. K-Means Clustering Algorithm: this clustering method [4] could be defined as an iterative process that repeatedly divides the image into different clusters. KM algorithm aims to allocate each data point to a particular cluster, xj , by calculating the distance between xj and each centroid position, μj , j = 1, . . . , K, through the minimization of an objective function, D, defined as shown in Eq. 1. D = M in

K   j=1 x∈Cj

d(x, μj ) = M in

K  

||x − μj ||2

(1)

j=1 x∈Cj

where K identifies the number of allocated clusters, Cj represents the cluster center of the j th cluster and d stands for the Euclidean distance.

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2. Fuzzy C-Means Clustering Technique: this technique performs the clustering process by splitting one set of data points into two or more clusters [5]. This technique does not place any hard constraints on the pixels while building the clusters. FCM technique works by setting a membership value for each data point that corresponds to the center of a cluster and is identified by the distance between the data point and the corresponding cluster. These membership values specify the degree of similarity. The closer the data points are to the cluster’s centroid, the greater their memberships towards the center of the cluster.

3

Proposed Cluster Analysis Method

Image clustering as an unsupervised machine learning task is one of the most widespread methods of data analysis. In effect, the clustering problem can be considered an NP-hard problem. As given above, this task represents grouping of data objects into clusters in a predefined manner, where the data sets of a certain cluster must have a large similarity index [11]. Basically, a distance criterion should be pre-defined and utilized to assess the similarity amidst data objects for a given set of points. In particular, the clustering problem could be clearly illustrated as follows: considered a number of objects, N , then allocate each object to one of predefined number of clusters, K, while decreasing the overall squared Euclidean distances between each identified object and the center of the corresponding cluster. This metric measure is defined in Eq. 2. F (O, C) =

N  K 

wji (Oj − Ci )

2

(2)

j=1 i=1

where (Oj − Ci ) identifies the norm of the Euclidean distance between the center of the ith cluster, Ci , and the j th object Oj , N represents the number of data objects, K is the number of clusters and wji is the weight of data object, Oj , which is associated with cluster i; this association weight is either 1 if object j is set to cluster i or 0 if object j is not set to cluster i. There are many clustering methods presented in literature to solve specific clustering problems for a variety of computer vision and medical applications. Each of these methods has its own advantages and disadvantages. Specifically, K-means is the most notorious classical clustering approach, which is attributed to its simplicity and efficacy [4]. However, K-means is suffered from two explicit problems : (1) the number of clusters must be defined a priori and before beginning the clustering process, (2) the performance of this technique relies largely on the initial centroids which may fall into local optimal solutions [4]. To cope with the flaws and shortcomings of classical clustering techniques such as K-means, many metaheuristics have been presented as clustering methods as given below.

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Metaheuristic Algorithms for Data Clustering

Broadly speaking, clustering itself is better to be addressed as an optimization problem, and thereby metaheuristic optimization algorithms can be exploited to perform the clustering problem. In fact, metaheuristics algorithms inspired by nature have become very potent and prevalent in addressing contemporary and real-world optimization problems [12]. An extended version of the clustering task, referred to as automatic clustering, can be easily addressed using methods developed based on swarm intelligence or evolutionary algorithms. In such an automatic clustering method, the number of clusters is not known as usual, so some of the conventional clustering approaches are feeble to perform a clustering task. On the contrary, identifying sensible objectives would be efficient in handling an automatic clustering task using metaheuristics algorithms, just as the normal clustering. For example, Particle Swarm Optimization (PSO) [13], Genetic Algorithm (GA) [14] as well as many nature-based algorithms have been broadly used for data clustering tasks in a range of applications. In addition, different types of clustering approaches have been applied in several fields of study suchlike document clustering [15] and human contour detection [16]. In this paper, we proposed CSA and FPA to perform particular medical image clustering tasks for brain tumors and breast cancer in MR images, as briefed below.

5

Proposed Clustering Method

Crow search and flower pollination algorithms are two metaheuristic optimization algorithms extensively used for optimization problems [8,12]. 5.1

Crow Search Algorithm

CSA emerged from the imitation of the social behavior of crows besides their cunning ways of concealing food [7]. Basically, the following four key rules proposed by Askarzadeh [7] outline the basic assumptions of CSA: • Crows live in flocks, so CSA is considered a population-based algorithm. • Crows are able to recall the localities where they stow their food and can retrieve it. • Crows are able to monitor other animals when they robbed their concealed food. • Crows are able to administer themselves to conserve their invisible food. 5.2

Flower Pollination Algorithm

FPA was inspired from the flow pollination process, flower fastness and the pollinator behavior of flowering plants [8]. In sum, the following key rules present the inspiration points proposed by Yang [8], during the development of FPA:

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• Biotic and cross-pollination can be regarded a global pollination process, and pollen-carrying pollinators proceed in a way that conforms with l´evy flights. • Self-pollination and abiotic are considered as local pollination. • Pollinators such as insects can cause the stability of the flowers, which is equivalent to the probability of reproduction. • A switch probability was suggested to switch between local and global pollination. This probability takes a value from 0 to 1 with a slight bias towards local pollination.

6

Problem Formulation

The proposed clustering problem using CSA and FPA is defined as an optimization problem, where the fitness function is given in Eq. 2. The candidate solution of the proposed clustering method by the metaheuristics presented in this work was implemented by a one-dimensional vector. Similarly, the individual unit in the proposed vector solution is considered as the dimension of the cluster centroid. Figure 1 shows a candidate solution for the proposed clustering method, where there are three clusters and five features in each object.

Fig. 1. A vector representing a candidate solution for an image containing three clusters and five features in each object.

The wiener filter, which represents a linear estimate of the image being processed, was used as a pre-step to lessen the mean square error in the process of noise smoothing and inverse filtering in the clustering problem.

7

Experimental Results

The proposed image clustering approach was applied to two benchmark datasets gathered with a diversity of complexity. The datasets are MR images of brain tumors and breast cancer, which are publicly available in the repository of Kaggle at1 . The key properties of the employed medical datasets are given in Table 1. The number of generations and population size are respectively 200 and 50 for both CSA and FPA. The probability of awareness and flight length of CSA are 0.5 and 2, respectively and the switch probability of FPA is 0.8.

1

https://www.kaggle.com.

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Table 1. The main characteristics of the benchmark datasets used in this research. Database

No. of clusters No. of features No. of objects No. of images

Brain MRI images

3

2

893

556

Breast cancer images 2

4

771

417

106

1.7

1.6

1.5

1.4

1.3

1.2

1.1

0

20

40

60

80

100

120

140

160

180

200

Fig. 2. Best convergence characteristic curves of the evolutionary process of several metaheuristic algorithms over ten evaluation experiments.

The convergence curves of Fig. 2 shows the best evolutionary convergence processes of CSA, FPA, PSO, GA and DE during executing the proposed clustering method for brain tumors in MR gray-scale images. The curves in Fig. 2 show a measure of the fitness function defined in Eq. 2. Figures 3 and 4 exemplify the clustering results of two examples of MR images of human brain based on the clustering methods presented in this work. In fact, it is difficult to appropriately judge results visually between different clustering methods. As aforementioned, this problem is an NP-hard problem, and although there are many clustering methods, a direct visual comparison is problematic because different clustering methods, tailored to the strengths of specific applications, are frequently used in evaluation. Hence, a proper metric measure is needed to judge the efficiency of the clustering methods as given next. The performance of the proposed image clustering method using FPA and CSA was compared with two well-known classical and four promising metaheuristic algorithms when used to implement the same clustering approach. The classical methods include KM [4] and FCM [5]. The metaheuristics include GA [14], differential evolution [17] and PSO [13]. The computational accuracy of the proposed clustering methods was assessed using two relevant evaluation methods:

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Fig. 3. Clustering results: (a) An MR image pre-processed using wiener filter, (b) Kmeans, (c) Fuzzy-C means, (d) GA, (e) DE, (f) PSO, (g) CSA, (h) FPA.

Fig. 4. Clustering results: (a) An MR image pre-processed using wiener filter, (b) Kmeans, (c) Fuzzy-C means, (d) GA, (e) DE, (f) PSO, (g) CSA, (h) FPA.

– Sum of Intra-Cluster Distances (SICD) was used to assess the internal quality of the created clusters: the distance between each detected object and the centroid of the corresponding cluster is calculated and added together, as given in Eq. 2. Obviously, the lower the SICD, the greater the adequacy and quality of the clustering results. Further, the sum of intra-cluster distances is used as an assessment of the fitness. – Error Rate (ER) was used to assess the external quality of the established clusters: this assessment measure is defined in Eq. 3.

Clustering of Brain and Breast Tumors

ER =

number of mislaid objects after clustering × 100 The whole number of objects within the database

611

(3)

Table 2 presents the intra-cluster distances reported by the clustering methods presented in this work. The solutions shown in Table 2 for ten simulation runs are the best, average, worst and standard deviation. Table 2. The sum of intra-cluster distances obtained by the clustering methods on two medical databases. Criteria KM

FCM

PSO

GAs

DE

CSA

FPA

539605 2.13e+6 6.86e+6 1.59e+6

502629 1.97e+6 5.77e+6 1.37e+6

502621 1.97e+6 5.76e+6 1.34e+6

502633 1.97e+6 5.78e+6 1.37e+6

502270 1.96e+6 5.74e+6 1.32e+6

501875 1.94e+6 5.72e+6 1.30+6

751668 3.62e+6 6.69e+6 2.70e+6

751379 3.59e+6 6.68e+6 2.69e+6

751677 3.62e+6 6.69e+6 2.71e+6

751389 3.53e+6 6.63e+6 2.60e+6

751034 3.50e+6 6.61e+6 2.43e+6

Brain MR images Best Average Worst STD

554323 2.18e+6 7.03e+6 1.63e+6

Breast cancer images Best Average Worst STD

863893 4.16e+6 7.38e+6 2.91e+6

816764 3.98e+6 7.30e+6 2.93e+6

As evidenced from the results in Table 2, FPA attained the best computational results among the other algorithms. For the brain MRI images, the best, worst and SD values reported by FPA are 501875, 5.72e + 06 and 1.30 + 06, respectively, which are conspicuously better than the clustering results of KM, FCM and the other evaluated algorithms. As realized from the results of the breast cancer image set shown in Table 2, clustering results of FPA are much better than those of the other tested algorithms. For the breast cancer image set, the worst solution reported by FPA is 6.60e + 06, which is much higher than the worst solutions acquired by the other algorithms. For the brain MRI and breast cancer datasets, the outcomes attained by FPA and CSA outperformed the computational results obtained by KM and FCM methods; however, the results obtained by the clustering approaches implemented by GA, PSO and DE are nearly analogous to each other. For the brain MRI dataset, FPA reached an average solution of 1.94e + 06, while the other evaluated algorithms were incapable to report this solution even once during the ten evaluation runs. In the breast cancer dataset, CSA provided reasonable best and average solutions with small SD compared to the traditional KM and FCM techniques. The average ER found by the clustering methods from the ten runs on the test brain tumor and breast cancer datasets are displayed in Table 3. As clearly observed from the results in Table 3, FPA offered a lower average error rate in all test datasets.

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K-means FCM PSO GAs

Brain MRI images

25.24

Breast cancer images

8

DE

CSA FPA

22.79 19.15 18.96 19.17 18.67 18.25

6.89

5.79

3.64

3.26

3.66

3.05

2.91

Computational Statistical Analysis

Statistical analysis was conducted to locate the importance of differences between the clustering results obtained by each clustering method. Friedman statistical test was used to ascertain if there are noticeable differences between the outcomes of the clustering methods [18]. In case of the existence of the statistically worthy differences between the results, the Holm procedure [18] will be preceded as a post hoc statistical method that is typically utilized to compare the best performing method (i.e. the control method) versus the remaining algorithms. The confidence level, referred to as α, was 0.05 in all test cases. The average order of the clustering methods attained by Friedman’s test according to the criterion defined in Eq. 2 is given in Table 4. The clustering method is ranked by FPA in the first place, followed order by CSA, GA, PSO, DE, FCM and KM. Table 4. Average order of the clustering methods based on Friedman’s test using the evaluation method in Eq. 2. Algorithm K-means FCM PSO GAs DE CSA FPA Ranking

7.0

6.0

4.0

3.0

5.0 2.0

1.0

The p-value calculated by Friedman’s test is presented in Table 5, where the null hypothesis of equivalent accuracy is rejected to affirm if there are statistically remarkable differences between the accuracy of all tested. Table 5. Friedman’s and Iman–Davenport’s tests results obtained on the basis of the evaluation method in Eq. 2. Statistical method Statistical value p-value

Hypothesis

Friedman

11.78571

0.06692

Not rejected

Iman–Davenport

54.99999

5.54280E − 5 Not rejected

The Holm’s procedure is then applied as a post-test method to identify if there are efficacious statistical differences between FPA, that is, the method that has the lowest rank based on Friedman’s test, and the remaining methods.

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The results based on Holm’s method are given in Table 6, where FPA is the control algorithm. Table 6. Results of Holm’s method based on the evaluation method in Eq. 2. i Method

z

p-value α ÷ i

6 K-means 2.77746 0.00547 0.00833

Hypothesis Rejected

5 FCM

2.31455 0.02063 0.01

Not rejected

4 DE

1.85164 0.06407 0.0125

Not rejected

3 PSO

1.38873 0.16491 0.016666 Not rejected

2 GAs

0.92582 0.35453 0.025

Not rejected

1 CSA

0.46291 0.64342 0.05

Not rejected

Holm’s method rejects those hypotheses that have a p-value ≤ 0.01. The findings of Holm’s method divulge that FPA (the control algorithm) is statistically better than the clustering methods KM, FCM, CSA, GA, PSO and DE.

9

Discussion of the Results

Substantially, FPA and CSA are superior to the other metaheuristic algorithms as well as the traditional clustering algorithms. In addition to this, CSA and FPA can locate high quality solutions and provide plausible standard deviation values. In other words, FPA and CSA converge to global optima solutions in all the ten simulation runs, while the other clustering methods such as KM and FCM may fall into local solutions. In the images of the breast cancer, FPA reported a slightly better solution than the solution reported by CSA. Even in this dataset, CSA captured high quality results compared to the remaining evaluated algorithms. The findings in Table 3 confirm the sensibility and suitability of CSA and FPA in clustering medical brain tumor and breast cancer.

10

Conclusion and Future Work

Simulating and modeling natural phenomena to solve complex real issues has been a magnificent area of research for decades. In this research, we have used Crow Search Algorithm (CSA) and Flower pollination Algorithm (FPA), as two metaheuristic algorithms, to accomplish a medical image clustering problem. This clustering problem was conducted to brain human and breast cancer in Magnetic Resonance (MR) images. The accuracy level of the clustering methods was assessed using the percentage of misplacing objects and the total distances between each object and the centroid of its corresponding cluster. Experimental results on the tested benchmark show that CSA and FPA outdo the other test algorithms. For further work, the presented metaheuristics can also be utilized for

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a wide range of applications as well as other datasets that contain different types of diseases. The use of CSA in hybridization with FPA or other metaheuristics may be effectual.

References 1. Braik, M., Sheta, A.: A new approach for potentially breast cancer detection using extracted features and artificial neural networks. J. Intell. Comput. 2(2), 55 (2011) 2. Bezdek, J.C., Hall, L., Clarke, L.: Review of MR image segmentation techniques using pattern recognition. Med. Phys. 20(4), 1033–1048 (1993) 3. Patil, D.D., Deore, S.G.: Medical image segmentation: a review. Int. J. Comput. Sci. Mob. Comput. 2(1), 22–27 (2013) 4. Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010) 5. Hanping, M., Yancheng, Z., Bo, H.: Segmentation of crop disease leaf images using fuzzy c–means clustering algorithm. Trans. Chin. Soc. Agric. Eng. 9, 2008 (2008) 6. Braik, M., Sheta, A.F., Ayesh, A.: Image enhancement using particle swarm optimization. In: World Congress on Engineering, vol. 1, pp. 978–988 (2007) 7. Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016) 8. Yang, X.-S.: Flower pollination algorithm for global optimization. In: International Conference on Unconventional Computing and Natural Computation, pp. 240–249 (2012) 9. Braik, M., Sheta, A.: Exploration of genetic algorithms and particle swarm optimization in improving the quality of medical images (2011) 10. Sheta, A., Alkasassbeh, M., Braik, M., Ayyash, H.A.: Detection of oil spills in SAR images using threshold segmentation algorithms. Int. J. Comput. Appl. 57(7), 10– 15 (2012) 11. Kumar, S., Fred, A.L., Varghese, P.S.: Suspicious lesion segmentation on brain, mammograms and breast mr images using new optimized spatial feature based super-pixel fuzzy c-means clustering. J. Dig. Imag. 32(2), 322–335 (2019) 12. Sheta, A., Faris, H., Braik, M., Mirjalili, S.: Nature-inspired metaheuristics search algorithms for solving the economic load dispatch problem of power system: a comparison study. In: Applied Nature-Inspired Computing: Algorithms and Case Studies, pp. 199–230 (2020) 13. Kuo, R., Syu, Y., Chen, Z.-Y., Tien, F.-C.: Integration of particle swarm optimization and genetic algorithm for dynamic clustering. Inf. Sci. 195, 124–140 (2012) 14. Sheta, A., Braik, M.S., Aljahdali, S.: Genetic algorithms: a tool for image segmentation. In: 2012 International Conference on Multimedia Computing and Systems, pp. 84–90. IEEE (2012) 15. Rashaideh, H., Sawaie, A., Al-Betar, M.A., Abualigah, L.M., Al-Laham, M.M., Ra’ed, M., Braik, M.: A grey wolf optimizer for text document clustering. J. Intell. Syst. (2018) 16. Braik, M., Al-Zoubi, H., Al-Hiary, H.: Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier. In: Pattern Analysis and Applications, pp. 1–19 (2019) 17. Das, S., Abraham, A., Konar, A.: Automatic hard clustering using improved differential evolution algorithm. In: Metaheuristic Clustering, pp. 137–174 (2009) 18. Mendenhall, W., Beaver, R.J., Beaver, B.M.: Introduction to Probability and Statistics. Cengage Learning (2012)

Biometric Person Authentication Using a Wireless EEG Device Jordan Ortega(&), Kevin Martín-Chinea, José Francisco Gómez-González, and Ernesto Pereda Department of Industrial Engineering, University of La Laguna, 38071 La Laguna, Tenerife, Spain {jortegar,kmartinc,jfcgomez,eperdepa}@ull.edu.es

Abstract. The objective of this work is the study of the capabilities that a portable electroencephalography is able to offer for the biometric recognition of people through the implementation of automatic classification and learning algorithms to the acquired electrophysiological brain signals, since it is one of the safest and almost impossible to counterfeit biometric measures that exist to secure systems. To accomplish this purpose, a commercial wireless EEG device is used by focusing in the alpha and beta frequency bands during the performance of different mental tasks using the information about frequency spectrum potentials and the degrees of asymmetry between the two cerebral hemispheres, being able to identify a person among a group of registered users with a high success rate. Keywords: EEG


Biometric
Classification
Machine learning

1 Introduction Biometrics consists of those techniques of individual identification of people based on their physical or biological features, which result in unique and irreplaceable information, this is the reason because it is widely used as a security system. Biometric features commonly in use include fingerprints, DNA, or facial recognition, among others. However, in the last decade the characterization of brain activity such as electroencephalography (EEG) has been proposed as an alternative, since it is a tool of identification and recognition of the highly useful and unforgettable individual [1]. The most recent works show a clear trend towards using neural networks, support vector machines (SVM) or linear discriminant analysis (LDA) as the main classification algorithms [2–7]. The aim of the present work is to study the possibilities of a commercial, wireless and low-cost EEG recording device to analyze the brain waves acquired to be used in a biometric identification system and authentication of individuals and uniquely characterize a user among a set of previously registered individuals.

© Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 615–620, 2020. https://doi.org/10.1007/978-3-030-36778-7_67

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2 Materials and Methods 2.1

Experimental Protocol and Data Acquisition

A wireless EEG headset named Emotiv EPOC+ is used. It is composed of 16 sensors positioned according to the 10–20 system (14 channels and 2 reference electrodes), which send the data to a USB receiver. This device allows an effective acquisition of the signal in the 0.2–45 Hz band with a sampling frequency of 128 Hz. A resolution of 0.51 µV with a bandwidth of 16 bits (14 effective) manage the signal conditions. Eleven healthy subjects (without any motor pathology) volunteered for the study. All of them were between 18 and 50 years old. They are asked to perform two different tasks: to squeeze a flexible object and to imagine that they are squeezing the object. These tasks are done with the right and the left hand independently. For each task, we asked the users to repeat the protocol four times with each hand, thereby obtaining sixteen records per subject. Each record lasted 50 s: 20 s where the basal state is recorded, 10 s as a transition period and 20 s after the stimulus starts, during which the task takes place. We use the commercial Emotiv Xavier TestBench v3.1.21 software to record the EEG data. Therefore, an EEG database with all these records is generated, which is used by different classifiers to identify each user. To analyze the obtained EEG records, the toolbox FieldTrip [8] for Matlab is used. It allows working with diverse analysis methods of EEG, MEG and invasive electrophysiological data. 2.2

Data Pre-processing

Once the EEG signals are acquired, it is necessary to prepare the data in an appropriate way to facilitate the classification process. This step includes reducing the noise that may exist in it by applying a band pass filter between 5 and 40 Hz and cleaning the signal to eliminate possible artifacts obtained during recordings. For that purpose, Fieldtrip obtains the z-score of each channel, which is defined as a normalization on its mean and standard deviation. After the z-score of each channel is obtained, the summation is carried out, obtaining a single z-score signal. The mean and standard deviation of this signal is used to define a threshold to remove artifacts from the signal. 2.3

Feature Extraction

A selection of features is carried out, whose purpose is to search for patterns that allow to classify the signal information to differentiate diverse users. We extract information from 14 channels, focusing our study on the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands, which contain important information about our actions of interest. Because of that, for each user, the feature table contains 29 features, that is, information of fourteen channels for each of the two bands and the label corresponding to the specific user.

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The method chosen for the frequency analysis is mtmfft, which analyzes an entire spectrum for the entire data length and implements multitaper frequency transformation based on discrete prolate spheroidal sequences, obtaining a frequency analysis on the time series. Besides to taking the spectral information, in order to improve the classification, the asymmetry index between left and right cerebral regions is calculated [9]:  AsymetryIndex ¼ ln

channelleft channelright

 ð1Þ

Thereby, by applying the hemispheric asymmetry on the channels on different frequencies, fourteen new features for each user are obtained for training. Considering that we have a database composed by eleven users, classification model finally deals with a totally of forty-two features and eleven labels. 2.4

Classification

Regarding the study of the classification, generating the models and comparing the accuracy results, the Matlab Classification Learner application is used. This application consists of a graphical interface for the comparative study of results of the different automatic learning classifiers more common to our features selection. For this purpose, we select a series of classification algorithms in search of the one that offers the best results. The selected algorithms are: Decision Trees, Discriminant Analysis, Support Vector Machines (SVM) and Nearest Neighbor Classifiers (KNN).

3 Results and Discussion After having carried out the process of signal analysis and processing, feature extraction, and comparing the best common classification algorithms, it has been possible to appreciate differences in the brain activity between different users during several mental tasks, which seems to characterize each one individually. Figure 1 shows an example of how the brain activity of two different users are so different for the same mental task. In this example, some power spectrum topographic maps from two users at different times are represented for the alpha and beta frequency bands. The topographic map at 0 s is calculated before the stimulus at the basal state. In addition, the corresponding hemispheric asymmetry graphs for each pair of channels have also been shown for each user. As it can be appreciated, the brain activity between two users remains without significant changes at channels activity, although it is distinguishable for the same task during the time in which it performed.

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Fig. 1. Power spectrum and asymmetry representation among time in Alpha and Beta bands for two different users during an imaginary right-hand squeeze task.

Table 1 shows a distribution of the percentages of precision by different types of classifiers proposed, making a comparison of results by choosing different characteristics: on the one hand, only calculating the power spectrum to each canal or, by the

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619

other hand, adding to this the calculation of the hemispheric asymmetry. As it can be seen, good classification results have been obtained for this small population of eleven users. Table 1. Comparison of classifiers accuracy results by choosing different features and mental tasks, where MI Right and MI Left are the motor imagery of the right and the left hand, and MA Right and MA Left are the motor action of right and left hand, respectively. Classifier

Accuracy (%) Only power spectrum MI MI MA right left right SVM Quadratic 99.8 98.9 98.4 Linear 98.9 98 98 Cubic 99.5 98 98.6 Decision Fine 91.1 89.5 91.4 Tree Medium 91.1 89.5 91.1 Discriminant Quadratic 95 94.5 94.8 Linear 82.5 85.7 95.5 KNN Fine 95.9 94.1 97 Medium 93.6 94.3 95.5

MA left 98.4 97.2 98.6 89.8 89.8 96.3 96 94.4 94.2

Power MI right 99.8 99.3 99.5 92.5 92.5 97.7 98.4 98.2 96.1

spectrum & asymmetry MI MA MA left right left 98.6 98.2 98.1 99.1 98 97 98.2 98.2 98.1 88.2 92.3 91.6 88.2 92.3 91.6 97.3 97.5 94.7 97.3 97 94.5 94.3 96.8 94.2 95.9 96.6 93.5

Regarding the selection of characteristics with which the classifier operates, there is a slight improvement by applying the calculation of the asymmetry with respect to only using the power spectrum to each canal. Applying the asymmetry information, an increase at the average precision of the classifiers studied of approximately 2.08% is obtained, achieving up to 99.8% accuracy with Quadratic SVM classification algorithm for the motor imagery task of the right hand. To this classification algorithm, it is obtained a receiver operating characteristic curves (ROC) with an area under curve (AUC) of 1.00. Although with some exceptions, most classifiers achieve accuracy rates higher than 92% with any of the four chosen mental tasks. However, the classifiers have better fidelity in the case of the imaginary movement using the right hand, with an accuracy result of 99.8% by Quadratic SVM.

4 Conclusions and Future Works In this work, the possibility of using the EEG information as a biometric measure for the identification of different users harnessing of a wireless low-cost commercial device has been shown. The support vector machine algorithms obtained a higher accuracy percentage around 99.8% for a sample of eleven users if the hemispheric asymmetry of the brain and the power spectrum information are combined to classify.

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Our short-term objectives focus on several aspects. It is necessary to study the optimal number of channel combination, to improve the extraction of new features and to study the possibility of introducing functional connectivity information. Furthermore, it is important to carry out the study for a wider population and the classification efficiency must be analyzed with each user data over different sessions along time. All of this in order to improve the obtained results, so that it can be applied as support in some biotechnology research fields such as security systems. Acknowledgments. This work was conducted under the auspices of Research Project ProID2017010100, supported by Consejería de Economía, Industria, Comercio y Conocimiento from Canary Government (Spain) and European Regional Development Fund (ERDF), and the Research Project TEC2016-80063-C3-2-R, supported by Spanish Ministerio de Economía y Competitividad. Jordan Ortega has a fellowship by Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI) from Canary Government (Spain).

References 1. Del Pozo-Baños, M., Alonso, J.B., Ticay-Rivas, J.R., Travieso, C.M.: Electroencephalogram Subject identification: a review. Expert Syst. Appl. 41, 6537–6554 (2014) 2. Chan, H.-L., Kuo, P.-C., Cheng, C.-Y., Chen, Y.-S.: Challenges and future perspectives on electroencephalogram-based biometrics in person recognition. Front. Neuroinf. 12, 66 (2018) 3. Thomas, K.P., Vinod, A.P.: EEG-based biometric authentication using gamma band power during rest state. Circ. Syst. Sign. Process. 37, 277–289 (2017) 4. Mao, C., Hu, B., Wang, M., Moore, P.: EEG-based biometric identification using local probability centers. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE Press, Killarney (2015) 5. Yang, S., Deravi, F.: On the usability of electroencephalographic signals for biometric recognition: a survey. IEEE Trans. Hum. Mach. Syst. 47, 958–969 (2017) 6. Blinowska, K.J., Rakowski, F., Kaminski, M., Fallani, F., Del Precio, C., Lizio, R., Babiloni, C.: Functional and effective brain connectivity for discrimination between Alzheimer’s patients and healthy individuals: a study on resting state EEG rhythms. Clin. Neurophysiol. 128, 667–680 (2016) 7. Ruiz-Blondet, M.V., Jin, Z., Laszlo, S.: Permanence of the CEREBRE brain biometric protocol. Pattern Recogn. Lett. 95, 37–43 (2017) 8. Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.-M.: FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011) 9. Bai, O., Mari, Z., Vorbach, S., Hallett, M.: Asymmetric spatiotemporal patterns of eventrelated desynchronization preceding voluntary sequential finger movements: a high-resolution EEG study. Clin. Neurophysiol. 116(5), 1213–1221 (2005)

Quadcopter Attitude Stabilization in a Gyroscopic Testbench Yassine El Houm(B) , Ahmed Abbou, Ali Mousmi, and Moussa Labbadi Mohamed V University in Rabat, Mohammadia School’s of Engineers, Street Ibn Sina B, P 765, Agdal Rabat, Morocco [email protected], [email protected]

Abstract. This paper aim to present the benefit of using a gyroscopic test bench to evaluate angular stability, test and tune different control algorithms in real time. Quadcopter model dynamics are studied in first place for designing system requirement, which is then used to design the controller. This later is implemented on a DSP F28379D hardware target and communicate with the host via a serial interface. The proposed controller scheme is a gain scheduling PID controller where its parameters changes according to the operating point. To further fine tune those parameters, the paper propose a hardware in the loop simulation using matlab where PID gains are updated from the host and loaded to the target at each sample time. Keywords: Quadrotor · Testbench · Attitude control · Gain scheduling PID · Hardware in the loop · Pose estimation

1

Introduction

Recently Quadcopters has become very significantly involved in many area and application due to their fairly affordable price, enormous capability, maneuverability and guidance simplicity. Quad-copters find its use in multiples disciplines such as agriculture, military intervention, goods transport and academic field such as research and development. In Control system engineering where quadcopters are used a platform, researchers tend to test multiples algorithms structures and evaluate whether the robustness and stability are within the margin of the defined system requirement. Operating a drone in such environment can lead to a serious damages and injuries if not used properly. That’s why a test bench is strongly recommended in this stage. Although there are multiple topologies and structures of test benches that can be used for this purpose, only two are widely used in the literature: – Gyroscopic test bench: consist of two rings attached with two axis of rotation in the extremity, the inner ring is attached to the quad-copter while the outer ring is a fixed to the stand [2,6]. c Springer Nature Switzerland AG 2020  M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 621–630, 2020. https://doi.org/10.1007/978-3-030-36778-7_68

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– 4DOF test bench: a metal rod connected to a plate where the drone is placed, which offer a 3 rotational degrees of freedom and one translation over z-axis. The roll and pitch angles are limited generally to an angles less than 45◦ . The paper propose to use the gyroscopic test bench to evaluate attitude stability (roll, pitch and yaw angles) by feeding an input command form a host computer to a target hardware (dual core DSP F28379D) and receiving the corresponding angle command via a serial communication. Section 2 consist of developing and studding the quadcopter 3 degree of freedom mathematical model, which is derived from Newton-Euler equations of motion. The third section focuses on describing the overall test bench structure and demonstrate the interaction between different hardware component such as microcontroller, inertial measurement sensor and Xbee communication interface. The fourth section describe the process of extracting, filtering and merging data from different sensors for pose estimation using conventional filtering techniques like complementary and Kalman filters. The fifth section illustrate the control architecture used to stabilize the quadcopter, the implementation process and the real time data exchange for tuning algorithms and displaying results in the host computer. The last section is for the conclusion.

2

Mathematical Modeling

This section presents the working principle of a 3 DOF quadcopter with a full detailed description of its dynamics, the mathematical model system is obtained using newton euler equations of motion with the following assumption: – – – –

The components of the system are rigid bodies. The body mass is concentrated at the center of gravity. The body axes are the principal axes for the quadcopter. The rotational speed of the rotors relative to the ground is not taken in consideration. – The lift and the drag are proportional to the square of the speed rotation of the motors.

2.1

Mathematical Modeling

Figure 1 illustrate the frames of reference adopted to locate the UAV. The world of reference is a fixed frame FE where roll, pitch and yaw angles are measured in, while The frame FB is a body frame attached the quadcopter where some proprieties like linear accelerations are measured in. The transition between the two coordinate frames is assured by introducing three elementary rotation angles φ, θ, ψ which are defined in the following way: Rotation of φ(t) around the x-axis (angle: −π/2 ≤ φ ≤ π/2) Rotation of θ(t) around the y-axis (angle: −π/2 ≤ θ ≤ π/2)

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Z (X, Y, Z) Y

X

Fig. 1. Quadcopter body and reference frames

Rotation of ψ(t) around the z-axis (angle:

−π ≤ ψ ≤ π)

The rotation matrices are given by: ⎛

⎞ ⎛ ⎞ ⎛ ⎞ 1 0 0 cos(θ) 0 sin(θ) cos(ψ) sin(ψ) 0 ⎝ ⎠ ⎝ ⎠ ⎝ 0 1 0 R(x, φ) = 0 cos(φ) −sin(φ) R(y, θ) = R(z, ψ) = −sin(ψ) cos(ψ) 0 ⎠ 0 sin(φ) cos(φ) −sin(θ) 0 cos(θ) 0 0 1

The combination of the tree matrices lead to the transformation from the world of reference frame to the body frame Eq. 1: ⎞ c(ψ)c(θ) s(ψ)c(θ) s(θ) R = ⎝ c(ψ)s(θ)s(φ) − s(ψ)c(φ) s(ψ)s(θ)s(φ) + c(ψ)c(φ) −c(θ)s(φ) ⎠ (1) −c(φ)c(ψ)s(θ) − s(φ)s(ψ) c(ψ)s(φ) − c(φ)s(ψ)(θ)s(ψ) c(φ)c(θ) ⎛

Newton - Euler Equations of Motion The equations of translational and rotational dynamics of a rigid body can be for formulated using Newton - Euler formalism [3,4]: B B

F = m.( B

B

B

v˙ +

M = I. ω˙ +

B B

ω × Bv ) B

(2)

B

ω × ( I . ω)

(3)

B

F : Force vector in the body frame. M : Moment vector in the body frame. B v: Translational velocity in the body frame. B ω: Angular velocity in the body frame. I: 3×3 identity matrix. B

The Euler rate vector can be related to⎛the⎞body angular rate by the⎞following ⎛⎛ ⎞ ⎞ ⎛ ⎛ ⎞ ⎛ ⎞ φ˙

0

0

1

0

0

ψ˙

0 sinφ cosφcosθ

0

sinθ

φ˙

relationship: B ω = ⎝ 0 ⎠ + Rφ ⎝⎝ θ˙ ⎠ + Rθ ⎝ 0 ⎠⎠ = ⎝ 0 cosφ −sinφcosθ ⎠ ⎝ θ˙ ⎠ ψ˙

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System Dynamics The system dynamic can be derived from Eqs. 2 and 3: The roll, pitch and yaw torques are: √

τθ =

2 2 l(T1

+ T3 − T2 − T4 ); τφ =

√ 2 2 l(T2

+ T3 − T1 − T4 ); τψ = m1 + m2 − m3 − m4

⎞ ⎞ ⎛ K1 x˙ fx The air drag can be reprsented by the flollowing equations: ⎝ fy ⎠ = ⎝ K2 y˙ ⎠ fz K3 z˙ ⎛

The quadcopter position and angular equations can be expressed as: ⎤ ⎡ ⎤ ⎡T ⎤ ⎡ ⎤ ⎡ Jy −Jz τφ τa f x ˙ x ¨ p˙ Jx qr + Jx − Jx m (−cosψsinθcosφ − sinψsinφ) − K1 x τ fy ⎥ τ J −J ⎣ y¨ ⎦ = ⎣ T (cosψsinφ − sinψsinθcosφ) − K2 y˙ ⎦ ; ⎣ q˙ ⎦ = ⎢ ⎣ zJy x pr + Jθy − aJy ⎦ (4) m T τ J −J τa f z ψ x y z¨ r˙ m cosφcosθ − g − K3 z˙ Jz pq + Jz − Jz

where J = diag[Jx , Jy , Jz ] is quadrotor moments of inertia related to 3 axes of body coordinate system and τa f = Ka f v is the aerodynamic drag torque. The system dynamic can be regrouped in the following form: ⎤ ⎡ ⎤ ⎡ vx x˙ ⎥ vy ⎢ y˙ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ v z ⎢ z˙ ⎥ ⎢ ⎢ ⎥ ⎢ T (−cosψsinθcosφ − sinψsinφ) − K v ⎥ 1 x⎥ ⎢ v˙x ⎥ ⎢ m ⎢ ⎥ ⎢ T (cosψsinφ − sinψsinθcosφ) − K v ⎥ ⎢ v˙y ⎥ ⎢ m 2 y ⎥ ⎥ ⎢ ⎥ ⎢ T ⎥ cosφcosθ − g − K v ⎢ v˙z ⎥ ⎢ 3 z m ⎥ ⎢ ⎥=⎢ (5) ⎥ p + (sinφtanθ)q − (cosφtanθ)r ˙ ⎢φ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ qcosφ + rsinφ ⎢ θ˙ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ − sinφ q + cosφ r ⎢ ψ˙ ⎥ ⎢ ⎥ cosθ cosθ ⎢ ⎥ ⎢ τφ Jy −Jz ⎥ ⎢ p˙ ⎥ ⎢ qr + − K p 4 ⎥ Jx Jx ⎢ ⎥ ⎢ ⎥ τ J −J θ z x ⎣ q˙ ⎦ ⎣ pr + − K q ⎦ 5 Jy Jy τψ Jx −Jy r˙ Jz pq + Jz − K6 r

3

Expreremental Setup

The test bench is designed in such a way that the drone can rotate freely on the three axes (roll, pitch and yaw). It consists of two fine rings attached to each other by bearings. The schematics in Fig. 2 illustrate the different hardware setup used in this experiment: The main controller is a DSP F28379D dual core CPU low cost evaluation board from Texas instrument, which is a very capable microcontroller running at 200 MHz clock speed for each core, it has several advanced peripherals such as: 16 bit resolution ADC, high resolution PWM (HRPWM), I2C, CAN, Inter-processor communication (for sharing data between multiple cores), Control law accelerator (each core has a CLA where

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BMI160 : 3 axis gyro + 3 axis accelerometer BMM150 : 3 axis auxiliary magnetometer

Sensor board I²C communication 5V power

4 control input (PWM 20 ms width) Gnd

30A ESC + BLDC MOTOR

30A ESC + BLDC MOTOR

serial 4 pins (RX ,TX, VCC,GND)

LAUNCHXL-F28379D Microcontroller

Xbee wireless communication

30A ESC + BLDC MOTOR

30A ESC + BLDC MOTOR

Lipo battery pack 4s 4000mah Power distribution board

Fig. 2. Quadcopter hardware component schematic

interrupts can run independently from main CPU core.), 1 MB flash memory and many others features. The Inertial measurement unit (sensor board) combine two ships inside: a BMI160 gyro and accelerometer unit and a BMM150 magnetometer unit, this board can be interfaced with the microcontroller using I2c protocol interface. It should be noted that only BMI160 is accessible directly via I2C since BMM150 is connected via an auxiliary I2C interface to the BMI160, meaning that any reading or writing to the chips has to be done via indirect addressing mode through the primary interface of BMI160. In order to exchange data in real time a communication interface has to be established, a typical serial interface can be used to communicate data between the hardware target and the host computer using a USB cable, while this solution is cheap and simple to use it cannot be considered in this case since it can interact with the movement of the test bench. A wireless communication should take place. In this case an Xbee communication is considered. Since we operate in indoor environment range is not important, Two Xbee s1 with a 50 m range are sufficient for the operation. Both devices are configured to AT mode (also referred to as “Transparent Serial”) this make it behave as a typical serial line. Figure 3a illustrate the positioning of a quadcopter in the test bench. An X configuration has been adopted since roll and pitch torques are higher compared to the + configuration, this is due to the contribution of four rotors in rolling and pitching, while in + configuration only two rotors affect roll and pitch dynamics. In Fig. 3b the host communicate with the target via an Xbee module, the baud

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(a) X configuration drone in testbench

(b) Host computer with Xbee module

Fig. 3. Hardware setup

rate speed is set to 115200 baud/s. Tunable parameters are sent in real time, while received angle response (roll, pitch and yaw) are buffered and displaced each 0.1 s (buffer size is set to 100 sample). This technique help to maintain the original target sample time (1 khz) and transfer signals to the host with a maximum delay of 0.1 s.

4

Sensors Filtering: Fusion Algorithms

4.1

Complimentary Filter

The problem of pose estimation [5,7] using onboard inertial measurement unit sensor which is equipped with gyro, accelerometer and magnetometer can be formulated using the following reasoning: – Gyroscope: Theoretically a gyroscope can measure roll and pitch angles since it can measure angular rate, a straightforward solution rely on integrating the angular rate over time in order to estimate the angular position. However in real application a gyro has some sort of noise and errors characteristics, meaning that integrating the gyro rate will amplify this error over times leading to a significant drifting from the actual angle. – Accelerometer: accelerometer can measure acceleration in each axes direction, since in stationary condition only acceleration due to the gravity is present, the angle estimation can be represented by the following relationship:

X Y Ay = arctan √ Ax = arctan √ X2 + Z2 X2 + Z2 where X, Y and Z represent raw measurement; Ax and Ay represent roll and pitch measurements.

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Fig. 4. Complementary and Kalman filter structures.

This measurement is not very precise on its own, because each time the drone accelerate garavity is no longer the only acting factor, meaning that for quick and short movement the information from the accelerometer is not reliable, while in long term is stable since the acceleration due to the gravity remain unchanged. A conventional approach rely on fusing sensors data such as complimentary filter can be beneficial in this case. The structure of the filter is simple and can be implemented easily Fig. 4a. 4.2

Kalman Filter

Kalman filter is an optimal estimation algorithm that can be used to estimate a system state when it cannot be measured directly. The filter can be used also to combine nosy data from different sensors such as an inertial measurement unit and produce an accurate estimate of angular position (attitude). There are two principles steps Fig. 4b: 1. Predict the current state by using the stat estimate from previous time step and the current input. 2. Using the measurement y and integrate with the prediction for updating the a priori estimate to constitute the a posteriori estimate. A and B: are respectively the stat and the control input matrices. C = [1 0]: the obeservation matrix maping the stat space to the obeserved spcae. Q, R, P and K: are respectively the procsess noise, observation noise, estimate matrix covariances and kalman gain. The first step consist of calculating a priori state estimate and its error covariance at step time k using previous information of state estimate and error covariance at time step k − 1. Initials estimates values has to be applied at the startup of the algorithm. Based on the a priori estimate and error covariance, an update step take place by calculating a Kalman gain and incorporate it in a posteriori state estimate and error covariance equations at time step k. The algorithm repeat itself by calculating the new a priori state estimate and its error covariance. The implementation process can be done using the following steps:

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The prediction part can be expressed using Eqs. 6 and 7. xk−1 + B φ˙ k x ˆ− k = Aˆ       −   φ φ 1 −Δt Δt ˙ φ + Δt(φ˙ − φ˙ b ) = + = φ k 0 1 0 φ˙ b k−1 φ˙ b k φ˙ b 

P00 P10

Pk− = APk−1 AT + Qk  −       Qφ 0 1 −Δt P00 P01 P01 1 0 = Δt + 0 Qφ˙ b 0 1 P1 k P10 P1 k−1 −Δt 1

(6)

(7)

The update phase is expressed by Eqs. 8, 9 and 10.  −   P00 P01 1   −  P10 P1 k 0 P00 K0 − = /(P00k + R) (8) Kk = ⇔     K1 k P10 k   P00 P01 − 1 10 +R P10 P1 k 0    −    − φ φ φ K0 (y − φ− ) − x ˆk = x ˆ− + K (y − φ ) ⇔ = + (9) k k k k K1 (y − φ− ) k φ˙ b k φ˙ b k φ˙ b k    −   P00 P01 P00 P01 K0 P00 K0 P01 − Pk = (I − Kk C)Pk ⇔ = − (10) P10 P11 k P10 P11 k K1 P00 K1 P01

5

Controller Implementation and Results

This section deal with the implementation of a gain scheduling PID controller [1] where a proportional, integral and derivatives actions can changes according to on the angular position. The implementation of the controller can be done using a matlab 1-D Lookup Table where gains are mapped to each angular position range. Two angle range are considered in the experiment: Angle between [−15 , +15] degree. Angle less than −15 or higher than 15◦ limted to ±35. The first range is considered due to the linearize dynamics model near hover position, While the second marge is considered due to the change of operating point. The D term is shutdown if the reference is not changing within a specific time and static error is less than ±2◦ . This action reduce the noise coming from the derivative while keeping its effectiveness of rejecting disturbances. 5.1

Hardware Implementation

The implementation is carried out in Simulink using c2000 Texas instrument support package, this help to generate an optimized C/C++ code directly to the target using supported Simulink blocks. The principle algorithm is running main core cpu, while any subroutine call functions are executed by the co-processor cpu (CLA) which help to separate tasks and run them in parallel. The main controller is running in cpu core C28x at a sampling time of 1khz, where four task are executed:

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1. Reading Inertial measurement raw data: reading 9 DOF raw data from the slave device using I 2 C communication. 2. Calibration process: Each reading from the gyroscope and accelerometer are calibrated by subtracting a pre-calculated bias form raw data (bias calculated by summing buffed raw data and dividing it by its buffer size). The magnetometer hard and soft iron calibration process is done by injecting a pre-calculated transformation matrix and bias using the following equation: [Ac ] = [M ] × ([An c] − [B]) where [Ac ], [An c] and [B] are respectively 3 × 1 matrices of calibrated data, non calibrated data and Bias. [M ] is a 3 × 3 transformation matrix. 3. compute angles φ, θ and ψ using KALMAN filter. 4. adjusting motors speed using a Gain-scheduling PID controller. A subroutine function call is caried out by the CLA where the battery voltage is read every 0.1 second for compensating the throttle and toggling indicator LEDS. In order to exchange data with the host computer, buffered and multiplexed data from main CPU and CLA are sent over serial communication. Figure 5 illustrate the overall block diagram.

Fig. 5. Implementation schematic

5.2

Experimental Results

The experimental results are shown in Fig. 6, where a, b and c represent, respectively roll angle response including controller effort, pitch angle and yaw angle

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responses. Figure 6d and e represent, respectively complementary filtered angle and KALMAN filtered angle. Refrence angle: Roll[15, −15, 0, −15, 15]; Pitch[0, 2, 7, −1, 10]; Yaw[5, 0, 10, −1, 5].

Fig. 6. Attitude angles responses, complementary and kalman filtered angles

6

Conclusion

The paper shows a practical solution for evaluating attitude stability of a quadcopter using a 3 degree of freedom gyroscopic test bench. This platform shows its effectiveness in studying, testing and validating experimental data in a secure and compact environment. The test bench can be exploited in future work for model based control design such as MPC, where an accurate mathematical model dynamic (obtained using system identification) is required for updating the controller.

References 1. Blanchett, T.P., Kember, G.C., Dubay, R.: Pid gain scheduling using fuzzy logic. ISA Trans. 39(3), 317–325 (2000) 2. Bouabdallah, S., Murrieri, P., Siegwart, R.: Design and control of an indoor micro quadrotor. In: IEEE International Conference on Robotics and Automation, 2004 Proceedings, ICRA 2004, vol. 5, pp. 4393–4398. IEEE (2004) 3. Luukkonen, T.: Modelling and control of quadcopter. Independent research project in applied mathematics, Espoo, vol. 22 (2011) 4. Quan, Q.: Dynamic Model and Parameter Measurement, pp. 121–143. Springer, Singapore (2017) 5. Quan, Q.: State Estimation, pp. 199–224. Springer, Singapore (2017) 6. Santos, M.F., Silva, M.F., Vidal, V.F., Hon´ orio, L.M., Lopes, V.L.M., Silva, L.A.Z., Rezende, H.B., Ribeiro, J.M.S., Cerqueira, A.S., Pancoti, A.A.N., et al.: Experimental validation of quadrotors angular stability in a gyroscopic test bench. In: 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC), pp. 783–788. IEEE (2018) 7. Tran, L.D.: Data fusion with 9 degrees of freedom inertial measurement unit to determine objects orientation (2017)

A Novel Multicore Multicasting Scheme for PIM-SM Indranil Roy1, Banafshah Rekabdar1, Swathi Kaluvakuri1(&), Koushik Maddali1, Bidyut Gupta1, and Narayan Debnath2 1

Department of Computer Science, Southern Illinois University Carbondale, Carbondale, IL, USA {indranil.roy,swathi.kaluvakuri,koushik}@siu.edu, {brekabdar,bidyut}@cs.siu.edu 2 School of Computing and Information Technology, Eastern International University, Thủ Dầu Một, Vietnam [email protected]

Abstract. Shared tree multicast uses a single core to handle entire multicast traffic load in a domain. In this paper, we present a new multicast approach with multiple cores to reduce the traffic load. Main objective of our present work is to create a group-to-core mapping table a priori, i.e. even before a multicast session begins; it is done immediately after the network is booted. To the best of our knowledge, there does not exist any such multicast architecture for PIM SM related to load sharing. We select statically a set of k primary cores for possible load share with complexity O(n2). This selection is done immediately after the network is booted. At the same time, we create statically k partitions on all possible multicast addresses. The ith partition maps to the ith core in the list of k cores – this means that any new multicast session with multicast group address belonging to the ith partition will use the ith core from the list of k cores to implement PIM SM. In addition, we have incorporated fault tolerance in our approach to tackle the problem of any number of primary core failures. Keywords: Static partition multicast
Load sharing




Core selection




Pseudo diameter




Multicore

1 Introduction Multicast communication protocols [4] are classified into two categories, namely, sourcebased trees [1, 12, 15] and core based trees (CBT) [2]. A problem associated with sourcebased-tree routing is that a router has to keep the pair information (source, group) and it is a one tree per source. In reality the Internet is a complex heterogeneous environment, which potentially has to support many thousands of active groups, each of which may be sparsely distributed; this technique clearly does not scale. Shared tree based approaches like CBT [2, 11] and protocol independent multicasting – sparse mode (PIM-SM) [6] offer an improvement in scalability by a factor of the number of active sources. In a core-based tree/shared tree [2] the tree branches are made up of other routers, so-called non-core routers, which form a shortest path between a member-host’s © Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 631–638, 2020. https://doi.org/10.1007/978-3-030-36778-7_69

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directly attached router and the core. A core need not be topologically centered, since multicasts vary in nature and therefore, the form of a core-based tree also can vary [2]. CBT is attractive compared to source based tree because of its key architectural features like scaling, tree creation, and unicast routing separation. The major concerns of shared tree approach are; core selection and core as a single point failure. Core selection [2, 7] is the problem of appropriate placement of a core or cores in a network for the purpose of improving the performance of the tree(s) constructed around these core(s). In static networks core selection depends on knowledge of entire network topology. It involves all routers in the network. There exist several important works [3, 5, 13, 14, 21], which take into account network topology while selecting a core. 1.1

Related Works

In case of single core-tree based multicast, the core has to handle all traffic load. It degrades the performance. To overcome the problem, shared tree multicast using multiple cores is the only solution. It distributes the total traffic load on the cores resulting in improved load balancing and thereby causing improved multicast performance. There exist in the literature some important contributions in this area of multicore-based multicasting [16–18, 21]. The goal of these works is to achieve load balancing. In [16], Jia et al. have presented a Multiple Shared-Trees (MST) based approach to multicast routing. In their approach, the tree roots are formed into an anycast group so that the multicast packets can be anycast to the nearest node at one of the shared trees. However, load balancing is at the level of each source choosing the best core closest to it rather than attempting to utilize all the candidate cores simultaneously. This may lead to congestion in a core if multiple sources choose that core based on their shortest delay metric. In all these works, a core is assigned to a multicast group during a multicast session. In [17], a unique tree consisting of multiple cores is maintained with one of the cores being the root. The objective of the work is to develop a loop free multi-core based tree by assigning level numbers to the cores and the nodes to join the tree to help maintain the tree structure. The cores need to coordinate with one another for their operations. Zappala et al. [18] have considered two different approaches for multicore load shared multicasting. The first one is senders-to-many scheme; it partitions the receivers of a group among the trees rooted at different cores so that each receiver is exactly on one core tree at a time. Therefore, one core tree spans some of the group members only. Even though it offers less routing state, yet it has the complex task to take care of newly arriving group members, i.e. partition them appropriately to the different core trees. In their second scheme, each core tree spans over the entire receiver group. To transmit data, different senders in a multicast group can use different trees with respect to the proximity of the source to the tree; it helps in balancing the traffic load and improve performance. The trees are maintained independently unlike the work in [17]. The core distribution method follows the hash based scheme of [19]. Note that a different kind of load sharing (not load balancing) approach exists for splitting the load over Equal Cost Multipath (ECMP). Multicast traffic from different sources or from different sources and groups are load split across equal-cost paths provided such equal cost paths exist and are recognized as well [20]. The idea of core-based multicasting has been extended to allow migration of cores [21]. When the performance at an alternate core is

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‘reasonably’ better than that at the current core, migration takes place to the alternate core. It helps in controlling multicast latency and load sharing. 1.2

Our Contribution

In this paper, we have considered shared tree multicast with multiple cores in PIM-SM domain. The motivation of the work is to improve multicast performance via load sharing. We prefer the term ‘sharing’ to ‘balancing’ because the objective of the work is to engage all cores whenever possible and more so because it is neither possible to know a priori the duration of different multicast sessions running at a given time, nor it is possible to know a priori the number of possible senders per multicast group. Main objective of our present work is to create a group-to-core mapping table a priori, i.e. even before a multicast session begins; it is done statically and immediately after the network is booted. To the best of our knowledge, there does not exist any such multicast architecture for PIM SM related to load sharing. We select statically a set of k primary cores for possible load share with complexity O(n2). This selection is done immediately after the network is booted. At the same time, we create statically k partitions on all possible multicast addresses. The ith partition maps to the ith core in the list of k cores – this means that any multicast session with multicast group address belonging to the ith partition will use the ith core from the list of k cores to implement PIM SM. In addition, we have incorporated fault tolerance in our approach to tackle the problem of any number of primary core failures. Organization of the paper is as follows. In Sect. 2, we state briefly the existing concept of pseudo diameter [8–10]. In Sect. 3, we present the proposed static partitionbased multicore multicasting. In Sect. 4, we have considered core failures. Finally, Sect. 5 draws the conclusion.

2 Preliminaries Two widely used unicast routing protocols are distance vector routing (DVR) and link state routing (LSR). In the former one, routers do not have the knowledge of network topology, whereas in the later routers have this knowledge. The concept of pseudo diameter is independent of the underlying unicast routing protocol. We denote the unicast routing table by UCTi for some router ri; it can be either the DVR table or the LSR table of the router depending on the unicast protocol used. Pseudo diameter of a router ri denoted as Pd(ri) is defined as follows [8–10]. Pd (ri) = max {ci,j}, where ci,j = cost (ri, rj ), [1 ≤ j ≤ n, j ≠ i] and ci,j ϵ UCTi and n = number of routers in the network

In words, pseudo diameter of router ri denoted as Pd(ri), is the maximum value among the costs (as present in its routing table UCTi) to reach from ri all other routers in a network. The implication of pseudo-diameter is that any other router is reachable from router ri within the distance (i.e. no. of hops/delay etc.) equal to the pseudo

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diameter Pd (ri) of router ri, It thus directly relates to the physical location of router ri. Pseudo diameter is not the actual diameter of the network, because it depends on the location of router ri in the network. So different routers in the network may have different values for their respective pseudo diameters. Therefore, pseudo diameter Pd is always less than or equal to the actual diameter of a network. As an example [21], consider the network shown in Fig. 1. Without any loss of generality, we have considered DVR protocol as the underlying unicast protocol used in the network. Note that the diameter of the network is 90. From router A’s DVR table (Fig. 2), it is seen that its pseudo diameter is 90, which is incidentally equal to the network diameter, whereas for router C it is 70 as is seen from C’s DVR table in Fig. 2. It means that if C is the source of communication, the maximum cost to reach any other router will be 70, which is less than the network diameter of 90. Observe that in this network router E has the minimum pseudo diameter, viz. 60. A

B

40

30

30

C

20

50

D

20

20 G

50

E

F

20

20 H

Fig. 1. An 8-router network

Fig. 2. DVR tables of the routers

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3 Static Partition-Based Multicore Multicasting In the proposed approach, we first select statically the candidate cores; followed by the proposed partitioning scheme of all possible multicast addresses. The number of partitions is equal to the number of the candidate cores selected. All first-hop routers perform this partitioning immediately after the network is booted. They do it independently. During a multicast session, a first-hop router connected to a multicast source uses the knowledge of the partitions and the cores to accomplish its responsibility following the principle of PIM-SM. 3.1

Selection of Candidate Cores – A Distributed Approach

We exclude any first hop router from a possible set of candidate cores. The reason for such exclusion is that a first hop router’s main responsibility is to connect LAN users to the rest of the network domain. We present a modification of the scheme proposed in [21] to select candidate cores to be used for multicore multicasting. Cores are selected statically using the routing information of all routers (except all first hop routers) in the underlying domain. This core selection is independent of any multicast group. Candidate cores selection process 1. Each non-first hop router Ri determines its Pd (Ri) from its unicast routing table. 2. It broadcasts its Pd (Ri) in the network using pseudo diameter based broadcasting [10]. 3. Every router Rm, 1  m  n receives all Pd (Ri) from every non-first hop router Ri. 4. Router Rm creates a list of k routers out of all non-first hop routers, sorted in ascending order based on their Pd values. //These k routers are used for multicore multicast. 5. Logical address of the ith core in the list of k cores is assigned with the value i. Remark 1. Every router creates identical sorted list of the k cores. Remark 2. Message complexity of the core selection process is O(n2). Remark 3. In the core selection process, a router does not need to know the entire topology. 3.2

Responsibility of Each First Hop Router in the Domain Immediately After the Network Is Booted

Static Partitioning of Multicast Addresses 1. Each first hop router Rj divides all group addresses into k distinct ranges (partitions) with starting address of the ith partition as SAi, 1  i  k. 2. Router Rj creates a vector (VSA) consisting of the starting addresses of the partitions. It is: VSA =

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3. Router Rj maintains a core map table (CMT) such that its ith entry CMT(i) = . //SAi+1 > any multicast with group address  SAi will use Core i. Remark 4. Every first hop router creates identical VSA. 3.3

Responsibility of a First Hop Router Rj During a Multicast Session

Multicast Session 1. First hop router Rj receives a multicast packet with group address Gm from a source connected to it. 2. Rj identifies the largest SAi in the vector VSA such that SAi  Gm. 3. Rj identifies the core to be used from the entry CMT(i) = in the core map table (CMT). 4. Rj unicasts the multicast packet to core i.

4 Fault Tolerant Multicast In the proposed fault- model, we assume any number (  k) of core failures. To incorporate fault-tolerance, we select 2 k number of cores instead of k cores. The core selection method is the same as that stated in the previous section. The proposed faulttolerant approach uses the following idea: According to the positions in the sorted list all odd-numbered cores can be used for multicasting in absence of any core failures. Every odd-numbered core will have its standby as the even numbered core that immediately follows it in the sorted list of the cores; this standby core selection utilizes the proximity between these two cores from the viewpoint of their pseudo diameter values. 4.1

Modified Partitioning Approach

Static Partitioning of Multicast Addresses 1. Each first hop router Rj divides all group addresses into k distinct ranges (partitions) with starting address of the ith partition as SAi, 1  i  k. 2. Router Rj creates a vector (VSA) consisting of the starting addresses of the partitions. It is: VSA = 3. Router Rj maintains a core map table (CMT) such that its ith entry (1  i  k) in CMT consists of two tuples: t1i and t2i such that t1i = < SAi (2i − 1), IP address of core (2i-1) > and t2i = < SAi(2i), IP address of core (2i)>.

A Novel Multicore Multicasting Scheme for PIM-SM

4.2

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Fault-Tolerant Multicast Session 1. 2. 3.

First hop router Rj receives a multicast packet with group address Gm from a source connected to it. Rj identifies the largest SAi in the vector VSA such that SAi ≤ Gm for (1≤ i ≤ k) If core (2i – 1) is not faulty Rj unicasts the multicast packet to core (2i - 1) // tuple ti1 = < SAi, (2i -1), IP address of core (2i -1) > in CMT is used to identify the core Else Rj unicasts the multicast packet to core 2i. // tuple ti2 = < SAi,(2i), IP address of core (2i) > in CMT is used to identify the core

5 Conclusion In this paper, we have presented a novel approach for shared tree multicast with multiple cores in PIM-SM domain. The motivation of the work has been to improve multicast performance via load sharing. We have presented a method to select statically a set of k primary cores for possible load share with complexity O(n2). This selection is done immediately after the network is booted. At the same time, we have presented a simple scheme to create statically k partitions on all possible multicast addresses. The ith partition maps to the ith core in the list of k cores – this means that any new multicast session with multicast group address belonging to the ith partition will use the ith core from the list of k cores to implement PIM-SM. To the best of our knowledge, there does not exist any such multicast architecture for PIM SM that uses static partitioning of all possible multicast addresses for load sharing. In addition, we have incorporated fault tolerance in our approach to tackle the problem of any number of primary core failures. Future work is directed at reducing further the load on any member core.

References 1. Adams, A., Nicholas, J., Siadak, W.: Protocol independent multicast - dense mode (PIMDM). Internet Engineering Task Force (IETF), RFC-3973, January 2005 2. Ballardie, T.A.: Core based tree multicast routing architecture. Internet Engineering Task Force (IETF), RFC 2201, September 1997 3. Karaman, A., Hassanein, H.: Core selection algorithms in multicast routing – comparative and complexity analysis. J. Comput. Commun. 29(8), 998–1014 (2006) 4. Deering, S.E., Cheriton, D.R.: Multicast routing in datagram internetworks and extended LANs. ACM Trans. Comput. Syst. (TOCS) 8(2), 85–110 (1990)

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5. Rouskas, G.N., Baldine, I.: Multicast routing with end-to-end delay and delay variation constraints. IEEE J. Sel. Areas Commun. 15(3), 346–356 (1997) 6. Fenner, B., Handley, M., Holbrook, H., Kouvelas, I.: Protocol independent multicast - sparse mode (PIM-SM), Internet Engineering Task Force (IETF), RFC-4601, August 2006 7. Jia, W., Zhao, W., Xuan, D., Xu, G.: An efficient fault-tolerant multicast routing protocol with core - based tree techniques. IEEE Trans. Parallel Distribut. Syst. 10(10), 984–1000 (1999) 8. Koneru, S., Gupta, B., Rahimi, S., Liu, Z.: Hierarchical pruning to improve bandwidth utilization of RPF-based broadcasting. In: IEEE Symposium on Computers and Communications (ISCC), Split, Croatia, pp. 96–100, July 2013 9. Koneru, S., Gupta, B., Debnath, N.: A novel DVR based multicast routing protocol with hierarchical pruning. Int. J. Comput. Appl. (IJCA) 20(3), 184–191 (2013) 10. Koneru, S., Gupta, B., Rahimi, S., Liu, Z., Debnath, N.: A highly efficient RPF-based broadcast protocol using a new two-level pruning mechanism. J. Comput. Sci. (JOCS) 5(3) (2014). SpringerLink, vol. 2345, pp. 1045–1056, Berlin, Heidelberg (2002) 11. Lin, H.-C., Lai, S.-C.: A simple and effective core placement method for the core-based tree multicast routing architecture. In: Proceedings of IEEE International Conference on Performance, Computing, and Communications, pp. 215–219, February 2000 12. Pusateri, T.: Distance vector multicast routing protocol, Juniper Networks, Internet Engineering Task Force (IETF), draft-ietf-idmr-dvmrp-v3-11.txt, October 2003 13. Shim, Y.-C., Kang, S.-K.: New center location algorithms for shared multicast trees. Lecture Notes in Computer Science, Vol. 2345, pp. 1045–1056. Springer, Heidelberg (2002) 14. Thaler, D.G., Ravishankar, C.V.: Distributed center-location algorithms. IEEE J. Sel. Areas Commun. 15(3), 291–303 (1997) 15. Waitzman, D., Partridge, C., Deering, S.E.: Distance vector multicast routing protocol (DVMRP), Internet Engineering Task Force (IETF), RFC 1075, November 1988 16. Jia, W., Tu, W., Zhao, W., Xu, G.: Multi-shared-trees based multicast routing control protocol using anycast selection. Int. J. Parallel Emerg. Distrib. Syst. 20(4), 69–84 (2005) 17. Shields, C., Garcia-Luna-Acevez, J.J.: The ordered core-based tree protocol. In: Proceedings of IEEE INFOCOM 1997 (1997) 18. Zappala, D., Fabbri, A., Lo, V.: An evaluation of shared multicast trees with multiple active cores. J. Telecommun. Syst. 19, 461–479 (2002) 19. Estrin, D., Handley, M., Helmy, A., Huang, P.: A dynamic bootstrap mechanism for rendezvous-based multicast routing. In: Proceedings of IEEE INFOCOM (1999) 20. Load Splitting IP Multicast Traffic over ECMP, Cisco, March 2015. www.cisco.com/c/en/us/ td/docs/ios/12_4t/ip_mcast/configuration/guide/mctlsplt.html 21. Gupta, B., Alyanbaawi, A., Rahimi, N., Sinha, K., Liu, Z.: Novel low latency load shared multicore multicasting schemes─an extension to core migration. IJCA 25(3) (2018)

Author Index

A Aballo, Onyonkiton Théophile, 281 Abbou, Ahmed, 621 Abdar, Moloud, 480 Abghour, Nourdinne, 271 Abik, Mounia, 194 Abouqora, Youness, 426 Acosta, Leopoldo, 337 Adraoui, Meriem, 75 Ait Daoud, Mohammed, 27 Ait El Mouden, Zakariyaa, 176 Ait Touil, Abdelhak, 116 Ali, Brahim Ait Ben, 203 Ali, Noor, 442 Aljahdali, Sultan, 523, 603 Allali, Hakim, 469 ALRashdi, Reem, 124 Amankwa, Eric, 458 Aouabed, Zahia, 480 Arezki, Sara, 203 Ayres, Fabiane, 243 Ayres, Franklin, 243 Azizi, Abdelmalek, 551 Azzam-Jai, Asmae, 572 B Babori, Abdelghani, 40 Bahbouhi, Jalal Eddine, 500 Bahij, Mouad, 155 Ballesteros-Ricaurte, Javier Antonio, 184 Barão, Alexandre, 243 Bazi, Ismail El, 203 Belabbas, Yagoubi, 321 Belkasmi, Mohammed Ghaouth, 436 Benko, Ľubomír, 400

Bennani, Samir, 65, 75, 85 Berrios-Aguayo, Beatriz, 14 Beyyoudh, Mohammed, 85 Bouattane, Omar, 469 Bouchair, Abderrahim, 321 Bouchentouf, Toumi, 436 Boutahar, Mohamed, 176 Braik, Malik, 603 C Cabral, Bruno, 145 Camara, Mamadou Samba, 451 Canlas, Ferddie Quiroz, 343 Castillo, David, 331 Chafi, Hassan, 260 Chakir, Houssam Eddine, 102 Champagne Gareau, Jaël, 480 Cherkaoui, Mohamed, 155 Cherradi, Bouchaib, 368 Chiadmi, Dalila, 214, 260 Chouay, Yassine, 533 Chtouki, Ihssane, 102 Collicchio, Bruno, 102 Corral, José María Rodríguez, 55 D Dargham, Abdelmajid, 32 Debnath, Narayan, 248, 312, 631 Déguénonvo, Roland, 281 Díaz-Aleman, Drago, 362 Díaz-González, Elisa, 362 Diop, Idy, 451 Dokic, Kristian, 165 Durán-Vaca, Mónica Katherine, 184

© Springer Nature Switzerland AG 2020 M. Serrhini et al. (Eds.): EMENA-ISTL 2019, LAIS 7, pp. 639–641, 2020. https://doi.org/10.1007/978-3-030-36778-7

640 E El Abbadi, Jamal, 292 El Bouroumi, Jamal, 515 El Houm, Yassine, 621 Elfilali, Sanaa, 27 Elhassani, Ibtissam, 506 Elmiad, Aissa Kerkour, 378, 410 Elomri, Amina, 271 Ettifouri, El Hassane, 436 Ezzouak, Siham, 551 F Farahat, Zineb, 390 Figueira, Álvaro, 145 Firmli, Soukaina, 214 Fouad, Mountassir, 235 Fournier, Helene, 442 Fuentes-Porto, Alba, 362 G Gadi, Taoufiq, 426 Galindo, José, 55 Galindo, Patricia, 55 Gomez A, Hector F., 331 Gómez-González, José Francisco, 337, 615 Gomis, François Kasséné, 451 Guemana, Mouloud, 563 Guermah, Hatim, 515 Guerss, Fatima Zahra, 27 Guibert, Denis, 135 Gupta, Bidyut, 312, 631 H Hadi, Hajar, 506 Hafaifa, Ahmed, 563 Hajar, Moha, 176 Hamida, Soufiane, 368 Hammouche, Amar, 490 Hasni, Mouad, 390 Hassani-Alaoui, Fatima Zahra, 292 Herouane, Omar, 426 Hong, Sungpack, 260 I Idrissi, Mohammed Khalidi, 65, 75, 85 Ihya, Rachida, 27 Imed, Kaid, 563 J Jabraoui, Siham, 116 Jaddar, Abdessamad, 378 Jaillet, Alain, 47 Jakimi, Abdeslam, 176 Jayavel, Kayalvizhi, 302

Author Index Jebari, Sihem, 593 Jenkins, Marcelo, 92 K Kaluvakuri, Swathi, 312, 631 Kapusta, Jozef, 400 Kasakula, Willie, 302 Kasaš, Karol, 416 Khalfaoui, Ibtissam, 490 Kondratova, Irina, 442 Kouaiba, Ghizlane, 378 Kriouile, Abdelaziz, 515 Kritzinger, Elmarie, 458 Kumaran, Santhi, 302 L Laachfoubi, Nabil, 203 Laaji, El Hassane, 551 Labbadi, Moussa, 155, 621 Lamghari, Zineb, 352 Loock, Marianne, 458 Loola Bokonda, Patrick, 543 Louati, Aymen, 593 M Maazouz, Hamid El, 260 Maddali, Koushik, 312, 631 Makarenkov, Vladimir, 480 Makhlouf, Sid Ahmed, 321 Mali, Reda, 235 Mandusic, Dubravka, 165 Martín-Chinea, Kevin, 337, 615 Martinez, Carlos, 331 Mazzi, Mouna, 248 Mbise, Esther Rosinner, 1 Megdiche, Kawtar, 390 Mellah, Youssef, 436 Mihi, Soukaina, 203 Mikeka, Chomora, 302 Miloud, Jaara El, 224 Mohamed, Ikan, 378 Mohammed, Ouazzani Jamil, 224 Molyneaux, Heather, 442 Montejano, Germán, 248 Montero, Calkin Suero, 1 Moumoun, Lahcen, 426 Mousaid, Khalid, 271 Mousmi, Ali, 621 Moussa, Najem, 500 Mtonga, Kambombo, 302 Munk, Michal, 400, 416 Munková, Daša, 416 Mwandosya, Godfrey Isaac, 1

Author Index N Namir, Abdelwahed, 27 Nassar, Mahmoud, 515 Ngote, Nabil, 390, 583 Nsenga, Jimmy, 302 O O’Keefe, Simon, 124 Ortega, Jordan, 337, 615 Ouajji, Hassan, 368 Ouassaid, Mohammed, 533, 572, 583 Ouazzani-Touhami, Khadija, 543 Oyelere, Solomon Sunday, 1 P Panchoo, Shireen, 47 Pantoja-Vallejo, Antonio, 14 Pech-Gourg, Nicolas, 135 Peralta, Mario, 248 Pereda, Ernesto, 337, 615 Pr.Bousmah, Mohamed, 235 Q Quesada-Lopez, Cristian, 92 R Radgui, Maryam, 352 Radisic, Bojan, 165 Rahmani, Moulay Driss, 352 Raihani, Abdelhadi, 368 Rakrak, Said, 286 Rekabdar, Banafshah, 631 Rekabdar, Banafsheh, 312 Retbi, Asmaâ, 65, 75 Rida, Mohamed, 271 Riesco, Daniel, 248 Rivera, Ruth Maldonado, 331 Roqué, Luis, 248 Roy, Indranil, 631

641 S Sabir, Badr Eddine, 469 Sabir, Hamza, 583 Saidi, Rajaa, 352 Salam, Abudura, 563 Salgado, Carlos, 248 Sekkat, Souhail, 506 Serieye, Thibaud, 135 Serrhini, Mohammed, 32 Sevenich, Martin, 260 Sheta, Alaa, 523, 603 Simohammed, Serrhini, 224 Skalka, Ján, 416 Smiti, Abir, 593 Souabi, Sonia, 65 Souilkat, Anass, 271 Souissi, Nissrine, 390, 543 T Tahiri, Nadia, 480 Tamri, Rajae, 286 Tarik, Hajji, 224 Tawfik, Masrour, 224 Toledo, Jonay, 337 Turabieh, Hamza, 523 V Vianou, Antoine, 281 W Wachsmuth, Guido, 260 Wira, Patrice, 102 Y Youssef, Douzi, 224 Youssfi, Mohamed, 469 Z Zarnoufi, Randa, 194 Zazi, Malika, 102