Intelligent Computing Theories and Application: 15th International Conference, ICIC 2019, Nanchang, China, August 3–6, 2019, Proceedings, Part II [1st ed. 2019] 978-3-030-26968-5, 978-3-030-26969-2

Table of contents :
Front Matter ....Pages i-xxi
Particle Swarm Optimization-Based Power Allocation Scheme for Secrecy Sum Rate Maximization in NOMA with Cooperative Relaying (Carla E. Garcia, Mario R. Camana, Insoo Koo, Md Arifur Rahman)....Pages 1-12
A Novel Binary Particle Swarm Optimization for Multiple Sequence Alignment (Yanlian Du, Jingxuan He, Changcun Du)....Pages 13-25
A Diversity Based Competitive Multi-objective PSO for Feature Selection (Jianfeng Qiu, Fan Cheng, Lei Zhang, Yi Xu)....Pages 26-37
A Decomposition-Based Hybrid Estimation of Distribution Algorithm for Practical Mean-CVaR Portfolio Optimization (Yihua Wang, Wanyi Chen)....Pages 38-50
CBLNER: A Multi-models Biomedical Named Entity Recognition System Based on Machine Learning (Gong Lejun, Liu Xiaolin, Yang Xuemin, Zhang Lipeng, Jia Yao, Yang Ronggen)....Pages 51-60
Dice Loss in Siamese Network for Visual Object Tracking (Zhao Wei, Changhao Zhang, Kaiming Gu, Fei Wang)....Pages 61-70
Fuzzy PID Controller for Accurate Power Sharing in DC Microgrid (Duy-Long Nguyen, Hong-Hee Lee)....Pages 71-80
Precipitation Modeling and Prediction Based on Fuzzy-Control Multi-cellular Gene Expression Programming and Wavelet Transform (YuZhong Peng, ChuYan Deng, HongYa Li, DaoQing Gong, Xiao Qin, Li Cai)....Pages 81-92
Integrative Enrichment Analysis of Intra- and Inter- Tissues’ Differentially Expressed Genes Based on Perceptron (Xue Jiang, Weihao Pan, Miao Chen, Weidi Wang, Weichen Song, Guan Ning Lin)....Pages 93-104
Identifying Differentially Expressed Genes Based on Differentially Expressed Edges (Bolin Chen, Li Gao, Xuequn Shang)....Pages 105-115
Gene Functional Module Discovery via Integrating Gene Expression and PPI Network Data (Fangfang Zhu, Juan Liu, Wenwen Min)....Pages 116-126
A Novel Framework for Improving the Prediction of Disease-Associated MicroRNAs (Wenhe Zhao, Jiawei Luo, Nguyen Hoang Tu)....Pages 127-137
Precise Prediction of Pathogenic Microorganisms Using 16S rRNA Gene Sequences (Yu-An Huang, Zhi-An Huang, Zhu-Hong You, Pengwei Hu, Li-Ping Li, Zheng-Wei Li et al.)....Pages 138-150
Learning from Deep Representations of Multiple Networks for Predicting Drug–Target Interactions (Pengwei Hu, Yu-an Huang, Zhuhong You, Shaochun Li, Keith C. C. Chan, Henry Leung et al.)....Pages 151-161
Simulation of Complex Neural Firing Patterns Based on Improved Deterministic Chay Model (Zhongting Jiang, Dong Wang, Huijie Shang, Yuehui Chen)....Pages 162-169
Knowledge Based Helix Angle and Residue Distance Restraint Free Energy Terms of GPCRs (Huajing Ling, Hongjie Wu, Jiayan Han, Jiwen Ding, Weizhong Lu, Qiming Fu)....Pages 170-176
Improved Spectral Clustering Method for Identifying Cell Types from Single-Cell Data (Yuanyuan Li, Ping Luo, Yi Lu, Fang-Xiang Wu)....Pages 177-189
A Novel Weight Learning Approach Based on Density for Accurate Prediction of Atherosclerosis (Jiang Xie, Ruiying Wu, Haitao Wang, Yanyan Kong, Haozhe Li, Wu Zhang)....Pages 190-200
An Effective Approach of Measuring Disease Similarities Based on the DNN Regression Model (Shuhui Su, Xiaoxiao(X.X.) Zhang, Lei Zhang, Jian Liu)....Pages 201-212
Herb Pair Danggui-Baishao: Pharmacological Mechanisms Underlying Primary Dysmenorrhea by Network Pharmacology Approach (Li-Ting Li, Hai-Yan Qiu, Mi-Mi Liu, Yong-Ming Cai)....Pages 213-225
End-to-End Learning Based Compound Activity Prediction Using Binding Pocket Information (Toshitaka Tanebe, Takashi Ishida)....Pages 226-234
A Novel Approach for Predicting LncRNA-Disease Associations by Structural Perturbation Method (Zhi Cao, Jun-Feng Zhang, Shu-Lin Wang, Yue Liu)....Pages 235-246
Improved Inductive Matrix Completion Method for Predicting MicroRNA-Disease Associations (Xin Ding, Jun-Feng Xia, Yu-Tian Wang, Jing Wang, Chun-Hou Zheng)....Pages 247-255
A Link and Weight-Based Ensemble Clustering for Patient Stratification (Yuan-Yuan Zhang, Chao Yang, Jing Wang, Chun-Hou Zheng)....Pages 256-264
HGMDA: HyperGraph for Predicting MiRNA-Disease Association (Qing-Wen Wu, Yu-Tian Wang, Zhen Gao, Ming-Wen Zhang, Jian-Cheng Ni, Chun-Hou Zheng)....Pages 265-271
Discovering Driver Mutation Profiles in Cancer with a Local Centrality Score (Ying Hui, Pi-Jing Wei, Jun-Feng Xia, Hong-Bo Wang, Jing Wang, Chun-Hou Zheng)....Pages 272-282
LRMDA: Using Logistic Regression and Random Walk with Restart for MiRNA-Disease Association Prediction (Zhengwei Li, Ru Nie, Zhuhong You, Yan Zhao, Xin Ge, Yang Wang)....Pages 283-293
Distinguishing Driver Missense Mutations from Benign Polymorphisms in Breast Cancer (Xiyu Zhang, Ruoqing Xu, Yannan Bin, Zhenyu Yue)....Pages 294-302
A Novel Method to Predict Protein Regions Driving Cancer Through Integration of Multi-omics Data (Xinguo Lu, Xinyu Wang, Ping Liu, Zhenghao Zhu, Li Ding)....Pages 303-312
In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information (Hai-Cheng Yi, Zhu-Hong You, Yan-Bin Wang, Zhan-Heng Chen, Zhen-Hao Guo, Hui-Juan Zhu)....Pages 313-323
Effective Analysis of Hot Spots in Hub Protein Interfaces Based on Random Forest (Xiaoli Lin, Fengli Zhou)....Pages 324-332
Prediction of Human LncRNAs Based on Integrated Information Entropy Features (Junyi Li, Huinian Li, Li Zhang, Qingzhe Xu, Yuan Ping, Xiaozhu Jing et al.)....Pages 333-343
A Gated Recurrent Unit Model for Drug Repositioning by Combining Comprehensive Similarity Measures and Gaussian Interaction Profile Kernel (Tao Wang, Hai-Cheng Yi, Zhu-Hong You, Li-Ping Li, Yan-Bin Wang, Lun Hu et al.)....Pages 344-353
A Novel Approach to Predicting MiRNA-Disease Associations (Guo Mao, Shu-Lin Wang)....Pages 354-365
Hierarchical Attention Network for Predicting DNA-Protein Binding Sites (Wenbo Yu, Chang-An Yuan, Xiao Qin, Zhi-Kai Huang, Li Shang)....Pages 366-373
Motif Discovery via Convolutional Networks with K-mer Embedding (Dailun Wang, Qinhu Zhang, Chang-An Yuan, Xiao Qin, Zhi-Kai Huang, Li Shang)....Pages 374-382
Whole-Genome Shotgun Sequence of Natronobacterium gregoryi SP2 (Lixu Jiang, Hao Xu, Zhixi Yun, Jiayi Yin, Juanjuan Kang, Bifang He et al.)....Pages 383-393
The Detection of Gene Modules with Overlapping Characteristic via Integrating Multi-omics Data in Six Cancers (Xinguo Lu, Qiumai Miao, Ping Liu, Li Ding, Zhenghao Zhu, Min Liu et al.)....Pages 394-405
Combining High Speed ELM with a CNN Feature Encoding to Predict LncRNA-Disease Associations (Zhen-Hao Guo, Zhu-Hong You, Li-Ping Li, Yan-Bin Wang, Zhan-Heng Chen)....Pages 406-417
A Prediction Method of DNA-Binding Proteins Based on Evolutionary Information (Weizhong Lu, Zhengwei Song, Yijie Ding, Hongjie Wu, Hongmei Huang)....Pages 418-429
Research on RNA Secondary Structure Prediction Based on Decision Tree (Weizhong Lu, Yan Cao, Hongjie Wu, Hongmei Huang, Yijie Ding)....Pages 430-439
Improving Hot Region Prediction by Combining Gaussian Naive Bayes and DBSCAN (Jing Hu, Longwei Zhou, Xiaolong Zhang, Nansheng Chen)....Pages 440-452
An Efficient LightGBM Model to Predict Protein Self-interacting Using Chebyshev Moments and Bi-gram (Zhao-Hui Zhan, Zhu-Hong You, Yong Zhou, Kai Zheng, Zheng-Wei Li)....Pages 453-459
Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins (Yan-Bin Wang, Zhu-Hong You, Hai-cheng Yi, Zhan-Heng Chen, Zhen-Hao Guo, Kai Zheng)....Pages 460-467
Identification of DNA-Binding Proteins via Fuzzy Multiple Kernel Model and Sequence Information (Yijie Ding, Jijun Tang, Fei Guo)....Pages 468-479
Research on HP Model Optimization Method Based on Reinforcement Learning (Zhou Fengli, Lin Xiaoli)....Pages 480-492
EnsembleKQC: An Unsupervised Ensemble Learning Method for Quality Control of Single Cell RNA-seq Sequencing Data (Anqi Ma, Zuolang Zhu, Meiqin Ye, Fei Wang)....Pages 493-504
Regulatory Sequence Architecture of Stress Responsive Genes in Oryza Sativa (Mohsin Ali Nasir, Samia Nawaz, Farrukh Azeem, Sajjad Haider)....Pages 505-516
Identifying Cancer Biomarkers from High-Throughput RNA Sequencing Data by Machine Learning (Zishuang Zhang, Zhi-Ping Liu)....Pages 517-528
Identification of Prognostic and Heterogeneous Breast Cancer Biomarkers Based on Fusion Network and Multiple Scoring Strategies (Xingyi Li, Ju Xiang, Jianxin Wang, Fang-Xiang Wu, Min Li)....Pages 529-534
A Novel Differential Essential Genes Prediction Method Based on Random Forests Model (Jiang Xie, Jiamin Sun, Jiaxin Li, Fuzhang Yang, Haozhe Li, Jiao Wang)....Pages 535-546
Identification of Candidate Biomarkers and Pathways Associated with Liver Cancer by Bioinformatics Analysis (Zhen-Bo Tian, Xu-Qing Tang)....Pages 547-557
Real-Time Pedestrian Detection in Monitoring Scene Based on Head Model (Panpan Lu, Kun Lu, Wenyan Wang, Jun Zhang, Peng Chen, Bing Wang)....Pages 558-568
CNN and Metadata for Classification of Benign and Malignant Melanomas (José-Sergio Ruiz-Castilla, Juan-José Rangel-Cortes, Farid García-Lamont, Adrián Trueba-Espinosa)....Pages 569-579
An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets (Mohammed Khalaf, Abir Jaafar Hussain, Omar Alafandi, Dhiya Al-Jumeily, Mohamed Alloghani, Mahmood Alsaadi et al.)....Pages 580-591
Fully Convolutional Neural Networks for 3D Vehicle Detection Based on Point Clouds (Lihua Wen, Kang-Hyun Jo)....Pages 592-601
Illegally Parked Vehicle Detection Based on Haar-Cascade Classifier (Aapan Mutsuddy, Kaushik Deb, Tahmina Khanam, Kang-Hyun Jo)....Pages 602-614
Graph-SLAM Based Hardware-in-the-Loop-Simulation for Unmanned Aerial Vehicles Using Gazebo and PX4 Open Source (Khoa Dang Nguyen, Trong-Thang Nguyen, Cheolkeun Ha)....Pages 615-627
Evaluating CNNs for Military Target Recognition (Xiao Ding, Lei Xing, Tao Lin, Jiabao Wang, Yang Li, Zhuang Miao)....Pages 628-638
Detection and Segmentation of Kidneys from Magnetic Resonance Images in Patients with Autosomal Dominant Polycystic Kidney Disease (Antonio Brunetti, Giacomo Donato Cascarano, Irio De Feudis, Marco Moschetta, Loreto Gesualdo, Vitoantonio Bevilacqua)....Pages 639-650
Research of Formal Analysis Based on Extended Strand Space Theories (Meng-meng Yao, Jun Zhang, Xi Weng)....Pages 651-661
The Application of Artificial Intelligence in Music Education (Mingli Shang)....Pages 662-668
Single-Machine Green Scheduling Problem of Multi-speed Machine (Ai Yang, Bin Qian, Rong Hu, Ling Wang, Shang-Han Li)....Pages 669-677
Whale Optimization Algorithm with Local Search for Open Shop Scheduling Problem to Minimize Makespan (Hui-Min Gu, Rong Hu, Bin Qian, Huai-Ping Jin, Ling Wang)....Pages 678-687
Water Wave Optimization for the Multidimensional Knapsack Problem (Hong-Fang Yan, Ci-Yun Cai, De-Huai Liu, Min-Xia Zhang)....Pages 688-699
A Complex-Valued Firefly Algorithm (Chuandong Song)....Pages 700-707
Gene Expression Prediction Based on Hybrid Evolutionary Algorithm (Haifeng Wang, Sanrong Liu)....Pages 708-716
MPdeep: Medical Procession with Deep Learning (Qi Liu, Wenzheng Bao, Zhuo Wang)....Pages 717-725
Predicting Potential Drug-Target Interactions with Multi-label Learning and Ensemble Learning (Lida Zhu, Jun Yuan)....Pages 726-735
Robust Circulating Tumor Cells Detection in Scanned Microscopic Images with Cascaded Morphological and Faster R-CNN Deep Detectors (Yun-Xia Liu, An-Jie Zhang, Qing-Fang Meng, Ying-Jie Chen, Yang Yang, Yue-Hui Chen)....Pages 736-746
Electrocardiogram Diagnosis Based on SMOTE+ENN and Random Forest (Li Sun, Ziwei Shang, Qing Cao, Kang Chen, Jiyun Li)....Pages 747-757
Complex Leaves Classification with Features Extractor (Daniel Ayala Niño, José S. Ruíz Castilla, Ma. Dolores Arévalo Zenteno, Laura D. Jalili)....Pages 758-769
IDP - OPTICS: Improvement of Differential Privacy Algorithm in Data Histogram Publishing Based on Density Clustering (Lina Ge, Yugu Hu, Hong Wang, Zhonghua He, Huazhi Meng, Xiong Tang et al.)....Pages 770-781
Back Matter ....Pages 783-790

Citation preview

LNCS 11644

De-Shuang Huang Kang-Hyun Jo Zhi-Kai Huang (Eds.)

Intelligent Computing Theories and Application 15th International Conference, ICIC 2019 Nanchang, China, August 3–6, 2019 Proceedings, Part II

Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA

Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA

11644

More information about this series at http://www.springer.com/series/7409

De-Shuang Huang Kang-Hyun Jo Zhi-Kai Huang (Eds.) •

•

Intelligent Computing Theories and Application 15th International Conference, ICIC 2019 Nanchang, China, August 3–6, 2019 Proceedings, Part II

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Editors De-Shuang Huang Tongji University Shanghai, China

Kang-Hyun Jo University of Ulsan Ulsan, South Korea

Zhi-Kai Huang Nanchang Institute of Technology Nanchang, China

ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-26968-5 ISBN 978-3-030-26969-2 (eBook) https://doi.org/10.1007/978-3-030-26969-2 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © Springer Nature Switzerland AG 2019 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

The International Conference on Intelligent Computing (ICIC) was started to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, pattern recognition, bioinformatics, and computational biology. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing. ICIC 2019, held in Nanchang, China, during August 3–6, 2019, constituted the 15th International Conference on Intelligent Computing. It built upon the success of previous ICIC conferences held in Wuhan, China (2018), Liverpool, UK (2017), Lanzhou, China (2016), Fuzhou, China (2015), Taiyuan, China (2014), Nanning, China (2013), Huangshan, China (2012), Zhengzhou, China (2011), Changsha, China (2010), Ulsan, Republic of Korea (2009), Shanghai, China (2008), Qingdao, China (2007), Kunming, China (2006), and Hefei, China (2005). This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced Intelligent Computing Technology and Applications.” Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology. ICIC 2019 received 609 submissions from 22 countries and regions. All papers went through a rigorous peer-review procedure and each paper received at least three review reports. Based on the review reports, the Program Committee finally selected 217 high-quality papers for presentation at ICIC 2019, included in three volumes of proceedings published by Springer: two volumes of Lecture Notes in Computer Science (LNCS), and one volume of Lecture Notes in Artificial Intelligence (LNAI). This volume of Lecture Notes in Computer Science (LNCS) includes 73 papers. The organizers of ICIC 2019, including Tongji University, Nanchang Institute of Technology, and East China Institute of Technology, as well as Shandong University at Weihai, made an enormous effort to ensure the success of the conference. We hereby would like to thank the members of the Program Committee and the referees for their collective effort in reviewing and soliciting the papers. We would like to thank Alfred Hofmann, executive editor from Springer, for his frank and helpful advice and guidance throughout, and for his continuous support in publishing the proceedings. In particular, we would like to thank all the authors for contributing their papers. Without the high-quality submissions from the authors, the success of the conference would not

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Preface

have been possible. Finally, we are especially grateful to the International Neural Network Society, and the National Science Foundation of China for their sponsorship. June 2019

De-Shuang Huang Kang-Hyun Jo Zhi-Kai Huang

ICIC 2019 Organization

General Co-chairs De-Shuang Huang, China Shengqian Wang, China

Program Committee Co-chairs Kang-Hyun Jo, South Korea Phalguni Gupta, India

Organizing Committee Co-chairs Chengzhi Deng, China Zhikai Huang, China Yusen Zhang, China

Organizing Committee Members Shumin Zhou, China Wei Tian, China Yan Li, China Keming Liu, China Shaoquan Zhang, China Liling Zhang, China

Award Committee Chair Vitoantonio Bevilacqua, Italy

Tutorial Chair M. Michael Gromiha, India

Publication Chair Ling Wang, China

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ICIC 2019 Organization

Special Session Chair Abir Hussain, UK

Special Issue Chair Kyungsook Han, South Korea

International Liaison Chair Prashan Premaratne, Australia

Workshop Co-chairs Jair Cervantes Canales, Mexico Michal Choras, Poland

Publicity Co-chairs Valeriya Gribova, Russia Laurent Heutte, France Chun-Hou Zheng, China

Exhibition Contact Chair Di Wu, Tongji University, China

Program Committee Members Abir Hussain Khalid Aamir Kang-Hyun Jo Angelo Ciaramella Wenzheng Bao Binhua Tang Bin Qian Bingqiang Liu Bin Liu Li Chai Chin-Chih Chang Wen-Sheng Chen Michal Choras Xiyuan Chen Jieren Cheng Chengzhi Liang

Dah-Jing Jwo Shaoyi Du Dunwei Gong Xiaoheng Deng Meng Joo Er Eros Pasero Evi Syukur Fengfeng Zhou Francesco Pappalardo Gai-Ge Wang LJ Gong Valeriya Gribova Michael Gromiha Maria Naijie Gu Guoliang Li Fei Han

Tianyong Hao Mohd Helmy Abd Wahab Hao Lin Hongmin Cai Xinguo Lu Hongjie Wu Jianbo Fan Jair Cervantes Junfeng Xia Juan Carlos Figueroa-Jiangning Song Joo M. C. Sousa Ju Liu Ka-Chun Wong Kyungsook Han Seeja K. R.

ICIC 2019 Organization

Yoshinori Kuno Laurent Heutte Xinyi Le Bo Li Yunxia Liu Zhendong Liu Hu Lu Fei Luo Haiying Ma Mingon Kang Marzio Pennisi Gaoxiang Ouyang Seiichi Ozawa Shaoliang Peng Prashan Premaratne Boyang Qu Rui Wang Wei-Chiang Hong Xiangwei Zheng Shen Yin Sungshin Kim

Surya Prakash TarVeli Mumcu Vasily Aristarkhov Vitoantonio Bevilacqua Ling Wang Xuesong Wang Waqas Haider Khan Bangyal Bing Wang Wenbin Liu Weidong Chen Wei Jiang Wei Wei Weining Qian Takashi Kuremoto Shitong Wang Xiao-Hua Yu Jing Xiao Xin Yin Xingwen Liu Xiujuan Lei

Xiaoke Ma Xiaoping Liu Xiwei Liu Yonggang Lu Yongquan Zhou Zu-Guo Yu Yuan-Nong Ye Jianyang Zeng Tao Zeng Junqi Zhang Le Zhang Wen Zhang Qi Zhao Chunhou Zheng Zhan-Li Sun Zhongming Zhao Shanfeng Zhu Quan Zou Zhenran Jiang

Jialing Li Kai Qian Huichao Zhong Huiyan Jiang Lei Wang Yuanyuan Wang Biao Zhang Ta Zhou Wei Liao Bin Qin Jiazhou Chen Mengze Du Sheng Ding Dongliang Qin Syed Sadaf Ali Zheng Chenc Shang Xiang Xia Lin Yang Wu Xiaoming Liu Jing Lv

Lin Weizhong Jun Li Li Peng Hongfei Bao Zhaoqiang Chen Ru Yang Jiayao Wu Dadong Dai Guangdi Liu Jiajia Miao Xiuhong Yang Xiwen Cai Fan Li Aysel Ersoy Yilmaz Agata Giełczyk Akila Ranjith Xiao Yang Cheng Liang Alessio Ferone José Alfredo Costa Ambuj Srivastava

Additional Reviewers Huijuan Zhu Yizhong Zhou Lixiang Hong Yuan Wang Mao Xiaodan Ke Zeng Xiongtao Zhang Ning Lai Shan Gao Jia Liu Ye Tang Weiwei Cai Yan Zhang Yuanpeng Zhang Han Zhu Wei Jiang Hong Peng Wenyan Wang Xiaodan Deng Hongguan Liu Hai-Tao Li

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ICIC 2019 Organization

Mohamed Abdel-Basset Angelo Ciaramella Anthony Chefles Antonino Staiano Antonio Brunetti Antonio Maratea Antony Lam Alfredo Pulvirenti Areesha Anjum Athar Ali Moinuddin Mohd Ayyub Khan Alfonso Zarco Azis Ciayadi Brendan Halloran Bin Qian Wenbin Song Benjamin J. Lang Bo Liu Bin Liu Bin Xin Guanya Cai Casey P. Shannon Chao Dai Chaowang Lan Chaoyang Zhang Chuanchao Zhang Jair Cervantes Bo Chen Yueshan Cheng Chen He Zhen Chen Chen Zhang Li Cao Claudio Loconsole Cláudio R. M. Silva Chunmei Liu Yan Jiang Claus Scholz Yi Chen Dhiya AL-Jumeily Ling-Yun Dai Dongbo Bu Deming Lei Deepak Ranjan Nayak Dong Han Xiaojun Ding

Domenico Buongiorno Haizhou Wu Pingjian Ding Dongqing Wei Yonghao Du Yi Yao Ekram Khan Miao Jiajia Ziqing Liu Sergio Santos Tomasz Andrysiak Fengyi Song Xiaomeng Fang Farzana Bibi Fatih Adıgüzel Fang-Xiang Wu Dongyi Fan Chunmei Feng Fengfeng Zhou Pengmian Feng Feng Wang Feng Ye Farid Garcia-Lamont Frank Shi Chien-Yuan Lai Francesco Fontanella Lei Shi Francesca Nardone Francesco Camastra Francesco Pappalardo Dongjie Fu Fuhai Li Hisato Fukuda Fuyi Li Gai-Ge Wang Bo Gao Fei Gao Hongyun Gao Jianzhao Gao Jianzhao Gao Gaoyuan Liang Geethan Mendiz Geethan Mendiz Guanghui Li Giacomo Donato Cascarano

Giorgio Valle Giovanni Dimauro Giulia Russo Linting Guan Ping Gong Yanhui Gu Gunjan Singh Guohua Wu Guohui Zhang Guo-Sheng Hao Surendra M. Gupta Sandesh Gupta Gang Wang Hafizul Fahri Hanafi Haiming Tang Fei Han Hao Ge Kai Zhao Hangbin Wu Hui Ding Kan He Bifang He Xin He Huajuan Huang Jian Huang Hao Lin Ling Han Qiu Xiao Yefeng Li Hongjie Wu Hongjun Bai Hongtao Lei Haitao Zhang Huakang Li Jixia Huang Pu Huang Sheng-Jun Huang Hailin Hu Xuan Huo Wan Hussain Wan Ishak Haiying Wang Il-Hwan Kim Kamlesh Tiwari M. IkramUllah Lali Ilaria Bortone H. M. Imran

ICIC 2019 Organization

Ingemar Bengtsson Izharuddin Izharuddin Jackson Gomes Wu Zhang Jiansheng Wu Yu Hu Jaya Sudha Jianbo Fan Jiancheng Zhong Enda Jiang Jianfeng Pei Jiao Zhang Jie An Jieyi Zhao Jie Zhang Jin Lu Jing Li Jingyu Hou Joe Song Jose Sergio Ruiz Jiang Shu Juntao Liu Jiawen Lu Jinzhi Lei Kanoksak Wattanachote Juanjuan Kang Kunikazu Kobayashi Takashi Komuro Xiangzhen Kong Kulandaisamy A. Kunkun Peng Vivek Kanhangad Kang Xu Kai Zheng Kun Zhan Wei Lan Laura Yadira Domínguez Jalili Xiangtao Chen Leandro Pasa Erchao Li Guozheng Li Liangfang Zhao Jing Liang Bo Li

Feng Li Jianqiang Li Lijun Quan Junqing Li Min Li Liming Xie Ping Li Qingyang Li Lisbeth Rodríguez Shaohua Li Shiyong Liu Yang Li Yixin Li Zhe Li Zepeng Li Lulu Zuo Fei Luo Panpan Lu Liangxu Liu Weizhong Lu Xiong Li Junming Zhang Shingo Mabu Yasushi Mae Malik Jahan Khan Mansi Desai Guoyong Mao Marcial Guerra de Medeiros Ma Wubin Xiaomin Ma Medha Pandey Meng Ding Muhammad Fahad Haiying Ma Mingzhang Yang Wenwen Min Mi-Xiao Hou Mengjun Ming Makoto Motoki Naixia Mu Marzio Pennisi Yong Wang Muhammad Asghar Nadeem Nadir Subasi

Nagarajan Raju Davide Nardone Nathan R. Cannon Nicole Yunger Halpern Ning Bao Akio Nakamura Zhichao Shi Ruxin Zhao Mohd Norzali Hj Mohd Nor Surayahani Suriani Wataru Ohyama Kazunori Onoguchi Aijia Ouyang Paul Ross McWhirter Jie Pan Binbin Pan Pengfei Cui Pu-Feng Du Kunkun Peng Syed Sadaf Ali Iyyakutti Iyappan Ganapathi Piyush Joshi Prashan Premaratne Peng Gang Sun Puneet Gupta Qinghua Jiang Wangren Qiu Qiuwei Li Shi Qianqian Zhi-Xian Liu Raghad AL-Shabandar Rafał Kozik Raffaele Montella Woong-Hee Shin Renjie Tan Rodrigo A. Gutiérrez Rozaida Ghazali Prabakaran Jue Ruan Rui Wang Ruoyao Ding Ryuzo Okada Kalpana Shankhwar Liang Zhao Sajjad Ahmed

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ICIC 2019 Organization

Sakthivel Ramasamy Shao-Lun Lee Wei-Chiang Hong Hongyan Sang Jinhui Liu Stephen Brierley Haozhen Situ Sonja Sonja Jin-Xing Liu Haoxiang Zhang Sebastian Laskawiec Shailendra Kumar Junliang Shang Wei-Feng Guo Yu-Bo Sheng Hongbo Shi Nobutaka Shimada Syeda Shira Moin Xingjia Lu Shoaib Malik Feng Shu Siqi Qiu Boyu Zhou Stefan Weigert Sameena Naaz Sobia Pervaiz Somnath Dey Sotanto Sotanto Chao Wu Yang Lei Surya Prakash Wei Su Qi Li Hotaka Takizawa FuZhou Tang Xiwei Tang Li-Na Chen Yao Tuozhong Qing Tian Tianyi Zhou Junbin Fang Wei Xie Shikui Tu Umarani Jayaraman Vahid Karimipour Vasily Aristarkhov

Vitoantonio Bevilacqua Valeriya Gribova Guangchen Wang Hong Wang Haiyan Wang Jingjing Wang Ran Wang Waqas Haider Bangyal Pi-Jing Wei Wei Lan Fangping Wan Jue Wang Minghua Wan Qiaoyan Wen Takashi Kuremoto Chuge Wu Jibing Wu Jinglong Wu Wei Wu Xiuli Wu Yahui Wu Wenyin Gong Wu Zhang Zhanjun Wang Xiaobing Tang Xiangfu Zou Xuefeng Cui Lin Xia Taihong Xiao Xing Chen Lining Xing Jian Xiong Yi Xiong Xiaoke Ma Guoliang Xu Bingxiang Xu Jianhua Xu Xin Xu Xuan Xiao Takayoshi Yamashita Atsushi Yamashita Yang Yang Zhengyu Yang Ronggen Yang Xiao Yang Zhengyu Yang

Yaolai Wang Yaping Yang Yue Chen Yongchun Zuo Bei Ye Yifei Qi Yifei Sun Yinglei Song Ying Ling Ying Shen Yingying Qu Lvjiang Yin Yiping Liu Wenjie Yi Jianwei Yang Yu-Jun Zheng Yonggang Lu Yan Li Yuannong Ye Yong Chen Yongquan zhou Yong Zhang Yuan Lin Yuansheng Liu Bin Yu Fang Yu Kumar Yugandhar Liang Yu Yumin Nie Xu Yu Yuyan Han Yikuan Yu Yong Wang Ying Wu Ying Xu Zhiyong Wang Shaofei Zang Chengxin Zhang Zehui Cao Tao Zeng Shuaifang Zhang Yan Zhang Liye Zhang Zhang Qinhu Sai Zhang Sen Zhang

ICIC 2019 Organization

Shan Zhang Shao Ling Zhang Wen Zhang Wei Zhao Bao Zhao Zheng Tian Sijia Zheng Zhenyu Xuan Fangqing Zhao Zhao Fangqing

Zhipeng Cai Xing Zhou Xiong-Hui Zhou Lida Zhu Ping Zhu Qi Zhu Zhong-Yuan Zhang Ziding Zhang Junfei Zhao Zhe Li

Juan Zou Quan Zou Qian Zhu Zunyan Xiong Zeya Wang Yatong Zhou Shuyi Zhang Zhongyi Zhou

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Contents – Part II

Particle Swarm Optimization-Based Power Allocation Scheme for Secrecy Sum Rate Maximization in NOMA with Cooperative Relaying . . . . . . . . . . . Carla E. Garcia, Mario R. Camana, Insoo Koo, and Md Arifur Rahman

1

A Novel Binary Particle Swarm Optimization for Multiple Sequence Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanlian Du, Jingxuan He, and Changcun Du

13

A Diversity Based Competitive Multi-objective PSO for Feature Selection . . . Jianfeng Qiu, Fan Cheng, Lei Zhang, and Yi Xu A Decomposition-Based Hybrid Estimation of Distribution Algorithm for Practical Mean-CVaR Portfolio Optimization . . . . . . . . . . . . . Yihua Wang and Wanyi Chen CBLNER: A Multi-models Biomedical Named Entity Recognition System Based on Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gong Lejun, Liu Xiaolin, Yang Xuemin, Zhang Lipeng, Jia Yao, and Yang Ronggen

26

38

51

Dice Loss in Siamese Network for Visual Object Tracking. . . . . . . . . . . . . . Zhao Wei, Changhao Zhang, Kaiming Gu, and Fei Wang

61

Fuzzy PID Controller for Accurate Power Sharing in DC Microgrid . . . . . . . Duy-Long Nguyen and Hong-Hee Lee

71

Precipitation Modeling and Prediction Based on Fuzzy-Control Multi-cellular Gene Expression Programming and Wavelet Transform . . . . . . YuZhong Peng, ChuYan Deng, HongYa Li, DaoQing Gong, Xiao Qin, and Li Cai Integrative Enrichment Analysis of Intra- and Inter- Tissues’ Differentially Expressed Genes Based on Perceptron . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xue Jiang, Weihao Pan, Miao Chen, Weidi Wang, Weichen Song, and Guan Ning Lin

81

93

Identifying Differentially Expressed Genes Based on Differentially Expressed Edges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bolin Chen, Li Gao, and Xuequn Shang

105

Gene Functional Module Discovery via Integrating Gene Expression and PPI Network Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fangfang Zhu, Juan Liu, and Wenwen Min

116

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Contents – Part II

A Novel Framework for Improving the Prediction of Disease-Associated MicroRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenhe Zhao, Jiawei Luo, and Nguyen Hoang Tu Precise Prediction of Pathogenic Microorganisms Using 16S rRNA Gene Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu-An Huang, Zhi-An Huang, Zhu-Hong You, Pengwei Hu, Li-Ping Li, Zheng-Wei Li, and Lei Wang Learning from Deep Representations of Multiple Networks for Predicting Drug–Target Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pengwei Hu, Yu-an Huang, Zhuhong You, Shaochun Li, Keith C. C. Chan, Henry Leung, and Lun Hu Simulation of Complex Neural Firing Patterns Based on Improved Deterministic Chay Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhongting Jiang, Dong Wang, Huijie Shang, and Yuehui Chen Knowledge Based Helix Angle and Residue Distance Restraint Free Energy Terms of GPCRs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huajing Ling, Hongjie Wu, Jiayan Han, Jiwen Ding, Weizhong Lu, and Qiming Fu Improved Spectral Clustering Method for Identifying Cell Types from Single-Cell Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuanyuan Li, Ping Luo, Yi Lu, and Fang-Xiang Wu A Novel Weight Learning Approach Based on Density for Accurate Prediction of Atherosclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiang Xie, Ruiying Wu, Haitao Wang, Yanyan Kong, Haozhe Li, and Wu Zhang

127

138

151

162

170

177

190

An Effective Approach of Measuring Disease Similarities Based on the DNN Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuhui Su, Xiaoxiao(X.X.) Zhang, Lei Zhang, and Jian Liu

201

Herb Pair Danggui-Baishao: Pharmacological Mechanisms Underlying Primary Dysmenorrhea by Network Pharmacology Approach . . . . . . . . . . . . Li-Ting Li, Hai-Yan Qiu, Mi-Mi Liu, and Yong-Ming Cai

213

End-to-End Learning Based Compound Activity Prediction Using Binding Pocket Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Toshitaka Tanebe and Takashi Ishida

226

A Novel Approach for Predicting LncRNA-Disease Associations by Structural Perturbation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhi Cao, Jun-Feng Zhang, Shu-Lin Wang, and Yue Liu

235

Contents – Part II

Improved Inductive Matrix Completion Method for Predicting MicroRNA-Disease Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Ding, Jun-Feng Xia, Yu-Tian Wang, Jing Wang, and Chun-Hou Zheng

xvii

247

A Link and Weight-Based Ensemble Clustering for Patient Stratification . . . . Yuan-Yuan Zhang, Chao Yang, Jing Wang, and Chun-Hou Zheng

256

HGMDA: HyperGraph for Predicting MiRNA-Disease Association . . . . . . . . Qing-Wen Wu, Yu-Tian Wang, Zhen Gao, Ming-Wen Zhang, Jian-Cheng Ni, and Chun-Hou Zheng

265

Discovering Driver Mutation Profiles in Cancer with a Local Centrality Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ying Hui, Pi-Jing Wei, Jun-Feng Xia, Hong-Bo Wang, Jing Wang, and Chun-Hou Zheng

272

LRMDA: Using Logistic Regression and Random Walk with Restart for MiRNA-Disease Association Prediction . . . . . . . . . . . . . . . . . . . . . . . . Zhengwei Li, Ru Nie, Zhuhong You, Yan Zhao, Xin Ge, and Yang Wang

283

Distinguishing Driver Missense Mutations from Benign Polymorphisms in Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiyu Zhang, Ruoqing Xu, Yannan Bin, and Zhenyu Yue

294

A Novel Method to Predict Protein Regions Driving Cancer Through Integration of Multi-omics Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinguo Lu, Xinyu Wang, Ping Liu, Zhenghao Zhu, and Li Ding

303

In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information . . . . . . . . . . . . . . . . . Hai-Cheng Yi, Zhu-Hong You, Yan-Bin Wang, Zhan-Heng Chen, Zhen-Hao Guo, and Hui-Juan Zhu Effective Analysis of Hot Spots in Hub Protein Interfaces Based on Random Forest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoli Lin and Fengli Zhou Prediction of Human LncRNAs Based on Integrated Information Entropy Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junyi Li, Huinian Li, Li Zhang, Qingzhe Xu, Yuan Ping, Xiaozhu Jing, Wei Jiang, Bo Liu, and Yadong Wang A Gated Recurrent Unit Model for Drug Repositioning by Combining Comprehensive Similarity Measures and Gaussian Interaction Profile Kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Wang, Hai-Cheng Yi, Zhu-Hong You, Li-Ping Li, Yan-Bin Wang, Lun Hu, and Leon Wong

313

324

333

344

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Contents – Part II

A Novel Approach to Predicting MiRNA-Disease Associations. . . . . . . . . . . Guo Mao and Shu-Lin Wang Hierarchical Attention Network for Predicting DNA-Protein Binding Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenbo Yu, Chang-An Yuan, Xiao Qin, Zhi-Kai Huang, and Li Shang

354

366

Motif Discovery via Convolutional Networks with K-mer Embedding . . . . . . Dailun Wang, Qinhu Zhang, Chang-An Yuan, Xiao Qin, Zhi-Kai Huang, and Li Shang

374

Whole-Genome Shotgun Sequence of Natronobacterium gregoryi SP2 . . . . . Lixu Jiang, Hao Xu, Zhixi Yun, Jiayi Yin, Juanjuan Kang, Bifang He, and Jian Huang

383

The Detection of Gene Modules with Overlapping Characteristic via Integrating Multi-omics Data in Six Cancers . . . . . . . . . . . . . . . . . . . . . Xinguo Lu, Qiumai Miao, Ping Liu, Li Ding, Zhenghao Zhu, Min Liu, and Shulin Wang Combining High Speed ELM with a CNN Feature Encoding to Predict LncRNA-Disease Associations . . . . . . . . . . . . . . . . . . . . . . . . . . Zhen-Hao Guo, Zhu-Hong You, Li-Ping Li, Yan-Bin Wang, and Zhan-Heng Chen A Prediction Method of DNA-Binding Proteins Based on Evolutionary Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weizhong Lu, Zhengwei Song, Yijie Ding, Hongjie Wu, and Hongmei Huang

394

406

418

Research on RNA Secondary Structure Prediction Based on Decision Tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weizhong Lu, Yan Cao, Hongjie Wu, Hongmei Huang, and Yijie Ding

430

Improving Hot Region Prediction by Combining Gaussian Naive Bayes and DBSCAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jing Hu, Longwei Zhou, Xiaolong Zhang, and Nansheng Chen

440

An Efficient LightGBM Model to Predict Protein Self-interacting Using Chebyshev Moments and Bi-gram . . . . . . . . . . . . . . . . . . . . . . . . . . Zhao-Hui Zhan, Zhu-Hong You, Yong Zhou, Kai Zheng, and Zheng-Wei Li Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins. . . . . . . . . . . . . . . . . . Yan-Bin Wang, Zhu-Hong You, Hai-cheng Yi, Zhan-Heng Chen, Zhen-Hao Guo, and Kai Zheng

453

460

Contents – Part II

xix

Identification of DNA-Binding Proteins via Fuzzy Multiple Kernel Model and Sequence Information . . . . . . . . . . . . . . . . . . . . . . . . . . Yijie Ding, Jijun Tang, and Fei Guo

468

Research on HP Model Optimization Method Based on Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhou Fengli and Lin Xiaoli

480

EnsembleKQC: An Unsupervised Ensemble Learning Method for Quality Control of Single Cell RNA-seq Sequencing Data . . . . . . . . . . . Anqi Ma, Zuolang Zhu, Meiqin Ye, and Fei Wang

493

Regulatory Sequence Architecture of Stress Responsive Genes in Oryza Sativa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohsin Ali Nasir, Samia Nawaz, Farrukh Azeem, and Sajjad Haider

505

Identifying Cancer Biomarkers from High-Throughput RNA Sequencing Data by Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zishuang Zhang and Zhi-Ping Liu

517

Identification of Prognostic and Heterogeneous Breast Cancer Biomarkers Based on Fusion Network and Multiple Scoring Strategies . . . . . . . . . . . . . . Xingyi Li, Ju Xiang, Jianxin Wang, Fang-Xiang Wu, and Min Li

529

A Novel Differential Essential Genes Prediction Method Based on Random Forests Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiang Xie, Jiamin Sun, Jiaxin Li, Fuzhang Yang, Haozhe Li, and Jiao Wang Identification of Candidate Biomarkers and Pathways Associated with Liver Cancer by Bioinformatics Analysis . . . . . . . . . . . . . . . . . . . . . . Zhen-Bo Tian and Xu-Qing Tang Real-Time Pedestrian Detection in Monitoring Scene Based on Head Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Panpan Lu, Kun Lu, Wenyan Wang, Jun Zhang, Peng Chen, and Bing Wang CNN and Metadata for Classification of Benign and Malignant Melanomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . José-Sergio Ruiz-Castilla, Juan-José Rangel-Cortes, Farid García-Lamont, and Adrián Trueba-Espinosa An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets . . . . . . . . . . . . Mohammed Khalaf, Abir Jaafar Hussain, Omar Alafandi, Dhiya Al-Jumeily, Mohamed Alloghani, Mahmood Alsaadi, Omar A. Dawood, and Dhafar Hamed Abd

535

547

558

569

580

xx

Contents – Part II

Fully Convolutional Neural Networks for 3D Vehicle Detection Based on Point Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lihua Wen and Kang-Hyun Jo Illegally Parked Vehicle Detection Based on Haar-Cascade Classifier . . . . . . Aapan Mutsuddy, Kaushik Deb, Tahmina Khanam, and Kang-Hyun Jo Graph-SLAM Based Hardware-in-the-Loop-Simulation for Unmanned Aerial Vehicles Using Gazebo and PX4 Open Source . . . . . . . . . . . . . . . . . Khoa Dang Nguyen, Trong-Thang Nguyen, and Cheolkeun Ha Evaluating CNNs for Military Target Recognition . . . . . . . . . . . . . . . . . . . . Xiao Ding, Lei Xing, Tao Lin, Jiabao Wang, Yang Li, and Zhuang Miao Detection and Segmentation of Kidneys from Magnetic Resonance Images in Patients with Autosomal Dominant Polycystic Kidney Disease . . . . Antonio Brunetti, Giacomo Donato Cascarano, Irio De Feudis, Marco Moschetta, Loreto Gesualdo, and Vitoantonio Bevilacqua

592 602

615 628

639

Research of Formal Analysis Based on Extended Strand Space Theories . . . . Meng-meng Yao, Jun Zhang, and Xi Weng

651

The Application of Artificial Intelligence in Music Education . . . . . . . . . . . . Mingli Shang

662

Single-Machine Green Scheduling Problem of Multi-speed Machine . . . . . . . Ai Yang, Bin Qian, Rong Hu, Ling Wang, and Shang-Han Li

669

Whale Optimization Algorithm with Local Search for Open Shop Scheduling Problem to Minimize Makespan . . . . . . . . . . . . . . . . . . . . . . . . Hui-Min Gu, Rong Hu, Bin Qian, Huai-Ping Jin, and Ling Wang

678

Water Wave Optimization for the Multidimensional Knapsack Problem . . . . . Hong-Fang Yan, Ci-Yun Cai, De-Huai Liu, and Min-Xia Zhang

688

A Complex-Valued Firefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chuandong Song

700

Gene Expression Prediction Based on Hybrid Evolutionary Algorithm . . . . . . Haifeng Wang and Sanrong Liu

708

MPdeep: Medical Procession with Deep Learning . . . . . . . . . . . . . . . . . . . . Qi Liu, Wenzheng Bao, and Zhuo Wang

717

Predicting Potential Drug-Target Interactions with Multi-label Learning and Ensemble Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lida Zhu and Jun Yuan

726

Contents – Part II

Robust Circulating Tumor Cells Detection in Scanned Microscopic Images with Cascaded Morphological and Faster R-CNN Deep Detectors . . . Yun-Xia Liu, An-Jie Zhang, Qing-Fang Meng, Ying-Jie Chen, Yang Yang, and Yue-Hui Chen Electrocardiogram Diagnosis Based on SMOTE+ENN and Random Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Li Sun, Ziwei Shang, Qing Cao, Kang Chen, and Jiyun Li Complex Leaves Classification with Features Extractor . . . . . . . . . . . . . . . . Daniel Ayala Niño, José S. Ruíz Castilla, Ma. Dolores Arévalo Zenteno, and Laura D. Jalili IDP - OPTICS: Improvement of Differential Privacy Algorithm in Data Histogram Publishing Based on Density Clustering . . . . . . . . . . . . . Lina Ge, Yugu Hu, Hong Wang, Zhonghua He, Huazhi Meng, Xiong Tang, and Liyan Wu Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Particle Swarm Optimization-Based Power Allocation Scheme for Secrecy Sum Rate Maximization in NOMA with Cooperative Relaying Carla E. Garcia1

, Mario R. Camana1 , Insoo Koo1(&) and Md Arifur Rahman2

,

1

2

University of Ulsan, Ulsan, South Korea [email protected] Campus of Rennes, Cesson Sevigne, France

Abstract. In this paper, we study a particle swarm optimization (PSO)-based power allocation scheme for physical-layer security under downlink nonorthogonal multiple-access (NOMA) with cooperative relaying where a source node communicates directly with a nearby user and with a distant user via decode-and-forward relay. First, we formulate the optimization problem to maximize the secrecy sum rate (SSR) under the constraints of a minimum rate at each user and maximum transmission power at the source and the relay. Then, we stablish the lower and upper boundaries of the variables that need to be optimized, and we derive a solution via the proposed low computationalcomplexity scheme based on PSO algorithm. Next, we compare the SSR performance of the proposed scheme with two baseline schemes: orthogonal multiple access (OMA) and NOMA without cooperative relaying. Finally, simulation results show that NOMA with cooperative relaying can increase the SSR, compared to OMA and NOMA without cooperative relaying. Keywords: Particle swarm optimization
Non-orthogonal multiple-access Physical-layer security
Secrecy sum rate
Orthogonal multiple access
Cooperative relaying




1 Introduction The rapid advancement of wireless communications networks has led to tremendous increases in the data traffic of the networks. Fifth-generation (5G) technology needs to support massive connectivity of users and Internet of Things devices to meet the demand for high data rates, high-energy efficiency, low latency, and improved quality of service (QoS). Multiple access techniques in wireless communications can be broadly categorized into two different approaches: orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) [1]. In OMA, the same frequency channel is shared by all users at different times, so that it is not possible to use the same resource elements (e.g., frequency bands, time slots) in order to perform multiple access among several users. In contrast to OMA, NOMA allows allocation of one © Springer Nature Switzerland AG 2019 D.-S. Huang et al. (Eds.): ICIC 2019, LNCS 11644, pp. 1–12, 2019. https://doi.org/10.1007/978-3-030-26969-2_1

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frequency channel to multiple users within the same cell at the same time in order to obtain advantages like improved spectral efficiency, higher cell-edge throughput, relaxed channel feedback, and low transmission latency [2]. In power-domain NOMA, different levels of transmission power are assigned to different users according to their channel conditions in order to obtain high system performance. In downlink NOMA, multiple users’ information signals are overlaid at the transmitter, and successive interference cancellation (SIC) is applied at the receiver to decode the signals one by one until the desired users’ information is obtained. Cooperative communications (CC) uses a cooperative user to transmit a base station signal to the intended user, and it can also enhance the coverage of the network. To improve the transmission reliability, different cooperative NOMA schemes have been proposed in [3, 4] to evaluate the performance in outage probability and data rates of the systems. With the concept of CC in NOMA, a dynamic power allocation (PA) scheme was proposed in [5], where a closed form approximation is derived for outage probability, and a dynamic PA scheme is proposed to improve the throughput of the network. In wireless communications, due to the broadcasting nature, secure information exchange between the sender and the receiver is critical when an eavesdropper coexists in the network to intercept the legitimate users’ communications. CC is a very useful method to improve the physical-layer security (PLS) of wireless communications [6–8], in which the secrecy rate (SR) of the networks is maximized based on solving the relay selection and the PA problems of the networks. Similarly, the concept of PLS enhancement can be applied in NOMA to recognize robust secure transmission. SSR maximization in NOMA was studied in the literature [9, 10] through a closedform solution of an optimal PA policy that maximizes the SSR. The results validated significant SSR improvement by NOMA, compared to OMA. However, neither study considered a cooperative relaying system. SR performance in a downlink NOMA system with relay was analyzed in [11, 12] based on evaluation of the secrecy outage probability. Simulation results demonstrated that cooperative relay in a NOMA system can be very useful in improving the secrecy performance of NOMA users. In [13], the authors proposed maximizing the achievable SSR under NOMA with an amplify-and-forward (AF) relay system. The solution consists of decoupling the optimization problem into two subproblems and solving PA and relay beamforming in an iterative manner, which has high computational complexity. That is unlike our paper, where we use a decode-and-forward (DF) relay and an optimization solution based on the low computational-complexity particle swarm optimization (PSO) method [14]. A low complexity PA scheme is essentially required for SSR maximization in NOMA with cooperative relaying, which motivates us to propose a PSO-based PA scheme which can converge faster than the exhaustive search-based (ES) method. Exhaustive search is useful to obtain the optimal optimization solution. However, its computational complexity is very high when the number of users is increased in the network. Our main contributions are summarized as follows.

Particle Swarm Optimization-Based Power Allocation Scheme

3

– We provide a solution to maximize the SSR of NOMA system with cooperative relaying by applying a low-complexity PA scheme based on the PSO algorithm, which greatly decreases the complexity of the optimization problem. – Maximization of the SSR under NOMA with cooperative relaying system is analyzed by satisfying the minimum rate for users and the maximum transmission power requirements of the network. – Simulation results reveal that NOMA achieves significant SSR improvement in comparison with conventional OMA. Moreover, the proposed scheme can reach near-optimal performance, compared with an (ES)-based scheme with much lower complexity. – We also verify through simulation results that the proposed PSO-based scheme with cooperative relaying significantly improves SSR performance compared to a scheme without cooperative relaying in NOMA systems. The remainder of this paper is organized as follows. The system model is briefly discussed in Sect. 2. The problem formulation of NOMA with cooperative relaying and the PSO-based PA scheme are described in Sect. 3. Simulation results of the proposed scheme and a comparison with the OMA scheme are presented in Sect. 4. Finally, the conclusion is presented in Sect. 5.

2 System Model We consider a downlink NOMA with cooperative relaying system comprised of one source, S; two users, one half duplex relay, R; employing a DF protocol, and an eavesdropper, E; as shown in Fig. 1. The nearby user is denoted as u1 and the distant user is denoted u2 : Assume that each node only has a single antenna and there is no direct transmission link between the source and the distant user due to obstacles or shadowing effect. The channel coefficients of S to R, S to u1 , S to E, R to u2 , R to u1 , and R to E are denote as hSR ; hSu1 ; hSE ; hRu2 ; hRu1 ; hRE : The Additive white Gaussian noise (AWGN) at all nodes is denoted as zj and is assumed to be independent, with variances of r2j , j 2 fu1 ; u2 ; R and Eg: In this paper, we also assume that the variance in the noise of the users, the eavesdropper, and the relays is the same as the variance for r2 , where r2 ¼ r2E ¼ r2R ¼ r2u1 ¼ r2u2 . NOMA with a cooperative relaying system involves two stages: Stage A and Stage B. In Stage A, the source transmits nearby- and distant-users’ messages, and then relay, nearby user, and eavesdropper receive them. According to NOMA principles, the nearby user performs SIC to remove the distant-user message. In Stage B, the relay transmits the distant-user message, and the eavesdropper, nearby user, and distant user receive it. In the following, we explain the operations in stage A and B in more details. pffiffiffiffiffi pffiffiffiffiffi In Stage A, the source sends the superimposed signal, x ¼ p1 x1 þ p2 x2 where x1 ; x2 2 C; are the information-bearing messages for the nearby user and the distant user, respectively, with corresponding transmit power p1 and p2 :

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Fig. 1. Transmission under NOMA with cooperative relaying.

The received signal at nearby user u1 ; the relay, and the eavesdropper are given by yAj ¼

pffiffiffiffiffi pffiffiffiffiffi p1 x1 hSj þ p2 x2 hSj þ zj ;

ð1Þ

where j 2 fu1 ; R; Eg: The rate of the nearby-user data at u1 and at eavesdropper can be written as (2) and (3), respectively. p1 h2Su1 1 Rx1 ;Su1 ¼ log2 ð1 þ Þ: 2 r2

ð2Þ

1 p1 h2SE Þ: Rx1 ;SE ¼ log2 ð1 þ 2 p2 h2SE þ r2

ð3Þ

Hence, the achievable secrecy rate of the nearby user is the rate of nearby-user data at u1 minus the rate of nearby-user data at eavesdropper [10, 12], given by  þ Ru1 ¼ Rx1 ;Su1  Rx1 ;SE ;

ð4Þ

where ½ x þ ¼ maxð0; xÞ: In Stage B, the relay decodes x2 by treating x1 as noise, and then, the relay sends x2 ; which is received by the eavesdropper, nearby user u1 ; and distant user u2 : Accordingly, the received signal in Stage B at u1 ; u2 ; and the eavesdropper is given by yBj ¼

pffiffiffiffiffi pr x2 hRj þ nj ;

ð5Þ

where pr is the transmission power at relay and j 2 fu1 ; u2 ; Eg: The rate of the distantuser data at u1 and at the eavesdropper can be written as p2 h2Sj pr h2Rj 1 Rx2 ;j ¼ log2 ð1 þ þ Þ; 2 2 r2 p1 hSj þ r2

ð6Þ

where j 2 fu1 ; Eg: The rate of the distant-user data at u2 and at the relay is given by (7) and (8), respectively.

Particle Swarm Optimization-Based Power Allocation Scheme

5

pr h2Ru2 1 Rx2 ;Ru2 ¼ log2 ð1 þ Þ: 2 r2

ð7Þ

1 p2 h2SR Þ: Rx2 ;SR ¼ log2 ð1 þ 2 p1 h2SR þ r2

ð8Þ

Since the relay executes the DF protocol, and nearby user u1 applies SIC, the achievable rate and the achievable secrecy rate for distant user u2 are given by (9) and (10), respectively. Ru2 ;min ¼ minðRx2 ;u1 ; Rx2 ;Ru2 ; Rx2 ;SR Þ:

ð9Þ

 þ Ru2 ¼ Ru2 ;min  Rx2 ;E :

ð10Þ

3 Problem Formulation and PSO-Based PA Scheme for Secrecy Sum Rate Maximization In this paper, we focus on finding a low-complexity PA scheme for secrecy sum rate maximization in downlink NOMA with cooperative relaying, which is equivalent to maximizing Ru1 þ Ru2 under the constraint of a minimum rate at each user and the maximum transmission power at the source and the relay, as follows P1 :

max Ru1 þ Ru2

p1 ;p2 ;pr

ð11aÞ

Rx1 ;Su1  c1 ;

ð11bÞ

Ru2 ;min  c2 ;

ð11cÞ

p1 þ p2  Pmax T ;

ð11dÞ

pr  Pmax R ;

ð11eÞ

where c1 is the minimum rate at nearby user u1 ; c2 is the minimum rate at distant user u2 ; Pmax is the maximum transmission power at the source, and Pmax is the maximum T R transmission power at the relay. 3.1

PSO-Based PA Scheme for Secrecy Sum Rate Maximization in NOMA with Cooperative Relaying

In this paper, we propose a low-complexity PA scheme based on a PSO algorithm [14], which tries to find the approximately optimal solution in the search space base on a swarm of particles that updates from iteration to iteration. We first establish the lower and upper boundaries of the variables p1 ; p2 and pr ; which is needed to be optimized for SSR maximization. The minimum transmission

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power of the nearby user is calculated based on constraint (11b), the minimum transmission power of the distant user and the minimum transmission power of relay is calculated based on the constraint (11c) and are given by (12), (13) and (14), respectively.

where

h1 ¼ ð22c2  1Þ

ð22c2  1Þ hr2

2

Ru2

  r2 p1min ¼ 22c1  1 2 : hSu1

ð12Þ

p2min ¼ maxð0; h1 ; h2 Þ;

ð13Þ

prmin ¼ maxð0; u1 ; u2 Þ;

ð14Þ

  p1min h2Su þ r2 Pmax h2 1 22c2  1  R r2 Ru1 : h2su 1  Pmax h2 2  1  p1minTh2 Suþ1 r2 hr2 :

p1min h2SR þ r2 h2SR

 ; and u2 ¼ 22c2

;

h2 ¼

Su1

u1 ¼

Ru1

The procedure of the proposed PSO-based algorithm in solving SSR maximization problem P1 is described in Table 1, where the input parameters of PSO correspond to the maximum iteration number, the number of particles in a swarm, the inertia weight for velocity update, and the cognitive and social parameters, denoted as MI , NP ; IW ; c1 ; and c2 ; respectively. Each of the m-th particle’s position  is a vector  of  three elements,  ; p2 min ; Pmax and p1 ; p2 and pr ; the feasible search region is denoted as p1 min ; Pmax T T   max pr min ; PR , respectively. The position of the m-th particle ðm ¼ 1; 2; 3; . . .; NP Þ has the elements xp1 ;m ; xp2 ;m ; and xpr ;m as follows:

xm ¼ xp1 ;m ; xp2 ;m ; xpr ;m

ð15Þ

The initial global best position is assigned by selecting the maximum objective value from among all the initial positions of the particles. The velocities of the particles are updated, based on [15], can be given by (16) and the positions of the particles are accelerated can be given by (17).   ðk Þ vðmk þ 1Þ ¼ IW vðmkÞ þ j1 c1 pbm  xðmkÞ þ j2 c2 gb  xðmkÞ ;

ð16Þ

xðmk þ 1Þ ¼ xðmkÞ þ vðmk þ 1Þ :

ð17Þ

where vðmkÞ is the velocity of the m-th particle in the k-th iteration, and j1 and j2 are random numbers between 0 and 1. Moreover, xðmkÞ is the position of the m-th particle in the k-th iteration. The individual best position of the m-th particle is denoted by pbm and the global best position of all particles in the k-th iteration is denoted by gb: In order to obtain the global optimum values of the p1 ; p2 and pr variables, each particle is recalculated by updating the velocity and position of each particle and by evaluating the global best position of all particles until finding the best m-th particle to achieve the maximum value of SSR. The process is repeated until meeting the stopping criteria.

Particle Swarm Optimization-Based Power Allocation Scheme Table 1. The proposed algorithm based on PSO to solve the problem P1.

1: 2: 3:

Input parameters of PSO Set iteration count k =1 Assign the initial particles’ positions. ⎧⎪⎛ ( k ) ( k ) ( k ) ⎞ ⎛ ( k ) ( k ) , x( k ) ⎞⎪⎫ ,x ,x ,x x( k ) = ⎨⎜ x ⎟,...,⎜ x ⎟⎬ ⎪⎩⎝ p1 ,1 p2 ,1 pr ,1 ⎠ ⎝ p1 , m p2 , m pr , m ⎠ ⎪⎭

4:

Assign the initial particles' velocities. ⎛ (k ) (k ) (k ) ⎞ v( k ) = v ,v ,..., v ⎜ 1 ⎝

2

m ⎟ ⎠

5: Calculate SSR by computing Ru1 + Ru2 for each m-th particle,

( )

and denoting by SSR xkm ,∀m . 6:

Set the initial best particles’ positions,

: pb m = x(m ) k

⎛ (k ) ⎞ Set the initial global best position. gb = arg max SSR⎜ x m ⎟. ⎝ ⎠ 1≤ m≤ NP 8: Increase the set iteration count k = k +1. 9: For each particle m do 10: From (16), update the particles’ velocities 11: From (17), update the particles’ positions 12: Update the best particles’ positions.

7:

( )

if SSR xkm > SSR( pbm ) then

(k ) pb m = xm 13:

end if Update the global best particles’ positions, ∀m.

( )

if SSR xkm > SSR( gb )

then

(k ) gb = xm end if 14: end for 15: if k < M I then k = k +1 and go to step 9 else go to step 16 end if

{

}

16: Return the best values p1* , p*2 , p*r =gb to obtain the maximum value of SSR indicated in the problem P1.

7

8

3.2

C. E. Garcia et al.

Baseline Scheme to Solve the Secrecy Sum Rate Maximization Problem in OMA

In this subsection, we describe the baseline scheme to find a low-complexity PA scheme for secrecy sum rate maximization problem in OMA with cooperative relaying. In OMA, in the first time slot, the message x1 is sent from the source to the nearby user u1 . Then, in the second time slot, the message x2 is sent from the source to the relay. After the relay receives the message x2 ; in the third time slot, the relay forwards this message to the distant user u2 : Accordingly, the rate of nearby-user data at u1 and at eavesdropper can be written as (18) and (19), respectively. Rx1 ;Su1 Rx1 ;SE

OMA

OMA

  p1 h2Su1 1 ¼ log2 1 þ 3 r2

ð18Þ

  1 p1 h2SE ¼ log2 1 þ 3 r2

ð19Þ

Consequently, the achievable secrecy rate of the nearby user is given by Ru1

h ¼ Rx1 ;Su1

OMA

OMA

 Rx1 ;SE

iþ OMA

:

ð20Þ

The rate of the distant-user data at u2 and at relay are given by (21) and (22), respectively: Rx2 ;Su2 Rx2 ;SR

OMA

OMA

  pr h2Ru2 1 ¼ log2 1 þ 3 r2

ð21Þ

  1 p2 h2SR ¼ log2 1 þ 3 r2

ð22Þ

The achievable rate of distant user u2 is expressed as follows: Ru2 ;min

OMA

 ¼ min Rx2 ;Su2

; Rx2 ;SR OMA

OMA

ð23Þ

The rate of the distant-user data at the eavesdropper is given by Rx2 ;E

OMA

p2 h2Sj pr h2Rj 1 ¼ log2 1 þ 2 þ 2 3 r r

! ð24Þ

Consequently, the achievable secrecy rate of the distant user is expressed as follows: Ru2

OMA

 ¼ Ru2 ;min

OMA

 Rx2 ;E

þ OMA

:

ð25Þ

Particle Swarm Optimization-Based Power Allocation Scheme

9

In the same manner as P1; our aim is to maximize the secrecy sum rate, subject to the minimum rate requirements of the users and by satisfying the transmission power constraints of the source and the relay. Therefore, the optimization problem considering OMA with cooperative relaying is formulated as follows: P2 :

max Ru1

p1 ;p2 ;pr

Rx1 ;Su1 Ru2 ;min

OMA

OMA

þ Ru2

 c1 ;

OMA

 c2 ;

OMA

ð26aÞ ð26bÞ ð26cÞ

p1  Pmax T ;

ð26dÞ

p2  Pmax T ;

ð26eÞ

pr  Pmax R ;

ð26fÞ

Similar to the previous case, we propose a low-complexity PA scheme based on a PSO method to solve secrecy sum rate maximization problem P2:

4 Simulation Results In this section, we present the simulation results of the proposed PA scheme for secrecy sum rate maximization in downlink NOMA with cooperative relaying programmed in the MATLAB software. Moreover, we provide a performance comparison among the proposed scheme, conventional OMA, and NOMA without cooperative relaying. 

v In the simulations, the channel coefficients are modeled as hSu1 : CN 0; dSu ; 1    v    v v v ; where dij hSE : CN 0; dSE ; hRu2 : CN 0; dRu ; hRu1 : CN 0; dRu ; hRE : CN 0; dRE 2 1

denotes the distance between node i and j; and v is the path-loss exponent. We used the value of the path-loss exponent at v ¼ 4. The noise power was r2E ¼ r2R ¼ r2u1 ¼ r2u2 ¼ r2 ¼ 60 dBm. The distances between the nodes were as follows: dSR ¼ 50; dSu1 ¼ 30; dSE ¼ 50; dRu2 ¼ 50; dRu1 ¼ 30; and dRE ¼ 30 in meters. Figure 2 shows the convergence of the proposed low-complexity PSO-based algorithm given in Table 1 in terms of the iteration index. We see that the SSR improves as the iteration index is increased. Furthermore, we can observe that the SSR can converge in less than 100 iterations. Consequently, we set the maximum number of iterations, MI to be 60 for further simulations. The rest of the simulation parameters for the PSO method were chosen, based on [16], as follows: number of particles NP ¼ 15; inertia weight for velocity update IW ¼ 0:7; and scaling factors c1 ¼ 1:494 and c2 ¼ 1:494:

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Fig. 2. Convergence of the PSO-based algorithm with different data rate requirements at user 1 ðc1 Þ and user 2 ðc2 Þ:

Figure 3(a) shows the SSR performance according to the minimum data rate at nearby user 1, c1 ; and at distant user 2, c2 ; when the maximum transmission power at the source and the maximum transmission power at the relay are limited to Pmax ¼ T max PR ¼ 40 dBm. The SSR performance was obtained under the proposed scheme and the OMA scheme, and we can observe that the proposed scheme is superior to the traditional OMA. This is because in OMA, the users share the same frequency channel at different times. By contrast, NOMA users can utilize the same frequency channel at the same time. This fact improves the spectral efficiency in the NOMA system. Moreover, we see that the simulation result of the proposed PSO-based algorithm is very close to that obtained by the ES method, which is useful for obtaining the optimal optimization solution. However, the computational complexity of ES is very high when the number of users in the network is increased. Therefore, we use PSO-based PA scheme, which permits obtaining an approximately optimal solution and can converge faster than the ES-based method. To show the performance of the ES scheme, we use 10 channel realizations because of the long computational time. However, we use 1000 channel realizations for the rest of the simulations.

Fig. 3. SSR performance comparison according to the minimum data rate requirements at user 1 ðc1 Þ and user 2 ðc2 Þ: (a) for the exhaustive search, NOMA and OMA (b) between NOMA with cooperative relaying and NOMA without cooperative relaying.

Particle Swarm Optimization-Based Power Allocation Scheme

11

Based on the minimum data rates at nearby user 1 ðc1 Þ and distant user 2 ðc2 Þ; Fig. 3(b) shows a comparison of SSR performance between our scheme using cooperative relaying and a NOMA scheme without employing comparative relaying when the maximum transmission power at the source and the maximum transmission power at the relay are limited to Pmax ¼ Pmax ¼ 30 dBm. The results show that the proposed T R scheme achieves higher SSR by using NOMA with cooperative relaying than by using NOMA without cooperative relaying. This behavior is because the distant user can easily get the QoS requirement by utilizing the relaying, especially when this user is far from the source. In the NOMA scheme without cooperative relaying, the source provides more transmission power to satisfy the QoS requirement of the distant user, causing that the SINR increases at the eavesdropper, and hence, the SSR decreases.

5 Conclusion In this paper, we studied a low-complexity PA scheme based on a PSO algorithm to maximize the SSR of NOMA cooperative relaying. SSR is maximized based on the minimum rate requirements of the users and by satisfying the transmission power constraints of the source and the relay in a downlink NOMA system. We compared our proposed scheme with the optimal ES-based scheme and showed that the proposed scheme can achieve near-optimal performance with much lower computational complexity. In addition, we compared the performance of NOMA with a baseline OMA scheme to show the effectiveness of NOMA at improving the SSR of the system. We also showed that NOMA with cooperative relaying provides better performance than NOMA without cooperative relaying. Acknowledgment. This work was supported by the National Research Foundation of Korea (NRF) grant through the Korean Government (MSIT) under Grant NRF-2018R1A2B6001714.

References 1. Wang, P., Xiao, J., Ping, L.: Comparison of orthogonal and nonorthogonal approaches to future wireless cellular systems. IEEE Vehi. Techn. 1(3), 4–11 (2006) 2. Wan, D., Wen, M., Ji, F., Yu, H., Chen, F.: Non-orthogonal multiple access for cooperative communications: challenges, opportunities, and trends. IEEE Wireless Commun. 25(2), 109–117 (2018) 3. Choi, J.: Non-orthogonal multiple access in downlink coordinated two-point systems. IEEE Commun. 18(2), 313–316 (2014) 4. Ding, Z., Peng, M., Poor, H.: Cooperative non-orthogonal multiple access in 5G systems. IEEE Commun. Lett. 19(8), 1462–1465 (2015) 5. Li, Y., Li, Y., Chen, Y., Ye, Y., Zhang, H.: Performance analysis of cooperative NOMA with a shared AF relay. IET Commun. 12(19), 2438–2447 (2018) 6. Wang, D., Bai, B., Chen, W., Han, Z.: Secure green communication via untrusted two-way relaying: a physical layer approach. IEEE Trans. Commun. 64(5), 1861–1874 (2016) 7. Wang, D., Bai, B., Chen, W., Han, Z.: Achieving high energy efficiency and physical-layer security in AF relaying. IEEE Trans. Wireless Commun. 15(1), 740–752 (2016)

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8. Wang, D., Bai, B., Chen, W., Han, Z.: Energy efficient secure communication over decodeand-forward relay channels. IEEE Trans. Commun. 63(3), 892–905 (2015) 9. Zhang, Y., Wang, H., Yang, Q., Ding, Z.: Secrecy sum rate maximization in non-orthogonal multiple access. IEEE Commun. Lett. 20(5), 930–933 (2016) 10. Tang, J., et al.: Optimization for maximizing sum secrecy rate in SWIPT-enabled NOMA systems. IEEE Access 6, 43440–43449 (2018) 11. Liu, C., Zhang, L., Xiao, M., Chen, Z., Li, S.: Secrecy performance analysis in downlink NOMA systems with cooperative full-duplex relaying. In: 2018 IEEE International Conference on Communications Workshops (ICC Workshops), Kansas City, USA, pp. 1– 6 (2018) 12. Abbasi, O., Ebrahimi, A.: Secrecy analysis of a NOMA system with full duplex and half duplex relay. In: 2017 Iran Workshop on Communication and Information Theory (IWCIT), Tehran, pp. 1–6 (2017) 13. Tian, M., Zhao, S., Li, Q., Qin, J.: Secrecy sum rate optimization in nonorthogonal multiple access AF relay networks. IEEE Syst. J., 1–4 (2018). https://doi.org/10.1109/JSYST.2018. 2867365 14. Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Prob. Eng. 501, 931256 (2015) 15. Rahman, M., Lee, Y., Koo, I.: Energy-efficient power allocation and relay selection schemes for relay-assisted D2D communications in 5G wireless networks. Sensors 18(9), 2865 (2018) 16. Tuan, P., Koo, I.: Optimal multiuser MISO beamforming for power-splitting SWIPT cognitive radio networks. IEEE Access 5, 14141–14153 (2017)

A Novel Binary Particle Swarm Optimization for Multiple Sequence Alignment Yanlian Du(&), Jingxuan He, and Changcun Du Hainan University, Haikou 570228, Hainan, China [email protected]

Abstract. Multiple sequence alignment (MSA) is a basic operation in bioinformatics, which is known to be an NP-complete problem. With the advent of the post genetic era, the number of biological sequences is increasing exponentially, it is urgent to develop high-efficiency heuristic parallel algorithms to solve MSA. In this paper, a novel binary particle swarm optimization (NBPSO) is proposed for MSA. Firstly, a novel binary code is designed to encode particle to adapt PSO to MSA, velocity and position updating formulas are refined accordingly. Regarding of the ‘illegal’ phenomenon of MSA after positions updating, based on probability theory, an auxiliary particle re-initialization strategy is proposed. Multi-swarm PSO is used to make the searching more parallel, diversity index dt is proposed based on Hamming Distance, mutation takes place when dt falls below threshold. The proposed NBPSO-MSA is operated on two benchmarks, i.e., OX-Bench and BAliBASE. Experiments show that, NBPSO-MSA outperforms other 12 state-of-the art aligners in accuracy, the trajectories of particles show that, diversity index and mutation help multi-swarm NBPSO jump out of local optima and lead to rapid convergence. Keywords: Multiple sequence alignment
Particle swarm optimization Novel binary particle swarm optimization
Diversity index




1 Introduction Multiple sequence alignment (MSA) is a basic operation in Bioinformatics. It plays an important role in discovering sequence motifs, analyzing phylogeny and predicting structure. As a basic subject of Bioinformatics, MSA algorithms have been around for 40 years. So far, many aligners have been proposed, either based on Dynamic Programming (DP), or based on heuristic algorithm. DP is an accurate aligner, the cost is high complexity, with O(ns) time and space [1]. Most of the aligners may have high precision in alignment effect, but either with high complexity or with numerous parameters. When changing to other sequences, new parameters need to be reset. With the advent of the post-genetic era, the number of biological sequences is increasing exponentially, it is urgent to develop high-efficiency heuristic algorithms to solve it. In recent years, Genetic Algorithm, Ant Colony Algorithm, Particle Swarm Optimization (PSO) and other algorithms have been extended to MSA, trying to solve the problems of accuracy, inherent complexity and parameters selection. PSO, which was © Springer Nature Switzerland AG 2019 D.-S. Huang et al. (Eds.): ICIC 2019, LNCS 11644, pp. 13–25, 2019. https://doi.org/10.1007/978-3-030-26969-2_2

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proposed in 1995 [2], has been successfully applied to various fields, but there is little research on MSA using binary parallel PSO. In [3], PSO is designed to use binary code to encode the biological sequences, and a probability model is introduced. However, in the iteration process, all particles share the same probability matrix, which does not conform to the idea of PSO that, particles fly according to their velocity, personal best (pbest), and global best (gbest), leading to problems such as local optima, premature convergence etc. In [4], Sun Jun et al. use PSO to train parameters of HMM, then HMM is used to resolve MSA. In [5], Zhan et al. first optimize the parameters of HMM by PSO, then HMM is combined with partition function to resolve MSA. In both [4, 5], PSO is used to resolve the parameters of HMM instead of resolving MSA directly, this reduces the feasibility of the algorithm, in the way that different models have to be trained again according to different sequences and different objective functions, what’s more, learning HMM is itself a difficult task, which inherently increases the propagation of error. In [6], bi-level discrete PSO based on set-theory is proposed to solve MSA, level one optimizes optimal sequence length, whereas level two optimizes optimum gap positions for best alignment. In [7, 8], a characteristic-based framework for MSA is proposed, PSO was used to train the parameter configuration of the framework. In this paper, inspired by the idea of PSO tracking the optimal particle in an iterative process [2, 9], the problem of finding the optimal MSA is transformed into a problem of finding the optimal particle. The particle is encoded as a 0/1 position matrix, the components of position are “1” or “0”, “1” represents a gap inserted, while “0” represents a base (or residue), and all “0” components from the ith row corresponds to the ith sequence to be aligned, in this way, each particle represents a MSA. The goal is to find the significant locations for “1” in the optimal particle. Velocity is designed to estimate the probability that, the value of the corresponding component in position matrix will change, it is quantified by the distance between the individual and the optimal, which represents the pace of the individual and the optimal. The proposed algorithm gives a new definition of velocity and position, designs a novel binary coding according to the problem of MSA, while the idea of NBPSO is exactly the same with the original PSO, and shares the same parameter settings, so all variants of PSO can be used to solve MSA. The main contributions of this paper are as follows: (1) Based on the binary code, we design a novel binary particle swarm optimization for multiple sequence alignment (NBPSO-MSA). (2) According to the ‘illegal’ phenomenon, an auxiliary position re-initialization strategy is proposed, to make the search more effective. (3) Multi-swarm PSO is used to make the search parallel, diversity index dt is proposed based on Hamming Distance. To preserve the diversity of the population, when dt falls below threshold, mutation takes place in each sub-swarm accordingly. (4) NBPSO-MSA is operated on OX-Bench and BAliBASE benchmarks. The experimental results of NBPSO-MSA are compared with 12 well-known aligners. The rest of the paper is organized as follows. Section 2 introduces the conventional PSO and its variants. Section 3.1 gives a mathematical description of MSA. Section 3.2 presents the proposed NBPSO-MSA, including the representation scheme, related operators, an auxiliary position re-initialization strategy and diversity index.

A Novel Binary Particle Swarm Optimization for Multiple Sequence Alignment

15

Section 3.3 presents the experimental results and the comparisons between NBPSOMSA and other aligners. Section 5 concludes the paper and outlines future work.

2 Particle Swarm Optimization 2.1

Continuous Particle Swarm Optimization

Particle swarm optimization (PSO) was first proposed by Kennedy and Eberhart in 1995 [2], as a swarm-based heuristic random search algorithm, inspired by behavior patterns such as bird flocking and fish foraging. It was originally designed to find optimal solutions in a continuous space. In the original PSO, m particles cooperate to search for the global optimum in the n-dimensional search space. Every particle maintains a position. In the iterative process, each particle uses its own search experience (self-cognitive) and the whole swarm’s search experience (social-influence) to update the velocity and fly to a new position. The updating rules are as follows: vi þ 1 ¼ vi þ c1 r1 ðpbesti  xi Þ þ c2 r2 ðgbest  xi Þ

ð1Þ

xi þ 1 ¼ xi þ vi þ 1

ð2Þ

where pbesti is the best solution yielded by the ith particle and gbest is the best-so-far solution obtained by the whole swarm. c1 and c2 are two parameters to weigh the importance of self-cognitive and social-influence, respectively. r1 and r2 are random numbers uniformly distributed in [0, 1]. As the original PSO is easy to implement and effective, an amount of research effort has been made to improve its performance in various ways. The first successor is introduced by updating the inertia weight x according to the following rule (3) [10]: vi þ 1 ¼ wvi þ c1 r1 ðpbesti  xi Þ þ c2 r2 ðgbest  xi Þ

ð3Þ

This work indicates that, a relatively large inertia weight is better for global search, while a small one enhances the ability of local search. A scheme to decrease x linearly from 0.9 to 0.4 was proposed accordingly. Constriction-factor variant was proposed, it is basically the same as inertia-weight variant, except the velocity updating formula (4) [11]. In [12], Clerc and Kennedy analyze the convergence behavior of PSO in detail and introduce a constriction factor, to guarantee the convergence of the algorithm. vi þ 1 ¼ vðvi þ c1 r1 ðpbesti  xi Þ þ c2 r2 ðgbest  xi ÞÞ

ð4Þ

Another active research area is to develop different topologies. Kennedy and Mendes suggest using the local best position lbest of a neighborhood and modify the velocity updating rule into formula (5) [13], vi þ 1 ¼ wvi þ c1 r1 ðpbesti  xi Þ þ c2 r2 ðlbest  xi Þ

ð5Þ

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The neighborhood can be defined by different topologies, such as Ring, URing, von Neumann and so on. In general, a large neighborhood is better for simple problems, and a small neighborhood is more suitable for complex problems. Meanwhile, many researchers have developed variants of PSO to prevent premature convergence by modifying the learning strategies of particles or combining PSO with other search techniques. A representative PSO variant is the CLPSO proposed by Liang et al. [14]. The algorithm updates velocity using formula (6),
 vijþ 1 ¼ wvij þ cr j pbestfjiðjÞ  xij

ð6Þ

where c is a parameter, r j is a random number in [0, 1], and pbestfjið jÞ means the jth dimension of the pbestfjið jÞ position of the particle fi ð jÞ, pbestfið jÞ can be pbesti or any other particles’ pbest position. CLPSO has been shown to be excellent for complex multimodal function optimization problems. The third group uses hybrid strategies and the fourth uses multi-swarm techniques. 2.2

Discrete Particle Swarm Optimization (DPSO)

PSO can also be applied to some discrete optimization problems of integer variables. Many DPSO algorithms have been proposed [9, 15, 16]. Chen et al. classify the existing DPSO algorithms into four types. Swap operator based PSO uses a permutation of numbers as position and a set of swaps as velocity. Space transformation based PSO uses various techniques to transform the position, defined as a vector of real numbers, to the corresponding solution in discrete space. Fuzzy matrix based PSO defines the position and velocity as a fuzzy matrix, and encode it to a feasible solution. Incorporating based PSO consists of hybrid methods incorporating some heuristic or problem dependent techniques. Chen et al. propose a discrete set-based PSO (S-PSO) with probabilities, velocity is defined as a set with probabilities, operators are replaced by procedures defined on the set. Experiments on TSP and MKP show that S-PSO fits for combinatorial optimization problems very well [15]. Wu et al. then adapt S-PSO to generate covering arrays, and propose two auxiliary strategies to enhance the performance of S-PSO [16]. 2.3

Mathematical Description of Multiple Sequence Alignment

A sequence S is a string consisting of a number of characters from an alphabet, e.g., S ¼ s1 s2


sl ; si 2 K; i ¼ 1; 2;


; l, where K is a set with finite number of characters, l is the length of the sequence S. For DNA sequence, n = 4, K = {A, C, G,T}, which representing four distinct nucleotides; for protein sequences, n = 20, each character represents a unique amino acid in K = {A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, V}, A sequence set is a set consisting of a given number of sequences, e.g., U ¼ ðS1 ; S2 ;


; Sm Þ, where Si ¼ si1 si2


sili ; i ¼ 1; 2;


; m, sij 2 K, li is the length of the ith sequence Si in sequence set U. Then the sequence alignment of U can be

A Novel Binary Particle Swarm Optimization for Multiple Sequence Alignment

17

  defined as a matrix Y ¼ yij mn satisfying the following conditions: 1. 2. 3. 4.

yij 2 K [ fg, where “ − ” indicates a gap inserted; If delete all “ − ” in the ith row of Y, the left yij of Y corresponds to Si ; P No column in Y consisting only of “ − ”, i.e., m i xij \m; 8j ¼ 1; 2;


; n. When m = 2, it is called pairwise sequence alignment (PSA), else it is called MSA while m > 2. Based on the above discussion, MSA can be described as a Maximum problem (7): Max f ðU Þ

ð7Þ

s:t: U  V ¼ Y y1j þ y2j þ


þ ymj \m; 8j ¼ 1; 2;


; n li þ ri ¼ n; 8i ¼ 1; 2;


; m uik 2 K; yij 2 K [ fg; vit 2 N i ¼ 1; 2;


; m; k ¼ 1; 2;


; li ; j ¼ 1; 2;


; n; t ¼ 1; 2;


; ri : where V records the indexes of gaps inserted, the operator  describes how to insert gaps into the sequences in U, e.g., ‘ATGATC’  (0, 3, 6) = ‘-ATG-ATC-’. f is the objective function, i.e., the scoring function of MSA. ri is the number of gaps inserted in the ith sequence Si . To assess the quality of a test alignment with respect to a reference alignment, in this paper, we take into account two well-known scoring methods [7, 8]: Q-Score (also known as Sum-of-Pairs Score, QS): Defines pijk as 1 if yji ¼ yki , 0 else; the score for the ith column pi is defined as (8), the Q-score for the MSA is equal to (9), pi ¼

Xn Xn j¼1

k¼1;k6¼j

Pn pi QS ¼ Prni¼1 rp i i¼1

pijk

ð8Þ ð9Þ

where rn is the number of columns in the reference alignment, rpi is the score for the ith column in the reference alignment. Total Column Score (TC-Score): Defines a score ci for the ith column in the alignment: ci ¼ 1 if all the residues are aligned with the reference alignment; otherwise, ci ¼ 0. The TC-score for the MSA is then calculated as formula (10), TC ¼

Prn

i¼1 ci

n

ð10Þ

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3 The Novel Binary PSO for MSA (NBPSO-MSA) In this section, the NBPSO-MSA is described. To adapt the idea of PSO to solve MSA, NBPSO-MSA applies a novel binary representation scheme for MSA, and redefines the term “position” and “velocity” and all related operators for the discrete space according to MSA. In order to overcome the disadvantages of PSO, diversity index is proposed based on Hamming Distance, mutation is activated when the diversity index dt falls below the threshold dlow. The pseudo code of NBPSO-MSA is showed in Algorithm 1. 3.1

Representation Scheme

For MSA, two problems are significant: (1). How many gaps should be inserted; (2). Which locations of particle should be gaps. According to problem (1), in this paper, OX-bench and BAliBASE benchmarks are used, which provided with reference alignments, the lengths of the aligned sequences are consistent with those in reference alignments. For problem (2), the key problem of MSA is how to detect locations of gaps that satisfies conditions (1)–(3). Exhaustive alignment covers exponential schemes of the system. With exponential time and space complexities, alignment becomes prohibitive as the number of sequences and the lengths of the sequences increase. Moreover, only a few locations are significant, in other words, exploring key locations can lead to rapid convergence. From this point of view, in this paper, each particle represents a candidate optimal MSA, a population of m particles search in parallel for the optimal MSA. Table 1. State of pbestij  xij & gbestij  xij X 0 1

pbest or gbest 0 1 0 1 −1 0

Table 2. State of jpbestij  xij j þ jgbestij  xij j

pbest

1 0

X 1 0 1 1 2

0 2 1 1 0

1 0 1 0

gbest gbest

The position of each particle is encoded as a matrix Xsn , where s is the number of sequences to be aligned, n is the length of the aligned sequences, its components xij , indicates whether a gap is inserted or not at the jth location of the ith sequence, with a range of {0, 1}. The velocity of each particle is encoded as a matrix Vsn of the same type as the position matrix, and its components vij estimates the probability of the change of the corresponding component in position matrix, with a range of [0,1]. The range of the pbestij  xij and the gbestij  xij both are {−1,0,1}, as shown in Table 1, “−1” indicates that the pbestij or gbestij is not gap while xij do, in this case, vij should be large to increase the probability that xij switch to 0; “1” indicates that the pbestij or gbestij is gap while xij do not, in this case, xij should switch to “1”, vij should also be increased to make itself consistent with the optimal particle; differently, “0” indicates that the pbestij or gbestij and xij are both gap or both not, in that case, xij has been consistent with pbestij and gbestij , then vij should be decreased, preventing it from going away from the optimal.

A Novel Binary Particle Swarm Optimization for Multiple Sequence Alignment

3.2

19

Velocity Updating

In NBPSO, velocity is a matrix Vsn , whose components represent the probabilities that, the corresponding components in Xsn switch from “1” to “0”, or “0” to “1”. As is presented in Table 1, the states of pbestij  xij or gbestij  xij is 1 or 0. Furthermore, the states of jpbestij  xij j þ jgbestij  xij j is presented in Table 2. 0 conveys the fact that, the component of the current particle is consistent with both the pbest and gbest, change is not needed; 1 indicates that the current particle is consistent with only one of the pbest and gbest, change is needed; while 2 represents the fact that the current particle is inconsistent with pbest or gbest, urgent change is needed. Consequently, velocity is designed to represent probability of the change of the particle, it is firstly quantified by the distance between the individual and the optimal, then, calculated by formula (11), finally serves as a probability that stimulates the particle, guides the swarm to fly to gbest. Velocity and position updating rules are as followed (11) and (12). vt þ 1 ¼ wt vt þ c1 r1 jpbestt  xt j þ c2 r2 jgbest  xt j  if ðrandðÞ \ Sðvt ÞÞ then xt þ 1 ¼ 1  xt else xt þ 1 ¼ xt

ð11Þ ð12Þ

where rand() is a quasi-random number uniformly distributed in [0,1], S (v) is as formula (13), wt linearly decreases from 0.9 to 0.4, c1 ¼ 2, c2 ¼ 2, vmax ¼ 6. SðvÞ ¼ Sigmoid ðvÞ ¼

1 1 þ ev

ð13Þ

Algorithm 1 NBPSO-MSA 1: 2: 3: 4: 5: 6: 7: 8:

3.3

Initialize while the termination condition is not met, do Each sub-swarm operates PSO independently Mutation Migration t=t+1 end while return

Position Updating

As velocity is designed to represent the probability of the change of the current particle, acts as a compass to guide the swarms. After velocity updating, particles update their position value by formula (12). The larger the velocity, the more probably it will switch, either from “1” to “0” or “0” to “1”; the smaller the velocity, the more probably it remains the same. After all components are updated, the particle probably becomes illegal. That is, some particles fail to satisfy conditions (1)–(3) in three ways, the

20

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number of “1” components from the same row exceeds maximum, the number of “1” components from the same row is less than needed, the third is, some columns are all “1”. In order to overcome these, an auxiliary particle re-initialization strategy based on probability theory is proposed for MSA. As the populations evolve, pbest and gbest become more and more optimal. There must be some significant locations that frequently contribute to high fitness, e.g., type A locations which frequently keep “1” in gbest. For this sake, type A location is defined as definition 1. Position re-initialization is designed as Algorithm 2. Algorithm 2 Position re-initialization 1: for each particle 2:

do

for the jth sequence

of particle

3:

the indexes of

4: 5:

for each base or gap k if

do

in descending order

6: 7: 8: 9:

end if end for if sum(

then

10:

d=

11:

Set={locations of 1 in

12: 13:

q=size(Set) Randomly generate a permutation

14: 15:

for each dimension k

16:

do

end for

17: 18:

end if if sum(

19:

d

then

20:

Set={locations of 0 in

21:

q=size(Set)

22: 23:

Randomly generate a permutation k=1,kk=1

24: 25:

while k jS  jS2 jj  jjS1 j  jS2 jj, if D1 and D2 are equal, then jS  jS1 jj = jS  jS2 jj  jjS1 j  jS2 jj, so jS  jS1 jj  jS  jS2 jj  jjS1 j  jS2 jj. (3) According to the sum of the two sides is smaller than the third, jjS1 j  jS1 jj\jS1 S2 j, if D1 and D2 are equal, then jjS1 j  jS1 jj¼jS1 S2 j, so jjS1 j  jS1 jj  jS1 S2 j. Thus it is proved that P[S1 2S]/P[S2 2S]  exp (eÞ. It is proved that the IDP-OPTICS algorithm satisfies (e, 0)-differential privacy. □

5 Experimental Evaluation 5.1

Example Simulation

Let us illustrate with a concrete example: We assume that the neighborhood radius E = 2, the point within the minimum neighborhood radius MinPts = 5, and there are 11 points A(10,11), B(10,10), C(11,11), D(10,12), E(10,13), F(11,12), G(12,12), H(100,200), I(100,201), J(101,200), K (200,300) in the data set and calculate the Euclidean distance between points. It can be known that A, C, D, and F points are core objects; B, E, and G belong to the points in their clusters; H, I, and J are not core objects, but the compression condition is satisfied at a radius of E = 2. Therefore, H, I, and J can be classified into one cluster, but K is a discrete point because it does not satisfy the compression condition. We use adjacency tables to store. From the adjacency list shown in Fig. 1 we can see that A, B, C, D, E, F, G are high frequency points and H, I, J, K are low frequency points.

Fig. 1. Example of using IDP-OPTICS clustering.

5.2

Analysis of Experimental Data for Improved Algorithms

The configuration of computer is Windows 7, AMD Athlon(tm) II X4 640 processor and 4 GB of memory. We ran the experiment on MATLAB R2012 (a), Weka 3.6 version, Eclipse 3.7.0 and the data set is from the credit-g.arff data set in the data folder of Weka 3.6 version, Relation: german_credit; Instances: 1000; Attributes: 21.

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We use the DP-DBSCAN, the IDP-OPTICS and the OPTICS-based differential privacy algorithm to perform multiple experiments on 1000 records in the credit-g.arff data set. We take values for E and MinPts {(0.1,5), (1,10), (5,10), (10,50), (0.5,8), (2,10), (10,20), (20, 50)} and take the average of 150 experiments. Through the noise processing of high-frequency data and low-frequency data, by experience, we allocation the e as ei :ej = 20:1. So ei = 0.475, ej = 0.025. Comparative analysis is shown in Fig. 2. When IDP-OPTICS achieves relatively small values in E and MinPts, the clustering is better in terms of locality; overall, it is better than differential privacy and DP-DBSCAN algorithm based on OPTICS clustering.

Instances

Clustering Result Comparison 1200 1000 800 600 400 200 0

DP-DBSCAN

IDP-OPTICS

OPTICS-based differen al privacy

1 2 3 4 5 6 7 8 The Same Value of E, MinPts and ε

Fig. 2. OPTICS-based differential privacy and IDP-OPTICS clustering results under the same E, MinPts and e.

In the case where (E, MinPts, e) are respectively taken {(0.1,5,0.1), (1,10,0.3), (2,15,0.5), (4,18,1.0), (6,20,1.2), (10,20,1.3), (15,25,1.5), (20,25,20)}, we add the same-variance Laplace noise to the high-frequency data and add the heteroscedastic Laplace noise to the low-frequency data. The high-frequency and the low-frequency data still add noise in a ratio of 1:20. Their relative errors are shown in Fig. 3. From Fig. 3, we can clearly analyze that when adding noise with the same variance method, the more clustered high-frequency data, the greater the relative error; while the heteroscedastic method is used to add noise and the overall error is relatively small. Therefore, adding the heteroscedasticity method is better than the same variance method and has better performance in processing data.

IDP - OPTICS: Improvement of Differential Privacy Algorithm

779

Add the same/heteroscedastic relative error comparison

Relative Error

1.5 1 Add the same variance noise to IDP-OPTICS

0.5

Add the heteroscedas c noise to IDP-OPTICS

0 0.1 0.3 0.5 1 1.2 1.3 1.5 2 Privacy Parameter ε

Fig. 3. Comparison of IDP-OPTICS relative errors for adding two different modes of noise.

In the case where e = 0.5 and (E, MinPts) are respectively taken {(0.1,5), (1,10), (3,13), (5,15), (10,15), (15,20), (20, 25)}, we use IDP-OPTICS, DP-DBSCAN and OPTICS-based differential privacy algorithm. After several experiments, 150 experimental averages were obtained, and the time comparison of the three was as shown in Fig. 4. The OPTICS-based differential privacy algorithm is time-consuming compared to DP-DBSCAN and IDP-OPTICS. Since IDP-OPTICS adopts the adjacency list form, it is superior to the DP-DBSCAN algorithm in time complexity. Overall, the IDPOPTICS algorithm is still feasible.

Running Time Comparison Running Time (s)

0.8 0.6 OPTICS-based differen al privacy

0.4

IDP-OPTICS

0.2 DP-DBSCAN

0 0.1 0.3 0.5 1 1.2 1.3 1.5 2 Privacy Parameter ε

Fig. 4. Three algorithms time consumption.

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The credit-g.arff data set is evenly distributed. In the case of the central position parameter l = 0, L = 0.5, the probability of the attacker’s success in querying the result data set is q¼ 1  e^ ð  e=2Þ=2. The upper bound of the e satisfies e  ln2(1 - qÞDf/L can ensure the privacy security of data: It can be calculated that the upper bound of e is 0.42. In the case of e ¼ 0:42, we select the credit_history, employment, personal_status, job and other information in the credit-g.arff data set, and use the IDP-OPTICS algorithm for processing. Through compare and analyze with the original data, as shown in Fig. 5, the abscissa is a division distance according to a histogram of a constant width, and the ordinate is a clustering result of one thousandth. Raw data and private data processed by DP-OPTICS algorithm

Instances The width of the histogram.

Fig. 5. Comparison of the original data with the privacy protection data processed by the IDPOPTICS algorithm.

6 Conclusions In this paper, we propose IDP-OPTICS, a differential privacy algorithm which is based on the DP-DBSCAN and the differential privacy algorithm based on OPTICS clustering. It has higher data availability, better data set clustering and can effectively balance the distribution of e. Considering the probability of users querying data and determining the maximum e that an attacker can successfully attack dataset records, we designed a heteroscedastic noise addition scheme for IDP-OPTICS to add noise to high frequency data and low frequency data. The privacy analysis proves that IDP-OPTICS satisfies differential privacy. Experiments on real data show that IDP-OPTICS is outperform other methods and achieves both utility and privacy required. In future research work, we will consider applying the IDP-OPTICS algorithm to the recommendation system, which will broaden the application scope of differential privacy. Acknowledgements. This work is supported by the National Science Foundation of China under Grant No. 61862007, Guangxi Natural Science Foundation under Grant No. 2018GXNSFAA138147, Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence under Grant No. 2016CSCI09, and Teaching Reform Project of Guangxi University for Nationalities under Grant No. 2016XJGY33 and No. 2016XJGY34.

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References 1. Dwork, C.: Differential privacy. In: Proceedings of the 33rd International colloquium on Automata, Languages and Programming, pp. 1–12 (2006) 2. Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14 3. Mcsherry, F., Talwar, K.: Mechanism design via differential privacy. In: IEEE Symposium on Foundations of Computer Science, Providence, RI, USA, pp. 94–103 (2007) 4. Hay, M., Rastogi, V., Miklau, G., et al.: Boosting the accuracy of differentially private histograms through consistency. Proc. VLDB Endow. 3(1–2), 1021–1032 (2010) 5. Peng, S., Yang, Y., Zhang, Z., et al.: DP-tree: indexing multi-dimensional data under differential privacy (abstract only), pp. 864–864. ACM (2012) 6. Xiao, Y., Xiong, L., Yuan, C.: Differentially private data release through multidimensional partitioning. In: Jonker, W., Petković, M. (eds.) SDM 2010. LNCS, vol. 6358, pp. 150–168. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15546-8_11 7. Xiao, X., Wang, G., Gehrke, J.: Differential privacy via wavelet transforms. IEEE Trans. Knowl. Data Eng. 23(8), 1200–1214 (2010) 8. Xiong, P., Zhu, T.Q., Jin, D.W., et al.: A differential privacy protection algorithm for decision tree construction. 31(10), 3108–3112 (2014) 9. Wu, Y.J.: Privacy Protection Data Release: Models and Algorithms, pp. 258–262. Tsinghua University Press, China (2015) 10. Wang, H., Ge, L.N., Wang, L.Y., et al.: Survey of differential privacy for data histogram publication. Appl. Res. Comput. 34(6), 1609–1612 (2017) 11. Blum, A., Dwork, C., Mcsherry, F., et al.: Practical privacy: the SuLQ framework. In: Twenty-Fourth ACM Sigact-Sigmod-Sigart Symposium on Principles of Database Systems, DBLP, Baltimore, Maryland, USA, vol. 6, no. 13–15, pp. 128–138 (2005) 12. Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011) 13. Wu, W.M., Huang, H.K.: Research on DP-DBScan clustering algorithm based on differential privacy protection. Comput. Eng. Sci. 37(4), 830–834 (2015) 14. André, L.C.M., Brito, F.T., Linhares, L.S., et al.: DiPCoDing: a differentially private approach for correlated data with clustering. In: International Database Engineering & Applications Symposium. ACM (2017) 15. Ni, L., Li, C.: DP-MCDBSCAN: differential privacy preserving multi-core DBSCAN clustering for network user data. IEEE Access 6, 21053–21063 (2018) 16. Nissim, K., Raskhodnikova, S., Smith, A.: Smooth sensitivity and sampling in private data analysis. In: Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing, pp. 75–84. ACM (2007) 17. Jagadish, H.V., Poosala, V., Koudas, N., et al.: Optimal histograms with quality guarantees. In: Gupta, A., Shmueli, O., Widom, J. (eds.) VLDB 1998, Proceedings of the 24th Internationa1 Conference on Very Large Data Bases, pp. 275–286. Morgan Kaufmann, New York (1998) 18. Poosala, V., Haas, P.J., Ioannidis, Y.E., et al.: Improved histograms for selectivity estimation of range predicates. ACM Sigmod Rec. 25(2), 294–305 (1999)

Author Index

Abd, Dhafar Hamed II-580 Ahmad, Tanvir III-99 Alafandi, Omar II-580 Alaskar, Haya III-784 Ali, Sarwar III-32 Al-Jumeily, Dhiya I-77, II-580, III-593 Alloghani, Mohamed II-580 Almarshed, Fatma III-784 Alsaadi, Mahmood II-580 Alsaadi, Mohmood III-581 Al-Taei, May I-77 Alzhrani, Nada III-784 An, Jie I-520 Arévalo Zenteno, Ma. Dolores II-758 Ayala Niño, Daniel II-758 Azeem, Farrukh II-505 Babiloni, Claudio III-692 Bai, Yu-Cai I-238 Baker, Thar I-77 Balogh, Zoltán I-400, III-142 Bao, Changchun I-111 Bao, Wenzheng II-717 Bao, Xianyu III-455 Barsotti, Michele III-739 Battini, Elena III-704 Bevilacqua, Vitoantonio II-639, III-692, III-704, III-716, III-727, III-739, III-751 Bin, Yannan II-294 Bisong, O. Ekaba III-201 Brunetti, Antonio II-639, III-692, III-727, III-751 Buongiorno, Domenico III-704, III-727, III-739, III-751 Cai, Ci-Yun II-688 Cai, Jie III-479 Cai, Li II-81 Cai, Yong-Ming II-213 Cai, Yunfeng I-197 Camana, Mario R. I-634, II-1 Camardella, Cristian III-739 Cao, Jingyuan III-629

Cao, Qing II-747 Cao, Yan II-430 Cao, Yi I-388 Cao, Zhi II-235 Cascarano, Giacomo Donato II-639, III-727, III-751 Catania, Valentina III-692 Chan, Keith C. C. II-151 Chang, Jie I-45 Chang, Zheng III-347 Che, Xiangyang I-25 Chen, Bolin II-105 Chen, Chao I-88 Chen, Debao I-35 Chen, Duke III-327 Chen, Jinzhuo III-640 Chen, Jun I-228 Chen, Kang II-747 Chen, Miao I-238, I-529, II-93 Chen, Ming III-617 Chen, Nansheng II-440 Chen, Peng I-274, I-543, I-553, II-558, III-443, III-468 Chen, Wanyi II-38 Chen, Xiao I-197 Chen, Yan-Ping I-326, III-399 Chen, Yiming I-367 Chen, Ying-Jie II-736 Chen, Yue I-88 Chen, Yuehui I-388, II-162 Chen, Yue-Hui II-736 Chen, Yuxuan I-470, I-481 Chen, Zhan-Heng I-166, II-313, II-406, II-460, III-369 Chen, Zhexin III-606 Cheng, Fan II-26 Chi, Der-Jang III-327 Chia, E. T. III-617 Chong, Yanwen I-54 Choraś, Michał I-749 Chu, Chien-Chou III-327 Cui, Xue III-1 Cui, Zhen III-318 Curbelo Montanez, Casimiro A. III-593

784

Author Index

D. Jalili, Laura II-758 Dai, Bing III-468 Dawood, Omar A. II-580 de Arquer Rilo, Javier I-77 De Feudis, Irio II-639, III-727, III-751 De Meo, Claudio III-692 Deb, Kaushik II-602 Debitonto, Francesco Saverio III-727 Deng, ChuYan II-81 Ding, Jiwen II-170 Ding, Li II-303, II-394 Ding, Xiao II-628 Ding, Xin II-247 Ding, Yijie II-418, II-430, II-468 Ding, Zuohua I-501 Draicchio, Francesco III-704 Du, Changcun II-13 Du, Hongyi III-555 Du, Ji-Xiang III-568 Du, Yanlian II-13 Du, Yi-Chen III-771 Egger, Jan III-716 Fan, Chencheng I-719 Fang, Tao I-553 Fang, Weichuang I-131 Fengli, Zhou II-480, III-503 Ferri, Raffaele III-692 Frisoli, Antonio III-739 Fu, Qiming II-170 Fukuda, Hisato III-9, III-32, III-43, III-74 Gao, Li II-105 Gao, Ming-Ming III-318 Gao, Ying-Lian III-318 Gao, Yuanyuan I-739 Gao, Zhen II-265 Garcia, Carla E. I-634, II-1 García-Lamont, Farid II-569 Ge, Lina II-770, III-109 Ge, Xin II-283 Gelardi, Donatella III-692 Gesualdo, Loreto II-639, III-727 Ghaleb, O. III-130 Giansanti, Daniele III-704 Gong, DaoQing II-81 Gong, Lejun III-235 Grigioni, Mauro III-704 Gsaxner, Christina III-716

Gu, Hui-Min II-678 Gu, Kaiming II-61 Gu, Naijie I-45 Guerriero, Andrea III-727 Guo, Chen III-660 Guo, Chuang III-432 Guo, Fei II-468 Guo, Pengfei I-262 Guo, Zhen-Hao I-166, II-313, II-406, II-460 Ha, Cheolkeun I-593, I-604, II-615 Ha, Cheolkuen III-244, III-257 Haider, Sajjad II-505 Halloran, Brendan I-583, III-86 Han, Jiayan II-170 Hang, Jie I-274 He, Bifang II-383 He, Dengxu I-707 He, Jingxuan II-13 He, Rongmao III-306 He, Zhonghua II-770, III-109 Hoang, Duy Tang III-283 Hong, Zijing III-555 Hu, Haoruo III-617 Hu, Jing II-440 Hu, Lun II-151, II-344 Hu, Peng-Wei I-166 Hu, Pengwei II-138, II-151 Hu, Rong I-665, I-676, II-669, II-678 Hu, Shaohan I-448 Hu, Yugu II-770, III-109 Huang, De-Shuang I-304 Huang, Hongmei II-418, II-430 Huang, Jian II-383 Huang, Jin-Long III-568 Huang, Mengmeng III-455 Huang, Qian-Jing I-187, III-399 Huang, Qinhua III-535 Huang, Tao I-617, I-626 Huang, Wei III-479 Huang, Yu-An II-138 Huang, Yu-an II-151 Huang, Zhengyu I-436 Huang, Zhenkun I-88 Huang, Zhi-An II-138 Huang, Zhi-Kai I-320, II-366, II-374, III-1 Hui, Ying II-272 Hurst, William III-593 Hussain, Abir I-77, III-411, III-784 Hussain, Abir Jaafar II-580

Author Index

Ishida, Takashi II-226 Islam, Md. Matiqul III-9 Jabeen, Hajira I-424 Ji, Xiaoyu I-653 Jia, Maoshen I-111 Jia, Zhigang I-197 Jia, Zhijuan I-346 Jiang, Han-Jing I-166, III-369, III-392 Jiang, Jingzhou III-651 Jiang, Lixu II-383 Jiang, Shukun III-651 Jiang, Wei I-356, II-333 Jiang, Xue I-529, II-93 Jiang, Zhongting II-162 Jiao, Zhihao I-312 Jin, Huai-Ping II-678 Jing, Xiaozhu II-333 Jo, Kang-Hyun II-592, II-602 Jo, Kanghyun III-762 Kadhim, Inas III-86 Kang, Hee Jun III-283 Kang, Hee-Jun III-20, III-155, III-167, III-294 Kang, Juanjuan II-383 Kang, Peiyu I-144 Ke, Bowen III-213 Khalaf, Mohammed II-580, III-581 Khanam, Tahmina II-602 Kim, Myung Kyun III-118 Kleanthous, Natasa III-411 Kobayashi, Yoshinori III-9, III-32, III-43, III-74 Kohútek, Michal I-400 Kong, Shan I-653 Kong, Yanyan II-190 Koo, Insoo I-634, II-1 Koprda, Štefan I-400, III-142 Kozik, Rafał I-749 Kuno, Yoshinori III-9, III-32, III-43, III-74 Lam, Antony III-9, III-32, III-43, III-74 Le, Phu-Nguyen III-20 Le, Quang Dan III-294 Le, Tien Dung III-155 Le, Van-Tien III-271 Lee, Chang Boon Patrick III-660 Lee, Hong-Hee II-71, III-271 Lee, Youlkyeong III-762

Lehmann, Jens I-424 Lei, Mengyi I-739 Lejun, Gong II-51 Lemma, Ruggero III-727 Leng, Rui I-617, I-626 Leung, Henry II-151 Li, Andong I-304 Li, Bing III-795 Li, Bo I-296, I-337 Li, Cong III-606 Li, Feng III-318 Li, Gang I-436 Li, Haozhe II-190, II-535 Li, HongYa II-81 Li, Huijun III-225 Li, Huinian II-333 Li, Jiaxin II-535 Li, Jiyun I-686, II-747 Li, Junyi II-333 Li, Li-Ping I-166, II-138, II-344, II-406 Li, Li-Ting II-213 Li, Min II-529 Li, Pingrong III-545 Li, Shang-Han II-669 Li, Shaochun II-151 Li, Suiyi I-470, I-481 Li, Wei I-617 Li, Xiangchen I-131 Li, Xiaohang III-179 Li, Xiaoqing I-250 Li, Xingyi II-529 Li, Yafei III-381 Li, Yang I-665, II-628, III-682 Li, Yinqi III-381 Li, Yi-Ran III-392 Li, Yuanyuan II-177 Li, Zheng-Wei II-138, II-453 Li, Zhengwei II-283 Liang, Jun I-460 Liang, Liang I-377 Liang, Yingshi III-606 Liang, Yi-wen III-491 Liang, Yunyu III-651 Lin, Chuanwen I-45 Lin, Guan Ning I-529, II-93 Lin, Hong-Li I-228 Lin, Jingsheng I-686 Lin, Tao II-628 Lin, Xiaoli II-324 Lin, Zehui I-367

785

786

Author Index

Ling, Huajing II-170 Lipeng, Zhang II-51 Lisitsa, Alexei III-581 Liu, Bing I-501 Liu, Bo I-665, II-333 Liu, Chang I-156, III-189 Liu, Chuanyi I-412, III-189 Liu, De-Huai II-688 Liu, Dong I-337 Liu, Guoliang III-528 Liu, Guoquan I-367 Liu, Jian II-201 Liu, Jin-Xing III-318 Liu, Juan II-116 Liu, Jun III-491 Liu, Mi-Mi II-213 Liu, Min II-394 Liu, Ping II-303, II-394 Liu, Qi II-717 Liu, Sanrong II-708 Liu, Tengjun III-640 Liu, Xiaoming I-337 Liu, Xinxin I-346 Liu, Yanmin I-617, I-626 Liu, Yudong I-460 Liu, Yue II-235 Liu, Yunxia I-653 Liu, Yun-Xia II-736 Liu, Zhi-Ping II-517 Liu, Zhongbao III-337 Long, Hongming III-468 Lu, Jing I-436 Lu, Kun II-558 Lu, Miao I-707 Lu, Panpan II-558 Lu, Weizhong II-170, II-418, II-430 Lu, Xinguo II-303, II-394 Lu, Xue-Qin III-771 Lu, Yi II-177 Lu, Yonggang I-144 Luan, Yushi III-347, III-358 Luo, Jiawei II-127, III-479 Luo, Ping II-177 Luo, Qifang I-719, I-739 Luo, Wuyang I-470, I-481 Lv, Li I-707 Ma, Anqi II-493 Ma, Jinwen I-1, I-131, I-250, I-520 Ma, Yinglong III-99

Maccioni, Giovanni III-704 Magdin, Martin I-400, III-142 Mai, Dinh Loc III-118 Mao, Guo II-354 Mao, Hao I-412, III-189 Maqbool, Fahad I-424 Mason, Alex III-411 Mazzoleni, Stefano III-704 Mei, Kuizhi III-381 Meng, Huazhi II-770 Meng, Jun III-347, III-358 Meng, Qing-Fang II-736 Mi, Jian-Xun I-284 Miao, Qiumai II-394 Miao, Zhuang II-628, III-682 Min, Wenwen II-116 Moschetta, Marco II-639 Murciego, Luis Pelaez III-739 Mutsuddy, Aapan II-602 Nagpal, Abhinav III-515 Nasir, Mohsin Ali II-505 Nawaz, Samia II-505 Nguyen, Duy-Long II-71 Nguyen, Khoa Dang II-615 Nguyen, Trong-Thang II-615 Nguyen, Van-Cuong III-167 Ni, Jian-Cheng II-265 Nie, Ru II-283 Niu, Ben III-660, III-672 Niu, Yanbiao I-729 Oommen, B. John III-130, III-201 Ouyang, Aijia I-617, I-626 Ouyang, Weimin III-535 Pan, Diqi I-144 Pan, Shaoming I-54 Pan, Wei I-697 Pan, Weihao II-93 Panerai, Simonetta III-692 Pang, Mo III-629 Pawlicki, Marek I-749 Peng, Chengwei I-54 Peng, Lu I-98 Peng, Xiao-Xiao III-568 Peng, YuZhong II-81 Pepe, Antonio III-716 Pesce, Francesco III-727 Phyo, Aye Su III-43

Author Index

Ping, Yuan II-333 Posteraro, Federico III-704 Premaratne, Prashan I-583, III-86 Pu, Yi-Fei I-238 Qasaimeh, Malik III-581 Qian, Bin I-665, I-676, II-669, II-678 Qin, Jiafeng III-555 Qin, Wei III-1 Qin, Xiao I-312, II-81, II-366, II-374, III-1 Qiu, Hai-Yan II-213 Qiu, Jianfeng II-26 Rahman, Md Arifur II-1 Rahman, Md. Azizur I-1 Rangel-Cortes, Juan-José II-569 Rao, Xiaosong I-460 Rao, Yundi I-707 Razzaq, Saad I-424 Ren, Fei I-460 Ren, Qiang-Qiang III-65 Rong, Yuelin III-640 Ronggen, Yang II-51 Rossini, Michele III-727 Rugamba, Jules Pascal Gilles III-118 Ruíz Castilla, José S. II-758 Ruiz-Castilla, José-Sergio II-569 Rundo, Francesco III-692

Su, Wei I-412, III-189 Sun, Jiamin II-535 Sun, Li I-686, II-747 Sun, Yi I-436 Sun, Yueru I-284 Sun, Zhan-li I-208 Sylos Labini, Mauro III-716 Taguchi, Y-h. I-565 Tan, Lijing III-606, III-617, III-640, III-651 Tanebe, Toshitaka II-226 Tang, Chao I-326, III-399 Tang, Chengmei I-739 Tang, Heng III-660 Tang, Jijun II-468 Tang, Qing III-762 Tang, Xiong II-770 Tang, Xu-Qing II-547 Tang, Zhonghua I-729 Tian, Wei-Dong III-65 Tian, Weidong I-175, III-432 Tian, Zhen-Bo II-547 Tong, Xuefeng III-53 Tran, Huy Q. I-593, I-604 Tripathy, Balakrushna III-515 Trueba-Espinosa, Adrián II-569 Tu, Nguyen Hoang II-127 Uddin, Md. Kamal

Shang, Huijie II-162 Shang, Li I-208, I-312, I-320, II-366, II-374, III-1 Shang, Lijuan I-262 Shang, Mingli II-662 Shang, Xuequn II-105 Shang, Ziwei II-747 Sharma, Vasudev III-515 Shi, Baorong I-460 Shi, Fanhuai III-53 Shi, Wenhao III-347 Shiranthika, C. I-583 Sneddon, Jennifer III-411 Song, Chuandong II-700 Song, Liang-Tu I-326, III-399 Song, Weichen I-529, II-93 Song, Xi III-617 Song, Zhengwei II-418 Su, Jie I-388 Su, Shuhui II-201

787

III-74

Van Nguyen, Tan III-244, III-257 Vial, Peter James III-86 Vo, Anh Tuan III-155 Vo, Anh-Tuan III-167 Wallner, Juergen III-716 Wan, Si-Zhe III-1 Wan, Xiaohua I-448 Wang, Bao-Cheng I-296 Wang, Bing I-274, I-543, I-553, II-558, III-443, III-468 Wang, Bo III-235 Wang, Chao I-14 Wang, Dailun II-374 Wang, Dong II-162 Wang, Fei II-61, II-493 Wang, Guangyu III-443, III-468 Wang, Haifeng II-708 Wang, Haitao II-190

788

Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wang,

Author Index

Hong II-770, III-629, III-672 Hong-Bo II-272 Jiabao II-628, III-682 Jianxin II-529 Jiao II-535 Jing II-247, II-256, II-272 Jixiao III-682 Lei II-138, III-392 Li I-121 Lijuan III-109 Ling I-676, II-669, II-678 Pengchuan I-719 Qi I-111 Shaocheng I-337 Shu-Lin I-228, II-235, II-354 Shulin II-394, III-479 Tao II-344 Weidi I-529, II-93 Weipeng I-686 Wenyan II-558, III-443, III-468 Wenyu III-528 Xianfeng I-511 Xian-feng I-646 Xiao III-381 Xiao-Feng I-187, I-326, III-399 Xinyu II-303 Ya I-131 Yadong II-333 Yan-Bin II-313, II-344, II-406, II-460, III-392 Wang, Yang II-283 Wang, Yanjiao I-25 Wang, Yibo III-225 Wang, Yichen III-235 Wang, Yihua II-38 Wang, Yu-Tian II-247, II-265 Wang, Yuting III-53 Wang, Zhen I-511, I-646 Wang, Zhongmin I-729 Wang, Zhuo II-717 Wei, Hangui III-109 Wei, Pi-Jing II-272 Wei, Zhao II-61 Wekesa, Jael Sanyanda III-358 Wen, Lihua II-592 Weng, Xi II-651 Wong, Leon II-344 Wu, Di I-304 Wu, Fang-Xiang II-177, II-529 Wu, Gongqing III-455

Wu, Wu, Wu, Wu, Wu, Wu, Wu, Wu, Wu, Wu,

Hongjie II-170, II-418, II-430 Jia-Yu III-771 Jingfeng I-520 Li-Ping I-665 Liyan II-770 Ningning I-412 Qing-Wen II-265 Ruiying II-190 Xue III-771 Yunzhi III-528

Xia, Chuan III-528 Xia, Haibang I-501 Xia, Jiazhu III-455 Xia, Jun-Feng II-247, II-272 Xia, Junfeng I-575 Xia, Yi I-543 Xiang, Feng-Hong I-676 Xiang, Ju II-529 Xiang, Tiange I-460 Xiang, Zhehong I-719 Xiao, Binggang III-179 Xiaoli, Lin II-480, III-503 Xiaolin, Liu II-51 Xie, Jiang II-190, II-535 Xie, Yang III-432 Xing, Lei II-628 Xu, Erfeng I-346 Xu, Hao II-383 Xu, Qingzhe II-333 Xu, Ran I-14 Xu, Ruoqing II-294 Xu, Shoutao I-65 Xu, Xiao-Tao I-543 Xu, Yi II-26 Xu, Zhongfu III-306 Xue, Fangxia III-306 Xue, Li-Yuan I-491 Xuemin, Yang II-51 Yan, Hong-Fang II-688 Yan, Kunpeng III-423 Yan, Rui I-460 Yang, Ai II-669 Yang, Chao II-256 Yang, Fan I-98, I-356, III-179, III-491 Yang, Fuzhang II-535 Yang, Hua-yong I-356 Yang, Jiansheng I-250

Author Index

Yang, Ronggen III-235 Yang, Sheng III-479 Yang, Su I-436, I-470, I-481 Yang, Xiaojun III-306 Yang, Xuesen III-672 Yang, Yang II-736 Yang, Yanxia III-213 Yang, Yanyan I-346 Yang, Yi III-555 Yao, Jia II-51 Yao, Meng-meng II-651 Ye, Hong I-45 Ye, Hongyan I-626 Ye, Jianxiong III-795 Ye, Meiqin II-493 Ye, Minquan I-45 Ye, Zhi III-606 Ye, Zhixia III-225 Yi, Hai-Cheng II-313, II-344 Yi, Hai-cheng II-460 Yi, Wenhui I-367 Yin, Jiayi II-383 You, Zhu-Hong I-166, II-138, II-313, II-344, II-406, II-453, II-460, III-369, III-392 You, Zhuhong II-151, II-283 Yu, Qiming I-653 Yu, Wenbo II-366 Yuan, Chang-An I-312, I-320, II-366, II-374, III-1 Yuan, Jun II-726 Yuan, Lian I-617, I-626 Yue, Yi III-528 Yue, Zhenyu II-294 Yun, Lijun III-225 Yun, Zhixi II-383 Zeng, Rong-Qiang I-491 Zhan, Zhao-Hui II-453 Zhang, An-Jie II-736 Zhang, Boheng I-448 Zhang, Boyan I-218 Zhang, Changhao II-61 Zhang, Chen I-326, III-399 Zhang, Dexiang I-274 Zhang, Enze I-448 Zhang, Fa I-448, I-460 Zhang, Fan I-218 Zhang, Hong-Bo III-568 Zhang, Hua-li I-98, I-356 Zhang, Jiaming I-111

789

Zhang, Jingchao III-225 Zhang, Jingjing I-54 Zhang, Jun I-274, I-543, I-553, II-558, II-651, III-443, III-468 Zhang, Jun-Feng II-235 Zhang, Kai III-528 Zhang, Lei II-26, II-201 Zhang, Li II-333 Zhang, Lijun I-304, I-312 Zhang, Liling III-795 Zhang, Lingling I-460 Zhang, Min-Xia II-688, III-771 Zhang, Ming-Wen II-265 Zhang, Peng III-347 Zhang, Qinhu II-374 Zhang, Shan-wen I-646 Zhang, Shanwen I-511 Zhang, Sijia I-575 Zhang, Wu II-190 Zhang, Xiaoci I-45 Zhang, Xiaolong II-440 Zhang, Xiaoxiao(X.X.) II-201 Zhang, Xin I-156 Zhang, Xinfeng III-423 Zhang, Xingyi I-14 Zhang, Xinhong I-218 Zhang, Xiyu II-294 Zhang, Yangshuo III-682 Zhang, Youhua III-528 Zhang, Yuan-Yuan II-256 Zhang, Zishuang II-517 Zhao, Le I-575 Zhao, Meixiang I-197 Zhao, Wenhe II-127 Zhao, Yan II-283 Zhao, Yongqi I-35 Zhao, Zerong III-109 Zhao, Zhong-Qiu I-65, I-175, III-65 Zhao, Zhongqiu III-432 Zheng, Canhua III-651 Zheng, Chun-Hou II-247, II-256, II-265, II-272 Zheng, Chunhou I-460 Zheng, Jian-Feng III-568 Zheng, Kai II-453, II-460, III-369, III-392 Zheng, Yang I-320 Zheng, Yi-Fan I-676 Zheng, Yuting III-640 Zheng, Z. I-583 Zhong, Albert I-131

790

Author Index

Zhou, Zhou, Zhou, Zhou, Zhou, Zhou, Zhou, Zhou, Zhou, Zhou,

Fengli II-324 Guo I-697 Haoran III-358 Hongjuan III-432 Jian I-187 Longwei II-440 Tongxu I-121 Yan I-208 Yong II-453 Yongquan I-697, I-719, I-729, I-739

Zhu, Zhu, Zhu, Zhu, Zhu, Zhu, Zhu, Zhu, Zou, Zou,

Fangfang II-116 Hui-Juan II-313 Lida II-726 Liyao I-156 Qian I-377 Xijing I-175 Zhenghao II-303, II-394 Zuolang II-493 Feng I-35 Le I-187, I-326, III-399