Chu Kiong Loo Keem Siah YapKok Wai Wong Andrew TeohKaizhu Huang (Eds.)

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LNCS

883

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21st International Conference, ICONIP 2014Kuching, Malaysia, November 36, 2014Proceedings, Part I

NeuralInformation Processing

Lecture Notes in Computer Science 8834Commenced Publication in 1973Founding and Former Series Editors:Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

Editorial Board

David HutchisonLancaster University, UK

Takeo KanadeCarnegie Mellon University, Pittsburgh, PA, USA

Josef KittlerUniversity of Surrey, Guildford, UK

Jon M. KleinbergCornell University, Ithaca, NY, USA

Friedemann MatternETH Zurich, Switzerland

John C. MitchellStanford University, CA, USA

Moni NaorWeizmann Institute of Science, Rehovot, Israel

C. Pandu RanganIndian Institute of Technology, Madras, India

Bernhard SteffenTU Dortmund University, Germany

Demetri TerzopoulosUniversity of California, Los Angeles, CA, USA

Doug TygarUniversity of California, Berkeley, CA, USA

Gerhard WeikumMax Planck Institute for Informatics, Saarbruecken, Germany

Chu Kiong Loo Keem Siah YapKok Wai Wong Andrew TeohKaizhu Huang (Eds.)

NeuralInformation Processing

21st International Conference, ICONIP 2014Kuching, Malaysia, November 3-6, 2014Proceedings, Part I

1 3

Volume Editors

Chu Kiong LooUniversity of Malaya, Kuala Lumpur, MalaysiaE-mail: ckloo.um@um.edu.my

Keem Siah YapUniversiti Tenaga Nasional, Selangor, MalaysiaE-mail: yapkeem@uniten.edu.my

Kok Wai WongMurdoch University, Murdoch, WA, AustraliaE-mail: k.wong@murdoch.edu.au

Andrew TeohYonsei University, Seoul, South KoreaE-mail: bjteoh@yonsei.ac.kr

Kaizhu HuangXian Jiaotong-Liverpool University, Suzhou, ChinaE-mail: kaizhu.huang@xjtlu.edu.cn

ISSN 0302-9743 e-ISSN 1611-3349ISBN 978-3-319-12636-4 e-ISBN 978-3-319-12637-1DOI 10.1007/978-3-319-12637-1Springer Cham Heidelberg New York Dordrecht London

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Preface

This volume is part of the three-volume proceedings of the 21st InternationalConference on Neural Information Processing (ICONIP 2014), which was heldin Kuching, Malaysia, during November 36, 2014. The ICONIP is an annualconference of the Asia Pacific Neural Network Assembly (APNNA). This series ofICONIP conferences has been held annually since 1994 in Seoul and has becomeone of the leading international conferences in the area of neural networks.

ICONIP 2014 received a total of 375 submissions by scholars from 47 coun-tries/regions across six continents. Based on a rigorous peer-review process whereeach submission was evaluated by at least two qualified reviewers, a total of231 high-quality papers were selected for publication in the reputable series ofLecture Notes in Computer Science (LNCS). The selected papers cover majortopics of theoretical research, empirical study, and applications of neural infor-mation processing research. ICONIP 2014 also featured a pre-conference event,namely, the Cybersecurity Data Mining Competition and Workshop (CDMC2014) which was held in Kuala Lumpur. Nine papers from CDMC 2014 wereselected for a Special Session of the conference proceedings.

In addition to the contributed papers, the ICONIP 2014 technical programincluded a keynote speech by Shun-Ichi Amari (RIKEN Brain Science Institute,Japan), two plenary speeches by Jacek Zurada (University of Louisville, USA)and Jurgen Schmidhuber (Istituto Dalle Molle di Studi sullIntelligenza Arti-ficiale, Switzerland). This conference also featured seven invited speakers, i.e.,Akira Hirose (The University of Tokyo, Japan), Nikola Kasabov (Auckland Uni-versity of Technology, New Zealand), Soo-Young Lee (KAIST, Korea), DerongLiu (Chinese Academy of Sciences, China; University of Illinois, USA), Kay ChenTan (National University of Singapore), Jun Wang (The Chinese University ofHong Kong), and Zhi-Hua Zhou (Nanjing University, China).

We would like to sincerely thank Honorary Chair Shun-ichi Amari, MohdAmin Jalaludin, the members of the Advisory Committee, the APNNA Gov-erning Board for their guidance, the members of the Organizing Committee forall their great efforts and time in organizing such an event. We would also liketo take this opportunity to express our deepest gratitude to all the technicalcommittee members for their professional review that guaranteed high qualitypapers.

We would also like to thank Springer for publishing the proceedings in theprestigious LNCS series. Finally, we would like to thank all the speakers, authors,

VI Preface

and participants for their contribution and support in making ICONIP 2014 asuccessful event.

November 2014 Chu Kiong LooKeem Siah YapKok Wai WongAndrew TeohKaizhu Huang

Organization

Honorary Chairs

Shun-Ichi Amari RIKEN, JapanMohd Amin Jalaludin University of Malaya, Malaysia

General Chair

Chu Kiong Loo University of Malaya, Malaysia

General Co-chairs

Yin Chai Wang University Malaysia Sarawak, MalaysiaWeng Kin Lai Tunku Abdul Rahman University College,

Malaysia

Program Chairs

Kevin Kok Wai Wong Murdoch University, AustraliaAndrew Teoh Yonsei University, KoreaKaizhu Huang Xian Jiaotong-Liverpool University, China

Publication Chairs

Lakhmi Jain University of South Australia, AustraliaChee Peng Lim Deakin University, AustraliaKeem Siah Yap Universiti Tenaga Nasional, Malaysia

Registration Chair and Webmaster

Yun Li Lee Sunway University, Malaysia

Local Organizing Chairs

Chong Eng Tan University Malaysia Sarawak, MalaysiaKai Meng Tay University Malaysia Sarawak, Malaysia

VIII Organization

Workshop and Tutorial Chairs

Chen Change Loy Chinese University of Hong Kong, SAR ChinaYing Wah Teh University of Malaya, MalaysiaSaeed Reza University of Malaya, MalaysiaTutut Harewan University of Malaya, Malaysia

Special Session Chairs

Thian Song Ong Multimedia University, MalaysiaSiti Nurul Huda Sheikh

Abdullah Universiti Kebangsaan Malaysia, Malaysia

Financial Chair

Ching Seong Tan Multimedia University, Malaysia

Sponsorship Chairs

Manjeevan Seera University of Malaya, MalaysiaJohn See Multimedia University, MalaysiaAamir Saeed Malik Universiti Teknologi Petronas, Malaysia

Publicity Chairs

Siong Hoe Lau Multimedia University, MalaysiaKhairul Salleh Mohamed

Sahari Universiti Tenaga Nasional, Malaysia

Asia Liaison Chairs

ShenShen Gu Shanghai University, China

Europe Liaison Chair

Wlodzislaw Duch Nicolaus Copernicus University, Poland

America Liaison Chair

James T. Lo University of Maryland, USA

Organization IX

Advisory Committee

Lakhmi Jain, AustraliaDavid Gao, AustraliaBaoLiang Lu, ChinaYing Tan, ChinaJin Xu, China.Irwin King, Hong Kong, SAR ChinaJun Wang, Hong Kong, SAR ChinaP. Balasubramaniam, IndiaKunihiko Fukushima, JapanShiro Usui, JapanMinho Lee, KoreaMuhammad Leo Michael Toyad

Abdullah, MalaysiaMustafa Abdul Rahman, MalaysiaNarayanan Kulathuramaiyer,

MalaysiaDavid Ngo, Malaysia

Siti Salwah Salim, MalaysiaWan Ahmad Tajuddin Wan Abdullah,

MalaysiaWan Hashim Wan Ibrahim, MalaysiaDennis Wong, MalaysiaNik Kasabov, New ZealandArnulfo P. Azcarraga, PhillipinesWlodzislaw Duch, PolandTingwen Huang, QatarMeng Joo Err, SingaporeXie Ming, SingaporeLipo Wang, SingaporeJonathan H. Chan, ThailandRon Sun, USADe-Liang Wang, USADe-Shuang Huang, China

Technical Committee

Ahmad Termimi Ab GhaniMark AbernethyAdel Al-JumailyLeila AliouaneCesare AlippiOgnjen ArandjelovicSabri ArikMian M. AwaisEmili Balaguer-BallesterValentina Emilia BalasTao BanSang-Woo BanYounes BennaniAsim BhattiJanos BotzheimSalim BouzerdoumIvo BukovskyJinde CaoJiang-Tao CaoChee Seng ChanLong ChengGirija Chetty

Andrew ChiouPei-Ling ChiuSung-Bae ChoTodsanai ChumwatanaPau-Choo ChungJose Alfredo Ferreira CostaJustin DauwelsMingcong DengM.L. Dennis WongHongli DongHiroshi DozonoEl-Sayed M. El-AlfyZhouyu FuDavid GaoTom GedeonVik Tor GohNistor GrozavuPing GuoMasafumi HagiwaraOsman Hassab ElgawiShan HeHaibo He

X Organization

Sven HellbachJer Lang HongJinglu HuXiaolin HuKaizhu HuangAmir HussainKazushi IkedaPiyasak JeatrakulSungmoon JeongYaochu JinZsolt Csaba JohanyakYouki KadobayashiHiroshi KageJoarder KamruzzamanShinIchiro KanohNikola KasabovRhee Man KilKyung-Joong KimKyung-Hwan KimDaeeun KimLaszlo T. KoczyMarkus KoskelaSzilveszter KovacsNaoyuki KubotaTakio KuritaOlcay KursunJames KwokSungoh KwonWeng Kin LaiSiong Hoe LauYun Li LeeMinho LeeNung Kion LeeChin Poo LeeVincent LemaireL. LengYee Tak LeungBin LiYangming LiMing LiXiaofeng LiaoMeng-Hui LimC.P. LimChee Peng LimKim Chuan Lim

Hsuan-Tien LinHuo Chong LingDerong LiuZhi-Yong LiuChu Kiong LooWenlian LuZhiwu LuBao-Liang LuShuangge MaMufti MahmudKenichiro MiuraHiroyuki NakaharaKiyohisa NatsumeVinh NguyenTohru NittaYusuke NojimaAnto Satriyo NugrohoTakenori OboToshiaki OmoriTakashi OmoriThian Song OngSid-Ali OuadfeulSeiichi OzawaWorapat PaireekrengPaul PangYing Han PangShaoning PangHyung-Min ParkShri RaiMallipeddi RammohanAlexander RastJinchang RenMehdi RoopaeiKo SakaiYasuomi SatoNaoyuki SatoShunji SatohManjeevan SeeraSubana ShanmuganathanBo ShenYang ShiTomohiro ShibataHayaru ShounoJennie SiJungsuk Song

Organization XI

Kingkarn SookhanaphibarnIndra Adji SulistijonoChangyin SunJun SunMasahiro TakatsukaTakahiro TakedaShing Chiang TanSyh Yuan TanChing Seong TanKen-Ichi TanakaKatsumi TatenoKai Meng TayConnie TeeGuo TengAndrew TeohHeizo TokutakaDat TranBoris TudjarovEiji UchinoKalyana C. VeluvoluMichel VerleysenLipo WangHongyuan Wang

Frank WangYin Chai WangYoshikazu WashizawaKazuho WatanabeBunthit WatanapaKevin WongKok Seng WongZenglin XuYoko YamaguchKoichiro YamauchiHong YanWei Qi YanKun YangPeipei YangBo YangXu-Cheng YinZhigang ZengMin-Ling ZhangChao ZhangRui ZhangYanming ZhangRui ZhangDing-Xuan Zhou

Table of Contents Part I

Cognitive Science

Transfer Entropy and Information Flow Patterns in Functional BrainNetworks during Cognitive Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Md. Hedayetul Islam Shovon, D. (Nanda) Nandagopal,Ramasamy Vijayalakshmi, Jia Tina Du, and Bernadine Cocks

Human Implicit Intent Discrimination Using EEG and EyeMovement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Ukeob Park, Rammohan Mallipeddi, and Minho Lee

Towards Establishing Relationships between Human Arousal Level andMotion Mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Sven Nomm, Tiit Konnusaar, and Aaro Toomela

Estimating Nonlinear Spatiotemporal Membrane Dynamics in ActiveDendrites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Toshiaki Omori

Inter Subject Correlation of Brain Activity during Visuo-MotorSequence Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Krishna Prasad Miyapuram, Ujjval Pamnani, Kenji Doya,and Raju S. Bapi

An Agent Response System Based on Mirror Neuron and Theoryof Mind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Kyon-Mo Yang and Sung-Bae Cho

Dynamic of Nitric Oxide Diffusion in Volume Transmission: Model andValidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Fernandez Lopez Pablo, Garca Baez Patricio,and Suarez Araujo Carmen Paz

A Computational Model of the Relation between Regulation ofNegative Emotions and Mood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Altaf Hussain Abro, Michel C.A. Klein, Adnan R. Manzoor,Seyed Amin Tabatabaei, and Jan Treur

Neural Networks and Learning Systems - Theoryand Design

Low-Cost Representation for Restricted Boltzmann Machines . . . . . . . . . . 69Son N. Tran and Artur dAvila Garcez

XIV Table of Contents Part I

Add-if-Silent Rule for Training Multi-layered Convolutional NetworkNeocognitron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Kunihiko Fukushima

Posterior Distribution Learning (PDL): A Novel Supervised LearningFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Enmei Tu, Jie Yang, Zhenghong Jia, and Nicola Kasabov

Computational Model of Neocortical Learning Process: Prototype . . . . . . 95Jing Xian Teo and Henry Lee Seldon

Active Learning with Maximum Density and Minimum Redundancy . . . . 103Yingjie Gu, Zhong Jin, and Steve C. Chiu

One-to-Many Association Ability of Chaotic QuaternionicMultidirectional Associative Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Takumi Okutsu and Yuko Osana

An Entropy-Guided Adaptive Co-construction Method of State andAction Spaces in Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Masato Nagayoshi, Hajime Murao, and Hisashi Tamaki

A Nodes Reduction Procedure for RBFNDDA through Histogram . . . . . . 127Pey Yun Goh, Shing Chiang Tan, and Wooi Ping Cheah

Toroidal Approximate Identity Neural Networks Are UniversalApproximators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Saeed Panahian Fard and Zarita Zainuddin

Self-organizing Neural Grove . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Hirotaka Inoue

Transfer Learning Using the Online FMM Model . . . . . . . . . . . . . . . . . . . . . 151Manjeevan Seera, Chee Peng Lim, and Chu Kiong Loo

A Supervised Methodology to Measure the Variables Contribution to aClustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Oumaima Alaoui Ismaili, Vincent Lemaire, and Antoine Cornuejols

Coupling between Spatial Consistency of Neural Firing and Local FieldPotential Coherence: A Computational Study . . . . . . . . . . . . . . . . . . . . . . . . 167

Naoyuki Sato

Fading Channel Prediction Based on Self-optimizing NeuralNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

Tianben Ding and Akira Hirose

Invariant Multiparameter Sensitivity of Oscillator Networks . . . . . . . . . . . 183Kenzaburo Fujiwara, Takuma Tanaka, and Kiyohiko Nakamura

Table of Contents Part I XV

Spatial-Temporal Saliency Feature Extraction for Robust Mean-ShiftTracker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Suiwu Zheng, Linshan Liu, and Hong Qiao

BOOSTRON: Boosting Based Perceptron Learning . . . . . . . . . . . . . . . . . . . 199Mirza M. Baig, Mian.M. Awais, and El-Sayed M. El-Alfy

G-Stream: Growing Neural Gas over Data Stream . . . . . . . . . . . . . . . . . . . . 207Mohammed Ghesmoune, Hanene Azzag, and Mustapha Lebbah

Combining Active Learning and Semi-supervised Learning Using Localand Global Consistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

Yingjie Gu, Zhong Jin, and Steve C. Chiu

Complex-Valued Neural Networks Recent Progress and FutureDirections (Invited Paper) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Akira Hirose

A Cascade System of Simple Dynamic Binary Neural Networks and ItsSparsification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

Jungo Moriyasu and Toshimichi Saito

A Model of V4 Neurons Based on Sparse Coding . . . . . . . . . . . . . . . . . . . . . 239Hui Wei, Zheng Dong, and Qiang Li

A Fast and Memory-Efficient Hierarchical Graph ClusteringAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

Laszlo Szilagyi, Sandor Miklos Szilagyi, and Beat Hirsbrunner

Hopfield-Type Associative Memory with Sparse Modular Networks . . . . . 255Gouhei Tanaka, Toshiyuki Yamane, Daiju Nakano,Ryosho Nakane, and Yasunao Katayama

Concept Drift Detection Based on Anomaly Analysis . . . . . . . . . . . . . . . . . 263Anjin Liu, Guangquan Zhang, and Jie Lu

Online Learning for Faulty RBF Networks with the Concurrent Fault . . . 271Wai Yan Wan, Chi-Sing Leung, Zi-Fa Han, and Ruibin Feng

The Performance of the Stochastic DNN-kWTA Network . . . . . . . . . . . . . 279Ruibin Feng, Chi-Sing Leung, Kai-Tat Ng, and John Sum

Modularity Maximization Adjusted by Neural Networks . . . . . . . . . . . . . . 287Desiree Maldonado Carvalho, Hugo Resende,and Maria C.V. Nascimento

A Dynamic Pruning Strategy for Incremental Learning on a Budget . . . . 295Yusuke Kondo and Koichiro Yamauchi

XVI Table of Contents Part I

Neural Computing with Concurrent Synchrony . . . . . . . . . . . . . . . . . . . . . . 304Victor Parque, Masakazu Kobayashi, and Masatake Higashi

A Line-Partitioned Heteroassociative Memory for Storing BinaryFresnel Hologram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312

Peter Wai Ming Tsang and Chi-Sing Leung

A Unified Framework for Privacy Preserving Data Clustering . . . . . . . . . . 319Wenye Li

Spiking Neural Network with Lateral Inhibition for Reward-BasedAssociative Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327

Nooraini Yusoff and Farzana Kabir Ahmad

Fuzzy Signature Neural Networks for Classification: Optimising theStructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335

Tom Gedeon, Xuanying Zhu, Kun He, and Leana Copeland

Self-organizing Map-Based Probabilistic Associative Memory . . . . . . . . . . 342Yuko Osana

Neural Networks and Learning Systems -Applications

A Causal Model for Disease Pathway Discovery . . . . . . . . . . . . . . . . . . . . . . 350Ruichu Cai, Chang Yuan, Zhifeng Hao, Wen Wen, Lijuan Wang,Weiqi Chen, and Zhihao Li

Enhanced Non-linear Features for On-line Handwriting RecognitionUsing Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358

Qing Zhang, Minhua Wu, Zhenbo Luo, and Youxin Chen

Recognizing Human Actions by Using the Evolving Remote SupervisedMethod of Spiking Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366

Xiurui Xie, Hong Qu, Guisong Liu, and Lingshuang Liu

A Neural Networks Committee for the Contextual Bandit Problem . . . . . 374Robin Allesiardo, Raphael Feraud, and Djallel Bouneffouf

Multi-step Predictions of Landslide Displacements Based on Echo StateNetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382

Wei Yao, Zhigang Zeng, Cheng Lian, Huiming Tang,and Tingwen Huang

Discrete-Time Nonlinear Generalized Policy Iteration for OptimalControl Using Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389

Qinglai Wei, Derong Liu, and Xiong Yang

Table of Contents Part I XVII

ANFIS-Based Model for Improved Paraphrase Rating Prediction . . . . . . . 397El-Sayed M. El-Alfy

Contextual Bandit for Active Learning: Active Thompson Sampling . . . . 405Djallel Bouneffouf, Romain Laroche, Tanguy Urvoy,Raphael Feraud, and Robin Allesiardo

Choosing the Best Auto-Encoder-Based Bagging Classifier:An Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413

Yifan Nie, Wenge Rong, Yikang Shen, Chao Li, and Zhang Xiong

Classification of fMRI Data in the NeuCube Evolving Spiking NeuralNetwork Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421

Norhanifah Murli, Nikola Kasabov, and Bana Handaga

A Hybrid Approach to Pixel Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . 429Subana Shanmuganathan

A Novel SOH Prediction Framework for the Lithium-ion Battery UsingEcho State Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438

Jianmin Wang, Zhe Li, Xiao Li, and Youyi Zhao

Significance of Non-edge Priors in Gene Regulatory NetworkReconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446

Ajay Nair, Madhu Chetty, and Pramod P. Wangikar

Robust Lane Detection Based on Convolutional Neural Network andRandom Sample Consensus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454

Jihun Kim and Minho Lee

Learning Local Receptive Fields in Deep Belief Networks for VisualFeature Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462

Diana Turcsany and Andrzej Bargiela

Adaptive Wavelet Extreme Learning Machine (AW-ELM) for IndexFinger Recognition Using Two-Channel Electromyography . . . . . . . . . . . . 471

Khairul Anam and Adel Al-Jumaily

Text Categorization Using an Automatically Generated LabelledDataset: An Evaluation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479

Dengya Zhu and Kok Wai Wong

Online Recommender System Based on Social NetworkRegularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487

Zhuo Wang and Hongtao Lu

A Nonlinear Cross-Site Transfer Learning Approach for RecommenderSystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495

Xin Xin, Zhirun Liu, and Heyan Huang

XVIII Table of Contents Part I

Deep Learning of Multifractal Attributes from Motor Imagery InducedEEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503

Junhua Li and Andrzej Cichocki

A Fast Neural-Dynamical Approach to Scale-Invariant ObjectDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

Kasim Terzic, David Lobato, Mario Saleiro, and J.M.H. du Buf

Improving Quantization Quality in Brain-Inspired Self-organization forNon-stationary Data Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519

Kasun Gunawardana, Jayantha Rajapakse,and Damminda Alahakoon

Utilizing High-Dimensional Neural Networks for Pseudo-orthogonalization of Memory Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527

Toshifumi Minemoto, Teijiro Isokawa, Haruhiko Nishimura, andNobuyuki Matsui

Adaptive Noise Schedule for Denoising Autoencoder . . . . . . . . . . . . . . . . . . 535B. Chandra and Rajesh Kumar Sharma

Modeling Bi-directional Tree Contexts by Generative Transductions . . . . 543Davide Bacciu, Alessio Micheli, and Alessandro Sperduti

A Novel Architecture for Capturing Discrete Sequences UsingSelf-organizing Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551

Manjusri Ishwara, Jayantha Rajapakse, and Damminda Alahakoon

An Improved Gbest Guided Artificial Bee Colony (IGGABC) Algorithmfor Classification and Prediction Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559

Habib Shah, Tutut Herawan, Rozaida Ghazali, Rashid Naseem,Maslina Abdul Aziz, and Jemal H. Abawajy

Artifact Removal from EEG Using a Multi-objective IndependentComponent Analysis Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 570

Sim Kuan Goh, Hussein A. Abbass, Kay Chen Tan, andAbdullah Al Mamun

Cooperative Feature Level Data Fusion for Authentication UsingNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578

Mark Abernethy and Shri M. Rai

Fuzzy Output Error as the Performance Function for Training ArtificialNeural Networks to Predict Reading Comprehension from Eye Gaze . . . . 586

Leana Copeland, Tom Gedeon, and Sumudu Mendis

Part-Based Tracking with Appearance Learning and StructuralConstrains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594

Wei Xiang and Yue Zhou

Table of Contents Part I XIX

Estimation of Hidden Markov Chains by a Neural Network . . . . . . . . . . . . 602Yoshifusa Ito, Hiroyuki Izumi, and Cidambi Srinivasan

Corporate Leaders Analytics and Network System (CLANS):Constructing and Mining Social Networks among Corporations andBusiness Elites in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610

Yuanyuan Man, Shuai Wang, Tianyu Zhang, T.J. Wong,and Irwin King

Characteristics and Potential Developments of Multiple-MLP EnsembleRe-RX Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619

Yoichi Hayashi, Yuki Tanaka, Tomoki Izawa, and Shota Fujisawa

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629

Table of Contents Part II

Kernel and Statistical Methods

A New Ensemble Clustering Method Based on Dempster-ShaferEvidence Theory and Gaussian Mixture Modeling . . . . . . . . . . . . . . . . . . . . 1

Yi Wu, Xiabi Liu, and Lunhao Guo

Extraction of Dimension Reduced Features from Empirical KernelVector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Takio Kurita and Yayoi Harashima

Method of Evolving Non-stationary Multiple Kernel Learning . . . . . . . . . 17Peng Wu, Qian Yin, and Ping Guo

A Kernel Method to Extract Common Features Based on MutualInformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Takamitsu Araki, Hideitsu Hino, and Shotaro Akaho

Properties of Text-Prompted Multistep Speaker Verification UsingGibbs-Distribution-Based Extended Bayesian Inference for RejectingUnregistered Speakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Shuichi Kurogi, Takuya Ueki, Satoshi Takeguchi, and Yuta Mizobe

Non-monotonic Feature Selection for Regression . . . . . . . . . . . . . . . . . . . . . 44Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu

Non-negative Matrix Factorization with Schatten p-normsReguralization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Ievgen Redko and Younes Bennani

A New Energy Model for the Hidden Markov Random Fields . . . . . . . . . . 60Jeremie Sublime, Antoine Cornuejols, and Younes Bennani

Online Nonlinear Granger Causality Detection by Quantized KernelLeast Mean Square . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Hong Ji, Badong Chen, Zejian Yuan, Nanning Zheng, Andreas Keil,and Jose C. Prncipe

A Computational Model of Anti-Bayesian Sensory Integration in theSize-Weight Illusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Yuki Ueyama

Unsupervised Dimensionality Reduction for Gaussian Mixture Model . . . 84Xi Yang, Kaizhu Huang, and Rui Zhang

XXII Table of Contents Part II

Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism TestExtensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Giovanni Da San Martino, Nicolo Navarin, and Alessandro Sperduti

Texture Analysis Based Automated Decision Support System forClassification of Skin Cancer Using SA-SVM . . . . . . . . . . . . . . . . . . . . . . . . 101

Ammara Masood, Adel Al-Jumaily, and Khairul Anam

In-attention State Monitoring for a Driver Based on Head Pose andEye Blinking Detection Using One Class Support Vector Machine . . . . . . 110

Hyunrae Jo and Minho Lee

An Improved Separating Hyperplane Method with Application toEmbedded Intelligent Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Yanjun Li, Ping Guo, and Xin Xin

Fine-Grained Air Quality Monitoring Based on Gaussian ProcessRegression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

Yun Cheng, Xiucheng Li, Zhijun Li, Shouxu Jiang,and Xiaofan Jiang

Retrieval of Experiments by Efficient Comparison of MarginalLikelihoods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Sohan Seth, John Shawe-Taylor, and Samuel Kaski

Evolutionary Computation and Hybrid IntelligentSystems

A New Approach of Diversity Enhanced Particle Swarm Optimizationwith Neighborhood Search and Adaptive Mutation . . . . . . . . . . . . . . . . . . . 143

Dang Cong Tran, Zhijian Wu, and Hui Wang

Data Clustering Based on Particle Swarm Optimization withNeighborhood Search and Cauchy Mutation . . . . . . . . . . . . . . . . . . . . . . . . . 151

Dang Cong Tran and Zhijian Wu

Accuracy Improvement of Localization and Mapping of ICP-SLAM viaCompetitive Associative Nets and Leave-One-Out Cross-Validation . . . . . 160

Shuichi Kurogi, Yoichiro Yamashita, Hikaru Yoshikawa,and Kotaro Hirayama

Saliency Level Set Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170Jincheng Mei and Bao-Liang Lu

Application of Cuckoo Search for Design Optimization of HeatExchangers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178

Rihanna Khosravi, Abbas Khosravi, and Saeid Nahavandi

Table of Contents Part II XXIII

A Hybrid Method to Improve the Reduction of BallistocardiogramArtifact from EEG Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

Ehtasham Javed, Ibrahima Faye, Aamir Saeed Malik,and Jafri Malin Abdullah

VLGAAC: Variable Length Genetic Algorithm Based AlternativeClustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

Moumita Saha and Pabitra Mitra

Social Book Search with Pseudo-Relevance Feedback . . . . . . . . . . . . . . . . . 203Bin Geng, Fang Zhou, Jiao Qu, Bo-Wen Zhang, Xiao-Ping Cui,and Xu-Cheng Yin

A Random Key Genetic Algorithm for Live Migration of MultipleVirtual Machines in Data Centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212

Tusher Kumer Sarker and Maolin Tang

Collaboration of the Radial Basis ART and PSO in Multi-SolutionProblems of the Hnon Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221

Fumiaki Tokunaga, Takumi Sato, and Toshimichi Saito

Reconstructing Gene Regulatory Network with Enhanced ParticleSwarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

Rezwana Sultana, Dilruba Showkat, Mohammad Samiullah,and Ahsan Raja Chowdhury

Neural Network Training by Hybrid Accelerated Cuckoo ParticleSwarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

Nazri Mohd Nawi, Abdullah Khan, M.Z. Rehman,Maslina Abdul Aziz, Tutut Herawan, and Jemal H. Abawajy

An Accelerated Particle Swarm Optimization Based LevenbergMarquardt Back Propagation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

Nazri Mohd Nawi, Abdullah Khan, M.Z. Rehman,Maslina Abdul Aziz, Tutut Herawan, and Jemal H. Abawajy

Fission-and-Recombination Particle Swarm Optimizers for Search ofMultiple Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

Takumi Sato and Toshimichi Saito

Fast Generalized Fuzzy C-means Using Particle Swarm Optimizationfor Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263

Dang Cong Tran, Zhijian Wu, and Van Hung Tran

Evolutionary Learning and Stability of Mixed-Rule CellularAutomata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271

Ryo Sawayama and Toshimichi Saito

XXIV Table of Contents Part II

Pattern Recognition Techniques

Radical-Enhanced Chinese Character Embedding . . . . . . . . . . . . . . . . . . . . 279Yaming Sun, Lei Lin, Nan Yang, Zhenzhou Ji, and Xiaolong Wang

Conditional Multidimensional Parameter Identification withAsymmetric Correlated Losses of Estimation Errors . . . . . . . . . . . . . . . . . . 287

Piotr Kulczycki and Malgorzata Charytanowicz

Short Text Hashing Improved by Integrating Topic Featuresand Tags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295

Jiaming Xu, Bo Xu, Jun Zhao, Guanhua Tian, Heng Zhang,and Hongwei Hao

Synthetic Test Data Generation for Hierarchical Graph ClusteringMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303

Laszlo Szilagyi, Levente Kovacs, and Sandor Miklos Szilagyi

Optimal Landmark Selection for Nystrom Approximation . . . . . . . . . . . . . 311Zhouyu Fu

Privacy Preserving Clustering: A k -Means Type Extension . . . . . . . . . . . . 319Wenye Li

Stream Quantiles via Maximal Entropy Histograms . . . . . . . . . . . . . . . . . . 327Ognjen Arandjelovic, Ducson Pham, and Svetha Venkatesh

A Unified Framework for Thermal Face Recognition . . . . . . . . . . . . . . . . . . 335Reza Shoja Ghiass, Ognjen Arandjelovic, Hakim Bendada,and Xavier Maldague

Geometric Feature-Based Facial Emotion Recognition Using Two-StageFuzzy Reasoning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344

Md. Nazrul Islam and Chu Kiong Loo

Human Activity Recognition by Matching Curve Shapes . . . . . . . . . . . . . . 352Poorna Talkad Sukumar and K. Gopinath

Sentiment Analysis of Chinese Microblogs Based on Layered Features . . . 361Dongfang Wang and Fang Li

Feature Group Weighting and Topological Biclustering . . . . . . . . . . . . . . . . 369Tugdual Sarazin, Mustapha Lebbah, Hanane Azzag,and Amine Chaibi

A Label Completion Approach to Crowd Approximation . . . . . . . . . . . . . . 377Toshihiro Watanabe and Hisashi Kashima

Multi-label Linear Discriminant Analysis with Locality Consistency . . . . 386Yuzhang Yuan, Kang Zhao, and Hongtao Lu

Table of Contents Part II XXV

Hashing for Financial Credit Risk Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 395Bernardete Ribeiro and Ning Chen

MAP Inference with MRF by Graduated Non-Convexity and ConcavityProcedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

Zhi-Yong Liu, Hong Qiao, and Jian-Hua Su

Two-Phase Approach to Link Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413Srinivas Virinchi and Pabitra Mitra

Properties of Direct Multi-Step Ahead Prediction of Chaotic TimeSeries and Out-of-Bag Estimate for Model Selection . . . . . . . . . . . . . . . . . . 421

Shuichi Kurogi, Ryosuke Shigematsu, and Kohei Ono

Multi-document Summarization Based on Sentence Clustering . . . . . . . . . 429Hai-Tao Zheng, Shu-Qin Gong, Hao Chen, Yong Jiang,and Shu-Tao Xia

An Ontology-Based Approach to Query Suggestion Diversification . . . . . . 437Hai-Tao Zheng, Jie Zhao, Yi-Chi Zhang, Yong Jiang,and Shu-Tao Xia

Sensor Drift Compensation Using Fuzzy Interference System andSparse-Grid Quadrature Filter in Blood Glucose Control . . . . . . . . . . . . . . 445

Peter Szalay, Laszlo Szilagyi, Zoltan Benyo, and Levente Kovacs

Webpage Segmentation Using Ontology and Word Matching . . . . . . . . . . . 454Huey Jing Toh and Jer Lang Hong

Continuity of Discrete-Time Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . 462Takashi Mitsuishi, Takanori Terashima, Koji Saigusa,Nami Shimada, Toshimichi Homma, Kiyoshi Sawada,and Yasunari Shidama

Sib-Based Survival Selection Technique for Protein Structure Predictionin 3D-FCC Lattice Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470

Rumana Nazmul and Madhu Chetty

Tensor Completion Based on Structural Information . . . . . . . . . . . . . . . . . . 479Zi-Fa Han, Ruibin Feng, Long-Ting Huang, Yi Xiao,Chi-Sing Leung, and Hing Cheung So

Document Versioning Using Feature Space Distances . . . . . . . . . . . . . . . . . 487Wei Lee Woon, Kuok-Shoong Daniel Wong, Zeyar Aung,and Davor Svetinovic

XXVI Table of Contents Part II

Separation and Classification of Crackles and Bronchial Breath Soundsfrom Normal Breath Sounds Using Gaussian Mixture Model . . . . . . . . . . . 495

Ali Haider, M. Daniyal Ashraf, M. Usama Azhar,Syed Osama Maruf, Mehdi Naqvi, Sajid Gul Khawaja, andM. Usman Akram

Combined Features for Face Recognition in Surveillance Conditions . . . . 503Khaled Assaleh, Tamer Shanableh, and Kamal Abuqaaud

Sparse Coding on Multiple Manifold Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 515Hanchao Zhang and Jinhua Xu

Mutual Information Estimation with Random Forests . . . . . . . . . . . . . . . . 524Mike Koeman and Tom Heskes

Out-Of-Vocabulary Words Recognition Based on Conditional RandomField in Electronic Commerce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532

Yanfeng Yang, Yanqin Yang, Hu Guan, and Wenchao Xu

Least Angle Regression in Orthogonal Case . . . . . . . . . . . . . . . . . . . . . . . . . 540Katsuyuki Hagiwara

Evaluation Protocol of Early Classifiers over Multiple Data Sets . . . . . . . 548Asma Dachraoui, Alexis Bondu, and Antoine Cornuejols

Exploiting Level-Wise Category Links for Semantic RelatednessComputing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556

Hai-Tao Zheng, Wenzhen Wu, Yong Jiang, and Shu-Tao Xia

Characteristic Prediction of a Varistor in Over-Voltage ProtectionApplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565

Kohei Nagatomo, Muhammad Aziz Muslim, Hiroki Tamura,Koichi Tanno, and Wijono

Optimizing Complex Building Renovation Process with Fuzzy SignatureState Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573

Gergely I. Molnarka and Laszlo T. Koczy

News Title Classification with Support from Auxiliary Long Texts . . . . . 581Yuanxin Ouyang, Yao Huangfu, Hao Sheng, and Zhang Xiong

Modelling Mediator Intervention in Joint Decision Making ProcessesInvolving Mutual Empathic Understanding . . . . . . . . . . . . . . . . . . . . . . . . . . 589

Rob Duell

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597

Table of Contents Part III

Signal and Image Processing

Real Time Crowd Counting with Human Detection and HumanTracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Xinjian Zhang and Liqing Zhang

A New Method for Removing Random-Valued Impulse Noise . . . . . . . . . . 9Qiyu Jin, Li Bai, Jie Yang, Ion Grama, and Quansheng Liu

CTR Prediction for DSP with Improved Cube Factorization Modelfrom Historical Bidding Log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Lili Shan, Lei Lin, Di Shao, and Xiaolong Wang

Image Retrieval Using a Novel Color Similarity Measurement andNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Cheng Yang and Xiaodong Gu

Bottom-Up Visual Saliency Using Binary Spectrum of Walsh-HadamardTransform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Ying Yu, Jie Lin, and Jian Yang

Sonification for EEG Frequency Spectrum and EEG-Based EmotionFeatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Yuxi Zhang, Yifeng Huang, Junwei Yue, and Liqing Zhang

Forecasting Crowd State in Video by an Improved Lattice BoltzmannModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Ye Tao, Peng Liu, Wei Zhao, and XiangLong Tang

Properties of Multiobjective Robust Controller Using DifferenceSignals and Multiple Competitive Associative Nets in Control of LinearSystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Weicheng Huang, Yuki Ishiguma, and Shuichi Kurogi

Calibrating Independent Component Analysis with Laplacian Referencefor Real-Time EEG Artifact Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Hussein A. Abbass

Unsupervised Segmentation Using Cluster Ensembles . . . . . . . . . . . . . . . . . 76Wei Zhang, Jie Yang, Wenjing Jia, Nikola Kasabov, Zhenhong Jia,and Lei Zhou

XXVIII Table of Contents Part III

Similar-Video Retrieval via Learned Exemplars and Time-WarpedAlignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Teruki Horie, Masafumi Moriwaki, Ryota Yokote, Shota Ninomiya,Akihiro Shikano, and Yasuo Matsuyama

Automatic Image Annotation Exploiting Textual and Visual Saliency . . . 95Yun Gu, Haoyang Xue, Jie Yang, and Zhenhong Jia

Classification of Fish Ectoparasite Genus Gyrodactylus SEM ImagesUsing ASM and Complex Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Rozniza Ali, Bo Jiang, Mustafa Man, Amir Hussain, and Bin Luo

Linked Tucker2 Decomposition for Flexible Multi-block DataAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Tatsuya Yokota and Andrzej Cichocki

Celebrity Face Image Retrieval Using Multiple Features . . . . . . . . . . . . . . . 119Jie Jin and Liqing Zhang

A Novel Adaptive Shrinkage Threshold on Shearlet Transform forImage Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Sheikh Md. Rabiul Islam, Xu Huang, and Kim Le

Perception of Symmetry in Natural Images: A Cortical Representationof Shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Ko Sakai, Ken Kurematsu, and Shouhei Matsuoka

Image Denoising with Rectified Linear Units . . . . . . . . . . . . . . . . . . . . . . . . 142Yangwei Wu, Haohua Zhao, and Liqing Zhang

Shape Preserving RGB-D Depth Map Restoration . . . . . . . . . . . . . . . . . . . . 150Wei Liu, Haoyang Xue, Yun Gu, Jie Yang, Qiang Wu,and Zhenhong Jia

Online Detection of Concept Drift in Visual Tracking . . . . . . . . . . . . . . . . . 159Yichen Liu and Yue Zhou

Temporally Regularized Filters for Common Spatial Patterns byPreserving Locally Linear Structure of EEG Trials . . . . . . . . . . . . . . . . . . . 167

Minmin Cheng, Haixian Wang, Zuhong Lu, and Deji Lu

Interactive Color Correction of Display by Dichromatic User . . . . . . . . . . . 175Hiroki Takagi, Hiroaki Kudo, Tetsuya Matsumoto,Yoshinori Takeuchi, and Noboru Ohnishi

A Neural Ensemble Approach for Segmentation and Classificationof Road Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

Tejy Kinattukara and Brijesh Verma

Table of Contents Part III XXIX

Using Biologically-Inspired Visual Features to Model the RestorativePotential of Scenes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

James Mountstephens

Classification of Stroke Patients Motor Imagery EEG withAutoencoders in BCI-FES Rehabilitation Training System . . . . . . . . . . . . 202

Mushangshu Chen, Ye Liu, and Liqing Zhang

Real-Time Patch-Based Tracking with Occlusion Handling . . . . . . . . . . . . 210Jian Tian and Yue Zhou

Blood Cell Image Retrieval System Using Color, Shape and Bagof Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

Mohammad Reza Zare and Woo Chaw Seng

Analysis of OCT Images for Detection of Choroidal Neovascularizationin Retinal Pigment Epithelial Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226

Sadaf Ayaz, Sadaf Sahar, Madeeha Zafar,Muhammad Usman Akram, and Yasser Nadeem

Online Object Tracking Based on Depth Image with Sparse Coding . . . . 234Shan-Chun Shen, Wei-Long Zheng, and Bao-Liang Lu

Real-Time Compressive Tracking with a Particle Filter Framework . . . . . 242Xuan Yao and Yue Zhou

Image Super-Resolution with Fast Approximate Convolutional SparseCoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

Christian Osendorfer, Hubert Soyer, and Patrick van der Smagt

Sparse Coding for Improved Signal-to-Noise Ratio in MRI . . . . . . . . . . . . . 258Fuleah A. Razzaq, Shady Mohamed, Asim Bhatti,and Saeid Nahavandi

Scalable Video Coding Using Hybrid DCT/Wavelets Architectures . . . . . 266Tamer Shanableh

Image Enhancement Using Geometric Mean Filter and GammaCorrection for WCE Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276

Shipra Suman, Fawnizu Azmadi Hussin, Aamir Saeed Malik,Nicolas Walter, Khean Lee Goh, Ida Hilmi, and Shiaw hooi Ho

Autoencoder-Based Collaborative Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . 284Yuanxin Ouyang, Wenqi Liu, Wenge Rong, and Zhang Xiong

Extended Laplacian Sparse Coding for Image Categorization . . . . . . . . . . 292Mouna Dammak, Mahmoud Mejdoub, and Chokri Ben Amar

XXX Table of Contents Part III

The 2014 Cybersecurity Data Mining Competitionand Workshop (CDMC2014)

Stochastic Decision Making in Learning Classifier Systems through aNatural Policy Gradient Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300

Gang Chen, Mengjie Zhang, Shaoning Pang, and Colin Douch

Quantum Inspired Evolutionary Algorithm by Representing CandidateSolution as Normal Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308

Sreenivas Sremath Tirumala, Gang Chen, and Shaoning Pang

Text Categorization with Diversity Random Forests . . . . . . . . . . . . . . . . . . 317Chun Yang, Xu-Cheng Yin, and Kaizhu Huang

Unknown Attack Detection by Multistage One-Class SVM Focusing onCommunication Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325

Shohei Araki, Yukiko Yamaguchi, Hajime Shimada,and Hiroki Takakura

Analysis and Configuration of Boundary Difference Calculations . . . . . . . 333Simon Dacey, Lei Song, Lei Zhu, and Shaoning Pang

Morphological Associative Memory Employing a Split Store Method . . . . 341Hakaru Tamukoh, Kensuke Koga, Hideaki Harada,and Takashi Morie

A Novel Hybrid Approach for Combining Deep and Traditional NeuralNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349

Rui Zhang, Shufei Zhang, and Kaizhu Huang

A Classification Method of Darknet Traffic for Advanced SecurityMonitoring and Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357

Sangjun Ko, Kyuil Kim, Younsu Lee, and Jungsuk Song

Detecting Malicious Spam Mails: An Online Machine LearningApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365

Yuli Dai, Shunsuke Tada, Tao Ban, Junji Nakazato,Jumpei Shimamura, and Seiichi Ozawa

Intelligent Systems for Supporting Decision-MakingProcesses: Theories and Applications

Freshness-Aware Thompson Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373Djallel Bouneffouf

Condition Monitoring of Broken Rotor Bars Using a Hybrid FMM-GAModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381

Manjeevan Seera, Chee Peng Lim, and Chu Kiong Loo

Table of Contents Part III XXXI

Employing Genetic Algorithm to Construct Epigenetic Tree-BasedFeatures for Enhancer Region Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390

Pui Kwan Fong, Nung Kion Lee, and Mohd Tajuddin Abdullah

Model and Algorithm for Multi-follower Tri-level HierarchicalDecision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398

Jialin Han, Guangquan Zhang, Jie Lu, Yaoguang Hu,and Shuyuan Ma

A Fuzzy ART-Based Approach for Estimation of High PerformanceConcrete Mix Proportion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407

Fei Ha Chiew, Kok Chin Chai, Chee Khoon Ng, and Kai Meng Tay

A New Application of an Evolving Tree to Failure Mode and EffectAnalysis Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415

Wui Lee Chang, Kai Meng Tay, and Chee Peng Lim

An Application of Fuzzy Adaptive Resonance Theory to EngineeringEducation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423

See Hung Lau, Kai Meng Tay, and Chee Khoon Ng

Augmented Query Strategies for Active Learning in Stream DataMining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431

Mustafa Amir Faisal, Zeyar Aung, Wei Lee Woon,and Davor Svetinovic

Feature Selection and Mass Classification Using Particle SwarmOptimization and Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . 439

Man To Wong, Xiangjian He, Wei-Chang Yeh, Zaidah Ibrahim,and Yuk Ying Chung

Strategic Decision Support in Waste Management Systems by StateReduction in FCM Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447

Miklos F. Hatwagner, Adrienn Buruzs, Peter Foldesi,and Laszlo T. Koczy

An ELM Based Multi Agent Systems Using Certified Beliefin Strength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458

Chong Tak Yaw, Keem Siah Yap, Hwa Jen Yap,and Ungku Anisa Ungku Amirulddin

ConstrainedOptimization-Based Bayesian Posterior ProbabilityExtreme Learning Machine for Pattern Classification . . . . . . . . . . . . . . . . . 466

Shen Yuong Wong, Keem Siah Yap, and Hwa Jen Yap

XXXII Table of Contents Part III

Neuroengineering and Neuralcomputing

Adaptive Translational Cueing Motion Algorithm Using Fuzzy BasedTilt Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474

Houshyar Asadi, Arash Mohammadi, Shady Mohamed,and Saeid Nahavandi

Adaptive Washout Algorithm Based Fuzzy Tuning for ImprovingHuman Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483

Houshyar Asadi, Arash Mohammadi, Shady Mohamed,Delpak Rahim Zadeh, and Saeid Nahavandi

Neurophysiology of Insects Using Microelectrode Arrays: CurrentTrends and Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

Julie Gaburro, Jean-Bernard Duchemin, Asim Bhatti, Peter Walker,and Saeid Nahavandi

Neurons Spikes Noise Level Classification Using Hidden MarkovModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501

Sherif Haggag, Shady Mohamed, Asim Bhatti, Hussein Haggag,and Saeid Nahavandi

Improved Robust Kalman Filtering for Uncertain Systems with MissingMeasurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509

Hossein Rezaei, Shady Mohamed, Reza Mahboobi Esfanjani,and Saeid Nahavandi

Motor Imagery Data Classification for BCI Application Using WaveletPacket Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519

Imali Thanuja Hettiarachchi, Thanh Thi Nguyen,and Saeid Nahavandi

Adaptive-Multi-Reference Least Means Squares Filter . . . . . . . . . . . . . . . . 527Luke Nyhof, Imali Hettiarachchi, Shady Mohammed,and Saeid Nahavandi

sEMG-Based Single-Joint Active Training with iLegA HorizontalExoskeleton for Lower Limb Rehabilitation . . . . . . . . . . . . . . . . . . . . . . . . . . 535

Jin Hu, Zeng-Guang Hou, Liang Peng, Long Peng, and Nong Gu

Cognitive Robotics

Find Rooms for Improvement: Towards Semi-automatic Labeling ofOccupancy Grid Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543

Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel,Thomas Villmann, and Hans-Joachim Bohme

Table of Contents Part III XXXIII

Understanding Dynamic Environments with Fuzzy Perception . . . . . . . . . 553Frank Bahrmann, Sven Hellbach, Sabrina Keil,and Hans-Joachim Bohme

Towards Real-World Neurorobotics: Integrated Neuromorphic VisualAttention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563

Samantha V. Adams, Alexander D. Rast, Cameron Patterson,Francesco Galluppi, Kevin Brohan, Jose-Antonio Perez-Carrasco,Thomas Wennekers, Steve Furber, and Angelo Cangelosi

On the Role of Working Memory in Trading-Off Skills and SituationAwareness in Sudoku . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571

George Leu, Jiangjun Tang, and Hussein Abbass

GA-Tetris Bot: Evolving a Better Tetris Gameplay Using AdaptiveEvaluation Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579

Somnuk Phon-Amnuaisuk

An Effectiveness of Model-Based Development with User Model inConsideration of Human . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587

Yoshinobu Akimoto, Eri Sato-Shimokawara, Yasunari Fujimoto,and Toru Yamaguchi

Dynamic Programming for Guided Gene Transfer in Bacterial MemeticAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596

Tiong Yew Tang, Simon Egerton, Janos Botzheim, andNaoyuki Kubota

Topological Gaussian Adaptive Resonance Associative Memory withFuzzy Motion Planning for Place Navigation . . . . . . . . . . . . . . . . . . . . . . . . 604

Wei Hong Chin and Chu Kiong Loo

Security in Signal Processing and Machine Learning

Anomaly Based Intrusion Detection through Temporal Classification . . . 612Shih Yin Ooi, Shing Chiang Tan, and Wooi Ping Cheah

Threshold Visual Secret Sharing Based on Boolean Operations andRandom Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620

Xuehu Yan, Shen Wang, and Xiamu Niu

Wavelet Based SDA for Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . 628Goh Fan Ling, Pang Ying Han, Liew Yee Ping, Ooi Shih Yin,and Loo Chu Kiong

Essential Visual Cryptographic Scheme with Different Importance ofShares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636

Xuehu Yan, Shen Wang, Xiamu Niu, and Ching-Nung Yang

XXXIV Table of Contents Part III

Improved Biohashing Method Based on Most Intensive HistogramBlock Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644

Munalih Ahmad Syarif, Thian Song Ong, Andrew Beng Jin Teoh,and Connie Tee

2.5D Face Recognition under Tensor Manifold Metrics . . . . . . . . . . . . . . . . 653Lee-Ying Chong, Andrew Beng Jin Teoh, Thian-Song Ong,and Siew-Chin Chong

Learning Systems for Social Network and WebMining

Predicting Mobile Subscribers Behaviour from Contextual InformationExtraction: SMS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661

Ayesha Javed Butt, Naveed Anwer Butt, Rabia Ghias Butt,and Muhammad Touseef Ikram

Discovering Plain-Text-Described Services Based on OntologyLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673

Hai Dong, Farookh Khadeer Hussain, and Athman Bouguettaya

A Fuzzy VSM-Based Approach for Semantic Service Retrieval . . . . . . . . . 682Supannada Chotipant, Farookh Khadeer Hussain, Hai Dong,and Omar Khadeer Hussain

Maintaining Trust in Cloud Computing through SLA Monitoring . . . . . . 690Walayat Hussain, Farookh Khadeer Hussain,and Omar Khadeer Hussain

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699

C.K. Loo et al. (Eds.): ICONIP 2014, Part I, LNCS 8834, pp. 110, 2014. Springer International Publishing Switzerland 2014

Transfer Entropy and Information Flow Patterns in Functional Brain Networks during Cognitive Activity

Md. Hedayetul Islam Shovon1, D (Nanda) Nandagopal1, Ramasamy Vijayalakshmi2, Jia Tina Du1, and Bernadine Cocks1

1 Cognitive Neuroengineering Laboratory, Division of IT, Engineering and the Environments, University of South Australia, Adelaide, Australia

2 Department of Applied Mathematics and Computational Science, PSG College of Technology, Coimbatore, Tamil Nadu, India

shomy004@mymail.unisa.edu.au

Abstract. Most previous studies of functional brain networks have been con-ducted on undirected networks despite the direction of information flow able to provide additional information on how one brain region influences another. The current study explores the application of normalized transfer entropy to EEG data to detect and identify the patterns of information flow in the functional brain networks during cognitive activity. Using a mix of signal processing, information and graph-theoretic techniques, this study has identified and cha-racterized the changing connectivity patterns of the directed functional brain networks during different cognitive tasks. The results demonstrate not only the value of transfer entropy in evaluating the directed functional brain networks but more importantly in determining the information flow patterns and thus providing more insights into the dynamics of the neuronal clusters underpinning cognitive function.

Keywords: Transfer entropy, directed functional brain network, EEG, cognitive load, graph theory.

1 Introduction

The human brain is a complex and dense network of billions of interconnected neu-rons. To quantify the topological features of this network, graph theoretical analysis has been successfully employed by researchers in the recent past [1-3]. Most of this graph theoretical analysis has, however, been applied to undirected networks. Such analysis can, however, be applied to directed networks [2]. This is perhaps, surprising given that directed networks exhibit more prominent features by providing directional interactions between pairwise elements thereby enabling more detailed analysis. Al-though Granger causality is often used to identify causal relationship in electroence-phalogram (EEG) data, it is limited to the linear model of interaction [4]. As a result, it fails to accurately identify causal relationships in highly nonlinear systems such as the human brain. By comparison, the information theoretical measure of transfer en-tropy (TE) determines the direction and quantifies the information transfer between two processes [5]. TE estimates the amount of activity of a system which is not

2 M.H.I. Shovon et al.

dependent on its own past activity but on the past activity of another system. It does not require a model of the interaction and inherently non-linear [4]. As a consequence, TE has been used in various applications such as identifying information transfer between auditory cortical neurons using spike train data, investigating the influence of heart rate on breath rate and vice versa, and for the localization of the epileptic focus of epileptic patients on EEG data [5-8]. TE has however not been applied in the con-struction and analysis of directed functional brain networks (FBN) during different cognitive states. The current study reported in this paper aims to investigate the appli-cation of normalized TE to construct a directed FBN. It further explores graph theo-retic and statistical analysis to characterize this directed FBN and its patterns of in-formation flow during cognitive tasks. Complex network metrics have also been used to delineate the cognitive tasks and the comparative results are presented. The re-search literature on TE and graph theoretical analysis are reviewed and described in the following sections.

2 Transfer Entropy

Given two processes and , the TE from to is shown in Equation 1 [9]: , , log , , ., . , , , 1 Here, denotes the status (value) of signal/system at time , denotes the status of signal at time and denotes the status of signal at time 1. The value of is calculated by summing over all possible combination of , , and . TE is in the range 0 . Here, . There is another similar equation for . In practice, for the calculation of TE, two additional steps were included to improve the calculation accuracy [7, 10]. Due to the finite size and non-stationarity of data, TE matrices usually contain much noise. In the existing literature, noise/bias has been removed from the estimate of TE by subtracting the average trans-fer entropy from to using shuffled version of denoted by , over several shuffles [10]. contains the same symbol as in but those sym-bols are rearranged in a randomly shuffled order. Then, normalized transfer entropy is calculated from to with respect to the total information in sequence itself. This will represent the relative amount of information transferred by . The normalized transfer entropy (NTE) is shown in Equation 2 as follows [7]: | 2 In equation 2, | represents the conditional entropy of process at time 1 given its value at time as shown in equation 3. | , log ,, 3

Transfer Entropy and Information Flow Patterns in Functional Brain Networks 3

NTE is in the range 0 1 . NTE is 0 when transfers no information to , and is 1 when transfers maximal information to .

3 Functional Brain Networks and Graph Theoretical Analysis

To capture the dynamic interactions between the neuronal elements of human brain, FBNs can be derived from time series observations of EEG signals [2]. As EEG is cheap non-invasive method with high temporal resolution, it has been used extensively in research, medical diagnosis and brain computer interaction [3, 11]. In the present study, the FBNs are constructed by computing the NTE between EEG channels. Ac-cording to the graph theory, graph is a mathematical model that consists of vertices (nodes) where the connection between each pairs of vertices is called an edge (link) [1, 2]. In the case of FBNs, scalp electrodes are considered as vertices and the connec-tions/links between electrodes are measured using correlation. The following graph (or complex network) metrics are also used in this study: connectivity density representing the actual number of edges as a proportion to the total number of possible edges [1]; reciprocity representing the ratio of the number of pairs with a reciprocated edges rela-tive to the total number of edges [12]; clustering coefficient quantifying the fraction of triangles around a node [13]; average of shortest path length between all node pairs known as the characteristic path length [14]; small world representing both high cluster-ing and short characteristics path length [14]; node eccentricity representing the maxim-al shortest path length between any two nodes [12]; and node strength representing the total of all incoming and outgoing link weights [15].

4 Methods

4.1 Participants and EEG Data Acquisition

Six healthy, right handed adults (4 males, 2 females) volunteered for EEG data collec-tion (age range 19-59) at the Cognitive Neuroengineering Laboratory of University of South Australia. The participants were recruited from staff and students cohort of University of South Australia. All reported normal hearing, normal or corrected-to-normal vision, with none reporting any psychological, neurological or psychiatric disorder. EEG data were acquired at a sampling rate of 1000 Hz through a 40 channel Neuroscan Nuamps amplifier using Curry 7 software. The 30 electrode sites used in the current study were based on the international 10-20 convention: FP1, FP2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, T3, C3, Cz, C4, T4, TP7, CP3, CPz, CP4, TP8, T5, P3, Pz, P4, T6, O1, Oz and O2. Continuous EEG data were collected as each par-ticipant undertook three tasks in a Simuride Driving Simulator. In Task 1 (baseline), data were collected for 2 minutes (each) in both eyes open and eyes closed conditions. In Task 2, participants were asked to drive normally on a virtual winding road for approximately 4 minutes. In Task 3, participants were asked to drive, as per Task 2, while also responding to auditory stimuli. All stimulus onsets and participant res-ponses were time-marked on the EEG record using STIM 2 software.

4 M.H.I. Shovon et al.

4.2 EEG Signal Pre-processing

A band pass filter of 1-70 Hz and a notch filter of 50 Hz were applied to the EEG data. Eye blinks were removed using principal component analysis (PCA), with any residual. Bad blocks were removed manually. For Tasks 1 and 2, two seconds epoched averaged data were extracted by applying back-to-back epoching process of Curry software using epoch length of 2 seconds, and then averaging those epochs. For tasks 3, two seconds epochs were extracted by identifying all two seconds epochs from stimulus onsets, where reaction times were between 1.5 and 3 seconds, and then averaging those epochs.

4.3 Proposed Methodology for the Analysis of Directed Functional Brain Network

The pre-processed EEG data during eyes open (EOP), driving only (Drive), and driv-ing with audio distraction (DriveAdo) were used for the construction of TE matrices, where each cell of the TE matrices represents the TE value from one electrode to another. For noise removal, an average shuffled TE matrix (noise matrix) was calcu-lated and subtracted from the original matrix. The data, information processing and associated computational steps are illustrated in Figure 1.

Fig. 1. Framework for transfer entropy and information flow patterns during cognition

Raw EEG Data

Baseline Eyes Open/ Eyes Closed Cognitive load Driving, Driving Audio

EEG Data Acquisition Participant

Pre-processing

Filtering (Band Pass, Notch) Eye Blink Removal (PCA) Epoching and Averaging

EEG Signal Pre-processing Raw EEG Data

Pre-processed EEG Data

-

Transfer Entropy Matrix Noise/ Bias Matrix Normalized Transfer

Entropy matrix

Pre-processed EEG Data

Normalization

Graph Database Construction

(Subtraction)

Results/ Visualization / Graph Theoretical Analysis /Statistical Analysis FP1 FP2

F7F3 Fz F4

F8

FT7FC3 FCz FC4

FT8

T3 C3 Cz C4 T4

TP7CP3 CPz CP4

TP8

T5P3 Pz P4

T6

O1 Oz O2

FP1 FP2

F7F3 Fz F4

F8

FT7FC3 FCz FC4

FT8

T3 C3 Cz C4 T4

TP7CP3 CPz CP4

TP8

T5P3 Pz P4

T6

O1 Oz O2

FP1 FP2

F7F3 Fz F4

F8

FT7FC3 FCz FC4

FT8

T3 C3 Cz C4 T4

TP7CP3 CPz CP4

TP8

T5P3 Pz P4

T6

O1 Oz O2

(a) (b) (c)

Graph Database Weighted/Binary/

Binary (Influential edge)

Transfer Entropy and Information Flow Patterns in Functional Brain Networks 5

The Normalized TE (NTE) matrices were used for the construction of both binary and weighed directed FBNs. The objectives of the proposed methodology are twofold:

1. To quantify the topological features of binary and weighted directed FBNs during different cognitive states; and

2. To determine information flow patterns during different cognitive states.

To investigate the topological features and information flow patterns from con-structed FBNs, the following complex network metrics were used. Connectivity den-sity is calculated by counting the actual number of edges in a graph and then diving by all possible number of edges in that graph. For a directed graph with nodes where there is no self-connections/loop, the total number of possible connections are 1 . Reciprocity of a graph is calculated by finding the ratio of the total num-ber of bidirectional edges relative to the total number of any directional edges be-tween the node pairs. Clustering coefficient has been calculated using the technique of directed variant of clustering coefficient developed by Fagiolo [13]. In a directed graph, 3 nodes can generate up to 8 triangles. The clustering coefficient for node represents the ratio between all directed triangles actually formed by and the number of all possible triangles that could form. The experimental results and analysis are presented in the following section.

5 Results and Discussion

The NTE matrices (each are 30 by 30 in size) during EOP, Drive and DriveAdo states are presented in Figure 2. The increased information flow during cognitive load con-dition is demonstrated by the appearance of more cluttered brighter pixels (see Figure 2b and 2c). From this experiment, it can be inferred that, during cognitive load, in-formation flow generally increases between the electrodes than in baseline condition (EOP).

Fig. 2. Normalized transfer entropy matrix during a) EOP, b) Drive and c) DriveAdo

5.1 Analysis of Binary Directed Functional Brain Network

The binary directed FBNs constructed using NTE matrices with threshold=0.002 was utilized in this study and as shown in Figure 3 demonstrating increased connectivity between the electrodes during the cognitive load. Due to space limitations the result of graph metrics analysis is shown only for one participant. As indicated in Figure 4a connectivity density is higher in the cognitive load than the baseline condition, infer-ring more connections are established to facilitate more active information flow.

0.0 (b) (c)

0.1

(a)

6 M.H.I. Shovon et al.

Figure 4b shows that reciprocity is higher in the cognitive load condition, which further suggests that most electrode pairs are trying to establish mutual connections to facilitate effective information transmission within the network.

Fig. 3. Binary directed functional brain network during a) EOP, b) Drive and c) DriveAdo

Fig. 4. Comparison of a) Connectivity density, and b) Reciprocity of the brain network during EOP, Drive and DriveAdo

Clustering coefficient value increases in almost all of the electrodes during cogni-tive load (Figure 5). This indicates that each nodes nearest neighbors directly communicate and form clusters. This type of segregated neural processing of brain network during cognitive load would increases the local efficiency of the information transfer. The electrodes F3, F8, FT7, FT8 and TP8 are not showing that trend which need to explore in further research.

Fig. 5. Clustering coefficient across electrodes during EOP, Drive and DriveAdo

(a) (b)

00.20.40.6

EOP Drive DriveAdo

Density

0

0.2

0.4

EOP Drive DriveAdo

Reciprocity

Electrodes

Clu

ster

ing

Coe

ffici

ent

FP1 FP2

F7F3 Fz F4

F8

FT7FC3 FCz FC4

FT8

T3 C3 Cz C4 T4

TP7CP3 CPz CP4

TP8

T5P3 Pz P4

T6

O1 Oz O2

FP1 FP2

F7F3 Fz F4

F8

FT7FC3 FCz FC4

FT8

T3 C3 Cz C4 T4

TP7CP3 CPz CP4

TP8

T5P3 Pz P4

T6

O1 Oz O2

FP1 FP2

F7F3 Fz F4

F8

FT7FC3 FCz FC4

FT8

T3 C3 Cz C4 T4

TP7CP3 CPz CP4

TP8

T5P3 Pz P4

T6

O1 Oz O2

(a) (b) (c)

Transfer Entropy and Information Flow Patterns in Functional Brain Networks 7

Between the regular lattice and completely random networks there is a class of networks which has the properties of high clustering and short path length. That means, most of the nodes are not neighbors but most of the nodes can traverse each others by small number of edges. This class of network is called as small world network [14]. Small world network can be also characterized as a set of network with both high local and global efficiency [16]. Efficiency estimates how well the information propagates over the network. The small world properties of the directed FBNs during different cognitive tasks are illustrated in Table 1.

Table 1. Small-worldness of directed FBNs during EOP, Drive and DriveAdo

Cognitive Tasks

/

/ / EOP 0.4735 0.4708 1.5345 1.5264 1.0056 1.0053 1.0003 Drive 0.5736 0.4882 1.4356 1.5126 1.1749 0.9491 1.2680

DriveAdo 0.7930 0.4905 1.2425 1.5011 1.6167 0.8277 1.9532

Here, and are the clustering coefficient of a tested and random network re-spectively; and are the characteristic path length of a tested and random net-work respectively. The results reveal that characteristics path length decreases during cognitive load which would suggest a high global efficiency of information transfer in the FBN. The directed FBNs during EOP, Drive and DriveAdo all exhibit small-worldness ( 1 , however, the FBN constructed during cognitive load exhibits more small world properties than that of the baseline condition suggesting that the FBN facilitates both high local and global efficiency of information transfer.

5.2 Analysis of Binary Directed Functional Brain Network (Influential Edges Only)

Binary FBNs were constructed by keeping only the influential edges between each electrode pair during different cognitive states; namely, two electrodes and , may have two possible edges: and . In the current study, the maximum weight values edge or was chosen.

Fig. 6. Comparison of node eccentricity across electrodes during EOP, Drive and DriveAdo

Electrodes

Ecce

ntric

ity

8 M.H.I. Shovon et al.

Eccentricity was then calculated for different cognitive states (See Figure 6). The maximum number of edges traversed to reach one node from another during EOP, Drive and DriveAdo are 6, 5 and 4 edges, respectively. It may be concluded that, during cognitive load, information may travel pretty quickly in FBN than in baseline condition.

5.3 Analysis of Weighted Directed Functional Brain Network

NTE matrices without applying any threshold were also used to construct weighted FBN. Figure 7 represents the comparison of the node strengths during different cogni-tive states computed using the weighted network. As shown, almost all the electrodes have higher strength values during cognitive load, which indicates that each electrode sends and receives more information during cognitive load.

Fig. 7. Comparison of strength across electrodes during EOP, Drive and DriveAdo

5.4 Statistical Analysis

The following experiments were conducted to demonstrate the statistical significance of information flow during various cognitive states. For the three cognitive states, the total information flow of each electrode to all other electrodes was calculated. One-way analysis of variance (ANOVA) was applied to compare the mean information flow of each group and the multi-comparison results are shown in Figure 8.

Fig. 8. Multi-comparison of mean total information flow of each electrode to all other elec-trodes during different cognitive states for 3 participants (a) DP1, (b) DP2 and (c) DP3 (Y-Axis: Cognitive States 1 for EOP, 2 for Drive, and 3 for DriveAdo)

Electrodes

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ngth

Mean of Total Information Flow

(b)

Mean of Total Information Flow

(c)

0.02 0.04 0.06 0.08 0.1 0.12

3

2

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0.06 0.08 0.1 0.12 0.14 0.16

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0.02 0.03 0.04 0.05 0.06 0.07 0.08

3

2

1

12 00

3

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0.02 0.04 0.06 0.08 0.1 0.1.11212 00Mean of Total Information Flow (a)

Transfer Entropy and Information Flow Patterns in Functional Brain Networks 9

The statistical significance of the differences between means is calculated using t-test at 0.05 (two-tailed) and the results are shown in Table 2. The results dem-onstrate that mean of total information flow is significantly different in EOP and Dri-veAdo experiments for all participants.

Table 2. Statistical validation of mean information flow difference in different cognitive states

States DP 1 DP 2 DP 3 Mean Diff 95% CI Mean Diff 95% CI Mean Diff 95% CI

Drive EOP 0.0244 [0.0244,0.0651] 0.0218 [-0.0033,0.0469] 0.0155* [ 0.0053, 0.0258] DriveAdo Drive -0.0108 [-0.0329,0.0113] 0.0239 [-0.0071,0.0550] 0.0235* [ 0.0110, 0.0360] DriveAdo EOP 0.0339* [0.0191,0.0487] 0.0457* [0.0200,0.0715] 0.039* [0.0285, 0.0495]

*Mean difference is significant at p< .05 level.

6 Conclusion

In this study the information theoretical NTE measure has been applied to construct EEG based directed FBNs in baseline and cognitive load conditions. Using the tech-niques of signal processing, information and graph theoretic measures, and inferential statistics, information flow patterns during cognition have been detected and identi-fied. The overall results demonstrate that the directed FBNs constructed using NTE are sensitive to cognitive load. This sensitivity of NTE based FBN has the potential to assist the development of quantitative metrics to measure cognition. The NTE ap-proach may have likely application in the clinical diagnosis of cognitive impairments in future. Future research should address the application of various graph mining al-gorithms on the constructed directed functional brain networks to detect and track possible patterns as well as quantum of information flow during cognitive activity. This may lead to deeper understanding of cognitive function. Acknowledgement. The authors wish to acknowledge partial support provided by the Defence Science and Technology Organisation (DSTO), Australia. The assistance and technical support provided by fellow researchers Mr Nabaraj Dahal and Mr Naga Dasari are greatly appreciated.

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8. Sabesan, S., Narayanan, K., Prasad, A., Iasemidis, L., Spanias, A., Tsakalis, K.: Informa-tion flow in coupled nonlinear systems: Application to the epileptic human brain. In: Data Mining in Biomedicine, pp. 483503. Springer (2007)

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C.K. Loo et al. (Eds.): ICONIP 2014, Part I, LNCS 8834, pp. 1118, 2014. Springer International Publishing Switzerland 2014

Human Implicit Intent Discrimination Using EEG and Eye Movement

Ukeob Park1, Rammohan Mallipeddi2, and Minho Lee2,*

1 Department of Robot Engineering, Kyungpook National University 2 School of Electronics Engineering,

Kyungpook National University 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, South Korea

uepark@ee.knu.ac.kr, {mallipeddi.ram,mholee}@gmail.com

Abstract. In this paper, we propose a new human implicit intent understanding model based on multi-modal information, which is a combination of eye movement data and brain wave signal obtained from eye-tracker and Electroen-cephalography (EEG) sensors respectively. From the eye movement data, we extract human implicit intention related to features such as fixation count and fixation duration corresponding to the areas of interest (AOI). Also, we analyze the EEG signals based on phase synchrony method. Combining the eye move-ment and EEG information, we train several classifiers such as support vector machine classifier, Gaussian Mixture Model and Nave Bayesian, which can successfully identify the human's implicit intention into two defined categories, i.e. navigational and informational intentions. Experimental results show that the human implicit intention can be better understood using multimodal infor-mation.

Keywords: brain-computer interface (BCI), electroencephalographic (EEG), eye movement, phase synchrony, intent recognition, multi-modality.

1 Introduction

Human intent recognition is crucial for an efficient non-verbal human computer inte-raction. In cognitive science, intention modeling and recognition is considered to create a new paradigm in human computer interface (HCI) and human robot interac-tion (HRI) [1, 2]. In Psychology, human intention can be divided into 2 types expli-cit and implicit [3]. In last few years, several attempts have been made to understand and distinctly recognize subjects implicit intention using techniques like eye move-ment, Electroencephalography (EEG), etc. [4].

In this work, we propose a method to integrate two different modalities, eyeball movement data and EEG data, in order to improve the classification of human's im-plicit intention in real-world environment. Using the multimodal information, we compare performance of several classifiers that is trained to classify human's implicit

* Corresponding author.

12 U. Park, R. Malliped

intention as navigational ament, we compare performsignal) with multi-modal (b

This paper is organized aplicit intentions and presenIn addition, we briefly summ(PLV). In section 3, we pmodel. Section 4 concludes

2 Methods

The proposed method is bmovement patterns and braproposed method. As the mdata from different tools, wthe data simultaneously. InEEG data can be analyzedmultimodal data are collectsify human intention into na

Fig.