Smart Innovation, Systems and Technologies978-3-319-13545-8/1.pdf · Japan Robert J. Howlett KES...

Smart Innovation, Systems and Technologies

Volume 30

Series editors

Robert J. Howlett, KES International, Shoreham-by-Sea, UKe-mail: [email protected]

Lakhmi C. Jain, University of Canberra, Canberra, Australia andUniversity of South Australia, Australiae-mail: [email protected]

About this Series

The Smart Innovation, Systems and Technologies book series encompasses the top-ics of knowledge, intelligence, innovation and sustainability. The aim of the series isto make available a platform for the publication of books on all aspects of single andmulti-disciplinary research on these themes in order to make the latest results avail-able in a readily-accessible form. Volumes on interdisciplinary research combiningtwo or more of these areas is particularly sought.

The series covers systems and paradigms that employ knowledge and intelligencein a broad sense. Its scope is systems having embedded knowledge and intelligence,which may be applied to the solution of world problems in industry, the environmentand the community. It also focusses on the knowledge-transfer methodologies andinnovation strategies employed to make this happen effectively. The combination ofintelligent systems tools and a broad range of applications introduces a need for asynergy of disciplines from science, technology, business and the humanities. Theseries will include conference proceedings, edited collections, monographs, hand-books, reference books, and other relevant types of book in areas of science andtechnology where smart systems and technologies can offer innovative solutions.

High quality content is an essential feature for all book proposals accepted forthe series. It is expected that editors of all accepted volumes will ensure that con-tributions are subjected to an appropriate level of reviewing process and adhere toKES quality principles.

More information about this series at http://www.springer.com/series/8767

Jeffrey W. Tweedale · Lakhmi C. JainJunzo Watada · Robert J. HowlettEditors

Knowledge-Based InformationSystems in Practice

ABC

EditorsJeffrey W. TweedaleAir Operations Division Defence Science

and Technology OrganisationEdinburgh South AustraliaAustralia

Lakhmi C. JainUniversity of Canberra, Australia and

University of South AustraliaSouth AustraliaAustralia

Junzo WatadaWaseda UniversityFukuokaJapan

Robert J. HowlettKES InternationalShoreham-by-seaUnited Kingdom

ISSN 2190-3018 ISSN 2190-3026 (electronic)Smart Innovation, Systems and TechnologiesISBN 978-3-319-13544-1 ISBN 978-3-319-13545-8 (eBook)DOI 10.1007/978-3-319-13545-8

Library of Congress Control Number: 2014956101

Springer Cham Heidelberg New York Dordrecht Londonc© Springer International Publishing Switzerland 2015

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part ofthe 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 informationstorage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodologynow known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoes not imply, even in the absence of a specific statement, that such names are exempt from the relevantprotective 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 bookare believed to be true and accurate at the date of publication. Neither the publisher nor the authors orthe editors give a warranty, express or implied, with respect to the material contained herein or for anyerrors or omissions that may have been made.

Printed on acid-free paper

Springer International Publishing AG Switzerland is part of Springer Science+Business Media(www.springer.com)

This book is dedicated to the valuablecontribution made by all researchers in thefield Knowledge-Based and IntelligentInformation and Engineering Systems.Society continues to benefit from their hardwork and inspiration. The editors also extendtheir unwavering support to those nowinvolved in pursuing classifiers, data mining,knowledge management, AdvancedInformation Processing (AIP) and aknowledge management models. There ishope that these innovations will stimulateincreased autonomy within the digital realm.

Foreword

This book serves a unique need in the information systems research literature bydemonstrating the impact of knowledge-based and intelligent information and engi-neering systems toward solving complex real-world problems. Society is becomingincreasingly sophisticated in the use of intelligent technologies in business, socialand professional arenas to provide solutions that holistically address difficult prob-lems. The ability to combine approaches and technologies has reinvigorated interestin research and innovation centred on knowledge management. The new perspec-tives and interest have generated exciting and active research agendas that underpinnew applications.

Renewed interest in knowledge management is being led by domain experts whoare developing novel intelligent methods or techniques to improve processing andoperations within their industries. Modern technologies such as increased process-ing power, new communication infrastructures, and distributed paradigms facili-tate greater access to data and enable innovative techniques to process them. Theknowledge-based and intelligent information and engineering systems domain is di-verse with many sub-domains that are represented in numerous frontiers. Examplesinclude signal processing, knowledge representation (of structured and unstructureddata), computational modelling, pattern recognition, data analytics, big data, be-havioural modelling, and knowledge discovery.

Although most associated research areas include data mining and artificial intel-ligence domains, the modern paradigm of distributed computing is central to theconnection of systems, organisations, computers, individuals and their communi-ties. Distributed systems add new challenges for knowledge managers and requireintelligent solutions. Although artificial intelligence techniques can be enormouslyeffective when managing a wide breadth of information processing, the problem isoften complicated by a mix of structured and ill-structured (or incomplete) data.The methods highlighted in this book have produced extraordinary success storiesand should serve to spark interest in the growing technical capacity of researchersto advance solutions to applied problems.

The editors are leading contributors to knowledge-based and intelligent informa-tion and engineering systems research. They have compiled a compendium of recent

VIII Foreword

contributions to knowledge management research based on industry and real-worldapplications. They are accomplished researchers in core areas of artificial intelli-gence, and this selection of chapters provides a diverse perspective of their un-derstanding of this domain displayed through recent research to model and solvecomplex real-world problems. The chapters selected for this book provide a refresh-ing approach, covering knowledge management applications that serve to motivatenew research directions in knowledge-based and intelligent information and engi-neering systems.

The first chapter introduces recent research and models. Each of the subse-quent chapters reveals leading-edge research and innovative solutions that employknowledge-based and intelligent information and engineering system techniqueswith an applied perspective. The problems include classifiers, data mining, knowl-edge management, advanced information processing and models that can be usedto solve industrial-level problems. All of the chapters are novel applications thatillustrate how modern research contributes to solving extremely difficult appliedproblems that benefit the community, industry and society. I believe that the readerwill discover that this book will spark new ideas and future research directions forresearchers and practitioners alike.

Gloria Phillips-Wren, Ph.D.Professor of Information Systems

Loyola University Maryland

Preface

Knowledge-Based and Intelligent Information and Engineering Systems is a fieldof study related to innovative knowledge maangement to achieve intelligent tasks.Research is predominantly software based, however requires and incorporates tech-nology to build new or innovative tools that ultimately benefit society. Industry con-tinues to invest in Knowledge-Based and Intelligent Information and EngineeringSystems, with many creating internal reaserch facilities. During the 20th century,Industry has been heavy invester in this sector and the trend is increasing. Thishigfhlights the evolution towards a mechanized workforce, resulting in a shift to-wards more intelligent robots and unmanned systems. A similarly growth area isalso being reflecting in agriculture, medicine and portable health care products.

Intelligent Systems are becoming ubiquitous in a wide range of situations. Theseinclude facets of simple everyday actions on mobile devices and advanced enter-prise level applications. Society continues to benefits from application of innovativeknowledgement management techniques and expert decision making in knowledge-based systems. Although knowledge engineering relies on the exploitation of Ar-tificial Intelligence techniques, the field relies on innovative researchers exploringsolutions to an ever increasing span of industrial level problems.

This evolution in knowledgement management techniques has become a perva-sive phenomenon wihtin the community. These techniques are rapidly being em-ployed within the mobile computing domain and continue to promote the unbiquo-tous access to information resources. Technolgy is enabling increased processingcapabilities to hand-held devices, forcing more innovative access techniques to ex-isting intelligent systems. Society is begining to demand everyday applications thatprovide convenient access to the wealth of information processing systems servingthe public. To achieve this, we must take advantage of the most recent research ininformation technologies.

We have choosen a handfull of world class contributions from leading-edge re-searchers to provide readers with the abilitiy explore cutting edge examples of thisevolvution in a single volume. These experts share a combined knowledge of overa century of experience in promoting and sharing advancements in both ArtificialIntelligence and Knowledge-based engineering. The editors are proud to offer this

X Preface

ensemble of contributions and the reader is encouraged to explore the introductionto orient their expectations prior to focusing on any specific topic(s) of interest. Theyare also encouraged to extend there interest by exploring the remaining chapters toobtain an up to date exposure of a diverse range of Knowledge-Based and Intelli-gent Information and Engineering Systems topics and techniques. Several chaptersare also dedicated to employing knowledge management methodologies that helpsolve a diverse range of problems experienced by industry. We hope you enjoy theinovations presentated as much as we did shepherding these contributuons into print.

July 2014 Jeffrey W. Tweedale,Lakhmi C. Jain,

Junzo Watada,Robert J. Howlett

Acknowledgements

This publication would not be possible without the dedication and innovation ofresearchers within the field of Knowledge-Based and Intelligent Information andEngineering Systems. The continuous evolution of technicques and methodolgiesare rapidly being adapted to more diverse problems, ultimately enhancing the fea-tures in modern day applicaitons. This culmination of mutli-disciplined skills are be-ing employed to create innovative solutions for classifiers, data mining, knowledgemanagement, Advanced Information Processing (AIP) and a number of models thatcan be used to solve industrial level problems. Obviously without the contributionof the founding subject matter experts, these existing researchers may have pursuedcompletely different disciplines. This book provide prospective researchers with avital source to motiviation and help formulate their vision to ultimately produceinnovative solutions in the future.

Similarly, without the efforts of the chairs who organised the 17th annual confer-ence on Knowledge-Based and Intelligent Information and Engineering Systems1,the ideas for this publication would not exist. The scene for this topic was stimu-lated through the rich source of contributions to this anual event. The authors wereselected based on their initial contributions published in those proceedings in theProcedia Computer Science [1]. These include topics relating to:

• Multi-Step-Ahead Reservoir Inflow Forecasting by Artificial Neural Networks[2]

• Data Jackets for Synthesizing Values in the Market of Data [3]• Efficient Maximum Range Search on Remote Spatial Databases Using k-Nearest

Neighbor Queries [4]• Motion estimations based on invariant moments for frames interpolation in stere-

ovision [5]• Dual Decomposition for Vietnamese Part-of-Speech Tagging [6]• A Long-term Data Collection System for Life Pattern Sensor [7]• A Greedy Algorithm for k-Member Co-clustering and Its Applicability to Col-

laborative Filtering [8]

1 Held in Kitakyushu, Japan from 9–11 September 2013.

XII Acknowledgements

• Extended Interval-Valued Confidence for Inference of Knowware System usingHybrid Logic [9]

• An effective method for habitual behavior extraction from the Internet [10]• Method of Embodying the Meaning of Headlines using News Articles [11]• Integrating Robot Task Planner with Common-Sense Knowledge Base to Im-

prove the Efficiency of Planning [12]• Using Multi-Agent Systems to Pursue Autonomy with Automated Components

[13]• Application Methods for Genetic Algorithms for the Search of Feed Positions in

the Design of a Reactive Distillation Process [14]• Scalable Adaptive Group Communication for Collaboration Framework of Cloud-

Enable Robots [15]• Extraction of Daily Life Log Measured by Smart Phone Sensors using Neural

Computing [16]• Shape from Endoscope Image based on Photometric and Geometric Constraints

[17]• Towards Trial Simulation of Homogeneous Behavior [18]• Computational Techniques for Characterizing Cognition using EEG - New Ap-

proaches [19]• Interlinking documents based on semantic graphs [20]• Self-destruction Dynamics of HIV-1 Quasi-species Population in the Presence of

Mutagenic Activities [21]• Modified Hybridized Multi-Agent Oriented Approach to Analyze Work-stress

Data Providing Feedback in Real Time [22]

References

[1] Watada, J., Jain, L.C., Howlett, R.J., Mukai, N., Asakura, K. (eds.): 17th InternationalConference in Knowledge Based and Intelligent Information and Engineering Sys-tems, Kitakyushu, Japan, September 9-11. Procedia Computer Science, vol. 22. Elsevier(2013)

[2] Chang, F.J., Lo, Y.C., Chang, L.C., Shieh, M.C.: Multi-step-ahead reservoir inflow fore-casting by artificial neural networks. In: 17th International Conference in KnowledgeBased and Intelligent Information and Engineering Systems. Procedia Computer Sci-ence, vol. 22, pp. 412–421 (2013)

[3] Ohsawa, Y., Kido, H., Hayashi, T., Liu, C.: Data jackets for synthesizing values in themarket of data. In: 17th International Conference in Knowledge Based and IntelligentInformation and Engineering Systems. Procedia Computer Science, vol. 22, pp. 709–716 (2013)

[4] Sato, H., Narita, R.: Efficient maximum range search on remote spatial databases usingk-nearest neighbor queries. In: 17th International Conference in Knowledge Based andIntelligent Information and Engineering Systems. Procedia Computer Science, vol. 22,pp. 836–845 (2013)

Acknowledgements XIII

[5] Favorskaya, M., Pyankov, D., Popov, A.: Motion estimations based on invariant mo-ments for frames interpolation in stereovision. In: 17th International Conference inKnowledge Based and Intelligent Information and Engineering Systems. Procedia Com-puter Science, vol. 22, pp. 1102–1111 (2013)

[6] Bach, N.X., Hiraishi, K., Minh, N.L., Shimazu, A.: Dual decomposition for vietnamesepart-of-speech tagging. In: 17th International Conference in Knowledge Based and In-telligent Information and Engineering Systems. Procedia Computer Science, vol. 22,pp. 123–131 (2013)

[7] Kushiro, N., Ide, T., Katsukura, M., Higuma, T.: A long-term data collection system forlife pattern sensor. In: 17th International Conference in Knowledge Based and Intelli-gent Information and Engineering Systems. Procedia Computer Science, vol. 22, pp.485–493 (2013)

[8] Kawano, A., Honda, K., Kasugai, H., Notsu, A.: A greedy algorithm for k-memberco-clustering and its applicability to collaborative filtering. In: 17th International Con-ference in Knowledge Based and Intelligent Information and Engineering Systems. Pro-cedia Computer Science, vol. 22, pp. 477–484 (2013)

[9] Ding, L., Lo, S.L.: Extended interval-valued confidence for inference of knowware sys-tem using hybrid logic. In: 17th International Conference in Knowledge Based andIntelligent Information and Engineering Systems. Procedia Computer Science, vol. 22,pp. 873–882 (2013)

[10] Suzuki, N., Tsuda, K.: An effective method for habitual behavior extraction from theinternet. In: 17th International Conference in Knowledge Based and Intelligent Infor-mation and Engineering Systems. Procedia Computer Science, vol. 22, pp. 599–605(2013)

[11] Imono, M., Yoshimura, E., Tsuchiya, S., Watabe, H.: Method of embodying the mean-ing of headlines using news articles. In: 17th International Conference in KnowledgeBased and Intelligent Information and Engineering Systems. Procedia Computer Sci-ence, vol. 22, pp. 260–268 (2013)

[12] Al-Moadhen, A., Qiu, R., Packianather, M., Ji, Z., Setchi, R.: Integrating robot taskplanner with common-sense knowledge base to improve the efficiency of planning. In:17th International Conference in Knowledge Based and Intelligent Information and En-gineering Systems. Procedia Computer Science, vol. 22, pp. 211–220 (2013)

[13] Tweedale, J.W.: Using multi-agent systems to pursue autonomy with automated com-ponents. In: 17th International Conference in Knowledge Based and Intelligent Infor-mation and Engineering Systems. Procedia Computer Science, vol. 22, pp. 1369–1378(2013)

[14] Tun, L.K., Matsumoto, H.: Application methods for genetic algorithms for the searchof feed positions in the design of a reactive distillation process. In: 17th InternationalConference in Knowledge Based and Intelligent Information and Engineering Systems.Procedia Computer Science, vol. 22, pp. 623–632 (2013)

[15] Mateo, R.M.A.: Scalable adaptive group communication for collaboration frameworkof cloud-enabled robots. In: 17th International Conference in Knowledge Based andIntelligent Information and Engineering Systems. Procedia Computer Science, vol. 22,pp. 1239–1248 (2013)

[16] Botzheim, J., Tang, D., Yusuf, B., Obo, T., Kubota, N., Yamaguchi, T.: Extraction ofdaily life log measured by smart phone sensors using neural computing. In: 17th Inter-national Conference in Knowledge Based and Intelligent Information and EngineeringSystems. Procedia Computer Science, vol. 22, pp. 883–892 (2013)

XIV Acknowledgements

[17] Tatematsu, K., Iwahori, Y., Nakamura, T., Fukui, S., Woodham, R.J., Kasugai, K.: Shapefrom endoscope image based on photometric and geometric constraints. In: 17th Inter-national Conference in Knowledge Based and Intelligent Information and EngineeringSystems. Procedia Computer Science, vol. 22, pp. 1285–1293 (2013)

[18] Nomakuchi, T., Kuroki, H., Takahashi, M.: Towards trial simulation of homogeneousbehavior. In: 17th International Conference in Knowledge Based and Intelligent Infor-mation and Engineering Systems. Procedia Computer Science, vol. 22, pp. 1336–1343(2013)

[19] Nandagopal, N., Vijayalakshmi, R., Cocks, B., Dahal, N., Dasari, N., Thilaga, M., Dhar-wez, S.S.: Computational techniques for characterizing cognition using EEG data – newapproaches. In: 17th International Conference in Knowledge Based and Intelligent In-formation and Engineering Systems. Procedia Computer Science, vol. 22, pp. 699–708(2013)

[20] Nunes, B.P., Kawase, R., Fetahu, B., Dietze, S., Casanova, M.A., Maynard, D.: In-terlinking documents based on semantic graphs. In: 17th International Conference inKnowledge Based and Intelligent Information and Engineering Systems. Procedia Com-puter Science, vol. 22, pp. 231–240 (2013)

[21] Harada, K.: Self-destruction dynamics of hiv-1 quasi-species population in the presenceof mutagenic activities. In: 17th International Conference in Knowledge Based and In-telligent Information and Engineering Systems. Procedia Computer Science, vol. 22,pp. 1259–1265 (2013)

[22] Ghosh, A., Tweedale, J.W., Nafalski, A.: Modified hybridized multi-agent oriented ap-proach to analyze work-stress data providing feedback in real time. In: 17th Interna-tional Conference in Knowledge Based and Intelligent Information and EngineeringSystems. Procedia Computer Science, vol. 22, pp. 1092–1101 (2013)

Contents

1 Advances in Knowledge-Based Information Systems . . . . . . . . . . . . . . 1Jeffrey W. Tweedale, Lakhmi C. Jain1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Advanced Information Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3 Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4.1 Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.4.2 Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.4.3 Knowledge Management . . . . . . . . . . . . . . . . . . . . . . . . . . 101.4.4 Advanced Information Processing . . . . . . . . . . . . . . . . . . . 121.4.5 Modelling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Part I: Classification

2 A Basic Study for Realizing Life Event Sensor for Home EnergyManagement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Noriyuki Kushiro2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.2 Life Event Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.2.1 Overview of Life Event Sensor . . . . . . . . . . . . . . . . . . . . . 222.2.2 Field Test Results for the Prototype of the Sensor . . . . . . 25

2.3 Long Term Filed Data Collection System . . . . . . . . . . . . . . . . . . . . 252.3.1 Long Term Data Collection System . . . . . . . . . . . . . . . . . 262.3.2 Data Analysis Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.4 Discussion about Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.4.1 Can Current Waveforms Be Assumed to Be in a

Stable Condition in a House? . . . . . . . . . . . . . . . . . . . . . . 272.4.2 Can Sequence of Electronic Activity Indicate all

Residents’ Real Life Events? . . . . . . . . . . . . . . . . . . . . . . . 32

XVI Contents

2.4.3 Can Residents Manage Energy by Knowing TheirOwn Life Events or Life Pattern? . . . . . . . . . . . . . . . . . . . 34


3 A Greedy Fuzzy k-Member Co-clustering Algorithmand Collaborative Filtering Applicability . . . . . . . . . . . . . . . . . . . . . . . . 39Katsuhiro Honda, Arina Kawano, Akira Notsu3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.2 k-Member Clustering-Based k-Anonymization

and Co-clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.2.1 k-Member Clustering and Its Fuzzy Variant . . . . . . . . . . . 403.2.2 Co-clustering and k-Member Co-clustering . . . . . . . . . . . 41

3.3 Fuzzy k-Member Co-clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433.4 Numerical Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.4.1 Comparison of Aggregation Quality . . . . . . . . . . . . . . . . . 453.4.2 Comparison of Applicability of k-Member

Co-clusters in Collaborative Filtering . . . . . . . . . . . . . . . . 463.4.3 Comparison of Applicability of Published Data in

Hybrid Use with GroupLens Recommendation . . . . . . . . 473.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4 Method of Embodying the Newspaper Headlines by Using Wordsand Phrases in the Article . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Misako Imono, Eriko Yoshimura, Seiji Tsuchiya, Hirokazu Watabe4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.2 Background and Outline of the Proposed Method . . . . . . . . . . . . . 524.3 Association System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.3.1 Concept Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.3.2 The Degree of Association . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.4 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.4.1 Analyzing Five W’s and the Verb . . . . . . . . . . . . . . . . . . . 564.4.2 Adding the Verb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574.4.3 Adding WHEN and WHERE . . . . . . . . . . . . . . . . . . . . . . . 584.4.4 Replacing WHO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.5 Evaluation and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5 3D Shape Recovery from Endoscope Image Based on BothPhotometric and Geometric Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 65Yuji Iwahori, Keita Tatematsu, Tsuyoshi Nakamura, Shinji Fukui,Robert J. Woodham, Kunio Kasugai5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665.2 Fast Marching Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Contents XVII

5.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685.3.1 Photometric Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695.3.2 Geometric Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715.3.3 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715.3.4 Shape Recovery by Optimization . . . . . . . . . . . . . . . . . . . 735.3.5 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745.4.1 Computer Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745.4.2 Experiments with Real Data . . . . . . . . . . . . . . . . . . . . . . . . 75


Part II: Data Mining and Classification

6 Innovators Marketplace on Data Jackets, for Valuating, Sharing,and Synthesizing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Yukio Ohsawa, Hiroyuki Kido, Teruaki Hayashi, Chang Liu,Kazuhiro Komoda6.1 Introduction: Why We Need the Market of Data . . . . . . . . . . . . . . . 836.2 One Way to Go: Innovators Marketplace on Data Jackets . . . . . . . 84

6.2.1 Innovators Marketplace as the Model of ValueCo-creative Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

6.3 A Preliminary Experimental Workshop as a Background forDiscussing Technologies Desired for Realizing IMDJ . . . . . . . . . . 896.3.1 Mining and Visualizing Text – Steps (1) and (2) . . . . . . . 896.3.2 Semantic Information Processing – Visualization of

Step (3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916.3.3 Aiding Market-Oriented Co-creative

Communication, Correspondingto Step (3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6.4 Tools as Fruits of a Case of IMDJ . . . . . . . . . . . . . . . . . . . . . . . . . . . 926.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

7 Regular Polygon Based Search Algorithm for ProcessingMaximum Range Queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Hideki Sato, Ryoichi Narita7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1007.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1017.3 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027.4 Search Algorithm for Aggregate Range Query . . . . . . . . . . . . . . . . 104

7.4.1 Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047.4.2 Regular Polygon Based Search Algorithm . . . . . . . . . . . . 106

7.5 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1077.5.1 Influence of Regular Polygon in Performance . . . . . . . . . 108

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7.5.2 Performance of Regular Polygon based SearchAlgorithm over Synthetic Datasets . . . . . . . . . . . . . . . . . . 108

7.5.3 Case Study: Maximum k-th Range Queries overPublic Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110


8 Computational Neuroengineering Approaches to CharacteriseCognitive Activity in EEG Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115D. Nandagopal, R. Vijayalakshmi, Bernie Cocks, Nabaraj Dahal,Naga Dasari, M. Thilaga8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1168.2 Review of Computational Techniques to Identify Cognitive

Activity Using EEG Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1178.2.1 Graph Theoretic Approach . . . . . . . . . . . . . . . . . . . . . . . . . 1188.2.2 Pearson’s Correlation Coefficient . . . . . . . . . . . . . . . . . . . 1188.2.3 Magnitude Squared Coherence . . . . . . . . . . . . . . . . . . . . . 1198.2.4 Entropy and Mutual Information . . . . . . . . . . . . . . . . . . . . 1198.2.5 Approximate Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1218.2.6 Voronoi Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1228.2.7 Centrality Measures in Complex Networks . . . . . . . . . . . 1228.2.8 Degree Centrality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1228.2.9 Eigenvector Centrality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1238.2.10 Closeness Centrality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1238.2.11 Betweenness Centrality . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

8.3 Methodology to Characterise Functional Brain Networksduring Cognitive Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1248.3.1 Participants and Data Acquisition . . . . . . . . . . . . . . . . . . . 1248.3.2 EEG Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1258.3.3 Change Detection in Functional Brain Networks . . . . . . . 127

8.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1288.5 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

9 Interlinking Documents Based on Semantic Graphs with anApplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139Bernardo Pereira Nunes, Besnik Fetahu, Ricardo Kawase,Stefan Dietze, Marco Antonio Casanova, Diana Maynard9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1409.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1419.3 Document Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

9.3.1 A Novel Approach to Document Connectivity . . . . . . . . . 1439.4 Evaluation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

9.4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1469.4.2 Gold Standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1469.4.3 Evaluation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

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9.4.4 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1489.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

9.5.1 Document Connectivity Results . . . . . . . . . . . . . . . . . . . . 1499.5.2 Analysis of the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

9.6 Use Case Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1519.6.1 Exploratory Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1519.6.2 Semantic Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1519.6.3 Search Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . 1529.6.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

9.7 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Part III: Knowledge Management

10 An Interval-Valued Confidence for Inference in HybridKnowledge-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Liya Ding, Sio-Long Lo10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16010.2 Inference in Knowware System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

10.2.1 KWS Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16110.2.2 Truth Value Flow Inference on Knowledge

Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16210.2.3 Hybrid Logic Applied in KWS . . . . . . . . . . . . . . . . . . . . . 163

10.3 Inference with Interval-Valued Confidence . . . . . . . . . . . . . . . . . . . 16610.3.1 Interval-Valued Confidence . . . . . . . . . . . . . . . . . . . . . . . . 16610.3.2 Operations on IVC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16810.3.3 Example 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

10.4 Extended Interval-Valued Confidence for Inference in HybridLogic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17410.4.1 Extended Interval-Valued Confidence . . . . . . . . . . . . . . . . 17410.4.2 Joint Credibility Represented as Extended IVC . . . . . . . . 17610.4.3 Inference in Hybrid Logic with EIVC . . . . . . . . . . . . . . . . 17710.4.4 Example 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178


11 Improving the Efficiency of Robot Task Planning byAutomatically Integrating Its Planner and Common-SenseKnowledge Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185Ahmed Al-Moadhen, Michael Packianather, Renxi Qiu, Rossi Setchi,Ze Ji11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18611.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18711.3 Planning System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

11.3.1 Semantic Action Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 18811.3.2 Common-Sense Knowledge Base . . . . . . . . . . . . . . . . . . . 190

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11.3.3 Semantic Realization and Refreshment Module . . . . . . . 19111.3.4 Semantic Action to Domain Definition Algorithm . . . . . 19211.3.5 Planner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

11.4 Problem Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19211.5 Extending Initial State and Action Details by Semantic

Realization and Refreshment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19311.6 Plan Accuracy Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19311.7 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

11.7.1 Navigation Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19511.7.2 Manipulation Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19611.7.3 Bringing Water Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 196

11.8 Conclusions and Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

12 Scalable Group Communication for Efficient Knowledge Sharingin Cloud-Enabled Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201Romeo M. Mateo, Bobby Gerardo, Jaewan Lee12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20112.2 Background and Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

12.2.1 Networked Robots and Cloud-Enabled Robots . . . . . . . . 20312.2.2 Group Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20312.2.3 Proposed Group Communication for Cloud-Enabled

Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20412.3 Knowledge-Sharing Model for Cloud-Enabled Robots . . . . . . . . . 204

12.3.1 Robot Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20512.3.2 Robot Ontology and Cloud Collaboration

Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20712.4 Scalable Grouping and Adaptive Scheme in CCF . . . . . . . . . . . . . . 208

12.4.1 Grouping Cloud Robots Based on Fuzzy Algorithm . . . . 20812.4.2 Adaptive Communication Based on Brownian Agent . . . 210

12.5 Implementation and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21112.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

13 A Theoretical Basis for an Acquired ImmunodeficiencySyndrome Treatment by a Mutagenesis Approach . . . . . . . . . . . . . . . . 217Kouji Harada13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21713.2 HIV Mathematical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

13.2.1 Phenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22013.2.2 HIV Infection, Mutation, and Replication Processes . . . . 220

13.3 Dynamical Analyses of Viral Dynamics under Control ofMutation Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22213.3.1 Viral Destruction Event . . . . . . . . . . . . . . . . . . . . . . . . . . . 22213.3.2 The Asymptotic Stability Analysis of the Viral

Destruction State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224

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13.3.3 A Relationship between Viral Productivity R and theViral Destruction Event . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

13.4 Combinational Effects of Anti-HIV Drugs and a Mutagen . . . . . . 22613.4.1 HIV Reverse Transcriptase Inhibitor . . . . . . . . . . . . . . . . . 22613.4.2 HIV Protease Inhibitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22713.4.3 Both Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228


Part IV: Advanced Information Processes

14 Multi-Step-Ahead Reservoir Inflow Forecasting by ArtificialIntelligence Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235F.J. Chang, Y.C. Lo, P.A. Chen, L.C. Chang, M.C. Shieh14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23614.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

14.2.1 Pearson Correlation Coefficient . . . . . . . . . . . . . . . . . . . . . 23714.2.2 Back Propagation Neural Network . . . . . . . . . . . . . . . . . . 23814.2.3 Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . 239

14.3 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24014.3.1 Study Area and Data Collection . . . . . . . . . . . . . . . . . . . . 24014.3.2 Evaluation Criteria for Model Performance . . . . . . . . . . . 241

14.4 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24214.4.1 Time Delay and Duration Analysis . . . . . . . . . . . . . . . . . . 24214.4.2 Performance of Multi-Step-Ahead Real-Time

Reservoir Inflow Forecasting . . . . . . . . . . . . . . . . . . . . . . 24314.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248

15 Enhancing the Degree of Autonomy by Creating AutomatedComponents within a Multi-Agent System Framework . . . . . . . . . . . . 251Jeffrey W. Tweedale15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25215.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25415.3 The Need for Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25615.4 Cognitive Processing within Machines . . . . . . . . . . . . . . . . . . . . . . . 25915.5 Multi-Agent System Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26115.6 Component Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26315.7 Cognitive Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26415.8 Candidate Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26615.9 Conclusion and Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272

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16 Structured Learning for Extraction of Daily Life Log Measuredby Smart Phone Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277Janos Botzheim, Naoyuki Kubota16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27716.2 Information Support by Daily Life Log . . . . . . . . . . . . . . . . . . . . . . 278

16.2.1 Informationally Structured Space . . . . . . . . . . . . . . . . . . . 27916.2.2 Sensory Inputs from a Smart Phone . . . . . . . . . . . . . . . . . 280

16.3 Structured Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28116.3.1 Growing Neural Gas for Information Extraction . . . . . . . 28116.3.2 Spiking Neural Network for Activity Estimation . . . . . . . 28516.3.3 External Input of the SNN . . . . . . . . . . . . . . . . . . . . . . . . . 28616.3.4 Output of the SNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288

16.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28916.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292

17 Optimizing Stressors Using Genetic Algorithm to MinimizeWork-Related Stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295A. Ghosh, J.W. Tweedale, A. Nafalski17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29517.2 Background Knowledge Used to Design and Develop

IMADA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29617.2.1 Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29717.2.2 Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . 29817.2.3 Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29917.2.4 Hybrid Intelligent System . . . . . . . . . . . . . . . . . . . . . . . . . 29917.2.5 Work-Related Stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299

17.3 IMADA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30017.4 Computational Complexity of the Model . . . . . . . . . . . . . . . . . . . . . 30317.5 Overview of Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30417.6 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30617.7 The Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30717.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310

18 Application Methods for a Niche Genetic Algorithm for Design ofReactive Distillation Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313Hideyuki Matsumoto, Kai Tun Lim, Chiaki Kuroda, Takehiro Yamaki,Keigo Matsuda18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31418.2 Methodology for Optimization Using a Niche GA . . . . . . . . . . . . . 315

18.2.1 Coding of GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31518.2.2 Structural Representation of RD Column . . . . . . . . . . . . . 31718.2.3 Mutation and Crossover Operations . . . . . . . . . . . . . . . . . 31718.2.4 Niche Selection and Replacement . . . . . . . . . . . . . . . . . . . 318

18.3 Case Study for RD Process with Multiple Feeds . . . . . . . . . . . . . . . 319

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18.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32118.4.1 Optimization of RD Process with Multiple Feeds . . . . . . 32118.4.2 Utilization of Preferable Solutions for Sensitivity

Analysis of RD Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 32218.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325

Part V: Modelling and Simulation

19 Accurate Motion Estimation Based on Moment Invariants andHigh Order Statistics for Frames Interpolation in Stereo Vision . . . . 329Margarita Favorskaya, Dmitriy Pyankov, Aleksei Popov19.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32919.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33119.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33519.4 Background Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33519.5 Accurate Motion Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337

19.5.1 Block-Matching Motion Estimation . . . . . . . . . . . . . . . . . 33719.5.2 Motion Estimation Based on Hu and Zernike

Moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33919.5.3 Motion Estimation Based on Kurtosis . . . . . . . . . . . . . . . . 341

19.6 Frames Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34319.7 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34419.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348

20 A Joint Model for Vietnamese Part-of-Speech Tagging UsingDual Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353Ngo Xuan Bach, Kunihiko Hiraishi, Nguyen Le Minh, Akira Shimazu20.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35320.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355

20.2.1 Characteristics of Vietnamese Words . . . . . . . . . . . . . . . . 35520.2.2 Maximum Entropy Models . . . . . . . . . . . . . . . . . . . . . . . . 35620.2.3 Lagrangian Relaxation and Dual Decomposition for

Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35720.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359

20.3.1 Base Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35920.3.2 Dual Decomposition for Vietnamese POS Tagging . . . . . 359

20.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36220.4.1 Data and Experiment Setting . . . . . . . . . . . . . . . . . . . . . . . 36220.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36220.4.3 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36320.4.4 Running Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364

20.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365

XXIV Contents

21 Habitual Behavior Extraction with Statistical Topic Model fromthe Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369S. Nobuo, T. Kazuhiko21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36921.2 Methods of Extracting Behavioral Information . . . . . . . . . . . . . . . . 37021.3 Extraction of the Habitual Behavioral Information by Using

LDA and PMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37121.3.1 The Habitual Behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . 37121.3.2 Extracting Candidates of the Habitual Behaviors

with LDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37121.3.3 Selecting the Candidates by PMI . . . . . . . . . . . . . . . . . . . . 372

21.4 The Evaluation Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37321.4.1 Question-Answering Sites of Telecommunication

Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37421.4.2 Question-Answering Sites of Transportation . . . . . . . . . . 376


22 A Study on the Influence of the Competitive Environment on theCorporate Strategic Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381Takao Nomakuchi, Hiroshi Kuroki, Masakazu Takahashi22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381

22.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38222.1.2 Multi-Agent System Simulation . . . . . . . . . . . . . . . . . . . . 382

22.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38322.2.1 Homogeneous Behavior in the Theory of

Management Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38322.2.2 Homogeneous Behavior in MAS . . . . . . . . . . . . . . . . . . . . 385

22.3 Design and Development of Homogeneous Behavior MAS . . . . . 38622.4 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389

22.4.1 MAS Experiment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39022.4.2 MAS Experiment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39022.4.3 Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391


Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393

Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395

Acronyms

ACCESS Agents Channelling ContExt Sensitive ServicesACL Agent Communication LanguagesAcOH Acetate AcidACTR Adaptive Character of Thought – RationalACT Australian Capital TeritorryA/C Air ConditionerAFAPL Agent Factory Agent Programming LanguageAFSE Agent Factory Standard EditionAIDS Acquired ImmunoDeficiency SyndromeAI Artificial IntelligenceALU Arithmetic Logic UnitAgNN Aggregate Nearest NeighborANN Artificial Neural NetworksANOVA ANalysis Of VArianceAOS Agent Oriented SoftwareApEn Approximate EntropyAPI Application Program InterfaceARV AntiRetroViralAWB Australian Workplace BarometerBBGDS Block-Based Gradient Descent SearchBC Betweenness CentralityBDI Beliefs, Desires, IntentionsBISC Berkeley Initiative in Soft ComputingBMA Block-Matching AlgorithmBPMN Business Process Management NotationBPNN Back Propagation Neural NetworkC2 Command and ControlCAF Cognitive Analysis FrameworkcART Combination AntiRetoviral TherapyCAST Collaborative Agent for Simulating TeamworkCATI Computer Aided Telephone Interviews

XXVI Acronyms

CBM Co-occurrence-based methodCBR Cased Based ReasoningCCF Cloud Collaboration FrameworkCCR5 C-C chemokine receptor 5CC Closeness CentralityCoC Correlation CoefficientCDS Cross-Diamond SearchCELEST Center of Excellence for Learning in Education, Science and

TechnologyCEO Chief Executive OfficerCE Coefficient of EfficiencyCI Computational IntelligenceCMOS Complimentary Metal Oxide SemiconductorCogLoad Cognitive LoadsCPU Central Processing UnitCQPlist Candidate Query Point listCRF Conditional Random FieldsCRUD Creation, Reading, Updating and DeletingCWB Central Weather BureauDAI Distributed Artificial IntelligenceDARPA Defence Advanced Research Project AgencyDA Decision AnalyzerDC Degree CentralityDE Differential EvolutionDFD Displaced Frame DifferenceDIARG Dynamic Inter-Agent Rule GenerationdMars distributed Multi-Agent Reasoning SystemDMS Decisional Model of ServiceDNA DeoxyriboNucleic AcidDoA Degree of AssociationDPS Distributed Problem SolvingDSS Decision Support SystemDS Diamond SearchEAS Extended Action SetECl Eyes ClosedEEG ElectroencephalographyEIS Extended Initial StateEIVC Extended Interval-Valued ConfidenceEJB Enterprise Java BeansEOP Eyes OPenEOQ Economic Order QuantityEP Evolutionary ProgrammingESA Explicit Semantic AnalysisEtOAc Ethyl AcetateEtOH Ethanol

Acronyms XXVII

ETVFI Extended Truth Value Flow InferenceEVC Eigen Vector CentralityFAST-ER FAST-Enhanced RepeatabilityFAST Features from Accelerated Segment TestFCCM Fuzzy Clustering for Categorical Multivariate dataFCM Fuzzy c-MeansFFS Fast Full SearchFFT Fast Fourier-TransformFIPA Foundation of Intelligent Physical AgentsFLR Fuzzy Lattice ReasoningFL Fuzzy LogicFMM Fast Marching MethodfMRI functional Magnetic Resonance ImagingFPGA Field Programmable Grid or Gate ArraysFSS Four Step SearchFS Full SearchGA Genetic AlgorithmGBD Generalised Benders DecompositionGCS Growing Cell StructuresGE General Electric CompanyGNG Growing Neural GasGPS Global Positioning SystemGPU Graphics Processing UnitGP Genetic ProgrammingGSN Goal Structuring NotationGUI Graphical User InterfaceHAART Highly Active AntiRetroviral TherapyHCI Human Computer InteractionHDD Hard Disk DriveHEMS Home Energy Management SystemHIL human-in-the-loopHIV Human Immunodeficiency VirusHP Hewlett PackardHR high-resolutionHTML HyperText Markup LanguageHTN Hierarchical Task NetworkHTTP Hyper Text Transfer ProtocolHTTPS HyperText Transfer Protocol SecureIA Intelligent AgentIAF Intelligent Agent FrameworkICA Independent Component AnalysisIDC Intelligent Design ChoiceIDE Integrated Development EnvironmentIDL Interface Description LanguageIDSS Intelligent Decision Support System

XXVIII Acronyms

iLand The Individual-based LANdscape and Disturbance modelIMADA Intelligent Multi-Agent Decision AnalyserIMM Individual Mental ModelIMS Information Management SystemINSTI INtegrase Strand Transfer InhibitorsINS Inertial Navigation SystemIQ Intelligence QuotientIR Information RetrievalISS Informationally Structured SpaceIS Information SystemIT Information TechnologyIVC Interval-Valued ConfidenceJACK Java Agent Compiler and KernelJADE Java Agent DEvelopment frameworkJAF JavaBeans Activation FrameworkJAM Java Agent ModelJARE Java Automatic Reasoning EngineJAX-RPC Java API for XML based RPCJAX-WS Java XMLWeb ServicesJAXM Java API XML MessagingJAXP Java API for XML ProcessingJCE Java Cryptography ExtensionJCS Joint Cognitive SystemsJDBC Java Data Base ConnectivityJDIC JDesktop Integration ComponentsJDK Java Developers KitJDL Joint Director of LaboratoriesJSR Java Specification ProgramJVM Java Virtual Machinek-ANN Aggregate k-Nearest Neighbork-NN k-Nearest NeighborKAOS Keep All Objectives SatisfiedKBS Knowledge-Based SystemKB Knowledge BaseKCDS Kite Cross Diamond SearchKES Knowledge-Based Intelligent Information and Engineering SystemsKIF Knowledge Interchange FormatKQML Knowledge Query Manipulation LanguageKWS Knowware SystemLAN Local Area NetworkLBS Location Based ServicesLCD Liquid Crystal DisplayLC Learning ComponentLDA Latent Dirichlet AllocationLDA-C Latent Dirichlet Allocation in C

Acronyms XXIX

LEAP Lightweight Extensible Agent PlatformLH Left HemisphereLISP LISt ProcessingLOA Level of AutomationLOD Levels Of DetailLP Lagrangian RelaxationLR low-resolutionLSME Level-Set-based Motion EstimationM2M Machine to MachineMALLET Multi-Agent Logic Language for Encoding NetworkMASS Multi-Agent System SimulationMAS Multi-Agent SystemM&A Mergers and AcquisitionsMDP Markov Decision ProcessMEM Maximum Entropy ModelMF Membership FunctionMIMD Multiple Instruction, Multiple Data streamsMINLP Mixed Integer Nonlinear ProgrammingMI Machine IntelligenceML Machine LearningMLP Multilayer PerceptronMNC-GA Multi-Niche Crowding – Genetic AlgorithmMoNETA Modular Neural Exploring Travelling AgentMQ Machine QuotientMSC Magnitude Squared CoherenceMSI-GA Most Similar Individual – Genetic AlgorithmMURI Multidisciplinary Research Program of the University Research

InitiativeNASA National Aeronautics and Space AdministrationNER Named Entity RecognitionNGD Normalised Google DistanceNG Neural GasNLP Natural Language ProcessingNMI National Measurement InstituteNN1 Nearest neighbour based methodNN2 Nearest and second nearest neighbour based methodNNRTI Non-Nucleoside Reverse Transcriptase InhibitorsNeN Nearest NeighborNN Neural NetworkNOAA National Oceanic and Atmospheric AgencyNOR Number of RequestsNRTI Nucleotide Reverse Transcriptase InhibitorsNSSL National Severe Storms LaboratoryNSS N-Step SearchNSW New South Wales

XXX Acronyms

NTSS New Three Step SearchNT Northern TerritoryOMICS Open Mind In-door Common Sense projectOODA Observe, Orient, Decide and ActORiN Open Robot/Resource interface for the NetworkOROCOS Open RObot Control SoftwareOS Operating SystemOWL Ontology Web Language or Web Ontology LanguageOWL-S OWL-ServicesP2P Peer-to-PeerPAI Parallel Artificial IntelligencePCG Preconditioned Conjugate GradientPC Personal ComputerPDDL Planning Domain Definition LanguageP.F. Power FactorPHP Hypertext PreprocessorPI Protease InhibitorsPIMS Profit Impact of Marketing StrategiesPMI Point-wise Mutual InformationPMM Process Manager ModulePOI Points of InterestsPOS Part-of-speechPPE Power Processor ElementPROLOG PROcedural LOGicPROM Profitability Optimization ModelPRS Procedural Reasoning SystemPSNR Peak Signal Noise RatioPSO Particle Swarm OptimizationPSP Post Synaptic PotentialQPESUMS Quantitative Precipitation Estimation and Segregation Using Multi-

ple SensorsRaaS Robot-as-a-SoftwareRAM Random Access MemoryRBF Radial Basis FunctionRBS Rule Based SystemsRD Reactive DistillationRDF Resource Description FrameworkREST Representational State TransferRH Right HemisphereRMI Remote Method InvocationRMSE Root Mean Square ErrorRNA RiboNucleic AcidRNN Recurrent Neural NetworkROE Rules Of EngagementROI Return on Investment

Acronyms XXXI

RPD Recognition-Primed DecisionRPSA Regular Polygon based Search AlgorithmRST rotation, scale and translationSAGC Scalable Adaptive Group CommunicationSAM Semantic Action ModelSA South AustraliaSCA Space Colonization AlgorithmSCDS Small Cross-Diamond SearchSCS d Semantic Connectivity ScoreSCS e Semantic Connectivity ScoreSD Standard DeviationSfS Shape from ShadingSHARSHA State Hierarchy, Action, Reward, State Hierarchy, ActionSIFT Scale Invariant Feature TransformSIMD Single Instruction, Multiple Data streamsSI Systeme International d’unitesSL Structured LearningSME Subject Matter ExpertSMM Shared Mental ModelSNN Spiking Neural NetworkSOAda Service Oriented Architecture with decisional aspectSoaML Service oriented architecture Modeling LanguageSOAP Simple Object Access ProtocolSOAR State, Operator And ResultSOA Service-Oriented ArchitectureSOM Self-Organising MapsSRRM Semantic Realization and Refreshment ModuleSSD Sum of Squared DifferencesSTRIPS Stanford Research Institute Planning SystemSURF Speed-Up Robust FeatureSVM Support Vector MachineSyNAPSE Systems of Neuromorphics Adaptive Plastic Scalable ElectronicsTAS TasmaniaTAT Trans-Activator of TranscriptionTF-IDF Term Frequency, Inverse Document FrequencyTNC Trust, Negotiation and CommunicationTSS Three-Step SearchTVFI Truth Value Flow InferenceUAV Unmanned Aerial VehicleUMHexagon Non-Symmetrical Crossed Search by HexagonUML Unified Modeling LanguageURI Universal Resource IdentifierVBA Visual Basic for ApplicationsWA Western AustraliaWSDL Web-Services Description Language

XXXII Acronyms

WWW World Wide WebXHTML Extensible HyperText Markup LanguageXML Extensible Markup LanguageXOM XML Object ModelXOR eXclusive ORXSD XML Schema Definition

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