Behavior Engineering and Applications

1. Front Matter

   Pages i-viii

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2. Benchmarking Swarm Rebalancing Algorithm for Relieving Imbalanced Machine Learning Problems

   • Jinyan Li, Simon Fong

   Pages 1-40

3. Performance Evaluation of Shadow Features as a Data Preprocessing Method in Data Mining for Human Activities Recognitions

   • Simon Fong, Shimin Hu, Ni Ren, Wei Song, Kyungeun Cho, Raymond Wong et al.

   Pages 41-66

4. Fast Plagiarism Detection Using Approximate String Matching and Vector Representation of Words

   • Kensuke Baba

   Pages 67-79

5. A Psychologically-Realistic Personality Model for Virtual Agents

   • Curtis L. Gittens

   Pages 81-99

6. Online Impulse Buying: An Investigation into Consumer Decision Making

   • Arunima Rana

   Pages 101-128

7. eHealth Consumer Behavior

   • Md. Nazmul Hossain, Hiroshi Okajima, Hironobu Kitaoka, Fumihiko Yokota, Ashir Ahmed

   Pages 129-147

8. Guiding Usability Newcomers to Understand the Context of Use: Towards Models of Collaborative Heuristic Evaluation

   • André de Lima Salgado, Flávia de Souza Santos, Renata Pontin de Mattos Fortes, Patrick C. K. Hung

   Pages 149-168

9. Forecasting China Future MNP by Deep Learning

   • Shimin Hu, Mengyu Liu, Simon Fong, Wei Song, Nilanjan Dey, Raymond Wong

   Pages 169-210

10. Gender Differences in Affective Response Toward Pictorials: Affective Warning Pictorials on Cigarette Label

    • Arisara Jiamsanguanwong, Pat-Arin Chanduen

    Pages 211-222

11. A Web-Based System with Spatial Clustering to Observe the Changes of Emergency Distribution Using Social Big Data

    • Yilang Wu, Junbo Wang

    Pages 223-240

Many industrial applications built today are increasingly using emerging behavior engineering technologies: this book looks at various research and practical issues  for researchers and students working in computer science and engineering, and for industry technology providers interested in behavior engineering and applications. Behavior Engineering and Applications encompasses intelligent and efficient computational solutions, including models, architectures, algorithms and specific applications, focused on processing, discovering, understanding and analyzing the behavior captured by the above data. Focusing on applying any engineering paradigm to systemically process, discover, understand and analyze these data, this book also addresses problems in a variety of areas and applications that related to behavior engineering.

This book includes chapters derived from selected papers from The 2016 International Conference on Behavior Engineering (ICBE), as well as separate contributions the editors selected cutting-edge research related to behavior engineering.

• Behavior engineering
• Data analysis
• System architecture
• System design
• Big data

• School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

  Raymond Wong

• CSIRO, Hobart, Australia

  Chi-Hung Chi

• Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, Canada

  Patrick C. K. Hung

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