PAKDD-2003 CALL FOR PARTICIPATION

                      PAKDD-2003   CALL FOR PARTICIPATION

**************************************************************************
The Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining 
**************************************************************************

                   April 30-May 2, 2003, Seoul, Korea
                   http://aitrc.kaist.ac.kr/~pakdd03

                             Organized by
   Advanced Information Technology Research Center (AITrc), KAIST, Korea
    Statistical Research Center for Complex Systems (SRCCS), SNU, Korea

                         In Cooperation with 
                              ACM SIGKDD
 
                             Sponsored by
           Air Force Office of Scientific Research (AFOSR)
        Asian Office of Aerospace Research & Development (AOARD)
       Korea Advanced Institute of Science and Technology (KAIST)
                        The Korea Telecom (KT)

                             Academic Sponsors
                   Korea Information Science Society
                       Korean Datamining Society

            Workshops to be Held in Conjunction with PAKDD 2003
        Workshop on Mining Data for Actionable Knowledge (DMAK2003)
              (April 30, 2003, http://dmak.hanyang.ac.kr/)
                Workshop on Biological Data Mining (BDM)
              (April 30, 2003, http://bi.snu.ac.kr/bdm2003/)

-----------------------------
** AIMS OF THE CONFERENCE **
-----------------------------
   The Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining 
   (PAKDD-03) aims to provide a forum for academic researchers and industry 
   practitioners to share original research results and development experiences 
   from different KDD related areas such as data mining, data warehousing, 
   machine learning, databases, statistics, knowledge acquisition and 
discovery, 
   data visualization, and knowledge-based systems. The conference solicits 
   research papers as well as proposals for tutorials and workshops on all 
   aspects of knowledge discovery and data mining. 

--------------------------------------------
** REGISTRATION, ACCOMODATION, and VISA  **
--------------------------------------------

   Visit http://aitrc.kaist.ac.kr/~pakdd03 for detailed information.


-------------------------
** CONFERENCE PROGRAM **
-------------------------

April 30, 2003 (Wednesday)

09:00 - 12:30

TUTORIAL 1 : Data Mining for Intrusion Detection
             Aleksandar Lazarevic, Jaideep Srivastava, Vipin Kumar
             (University of Minnesota, USA)
   
   Modern society depends critically on the information infrastructure, and
   is becoming increasingly vulnerable to attacks against the
   infrastructure. The escalating magnitude of this threat is evident from
   the increasing rate of cyber attacks against our computers in the past few
   years. According to a recent survey by CERT/CC (Computer Emergency
   Response Team/Coordination Center), the rate of cyber attacks has been
   more than doubling every year in recent times. Intrusion detection, as a
   special form of cyber threat analysis, includes identifying a set of
   malicious actions that "compromise the integrity, confidentiality, and
   availability of information resources". The tremendous increase of novel
   cyber attacks has made data mining based intrusion detection techniques
   extremely useful in their detection.

   This tutorial provides an up-to-date introduction to the increasingly
   important field of the data mining in intrusion detection, as well as an
   overview of research directions in this field. This tutorial will help
   researchers, officers from federal and military/agency organizations, and
   practitioners from industry and financial organizations to understand the
   key practical and research issues related to building a successful
   intrusion detection system.


14:00 - 17:30

TUTORIAL 2: Analyzing and Mining Data Streams
            Sudipto Guha (University of Pennsylvania, USA)
            Nick Koudas (AT&T Shannon Lab, USA)
            Kyuseok Shim (Seoul National University, Korea)

   For many recent applications, the concept of a data stream is more 
appropriate 
   than a data set. A data stream is an appropriate model when a large volume 
of 
   data is arriving continuously and it is either unnecessary or impractical to 
store
   the entire data in some form of memory. Many applications naturally generate 
   streams of data as opposed to simple data sets. Astronomers, 
   telecommunications companies, banks, stock-market analysts, and news 
   organizations, for example, have vast amounts of data arriving continuously.

   Data Mining of streams is thus a necessary ingredient for many successful 
   applications. The stream view challenges basic assumptions in data mining 
   like random access to data. It also raises several fundamental questions 
   like are there effective techniques for mining streams?

   In this tutorial we will present a survey of algorithms and applications 
related 
   to data streams. 


----------------------------------------------------------------------
May 1, 2003 (Thursday)

09:30-10:30

    KEYNOTE I : Privacy Aware  Data Management and Analytics
                Rakesh Agrawal (IBM Almaden Research Center)

   The explosive progress in networking, storage, and processor technologies
   is resulting in an unprecedented amount of digitization of information. In
   concert with this dramatic increase in digital data, concerns about the
   privacy of personal information have emerged globally.  The concerns over
   massive collection of data are naturally extending to analytic tools
   applied to data.  Data mining, with its promise to efficiently discover
   valuable, non-obvious information from large databases, is particularly
   vulnerable to misuse.

   Inspired by the privacy tenet of the Hippocratic Oath, we argue that future
   database systems must include responsibility for the privacy of data they
   manage as a founding tenet.  We enunciate the key principles for such
   Hippocratic database systems, distilled from the principles behind current
   privacy legislations and guidelines.  We identify the technical challenges
   and problems in designing Hippocratic databases, and also outline some
   solution approaches.

   One way of preserving privacy of individual data records would be to
   perturb them. Since the primary task in data mining is the development of
   models about aggregated data, we explore if we can develop accurate models
   without access to precise information in individual data records.   We
   consider the concrete case of building a decision-tree classifier from
   perturbed data.  While it is not possible to accurately estimate original
   values in individual data records, we describe a reconstruction procedure
   to accurately estimate the distribution of original data values.  By using
   these reconstructed distributions, we are able to build classifiers whose
   accuracy is comparable to the accuracy of classifiers built with the
   original data.

   We will conclude by pointing out some open research problems.


10:30-11:00

    Industrial Talk I : Data Mining as an Atomated Service
                        Paul Bradley  (Bradley Data Consulting)

   An automated data mining service offers an out-sourced, cost-effective 
   analysis option for clients desiring to leverage their data resources for 
   decision support and operational improvement. In the context of the 
   service model, typically the client provides the service with data and 
   other information likely to aid in the analysis process (e.g. domain
   knowledge, etc.). In return, the service provides analysis results to the
   client. We describe the required processes, issues, and challenges in 
   automating the data mining and analysis process when the high-level goals 
   are: (1) to provide the client with a high quality, pertinent analysis 
   result; and (2) to automate the data mining service, minimizing the amount
   of human analyst effort required and the cost of delivering the service.
   We argue that by focusing on client problems within market sectors, both
   of these goals may be realized.
   
11:30-12:30

   SESSION 1A : Stream Mining I
           Finding Event-Oriented Patterns in Long Temporal Sequences
                  Xingzhi Sun, Maria E Orlowska, Xiaofang Zhou
           Mining Frequent Episodes for relating Financial Events and 
           Stock Trends
                  Anny Ng, Ada Wai-chee Fu

   SESSION 1B: Graph Mining
          An Efficient Algorithm of Frequent Connected Subgraph Extraction
                  Mingsheng Hong, Haofeng Zhou, Wei Wang, Baile Shi
          Classifier Construction by Graph-Based Induction for 
          Graph-Structured Data
                  Warodom Geamsakul, Takashi Matsuda, Tetsuya Yoshida, 
                  Hiroshi Motoda, Takashi Washio

   SESSION 1C: Clustering I
           Comparison of the Performance of Center-Based Clustering Algorithms
                  Bin Zhang
           Automatic Extraction of Clusters from Hierarchical Clustering 
           Representations
                  Jorg Sander, Xuejie Qin, Zhiyong Lu, Nan Niu, Alex Kovarsky

14:00-15:45

   SESSION 2A: Text Mining
           Large Scale Unstructured Document Classification Using Unlabeled 
           Data and Syntactic Information
                  Seong-Bae Park, Byoung-Tak Zhang
           Extracting Shared Topics of Multiple Documents
                  Xiang Ji, Hongyuan Zha
           An Empirical Study on Dimensionality Optimization in Text Mining 
           for Linguistic Knowledge Acquisition
                  Yu-Seop Kim, Jeong-Ho Chang, Byung-Tak Zhang
           A Semi-supervised Algorithm for Pattern Discovery in Information 
           Extraction from Textual Data
                  Tianhao Wu, William M. Pottenger

   SESSION 2B: Bio Mining
           Mining Patterns of Dyspepsia Symptoms Across Time Points Using 
           Constraint Association Rules
                  Annie Lau, Siew Siew Ong, Ashesh Mahidadia, Achim Hoffmann, 
                  Johanna Westbrook, Tatjana Zrimec
           Predicting Protein Structural Class from Closed Protein Sequences
                  N. Rattanakronkul, T. Wattarujeekrit, K. Waiyamai
           Learning Rules to Extract Protein Interactions from Biomedical Text
                  Tu Minh Phuong, Doheon Lee, Kwang Hyung Lee
           Predicting Protein Interactions in Human by Homologous Interactions 
           in Yeast
                  Hyongguen Kim, Jong Park, Kyungsook Han

   SESSION 2C: Web Mining
           Mining the Customer's Up-To-Moment Preferences for E-Commerce 
           Recommendation
                  Yi-Dong Shen, Qiang Yang, Zhong Zhang, Hongjun Lu
           A Graph-based Optimization Algorithm for Website Topology Using 
           Interesting Association Rules
                  Edmond H. Wu, Michael K. Ng
           A Markovian Approach For Web User Profiling and Clustering
                  Younes Hafri, Chabane Djeraba, Peter Stanchev, Bruno Bachimont
           Extracting User Interests From Bookmarks on the Web
                  Jason J. Jung, Geun-Sik Jo

16:15-17:30

   SESSION 3A: Stream Mining II
           Mining Frequent Instances on Workflows
                  Gianluigi Greco, Antonella Guzzo, Giuseppe Manco,  
                  Domenico Sacca
           Real Time Video Data Mining for Surveillance Video Streams
                  JungHwan Oh, JeongKyu Lee, Sanjaykumar Kote
           Distinguishing Causal and Acausal Temporal Relations
                  Kamran Karimi, Howard J. Hamilton

   SESSION 3B: Bayesian Networks
           Online Bayes Point Machines
                  Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John Platt, 
                  Robert C. Williamson
           Exploiting Hierarchical Domain Values for Bayesian Learning
                  Yiqiu Han, Wai Lam
           A New Restricted Bayesian Network Classifier
                  Hongbo Shi, Zhihai Wang, Geoff Webb, Houkuan Huang

   SESSION 3C: Clustering II
          AGRID: An Efficient Algorithm for Clustering Large            
          High-Dimensional Datasets
                   Yanchang Zhao, Junde Song 
          Multi-Level Clustering and Reasoning about its Clusters Using 
          Region Connection Calculus
                  Ickjai Lee, Mary-Anne Williams
           An Efficient Cell-based Clustering Method for Handling Large, 
           High-Dimensional Data 
                  Jae-Woo Chang

May 2, 2003 (Friday)   

09:00-10:00
 
   KEYNOTE II :  Web Mining - Accomplishments & Future Directions
                 Jaideep Srivastava (University of Minnesota, USA)
 
   From its very beginning, the potential of extracting valuable knowledge 
   from the Web has been quite evident. Web mining - i.e. the application 
   of data mining techniques to extract knowledge from Web content, structure, 
   and usage - is the collection of technologies to fulfill this potential. 
   Interest in Web mining has grown rapidly in its short existence, both in 
   the research and practitioner communities. A number of new concepts, e.g. 
   PageRank, hubs & authorities, web communities, web interestingness measures, 
   etc., and techniques to compute them have been developed. In addition, 
   a wide variety of commercial enterprises regularly use Web mining in their 
   daily operations, e.g. Amazon, Yahoo, Google, etc. This talk provides 
   an overview of the accomplishments of the field - both in terms of 
   technologies and applications - and outlines key future research directions.
 

10:00-10:30

    Industrial Talk II : Trends and Challenges in the Industrial Applications 
of KDD
               Ramasamy Uthurasamy (General Motors Corporation, USA)

   As an applications driven field, Knowledge Discovery in Databases and Data 
   Mining (KDD) techniques have made considerable progress towards meeting the 
   needs of these industrial and business specific applications.  However, 
there 
   are still considerable challenges facing this multidisciplinary field.  
   Drawing from some industry specific applications this talk will cover the 
   trends and challenges facing the researchers and practitioners of this 
rapidly 
   evolving area. In particular, this talk will outline a set of issues that 
   inhibit or delay the successful completion of an industrial application of 
KDD.
   This talk will also point out emerging and significant application areas 
that 
   demand development of new KDD techniques by the researchers and 
practitioners. 
   One such area is discovering patterns in temporal data. Another is the 
evolution 
   of discovery algorithms that respond to changing data forms and streams.
   Finally, this talk will outline the emerging vertical solutions arena that 
is 
   driven by business value, which is measured as a progress towards minimizing 
   the gap between the needs of the business user and the accessibility and 
   usability of analytic tools.

11:00-12:30

   SESSION 4A: Association Rules I
           Enhancing SWF for Incremental Association Mining by Itemset 
           Maintenance
                  Chia-Hui Chang, Shi-Hsan Yang
           Reducing Rule Covers with Deterministic Error Bounds
                  Vikram Pudi, Jayant R. Haritsa
           Evolutionary Approach for Mining Association Rules on Dynamic 
           Databases
                  P. Deepa Shenoy, K.G Srinivasa, K.R Venugopal, L.M. Patnaik

   SESSION 4B: Semi-Structured Data Mining
           Position Coded Pre-Order Linked WAP-Tree for Web Log Sequential 
           Pattern Mining
                  Yi Lu, C.I. Ezeife  
           An Integrated System of Mining HTML Texts and Filtering Structured 
           Documents
                  B-H. Yun, M-E. Lim, S-H. Park
           A New Sequential Mining Approach to XML Document Similarity 
           Computation
                  Ho-pong Leung, Fu-lai Chung, Stephen Chi-fai Chan

   SESSION 4C: Classification I
           Optimization of Fuzzy Rules for Classification Using Genetic 
           Algorithm
                  Myung Won Kim, Joung Woo Ryu, Samkeun Kim, Joong Geun Lee
           Fast Pattern Selection for Support Vector Classifiers
                  Hyunjung Shin, Sungzoon Cho
           Averaged Boosting: A Noise-Robust Ensemble Method
                  Yongdai Kim               
           Improving Performance of Decision Tree Algorithms with 
           Multi-Edited Nearest Neighbor Rule
                  Chen-Zhou Ye, Jie Yang, Li-Xiu Yao, Nian-Yi Chen

14:00-15:45

   SESSION 5A: Data Analysis
           HOT: Hypergraph-based Outlier Test for Categorical Data
                  Li Wei, Weining Qian, Aoying Zhou, Wen Jin
           A Method for Aggregating Partitions, Applications in K.D.D.
                  Pierre-Emmanuel Jouve, Nicolas Nicoloyannis
           Efficiently Computing Iceberg Cubes with Complex Constraints 
           Through Bounding
                  Pauline LienHua Chou, Xiuzhen Zhang
           Extraction of Tag Tree Patterns with Contractible Variables from 
           Irregular Semistructured Data
                  Tetsuhiro Miyahara, Yusuke Suzuki, Takayoshi Shoudai, 
                  Tomoyuki Uchida, Sachio Hirokawa, Kenichi Takahashi, 
                  Hiroaki Ueda

   SESSION 5B: Association Rules II
           Step-By-Step Regression: A More Efficient Alternative for 
           Polynomial Multiple Linear Regression in Stream Cube
                  Chao Liu, Ming Zhang, Minrui Zheng, Yixin Chen
           Progressive Weighted Miner: An Efficient Method for 
           Time-Constraint Mining
                  Chang-Hung Lee, Jian-Chih Ou, Ming-Syan Chen
           Mining Open Source Software(OSS) Data Using Associaton Rules 
           Network
                  Sanjay Chawla, Bavani Arunasalam, Joseph Davis
           Parallel FP-growth on PC cluster
                  Iko Pramudiono, Masaru Kitsuregawa

   SESSION 5C: Feature Selection
            Active Feature Selection Using Classes 
                  Huan Liu, Lei Yu, Manoranjan Dash, Hiroshi Motoda
           Electricity Based External Similarity of Categorical Attributes
                  Christopher R. Palmer, Christos Faloutsos
           Weighted Proportional k-Interval Discretization for Naive-Bayes 
           Classifiers
                  Ying Yang, Geoffrey I. Webb
           Dealing with Relative Similarity in Clustering: An Indiscernibility 
           Based Approach
                  Shoji Hirano, Shusaku Tsumoto

16:15-17:30

   SESSION 6A: Stream Mining III
           Considering Correlation Between Variables to Improve Spatiotemporal 
           Forecasting
                  Zhigang Li, Liangang Liu, Margaret H. Dunham
           Correlation Analysis of Spatial Time Series Datasets: 
           A Filter-and-Refine Approach
                  Pusheng Zhang, Yan Huang, Shashi Shekhar, Vipin Kumar
           When to Update the Sequential Patterns of Stream Data?
                  Qingguo Zheng, Ke Xu, Shilong Ma

   SESSION 6B: Clustering III
           A New Clustering Algorithm For Transaction Data via Caucus
                  Jinmei Xu, Hui Xiong, Sam Yuan Sung, Vipin Kumar
           DBRS: A Density-Based Spatial Clustering Method with Random 
           Sampling
                  Xin Wang, Howard J. Hamilton
           Optimized Clustering for Anomaly Intrusion Detection
                  Sang Hyun Oh, Won Suk Lee

   SESSION 6C: Classification II
           Finding Frequent Subgraphs from Graph Structured Data with 
           Geometric Information and Its Application to Lossless Compression
                  Yuko Itokawa, Tomoyuki Uchida, Takayoshi Shodai, 
                  Tetsuhiro Miyahara, Yasuaki Nakamura
           Upgrading ILP Rules to First-Order Bayesian Networks 
                  Ratthachat Chatpatanasiri, Boonserm Kijsirikul
           A Clustering Validity Assessment Index
                  YoungOk Kim, SooWon Lee

----------------------------------
** PROGRAM COMMITTEE CO-CHAIRS **
----------------------------------
   Prof. Kyuseok Shim
   School of Electrical Engineering and Computer Science
   Seoul National University / AITrc , Korea
   Kwanak P.O. Box 34, Seoul, 151-742, KOREA
   Email: shim-2qiS7stYTIJOKQ8SreQ7dg@xxxxxxxxxxxxxxxx 
   Phone: +82-2-880-7269
   Fax: +82-2-871-5974

   Prof. Jaideep Srivastava
   Department of Computer Science and Engineering 
   University of Minnesota
   4-192 EE/CSci Building 
   200 Union Street SE 
   Minneapolis, MN 55455 , USA
   Email: srivasta-k0Ej6N9cQa43uPMLIKxrzw@xxxxxxxxxxxxxxxx
   Phone: +1-612-625-4002 
   Fax: +1-612-625-0572 

------------------------
** PROGRAM COMMITTEE **
------------------------
   Raj Acharya, Pennsylvania State University, USA
   Roberto Bayardo, IBM Almaden Research, USA
   Elisa Bertino, University of Milano, Italy
   Bharat Bhasker, IIM Lucknow, India
   Paul Bradley, DigiMine, USA
   Arbee Chen, National Tsing Hua University, Taiwan
   Ming-Syan Chen, National Taiwan University, Taiwan
   David Cheung,The University Hong Kong, Hong Kong
   Daewoo Choi, Hankuk University of FS, Korea
   Gautam Das, Microsoft Research, USA
   Umesh Dayal, HP Lab, USA
   Usama Fayyad, DigiMine, USA
   Venkatesh Ganti, Microsoft Research, USA
   Minos Garofalakis, Bell Labs, USA
   Johannes Gehrke, Cornell University, USA
   Joydeep Ghosh, UT Austin, USA
   Dimitrios Gunopoulos, UC Riverside, USA
   Shyam Kumar Gupta, IIT Delhi, India
   Sudipto Guha, University of Pennsylvania, USA
   Jayant R. Haritsa, Indian Institute of Science, India
   San-Yih Hwang, National Sun-Yat Sen University, Taiwan 
   Soon Joo Hyun, Information and Communication University, Korea 
   H. V. Jagadish, University of Michigan, USA
   Myoung Ho Kim, KAIST, Korea 
   Masaru Kitsuregawa, The University of Tokyo, Japan
   Rao Kotagiri, University of Melbourne, Australia 
   Nick Koudas, AT&T Research, USA 
   Vipin Kumar, University of Minnesota, USA
   Laks V.S. Lakshmanan, UBC, Canada 
   Aleks Lazarevic, University of Minnesota, USA 
   Kyuchul Lee, Chungnam National University, Korea 
   Sangho Lee, Soongsil University, Korea 
   Sang-goo Lee, Seoul National University, Korea 
   Yoon-Joon Lee, KAIST, Korea 
   Yugi Lee, Univ. of Missouri-Kansas city, USA 
   Jianzhong Li, Harbin Institute of Technology, China 
   Ee-Peng Lim, Nanyang Technological University, Singapore 
   Tok Wang Ling, National University of Singapore, Singapore
   Bing Liu, University of Chicago, USA 
   Huan Liu, Arizona State University, USA 
   Hongjun Lu, Hong Kong UST, Hong Kong 
   Akifumi Makinouch, Kyushu University, Japan
   Heikki Mannila, Nokia, Finland
   Yoshifumi Masunaga, Ochanomizu University, Japan
   Hiroshi Motoda, Osaka University, Japan
   Rajeev Motwani, Stanford University, USA
   Sham Navathe, Georgia Institute of Technology, USA
   Raymond Ng, UBC, Canada
   Beng Chin Ooi, National University. of Singapore, Singapore
   T. V. Prabhakar, IIT Kanpur, India
   Raghu Ramakrishnan, University of Wisconsin, USA
   Keun Ho Ryu, Chungbuk National University, Korea
   Shashi Shekhar, University of Minnesota, USA
   Kian-Lee Tan, National University of Singapore, Singapore
   Pang-Ning Tan, University of Minnesota, USA
   Takao Terano, University of Tsukuba, Japan
   Bhavani Thuraisingham, NSF, USA
   Hannu T. T. Toivonen, Nokia Research, Finland
   Shan Wang, Renmin University of China, China
   You-Jip Won, Hanyang University, Korea
   Xingdong Wu, Colorado School of Mine, USA
   Yong-Ik Yoon, Sookmyung Women¡¦s University, Korea
   Philip S. Yu, IBM Watson Research Center, USA
   Ning Zhong, Maebashi Institute of Technology, Japan
   Aoying Zhou, Fudan University, China
   Lizhu Zhou, Tsinghua University, China 
   Jau-Hwang Wang, Central Police University, Taiwan
   Richard Weber, University of Chile, Chile
   Graham Williams, CSIRO, Australia
   Mohammed Zaki, Rensselaer Polytechnic Institute, USA

---------------------------
** ORGANIZING COMMITTEE **
---------------------------
Honorary Chairs  
   Won Kim, Cyber Database Solutions, USA/Ewha Womans University, Korea
   Sukho Lee, Seoul National University, Korea
 
International Advisory Committee  
   Rakesh Agrawal, IBM Almaden Lab., USA
   Jiawei Han, University of Illinois, Urbana-Champaign, USA
   Kuan-Tsae Huang, Taskco Corporation, Taiwan
   Jongwoo Jeon, Seoul National University/SRCCS, Korea
 
General Chair  
   Kyu-Young Whang, KAIST/AITrc, Korea
 
Tutorial Chairs  
   Sang Kyun Cha, Seoul National University, Korea
   Rajeev Rastogi, Lucent Bell Labs, USA
 
Workshop Chairs  
   Doheon Lee, KAIST, Korea
   Bongki Moon, Univ. of Arizona, USA
 
Industrial Program Chairs  
   Jaehee Cho, Kwangwoon University, Korea
   Ramakrishnan Srikant, IBM Almaden, USA
 
Organization Committee  
   Eui Kyeong Hong, University of Seoul/AITrc, Korea
   Won Chul Jhee, Hongik University, Korea
 
Publication Chair  
   Byoung-Kee Yi, POSTECH, Korea
 
Registration Chair  
   Byung Yeon Hwang, The Catholic University of Korea, Korea
 
Publicity Chairs  
   Myung Kim, Ewha Womans University, Korea
   Byung S. Lee, Univ. of Vermont, USA
 
Local Arrangements  
   Yong Chul Oh(Chair), Korea Polytechnic University, Korea
   Sungzoon Cho, Seoul National University, Korea
   Jinho Kim, Kangwon National University/AITrc
 
Steering Committee 
   Hongjun Lu , Hong Kong University of Science & Technology, Hong Kong (Chair)
   Hiroshi Motoda , Osaka University, Japan (Co-chair)
   Arbee L. P. Chen , National Tsing Hua Univercity, Taiwan
   Ming-Syan Chen , National Taiwan Univercity, Taiwan
   David W. Cheung , The Univerity Hong Kong, Hong Kong
   Masaru Kitsuregawa , The University of Tokyo, Japan
   Rao Kotagiri , University of Melbourne, Australia
   Huan Liu, Arizona State University, USA
   Takao Terano, University of Tsukuba, Japan
   Graham Williams , CSIRO, Australia
   Ning Zhong, Maebashi Institute of Technology, Japan
   Lizhu Zhou, Tsinghua University, China 

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