2010 The 2nd International Conference on Computer Engineering and Technology (ICCET 2010)

Organized by the International Association of Computer Science and Information Technology (IACSIT)
April 16-18, 2010, Chengdu, Sichuan, China

16-18 April 2010, Chengdu, China

Keynote Speakers

Keynote Speaker I

Chinese Academic of Engineering Fellow, Prof. Li Lemin
University of Electronic Science and Technology of China
中国工程院院士 电子科技大学 李乐民 教授

Research on Future Network Architecture

Abstract: The Internet has created great influence on our society, but there are many problems, such as scalable, manageable, controllable, and dependable, to be further solved. It is very important to study the future of the Internet, and this is a hot topic in the world. In this talk, the Chinese activity of research on future networks will be presented. The technical problems related to future network architecture, such as identifier and locator split, edge and core network split, and various possible schemes will be discussed.

Keynote Speaker II

IEEE Fellow, Prof. Wang Jun
The Chinese University of Hong Kong

• Professor, The Chinese University of Hong Kong, Hong Kong, 2002 to present.
• Associate Professor, The Chinese University of Hong Kong, Hong Kong, 1995 to 2001.
• Associate Professor,  University of North Dakota, Grand Forks, North Dakota, USA, 1993-1997.
• Assistant Professor, University of North Dakota, Grand Forks, North Dakota, 1990-1993.

• Associate Editor, IEEE Transactions on Neural Networks, 1999 to present.
• Associate Editor, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Bybernetics, 2003 to present.
• Associate Editor, IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and reviews, 2001-2005.
• Member of Editorial Advisory Board, International Journal of Neural Systems, 2006-2008.
• Guest Co-editor, special issues in European Journal of Operational Research (vol. 93, no. 2, 1996), International Journal of Neural Systems (vol. 17, no. 6, 2007), and Neurocomputing (vol. 71, nos. 16-18., 2008).
• Various positions at numerous international conferences such as ISNN2004, ISNN2005, IROS2005, ISNN2006, WCCI2006, ICONIP2006, and WCCI2008.
• Past President of the Asia Pacific Neural Network Assembly (APNNA).

Keynote Speaker III

IEEE and IAPR Fellow, Prof. B. John Oommen
School of Computer Science, Carleton University, Canada

Dr. John Oommen was born in Coonoor, India on September 9, 1953. He obtained his B.Tech. degree from the Indian Institute of Technology, Madras, India in 1975. He obtained his M.E. from the Indian Institute of Science in Bangalore, India in 1977. He then went on for his M.S. and Ph. D. which he obtained from Purdue University, in West Lafayettte, Indiana in 1979 and 1982 respectively. He joined the School of Computer Science at Carleton University in Ottawa, Canada, in the 1981-82 academic year. He is still at Carleton and holds the rank of a Full Professor. Since July 2006, he has been awarded the honorary rank of Chancellor's Professor, which is a lifetime award from Carleton University. His research interests include Automata Learning, Adaptive Data Structures, Statistical and Syntactic Pattern Recognition, Stochastic Algorithms and Partitioning Algorithms. He is the author of more than 325 refereed journal and conference publications, and is a Fellow of the IEEE and a Fellow of the IAPR. Dr. Oommen has also served on the Editorial Board of the IEEE Transactions on Systems, Man and Cybernetics, and Pattern Recognition.

Keynote Talk: ICCET 2010
Title: Recent Advances in Learning Automata Systems

Abstract
This keynote talk will present the most recent advances in the theory and applications of Learning Automata (LA) systems. Initially, it will concentrate on the general area of stochastic LA, which are probabilistic finite state machines that have been used to model how biological systems can learn. The structure of such a machine can be fixed, or it can be changing with time. We will explain how LA can also be implemented using action (choosing) probability updating rules, which, in turn, may or may not depend on estimates from the Environment being investigated.
While, traditionally, these updating rules have worked with the continuous probability space, we will also describe how LA can be designed by discretizing the probability space. The talk will describe the design and analysis of both continuous and discretized LA, and will highlight the subtle differences between the corresponding learning machines, their convergence properties, and their learning capabilities.
The talk will then discuss the most recent developments such as the Generalized Thathachar-Sastry estimator scheme.
The talk will include a comprehensive list of the applications in which LA have proven their powerful potential.

Keynote Speaker IV

Dean Prof. Pan Wei
Southwest Jiaotong University
西南交通大学 信息科学与技术学院院长 潘炜 教授

Keynote Speaker V

Prof. Michal Wozniak, Ph.D., D.Sc.
Wrocsaw University of Technology,
Chair of Systems and Computer Networks, Faculty of Electronics,
Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland

Michal Wozniak is Professor of Computer Science in the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Poland. He received an M.S. degree in Biomedical Engineering in 1992 and Ph.D. degrees in Computer Science in 1996 from the Wroclaw University of Technology. His research focuses on multiple classifier systems, machine learning, data and web mining, Bayes compound theory, distributed algorithms, computer and networks security and teleinformatics.
Prof. Wozniak has published over 100 papers, 2 books and edited 3 books Computer Recognition Systems (Springer). He is editor in chief of International Journal of Computer Networks and Communications and associate editor of several international journals including Pattern Analysis and Applications, Expert Systems, Computational Intelligence, and International Journal of Communication Networks and Distributed Systems.  He serves on program committees of numerous international conferences. His works have been transitioned into commercial applications.
See detail profile at: http://www.kssk.pwr.wroc.pl/pracownicy/michal.wozniak-en

Combining pattern recognition algorithms – chances and limits

Problem of pattern recognition  is accompanying our whole life. We start learn how to recognize simple objects like “dog”, “flower”, “car” when we are young and more sophisticated ones when we are growing up. Therefore methods of automatic pattern recognition is one of the main trend in Artificial Intelligence. The aim of such task is to classify the object to one of predefined categories, on the basis of observation of its features. Such methods are applied to many practical areas like prediction of customer behavior, fraud detection, medical diagnosis etc. Numerous approaches have been proposed to construct efficient, high quality classifiers like neural networks, statistical and symbolic learning. There is much current research into developing even more efficient and accurate recognition algorithms. Multiple classifier systems are currently the focus of intense research. The subject matter has been known for over 15 years. Some works in this field were published as early as the ’60 of the XX century, when it was shown that the common decision of independent classifiers is optimal, when chosen weights are inversely proportional to errors made by the classifiers. In many review articles this trend has been mentioned as one of the most promising in the field of the pattern recognition. In the beginning in literature one could find only majority vote, but in later works more advanced methods of finding a common solution to the classifier group problem were proposed. Estimation accuracy of the classifier committee is one of fundamental importance. Known conclusions, derived on analytic way, concern particular case of the majority vote when classifier committee is formed on the basis of independent classifiers. Unfortunately this case has only theoretical character  and is not useful in practice. The weighted voting is taken into consideration, but a problem of establishing weights for mentioned voting procedure is not simple. Many of authors have proposed treating the voting block as a kind of classifier but the general question is “does the fuser need to be trained?”. An alternative way of common classifier construction is combination of discriminants of available classifiers in order to obtain set of common discriminants, e.g. via linear combination.

The proposed speech will present short review of the main methods of combined pattern recognition, comparative analysis of some methods of classifier fusion based on weighted voting of classifiers’ responses and combination of classifiers’ discriminants. The presented remarks will be illustrated by the results of experimental based on real classification problems.

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