Soft Granular Mining and Pattern Recognition: A Way to Natural Computing
Sankar K. Pal
Center for Soft Computing Research
Indian Statistical Institute
Kolkata 700108, India

 [Abstract]

Different components of machine intelligence are explained. The role of rough sets in uncertainty handling and granular computing is described. The relevance of its integration with fuzzy sets, namely, rough-fuzzy computing, as a stronger paradigm for uncertainty handling, is explained. Various applications of rough granules, significance of f-granulation and other important issues in their implementations are stated. Generalized rough sets using the concept of fuzziness in granules and sets, rough-fuzzy entropy measures, fuzzy equivalence partition matrix and f-information measures are defined. Various tasks such as case generation, class-dependent rough-fuzzy granulation for classification, rough-fuzzy clustering, feature selection, and measuring image ambiguity measures for mining are then addressed, explaining the nature and characteristics of granules used therein. Concept of fuzzy granular computing and granular fuzzy computing are explained.

While the method of case generation with variable reduced dimension is useful for mining data sets with large dimension and size, class dependent granulation coupled with neighbourhood rough sets for feature selection is efficient in modelling overlapping classes. Rough-fuzzy clustering is superior in terms of performance and speed. Entropy and mutual information defined on class independent fuzzy approximation space of attribute sets are useful for measuring relevance of a conditional attribute with respect to decision attribute and redundancy among conditional attributes. Effectiveness of these features is demonstrated for image analysis, and bioinformatics problems e.g., determination of bio-bases from protein sequence and selection of relevant genes from micro-array data. Significance of the measure "dispersion" of classification performance, which focuses on confused classes for higher level analysis, is explained in this regard. The talk concludes with stating the future directions of research and challenges with other applications including natural computing.

 [Biography]

Sankar K. Pal (www.isical.ac.in/~sankar) is a Distinguished Scientist of the Indian Statistical Institute and a former Director. He is also a J.C. Bose Fellow of the Govt. of India. He founded the Machine Intelligence Unit and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta. He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical Engineering along with DIC from Imperial College, University of London in 1982. He joined his Institute in 1975 as a CSIR Senior Research Fellow where he later became a Full Professor in 1987, Distinguished Scientist in 1998 and the Director for the term 2005-10.

He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he has been serving as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held several visiting positions in Italy, Poland, Hong Kong and Australian universities.

Prof. Pal is a Fellow of the IEEE, USA, the Academy of Sciences for the Developing World (TWAS), Italy, International Association for Pattern recognition, USA, International Association of Fuzzy Systems, USA, and all the four National Academies for Science/Engineering in India. He is a co-author of seventeen books and more than four hundred research publications in the areas of Pattern Recognition and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms, Fuzzy Sets, Rough Sets and Bioinformatics.

He has received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), and many prestigious awards in India and abroad including the 1999 G.D. Birla Award, 1998 Om Bhasin Award, 1993 Jawaharlal Nehru Fellowship, 2000 Khwarizmi International Award from the Islamic Republic of Iran, 2000-2001 FICCI Award, 1993 Vikram Sarabhai Research Award, 1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award (USA), 1995 NASA Patent Application Award (USA), 1997 IETE-R.L. Wadhwa Gold Medal, the 2001 INSA-S.H. Zaheer Medal, 2005-06 Indian Science Congress-P.C. Mahalanobis Birth Centenary Award (Gold Medal) for Lifetime Achievement, 2007 J.C. Bose Fellowship of the Government of India and 2008 Vigyan Ratna Award from Science & Culture Organization, West Bengal.

Prof. Pal is/ was an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence (2002-06), IEEE Trans. Neural Networks [1994-98 & 2003-06], Neurocomputing (1995-2005), Pattern Recognition Letters (1993-2011), Int. J. Pattern Recognition & Artificial Intelligence, Applied Intelligence, Information Sciences, Fuzzy Sets and Systems, Fundamenta Informaticae, LNCS Trans. On Rough Sets, Int. J. Computational Intelligence and Applications, IET Image Processing, J. Intelligent Information Systems, and Proc. INSA-A; Editor-in-Chief, Int. J. Signal Processing, Image Processing and Pattern Recognition; a Book Series Editor, Frontiers in Artificial Intelligence and Applications, IOS Press, and Statistical Science and Interdisciplinary Research, World Scientific; a Member, Executive Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, Int. J. Computational Science & Engineering, and Int. J. Approximate Reasoning; and Guest Editor, IEEE Computer, and Theoretical Computer Science - C.

Darwin, Debussy an'Dante – A four-part bioinformatics symphony
R. Marshall
Plymouth State University, New Hampshire, USA

 [Abstract]
We present a method for modeling and simulating nucleotide sequences at an elemental chemical-structural level using passive electrical circuits comprised of resistors, inductors and capacitors and studying the behavior of these circuits when subject to user-specific sensory data such as speech and music, retinal scans and fingerprints after the data has been suitably transformed to serve as input signals. The circuits' responses are then used to generate distinct visual representations which can be used in a variety of applications including DNA sequence alignments and comparisons, protein sequence modeling, novel biometric identification schemes and computer/network security.

 [Biography]

R. Marshall has been teaching and conducting research in Computer Science for the past 30 years. He has taught at a variety of universities including Johns Hopkins University, Boston University, Loyola University and the University of Massachusetts. He was educated at IIT-Madras (B.Tech), Dalhousie University (MS), University of Nebraska (PhD) and McGill University (ABD). He is Professor of Computer Science at Plymouth State University in New Hampshire. He has published over 120 refereed journal and international conference articles and is the author of a monograph on natural language processing and co-author of a book on distributed database systems. He has been the recipient of two Fulbright Senior Scholar awards, McConnell Fellowship, Wachovia Research Award and Hanes Sigma Delta Theta distinguished professorship. He has obtained research grants from NASA, NSF, USAID and the Department of the Navy and has held several NASA/Navy-ASEE summer research fellowships at the Naval Research Laboratory, Naval Underwater Systems Center, NASA-Goddard Space Flight Center and the Applied Physics Lab.

Linguistic Geometry: From Ancient Warfare to Modern Adversarial Reasoning
Boris Stilman
University of Colorado Denver, USA
STILMAN Advanced Strategies, USA

 [Abstract]

In my talk I will put Linguistic Geometry (LG) in the historical prospective. At first, I will go backward in time: from the modern advanced applications to the past achievements in computer chess and, even further, to the ancient warfare of Alexander the Great and Hannibal. Then, I will change direction. I will go forward by tracing the development of LG from predecessors of computer chess to experiments with program PIONEER, to the major theoretical results and to the modern LG-based defense systems that are already considered vital to the national defense.

 [Biography]

Dr. Stilman is currently Professor of Computer Science at the University of Colorado Denver, USA and the Chairman & CEO at STILMAN Advanced Strategies, USA.

Boris Stilman received MS in Mathematics from Moscow State University, USSR in 1972 and two Ph.Ds in Electrical Engineering and Computer Science from National Research Institute for Electrical Engineering, Moscow, USSR in 1984. In 1972-1988, in Moscow, he was involved in the advanced research project PIONEER led by a former World Chess Champion Professor Mikhail Botvinnik. The goal of the project was to discover and formalize an approach utilized by the most advanced chess experts in solving chess problems almost without search. While program PIONEER has never played complete chess games, it solved a number of complex endgames and positions from the games of World Chess Champions. Based on these experiences over a number of years, in Moscow, Dr. Stilman developed experimental and mathematical foundations of the new approach to search problems in Artificial Intelligence. In 1990-91, while at McGill University, Montreal, Canada, based on this approach, he originated Linguistic Geometry (LG), a new theory for solving abstract board games. LG allows us to avoid combinatorial explosion by changing the paradigm from search to construction. It is scalable to solving complex real world problems that are considered intractable by conventional approaches.

Since 1991, Dr. Stilman was developing the theory and applications of LG at the University of Colorado Denver (UC Denver). A leap in the development LG was made in 1999, when he (with a group of scientists and engineers) founded STILMAN Advanced Strategies, LLC (STILMAN). A growing number of applications of LG developed at STILMAN have passed comprehensive testing and are currently being transitioned to the real world command and control systems and already considered vital to the US national defense. Thirteen years of highly successful application of LG, its unmatched scalability and accuracy, permitted to conclude that LG is a lot more fundamental than simply yet another mathematical theory of efficient wargaming. Every LG application generated new ideas that experts evaluated as brilliant. It appears that LG is a mathematical model of human thinking about armed conflict, a mental reality that existed for millions of years. For example, LG is applicable for what-if analysis of the battles of Alexander the Great and Hannibal. Moreover, LG as an evolutionary product of millions of years of human warfare served, in its turn, as the principle mover for evolution of human intelligence. It appears that the game of chess served as a means for discovering human methodology of efficient warfare.

Dr. Stilman published several books (including "Linguistic Geometry: From Search to Construction") and contributions to books, and over 200 research papers. He is a recipient of numerous R&D awards, including the top research awards at University of Colorado, grants from the former USSR Academy of Sciences, substantial grants from the US government agencies such as major multiple awards from DARPA, US Dept. of Energy, US Army, US Navy, US Air Force, etc.; Ministry of Defence of UK; from the world leading defense companies such as Boeing (USA), Rockwell (USA), BAE Systems (UK), SELEX/Finmeccanica (Italy-UK) and Fujitsu (Japan). More information about Dr. Stilman, history of LG and projects including several
narrated movies can be found at www.stilman-strategies.com