Hamid R. Arabnia received a Ph.D. degree in Computer Science from the University of Kent (Canterbury, England) in 1987. In 1987, he worked as a Consultant for Caplin Cybernetics Corporation (London, England), where he helped in the design of a number of image processing algorithms that were targeted at a particular parallel machine architecture. Prof. Arabnia is currently a Full Professor of Computer Science at University of Georgia (Georgia, USA), where he has been since October 1987. His research interests include Parallel and distributed processing techniques and algorithms, interconnection networks, and applications (in particular, in image processing, medical imaging, and other computational intensive problems). Prof. Arabnia is the founding chair of WORLDCOMP Congress. He is Editor-in-Chief of The Journal of Supercomputing (Springer) and is on the editorial and advisory boards of 17 other journals and magazines. He is the chair of the world committee of PDPTA (Parallel and Distributed Processing Techniques and Applications research organization: PDPTA is composed of 28 task forces with over 2,800 active participants) and is on the Advisory Board of IEEE Technical Committee on Scalable Computing (TCSC).

Prof. Arabnia is the recipient of William F. Rockwell, Jr. Medal for promotion of multi-disciplinary research (Rockwell Medal is International Technology Institute's highest honor). In 2000, Prof. Arabnia was inducted to the World Level of the Hall of Fame for Engineering, Science and Technology (The World Level is the highest possible level for a living person; since 1982, 41 other individuals have been inducted at this level, including: Allen E. Puckett, CEO of Hughes Aircraft Company; Dr. Robert C. Seamans, Chairman of the board of trustees of Aerospace Corp.; Dr. James C. Fletcher, 7th head of NASA; Dr. Ralph E. Gomory, IBM Director of Research; Dr. Richard M. Cyert, President of Carnegie Mellon University; Dr. John R. Koza, Stanford University; Dr. George M. Reed, Oxford University; Dr. Andrew S. Grove, CEO of Intel Corp.; Dr. Irwin M. Jacobs, CEO of Qualcomm, Inc.; Bill Gates, Chairman of Microsoft Corp.; see http://www.hofest.org/inductee-wlh.asp for the complete list) He has received a number of other awards, including, The Johns Hopkins University National Search in recognition of his contributions to the national program for enhancing the quality of life for people with disabilities through the application of computing technology (presented to him in December 1991 - signatories: co-directors of the National Search and President of Johns Hopkins U.) In 2006, Prof. Arabnia received the Distinguished Service Award in recognition and appreciation of his contributions to the profession of computer science and his assistance and support to students and scholars from all over the world; this award was formally presented to him on June 26, 2006 by Professor Barry Vercoe (Massachusetts Institute of Technology / MIT). Most recently (October 14, 2007), Prof. Arabnia received an "Outstanding Achievement Award in Recognition of His Leadership and Outstanding Research Contributions to the Field of Supercomputing". This award was formally presented to him at Harvard University Medical School (signatories: Lawrence O. Hall, President of IEEE/SMC; Zhi-Pei Liang, Vice President of IEEE/EMB; Jack. Y. Yang, General Chair of IEEE BIBE and Harvard University; Mary Qu Yang, Chair of Steering Committee, IEEE BIBE and NIH). Prof. Arabnia has published extensively in journals and refereed conference proceedings. He has over 250 research publications (journals, proceedings, editorship) in his area of research. Prof. Arabnia has been a Co-PI on $7,139,525 externally funded projects/initiatives (mainly via Yamacraw and includes significant UGA matching) and on $103,453 internally funded projects (as of October 2007). He has also contributed projects for justification for equipment purchase (grant proposals worth over $3 Million - awarded).

Model-Driven Architecture (MDA) is currently one of the most exciting approaches for accelerating code development and improving the quality of software in complex systems like embedded systems in ubiquitous era. MDA is an approach to the full lifecycle integration of enterprise systems comprised of software, hardware, humans, and business practices. It provides a systematic framework to understand, design, operate, and evolve all aspects of such enterprise systems, using engineering methods and tools. MDA utilizes models and a generalized idea of architecture standards to address integration of enterprise systems in the face of heterogeneous and evolving technology and embedded business domains. MDA combines computer-aided verification and machine intelligence during modeling to discover and remove design bugs before code reviews and testing. MDA represents an evolutionary step forward from previous development approaches. It’s built on the solid foundation of well established standards, including Unified Modeling Language (UML), the ubiquitous modeling notation used and supported by every major company in the software industry, and XML Metadata Interchange (XMI), the standard for storing and exchanging models using XML.

MDA is well-suited for embedded software development because it separates functional logic from implementation details and with the right MDA technology, automates the generation and testing of any embedded application architecture. MDA provides embedded software developers with a fundamentally different and higher-level way to accommodate changing requirements, increase reuse and extend system longevity.

In my talks, we will discuss the MDA and will help you understand what MDA is, how best to adopt it and the benefits it provides when implemented the embedded software with a model automation and transformation environment. We will also deliver the advantages of this approach as faster, more predictable software delivery cycles, minimizing the impact of requirements changes on development schedules, greater component reuse & implementation consistency and architectural flexibility and platform independences.

Components of Machine Intelligence and their relation with Computational Theory of Perception (CTP) is explained. The significance of fuzzy-granulation in CTP is stated. The role of rough fuzzy computing in the said framework along with the relevance of integration is explained. Two examples of such integration are described for problems of clustering and generating class prototypes. Here rough sets are used for granular computing using information granules and for uncertainty handling in defining cluster shapes using lower and upper approximation, whereas fuzzy sets are used for linguistic representation and fuzzy granulation, and modeling uncertainty arising from overlapping regions. In effect, rough-fuzzy clustering provides a balanced compromise between restrictive (hard) and descriptive (fuzzy) representations of class belonging for overlapping regions, while rough-fuzzy case generation helps in determining class prototyped which are efficient in terms of generation time, retrieval time, classification accuracy and average features required per case for representation. Merits of rough-fuzzy case generation as knowledge encoding are demonstrated for multi-spectral image segmentation. Features and merits of both rough-fuzzy c-means and c-medoids are demonstrated, with quantitative indices, for segmentation of brain MR images and determining bio-bases in encoding protein sequence for analysis respectively. The talk concludes with the possible future uses in data mining and bioinformatics.

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 Hong Kong and Australian universities.

Prof. Pal is a Fellow of the IEEE, USA, The Academy of Sciences for the Developing World, Italy, International Association for Pattern recognition, USA, and all the four National Academies for Science/Engineering in India. He is a co-author of fourteen books and about three 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 Meda, and 2005-06 ISC-P.C. Mahalanobis Birth Centenary Award (Gold Medal) for Lifetime Achievement.

Prof. Pal is an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Neural Networks [1994-98, 2003-2006], Pattern Recognition Letters, Neurocomputing (1995-2005), Applied Intelligence, Information Sciences, Fuzzy Sets and Systems, Fundamenta Informaticae, LNCS Trans. On Rough Sets, Int. J. Computational Intelligence and Applications, and Proc. INSA-A; a Member, Executive Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, and Int. Journal of Approximate Reasoning; and a Guest Editor of IEEE Computer.

Spacelog is a continuous collection of digital records on existence states and activity experiences of multiple entities in a real space or physical environment such as a laboratory, a classroom, a library, a home, a clinic, a shop, a restaurant, a farmland, or a construction field. The entities in a space can be people, facilities, artifacts, and space-related states, e.g., temperature distribution, air quality, noise level, sound source, etc. This variety of possible entities in a space marks one essential difference of the spacelog from the lifelog, which the latter refers specifically to activity records of a person - single entity. Furthermore, while monitoring cameras are widely used to record raw video data for security surveillance, the spacelog is to automatically collect different kinds of data related to multiple entities via various sensors distributed in a space, and efficiently keep the data in an integrated database.
That is, spacelog can be regarded conceptually as a special database of a physical environment to automatically record what occurred inside the environment. Actually, spacelog can also serve as a context history database in a smart space system, and function as a “memory organ” of a smart space.
The spacelog concept, first proposed by us, was motivated by the outcomes of our three previous research streams: (1) context-aware smart applications, (2) robot-integrated smart space, and (3) outdoor lifelog analysis. The spacelog can be exploited to extend functions of physical environments and provide many novel services, including but not limited to (a) effective retrievals of past events that happened in a space; (b) systematic summaries of entities’ experiences which occurred in a specified period; and (c) automatic detections of important events or changes for prompt reactions. Furthermore, the intelligent capability of smart spaces/environments can be greatly enhanced when incorporated with the spacelog, and the whole individual’s lifelog may be conveniently collected by integrating spacelogs in different environments the individual has stayed.
This talk will first clarify the spacelog concept from various aspects, next discuss the related technical issues for making spacelog systems, then depict the possible novel u-services using spacelog, and finally show our preliminary study on some specific spacelog system prototypes: HomeLog and LabLog in the robot-integrated smart environments.

Dr. Ma is the Co-Editor-in-Chief of three international journals: Journal of Ubiquitous Computing and Intelligence (JUCI), Journal of Mobile Multimedia (JMM) and Journal of Autonomic and Trusted Computing (JoATC), and the Assistant Editor-in-Chief of International Journal of Pervasive Computing and Communications (JPCC). He is on the editorial board of IJCPOL, IJDET, IJWMC, and IJSH, and has edited more than 10 journal special issues as a Guest Editor. He organized the 6th Int'l Conference on Distributed Multimedia Systems (DMS'99) as PC Co-Chair, the 1st Int'l Conference on Cyber Worlds (CW'02) as one of founders and PC Co-Chairs, and the 18th IEEE Int'l Conference on Advanced Information Networks and Applications as a General Co-Chair. He is a founder of Int'l Conf. on Ubiquitous Intelligence and Computing (UIC) and Int'l Conf. on Autonomic and Trusted Computing (ATC). He has severed many other conferences/workshops as various chairs and committee members.

Dr. Ma obtained his B.S, M.S. and Ph.D in 1982, 1985 and 1990, respectively. He was awarded as the Excellent Graduated Student by NUDT in 1982. He received the Annual Excellent Paper Awards from China Information Theory Society, Electronics Society, and Association of Hunan Science and Technology, respectively. He received the Best Paper Award from the Int'l Conference on Information Society in the 21st Century (IS2000), and the Highly Commended Paper Award from the IEEE Int'l Conference on Advanced Information Networking and Applications (AINA2004). He received the Appreciation Certificate from IEEE Computer Society in 2004, 2005 and 2006, respectively.

Finding hidden patterns in data or grouping data is essential for making sense of present-day biomedical research involving multi-dimensional, multi-scope large data sets. A promising method to achieve this goal is semi-supervised learning that can discover almost unknown knowledge. The main advantage of this machine learning approach is the ability to adapt its structure using the data set features (i.e. learn), thus making it immensely useful for many applications including work of the presenter in Environmental Genomics and microarray data analysis.

Once business deal mode will be very frequent in the world along with the economy development. It is very difficult for an entity to make correct judgment for a strange adversary according to own previous experiences. Thus, the reputation of an entity among the group and its trustworthiness for other entities will be key factors for its success and making its benefits. How to build a good reputation system makes that the cooperation among strange entities is successful. In our study, we explore the problem and find that the use of indirect reciprocity can provide an efficient module for the cooperation and trade in strange entities among deal groups.

We first classify two new types of indirect reciprocity based M. Nowak and K. Sigmund (1998). Then a new mathematics module with indirect reciprocity is provided for a complicated reputation system, and the different functions are used to score the increase and the decrease of an reputation in our module, respectively, so that they are more closed to the cognizant criterion in our human society, and a punishment mechanism with arbitration is also provided to guarantee the stabilization of indirect reciprocity and to restrain vicious or revengeful punishments. Our experiments also show that the stabilization of indirect reciprocity in complicated reputation is good and our punishment mechanism would bring useful effect on indirect reciprocity.