SATCHMORE: SATCHMO with RElevancy

We introduce a relevancy detection algorithm to be used in conjunction with the SATCHMO prover. The version of SATCHMO considered here is essentially a bidirectional prover, utilizing Prolog (back chaining) on Horn clauses and forward chaining on non-Horn clauses. Our extension, SATCHMORE (SATCHMO with RElevancy), addresses the major weakness of SATCHMO: the uncontrolled use of forward chaining. By marking potentially relevant clause head literals, and then requiring that all the head literals be marked relevant (be totally relevant) before a clause is used for forward chaining, SATCHMORE is able to guide the use of these rules. Furthermore, the relevancy testing is performed without extending the proof search beyond what is done in SATCHMO. A simple implementation of the extended SATCHMO can be written in Prolog. We describe our relevancy testing approach, present the implementation, prove soundness and completeness, and provide examples that demonstrate the power of relevancy testing.This research was partially supported by NSF Grants IRI-8805696 and CCR-9116203. This paper is a major revision of Wilson and Loveland (1989).     

I—SATCHMORE：An Improvement of A—SATCHMORE

This paper presents an improvement of A-SATCHMORE (SATCHMORE with Availability).A-SATCHMORE incorporates relevancy testing and availability checking into SATCHMO to prune away irrelevant forward chaining.However ,considering every consequent atom of those non-Horn clauses being derivable,A-SATCHMORE may suffer from a potential explosion of the search space when some of such consequent atoms are actually underivable.This paper introduces a solution for this problem and shows its correctness.     

A-SATCHMORE: SATCHMORE with availability checking

We present an improvement of SATCHMORE, calledA-SATCHMORE, by incorporating availability checking into relevancy. Because some atoms unavailable to the further computation are also marked relevant, SATCHMORE suffers from a potential explosion of the search space. Addressing this weakness of SATCHMORE, we show that an atom does not need to be marked relevant unless it is available to the further computation and no non-Horn clause needs to be selected unless all its consequent atoms are marked availably relevant, i.e., unless it is totally availably relevant. In this way,A-SATCHMORE is able to further restrict the ues of non-Horn clauses (therefore to reduce the search space) and makes the proof more goal-oriented. Our theorem prover,A-SATCHMORE, can be simply implemented in PROLOG based on SATCHMORE. We discuss how to incorporate availability cheeking into relevancy, describe our improvement and present the implementation. We also prove that our theorem prover is sound and complete, and provide examples to show the power of our availability approach. This research is supported in part by the Japanese Ministry of Education and the Artificial Intelligence Research Promotion Foundation. Lifeng He, Ph.D: He received the B. E. degree from Northwest Institute of Light Industry, China, in 1982, the M. S. and Ph.D. degrees in AI and computer science from Nagoya Institute of Technology, Japan, in 1994 and 1997, respectively. He currently works at the Institute of Open System in Nagoya, Japan. His research interests include automated reasoning, theorem proving, logic programming, knowledge bases, multi-agent cooperation and modal logic. Yuyan Chao, M. S.: She received the B. E. degree from Northwest Institute of Light Industry, China, in 1984, and the M. S. degree from Nagoya University, Japan, in 1997. She is currently a doctoral candidate in the Department of Human Information, Nagoya University. Her research interests include image processing, graphic understanding, CAD and theorem proving. Yuka Shimajiri, M. S.: She currently works as a Assistant Professor in Department of Artificial Intelligence and Computer Science at the Nagoya Institute of Technology. She received her B.Eng. and M.Eng. from the Nagoya Institute of Technology in 1994 and 1996, respectively. Her current research interests include logic programming and automated deduction. She is a member of IPSJ and JSAI. Hirohisa Seki, Ph.D.: He received the B. E., M. E. and Ph.D degrees from the University of Tokyo in 1979, 1981 and 1991 respectively. He joined the Central Research Laboratory of Mitsubishi Electric Corporation in 1981. From 1985 to 1989, he was with the Institute for New Generation Computer Technology (ICOT). Since 1992, he has been an Associate Professor in the Department of AI and Computer Science at Nagoya Institute of Technology. His current research interests include logic programming, deductive databases and automated deduction. He is a member of ACM, IEEE, IPSJ and JSAI. Hidenori Itoh, Ph.D.: He received the B. S. degree from Fukui University, in 1969, the M. S. degree and Ph.D degree from Nagoya University, Japan, in 1971 and 1974, respectively. From 1974 to 1985, he worked at Nippon Telephone and Telegraph Laboratories, developing operating systems. From 1985 to 1989, he was with the Institute for New Generation Computer Technology, developing knowledge base systems. Since 1989, he has become a professor at the Nagoya Institute of Technology. His current research interests include image processing, parallel computing, fuzzy logic and knowledge processing.     

On High Dimensional Projected Clustering of Data Streams

The data stream problem has been studied extensively in recent years, because of the great ease in collection of stream data. The nature of stream data makes it essential to use algorithms which require only one pass over the data. Recently, single-scan, stream analysis methods have been proposed in this context. However, a lot of stream data is high-dimensional in nature. High-dimensional data is inherently more complex in clustering, classification, and similarity search. Recent research discusses methods for projected clustering over high-dimensional data sets. This method is however difficult to generalize to data streams because of the complexity of the method and the large volume of the data streams.In this paper, we propose a new, high-dimensional, projected data stream clustering method, called HPStream. The method incorporates a fading cluster structure, and the projection based clustering methodology. It is incrementally updatable and is highly scalable on both the number of dimensions and the size of the data streams, and it achieves better clustering quality in comparison with the previous stream clustering methods. Our performance study with both real and synthetic data sets demonstrates the efficiency and effectiveness of our proposed framework and implementation methods.Charu C. Aggarwal received his B.Tech. degree in Computer Science from the Indian Institute of Technology (1993) and his Ph.D. degree in Operations Research from the Massachusetts Institute of Technology (1996). He has been a Research Staff Member at the IBM T. J. Watson Research Center since June 1996. He has applied for or been granted over 50 US patents, and has published over 75 papers in numerous international conferences and journals. He has twice been designated Master Inventor at IBM Research in 2000 and 2003 for the commercial value of his patents. His contributions to the Epispire project on real time attack detection were awarded the IBM Corporate Award for Environmental Excellence in 2003. He has been a program chair of the DMKD 2003, chair for all workshops organized in conjunction with ACM KDD 2003, and is also an associate editor of the IEEE Transactions on Knowledge and Data Engineering Journal. His current research interests include algorithms, data mining, privacy, and information retrieval.Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. He has been working on research into data mining, data warehousing, stream and RFID data mining, spatiotemporal and multimedia data mining, biological data mining, social network analysis, text and Web mining, and software bug mining, with over 300 conference and journal publications. He has chaired or served in many program committees of international conferences and workshops, including ACM SIGKDD Conferences (2001 best paper award chair, 1996 PC co-chair), SIAM-Data Mining Conferences (2001 and 2002 PC co-chair), ACM SIGMOD Conferences (2000 exhibit program chair), International Conferences on Data Engineering (2004 and 2002 PC vice-chair), and International Conferences on Data Mining (2005 PC co-chair). He also served or is serving on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Journal of Computer Science and Technology, and Journal of Intelligent Information Systems. He is currently serving on the Board of Directors for the Executive Committee of ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). Jiawei has received three IBM Faculty Awards, the Outstanding Contribution Award at the 2002 International Conference on Data Mining, ACM Service Award (1999) and ACM SIGKDD Innovation Award (2004). He is an ACM Fellow (since 2003). He is the first author of the textbook “Data Mining: Concepts and Techniques” (Morgan Kaufmann, 2001).Jianyong Wang received the Ph.D. degree in computer science in 1999 from the Institute of Computing Technology, the Chinese Academy of Sciences. Since then, he ever worked as an assistant professor in the Department of Computer Science and Technology, Peking (Beijing) University in the areas of distributed systems and Web search engines (May 1999–May 2001), and visited the School of Computing Science at Simon Fraser University (June 2001–December 2001), the Department of Computer Science at the University of Illinois at Urbana-Champaign (December 2001–July 2003), and the Digital Technology Center and Department of Computer Science and Engineering at the University of Minnesota (July 2003–November 2004), mainly working in the area of data mining. He is currently an associate professor in the Department of Computer Science and Technology, Tsinghua University, Beijing, China.Philip S. Yuis the manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. The current focuses of the project include the development of advanced algorithms and optimization techniques for data mining, anomaly detection and personalization, and the enabling of Web technologies to facilitate E-commerce and pervasive computing. Dr. Yu,s research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, disk arrays, computer architecture, performance modeling and workload analysis. Dr. Yu has published more than 340 papers in refereed journals and conferences. He holds or has applied for more than 200 US patents. Dr. Yu is an IBM Master Inventor.Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He will become the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering on Jan. 2001. He is an associate editor of ACM Transactions of the Internet Technology and also Knowledge and Information Systems Journal. He is a member of the IEEE Data Engineering steering committee. He also serves on the steering committee of IEEE Intl. Conference on Data Mining. He received an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts”. Philip S. Yu received the B.S. Degree in E.E. from National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University.     

Catch the moment: maintaining closed frequent itemsets over a data stream sliding window

This paper considers the problem of mining closed frequent itemsets over a data stream sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent itemsets at any time. Due to time and memory constraints, the synopsis data structure cannot monitor all possible itemsets. However, monitoring only frequent itemsets will make it impossible to detect new itemsets when they become frequent. In this paper, we introduce a compact data structure, the closed enumeration tree (CET), to maintain a dynamically selected set of itemsets over a sliding window. The selected itemsets contain a boundary between closed frequent itemsets and the rest of the itemsets. Concept drifts in a data stream are reflected by boundary movements in the CET. In other words, a status change of any itemset (e.g., from non-frequent to frequent) must occur through the boundary. Because the boundary is relatively stable, the cost of mining closed frequent itemsets over a sliding window is dramatically reduced to that of mining transactions that can possibly cause boundary movements in the CET. Our experiments show that our algorithm performs much better than representative algorithms for the sate-of-the-art approaches. Yun Chi is currently a Ph.D. student at the Department of Computer Science, UCLA. His main areas of research include database systems, data mining, and bioinformatics. For data mining, he is interested in mining labeled trees and graphs, mining data streams, and mining data with uncertainty. Haixun Wang is currently a research staff member at IBM T. J. Watson Research Center. He received the B.S. and the M.S. degree, both in computer science, from Shanghai Jiao Tong University in 1994 and 1996. He received the Ph.D. degree in computer science from the University of California, Los Angeles in 2000. He has published more than 60 research papers in referred international journals and conference proceedings. He is a member of the ACM, the ACM SIGMOD, the ACM SIGKDD, and the IEEE Computer Society. He has served in program committees of international conferences and workshops, and has been a reviewer for some leading academic journals in the database field. Philip S. Yureceived the B.S. Degree in electrical engineering from National Taiwan University, the M.S. and Ph.D. degrees in electrical engineering from Stanford University, and the M.B.A. degree from New York University. He is with the IBM Thomas J. Watson Research Center and currently manager of the Software Tools and Techniques group. His research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, and performance modeling. Dr. Yu has published more than 430 papers in refereed journals and conferences. He holds or has applied for more than 250 US patents.Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He is associate editors of ACM Transactions on the Internet Technology and ACM Transactions on Knowledge Discovery in Data. He is a member of the IEEE Data Engineering steering committee and is also on the steering committee of IEEE Conference on Data Mining. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001–2004), an editor, advisory board member and also a guest co-editor of the special issue on mining of databases. He had also served as an associate editor of Knowledge and Information Systems. In addition to serving as program committee member on various conferences, he will be serving as the general chairman of 2006 ACM Conference on Information and Knowledge Management and the program chairman of the 2006 joint conferences of the 8th IEEE Conference on E-Commerce Technology (CEC' 06) and the 3rd IEEE Conference on Enterprise Computing, E-Commerce and E-Services (EEE' 06). He was the program chairman or co-chairs of the 11th IEEE International Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, the 2nd IEEE International Workshop on Research Issues on Data Engineering:Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases, and the 2nd IEEE International Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chairman of the 14th IEEE International Conference on Data Engineering and the general co-chairman of the 2nd IEEE International Conference on Data Mining. He has received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 84th plateau of Invention Achievement Awards. He received an Outstanding Contributions Award from IEEE International Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts" in 1999. Dr. Yu is an IBM Master Inventor. Richard R. Muntz is a Professor and past chairman of the Computer Science Department, School of Engineering and Applied Science, UCLA. His current research interests are sensor rich environments, multimedia storage servers and database systems, distributed and parallel database systems, spatial and scientific database systems, data mining, and computer performance evaluation. He is the author of over one hundred and fifty research papers.Dr. Muntz received the BEE from Pratt Institute in 1963, the MEE from New York University in 1966, and the Ph.D. in Electrical Engineering from Princeton University in 1969. He is a member of the Board of Directors for SIGMETRICS and past chairman of IFIP WG7.3 on performance evaluation. He was a member of the Corporate Technology Advisory Board at NCR/Teradata, a member of the Science Advisory Board of NASA's Center of Excellence in Space Data Information Systems, and a member of the Goddard Space Flight Center Visiting Committee on Information Technology. He recently chaired a National Research Council study on “The Intersection of Geospatial Information and IT” which was published in 2003. He was an associate editor for the Journal of the ACM from 1975 to 1980 and the Editor-in-Chief of ACM Computing Surveys from 1992 to 1995. He is a Fellow of the ACM and a Fellow of the IEEE.     

A multi-threshold online smoothing technique for variable rate multimedia streams

Variable bit rate (VBR) compression for media streams allocates more bits to complex scenes and fewer bits to simple scenes. This results in a higher and more uniform visual and aural quality. The disadvantage of the VBR technique is that it results in bursty network traffic and uneven resource utilization when streaming media. In this study we propose an online media transmission smoothing technique that requires no a priori knowledge of the actual bit rate. It utilizes multi-level buffer thresholds at the client side that trigger feedback information sent to the server. This technique can be applied to both live captured streams and stored streams without requiring any server side pre-processing. We have implemented this scheme in our continuous media server and verified its operation across real world LAN and WAN connections. The results show smoother transmission schedules than any other previously proposed online technique. This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC), and IIS-0082826, DARPA and USAF under agreement nr. F30602-99-1-0524, and unrestricted cash/equipment gifts from NCR, IBM, Intel and SUN. Roger Zimmermann is currently a Research Assistant Professor with the Computer Science Department and a Research Area Director with the Integrated Media Systems Center (IMSC) at the University of Southern California. His research activities focus on streaming media architectures, peer-to-peer systems, immersive environments, and multimodal databases. He has made significant contributions in the areas of interactive and high quality video streaming, collaborative large-scale group communications, and mobile location-based services. Dr. Zimmermann has co-authored a book, a patent and more than seventy conference publications, journal articles and book chapters in the areas of multimedia and databases. He was the co-chair of the ACM NRBC 2004 workshop, the Open Source Software Competition of the ACM Multimedia 2004 conference, the short paper program systems track of ACM Multimedia 2005 and will be the proceedings chair of ACM Multimedia 2006. He is on the editorial board of SIGMOD DiSC, the ACM Computers in Entertainment magazine and the International Journal of Multimedia Tools and Applications. He has served on many conference program committees such as ACM Multimedia, SPIE MMCN and IEEE ICME. Cyrus Shahabi is currently an Associate Professor and the Director of the Information Laboratory (InfoLAB) at the Computer Science Department and also a Research Area Director at the NSF's Integrated Media Systems Center (IMSC) at the University of Southern California. He received his M.S. and Ph.D. degrees in Computer Science from the University of Southern California in May 1993 and August 1996, respectively. His B.S. degree is in Computer Engineering from Sharif University of Technology, Iran. He has two books and more than hundred articles, book chapters, and conference papers in the areas of databases and multimedia. Dr. Shahabi's current research interests include Peer-to-Peer Systems, Streaming Architectures, Geospatial Data Integration and Multidimensional Data Analysis. He is currently an associate editor of the IEEE Transactions on Parallel and Distributed Systems (TPDS) and on the editorial board of ACM Computers in Entertainment magazine. He is also the program committee chair of ICDE NetDB 2005 and ACM GIS 2005. He serves on many conference program committees such as IEEE ICDE 2006, ACM CIKM 2005, SSTD 2005 and ACM SIGMOD 2004. Dr. Shahabi is the recipient of the 2002 National Science Foundation CAREER Award and 2003 Presidential Early Career Awards for Scientists and Engineers (PECASE). In 2001, he also received an award from the Okawa Foundations. Kun Fu is currently a Ph.D candidate in computer science from the University of Southern California. He did research at the Data Communication Technology Research Institute and National Data Communication Engineering Center in China prior to coming to the United States and is currently working on large scale data stream recording architectures at the NSF's Integrated Media System Center (IMSC) and Data Management Research Laboratory (DMRL) at the Computer Science Department at USC. He received an MS in engineering science from the University of Toledo. He is a member of the IEEE. His research interests are in the area of scalable streaming architectures, distributed real-time systems, and multimedia computing and networking. Mehrdad Jahangiri was born in Tehran, Iran. He received the B.S. degree in Civil Engineering from University of Tehran at Tehran, in 1999. He is currently working towards the Ph.D. degree in Computer Science at the University of Southern California. He is currently a research assistant working on multidimensional data analysis at Integrated Media Systems Center (IMSC)—Information Laboratory (InfoLAB) at the Computer Science Department of the University of Southern California.     

Algebraic retrieval of fragmentarily indexed video

When dealing with long video data, the task of identifying and indexing all meaningful subintervals that become answers to some queries is infeasible. It is infeasible not only when done by hand but even when done by using latest automatic video indexing techniques. Whether manually or automatically, it is only fragmentary video intervals that we can identify in advance of any database usage. Our goal is to develop a framework for retrieving meaningful intervals from such fragmentarily indexed video data. We propose a set of algebraic operations that includes ourglue join operations, with which we can dynamically synthesize all the intervals that are conceivably relevant to a given query. In most cases, since these operations also produce irrelevant intervals, we also define variousselection operations that are useful in excluding them from the answer set. We also show the algebraic properties possessed by those operations, which establish the basis of an algebraic query optimization. Katsumi Tanaka, D. Eng.: He received his B.E., M.E., and D.Eng. degrees in information science from Kyoto University, in 1974, 1976, and 1981, respectively. Since 1994, he is a professor of the Department of Computer and Systems Engineering and since 1997, he is a professor of the Division of Information and Media Sciences, Graduate School of Science and Technology, Kobe University. His research interests include object-oriented, multimedia and historical databases abd multimedia information systems. He is a member of the ACM, IEEE Computer Society and the Information Processing Society of Japan. Keishi Tajima, D.Sci.: He received his B.S, M.S., and D.S. from the department of information science of University of Tokyo in 1991, 1993, and 1996 respectively. Since 1996, he is a Research Associate in the Department of Computer and Systems Engineering at Kobe University. His research interests include data models for non-traditional database systems and their query languages. He is a member of ACM, ACM SIGMOD, Information Processing Society of Japan (IPSJ), and Japan Society for Software Science and Technology (JSSST). Takashi Sogo, M.Eng.: He received B.E. and M.E. from the Department of Computer and Systems Engineering, Kobe University in 1998 and 2000, respectively. Currently, he is with USAC Systems Co. His research interests include video database systems. Sujeet Pradhan, D.Eng.: He received his BE in Mechanical Engineering from the University of Rajasthan, India in 1988, MS in Instrumentation Engineering in 1995 and Ph.D. in Intelligence Science in 1999 from Kobe University, Japan. Since 1999 May, he is a lecturer of the Department of Computer Science and Mathematics at Kurashiki University of Science and the Arts, Japan. A JSPS (Japan Society for the Promotion of Science) Research Fellow during the period between 1997 and 1999, his research interests include video databases, multimedia authoring, prototypebased languages and semi-structured databases. Dr. Pradhan is a member of Information Processing Society of Japan.     

Handicapping attacker's confidence: an alternative to <Emphasis Type="Italic">k</Emphasis>-anonymization

We present an approach of limiting the confidence of inferring sensitive properties to protect against the threats caused by data mining abilities. The problem has dual goals: preserve the information for a wanted data analysis request and limit the usefulness of unwanted sensitive inferences that may be derived from the release of data. Sensitive inferences are specified by a set of “privacy templates". Each template specifies the sensitive property to be protected, the attributes identifying a group of individuals, and a maximum threshold for the confidence of inferring the sensitive property given the identifying attributes. We show that suppressing the domain values monotonically decreases the maximum confidence of such sensitive inferences. Hence, we propose a data transformation that minimally suppresses the domain values in the data to satisfy the set of privacy templates. The transformed data is free of sensitive inferences even in the presence of data mining algorithms. The prior k-anonymization k has been italicized consistently throughout this article. focuses on personal identities. This work focuses on the association between personal identities and sensitive properties. Ke Wang received Ph.D. from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser University. Before joining Simon Fraser, he was an associate professor at National University of Singapore. He has taught in the areas of database and data mining. Dr. Wang’s research interests include database technology, data mining and knowledge discovery, machine learning, and emerging applications, with recent interests focusing on the end use of data mining. This includes explicitly modeling the business goal (such as profit mining, bio-mining and web mining) and exploiting user prior knowledge (such as extracting unexpected patterns and actionable knowledge). He is interested in combining the strengths of various fields such as database, statistics, machine learning and optimization to provide actionable solutions to real-life problems. He is an associate editor of the IEEE TKDE journal and has served program committees for international conferences. Benjamin C. M. Fung received B.Sc. and M.Sc. degrees in computing science from Simon Fraser University. Received the postgraduate scholarship doctoral award from the Natural Sciences and Engineering Research Council of Canada (NSERC), Mr. Fung is currently a Ph.D. candidate at Simon Fraser. His recent research interests include privacy-preserving data mining, secure distributed computing, and text mining. Before pursuing his Ph.D., he worked in the R&D Department at Business Objects and designed reporting systems for various Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems, including BaaN, Siebel, and PeopleSoft. Mr. Fung has published in data engineering, data mining, and security conferences, journals, and books, including IEEE ICDE, IEEE ICDM, IEEE ISI, SDM, KAIS, and the Encyclopedia of Data Warehousing and Mining. Philip S. Yu received B.S. degree in E.E. from National Taiwan University, M.S. and Ph.D. degrees in E.E. from Stanford University, and M.B.A. degree from New York University. He is with IBM T.J. Watson Research Center and currently manager of the Software Tools and Techniques group. Dr. Yu has published more than 450 papers in refereed journals and conferences. He holds or has applied for more than 250 US patents. Dr. Yu is a Fellow of the ACM and the IEEE. He has received several IBM honors including two IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, two Research Division Awards and the 85th plateau of Invention Achievement Awards. He received a Research Contributions Award from IEEE International Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts” in 1999. Dr. Yu is an IBM Master Inventor.     

Higher order feature selection for text classification

In this paper. we present the MIFS-C variant of the mutual information feature-selection algorithms. We present an algorithm to find the optimal value of the redundancy parameter, which is a key parameter in the MIFS-type algorithms. Furthermore, we present an algorithm that speeds up the execution time of all the MIFS variants. Overall, the presented MIFS-C has comparable classification accuracy (in some cases even better) compared with other MIFS algorithms, while its running time is faster. We compared this feature selector with other feature selectors, and found that it performs better in most cases. The MIFS-C performed especially well for the breakeven and F-measure because the algorithm can be tuned to optimise these evaluation measures. Jan Bakus received the B.A.Sc. and M.A.Sc. degrees in electrical engineering from the University of Waterloo, Waterloo, ON, Canada, in 1996 and 1998, respectively, and Ph.D. degree in systems design engineering in 2005. He is currently working at Maplesoft, Waterloo, ON, Canada as an applications engineer, where he is responsible for the development of application specific toolboxes for the Maple scientific computing software. His research interests are in the area of feature selection for text classification, text classification, text clustering, and information retrieval. He is the recipient of the Carl Pollock Fellowship award from the University of Waterloo and the Datatel Scholars Foundation scholarship from Datatel. Mohamed S. Kamel holds a Ph.D. in computer science from the University of Toronto, Canada. He is at present Professor and Director of the Pattern Analysis and Machine Intelligence Laboratory in the Department of Electrical and Computing Engineering, University of Waterloo, Canada. Professor Kamel holds a Canada Research Chair in Cooperative Intelligent Systems. Dr. Kamel's research interests are in machine intelligence, neural networks and pattern recognition with applications in robotics and manufacturing. He has authored and coauthored over 200 papers in journals and conference proceedings, 2 patents and numerous technical and industrial project reports. Under his supervision, 53 Ph.D. and M.A.Sc. students have completed their degrees. Dr. Kamel is a member of ACM, AAAI, CIPS and APEO and has been named s Fellow of IEEE (2005). He is the editor-in-chief of the International Journal of Robotics and Automation, Associate Editor of the IEEE SMC, Part A, the International Journal of Image and Graphics, Pattern Recognition Letters and is a member of the editorial board of the Intelligent Automation and Soft Computing. He has served as a consultant to many Companies, including NCR, IBM, Nortel, VRP and CSA. He is a member of the board of directors and cofounder of Virtek Vision International in Waterloo.     

Privacy-preserving SVM classification

Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the nondisclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from data distributed at multiple parties, without disclosing the data of each party to others. Solutions are sketched out for data that is vertically, horizontally, or even arbitrarily partitioned. We quantify the security and efficiency of the proposed method, and highlight future challenges. Jaideep Vaidya received the Bachelor’s degree in Computer Engineering from the University of Mumbai. He received the Master’s and the Ph.D. degrees in Computer Science from Purdue University. He is an Assistant Professor in the Management Science and Information Systems Department at Rutgers University. His research interests include data mining and analysis, information security, and privacy. He has received best paper awards for papers in ICDE and SIDKDD. He is a Member of the IEEE Computer Society and the ACM. Hwanjo Yu received the Ph.D. degree in Computer Science in 2004 from the University of Illinois at Urbana-Champaign. He is an Assistant Professor in the Department of Computer Science at the University of Iowa. His research interests include data mining, machine learning, database, and information systems. He is an Associate Editor of Neurocomputing and served on the NSF Panel in 2006. He has served on the program committees of 2005 ACM SAC on Data Mining track, 2005 and 2006 IEEE ICDM, 2006 ACM CIKM, and 2006 SIAM Data Mining. Xiaoqian Jiang received the B.S. degree in Computer Science from Shanghai Maritime University, Shanghai, 2003. He received the M.C.S. degree in Computer Science from the University of Iowa, Iowa City, 2005. Currently, he is pursuing a Ph.D. degree from the School of Computer Science, Carnegie Mellon University. His research interests are computer vision, machine learning, data mining, and privacy protection technologies.     

Surface detail capturing for realistic facial animation

In this paper, a facial animation system is proposed for capturing both geometrical information and illumination changes of surface details, called expression details, from video clips simultaneously, and the capture ddata can be widely applied to different 2D face images and 3D face models. While tracking the geometric data, we record the expression details by ratio images. For 2D facial animation synthesis, these ratio images are used to generate dynamic textures. Because a ratio image is obtained via dividing colors of an expressive face by those of a neutral face, pixels with ratio value smaller than one are where a wrinkle or crease appears. The refore, thegradients of the ratio value at each pixel in ratio images are regarded as changes of a face surface, and original normals on the surface can be adjusted according to these gradients. Based on this idea, we can convert the ratio images into a sequence of normal maps and then apply them to animated 3D model rendering. With the expression detail mapping, the resulted facial animations are more life-like and more expressive.     

Performance evaluation of a parallel cascade semijoin algorithm for computing path expressions in object database systems

With the emerging of new applications,especially in Web,Such as E-Commerce,Digital Library and DNA Bank,object database systems show their stronger funcitons than other kinds of database systems due to their powerful representation ability on complex semantics and relationshiop.One distinguished feature of object database systems is path expression,and most queries on an object database ar based on path expression because it is the most natural and convenient way to access the object databse,for example,to navigate the hyper-links in a web-based database,The execution of path expression is usually extremely expensive on a very large database.Therefore,the improvement of path expression eecution efficiency is critical for the performance ofobject databases.As an importan approach realizing high-performance query processing ,the parallel processing of path expression on distributed object databases is explored in this paper.Up to now,some algorithms about how to compute path expressions and how to optimize path expression processing have been proposed for centralizedenvironments.But,few approaches have been presented for computing path expressions in parallel.In this paper,a new paralle algorithm for computing path expression named Parallel Cascade Semijoin(PCSJ)is proposed.Moreover,a new scheduling strategy called right-deep zigzag tree is designed to further improve the performance of the PCSJ algorithm.The exper-iments have been implemented in an NOW distributed and parallel environment.The results show that the PCSJ algorithm outperforms the other two parallel algorithms(the parallel version of forward pointer chasing algorithm(PFPC)and the index splitting parallel algorithm(IndexSplit) when computing path expressions with restrictive predicates and that the right-deep zigzage tree scheduling strategy has better performance than the right-deep tree scheduling strategy.     

Constructive negation and constraint logic programming with sets

The aim of this paper is to extend theConstructive Negation technique to the case ofCLP(SεT), a Constraint Logic Programming (CLP) language based on hereditarily (and hybrid) finite sets. The challenging aspects of the problem originate from the fact that the structure on whichCLP(SεT) is based is notadmissible closed, and this does not allow to reuse the results presented in the literature concerning the relationships betweenCLP and constructive negation. We propose a new constraint satisfaction algorithm, capable of correctly handling constructive negation for large classes ofCLP(SεT) programs; we also provide a syntactic characterization of such classes of programs. The resulting algorithm provides a novel constraint simplification procedure to handle constructive negation, suitable to theories where unification admits multiple most general unifiers. We also show, using a general result, that it is impossible to construct an interpreter forCLP(SεT) with constructive negation which is guaranteed to work for any arbitrary program; we identify classes of programs for which the implementation of the constructive negation technique is feasible. Agostino Dovier, Ph.D.: He is a researcher in the Department of Science and Technology at the University of Verona, Italy. He obtained his master degree in Computer Science from the University of Udine, Italy, in 1991 and his Ph.D. in Computer Science from the University of Pisa, Italy, in 1996. His research interests are in Programming Languages and Constraints over complex domains, such as Sets and Multisets. He has published over 20 research papers in International Journals and Conferences. He is teaching a course entitled “Special Languages and Techniques for Programming” at the University of Verona. Enrico Pontelli, Ph.D.: He is an Assistant Professor in the Department of Computer Science at the New Mexico State University. He obtained his Laurea degree from the University of Udine (Italy) in 1991, his Master degree from the University of Houston in 1992, and his Ph.D. degree from New Mexico State University in 1997. His research interests are in Programming Languages, Parallel Processing, and Constraint Programming. He has published over 50 papers and served on the program committees of different conferences. He is presently the Associate Director of the Laboratory for Logic, Databases, and Advanced Programming. Gianfranco Rossi, Ph.D.: He received his degree in Computer Science from the University of Pisa in 1979. From 1981 to 1983 he was employed at Intecs Co. System House in Pisa. From November 1983 to February 1989 he was a researcher at the Dipartimento di Informatica of the University of Turin. Since March 1989 he is an Associate Professor of Computer Science, currently with the University of Parma. He is the author of several papers dealing mainly with programming languages, in particular logic programming languages and Prolog, and extended unification algorithms. His current research interests are (logic) programming languages with sets and set unification algorithms.     

Secure software infrastructure in the internet age

The rapid growth and penetration of the Internet are now leading us to a world where networks are ubiquitous and everything is connected. Breaking the distance barrier by the ubiquitous connection, however, is a two-edged sword. Our network infrastructure today is still fragile and thus “everything is connected” may simply mean “everything can be attacked from whatever place on the earth.” In this paper, we first point out the importance and inherent problems of software systems that underlay open and extensible networks, especially the Internet. We put emphasis on software since software vulnerabilities account for most attacks, incidents, or even disasters on the Internet today. Next we present general ideas of promising techniques in defense of software systems, including theoretical, language-based, and runtime solutions. Finally, we show our experience in developing a secure mail system. Etsuya Shibayama, D.Sc.: He is a professor of the Graduate School of Information Science and Engineering at Tokyo Institute of Technology. He received B.Sc. and M.Sc. in mathematical sciences from Kyoto University in 1981 and 1983, respectively, and D.Sc. in information science from the University of Tokyo in 1991. He is interested in various topics in software including design and implementation of textual and visual programming languages, system software, and user interface software. Recently, he has been doing research on language-based software security and methodologies for building secure software. Akinori Yonezawa, Ph.D.: He is a Professor of computer science at Department of Computer Science, the University of Tokyo. He received his Ph.D. in Computer Science form the Massachusetts Institute of Technology in 1977. His current major research interests are in the areas of concurrent/parallel computation models, programming languages, object-oriented computing and distributed computing. He is the designer of and object-oriented concurrent language ABCL/1 and the editor of several books and served as an associate editor of ACM Transaction of Programming Language and Systems (TOPLAS). Since 1998, he has been an ACM Fellow.     

Scheduling real-time requests in on-demand data broadcast environments

On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. In this paper, we propose a new online preemptive scheduling algorithm, called PRDS that incorporates urgency, data size and number of pending requests for real-time on-demand broadcast system. Furthermore, we use pyramid preemption to optimize performance and reduce overhead. A series of simulation experiments have been performed to evaluate the real-time performance of our algorithm as compared with other previously proposed methods. The experimental results show that our algorithm substantially outperforms other algorithms over a wide range of workloads and parameter settings. The work described in this paper was partially supported by grants from CityU (Project No. 7001841) and RGC CERG Grant No. HKBU 2174/03E. This paper is an extended version of the paper “A preemptive scheduling algorithm for wireless real-time on-demand data broadcast” that appeared in the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications. Victor C. S. Lee received his Ph.D. degree in Computer Science from the City University of Hong Kong in 1997. He is now an Assistant Professor in the Department of Computer Science of the City University of Hong Kong. Dr. Lee is a member of the ACM, the IEEE and the IEEE Computer Society. He is currently the Chairman of the IEEE, Hong Kong Section, Computer Chapter. His research interests include real-time data management, mobile computing, and transaction processing. Xiao Wu received the B.Eng. and M.S. degrees in computer science from Yunnan University, Kunming, China, in 1999 and 2002, respectively. He is currently a Ph.D. candidate in the Department of Computer Science at the City University of Hong Kong. He was with the Institute of Software, Chinese Academy of Sciences, Beijing, China, between January 2001 and July 2002. From 2003 to 2004, he was with the Department of Computer Science of the City University of Hong Kong, Hong Kong, as a Research Assistant. His research interests include multimedia information retrieval, video computing and mobile computing. Joseph Kee-Yin NG received a B.Sc. in Mathematics and Computer Science, a M.Sc. in Computer Science, and a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in the years 1986, 1988, and 1993, respectively. Prof. Ng is currently a professor in the Department of Computer Science at Hong Kong Baptist University. His current research interests include Real-Time Networks, Multimedia Communications, Ubiquitous/Pervasive Computing, Mobile and Location- aware Computing, Performance Evaluation, Parallel and Distributed Computing. Prof. Ng is the Technical Program Chair for TENCON 2006, General Co-Chair for The 11th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2005), Program Vice Chair for The 11th International Conference on Parallel and Distributed Systems (ICPADS 2005), Program Area-Chair for The 18th & 19th International Conference on Advanced Information Networking and Applications (AINA 2004 & AINA 2005), General Co-Chair for The International Computer Congress 1999 & 2001 (ICC’99 & ICC’01), Program Co-Chair for The Sixth International Conference on Real-Time Computing Systems and Applications (RTCSA’99) and General Co-Chair for The 1999 and 2001 International Computer Science Conference (ICSC’99 & ICSC’01). Prof. Ng is a member of the Editorial Board of Journal of Pervasive Computing and Communications, Journal of Ubiquitous Computing and Intelligence, Journal of Embedded Computing, and Journal of Microprocessors and Microsystems. He is the Associate Editor of Real-Time Systems Journal and Journal of Mobile Multimedia. He is also a guest editor of International Journal of Wireless and Mobile Computing for a special issue on Applications, Services, and Infrastructures for Wireless and Mobile Computing. Prof. Ng is currently the Region 10 Coordinator for the Chapter Activities Board of the IEEE Computer Society, and is the Coordinator of the IEEE Computer Society Distinguished Visitors Program (Asia/Pacific). He is a senior member of the IEEE and has been a member of the IEEE Computer Society since 1991. Prof. Ng has been an Exco-member (1993–95), General Secretary (1995–1997), Vice-Chair (1997–1999), Chair (1999–2001) and the Past Chair of the IEEE, Hong Kong Section, Computer Chapter. Prof. Ng received the Certificate of Appreciation for Services and Contribution (2004) from IEEE Hong Kong Section, the Certificate of Appreciation for Leadership and Service (2000–2001) from IEEE Region 10 and the IEEE Meritorious Service Award from IEEE Computer Society at 2004. He is also a member of the IEEE Communication Society, ACM and the Founding Member for the Internet Society (ISOC)-Hong Kong Chapter.     

Specification and analysis of a data transfer protocol using systems of communicating machines

Summary A model for communication protocols calledsystems of communicating machines is used to specify a data transfer protocol with variable window size (e.g., HDLC), which is an arbitrary nonnegative integer, and to analyze it for freedom from deadlocks. The model uses a combination of finite state machines and variables. This allows the size of the specification (i.e., number of states and variables) to be linear in the window size, a considerable reduction from the pure finite state machine model. A new type of analysis is demonstrated which we callsystem state analysis. This is similar to thereachability analysis used in the pure finite state model, but it provides substantial simplication by reducing the number of states generated. For example, with the protocol in this paper, ifw is the window size, then the global analysis producesO(w ⁵) states, while the system state analysis producesO(w ³) states. The system state analysis is then combined with an inductive proof, extending the analysis to all nonnegative integersw. Gilbert M. Lundy, Jr was born in New Orleans, Louisiana, in 1954. After completing schools in Plano, Texas, he attended Texas A & M University, receiving the B.A. in mathematics (1976). From 1977–81 he served as a Lieutenant in the U.S. Army, based at Fort Ord, California. From 1981–84 he was a software engineer at E-Systems, in Dallas, Texas. During this period he also completed the M.S. program in Computer Science at the University of Texas at Dallas. From 1984 to 1988, he was a graduate student at Georgia Institute of Technology, receiving the Ph.D. in 1988. His research was in the formal modeling of communication protocols for computer networks. Since September 1988, he has been an Assistant Professor of computer science at the U.S. Naval Postgraduate School in Monterey, CA. He teaches classes and performs research in computer networks and communications. Raymond E. Miller received his Ph.D. degree from the University of Illinois, Urbana-Champaign, in 1957. He was a Research Staff Member at IBM Thomas J. Watson Research Center, Yorktown, Heights, NY, from 1957 to 1980, Director of the School of Information and Computer Science at Georgia Tech from 1980 to 1987, and is currently a Professor of Computer Science at the University of Maryland, College Park, and Director of the NASA Center of Excellence in Space Data and Information Sciences at Goddard Space Flight Center. He has written over 90 technical papers in areas of theory of computation, machine organization, parallel computation, and communication protocols. Dr. Miller is a Fellow of the American Association for the Advancement of Science, a Fellow of the IEEE and a member of ACM. Among his numerous society activities he served as an ACM Council Member-at-Large from 1976–1982, Editor in Chief of the Journal of the ACM from 1972–1976, a Board Member of the Computing Research Association from 1983–1991, and President of the Computing Sciences Accreditation Board from 1985–1987. Currently he is a member of the Board of Governors of the IEEE Computer Society and Vice President for Educational Activities.This research was performed while the authors were at Georgia Institute of Technology     

GigaE PM: a high performance communication facility using a Gigabit Ethernet

A high performance communication facility, called theGigaE PM, has been designed and implemented for parallel applications on clusters of computers using a Gigabit Ethernet. The GigaE PM provides not only a reliable high bandwidth and low latency communication, but also supports existing network protocols such as TCP/IP. A reliable communication mechanism for a parallel application is implemented on the firmware on a NIC while existing network protocols are handled by an operating system kernel. A prototype system has been implemented using an Essential Communications Gigabit Ethernet card. The performance results show that a 58.3 μs round trip time for a four byte user message, and 56.7 MBytes/sec bandwidth for a 1,468 byte message have been achieved on Intel Pentium II 400 MHz PCs. We have implemented MPICH-PM on top of the GigaE PM, and evaluated the NAS parallel benchmark performance. The results show that the IS class S performance on the GigaE PM is 1.8 times faster than that on TCP/IP. Shinji Sumimoto: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. He received BS degree in electrical engineering from Doshisha University. His research interest include parallel and distributed systems, real-time systems, and high performance communication facilities. He is a member of Information Processing Society of Japan. Hiroshi Tezuka: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. His research interests include real-time systems and operating system kernel. He is a member of the Information Processing Society of Japan, and Japan Society for Software Science and Technology. Atsushi Hori, Ph.D.: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. His current research interests include parallel operating system. He received B.S. and M.S. degrees in Electrical Engineering from Waseda University, and received Ph.D. from the University of Tokyo. He worked as a researcher in Mitsubishi Research Institute from 1981 to 1992. Hiroshi Harada: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. His research interests include distributed/parallel systems and distributed shared memory. He received BS degree in physics from Science University of Tokyo. He is a member of ACM and Information Processing Society of Japan. Toshiyuki Takahashi: He is a Researcher at Real World Computing Partnership since 1998. He received his B.S. and M.S. from the Department of Information Sciences of Science University of Tokyo in 1993 and 1995. He was a student of the Information Science Department of the University of Tokyo from 1995 to 1998. His current interests are in meta-level architecture for programming languages and high-performance software technologies. He is a member of Information Processing Society of Japan. Yutaka Ishikawa, Ph.D.: He is the chief of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. He is currently temporary retirement from Electrotechnical Laboratory, MITI. His research interests include distributed/parallel systems, object-oriented programming languages, and real-time systems. He received the B.S., M.S. and Ph.D degrees in electrical engineering from Keio University. He is a member of the IEEE Computer Society, ACM, Information Processing Society of Japan, and Japan Society for Software Science and Technology.     

A challenge towards next-generation research infrastructure for advanced life science

Recently, life scientists have expressed a strong need for computational power sufficient to complete their analyses within a realistic time as well as for a computational power capable of seamlessly retrieving biological data of interest from multiple and diverse bio-related databases for their research infrastructure. This need implies that life science strongly requires the benefits of advanced IT. In Japan, the Biogrid project has been promoted since 2002 toward the establishment of a next-generation research infrastructure for advanced life science. In this paper, the Biogrid strategy toward these ends is detailed along with the role and mission imposed on the Biogrid project. In addition, we present the current status of the development of the project as well as the future issues to be tackled. Haruki Nakamura, Ph.D.: He is Professor of Protein Informatics at Institute for Protein Research, Osaka University. He received his B.S., M.A. and Ph.D. from the University of Tokyo in 1975, 1977 and 1980 respectively. His research field is Biophysics and Bioinformatics, and has so far developed several original algorithms in the computational analyses of protein electrostatic features and folding dynamics. He is also a head of PDBj (Protein Data Bank Japan) to manage and develop the protein structure database, collaborating with RCSB (Research Collaboratory for Structural Bioinformatics) in USA and MSD-EBI (Macromolecular Structure Database at the European Bioinformatics Institute) in EU. Susumu Date, Ph.D.: He is Assistant Professor of the Graduate School of Information Science and Technology, Osaka University. He received his B.E., M.E. and Ph.D. degrees from Osaka University in 1997, 2000 and 2002, respectively. His research field is computer science and his current research interests include application of Grid computing and related information technologies to life sciences. He is a member of IEEE CS and IPSJ. Hideo Matsuda, Ph.D.: He is Professor of the Department of Bioinformatic Engineering, the Graduate School of Information Science and Technology, Osaka University. He received his B.S., M.Eng. and Ph.D. degrees from Kobe University in 1982, 1984 and 1987 respectively. For M.Eng. and Ph.D. degrees, he majored in computer science. His research interests include computational analysis of genomic sequences. He has been involved in the FANTOM (Functional Annotation of Mouse) Project for the functional annotation of RIKEN mouse full-length cDNA sequences. He is a member of ISCB, IEEE CS and ACM. Shinji Shimojo, Ph.D.: He received M.E. and Ph.D. degrees from Osaka University in 1983 and 1986 respectively. He was an Assistant Professor with the Department of Information and Computer Sciences, Faculty of Engineering Science at Osaka University from 1986, and an Associate Professor with Computation Center from 1991 to 1998. During the period, he also worked as a visiting researcher at the University of California, Irvine for a year. He has been Professor with Cybermedia Center (then Computation Center) at Osaka University since 1998. His current research work focus on a wide variety of multimedia applications, peer-to-peer communication networks, ubiquitous network systems and Grid technologies. He is a member of ACM, IEEE and IEICE.     

Performance Analysis of the JPEG 2000 Image Coding Standard

Some of the major objectives of the JPEG 2000 still image coding standard were compression and memory efficiency, lossy to lossless coding, support for continuous-tone to bi-level images, error resilience, and random access to regions of interest. This paper will provide readers with some insight on various features and functionalities supported by a baseline JPEG 2000-compliant codec. Three JPEG 2000 software implementations (Kakadu, JasPer, JJ2000) are compared with several other codecs, including JPEG, JBIG, JPEG-LS, MPEG-4 VTC and H.264 intra coding. This study can serve as a guideline for users to estimate the effectiveness of JPEG 2000 for various applications, and to select optimal parameters according to specific application requirements.Hong Man received his Ph.D. degree from Georgia Institute of Technology in 1999, in Electrical Engineering. He joined Stevens Institute of Technology in 2000, and currently he is an assistant professor in the Department of Electrical and Computer Engineering. He is serving as the director for Visual Information Environment Laboratory at Stevens, the director for Computer Engineering undergraduate program in the ECE department, and the coordinator for NSA Center of Academic Excellence in Information Assurance in the School of Engineering. He is a member of the IEEE and ACM. He served as member of organizing committee for IEEE International Workshop on Multimedia and Signal Processing (MMSP) 2002 and 2005, member of technical program committee for IEEE Vehicular Technology Conference (VTC) Fall 2003, and IEEE/ACM International Conference on E-Business and Telecommunication Networks (ICETE) 2004 and 2005. He is a committee member on IEEE SPS TC for Education. He was an active contributor to the ISO/ITU JPEG 2000 image coding standard.Alen Docef received his Diploma of Engineer from the Polytechnic Institute of Bucharest, Romania, in 1991. He obtained an M.S.E.E degree in 1992 and a Ph.D. degree in 1998 from the Georgia Institute of Technology, Atlanta, Georgia, all in electrical engineering. From 1998 to 1999 he worked as a research engineer in the Signal Processing and Multimedia Group of the University of British Columbia. In 2000 he joined the Virginia Commonwealth University School of Engineering as an Assistant Professor. His research interests include multimedia signal compression, medical image processing, and real-time implementation of DSP algorithms. He has been a member of the IEEE since 1995.Faouzi Kossentini received the B.S., M.S., and Ph.D. degrees from the Georgia Institute of Technology, Atlanta, in 1989, 1990, and 1994, respectively. He is presently the President and CEO of UB Video Inc., a company in Vancouver (Canada) that develops video communication products for the video conferencing and broadcast markets. Before the year 2004, he had been an associate professor in the Department of Electrical and Computer Engineering at the University of British Columbia, where he was involved in research in the areas of signal processing, communications and multimedia. He has co-authored more than two hundred journal papers, conference papers and book chapters. Dr. Kossentini is a senior member of the IEEE. He has served as a Vice General Chair for ICIP-2000, and he has also served as an associate editor for the IEEE transactions on Image Processing and the IEEE transactions on Multimedia.     

A grid-oriented genetic algorithm framework for bioinformatics

In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running on the Grid, named “Grid-Oriented Genetic algorithms (GOGAs)”, and actually “Gridify” a GA for estimating genetic networks, which is being developed by our group, in order to examine the usability of the proposed GOGA framework. We also evaluate the scalability of the “Gridified” GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed in our laboratory. Hiroaki Imade: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2001. He received the M.S. degree in information systems from the Graduate School of Engineering, The University of Tokushima in 2003. He is now in Doctoral Course of Graduate School of Engineering, The University of Tokushima. His research interests include evolutionary computation. He currently researches a framework to easily develop the GOGA models which efficiently work on the grid. Ryohei Morishita: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2002. He is now in Master Course of Graduate School of Engineering, The University of Tokushima, Tokushima. His research interest is evolutionary computation. He currently researches GA for estimating genetic networks. Isao Ono, Ph.D.: He received his B.S. degree from the Department of Control Engineering, Tokyo Institute of Technology, Tokyo, Japan, in 1994. He received Ph.D. of Engineering at Tokyo Institute of Technology, Yokohama, in 1997. He worked as a Research Fellow from 1997 to 1998 at Tokyo Institute of Technology, and at University of Tokushima, Tokushima, Japan, in 1998. He worked as a Lecturer from 1998 to 2001 at University of Tokushima. He is now Associate Professor at University of Tokushima. His research interests include evolutionary computation, scheduling, function optimization, optical design and bioinformatics. He is a member of JSAI, SCI, IPSJ and OSJ. Norihiko Ono, Ph.D.: He received his B.S. M.S. and Ph.D. of Engineering in 1979, 1981 and 1986, respectively, from Tokyo Institute of Technology. From 1986 to 1989, he was Research Associate at Faculty of Engineering, Hiroshima University. From 1989 to 1997, he was an associate professor at Faculty of Engineering, University of Tokushima. He was promoted to Professor in the Department of Information Science and Intelligent Systems in 1997. His current research interests include learning in multi-agent systems, autonomous agents, reinforcement learning and evolutionary algorithms. Masahiro Okamoto, Ph.D.: He is currently Professor of Graduate School of Systems Life Sciences, Kyushu University, Japan. He received his Ph.D. degree in Biochemistry from Kyushu University in 1981. His major research field is nonlinear numerical optimization and systems biology. His current research interests cover system identification of nonlinear complex systems by using evolutional computer algorithm of optimization, development of integrated simulator for analyzing nonlinear dynamics and design of fault-tolerant routing network by mimicking metabolic control system. He has more than 90 peer reviewed publications.