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.     

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.     

Image hiding scheme using modulus function and optimal substitution table

The simple least-significant-bit (LSB) substitution technique is the easiest way to embed secret data in the host image. To avoid image degradation of the simple LSB substitution technique, Wang et al. proposed a method using the substitution table to process image hiding. Later, Thien and Lin employed the modulus function to solve the same problem. In this paper, the proposed scheme combines the modulus function and the optimal substitution table to improve the quality of the stego-image. Experimental results show that our method can achieve better quality of the stego-image than Thien and Lin’s method does. The text was submitted by the authors in English. Chin-Shiang Chan received his BS degree in Computer Science in 1999 from the National Cheng Chi University, Taipei, Taiwan and the MS degree in Computer Science and Information Engineering in 2001 from the National Chung Cheng University, ChiaYi, Taiwan. He is currently a Ph.D. student in Computer Science and Information Engineering at the National Chung Cheng University, Chiayi, Taiwan. His research fields are image hiding and image compression. Chin-Chen Chang received his BS degree in applied mathematics in 1977 and his MS degree in computer and decision sciences in 1979, both from the National Tsing Hua University, Hsinchu, Taiwan. He received his Ph.D. in computer engineering in 1982 from the National Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering at the National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan. Dr. Chang is a Fellow of IEEE, a Fellow of IEE and a member of the Chinese Language Computer Society, the Chinese Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include computer cryptography, data engineering, and image compression. Yu-Chen Hu received his Ph.D. degree in Computer Science and Information Engineering from the Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan in 1999. Dr. Hu is currently an assistant professor in the Department of Computer Science and Information Engineering, Providence University, Sha-Lu, Taiwan. He is a member of the SPIE society and a member of the IEEE society. He is also a member of the Phi Tau Phi Society of the Republic of China. His research interests include image and data compression, information hiding, and image processing.     

A New Challenge for Applying Time Series Metrics Data to Software Quality Estimation

In typical software development, a software reliability growth model (SRGM) is applied in each testing activity to determine the time to finish the testing. However, there are some cases in which the SRGM does not work correctly. That is, the SRGM sometimes mistakes quality for poor quality products. In order to tackle this problem, we focussed on the trend of time series data of software defects among successive testing phases and tried to estimate software quality using the trend. First, we investigate the characteristics of the time series data on the detected faults by observing the change of the number of detected faults. Using the rank correlation coefficient, the data are classified into four kinds of trends. Next, with the intention of estimating software quality, we investigate the relationship between the trends of the time series data and software quality. Here, software quality is defined by the number of faults detected during six months after shipment. Finally, we find a relationship between the trends and metrics data collected in the software design phase. Using logistic regression, we statistically show that two review metrics in the design and coding phase can determine the trend. Sousuke Amasakireceived the B.E. degree in Information and Computer Sciences from Okayama Prefectural University, Japan, in 2000 and the M.E. degree in Information and Computer Sciences from Graduate School of Information Science and Technology, Osaka University, Japan, in 2003. He has been in Ph.D. course of Graduate School of Information Science and Technology at Osaka University. His interests include the software process and the software quality assurance technique. He is a student member of IEEE and ACM. Takashi Yoshitomireceived the B.E. degree in Information and Computer Sciences from Osaka University, Japan, in 2002. He has been working for Hitachi Software Engineering Co., Ltd. Osamu Mizunoreceived the B.E., M.E., and Ph.D. degrees in Information and Computer Sciences from Osaka University, Japan, in 1996, 1998, and 2001, respectively. He is an Assistant Professor of the Graduate School of Information Science and Technology at Osaka University. His research interests include the improvement technique of the software process and the software risk management technique. He is a member of IEEE. Yasunari Takagireceived the B.E. degree in Information and Computer Science, from Nagoya Institute of Technology, Japan, in 1985. He has been working for OMRON Corporation. He has been also in Ph.D. course of Graduate School of Information Science and Technology at Osaka University since 2002. Tohru Kikunoreceived the B.E., M.Sc., and Ph.D. degrees in Electrical Engineering from Osaka University, Japan, in 1970, 1972, and 1975, respectively. He joined Hiroshima University from 1975 to 1987. Since 1990, he has been a Professor of the Department of Information and Computer Sciences at Osaka University. His research interests include the analysis and design of fault-tolerant systems, the quantitative evaluation of software development processes, and the design of procedures for testing communication protocols. He is a member of IEEE and ACM.     

A separation method for DNA computing based on concentration control

A separation method for DNA computing based on concentration control is presented. The concentration control method was earlier developed and has enabled us to use DNA concentrations as input data and as filters to extract target DNA. We have also applied the method to the shortest path problems, and have shown the potential of concentration control to solve large-scale combinatorial optimization problems. However, it is still quite difficult to separate different DNA with the same length and to quantify individual DNA concentrations. To overcome these difficulties, we use DGGE and CDGE in this paper. We demonstrate that the proposed method enables us to separate different DNA with the same length efficiently, and we actually solve an instance of the shortest path problems. Masahito Yamamoto, Ph.D.: He is associate professor of information engineering at Hokkaido University. He received Ph.D. from the Graduate School of Engineering, Hokkaido University in 1996. His current research interests include DNA computing based the laboratory experiments. He is a member of Operations Research Society of Japan, Japanese Society for Artificial Intelligence, Information Processing Society of Japan etc. Atsushi Kameda, Ph.D.: He is the research staff of Japan Science and Technology Corporation, and has participated in research of DNA computing in Hokkaido University. He received his Ph.D. from Hokkaido University in 2001. For each degree he majored in molecular biology. His research theme is about the role of polyphosphate in the living body. As one of the researches relevant to it, he constructed the ATP regeneration system using two enzyme which makes polyphosphate the phosphagen. Nobuo Matsuura: He is a master course student of Division of Systems and Information Engineering of Hokkaido University. His research interests relate to DNA computing with concentration control for shortest path problems, as a means of solution of optimization problems with bimolecular. Toshikazu Shiba, Ph.D.: He is associate, professor of biochemical engineering at Hokkaido University. He received his Ph.D. from Osaka University in 1991. He majored in molecular genetics and biochemistry. His research has progressed from bacterial molecular biology (regulation of gene expression of bacterial cells) to tissue engineering (bone regeneration). Recently, he is very interested in molecular computation and trying to apply his biochemical idea to information technology. Yumi Kawazoe: She is a master course student of Division of Molecular Chemistry of Hokkaido University. Although her major is molecular biology, she is very interested in molecular computation and bioinformatics. Azuma Ohuchi, Ph.D.: He is professor of Information Engineering at the University of Hokkaido, Sapporo, Japan. He has been developing a new field of complex systems engineering, i.e., Harmonious Systems Engineering since 1995. He has published numerous papers on systems engineering, operations research, and computer science. In addition, he is currently supervising projects on DNA computing, multi-agents based artificial market systems, medical informatics, and autonomous flying objects. He was awarded “The 30th Anniversary Award for Excellent Papers” by the Information Processing Society of Japan. He is a member of Operations Research Society of Japan, Japanese Society for Artificial Intelligence, Information Processing Society of Japan, Japan Association for Medical Informatics, IEEE Computer Society, IEEE System, Man and Cybernetics Society etc. He received PhD from Hokkaido University in 1976.     

Web information visualization method employing immune network model for finding topic stream from document-set sequence

A Web information visualization method based on the document set-wise processing is proposed to find the topic stream from a sequence of document sets. Although the hugeness as well as its dynamic nature of the Web is burden for the users, it will also bring them a chance for business and research if they can notice the trends or movement of the real world from the Web. A sequence of document sets found on the Web, such as online news article sets is focused on in this paper. The proposed method employs the immune network model, in which the property of memory cell is used to find the topical relation among document sets. After several types of memory cell models are proposed and evaluated, the experimental results show that the proposed method with memory cell can find more topic streams than that without memory cell. Yasufumi Takama, D.Eng.: He received his B.S., M.S. and Dr.Eng. degrees from the University of Tokyo in 1994, 1996, and 1999, respectively. From 1999 to 2002 he was with Tokyo Institute of Technology, Japan. Since 2002, he has been Associate Professor of Department of Electronic Systems and Engineering, Tokyo Metropolitan Institute of Technology, Tokyo, Japan. He has also been participating in JST (Japan Science and Technology Corporation) since October 2000. His current research interests include artificial intelligence, Web information retrieval and visualization systems, and artificial immune systems. He is a member of JSAI (Japanese Society of Artificial Intelligence), IPS J (Information Processing Society of Japan), and SOFT (Japan Society for Fuzzy Theory and Systems). Kaoru Hirota, D.Eng.: He received his B.E., M.E. and Dr.Eng. degrees in electronics from Tokyo Institute of Technology, Tokyo, Japan, in 1974, 1976, and 1979, respectively. From 1979 to 1982 and from 1982 to 1995 he was with the Sagami Institute of Technology and Hosei University, respectively. Since 1995, he has been with the Interdisciplinary Graduate School of Science and Technology, Tokyo Institute of Technology, Yokohama, Japan. He is now a department head professor of Department of Computational Intelligence and Systems Science. Dr.Hirota is a member of IFSA (International Fuzzy Systems Association (Vice President 1991–1993), Treasurer 1997–2001), IEEE (Associate Editors of IEEE Transactions on Fuzzy Systems (1993–1995) and IEEE Transactions on Industrial Electronics (1996–2000)) and SOFT (Japan Society for Fuzzy Theory and Systems (Vice President 1995–1997, President 2001–2003)), and he is an editor in chief of Int. J. of Advanced Computational Intelligence.     

A study of the model and algorithms for handling location-dependent continuous queries

Advances in wireless and mobile computing environments allow a mobile user to access a wide range of applications. For example, mobile users may want to retrieve data about unfamiliar places or local life styles related to their location. These queries are called location-dependent queries. Furthermore, a mobile user may be interested in getting the query results repeatedly, which is called location-dependent continuous querying. This continuous query emanating from a mobile user may retrieve information from a single-zone (single-ZQ) or from multiple neighbouring zones (multiple-ZQ). We consider the problem of handling location-dependent continuous queries with the main emphasis on reducing communication costs and making sure that the user gets correct current-query result. The key contributions of this paper include: (1) Proposing a hierarchical database framework (tree architecture and supporting continuous query algorithm) for handling location-dependent continuous queries. (2) Analysing the flexibility of this framework for handling queries related to single-ZQ or multiple-ZQ and propose intelligent selective placement of location-dependent databases. (3) Proposing an intelligent selective replication algorithm to facilitate time- and space-efficient processing of location-dependent continuous queries retrieving single-ZQ information. (4) Demonstrating, using simulation, the significance of our intelligent selective placement and selective replication model in terms of communication cost and storage constraints, considering various types of queries. Manish Gupta received his B.E. degree in Electrical Engineering from Govindram Sakseria Institute of Technology & Sciences, India, in 1997 and his M.S. degree in Computer Science from University of Texas at Dallas in 2002. He is currently working toward his Ph.D. degree in the Department of Computer Science at University of Texas at Dallas. His current research focuses on AI-based software synthesis and testing. His other research interests include mobile computing, aspect-oriented programming and model checking. Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China, in 1996, and a Master's Degree in Computer Science from the University of Texas at Dallas 2001. He is currently working toward the Ph.D. degree in the Department of Computer Science at the University of Texas at Dallas. Mr. Tu's research interests include distributed systems, wireless communications, mobile computing, and reliability and performance analysis. His Ph.D. research work focuses on the dependent and secure data replication and placement issues in network-centric systems. Latifur R. Khan has been an Assistant Professor of Computer Science department at University of Texas at Dallas since September 2000. He received his Ph.D. and M.S. degrees in Computer Science from University of Southern California (USC) in August 2000 and December 1996, respectively. He obtained his B.Sc. degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in November of 1993. Professor Khan is currently supported by grants from the National Science Foundation (NSF), Texas Instruments, Alcatel, USA, and has been awarded the Sun Equipment Grant. Dr. Khan has more than 50 articles, book chapters and conference papers focusing in the areas of database systems, multimedia information management and data mining in bio-informatics and intrusion detection. Professor Khan has also served as a referee for database journals, conferences (e.g. IEEE TKDE, KAIS, ADL, VLDB) and he is currently serving as a program committee member for the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD2005), ACM 14th Conference on Information and Knowledge Management (CIKM 2005), International Conference on Database and Expert Systems Applications DEXA 2005 and International Conference on Cooperative Information Systems (CoopIS 2005), and is program chair of ACM SIGKDD International Workshop on Multimedia Data Mining, 2004. Farokh Bastani received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Bombay, and the M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley. He is currently a Professor of Computer Science at the University of Texas at Dallas. Dr. Bastani's research interests include various aspects of the ultrahigh dependable systems, especially automated software synthesis and testing, embedded real-time process-control and telecommunications systems and high-assurance systems engineering. Dr. Bastani was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (IEEE-TKDE). He is currently an emeritus EIC of IEEE-TKDE and is on the editorial board of the International Journal of Artificial Intelligence Tools, the International Journal of Knowledge and Information Systems and the Springer-Verlag series on Knowledge and Information Management. He was the program cochair of the 1997 IEEE Symposium on Reliable Distributed Systems, 1998 IEEE International Symposium on Software Reliability Engineering, 1999 IEEE Knowledge and Data Engineering Workshop, 1999 International Symposium on Autonomous Decentralised Systems, and the program chair of the 1995 IEEE International Conference on Tools with Artificial Intelligence. He has been on the program and steering committees of several conferences and workshops and on the editorial boards of the IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering and the Oxford University Press High Integrity Systems Journal. I-Ling Yen received her B.S. degree from Tsing-Hua University, Taiwan, and her M.S. and Ph.D. degrees in Computer Science from the University of Houston. She is currently an Associate Professor of Computer Science at University of Texas at Dallas. Dr. Yen's research interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce and self-stabilising systems. She has published over 100 technical papers in these research areas and received many research awards from NSF, DOD, NASA and several industry companies. She has served as Program Committee member for many conferences and Program Chair/Cochair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International Symposium on Autonomous Decentralized Systems. She has also served as a guest editor for a theme issue of IEEE Computer devoted to high-assurance systems.     

AgentTeamwork: Coordinating grid-computing jobs with mobile agents

AgentTeamwork is a grid-computing middleware system that dispatches a collection of mobile agents to coordinate a user job over remote computing nodes in a decentralized manner. Its utmost focus is to maintain high availability and dynamic balancing of distributed computing resources to a parallel-computing job. For this purpose, a mobile agent is assigned to each process engaged in the same job, monitors its execution at a different machine, takes its periodical execution snapshot, moves it to a lighter-loaded machine, and resumes it from the latest snapshot upon an accidental crash. The system also restores broken inter-process communication involved in the same job using its error-recoverable socket and mpiJava libraries in collaboration among mobile agents. We have implemented the first version of our middleware including a mobile agent execution platform, error-recoverable socket and mpiJava API libraries, a job wrapper program, and several types of mobile agents such as commander, resource, sentinel, and bookkeeper agents, each orchestrating, allocating resources to, monitoring and maintaining snapshots of a user process respectively. This paper presents AgentTeamwork’s execution model, its implementation techniques, and our performance evaluation using the Java Grande benchmark test programs. Munehiro Fukuda received a B.S. from the College of Information Sciences and an M.S. from the Master’s Program in Science and Enginnering at the University of Tsukuba in 1986 and 1988. He received his M.S. and Ph.D. in Information and Computer Science at the University of California at Irvine in 1995 and 1997, respectively. He worked at IBM Tokyo Research Laboratory from 1988 to 1993 and taught at the University of Tsukuba from 1998 to 2001. Since 2001, he has been an assistant professor at Computing & Software Systems, the University of Washington, Bothell. His research interests include mobile agents, multi-threading, cluster computing, grid computing and distributed simulations. Koichi Kashiwagi received a Bachelor of Science degree from the Faculty of Science, Ehime University in 2000 and a Master of Engineering degree from the Department of Compter Science, Ehime University in 2002. In 2004 he became a research assistant in Department of Compter Science, Ehime University. His research interests include distributed computing, job scheduling, and grid computing. Shin-ya Kobayashi received the B.E. degree, M.E. degree, and Dr.E. degree in Communication Engineering from Osaka University in 1985, 1988, and 1991 respectively. From 1991 to 1999, he was on the faculty of Engineering at Kanazawa University, Japan. From 1999 to 2004, He was an Associate Professor in the Department of Computer Science, Ehime University. He is a Professor at Graduate School of Science and Engineering, Ehime University. His research interests include distributed processing, and parallel processing. He is a member of the Information Processing Society of Japan, the Institute of Electrical Engineers of Japan, IEEE, and ACM.     

Toward a real-world knowledge medium: Building a guidance system for exhibition tours

We propose a notion of a real-world knowledge medium by presenting our ongoing project to build a guidance system for exhibition tours. In order to realize a knowledge medium usable in the real world, we focus on the context-awareness of users and their environments. Our system is a personal mobile assistant that provides visitors touring exhibitions with information based on their spatial/temporal locations and individual interests. We also describe an application of knowledge sharing used in the actual exhibition spaces. Yasuyuki Sumi, Ph.D.: He has been a researcher at ATR Media Integration & Communications Research Laboratories since 1995. His research interests include knowledge-based systems, creativity supporting systems, and their applications for facilitating human collaboration. He received his B. Eng. degree from Waseda University in 1990, and M. Eng. and D. Eng. degrees in information engineering from the University of Tokyo in 1992 and 1995, respectively. He is a member of Institutes of Electronics, Information and Communication Engineers (IEICE) of Japan, the Information Processing Society of Japan (IPSJ), the Japanese Society for Artificial Intelligence (JSAI), and American Association for Artificial Intelligence (AAAI). Kenji Mase, Ph.D.: He received the B.S. degree in Electrical Engineering and the M.S. and Ph.D. degrees in Information Engineering from Nagoya University in 1979, 1981 and 1992 respectively. He has been with ATR (Advanced Telecommunications Research Institute) Media Integration & Communications Research Laboratories since 1995 and is currently the head of Department 2. He joined the Nippon Telegraph and Telephone Corporation (NTT) in 1981 and had been with the NTT Human Interface Laboratories. He was a visiting researcher at the Media Laboratory, MIT in 1988–1989. His research interests include image sequence processing of human actions, computer graphics, computer vision, artificial intelligence and their applications for computer-aided communications and human-machine interfaces. He is a member of the Information Processing Society of Japan (IPSJ), Institutes of Electronics, Information and Communication Engineers (IEICE) of Japan and IEEE Computer Society.     

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.     

Supervised tensor learning

Tensor representation is helpful to reduce the small sample size problem in discriminative subspace selection. As pointed by this paper, this is mainly because the structure information of objects in computer vision research is a reasonable constraint to reduce the number of unknown parameters used to represent a learning model. Therefore, we apply this information to the vector-based learning and generalize the vector-based learning to the tensor-based learning as the supervised tensor learning (STL) framework, which accepts tensors as input. To obtain the solution of STL, the alternating projection optimization procedure is developed. The STL framework is a combination of the convex optimization and the operations in multilinear algebra. The tensor representation helps reduce the overfitting problem in vector-based learning. Based on STL and its alternating projection optimization procedure, we generalize support vector machines, minimax probability machine, Fisher discriminant analysis, and distance metric learning, to support tensor machines, tensor minimax probability machine, tensor Fisher discriminant analysis, and the multiple distance metrics learning, respectively. We also study the iterative procedure for feature extraction within STL. To examine the effectiveness of STL, we implement the tensor minimax probability machine for image classification. By comparing with minimax probability machine, the tensor version reduces the overfitting problem. We focus on the convex optimization-based binary classification learning algorithms in this paper. This is because the solution to a convex optimization-based learning algorithm is unique. Dacheng Tao received the B.Eng. degree from the University of Science and Technology of China (USTC), the MPhil degree from the Chinese University of Hong Kong (CUHK) and the PhD from the University of London (Birkbeck). He will join the Department of Computing in the Hong Kong Polytechnic University as an assistant professor. His research interests include biometric research, discriminant analysis, support vector machine, convex optimization for machine learning, multilinear algebra, multimedia information retrieval, data mining, and video surveillance. He published extensively at TPAMI, TKDE, TIP, TMM, TCSVT, CVPR, ICDM, ICASSP, ICIP, ICME, ACM Multimedia, ACM KDD, etc. He gained several Meritorious Awards from the Int’l Interdisciplinary Contest in Modeling, which is the highest level mathematical modeling contest in the world, organized by COMAP. He is a guest editor for special issues of the Int’l Journal of Image and Graphics (World Scientific) and the Neurocomputing (Elsevier). Xuelong Li works at the University of London. He has published in journals (IEEE T-PAMI, T-CSVT, T-IP, T-KDE, TMM, etc.) and conferences (IEEE CVPR, ICASSP, ICDM, etc.). He is an Associate Editor of IEEE T-SMC, Part C, Neurocomputing, IJIG (World Scientific), and Pattern Recognition (Elsevier). He is also an Editor Board Member of IJITDM (World Scientific) and ELCVIA (CVC Press). He is a Guest Editor for special issues of IJCM (Taylor and Francis), IJIG (World Scientific), and Neurocomputing (Elsevier). He co-chaired the 5th Annual UK Workshop on Computational Intelligence and the 6th the IEEE Int’l Conf. on Machine Learning and Cybernetics. He was also a publicity chair of the 7th IEEE Int’l Conf. on Data Mining and the 4th Int’l Conf. on Image and Graphics. He has been on the program committees of more than 50 conferences and workshops. Xindong Wu is a Professor and the Chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW, as well as 12 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the Founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AIKP). He is the 2004 ACM SIGKDD Service Award winner. Weiming Hu received the Ph.D. degree from the Department of Computer Science and Engineering, Zhejiang University. From April 1998 to March 2000, he was a Postdoctoral Research Fellow with the Institute of Computer Science and Technology, Founder Research and Design Center, Peking University. Since April 1998, he has been with the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. Now he is a Professor and a Ph.D. Student Supervisor in the laboratory. His research interests are in visual surveillance, neural networks, filtering of Internet objectionable information, retrieval of multimedia, and understanding of Internet behaviors. He has published more than 80 papers on national and international journals, and international conferences. Stephen J. Maybank received a BA in Mathematics from King’s college, Cambridge in 1976 and a PhD in Computer Science from Birkbeck College, University of London in 1988. He was a research scientist at GEC from 1980 to 1995, first at MCCS, Frimley and then, from 1989, at the GEC Marconi Hirst Research Centre in London. In 1995 he became a lecturer in the Department of Computer Science at the University of Reading and in 2004 he became a professor in the School of Computer Science and Information Systems at Birkbeck College, University of London. His research interests include camera calibration, visual surveillance, tracking, filtering, applications of projective geometry to computer vision and applications of probability, statistics and information theory to computer vision. He is the author of more than 90 scientific publications and one book. He is a Fellow of the Institute of Mathematics and its Applications, a Fellow of the Royal Statistical Society and a Senior Member of the IEEE. For further information see http://www.dcs.bbk.ac.uk/~sjmaybank.     

Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7]. Shi-Jay Chen was born in 1972, in Taipei, Taiwan, Republic of China. He received the B.S. degree in information management from the Kaohsiung Polytechnic Institute, Kaohsiung, Taiwan, and the M.S. degree in information management from the Chaoyang University of Technology, Taichung, Taiwan, in 1997 and 1999, respectively. He received the Ph.D. degree at the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, in October 2004. His research interests include fuzzy systems, multicriteria fuzzy decisionmaking, and artificial intelligence. Shyi-Ming Chen was born on January 16, 1960, in Taipei, Taiwan, Republic of China. He received the Ph.D. degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in June 1991. From August 1987 to July 1989 and from August 1990 to July 1991, he was with the Department of Electronic Engineering, Fu-Jen University, Taipei, Taiwan. From August 1991 to July 1996, he was an Associate Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1996 to July 1998, he was a Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1998 to July 2001, he was a Professor in the Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. Since August 2001, he has been a Professor in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He was a Visiting Scholar in the Department of Electrical Engineering and Computer Science, University of California, Berkeley, in 1999. He was a Visiting Scholar in the Institute of Information Science, Academia Sinica, Republic of China, in 2003. He has published more than 250 papers in referred journals, conference proceedings and book chapters. His research interests include fuzzy systems, information retrieval, knowledge-based systems, artificial intelligence, neural networks, data mining, and genetic algorithms. Dr. Chen has received several honors and awards, including the 1994 Outstanding Paper Award o f the Journal of Information and Education, the 1995 Outstanding Paper Award of the Computer Society of the Republic of China, the 1995 and 1996 Acer Dragon Thesis Awards for Outstanding M.S. Thesis Supervision, the 1995 Xerox Foundation Award for Outstanding M.S. Thesis Supervision, the 1996 Chinese Institute of Electrical Engineering Award for Outstanding M.S. Thesis Supervision, the 1997 National Science Council Award, Republic of China, for Outstanding Undergraduate Student's Project Supervision, the 1997 Outstanding Youth Electrical Engineer Award of the Chinese Institute of Electrical Engineering, Republic of China, the Best Paper Award of the 1999 National Computer Symposium, Republic of China, the 1999 Outstanding Paper Award of the Computer Society of the Republic of China, the 2001 Institute of Information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the 2001 Outstanding Talented Person Award, Republic of China, for the contributions in Information Technology, the 2002 Institute of information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the Outstanding Electrical Engineering Professor Award granted by the Chinese Institute of Electrical Engineering (CIEE), Republic of China, the 2002 Chinese Fuzzy Systems Association Best Thesis Award for Outstanding M.S. Thesis Supervision, the 2003 Outstanding Paper Award of the Technological and Vocational Education Society, Republic of China, the 2003 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision, the 2005 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 Taiwan Fuzzy Systems Association Award for Outstanding Ph.D. Dissertation Supervision, and the 2006 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision. Dr. Chen is currently the President of the Taiwanese Association for Artificial Intelligence (TAAI). He is a Senior Member of the IEEE, a member of the ACM, the International Fuzzy Systems Association (IFSA), and the Phi Tau Phi Scholastic Honor Society. He was an administrative committee member of the Chinese Fuzzy Systems Association (CFSA) from 1998 to 2004. He is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics - Part C, an Associate Editor of the IEEE Computational Intelligence Magazine, an Associate Editor of the Journal of Intelligent & Fuzzy Systems, an Editorial Board Member of the International Journal of Applied Intelligence, an Editor of the New Mathematics and Natural Computation Journal, an Associate Editor of the International Journal of Fuzzy Systems, an Editorial Board Member of the International Journal of Information and Communication Technology, an Editorial Board Member of the WSEAS Transactions on Systems, an Editor of the Journal of Advanced Computational Intelligence and Intelligent Informatics, an Associate Editor of the WSEAS Transactions on Computers, an Editorial Board Member of the International Journal of Computational Intelligence and Applications, an Editorial Board Member of the Advances in Fuzzy Sets and Systems Journal, an Editor of the International Journal of Soft Computing, an Editor of the Asian Journal of Information Technology, an Editorial Board Member of the International Journal of Intelligence Systems Technologies and Applications, an Editor of the Asian Journal of Information Management, an Associate Editor of the International Journal of Innovative Computing, Information and Control, and an Editorial Board Member of the International Journal of Computer Applications in Technology. He was an Editor of the Journal of the Chinese Grey System Association from 1998 to 2003. He is listed in International Who's Who of Professionals, Marquis Who's Who in the World, and Marquis Who's Who in Science and Engineering.     

Cache modeling and optimization for portable devices running MPEG-4 video decoder

There are increasing demands on portable communication devices to run multimedia applications. ISO (an International Organization for Standardization) standard MPEG-4 is an important and demanding multimedia application. To satisfy the growing consumer demands, more functions are added to support MPEG-4 video applications. With improved CPU speed, memory sub-system deficiency is the major barrier to improving the system performance. Studies show that there is sufficient reuse of values for caching that significantly reduce the memory bandwidth requirement for video data. Software decoding of MPEG-4 video data generates much more cache-memory traffic than required. Proper understanding of the decoding algorithm and the composition of its data set is obvious to improve the performance of such a system. The focus of this paper is cache modeling and optimization for portable communication devices running MPEG-4 video decoding algorithm. The architecture we simulate includes a digital signal processor (DSP) for running the MPEG-4 decoding algorithm and a memory system with two levels of caches. We use VisualSim and Cachegrind simulation tools to optimize cache sizes, levels of associativity, and cache levels for a portable device decoding MPEG-4 video. Abu Asaduzzaman is, currently, a PhD candidate in the department of Computer Science and Engineering (CSE), Florida Atlantic University (FAU), Boca Raton, Florida. He received his MS degree in computer engineering from FAU in 1997. Mr. Asaduzzaman worked for ECI Telecom as a software engineer from 1998 to 2001. From 2001 to 2003, he worked for BlueCross and BlueShield of Florida and SunPass (FDoT) as an IT Consultant. Currently, he is working as a research assistant at CSE Dept, FAU. His research interests include cache optimization, architecture exploration, embedded system evaluation, and networks-on-a-chip (NoC). He has published several research papers in these areas. Abu is a member of the honor society of Phi Kappa Phi, Tau Beta Pi, Upsilon Phi Epsilon, and the Association for Computing Machinery (ACM) FAU Chapter. Imad Mahgoub received the MS degree in applied mathematics and MS degree in electrical and computer engineering, both from North Carolina State University, Raleigh in 1983 and 1986 respectively and the PhD degree in computer engineering from the Pennsylvania State University, University Park, PA in 1989. Dr. Mahgoub joined Florida Atlantic University (FAU), Boca Raton, Florida in 1989. Currently he is a full professor of Computer Science and Engineering department and the director of the Mobile Computing Laboratory. His research interests include performance evaluation, mobile computing, sensor networks, and parallel and distributed processing. He has published over 80 research papers in these areas. He is the co-editor of the Mobile Computing Handbook and the Handbook of Sensor Networks. Dr. Mahgoub has served on the program committees of numerous conferences. He has been the vice-chair for the Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS) since 2003. He is a senior member of the IEEE. He is also a member of Tau Beta Pi, Upsilon Pi Epsilon, the IEEE Computer Society, and the ACM.     

Cache modeling and optimization for portable devices running MPEG-4 video decoder

There are increasing demands on portable communication devices to run multimedia applications. ISO (an International Organization for Standardization) standard MPEG-4 is an important and demanding multimedia application. To satisfy the growing consumer demands, more functions are added to support MPEG-4 video applications. With improved CPU speed, memory sub-system deficiency is the major barrier to improving the system performance. Studies show that there is sufficient reuse of values for caching that significantly reduce the memory bandwidth requirement for video data. Software decoding of MPEG-4 video data generates much more cache-memory traffic than required. Proper understanding of the decoding algorithm and the composition of its data set is obvious to improve the performance of such a system. The focus of this paper is cache modeling and optimization for portable communication devices running MPEG-4 video decoding algorithm. The architecture we simulate includes a digital signal processor (DSP) for running the MPEG-4 decoding algorithm and a memory system with two levels of caches. We use VisualSim and Cachegrind simulation tools to optimize cache sizes, levels of associativity, and cache levels for a portable device decoding MPEG-4 video. Abu Asaduzzaman is, currently, a PhD candidate in the department of Computer Science and Engineering (CSE), Florida Atlantic University (FAU), Boca Raton, Florida. He received his MS degree in computer engineering from FAU in 1997. Mr. Asaduzzaman worked for ECI Telecom as a software engineer from 1998 to 2001. From 2001 to 2003, he worked for BlueCross and BlueShield of Florida and SunPass (FDoT) as an IT Consultant. Currently, he is working as a research assistant at CSE Dept, FAU. His research interests include cache optimization, architecture exploration, embedded system evaluation, and networks-on-a-chip (NoC). He has published several research papers in these areas. Abu is a member of the honor society of Phi Kappa Phi, Tau Beta Pi, Upsilon Phi Epsilon, and the Association for Computing Machinery (ACM) FAU Chapter. Imad Mahgoub received the MS degree in applied mathematics and MS degree in electrical and computer engineering, both from North Carolina State University, Raleigh in 1983 and 1986 respectively and the PhD degree in computer engineering from the Pennsylvania State University, University Park, PA in 1989. Dr. Mahgoub joined Florida Atlantic University (FAU), Boca Raton, Florida in 1989. Currently he is a full professor of Computer Science and Engineering department and the director of the Mobile Computing Laboratory. His research interests include performance evaluation, mobile computing, sensor networks, and parallel and distributed processing. He has published over 80 research papers in these areas. He is the co-editor of the Mobile Computing Handbook and the Handbook of Sensor Networks. Dr. Mahgoub has served on the program committees of numerous conferences. He has been the vice-chair for the Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS) since 2003. He is a senior member of the IEEE. He is also a member of Tau Beta Pi, Upsilon Pi Epsilon, the IEEE Computer Society, and the ACM.     

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.     

Trends of internet auctions and agent-mediated Web commerce

Electronic Commerce (EC) is a promising field for applying agent and Artificial Intelligence technologies. In this article, we give an overview of the trends of Internet auctions and agent-mediated Web commerce. We describe the theoretical backgrounds of auction protocols and introduce several Internet auction sites. Furthermore, we describe various activities aimed toward utilizing agent technologies in EC and the trends in standardization efforts on agent technologies. Makoto Yokoo, Ph.D.: He received the B.E. and M.E. degrees in electrical engineering, in 1984 and 1986, respectively, from the University of Tokyo, Japan, and the Ph.D. degree in information and communication engineering in 1995 from the University of Tokyo, Japan. He is currently a distinguished technical member in NTT Communication Science Laboratories, Kyoto, Japan. He was a visiting research scientist at the Department of Electrical Engineering and Computer Science, the University of Michigan, Ann Arbor, from 1990 to 1991. His current research interests include multi-agent systems, search, and constraint satisfaction. Satoru Fujita, D.Eng.: He received his B.E. and M.E. degrees in electronic engineering from the University of Tokyo in 1984 and 1986, respectively. He also received his D.Eng. from the University of Tokyo in 1989 for his research on context comprehension in natural language understanding. He joined NEC Corporation in 1989, and is now a principal researcher of Internet Systems Research Laboratories of NEC. He is engaged in research on mobile agents, distributed systems and Web services.     

Finite-horizon scheduling of radar dwells with online template construction

Timing constraints for radar tasks are usually specified in terms of the minimum and maximum temporal distance between successive radar dwells. We utilize the idea of feasible intervals for dealing with the temporal distance constraints. In order to increase the freedom that the scheduler can offer a high-level resource manager, we introduce a technique for nesting and interleaving dwells online while accounting for the energy constraint that radar systems need to satisfy. Further, in radar systems, the task set changes frequently and we advocate the use of finite horizon scheduling in order to avoid the pessimism inherent in schedulers that assume a task will execute forever. The combination of feasible intervals and online dwell packing allows modular schedule updates whereby portions of a schedule can be altered without affecting the entire schedule, hence reducing the complexity of the scheduler. Through extensive simulations we validate our claims of providing greater scheduling flexibility without compromising on performance when compared with earlier work based on templates constructed offline. We also evaluate the impact of two parameters in our scheduling approach: the template length (or the extent of dwell nesting and interleaving) and the length of the finite horizon. Sathish Gopalakrishnan is a visting scholar in the Department of Computer Science, University of Illinois at Urbana-Champaign, where he defended his Ph.D. thesis in December 2005. He received an M.S. in Applied Mathematics from the University of Illinois in 2004 and a B.E. in Computer Science and Engineering from the University of Madras in 1999. Sathish’s research interests concern real-time and embedded systems, and the design of large-scale reliable systems. He received the best student paper award for his work on radar dwell scheduling at the Real-Time Systems Symposium 2004. Marco Caccamo graduated in computer engineering from the University of Pisa in 1997 and received the Ph.D. degree in computer engineering from the Scuola Superiore S. Anna in 2002. He is an Assistant Professor of the Department of Computer Science at the University of Illinois. His research interests include real-time operating systems, real-time scheduling and resource management, wireless sensor networks, and quality of service control in next generation digital infrastructures. He is recipient of the NSF CAREER Award (2003). He is a member of ACM and IEEE. Chi-Sheng Shih is currently an assistant professor at the Graduate Institute of Networking and Multimedia and Department of Computer Science and Information Engineering at National Taiwan University since February 2004. He received the B.S. in Engineering Science and M.S. in Computer Science from National Cheng Kung University in 1993 and 1995, respectively. In 2003, he received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. His main research interests are embedded systems, hardware/software codesign, real-time systems, and database systems. Specifically, his main research interests focus on real-time operating systems, real-time scheduling theory, embedded software, and software/hardware co-design for system-on-a-chip. Chang-Gun Lee received the B.S., M.S. and Ph.D. degrees in computer engineering from Seoul National University, Korea, in 1991, 1993 and 1998, respectively. He is currently an Assistant Professor in the Department of Electrical Engineering, Ohio State University, Columbus. Previously, he was a Research Scientist in the Department of Computer Science, University of Illinois at Urbana-Champaign from March 2000 to July 2002 and a Research Engineer in the Advanced Telecomm. Research Lab., LG Information & Communications, Ltd. from March 1998 to February 2000. His current research interests include real-time systems, complex embedded systems, QoS management, and wireless ad-hoc networks. Chang-Gun Lee is a member of the IEEE Computer Society. Lui Sha graduated with the Ph.D. degree from Carnegie-Mellon University in 1985. He was a Member and then a Senior Member of Technical Staff at Software Engineering Institute (SEI) from 1986 to 1998. Since Fall 1998, he has been a Professor of Computer Science at the University of Illinois at Urbana Champaign, and a Visiting Scientist of the SEI. He was the Chair of IEEE Real Time Systems Technical Committee from 1999 to 2000, and has served on its Executive Committee since 2001. He was a member of National Academy of Science’s study group on Software Dependability and Certification from 2004 to 2005, and is an IEEE Distinguished Visitor (2005 to 2007). Lui Sha is a Fellow of the IEEE and the ACM.     

Evolutionary fusion of a multi-classifier system for efficient face recognition

In this paper an evolutionary classifier fusion method inspired by biological evolution is presented to optimize the performance of a face recognition system. Initially, different illumination environments are modeled as multiple contexts using unsupervised learning and then the optimized classifier ensemble is searched for each context using a Genetic Algorithm (GA). For each context, multiple optimized classifiers are searched; each of which are referred to as a context based classifier. An evolutionary framework comprised of a combination of these classifiers is then applied to optimize face recognition as a whole. Evolutionary classifier fusion is compared with the simple adaptive system. Experiments are carried out using the Inha database and FERET database. Experimental results show that the proposed evolutionary classifier fusion method gives superior performance over other methods without using evolutionary fusion. Recommended by Guest Editor Daniel Howard. This work was supported by INHA UNIVERSITY Research Grant. Zhan Yu received the B.E. degree in Software Engineering from Xiamen University, China, in 2008. He is currently a master student in Intelligent Technology Lab, Computer and Information Department, Inha University, Korea. He has research interests in image processing, pattern recognition, computer vision, machine learning and statistical inference and computating. Mi Young Nam received the B.Sc. and M.Sc. degrees in Computer Science from the University of Silla Busan, Korea in 1995 and 2001 respectively and the Ph.D. degree in Computer Science & Engineering from the University of Inha, Korea in 2006. Currently, She is Post-Doctor course in Intelligent Technology Laboratory, Inha University, Korea. She’s research interest includes biometrics, pattern recognition, computer vision, image processing. Suman Sedai received the M.S. degree in Software Engineering from Inha University, China, in 2008. He is currently a Doctoral course in Western Australia University, Australia. He has research interests in image processing, pattern recognition, computer vision, machine learning. Phill Kyu Rhee received the B.S. degree in Electrical Engineering from the Seoul University, Seoul, Korea, the M.S. degree in Computer Science from the East Texas State University, Commerce, TX, and the Ph.D. degree in Computer Science from the University of Louisiana, Lafayette, LA, in 1982, 1986, and 1990 respectively. During 1982–1985 he was working in the System Engineering Research Institute, Seoul, Korea as a research scientist. In 1991 he joined the Electronic and Telecommunication Research Institute, Seoul, Korea, as a Senior Research Staff. Since 1992, he has been an Associate Professor in the Department of Computer Science and Engineering of the Inha University, Incheon, Korea and since 2001, he is a Professor in the same department and university. His current research interests are pattern recognition, machine intelligence, and parallel computer architecture. dr. rhee is a Member of the IEEE Computer Society and KISS (Korea Information Science Society).     

A quantum algorithm using NMR computers to break secret-key cryptosystems

In this paper, we discuss quantum algorithms that, for a given plaintextm _o and a given ciphertextc _o, will find a secret key,k _o, satisfyingc _o=E(k _o,m _o), where an encryption algorithm,E, is publicly available. We propose a new algorithm suitable for an NMR (Nuclear Magnetic Resonance) computer based on the technique used to solve the counting problem. The complexity of, our algorithm decreases as the measurement accuracy of the NMR computer increases. We discuss the possibility that the proposed algorithm is superior to Grover’s algorithm based on initial experimental results. Kazuo Ohta, Dr.S.: He is Professor of Faculty of Electro-Communications at the University of Electro-Communications, Japan. He received B.S., M.S., and Dr. S. degrees from Waseda University, Japan, in 1977, 1979, and 1990, respectively. He was researcher of NTT (Nippon Telegraph and Telephone Corporation) from 1979 to 2001, and was visiting scientist of Laboratory for Computer Science e of MIT (Massachusetts Institute of Technology) in 1991–1992 and visiting Professor of Applied Mathematics of MIT in 2000. He is presently engaged in research on Information Security, and theoretical computer science. Dr. Ohta is a member of IEEE, the International Association for Cryptologic Research, the Institute of Electronics, Information and Communication Engineers and the Information Processing Society of Japan. Tetsuro Nishino,: He received the B.S., M.S. and, D.Sc. degrees in mathematics from Waseda University, in 1982, 1984, and 1991 respectively. From 1984 to 1987, he joined Tokyo Research Laboratory, IBM Japan. From 1987 to 1992, he was a Research Associate of Tokyo Denki University, and from 1992 to 1994, he was an Associate Professor of Japan Advanced Institute of Science and Technology, Hokuriku. He is presently an Associate Professor in the Department of Communications and Systems Engineering, the University of Electro-Communications. His main interests are circuit complexity theory, computational learning theory and quantum complexity theory. Seiya Okubo,: He received the B.Eng. and M.Eng. degrees from the University of Electro-Communications in 2000 and 2002, respectively. He is a student in Graduate School of Electro-Communications, the University of Electro-Communications. His research interests include quantum complexity theory and cryptography. Noboru Kunihiro, Ph.D.: He is Assistant Professor of the University of Electro-Communications. He received his B. E., M. E. and Ph. D. in mathematical engineering and information physics from the University of Tokyo in 1994, 1996 and 2001, respectively. He had been engaged in the research on cryptography and information security at NTT Communication Science Laboratories from 1996 to 2002. Since 2002, he has been working for Department of Information and Communication Engineering of the University of Elector-Communications. His research interests include cryptography, information security and quantum computations. He was awarded the SCIS’97 paper prize.