Interactive learning and management of visual information via human-like software robot

To achieve smooth real-world interaction between people and computers, we developed a system that displays a three-dimensional computer-graphic human-like image from the waist up (anthropomorphic software robot: hereinafter “robot”) on the display, that interactively sees and hears, and that has fine and detailed control functions such as facial expressions, line of sight, and pointing at targets with its finger. The robot visually searches and identifies persons and objects in real space that it has learned in advance (registered space, which was our office in this case), manages the history information of the places and times it found objects and/or persons, and tells the user, indicating their three-dimensional positions with line of sight and its finger. It interactively learns new objects and persons with line of with their names and owners. By using this function, the robot can engage in simple dialogue (do a task) with the user. Osamu Hasegawa, Ph.D.: He received the B.E. and M.E. degrees from the Science University of Tokyo, in 1988, 1990 respectively. He received Ph.D. degree from the University of Tokyo, in 1993. Currently, he is a senior research scientist at the Electrotechnical Laboratory (ETL), Tsukuba, Japan. His research interests include Computer Vision and Multi-modal Human Interface. Dr. Hasegawa is a member of the AAAI, the Institute of Electronics, Information and Communication Engineers, Japan (IEICE), Information Processing Society of Japan and others. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from the University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information Processing Society of Japan. He is a member of IEICE, IEEE, IPSJ, ITE. Satoru Hayamizu, Ph.D.: He is a leader of Interactive Intermodal Integration Lab. at Electrotechnical Laboratory. He received the B.E., M.E., Ph.D. degrees from Tokyo University. Since 1981, he has been working on speech recognition, spoken dialogue, and communication with artifacts. From 1989 to 1990, he was a visiting scholar in Carnegie Mellon University and in 1994 a visiting scientist in LIMSI/CNRS.     

An algorithm for similar utterance section extraction for managing spoken documents

This paper proposes a new, efficient algorithm for extracting similar sections between two time sequence data sets. The algorithm, called Relay Continuous Dynamic Programming (Relay CDP), realizes fast matching between arbitrary sections in the reference pattern and the input pattern and enables the extraction of similar sections in a frame synchronous manner. In addition, Relay CDP is extended to two types of applications that handle spoken documents. The first application is the extraction of repeated utterances in a presentation or a news speech because repeated utterances are assumed to be important parts of the speech. These repeated utterances can be regarded as labels for information retrieval. The second application is flexible spoken document retrieval. A phonetic model is introduced to cope with the speech of different speakers. The new algorithm allows a user to query by natural utterance and searches spoken documents for any partial matches to the query utterance. We present herein a detailed explanation of Relay CDP and the experimental results for the extraction of similar sections and report results for two applications using Relay CDP. Yoshiaki Itoh has been an associate professor in the Faculty of Software and Information Science at Iwate Prefectural University, Iwate, Japan, since 2001. He received the B.E. degree, M.E. degree, and Dr. Eng. from Tokyo University, Tokyo, in 1987, 1989, and 1999, respectively. From 1989 to 2001 he was a researcher and a staff member of Kawasaki Steel Corporation, Tokyo and Okayama. From 1992 to 1994 he transferred as a researcher to Real World Computing Partnership, Tsukuba, Japan. Dr. Itoh's research interests include spoken document processing without recognition, audio and video retrieval, and real-time human communication systems. He is a member of ISCA, Acoustical Society of Japan, Institute of Electronics, Information and Communication Engineers, Information Processing Society of Japan, and Japan Society of Artificial Intelligence. Kazuyo Tanaka has been a professor at the University of Tsukuba, Tsukuba, Japan, since 2002. He received the B.E. degree from Yokohama National University, Yokohama, Japan, in 1970, and the Dr. Eng. degree from Tohoku University, Sendai, Japan, in 1984. From 1971 to 2002 he was research officer of Electrotechnical Laboratory (ETL), Tsukuba, Japan, and the National Institute of Advanced Science and Technology (AIST), Tsukuba, Japan, where he was working on speech analysis, synthesis, recognition, and understanding, and also served as chief of the speech processing section. His current interests include digital signal processing, spoken document processing, and human information processing. He is a member of IEEE, ISCA, Acoustical Society of Japan, Institute of Electronics, Information and Communication Engineers, and Japan Society of Artificial Intelligence. Shi-Wook Lee received the B.E. degree and M.E. degree from Yeungnam University, Korea and Ph.D. degree from the University of Tokyo in 1995, 1997, and 2001, respectively. Since 2001 he has been working in the Research Group of Speech and Auditory Signal Processing, the National Institute of Advanced Science and Technology (AIST), Tsukuba, Japan, as a postdoctoral fellow. His research interests include spoken document processing, speech recognition, and understanding.     

Pitch-Dependent Identification of Musical Instrument Sounds

This paper describes a musical instrument identification method that takes into consideration the pitch dependency of timbres of musical instruments. The difficulty in musical instrument identification resides in the pitch dependency of musical instrument sounds, that is, acoustic features of most musical instruments vary according to the pitch (fundamental frequency, F0). To cope with this difficulty, we propose an F0-dependent multivariate normal distribution, where each element of the mean vector is represented by a function of F0. Our method first extracts 129 features (e.g., the spectral centroid, the gradient of the straight line approximating the power envelope) from a musical instrument sound and then reduces the dimensionality of the feature space into 18 dimension. In the 18-dimensional feature space, it calculates an F0-dependent mean function and an F0-normalized covariance, and finally applies the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments shows that the proposed method improved the recognition rate from 75.73% to 79.73%. This research was partially supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Grant-in-Aid for Scientific Research (A), No.15200015, and Informatics Research Center for Development of Knowledge Society Infrastructure (COE program of MEXT, Japan). Tetsuro Kitahara received the B.S. from Tokyo University of Science in 2002 and the M.S. from Kyoto University in 2004. He is currently a Ph.D. course student at Graduate School of Informatics, Kyoto University. Since 2005, he has been a Research Fellow of the Japan Society for the Promotion of Science. His research interests include music informatics. He recieved IPSJ 65th National Convention Student Award in 2003, IPSJ 66th National Convention Student Award and TELECOM System Technology Award for Student in 2004, and IPSJ 67th National Convention Best Paper Award for Young Researcher in 2005. He is a student member of IPSJ, IEICE, JSAI, ASJ, and JSMPC. Masataka Goto received his Doctor of Engineering degree in Electronics, Information and Communication Engineering from Waseda University, Japan, in 1998. He then joined the Electrotechnical Laboratory (ETL; reorganized as the National Institute of Advanced Industrial Science and Technology (AIST) in 2001), where he has been engaged as a researcher ever since. He served concurrently as a researcher in Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Corporation (JST) from 2000 to 2003, and an associate professor of the Department of Intelligent Interaction Technologies, Graduate School of Systems and Information Engineering, University of Tsukuba since 2005. His research interests include music information processing and spoken language processing. Dr. Goto received seventeen awards including the IPSJ Best Paper Award and IPSJ Yamashita SIG Research Awards (MUS and SLP) from the Information Processing Society of Japan (IPSJ), Awaya Prize for Outstanding Presentation and Award for Outstanding Poster Presentation from the Acoustical Society of Japan (ASJ), Award for Best Presentation from the Japanese Society for Music Perception and Cognition (JSMPC), WISS 2000 Best Paper Award and Best Presentation Award, and Interaction 2003 Best Paper Award. He is a member of the IPSJ, ASJ, JSMPC, Institute of Electronics, Information and Communication Engineers (IEICE), and International Speech Communication Association (ISCA). Hiroshi G. Okuno received the B.A. and Ph.D from the University of Tokyo in 1972 and 1996, respectively. He worked for Nippon Telegraph and Telephone, Kitano Symbiotic Systems Project, and Tokyo University of Science. He is currently a professor at the Department of Intelligence Technology and Science, Graduate School of Informatics, Kyoto University. He was a visiting scholar at Stanford University, and a visiting associate professor at the University of Tokyo. He has done research in programming languages, parallel processing, and reasoning mechanism in AI, and he is currently engaged in computational auditory scene analysis, music scene analysis and robot audition. He received the best paper awards from the Japanese Society for Artificial Intelligence and the International Society for Applied Intelligence, in 1991 and 2001, respectively. He edited with David Rosenthal “Computational Auditory Scene Analysis” from Lawrence Erlbaum Associates in 1998 and with Taiichi Yuasa “Advanced Lisp Technology” from Taylor and Francis Inc. in 2002. He is a member of IPSJ, JSAI, JSSST, JSCS, ACM, AAAI, ASA, and IEEE.     

Music composition by interaction between human and computer

This paper aims at constructing a music composition system that composes music by the interaction between human and a computer. Even users without special musical knowledge can compose 16-bar musical works with one melody part and some backing parts using this system. The interactive Genetic Algorithm is introduced to music composition so that users’ feeling toward music is reflected in the composed music. One chromosome corresponds to 4-bar musical work information. Users participate in music composition by evaluating composed works after GA operators such as crossover, mutation, virus infection are applied to chromosomes based on the evaluation results. From the experimental results, it is found that the users’ evaluation values become high over the progress of generations. That is, the system can compose 16-bar musical works reflecting users’ feeling. Muneyuki Unehara: He received his M.S. in Engineering in 2002 from Institute of Science and Engineering, University of Tsukuba. Currently, he is a Ph.D. candidate of Graduate School of Systems and Information Engineering, University of Tsukuba. His research interests include the construction of intelligent systems by considering soft computing techniques and human interface. Takehisa Onisawa, Ph.D.: He received Dr.Eng. in Systems Science in 1986 from Tokyo Institute of Technology. Currently, he is a Professor in the Graduate School of Systems and Information Engineering, University of Tsukuba. His research interests include applications of soft computing techniques to human centered systems thinking. He is a member of IEEE and IFSA.     

A method of model improvement for spotting recognition of gestures using an image sequence

We have developed a real-time gesture recognition system whose models can be taught by only one instruction. Therefore the system can adapt to new gesture performer quickly but it can not raise the recognition rates even if we teach gestures many times. That is because the system could not utilize all the teaching data. In order to cope with the problem, averages of teaching data are calculated. First, the best frame correspondence of the teaching data and the model is obtained by Continuous DP. Next the averages and variations are calculated for each frame of the model. We show the effectiveness of our method in the experiments. Takuichi Nishimura: He is a researcher of Multi-modal Function Tsukuba Laboratory and Information Basis Function Laboratory at the Real World Computing Partnership. He has engaged in motion image understanding, multi-modal human computer interface, multi-modal information retrieval, and mobile robot navigation. He completed the master’s course of the University of Tokyo in 1992. Hiroaki Yabe: He is from SHARP corporation working as a researcher of Multi-modal Function Tsukuba Laboratory and Information Basis Function Tsukuba Laboratory at the Real World Computing Partnership. He has engaged in motion image understanding, multi-modal human computer interface, multi-modal information retrieval. He completed the master’s course of the University of Tokyo in 1995. Ryuichi Oka, Ph.D.: He is a chief of Multi-modal Function Tsukuba Laboratory and Information Basis Function Laboratory at Tsukuba Research Center of the Real World Computing Partnership (RWC Japan) which started in 1992. His research interests include motion image understanding, spontaneous speech understanding, self-organisation information base, multi-modal human computer interface, multi-modal information retrieval, mobile robot, integration of symbol and pattern, and super parallel computation. He received his Ph.D degree in Engineering from the University of Tokyo.     

PAI: Automatic indexing for extracting asserted keywords from a document

This paper proposes an automatic indexing method named PAI (Priming Activation Indexing) that extracts keywords expressing the author’s main point from a document based on the priming effect. The basic idea is that since the author writes a document emphasizing his/her main point, impressive terms born in the mind of the reader could represent the asserted keywords. Our approach employs a spreading activation model without using corpus, thesaurus, syntactic analysis, dependency relations between terms or any other knowledge except for stop-word list. Experimental evaluations are reported by applying PAI to journal/conference papers. Naohiro Matsumura: He received his B.S. and M.S. in Engineering Science from Osaka University in 1998 and 2000. Currently, he is a Ph.D. candidate in Engineering at the University of Tokyo and a research staff of PRESTO of Japan Science and Technology Corporation (2000–). His research interests include chance discovery, computer-mediated communication, and user-oriented data mining/text mining. Yukio Ohsawa, Ph.D.: BS, U. Tokyo, 1990, MS, 1992, DS, 1995. Research associate Osaka U. (1995). Associate prof. Univ. of Tsukuba (1999–) and also researcher of Japan Science and Technology Corp (2000–). He has been working for the program com. of the Workshop on Multiagent and Cooperative Computation, Annual Conf. Japanese Soc. Artificial Intelligence, International Conf. MultiAgent Systems, Discovery Science, Pacific Asia Knowledge Discovery and Data Mining, International Conference on Web Intelligence, etc. He chaired the First International Workshop of Japanese Soc. on Artificial Intelligence, Chance Discovery International Workshop Series and the Fall Symposium on Chance Discovery from AAAI. Guest editor of Special Issues on Chance Discovery for the Journal of Contingencies and Crisis Management, Journal of Japan Society for Fuzzy Theory and intelligent informatics, regular member of editorial board for Japanese Society of Artificial Intelligence. Currently he is authoring book “Chance Discovery” from Springer Verlag, “Knowledge Managament” from Ohmsha etc. Mitsuru Ishizuka, Ph.D.: He is a professor at the Dept. of Infomation and Communication Eng., School of Information Science and Thechnology, the Univ. of Tokyo. Prior to this position, he worked at NTT Yokosuka Lab. and the Institute of Industrial Science, the Univ. of Tokyo. He earned his B.S., M.S. and Ph.D. in electronic engineering from the Univ. of Tokyo. His research interests include artificial intelligence, WWW intelligence, and multimodal lifelike agents. He is a member of IEEE, AAAI, IEICE Japan, IPS Japan, and Japanese Society for AI.     

An approach to discovering risks in development process of large and complex systems

When building a large and complex system, such as satellites, all sorts of risks have to be managed if it were to be successful. For risks in the design of an artifact, various reliability analysis techniques such as FTA or FMEA have been employed in the engineering domain. However, risks exist as well in the development process, and they could result in a failure of the system. In this paper, we present an approach to discovering risks in development process by collecting and organizing information produced during development process at low cost. We describe a prototype system called IDIMS, and show how it can be used to discover risks from e-mail communications between developers. The motivation of our work is to overcome thecapture bottleneck problem, and utilize now wasted information to improve development process. Yoshikiyo Kato: He received his B. Eng. (1998) and M.Eng. (2000) degrees in aeronautics and astronautics from The University of Tokyo. From September 1998 to July 1999, he was an exchange student at Department of Computer Science and Engineering of University of California, San Diego, and worked on software engineering tools. From May 2001 to July 2002, he was a research assistant at National Institute of Informatics (Japan). He is currently a Ph.D. student at Department of Advanced Interdisciplinary Studies of the University of Tokyo. His research interests include knowledge management, CSCW, HCI and software engineering He is a member of AAAI and JSAI. Takahiro Shirakawa: He received his B.Eng. (2000) and M.Eng. (2002) degrees in aeronautics and astronautics from the University of Tokyo. He is currently an assistant examiner at Japan Patent Office. Kohei Taketa: He received his B.Eng. (2000) and M.Eng. (2002) degrees in aeronautics and astronautics from the University of Tokyo. He is currently a software engineer at NTT Data Corp. Koichi Hori, Dr.Eng.: He received B.Eng, M.Eng, and Dr.Eng. degrees in electronic engineering from the University of Tokyo, Japan, in 1979, 1981, and 1984, respectively. In 1984, he joined National Institute of Japanese Literature where he developed AI systems for literature studies. Since 1988, he has been with the U University of Tokyo. He is currently a professor with Department of Advanced Interdisciplinary Studies, The University of Tokyo. From September 1989 to January 1990, he also held a visiting position at University of Compiegne, France. His current research interests include AI technology for supporting human creative activities, cognitive engineering, and Intelligent CAD systems. He is a member of IEEE, ACM, IEICE, IPSJ, JSAI, JSSST and JCSS.     

View-based virtual learning and recognition of 3D object using view model obtained by motion-stereo

View-based approach for learning and recognition of 3D object and its pose detection was proved to be affective and efficient, except its high learning cost. In this research, we propose a virtual learning approach which generates learning samples of views of an object from its 3D view model obtained by motion-stereo method. From the generated learning sample views, features of high-order autocorrelation are extracted, and discriminant feature spaces for object recognition and pose detection are built. Recognition experiments on real objects are carried out to show the effectiveness of the proposed method. Caihua Wang, Ph.D.: He received his B.S. in mathematics and M.E. in electronic engineering from Renmin University of China, Beijing, China in 1983 and 1986, and his Ph. D. from Shizuoka University, Hamamatsu, Japan in 1996. He is a JST domestic fellow and is doing his post doctoral research at Electrotechnical Laboratory. His research interests are computer vision and image processing. He is a member of IEICE and IPSJ. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information.     

A study on effects of market systems in an artificial market

In an artificial market approach with multi-agent systems, the static equilibrium concept is often used in market systems to approximate continuous market auctions. However, differences between the static equilibrium concept and continuous auctions have not been discussed in the context of an artificial market study. In this paper, we construct an artificial market model with both of them, namely, the Itayose and Zaraba method, and show simple characteristic differences between these methods based on computer simulations. The result indicates the further need to model the market system by studying artificial markets. Hidenori Kawamura, Ph.D.: He received Ph.D. degree from Division of Systems and Information Engineering, Graduate School of Engineering, Hokkaido University, Japan in 2000. He is currently an instructor in Graduate School of Information Science and Technology, Hokkaido University, Japan. His research interests include multiagent systems, mass user support, artificial intelligence, complex systems, and tourism informatics. He is a member of IPSJ, JSAI, IEICE, ORSJ, JSTI and AAAI. Yasushi Okada, Ph.D.: He is a master course student in Graduate School of Engineering, Hokkaido University, Japan. He studies multiagent systems. Azuma Ohuchi, Ph.D.: He received his Ph.D. degree in 1974 from Hokkaido University. He is currently the professor in Graduate School of Information Science and Technology, Hokkaido University Japan. His research interstes include systems information engineering, artificial intelligence, complex systems, tourism informatics and medical systems. He is a member of the IPSJ, JSAI, IEEJ, ORSJ, Soc. Contr. Eng., Jap. OR Soc., Soc. Med. Informatics, Hosp. Manag., JSTI and IEEE-SMC. Koichi Kurumatani, Ph.D.: He received his Ph.D. Degree in 1989 from The University of Tokyo. He is currently a leader of Multiagent Research Team in Cyber Assist Research Center (CARC), National Institute of Advanced Industrial Science and Technology (AIST), Japan. His research interests include multiagent systems and mass user support. He is a member of JSAI, IPSJ, JSTI and AAAI.     

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.     

Chance discoveries for making decisions in complex real world

Chance discovery is to become aware of a chance and to explain its significance, especially if the chance is rare and its significance is unnoticed. This direction matches with various real requirements in human life. This paper presents the significance, viewpoints, theories, methods, and future work of chance discovery. Three keys for the progress are extracted from fundamental discussions on how to realize chance discovery: (1) communication, (2) imagination, and (3) data mining. As an approach to chance discovery, visualized data mining methods are formalized as tools aiding chance discoveries on the basis of these keys. Yukio Ohsawa, Ph.D.: He received Bechelor of Engineering (1990) from Faculty of Engineering, Master of Engineering (1992) and Ph.D. (1995) from Graduate School of Engineering, respectively of The University of Tokyo. In the doctoral course he began artificial intelligence research, especially of abductive inference. He was a research associate (1995–1999) in Osaka University on studies of text mining and related issues, and moved to the current position, associate professor in the University of Tsukuba in 1999. From 2001, he is also a researcher of TRESTO (changed to PRESTO) in Japan Science and Technology Corporation. He received best paper awards in two Annual Conferences of Japasese Society of AI (1994 and 1998), and a Journal Paper Award from JSAI in 1998. His social activities are committees of conferences e.g., International Conference of Multi-Agent Systems (ICMAS) since 1998 and Discovery Science (DS) since 2001, program chair of MultiAgent and Cooporative Computations (MACC, in Japan) in 1999, and committes of meetings including ones on Chance Discovery.     

Object-spatial layout-route based hybrid map and global localization for mobile robots

This paper presents new object-spatial layout-route based hybrid map representation and global localization approaches using a stereo camera. By representing objects as high-level features in a map, a robot can deal more effectively with different contexts such as dynamic environments, human-robot interaction, and semantic information. However, the use of objects alone for map representation has inherent problems. For example, it is difficult to represent empty spaces for robot navigation, and objects are limited to readily recognizable things. One way to overcome these problems is to develop a hybrid map that includes objects and the spatial layout of a local space. The map developed in this research has a hybrid structure that combines a global topological map and a local hybrid map. The topological map represents the spatial relationships between local spaces. The local hybrid map combines the spatial layout of the local space with the objects found in that space. Based on the proposed map, we suggest a novel coarse-to-fine global localization method that uses object recognition, point cloud fitting and probabilistic scan matching. This approach can accurately estimate robot pose with respect to the correct local space. Recommended by Editor Jae-Bok Song. This research was performed for the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. Soonyong Park received the B.S. and M.S. degrees from the Department of Mechanical Engineering, Kyunghee University, Seoul, Korea, in 2001 and 2003, respectively. He is currently working toward the Ph.D. degree in the Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. Since 2001, he has been a student researcher in the Center for Cognitive Robotics Research, Korea Institute of Science and Technology (KIST), Seoul, Korea. His research interests include mobile robot navigation and computer vision. Mignon Park received the B.S. and M.S. degrees in Electronics from Yonsei University, Seoul, Korea, in 1973 and 1977, respectively. He received the Ph.D. degree in University of Tokyo, Japan, 1982. He was a researcher with the Institute of Biomedical Engineering, University of Tokyo, Japan, from 1972 to 1982, as well as at the Massachusetts Institute of Technology, Cambridge, and the University of California Berkeley, in 1982. He was a visiting researcher in Robotics Division, Mechanical Engineering Laboratory, Ministry of International Trade and Industry, Tsukuba, Japan, from 1986 to 1987. He has been a Professor in the Department of Electrical and Electronic Engineering in Yonsei University, since 1982. His research interests include fuzzy control and application, robotics, and fuzzy biomedical system. Sung-Kee Park is a principal research scientist for Korea Institute of Science and Technology (KIST). He received the B.S. and M.S. degrees in Mechanical Design and Production Engineering from Seoul National University, Seoul, Korea, in 1987 and 1989, respectively. He received the Ph.D. degree (2000) from Korea Advanced Institue of Science and Technology (KAIST), Korea, in the area of computer vision. Since then, he has been working for the center for cognitive robotics research at KIST. During his period at KIST, he held a visiting position at the Robotics Institute of Carnegie Mellon University in 2005, where he did research on object recognition. His recent work has been on cognitive visual processing, object recognition, visual navigation, and human-robot interaction.     

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.     

A futures market-based model for dynamic network resource allocation

Management of telecommunication network requires quick, continuous and decentralized allocation of network bandwidth to various sorts of demands. So as to achieve the efficient network resource allocation, this paper describes a market-based model combining futures market with the agent-based approach. That is, utilization time is divided into many timeslots, and futures markets in hereafter use of bandwidth are opened. In our model, all market participants (software agents) observe only market prices and decide to buy or sell bandwidth trying to maximize their utilities over time so that they can secure enough network resources. The authors discuss network resource allocation through simulation using the proposed model. Masayuki Ishinishi, Ph.D.: He graduated from National Defense Academy in 1995 and 2000. He received the B.E. (1995) and M.E.(2000) degrees in computer science from National Institution for Academic Degrees (NIAD). He received his Ph.D. degree from Tokyo Institute of Technology in 2003. He has been a communications officer at Air Communications and Systems Wing in Japan Air Self-Defence Force (JASDF) since 2003. His research interests include information assurance, agent-based modeling and simulation, multi-agent system and market-based control. He is a member of IEEJ, IPSJ and JSAI. Yuhsuke Koyama, Ph.D.: He received the B.Econ., M.Econ., and Ph.D. degrees in economics from Kyoto University, in 1996, 1998, 2002, respectively. He has been a research associate of Tokyo Institute of Technology since 2002. His research field is evolutionary economics, mathematical sociology and experimental economics. He is a member of JAFEE, JAMS, JASESS and JASAG. Hiroshi Deguchi, Ph.D.: He received his Ph.D. degree in systems science from Tokyo Institute of Technology, in 1986. He also received the Dr. Econ. degree from Kyoto University in 2001. He has been a Professor of Tokyo Institute of Technology since 2002. His research field is evolutionary economics, computational organization theory, agent-based modeling, social system theory, gaming simulation, and philosophy of science. He is a member of SICE, JAMS, IPSJ, PHSC, JASAG and JAFEE. Hajime Kita, Ph.D.: He received the B.E., M.E., and Ph.D. degrees in electrical engineering from Kyoto University, in 1982, 1984, 1991, respectively. He has been a Professor of Kyoto University since 2003, His research field is systems science/engineering, and his research interests are evolutionary computation, neural networks and socio-economic analysis of energy systems, and agentbased modeling. He is a member of IEEJ, IEICE, ISCIE, JNNS, JSER, ORSJ and SICE.     

Public opinion channel for communities in the information age

In this paper, we propose as a new challenge a public opinion channel which can provide a novel communication medium for sharing and exchanging opinions in a community. Rather than simply developing a means of investigating public opinion, we aim at an active medium that can facilitate mutual understanding, discussion, and public opinion formation. First, we elaborate the idea of public opinion channels and identify key issues. Second, we describe our first step towards the goal using the talking virtualized egos metaphor. Finally, we discuss a research agenda towards the goal. Toyoaki Nishida, Dr.Eng.: He is a professor of Department of Information and Communication Engineering, School of Engineering, The University of Tokyo. He received the B.E., the M.E., and the Doctor of Engineering degrees from Kyoto University in 1977, 1979, and 1984 respectively. His research centers on artificial intelligence in general. His current research focuses on community computing and support systems, including knowledge sharing, knowledge media, and agent technology. He has been leading the Breakthrough 21 Nishida Project, sponsored by Ministry of Posts and Telecommunications, Japan, aiming at understanding and assisting networked communities. Since 1997, he is a trustee for JSAI (Japanese Society for Artificial Intelligence), and serves as the program chair of 1999 JSAI Annual Convention. He is an area editor (intelligent systems) of New Generation Computing and an editor of Autonomous Agents and Multiagent Systems. Nobuhiko Fujihara, Ph.D.: He is a fellow of Breakthrough 21 Nishida project, Communications Research Laboratory sponsored by Ministry of Posts and Telecommunications, Japan. He received the B.E., the M.E., and the Ph.D. in Human Sciences degrees from Osaka University in 1992, 1994, and 1998 respectively. He has a cognitive psychological background. His current research focuses on: (1) cognitive psychological analysis of human behavior in a networked community, (2) investigation of information comprehension process, (3) assessment and proposition of communication tools in networking society. Shintaro Azechi: He is a fellow of Breakthrough 21 Nishida project, Communications Research Laboratory sponsored by Ministry of Posts and Telecommunications, Japan. He received the B.E. and the M.E. of Human Sciences degrees from Osaka University in 1994 and 1996 respectively. He is a Doctoral Candidate of Graduate School of Human Sciences, Osaka University. His current researches focus on (1) human behavior in networking community (2) social infomation process in human mind (3) development of acessment technique for communication tools in networkingsociety. His approach is from social psychological view. Kaoru Sumi, Dr.Eng.: She is a Researcher of Breakthrough 21 Nishida Project. She received her Bachelor of Science at School of Physics, Science University of Tokyo. She received her Master of Systems Management at Graduate School of Systems Management, The university of Tsukuba. She received her Doctor of engineering at Graduate School of Engineering, The University of Tokyo. Her research interests include knowledge-based systems, creativity supporting systems, and their applications for facilitating human collaboration. She is a member of the Information Processing Society of Japan (IPSJ), the Japanese Society for Artificial Intelligence (JSAI). Hiroyuki Yano, Dr.Eng.: He is a senior research official of Kansai Advanced Research Center, Communications Research Laboratory, Ministry of Posts and Telecommunications. He received the B.E., the M.E., and the Doctor of Engineering degrees from Tohoku University in 1986, 1988, and 1993 respectively. His interests of research include cognitive mechanism of human communications. His current research focuses on discourse structure, human interface, and dialogue systems for human natural dialogues. He is a member of the Japanese Society for Artificial Intelligence, the Association for Natural Language Processing, and the Japanese Cognitive Science Society. Takashi Hirata: He is a doctor course student in Graduate School of Information Scienc at Nara Institute of Science and Technology (NAIST). He received a master of engineering from NAIST in 1998. His research interest is knowledge media and knowledge sharing. He is a member of Information Processing Society of Japan (IPSJ), Japan Association for Artificial Intelligence (JSAI) and The Institute of Systems, Control and Information Engineers (ISCIE).     

Calculating accumulations

Theaccumulation strategy consists of generalizing a function over an algebraic data structure by inclusion of an extra parameter, anaccumulating parameter, for reusing and propagating intermediate results. However, there remain two major difficulties in this accumulation strategy. One is to determinewhere andwhen to generalize the original function. The other, surprisingly not yet receiving its worthy consideration, is how to manipulate accumulations. To overcome these difficulties, we propose to formulate accumulations ashigher order catamorphisms, and provide several general transformation rules for calculating accumulations (i.e., finding and manipulating accumulations) bycalculation-based (rather than a search-based) program transformation methods. Some examples are given for illustration. Zhenjiang Hu, Dr.Eng.: He is an Assistant Professor in Information Engineering at the University of Tokyo. He received his BS and MS in Computer Science from Shanghai Jiao Tong University in 1988 and 1990 respectively, and his Dr. Eng. degree in Information Engineering from the University of Tokyo in 1996. His current research concerns programming languages, functional programming, program transformation, and parallel processing. Hideya Iwasaki, Dr.Eng.: He is an Associate Professor in Information Engineering at the University of Tokyo. He received the M.E. degree in 1985, the Dr. Eng. degree in 1988 from the University of Tokyo. His research interests are list processing languages, functional languages, parallel processing, and constructive algorithmics. Masato Takeichi, Dr.Eng.: He is Professor in Mathematical Engineering and Information Engineering at the University of Tokyo since 1993. After graduation from the University of Tokyo, he joined the faculty at the University of Electro-Communications in Tokyo before he went back to work at the University of Tokyo in 1987. His research concerns the design and implementation of functional programming languages, and calculational program transformation systems.     

An Efficient Support Vector Machine Learning Method with Second-Order Cone Programming for Large-Scale Problems

In this paper we propose a new fast learning algorithm for the support vector machine (SVM). The proposed method is based on the technique of second-order cone programming. We reformulate the SVM's quadratic programming problem into the second-order cone programming problem. The proposed method needs to decompose the kernel matrix of SVM's optimization problem, and the decomposed matrix is used in the new optimization problem. Since the kernel matrix is positive semidefinite, the dimension of the decomposed matrix can be reduced by decomposition (factorization) methods. The performance of the proposed method depends on the dimension of the decomposed matrix. Experimental results show that the proposed method is much faster than the quadratic programming solver LOQO if the dimension of the decomposed matrix is small enough compared to that of the kernel matrix. The proposed method is also faster than the method proposed in (S. Fine and K. Scheinberg, 2001) for both low-rank and full-rank kernel matrices. The working set selection is an important issue in the SVM decomposition (chunking) method. We also modify Hsu and Lin's working set selection approach to deal with large working set. The proposed approach leads to faster convergence. Rameswar Debnath is a Ph.D candidate at the University of Electro-Communications, Tokyo, Japan and also a lecturer of the Computer Science & Engineering Discipline at Khulna University, Bangladesh. He received the bachelor's degree in computer science and engineering from Khulna University in 1997 and masters of engineering degree in communication and systems from the University of Electro-Communications in 2002. His research interests include support vector machines, artificial neural networks, pattern recognition, and image processing. Masakazu Muramatsu is an associate professor of the Department of Computer Science at the University of Electro-Communications, Japan. He received a bachelor's degree from the University of Tokyo in 1989, master's degree in engineering from University of Tokyo in 1991, and Ph.D from the Graduate University for Advanced Studies in 1994. He was an assistant professor of the Department of Mechanical Engineering at Sophia University from 1994 to 2000, when he moved to the current university. His research interests include mathematical programming, second-order cone programming and its application to machine learning. Haruhisa Takahashi was born in Shizuoka Prefecture Japan, on March 31, 1952. He graduated from the University of Electro-Communications. He received the Dr Eng. degree from Osaka University. He was a faculty member of the Department of Computer Science and Engineering at Toyohashi University of Technology from 1980 to 1986. Since 1986, he has been with the University of Electro-Communications where he is currently professor of the Department of Information and Communication Engineering. He was previously engaged in the fields of nonlinear network theory, queueing theory and performance evaluation of communication systems. His current research includes learning machines, artificial neural networks, and cognitive science.     

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.     

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.