Stable methods for recognizing acronym-expansion pairs: from rule sets to hidden Markov models

A significant portion of currently available documents exist in the form of images, for instance, as scanned documents. Electronic documents produced by scanning and OCR software contain recognition errors. This paper uses an automatic approach to examine the selection and the effectiveness of searching techniques for possible erroneous terms for query expansion. The proposed method consists of two basic steps. In the first step, confused characters in erroneous words are located and editing operations are applied to create a collection of erroneous error-grams in the basic unit of the model. The second step uses query terms and error-grams to generate additional query terms, identify appropriate matching terms, and determine the degree of relevance of retrieved document images to the user's query, based on a vector space IR model. The proposed approach has been trained on 979 document images to construct about 2,822 error-grams and tested on 100 scanned Web pages, 200 advertisements and manuals, and 700 degraded images. The performance of our method is evaluated experimentally by determining retrieval effectiveness with respect to recall and precision. The results obtained show its effectiveness and indicate an improvement over standard methods such as vectorial systems without expanded query and 3-gram overlapping. Youssef Fataicha received his B.Sc. degree from Université de Rennes1, Rennes, France, in 1982. In 1984 he obtained his M.Sc. in computer science from Université de Rennes1, France. Between 1984 and 1986 he was a lecturer at the Université de Rennes1, France. He then served as engineer, from 1987 to 2000, at {Office de l'eau potable et de l'électricité} in Morocco. Since 2001 has been a Ph.D. student at the {école de Technologie Supérieure de l'Université du Québec} in Montreal, Québec, Canada. His research interests include pattern recognition, information retrieval, and image analysis. Mohamed Cheriet received his B.Eng. in computer science from {Université des Sciences et de Technologie d'Alger} (Bab Ezouar, Algiers) in 1984 and his M.Sc. and Ph.D., also in computer science, from the University of Pierre et Marie Curie (Paris VI) in 1985 and 1988, respectively. Dr. Cheriet was appointed assistant professor in 1992, associate professor in 1995, and full professor in 1998 in the Department of Automation Engineering, {école de Technologie Supérieure} of the University of Québec, Montreal. Currently he is the director of LIVIA, the Laboratory for Imagery, Vision and Artificial Intelligence at ETS, and an active member of CENPARMI, the Centre for Pattern Recognition and Machine Intelligence. Professor Cheriet's research focuses on mathematical modeling for signal and image processing (scale-space, PDEs, and variational methods), pattern recognition, character recognition, text processing, document analysis and recognition, and perception. He has published more than 100 technical papers in these fields. He was the co-chair of the 11th and the 13th Vision Interface Conferences held respectively in Vancouver in 1998 and in Montreal in 2000. He was also the general co-chair of the 8th International Workshop on Frontiers on Handwriting Recognition held in Niagara-on-the-Lake in 2002. He has served as associate editor of the International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI) since 2000. Dr. Cheriet is a senior member of IEEE. Jian Yun Nie is a professor in the computer science department (DIRO), Université de Montreal, Québec, Canada. His research focuses on problems related to information retrieval, including multilingual and multimedia information retrieval, as well as natural language processing. Ching Y. Suen received his M.Sc. (Eng.) from the University of Hong Kong and Ph.D. from the University of British Columbia, Canada. In 1972 he joined the Department of Computer Science of Concordia University, where he became professor in 1979 and served as chairman from 1980 to 1984 and as associate dean for research of the Faculty of Engineering and Computer Science from 1993 to 1997. He has guided/hosted 65 visiting scientists and professors and supervised 60 doctoral and master's graduates. Currently he holds the distinguished Concordia Research Chair in Artificial Intelligence and Pattern Recognition and is the Director of CENPARMI, the Centre for Pattern Recognition and Machine Intelligence.Professor Suen is the author/editor of 11 books and more than 400 papers on subjects ranging from computer vision and handwriting recognition to expert systems and computational linguistics. A Google search on “Ching Y. Suen” will show some of his publications. He is the founder of the International Journal of Computer Processing of Oriental Languages and served as its first editor-in-chief for 10 years. Presently he is an associate editor of several journals related to pattern recognition.A fellow of the IEEE, IAPR, and the Academy of Sciences of the Royal Society of Canada, he has served several professional societies as president, vice-president, or governor. He is also the founder and chair of several conference series including ICDAR, IWFHR, and VI. He has been the general chair of numerous international conferences, including the International Conference on Computer Processing of Chinese and Oriental Languages in August 1988 held in Toronto, International Conference on Document Analysis and Recognition held in Montreal in August 1995, and the International Conference on Pattern Recognition held in Québec City in August 2002.Dr. Suen has given 150 seminars at major computer companies and various government and academic institutions around the world. He has been the principal investigator of 25 industrial/government research contracts and is a grant holder and recipient of prestigious awards, including the ITAC/NSERC award from the Information Technology Association of Canada and the Natural Sciences and Engineering Research Council of Canada in 1992 and the Concordia “Research Fellow” award in 1998.     

On the suitability of Biological structure determination by electron microscopy to Grid Computing

The present contribution describes a potential application of Grid Computing in Bioinformatics. High resolution structure determination of biological specimens is critical in BioSciences to understanding the biological function. The problem is computational intensive. Distributed and Grid Computing are thus becoming essential. This contribution analyzes the use of Grid Computing and its potential benefits in the field of electron microscope tomography of biological specimens. Jose-Jesus Fernandez, Ph.D.: He received his M.Sc. and Ph.D. degrees in Computer Science from the University of Granada, Spain, in 1992 and 1997, respectively. He was a Ph.D. student at the Bio-Computing unit of the National Center for BioTechnology (CNB) from the Spanish National Council of Scientific Research (CSIC), Madrid, Spain. He became an Assistant Professor in 1997 and, subsequently, Associate Professor in 2000 in Computer Architecture at the University of Almeria, Spain. He is a member of the supercomputing-algorithms research group. His research interests include high performance computing (HPC), image processing and tomography. Jose-Roman Bilbao-Castro: He received his M.Sc. degree in Computer Science from the University of Almeria in 2001. He is currently a Ph.D. student at the BioComputing unit of the CNB (CSIC) through a Ph.D. CSIC-grant in conjuction with Dept. Computer Architecture at the University of Malaga (Spain). His current research interestsinclude tomography, HPC and distributed and grid computing. Roberto Marabini, Ph.D.: He received the M.Sc. (1989) and Ph.D. (1995) degrees in Physics from the University Autonoma de Madrid (UAM) and University of Santiago de Compostela, respectively. He was a Ph.D. student at the BioComputing Unit at the CNB (CSIC). He worked at the University of Pennsylvania and the City University of New York from 1998 to 2002. At present he is an Associate Professor at the UAM. His current research interests include inverse problems, image processing and HPC. Jose-Maria Carazo, Ph.D.: He received the M.Sc. degree from the Granada University, Spain, in 1981, and got his Ph.D. in Molecular Biology at the UAM in 1984. He left for Albany, NY, in 1986, coming back to Madrid in 1989 to set up the BioComputing Unit of the CNB (CSIC). He was involved in the Spanish Ministry of Science and Technology as Deputy General Director for Research Planning. Currently, he keeps engaged in his activities at the CNB, the Scientific Park of Madrid and Integromics S.L. Immaculada Garcia, Ph.D.: She received her B.Sc. (1977) and Ph.D. (1986) degrees in Physics from the Complutense University of Madrid and University of Santiago de Compostela, respectively. From 1977 to 1987 she was an Assistant professor at the University of Granada, from 1987 to 1996 Associate professor at the University of Almeria and since 1997 she is a Full Professor and head of Dept. Computer Architecture. She is head of the supercomputing-algorithms research group. Her research interest lies in HPC for irregular problems related to image processing, global optimization and matrix computation.     

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.     

Determination of user interfaces in adaptive systems using a rough classification-based method

This paper presents a novel method for user classification in adaptive systems based on rough classification. Adaptive systems could be used in many areas, for example in a user interface construction or e-Learning environments for learning strategy selection. In this paper the adaptation of web-based system user interface is presented. The goal of rough user classification is to select the most essential attributes and their values that group together users who are very much alike concerning the system logic. In order to group users we exploit their usage data taken from the user model of the adaptive web-based system user interface. We presented three basic problems for attribute selection that generates the following partitions: that is included, that includes and that is the closest to the given partition. Ngoc Thanh Nguyen, Ph.D., D.Sc.: He currently works as an associate professor at the Faculty of Computer Science and Management, Wroclaw University of Technology in Poland. He received his diplomas of M.Sc, Ph.D. and D.Sc. in Computer Science in 1986, 1989 and 2002, respectively. Actually, he is working on intelligent technologies for conflict resolution and inconsistent knowledge processing and e-learning methods. His teaching interests consist of database systems and distributed systems. He is a co-editor of 4 special issues in international journals, author of 3 monographs, editor of one book and about 110 other publications (book chapters, journal and refereed conference papers). He is an associate editor of the following journals: “International Journal of Computer Science & Application”; “Journal of Information Knowledge System Management”; and “International Journal of Knowledge-Based & Intelligent Engineering Systems”. He is a member of societies: ACM, IFIP WG 7.2, ISAI, KES International, and WIC. Janusz Sobecki, Ph.D.: He is an Assistant Professor in Institute of Applied Informatics (IAI) at Wroclaw University of Technology (WUT). He received his M. Sc. in Computer Science from Faculty of Computer Science and Management at WUT in 1986 and Ph.D. in Computer Science from Faculty of Electronics at WUT in 1994. For 1986–1996 he was an Assistant at the Department of Information Systems (DIS) at WUT. For 1988–1996 he was also a head of the laboratory at DIS. For 1996–2004 he was an Assistant Professor in DIS and since fall of 2004 at IAI, both at WUT. His research interests include information retrieval, multimedia information systems, system usability and recommender systems. He is on the editorial board of New Generation Computing and was a co-editor of two journal special issues. He is a member of American Association of Machinery.     

An operator net model for distributed systems

An operator net is a graph consisting of nodes and directed arcs. While operator nets are syntactically similar to dataflow nets, they completely separate the operational semantics from the mathematical semantics. In this paper we define an operational semantics for operator nets that intuitively corresponds to communication in a distributed system. The operational semantics of operator ator nets provide a formal model for a distributed system that is an intermediate point between the actual system and a mathematical model. Abstract properties are expressed using relations on events and messages of an operator net. Corresponding operational specifications can be written using Lucid equations that define a node as a mathematical function on infinite history sequences. The operational specifications are executable and can be easily transformed into a practical implementation of the system. Examples of such specifications are included in the paper.Janice Glasgow is an associate professor in the Department of Computing and Information Science at Queen's University. She received her M. Math and Ph.D. degrees from the University of Waterloo. Dr. Glasgow's current research interests include programming language semantics and logics for reasoning about programming.Glenn H. MacEwen r received the B.Eng. degree in electrical engineering from McGill University, Montreal, P.Q., Canada, in 1962 and the M.Sc. and Ph.D. degrees in computer science from the University of Toronto, Toronto, Ont., Canada in 1967 and 1971, respectively. Since 1970 he has been with the Department of Computing and Information Science at Queen's University, Kingston, Ontario. He served as Head from 1982 to 1987 and is currently a Professor in the department. He is also a director and consultant to Andyne Computing Limited. His research interests include software engineering, computer security, and real-time systems. Dr. MacEwen is a senior member of the Institute of Electrical and Electronics Engineers, and a member of the Association for Computing Machinery and the Association of Professional Engineers of Ontario.     

Delay-dependent robust stability criteria for delay neural networks with linear fractional uncertainties

This article investigates the problem of robust stability for neural networks with time-varying delays and parameter uncertainties of linear fractional form. By introducing a new Lyapunov-Krasovskii functional and a tighter inequality, delay-dependent stability criteria are established in term of linear matrix inequalities (LMIs). It is shown that the obtained criteria can provide less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach. Recommended by Editorial Board member Naira Hovakimyan under the direction of Editor Young-Hoon Joo. This work was supported by the National Science foundation of China under Grant no. 60774013 and Key Laboratory of Education Ministry for Image Processing and Intelligent Control under grant no. 200805. Tao Li received the Ph.D. degree in The Research Institute of Automation Southeast University, China. Now He is an Assistant Professor in Department of Information and Communication, Nanjing University of Information Science and Technology. His current research interests include time-delay systems, neural networks, robust control, fault detection and diagnosis. Lei Guo received the Ph.D. degree in the Research Institute of Automation Southeast University, China. From 1999 to 2004, he has worked at Hong Kong University, IRCCyN (France), Glasgow University, Loughborough University and UMIST, UK. Now He is a Professor in School of Instrument Science and Opto-Electronics Engineering, Beihang University. His current research interests include robust control, fault detection and diagnosis. Lingyao Wu received the Ph.D. degree in The Research Institute of Automation Southeast University, China. Now He is an Assistant Professor in the Research Institute of Automation Southeast University. His current research interests include time-delay systems, neural networks, robust control, fault detection and diagnosis. Changyin Sun received the Ph.D. degree in the Research Institute of Automation Southeast University, China. Now He is a Professor in the Research Institute of Automation Southeast University. His current research interests include timedelay systems, neural networks.     

Rank order filters and priority queues

Summary A derivation of a parallel algorithm for rank order filtering is presented. Both derivation and result differ from earlier designs: the derivations are less complicated and the result allows a number of different implementations. The same derivation is used to design a collection of priority queues. Both filters and priority queues are highly efficient: they have constant response time and small latency. Anne Kaldewaij received an M.Sc. degree in Mathematics from the University of Utrecht (The Netherlands) and a Ph.D. degree in Computing Science from the Eindhoven University of Technology. Currently, he is associate professor in Computing Science at Eindhoven University. His research includes parallel programming and the design of algorithms and data structures. He enjoys teaching and he has written a number of textbooks on mathematics and programming. Jan Tijmen Udding received an M.Sc. degree in Mathematics in 1980 and a Ph.D. degree in Computing Science in 1984 from Eindhoven University of Technology. Currently, he is associate professor at Groningen University. His main research interests are mathematical aspects of VLSI, program derivation and correctness, and functional programming.     

Design of an H ∞ PID controller using particle swarm optimization

This paper proposes a novel method to designing an H _∞ PID controller with robust stability and disturbance attenuation. This method uses particle swarm optimization algorithm to minimize a cost function subject to H _∞-norm to design robust performance PID controller. We propose two cost functions to design of a multiple-input, multiple-output (MIMO) and single-input, single-output (SISO) robust performance PID controller. We apply this method to a SISO flexible-link manipulator and a MIMO super maneuverable F18/HARV fighter aircraft system as two challenging examples to illustrate the design procedure and to verify performance of the proposed PID controller design methodology. It is shown with the MIMO super maneuverable F18/HARV fighter system that PSO performs well for parametric optimization functions and performance of the PSO-based method without prior domain knowledge is superior to those of existing GA-based and OSA-based methods for designing H _∞ PID controllers. Recommended by Editorial Board member Jietae Lee under the direction of Editor Young-Hoon Joo. This work was supported by the Iranian Telecommunication Research Center (ITRC) under Grant T500-11629. Majid Zamani received the B.Sc. and M.Sc. degrees in Electrical Engineering in 2005 and 2007 from Isfahan University of Technology, and Sharif University of Technology, Iran, respectively. Currently, He is a Ph.D. student in Electrical Engineer-ing Department of University of California, Los Angeles, U.S.A. Nasser Sadati was born in Iran in 1960. He received the B.S. degree from Oklahoma State University, Stillwater, in 1982, and the M.S. and Ph.D. degrees from Cleveland State University, Cleveland, OH, USA, in 1985 and 1989, respectively, all in Electrical Engineering. From 1986 to 1987, he was with the NASA Lewis Research Center, Cleveland, to study the albedo effects on space station solar array. In 1989, he conducted postgraduate research at Case Western Reserve University, Cleveland, OH. Since 1990, he has been with the Sharif University of Technology, Tehran, Iran, where he is currently a Full Professor in the Department of Electrical Engineering, the Head of Control Group, and the Director of the Intelligent Systems Laboratory and the Co-Director of Robotics and Machine Vision Laboratory. He was the first to introduce the subject of fuzzy logic and intelligent control as course work in the universities engineering program in Iran. He has published two books in Persian and over 200 technical papers in peer-reviewed journals and conference proceedings, and is currently working on two more books in English (Intelligent Control of Large-Scale Systems) and Persian (Neural Networks). His research interests include intelligent control and soft computing, large-scale systems, robotics and pattern recognition. Dr. Sadati was the recipient of the Academic Excellence Award for 1998–1999 from the Sharif University of Technology. He is a Founding Member of the Iranian Journal of Fuzzy Systems (IJFS). He is the Founder and Chairman of the First Symposiums on Fuzzy Logic, and Intelligent Control and Soft Computing in Iran. He is the editorial board members of International Journal of Advances in Fuzzy Mathematics (AFM) and the Journal of Iranian Association of Electrical and Electronics Engineers (IAEEE). He also has served as the Co-Chair of the First International Conference on Intelligent and Cognitive Systems (ICICS’96). Dr. Sadati is a Founding Member of the Center of Excellence in Power System Management and Control (CEPSMC), Sharif University of Technology, Tehran, Iran and the Foreign Member of the Institute of Control, Robotics, and Systems (ICROS), Korea. Masoud Karimi Ghartemani received the B.Sc. and M.Sc. in Electrical Engineering in 1993 and 1995 from Isfahan University of Technology, Iran, where he continued to work as a Teaching and Research Assistant until 1998. He received the Ph.D. degree in Electrical Engineering from University of Toronto in 2004. He was a Research Associate and a Post-doctoral Researcher in the Department of Electrical and Computer Engineering of the University of Toronto from 1998 to 2001 and from 2004 to 2005, respectively. He joined Sharif University of Technology, Tehran, Iran, in 2005 as a Faculty Member. His research topics include nonlinear and optimal control, novel control and signal processing techniques/algorithms for control and protection of modern power systems, power electronics, power system stability and control, and power quality.     

Knowledge in shared memory systems

Summary We study the relation between knowledge and space. That is, we analyze how much shared memory space is needed in order to learn certain kinds of facts. Such results are useful tools for reasoning about shared memory systems. In addition we generalize a known impossibility result, and show that results about how knowledge can be gained and lost in message passing systems also hold for shared memory systems. Michael Merritt received a B.S. degree in Philosophy and in Computer Science from Yale College in 1978, the M.S. and Ph.D. degrees in Information and Computer Science in 1980 and 1983, respectively, from the Georgia Institute of Technology. Since 1983 he has been a member of technical staff at AT & T Bell Laboratories, and has taught as an adjunct or visiting lecturer at Stevens Institute of Technology, Massachusetts Institute of Technology, and Columbia University. In 1989 he was program chair for the ACM Symposium on Principles of Distributed Computing. His research interests include distributed and concurrent computation, both algorithms and formal methods for verifying their correctness, cryptography, and security. He is an editor for Distributed Computing and for Information and Computation, recently co-authored a book on database concurrency control algorithms, and is a member of the ACM and of Computer Professionals for Social Responsibility. Gadi Taubenfeld received the B.A., M.Sc. and Ph.D. degrees in Computer Science from the Technion (Israel Institute of Technology), in 1982, 1984 and 1988, respectively. From 1988 to 1990 he was a research scientist at Yale University. Since 1991 he has been a member of technical staff at AT & T Bell Laboratories. His primary research interests are in concurrent and distributed computing.A preliminary version of this work appeared in the Proceedings of the Tenth Annual ACM Symposium on Principles of Distributed Computing, pages 189–200, Montreal, Canada, August 1991     

Atomicm-register operations

Summary We investigate systems where it is possible to access several shared registers in one atomic step. We characterize those systems in which the consensus problem can be solved in the presence of faults and give bounds on the space required. We also describe a fast solution to the mutual exclusion problem using atomicm-register operations. Michael Merritt received a B.S. degree in Philosophy and in Computer Science from Yale College in 1978, the M.S. and Ph. D. degrees in Information and Computer Science in 1980 and 1983, respectively, from the Georgia Institute of Technology. Since 1983 he has been a member of technical staff at AT&T Bell Laboratories, and has taught as an adjunct or visiting lecturer at Stevens Institute of Technology and Columbia University. In 1989 he was program chair for the ACM Symposium on Principles of Distributed Computing. His research interests include distributed and concurrent computation, both algorithms and formal methods for verifying their correctness, cryptography, and security. He is an editor for Distributed Computing and for Information and Computation, recently coauthored a book on database concurrency control algorithms, and is a member of the ACM and of Computer Professionals for Social Responsibility. Gadi Taubenfeld received the B.A., M.Sc. and Ph.D. degrees in Computer Science from the Technion (Israel Institute of Technology), in 1982, 1984 and 1988, respectively. From 1988 to 1990 he was a research scientist at Yale University. Since 1991 he has been a member of technical staff at AT&T Bell Laboratories. His primary research interests are in concurrent and distributed computing.A preliminary version of this workappeared in theProceedings of the Fifth International Workshop on Distributed Algorithms, Delphi, Greece, October 1991, pp 289–294     

Multiagent learning towards RoboCup

This article describes the issues in multiagent learning towards RoboCup,^1≈3) especially for the real robot leagues. First, the review of the issue in the context of the related area is given, then related works from several viewpoints are reviewed. Next, our approach towards RoboCup Initiative is introduced and finally future issues are given. Minoru Asada, Ph.D.: He received B.E., M.Sc., and Ph.D., degrees in control engineering from Osaka University, in 1977, 1979, and 1982, respectively. From 1982 to 1988, he was a research associate of Control Engineering, Osaka University. In 1989, he became an associate professor of Mechanical Engineering for Computer-Controlled Machinery, Osaka University. In 1995 he became a professor of the department of Adaptive Machine Systems at the same university. From 1986 to 1987, he was a visiting researcher of Center for Automation Research, University of Maryland, College Park, MD. He received the 1992 best paper award of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS92), and the 1996 best paper award of RSJ (Robotics Society of Japan). Also, his paper was one of the finalists of IEEE Robotics and Automation Society 1995 Best Conference Paper Award. He was a general chair of IEEE/RSJ 1996 International Conference on Intelligent Robots and Systems (IROS96). Since early 1990, he has been involved in RoboCup activities and his team was the first champion team with USC team in the middle size league of the first RoboCup held in conjunction with IJCAI-97, Nagoya, Japan. Eiji Uchibe, Ph.D.: He received a Ph.D. degree in mechanical engineering from Osaka University in 1999. He is currently a research associate of the Japan Society for the Promotion of Science, in Research for the Future Program titled Cooperative Distributed Vision for Dynamic Three Dimensional Scene Understanding. His research interests are in reinforcement learning, evolutionary computation, and their applications. He is a member of IEEE, AAAI, RSJ, and JSAI.     

Evolving comprehensible neural network trees using genetic algorithms

This paper presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it can be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem. The article is published in the original. Elsayed Mostafa. Received the B.Sc. degree in electrical (Communication) Eng., Cairo University at 1967. Dipl.-Ing, and Dr-Ing. from Stuttgart University at 1977, 1981 respectively. He is a member of ECS and EEES. He is currently a professor of electronic circuits, Faculty of Engineering, University of Helwan. Amr Kamel. Graduated from Computer Department, Faculty of Engineering of Ain Shams University, Egypt in 1999, and studying M.Sc. degree in computer engineering from the Faculty of Engineering of Helwan University. His special fields of interest include neural networks and genetic algorithms. Alaa Hamdy. Was born in Giza in Egypt, on August 17, 1966. He graduated from the Telecommunications and Electronics Department, Faculty of Engineering and Technology of Helwan University, Cairo, Egypt in 1989. He received the M.Sc. degree in computer engineering from the same university in 1996 and the Ph.D. degree from the Faculty of Electrical Engineering, Poznan University of Technology, Poland in 2004. Currently he is working as a lecturer in the Faculty of Engineering of Helwan University. His special fields of interest, include image processing, pattern analysis, and machine vision.     

Stability of feature selection algorithms: a study on high-dimensional spaces

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorithm. Alexandros Kalousis received the B.Sc. degree in computer science, in 1994, and the M.Sc. degree in advanced information systems, in 1997, both from the University of Athens, Greece. He received the Ph.D. degree in meta-learning for classification algorithm selection from the University of Geneva, Department of Computer Science, Geneva, in 2002. Since then he is a Senior Researcher in the same university. His research interests include relational learning with kernels and distances, stability of feature selection algorithms, and feature extraction from spectral data. Julien Prados is a Ph.D. student at the University of Geneva, Switzerland. In 1999 and 2001, he received the B.Sc. and M.Sc. degrees in computer science from the University Joseph Fourier (Grenoble, France). After a year of work in industry, he joined the Geneva Artificial Intelligence Laboratory, where he is working on bioinformatics and datamining tools for mass spectrometry data analysis. Melanie Hilario has a Ph.D. in computer science from the University of Paris VI and currently works at the University of Geneva’s Artificial Intelligence Laboratory. She has initiated and participated in several European research projects on neuro-symbolic integration, meta-learning, and biological text mining. She has served on the program committees of many conferences and workshops in machine learning, data mining, and artificial intelligence. She is currently an Associate Editor of theInternational Journal on Artificial Intelligence Toolsand a member of the Editorial Board of theIntelligent Data Analysis journal.     

Spontaneous Speech Parsing in Travel Information Inquiring and Booking Systems

Grammar-based parsing is a prevalent method for natural language understanding(NLU)and has been introduced into dialogue systems for spoken language processing (SLP).A robust parsing scheme is proposed in this paper to overcome the notorious phenomena,such as garbage,ellipsis,word disordering,fragment ,and ill-form,which frequently occur in splien utterances,Keyword categories are used as terminal symbols,and the definition of grammar is extended by introducing three new rule types,by-passing,up-messing and overcrossing,in addition to the general rules called up-tying in this paper,and the use of semantic items simplifies the semantics extraction.The corresponding parser marionette,which is essentially a partial chart parser,is enhanced to parse the semantic grammar.The robust parsing scheme integrating the above methods has been adopted in an air traveling information service system called EasyFlight,and has achieved a high performance when used for parsing spontaneous speeches.     

A Prolog simulator for deterministic P systems with active membranes

In this paper we propose a new way to represent P systems with active membranes based on Logic Programming techniques. This representation allows us to express the set of rules and the configuration of the P system in each step of the evolution as literals of an appropriate language of first order logic. We provide a Prolog program to simulate, the evolution of these P systems and present some auxiliary tools to simulate the evolution of a P system with active membranes using 2-division which solves the SAT problem following the techniques presented in Reference.¹⁰ Andrés Cordón-Franco: He is a member of the Department of Computer Science and Artificial Intelligence at the University of Sevilla (Spain). He is also a member of the research group on Natural Computing of the University of Seville. His research interest includes Mathematical Logic, Logic in Computer Science, and Membrane Computing, both from a theoretical and from a practical (software implementation) point of view. Miguel A. Gutiérrez-Naranjo: He is an assistant professor in the Computer Science and Artificial Intelligence Department at University of Sevilla, Spain. He is also a member of the Research Group on Natural Computing of the University of Seville. His research interest includes Machine Learning, Logic Programming and Membrane Computing, both from a theoretical and a practical point of view. Mario J. Pérez-Jiménez, Ph.D.: He is professor of Department of Computer Science and Artificial Intelligence at University of Seville, where he is the head of the Group of Research on Natural Computing, He has published 8 books of Mathematics and Computation, and more than 90 scientific articles in prestigious scientific journals. He is member of European Molecular Computing Consortium. Fernando Sancho-Caparrini: He is a member of the Department of Computer Science and Artificial Intelligence at the University of Sevilla (Spain). He is also a member of the research group on Natural Computing of the University of Seville. His research interest includes Complex Systems, DNA Computing, Logic in Computer Science, and Membrane Computing, both from a theoretical and from a practical point of view.     

Leakage current estimation of CMOS circuit with stack effect

Leakage current of CMOS circuit increases dramatically with the technology scaling down and has become a critical issue of high performance system. Subthreshold, gate and reverse biased junction band-to-band tunneling (BTBT) leakages are considered three main determinants of total leakage current. Up to now, how to accurately estimate leakage current of large-scale circuits within endurable time remains unsolved, even though accurate leakage models have been widely discussed. In this paper, the authors first dip into the stack effect of CMOS technology and propose a new simple gate-level leakage current model. Then, a table-lookup based total leakage current simulator is built up according to the model. To validate the simulator, accurate leakage current is simulated at circuit level using popular simulator HSPICE for comparison. Some further studies such as maximum leakage current estimation, minimum leakage current generation and a high-level average leakage current macromodel are introduced in detail. Experiments on ISCAS85 and ISCAS89 benchmarks demonstrate that the two proposed leakage current estimation methods are very accurate and efficient.     

An inexpensive unstructured platform for wireless mobile peer-to-peer networks

In this paper, we propose an unstructured platform, namely I nexpensive P eer-to- P eer S ubsystem (IPPS), for wireless mobile peer-to-peer networks. The platform addresses the constraints of expensive bandwidth of wireless medium, and limited memory and computing power of mobile devices. It uses a computationally-, memory requirement- and communication- wise inexpensive gossip protocol as the main maintenance operation, and exploits location information of the wireless nodes to minimize the number of link-level messages for communication between peers. As a result, the platform is not only lightweight by itself, but also provides a low cost framework for different peer-to-peer applications. In addition, further enhancements are introduced to enrich the platform with robustness and tolerance to failures without incurring any additional computational and memory complexity, and communication between peers. In specific, we propose schemes for a peer (1) to chose a partner for a gossip iteration, (2) to maintain the neighbors, and (3) to leave the peer-to-peer network. Simulation results are given to demonstrate the performance of the platform.

Deterioration of visual information in face classification using Eigenfaces and Fisherfaces

In the area of biometrics, face classification becomes one of the most appealing and commonly used approaches for personal identification. There has been an ongoing quest for designing systems that exhibit high classification rates and portray significant robustness. This feature becomes of paramount relevance when dealing with noisy and uncertain images. The design of face recognition classifiers capable of operating in presence of deteriorated (noise affected) face images requires a careful quantification of deterioration of the existing approaches vis-à-vis anticipated form and levels of image distortion. The objective of this experimental study is to reveal some general relationships characterizing the performance of two commonly used face classifiers (that is Eigenfaces and Fisherfaces) in presence of deteriorated visual information. The findings obtained in our study are crucial to identify at which levels of noise the face classifiers can still be considered valid. Prior knowledge helps us develop adequate face recognition systems. We investigate several typical models of image distortion such as Gaussian noise, salt and pepper, and blurring effect and demonstrate their impact on the performance of the two main types of the classifiers. Several distance models derived from the Minkowski family of distances are investigated with respect to the produced classification rates. The experimental environment concerns a well-known standard in this area of face biometrics such as the FERET database. The study reports on the performance of the classifiers, which is based on a comprehensive suite of experiments and delivers several design hints supporting further developments of face classifiers. Gabriel Jarillo Alvarado obtained his B.Sc. degree in Biomedical Engineering from the Universidad Iberoamericana, Mexico. In 2003 he obtained his M.Sc. degree from the University of Alberta at the Department of Electrical and Computer Engineering, he is currently enrolled in the Ph.D. program at the same University. His research interests involve machine learning, pattern recognition, and evolutionary computation with particular interest to biometrics for personal identification. Witold Pedrycz is a Professor and Canada Research Chair (CRC) in Computational Intelligence) in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. His research interests involve Computational Intelligence, fuzzy modeling, knowledge discovery and data mining, fuzzy control including fuzzy controllers, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 9 research monographs. Witold Pedrycz has been a member of numerous program committees of conferences in the area of fuzzy sets and neurocomputing. He currently serves on editorial board of numereous journals including IEEE Transactions on Systems Man and Cybernetics, Pattern Recognition Letters, IEEE Transactions on Fuzzy Systems, Fuzzy Sets & Systems, and IEEE Transactions on Neural Networks. He is an Editor-in-Chief of Information Sciences. Marek Reformat received his M.Sc. degree from Technical University of Poznan, Poland, and his Ph.D. from University of Manitoba, Canada. His interests were related to simulation and modeling in time-domain, as well as evolutionary computing and its application to optimization problems For three years he worked for the Manitoba HVDC Research Centre, Canada, where he was a member of a simulation software development team. Currently, Marek Reformat is with the Department of Electrical and Computer Engineering at University of Alberta. His research interests lay in the areas of application of Computational Intelligence techniques, such as neuro-fuzzy systems and evolutionary computing, as well as probabilistic and evidence theories to intelligent data analysis leading to translating data into knowledge. He applies these methods to conduct research in the areas of Software and Knowledge Engineering. He has been a member of program committees of several conferences related to Computational Intelligence and evolutionary computing. Keun-Chang Kwak received B.Sc., M.Sc., and Ph.D. degrees in the Department of Electrical Engineering from Chungbuk National University, Cheongju, South Korea, in 1996, 1998, and 2002, respectively. During 2002–2003, he worked as a researcher in the Brain Korea 21 Project Group, Chungbuk National University. His research interests include biometrics, computational intelligence, pattern recognition, and intelligent control.     

DocXS distributed operator construction and execution system

In the field of computer vision and pattern recognition, data processing and data analysis tasks are often implemented as a consecutive or parallel application of more-or-less complex operations. In the following we will present DocXS, a computing environment for the design and the distributed and parallel execution of such tasks. Algorithms can be programmed using an Eclipse-based user interface, and the resulting Matlab and Java operators can be visually connected to graphs representing complex data processing workflows. DocXS is platform independent due to its implementation in Java, is freely available for noncommercial research, and can be installed on standard office computers. One advantage of DocXS is that it automatically takes care about the task execution and does not require its users to care about code distribution or parallelization. Experiments with DocXS show that it scales very well with only a small overhead. The text was submitted by the authors in English. Steffen Wachenfeld received B.Sc. and M.Sc. (honors) degrees in Information Systems in 2003 and 2005 from the University of Muenster, Germany, and an M.Sc. (honors) degree in Computer Science in 2003 from the University of Muenster. He is currently a research fellow and PhD student in the Computer Science at the Dept. of Computer Science, University of Muenster. His research interests include low resolution text recognition, computer vision on mobile devices, and systems/system architectures for computer vision and image analysis. He is author or coauthor of more than ten scientific papers and a member of IAPR. Tobias Lohe, M.Sc. degree in Computer Science in 2007 from the University of Muenster, Germany, is currently a research associate and PhD student in Computer Science at the Institute for Robotics and Cognitive Systems, University of Luebeck, Germany. His research interests include medical imaging, signal processing, and robotics for minimally invasive surgery. Michael Fieseler is currently a student of Computer Science at the University of Muenster, Germany. He has participated in research in the field of computer vision and medical imaging. Currently he is working on his Master thesis on depth-based image rendering (DBIR). Xiaoyi Jiang studied Computer Science at Peking University, China, and received his PhD and Venia Docendi (Habilitation) degree in Computer Science from the University of Bern, Switzerland. In 2002 he became an associate professor at the Technical University of Berlin, Germany. Since October 2002 he has been a full professor at the University of Münster, Germany. He has coauthored and coedited two books published by Springer and has served as the co-guest-editor of two special issues in international journals. Currently, he is the Coeditor-in-Chief of the International Journal of Pattern Recognition and Artificial Intelligence. In addition he also serves on the editorial advisory board of the International Journal of Neural Systems and the editorial board of IEEE Transactions on Systems, Man, and Cybernetics—Part B, the International Journal of Image and Graphics, Electronic Letters on Computer Vision and Image Analysis, and Pattern Recognition. His research interests include medical image analysis, vision-based man-machine interface, 3D image analysis, structural pattern recognition, and mobile multimedia. He is a member of IEEE and a Fellow of IAPR.