A Framework for the Generation of Adaptive Courses Based on Semantic Metadata

This approach proposes the creation and management of adaptive learning systems by combining component technology, semantic metadata, and adaptation rules. A component model allows interaction among components that share consistent assumptions about what each provides and each requires of the other. It allows indexing, using, reusing, and coupling of components in different contexts powering adaptation. Our claim is that semantic metadata are required to allow a real reusing and assembling of educational component. Finally, a rule language is used to define strategies to rewrite user query and user model. The former allows searching components developing concepts not appearing in the user query but related with user goals, whereas the last allow inferring user knowledge that is not explicit in user model.John Freddy Duitama received his M.Sc. degree in system engineering from the University of Antioquia -Colombia (South America). He is currently a doctoral candidate in the GET – Institut National des Télécommunications, Evry France. This work is sponsored by the University of Antioquia, where he is assistant professor.His research interest includes semantic web and web-based learning systems, educational metadata and learning objects.Bruno Defude received his Ph.D. in Computer Science from the University of Grenoble (I.N.P.G) in 1986. He is currently Professor in the Department of Computer Science at the GET - Institut National des Télécommunications, Evry France where he leads the SIMBAD project (Semantic Interoperability for MoBile and ADaptive applications).His major field of research interest is databases and semantic web, specifically personalized data access, adaptive systems, metadata, interoperability and semantic Peer-to-peer systems with elearning as a privileged application area.He is a member of ACM SIGMOD.Amel Bouzeghoub received a degree of Ph.D. in Computer Sciences at Pierre et Marie Curie University, France.In 2000, she joined the Computer Sciences Department of GET-INT (Institut National des Telecommunications) at Evry (France) as an associate professor.Her research interests include topics related to Web-based Learning Systems, Semantic Metadata for learning resources, Adaptive Learning Systems and Intelligent Tutoring Systems.Claire Lecocq received an Engineer Degree and a Ph.D. in Computer Sciences respectively in 1994 and 1999. In 1997, she joined the Computer Sciences Department at GET-INT (Institut National des Télécommunications) of Evry, France, as an associate professor. Her first research interests included spatial databases and visual query languages. She is now working on adaptive learning systems, particularly on semantic metadata and user models.     

An integrated edge detection method using mathematical morphology

This paper presents an edge detection method based on mathematical morphology. The proposed scheme consists of four steps: preprocessing, edge extraction, edge decision, and postprocessing. In the preprocessing step, a morphological central transformation is applied to remove noise. In the edge extraction and decision steps, a morphological edge extractor is designed to estimate the edge information of an image, and an edge decision criterion is followed to determine whether a pixel is an edge or not. In the postprocessing step, the morphological hit-or-miss transformation is utilized to improve the correctness of the detected edges. It is proved theoretically for the correctness and effectiveness for detecting ideal edges. Experimental results show that the proposed method works well on both artificial and real images. The text was submitted by the authors in English. Chin-Pan Huang was born in 1959 in Taiwan, Republic of China. He received the B.S. and M.S. degrees in electrical engineering from Chung Cheng Institute of Technology, Taiwan, in 1981 and in 1985, respectively. In 1996, he received the Ph.D. degree in electrical engineering from the University of Pittsburgh in the United States. From 1996 to 2002, he was an associate scientist of the Electronic System Division in Chung Shan Institute of Science and Technology. He then joined the Department of Computer and Communication Engineering at Ming Chuan University in August 2002 and is currently an assistant professor there. His recent research interests include data compression, computer vision, digital image processing, and pattern recognition. Ran-Zan Wang was born in 1972 in Fukien, Republic of China. He received his B.S. degree in computer engineering and science in 1994 and M.S. degree in electrical engineering and computer science in 1996, both from Yuan-Ze University. In 2001, he received his Ph.D. degree in computer and information science from National Chiao Tung University. In 2001–2002, he was an assistant professor at the Department of Computer Engineering at the Van Nung Institute of Technology. He joined the Department of Computer and Communication Engineering at Ming Chuan University in August 2002 and is currently an assistant professor there. His recent research interests include data hiding and digital watermarking, image processing, and pattern recognition. Dr. Wang is a member of the Phi Tau Phi Scholastic Honor Society.     

An experimental analysis of DCT-based approaches for fine-grained multiresolution video

In the future, video-streaming systems will have to support adaptation over an extremely large range of display requirements (e.g., 90×60 to 1920×1080). This paper presents the architectural trade-offs of bandwidth efficiency, computational cost, and storage cost to support fine-grained multiresolution video over a large set of resolutions. While several techniques have been proposed, they have focused mainly on limited spatial resolution adaptation. In this paper, we examine the ability of current techniques to support wide-range spatial resolution adaptation. Based upon experiments with real video, we propose an architecture that can support wide-range adaptation efficiently. Our results indicate that multiple encodings with limited spatial adaptation from each encoding provide good trade-offs between efficient coding and the ability to adapt the stream to various resolutions. Jie Huang received her BS in computer and communications and MS in computer science from Beijing University of Posts and Telecommunications, Beijing, China, in 1992 and 1995 respectively, where she was an assistant professor from 1995 to 1999. Since 1999, she has been pursuing her PhD at OGI school of Science and Engineering at Oregon Health and Science University (from 1999 to 2004) and Portland State University (since 2004). Her research interests include multimedia networking and software engineering. Wu-chi Feng received his Ph.D. in Computer Science and Engineering from the University of Michigan in 1996. ~His research interests include multimedia systems, video-based sensor networking technologies, and networking. ~He currently serves as an Editor for the Springer-ACM Multimedia Systems Journal. ~He also serves on the national Orion Cyberinfrastructure Advisory committee. Jonathan Walpole received his Ph.D. degree in Computer Science from Lancaster University, UK. He is a Professor in the Computer Science Department at Portland State University. Prior to joining PSU he was a Professor and Director of the Systems Software Laboratory at the OGI School of Science and Engineering at Oregon Health & Science University. His research interests are in operating systems, networking, distributed systems and multimedia computing. He has pioneered research in adaptive resource management and the integration of application and system-level quality of service management. He has also done leading edge research on dynamic specialization for enhanced performance, survivability and evolvability of large software systems. His research on distributed multimedia systems began in 1988, and in the early 1990s he lead the development of one of the first QoS-adaptive Internet streaming video players.     

Temporal disjunctive logic programming

In this paper we introduce the logic programming languageDisjunctive Chronolog which combines the programming paradigms of temporal and disjunctive logic programming. Disjunctive Chronolog is capable of expressing dynamic behaviour as well as uncertainty, two notions that are very common in a variety of real systems. We present the minimal temporal model semantics and the fixpoint semantics for the new programming language and demonstrate their equivalence. We also show how proof procedures developed for disjunctive logic programs can be easily extended to apply to Disjunctive Chronolog programs. Manolis Gergatsoulis, Ph.D.: He received his B.Sc. in Physics in 1983, the M.Sc. and the Ph.D. degrees in Computer Science in 1986 and 1995 respectively all from the University of Athens, Greece. Since 1996 he is a Research Associate in the Institute of Informatics and Telecommunications, NCSR ‘Demokritos’, Athens. His research interests include logic and temporal programming, program transformations and synthesis, as well as theory of programming languages. Panagiotis Rondogiannis, Ph.D.: He received his B.Sc. from the Department of Computer Engineering and Informatics, University of Patras, Greece, in 1989, and his M.Sc. and Ph.D. from the Department of Computer Science, University of Victoria, Canada, in 1991 and 1994 respectively. From 1995 to 1996 he served in the Greek army. From 1996 to 1997 he was a visiting professor in the Department of Computer Science, University of Ioannina, Greece, and since 1997 he is a Lecturer in the same Department. In January 2000 he was elected Assistant Professor in the Department of Informatics at the University of Athens. His research interests include functional, logic and temporal programming, as well as theory of programming languages. Themis Panayiotopoulos, Ph.D.: He received his Diploma on Electrical Engineering from the Department of Electrical Engineering, National Technical Univesity of Athens, in 1984, and his Ph.D. on Artificial Intelligence from the above mentioned department in 1989. From 1991 to 1994 he was a visiting professor at the Department of Mathematics, University of the Aegean, Samos, Greece and a Research Associate at the Institute of Informatics and Telecommunications of “Democritos” National Research Center. Since 1995 he is an Assistant Prof. at the Department of Computer Science, University of Piraeus. His research interests include temporal programming, logic programming, expert systems and intelligent agent architectures.     

Self-stabilization of dynamic systems assuming only read/write atomicity

Summary Three self-stabilizing protocols for distributed systems in the shared memory model are presented. The first protocol is a mutual-exclusion prootocol for tree structured systems. The second protocol is a spanning tree protocol for systems with any connected communication graph. The thrid protocol is obtianed by use offair protoco combination, a simple technique which enables the combination of two self-stabilizing dynamic protocols. The result protocol is a self-stabilizing, mutualexclusion protocol for dynamic systems with a general (connected) communication graph. The presented protocols improve upon previous protocols in two ways: First, it is assumed that the only atomic operations are either read or write to the shared memory. Second, our protocols work for any connected network and even for dynamic network, in which the topology of the network may change during the excution. Shlomi Dolev received his B.Sc. in Civil Engineering and B.A. in Computer Science in 1984 and 1985, and his M.Sc. and Ph.D. in computer Sciene in 1989 and 1992 from the Technion Israel Institute of Technology. He is currently a post-dotoral fellow in the Department of Computer Science at Texas A & M Univeristy. His current research interests include the theoretical aspects of distributed computing and communcation networks. Amos Israeli received his B.Sc. in Mathematics and Physics from Hebrew University in 1976, and his M.Sc. and D.Sc. in Computer Science from the Weizmann Institute in 1980 and the Technion in 1985, respectively. Currently he is a sensior lecturer at the Electrical Engineering Department at the Technion. Prior tot his he was a postdoctoral fellow at the Aiken Computation Laboratory at harvard. His research interests are in Parellel and Distributed Computing and in Robotics. In particular he has worked on the design and analysis of Wait-Free and Self-Stabilizing distributed protocols. Shlomo Moran received his B.Sc. and D.Sc. degrees in matheamtics from Technion, Israel Institute of Technology, Haifa, in 1975 and 1979, respectively. From 1979 to 1981 he was assistant professors and a visiting research specialist at the University of Minnesota, Minneapolis. From 1981 to 1985 he was a senior lecturer at the Department of Computer Science. Technion, and from 1985 to 1986 he visted at IBM Thoas J. Watson Research Center, Yorktown Heights. From 1986 to 1993 he was an associated professor at the Department of Computer Science, Technin. in 1992–3 he visited at AT & T Bell Labs at Murray Hill and at Centrum voor Wiskunde en Informatica, Amsterdam. From 1993 he is a full professor at the Department of Computer Science, Technion. His researchinterests include distributed algorithm, computational complexity, combinatorics and grapth theory.Part of this research was supported in part by Technion V.P.R. Funds — Wellner Research Fund, and by the Foundation for Research in Electronics, Computers and Communictions, administrated by the Israel Academy of Sciences and Humanities.     

Hiding collaborative recommendation association rules

The concept of Privacy-Preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. For example, through data mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. There have been two types of privacy concerning data mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance [10, 18–21, 24, 27]. This selection of rules would require data mining process to be executed first. Based on the discovered rules and privacy requirements, hidden rules or patterns are then selected manually. However, for some applications, we are interested in hiding certain constrained classes of association rules such as collaborative recommendation association rules [15, 22]. To hide such rules, the pre-process of finding these hidden rules can be integrated into the hiding process as long as the recommended items are given. In this work, we propose two algorithms, DCIS (Decrease Confidence by Increase Support) and DCDS (Decrease Confidence by Decrease Support), to automatically hiding collaborative recommendation association rules without pre-mining and selection of hidden rules. Examples illustrating the proposed algorithms are given. Numerical simulations are performed to show the various effects of the algorithms. Recommendations of appropriate usage of the proposed algorithms based on the characteristics of databases are reported. Leon Wang received his Ph.D. in Applied Mathematics from State University of New York at Stony Brook in 1984. From 1984 to 1987, he was an assistant professor in mathematics at University of New Haven, Connecticut, USA. From 1987 to 1994, he joined New York Institute of Technology as a research associate in the Electromagnetic Lab and assistant/associate professor in the Department of Computer Science. From 1994 to 2001, he joined I-Shou University in Taiwan as associate professor in the Department of Information Management. In 1996, he was the Director of Computing Center. From 1997 to 2000, he was the Chairman of Department of Information Management. In 2001, he was Professor and director of Library, all in I-Shou University. In 2002, he was Associate Professor and Chairman in Information Management at National University of Kaohsiung, Taiwan. In 2003, he rejoined New York Institute of Technology. Dr.Wang has published 33 journal papers, 102 conference papers, and 5 book chapters, in the areas of data mining, machine learning, expert systems, and fuzzy databases, etc. Dr. Wang is a member of IEEE, Chinese Fuzzy System Association Taiwan, Chinese Computer Association, and Chinese Information Management Association. Ayat Jafari received the Ph.D. degree from City University of New York. He has conducted considerable research in the areas of Computer Communication Networks, Local Area Networks, and Computer Network Security, and published many technical articles. His interests and expertise are in the area of Computer Networks, Signal Processing, and Digital Communications. He is currently the Chairman of the Computer Science and Electrical Engineering Department of New York Institute of Technology. Tzung-Pei Hong received his B.S. degree in chemical engineering from National Taiwan University in 1985, and his Ph.D. degree in computer science and information engineering from National Chiao-Tung University in 1992. He was a faculty at the Department of Computer Science in Chung-Hua Polytechnic Institute from 1992 to 1994, and at the Department of Information Management in I-Shou University from 1994 to 2001. He was in charge of the whole computerization and library planning for National University of Kaohsiung in Preparation from 1997 to 2000, and served as the first director of the library and computer center in National University of Kaohsiung from 2000 to 2001 and as the Dean of Academic Affairs from 2003 to 2006. He is currently a professor at the Department of Electrical Engineering and at the Department of Computer Science and Information Engineering. His current research interests include machine learning, data mining, soft computing, management information systems, and www applications. Springer     

ScaleSpaceViz: α-Scale spaces in practice

Kernels of the so-called α-scale space have the undesirable property of having no closed-form representation in the spatial domain, despite their simple closed-form expression in the Fourier domain. This obstructs spatial convolution or recursive implementation. For this reason an approximation of the 2D α-kernel in the spatial domain is presented using the well-known Gaussian kernel and the Poisson kernel. Experiments show good results, with maximum relative errors of less than 2.4%. The approximation has been successfully implemented in a program for visualizing α-scale spaces. Some examples of practical applications with scale space feature points using the proposed approximation are given. The text was submitted by the authors in English. Frans Kanters received his MSc degree in Electrical Engineering in 2002 from the Eindhoven University of Technology in the Netherlands. Currently he is working on his PhD at the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. His PhD work is part of the “Deep Structure, Singularities, and Computer Vision (DSSCV)” project sponsored by the European Union. His research interests include scale space theory, image reconstruction, image processing algorithms, and hardware implementations thereof. Luc Florack received his MSc degree in theoretical physics in 1989 and his PhD degree cum laude in 1993 with a thesis on image structure, both from Utrecht University, the Netherlands. During the period from 1994 to 1995, he was an ERCIM/HCM research fellow at INRIA Sophia-Antipolis, France, and IN-ESC Aveiro, Portugal. In 1996 he was an assistant research professor at DIKU, Copenhagen, Denmark, on a grant from the Danish Research Council. From 1997 to June 2001, he was an assistant research professor at Utrecht University in the Department of Mathematics and Computer Science. Since June 1, 2001, he has been working as an assistant professor and, then, as an associate professor at Eindhoven University of Technology, Department of Biomedical Engineering. His interest includes all multiscale structural aspects of signals, images, and movies and their applications to imaging and vision. Remco Duits received his MSc degree (cum laude) in Mathematics in 2001 from the Eindhoven University of Technology, the Netherlands. Today he is a PhD student at the Department of Biomedical Engineering at the Eindhoven University of Technology on the subject of multiscale perceptual organization. His interest subtends functional analysis, group theory, partial differential equations, multiscale representations and their applications to biomedical imaging and vision, perceptual grouping. Currently, he is finishing his thesis “Perceptual Organization in Image Analysis (A Mathematical Approach Based on Scale, Orientation and Curvature).” During his PhD work, several of his submissions at conferences were chosen as selected or best papers—in particular, at the PRIA 2004 conference on pattern recognition and image analysis in St. Petersburg, where he received a best paper award (second place) for his work on invertible orientation scores. Bram Platel received his Masters Degree cum laude in biomedical engineering from the Eindhoven University of Technology in 2002. His research interests include image matching, scale space theory, catastrophe theory, and image-describing graph constructions. Currently he is working on his PhD in the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. Bart M. ter Haar Romany is full professor in Biomedical Image Analysis at the Department of Biomedical Engineering at Eindhoven University of Technology. He has been in this position since 2001. He received a MSc in Applied Physics from Delft University of Technology in 1978, and a PhD on neuromuscular nonlinearities from Utrecht University in 1983. After being the principal physicist of the Utrecht University Hospital Radiology Department, in 1989 he joined the department of Medical Imaging at Utrecht University as an associate professor. His interests are mathematical aspects of visual perception, in particular linear and non-linear scale-space theory, computer vision applications, and all aspects of medical imaging. He is author of numerous papers and book chapters on these issues; he edited a book on non-linear diffusion theory and is author of an interactive tutorial book on scale-space theory in computer vision. He has initiated a number of international collaborations on these subjects. He is an active teacher in international courses, a senior member of IEEE, and IEEE Chapter Tutorial Speaker. He is chairman of the Dutch Biophysical Society.     

Image hiding scheme using modulus function and optimal substitution table

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

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models. Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University, Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles at international journals. He is currently working on applications of artificial intelligence and adaptive control theory in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor manufacturing. Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management, inventory control, production planning and scheduling. Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively. From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis, and ubiquitous logistics information systems. An erratum to this article can be found at     

Adaptive receiver-driven streaming from multiple senders

This paper presents the design and evaluation of an adaptive streaming mechanism from multiple senders to a single receiver in Peer-to-Peer (P2P) networks, called P2P Adaptive Layered Streaming, or PALS. PALS is a receiver-driven mechanism. It enables a receiver peer to orchestrate quality adaptive streaming of a single, layer-encoded video stream from multiple congestion-controlled senders, and is able to support a spectrum of noninteractive streaming applications. The primary challenge in the design of a streaming mechanism from multiple senders is that available bandwidth from individual peers is not known a priori, and could significantly change during delivery. In PALS, the receiver periodically performs quality adaptation based on the aggregate bandwidth from all senders to determine: (i) the overall quality (i.e number of layers) that can be collectively delivered by all senders, and more importantly (ii) the specific subset of packets that should be delivered by individual senders in order to gracefully cope with any sudden change in their bandwidth. Our detailed simulation-based evaluations illustrate that PALS can effectively cope with several angles of dynamics in the system including: bandwidth variations, peer participation, and partially available content at different peers. We also demonstrate the importance of coordination among senders and examine key design tradeoffs for the PALS mechanism. Nazanin Magharei is currently a PhD student in the Computer Science Department at the University of Oregon. She received her BSc degree in Electrical Engineering from Sharif University of Technology, Iran in 2002. Her research interests include Peer-to-Peer streaming and multimedia caching. Reza Rejaie is currently an Assistant Professor at the Department of Computer and Information Science at the University of Oregon. From October 1999 to March 2002, he was a Senior Technical Staff member at AT&T Labs-Research in Menlo Park, California. He received a NSF CAREER Award for his work on P2P streaming in 2005. Reza has served on the editorial board of IEEE Communications Surveys & Tutorials, as well as the program committee of major networking conferences including INFOCOM, ICNP, Global Internet, ACM Multimedia, IEEE Multimedia, NOSSDAV, ICDCS, and MMCN. Reza received his MS and PhD degrees in Computer Science from the University of Southern California (USC) in 1996 and 1999, and his BS degree in Electrical Engineering from the Sharif University of Technology (Tehran, Iran) in 1991, respectively. Reza has been a member of both the ACM and IEEE since 1997.     

Self-stabilization over unreliable communication media

Summary A self-stabilizing system has the property that it will converge to a desirable state when started from any state. Most previous researchers assumed that processes in self-stabilizing systems may communicate through shared variables while those that studied meassage passing systems allowed messages with unbounded size. This paper discusses the development of self-stabilizing systems which communicate through message passing, and in which messages may be lost in transit. The systems presented all use fixed size message headers. First, a selfstabilizing version of theAlternating Bit Protocol, a fundamental communication protocol for transmitting data across an unreliable communication medium, is presented. Secondly, the alternating-bit protocol is used to construct a self-stabilizing token ring. Yehuda Afek received a B.Sc. in Electrical Engineering from the Technion and an M.S. and Ph.D. in Computer Science from the University of California, Los Angeles. In 1985 he joined the Distributed Systems research Department in AT&T Bell Laboratories and in 1988 he joined the Department of Computer Science in Tel-Aviv University. His interests include communication protocols, distributed systems, and asynchronous shared memories. Geoffrey M. Brown received the BS degree in Engineering from Swarthmore College in 1982, the MS degree in Electrical Engineering from Stanford University in 1983, and the Ph.D. degree in Electrical Engineering from the University of Texas at Austin in 1987. From 1983 to 1984 he worked for Motorola in Austin, TX. Currently he is an Assistant Professor in the School of Electrical Engineering at Cornell University. In 1990, Brown was named a Presidential Young Investigator by the National Science Foundation.This work supported in part by NSF grant CCR-9058180     

Preference update for e-commerce applications: Model,language, and processing

It is likely that customers issue requests based on out-of-date information in e-commerce application systems. Hence, the transaction failure rates would increase greatly. In this paper, we present a preference update model to address this problem. A preference update is an extended SQL update statement where a user can request the desired number of target data items by specifying multiple preferences. Moreover, the preference update allows easy extraction of criteria from a set of concurrent requests and, hence, optimal decisions for the data assignments can be made. We propose a group evaluation strategy for preference update processing in a multidatabase environment. The experimental results show that the group evaluation can effectively increase the customer satisfaction level with acceptable cost. Peng Li is the Chief Software Architect of didiom LLC. Before that, he was a visiting assistant professor of computer science department in Western Kentucky University. He received his Ph.D. degree of computer science from the University of Texas at Dallas. He also holds a B.Sc. and M.S. in Computer Science from the Renmin University of China. His research interests include database systems, database security, transaction processing, distributed and Internet computer and E-commerce. Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China in 1996, and a Master Degree in Computer Science from the University of Texas at Dallas 2001. He is currently working toward the PhD degree in the Department of Computer Science at the University of Texas at Dallas. Mr. Tu’s research interests include distributed systems, grid computing, information security, mobile computing, and scientific computing. His PhD research work focus on the data management in secure and high performance data grid. He is a student member of the IEEE. I-Ling Yen received her BS degree from Tsing-Hua University, Taiwan, and her MS and PhD degrees in Computer Science from the University of Houston. She is currently an Associate Professor of Computer Science at the University of Texas at Dallas. Dr. Yen’s research interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce, and self-stabilizing systems. She had published over 100 technical papers in these research areas and received many research awards from NSF, DOD, NASA, and several industry companies. She has served as Program Committee member for many conferences and Program Chair/Co-Chair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International Symposium on Autonomous Decentralized Systems. She is a member of the IEEE. Zhonghang Xia received the B.S. degree in applied mathematics from Dalian University of Technology in 1990, the M.S. degree in Operations Research from Qufu Normal University in 1993, and the Ph.D. degree in computer science from the University of Texas at Dallas in 2004. He is now an assistant professor in the Department of Computer Science, Western Kentucky University, Bowling Green, KY. His research interests are in the area of multimedia computing and networking, distributed systems, and data mining.     

Algebraic retrieval of fragmentarily indexed video

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

Using Catastrophe Theory to Derive Trees from Images

In order to investigate the deep structure of Gaussian scale space images, one needs to understand the behaviour of critical points under the influence of blurring. We show how the mathematical framework of catastrophe theory can be used to describe the different types of annihilations and the creation of pairs of critical points and how this knowledge can be exploited in a scale space hierarchy tree for the purpose of a topology based segmentation. A key role is played by scale space saddles and iso-intensity manifolds through them. We discuss the role of non-generic catastrophes and their influence on the tree and the segmentation. Furthermore it is discussed, based on the structure of iso-intensity manifolds, why creations of pairs of critical points don’t influence the tree. We clarify the theory with an artificial image and a simulated MR image.Arjan Kuijper received his M.Sc. degree in applied mathematics in 1995 with a thesis on the comparision of two image restoration techniques, from the University of Twente, The Netherlands. During the period 1996–1997 he worked at ELTRA Parkeergroep, Ede, The Netherlands. In the period 1997-2002 he has been a Ph.D. student and associate researcher at the Institute of Information and Computing Sciences of Utrecht University. In 2002 he received his Ph.D. degree with a thesis on “Deep Structure of Gaussian Scale Space Images” and worked as postdoc at Utrecht University on the project “Co-registration of 3D Images” on a grant of the Netherlands Ministry of Economic Affairs within the framework of the Innovation Oriented Research Programme. Since Januari 1st 2003 he has been working as an assistant research professor at the IT University of Copenhagen in Denmark funded by the IST Programme “Deep Structure, Singularities, and Computer Vision (DSSCV)” of the European Union. His interest subtends all mathematical aspects of image analysis, notably multiscale representations (scale spaces), catastrophe and singularity theory, medial axes and symmetry sets, and applications to medical imaging.Luc M.J. Florack received his M.Sc. degree in theoretical physics invv 1989, and his Ph.D. degree in 1993 with a thesis on image structure, both from Utrecht University, The Netherlands. During the period 1994–1995 he was an ERCIM/HCM research fellow at INRIA Sophia-Antipolis, France, and INESC Aveiro, Portugal. In 1996 he was an assistant research professor at DIKU, Copenhagen, Denmark, on a grant from the Danish Research Council. From 1997 until June 2001 he was an assistant research professor at Utrecht University at the Department of Mathematics and Computer Science. Since June 1st 2001 he is with Eindhoven University of Technology, Department of Biomedical Engineering, currenlty employed as an associate professor. His interest subtends all structural aspects of signals, images and movies, notably multiscale representations, and their applications to imaging and vision.     

Learning Horn definitions: Theory and an application to planning

A Horn definition is a set of Horn clauses with the same predicate in all head literals. In this paper, we consider learning non-recursive, first-order Horn definitions from entailment. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Finally, we apply our results to learning control knowledge for efficient planning in the form of goal-decomposition rules. Chandra Reddy, Ph.D.: He is currently a doctoral student in the Department of Computer Science at Oregon State University. He is completing his Ph.D. on June 30, 1998. His dissertation is entitled “Learning Hierarchical Decomposition Rules for Planning: An Inductive Logic Programming Approach.” Earlier, he had an M. Tech in Artificial Intelligence and Robotics from University of Hyderabad, India, and an M.Sc.(tech) in Computer Science from Birla Institute of Technology and Science, India. His current research interests broadly fall under machine learning and planning/scheduling—more specifically, inductive logic programming, speedup learning, data mining, and hierarchical planning and optimization. Prasad Tadepalli, Ph.D.: He has an M.Tech in Computer Science from Indian Institute of Technology, Madras, India and a Ph.D. from Rutgers University, New Brunswick, USA. He joined Oregon State University, Corvallis, as an assistant professor in 1989. He is now an associate professor in the Department of Computer Science of Oregon State University. His main area of research is machine learning, including reinforcement learning, inductive logic programming, and computational learning theory, with applications to classification, planning, scheduling, manufacturing, and information retrieval.     

A Sampling-Based Motion Planning Approach to Maintain Visibility of Unpredictable Targets

This paper deals with the surveillance problem of computing the motions of one or more robot observers in order to maintain visibility of one or several moving targets. The targets are assumed to move unpredictably, and the distribution of obstacles in the workspace is assumed to be known in advance. Our algorithm computes a motion strategy by maximizing the shortest distance to escape—the shortest distance the target must move to escape an observer's visibility region. Since this optimization problem is intractable, we use randomized methods to generate candidate surveillance paths for the observers. We have implemented our algorithms, and we provide experimental results using real mobile robots for the single target case, and simulation results for the case of two targets-two observers. Rafael Murrieta-Cid received the B.S degree in Physics Engineering (1990), and the M.Sc. degree in Automatic Manufacturing Systems (1993), both from “Instituto Tecnológico y de Estudios Superiores de Monterrey” (ITESM) Campus Monterrey. He received his Ph.D. from the “Institut National Polytechnique” (INP) of Toulouse, France (1998). His Ph.D research was done in the Robotics and Artificial Intelligence group of the LAAS/CNRS. In 1998–1999, he was a postdoctoral researcher in the Computer Science Department at Stanford University. From January 2000 to July 2002 he was an assistant professor in the Electrical Engineering Department at ITESM Campus México City, México. In 2002–2004, he was working as a postdoctoral research associate in the Beckman Institute and Department of Electrical and Computer Engineering of the University of Illinois at Urbana-Champaign. Since August 2004, he is director of the Mechatronics Research Center in the ITESM Campus Estado de México, México. He is mainly interested in sensor-based robotics motion planning and computer vision. Benjamin Tovar received the B.S degree in electrical engineering from ITESM at Mexico City, Mexico, in 2000, and the M.S. in electrical engineering from University of Illinois, Urbana-Champaign, USA, in 2004. Currently (2005) he is pursuing the Ph.D degree in Computer Science at the University of Illinois. Prior to M.S. studies he worked as a research assistant at Mobile Robotics Laboratory at ITESM Mexico City. He is mainly interested in motion planning, visibility-based tasks, and minimal sensing for robotics. Seth Hutchinson received his Ph. D. from Purdue University in West Lafayette, Indiana in 1988. He spent 1989 as a Visiting Assistant Professor of Electrical Engineering at Purdue University. In 1990 Dr. Hutchinson joined the faculty at the University of Illinois in Urbana-Champaign, where he is currently a Professor in the Department of Electrical and Computer Engineering, the Coordinated Science Laboratory, and the Beckman Institute for Advanced Science and Technology. Dr. Hutchinson is currently a senior editor of the IEEE Transactions on Robotics and Automation. In 1996 he was a guest editor for a special section of the Transactions devoted to the topic of visual servo control, and in 1994 he was co-chair of an IEEE Workshop on Visual Servoing. In 1996 and 1998 he co-authored papers that were finalists for the King-Sun Fu Memorial Best Transactions Paper Award. He was co-chair of IEEE Robotics and Automation Society Technical Committee on Computer and Robot Vision from 1992 to 1996, and has served on the program committees for more than thirty conferences related to robotics and computer vision. He has published more than 100 papers on the topics of robotics and computer vision.     

A distributable security management architecture for enterprise systems spanning multiple security domains

Administering security in modern enterprise systems may prove an extremely complex task. Their large scale and dynamic nature are the main factors that contribute to this fact. A robust and flexible model is needed in order to guarantee both the easy management of security information and the efficient implementation of security mechanisms. In this paper, we present the foundations and the prototypical implementation of a new access control framework. The framework is mainly targeted to highly dynamic, large enterprise systems (e.g., service provisioning platforms, enterprise portals etc.), which contain various independent functional entities. Significant advantages gained from the application of the designated framework in such systems are epitomized in the easiness of managing access to their hosted resources (e.g., services) and the possibility of applying distributable management schemes for achieving it. The proposed framework allows for multi-level access control through the support of both role-based and user-based access control schemes. Discussion is structured in three distinct areas: the formal model of the proposed framework, the data model for supporting its operation, and the presentation of a prototypical implementation. The development of the framework is based on open technologies like XML, java and Directory Services. At the last part of the paper the results of a performance assessment are presented, aiming to quantify the delay overhead, imposed by the application of the new framework in a real system. Ioannis Priggouris received his B.Sc. in Informatics from the Department of Informatics & Telecommunications of the University of Athens, Greece in 1997 and his M.Sc. in Communication Systems and Data Networks from the same Department in 2000. Over the last years he has been a PhD candidate in the department. Since 1999, he has been a member of the Communication Networks Laboratory (CNL) of the University of Athens. As a senior researcher of the CNL he has participated in several EU projects implemented in the context of IST, namely the EURO-CITI and the PoLoS projects. He has also been extensively involved in several National IT Research projects. His research interests are in the areas of mobile computing, QoS and mobility support for IP networks, and network security. He is the author of several papers and book chapters in the aforementioned areas. Stathes Hadjiefthymiades received his B.Sc. (honors) and M.Sc. in Informatics from the Dept. of Informatics, University of Athens, Greece, in 1993 and 1996 respectively. In 1999 he received his Ph.D. from the University of Athens (Dept. of Informatics and Telecommunications). In 2002 he received a joint engineering-economics M.Sc. from the National Technical University of Athens. In 1992 he joined the Greek consulting firm Advanced Services Group, Ltd., where he was involved in the analysis, design and implementation of telematic applications and other software systems. In 1995 he joined, as research engineer, the Communication Networks Laboratory (UoA-CNL) of the University of Athens. During the period September 2001-July 2002, he served as a visiting assistant professor at the University of Aegean, Dept. of Information and Communication Systems Engineering. On the summer of 2002 he joined the faculty of the Hellenic Open University (Dept. of Informatics), Patras, Greece, as an assistant professor. Since December 2003, he is in the faculty of the Dept. of Informatics and Telecommunications, University of Athens, where he is presently an assistant professor and coordinator of the Pervasive Computing Research Group. He has participated in numerous projects realized in the context of EU programs (ACTS, ORA, TAP, and IST), EURESCOM projects, as well as national initiatives. His research interests are in the areas of web engineering, wireless/mobile computing, and networked multimedia applications. He is the author of over 100 publications in the above areas.     

QoS Control Strategies for High-Quality Video Processing

Video processing in software is often characterized by highly fluctuating, content-dependent processing times, and a limited tolerance for deadline misses. We present an approach that allows close-to-average-case resource allocation to a single video processing task, based on asynchronous, scalable processing, and QoS adaptation. The QoS adaptation balances different QoS parameters that can be tuned, based on user-perception experiments: picture quality, deadline misses, and quality changes. We model the balancing problem as a discrete stochastic decision problem, and propose two solution strategies, based on a Markov decision process and reinforcement learning, respectively. We enhance both strategies with a compensation for structural (non-stochastic) load fluctuations. Finally, we validate our approach by means of simulation experiments, and conclude that both enhanced strategies perform close to the theoretical optimum.Clemens Wüst received the M.Sc. degree in mathematics with honors from the University of Groningen, The Netherlands. Since then, he has been with the Philips Research Laboratories in Eindhoven, The Netherlands, where he has been working mainly on QoS for resource-constrained real-time systems using stochastic optimization techniques. Currently, he is pursuing a Ph.D. degree at the Technische Universiteit Eindhoven.Liesbeth Steffens received her M.Sc. from Utrecht University (NL) in 1972. She spent most of her professional life in Philips Research in Eindhoven. She contributed to the design of a real-time distributed operating system, a video-on-demand server, a DVD player, a set-top box, and a QoS-based Resource-Management framework for streaming video. Her current focus is on characterization of resource requirements, resource reservation, and system-on-chip infrastructure.Wim F. J. Verhaegh received the mathematical engineering degree with honors in 1990 from the Technische Universiteit Eindhoven, The Netherlands. Since then, he is with the Philips Research Laboratories in Eindhoven, The Netherlands. From 1990 until 1998, he has been a member of the department Digital VLSI, where he has been working on high-level synthesis of DSP systems for video applications, with the emphasis on scheduling problems and techniques. Based on this work, he received a Ph.D. degree in 1995 from the Technische Universiteit Eindhoven. Since 1998, he is working on various optimization aspects of multimedia systems, networks, and applications. On the one hand, this concerns application-level resource management and scheduling, for optimization of quality of service of multimedia systems. On the other hand, this concerns adaptive algorithms and machine learning algorithms for user interaction issues, such as content filtering and automatic playlist generation.Reinder J. Bril received a B.Sc. and a M.Sc. (both with honors) from the Department of Electrical Engineering of the University of Twente, and a Ph.D. from the Technische Universiteit Eindhoven (TU/e), The Netherlands. He started his professional career at the Delft University of technology in the Department of Electrical Engineering. From May 1985 till August 2004, he has been with Philips. He has worked in both Philips Research as well as Philips Business Units, on various topics, including fault-tolerance, formal specifications, and software architecture analysis, and in different application domains. The last five years, he worked at Philips Research Laboratories Eindhoven (PRLE), the Netherlands, in the area of Quality of Service (QoS) for consumer devices, with a focus on dynamic resource management in receivers in broadcast environments (such as digital TV-sets and set-top boxes). In September 2004, he made a transfer to the Technische Universiteit Eindhoven (TU/e), Department of Mathematics and Computer Science, Group System Architecture and Networking (SAN), i.e. back to the academic world, after 19 years in industry.Christian Hentschel received his Dr.-Ing. (Ph.D.) in 1989 and Dr.-Ing. habil. in 1996 at the University of Technology in Braunschweig, Germany. He worked on digital video signal processing with focus on quality improvement. In 1995, he joined Philips Research in Briarcliff Manor, USA, where he headed a research project on moiré analysis and suppression for CRT based displays. In 1997, he moved to Philips Research in Eindhoven, The Netherlands, leading a cluster for Programmable Video Architectures. Later he held a position of a Principal Scientist and coordinated a project on scalable media processing with dynamic resource control between different research laboratories. In 2003, he became a full professor at the Brandenburg University of Technology in Cottbus, Germany. Currently he chairs the department of Media Technology. He is a member of the Technical Committee of the International Conference on Consumer Electronics (IEEE) and a member of the FKTG in Germany.     

Anisotropic Curvature Motion for Structure Enhancing Smoothing of 3D MR Angiography Data

We propose a novel concept of shape prior for the processing of tubular structures in 3D images. It is based on the notion of an anisotropic area energy and the corresponding geometric gradient flow. The anisotropic area functional incorporates a locally adapted template as a shape prior for tubular vessel structures consisting of elongated, ellipsoidal shape models. The gradient flow for this functional leads to an anisotropic curvature motion model, where the evolution is driven locally in direction of the considered template. The problem is formulated in a level set framework, and a stable and robust method for the identification of the local prior is presented. The resulting algorithm is able to smooth the vessels, pushing solution toward elongated cylinders with round cross sections, while bridging gaps in the underlying raw data. The implementation includes a finite-element scheme for numerical accuracy and a narrow band strategy for computational efficiency. Oliver Nemitz received his Diploma in mathematics from the university of Duisburg, Germany in 2003. Then he started to work on his Ph.D. thesis in Duisburg. Since 2005 he is continuing the work on his Ph.D. project at the Institute for Numerical Simulation at Bonn University. His Ph.D. subject is fast algorithms for image manipulation in 3d, using PDE’s, variational methods, and level set methods. Martin Rumpf received his Ph.D. in mathematics from Bonn University in 1992. He held a postdoctoral research position at Freiburg University. Between 1996 and 2001, he was an associate professor at Bonn University and from 2001 until 2004 full professor at Duisburg University. Since 2004 he is now full professor for numerical mathematics and scientific computing at Bonn University. His research interests are in numerical methods for nonlinear partial differential equations, geometric evolution problems, calculus of variations, adaptive finite element methods, image and surface processing. Tolga Tasdizen received his B.S. degree in Electrical Engineering from Bogazici University, Istanbul in 1995. He received the M.S. and Ph.D. degrees in Engineering from Brown University in 1997 and 2001. From 2001 to 2004 he was a postdoctoral research associate with the Scientific Computing and Imaging Institute at the University of Utah. Since 2004 he has been with the School of Computing at the University of Utah as a research assistant professor. He also holds an adjunct assistant professor position with the Department of Neurology and the Center for Alzheimer’s Care, Imaging and Research, and a research scientist position with the Scientific Computing and Imaging Institute at the University of Utah. Ross Whitaker received his B.S. degree in Electrical Engineering and Computer Science from Princeton University in 1986, earning Summa Cum Laude. From 1986 to 1988 he worked for the Boston Consulting Group, entering the University of North Carolina at Chapel Hill in 1989. At UNC he received the Alumni Scholarship Award, and completed his Ph.D. in Computer Science in 1994. From 1994–1996 he worked at the European Computer-Industry Research Centre in Munich Germany as a research scientist in the User Interaction and Visualization Group. From 1996–2000 he was an Assistant Professor in the Department of Electrical Engineering at the University of Tennessee. He is now an Associate Professor at the University of Utah in the College of Computing and the Scientific Computing and Imaging Institute.