A metamodel for modeling system features and their refinement, constraint and interaction relationships

This paper presents a metamodel for modeling system features and relationships between features. The underlying idea of this metamodel is to employ features as first-class entities in the problem space of software and to improve the customization of software by explicitly specifying both static and dynamic dependencies between system features. In this metamodel, features are organized as hierarchy structures by the refinement relationships, static dependencies between features are specified by the constraint relationships, and dynamic dependencies between features are captured by the interaction relationships. A first-order logic based method is proposed to formalize constraints and to verify constraints and customization. This paper also presents a framework for interaction classification, and an informal mapping between interactions and constraints through constraint semantics. Hong Mei received the BSc and MSc degrees in computer science from the Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1984 and 1987, respectively, and the PhD degree in computer science from the Shanghai Jiao Tong University in 1992. He is currently a professor of Computer Science at the Peking University, China. His current research interests include Software Engineering and Software Engineering Environment, Software Reuse and Software Component Technology, Distributed Object Technology, and Programming Language. He has published more than 100 technical papers. Wei Zhang received the BSc in Engineering Thermophysics and the MSc in Computer Science from the Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1999 and 2002, respectively. He is currently a PhD student at the School of Electronics Engineering and Computer Science of the Peking University, China. His research interests include feature-oriented requirements modeling, feature-driven software architecture design and feature-oriented software reuse. Haiyan Zhao received both the BSc and the MSc degree in Computer Science from the Peking Univeristy, China, and the Ph.D degree in Information Engineering from the University of Tokyo, Japan. She is currently an associate professor of Computer Science at the Peking University, China. Her research interests include Software Reuse, Domain Engineering, Domain Specific Languange and Program Transformation.     

Information access in multimedia databases based on feature models

With the increasing popularity of the WWW, the main challenge in computer science has become content-based retrieval of multimedia objects. Access to multimedia objects in databases has long been limited to the information provided in manually assigned keywords. Now, with the integration of feature-detection algorithms in database systems software, content-based retrieval can be fully integrated with query processing. We describe our experimentation platform under development, making database technology available to multimedia. Our approach is based on the new notion of feature databases. Its architecture fully integrates traditional query processing and content-based retrieval techniques. Arjen P. de Vries, Ph.D.: He received his Ph.D. in Computer Science from the University of Twente in 1999, on the integration of content management in database systems. He is especially interested in the new requirements on the design of database systems to support content-based retrieval in multimedia digital libraries. He has continued to work on multimedia database systems as a postdoc at the CWI in Amsterdam as well as University of Twente. Menzo Windhouwer: He received his MSc in Computer Science and Management from the University of Amsterdam in 1997. Currently he is working in the CWI Database Research Group on his Ph.D., which is concerned with multimedia indexing and retrieval using feature grammars. Peter M.G. Apers, Ph.D.: He is a full professor in the area of databases at the University of Twente, the Netherlands. He obtained his MSc and Ph.D. at the Free University, Amsterdam, and has been a visiting researcher at the University of California, Santa Cruz and Stanford University. His research interests are query optimization in parallel and distributed database systems to support new application domains, such as multimedia applications and WWW. He has served on the program committees of major database conferences: VLDB, SIGMOD, ICDE, EDBT. In 1996 he was the chairman of the EDBT PC. In 2001 he will, for the second time, be the chairman of the European PC of the VLDB. Currently he is coordinating Editor-in-Chief of the VLDB Journal, editor of Data & Knowledge Engineering, and editor of Distributed and Parallel Databases. Martin Kersten, Ph.D.: He received his PhD in Computer Science from the Vrije Universiteit in 1985 on research in database security, whereafter he moved to CWI to establish the Database Research Group. Since 1994 he is professor at the University of Amsterdam. Currently he is heading a department involving 60 researchers in areas covering BDMS architectures, datamining, multimedia information systems, and quantum computing. In 1995 he co-founded Data Distilleries, specialized in data mining technology, and became a non-executive board member of the software company Consultdata Nederland. He has published ca. 130 scientific papers and is member of the editorial board of VLDB journal and Parallel and Distributed Systems. He acts as a reviewer for ESPRIT projects and is a trustee of the VLDB Endowment board.     

New Meta-Heuristic for Combinatorial Optimization Problems: Intersection Based Scaling

Combinatorial optimization problems are found in many application fields such as computer science,engineering and economy. In this paper, a new efficient meta-heuristic, Intersection-Based Scaling (IBS for abbreviation), is proposed and it can be applied to the combinatorial optimization problems. The main idea of IBS is to scale the size of the instance based on the intersection of some local optima, and to simplify the search space by extracting the intersection from the instance, which makes the search more efficient. The combination of IBS with some local search heuristics of different combinatorial optimization problems such as Traveling Salesman Problem (TSP) and Graph Partitioning Problem (GPP) is studied, and comparisons are made with some of the best heuristic algorithms and meta-heuristic algorithms. It is found that it has significantly improved the performance of existing local search heuristics and significantly outperforms the known best algorithms.     

Efficient Computation of k-Medians over Data Streams Under Memory Constraints

In this paper, we study the problem of efficiently computing k-medians over high-dimensional and high speed data streams. The focus of this paper is on the issue of minimizing CPU time to handle high speed data streams on top of the requirements of high accuracy and small memory. Our work is motivated by the following observation： the existing algorithms have similar approximation behaviors in practice, even though they make noticeably different worst case theoretical guarantees. The underlying reason is that in order to achieve high approximation level with the smallest possible memory, they need rather complex techniques to maintain a sketch, along time dimension, by using some existing off-line clustering algorithms. Those clustering algorithms cannot guarantee the optimal clustering result over data segments in a data stream but accumulate errors over segments, which makes most algorithms behave the same in terms of approximation level, in practice. We propose a new grid-based approach which divides the entire data set into cells （not along time dimension）. We can achieve high approximation level based on a novel concept called （1 - ε）-dominant. We further extend the method to the data stream context, by leveraging a density-based heuristic and frequent item mining techniques over data streams. We only need to apply an existing clustering once to computing k-medians, on demand, which reduces CPU time significantly. We conducted extensive experimental studies, and show that our approaches outperform other well-known approaches.     

A Power-Aware Branch Predictor by Accessing the BTB Selectively

Microarchitects should consider power consumption, together with accuracy, when designing a branch predictor, especially in embedded processors. This paper proposes a power-aware branch predictor, which is based on the gshare predictor, by accessing the BTB （Branch Target Buffer） selectively. To enable the selective access to the BTB, the PHT （Pattern History Table） in the proposed branch predictor is accessed one cycle earlier than the traditional PHT if the program is executed sequentially without branch instructions. As a side effect, two predictions from the PHT are obtained through one access to the PHT, resulting in more power savings. In the proposed branch predictor, if the previous instruction was not a branch and the prediction from the PHT is untaken, the BTB is not accessed to reduce power consumption. If the previous instruction was a branch, the BTB is always accessed, regardless of the prediction from the PHT, to prevent the additional delay/accuracy decrease. The proposed branch predictor reduces the power consumption with little hardware overhead, not incurring additional delay and never harming prediction accuracy. The simulation results show that the proposed branch predictor reduces the power consumption by 29-47%.     

PAN: A portable,parallel Prolog: Its design,realisation and performance

PAN is a general purpose, portable environment for executing logic programs in parallel. It combines a flexible, distributed architecture which is resilient to software and platform evolution with facilities for automatically extracting and exploiting AND and OR parallelism in ordinary Prolog programs. PAN incorporates a range of compile-time and run-time techniques to deliver the performance benefits of parallel execution while rertaining sequential execution semantics. Several examples illustrate the efficiency of the controls that facilitate the execution of logic programs in a distributed manner and identify the class of applications that benefit from distributed platforms like PAN. George Xirogiannis, Ph.D.: He received his B.S. in Mathematics from the University of Ioannina, Greece in 1993, his M.S in Artificial Intelligence from the University of Bristol in 1994 and his Ph.D. in Computer Science from Heriot-Watt University, Edinburgh in 1998. His Ph.D. thesis concerns the automated execution of Prolog on distributed heterogeneous multi-processors. His research interests have progressed from knowledge-based systems to distributed logic programming and data mining. Currently, he is working as a senior IT consultant at Pricewaterhouse Coopers. He is also a Research Associate at the National Technical University of Athens, researching in knowledge and data mining. Hamish Taylor, Ph.D.: He is a lecturer in Computer Science in the Computing and Electrical Engineering Department of Heriot-Watt University in Edinburgh. He received M.A. and MLitt degrees in philosophy from Cambridge University and an M.S. and a Ph.D. degree in computer science from Heriot-Watt University, Scotland. Since 1985 he has worked on research projects concerned with implementing concurrent logic programming languages, developing formal models for automated reasoning, performance modelling parallel relational database systems, and visualisizing resources in shared web caches. His current research interests are in applications of collaborative virtual environments, parallel logic programming and networked computing technologies.     

Recent Progress on Selected Topics in Database Research ——-A Report by Nine Young Chinese Researchers Working in the United States

The study on database technologies, or more generally, the technologies of data and information management, is an important and active research field. Recently, many exciting results have been reported. In this fast growing field, Chinese researchers play more and more active roles. Research papers from Chinese scholars, both in China and abroad,appear in prestigious academic forums.In this paper,we, nine young Chinese researchers working in the United States, present concise surveys and report our recent progress on the selected fields that we are working on.Although the paper covers only a small number of topics and the selection of the topics is far from balanced, we hope that such an effort would attract more and more researchers,especially those in China,to enter the frontiers of database research and promote collaborations. For the obvious reason, the authors are listed alphabetically, while the sections are arranged in the order of the author list.     

S-Club: an overlay-based efficient service discovery mechanism in CROWN Grid

Information service plays a key role in grid system, handles resource discovery and management process. Employing existing information service architectures suffers from poor scalability, long search response time, and large traffic overhead. In this paper, we propose a service club mechanism, called S-Club, for efficient service discovery. In S-Club, an overlay based on existing Grid Information Service (GIS) mesh network of CROWN is built, so that GISs are organized as service clubs. Each club serves for a certain type of service while each GIS may join one or more clubs. S-Club is adopted in our CROWN Grid and the performance of S-Club is evaluated by comprehensive simulations. The results show that S-Club scheme significantly improves search performance and outperforms existing approaches. Chunming Hu is a research staff in the Institute of Advanced Computing Technology at the School of Computer Science and Engineering, Beihang University, Beijing, China. He received his B.E. and M.E. in Department of Computer Science and Engineering in Beihang University. He received the Ph.D. degree in School of Computer Science and Engineering of Beihang University, Beijing, China, 2005. His research interests include peer-to-peer and grid computing; distributed systems and software architectures. Yanmin Zhu is a Ph.D. candidate in the Department of Computer Science, Hong Kong University of Science and Technology. He received his B.S. degree in computer science from Xi’an Jiaotong University, Xi’an, China, in 2002. His research interests include grid computing, peer-to-peer networking, pervasive computing and sensor networks. He is a member of the IEEE and the IEEE Computer Society. Jinpeng Huai is a Professor and Vice President of Beihang University. He serves on the Steering Committee for Advanced Computing Technology Subject, the National High-Tech Program (863) as Chief Scientist. He is a member of the Consulting Committee of the Central Government’s Information Office, and Chairman of the Expert Committee in both the National e-Government Engineering Taskforce and the National e-Government Standard office. Dr. Huai and his colleagues are leading the key projects in e-Science of the National Science Foundation of China (NSFC) and Sino-UK. He has authored over 100 papers. His research interests include middleware, peer-to-peer (P2P), grid computing, trustworthiness and security. Yunhao Liu received his B.S. degree in Automation Department from Tsinghua University, China, in 1995, and an M.A. degree in Beijing Foreign Studies University, China, in 1997, and an M.S. and a Ph.D. degree in computer science and engineering at Michigan State University in 2003 and 2004, respectively. He is now an assistant professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology. His research interests include peer-to-peer computing, pervasive computing, distributed systems, network security, grid computing, and high-speed networking. He is a senior member of the IEEE Computer Society. Lionel M. Ni is chair professor and head of the Computer Science and Engineering Department at Hong Kong University of Science and Technology. Lionel M. Ni received the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, Indiana, in 1980. He was a professor of computer science and engineering at Michigan State University from 1981 to 2003, where he received the Distinguished Faculty Award in 1994. His research interests include parallel architectures, distributed systems, high-speed networks, and pervasive computing. A fellow of the IEEE and the IEEE Computer Society, he has chaired many professional conferences and has received a number of awards for authoring outstanding papers.     

A multimodal virtual reality interface for 3D interaction with VTK

The object-oriented visualization Toolkit (VTK) is widely used for scientific visualization. VTK is a visualization library that provides a large number of functions for presenting three-dimensional data. Interaction with the visualized data is controlled with two-dimensional input devices, such as mouse and keyboard. Support for real three-dimensional and multimodal input is non-existent. This paper describes VR-VTK: a multimodal interface to VTK on a virtual environment. Six degree of freedom input devices are used for spatial 3D interaction. They control the 3D widgets that are used to interact with the visualized data. Head tracking is used for camera control. Pedals are used for clutching. Speech input is used for application commands and system control. To address several problems specific for spatial 3D interaction, a number of additional features, such as more complex interaction methods and enhanced depth perception, are discussed. Furthermore, the need for multimodal input to support interaction with the visualization is shown. Two existing VTK applications are ported using VR-VTK to run in a desktop virtual reality system. Informal user experiences are presented. Arjan J. F. Kok is an assistant professor at the Department of Computer Science at the Open University of the Netherlands. He studied Computer Science at the Delft University of Technology, The Netherlands. He received his Ph.D. from the same university. He worked as a Scientist for TNO (Netherlands Organization for Applied Scientific Research) and as assistant professor at the Eindhoven University of Technology before he joined the Open University. His research interests are visualization, virtual reality, and computer graphics. Robert van Liere studied Computer Science at the Delft University of Technology, the Netherlands. He received his Ph.D. with the thesis “Studies in Interactive Scientific Visualization” at the University of Amsterdam. Since 1985, he has worked at CWI, the Center for Mathematics and Computer Science in Amsterdam in which he is the head of CWI’s visualization research group. Since 2004, he holds a part-time position as full professor at the Eindhoven University of Technology. His research interests are in interactive data visualization and virtual reality. He is a member of IEEE.     

A unified architecture for agent behaviors with selection of evolved neural network modules

To model complex systems for agent behaviors, genetic algorithms have been used to evolve neural networks which are based on cellular automata. These neural networks are popular tools in the artificial life community. This hybrid architecture aims at achieving synergy between the cellular automata and the powerful generalization capabilities of the neural networks. Evolutionary algorithms provide useful ways to learn about the structure of these neural networks, but the use of direct evolution in more difficult and complicated problems often fails to achieve satisfactory solutions. A more promising solution is to employ incremental evolution that reuses the solutions of easy tasks and applies these solutions to more difficult ones. Moreover, because the human brain can be divided into many behaviors with specific functionalities and because human beings can integrate these behaviors for high-level tasks, a biologically-inspired behavior selection mechanism is useful when combining these incrementally evolving basic behaviors. In this paper, an architecture based on cellular automata, neural networks, evolutionary algorithms, incremental evolution and a behavior selection mechanism is proposed to generate high-level behaviors for mobile robots. Experimental results with several simulations show the possibilities of the proposed architecture. Kyung-Joong Kim (Student Member, IEEE) received the B.S. and M.S. degree in computer science from Yonsei University, Seoul, Korea, in 2000 and 2002, respectively. Since 2002, he has been a Ph.D. student in the Department of Computer Science, Yonsei University. His research interests include evolutionary neural network, robot control, and agent architecture. Sung-Bae Cho (Member, IEEE) received the B.S. degree in computer science from Yonsei University, Seoul, Korea, in 1988 and the M.S. and Ph.D. degrees in computer science from Korea Advanced Institute of Science and Technology (KAIST), Taejeon, Korea, in 1990 and 1993, respectively. From 1991 to 1993, he worked as a Member of the Research Staff at the Center for Artificial Intelligence Research at KAIST. From 1993 to 1995, he was an Invited Researcher of Human Information Processing Research Laboratories at ATR (Advanced Telecommunications Research) Institute, Kyoto, Japan. In 1998, he was a Visiting Scholar at University of New South Wales, Canberra, Australia. Since 1995, he has been a Professor in the Department of Computer Science, Yonsei University. His research interests include neural networks, pattern recognition, intelligent man-machine interfaces, evolutionary computation, and artificial life. Dr. Cho is a Member of the Korea Information Science Society, INNS, the IEEE Computer Society, and the IEEE Systems, Man and Cybernetics Society. He was awarded outstanding paper prizes from the IEEE Korea Section in 1989 and 1992, and another one from the Korea Information Science Society in 1990. In 1993, he also received the Richard E. Merwin prize from the IEEE Computer Society. In 1994, he was listed in Who’s Who in Pattern Recognition from the International Association for Pattern Recognition and received the best paper awards at International Conference on Soft Computing in 1996 and 1998. In 1998, he received the best paper award at World Automation Congress. He was listed in Marquis Who’s Who in Science and Engineering in 2000 and in Marquis Who’s Who in the World in 2001.     

Test minimization for human-computer interaction

This paper introduces a model-based approach for minimization of test sets to validate the interaction of human-computer systems. The novelty of the approach is twofold: (i) Test cases generated and selected holistically cover both the behavioral model and the complementary, fault model of the system under test (SUT). (ii) Methods known from state-based conformance testing and graph theory are extended to construct efficient, heuristic search-based algorithms for minimizing the test sets that are constructed in step (i), considering also structural features. Experience shows that the approach can help to considerably save test costs, up to 60% Fevzi Belli received the M.S., Ph.D., and Habilitation degrees in electrical engineering and computer science from the Berlin Technical University. He is presently a Professor of Software Engineering in the Faculty of Computer Science, Electrical Engineering and Mathematics, University of Paderborn, Paderborn, Germany. Prior to this, he headed several projects at a software house in Munich, was a Professor of Computing Science at the Hochschule Bremerhaven and a faculty member of the University of Maryland, European Division. He chaired several international conferences, e.g., ISSRE 1998 and is author and co-author of more than 100 papers published in scientific journals and conference proceedings. His research interests are in testing/fault tolerance/reliability of software and programming techniques. Christof J. Budnik received the MS degree in electrical engineering and computer science in 2001 from the University of Paderborn. In 2002, he joined the Department of Computer Science, Electrical Engineering and Mathematics at the same University where he is currently a faculty member. His research interests are in the areas of software quality, testing of interactive systems and safety-critical user interfaces.     

Dual-coding representations for robot vision programming in Tekkotsu

We describe complementary iconic and symbolic representations for parsing the visual world. The iconic pixmap representation is operated on by an extensible set of “visual routines” (Ullman, 1984; Forbus et al., 2001). A symbolic representation, in terms of lines, ellipses, blobs, etc., is extracted from the iconic encoding, manipulated algebraically, and re-rendered iconically. The two representations are therefore duals, and iconic operations can be freely intermixed with symbolic ones. The dual-coding approach offers robot programmers a versatile collection of primitives from which to construct application-specific vision software. We describe some sample applications implemented on the Sony AIBO. David S. Touretzky is a Research Professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University. He earned his B.A. in Computer Science from Rutgers University in 1978, and his M.S. (1979) and Ph.D. (1984) in Computer Science from Carnegie Mellon. Dr. Touretzky’s research interests are in computational neuroscience, particularly representations of space in the rodent hippocampus and related structures, and high level primitives for robot programming. He is presently developing an undergraduate curriculum in cognitive robotics based on the Tekkotsu software framework described in this article. Neil S. Halelamien earned a B.S. in Computer Science and a B.S. in Cognitive Science at Carnegie Mellon University in 2004, and is currently pursuing his Ph.D. in the Computation & Neural Systems program at the California Institute of Technology. His research interests are in studying vision from both a computational and biological perspective. He is currently using transcranial magnetic stimulation to study visual representations and information processing in visual cortex. Ethan J. Tira-Thompson is a graduate student in the Robotics Institute at Carnegie Mellon University. He earned a B.S. in Computer Science and a B.S. in Human-Computer Interaction in 2002, and an M.S. in Robotics in 2004, at Carnegie Mellon. He is interested in a wide variety of computer science topics, including machine learning, computer vision, software architecture, and interface design. Ethan’s research has revolved around the creation of the Tekkotsu framework to enable the rapid development of robotics software and its use in education. He intends to specialize in mobile manipulation and motion planning for the completion of his degree. Jordan J. Wales is completing a Master of Studies in Theology at the University of Notre Dame. He earned a B.S. in Engineering (Swarthmore College, 2001), an M.Sc. in Cognitive Science (Edinburgh, UK, 2002), and a Postgraduate Diploma in Theology (Oxford, UK, 2003). After a year as a graduate research assistant in Computer Science at Carnegie Mellon, he entered the master’s program in Theology at Notre Dame and is now applying to doctoral programs. His research focus in early and medieval Christianity is accompanied by an interest in medieval and modern philosophies of mind and their connections with modern cognitive science. Kei Usui is a masters student in the Robotics Institute at Carnegie Mellon University. He earned his B.S. in Physics from Carnegie Mellon University in 2005. His research interests are reinforcement learning, legged locomotion, and cognitive science. He is presently working on algorithms for humanoid robots to maintain balance against unexpected external forces.     

An Attack-Finding Algorithm for Security Protocols

This paper proposes an automatic attack construction algorithm in order to find potential attacks on ecurity protocols.It is based on a dynamic strand space model,which enhances the original strand space model by introducing active nodes on strands so as to characterize the dynamic procedure of protocol execution.With exact causal dependency relations between messages considered in the model,this algorithm can avoid state space explo-sion caused by asynchronous composition.In order to get a finite state space,a new method called strand-added on demand is exploited,which extends a bundle in an incremental manner without requiring explicit configuration of protocol execution parameters.A finer granularity model of term structure is also introduced, in which subterms are divided into check subterms and data subterms .Moreover,data subterms can be further classified based on the compatible data subterm relation to obtain automatically the finite set of valid acceptable terms for an honest principal.In this algorithm,terms core is designed to represent the intruder‘s knowledge compactly,and forward search technology is used to simulate attack patterns easily.Using this algorithm,a new attack on the Dolve-Yao protocol can be found,which is even more harmful beeause the secret is revealed before the session terminates.     

A Novel Computer Architecture to Prevent Destruction by Viruses

In today‘s Internet computing world,illegal activities by crackers pose a serious threat to computer security.It is well known that computer viruses,Trojan horses and other intrusive programs may cause sever and often catastrophic consequences. This paper proposes a novel secure computer architecture based on security-code.Every instruction/data word is added with a security-code denoting its security level.External programs and data are automatically addoed with security-code by hadware when entering a computer system.Instruction with lower security-code cannot run or process instruction/data with higher security level.Security-code cannot be modified by normal instruction.With minor hardware overhead,then new architecture can effectively protect the main computer system from destruction or theft by intrusive programs such as computer viruses.For most PC systems it includes an increase of word-length by 1 bit on register,the memory and the hard disk.     

Fast multicast on multistage interconnection networks using multi-head worms

1IntroductionMulticastcommunication,whichreferstothedeliveryofamessagefromasinglesourcenodetoanumberofdestinationnodes,isfrequentlyusedindistributed-memoryparallelcomputersystemsandnetworks[1].Efficientimplementationofmulticastcommunicationiscriticaltotheperformanceofmessage-basedscalableparallelcomputersandswitch-basedhighspeednetworks.Switch-basednetworksorindirectnetworks,basedonsomevariationsofmultistageiDterconnectionnetworks(MINs),haveemergedasapromisingnetworkajrchitectureforconstruct…     

Finding centric local outliers in categorical/numerical spaces

Outlier detection techniques are widely used in many applications such as credit-card fraud detection, monitoring criminal activities in electronic commerce, etc. These applications attempt to identify outliers as noises, exceptions, or objects around the border. The existing density-based local outlier detection assigns the degree to which an object is an outlier in a numerical space. In this paper, we propose a novel mutual-reinforcement-based local outlier detection approach. Instead of detecting local outliers as noise, we attempt to identify local outliers in the center, where they are similar to some clusters of objects on one hand, and are unique on the other. Our technique can be used for bank investment to identify a unique body, similar to many good competitors, in which to invest. We attempt to detect local outliers in categorical, ordinal as well as numerical data. In categorical data, the challenge is that there are many similar but different ways to specify relationships among the data items. Our mutual-reinforcement-based approach is stable, with similar but different user-defined relationships. Our technique can reduce the burden for users to determine the relationships among data items, and find the explanations why the outliers are found. We conducted extensive experimental studies using real datasets. Jeffrey Xu Yu received his B.E., M.E. and Ph.D. in computer science, from the University of Tsukuba, Japan, in 1985, 1987 and 1990, respectively. Jeffrey Xu Yu was a research fellow in the Institute of Information Sciences and Electronics, University of Tsukuba (Apr. 1990–Mar. 1991), and held teaching positions in the Institute of Information Sciences and Electronics, University of Tsukuba (Apr. 1991–July 1992) and in the Department of Computer Science, Australian National University (July 1992–June 2000). Currently he is an Associate Professor in the Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong. His major research interests include data mining, data stream mining/processing, XML query processing and optimization, data warehouse, on-line analytical processing, and design and implementation of database management systems. Weining Qian is currently an assistant professor of computer science at Fudan University, Shanghai, China. He received his M.S. and Ph.D. degrees in computer science from Fudan University in 2001 and 2004, respectively. He was supported by a Microsoft Research Fellowship when he was doing the research presented in this paper, and he is supported by the Shanghai Rising Star Program. His research interests include data mining for very large databases, data stream query processing and mining and peer-to-peer computing. Hongjun Lu received his B.Sc. from Tsinghua University, China, and M.Sc. and Ph.D. from the Department of Computer Science, University of Wisconsin–Madison. He worked as an engineer in the Chinese Academy of Space Technology, and a principal research scientist in the Computer Science Center of Honeywell Inc., Minnesota, USA (1985–1987), and a professor at the School of Computing of the National University of Singapore (1987–2000), and is a full professor of the Hong Kong University of Science and Technology. His research interests are in data/knowledge-base management systems with an emphasis on query processing and optimization, physical database design, and database performance. Hongjun Lu is currently a trustee of the VLDB Endowment, an associate editor of the IEEE Transactions on Knowledge and Data Engineering (TKDE), and a member of the review board of the Journal of Database Management. He served as a member of the ACM SIGMOD Advisory Board in 1998–2002. Aoying Zhou born in 1965, is currently a professor of computer science at Fudan University, Shanghai, China. He won his Bachelor degree and Master degree in Computer Science from Sichuan University in Chengdu, Sichuan, China in 1985 and 1988. respectively, and a Ph.D. degree from Fudan University in 1993. He has served as a member or chair of the program committees for many international conferences such as VLDB, ER, DASFAA, WAIM, and etc. His papers have been published in ACM SIGMOD, VLDB, ICDE and some international journals. His research interests include data mining and knowledge discovery, XML data management, web query and searching, data stream analysis and processing and peer-to-peer computing.     

Block cipher based on reversible cellular automata

We propose a new encryption algorithm relying on reversible cellular automata (CA). The behavior complexity of CA and their parallel nature makes them interesting candidates for cryptography. The proposed algorithm belongs to the class of symmetric key systems. Marcin Seredynski: He is a Ph.D. student at University of Luxembourg and Polish Academy of Sciences. He received his M.S. in 2004 from Faculty of Electronics and Information Technology in Warsaw University of Technology. His research interests include cryptography, cellular automata, nature inspired algorithms and network security. Currently he is working on intrusion detection algorithms for ad-hoc networks. Pascal Bouvry, Ph.D.: He earned his undergraduate degree in Economical & Social Sciences and his Master degree in Computer Science with distinction (’91) from the University of Namur, Belgium. He went on to obtain his Ph.D. degree (’94) in Computer Science with great distinction at the University of Grenoble (INPG), France. His research at the IMAG laboratory focussed on Mapping and scheduling task graphs onto Distributed Memory Parallel Computers. Next, he performed post-doctoral researches on coordination languages and multi-agent evolutionary computing at CWI in Amsterdam. He gained industrial experience as manager of the technology consultant team for FICS in the banking sector (Brussels, Belgium). Next, he worked as CEO and CTO of SDC (Ho Chi Minh city, Vietnam) in the telecom, semi-conductor and space industry. After that, He moved to Montreal Canada as VP Production of Lat45 and Development Director for MetaSolv Software in the telecom industry. He is currently serving as Professor in the group of Computer Science and Communications (CSC) of the Faculty of Sciences, Technology and Communications of Luxembourg University and he is heading the Intelligent & Adaptive Systems lab. His current research interests include: ad-hoc networks & grid-computing, evolutionary algorithms and multi-agent systems.     

Optimal bandwidth utilization of all-optical ring with a converter of degree 4

In many models of all-optical routing,a set of communication paths in a network is given,and a wavelength is to be assigned to each path so that paths sharing an edge receive different wavelengths .The goal is to assign as few wavelengths as possible,in order to use the optical bandwidth efficiently.If a node of a network contains a wavelength converter,any path that passes through this node may change its wavelength .Having converters at some of the nodes can reduce the mumber of wavelengths required for routing,This paper presents a wavelength converter with degree 4and gives a routing algorithm which shows that any routing with load L can be realized with L wavelengths when a node of an all-optical ring hosts such a wavelength converter.It is also proved that 4 is the minimum degree of the converter to reach the full utilization of the available wavelengths if only one mode of an all-optical ring hosts a converter.     

Measuring class cohesion based on dependence analysis

Classes are the basic modules in object-oriented (OO) software, which consist of attributes and methods. Thus, in OO environment, the cohesion is mainly about the tightness of the attributes and methods of classes. This paper discusses the relationships between attributes and attributes, attributes and methods,methods and methods of a class based on dependence analysis. Then the paper presents methods to compute these dependencies. Based on these, the paper proposes a method to measure the class cohesion, which satisfies the properties that a good measurement should have. The approach overcomes the limitations of previous class cohesion measures, which consider only one or two of the three relationships in a class.