A Mixed-Mode BIST Scheme Based on Folding Compression

In this paper a new scheme of mixed mode scan-based BIST is presented with complete fault coverage,and some new concepts of folding set and computing are introduced.This scheme applies single feedback polynomial of LFSR for generation pseudo-random patterns as well as for compressing and extending seeds of folding sets and an LFSR, where we encode seed of folding set as an initial seed of LFSR .Moreover these new techniques are 100% compatible with scan design .Experimental results show that the proposed scheme outperforms previously published approaches based on the reseeding of LFRSRs.     

Estimating the number of clusters using a windowing technique

Clustering is the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters). A fundamental and unresolved issue in cluster analysis is to determine how many clusters are present in a given set of patterns. In this paper, we present the z-windows clustering algorithm, which aims to address this problem using a windowing technique. Extensive empirical tests that illustrate the efficiency and the accuracy of the propsoed method are presented. The text was submitted by the authors in English. Basilis Boutsinas. Received his diploma in Computer Engineering and Informatics in 1991 from the University of Patras, Greece. He also conducted studies in Electronics Engineering at the Technical Education Institute of Piraeus, Greece, and Pedagogics at the Pedagogical Academy of Lamia, Greece. He received his PhD on Knowledge Representation from the University of Patras in 1997. He has been an assistant professor in the Department of Business Administration at the University of Patras since 2001. His primary research interests include data mining, business intelligence, knowledge representation techniques, nonmonotonic reasoning, and parallel AI. Dimitris K. Tasoulis received his diploma in Mathematics from the University of Patras, Greece, in 2000. He attained his MSc degree in 2004 from the postgraduate course “Mathematics of Computers and Decision Making” from which he was awarded a postgraduate fellowship. Currently, he is a PhD candidate in the same course. His research activities focus on data mining, clustering, neural networks, parallel algorithms, and evolutionary computation. He is coauthor of more than ten publications. Michael N. Vrahatis is with the Department of Mathematics at the University of Patras, Greece. He received the diploma and PhD degree in Mathematics from the University of Patras in 1978 and 1982, respectively. He was a visiting research fellow at the Department of Mathematics, Cornell University (1987–1988) and a visiting professor to the INFN (Istituto Nazionale di Fisica Nucleare), Bologna, Italy (1992, 1994, and 1998); the Department of Computer Science, Katholieke Universiteit Leuven, Belgium (1999); the Department of Ocean Engineering, Design Laboratory, MIT, Cambridge, MA, USA (2000); and the Collaborative Research Center “Computational Intelligence” (SFB 531) at the Department of Computer Science, University of Dortmund, Germany (2001). He was a visiting researcher at CERN (European Organization of Nuclear Research), Geneva, Switzerland (1992) and at INRIA (Institut National de Recherche en Informatique et en Automatique), France (1998, 2003, and 2004). He is the author of more than 250 publications (more than 110 of which are published in international journals) in his research areas, including computational mathematics, optimization, neural networks, evolutionary algorithms, and artificial intelligence. His research publications have received more than 600 citations. He has been a principal investigator of several research grants from the European Union, the Hellenic Ministry of Education and Religious Affairs, and the Hellenic Ministry of Industry, Energy, and Technology. He is among the founders of the “University of Patras Artificial Intelligence Research Center” (UPAIRC), established in 1997, where currently he serves as director. He is the founder of the Computational Intelligence Laboratory (CI Lab), established in 2004 at the Department of Mathematics of University of Patras, where currently he serves as director.     

Interactive online learning

This paper describes a system for visual object recognition based on mobile augmented reality gear. The user can train the system to the recognition of objects online using advanced methods of interaction with mobile systems: Hand gestures and speech input control “virtual menus,” which are displayed as overlays within the camera image. Here we focus on the underlying neural recognition system, which implements the key requirement of an online trainable system—fast adaptation to novel object data. The neural three-stage architecture can be adapted in two modes: In a fast training mode (FT), only the last stage is adapted, whereas complete training (CT) rebuilds the system from scratch. Using FT, online acquired views can be added at once to the classifier, the system being operational after a delay of less than a second, though still with reduced classification performance. In parallel, a new classifier is trained (CT) and loaded to the system when ready. The text was submitted by the authors in English. Gunther Heidemann was born in 1966. He studied physics at the Universities of Karlsruhe and Münster and received his PhD (Eng.) from Bielefeld University in 1998. He is currently working within the collaborative research project “Hybrid Knowledge Representation” of the SFB 360 at Bielefeld University. His fields of research are mainly computer vision, robotics, neural networks, data mining, bonification, and hybrid systems. Holger Bekel was born in 1970. He received his BS degree from the University of Bielefeld, Germany, in 1997. In 2002 he received a diploma in Computer Science from the University of Bielefeld. He is currently pursuing a PhD program in Computer Science at the University of Bielefeld, working within the Neuroinformatics Group (AG Neuroinformatik) in the project VAMPIRE (Visual Active Memory Processes and Interactive Retrieval). His fields of research are active vision and data mining. Ingo Bax was born in 1976. He received a diploma in Computer Science from the University of Bielefeld in 2002. He is currently pursuing a PhD program in Computer Science at the Neuroinformatics Group of the University of Bielefeld, working within the VAMPIRE project. His fields of interest are cognitive computer vision and pattern recognition. Helge J. Ritter was born 1958. He studied physics and mathematics at the Universities of Bayreuth, Heidelberg and Munich. After a PhD in physics at Technical University of Munich in 1988, he visited the Laboratory of Computer Science at Helsinki University of Technology and the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Since 1990 he has headed the Neuroinformatics Group at the Faculty of Technology, Bielefeld University. His main interests are principles of neural computation and their application to building intelligent systems. In 1999, she was awarded the SEL Alcatel Research Prize, and in 2001, the Leibniz Prize of the German Research Foundation DFG.     

Stereophotogrammetric 3D real-time machine vision

Signal processing algorithms often have to be modified significantly for implementation in hardware. Continuous real-time image processing at high speed is a particularly challenging task. In this paper a hardware-software codesign is applied to a stereophotogrammetric system. To calculate the depth map, an optimized algorithm is implemented as a hierarchical-parallel hardware solution. By subdividing distances to objects and selecting them sequentially, we can apply 3D scanning and ranging over large distances. We designed processor-based object clustering and tracking functions. We can detect objects utilizing density distributions of disparities in the depth map (disparity histogram). Motion parameters of detected objects are stabilized by Kalman filters. The text was submitted by the authors in English. Michael Tornow was born in Magdeburg, Germany, in 1977. He received his diploma engineer degree (Dipl.-Ing.) in electrical engineering at the University of Magdeburg, Germany, in 2002. He is currently working on a PhD thesis focusing on hardware adapted image processing and vision based driver assistance. Robert W. Kuhn received his diploma engineer degree (Dipl.-Ing.) in geodesy at the Technical University of Berlin, Germany, in 2000. His current work on a PhD thesis focuses on calibration and image processing. Jens Kaszubiak was born in Blankenburg, Germany, in 1977. He received his diploma engineer degree (Dipl.-Ing.) in electrical engineering at the University of Magdeburg, Germany, in 2002. His current research work focuses on vision-based driver assistance and hardware-software codesign. Bernd Michaelis was born in Magdeburg, Germany, in 1947. He received a Masters Degree in Electronic Engineering from the Technische Hochschule, Magdeburg, in 1971 and his first PhD in 1974. Between 1974 and 1980 he worked at the Technische Hochschule, Magdeburg, and was granted a second doctoral degree in 1980. In 1993 he became Professor of Technical Computer Science at the Otto-von-Guericke University, Magdeburg. His research work concentrates on the field of image processing, artificial neural networks, pattern recognition, processor architectures, and microcomputers. Professor Michaelis is the author of more than 150 papers. Gerald Krell was born in Magdeburg, Germany, in 1964. He earned his diploma in electrical engineering in 1990 and his doctorate in 1995 at Otto-von-Guericke University of Magdeburg. Since then he has been a research assistant. His primary research interest is focused on digital image processing and compression, electronic hardware development, and artificial neural networks.     

Improving Customer Retention in E-Commerce through a Secure and Privacy-Enhanced Loyalty System

Loyalty systems provide an interesting possibility for vendors in customer relationship management. This holds for both real world and online vendors. Many vendors apply loyalty systems to collect customer-specific data that may be exploited for many reasons, e.g., price discrimination and direct marketing. As a consequence, beside some potential benefits of a loyalty system, customers may also fear an invasion of privacy, and thus often refuse to participate in such programs. Thus, a vendor may have problems to turn privacy sensitive people into loyal customers using a typical loyalty system. In this paper, we present two variants of a privacy-friendly loyalty system to be used by online vendors for issuing loyalty points. The systems prevent vendors from exploiting data for the creation of customer profiles by providing unconditional unlinkability of loyalty points with regard to purchases. We propose a simple token-based approach and a counter-based approach which is much more efficient while preserving the privacy and security properties. Furthermore, the counter-based loyalty system prevents pooling of loyalty points which were issued to distinct customers. Matthias Enzmann received his diploma in computer science from the Technical University of Darmstadt, Germany, in 1999. In 1996, he started working with the TKT institute of GMD - German National Research Centre for Information Technology GmbH which in 2001 became Fraunhofer Institute for Secure Telecooperation due to the merger of GMD and Fraunhofer Gesellschaft. Since 1999 he holds the position of a regular researcher at Fraunhofer SIT. Currently, his research interests focus on privacy protection in electronic business processes, agent based mediation, and pseudonym systems. Markus Schneider received his diploma in electrical engineering with specialization on communications engineering. Afterwards, he started to work in the area of information and communication security and received his Doctor degree in electrical engineering. Currently, he is with the Fraunhofer Institute for Secure Telecooperation (SIT) in Darmstadt, Germany. His research interests include the development and application of security technologies in communications, and security and privacy issues in electronic business processes.     

An LMI approach to robust reduced-order H ∞ filter design for polytopic uncertain systems

This paper proposes a method for robust reduced-order H _∞ filter design for polytopic uncertain systems, using linear matrix inequalities (LMIs). Sufficient LMI conditions for both robust full- and reduced-order H _∞ filter design are derived. Convex optimization problems are formulated and solved to obtain optimal H _∞ filters by using the resulting LMI conditions. The resulting conditions do not involve any non-convex rank constraints, and thus the proposed method for H _∞ filter design guarantees global optimum solutions. Numerical examples are presented to show the effectiveness of the proposed method. Recommended by Editorial Board member Huanshui Zhang under the direction of Editor Young Il Lee. This work was supported by the Brain Korea 21 Project and the Basic Research Program of the Korea Science and Engineering Foundation under grant R01-2006-000-11373-0. Hyoun-Chul Choi received the B.S., M.S., and Ph.D. degrees in Control and Instrumentation Engineering from Ajou University, Suwon, Korea, in 1995, 1997, and 2006, respectively. He was a Visiting Researcher at Griffith University, Brisbane, Australia, from 2001 to 2002, and a Postdoctoral researcher at Ajou University, Suwon, Korea, from 2006 to 2007. Since 2008, he has been with ASRI, School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea, where he is currently a Postdoctoral Researcher. His research interests include LMI-based control, optimal and robust control, network-based control, and mechatronics. Dongkyoung Chwa received the B.S. and M.S. degrees from the Department of Control and Instrumentation Engineering in 1995 and 1997, respectively, and the Ph.D. degree from the School of Electrical and Computer Engineering in 2001, all from Seoul National University, Seoul, Korea. From 2001 to 2003, he was a Postdoctoral Researcher with Seoul National University. In 2003, he was a Visiting Research Fellow at The University of New South Wales, Australian Defence Force Academy, and was the Honorary Visiting Academic at the University of Melbourne, Melbourne, Australia. In 2004, he was a BK21 Assistant Professor with Seoul National University. Since 2005, he has been an Assistant Professor with the Department of Electrical and Computer Engineering, Ajou University, Suwon, Korea. His research interests are nonlinear, robust, and adaptive control theories and their applications to the robotics, underactuated systems including wheeled mobile robots, underactuated ships, cranes, and guidance and control of flight systems. Suk-Kyo Hong received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from Seoul National University, Seoul, Korea, in 1971, 1973, and 1981, respectively. His major graduate research works were centered on speed control of induction motors. He was an Exchange Professor at Rensselaer Polytechnic Institute, Troy, NY, from 1982 to 1983, and at the Institut National de Recherche en Informatique et en Automatique, France, from 1988 to 1989. He has been with the faculty of the Department of Electrical and Computer Engineering, Ajou University, Suwon, Korea, since 1976, and was a Visiting Professor at Griffith University, Australia, in 2001 and 2002. His current research interests include robust robot control, microprocessor applications, factory automation, and computer integrated manufacturing.     

On the message complexity of binary byzantine agreement under crash failures

Summary The binary Byzantine Agreement problem requiresn–1 receivers to agree on the binary value broadcast by a sender even when some of thesen processes may be faulty. We investigate the message complexity of protocols that solve this problem in the case of crash failures. In particular, we derive matching upper and lower bounds on the total, worst and average case number of meassages needed in the failure-free executions of such protocols.More specifically, we prove that any protocol that tolerates up tot faulty processes requires a total of at leastn+t–1 messages in its failure-free executions —and, therefore, at least [(n+t–1)/2] messages in the worst case and min (P ₀,P ₁)·(n+t–1) meassages in the average case, whereP _v is the probability that the value of the bit that the sender wants to broadcast isv. We also give protocols that solve the problem using only the minimum number of meassages for these three complexity measures. These protocols can be implemented by using 1-bit messages. Since a lower bound on the number of messages is also a lower bound on the number of meassage bits, this means that the above tight bounds on the number of messages are also tight bounds on the number of meassage bits. Vassos Hadzilacos received a BSE from Princeton University in 1980 and a PhD from Harvard University in 1984, both in Computer Science. In 1984 he joined the Department of Computer Science at the University of Toronto where he is currently an Associate Professor. In 1990–1991 he was visiting Associate Professor in the Department of Computer Science at Cornell University. His research interests are in the theory of distributed systems. Eugene Amdur obtained a B. Math from the University of Waterloo in 1986 and a M.Sc. from the University of Toronto in 1988. He is currently employed by the Vision and Robotics group at the University of Toronto in both technical and research capacities. His current areas of interest are vision, robotics, and networking. Samuel Weber received his B.Sc. in Mathematics and Computer Science and his M.Sc. in Computer Science from the University of Toronto. Currently, he is at Cornell University as a Ph.D. student in Computer Science with a minor in Psychology. His research interests include distributed systems, and the semantics of programming languages.     

A data mining approach to database compression

Data mining can dig out valuable information from databases to assist a business in approaching knowledge discovery and improving business intelligence. Database stores large structured data. The amount of data increases due to the advanced database technology and extensive use of information systems. Despite the price drop of storage devices, it is still important to develop efficient techniques for database compression. This paper develops a database compression method by eliminating redundant data, which often exist in transaction database. The proposed approach uses a data mining structure to extract association rules from a database. Redundant data will then be replaced by means of compression rules. A heuristic method is designed to resolve the conflicts of the compression rules. To prove its efficiency and effectiveness, the proposed approach is compared with two other database compression methods. Chin-Feng Lee is an associate professor with the Department of Information Management at Chaoyang University of Technology, Taiwan, R.O.C. She received her M.S. and Ph.D. degrees in 1994 and 1998, respectively, from the Department of Computer Science and Information Engineering at National Chung Cheng University. Her current research interests include database design, image processing and data mining techniques. S. Wesley Changchien is a professor with the Institute of Electronic Commerce at National Chung-Hsing University, Taiwan, R.O.C. He received a BS degree in Mechanical Engineering (1989) and completed his MS (1993) and Ph.D. (1996) degrees in Industrial Engineering at State University of New York at Buffalo, USA. His current research interests include electronic commerce, internet/database marketing, knowledge management, data mining, and decision support systems. Jau-Ji Shen received his Ph.D. degree in Information Engineering and Computer Science from National Taiwan University at Taipei, Taiwan in 1988. From 1988 to 1994, he was the leader of the software group in Institute of Aeronautic, Chung-Sung Institute of Science and Technology. He is currently an associate professor of information management department in the National Chung Hsing University at Taichung. His research areas focus on the digital multimedia, database and information security. His current research areas focus on data engineering, database techniques and information security. Wei-Tse Wang received the B.A. (2001) and M.B.A (2003) degrees in Information Management at Chaoyang University of Technology, Taiwan, R.O.C. His research interests include data mining, XML, and database compression.     

Behavioural notions for elementary net systems

We study the relationships between a number of behavioural notions that have arisen in the theory of distributed computing. In order to sharpen the under-standing of these relationships we apply the chosen behavioural notions to a basic net-theoretic model of distributed systems called elementary net systems. The behavioural notions that are considered here are trace languages, non-sequential processes, unfoldings and event structures. The relationships between these notions are brought out in the process of establishing that for each elementary net system, the trace language representation of its behaviour agrees in a strong way with the event structure representation of its behaviour. M. Nielsen received a Master of Science degree in mathematics and computer science in 1973, and a Ph.D. degree in computer science in 1976 both from Aarhus University, Denmark. He has held academic positions at Department of Computer Science, Aarhus University, Denmark since 1976, and was visiting researcher at Computer Science Department, University of Edinburgh, U.K., 1977–79, and Computer Laboratory, Cambridge University, U.K., 1986. His research interest is in the theory of distributed computing. Grzegorz Rozenberg received a master of engineering degree from the Department of Electronics (section computers) of the Technical University of Warsaw in 1964 and a Ph.D. in mathematics from the Institute of Mathematics of the Polish Academy of Science in 1968. He has held acdeemic positions at the Institute of Mathematics of the Polish Academy of Science, the Department of Mathematics of Utrecht University, the Department of Computer Science at SUNY at Buffalo, and the Department of Mathematics of the University of Antwerp. He is currently Professor at the Department of Computer Science of Leiden University and Adjoint Professor at the Department of Computer Science of the University of Colorado at Boulder. His research interests include formal languages and automata theory, theory of graph transformations, and theory of concurrent systems. He is currently President of the European Association for Theoretical Computer Science (EATCS). P.S. Thiagarajan received the Bachelor of Technology degree from the Indian Institute of Technology, Madras, India in 1970. He was awarded the Ph.D. degree by Rice University, Houston Texas, U.S.A, in 1973. He has been a Research Associate at the Massachusetts Institute of Technology, Cambridge a Staff Scientist at the Geosellschaft für Mathematik und Datenverarbeitung, St. Augustin, a Lektor at Århus University, Århus and an Associate Professor at the Institute of Mathematical Sciences, Madras. He is currently a Professor at the School of Mathematics, SPIC Science Foundation, Madras. He research intest is in the theory of distributed computing.     

Group Theoretical Structure of Spectral Spaces

It is known that for every selection of illumination spectra there is a coordinate system such that all coordinate vectors of these illumination spectra are located in a cone. A natural set of transformations of this cone are the Lorentz transformations. In this paper we investigate if sequences of illumination spectra can be described by one-parameter subgroups of Lorentz-transformations. We present two methods to estimate the parameters of such a curve from a set of coordinate points. We also use an optimization technique to approximate a given set of points by a one-parameter curve with a minimum approximation error. In the experimental part of the paper we investigate series of blackbody radiators and sequences of measured daylight spectra and show that one-parameter curves provide good approximations for large sequences of illumination spectra.Reiner Lenz is associate professor at the Department of Science and Technology, Linköping University, Sweden. He held positions as invited researcher at the ZEISS, Germany, the Advanced Telecommunication Research Institute (ATR), Kyoto, Japan, the Mechanical Engineering Laboratory, Tsukuba, Japan, Rutgers University, USA and AIST, Tsukuba, Japan. He received an honorable mention for the Pattern Recognition Society Award and the SAAB-Combitech Award. He is associated editor for Pattern Recognition and the IEEE-Transactions on Image Processing. He is interested in the application of group-theoretical methods in signal-, color-processing.Thanh Hai Bui is currently a Ph.D. student at Media group, Institute of Science and Technology, Linköping University. He obtained his B.Sc. in Computer Science from Hanoi University of Technology in 1995, Post-graduate diploma in Manufacturing System Engineering from Asian Institute of Technology in 1999, Master of Applied Computer Science from Vrije Universiteit Brussel in 2000, and Ph. Licentiate in Media Technology from Linköping Universitet in 2003. His work has mainly focused on multispectral database analysis, and applications of group theoretical methods.Javier Hernández-Andrés received his Ph.D. degree in Physics from the University of Granada, Spain, in 1999. Since 2003 he is an associate professor in the Department of Optics at the same University. His research interests are color-image processing, multispectral color science, applied colorimetry, color vision and atmospheric optics.     

Privacy-preserving SVM classification

Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the nondisclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from data distributed at multiple parties, without disclosing the data of each party to others. Solutions are sketched out for data that is vertically, horizontally, or even arbitrarily partitioned. We quantify the security and efficiency of the proposed method, and highlight future challenges. Jaideep Vaidya received the Bachelor’s degree in Computer Engineering from the University of Mumbai. He received the Master’s and the Ph.D. degrees in Computer Science from Purdue University. He is an Assistant Professor in the Management Science and Information Systems Department at Rutgers University. His research interests include data mining and analysis, information security, and privacy. He has received best paper awards for papers in ICDE and SIDKDD. He is a Member of the IEEE Computer Society and the ACM. Hwanjo Yu received the Ph.D. degree in Computer Science in 2004 from the University of Illinois at Urbana-Champaign. He is an Assistant Professor in the Department of Computer Science at the University of Iowa. His research interests include data mining, machine learning, database, and information systems. He is an Associate Editor of Neurocomputing and served on the NSF Panel in 2006. He has served on the program committees of 2005 ACM SAC on Data Mining track, 2005 and 2006 IEEE ICDM, 2006 ACM CIKM, and 2006 SIAM Data Mining. Xiaoqian Jiang received the B.S. degree in Computer Science from Shanghai Maritime University, Shanghai, 2003. He received the M.C.S. degree in Computer Science from the University of Iowa, Iowa City, 2005. Currently, he is pursuing a Ph.D. degree from the School of Computer Science, Carnegie Mellon University. His research interests are computer vision, machine learning, data mining, and privacy protection technologies.     

Combination of clock-state and clock-rate correction in fault-tolerant distributed systems

This paper proposes the integration of internal and external clock synchronization by a combination of a fault-tolerant distributed algorithm for clock state correction with a central algorithm for clock rate correction. By means of hardware and simulation experiments it is shown that this combination improves the precision of the global time base in a distributed single cluster system while reducing the need for high-quality oscillators. Simulation results have shown that the rate-correction algorithm contributes not only in the internal clock synchronization of a single cluster system, but it can be used for external clock synchronization of a multi-cluster system with a reference clock. Therefore, deployment of the rate-correction algorithm integrates internal and external clock synchronization in one mechanism. Experimental results show that a failure in the clock rate correction will not hinder the distributed fault-tolerant clock state synchronization algorithm, since the state correction operates independently from the rate correction. The paper introduces new algorithms and presents experimental results on the achieved improvements in the precision measured in a time-triggered system. Results of simulation experiments of the new algorithms in single-cluster and multi-cluster configurations are also presented. Hermann Kopetz (Fellow, IEEE) received the Ph.D. degree in physics ísub auspiciis praesidentis from the University of Vienna, Vienna, Austria, in 1968. He was Manager of the Computer Process Control Department at Voest Alpine, Linz, Austria, and Professor of Computer Process Control, Technical University of Berlin, Berlin, Germany. He is currently Professor of Real-Time Systems, Vienna University of Technology, Vienna, Austria, and a Visiting Professor at the University of California, Irvine, and the University of California, Santa Barbara. In 1993, he was offered a position as Director of the Max Planck Institute, Saarbrcken, Germany. Prof. Kopetz is the key architect of the Time-Triggered Architecture. Astrit Ademaj (IEEE member) received the Dipl-Ing. degree (1995) at the University of Prishtina, Kosova, and a doctoral degree (2003) in computer science from the Technical University of Vienna. He is currently working as Assistant Professor at the Technical University of Vienna and as a Visiting Lecturer at the University of Prishtina. His research interests are design and validation of communication systems for safety-critical and real-time applications. He is a member of the IEEE Computer Society. Alexander Hanzlik received a diploma (1995) and a doctoral degree (2004) in computer science from the Technical University of Vienna. From 1995 to 1998, he was concerned with voice communication system design for air traffic control for the Service de Navigation Aérienne (STNA). Since 1998, his focus is on embedded systems in the fields of telecommunication, automation and process control. Since 2001, Dr. Hanzlik is a member of the Real-Time Systems Group and works as a research assistant at the Technical University of Vienna. His main research activities deal with fault-tolerant clock synchronization in distributed systems and simulation. Currently, he is working on SIDERA, a simulation model for time-triggered, dependable real-time architectures.     

Propagation Models for Trust and Distrust in Social Networks

Semantic Web endeavors have mainly focused on issues pertaining to knowledge representation and ontology design. However, besides understanding information metadata stated by subjects, knowing about their credibility becomes equally crucial. Hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. Our major contribution to Semantic Web trust management through this work is twofold. First, we introduce a classification scheme for trust metrics along various axes and discuss advantages and drawbacks of existing approaches for Semantic Web scenarios. Hereby, we devise an advocacy for local group trust metrics, guiding us to the second part which presents Appleseed, our novel proposal for local group trust computation. Compelling in its simplicity, Appleseed borrows many ideas from spreading activation models in psychology and relates their concepts to trust evaluation in an intuitive fashion. Moreover, we provide extensions for the Appleseed nucleus that make our trust metric handle distrust statements. Cai-Nicolas Ziegler is a post-doctoral researcher at DBIS, the Databases and Information Systems group of the University of Freiburg, Germany. He studied Computer Science at the University of Passau, Germany, and Université Laval, Québec, receiving his Diploma (equivalent to MSc) in 2003. Cai-Nicolas obtained his PhD in Computer Science from the University of Freiburg in 2005. His primary research interests cover collaborative filtering applications and recommender systems, as well as computational trust models on the verge of human-computer interaction. Georg Lausen is head of the research group on Databases and Information Systems (DBIS) at the University of Freiburg, Germany. He received his Diploma in Industrial Engineering in 1978, PhD in 1982, and his postdoctoral lecture qualification (Habilitation) in 1985 from the University of Karlsruhe (TH). He was associate professor for Information Technology and Integration Problems at the Technical University of Darmstadt from 1986 to 1987. From 1987 to 1994 he was full professor for databases and informations systems at the University of Mannheim, and since 1994 at the University of Freiburg. His current research interests comprise information integration, internet technologies, and Web services.     

The rise and fall of the competitive local exchange carriers in the U.S.: An institutional perspective

The debacle of the telecommunications industry at the turn of the millennium resulted in significant consequences for investors, workers, financial institutions, telecom companies, and the economy in general worldwide. In the midst of the telecom bubble, the CLECs (competitive local exchange carriers) adopted similar or identical business plans and saturated the market, which resulted in destructive competition. In this study, we investigate the isomorphic business models of the CLECs from the perspectives of the new institutional theory. We argue that the combined coercive, mimetic, and normative institutional forces exerted on the companies by the actors who controlled the funding, managed the business, and provided the information fashioned the isomorphic CLEC business models, which in turn contributed to the demise of these companies and thus the burst of the telecom bubble. Evidence of the institutional influences on CLECs and the actors exerted the influences are presented and their consequences are discussed. Qing Hu is Professor of Information Systems in the Department of Information Technology & Operations Management at Florida Atlantic University. He earned his Ph.D. in Computer Information Systems from the University of Miami. His research interests include economics of information technology (IT), IT strategy and management, and information security. His work has been published in leading academic journals including Information Systems Research, Journal of Management Information Systems, Communications of the ACM, Communications of the AIS, California Management Review, and IEEE Transactions on Software Engineering. He also serves as associate and guest-editors for a number of IS journals and major conferences. C. Derrick Huang is Assistant Professor in the Department of Information Technology & Operations Management in the College of Business at Florida Atlantic University. Previously, as a practitioner, he held executive-level positions in the area of marketing and strategic planning in a number of high-tech companies. Dr. Huang’s research interest lies in the business value and strategic impact of information technology in organizations, and his current focus is on the economics and management of information security investments. He holds Ph.D. from Harvard University.     

Testing Congruence for Mobile Processes

Testing equivalence on πprocesses has been studied in literature.The equivalence is not closed under the iuput prefix operator and is therefore not a congruence relation.This note takes a look at testing congruence on fipite π processes.A complete equational system is given for the congruence relation.     

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.     

Parameter scan of an effective group difference pseudopotential using grid computing

Computational modeling in the health sciences is still very challenging and much of the success has been despite the difficulties involved in integrating all of the technologies, software, and other tools necessary to answer complex questions. Very large-scale problems are open to questions of spatio-temporal scale, and whether physico-chemical complexity is matched by biological complexity. For example, for many reasons, many large-scale biomedical computations today still tend to use rather simplified physics/chemistry compared with the state of knowledge of the actual biology/biochemistry. The ability to invoke modern grid technologies offers the ability to create new paradigms for computing, enabling access of resources which facilitate spanning the biological scale. Wibke Sudholt: She is a postdoc with J. A. McCammon and K. Baldridge at the University of California, San Diego and a fellow of the German Academic Exchange Service (DAAD). She received her diploma (Dipl. Chem.) at the University Dortmund, Germany in 1996, and her doctoral degree in 2001 (Dr. rer. nat.) at Heinrich-Heine-University Duesseldorf, Germany with Wolfgang Domcke on theoretical studies of a charge-transfer process. Her current research interests include the combination of quantum chemistry, molecular mechanics and continum electrostatics to describe chemical reactions in complex molecular systems. Kim K. Baldridge: She is a theoretical and computational chemist with expertise in the design, development, and application of computational quantum chemical methodology for understanding chemical and biochemical reaction processes of broad interest. Efforts include development of computational tools and associated grid technologies for the broader scientific community. She is a Fellow of the APS and AAAS, and was the 2000 Agnes Fay Morgan Awardee for Research Achievement in Chemistry. She is the Program Director for Integrative Computational Sciences at SDSC, where she has worked since 1989, and additionally holds an adjunct professorship at UCSD. David Abramson: He is currently a professor of Computer Science in the School of Computer Science and Software Engineering (CSSE) at Monash University, Australia. He is a project leader in the Co-operative Research Centre for Distributed Systems Nimrod Project and Chief Investigator on two ARC funded research projects. He is a co-founder of Active Tools P/L with Dr. Rok Sosic, established to commercialize the Nimrod project, and Guardsoft, focused on commercializing the Guard project. Abramson’s current interests are in high performance computer systems design and software engineering tools for programming parallel, distributed supercomputers. Colin Enticott: He completed a BComp (Hons) degree mid. 2002 at Monash University, Australia. His project, done under the supervision of Professor David Abramson, “The Multi Site EnFuzion Client” dealt in the area of cluster-of-clusters computing that has lead him into Grid computing. Currently employed by DSTC (Distributed Systems Technology Centre, Melbourne, Australia) working on the user front-end of Nimrod (the Nimrod Portal) and cluster implementations. Slavisa Garic: He completed Bachelor of Computer Science (Hons) degree at Monash University, Australia in November 2001. His project, “Suburban Area Networks: Security” involved working on security aspects of wireless community and suburban networks. The beginning of year 2002, he joined Distributed Systems Technology Centre, Melbourne Australia, where he currently works as a core Nimrod/G developer.     

A Framework for Design Tradeoffs

Designs almost always require tradeoffs between competing design choices to meet system requirements. We present a framework for evaluating design choices with respect to meeting competing requirements. Specifically, we develop a model to estimate the performance of a UML design subject to changing levels of security and fault-tolerance. This analysis gives us a way to identify design solutions that are infeasible. Multi-criteria decision making techniques are applied to evaluate the remaining feasible alternatives. The method is illustrated with two examples: a small sensor network and a system for controlling traffic lights. Dr. Anneliese Amschler Andrews is Professor and Chair of the Department of Computer Science at the University of Denver. Before that she was the Huie Rogers Endowed Chair in Software Engineering at Washington State University. Dr. Andrews is the author of a text book and over 130 articles in the area of Software Engineering, particularly software testing and maintenance. Dr. Andrews holds an MS and PhD from Duke University and a Dipl.-Inf. from the Technical University of Karlsruhe. She served as Editor-in-Chief of the IEEE Transactions on Software Engineering. She has also served on several other editorial boards including the IEEE Transactions on Reliability, the Empirical Software Engineering Journal, the Software Quality Journal, the Journal of Information Science and Technology, and the Journal of Software Maintenance. She was Director of the Colorado Advanced Software Institute from 1995 to 2002. CASI's mission was to support technology transfer research related to software through collaborations between industry and academia. Ed Mancebo studied software engineering at Milwaukee School of Engineering and computer science at Washington State University. His masters thesis explored applying systematic decision making methods to software engineering problems. He is currently a software developer at Amazon.com. Dr. Per Runeson is a professor in software engineering at Lund University, Sweden. His research interests include methods to facilitate, measure and manage aspects of software quality. He received a PhD from Lund University in 1998 and has industrial experience as a consulting expert. He is a member of the editorial board of Empirical Software Engineering and several program committees, and currently has a senior researcher position funded by the Swedish Research Council. Robert France is currently a Full Professor in the Department of Computer Science at Colorado State University. His research interests are in the area of Software Engineering, in particular formal specification techniques, software modeling techniques, design patterns, and domain-specific modeling languages. He is an Editor-in-Chief of the Springer journal on Software and System Modeling (SoSyM), and is a Steering Committee member and past Steering Committee Chair of the MoDELS/UML conference series. He was also a member of the revision task forces for the UML 1.x standards.