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.     

Multi-objective optimisation of bijective s-boxes

In this paper we investigate the heuristic construction of bijective s-boxes that satisfy a wide range of cryptographic criteria including algebraic complexity, high nonlinearity, low autocorrelation and have none of the known weaknesses including linear structures, fixed points or linear redundancy. We demonstrate that the power mappings can be evolved (by iterated mutation operators alone) to generate bijective s-boxes with the best known tradeoffs among the considered criteria. The s-boxes found are suitable for use directly in modern encryption algorithms. Joanne Fuller, Ph.D.: She is a research associate at the Information Security Institute of Queensland University of Technology in Brisbane, Australia. She received her Ph.D. from out in 2004. William Millan, Ph.D.: He is a postdoctoral research fellow at the Information Security Institute of Queensland University of Technology in Brisbane, Australia. He pioneered the use of Evolutionary Computation techniques to develop new cryptographic primitives, notably new Boolean functions and S-boxes for block and stream ciphers, and has published many papers in this area. Ed Dawson, Ph.D.: He is the Director of the Information Security Institute of Queensland University of Technology in Brisbane, Australia. He has published more than 200 papers in Crypto conferences and Journals. He has served as Program Committee Member for more than 50 International Conferences in Cryptology and Network Security. He is currently a member of the Board of Directors of International Association for Cryptologic Research (IACR).     

Power Efficient Sub-Array in Reconfigurable VLSI Meshes

Given an m×n mesh-connected VLSI array with some faulty elements, the reconfiguration problem is to find a maximum-sized fault-free sub-array under the row and column rerouting scheme. This problem has already been shown to be NP-complete. In this paper, new techniques are proposed, based on heuristic strategy, to minimize the number of switches required for the power efficient sub-array. Our algorithm shows that notable improvements in the reduction of the number of long interconnects could be realized in linear time and without sacrificing on the size of the sub-array. Simulations based on several random and clustered fault scenarios clearly reveal the superiority of the proposed techniques.     

A hybrid ASM approach for sparse volumetric data segmentation

Three-Dimensional (3D) Active Shape Modeling (ASM) is a straightforward extension of 2D ASM. 3D ASM is robust when true volumetric data is considered. However, when the information in one dimension is sparse, pure 3D ASM tends to be less robust. We present a hybrid 2D + 3D methodology which can deal with sparse 3D data. 2D and 3D ASMs are combined to obtain a “global optimal” segmentation of the 3D object embedded in the data set, rather than the “locally optimal” segmentation on separate slices. Experimental results indicate that the developed approach shows equivalent precision on separate slices but higher consistency for whole volumes when compared to 2D ASM, while the results for whole volumes are improved when compared to the pure 3D ASM approach. The text was submitted by the authors in English. Stuart Michael Williams, born in 1967, graduated with BAHons in 1989, BMBCh in 1992 from Oxford University, UK; MRCP (1995), FRCR(1999); Stuart Michael Williams is currently the Consultant Radiologist of Norfolk and Norwich University Hospital, Norwich, UK. His research areas include oncological radiology with an interest in image analysis and medical education. Stuart Michael Williams has 24 publications (monographs and articles). He is a member of the Royal College of Radiologists; member of the European Congress of Radiology; and a member of the European Society of Magnetic Resonance in Medicine and Biology. Yanong Zhu, born in 1975, graduated with B. Sci. in 1997 and M. Sci. in 2002 from Northwest University, China and PhD in 2006 from the University of East Anglia, Norwich, UK. His research areas include computer vision, medical image understanding, and analysis. Yanong Zhu has eight publications (monographs and articles). Reyer Zwiggelaar, born in 1963, graduated with B. Sci. from State University Groningen, the Netherlands in 1989. He was awarded his PhD in 1993 by University College London, UK. Reyer Zwiggelaar is currently the Senior Lecturer at the University of Wales Aberystwyth, UK. Dr. Zwiggelaar has more than 80 publications (monographs and articles). His research areas include medical image understanding, especially concentrating on mammographic data, pattern recognition, statistical methods, and feature detection techniques.     

Evie - A developer toolkit for encoding service interaction patterns

Facilitation of collaborative business processes across organizational and infrastructural boundaries continues to present challenges to enterprise software developers. One of the greatest difficulties in this respect is achieving a streamlined pipeline from business modeling to execution infrastructures. In this paper we present Evie - an approach for rapid design and deployment of event driven collaborative processes based on significant language extensions to Java that are characterized by abstract and succinct constructs. The focus of this paper is to provide proof of concept of Evie’s expressability using a recent benchmark known as service interaction patterns. While the patterns encapsulate the breadth of required business process semantics the Evie language delivers a rapid means of encoding them at an abstract level, and subsequently compiling and executing them to create a fully fledged Java-based execution environment.

Capabilities of outlier detection schemes in large datasets,framework and methodologies

Outlier detection is concerned with discovering exceptional behaviors of objects. Its theoretical principle and practical implementation lay a foundation for some important applications such as credit card fraud detection, discovering criminal behaviors in e-commerce, discovering computer intrusion, etc. In this paper, we first present a unified model for several existing outlier detection schemes, and propose a compatibility theory, which establishes a framework for describing the capabilities for various outlier formulation schemes in terms of matching users'intuitions. Under this framework, we show that the density-based scheme is more powerful than the distance-based scheme when a dataset contains patterns with diverse characteristics. The density-based scheme, however, is less effective when the patterns are of comparable densities with the outliers. We then introduce a connectivity-based scheme that improves the effectiveness of the density-based scheme when a pattern itself is of similar density as an outlier. We compare density-based and connectivity-based schemes in terms of their strengths and weaknesses, and demonstrate applications with different features where each of them is more effective than the other. Finally, connectivity-based and density-based schemes are comparatively evaluated on both real-life and synthetic datasets in terms of recall, precision, rank power and implementation-free metrics. Jian Tang received an MS degree from the University of Iowa in 1983, and PhD from the Pennsylvania State University in 1988, both from the Department of Computer Science. He joined the Department of Computer Science, Memorial University of Newfoundland, Canada, in 1988, where he is currently a professor. He has visited a number of research institutions to conduct researches ranging over a variety of topics relating to theories and practices for database management and systems. His current research interests include data mining, e-commerce, XML and bioinformatics. Zhixiang Chen is an associate professor in the Computer Science Department, University of Texas-Pan American. He received his PhD in computer science from Boston University in January 1996, BS and MS degrees in software engineering from Huazhong University of Science and Technology. He also studied at the University of Illinois at Chicago. He taught at Southwest State University from Fall 1995 to September 1997, and Huazhong University of Science and Technology from 1982 to 1990. His research interests include computational learning theory, algorithms and complexity, intelligent Web search, informational retrieval, and data mining. Ada Waichee Fu received her BSc degree in computer science in the Chinese University of Hong Kong in 1983, and both MSc and PhD degrees in computer science in Simon Fraser University of Canada in 1986, 1990, respectively; worked at Bell Northern Research in Ottawa, Canada, from 1989 to 1993 on a wide-area distributed database project; joined the Chinese University of Hong Kong in 1993. Her research interests are XML data, time series databases, data mining, content-based retrieval in multimedia databases, parallel, and distributed systems. David Wai-lok Cheung received the MSc and PhD degrees in computer science from Simon Fraser University, Canada, in 1985 and 1989, respectively. He also received the BSc degree in mathematics from the Chinese University of Hong Kong. From 1989 to 1993, he was a member of Scientific Staff at Bell Northern Research, Canada. Since 1994, he has been a faculty member of the Department of Computer Science in the University of Hong Kong. He is also the Director of the Center for E-Commerce Infrastructure Development. His research interests include data mining, data warehouse, XML technology for e-commerce and bioinformatics. Dr. Cheung was the Program Committee Chairman of the Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2001), Program Co-Chair of the Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2005). Dr. Cheung is a member of the ACM and the IEEE Computer Society.     

Towards integrating semantics of multi-media resources and processes in e-Learning

Internet-based e-Learning has experienced a boom and bust situation in the past 10 years [32]. To bring in new forces to knowledge-oriented e-Learning, this paper addresses the semantic integration issue of multi-media resources and learning processes with theoretical learning supports in an integrated framework. This paper proposes a context-mediated approach that aims to enable semantic-based inter-operations across knowledge domains, even across the WWW and the Semantic Web [8]. The proposed semantic e-Learning framework enables intelligent operations of heterogeneous multi-media contents based on a generic semantic context intermediation model. This framework supports intelligent e-Learning with a knowledge network for knowledge object visualization, an enhanced Kolb's learning cycle [31] to guide learning practices, and a learning health care framework for personalized learning. W. Huang received his PhD in Computer Science from Nanjing University in 2001, MEng in Pattern Recognition and Intelligent Control and BEng in Automatic Control from Southeast University in 1998 and 1995, respectively. Dr. Huang is currently a senior lecturer with the Faculty of Computing, Information Systems and Mathematics at Kingston University London. Prior to this, he was a lecturer with the Centre for Internet Computing, The University of Hull, United Kingdom. Between October 2001 and September 2002, Dr. Huang was a post-doctoral research fellow at the University Lyon 1, France. His research interests include knowledge engineering and management, adaptive multimedia service, and pragmatic Semantic Web supporting technologies. His recent research focuses on semantic context aware computing and its applications in intelligent e-Services such as e-Learning and e-Enterprises. Dr. Huang is a member of ACM and IEEE Computer Society. E. Eze received his BS Degree in Computer Science from University of Nigeria in 1999. He is now a PhD student with the Centre for Internet Computing, The University of Hull, United Kingdom. His research interests include multimedia semantic modelling and representation and contextual knowledge engineering. D. Webster is currently studying for a PhD in Computer Science on the topic of trusted agents in the Semantic Web. He holds a 2-1 honours degree in Internet Computing from the University of Hull, UK and is a member of the British Computer Society. In addition to Web-based research, he also has an interest in graphics and has been involved in the development of graphics engines for video game projects on embedded and personal computing platforms.     

A motion compensated lifting wavelet codec for 3D video coding

A motion compensated lifting (MCLIFT) ramework for the 3D wavelet video coding is proposed in this paper,By using bi-directional motion compensation in each lifting step of the temporal direction,the video frames are effectively de-correlated,With the proper entropy coding and bit-stream packaging schemes,the MCLIFT wavelet video coder is scalable at frame rate and quality level .Experimental results show that the MCLIFT video coder outperforms the 3D wavelet video coder without motion by an average of 0.9-1.3dB,and outperforms MPEG-4 coder by an average of 0.2-0.6dB.     

Mathematical Analysis of An Extended Mumford-Shah Model for Image Segmentation

Morel and Solimini have established proofs of important properties of segmentations which can be seen as locally optimal for the simplest Mumford-Shah model in the continuous domain. A weakness of the latter is that it is not suitable for handling noisy images. We propose a Bayesian model to overcome these problems. We demonstrate that this Bayesian model indeed generalizes the original Mumford-Shah model, and we prove it has the same desirable properties as shown by Morel and Solimini. Trevor Tao was Born in 1977 in Adelaide, Australia, found to be autistic when he was two years old. He was the first autistic child in Australia to have started normal school at the same age as his peers. He later became interested in music, chess, and mathematics. He has been described as a musical savant, and has represented Australia in the International Chess Olympiad in 1994, and won a bronze medal in the International Mathematical Olympiad in 1995. In 2000 he completed a double degree in B.Sc (Mathematics & Computer Science) and B.Mus. (Performance & Composition) at the University of Adelaide. After a short vacation job at the Defence Science & Technology Organization in Adelaide, he became interested in Image Analysis, and studied for a Ph.D. in Applied Mathematics. He is expected to complete his thesis this year. Dr David J. Crisp graduated from the University of Adelaide (Australia) in 1993 with a Ph.D. in Mathematics. He held several different research positions from 1994 to 1998. In 1999 he joined Australia’s Defence Science & Technology Organisation (DSTO) as a research scientist. His current research at DSTO is focused on the automated detection of targets in synthetic aperture radar imagery. Dr John van der Hoek graduated from University of Adelaide (Australia) in 1975 with a PhD in Pure Mathematics. He is currently a senior lecturer in the Department of Applied Mathematics at University of Adelaide. His research interests are applied functional analysis, partial differential equations and free boundary value problems, stochastic processes, mathematical finance and signal processing.     

Constraining and summarizing association rules in medical data

Association rules are a data mining technique used to discover frequent patterns in a data set. In this work, association rules are used in the medical domain, where data sets are generally high dimensional and small. The chief disadvantage about mining association rules in a high dimensional data set is the huge number of patterns that are discovered, most of which are irrelevant or redundant. Several constraints are proposed for filtering purposes, since our aim is to discover only significant association rules and accelerate the search process. A greedy algorithm is introduced to compute rule covers in order to summarize rules having the same consequent. The significance of association rules is evaluated using three metrics: support, confidence and lift. Experiments focus on discovering association rules on a real data set to predict absence or existence of heart disease. Constraints are shown to significantly reduce the number of discovered rules and improve running time. Rule covers summarize a large number of rules by producing a succinct set of rules with high-quality metrics. Carlos Ordonez received a degree in applied mathematics (actuarial sciences) and an MS degree in computer science, both from the UNAM University, Mexico, in 1992 and 1996, respectively. He got a PhD degree in computer science from the Georgia Institute of Technology, USA, in 2000. Dr. Ordonez currently works for Teradata (NCR) conducting research on database and data mining technology. He has published more than 20 research articles and holds three patents. Norberto Ezquerra obtained his undergraduate degree in mathematics and physics from the University of South Florida, and his doctoral degree from Florida State University, USA. He is an associate professor at the College of Computing at the Georgia Institute of Technology and an adjunct faculty member in the Emory University School of Medicine. His research interests include computer graphics, computer vision in medicine, AI in medicine, modeling of physically based systems, medical informatics and telemedicine. He is associate editor of the IEEE Transactions on Medical Imaging Journal, and a member of the American Medical Informatics Association and the IEEE Engineering in Medicine Biology Society. Cesar A. Santana received his MD degree in 1984 from the Institute of Medical Science, in Havana, Cuba. In 1988, he finished his residency training in internal medicine, and in 1991, completed a fellowship in nuclear medicine in Havana, Cuba. Dr. Santana received a PhD in nuclear cardiology in 1996 from the Department of Cardiology of the Vall d' Hebron University Hospital in Barcelona, Spain. Dr. Santana is an assistant professor at the Emory University School of Medicine and conducts research in the Radiology Department at the Emory University Hospital.     

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.     

Efficient discovery of optimal word-association patterns in large text databases

We study efficient discovery of proximity word-association patterns, defined by a sequence of strings and a proximity gap, from a collection of texts with the positive and the negative labels. We present an algorithm that finds alld-stringsk-proximity word-association patterns that maximize the number of texts whose matching agree with their labels. It runs in expected time complexityO(k ^d−1n log ^d n) and spaceO(k ^d−1n) with the total lengthn of texts, if texts are uniformly random strings. We also show that the problem to find one of the best word-association patterns with arbitrarily many strings in MAX SNP-hard. Shinichi Shimozono, Ph.D.: He is an Associate Professor of the Department of Artificial Intelligence at Kyushu Institute of Technology Iizuka, Japan. He obtained the B.S. degree in Physics from Kyushu University, awarded M.S. degree from Graduate School of Information Science in Kyushu University, and his Dr. Sci. degree in 1996 from Kyushu University. His research interests are primarily in the design and analysis of algorithms for intractable problems. Hiroki Arimura, Ph.D.: He is an Associate Professor of the Department of Informatics at Kyushu University, Fukuoka, Japan. He is also a researcher with Precursory Research for Embryonic Science and Technology, Japan Science and Technology Corporation (JST) since 1999. He received the B.S. degree in 1988 in Physics, the M.S. degree in 1979 and the Dr.Sci. degree in 1994 in Information Systems from Kyushu University. His research interests include data mining, computational learning theory, and inductive logic programming. Setsuo Arikawa, Ph.D.: He is a Professor of the Department of Informatics and the Director of University Library at Kyushu University, Fukuoka, Japan. He received the B.S. degree in 1964, the M.S. degree in 1966 and the Dr.Sci. degree in 1969 all in Mathematics from Kyushu University. His research interests include Discovery Science, Algorithmic Learning Theory, Logic and Inference/Reasoning in AI, Pattern Matching Algorithms and Library Science. He is the principal investigator of the Discovery Science Project sponsored by the Grant-in Aid for Scientific Research on Priority Area from the Ministry of ESSC, Japan.     

Tradeoff and Sensitivity Analysis in Software Architecture Evaluation Using Analytic Hierarchy Process

Software architecture evaluation involves evaluating different architecture design alternatives against multiple quality-attributes. These attributes typically have intrinsic conflicts and must be considered simultaneously in order to reach a final design decision. AHP (Analytic Hierarchy Process), an important decision making technique, has been leveraged to resolve such conflicts. AHP can help provide an overall ranking of design alternatives. However it lacks the capability to explicitly identify the exact tradeoffs being made and the relative size of these tradeoffs. Moreover, the ranking produced can be sensitive such that the smallest change in intermediate priority weights can alter the final order of design alternatives. In this paper, we propose several in-depth analysis techniques applicable to AHP to identify critical tradeoffs and sensitive points in the decision process. We apply our method to an example of a real-world distributed architecture presented in the literature. The results are promising in that they make important decision consequences explicit in terms of key design tradeoffs and the architecture's capability to handle future quality attribute changes. These expose critical decisions which are otherwise too subtle to be detected in standard AHP results. Liming Zhu is a PHD candidate in the School of Computer Science and Engineering at University of New South Wales. He is also a member of the Empirical Software Engineering Group at National ICT Australia (NICTA). He obtained his BSc from Dalian University of Technology in China. After moving to Australia, he obtained his MSc in computer science from University of New South Wales. His principle research interests include software architecture evaluation and empirical software engineering. Aybüke Aurum is a senior lecturer at the School of Information Systems, Technology and Management, University of New South Wales. She received her BSc and MSc in geological engineering, and MEngSc and PhD in computer science. She also works as a visiting researcher in National ICT, Australia (NICTA). Dr. Aurum is one of the editors of “Managing Software Engineering Knowledge”, “Engineering and Managing Software Requirements” and “Value-Based Software Engineering” books. Her research interests include management of software development process, software inspection, requirements engineering, decision making and knowledge management in software development. She is on the editorial boards of Requirements Engineering Journal and Asian Academy Journal of Management. Ian Gorton is a Senior Researcher at National ICT Australia. Until Match 2004 he was Chief Architect in Information Sciences and Engineering at the US Department of Energy's Pacific Northwest National Laboratory. Previously he has worked at Microsoft and IBM, as well as in other research positions. His interests include software architectures, particularly those for large-scale, high-performance information systems that use commercial off-the-shelf (COTS) middleware technologies. He received a PhD in Computer Science from Sheffield Hallam University. Dr. Ross Jeffery is Professor of Software Engineering in the School of Computer Science and Engineering at UNSW and Program Leader in Empirical Software Engineering in National ICT Australia Ltd. (NICTA). His current research interests are in software engineering process and product modeling and improvement, electronic process guides and software knowledge management, software quality, software metrics, software technical and management reviews, and software resource modeling and estimation. His research has involved over fifty government and industry organizations over a period of 15 years and has been funded from industry, government and universities. He has co-authored four books and over one hundred and twenty research papers. He has served on the editorial board of the IEEE Transactions on Software Engineering, and the Wiley International Series in Information Systems and he is Associate Editor of the Journal of Empirical Software Engineering. He is a founding member of the International Software Engineering Research Network (ISERN). He was elected Fellow of the Australian Computer Society for his contribution to software engineering research.     

Singular value decomposition based data distortion strategy for privacy protection

Privacy-preserving is a major concern in the application of data mining techniques to datasets containing personal, sensitive, or confidential information. Data distortion is a critical component to preserve privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. We propose a sparsified Singular Value Decomposition (SVD) method for data distortion. We also put forth a few metrics to measure the difference between the distorted dataset and the original dataset and the degree of the privacy protection. Our experimental results using synthetic and real world datasets show that the sparsified SVD method works well in preserving privacy as well as maintaining utility of the datasets. Shuting Xu received her PhD in Computer Science from the University of Kentucky in 2005. Dr. Xu is presently an Assistant Professor in the Department of Computer Information Systems at the Virginia State University. Her research interests include data mining and information retrieval, database systems, parallel, and distributed computing. Jun Zhang received a PhD from The George Washington University in 1997. He is an Associate Professor of Computer Science and Director of the Laboratory for High Performance Scientific Computing & Computer Simulation and Laboratory for Computational Medical Imaging & Data Analysis at the University of Kentucky. His research interests include computational neuroinformatics, data miningand information retrieval, large scale parallel and scientific computing, numerical simulation, iterative and preconditioning techniques for large scale matrix computation. Dr. Zhang is associate editor and on the editorial boards of four international journals in computer simulation andcomputational mathematics, and is on the program committees of a few international conferences. His research work has been funded by the U.S. National Science Foundation and the Department of Energy. He is recipient of the U.S. National Science Foundation CAREER Award and several other awards. Dianwei Han received an M.E. degree from Beijing Institute of Technology, Beijing, China, in 1995. From 1995to 1998, he worked in a Hitachi company(BHH) in Beijing, China. He received an MS degree from Lamar University, USA, in 2003. He is currently a PhD student in the Department of Computer Science, University of Kentucky, USA. His research interests include data mining and information retrieval, computational medical imaging analysis, and artificial intelligence. Jie Wang received the masters degree in Industrial Automation from Beijing University of Chemical Technology in 1996. She is currently a PhD student and a member of the Laboratory for High Performance Computing and Computer Simulation in the Department of Computer Science at the University of Kentucky, USA. Her research interests include data mining and knowledge discovery, information filtering and retrieval, inter-organizational collaboration mechanism, and intelligent e-Technology.     

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.     

Fingerprint image enhancement and postprocessing based on the directional fields

Fingerprint recognition is based on minutiae matching. The matching correctness of the fingerprints is due to the effect of the accuracy of the minutiae. Fingerprint enhancement and postprocessing are used to reduce the false minutiae. In this paper, we propose methods on fingerprint enhancement and postprocessing, based on the directional fields of a fingerprint. We directly enhance the fingerprint on a gray-scale image and reduce most false minutiae in the postprocessing step. The achieved results are compared with other methods, and the reduction of false minutiae and the recovery of dropped minutiae are improved. The text was submitted by the authors in English. Gwo-Cheng Chao was born in Dasi, Taoyuan, Taiwan, in 1978. He received MS degrees in computer science and information engineering from Taiwan University of Science and Technology, Taiwan, in 2004. He is currently pursuing a PhD degree in networking and multimedia at National Taiwan University, Taipei, Taiwan. His research interests include pattern recognition, image processing, computer vision, biometrics, computer graphics, and multimedia systems. Shung-Shing Lee received BS and MS degrees in electronic engineering and a PhD degree in electrical engineering in 1980, 1987, and 1996, respectively, all from National Taiwan Institute of Technology, Taipei, Taiwan. Currently, he is an associate professor in the Department of Electrical Engineering, Ching Yun University, Jung-Li, Taiwan. His research interests include image processing, biometrics, embedded system design, SOPC, parallel computing, and parallel algorithms. Hung-Chuan Lai received his MS degree in computer science and information engineering from Chung-Hua University, Hsinchu, Taiwan, in 2002. He is currently pursuing a PhD degree at National Taiwan University of Science and Technology, Taipei, Taiwan. His research interests include image processing, VLSI, fault tolerance architecture, embedded system design, data compression, computer architecture and organization, and biometrics.     

A platform for virtual collaboration spaces and educational communities: the case of EVE

This paper presents the design, implementation and evaluation of EVE Community Prototype, which is an educational virtual community aiming to meet the requirements of a Virtual Collaboration Space and to support e-learning services. Furthermore, this paper describes the design and implementation of an integrated platform for Networked Virtual Environments, called EVE Platform, which supports the afore-mentioned educational community. This platform supports stable event sharing and creation of multi-user three dimensional (3D) places, H.323-based voice over IP services integrated in 3D spaces as well as multiple concurrent virtual worlds. Christos Bouras obtained his Diploma and PhD from the Department Of Computer Engineering and Informatics of Patras University (Greece). He is currently an Associate Professor in the above department. Also he is a scientific advisor of Research Unit 6 in Research Academic Computer Technology Institute (CTI), Patras, Greece. His research interests include Analysis of Performance of Networking and Computer Systems, Computer Networks and Protocols, Telematics and New Services, QoS and Pricing for Networks and Services, e-Learning Networked Virtual Environments and WWW Issues. He has extended professional experience in Design and Analysis of Networks, Protocols, Telematics and New Services. He has published 200 papers in various well-known refereed conferences and journals. He is a co-author of seven books in Greek. He has been a PC member and referee in various international journals and conferences. He has participated in R&D projects such as RACE, ESPRIT, TELEMATICS, EDUCATIONAL MULTIMEDIA, ISPO, EMPLOYMENT, ADAPT, STRIDE, EUROFORM, IST, GROWTH and others. Also he is member of experts in the Greek Research and Technology Network (GRNET), Advisory Committee Member to the World Wide Web Consortium (W3C), Member of WG3.3 and WG6.4 of IFIP, Task Force for Broadband Access in Greece, ACM, IEEE, EDEN, AACE and New York Academy of Sciences. Eleftheria Giannaka obtained her Diploma from the Informatics Department of the Aristotelian University of Thessaloniki (Greece) and her Masters Degree from the Computer Engineering and Informatics Department of Patras University. She is currently a PhD Candidate of the Department of Computer Engineer and Informatics of Patras University. Furthermore, she is working as an R&D Computer Engineer at the Research Unit 6 of the Computer Technology Institute in Patra (Greece). Her interests include Computer Networks, Virtual Networks, System Architecture, Internet Applications, Electronic Commerce, Database Implementation and Administration, Virtual Reality applications, Performance Evaluation and Programming. Alexandros Panagopoulos was born in Pyrgos, Greece, 1981. He obtained his Diploma, from the Computer Engineering and Informatics Department of Patras University (Greece). In 2000 he became a member of Research Unit 6 of the Computer Technology Institute (CTI). His interests include Computer Networks, Multiuser Virtual Environments, Telematics, and C/C++ and Java programming. Dr. Thrasyvoulos Tsiatsos obtained his Diploma, his Master's Degree and his PhD from the Computer Engineering and Informatics Department of Patras University (Greece). He is currently an R&D Computer Engineer at the Research Unit 6 of Computer Technology Institute, Patras, Greece. His research interests include Computer Networks, Telematics, Distributed Systems, Networked Virtual Environments, Multimedia and Hypermedia. More particular he is engaged in Distant Education with the use of Computer Networks, Real Time Protocols and Networked Virtual Environments. He has published nine papers in journals and 30 papers in well-known refereed conferences. He has participated in R&D projects such as OSYDD, RTS-GUNET, ODL-UP, VES, ODL-OTE, INVITE, VirRAD and EdComNet.     

Multiobject tracking in video using a trisection paradigm

This paper proposes a technique for analyzing the following three problems: (a) segmentation of moving objects, (b) feature extraction, and (c) the solution of the correspondence problem in multiobject tracking in video sequences. In (c), we use a paradigm to solve the correspondence problem and to determine a motion trajectory based on a trisectional structure. The paradigm distinguishes between real-world objects, extracts image features such as motion blobs and color patches, and abstracts objects such as meta objects that denote real-world physical objects. The efficiency of the proposed method for determining the motion trajectories of moving objects will be demonstrated in this paper on the basis of the analysis of real image sequences that are subjected to severe disturbances (e.g., increasing congestion, shadow casting, and lighting transitions). The text was submitted by the authors in English. Ayoub K. Al-Hamadi received his Masters Degree (Dipl.-Ing.) in Electrical Engineering and Information Technology in 1997 and his PhD in Technical Computer Science at the Otto von Guericke University of Magdeburg, Germany, in 2001. Since 2002, he has been Assistant Professor at the Institute for Electronics, Signal Processing, and Communications Technology at the University of Magdeburg. His research work concentrates on the field of image processing, tracking analysis, and pattern recognition. Dr. Al-Hamadi is the author of more than 22 articles. Robert Niese received his Masters Degree (Dipl.-Ing.) in Computer Science at the University of Magdeburg, Germany, in 2004. He is currently working on a PhD thesis focusing on image processing, tracking, and pattern recognition. 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 of 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.