A Metadata Service for Integrated Management of Knowledges Related to Coastal Areas

The research outlined in this paper is part of a wider research program named SYSCOLAG (Coastal and LAGoonal SYStems in Languedoc-Roussillon area, France) dedicated to sustainable coastal management. The main objective of this program is to build up a communication infrastructure to improve the exchange of information and knowledge between the various scientific disciplines involved in the research. In order to ensure the sharing of resources without affecting the autonomy and independance of the partners, we propose a three-level infrastructure (resources, federation and knowledge access) based on a metadata service (using ISO 19115 standard for geographic information metadata) completed by a common vocabulary (ontology).The Syscolag research program (COastal and LAGoonal SYStems) is funded by Languedoc Roussillon authority.Julien Barde is currently a Ph.D. student in Computer Science at the LIRMM (the Computer Science, Robotic and Microelectronic Laboratory of the University of Montpellier II, France) under the guidance of Thérèse Libourel and Pierre Maurel since 2002. He works for a research program of Integrated Coastal Management to improve knowledge sharing between the stakeholders of Languedoc Roussillon coastal area. He has received his engineer/M.Sc. degrees in Oceanology Sciences and Spatial Information Treatment from the National Superior Agronomic School of Rennes (ENSAR, Brittany, France) in 2000 and 2001. He has experience in Computer Science, Remote sensing, GIS and oceanology.Thérèse Libourel is a Senior Lecturer in Computer Science from the Conservatoire National des Arts et Métiers (CNAM), currently at the LIRMM (the Computer Science, Robotic and Microelectronic Laboratory of the University of Montpellier II, France) since 1994. She holds a Ph.D. and a habilitation thesis in Computer Science from the University of Montpellier II (France). Among others, her research interests are oriented towards object oriented design, reuse of software components, object oriented databases and evolution, and data models for spatial and temporal information systems.Pierre Maurel is a research engineer in Cemagref (France). He received his Diploma on Agronomy Engineering from ESAP high school (France) in 1986 and his M.Sc. on quantitative geography in 1990 from Avignon University (France). In the past, he performed research and teaching in satellite image processing and GIS for environmental and water applications. His current scientific interests include the development of methods for the design of multi-partners geographic information systems, the use of metadata within Spatial Data Infrastructures and the integration of Geographic Information technologies to support public participation in the field of Integrated River Basin Management (HarmoniCOP European project).     

A new method for implementing moment functions in a CMOS retina

We present in this paper a new method for implementing geometric moment functions in a CMOS retina. The principle is based on the similarity between geometric moment equations and the measurement of the correlation value between an image to analyze and a range of grey levels. The latter is approximated by a binary image called mask using a dithering algorithm in order to reduce hardware implementation cost. The correlation product between the mask and the image under analysis gives an approximated value of the geometric moment with an error less than 1% of the exact value. Finally, the results obtained by our approach have been applied to an object localization application and the localization error due to the approximated moment values reported. Olivier Aubreton was born in Vichy on August 31, 1973. He obtained the agrégation examination in June 2000 and received the D.E.A. degree (equivalent to a master degree) in image processing in June 2001. He is currently a lecturer working towards a Ph. D. degree at Laboratory LE2I in the IUT of Le Creusot in Burgundy. His research interests include the design, development implementation, and testing of silicon retinas for pattern matching and pattern recognition. Lew F.C. Lew Yan Voon received his Ph.D. degree in Computer Aided Design of VLSI circuits from Montpellier University, France, in March 1992. Since September 1993, he has been first assistant professor and then associate professor at the University of Burgundy. His research interests lie in the field of pattern recognition and in the design of silicon retinas in standard CMOS technology for real-time inspection by machine vision. Bernard Lamalle was born in Autun on May 1, 1946. He obtained the Ph.D. degree in 1973 from the Université de Bourgogne in Dijon. During 1980 to 2000 he has been Maître de conférences at the IUT of Le Creusot in Bourgogne. He joined the image processing team of laboratory Le2i in 1992. Since 2000, he has been appointed full professor of the University of Bourgogne. His field of interest is principally the study and design of silicon retinas dedicated to industrial control. He has in charge some industrial contracts in the field of quality control by artificial vision and he holds two patents in the field of image processing and smart sensors. Guy Cathébras was born in Uzès, France, in 1961. He received the French engineer degree from the Ecole Nationale Supérieure de l'Electronique et de ses Applications, Cergy, France, in 1984 and the Diplôme de Doctorat de l'Université de Montpellier, France, in 1990. Since 1992 he is an assistant professor of microelectronics at the Institut des Sciences de l'Ingénieur de Montpellier. His current research interests include the design of imagers and silicon retinas using standard CMOS technologies.     

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

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

Behavior of the Laplacian of Gaussian Extrema

This paper analyses the behavior in scale space of linear junction models (L, Y and X models), nonlinear junction models, and linear junction multi-models. The variation of the grey level is considered to be constant, linear or nonlinear in the case of linear models and constant for the other models. We are mainly interested in the extrema points provided by the Laplacian of the Gaussian function. Moreover, we show that for infinite models the Laplacian of the Gaussian at the corner point is not always equal to zero.Salvatore Tabbone received his Ph.D. in computer science from the Institut National Polytechnique de Lorraine (France) in 1994. He is currently an assistant professor at the University of Nancy2 (France) and a member of the QGAR research project on graphics recognition at the LORIA-INRIA research center. His research interests include computer vision, pattern recognition, content-based image retrieval, and document analysis and recognition.Laurent Alonso was a student of ENS Ulm from 1987 to 1991, he received the Ph.D. degree in Computer Science from the University of Paris XI, Orsay, France in 1992. From 1991 to 1995 he served as lecturer in the University of Nancy I (France). Actually, he is full researcher in INRIA (France). His research interests include realistic rendering, geometric algorithms and combinatorics.Djemel Ziou received the BEng Degree in Computer Science from the University of Annaba (Algeria) in 1984, and Ph.D. degree in Computer Science from the Institut National Polytechnique de Lorraine (INPL), France in 1991. From 1987 to 1993 he served as lecturer in several universities in France. During the same period, he was a researcher in the Centre de Recherche en Informatique de Nancy (CRIN) and the Institut National de Recherche en Informatique et Automatique (INRIA) in France. Presently, he is full Professor at the department of computer science at the University of Sherbrooke in Canada. He has served on numerous conference committees as member or chair. He heads the laboratory MOIVRE and the consortium CoRIMedia which he founded. His research interests include image processing, information retrieval, computer vision and pattern recognition.     

STAMP: A Model for Generating Adaptable Multimedia Presentations

The STAMP model addresses the dynamic generation of multimedia presentations in the domain of Multimedia Web-based Information Systems. STAMP allows the presentation of multimedia data obtained from XML compatible data sources by means of query. Assuming that the size and the nature of the elements of information provided by a data source is not known a priori, STAMP proposes templates which describe the spatial, temporal, navigational structuration of multimedia presentations whose content varies. The instantiation of a template is done with respect to the set of spatial and temporal constraints associated with the delivery context. A set of adaptations preserving the initial intention of the presentation is proposed.Ioan Marius Bilasco is a Ph.D. student at the University Joseph Fourier in Grenoble, France, since 2003. He received his BS degree in Computer Science form the University Babes Bolyai in Cluj-Napoca, Romania and his MS degree in Computer Science from the University Joseph Fourier in Grenoble, France. He joined the LSR-IMAG Laboratory in Grenoble in 2001. His research interests include adaptability in Web-based Information Systems, 3D multimedia data modelling and mobile communications.Jérôme Gensel is an Assistant Professor at the University Pierre Mendès France in Grenoble, France, since 1996. He received his Ph.D. in 1995 from the University of Grenoble for his work on Constraint Programming and Knowledge Representation in the Sherpa project at the French National Institute of Computer Sciences and Automatics (INRIA). He joined the LSR-IMAG Laboratory in Grenoble in 2001. His research interests include adaptability and cooperation in Web-based Information Systems, multimedia data (especially video) modeling, semi-structured and object-based knowledge representation and constraint programming.Marlène Villanova-Oliver is an Assistant Professor at the University Pierre Mendès France in Grenoble, France, since 2003. In 1999, she received her MS degree in Computer Science from the University Joseph Fourier of Grenoble and the European Diploma of 3rd cycle in Management and Technology of Information Systems (MATIS). She received her Ph.D. in 2002 from the National Polytechnic Institute of Grenoble (INPG). She is a member of the LSR-IMAG Laboratory in Grenoble since 1998. Her research interests include adaptability in Web-based Information Systems, user modeling, adaptable Web Services.     

Automatic fog detection and estimation of visibility distance through use of an onboard camera

In this paper, we will present a technique for measuring visibility distances under foggy weather conditions using a camera mounted onboard a moving vehicle. Our research has focused in particular on the problem of detecting daytime fog and estimating visibility distances; thanks to these efforts, an original method has been developed, tested and patented. The approach consists of dynamically implementing Koschmieder's law. Our method enables computing the meteorological visibility distance, a measure defined by the International Commission on Illumination (CIE) as the distance beyond which a black object of an appropriate dimension is perceived with a contrast of less than 5%. Our proposed solution is an original one, featuring the advantage of utilizing a single camera and necessitating the presence of just the road and sky in the scene. As opposed to other methods that require the explicit extraction of the road, this method offers fewer constraints by virtue of being applicable with no more than the extraction of a homogeneous surface containing a portion of the road and sky within the image. This image preprocessing also serves to identify the level of compatibility of the processed image with the set of Koschmieder's model hypotheses. Nicolas Hautiére graduated from the École Nationale des Travaux Publics de l'État, France (2002). He received his M.S. and Ph.D. degree in computer vision, respectively, in 2002 and 2005 from Saint-Étienne University (France). From 2002, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France. His research interests include trafic engineering, computer vision, and pattern recognition. Jean-Philippe Tarel graduated from the École Nationale des Ponts et Chaussées, Paris, France (1991). He received his Ph.D. degree in Applied Mathematics from Paris IX-Dauphine University in 1996 and he was with the Institut National de Recherche en Informatique et Automatique (INRIA) from 1991 to 1996. From 1997 to 1998, he was a research associate at Brown University, USA. From 1999, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France, and from 2001 to 2003 in the INRIA. His research interests include computer vision, pattern recognition, and shape modeling. Jean Lavenant graduated from the École Nationale des Travaux Publics de l'État, Lyon, France (2001). He received the M.S. degree in Computer Vision from Jean Monnet university of Saint-Étienne in 2001. In 2001, he was a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC). In 2002, he was a system engineer in Chicago (USA). He is currently an engineer for the french ministry of transports. Didier Aubert received the M.S. and Ph.D. degree, respectively, in 1985 and 1989 from the National Polytechnical Institut of Grenoble (INPG). From 1989--1990, he worked as a research scientist on the development of an automatic road following system for the NAVLAB at Carnegie Mellon University. From 1990–1994, he worked in the research department of a private company (ITMI). During this period he was the project leader of several projects dealing with computer vision. He is currently a researcher at INRETS since 1995 and works on Road traffic measurements, crowd monitoring, automated highway systems, and driving assistance systems for vehicles. He is an image processing expert for several companies, teaches at Universities (Paris VI, Paris XI, ENPC, ENST) and is at the editorial board of RTS (Research - Transport - Safety).     

IMSP: An information theoretic approach for multi-dimensional sequential pattern mining

Sequential pattern mining is an important data mining problem with broad applications. While the current methods are inducing sequential patterns within a single attribute, the proposed method is able to detect them among different attributes. By incorporating the additional attributes, the sequential patterns found are richer and more informative to the user. This paper proposes a new method for inducing multi-dimensional sequential patterns with the use of Hellinger entropy measure. A number of theorems are proposed to reduce the computational complexity of the sequential pattern systems. The proposed method is tested on some synthesized transaction databases. Dr. Chang-Hwan Lee is a full professor at the Department of Information and Communications at DongGuk University, Seoul, Korea since 1996. He has received his B.Sc. and M.Sc in Computer Science and Statistics from Seoul National University in 1982 and 1988, respectively. He received his Ph.D. in Computer Science and Engineering from University of Connecticut in 1994. Prior to joining DongGuk University in Korea, he had worked for AT&T Bell Laboratories, Middletown, USA. (1994-1995). He also had been a visiting professor at the University of Illinois at Urbana-Champaign (2000-2001). He is author or co-author of more than 50 refereed articles on topics such as machine learning, data mining, artificial intelligence, pattern recognition, and bioinformatics.     

The Application of the Commparable Corpora in Chinese—English Cross—lingual Information Retrieval

This paper proposes a novel Chinese-English Cross-Lingual Information Retrieval(CECLIR)model PME,in which bilingual dictionary and comparable corpora are used to translate the query terms.The proximity and mutual information of the term-paris in the CHinese and English comparable corpora are employed not only to resolve the translation ambiguities but also to perform the query expansion so as to deal with the out-of -vocabulary issues in the CECLIR.The evaluation results show that the query precision of PME algorithm is about 84.4% of the monolingual information retrieval.     

A Linear 3D Elastic Segmentation Model for Vector Fields. Application to the Heart Segmentation in MRI

In this paper a 3D elastic model for the segmentation of vector fields has been proposed and analyzed. Elastic models for segmentation usually involve minimization of internal and external energy. A problem we observed with standard internal and external energy is that the local or the global reached minima do not force the external energy to be zero. To eliminate this difficulty, we propose introducing a constraint. The constraint problem is proved to be mathematically well posed, and a simple algorithm which avoids computing the lagrange multiplier is provided. This algorithm is proved to be convergent. Then the algorithm is applied to the segmentation of cardiac magnetic resonance imaging, and its efficiency is shown with two experiments. Martine Picq is member of the Institute of Mathematics C. Jordan in National Institute of Applied Sciences in Lyon, where she is teaching mathematics since 1997. Jerome Pousin received a Ph.D. degree in Applied Mathematics from University of Paris 6 France in 1983 and Ph.D. degree in Mathematic Sciences from EPFL Switzerland in 1992. Since 1993 he is professor of Mathematics at the National Institute of Applied Sciences in Lyon. His research interests are approximation of nonlinear Partial Differential Equations with Finite Element Method; domain decomposition methods and image segmentation with deformable models. Youssef Rouchdy received a Ph.D. degree in Applied Mathematics from the National Institute of Applied Sciences in Lyon in 2005. He is currently a Postdoc at INRIA Sophia Antipolis France.     

An interlingua-based Chinese-English MT system

Chinese-English machine translation is a significant and challenging problem in information processing.The paper presents an interlingua-based Chinese-English natural language translation system(ICENT).It introduces the realization mechanism of Chinses language analysis,which contains syntactic parsing and semantic analyzing and gives the design of interlingua in details .Experimental results and system evaluation are given .The sesult is satisfying.     

Adapted Total Variation for Artifact Free Decompression of JPEG Images

The widely used JPEG lossy baseline coding system is known to produce, at low bit rates, blocking effects and Gibbs phenomenon. This paper develops a method to get rid of these artifacts without smoothing images and without removing perceptual features. This results in better looking pictures and improved PSNR. Our algorithm is based on an adapted total variation minimization approach constrained by the knowledge of the input intervals the unquantized cosine coefficients belong to. In this way, we reconstruct an image having the same quantized coefficients than the original one, but which is minimal in term of the total variation. This discourages blocking effects and Gibbs phenomenon to appear while edges are kept as sharp as possible. Although the proposed subgradient method is converging in infinite time, experiments show that best results are obtained with a very few number of iterations. This leads to a simple and fast algorithm that may be applied to the great set of JPEG images to decompress them more efficiently.This work was also supported in part by CNES, 18 avenue E. Belin, 31055 Toulouse Cedex, France and Science Pratique SA, 47 avenue Carnot, 94230 Cachan, France under Grant 762/00/CNES/8319; by CMLA, ENS Cachan, 61 avenue du Président Wilson, 94235 Cachan Cedex, France; by DOLabs, 3 rue Nationale, 92100 Boulogne-Billancourt, France and by MAP5, UFR mathématiques et informatique, Université Paris 5, 45 rue des Saints Pères, 75270 Paris Cedex 06, France. François Alter was at École Normale Supérieure Ulm in Paris from 1998 to 2003, and entered the Corps des Mines attached to the French minister in charge of industry in 2003. He received M.Sc. degree in Pure Mathematics from Paris 6 University in 2001. Since 2002, he has been preparing his Ph.D. in Image Processing and Analysis at CMLA in Cachan, France. His research interests are Geometric Partial Differential Equations and Stochastic Perception Theory. Sylvain Durand received his PhD in applied mathematics, in 1993, from Paris-Dauphine University, France. In 1994 and 1995, he held a postdoctoral position at Washington University of St. Louis, Missouri. He is currently assistant professor at Jules Verne University of Picardie, France. His research interests include mathematical aspects of image processing. Jacques Froment received the Ph.D. degree in applied mathematics from Paris-Dauphine University, France, in 1990. During the academic year 1990/1991, he was an associate research scientist at the Courant Institute of Mathematical Sciences in NYUs computer science department. From 1991 to 2002, he was an assistant professor in the department of mathematics at Paris 5 University, France. He is currently professor of applied mathematics at the University of Bretagne Sud, Vannes, France. His research interests include mathematical models in computer vision and representation of meaningful information with applications to image compression and restoration.This revised version was published online in June 2005 with correction to CoverDate     

Semantic Ontology Mapping for Interoperability of Learning Resource Systems using a rule-based reasoning approach

There is an increasing demand for sharing learning resources between existing learning resource systems to support reusability, exchangeability, and adaptability. The learning resources need to be annotated with ontologies into learning objects that use different metadata standards. These ontologies have introduced the problems of semantic and structural heterogeneity. This research proposes a Semantic Ontology Mapping for Interoperability of Learning Resource Systems. To enable semantic ontology mapping, this research proposes conflict detection and resolution techniques for both semantic and structural conflicts. The Semantic Bridge Ontology has been proposed as a core component for generating mapping rules to reconcile terms defined in local ontologies into terms defined in the target common ontology. This work defines the reasoning rules to classify related learning objects to enhance the powerful deductive reasoning capabilities of the system. As a consequence, ontology-based learning object metadata are generated and used by the semantic query engine to facilitate user queries of learning objects across heterogeneous learning resource systems.     

Applying language modeling to session identification from database trace logs

A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement.In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-labeled training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, learning from labeled data, learning from semi-labeled data and learning from unlabeled data, are introduced to learn language models from different types of training data. Finally, we report experimental results that show the effectiveness of the language model based method for identifying sessions from the trace logs of an OLTP database application and the TPC-C Benchmark. Xiangji Huang joined York University as an Assistant Professor in July 2003 and then became a tenured Associate Professor in May 2006. Previously, he was a Post Doctoral Fellow at the School of Computer Science, University of Waterloo, Canada. He did his Ph.D. in Information Science at City University in London, England, with Professor Stephen E. Robertson. Before he went into his Ph.D. program, he worked as a lecturer for 4 years at Wuhan University. He also worked in the financial industry in Canada doing E-business, where he was awarded a CIO Achievement Award, for three and half years. He has published more than 50 refereed papers in journals, book chapter and conference proceedings. His Master (M.Eng.) and Bachelor (B.Eng.) degrees were in Computer Organization & Architecture and Computer Engineering, respectively. His research interests include information retrieval, data mining, natural language processing, bioinformatics and computational linguistics. Qingsong Yao is a Ph.D. student in the Department of Computer Science and Engineering at York University, Toronto, Canada. His research interests include database management systems and query optimization, data mining, information retrieval, natural language processing and computational linguistics. He earned his Master's degree in Computer Science from Institute of Software, Chinese Academy of Science in 1999 and Bachelor's degree in Computer Science from Tsinghua University. Aijun An is an associate professor in the Department of Computer Science and Engineering at York University, Toronto, Canada. She received her Bachelor's and Master's degrees in Computer Science from Xidian University in China. She received her PhD degree in Computer Science from the University of Regina in Canada in 1997. She worked at the University of Waterloo as a postdoctoral fellow from 1997 to 1999 and as a research assistant professor from 1999 to 2001. She joined York University in 2001. She has published more than 60 papers in refereed journals and conference proceedings. Her research interests include data mining, machine learning, and information retrieval.     

Reference chromosome to overcome user fatigue in IEC

Evolutionary Computation encompasses computational models that follow a biological evolution metaphor. The success of these techniques is based on the maintenance of the genetic diversity, for which it is necessary to work with large populations. However, it is not always possible to deal with such large populations, for instance, when the adequacy values must be estimated by a human being (Interactive Evolutionary Computation, IEC). This work introduces a new algorithm which is able to perform very well with a very low number of individuals (micropopulations) which speeds up the convergence and it is solving problems with complex evaluation functions. The new algorithm is compared with the canonical genetic algorithm in order to validate its efficiency. Two experimental frameworks have been chosen: table and logotype designs. An objective evaluation measures has been proposed to avoid user interaction in the experiments. In both cases the results show the efficiency of the new algorithm in terms of quality of solutions and convergence speed, two key issues in decreasing user fatigue. Yago Saez: He received the Computer Engineering degree from the Universidad Pontificia de Salamanca in 1999 Spain. He now is a Ph.D. student and works as assistant professor at the EVANNAI Group at the Computer Science Department of CARLOS III, Madrid, Spain. His main research areas encompasses the interactive evolutionary computation, the design applications and the optimization problems. Pedro Isasi, Ph.D.: He received Computer Science degree and Ph.D. degree from the Universidad Politécnica de Madrid (UPM), Spain in 1994. He is now working as professor at the EVANNAI Group at the Computer Science Department of CARLOS III, Madrid, Spain. His main research areas are Machine Learning, Evolutionary, Computation and Neural Networks and Applications to Optimization Problems. Javier Segovia, Ph.D.: He is a receiving physicist, Ph.D. degree in Computer Science (with honours) from the Universidad Politécnica de Madrid (UPM). Currently Dean of the UPM School of Computer Science, and is editor and/or author of more than 70 scientific publications in the fields of genetic algorithms, data and web mining, artificial intelligence and intelligent interfaces. Julio C. Hernandez, Ph.D.: He has received degree in Maths, Ph.D. degree in Computer Science. His main research area is the artificial intelligence applied to criptography and net security. His unofficial hobbies are chess and go. Currently, he is working as invited researcher at INRIA, France.     

Strong stable properties in distributed systems

Summary A stable property in a distributed system is a global property which once true, remains true forever. This paper refines this notion by formally introducing the concept ofstrong stable properties. A strong stable property has the nice property that it can be correctly evaluated on the consistent part of uncoordinated snapshots. Termination and deadlock are shown to be strong stable properties, whereas distributed garbage is not. We also show how to derive a simple generic algorithm for the detection of a strong stable property. The generic algorithm is illustrated by two examples: termination detection and deadlock detection. Incidentally the paper presents a very simple algorithm for termination detection. Andre Schiper has been a professor of Computer Science at EPFL (Federal Institute of Technology in Lausanne, Switzerland) since 1985, leading the Operating Systems laboratory. He graduated in Physics from the Federal Institute of technology in Zürich and received his Ph.D. in Computer Science from EPFL in 1980. In 1981–82 he spent one year at the University of Rennes, France. From 1983 to 1985, he was professor at the Engineering School in Yverdon, Switzerland. Between 1989 and 1991 André Schiper was head of the Department of Computer Science of EPFL, and during the academic year 1992–93 he was on sabbatical leave at Cornell University, Ithaca (NY). His research interests are in the areas of operating systems, distributed and fault-tolerant distributed systems, and parallelism. He is currently involved in the European Esprit project BROADCAST whose objective is the design and implementation of large scale distributed computing systems. Alain Sandoz graduated in Mathematics from the University of Neuchâtel, Switzerland, in 1984 and in Computer Science from the Federal Institute of Technology in Lausanne, Switzerland, in 1988. He received his Ph.D. in Computer Science from the Federal Institute of Technology in Lausanne in 1992. His dissertation was concerned with modelling causal relationships between transactions in distributed and replicated database systems. From 1992 to 1994 he was involved in research on fault-tolerant and large scale distributed computing systems. He is currently working on the development of information systems for the Swiss government.     

Efficient mining of association rules by reducing the number of passes over the database

This paper introduces a new algorithm of mining association rules.The algorithm RP counts the itemsets with different sizes in the same pass of scanning over the database by dividing the database into m partitions.The total number of pa sses over the database is only(k 2m-2)/m,where k is the longest size in the itemsets.It is much less than k .