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1.
In this paper, we formulate the problem of summarization of a data set of transactions with categorical attributes as an optimization
problem involving two objective functions – compaction gain and information loss. We propose metrics to characterize the output
of any summarization algorithm. We investigate two approaches to address this problem. The first approach is an adaptation
of clustering and the second approach makes use of frequent itemsets from the association analysis domain. We illustrate one
application of summarization in the field of network data where we show how our technique can be effectively used to summarize
network traffic into a compact but meaningful representation. Specifically, we evaluate our proposed algorithms on the 1998
DARPA Off-Line Intrusion Detection Evaluation data and network data generated by SKAION Corp for the ARDA information assurance
program.
Vipin Kumar is currently William Norris Professor and Head of the Computer Science and Engineering Department at the University of Minnesota.
His research interests include high-performance computing and data mining. He has authored over 200 research articles, and
has coedited or coauthored nine books including the widely used text booksIntroduction to Parallel Computing andIntroduction to Data Mining, both published by Addison Wesley. He has served as chair/co-chair for many conferences/workshops in the area of data mining
and parallel computing, including the IEEE International Conference on Data Mining (2002) and the 15th International Parallel
and Distributed Processing Symposium (2001). He serves as the chair of the steering committee of the SIAM International Conference
on Data Mining, and is a member of the steering committee of the IEEE International Conference on Data Mining. Dr. Kumar serves
or has served on the editorial boards of several journals includingKnowledge and Information Systems,Journal of Parallel and Distributed Computing andIEEE Transactions of Data and Knowledge Engineering (1993–1997). He is a Fellow of the ACM and IEEE, and a member of SIAM.
Varun Chandola received his BTech degree in Computer Science from the Indian Institute of Technology, Madras, India, in 2002. He is currently
a PhD student in the Computer Science and Engineering Department at the University of Minnesota. His research interests include
data mining, cyber-security and machine learning. 相似文献
2.
James V. Hansen James B. McDonald Conan C. Albrecht Douglas L. Dean Bonnie B. Anderson 《Electronic Commerce Research》2007,7(1):5-16
This paper proposes an adaptive learning approach that yields decision models that can be applied by a transactions agent.
This model can learn effectively with a variety of data distributions. This research uses the Semantic Web as a data access
approach. The Semantic Web is a method that sellers can use to publish semantically meaningful information on Websites so
automated applications can reliably access that information. We implemented a Semantic Web composed of 30 vendors’ Web pages
and a spider to search those pages to obtain product and vendor information. This information was used to train a learning
agent, which then provided a decision model to a transaction agent.
James Hansen is J. Owen Cherrington Professor in the Information Systems Department of the Marriott School of Management at Brigham Young
University. He is an associate editor for IEEE Intelligent Systems and Information Systems Frontiers. His research is in machine learning and planning as model checking.
James B. McDonald is Professor of Economics at Brigham Young University. His research interests are in econometrics and quantitative methods.
He has recently published in Econometrica, Journal of the American Statistical Association, Management Science, and Journal of Business
Conan C. Albrecht is a professor of Information Systems at Brigham Young University. He teaches classes in enterprise development, middleware,
and business programming.
Conan researches computer-based fraud detection techniques, ecommerce platforms, and online group dynamics. He has published
articles on fraud detection and information theory in The Journal of Forensic Accounting, The Journal of Accounting, The Communications of the ACM, Decision Support Systems, Information and Management, and other academic and professional outlets.
Conan is currently working on an open source framework for computer-based fraud detection. The core of this research is detectlets,
which encode background and detection information for specific fraud schemes. He is researching with the United Nations and
the World Bank to use detectlets to prevent and detect fraud in third world countries. In the next few years, he hopes the
system will serve as the foundation of a large, online repository of detectlets about all types of fraud.
Douglas L. Dean is an Associate Professor at the Marriott School of Management at Brigham Young University. He is also research coordinator
for the Rollins Center for E-business. He received his Ph.D. in MIS from the University of Arizona in 1995. Dr. Dean’s research
interests include electronic commerce technology and strategy, online communities, requirements analysis, and collaborative
tools and methods. His work has been published in Management Science, Journal of Management Information Systems, Information and Management, The DATA BASE for Advances in Information Systems,
Communications of the AIS, Expert Systems with Applications, Group Decision and Negotiation, and IEEE Transactions on Systems,
Man, and Cybernetics.
Bonnie Brinton Anderson is the LeAnn Albrecht Fellow and an Assistant Professor in the Information Systems Department of the Marriott School at Brigham
Young University (Provo, UT). She received her Ph.D. from Carnegie Mellon University. Dr. Anderson has published in Decision Support Systems; IEEE Transactions on Systems, Man, and Cybernetics; Communications of the ACM; Journal of Accountancy, among others. She researches in the areas of knowledge management, information systems security, and intelligent agents. 相似文献
3.
Capabilities of outlier detection schemes in large datasets,framework and methodologies 总被引:1,自引:1,他引:0
Jian Tang Zhixiang Chen Ada Waichee Fu David W. Cheung 《Knowledge and Information Systems》2007,11(1):45-84
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. 相似文献
4.
We define three operations on strings and languages suggested by the process of gene assembly in hypotrichous ciliates. This
process is considered to be a prine example of DNA computing in vivo. This paper is devoted to some computational aspects
of these operations from a formal language point of view. The closure of the classes of regular and context-free languages
under these operations is settled. Then, we consider theld-macronuclear language of a given languageL, which consists of allld-macronuclear strings obtained from the strings ofL by iteratively applying the loop-direct repeat-excision. Finally, we discuss some open problems and further directions of
research.
Rudolf Freund: He received his master and doctor degree in computer science from the Vienna University of Technology, Austria, in 1980
and 1982, respectively. In 1986, he received his master degree in mathematics and physics from the University Vienna, Austria.
In 1988 he joined the Vienna University of Technology in Austria, where he became an Associate Professor in September 1995.
He has given various lectures in theoretical computer science, especially on formal languages and automata. His research interests
include array and graph grammars, regulated rewritung, infinite words, syntactic pattern recognition, neural networks, and
especially models and systems for biological computing. In these fields he is author of more than sixty scientific papers.
Carlos Martín-Vide: He is Professor and Head of the Research Group on Mathematical Linguistics at Rovira i Virgili University, Tarragona, Spain.
His specialities are formal language theory and mathematical linguistics. His last volume edited is Where Mathematics, Computer
Science, Linguistics and Biology Meet (Kluwer, 2001, with V. Mitrana). He published 150 papers in conference proceedings and
journals such as: Acta Informatica, BioSystems. Computational Linguistics, Computers and Artificial Intelligence, Information
Processing Letters, Information Sciences, International Journal of Computer Mathematics, New Generation Computing, Publicationes
Mathematicae Debrecen, and Theoretical Computer Science. He is the editor-in-chief of the journal Grammars (Kluwer), and the
chairman of the 1st International PhD School in Formal Languages and Applications (2001–2003).
Victor Mitrana, Ph.D.: He is Professor of Computer Science at the Faculty of Mathematics, University of Bucharest. He received his MSc and PhD
from the University of Bucharest in 1986 and 1993, respectively. In 1999 he was awarded with the “Gheorghe Lazar” Prize for
Mathematics of the Romanian Academy. His research interests include: formal language theory and applications, combinatorics
on words, computational models inspired from biology, mathematical linguistics. In these areas, he published three books,
more than 100 papers, and edited two books. He is an associate editor of “The Korean Journal of Computational and Applied
Mathematics” and an editor of “Journal of Universal Computer Science”. 相似文献
5.
Efficient string matching with wildcards and length constraints 总被引:1,自引:2,他引:1
Gong Chen Xindong Wu Xingquan Zhu Abdullah N. Arslan Yu He 《Knowledge and Information Systems》2006,10(4):399-419
This paper defines a challenging problem of pattern matching between a pattern P and a text T, with wildcards and length constraints, and designs an efficient algorithm to return each pattern occurrence in an online manner. In this pattern matching problem, the user can specify the constraints on the number of wildcards between each two consecutive letters of P and the constraints on the length of each matching substring in T. We design a complete algorithm, SAIL that returns each matching substring of P in T as soon as it appears in T in an O(n+klmg) time with an O(lm) space overhead, where n is the length of T, k is the frequency of P's last letter occurring in T, l is the user-specified maximum length for each matching substring, m is the length of P, and g is the maximum difference between the user-specified maximum and minimum numbers of wildcards allowed between two consecutive letters in P.SAIL stands for string matching with wildcards and length constraints.
Gong Chen received the B.Eng. degree from the Beijing University of Technology, China, and the M.Sc. degree from the University of Vermont, USA, both in computer science. He is currently a graduate student in the Department of Statistics at the University of California, Los Angeles, USA. His research interests include data mining, statistical learning, machine learning, algorithm analysis and design, and database management.
Xindong Wu is a professor and the chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM and WWW, as well as 12 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM),an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He is the 2004 ACM SIGKDD Service Award winner.
Xingquan Zhu received his Ph.D degree in Computer Science from Fudan University, Shanghai, China, in 2001. He spent 4 months with Microsoft Research Asia, Beijing, China, where he was working on content-based image retrieval with relevance feedback. From 2001 to 2002, he was a postdoctoral associate in the Department of Computer Science at Purdue University, West Lafayette, IN. He is currently a research assistant professor in the Department of Computer Science, the University of Vermont, Burlington, VT. His research interests include data mining, machine learning, data quality, multimedia computing, and information retrieval. Since 2000, Dr. Zhu has published extensively, including over 50 refereed papers in various journals and conference proceedings.
Abdullah N. Arslan got his Ph.D. degree in Computer Science in 2002 from the University of California at Santa Barbara. Upon his graduation he joined the Department of Computer Science at the University of Vermont as an assistant professor. He has been with the computer science faculty there since then. Dr. Arslan's main research interests are on algorithms on strings, computational biology and bioinformatics. Dr. Arslan earned his Master's degree in Computer Science in 1996 from the University of North Texas, Denton, Texas and his Bachelor's degree in Computer Engineering in 1990 from the Middle East Technical University, Ankara, Turkey. He worked as a programmer for the Central Bank of Turkey between 1991 and 1994.
Yu He received her B.E. degree in Information Engineering from Zhejiang University, China, in 2001. She is currently a graduate student in the Department of Computer Science at the University of Vermont. Her research interests include data mining, bioinformatics and pattern recognition. 相似文献
6.
Chunming Hu Yanmin Zhu Jinpeng Huai Yunhao Liu Lionel M. Ni 《Knowledge and Information Systems》2007,12(1):55-75
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. 相似文献
7.
This paper presents a metamodel for modeling system features and relationships between features. The underlying idea of this metamodel is to employ features as first-class entities in the problem space of software and to improve the customization of software by explicitly specifying both static and dynamic dependencies between system features. In this metamodel, features are organized as hierarchy structures by the refinement relationships, static dependencies between features are specified by the constraint relationships, and dynamic dependencies between features are captured by the interaction relationships. A first-order logic based method is proposed to formalize constraints and to verify constraints and customization. This paper also presents a framework for interaction classification, and an informal mapping between interactions and constraints through constraint semantics.
Hong Mei received the BSc and MSc degrees in computer science from the Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1984 and 1987, respectively, and the PhD degree in computer science from the Shanghai Jiao Tong University in 1992. He is currently a professor of Computer Science at the Peking University, China. His current research interests include Software Engineering and Software Engineering Environment, Software Reuse and Software Component Technology, Distributed Object Technology, and Programming Language. He has published more than 100 technical papers.
Wei Zhang received the BSc in Engineering Thermophysics and the MSc in Computer Science from the Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1999 and 2002, respectively. He is currently a PhD student at the School of Electronics Engineering and Computer Science of the Peking University, China. His research interests include feature-oriented requirements modeling, feature-driven software architecture design and feature-oriented software reuse.
Haiyan Zhao received both the BSc and the MSc degree in Computer Science from the Peking Univeristy, China, and the Ph.D degree in Information Engineering from the University of Tokyo, Japan. She is currently an associate professor of Computer Science at the Peking University, China. Her research interests include Software Reuse, Domain Engineering, Domain Specific Languange and Program Transformation. 相似文献
8.
A range query finds the aggregated values over all selected cells of an online analytical processing (OLAP) data cube where
the selection is specified by the ranges of contiguous values for each dimension. An important issue in reality is how to
preserve the confidential information in individual data cells while still providing an accurate estimation of the original
aggregated values for range queries. In this paper, we propose an effective solution, called the zero-sum method, to this
problem. We derive theoretical formulas to analyse the performance of our method. Empirical experiments are also carried out
by using analytical processing benchmark (APB) dataset from the OLAP Council. Various parameters, such as the privacy factor
and the accuracy factor, have been considered and tested in the experiments. Finally, our experimental results show that there
is a trade-off between privacy preservation and range query accuracy, and the zero-sum method has fulfilled three design goals:
security, accuracy, and accessibility.
Sam Y. Sung is an Associate Professor in the Department of Computer Science, School of Computing, National University of Singapore. He
received a B.Sc. from the National Taiwan University in 1973, the M.Sc. and Ph.D. in computer science from the University
of Minnesota in 1977 and 1983, respectively. He was with the University of Oklahoma and University of Memphis in the United
States before joining the National University of Singapore. His research interests include information retrieval, data mining,
pictorial databases and mobile computing. He has published more than 80 papers in various conferences and journals, including
IEEE Transaction on Software Engineering, IEEE Transaction on Knowledge & Data Engineering, etc.
Yao Liu received the B.E. degree in computer science and technology from Peking University in 1996 and the MS. degree from the Software
Institute of the Chinese Science Academy in 1999. Currently, she is a Ph.D. candidate in the Department of Computer Science
at the National University of Singapore. Her research interests include data warehousing, database security, data mining and
high-speed networking.
Hui Xiong received the B.E. degree in Automation from the University of Science and Technology of China, Hefei, China, in 1995, the
M.S. degree in Computer Science from the National University of Singapore, Singapore, in 2000, and the Ph.D. degree in Computer
Science from the University of Minnesota, Minneapolis, MN, USA, in 2005. He is currently an Assistant Professor of Computer
Information Systems in the Management Science & Information Systems Department at Rutgers University, NJ, USA. His research
interests include data mining, databases, and statistical computing with applications in bioinformatics, database security,
and self-managing systems. He is a member of the IEEE Computer Society and the ACM.
Peter A. Ng is currently the Chairperson and Professor of Computer Science at the University of Texas—Pan American. He received his Ph.D.
from the University of Texas–Austin in 1974. Previously, he had served as the Vice President at the Fudan International Institute
for Information Science and Technology, Shanghai, China, from 1999 to 2002, and the Executive Director for the Global e-Learning
Project at the University of Nebraska at Omaha, 2000–2003. He was appointed as an Advisory Professor of Computer Science at
Fudan University, Shanghai, China in 1999. His recent research focuses on document and information-based processing, retrieval
and management. He has published many journal and conference articles in this area. He had served as the Editor-in-Chief for
the Journal on Systems Integration (1991–2001) and as Advisory Editor for the Data and Knowledge Engineering Journal since
1989. 相似文献
9.
Recently, mining from data streams has become an important and challenging task for many real-world applications such as credit
card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier
from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a new dynamic
classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm
dynamically selects a single “best” classifier to classify each test instance at run time. Our scheme uses statistical information
from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure
that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute
values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the
highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative
studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams
with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or
have low confidence.
A preliminary version of this paper was published in the Proceedings of the 4th IEEE International Conference on Data Mining,
pp 305–312, Brighton, UK
Xingquan Zhu received his Ph.D. degree in Computer Science from Fudan University, Shanghai, China, in 2001. He spent four months with
Microsoft Research Asia, Beijing, China, where he was working on content-based image retrieval with relevance feedback. From
2001 to 2002, he was a Postdoctoral Associate in the Department of Computer Science, Purdue University, West Lafayette, IN.
He is currently a Research Assistant Professor in the Department of Computer Science, University of Vermont, Burlington, VT.
His research interests include Data mining, machine learning, data quality, multimedia computing, and information retrieval.
Since 2000, Dr. Zhu has published extensively, including over 40 refereed papers in various journals and conference proceedings.
Xindong Wu is a Professor and the Chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial
Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems,
and Web information exploration. He has published extensively in these areas in various journals and conferences, including
IEEE TKDE, TPAMI, ACM TOIS, IJCAI, ICML, KDD, ICDM, and WWW, as well as 11 books and conference proceedings. Dr. Wu is the
Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the founder and current Steering Committee Chair of the IEEE International Conference on
Data Mining (ICDM), an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP).
He is the 2004 ACM SIGKDD Service Award winner.
Ying Yang received her Ph.D. in Computer Science from Monash University, Australia in 2003. Following academic appointments at the
University of Vermont, USA, she currently holds a Research Fellow at Monash University, Australia. Dr. Yang is recognized
for contributions in the fields of machine learning and data mining. She has published many scientific papers and book chapters
on adaptive learning, proactive mining, noise cleansing and discretization. Contact her at yyang@mail.csse.monash.edu.au. 相似文献
10.
Web image indexing by using associated texts 总被引:1,自引:0,他引:1
In order to index Web images, the whole associated texts are partitioned into a sequence of text blocks, then the local relevance of a term to the corresponding image is calculated with respect to both its local occurrence in the block and the distance of the block to the image. Thus, the overall relevance of a term is determined as the sum of all its local weight values multiplied by the corresponding distance factors of the text blocks. In the present approach, the associated text of a Web image is firstly partitioned into three parts, including a page-oriented text (TM), a link-oriented text (LT), and a caption-oriented text (BT). Since the big size and semantic divergence, the caption-oriented text is further partitioned into finer blocks based on the tree structure of the tag elements within the BT text. During the processing, all heading nodes are pulled up in order to correlate with their semantic scopes, and a collapse algorithm is also exploited to remove the empty blocks. In our system, the relevant factors of the text blocks are determined by using a greedy Two-Way-Merging algorithm.
Zhiguo Gong is an associate Professor in the Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macao, China. He received his BS, MS, and PhD from the Hebei Normal University, Peking University, and the Chinese Academy of Science in 1983, 1988, and 1998, respectively. His research interests include Distributed Database, Multimedia Database, Digital Library, Web Information Retrieval, and Web Mining.
Leong Hou U is currently a Master Candidate in the Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macao, China. He received his BS from National Chi Nan University, Taiwan in 2003. His research interests include Web Information Retrieval and Web Mining.
Chan Wa Cheang is currently a Master Candidate in the Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macao, China. He received his BS from the National Taiwan University, Taiwan in 2003. His research interests include Web Information Retrieval and Web Mining. 相似文献
11.
Solving multi-instance problems with classifier ensemble based on constructive clustering 总被引:3,自引:0,他引:3
In multi-instance learning, the training set is composed of labeled bags each consists of many unlabeled instances, that is, an object is represented by a set of feature vectors instead of only
one feature vector. Most current multi-instance learning algorithms work through adapting single-instance learning algorithms
to the multi-instance representation, while this paper proposes a new solution which goes at an opposite way, that is, adapting
the multi-instance representation to single-instance learning algorithms. In detail, the instances of all the bags are collected
together and clustered into d groups first. Each bag is then re-represented by d binary features, where the value of the ith feature is set to one if the concerned bag has instances falling into the ith group and zero otherwise. Thus, each bag is represented by one feature vector so that single-instance classifiers can be
used to distinguish different classes of bags. Through repeating the above process with different values of d, many classifiers can be generated and then they can be combined into an ensemble for prediction. Experiments show that the
proposed method works well on standard as well as generalized multi-instance problems.
Zhi-Hua Zhou is currently Professor in the Department of Computer Science & Technology and head of the LAMDA group at Nanjing University.
His main research interests include machine learning, data mining, information retrieval, and pattern recognition. He is associate
editor of Knowledge and Information Systems and on the editorial boards of Artificial Intelligence in Medicine, International Journal of Data Warehousing and Mining, Journal of Computer Science & Technology, and Journal of Software. He has also been involved in various conferences.
Min-Ling Zhang received his B.Sc. and M.Sc. degrees in computer science from Nanjing University, China, in 2001 and 2004, respectively.
Currently he is a Ph.D. candidate in the Department of Computer Science & Technology at Nanjing University and a member of
the LAMDA group. His main research interests include machine learning and data mining, especially in multi-instance learning
and multi-label learning. 相似文献
12.
S. Wachenfeld T. Lohe M. Fieseler Xiaoyi Jiang 《Pattern Recognition and Image Analysis》2008,18(2):328-331
In the field of computer vision and pattern recognition, data processing and data analysis tasks are often implemented as
a consecutive or parallel application of more-or-less complex operations. In the following we will present DocXS, a computing
environment for the design and the distributed and parallel execution of such tasks. Algorithms can be programmed using an
Eclipse-based user interface, and the resulting Matlab and Java operators can be visually connected to graphs representing
complex data processing workflows. DocXS is platform independent due to its implementation in Java, is freely available for
noncommercial research, and can be installed on standard office computers. One advantage of DocXS is that it automatically
takes care about the task execution and does not require its users to care about code distribution or parallelization. Experiments
with DocXS show that it scales very well with only a small overhead.
The text was submitted by the authors in English.
Steffen Wachenfeld received B.Sc. and M.Sc. (honors) degrees in Information Systems in 2003 and 2005 from the University of Muenster, Germany,
and an M.Sc. (honors) degree in Computer Science in 2003 from the University of Muenster. He is currently a research fellow
and PhD student in the Computer Science at the Dept. of Computer Science, University of Muenster. His research interests include
low resolution text recognition, computer vision on mobile devices, and systems/system architectures for computer vision and
image analysis. He is author or coauthor of more than ten scientific papers and a member of IAPR.
Tobias Lohe, M.Sc. degree in Computer Science in 2007 from the University of Muenster, Germany, is currently a research associate and
PhD student in Computer Science at the Institute for Robotics and Cognitive Systems, University of Luebeck, Germany. His research
interests include medical imaging, signal processing, and robotics for minimally invasive surgery.
Michael Fieseler is currently a student of Computer Science at the University of Muenster, Germany. He has participated in research in the
field of computer vision and medical imaging. Currently he is working on his Master thesis on depth-based image rendering
(DBIR).
Xiaoyi Jiang studied Computer Science at Peking University, China, and received his PhD and Venia Docendi (Habilitation) degree in Computer Science from the University of Bern, Switzerland. In 2002 he became an associate professor
at the Technical University of Berlin, Germany. Since October 2002 he has been a full professor at the University of Münster,
Germany. He has coauthored and coedited two books published by Springer and has served as the co-guest-editor of two special
issues in international journals. Currently, he is the Coeditor-in-Chief of the International Journal of Pattern Recognition and Artificial Intelligence. In addition he also serves on the editorial advisory board of the International Journal of Neural Systems and the editorial board of IEEE Transactions on Systems, Man, and Cybernetics—Part B, the International Journal of Image and Graphics, Electronic Letters on Computer Vision and Image Analysis, and Pattern Recognition. His research interests include medical image analysis, vision-based man-machine interface, 3D image analysis, structural
pattern recognition, and mobile multimedia. He is a member of IEEE and a Fellow of IAPR. 相似文献
13.
This paper presents a novel method for user classification in adaptive systems based on rough classification. Adaptive systems
could be used in many areas, for example in a user interface construction or e-Learning environments for learning strategy
selection. In this paper the adaptation of web-based system user interface is presented. The goal of rough user classification
is to select the most essential attributes and their values that group together users who are very much alike concerning the
system logic. In order to group users we exploit their usage data taken from the user model of the adaptive web-based system
user interface. We presented three basic problems for attribute selection that generates the following partitions: that is
included, that includes and that is the closest to the given partition.
Ngoc Thanh Nguyen, Ph.D., D.Sc.: He currently works as an associate professor at the Faculty of Computer Science and Management, Wroclaw University of Technology
in Poland. He received his diplomas of M.Sc, Ph.D. and D.Sc. in Computer Science in 1986, 1989 and 2002, respectively. Actually,
he is working on intelligent technologies for conflict resolution and inconsistent knowledge processing and e-learning methods.
His teaching interests consist of database systems and distributed systems. He is a co-editor of 4 special issues in international
journals, author of 3 monographs, editor of one book and about 110 other publications (book chapters, journal and refereed
conference papers). He is an associate editor of the following journals: “International Journal of Computer Science & Application”; “Journal of Information Knowledge System Management”; and “International Journal of Knowledge-Based & Intelligent Engineering Systems”. He is a member of societies: ACM, IFIP WG 7.2, ISAI, KES International, and WIC.
Janusz Sobecki, Ph.D.: He is an Assistant Professor in Institute of Applied Informatics (IAI) at Wroclaw University of Technology (WUT). He received
his M. Sc. in Computer Science from Faculty of Computer Science and Management at WUT in 1986 and Ph.D. in Computer Science
from Faculty of Electronics at WUT in 1994. For 1986–1996 he was an Assistant at the Department of Information Systems (DIS)
at WUT. For 1988–1996 he was also a head of the laboratory at DIS. For 1996–2004 he was an Assistant Professor in DIS and
since fall of 2004 at IAI, both at WUT. His research interests include information retrieval, multimedia information systems,
system usability and recommender systems. He is on the editorial board of New Generation Computing and was a co-editor of
two journal special issues. He is a member of American Association of Machinery. 相似文献
14.
In software testing, developing effective debugging strategies is important to guarantee the reliability of software under
testing. A heuristic technique is to cause failure and therefore expose faults. Based on this approach mutation testing has
been found very useful technique in detecting faults. However, it suffers from two problems with successfully testing programs:
(1) requires extensive computing resources and (2) puts heavy demand on human resources. Later, empirical observations suggest
that critical slicing based on Statement Deletion (Sdl) mutation operator has been found the most effective technique in reducing
effort and the required computing resources in locating the program faults. The second problem of mutation testing may be
solved by automating the program testing with the help of software tools. Our study focuses on determining the effectiveness
of the critical slicing technique with the help of the Mothra Mutation Testing System in detecting program faults. This paper
presents the results showing the performance of Mothra Mutation Testing System through conducting critical slicing testing
on a selected suite of programs.
Zuhoor Abdullah Al-Khanjari is an assistant professor in the Computer Science Department at Sultan Qaboos University, Sultanate of Oman. She received
her BSc in mathematics and computing from Sultan Qaboos University, MSc and PhD in Computer Science (Software Engineering)
from the University of Liverpool, UK. Her research interests include software testing, database management, e-learning, human-computer
interaction, programming languages, intelligent search engines, and web data mining and development. ~Currently, she is the
coordinator of the software engineering research group in the Department of Computer Science, College of Science, Sultan Qaboos
University. She is also coordinating a program to develop e-learning based undergraduate teaching in the Department of Computer
Science. Currently she is holding the position of assistant dean for postgraduate studies and research in the College of Science,
Sultan Qaboos University, Sultanate of Oman.
Martin Woodward is a Senior Fellow in the Computer Science Department at the University of Liverpool in the UK. After obtaining BSc and Ph.D.
degrees in mathematics from the University of Nottingham, he was employed by the University of Oxford as a Research Assistant
on secondment to the UK Atomic Energy Authority at the Culham Laboratory. He has been at the University of Liverpool for many
years and initially worked on the so-called ‘Testbed’ project, helping to develop automated tools for software testing which
are now marketed successfully by a commercial organisation. His research interests include software testing techniques, the
relationship between formal methods and testing, and software visualisation. He has served as Editor of the journal ‘Software
Testing, Verification and Reliability’ for the past thirteen years.
Haider Ramadhan is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his BS and MS in Computer
Science from University of North Carolina, and the PhD in Computer Science and AI from Sussex University. His research interests
include visualization of software, systems, and process, system engineering, human-computer interaction, intelligent search
engines, and Web data mining and development. Currently, he is the chairman of the Computer Science Department, College of
Science, Sultan Qaboos University, Sultanate of Oman.
Swamy Kutti
(N. S. Kutti) is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his B.E. in Electronics
Engineering from the University of Madras, M.E. in Communication Engineering from Indian Institute of Science (Bangalore),
and the MSc in Computer Science from Monash University (Australia) and PhD in Computer Science from Deakin University (Australia).
His research interests include Real-Time Programming, Programming Languages, Program Testing and Verification, eLearning,
and Distributed Operating Systems. 相似文献
15.
The simple least-significant-bit (LSB) substitution technique is the easiest way to embed secret data in the host image. To
avoid image degradation of the simple LSB substitution technique, Wang et al. proposed a method using the substitution table
to process image hiding. Later, Thien and Lin employed the modulus function to solve the same problem. In this paper, the
proposed scheme combines the modulus function and the optimal substitution table to improve the quality of the stego-image.
Experimental results show that our method can achieve better quality of the stego-image than Thien and Lin’s method does.
The text was submitted by the authors in English.
Chin-Shiang Chan received his BS degree in Computer Science in 1999 from the National Cheng Chi University, Taipei, Taiwan and the MS degree
in Computer Science and Information Engineering in 2001 from the National Chung Cheng University, ChiaYi, Taiwan. He is currently
a Ph.D. student in Computer Science and Information Engineering at the National Chung Cheng University, Chiayi, Taiwan. His
research fields are image hiding and image compression.
Chin-Chen Chang received his BS degree in applied mathematics in 1977 and his MS degree in computer and decision sciences in 1979, both from
the National Tsing Hua University, Hsinchu, Taiwan. He received his Ph.D. in computer engineering in 1982 from the National
Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of
Computer Engineering at the National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied
Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute
of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked
as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan.
Dr. Chang is a Fellow of IEEE, a Fellow of IEE and a member of the Chinese Language Computer Society, the Chinese Institute
of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include
computer cryptography, data engineering, and image compression.
Yu-Chen Hu received his Ph.D. degree in Computer Science and Information Engineering from the Department of Computer Science and Information
Engineering, National Chung Cheng University, Chiayi, Taiwan in 1999. Dr. Hu is currently an assistant professor in the Department
of Computer Science and Information Engineering, Providence University, Sha-Lu, Taiwan. He is a member of the SPIE society
and a member of the IEEE society. He is also a member of the Phi Tau Phi Society of the Republic of China. His research interests
include image and data compression, information hiding, and image processing. 相似文献
16.
Privacy-preserving SVM classification 总被引:2,自引:2,他引:0
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. 相似文献
17.
Katsumi Tanaka Keishi Tajima Takashi Sogo Sujeet Pradhan 《New Generation Computing》2000,18(4):359-374
When dealing with long video data, the task of identifying and indexing all meaningful subintervals that become answers to
some queries is infeasible. It is infeasible not only when done by hand but even when done by using latest automatic video
indexing techniques. Whether manually or automatically, it is only fragmentary video intervals that we can identify in advance
of any database usage. Our goal is to develop a framework for retrieving meaningful intervals from such fragmentarily indexed
video data. We propose a set of algebraic operations that includes ourglue join operations, with which we can dynamically synthesize all the intervals that are conceivably relevant to a given query. In
most cases, since these operations also produce irrelevant intervals, we also define variousselection operations that are useful in excluding them from the answer set. We also show the algebraic properties possessed by those
operations, which establish the basis of an algebraic query optimization.
Katsumi Tanaka, D. Eng.: He received his B.E., M.E., and D.Eng. degrees in information science from Kyoto University, in 1974, 1976, and 1981, respectively.
Since 1994, he is a professor of the Department of Computer and Systems Engineering and since 1997, he is a professor of the
Division of Information and Media Sciences, Graduate School of Science and Technology, Kobe University. His research interests
include object-oriented, multimedia and historical databases abd multimedia information systems. He is a member of the ACM,
IEEE Computer Society and the Information Processing Society of Japan.
Keishi Tajima, D.Sci.: He received his B.S, M.S., and D.S. from the department of information science of University of Tokyo in 1991, 1993, and
1996 respectively. Since 1996, he is a Research Associate in the Department of Computer and Systems Engineering at Kobe University.
His research interests include data models for non-traditional database systems and their query languages. He is a member
of ACM, ACM SIGMOD, Information Processing Society of Japan (IPSJ), and Japan Society for Software Science and Technology
(JSSST).
Takashi Sogo, M.Eng.: He received B.E. and M.E. from the Department of Computer and Systems Engineering, Kobe University in 1998 and 2000, respectively.
Currently, he is with USAC Systems Co. His research interests include video database systems.
Sujeet Pradhan, D.Eng.: He received his BE in Mechanical Engineering from the University of Rajasthan, India in 1988, MS in Instrumentation Engineering
in 1995 and Ph.D. in Intelligence Science in 1999 from Kobe University, Japan. Since 1999 May, he is a lecturer of the Department
of Computer Science and Mathematics at Kurashiki University of Science and the Arts, Japan. A JSPS (Japan Society for the
Promotion of Science) Research Fellow during the period between 1997 and 1999, his research interests include video databases,
multimedia authoring, prototypebased languages and semi-structured databases. Dr. Pradhan is a member of Information Processing
Society of Japan. 相似文献
18.
Mining frequent patterns with a frequent pattern tree (FP-tree in short) avoids costly candidate generation and repeatedly
occurrence frequency checking against the support threshold. It therefore achieves much better performance and efficiency
than Apriori-like algorithms. However, the database still needs to be scanned twice to get the FP-tree. This can be very time-consuming
when new data is added to an existing database because two scans may be needed for not only the new data but also the existing
data. In this research we propose a new data structure, the pattern tree (P-tree in short), and a new technique, which can
get the P-tree through only one scan of the database and can obtain the corresponding FP-tree with a specified support threshold.
Updating a P-tree with new data needs one scan of the new data only, and the existing data does not need to be re-scanned.
Our experiments show that the P-tree method outperforms the FP-tree method by a factor up to an order of magnitude in large
datasets.
A preliminary version of this paper has been published in theProceedings of the 2002 IEEE International Conference on Data Mining (ICDM ’02), 629–632.
Hao Huang: He is pursuing his Ph.D. degree in the Department of Computer Science at the University of Virginia. His research interests
are Gird Computing, Data Mining and their applications in Bioinformatics. He received his M.S. in Computer Science from Colorado
School of Mines in 2001.
Xindong Wu, Ph.D.: He is Professor and Chair of the Department of Computer Science at the University of Vermont, USA. He holds a Ph.D. in Artificial
Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems,
and Web information exploration. He has published extensively in these areas in various journals and conferences, including
IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW. Dr. Wu is the Executive Editor (January 1, 1999-December
31, 2004) and an Honorary Editor-in-Chief (starting January 1, 2005) of Knowledge and Information Systems (a peer-reviewed
archival journal published by Springer), the founder and current Steering Committee Chair of the IEEE International Conference
on Data Mining (ICDM), a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP),
and the Chair of the IEEE Computer Society Technical Committee on Computational Intelligence (TCCI). He served as an Associate
Editor for the IEEE Transactions on Knowledge and Data Engineering (TKDE) between January 1, 2000 and December 31, 2003, and
is the Editor-in-Chief of TKDE since January 1, 2005. He is the winner of the 2004 ACM SIGKDD Service Award.
Richard Relue, Ph.D.: He received his Ph.D. in Computer Science from the Colorado School of Mines in 2003. His research interests include association
rules in data mining, neural networks for automated classification, and artificial intelligence for robot navigation. He has
been an Information Technology consultant since 1992, working with Ball Aerospace and Technology, Rational Software, Natural
Fuels Corporation, and Western Interstate Commission for Higher Education (WICHE). 相似文献
19.
In this paper, we shall propose a method to hide a halftone secret image into two other camouflaged halftone images. In our
method, we adjust the gray-level image pixel value to fit the pixel values of the secret image and two camouflaged images.
Then, we use the halftone technique to transform the secret image into a secret halftone image. After that, we make two camouflaged
halftone images at the same time out of the two camouflaged images and the secret halftone image. After overlaying the two
camouflaged halftone images, the secret halftone image can be revealed by using our eyes. The experimental results included
in this paper show that our method is very practicable.
The text was submitted by the authors in English.
Wei-Liang Tai received his BS degree in Computer Science in 2002 from Tamkang University, Tamsui, Taiwan, and his MS degree in Computer
Science and Information Engineering in 2004 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student
of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding,
digital watermarking, and image compression.
Chi-Shiang Chan received his BS degree in Computer Science in 1999 from National Cheng Chi University, Taipei, Taiwan, and his MS degree
in Computer Science and Information Engineering in 2001 from National Chung Cheng University, Chiayi, Taiwan. He is currently
a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are
image hiding and image compression.
Chin-Chen Chang received his BS degree in Applied Mathematics in 1977 and his MS degree in Computer and Decision Sciences in 1979, both from
National Tsing Hua University, Hsinchu, Taiwan. He received his PhD in Computer Engineering in 1982 from National Chiao Tung
University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering
at National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National
Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science
and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in
the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan.
Dr. Chang is a fellow of the IEEE, a fellow of the IEE, and a member of the Chinese Language Computer Society, the Chinese
Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests
include computer cryptography, data engineering, and image compression. 相似文献
20.
Chenggang Lu Zheru Chi Gang Chen Dagan Feng 《Journal of Mathematical Imaging and Vision》2006,26(3):301-307
This paper proposes a geometrical model for the Particle Motion in a Vector Image Field (PMVIF) method. The model introduces
a c-evolute to approximate the edge curve in the gray-level image. The c-evolute concept has three major novelties: (1) The locus of Particle Motion in a Vector Image Field (PMVIF) is a c-evolute of image edge curve; (2) A geometrical interpretation is given to the setting of the parameters for the method based
on the PMVIF; (3) The gap between the image edge’s critical property and the particle motion equations appeared in PMVIF is
padded. Our experimental simulation based on the image gradient field is simple in computing and robust, and can perform well
even in situations where high curvature exists.
Chenggang Lu received his Bachelor of Science and PhD degrees from Zhejiang University in 1996 and 2003, respectively. Since 2003, he
has been with VIA Software (Hang Zhou), Inc. and Huawei Technology, Inc. His research interests include image processing,
acoustic signaling processing, and communication engineering.
Zheru Chi received his BEng and MEng degrees from Zhejiang University in 1982 and 1985 respectively, and his PhD degree from the University
of Sydney in March 1994, all in electrical engineering. Between 1985 and 1989, he was on the Faculty of the Department of
Scientific Instruments at Zhejiang University. He worked as a Senior Research Assistant/Research Fellow in the Laboratory
for Imaging Science and Engineering at the University of Sydney from April 1993 to January 1995. Since February 1995, he has
been with the Hong Kong Polytechnic University, where he is now an Associate Professor in the Department of Electronic and
Information Engineering. Since 1997, he has served on the organization or program committees for a number of international
conferences. His research interests include image processing, pattern recognition, and computational intelligence. Dr. Chi
has authored/co-authored one book and nine book chapters, and published more than 140 technical papers.
Gang Chen received his Bachelor of Science degree from Anqing Teachers College in 1983 and his PhD degree in the Department of Applied
Mathematics at Zhejiang University in 1994. Between 1994 and 1996, he was a postdoctoral researcher in electrical engineering
at Zhejiang University. From 1997 to 1999, he was a visiting researcher in the Institute of Mathematics at the Chinese University
of Hong Kong and the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University. Since 2001,
he has been a Professor at Zhejiang University. He has been the Director of the Institute of DSP and Software Techniques at
Ningbo University since 2002. His research interests include applied mathematics, image processing, fractal geometry, wavelet
analysis and computer graphics. Prof. Chen has co-authored one book, co-edited five technical proceedings and published more
than 80 technical papers.
(David) Dagan Feng received his ME in Electrical Engineering & Computing Science (EECS) from Shanghai JiaoTong University in 1982, MSc in Biocybernetics
and Ph.D in Computer Science from the University of California, Los Angeles (UCLA) in 1985 and 1988 respectively. After briefly
working as Assistant Professor at the University of California, Riverside, he joined the University of Sydney at the end of
1988, as Lecturer, Senior Lecturer, Reader, Professor and Head of Department of Computer Science/School of Information Technologies,
and Associate Dean of Faculty of Science. He is Chair-Professor of Information Technology, Hong Kong Polytechnic University;
Honorary Research Consultant, Royal Prince Alfred Hospital, the largest hospital in Australia; Advisory Professor, Shanghai
JiaoTong University; Guest Professor, Northwestern Polytechnic University, Northeastern University and Tsinghua University.
His research area is Biomedical & Multimedia Information Technology (BMIT). He is the Founder and Director of the BMIT Research
Group. He has published over 400 scholarly research papers, pioneered several new research directions, made a number of landmark
contributions in his field with significant scientific impact and social benefit, and received the Crump Prize for Excellence
in Medical Engineering from USA. More importantly, however, is that many of his research results have been translated into
solutions to real-life problems and have made tremendous improvements to the quality of life worldwide. He is a Fellow of
ACS, HKIE, IEE, IEEE, and ATSE, Special Area Editor of IEEE Transactions on Information Technology in Biomedicine, and is
the current Chairman of IFAC-TC-BIOMED. 相似文献