报告人：香港大学教授代表团 Speaker: Professor Francis Yuk-lun Chin (钱玉麟教授) Title: Online Frequency Assignment in Wireless Communication Networks Abstract Wireless communication has many applications since its invention more than a century ago. The frequency spectrum used for communication is a scarce resource and the Frequency Assignment Problem (FAP), aiming for better utilization of the frequencies, has been extensively studied in the past 20-30 years. Because of the rapid development of new wireless applications such as digital cellular network, cellular phone, the FAP problem has become more important. In Frequency Division Multiplexing (FDM) networks, a geographic area is divided into small cellular regions or cells, usually regular hexagons in shape. Each cell contains one base station that communicates with other base stations via a high-speed wired network. Calls between any two clients (even within the same cell) must be established through base stations. When a call arrives, the nearest base station must assign a frequency from the available spectrum to the call without causing any interference with other calls. Interference may occur, which distorts the radio signals, when the same frequency is assigned to two different calls emanating from cells that are geographically close to each other. Thus the FAP problem can be viewed as a problem of multi-coloring a hexagon graph with the minimum number of colors when each vertex of the graph is associated with an integer that represents the number of calls in a cell. FAP has attracted more attention recently because of the following: New technology and application: Wideband Code-Division Multiple-Access (W-CDMA) technology is a new technology used for the implementation of 3G cellular system. Orthogonal Variable Spreading Factor (OVSF) codes are used to satisfy requests with different data rate requirements. FAP with OVSF code trees representing the frequency spectrum becomes an important problem. Biography Professor Chin received the B.A.Sc. degree from the University of Toronto, Canada, in 1972, and the M.S., M.A. and Ph.D. degrees from Princeton University, in 1974, 1975, and 1976, respectively. Since 1975, he has taught at the University of Maryland, Baltimore County, University of California, San Diego, University of Alberta, Chinese University of Hong Kong, and University of Texas at Dallas. He joined the University of Hong Kong (HKU) in 1985, where he is the Chair of the Department of Computer Science and was the founding Head of the department from its establishment until December 31, 1999. Between 1992-1996, he served as the Associate Dean of Graduate School. In 1996, Prof. Chin was elected to the grade of IEEE Fellow. Professor Chin is currently the Associate Dean of Engineering. He is also serving as Manager Editor of the International Journal of Foundations of Computer Science and on the editorial boards of several journals. He has served on the program committees and as conference chairman of numerous international workshops and conferences. Professor Chin's research interests include design and analysis of algorithms, on-line algorithms, and bioinformatics. Speaker: Professor David Wai-lok Cheung (张伟牢教授) Title: Security and Integrity in Outsourcing of Data Mining Abstract Outsourcing of data mining to an outside service provider brings important benefits to the data owner. These include (i) relief from the high mining cost, (ii) minimization of demands in resources, and (iii) effective centralized mining for multiple distributed owners. However, security and integrity are issues that must be tackled before enterprises can indeed outsource data mining task. The service provider should be prevented from accessing the actual data (security), and the results returned to the owner must be authentic (integrity). In this talk, we will present the result of a secure association rules mining algorithm that we have published recently to explain the outsourcing model and to illustrate the feasibility of an approach we used. In protecting the security in mining association rules, a substitution cipher technique has been proposed in the encryption of transactional data. After identifying the non-trivial threats to a straightforward one-to-one item mapping substitution cipher, we propose a novel secure encryption algorithm based on a one-to-n item mapping that transforms transactions non-deterministically, yet guarantees correct decryption. We will discuss other important problems that can be developed in this new area. Biography Professor David Wai-lok Cheung is Head of Department of Computer Science and Director of the Center for E-commerce Infrastructure Development (CECID) in The University of Hong Kong. He is an active researcher in database, data mining and e-commerce technologies. His recent research covers security and integrity in outsourcing of data mining, data interoperability theory and xml schema transformation, projected clustering, sequential OLAP, and semantic query and searching. He has published on many leading venues including SIGMOD, VLDB, ICDE and KDD conferences. He was the recipient of the HKU Outstanding Researcher Award. He was the program chairman of the 2001 and 2005 Pacific-Asia Knowledge Discovery and Data Mining Conferences, the conference chairman of the 2007 PAKDD Conference in Nanjing. He was the Program Vice Chair of ICDM 2006 and Program Chair of HKICC 2003. In applied research, under the directorship of Professor Cheung, CECID has received prestigious grants from the Hong Kong SAR Government in a total amout of HK$34M. He and his team has developed open-source ebXML gateway used by developers from more than 80+ countries. The open-source product has received the prominent awards at the Hong Kong 2004 IT Excellence Awards competition, the 2004 Asia-Pacific ICT Awards competition, and the 2005 Linux Business Awards competition. Speaker : Dr. Nikos Mamoulis (孟每思博士) Title: Evaluation of complex queries on spatial and multi-dimensional data Abstract In this talk, I will give an outline of our recent research projects at the University of Hong Kong related to the analysis of spatial and multi-dimensional data, which are managed in a database or are continuously produced by data streams. We are especially interested in complex queries and mining tasks applied on such information. The first problem that I will discuss is about the continuous processing of spatial join queries on event data that arrive from a stream. We will also see how such queries can be evaluated in a sensor network. Then, I will talk about top-k queries and the efficient aggregation of ranked inputs of partial object scores. Another problem that I will discuss is the evaluation of top-k dominating queries, which are a means of identifying popular objects based on their attribute values in a multi-dimensional space. Finally, the application of data mining tasks on uncertain data will be discussed. Biography Nikos Mamoulis received the diploma in computer engineering and informatics in 1995 from the University of Patras, Greece, and the PhD degree in computer science in 2000 from the Hong Kong University of Science and Technology. Since September 2001, he has been a faculty member of the Department of Computer Science at the University of Hong Kong, currently an associate professor. In the past, he has worked as a postdoctoral researcher at the Centrum voor Wiskunde en Informatica (CWI), the Netherlands. His research interests include complex data management, data mining, advanced indexing and query processing, and constraint satisfaction problems. He has published more than 75 articles in reputable international conferences and journals and served in the program committees of numerous database and data mining conferences. Speaker: Dr. Wenping Wang (王文平博士) Title: A Variational Approach to 3D Shape Segmentation for Bounding Volume Computation Abstract A new variational approach to computing the bounding volumes of a 3D shape via shape segmentation will be discussed. A novel functional with sound theoretical justification is given to govern an optimization process to obtain a partition with multiple components. Refinement of segmentation is driven by application-specific error measures to account for overlapping between bounding primitives, so that the final bounding volume meets pre-specified user requirements. Examples will be presented to demonstrate the effectiveness of the method and show that it works well for computing ellipsoidal bounding volumes as well as rectangular bounding boxes. Biography Dr. Wenping Wang is an Associate Professor of Computer Science at University of Hong Kong. His research covers computer graphics, geometric computing and visualization, and he has published widely in these areas. He got his Ph.D. in Computer Science from University of Alberta in 1992. He is associate editor of the journal Computer Aided Geometric Design, and has been program co-chair of several international conferences, including Geometric Modeling and Processing 2000, ACM Symposium on Virtual Reality Software and Technology, Pacific Graphics 2003, and ACM Symposium on Solid and Physical Modeling 2006. Speaker: Professor Francis Yuk-lun Chin (钱玉麟教授) Title: Finding Motifs Computationally Abstract Finding common patterns, or motifs, in the promoter regions of co-expressed genes is an important problem in bioinformatics. Despite years of efforts in solving this problem, less than 30% of the known motifs in TRASNSFAC can be found computationally. Motifs can be represented by strings or probability matrix. In this talk, we shall present approaches and formulations of this problem based on these two common motif representations. The planted (l,d)-motif problem (PMP) is formulated for string representation, where l is the length of the motif and d is the maximum Hamming Distance between the similar patterns. We shall introduce new algorithms to solve this motif problem and its variations. Finding motifs based on matrix representation is very difficult. Even for a motif length 6 or 7, there is no algorithm that can guarantee finding the exact optimal matrix from an infinite number of possible matrices. New advances in finding the optimal matrix will be discussed in this talk. Finally, some new motif discovering algorithms, which assume extra biological knowledge and other types of representations to greatly improve performance, will be highlighted. Biography Professor Chin received the B.A.Sc. degree from the University of Toronto, Canada, in 1972, and the M.S., M.A. and Ph.D. degrees from Princeton University, in 1974, 1975, and 1976, respectively. Since 1975, he has taught at the University of Maryland, Baltimore County, University of California, San Diego, University of Alberta, Chinese University of Hong Kong, and University of Texas at Dallas. He joined the University of Hong Kong (HKU) in 1985, where he is the Chair of the Department of Computer Science and was the founding Head of the department from its establishment until December 31, 1999. Between 1992-1996, he served as the Associate Dean of Graduate School. In 1996, Prof. Chin was elected to the grade of IEEE Fellow. Professor Chin is currently the Associate Dean of Engineering. He is also serving as Manager Editor of the International Journal of Foundations of Computer Science and on the editorial boards of several journals. He has served on the program committees and as conference chairman of numerous international workshops and conferences. Professor Chin's research interests include design and analysis of algorithms, on-line algorithms, and bioinformatics. Speaker: Professor Francis Chi-moon Lau (刘智满教授) Title: Object Co-location and Memory Reuse for Java Programs Abstract We introduce a new memory management system, STEMA, which can improve the execution time of Java programs. STEMA detects prolific types on-the-fly and co-locates their objects in a special memory space which supports reuse of memory. We argue and show that memory reuse and co-location of prolific objects can result in improved cache locality, reduced memory fragmentation, reduced GC time, and faster object allocation. We evaluate STEMA using 16 benchmarks. Experimental results show that STEMA performs 2.7%, 4.0%, and 8.2% on average better than MarkSweep, CopyMS, and SemiSpace. Biography Francis Lau is a professor in the Computer Science Department, The University of Hong Kong. He obtained his PhD degree in computer science from the University of Waterloo in 1986. His research interests include operating systems, parallel and distributed systems, pervasive computing, wireless networks, computer graphics, and computer music. More information can be found at http://www.cs.hku.hk/~fcmlau. |
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