Events - 2007

Departmental Colloquium

Information Hiding – Watermark Approach
Two approaches to watermark embedding: one is lossy and the other is lossless

Dr. Shi-Jinn Horng
Professor and Dean, College of Electrical Engineering and Computer Science
National United University, Taiwan
Professor, Department of Computer Science and Information Engineering
National Taiwan University of Science and Technology

Watermarking, which belongs to the information hiding field, has seen a lot of research interest recently. Watermarking is used for content protection, copyright management, content authentication and tamper detection. Once the watermark is embedded, it can experience several attacks because the multimedia object (e.g., image) can be digitally processed. The attacks can be unintentional (like low-pass filtering or compression) or intentional (like scaling or cropping). Hence the watermark has to be very robust against all these possible attacks. Watermarking can be blind or non-blind; also the embedded watermarked image can be lossy if the original image is modified during embedding and lossless if it is not. In this talk, for the lossless image, we propose a method based on the 1/T rate forward error correction. The watermark logo is first fused with noise bits to improve the security, and later XORed with the feature value of the image by 1/T rate FEC. During extraction, the watermark bits are determined by majority voting. For the lossy image, we propose a method based on the significant difference of wavelet coefficients. Every seven non-overlap wavelet coefficients of the host image are grouped into a block. The largest two coefficients in a block are called significant coefficients in this talk; and their difference is called the significant difference. We quantized the local maximum wavelet coefficient in a block so that the significant difference between watermark bit 0 and watermark bit 1 exhibits a large energy difference which can be used for watermark extraction. The experimental results show that the two proposed methods are quite robust under different kind of attacks.

About the Speaker: Shi-Jinn Horng is a Visiting Professor in the Department of Computer Science at Georgia State University. He is a Professor and Dean of the College of Electrical Engineering and Computer Science, National United University, Miaoli, Taiwan. He is also a Professor in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, where he is currently taking a leave of absence. He received the B.S. degree in Electronics Engineering from National Taiwan Institute of Technology, Taipei, the M.S. degree in Information Engineering from National Central University, Taiwan, and the Ph.D. degree in Computer Science from National Tsing Hua University, Taiwan, in 1980, 1984, and 1989, respectively. He spent his sabbatical years as a Visiting Professor at the Computer Science Department, University of Dayton, Ohio, in 2000; Institute of Information Science, Academia Sinica, in 2001; National Mongolia University, in 2004; Institute of Mobile Communications, Southwest Jiaotong University, in 2004. He also worked as a PMTS at AT&T Bell Laboratories from 1990 to 1991. His research interests include VLSI design, multiprocessing systems, and multi-medium and parallel algorithms. He has published more than 100 research papers and received many awards; especially, the Distinguished Research Award between 2004 and 2006 from the National Science Council in Taiwan; Outstanding I.T. Elite Award, in 2005; Outstanding EE Prof. Award, the Chinese Institute of Electrical Engineering; and the Outstanding Research and Invention Award between 2006 and 2008 from National Taiwan University of Science and Technology.

Wednesday, September 26, 2007
1:30 p.m.–2:30 p.m.
Department Conference Room

Departmental Colloquium

Reliability in Grid Computing

Dr. Yuanshun Dai
Department of Industrial and Information Engineering
Department of Electrical Engineering and Computer Science
University of Tennessee

Grid computing is widely implemented in today’s computer and network systems, which focuses on large-scale resource sharing and wide-area collaboration. However, a big challenge in grid computing is reliability, especially when collaborating with anonymous peers all over the global Internet. A variety of reliability models, algorithms, mechanisms, and optimizations are presented. Then, a new technique of self-healing and self-protection for autonomic improvement on reliability in grid computing is elaborated. Finally, some ongoing projects that applied the above technologies will be shown, including the NASA ANTS project.

About the Speaker: Yuan-Shun Dai is an assistant professor with both the Department of Electrical Engineering and Computer Science and the department of Industrial and Information Engineering at the University of Tennessee, Knoxville. Before that, he was an assistant professor with the Computer Science Department of Purdue University School of Science at IUPUI since 2004. He received his Ph.D. frmo the National University of Singapore in 2004 and his bachelor's degree from Tsinghua University in 2000. His research is in dependability, security, grid computing, and autonomic computing. He has published 4 books, 40 journal papers (including 15 in IEEE/ACM Transactions), and 70 articles in these areas. Dr. Dai is a guest editor for IEEE Transactions on Reliability. He is also program chair for the 12th IEEE Pacific Rim Symposium on Dependable Computing (PRDC2006). He is a founder of the conference series IEEE Symposium on Dependable Autonomic and Secure Computing (DASC), acting as general chair for the 1st/2nd/3rd (DASC ’05, '06, '07). This DASC series has become the flagship conference of the IEEE Task Force on Autonomous and Autonomic Systems (TF-AAS). He is also the leader for the technical area on “Autonomic Management of Scalable Computing Systems” in the IEEE Technical Committee on Scalable Computing (TCSC). He has also chaired many other conferences and is on the editorial board of some journals, e.g. he was a guest editor for Lecture Notes in Computer Science, for Journal of Computer Science, and for International Journal of Autonomic and Trusted Computing.

Friday, May 18, 2007
1:30 p.m.–2:30 p.m.
Department Conference Room

Departmental Colloquium

Multiobjective Control of Time-Discrete Systems and Dynamic Games on Networks

Dr. Dmitrii Lozovanu
Department of Mathematics & Computer Science
Moldova State University

We study multiobjective discrete control problems using a game-theoretic approach. We consider time-discrete systems with a finite set of states. The starting and the final states of the dynamical system are fixed. We assume that the dynamics of the system is controlled by p actors (players) and each of them intends to optimize his own integral-time cost of system's passages by a certain trajectory. Applying Stackelberg, Nash, and Pareto optimality principles for such a model we obtain multiobjective control problems, solutions of which correspond to solutions of hierarchical, noncooperative, and cooperative dynamic games, respectively. Necessary and sufficient conditions for the existence of Nash equilibrium and Pareto optimum in considered game control models are derived. Such conditions for stationary and nonstationary cases of the dynamic games are formulated. In the following we extend dynamic programming technique for determining Nash equilibrium and Pareto optimum for dynamic games in positional form, especially for dynamic games on networks. Efficient polynomial-time algorithms are elaborated for finding optimal strategies of players in dynamic games on networks. In addition computational complexity of the proposed algorithms for the considered class of dynamic problems is discussed. Some extensions and generalizations of obtained results are suggested.

Wednesday, May 2, 2007
4:30 p.m.–5:30 p.m.
Department Conference Room

Departmental Colloquium

Revealing Divergent Evolution, Identifying Circular Permutations, and
Detecting Active-Sites by Protein Structure Comparison

Dr. Luonan Chen
Professor, Department of Electrical Engineering and Electronics, Osaka Sangyo University
Institute of Industrial Science, The University of Tokyo
Institute of Systems Biology, Shanghai University

Protein structure comparison is one of the most important problems in computational biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction, and protein docking. In this talk, I will describe a novel method to compare protein structures in an accurate and efficient manner. Such a method can be used not only to reveal divergent evolution but also to identify circular permutations and further detect active-sites. Specifically, we define structure alignment as a multi-objective optimization problem, i.e., maximizing the number of aligned atoms and minimizing their root-mean-square distance. By controlling a single distance-related parameter, theoretically we can obtain a variety of optimal alignments corresponding to different optimal matching patterns, i.e., from a large matching portion to a small matching portion. The number of variables in our algorithm increases with the number of atoms of protein pairs in almost a linear manner. In addition to a solid theoretical background, numerical experiments demonstrated significant improvement of our approach over the existing methods in terms of quality and efficiency. Convergence of computation is shown in experiments and is also theoretically proven. In particular, we show that divergent evolution, circular permutations, and active-sites (or structural motifs) can be identified by our method. The SAMO software is available at http://intelligent.eic.osaka-sandai.ac.jp/chenen/samo.htm.

References:

[1] Chen, L., Wu, L-Y., Wang, Y., Zhang, S., Zhang, X-S. Revealing Divergent Evolution, Identifying Circular Permutations and Detecting Active-Sites by Protein Structure Comparison, BMC Structural Biology, doi:10.1186/1472-6807-6-18, 2006.
[2] Chen, L., Zhou, T., Tang, Y. Protein Structure Alignment by Deterministic Annealing, Bioinformatics, 21, 51-62, 2005
[3] Zhou, T., Chen, L., Tang, T., Zhang, X-S. Aligning Multiple Protein Structures by Deterministic Annealing, Journal of Bioinformatics and Computational Biology, 3, 837-860, 2005.

Friday, March 23, 2007
2:00 p.m.–3:00 p.m.
Department Conference Room

Departmental Colloquium

Membrane Systems: An Unconventional Model for Computation (and Simulation)

Dr. Andrei Paun
Institute for Micromanufacturing
Department of Computer Science
Louisiana Tech University

My research work is a step in the direction of better understanding the compartmentalized way that the eukaryotic cells process information. I am interested in the computing and simulation power of such a compartmental system that works in a nondeterministic maximally parallel manner. In this talk, I will review several such models that could form the basis of computing with cells and present new results in the field.

Details of the recent simulation results for simulation signal transduction, more specifically to simulating the FAS apoptotic pathway will be presented. We consider two different situations: healthy cells and cells in an HIV infection. I will also  provide a description of the proposed simulation technique and comparison with ODE and Gillespie simulators.

Wednesday, March 14, 2007
1:00 p.m.–2:00 p.m.
Department Conference Room

Departmental Colloquium

Multi-granular Waveband Switching in Optical Networks

Dr. Xiaojun Cao
Department of Networking, Security, and Systems Administration
B. Thomas Golisano College of Computing and Information Sciences
Rochester Institute of Technology

An optical network employing wavelength division multiplexing (WDM) is a promising solution to meet the high-bandwidth requirements of emerging applications such as IPTV, VoIP, e-science and e-healthcare. The rapid advances in dense WDM technology with hundreds of wavelengths per fiber and world-wide fiber deployment have brought about a tremendous increase in the size (i.e. number of ports) of optical cross-connects, as well as in the cost and difficulty associated with controlling such large cross-connects. The talk will introduce a new switching paradigm called multi-granular waveband switching (WBS), which, in conjunction with new Multi-Granular Optical Cross-connects (or MG-OXCs), can significantly reduce the port count, associated control complexity, and cost of optical cross-connects. In particular, it is shown that WBS is different from traditional wavelength routing in terms of challenges such as MG-OXC architecture design, how to satisfy static (offline) traffic, how to accommodate dynamic (online) traffic, how to efficient utilize wavelength/waveband conversion, and how to provide protection/restoration. Then the talk focuses on one particular challenge, i.e., how to satisfy a set of static traffic with minimal number of ports in multi-granular switching network, which is shown to be an NP-complete problem. To solve this, an integer linear programming model, efficient heuristic algorithms and numerical analysis are presented.

About the Speaker: Xiaojun Cao is an assistant professor in the College of Computing and Information Sciences at Rochester Institute of Technology. He received the B.S. degree in Engineering Physics from Tsinghua University, Beijing, China, in 1996, the M.S. degree in EE from Chinese Academy of Sciences in 1999, and the Ph.D. degree in Computer Science from The State University of New York at Buffalo in 2004. His research interests include modeling, analysis, and protocols/algorithms design for optical and wireless networks. Dr. Cao received the NSF CAREER award in 2006.

Monday, February 19, 2007
1:00 p.m.–2:00 p.m.
Department Conference Room

Departmental Colloquium

Protein Interaction Module Detection Using Graph Algorithms

Dr. Chris Ding
Staff Computer Scientist
Computational Research Division
Lawrence Berkeley National Laboratory

Proteins carry out most cellular processes as protein modules. Systematic identification of protein functional modules provide essential knowledge linking proteome dynamics to cellular function and phenotype. This is one of the most challenging tasks at present after most genomes are successfully sequenced and genes identified. We give a brief introduction to the rapidly evolving field of genomics and clarify the vital role of protein interaction studies. We then describe two graph algorithms for computing protein modules: the spectral clustering and clique/biclique finding. The spectral clustering formulates the problem as eigenvectors of the graph Laplacian. The maximal clique/biclique finding algorithms use a new type of constrained quadratic optimizations. Both algorithms have complexity of O(||E||) (# of edges) and can be efficiently implemented on parallel architectures. We present a large number of results on protein interaction modules discovered in S. cerevisiae (yeast), Pyrococcus, Sulfolobus, Halobacterium which are important micro-organisms for environmental studies. Some of these discovered protein complexes have been experimentally verified by our collaborators. We discuss the biological significance of the discovered protein modules. A number of uncharacterized proteins are found to be new members of important protein complexes.

About the Speaker: Chris Ding is a staff computer scientist at Lawrence Berkeley National Laboratory. His research focus is on bioinformatics, data mining, information retrieval, and high-performance computing. He earned a Ph.D. from Columbia University and worked at Caltech and the Jet Propulsion Lab before joining Berkeley Lab in 1996. He served on several NSF review panels, the program committees of many data mining and bioinformatics conferences, and the editorial board of International Journal of Data Mining and Bioinformatics.

Thursday, February 15, 2007
2:30 p.m.–3:30 p.m.
Department Conference Room

Departmental Colloquium

Ant Colony Optimization for Bioinformatics Problems

Dr. Ling Chen
Department of Computer Science
Information Technology College
Yangzhou University

Ant Colony Optimization (ACO) is a new evolutionary algorithm. It successfully solves the TSP problem by simulating the ants’ food-hunting activities. It has shown exceptional performance in solving complex optimization problems, especially NP-hard combinational optimization problems. The main thrust of this presentation is to demonstrate the applications of ACO for bioinformatics problems, including multiple sequence alignment, gene expressing data bi-clustering, and phylogenetic tree generation. Experimental results on these bioinformatics problems show that ACO can deliver high-quality solutions more efficiently than traditional methods.

About the Speaker: Ling Chen is a Professor in the Department of Computer Science and the Dean of the Information Technology College, Yangzhou University, China. He was a visiting Associate Professor in the Department of Computer Science, University of Pittsburgh from 1992–1993. His research interests include parallel and distributed computing, computer architecture, image processing, bioinformatics, and optimization algorithms. He has co-edited 6 books/proceedings, and published more than 190 research papers, including over 70 journal papers. He was awarded the Government Special Allowance by the State Council and the Award of Progress in Science and Technology by the Government of Anhui Province. He was named a “National Excellent Teacher” by the Chinese Ministry of Education, “Young and Middle-aged Experts with Outstanding Contributions” by the government of Jiangsu Province, and “Famous University Teacher” by the Jiangsu Province Government. His research has been supported by the National Science Foundation of China, the Science Foundation of Jiangsu Province, and the Foundation of Chinese National Key Lab of Novel Software Technology. He has organized several academic conferences and workshops and has also served as a program committee member for several major international conferences, such as ICDCS-ECS04 , PDSEC-04, ISPA-05, GrC06 and ICMLC2007. Prof. Chen is a member of the IEEE CS society and a member of the Academic Committee of Office Automation, Chinese Computer Society. He is also the chairman of the Yangzhou Computer Society, director of the Jiangsu Computer Application Society, a member of the Electronics Academic Committee, Jiangsu Electronics Society, a member of the Committee of Computer Education of the Chinese High Education Research Society, and director of the Society of College Electronics Education.

Tuesday, February 13, 2007
3:00 p.m.–4:00 p.m.
Department Conference Room

Departmental Colloquium

Hierarchical Classification of Genes and
Prediction of Functional Modules for Bacterial Genomes

Dr. Hongwei Wu
Post-Doctoral Research Associate
Computational Systems Biology Laboratory
Department of Biochemistry and Molecular Biology and Institute of Bioinformatics
University of Georgia

Since the late eighties and early nineties when the Human Genome Project was initiated, a large volume of genomic data of different organisms have been made available thanks to world-wide sequencing efforts; with the development of high-throughout experiment technologies, a lot of measurements about functional and structural properties of biological molecules have also been made available. In this post-genome era, the focuses of the field of computational biology and bioinformatics are to use computer-based methods to analyze and interpret these data.

Biological functions of genes can be described from two perspectives. One perspective is to describe the activities of genes and their products at the molecular level; the other perspective is to describe the roles of genes and their products in the biological processes in which they participate. Accordingly, there are generally two kinds of methods to predict biological functions of newly sequenced genes. One is to identify the genes in those well-investigated genomes that are similar to the unknown genes; the other is to identify the genes that are functionally related to the unknown genes.

This talk will focus on my work on the hierarchical classification of genes and the prediction of functional modules for bacterial genomes, which belong to the two different kinds of methods for the prediction of gene functions, respectively. Our studies on the hierarchical classification of genes can not only be used to provide functional annotations of newly sequenced genes from multiple resolution levels, but can also be used to reveal the evolutionary trace of genes and genomes. Whereas, our studies on the prediction of functional modules can not only be used to reveal the functional relatedness between genes, but also represent a key step towards deciphering biological networks/pathways in a systematic way.

About the Speaker: Hongwei Wu is currently a post-doctoral research associate with the Computational System Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia. She received her Ph.D. and M.S. degrees in Electrical Engineering from the University of Southern California in 2004 and 2002, respectively, and her M.Eng. and B.Eng. from Tsinghua University of China in 1999 and 1997, respectively. Her current research interests are in the broad areas of computational biology/bioinformatics with focuses on (1) comparative genomic analyses, (2) computationally reconstructing (modelling, estimating, simulating and predicting) gene pathways/networks, and (3) computational intelligence theories and applications to computational biology/bioinformatics, signal processing, and machine learning.

Friday, February 9, 2007
1:15 p.m.–2:15 p.m.
Department Conference Room

Molecular Basis of Disease Distinguished Lecture Series

Algorithms for Estimating and Reconstructing the
History of Meiotic Recombination in Populations

Daniel M. Gusfield
Professor
University of California, Davis
Editor-in-Chief
IEEE/ACM Transactions on Computational Biology and Bioinformatics

Abstract coming.

Thursday, February 1, 2007
10:00 a.m.–11:00 a.m.
441 Natural Science Center