Spontaneous Speech Parsing in Travel Information Inquiring and Booking Systems

Grammar-based parsing is a prevalent method for natural language understanding(NLU)and has been introduced into dialogue systems for spoken language processing (SLP).A robust parsing scheme is proposed in this paper to overcome the notorious phenomena,such as garbage,ellipsis,word disordering,fragment ,and ill-form,which frequently occur in splien utterances,Keyword categories are used as terminal symbols,and the definition of grammar is extended by introducing three new rule types,by-passing,up-messing and overcrossing,in addition to the general rules called up-tying in this paper,and the use of semantic items simplifies the semantics extraction.The corresponding parser marionette,which is essentially a partial chart parser,is enhanced to parse the semantic grammar.The robust parsing scheme integrating the above methods has been adopted in an air traveling information service system called EasyFlight,and has achieved a high performance when used for parsing spontaneous speeches.     

HarkMan—A Vocabulary-Independent Keyword Spotter for Spontaneons Chinese Speech

In this paper,a noverl technique adopted in HarkMan is introduced.HarkMan is a keywore-spotter designed to automatically spot the given words of a vocabulary-independent task in unconstrained Chinese telephone speech.The speaking manner and the number of keywords are not limited.This paper focuses on the novel technique which addresses acoustic modeling,keyword spotting network,search strategies,robustness,and rejection.The underlying technologies used in HarkMan given in this paper are useful not only for keyword spotting but also for continuous speech recognition.The system has achieved a figure-of-merit value over 90%.     

A Rejection Model Based on Multi-Layer Perceptrons for Mandarin Digit Recognition

High performance Mandarin digit recognition(MDR)is much more difficult to achieve than its English counterpart,especially on inexpensive hardware implementation.In this paper,a new ,Multi-Layer Perceptrons(MLP)based postprocessor,an a posteriori probability estimator is presented and used for the rejection model of the speaker independent Mandarin digit recognition system based on hidden Markov model(HMM).Poor utterances,which are recognized by HMMs but have low a posteriori probability,will be rejected.After rejecting about 4.9% of the tested utteraces,the MLP rejection model can boost the digit recognition accuracy from 97.1%to 99.6%,The performance is better than those rejection models based on linear discrimiantion,likelihood ratio or anti-digit.     

Segmentation of stick text based on sub connected area analysis

A new stick text segmentation method based on the sub connected area analysis is introduced in this paper.The foundation of this method is the sub connected area representation of text image that can represent all connected areas in an image efficiently.This method consists mainly of four steps:sub connected area classification,finding initial boundary following point,finding optimal segmentation point by boundary tracing,and text segmentaton.This method is similar to boundary analysis method but is more efficient than boundary analysis.     

Fast evaluation of Bounded Slice-line Grid

Bounded Slice-line Grid (BSG) is an elegant representation of block placement, because it is very intuitionistic and has the advantage of handling various placement constraints. However, BSG has attracted little attention because its evaluation is very time-consuming. This paper proposes a simple algorithm independent of the BSG size to evaluate the BSG representation in O(nloglogn) time, where n is the number of blocks. In the algorithm, the BSG-rooms are assigned with integral coordinates firstly, and then a linear sorting algorithm is applied on the BSG-rooms where blocks are assigned to compute two block sequences, from which the block placement can be obtained in O(n log logn) time. As a consequence, the evaluation of the BSG is completed in O(nloglogn) time, where n is the number of blocks. The proposed algorithm is much faster than the previous graph-based O(n^2) algorithm. The experimental results demonstrate the efficiency of the algorithm.     

Improving retrieval performance by region constraints and relevance feedback

In this paper, region features and relevance feedback are used to improve the performance of CBIR. Unlike existing region-based approaches where either individual regions are used or only simple spatial layout is modeled, the proposed approach simultaneously models both region properties and their spatial relationships in a probabilistic framework. Furthermore, the retrieval performance is improved by an adaptive filter based relevance feedback. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.     

An efficient method for geo-referenced video mosaicing for environmental monitoring

Environmental monitoring applications require seamless registration of optical data into large area mosaics that are geographically referenced to the world frame. Using frame-by-frame image registration alone, we can obtain seamless mosaics, but it will not exhibit geographical accuracy due to frame-to-frame error accumulation. On the other hand, the 3D geo-data from GPS, a laser profiler, an INS system provides a globally correct track of the motion without error propagation. However, the inherent (absolute) errors in the instrumentation are large for seamless mosaicing. The paper describes an effective two-track method for combining two different sources of data to achieve a seamless and geo-referenced mosaic, without 3D reconstruction or complex global registration. Experiments with real airborne video images show that the proposed algorithms are practical in important environmental applications. Zhigang Zhu received his B.E., M.E. and Ph.D. degrees, all in computer science from Tsinghua University, Beijing, in 1988, 1991 and 1997, respectively. He is currently an associate professor in the Department of Computer Science, the City College of the City University of New York. Previously, he was an associate professor at Tsinghua University, and a senior research fellow at the University of Massachusetts, Amherst. His research interests include 3D computer vision, HCI, virtual/augmented reality, video representation, and various applications in education, environment, robotics, surveillance and transportation. He has published over 90 technical papers in the related fields. He is a member of IEEE and ACM. Edward M. Riseman received his B.S. degree from Clarkson College of Technology in 1964 and his M.S. and Ph.D. degrees in electrical engineering from Cornell University in 1966 and 1969, respectively. He joined the Computer Science Department at UMass-Amherst as assistant professor in 1969, has been a professor since 1978, and served as chairman of the department from 1981 to 1985. Professor Riseman has conducted research in computer vision, artificial intelligence, learning, and pattern recognition, and has more than 200 publications. He has co-directed the Computer Vision Laboratory since its inception in 1975. Professor Riseman has been on the editorial boards of Computer Vision and Image Understanding (CVIU) from 1992 to 1997 and of the International Journal of Computer Vision (IJCV) from 1987 to the present. He is a senior member of IEEE, and a fellow of AAAI. Allen R. Hanson received his B.S. degree from Clarkson College of Technology in 1964 and his M.S. and Ph.D. degrees in electrical engineering from Cornell University in 1966 and 1969, respectively. He joined the Computer Science Department at UMass-Amherst as an associate professor in 1981, and has been a professor there since 1989. Professor Hanson has conducted research in computer vision, artificial intelligence, learning, and pattern recognition, and has more than 150 publications. He is co-director of the Computer Vision Laboratory at UMass-Amherst, and has been on the editorial boards of the following journals: Computer Vision, Graphics and Image Processing 1983–1990, Computer Vision, Graphics, and Image Processing—Image Understanding 1991–1994, and Computer Vision and Image Understanding 1995–present. Howard Schultz received a M.S. degree in physics from UCLA in 1974 and a Ph.D. in physical oceanography from the University of Michigan in 1982. Currently, he is a senior research fellow with the Computer Science Department at the University of Massachusetts, Amherst. His research interests include quantitative methods for image understanding and remote sensing. The current focus of his research activities are on developing automatic techniques for generating complex, 3D models from sequences of images. This research has found application in a variety of programs including real-time terrain modeling and video aided navigation. He is a member of the IEEE, the American Geophysical Union, and the American Society of Photogrammetry and Remote Sensing.     

An Optimum Placement Search Algorithm Based on Extended Corner Block List

A non-slicing approach,Corner Block List(CBL),has been presented recently.Since CBL only can represent floorplans without empty rooms,the algorithm based on CBL cannot get the optimum placement.In this paper,an extended corner block list,ECBLλ,is proposed.It can represent non-slicing floorplan including empty rooms.Based on the optimum solution theorem of BSG(bounded-sliceline grid),it is proved that the solution space of ECBLn,where n is the number of blocks,contains the optimum block placement with the minimum area.A placement algorithm based on ECBLλ,whose solution space can be controlled by setting λ,the extending ratio,is completed.Whenλ is set as n,the algorithm based on ECBLn is the optimum placement search algorithm.Experiments show that λ has a reasonable constant range for building block layout problem,so the algorithm can translate an ECBLλ representation to its corresponding placement in O(n) time,Experimental results on MCNC benchmarks show promising performance with 7% improvement in wire length and 2% decrease in dead space over algorthms based on CBL.Meanwhile,compared with other algorithms,the proposed algorithm can get better results with less runtime.     

S-Club: an overlay-based efficient service discovery mechanism in CROWN Grid

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.     

A new approach to fuzzy modeling and control for nonlinear dynamic systems: Neuro-fuzzy dynamic characteristic modeling and adaptive control mechanism

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example. Recommended by Editor Young-Hoon Joo. This work was jointly supported by National Natural Science Foundation of China under Grant 60604010, 90716021, and 90405017 and Foundation of National Laboratory of Space Intelligent Control of China under Grant SIC07010202. Xiong Luo received the Ph.D. degree from Central South University, Changsha, China, in 2004. From 2005 to 2006, he was a Postdoctoral Fellow in the Department of Computer Science and Technology at Tsinghua University. He currently works as an Associate Professor in the Department of Computer Science and Technology, University of Science and Technology Beijing. His research interests include intelligent control for spacecraft, intelligent optimization algorithms, and intelligent robot system. Zengqi Sun received the bachelor degree from Tsinghua University, Beijing, China, in 1966, and the Ph.D. degree from Chalmers University of the Technology, Gothenburg, Sweden, in 1981. He currently works as a Professor in the Department of Computer Science and Technology, Tsinghua University. His research interests include intelligent control of robotics, fuzzy neural networks, and intelligent flight control. Fuchun Sun received the Ph.D. degree from Tsinghua University, Beijing, China, in 1998. From 1998 to 2000, he was a Postdoctoral Fellow in the Department of Automation at Tsinghua University, where he is currently a Professor in the Department of Computer Science and Technology. His research interests include neural-fuzzy systems, variable structure control, networked control systems, and robotics.     

Word Spotting Based on a posterior Measur of Keyword Confidence

In this paper,an approach of keyword confidence estimation is developed that ewll combines acoustic layer scores and syllable-based statistical language model(LM)scores.An a posterioir(AP)confidence measure and its forward-backward calculating algorithm are deduced.A zero false alarm(ZFA) assumption is proposed for evaluating relative confidence measures by word spotting task.In a word spotting experiment with a vocabulary of 240 keywords,the keyword accuracy under the AP measure is above 94%,which well approaches its theoretical upper limit.In addition,a syllable lattice Hidden Markov Model(SLHMM) is formulated and a unified view of confidence estimation.word spotting,optimal path search,and N-best syllable re-scoring is presented ,The proposed AP measure can be easily applied to various speech recognition systems as well.     

Adaptive filtering under minimum information divergence criterion

Traditional filtering theory is always based on optimization of the expected value of a suitably chosen function of error, such as the minimum mean-square error (MMSE) criterion, the minimum error entropy (MEE) criterion, and so on. None of those criteria could capture all the probabilistic information about the error distribution. In this work, we propose a novel approach to shape the probability density function (PDF) of the errors in adaptive filtering. As the PDF contains all the probabilistic information, the proposed approach can be used to obtain the desired variance or entropy, and is expected to be useful in the complex signal processing and learning systems. In our method, the information divergence between the actual errors and the desired errors is chosen as the cost function, which is estimated by kernel approach. Some important properties of the estimated divergence are presented. Also, for the finite impulse response (FIR) filter, a stochastic gradient algorithm is derived. Finally, simulation examples illustrate the effectiveness of this algorithm in adaptive system training. Recommended by Editorial Board member Naira Hovakimyan under the direction of Editor Jae Weon Choi. This work was supported in part by the National Natural Science Foundation of China under grants 50577037 and 60604010. Badong Chen received the B.S. and M.S. degrees in Control Theory and Engineering from Chongqing University, Chongqing, China, in 1997 and 2003, respectively, and the Ph.D. degree in Computer Science and Technology from Tsinghua University, Beijing China, in 2008. He is currently a Postdoctor of the Institute of Manufacturing Engineering, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China. His research interests are in signal processing, adaptive control, and information theoretic aspects of control systems. Yu Zhu received the B.S. of Radio Electronics in 1983 at Beijing Normal University, and the M.S. of Computer Applications in 1993, and the Ph.D. of Mechanical Design and Theory in 2001 at China University of Mining & Technology. He is now a Professor of the Institute of Manufacturing Engineering of Department of Precision and Mechanology of Tsinghua University. His current research interests are parallel machanism and theory, two photon micro-fabrication, ultra-precision motion system and motion control. Jinchun Hu received the Ph.D. in Control Science and Engineering from Nanjing University of Science and Technology, Nanjing, China, in 1998. Since then, he has been a postdoctoral researcher in Nanjing University of Aeronautics and Astronautics in 1999 and Tsinghua University in 2002 respectively. His research interests are in flight control, aerial Robot and intelligent control. Dr. Hu is currently an Associate Professor of the Department of Computer Science and Technology of Tsinghua University, Beijing, China. Zengqi Sun received the B.S. degree from the Department of Automatic Control, Tsinghua University, Beijing, China, in 1966 and the Ph.D. degree in Control Engineering from the Chalmas University of Technology, Sweden, in 1981. He is currently a Professor of the Department of Computer Science and Technology, Tsinghua University, Beijing, China. He is the author or coauthor of more than 100 paper and eight books on control and robotics. His research interests include robotics, intelligent control, fuzzy system, neural networks, and evolutionary computation.     

Algebraic retrieval of fragmentarily indexed video

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.     

On some operations on strings suggested by gene assembly in ciliates

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”.     

Learning-based tracking of complex non-rigid motion

This paper describes a novel method for tracking complex non-rigid motions by learning the intrinsic object structure. The approach builds on and extends the studies on non-linear dimensionality reduction for object representation, object dynamics modeling and particle filter style tracking. First, the dimensionality reduction and density estimation algorithm is derived for unsupervised learning of object intrinsic representation, and the obtained non-rigid part of object state reduces even to 2-3 dimensions. Secondly the dynamical model is derived and trained based on this intrinsic representation. Thirdly the learned intrinsic object structure is integrated into a particle filter style tracker. It is shown that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle filter style tracker more robust and reliable.Extensive experiments are done on the tracking of challenging non-rigid motions such as fish twisting with selfocclusion, large inter-frame lip motion and facial expressions with global head rotation. Quantitative results are given to make comparisons between the newly proposed tracker and the existing tracker. The proposed method also has the potential to solve other type of tracking problems.     

On High Dimensional Projected Clustering of Data Streams

The data stream problem has been studied extensively in recent years, because of the great ease in collection of stream data. The nature of stream data makes it essential to use algorithms which require only one pass over the data. Recently, single-scan, stream analysis methods have been proposed in this context. However, a lot of stream data is high-dimensional in nature. High-dimensional data is inherently more complex in clustering, classification, and similarity search. Recent research discusses methods for projected clustering over high-dimensional data sets. This method is however difficult to generalize to data streams because of the complexity of the method and the large volume of the data streams.In this paper, we propose a new, high-dimensional, projected data stream clustering method, called HPStream. The method incorporates a fading cluster structure, and the projection based clustering methodology. It is incrementally updatable and is highly scalable on both the number of dimensions and the size of the data streams, and it achieves better clustering quality in comparison with the previous stream clustering methods. Our performance study with both real and synthetic data sets demonstrates the efficiency and effectiveness of our proposed framework and implementation methods.Charu C. Aggarwal received his B.Tech. degree in Computer Science from the Indian Institute of Technology (1993) and his Ph.D. degree in Operations Research from the Massachusetts Institute of Technology (1996). He has been a Research Staff Member at the IBM T. J. Watson Research Center since June 1996. He has applied for or been granted over 50 US patents, and has published over 75 papers in numerous international conferences and journals. He has twice been designated Master Inventor at IBM Research in 2000 and 2003 for the commercial value of his patents. His contributions to the Epispire project on real time attack detection were awarded the IBM Corporate Award for Environmental Excellence in 2003. He has been a program chair of the DMKD 2003, chair for all workshops organized in conjunction with ACM KDD 2003, and is also an associate editor of the IEEE Transactions on Knowledge and Data Engineering Journal. His current research interests include algorithms, data mining, privacy, and information retrieval.Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. He has been working on research into data mining, data warehousing, stream and RFID data mining, spatiotemporal and multimedia data mining, biological data mining, social network analysis, text and Web mining, and software bug mining, with over 300 conference and journal publications. He has chaired or served in many program committees of international conferences and workshops, including ACM SIGKDD Conferences (2001 best paper award chair, 1996 PC co-chair), SIAM-Data Mining Conferences (2001 and 2002 PC co-chair), ACM SIGMOD Conferences (2000 exhibit program chair), International Conferences on Data Engineering (2004 and 2002 PC vice-chair), and International Conferences on Data Mining (2005 PC co-chair). He also served or is serving on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Journal of Computer Science and Technology, and Journal of Intelligent Information Systems. He is currently serving on the Board of Directors for the Executive Committee of ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). Jiawei has received three IBM Faculty Awards, the Outstanding Contribution Award at the 2002 International Conference on Data Mining, ACM Service Award (1999) and ACM SIGKDD Innovation Award (2004). He is an ACM Fellow (since 2003). He is the first author of the textbook “Data Mining: Concepts and Techniques” (Morgan Kaufmann, 2001).Jianyong Wang received the Ph.D. degree in computer science in 1999 from the Institute of Computing Technology, the Chinese Academy of Sciences. Since then, he ever worked as an assistant professor in the Department of Computer Science and Technology, Peking (Beijing) University in the areas of distributed systems and Web search engines (May 1999–May 2001), and visited the School of Computing Science at Simon Fraser University (June 2001–December 2001), the Department of Computer Science at the University of Illinois at Urbana-Champaign (December 2001–July 2003), and the Digital Technology Center and Department of Computer Science and Engineering at the University of Minnesota (July 2003–November 2004), mainly working in the area of data mining. He is currently an associate professor in the Department of Computer Science and Technology, Tsinghua University, Beijing, China.Philip S. Yuis the manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. The current focuses of the project include the development of advanced algorithms and optimization techniques for data mining, anomaly detection and personalization, and the enabling of Web technologies to facilitate E-commerce and pervasive computing. Dr. Yu,s research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, disk arrays, computer architecture, performance modeling and workload analysis. Dr. Yu has published more than 340 papers in refereed journals and conferences. He holds or has applied for more than 200 US patents. Dr. Yu is an IBM Master Inventor.Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He will become the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering on Jan. 2001. He is an associate editor of ACM Transactions of the Internet Technology and also Knowledge and Information Systems Journal. He is a member of the IEEE Data Engineering steering committee. He also serves on the steering committee of IEEE Intl. Conference on Data Mining. He received an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts”. Philip S. Yu received the B.S. Degree in E.E. from National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University.     

Resource access control for dynamic priority distributed real-time systems

Many of today’s complex computer applications are being modeled and constructed using the principles inherent to real-time distributed object systems. In response to this demand, the Object Management Group’s (OMG) Real-Time Special Interest Group (RT SIG) has worked to extend the Common Object Request Broker Architecture (CORBA) standard to include real-time specifications. This group’s most recent efforts focus on the requirements of dynamic distributed real-time systems. One open problem in this area is resource access synchronization for tasks employing dynamic priority scheduling. This paper presents two resource synchronization protocols that meet the requirements of dynamic distributed real-time systems as specified by Dynamic Scheduling Real-Time CORBA 2.0 (DSRT CORBA). The proposed protocols can be applied to both Earliest Deadline First (EDF) and Least Laxity First (LLF) dynamic scheduling algorithms, allow distributed nested critical sections, and avoid unnecessary runtime overhead. These protocols are based on (i) distributed resource preclaiming that allocates resources in the message-based distributed system for deadlock prevention, (ii) distributed priority inheritance that bounds local and remote priority inversion, and (iii) distributed preemption ceilings that delimit the priority inversion time further. Chen Zhang is an Assistant Professor of Computer Information Systems at Bryant University. He received his M.S. and Ph.D. in Computer Science from the University of Alabama in 2000 and 2002, a B.S. from Tsinghua University, Beijing, China. Dr. Zhang’s primary research interests fall into the areas of distributed systems and telecommunications. He is a member of ACM, IEEE and DSI. David Cordes is a Professor of Computer Science at the University of Alabama; he has also served as Department Head since 1997. He received his Ph.D. in Computer Science from Louisiana State University in 1988, an M.S. in Computer Science from Purdue University in 1984, and a B.S. in Computer Science from the University of Arkansas in 1982. Dr. Cordes’s primary research interests fall into the areas of software engineering and systems. He is a member of ACM and a Senior Member of IEEE.