The first all-electronic computer ENIAC was built in 1945 at the University of Pennsylvania. After nearly 65 years, computer has permeated into every facet of our lives. During these 65 years, many breakthroughs in hardware and software technology bring computers to this status quo. However, where do we go from here? Can computers and computer industries serve again as the locomotive to save and drive the world civilization and economy into the future? In this talk, we will suggest and introduce some promising fields that could have a big impact on the research and development of computers in particular and on the mankind in general. Some of the ideas may be deemed crazy. But who knows? You will be the judge.

The rapid adoption of service-oriented architecture in many large-scale distributed applications, such as scientific computing, e-business and healthcare, is due to the characteristics of service-based software systems (SBS), including loosely-coupling, late-binding, composability, and adaptability. These characteristics allow rapid composition of SBS from services provided by various organizations, and adaptation of SBS to satisfy multiple QoS or new functional requirements. To do so, the SBS needs to be aware of the current service QoS and resource status, estimate possible future changes of service QoS and resource status, and adapt service configurations and allocate resources accordingly. How to estimate the QoS of an SBS based on the QoS of individual services composing the SBS, and properly allocate computing and communication resources to improve service QoS are not well-understood. Hence, new techniques for adaptive QoS and resource management for SBS are needed.

In this talk, the challenges for developing SBS to satisfy multiple QoS in dynamic operating environments and current techniques useful for the development, such as QoS-aware service composition, distributed resource management, autonomic computing, and software cybernetics, will be discussed. Important issues, such as establishing QoS models for SBS, optimal resource allocation for workflows in SBS, and development support for generating autonomic monitoring and adaptation capabilities, and the ideas to address these issues will be presented.

He served as the president of the Computer Society of the Institute of Electrical and Electronics Engineers (IEEE) and American Federation of Information-Processing Societies (AFIPS). He was on the IEEE Board of Directors, and the Board of Directors of Computing Research Association. He served as the editor-in-chief of IEEE COMPUTER magazine, and organized many national and international major conferences, including the 1974 National Computer Conference sponsored by AFIPS, Association of Computing Machinery, IEEE Computer Society, and Society for Computer Simulation, and the 1989 World Computer Congress sponsored by International Federation for Information Processing (IFIP). He founded the Annual International Computer Software and Applications Conference (COMPSAC) sponsored by the IEEE Computer Society, in 1977.

His current research includes service-based systems, trustworthy computing, software engineering, mobile ad hoc networks and ubiquitous computing. He has received many awards and recognition for his accomplishments, including the Tsutomu Kanai Award and Richard E. Merwin Award of the IEEE Computer Society, the IEEE Centennial Awards and Third Millennium Medal, the Outstanding Contributions Award of the Chinese Computer Federation, and the Louis E. Levy Medal of the Franklin Institute. He is a Life Fellow of the IEEE and a Fellow of the American Association for the Advancement of Science.

In this talk we examine how high performance computing has changed over the last 10-year and look toward the future in terms of trends. These changes have had and will continue to have a major impact on our software. Some of the software and algorithm challenges have already been encountered, such as management of communication and memory hierarchies through a combination of compile--time and run--time techniques, but the increased scale of computation, depth of memory hierarchies, range of latencies, and increased run--time environment variability will make these problems much harder.

We will look at five areas of research that will have an importance impact in the development of software.

We will focus on following themes:

• Redesign of software to fit multicore architectures
• Automatically tuned application software
• Exploiting mixed precision for performance
• The importance of fault tolerance
• Communication avoiding algorithms

He specializes in numerical algorithms in linear algebra, parallel computing, the use of advanced-computer architectures, programming methodology, and tools for parallel computers. His research includes the development, testing and documentation of high quality mathematical software. He has contributed to the design and implementation of the following open source software packages and systems: EISPACK, LINPACK, the BLAS, LAPACK, ScaLAPACK, Netlib, PVM, MPI, NetSolve, Top500, ATLAS, and PAPI. He has published approximately 200 articles, papers, reports and technical memoranda and he is coauthor of several books. He was awarded the IEEE Sid Fernbach Award in 2004 for his contributions in the application of high performance computers using innovative approaches and in 2008 he was the recipient of the first IEEE Medal of Excellence in Scalable Computing. He is a Fellow of the AAAS, ACM, IEEE, and SIAM and a member of the National Academy of Engineering.

Video Forgery is a technique for generating fake video by altering, combining, or creating new video contents. We change the behavior of actors in a video. For instance, the outcome of a 100-meter race in the Olympic Game can be falsified. We track objects and segment motions using a modified mean shift mechanism. The resulting video layers can be played in different speeds and at different reference points with respect to the original video. In order to obtain a smooth movement of target objects, a motion interpolation mechanism is proposed based on reference stick figures (i.e., a structure of human skeleton) and video inpainting mechanism. The video inpainting mechanism is performed in a quasi-3D space via guided 3D patch matching. Interpolated target objects and background layers are fused. It is hard to tell whether a falsified video is the original. We demonstrate the original and the falsified videos in our website at http://member.mine.tku.edu.tw/www/TIP2008/ and http://member.mine.tku.edu.tw/www/ACMMM08VideoDemo. Video falsifying may create a moral problem. Our intension is to create special effects in movie industry.

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. These environments should be aware of people's needs, customizing requirements and forecasting behaviors. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. Artificial intelligence research aims to include more intelligence in AmI environments, allowing better support for humans and access to the essential knowledge for making better decisions when interacting with these environments. In this talk I will present the challenging concept of Ambient Intelligence, seen from a point of view of Artificial Intelligence.

In this talk, we will discuss some data mining methods and their applications in bioinformatics domain, focusing on integrating text mining and predictive modeling to analyze biomolecular network. Our method consists of three phases. In phase 1, we discuss a semi-supervised efficient learning approach to automatically extract biological relationships such as protein-protein interaction, protein-gene interaction from the biomedical literature databases to construct the biomolecular network. In Phase 2, a novel scale-free network clustering approach is applied to the biomolecular network to obtain various sub-networks. In Phase 3, a computational model is generated for the sub-network and simulated to predict their behavior in the cellular context. We discuss and evaluate some of the advanced computational models, in particular, state-space model, probabilistic Boolean Network model, fuzzy logic model. The modeling results represent hypotheses that are tested against high-throughput datasets (microarrays and/or genetic screens) for both the natural system and perturbations. Experimental results on time-series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large biomolecular network.

Tony is a scientist, teacher and entrepreneur. He joined Drexel University in 2002, founded the International Journal of Data Mining and Bioinformatics (SCI indexed) in 2006, International Journal of Granular Computing, Rough Sets and Intelligent Systems in 2008. Earlier, he worked as a research scientist in the world-leading R&D centers such as Nortel Research Center, GTE labs and HP Labs. In 2001, he founded the DMW Software in Silicon Valley, California. His research ideas have been integrated into many commercial products and applications. Tony’s current research interests are in biomedical literature data mining, bioinformatics, text mining, semantic web mining and reasoning, rough set theory and application, information extraction and information retrieval. He has published more than 160 peer-reviewed research papers in various journals, conferences and books such as various IEEE/ACM Transactions. co-edited 14 books/proceedings. He has received a few prestigious awards including the 2005 National Science Foundation (NSF) Career award, the best paper award at the 2007 International Conference on Artificial Intelligence, the best paper award at the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, the 2007 IEEE Bioinformatics and Bioengineering Outstanding Contribution Award, the 2006 IEEE Granular Computing Outstanding Service Award, and the 2001 IEEE Data Mining Outstanding Service Award. His research projects are funded by the National Science Foundation (NSF), US Dept. of Education, and the PA Dept. of Health and he has obtained more than US$4.0 millions research grants in the past 4 years as PI or Co-PI.

The Web has changed the landscape of how humans interact socially. With the advent of Web 2.0, Social Computing has emerged as a new and innovative paradigm that changes the way we communicate, interact, and learn. Social Computing involves the investigation of collective intelligence by using computational techniques such as machine learning, data mining, natural language processing, etc. on social behavioral data collected from blogs, wikis, emails, instant messages, clickthrough data, query logs, social bookmarks, tags, etc. In this talk, I will first introduce Social Computing by outlining some of the unique characteristics and aspects that are found on the various social platforms. Applications in each of the platforms will be presented to further demonstrate the use of these new technologies to enhance and enrich our lives. Lastly, I will conclude with some current challenges and potential future promises of Social Computing.

Dr. King is an Associate Editor of the IEEE Transactions on Neural Networks (TNN) and IEEE Computational Intelligence Magazine (CIM). He is a member of the Editorial Board of the Open Information Systems Journal, Journal of Nonlinear Analysis and Applied Mathematics, and Neural Information Processing–Letters and Reviews Journal (NIP-LR). He has also served as Special Issue Guest Editor for Neurocomputing, International Journal of Intelligent Computing and Cybernetics (IJICC), Journal of Intelligent Information Systems (JIIS), and International Journal of Computational Intelligent Research (IJCIR). He is a senior member of IEEE and a member of ACM, International Neural Network Society (INNS), and Asian Pacific Neural Network Assembly (APNNA). Currently, he is serving the Neural Network Technical Committee (NNTC) and the Data Mining Technical Committee under the IEEE Computational Intelligence Society (formerly the IEEE Neural Network Society). He is also a Vice-President and Governing Board Member of the Asian Pacific Neural Network Assembly (APNNA).

Dr. King joined the Chinese University of Hong Kong in 1993. He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles.

Recent widespread use of mobile technologies and advancement in computing power prompted strong needs of database systems that can be used in small devices such as sensors, cellular phones, PDA, ultra PCs, and navigators. We call database systems that are customizable from small-scale applications for small devices to large-scale applications such as large-scale search engines ubiquitous database management systems (UDBMSs). In this talk, we first review requirements of UDBMSs. The requirements we identified include selective convergence (or “devicetization”), flash-optimized storage system, data synchronization, supportability of unstructured/semi-structured data, and complex database operations. We then review existing systems and research prototypes. We first review the functionality of UDBMSs including the footprint size, support of standard SQL, supported data types, transactions, concurrency control, indexing, and recovery. We then review the supportability of requirements by those UDBMSs surveyed. We highlight ubiquitous features of a family of Odysseus systems that have been under development at KAIST for over 19 years. Functionalities of Odysseus can be “devicetized” or customized depending on the device types and applications as in Odysseus/Mobile for small devices, Odysseus/XML for unstructured/semistructured data, Odysseus/GIS for map data, and Odysseus/IR for large-scale search engines. We finally present research topics that are related to the UDBMSs.