Ubiquitous/Pervasive Intelligence: Visions and Challenges

     Ubiquitous/Pervasive computers, networks and information are paving a road towards a smart world (SW) in which computational intelligence is distributed throughout the physical environment to provide trustworthy and relevant services to people. This ubiquitous/pervasive intelligence (UI/PI) will change the computing landscape because it will enable new breeds of applications and systems to be developed; the realm of computing possibilities will be significantly extended. By embedding digital intelligence in everyday objects, our workplaces, our homes and even ourselves, many tasks and processes could be simplified, made more efficient, safer and more enjoyable. Ubiquitous or pervasive computing composes these many "smart things/u-things" to create the environments that underpin the smart world.
     In this presentation, the potential trends towards smart world (SW) and ubiquitous/pervasive intelligence (UI/PI) from smart u-things to smart spaces and then to smart hyperspaces will be addressed, as well as, the challenges in smart u-things' research in terms of technical and real world complexity.
　

Dr. Laurence T. Yang is a professor at St Francis Xavier University, Canada. His research includes high performance computing, embedded systems, ubiquitous/pervasive computing and intelligence. He has published around 300 papers (including around 100 international journal papers such as IEEE and ACM Transactions, more than 250 papers are indexed by SCI/EI) in refereed journals, conference proceedings and book chapters in these areas. He has been involved in more than 100 conferences and workshops as a program/general/steering conference chair and more than 300 conference and workshops as a program committee member. He served as the vice-chair of IEEE Technical Committee of Supercomputing Applications (TCSA) until 2004, currently is the chair of IEEE Technical Committee of Scalable Computing (TCSC), the chair of IEEE Task force on Ubiquitous Computing and Intelligence. He is also in the executive committee of IEEE Technical Committee of Self-Organization and Cybernetics for Informatics, and of IFIP Working Group 10.2 on Embedded Systems, and of IEEE Technical Committee of Granular Computing. Currently he is also in the steering committee of IEEE/ACM Supercomputing conference (SCxx) series.
     In addition, he is the editors-in-chief of several international journals and few book series. He is serving as an editor for around 20 international journals. He has been acting as an author/co-author or an editor/co-editor of 30 books from Kluwer, Springer, Nova Science, American Scientific Publishers and John Wiley & Sons. He has won 5 Best Paper Awards (including the IEEE 20th International Conference on Advanced Information Networking and Applications (AINA-06)); 4 IEEE Best Paper Awards; 2 IEEE Outstanding Paper Awards; one Best Paper Nomination; Distinguished Achievement Award (2005); IEEE Distinguished Achievement Award (2009); Canada Foundation for Innovation Award (2003); He has been invited to give around 20 keynote talks at various international conferences and symposia.
　

     Most of the traditional AI models based computing will not work well when dealing with large-scale, dynamically changing, open and distributed information sources at Web scale and ubiquitous environment. The next major advances in artificial intelligence and Web intelligence are most likely to be brought by an in-depth understanding of human intelligence and its application in the design and implementation of intelligent computing systems with human-level intelligence. To prepare us ready for the great opportunity, this talk outlines a unified framework for the study of brain inspired Web intelligence (WI) by exploring the latest results from brain informatics (BI). This leads to profound advances in the analysis and understanding of data, knowledge, intelligence and wisdom, as well as their inter-relationships, organization and creation process. The fast-evolving WI research and development initiatives are now moving towards understanding the multi-facet nature of intelligence in depth and incorporating it on a Web scale and ubiquitous environment. The recently developed instrumentation (fMRI etc.) and advanced IT are causing an impending revolution in WI research and development, making it possible for us to pursue the new frontier of intelligence science and develop human-level Web intelligence.

     Dr. Ning Zhong received the Ph.D. degree in the Interdisciplinary Course on Advanced Science and Technology from the University of Tokyo. He is currently head of Knowledge Information Systems Laboratory, and a professor in Department of Life Science and Informatics at Maebashi Institute of Technology, Japan. He is also director and an adjunct professor in the International WIC Institute (WICI), Beijing University of Technology. He has conducted research in the areas of knowledge discovery and data mining, rough sets and granular-soft computing, Web intelligence, intelligent agents, brain informatics, and knowledge information systems, with over 200 journal and conference publications and 20 books. He is the editor-in-chief of the Web Intelligence and Agent Systems journal (IOS Press), and serves as associate editor/editorial board for several international journals and book series. He is the co-chair of Web Intelligence Consortium (WIC), chair of the IEEE Computer Society Technical Committee on Intelligent Informatics (TCII), chair of IEEE Computational Intelligence Society Task Force on Brain Informatics (TF-BI). He has served or is currently serving on the program committees of over 100 international conferences and workshops, including IEEE ICDM'02 (conference chair), IEEE ICDM'06 (program chair), IEEE/WIC WI-IAT'03 (conference chair), IEEE/WIC/ACM WI-IAT'04 (program chair), IJCAI'03 (advisory committee member), and Brain Informatics 2009 (program chair). He was awarded the best paper awards of AMT'06, JSAI'03, IEEE TCCI/ICDM Outstanding Service Award in 2004, and Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Most Influential Paper Award (1999-2008).

    With distinguished cost-effectiveness, agility for growth and high-availability, commodity multicore clusters have been the dominant architecture for building supercomputers and large-scale servers. User-friendly language and tool support for exploiting parallelism on clusters has, however, noticeably lagged behind. While relevant research efforts have shown notable convergence into the partitioned global address space (PGAS) programming model in recent years, it meets the performance goal by shifting the effort of controlling shared data distribution and affinity to programmers. Such an abstraction level seems adequate for engineering and scientific applications but certainly not the much wider application domains like web and enterprise applications.
     In this talk, we will introduce a generic and easy-to-use parallel programming model for realizing the PGAS paradigm, coming out from the latest research in distributed Java virtual machines (DJVM). The DJVM abstracts away the low-level clustering decisions and hides the physical boundaries across the cluster nodes from the application layer; while potential performance optimization could leverage on a couple of adaptive sampling techniques for tracking thread-thread and thread-object affinity in a lightweight manner. As the design of DJVM adheres to the standard JVM specification, ideally all applications that follow the original Java multithreaded programming model on a single machine can now be executed in the cluster environment. This approach makes it possible for ordinary programmers to scale out their applications on large-scale clusters without using PGAS constructs or other libraries instead. Thus developers can focus their efforts only on their business logics and software innovation. In view of Java’s overwhelming popularity, we believe extending the Java multithreaded programming model to realize the PGAS paradigm through an underlying distributed Java virtual machine (DJVM) is an attractive solution. The talk reports our recent progress on the development of a DJVM, called JESSICA3, and suggests future directions in this research.

     Dr. Cho-Li Wang received his B.S. degree in Computer Science and Information Engineering from National Taiwan University in 1985, and his M.S. and Ph.D. degrees in Computer Engineering from University of Southern California in 1990 and 1995 respectively. He is currently an associate professor of the Department of Computer Science at The University of Hong Kong. Dr. Wang's research mainly focus on virtualization techniques for cluster and grid computing, parallel programming on clusters, and software systems for pervasive/mobile computing. Dr. Wang was the program chair for Cluster’03, CCGrid09, InfoScale’09, and ICPADS’09.  Dr. Wang also serves as an editorial board member for several journals, including IEEE Transactions on Computers (TC), Multiagent and Grid Systems (MAGS), International Journal of Pervasive Computing and Communications (JPCC), Journal of Information Science and Engineering (JISE), and ICST Transactions on Scalable Information Systems.  He is the regional coordinator (Hong Kong) of IEEE Technical Committee on Scalable Computing (TCSC).

     Over the last 50 years, computational sciences have advanced the development of new and ever faster and better computers, algorithms, and software tools, and vice versa. But the better the technologies we build the more demanding our computational science applications. That’s why optimization is everywhere, at the level of technologies and computer architectures, at the middleware level, at the algorithm and application level, and even at the level of computing paradigms, where we came from mainframes, to vector and parallel computers, to grids and clouds, recently. We successfully optimized our algorithms and mapped them to the underlying architecture, e.g. with overlapping communication with computation, better load balancing through domain decomposition into parallel processes, or using library routines optimized for the specific architecture and processors.
     Still many applications are extremely performance-hungry, or they simply waste a lot of computing cycles and memory. The two alternatives to cope with these challenges are: always using the largest and fastest (and most expensive) supercomputers for jobs running thousands or even millions of core hours, and/or using self-adaptive methods such that the applications adjust themselves to the underlying architecture, dynamically, automatically during run-time.
     In our presentation, we will concentrate on the use of grids and clouds for HPC, with the aid of the European DEISA project, analyze different HPC loads and their suitability for grids and for clouds, and take a closer look at adaptive algorithms and their benefits for modern computer architectures.

     Prof. Wolfgang Gentzsch is Dissemination Advisor for the DEISA Distributed European Initiative for Supercomputing Applications, and Member at Large of the Board of Directors of the Open Grid Forum. Until recently, he was an adjunct professor of computer science at Duke University in Durham, and a visiting scientist at RENCI Renaissance Computing Institute at UNC Chapel Hill, both in North Carolina. From 2005 to 2007, he was the Chairman of the German D-Grid Initiative; Vice Chair of the e-Infrastructure Reflection Group e-IRG; Area Director of Major Grid Projects of the OGF Open Grid Forum Steering Group; and a member of the US President’s Council of Advisors for Science and Technology (PCAST). Before, Wolfgang was Managing Director of the MCNC Grid and Data Center Services in North Carolina; Sun's Senior Director of Grid Computing in Menlo Park, CA; President, CEO, and CTO of HPC software companies Genias and Gridware, and a professor of mathematics and computer science at the University of Applied Sciences in Regensburg, Germany. Wolfgang Gentzsch studied mathematics and physics at the Technical Universities in Aachen and Darmstadt, Germany.

In this talk, we shall first review recent development on fuzzy matrix theory. Based on those developments, we shall outline some new results to the fuzzy markov chains. In particular, we present new conditions to ensure the ergodic property for fuzzy markov chains. As an application, we present a new method to the page rank of web surfing.

Prof. Sy-Ming Guu obtained both his M.S. degree in statistics and Ph.D. degree in operations research from Stanford University, California, USA. Currently, he is affiliated with the College of Management, Yuan Ze University, Taoyuan, Taiwan and holds the 2009 Hsu Y-Z chair professorship. His research interests include optimization-related topics, matrix analysis, fuzzy dynamic system, and economic finance. He was the Dean of College of Management, Yuan Ze University from 2002-2007. He has been appointed as the Provost, Yuan Ze University since 2007.

Data Mining and Its Application in Software Engineering and Incident Management

Data mining and knowledge discovery (DMKD), which develops methods, algorithms, and techniques to extract useful information from huge amounts of data, emerged in 1990s and grew rapidly since then. Data mining techniques, such as classification, association, and clustering, can be used to analyze different types of data. This talk will cover theoretical background, implementation techniques, and applications, with particular focus on applications to data mining tasks in software engineering and incident information management. We will also present tools and data repositories that you can use in your research, with research experiences on mining software repositories and a data mining framework for incident decision support.

Prof. Gang Kou obtained his B.S. degree in Physics from Tsinghua University, China and his Ph.D. degree in Information Technology from University of Nebraska at Omaha, USA. Currently, he is affiliated with the University of Electronic Science and Technology of China, Chengdu, China. Meantime he is the managing editor of International Journal of Information Technology and Decision-Making. His research interests include Data Mining and Database, Financial Risk Analysis and Software Reliability Testing.