7th IFIP International Conference On

Intelligent Information Processing

12-15, October, 2012, Guilin,China

The IIP conference series provides a forum for engineers and scientists in academia, university and industry to present their latest research findings in any aspects of intelligent information processing. IIP2012 attempts to meet the needs of a large and diverse community.

Sponsored by

    International Federation for Information Processing, IFIP TC12

Co-Sponsored by

Chinese Association for Artificial Intelligence

Guilin University of Electronic Technology

Institute of Computing Technology, Chinese Academy of Sciences

Conference Organization

General Chairs

T. Dillon (Australia)                T. Gu (China)              A. Aamodt (Norway)

Program Chairs

Z. Shi (China)              D. Leake (USA)        S. Vadera (UK)

Keynotes Speakers

The AI Journey: the Road Traveled and the (Long) Road Ahead

Ramon Lopez de Mantaras

Artificial Intelligence Research Institute,

Spanish National Research Council (CSIC),Spain

Abstract:  In this talk I will first briefly summarize the many impressive results we have achieved along the road so far traveled in the field of AI including some concrete results obtained at the IIIA-CSIC. Next I will describe some of the future challenges to be faced along the (long) road we still have ahead of us with an emphasis on integrated systems, a necessary step towards human-level AI. Finally I will comment on the importance of interdisciplinary research to build such integrated systems (for instance, sophisticated robots having artificial cartilages, artificial muscles, artificial skin, etc) using some examples related to materials science.

Transfer learning and Applications

Qiang Yang

Department of Computer Science and Engineering,

Hong Kong University of Science and Technology, Hong Kong

Abstract:  In machine learning and data mining, we often encounter situations where we have an insufficient amount of high-quality data in a target domain, but we may have plenty of auxiliary data in related domains.  Transfer learning aims to exploit these additional data to improve the learning performance in the target domain. In this talk, I will give an overview on some recent advances in transfer learning for challenging data mining problems.  I will present some theoretical challenges to transfer learning, survey the solutions to them, and discuss several innovative applications of transfer learning, including learning in heterogeneous cross-media domains and in online recommendation, social media and social network mining.

Semantics of Cyber-Physical Systems

 Tharam   Dillon¹, Elizabeth   Chang², Jaipal Singh³, Omar Hussain²

La Trobe University, Australlia

² Department of Information Systems, Curtin University,Australia

³ Dept. of Electrical and Computer   Engineering, Curtin University, Australia 

Abstract:  The very recent development of Cyber-Physical Systems (CPS) provides a smart infrastructure connecting abstract computational artifacts with the physical world. The solution to CPS must transcend the boundary between the cyber world and the physical world by providing integrated models addressing issues from both worlds simultaneously. This needs new theories, conceptual frameworks and engineering practice. In this paper, we set out the key requirements that must be met by CPS systems, and review and evaluate the progress that has been made in the development of theory, conceptual frameworks and practical applications. We then discuss the need for semantics and a proposed approach for addressing this. Grand challenges to informatics posed by CPS are raised in the paper.

Big Data Mining in the Cloud

Zhongzhi Shi

Key Laboratory of Intelligent Information Processing

Institute of Computing Technology, Chinese Academy of Sciences, Beijing,China

Abstract:  Big Data is the growing challenge that organizations face as they deal with large and fast-growing sources of data or information that also present a complex range of analysis and use problems. Digital data production in many fields of human activity from science to enterprise is characterized by an exponential growth. Big data technologies will become a new generation of technologies and architectures which is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time.

Massive data sets are hard to understand, and models and patterns hidden within them cannot be identified by humans directly, but must be analyzed by computers using data mining techniques. The world of big data present rich cross-media contents, such as text, image, video, audio, graphics and so on. For cross-media applications and services over the Internet and mobile wireless networks, there are strong demands for cross-media mining because of the significant amount of computation required for serving millions of Internet or mobile users at the same time. On the other hand, with cloud computing booming, new cloud-based cross-media computing paradigm emerged, in which users store and process their cross-media application data in the cloud in a distributed manner. Cross-media is the outstanding characteristics of the age of big data with large scale and complicated processing task. Cloud-based Big Data platforms will make it practical to access massive compute resources for short time periods without having to build their own big data farms. We propose a framework for cross-media semantic understanding which contains discriminative modeling, generative modeling and cognitive modeling. In cognitive modeling, a new model entitled CAM is proposed which is suitable for cross-media semantic understanding. A Cross-Media Intelligent Retrieval System (CMIRS), which is managed by ontology-based knowledge system KMSphere, will be illustrated.

This talk also concerns Cloud systems which can be effectively employed to handle parallel mining since they provide scalable storage and processing services, as well as software platforms for developing and running data analysis environments. We exploit Cloud computing platforms for running big data mining processes designed as a combination of several data analysis steps to be run in parallel on Cloud computing elements. Finally, the directions for further researches on big data mining technology will be pointed out and discussed.

Research on Semantic Programming Language

Shi Ying

State Key Laboratory of Software Engineering,

Wuhan University, Wuhan,China

Abstract:  As technologies of Semantic Web Service are gradually matured, developing intelligent web applications with Semantic Web Services becomes an important research topic in Software Engineering. This speech introduces our efforts on Semantic Web Service oriented programming. Employing the concept of semantic computing into service-oriented programming, we proposed a programming language SPL, Semantic Programming Language, which supports the expression and process of semantic information. Based on collaboration of semantic space and information space, the running mechanism of SPL program is presented, which provides SPL program with higher flexibility and stronger adaptability to changes. Furthermore, with the introduction of semantic operators, a kind of searching conditional expression is offered to facilitate the search of Semantic Web Services with greater preciseness and higher flexibility. Besides, semantic based policy and exception mechanism are also brought in to improve the intelligence of policy inference and exception handing in SPL program. At the same time, a platform that supports design and running of SPL program is developed.