The 3rd IEEE International Conference on High Performance and Smart Computing
(IEEE HPSC 2017)
May 26th-28th, 2017, Beijing, China.

Keynote Speakers


Dr. Shikun Zhang
Professor, Peking University,
National Engineering Research Center for Software Engineering
Beijing, China

Bio: Shikun Zhang: born in October 1969, Professor, director of the national engineering software engineering research center of Peking University, director of Beijing City Engineering Laboratory, director of zhongguanchun laboratory. His main research interests include software engineering, software security, distributed application and application integration.
In recent years, he presided over the development of "high quality software development environment”, including the protection architecture of quality assurance system, source code static analysis tool support, widely used in the field of military forces, to promote the quality of software. In the field of application, presided over the development of public service platform for network personal management, more than 20000 users get used.
Published more than 70 academic papers. Won the national science and technology progress award, and Beijing science and technology progress award.

Topic: Static Analysis Techniques and Assessment Approaches for Software Vulnerability

Abstract: With the development of Internet technologies, various software systems have been applied all over the world, and the vulnerabilities that exist in those systems have also put the systems in more and more danger. Recently, there have been vulnerability-analysis platforms to aid vulnerability discovery, but they still require large amount of human operations. In addition, the platforms are difficult to extend, have high false positives, and only support a few number of vulnerability types. To correctly and highly efficiently discover and reproduce different types of vulnerabilities in software systems, we give the topic about: (1) The design of intermediate code to represent binary code; (2) The semantics-recovery techniques from binary code to intermediate code; (3) Common techniques for vulnerability discovery; (4) Open techniques for vulnerability discovery; (5) The system architecture about software vulnerability.




Prof. Ruqian Lu
中国科学院院士
Chinese Academy of Sciences and Academy of Mathematics and Systems Science (CAS), China
Beijing, China

Bio: Ruqian Lu is a professor of computer science of the Institute of Mathematics, Academy of Mathematics and Systems Science, at the same time an adjunct professor of Institute of Computing Technology, Chinese Academy of Sciences and Peking University. He is also a fellow of Chinese Academy of Sciences. His research interests include artificial intelligence, knowledge engineering, knowledge based software engineering, formal semantics of programming languages and quantum information processing. He has published more than 180 papers and 10 books. He has won two first class awards from the Chinese Academy of Sciences and a National second class prize from the Ministry of Science and Technology. He has also won the 2003 Hua Loo-keng Mathematics Prize from the Chinese Mathematics Society and the 2014 lifetime achievements award from the China’s Computer Federation.

Topic: Engineering the Big-Knowledge

Abstract: Recently, the topic of mining big data to obtain knowledge (called big data knowledge engineering) has become hot interest of researchers. Also the concept of big knowledge was coined in this process. The new challenge was to mine big knowledge (not just knowledge) from big data. While researchers used to explore the basic characteristics of big data in the past, it seems that very few or even no researcher has tried to approach the problem of defining or summarizing the basic characteristics of big knowledge. This talk will first provide a retrospective view on the research of big data knowledge engineering and then introduce formally the big knowledge concept with some major characteristics. Accordingly, the big-knowledge system and big-knowledge engineering concepts are also introduced and discussed. We then delineate the major differences between traditional knowledge engineering, big data knowledge engineering and big-knowledge engineering. Using these concepts, we investigate four large scale knowledge engineering projects: the Shanghai project of fifth comprehensive investigation on city’s traffic, the Xia-Shang-Zhou chronology project, the international human genome project and the currently very hot research on knowledge graphs. We show that some of them are big-knowledge projects but some aren’t We summarize some further characteristics of big knowledge from this investigation. At last some historical notes on big knowledge from the media will be reviewed.







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