学术交流

据本人刚刚收到的消息

电气电子工程师协会（IEEE）中文网站http://cn.ieee.org今天正式发布！

1984年IEEE在中国成立了首个分会-北京分会-并拥有100名会员。今天，IEEE在中国已经有超过5000名会员，7个分会，3个支分会，1个联合会和1个代表处，在中国的影响力不断扩大，并成为中国工程师和全球科技团体沟通和交流的纽带和桥梁。

IEEE中文网站的成立标志着IEEE为中国会员的服务提升至一个新的台阶。在这里，我们将为您提供IEEE的最新咨询，会议信息，中国境内活动，会员专享服务等等。我们会继续努力，也坚信我们的会员，为了人类美好明天而不断创新。

感谢加入我们一起庆祝IEEE中文网站的成立！欲知更多网站内容，敬请访问http://cn.ieee.org。

此致

IEEE中国代表处

2010年3月22日

Dear IEEE Members:

IEEE Chinese Website http://cn.ieee.org Announcement today!

IEEE formed its first Chinese Section-Beijing Section-in 1984 with barely 100 members. Today, IEEE has more than 5,000 members, seven sections, three sub-sections, one council and one representative office. IEEE has expanded its efforts to establish links between China’s engineers and the global technical community.

The Establishment of IEEE Chinese website indicates IEEE membership services for the Chinese to upgrade to a new level. Here we are providing IEEE news, conference information, activities in China and China’s exclusive membership services. As we move forward, we are confident that our members will continue to innovate in the service of mankind.

Thank you for joining with us to celebrate Chinese website announcement! For more information, please visit http://cn.ieee.org .

Sincerely,

IEEE China office

March 22, 2010

IEEE计算机学会南京分会近期将举行2次学术报告会，信息如下。欢迎参加。

IEEE CS Nanjing Chapter

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3月5日（星期五） 15:00-16:15,  南京大学蒙民伟楼404会议室

题目: Reconstruction from Randomized Graph via Low Rank Approximation

报告人: Xintao Wu

              Associate Professor, University of North Carolina at Charlotte

摘要：The privacy concerns associated with data analysis over social networks have spurred recent research on privacy-preserving social network analysis, particularly on privacy-preserving publishing of social network data. In this talk, we focus on whether we can reconstruct a graph  from the edge randomized graph such that accurate feature values can be recovered. In particular, we present a low rank approximation based reconstruction algorithm. We exploit spectral properties of the graph data and show why noise could be separated from the perturbed graph using low rank approximation. We also show key differences from previous findings of point-wise reconstruction methods on numerical data through empirical evaluations and theoretical justifications.

报告人简介：Dr. Xintao Wu is an Associate Professor of Software and Information Systems Department at University of North Carolina at Charlotte. He got his Ph.D. in Information Technology from George Mason University in August 2001, an ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997, and an BS degree in Information Science from the University of Science and Technology of China in 1994.  His major research interests include data mining, data privacy and security, and social network analysis. Dr. Wu is an editor of Springer’s Journal of Intelligent Information Systems and Spanish Higher Research Council’s Transaction on Data Privacy, and serves on program committees of many international conferences, including KDD, ICDM, SDM, PKDD, and PAKDD. He is serving as the program co-chair of the 2nd International Symposium on Data, Privacy and E-Commerce (ISDPE’10). Dr. Wu is a recipient of NSF CAREER Award.

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3月8日（星期一） 10:30-11:45,  南京大学蒙民伟楼404会议室

题目: Spectrum based Fraud Detection in Social Networks

报告人: Xintao Wu

              Associate Professor, University of North Carolina at Charlotte

摘要：Social networks have reached an important role in people’s lifes. Unfortunately, social networks are vulnerable to various attacks such as spam emails, viral marketing and the such. In this work we develop a spectrum based detection framework to discover the perpetrators of these attacks. In particular, we focus on Random Link Attacks (RLAs) in which the malicious user creates multiple false identities and interactions among those identities to later proceed to attack the regular members of the network. We show that RLA attackers can be filtered by using their spectral coordinate characteristics, which are hard to hide even after the efforts by the attackers of resembling as much as possible the rest of the network. We present an effective algorithm to detect RLAs using the set of suspects filtered by their spectral characteristics.  Experimental results show that our technique is very effective in detecting those attackers and outperforms techniques previously published.

报告人简介：Dr. Xintao Wu is an Associate Professor of Software and Information Systems Department at University of North Carolina at Charlotte. He got his Ph.D. in Information Technology from George Mason University in August 2001, an ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997, and an BS degree in Information Science from the University of Science and Technology of China in 1994.  His major research interests include data mining, data privacy and security, and social network analysis. Dr. Wu is an editor of Springer’s Journal of Intelligent Information Systems and Spanish Higher Research Council’s Transaction on Data Privacy, and serves on program committees of many international conferences, including KDD, ICDM, SDM, PKDD, and PAKDD. He is serving as the program co-chair of the 2nd International Symposium on Data, Privacy and E-Commerce (ISDPE’10). Dr. Wu is a recipient of NSF CAREER Award.