国际学术报告会

2015 IEEE 4th International Conference on Computer Science and   Network Technology (IEEE ICCSNT 2015)

日期：2015年12月19日

时间：8:30-11:30am

地点：黑龙江省哈尔滨市南岗区学府路52号哈尔滨翰林天悦大酒店 11楼

本次由计算机学院、软件学院主办的IEEE ICCSNT 2015邀请了美国奥本大学的顾维信教授、美国蒙大拿州立大学杨青教授、上海交通大学朱燕民教授作3个学术报告。欢迎感兴趣的老师同学们参加，并现场讨论。

Plenary Speech I:   Authentication of Spatial Queries in Both Vector Spaces and Spatial Networks

Session Chair: Jinbao Li

Biography:

Wei-Shinn (Jeff) Ku received his   Ph.D. degree in computer science from the University of Southern California   (USC) in 2007. He also obtained both the M.S. degree in computer science and   the M.S. degree in electrical engineering from USC in 2003 and 2006,   respectively. Wei-Shinn Ku is an associate professor of the Department of   Computer Science and Software Engineering at Auburn University, USA. His   research interests include data management systems, big data analytics,   geographic information systems, and mobile computing. He has published more   than 80 research papers in refereed international journals and conference   proceedings. He is a senior member of the IEEE.

Abstract:

With the popularity of   location-based services and the abundant usage of smart phones and   GPS-enabled devices, the necessity of outsourcing spatial data has grown   rapidly over the past few years. Meanwhile, the fast arising trend of Cloud   storage and Cloud computing services has provided a flexible and   cost-effective platform for hosting data from businesses and individuals,   further enabling many location-based applications. Nevertheless, in this   database outsourcing paradigm, the authentication of the query results at the   client remains a challenging problem. In this talk, I will focus on the   Outsourced Spatial Database (OSDB) model and introduce an efficient scheme,   called VN-Auth, which allows a client to verify the correctness and   completeness of the result set in both vector spaces and spatial networks.   The approach is based on neighborhood information derived from the Voronoi   diagram of the underlying spatial dataset and can handle fundamental spatial   query types, such as k nearest neighbor and range queries, as well   as more advanced query types like reverse k nearest neighbor,   aggregate nearest neighbor, and spatial skyline. We evaluated VN-Auth based   on real-world datasets using mobile devices (Google Droid smart phones with   Android OS) as query clients. Compared to the current state-of-the-art   approaches (i.e., methods based on Merkle hash trees), our experiments show   that VN-Auth produces significantly smaller verification objects and is more   computationally efficient.

Plenary Speech II:   Trust Assessment in Online Social Networks

Session Chair: Yan Yang

Biography:

Qing Yang, Ph.D, is a RightNow   Technologies Assistant Professor in the Department of Computer Science at   Montana State University. He received B.S. and M.S. degrees in Computer   Science from Nankai University and Harbin Institute of Technology, China, in   2003 and 2005, respectively. He received his Ph.D degree in Computer Science   from Auburn University in 2011. His research interests include Online Social   Network, Trust Management, Vehicular Network, and Wireless Sensor Networks   (WSNs). He has published papers in prestigious journals such as IEEE   Transactions on Dependable and Secure Computing, IEEE Network Magazine and   IEEE Communications Magazine, in prestigious conferences such as IEEE   INFOCOM, CNS and SECON.

Abstract:

Assessing trust in online social   networks (OSNs) is critical for many social network applications such as   online marketing but challenging due to the difficulties of handling complex   OSNs topology, in existing models such as subjective logic, and the lack of   effective validation methods. To address these challenges, we for the first   time properly define trust propagation and combination in arbitrary OSN   topologies by proposing 3VSL (Three-Valued Subjective Logic). The 3VSL   distinguishes the posteriori and priori uncertainties existing in trust, and   the difference between distorting and original opinions, thus be able to   compute multi-hop trusts in arbitrary graphs. We theoretically proved the   capability based on the Dirichlet distribution. Furthermore, an online survey   system is implemented to collect interpersonal trust data and validate the   correctness and accuracy of 3VSL in real world. Both experimental and   numerical results show that 3VSL is accurate in computing interpersonal trust   in OSNs.

Plenary Speech III:   Crowdsensing for smart cities: challenges and opportunities

Session Chair: Longjiang   Guo

Biography:

Yanmin Zhu is a professor in the   Department of Computer Science and Engineering at Shanghai Jiao Tong   University since 2009. Prior to joining Shanghai Jiao Tong University, he was   a Research Associate with the Department of Computing at the Imperial College   London. He received his PhD from the Department of Computer Science and   Engineering at the Hong Kong University of Science and Technology (HKUST) in   July 2007; and his bachelor from Xi'an Jiao Tong University (XJTU) in July   2002. His research interests include wireless sensor networks, vehicular ad   hoc networks, Internet of things (IoT), crowd sensing and smartphone sensing.   He severed TPC Co-Chairs of a few international conference, including IEEE   ICPADS 2014, COLLABORATECOM 2015, etc.

Abstract:

With the rapid development of smart   mobile devices and mobile Internet, crowd sensing has become increasingly   popular in recent years as a new paradigm for gathering various sensing data   from people’s   surroundings. The main advantages of crowd sensing, in contrast to   traditional sensor networks, include lower deployment and maintenance cost,   wider geographical coverage, richer sensing data, etc. Crowd sensing is   particularly instrumental to various applications of smart cities. This talk   first introduces the basics of crowd sensing, highlighting the main   components of a crowd sensing system and the typical process of employing   crowd sensing for building an application of smart cities. Then, the talk   discusses the key challenges facing crowd sensing, e.g., strategic behaviors   of data contributors, and sparseness of sensing data. Finally, the talk will   present some of our recent research projects of crowd sensing, including   incentive mechanisms, state estimate with sparse data, and map updating with   GPS trajectories.