2012“WISM’12 – AICI’12”国际会议学术报告
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题目: Can I ask you? Crowdsourcing tasks on Microblog Service Platforms
主讲人: Lei Chen
Wisdom of crowds has exhibited tremendous magic on solving human-intrinsic problems. Besides the usage on specific crowdsourcing marketplaces, the rapid development of social media makes it possible to migrate this wisdom onto a broader sphere. It is universal to see people obtain knowledge on micro-blog services by asking others decision making questions. In this talk, I will present our recent study on the Jury Selection Problem(JSP) by utilizing crowdsourcing for decision making tasks on micro-blog services. Specifically, the problem is to enroll a subset of crowd under a limited budget, whose aggregated wisdom via Majority Voting scheme has the lowest probability of drawing a wrong answer (Jury Error Rate-JER). The challenges of such problem reside in the procedure of calculating JER and finding the optimal subset under a limited budget. Due to various individual error-rates of the crowd, the calculation of JER is non-trivial. In our study, we propose two efficient algorithms: a dynamic programming-based algorithm and a divide-and-conquer algorithm. For JSP, we formally propose two models, one for altruistic users(AltrM) and the other one for incentive-requiring users(PayM) who require extra payment when enrolled into a task. Based on two models, we design efficient algorithms for JSP. The efficiency and effectiveness of our proposed algorithms are verified on both synthetic and real micro-blog data. At last, I will highlight some future work on crowdsourcing on Microblog Service Platforms.
Lei Chen received the BS degree in computer science and engineering from Tianjin University, Tianjin, China, in 1994, the MA degree from Asian Institute of Technology, Bangkok, Thailand, in 1997, and the PhD degree in computer science from the University of Waterloo, Waterloo, Ontario, Canada, in 2005. He is currently an associate professor and associate head in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. His research interests include crowd sourcing on social media, social media analysis, probabilistic and uncertain databases, and privacy-preserved data publishing. So far, he published more than 150 conference and journal papers. He got the best paper awards in DASFAA 2009 and 2010. He is PC Track chairs for ACM SIGMM 2011, ACM CIKM 2012, and IEEE ICDE 2012. He has served as PC members for SIGMOD, VLDB, ICDE, SIGMM, and WWW. Currently, he works as associated editors for IEEE Transactions on Knowledge and Data Engineering (TKDE) and Distributed and Parallel Databases (DAPD). He is a member of the ACM and IEEE. He also serves as the chairman of ACM Hong Kong Chapter.
题目: Reusability and Searchability of Learning Objects
主讲人: Timothy K. Shih
Learning Objects (LOs) are atomic elements of lecture materials in e-learning. Creating high quality LOs is time consuming. Automatic mechanisms to support LOs reuse must be investigated. When an author creates learning materials, in order to reuse LOs from another person, it is necessary to search for these LOs. Thus, reusability and searchability are co-related research issues. This keynote starts from an introduction of metadata, follows by a discussion of the requirements to build a distributed repository, where LOs can be stored and shared. The concept of “Reusability Tree” to represent the relationships among relevant LOs and an infrastructure of LO repository will be presented. Relevant information while users are utilizing LOs, such as citations and time period persisted as well as user feedbacks will be used as critical elements for evaluating significance degree of LOs. Through theses factors, a mechanism to weight and rank LOs is discussed. The LONET (Learning Object Network), as an extension of Reusability Tree, is addressed and constructed to clarify the vague reuse scenario in the past, and to summarize collaborative intelligence through past interactive usage experiences. As a practical contribution, an adaptive algorithm is proposed to mine the social structure in our repository. The algorithm generates adaptive routes, based on past usage experiences, by computing possible interactive input, such as search criteria and feedback from instructors, and assists them in generating specific lectures.
Dr. Shih is a Professor at the National Central University, Taiwan. He was the Dean of College of Computer Science, Asia University, Taiwan and the Department Chair of the CSIE Department at Tamkang University, Taiwan. Dr. Shih is a Fellow of the Institution of Engineering and Technology (IET). He is also the founding Chairman of the IET Taipei Interim Local Network. In addition, he is a senior member of ACM and a senior member of IEEE. Dr. Shih also joined the Educational Activities Board of the Computer Society. His current research interests include Multimedia Computing and Distance Learning. Dr. Shih has edited many books and published over 480 papers and book chapters, as well as participated in many international academic activities, including the organization of more than 60 international conferences. He was the founder and co-editor-in-chief of the International Journal of Distance Education Technologies, published by the Idea Group Publishing, USA. Dr. Shih is an associate editor of the IEEE Transactions on Learning Technologies. He was an associate editor of the ACM Transactions on Internet Technology and an associate editor of the IEEE Transactions on Multimedia. Dr. Shih has received many research awards, including research awards from National Science Council of Taiwan, IIAS research award from Germany, HSSS award from Greece, Brandon Hall award from USA, and several best paper awards from international conferences. Dr. Shih has been invited to give more than 40 keynote speeches and plenary talks in international conferences, as well as tutorials in IEEE ICME 2001 and 2006, and ACM Multimedia 2002 and 2007.
题目: A Computational Model of Intelligence
主讲人: Zhang Yi
In this talk, I will describe a computational model of intelligence using the memory-prediction framework of intelligence proposed by Harkins. It is well known that almost everything we think of as intelligence such as perception, language, imagination, mathematics, art, music, and planning occurs from the neocortex. My talk will start from the structure of neocortex and then show how a computational model of intelligence can be build. In this model, each cortical region is regarded as a computational unit and the whole neocortex is structured in hierarchy way. This computational model consists of three sub-models: perception model, memory model, and prediction model. In the perception model, I will discuss the sparse representations of the input information to the regions of the cortex. In the memory model, three methods for memory storage will be discussed: discrete attractors approach, continuous attractors approach, and trajectories learning approach. In the prediction model, it contains prediction, feedback, and learning mechanisms. This talk will mainly focus on the computational aspect.
Zhang Yi received the Ph.D. degree in mathematics from the Institute of Mathematics, Chinese Academy of Science, Beijing, China, in 1994. Currently, he is a Professor at Sichuan University and acts as the Dean of the College of Computer Science. He is the founder of the Machine Intelligence Laboratory in the College of Computer Science at Sichuan University. He has published more than 100 refereed articles. He is the coauthor of the books: Convergence Analysis of Recurrent Neural Networks (Norwell, MA: Kluwer, 2004), Neural Networks: Computational Models and Applications (Heidelberg, Germany: Springer-Verlag, 2007), and Subspace Learning (Boca Raton, FL: Chapman & Hall/CRC, 2011). His research interests include machine intelligence, neural networks, data mining, and machine learning. Currently, he is especially interested in neocortex computing towards intelligent machines. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems.