Number of co-authors:16
Number of publications with 3 favourite co-authors:Shaozi Li:1Julita Vassileva:1Yamini Upadrashta:1
Yang Cao's 3 most productive colleagues in number of publications:Zheng Chen:62Qiang Yang:34Hua-Jun Zeng:20
go to course
User Experience: The Beginner's Guide
91% booked. Starts in 4 days
go to course
User-Centred Design - Module 2
90% booked. Starts in 5 days
Marc Hassenzahl explains the fascinating concept of User Experience and Experience Design. Commentaries by Don Norman, Eric Reiss, Mark Blythe, and Whitney Hess
User Experience and Experience Design !
Our Latest Books
The Social Design of Technical Systems: Building technologies for communities. 2nd Edition
by Brian Whitworth and Adnan Ahmad
Gamification at Work: Designing Engaging Business Software
by Janaki Mythily Kumar and Mario Herger
The Social Design of Technical Systems: Building technologies for communities
by Brian Whitworth and Adnan Ahmad
The Encyclopedia of Human-Computer Interaction, 2nd Ed.
by Mads Soegaard and Rikke Friis Dam
Publications by Yang Cao (bibliography)
Ma, Shuai, Cao, Yang, Huai, Jinpeng and Wo, Tianyu (2012): Distributed graph pattern matching. In: Proceedings of the 2012 International Conference on the World Wide Web 2012. pp. 949-958. Available online
Graph simulation has been adopted for pattern matching to reduce the complexity and capture the need of novel applications. With the rapid development of the Web and social networks, data is typically distributed over multiple machines. Hence a natural question raised is how to evaluate graph simulation on distributed data. To our knowledge, no such distributed algorithms are in place yet. This paper settles this question by providing evaluation algorithms and optimizations for graph simulation in a distributed setting. (1) We study the impacts of components and data locality on the evaluation of graph simulation. (2) We give an analysis of a large class of distributed algorithms, captured by a message-passing model, for graph simulation. We also identify three complexity measures: visit times, makespan and data shipment, for analyzing the distributed algorithms, and show that these measures are essentially controversial with each other. (3) We propose distributed algorithms and optimization techniques that exploit the properties of graph simulation and the analyses of distributed algorithms. (4) We experimentally verify the effectiveness and efficiency of these algorithms, using both real-life and synthetic data.
© All rights reserved Ma et al. and/or ACM Press
Hu, Jian, Fang, Lujun, Cao, Yang, Zeng, Hua-Jun, Li, Hua, Yang, Qiang and Chen, Zheng (2008): Enhancing text clustering by leveraging Wikipedia semantics. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 179-186. Available online
Most traditional text clustering methods are based on "bag of words" (BOW) representation based on frequency statistics in a set of documents. BOW, however, ignores the important information on the semantic relationships between key terms. To overcome this problem, several methods have been proposed to enrich text representation with external resource in the past, such as WordNet. However, many of these approaches suffer from some limitations: 1) WordNet has limited coverage and has a lack of effective word-sense disambiguation ability; 2) Most of the text representation enrichment strategies, which append or replace document terms with their hypernym and synonym, are overly simple. In this paper, to overcome these deficiencies, we first propose a way to build a concept thesaurus based on the semantic relations (synonym, hypernym, and associative relation) extracted from Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance traditional content similarity measure for text clustering. The experimental results on Reuters and OHSUMED datasets show that with the help of Wikipedia thesaurus, the clustering performance of our method is improved as compared to previous methods. In addition, with the optimized weights for hypernym, synonym, and associative concepts that are tuned with the help of a few labeled data users provided, the clustering performance can be further improved.
© All rights reserved Hu et al. and/or ACM Press
Li, Shaozi, Cao, Yang and Chen, Huowang (2006): A New Migration Algorithm of Mobile Agent Based on Ant Colony Algorithm in P2P Network. In: Luo, Yuhua (ed.) Cooperative Design, Visualization, and Engineering, Third International Conference - CDVE 2006 Mallorca, Spain, 2006, September 17-20. pp. 107-114. Available online
Cao, Yang and Greer, Jim E. (2004): Facilitating Web-based Education using Intelligent Agent Technologies. In: Yao, Jingtao, Raghavan, Vijay V. and Wang, G. Y. (eds.) Proceedings of the 2nd International Workshop on Web-based Support Systems September 20, 2004, Beijing, China. pp. 37-44. Available online
Cao, Yang, Sharifi, Golha, Upadrashta, Yamini and Vassileva, Julita (2004): A Study of User Attitude Dynamics in a Computer Game. In: ICEIS 2004 2004. pp. 222-229.
Chi, Chi-Hung and Cao, Yang (2002): Progressive proxy-based multimedia transcoding system with maximum data reuse. In: ACM Multimedia 2002 2002. pp. 425-426. Available online
Join our community and advance:
Page maintainer: The Editorial Team