Search

Your search keyword '"Jun Yan"' showing total 56 results
Start Over Author "Jun Yan" Publication Type Books

Refine your results

56 results on '"Jun Yan"'

15. Cloud and Crowd Based Learning

Author

Tseng, Chun-Hsiung, primary, Huang, Ching-Lien, additional, Chen, Yung-Hui, additional, Chuang, Chu-Chun, additional, Syu, Han-Ci, additional, Jiang, Yan-Ru, additional, Tsai, Fang-Chi, additional, Su, Pin-Yu, additional, and Chen, Jun-Yan, additional

Published
2016
Full Text
View/download PDF

47. Finding Relations Among Linear Constraints.

Author

Jun Yan, Jian Zhang, Zhongxing Xu, Calmet, Jacques, Ida, Tetsuo, and Dongming Wang

Abstract

In program analysis and verification, there are some constraints that have to be processed repeatedly. A possible way to speed up the processing is to find some relations among these constraints first. This paper studies the problem of finding Boolean relations among a set of linear numerical constraints. The relations can be represented by rules. It is believed that we can not generate all the rules in polynomial-time. A search based algorithm with some heuristics to speed up the search process is proposed. All the techniques are implemented in a tool called MALL which can generate the rules automatically. Experimental results with various examples show that our method can generate enough rules in acceptable time. Our method can also handle other types of constraints if proper numeric solvers are available. [ABSTRACT FROM AUTHOR]

Published
2006
Full Text
View/download PDF

48. Towards Incompletely Specified Process Support in SwinDeW - A Peer-to-Peer Based Workflow System.

Author

Weiming Shen, Zongkai Lin, Barthès, Jean-Paul A., Tangqiu Li, Jun Yan, Yun Yang, and Raikundalia, Gitesh K.

Abstract

Due to increased complexity and flexibility of processes and lack of modelling information, workflow processes are not always defined completely before their execution. Support for incompletely specified processes which require on-the-fly articulation of processes has become a desirable feature of workflow management systems. Unfortunately, this aspect is rather weak in contemporary workflow research. This paper reports innovative research on incompletely specified process support carried out in the context of SwinDeW, a peer-to-peer based decentralised workflow system. In order to extend the SwinDeW architecture and system functions seamlessly for supporting incompletely specified processes, a hierarchical process modelling and execution approach is presented in this paper. This approach supports stepwise elaboration of incompletely-specified processes on-the-fly. Further elaboration of a process is innovatively modelled as essential steps towards the process goal, thus being scheduled to execute as ordinary tasks. [ABSTRACT FROM AUTHOR]

Published
2005
Full Text
View/download PDF

49. Supervised Semi-definite Embedding for Email Data Cleaning and Visualization.

Author

Yanchun Zhang, Tanaka, Katsumi, Jeffrey Xu Yu, Shan Wang, Minglu Li, Ning Liu, Fengshan Bai, Jun Yan, Benyu Zhang, Zheng Chen, and Wei-Ying Ma

Abstract

The Email systems are playing an important and irreplaceable role in the digital world due to its convenience, efficiency and the rapid growth of World Wide Web (WWW). However, most of the email users nowadays are suffering from the large amounts of irrelevant and noisy emails everyday. Thus algorithms which can clean both the noise features and the irrelevant emails are highly desired. In this paper, we propose a novel Supervised Semi-definite Embedding (SSDE) algorithm to reduce the dimension of email data so as to leave out the noise features of them and visualize these emails in a supervised manner to find the irrelevant ones intuitively. Experiments on a set of received emails of several volunteers during a period of time and some benchmark datasets show the comparable performance of the proposed SSDE algorithm. [ABSTRACT FROM AUTHOR]

Published
2005

50. A Similarity Reinforcement Algorithm for Heterogeneous Web Pages.

Author

Yanchun Zhang, Katsumi&Tanaka, Jeffrey Xu Yu, Shan Wang, Minglu Li, Ning Liu, Jun Yan, Fengshan Bai, Benyu Zhang, Wensi Xi, Weiguo Fan, Zheng Chen, Lei Ji, Chenyong Hu, and Wei-Ying Ma

Abstract

Many machine learning and data mining algorithms crucially rely on the similarity metrics. However, most early research works such as Vector Space Model or Latent Semantic Index only used single relationship to measure the similarity of data objects. In this paper, we first use an Intra- and Inter- Type Relationship Matrix (IITRM) to represent a set of heterogeneous data objects and their inter-relationships. Then, we propose a novel similarity-calculating algorithm over the Inter- and Intra- Type Relationship Matrix. It tries to integrate information from heterogeneous sources to serve their purposes by iteratively computing. This algorithm can help detect latent relationships among heterogeneous data objects. Our new algorithm is based on the intuition that the intra-relationship should affect the inter-relationship, and vice versa. Experimental results on the MSN logs dataset show that our algorithm outperforms the traditional Cosine similarity. [ABSTRACT FROM AUTHOR]

Published
2005

Catalog

Books, media, physical & digital resources
See catalog results