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38 results on '"Chen, Xiangping"'

14. Online Educational Resources Classification Using Visual Features

Author

Chen Xiangping, Yonghao Long, Yongsheng Rao, Chen Yancheng, Hao Guan, and Lin Mouguang

Subjects
Promotion (rank), Information retrieval, Computer science, business.industry, media_common.quotation_subject, Educational resources, Classification methods, The Internet, Graphics, business, media_common, Random forest
Abstract

With the promotion of the Internet, people can easily retrieve various kinds of education resources on the web. However, current education resources sharing platforms do not support the resources retrieval through the visual information. Therefore, we need to classify the resources which are related to visual characteristics into several categories. In this paper, we propose a novel classification method for resources on Netpad[ http://www.netpad.net.cn/]. We extract the important visual features including graphics features and text features. Then, we use the random forest algorithm to train a valuable model. The results of the experiments indicate that, using graphics features and text features, most of the data are classified correctly, which means that our proposed method can solve the classification problem of Netpad effectively.

Published
2018

18. Learning Code Context Information to Predict Comment Locations.

Author

Huang, Yuan, Hu, Xinyu, Jia, Nan, Chen, Xiangping, Xiong, Yingfei, and Zheng, Zibin

Subjects
SYSTEMS software, MACHINE learning, INFORMATION resources, SOURCE code, FEATURE extraction, FEASIBILITY studies
Abstract

Code commenting is a common programming practice of practical importance to help developers review and comprehend source code. In our developer survey, commenting has become an important, yet often-neglected activity when programming. Moreover, there is a lack of formal and automatic way in current practice to remind developers where to comment in the source code. To provide informative guidance on commenting during development, we propose a novel method CommentSuggester to recommend developers regarding appropriate commenting locations in the source code. Because commenting is closely related to the context information of source code, we identify this important factor to determine comment positions and extract it as structural context features, syntactic context features, and semantic context features. Subsequently, machine learning techniques are applied to identify possible commenting locations in the source code. We evaluated CommentSuggester using large datasets from dozens of open-source software systems in GitHub. The encouraging experimental results and user study demonstrated the feasibility and effectiveness of our commenting suggestion method. [ABSTRACT FROM AUTHOR]

Published
2020
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38. Performance Evaluation of Service Differentiating Internet Servers.

Author

Chen, Xiangping and Mohapatra, Prasant

Subjects
*INTERNET, *QUALITY of service
Abstract

Differentiated service approach has been proposed as a potential solution to provide Quality of Services (QoS) in the next generation Internet. The ultimate goal of end-to-end service differentiation can be achieved by complementing the network-level QoS with the service differentiation at the Internet servers. In this paper, we have presented a detailed study of the performance of service differentiating web servers (SDIS). Various aspects, such as admission control, scheduling, and task assignment schemes for SDIS, have been evaluated through real workload traces. The impact of these aspects has been quantified in the simulation-based study. Under high system utilization, a service differentiating server provides significantly better services to high priority tasks compared to a traditional Internet server. A combination of selective early discard and priority-based task scheduling and assignment is required to provide efficient service differentiation at the servers. The results of these studies could be used as a foundation for further studies on service differentiating Internet servers. [ABSTRACT FROM AUTHOR]

Published
2002
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