Browsing by Author "Luo, Yun-Cheng"

Now showing 1 - 3 of 3

A hybrid priority-based video-on-demand resource sharing scheme
(2008-06-25)
Huang, Chenn-Jung
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Chuang, Yi-Ta
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Guan, Chih-Tai
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Luo, Yun-Cheng
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Hu, Kai-Wen
Video-on-demand (VoD) environments frequently batch video requests to decrease I/O demand and increase throughput. Since users may leave due to waiting too long, a good video scheduling policy has to consider not only the batch size, but also the user defection probabilities and waiting times. Moreover, a practical VoD resource sharing scheme should endeavor to provide some free streams to serve a high-priority clients requests immediately, since the high-priority clients might pay for the requested video. To tackle these problems, this work proposes a hybrid resource sharing model that integrates controlled multicasting and hatching. The proposed hybrid model applies a bandwidth borrowing and reserving scheme to give high-priority clients a prompt service, while still providing low-priority clients with a reasonable service. Furthermore, a novel probability model-based scheduling policy is proposed to alleviate the user defection behavior and unfairness issue. Experimental results demonstrate that the proposed resource sharing scheme is effective and feasible in terms of blocking probability of high-priority clients, the defection probability, service delay time and fairness to low-priority users. (C) 2008 Elsevier B.V. All rights reserved.
Developing an Intelligent Diagnosis and Assessment E-learning Tool for Introductory Programming
(2008)
Huang, Chenn-Jung
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Chen, Chun-Hua
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Luo, Yun-Cheng
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Chen, Hong-Xin
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Chuang, Yi-Ta
Recently, a lot of open source e-learning platforms have been offered for free in the Internet. We thus incorporate the intelligent diagnosis and assessment tool into an open software e-learning platform developed for programming language courses, wherein the proposed learning diagnosis assessment tools based on text mining and machine learning techniques are employed to alleviate the loading of the teachers. Experiments were conducted in two introductory-undergraduate programming courses to examine the effectiveness of the proposed diagnosis and assessment tools. The learners' work including the source code and comments were processed by the proposed text mining and machine learning techniques. This system also provides immediate feedback and high-quality evaluation results to guide the learners with poor performance. Our experimental results reveal that the proposed work can effectively assist the low-ability learners.
Using Learning Style-Based Diagnosis Tool to Enhance Collaborative Learning in an Undergraduate Engineering Curriculum
(2011-12-01)
Huang, Chenn-Jung
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Liao, Jia-Jian
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Shen, Hung-Yen
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Aye, Nwe Ni
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Wang, Yu-Wu
In this study, an intelligent learning style aware diagnosis agent for computer-supported cooperative learning is proposed. Learners are first assigned to heterogeneous groups based on their learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. The proposed diagnosis agent then scrutinizes each learner's learning portfolio on e-learning platform and automatically issues feedback messages in case some learner's behavior that is unfitted to his/her learning styles or devious argument on discussion board or wiki is detected. The Moodle, an open-source software e-learning platform, is used to establish the cooperative learning environment for this study. The experimental results reveal that the proposed learning style aware diagnosis agent indeed boosts the performance of the learners. (C) 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 19: 739-746, 2011; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.20359