Browsing by Author "Wang, Hsiu-Wen"

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A Novel Real-Time Speech Summarizer System for the Learning of Sustainability
(2015-04-01)
Wang, Hsiu-Wen
;
Cheng, Ding-Yuan
;
Chen, Chi-Hua
;
Wu, Yu-Rou
;
Lo, Chi-Chun
As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures). Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase real-time speech summarizer for the learning of sustainability (RTSSLS) was proposed. Phase One involved selecting independent features (e.g., centrality, resemblance to the title, sentence length, term frequency, and thematic words) and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and provide a summary. In practical results, the accuracies of macro-average and micro-average for the RTSSLS were 70% and 73%, respectively. Therefore, using a RTSSLS can enable users to acquire key speech information for the learning of sustainability.
A Real-Time Pothole Detection Approach for Intelligent Transportation System
(2015-01-01)
Wang, Hsiu-Wen
;
Chen, Chi-Hua
;
Cheng, Ding-Yuan
;
Lin, Chun-Hao
;
Lo, Chi-Chun
In recent years, fast economic growth and rapid technology advance have led to significant impact on the quality of traditional transport system. Intelligent transportation system(ITS), which aims to improve the transport system, has become more and more popular. Furthermore, improving the safety of traffic is an important issue of ITS, and the pothole on the road causes serious harm to drivers' safety. Therefore, drivers' safety may be improved with the establishment of real-time pothole detection system for sharing the pothole information. Moreover, using the mobile device to detect potholes has been more popular in recent years. This approach can detect potholes with lower cost in a comprehensive environment. This study proposes a pothole detection method based on the mobile sensing. The accelerometer data is normalized by Euler angle computation and is adopted in the pothole detection algorithm to obtain the pothole information. Moreover, the spatial interpolation method is used to reduce the location errors from global positioning system (GPS) data. In experiments, the results show that the proposed approach can precisely detect potholes without false-positives, and the higher accuracy is performed by the proposed approach. Therefore, the proposed real-time pothole detection approach can be used to improve the safety of traffic for ITS.
Integrating expert profile, reputation and link analysis for expert finding in question-answering websites
(2013-01-01)
Liu, Duen-Ren
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Chen, Yu-Hsuan
;
Kao, Wei-Chen
;
Wang, Hsiu-Wen
;
資訊管理與財務金融系註：原資管所+財金所
Question answering websites are becoming an ever more popular knowledge sharing platform. On such websites, people may ask any type of question and then wait for someone else to answer the question. However, in this manner, askers may not obtain correct answers from appropriate experts. Recently, various approaches have been proposed to automatically find experts in question answering websites. In this paper, we propose a novel hybrid approach to effectively find experts for the category of the target question in question answering websites. Our approach considers user subject relevance, user reputation and authority of a category in finding experts. A user's subject relevance denotes the relevance of a user's domain knowledge to the target question. A user's reputation is derived from the user's historical question-answering records, while user authority is derived from link analysis. Moreover, our proposed approach has been extended to develop a question dependent approach that considers the relevance of historical questions to the target question in deriving user domain knowledge, reputation and authority. We used a dataset obtained from Yahoo! Answer Taiwan to evaluate our approach. Our experiment results show that our proposed methods outperform other conventional methods. (C) 2012 Elsevier Ltd. All rights reserved.
Integrating expert profile, reputation and link analysis for expert finding in question-answering websites
(2013-01-01)
Liu, Duen-Ren
;
Chen, Yu-Hsuan
;
Kao, Wei-Chen
;
Wang, Hsiu-Wen
;
資訊管理與財務金融系註：原資管所+財金所
Question answering websites are becoming an ever more popular knowledge sharing platform. On such websites, people may ask any type of question and then wait for someone else to answer the question. However, in this manner, askers may not obtain correct answers from appropriate experts. Recently, various approaches have been proposed to automatically find experts in question answering websites. In this paper, we propose a novel hybrid approach to effectively find experts for the category of the target question in question answering websites. Our approach considers user subject relevance, user reputation and authority of a category in finding experts. A user's subject relevance denotes the relevance of a user's domain knowledge to the target question. A user's reputation is derived from the user's historical question-answering records, while user authority is derived from link analysis. Moreover, our proposed approach has been extended to develop a question dependent approach that considers the relevance of historical questions to the target question in deriving user domain knowledge, reputation and authority. We used a dataset obtained from Yahoo! Answer Taiwan to evaluate our approach. Our experiment results show that our proposed methods outperform other conventional methods. (C) 2012 Elsevier Ltd. All rights reserved.