Browsing by Author "Ke, Hao-Ren"

Now showing 1 - 13 of 13

A two-level relevance feedback mechanism for image retrieval
(2008-04-01)
Cheng, Pei-Cheng
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Chien, Been-Chian
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Ke, Hao-Ren
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Yang, Wei-Pang
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資訊工程學系
Content-based image retrieval (CBIR) is a group of techniques that analyzes the visual features (such as color, shape, texture) of an example image or image subregion to find similar images in an image database. Relevance feedback is often used in a CBIR system to help users express their preference and improve query results. Traditional relevance feedback relies on positive and negative examples to reformulate the query. Furthermore, if the system employs several visual features for a query, the weight of each feature is adjusted manually by the user or system predetermined and fixed by the system. In this paper we propose a new relevance feedback model suitable for medical image retrieval. The proposed method enables the user to rank the results in relevance order. According to the ranking, the system can automatically determine the importance ranking of features, and use this ranking to automatically adjust the weight of each feature. The experimental results show that the new relevance feedback mechanism outperforms previous relevance feedback models. (c) 2007 Elsevier Ltd. All rights reserved.
Access Control in XML Repositories Using a Role-Based Approach
(2010-12-01)
Chen, Shihn-Yuarn
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Ke, Hao-Ren
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Tsai, Wen-Nung
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Yang, Wei-Pang
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資訊工程學系
Nowadays, more and more Internet and Web applications store data in XML documents, which are more structured than unstructured plain text files. Administrators of an XML repository want to control the accessibility of both an XML file and an element within an XML file. However, traditional access control methods do not consult the semi-structure characteristics of XML documents. In view of this, this work proposes Integrated Role-based and XML document Access Control (IRXAC), which encompasses the Role Based Access Control phase and the XML Document Access Control phase, as a solution of access control on an XML repository. The former phase performs file-level access control and decides the accessibility of an XML file. The latter phase performs element-level access control and decides the accessibility of an element. Besides the details of IRXAC, the comparisons of IRXAC with other access control methods are shown.
Access Control in XML Repositories Using a Role-Based Approach
(2010-12-01)
Chen, Shihn-Yuarn
;
Ke, Hao-Ren
;
Tsai, Wen-Nung
;
Yang, Wei-Pang
;
資訊工程學系
Nowadays, more and more Internet and Web applications store data in XML documents, which are more structured than unstructured plain text files. Administrators of an XML repository want to control the accessibility of both an XML file and an element within an XML file. However, traditional access control methods do not consult the semi-structure characteristics of XML documents. In view of this, this work proposes Integrated Role-based and XML document Access Control (IRXAC), which encompasses the Role Based Access Control phase and the XML Document Access Control phase, as a solution of access control on an XML repository. The former phase performs file-level access control and decides the accessibility of an XML file. The latter phase performs element-level access control and decides the accessibility of an element. Besides the details of IRXAC, the comparisons of IRXAC with other access control methods are shown.
Classifier design with feature selection and feature extraction using layered genetic programming
(2008-02-01)
Lin, Jung-Yi
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Ke, Hao-Ren
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Chien, Been-Chian
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Yang, Wei-Pang
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資訊工程學系
This paper proposes a novel method called FLGP to construct a classifier device of capability in feature selection and feature extraction. FLGP is developed with layered genetic programming that is a kind of the multiple-population genetic programming. Populations advance to an optimal discriminant function to divide data into two classes. Two methods of feature selection are proposed. New features extracted by certain layer are used to be the training set of next layer's populations. Experiments on several well-known datasets are made to demonstrate performance of FLGP. (C) 2007 Elsevier Ltd. All rights reserved.
College students' misunderstandings about copyright laws for digital library resources
(2010)
Wu, Huan-Chueh
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Chou, Chien
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Ke, Hao-Ren
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Wang, Mei-Hung
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教育研究所
Purpose - This paper has two-primary purposes: to explore common copyright-related problems that arise when librarians promote die use of digital library resources; and to investigate college students' misconceptions of copyright laws that arise when the students use these resources. Design/methodology/approach - Four librarians in charge of the management of digital library resources were interviewed regarding student-users' problematic copyright-infringement behaviors that these librarians often encountered when they promoted the use of digital library resources. Also, a semi-structured questionnaire with nine questions about copyright-related behaviors was developed and distributed to college students. Students needed not only to identify whether the behavior was acceptable, but also to explain the reasons for their identification. A total of 109 valid sets of data were collected from 18 universities or colleges, the sets comprising responses from 48 undergraduate, 56 postgraduate, and five doctoral students. Findings - The librarian-interview results indicate that students' problematic behaviors included systematic downloading, distribution to unauthorized users, and going beyond the purpose and character of academic use. The student-survey results indicate that students had four major areas of misunderstanding about copyright laws when using digital library resources: the digital resources should be shared; the downloaded digital resources are all legitimately authorized and permitted; all educational use is fair use; and any downloading is permitted as long as students are paying tuition. Originality/value - This paper explores students' understanding and misunderstandings that arose when students used the school digital library resources and discusses implications of these results for librarians and libraries with regard to the design of related instruction. The paper also presents interpretations of students' thoughts and conduct, as well as some future possible research topics.
Combining textual and visual features for cross-language medical image retrieval
(2006)
Cheng, Pei-Cheng
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Chien, Been-Chian
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Ke, Hao-Ren
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Yang, Wei-Pang
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資訊工程學系
In this paper we describe the technologies and experimental results for the medical retrieval task and automatic annotation task. We combine textual and content-based approaches to retrieve relevant medical images. The content-based approach containing four image features and the text-based approach using word expansion are developed to accomplish these tasks. Experimental results show that combining both the content-based and text-based approaches is better than using only one approach. In the automatic annotation task we use Support Vector Machines (SVM) to learn image feature characteristics for assisting the task of image classification. Based on the SVM model, we analyze which image feature is more promising in medical image retrieval. The results show that the spatial relationship between pixels is an important feature in medical image data because medical image data always has similar anatomic regions. Therefore, image features emphasizing spatial relationship have better results than others.
Concept Extraction and Clustering for Search Result Organization and Virtual Community Construction
(2012-01-01)
Chen, Shihn-Yuarn
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Chang, Chia-Ning
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Nien, Yi-Hsiang
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Ke, Hao-Ren
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資訊管理與財務金融系註：原資管所+財金所
This study proposes a concept extraction and clustering method, which improves Topic Keyword Clustering by using Log Likelihood Ratio for semantic correlation and Bisection K-Means for document clustering. Two value-added services are proposed to show how this approach can benefit information retrieval (IR) systems. The first service focuses on the organization and visual presentation of search results by clustering and bibliographic coupling. The second one aims at constructing virtual research communities and recommending significant papers to researchers. In addition to the two services, this study conducts quantitative and qualitative evaluations to show the feasibility of the proposed method; moreover, comparison with the previous approach is also performed. The experimental results show that the accuracy of the proposed method for search result organization reaches 80%, outperforming Topic Keyword Clustering. Both the precision and recall of virtual community construction are higher than 70%, and the accuracy of paper recommendation is almost 90%.
Designing a classifier by a layered multi-population genetic programming approach
(2007-08-01)
Lin, Jung-Yi
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Ke, Hao-Ren
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Chien, Been-Chian
;
Yang, Wei-Pang
;
資訊工程學系
This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions transform the training set to construct a new training set. The successive layer uses the new training set to obtain better discriminant functions. Moreover, because the functions generated by each layer will be composed to a long discriminant function, which is the result of LAGEP, every layer can evolve with short individuals. For each population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations. Several experiments are conducted with different settings of LAGEP and several real-world medical problems. Experiment results show that LAGEP achieves comparable accuracy to single population GP in much less time. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
iSpreadRank: Ranking sentences for extraction-based summarization using feature weight propagation in the sentence similarity network
(2008-10-01)
Yeh, Jen-Yuan
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Ke, Hao-Ren
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Yang, Wei-Pang
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資訊工程學系
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圖書館
Sentence extraction is a widely adopted text summarization technique where the most important sentences are extracted from document(s) and presented as a summary. The first step towards sentence extraction is to rank sentences in order of importance as in the summary. This paper proposes a novel graph-based ranking method, iSpreadRank, to perform this task. iSpreadRank models a set of topic-related documents into a sentence similarity network. Based on such a network model, iSpreadRank exploits the spreading activation theory to formulate a general concept from social network analysis: the importance of a node in a network (i.e., a sentence in this paper) is determined not only by the number of nodes to which it connects, but also by the importance of its connected nodes. The algorithm recursively re-weights the importance of sentences by spreading their sentence-specific feature scores throughout the network to adjust the importance of other sentences. Consequently, a ranking of sentences indicating the relative importance of sentences is reasoned. This paper also develops in approach to produce a generic extractive summary according to the inferred sentence ranking. The proposed summarization method is evaluated using the DUC 2004 data set, and found to perform well. Experimental results show that the proposed method obtains a ROUGE-1 score of 0.38068, which represents a slight difference of 0.00156, when compared with the best participant in the DUC 2004 evaluation. (C) 2007 Elsevier Ltd. All rights reserved.
Knowledge-based mobile learning framework for museums
(2006)
Hsu, Tien-Yu
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Ke, Hao-Ren
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Yang, Wei-Pang
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圖書館
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Library
Purpose - The purpose of this study is to propose a knowledge-based mobile learning framework that integrates various types of museum-wide content, and supports ubiquitous, context-aware, personalized learning for museums. Design/methodology/approach - A unified knowledge base with multi-layer reusable content structures serves as the kernel component to integrate content from exhibitions for education and collection in a museum. The How-Net approach is adopted to build a unified natural and cultural ontology. The ontology functions as a common and sharable knowledge concept that denotes each knowledge element in the unified knowledge base, and associates each learner's learning context and usage with a content and usage profile respectively. Data mining algorithms, e.g. association mining and clustering, are applied to discover useful patterns for ubiquitous personalization from these content and usage profiles. Findings - A pilot project based on the proposed framework has been successfully implemented in the Life Science Hall of the National Museum of Natural Science (NMNS), Taiwan, demonstrating the feasibility of this framework. Originality/value - This study proposes a mobile learning framework that can be replicated in many museums. This framework improves learners' learning experiences with rich related content, and with ubiquitous, proactive and adaptive services. Museums can also benefit from implementing this framework through outreach services for educational, promoting and usability needs from combining mobile and Internet communication technologies and learning services.
Social Tagging in Digital Archives
(2008)
Chen, Shihn-Yuarn
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Teng, Yu-Ying
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Ke, Hao-Ren
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資訊工程學系
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Department of Computer Science
Social trend tracking by time series based social tagging clustering
(2011-09-15)
Chen, Shihn-Yuarn
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Tseng, Tzu-Ting
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Ke, Hao-Ren
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Sun, Chuen-Tsai
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資訊工程學系
Social tagging is widely practiced in the Web 2.0 era. Users can annotate useful or interesting Web resources with keywords for future reference. Social tagging also facilitates sharing of Web resources. This study reviews the chronological variation of social tagging data and tracks social trends by clustering tag time series. The data corpus in this study is collected from Hemidemi.com. A tag is represented in a time series form according to its annotating Web pages. Then time series clustering is applied to group tag time series with similar patterns and trends in the same time period. Finally, the similarities between clusters in different time periods are calculated to determine which clusters have similar themes, and the trend variation of a specific tag in different time periods is also analyzed. The evaluation shows the recommendation accuracy of the proposed approach is about 75%. Besides, the case discussion also proves the proposed approach can track the social trends. (C) 2011 Elsevier Ltd. All rights reserved.
Structure clustering for Chinese patent documents
(2008-05-04)
Huang, Su-Hsien
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Ke, Hao-Ren
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Yang, Wei-Pang
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資訊工程學系
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Department of Computer Science
This paper aims to cluster Chinese patent documents with the structures. Both the explicit and implicit structures are analyzed to represent by the proposed structure expression. Accordingly, an unsupervised clustering algorithm called structured self-organizing map (SOM) is adopted to cluster Chinese patent documents with both similar content and structure. Structured SOM clusters the similar content of each sub-part structure, and then propagates the similarity to upper level ones. Experimental result showed the maps size and number of patents are proportional to the computing time, which implies the width and depth of structure affects the performance of structured SOM. Structured clustering of patents is helpful in many applications. In the lawsuit of copyright, companies are easy to find claim conflict in the existent patents to contradict the accusation. Moreover, decision-maker of a company can be advised to avoid hot-spot aspects of patents, which can save a lot of R&D effort. (c) 2007 Elsevier Ltd. All rights reserved.