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Taipei, Taiwan

Ming Chuan University is a selective private university in Shilin District, Taipei, Taiwan, and accredited in the United States of America. It was founded by Pao Teh-Ming and her husband Lee Ying-Chao. The university was named after the famous progressive Qing Dynasty governor of Taiwan Liu Mingchuan. Wikipedia.


Lee M.C.,Ming Chuan University
Expert Systems with Applications | Year: 2011

A novel sentence similarity measure for semantic based expert systems is presented. The well-known problem in the fields of semantic processing, such as QA systems, is to evaluate the semantic similarity between irregular sentences. This paper takes advantage of corpus-based ontology to overcome this problem. A transformed vector space model is introduced in this article. The proposed two-phase algorithm evaluates the semantic similarity for two or more sentences via a semantic vector space. The first phase built part-of-speech (POS) based subspaces by the raw data, and the latter carried out a cosine evaluation and adopted the WordNet ontology to construct the semantic vectors. Unlike other related researches that focused only on short sentences, our algorithm is applicable to short (4-5 words), medium (8-12 words), and even long sentences (over 12 words). The experiment demonstrates that the proposed algorithm has outstanding performance in handling long sentences with complex syntax. The significance of this research lies in the semantic similarity extraction of sentences, with arbitrary structures. © 2010 Elsevier Ltd. All rights reserved.


Su M.-Y.,Ming Chuan University
Computers and Security | Year: 2010

The infrastructure of a Mobile Ad hoc Network (MANET) has no routers for routing, and all nodes must share the same routing protocol to assist each other when transmitting messages. However, almost all common routing protocols at present consider performance as first priority, and have little defense capability against the malicious nodes. Many researches have proposed various protocols of higher safety to defend against attacks; however, each has specific defense objects, and is unable to defend against particular attacks. Of all the types of attacks, the wormhole attack poses the greatest threat and is very difficult to prevent; therefore, this paper focuses on the wormhole attack, and proposes a secure routing protocol based on the AODV (Ad hoc On-demand Distance Vector) routing protocol, which is named WARP (Wormhole-Avoidance Routing Protocol). WARP considers link-disjoint multipaths during path discovery, and provides greater path selections to avoid malicious nodes, but eventually uses only one path to transmit data. Based on the characteristic that wormhole nodes can easily grab the route from the source node to the destination node, WARP enables the neighbors of the wormhole nodes to discover that the wormhole nodes have abnormal path attractions. Then, the wormhole nodes would be gradually isolated by their normal neighboring nodes, and finally be quarantined by the whole network. © 2009 Elsevier Ltd. All rights reserved.


Tsai C.-W.,Ming Chuan University
Computers and Education | Year: 2010

As more and more people use computers to complete their work and solve problems in the workplace, computing education is emphasized for students of all levels and disciplines in Taiwan. However, the computing education in Taiwan can hardly be recognized as effective and satisfactory. Many inappropriate examples that lack context are used in teaching and textbooks that may result in employees with low competence and insufficient ability for collaborative working. Students who grow up in this learning context usually lack the ability to seek information and solve problems by themselves. In this regard, the author redesigned a course and adopted online collaborative learning with initiation to establish the essential knowledge for students' collaboration in the initial stage of a course. This study conducted an experiment that included 169 undergraduates from three class sections - the first two from an academic university (Case 1, n = 68; Case 2, n = 68) and the last one from a university of science and technology (Case 3, n = 33) - taught by the same teacher under the same course name and the same course website. The results show that students who received online collaborative learning with initiation had higher grades than those without. The author further discusses the implications for teachers, schools, and scholars who plan to provide online courses for their students, particularly computing courses. © 2009 Elsevier Ltd. All rights reserved.


Wu Y.-C.,Ming Chuan University
Information Sciences | Year: 2014

Semi-supervised machine learning methods have the features of both, integrating labeled and unlabeled training data. In most structural problems, such as natural language processing and image processing, developing labeled data for a specific domain requires considerable amount of human resources. In this paper, we present a cluster-based method to fuse labeled training and unlabeled raw data. We design a top-down divisive clustering algorithm that ensures maximal information gain in the use of unlabeled data via clustering similar words. To implement this idea, we design a top-down iterative K-means clustering algorithm to merge word clusters. Differently, the derived term groups are then encoded as new features for the supervised learners in order to improve the coverage of lexical information. Without additional training data or external materials, this approach yields state-of-the-art performance on the shallow parsing and base-chunking benchmark datasets (94.50 and 93.12 in F (β) rates). © 2014 Elsevier Inc. All rights reserved.


Huang S.-L.,Ming Chuan University
Electronic Commerce Research and Applications | Year: 2011

Recommender systems are useful in reducing information overload and improving decision making. Utility-based recommender systems provide recommendations based on the computation of the utility of each item for the user. Some utility-elicitation methods have been developed on the basis of multi-attribute utility theory (MAUT) to represent a decision maker's complete preference. This study investigates whether these utility-based techniques outperform the traditional content-based technique for online recommendations. A laboratory experiment was conducted in two e-commerce contexts to compare the decomposed and holistic utility-based methods, simple multi-attribute rating technique exploiting ranks (SMARTER) and radial basis function network (RBFN), with the content-based method vector space model (VSM) in terms of recommendation accuracy, time expense, and user perceptions. The results demonstrate that the performances of utility-based methods depend on recommendation contexts. Furthermore, this study proposes guidelines for choosing appropriate recommendation methods in different contexts. © 2011 Elsevier B.V. All rights reserved.

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