Meng Chang Chen

Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community

An online learning community enables learners to access up-to-date information via the Internet anytime-anywhere because of the ubiquity of the World Wide Web (WWW). Students can also interact with one another during the learning process. Hence, researchers want to determine whether such interaction produces learning synergy in an online learning community. In this paper, we take the Technology Acceptance Model as a foundation and extend the external variables as well as the Perceived Variables as our model and propose a number of hypotheses. A total of 436 Taiwanese senior high school students participated in this research, and the online learning community focused on learning English. The research results show that all the hypotheses are supported, which indicates that the extended variables can effectively predict whether users will adopt an online learning community. Finally, we discuss the implications of our findings for the future development of online English learning communities.

A framework of handoffs in wireless overlay networks based on mobile IPv6

Although there are various wireless access network technologies with different characteristics and performance level have been developed, no single network that can satisfy the anytime, anywhere, and any service wireless access needs of mobile users. A truly seamless mobile environment can only be realized by considering vertical and horizontal handoffs together. With the advantages of Mobile IPv6, a more comprehensive and integrated framework of heterogeneous networks can be developed. In this paper, we discuss the issues related to handoffs including horizontal and vertical handoffs. We present a scheme for integrating wireless local area network and wide area access networks, and propose a micromobility management method called HiMIPv6+. We also propose a QoS-based (quality-of-service-based) vertical handoff scheme and algorithm that consider wireless network transport capacity and user service requirement. Our prototype evaluations and the simulations show that our framework performs as expected.

An anonymous multi-server authenticated key agreement scheme based on trust computing using smart cards and biometrics

Password-based remote user authentication schemes are widely investigated, with recent research increasingly combining a users biometrics with a password to design a remote user authentication scheme that enhances the level of the security. However, these authentication schemes are designed for a single server environment and result in users needing to register many times when they want to access different application servers. To solve this problem, in this paper we propose an anonymous multi-server authenticating key agreement scheme based on trust computing using smart cards, password, and biometrics. Our scheme not only supports multi-server environments but also achieves many security requirements. In addition, our scheme is a lightweight authentication scheme which only uses the nonce and a hash function. From the subsequent analysis, the proposed scheme can be seen to resist several kinds of attacks, and to have more security properties than other comparable schemes.

Extracting classification knowledge of Internet documents with mining term associations: a semantic approach

In this paper, we present a system that extracts and generalizes terms from Internet documents to represent classification knowledge of a given class hierarchy. We propose a measurement to evaluate the importance of a term with respect to a class in the class hierarchy, and denote it as support. With a given threshold, terms with high supports are sifted as keywords of a class, and terms with low supports are filtered out. To further enhance the recall of this approach, Mining Association Rules technique is applied to mine the association between terms. An inference model is composed of these association relations and the previously computed supports of the terms in the class. To increase the recall rate of the keyword selection process. we then present a polynomialtime inference algorithm to promote a term, strongly associated to a known keyword, to a keyword. According to our experiment results on the collected Internet documents from Yam search engine, we show that the proposed methods In the paper contribute to refine the classification knowledge and increase the recall of keyword selection.

PVA: A Self-Adaptive Personal View Agent

In this paper, we present PVA, an adaptive personal view information agent system for tracking, learning and managing user interests in Internet documents. PVA consists of three parts: a proxy, personal view constructor, and personal view maintainer. The proxy logs the users activities and extracts the users interests without user intervention. The personal view constructor mines user interests and maps them to a class hierarchy (i.e., personal view). The personal view maintainer synchronizes user interests and the personal view periodically. When user interests change, in PVA, not only the contents, but also the structure of the user profile are modified to adapt to the changes. In addition, PVA considers the aging problem of user interests. The experimental results show that modulating the structure of the user profile increases the accuracy of a personalization system.

Life cycle modeling of news events using aging theory

In this paper, an adaptive news event detection method is proposed. We consider a news event as a life form and propose an aging theory to model its life span. A news event becomes popular with a burst of news reports, and it fades away with time. We incorporate the proposed aging theory into the traditional single-pass clustering algorithm to model life spans of news events. Experiment results show that the proposed method has fairly good performance for both long-running and short-term events compared to other approaches.

Proportional delay differentiation service based on weighted fair queuing

Differentiation service (Diffserv) is regarded as one of the practical architectures to realize quality of service (QoS) on the Internet. Relative differentiated service, which achieves relative QoS differentiation between traffic classes, is a simple and easily-deployed service model. Based on the concept of relative differentiated service, Dovrolis et al. (ACM SIGMETRICS Performance Evaluation Review vol.27, no.1, pp.204-5, 1999; IEEE Network, September 1999; ACM SIGCOMM-99, September 1999) proposed a proportional differentiation service model which guarantees the ratios of service differences between classes. They claimed that weighted fair queuing (WFQ) is not suitable for implementing relative differentiation service and employed priority-based scheduling algorithms in their model. In this paper, we extend WFQ and apply it to proportional delay differentiation service. The extended WFQ algorithm adjusts the weighting of each class dynamically so that the delay differences between classes can be well controlled. Simulations show that the proposed methods can realize proportional delay differentiation service effectively and efficiently.

Using Incremental PLSI for Threshold-Resilient Online Event Analysis

The goal of online event analysis is to detect events and track their associated documents in real time from a continuous stream of documents generated by multiple information sources. Unlike traditional text categorization methods, event analysis approaches consider the temporal relations among documents. However, such methods suffer from the threshold-dependency problem, so they only perform well for a narrow range of thresholds. In addition, if the contents of a document stream change, the optimal threshold (that is, the threshold that yields the best performance) often changes as well. In this paper, we propose a threshold-resilient online algorithm, called the incremental probabilistic latent semantic indexing (IPLSI) algorithm, which alleviates the threshold-dependency problem and simultaneously maintains the continuity of the latent semantics to better capture the story line development of events. The IPLSI algorithm is theoretically sound and empirically efficient and effective for event analysis. The results of the performance evaluation performed on the topic detection and tracking (TDT)-4 corpus show that the algorithm reduces the cost of event analysis by as much as 15 percent ~ 20 percent and increases the acceptable threshold range by 200 percent to 300 percent over the baseline.

ACIRD: intelligent Internet document organization and retrieval

This paper presents an intelligent Internet information system, Automatic Classifier for the Internet Resource Discovery (ACIRD), which uses machine learning techniques to organize and retrieve Internet documents. ACIRD consists of a knowledge acquisition process, document classifier, and two-phase search engine. The knowledge acquisition process of ACIRD automatically learns classification knowledge from classified Internet documents. The document classifier applies learned classification knowledge to classify newly collected Internet documents into one or more classes. Experimental results indicate that ACIRD performs as well or better than human experts in both knowledge acquisition and document classification. By using the learned classification knowledge and the given class lattice, the ACIRD two-phase search engine responds to user queries with hierarchically structured navigable results (instead of a conventional flat ranked document list), which greatly aids users in locating information from numerous, diversified Internet documents.

An Aging Theory for Event Life-Cycle Modeling

An event can be described by a sequence of chronological documents from several information sources that together describe a story or happening. The goal of event detection and tracking is to automatically identify events and their associated documents during their life cycles. Conventional document clustering and classification techniques cannot effectively detect and track sequential events, as they ignore the temporal relationships among documents related to an event. The life cycle of an event is analogous to living beings. With abundant nourishment (i.e., related documents for the event), the life cycle is prolonged; conversely, an event or living fades away when nourishment is exhausted. Improper tracking algorithms often unnecessarily prolong or shorten the life cycle of detected events. In this paper, we propose an aging theory to model the life cycle of sequential events, which incorporates a traditional single-pass clustering algorithm to detect and track events. Our experiment results show that the proposed method achieves a better overall performance for both long-running and short-term events than previous approaches. Moreover, we find that the aging parameters of the aging schemes are profile dependent and that using proper profile-specific aging parameters improves the detection and tracking performance further