Shun-Chih Chang

A group intelligence-based asynchronous argumentation learning-assistance platform

Structured argumentation support environments have been built and used in scientific discourse in the literature. However, to the best our knowledge, there is no research work in the literature examining whether student’s knowledge has grown during learning activities with asynchronous argumentation. In this work, an intelligent computer-supported collaborative argumentation-based learning platform that detects whether the learners address the expected discussion issues is proposed. After each learner presents an argument, a term weighting method is adopted to derive input parameters of a one-class support vector machines classifier which determines if the learners’ arguments are related to the discussion topics. Notably, a peer review mechanism is established to improve the quality of the classifier. Besides, a feedback module is used to issue feedback messages to the learners if the learners have gone off on a tangent. The experimental results revealed that the students were benefited by the proposed learning-assistance platform.

An Adaptive Live Streaming Mechanism for Heterogeneous Vehicular Networks

With the popularity of vehicular networks, how to maintain high quality of the seamless live streaming service is a great challenge. In this work, an adaptive seamless live streaming dissemination system for heterogeneous vehicular networks to tackle this challenging issue is presented. First, differential service is presented in this work to ensure that paid users can satisfy the live steaming service. Based on users’ service-level agreement, an adaptive bandwidth allocation policy is proposed in this work to attain seamless handoff. In addition, because vehicles enter into the areas of hotspots, we also present a mechanism not only to prevent the insufficient bandwidth in advance, but also to make sure the paid users have higher priority than free users to obtain the seamless streaming service. When an unavoidable congestion occurs, we compress the streaming videos with a Region of Interest approach based on the content and characters of the video in order to maintain the service quality of paid users and reduce the required bandwidth for the streaming services. A series of simulation results show that our mechanism achieves better performance in terms of bandwidth utilization, packet loss ratio, and blocking probability. The capability of self-adaption in volatile real-time vehicular environment assists in the effectiveness and practicability of our proposed approach.