Search

Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm's effectiveness in meeting EV users' bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved. [ABSTRACT FROM AUTHOR]

With the rapid advancement of autonomous driving and network technologies, future vehicles will function as network nodes, facilitating information transmission. Concurrently, in-vehicle entertainment systems will undergo substantial enhancements. Beyond traditional broadcasting and video playback, future systems will integrate immersive applications featuring 360-degree views and six degrees of freedom (6DoF) capabilities. As autonomous driving technology matures, vehicle passengers will be able to engage in a broader range of entertainment activities while on the move. However, this evolution in video applications will significantly increase bandwidth demand for vehicular networks, potentially leading to bandwidth shortages in congested traffic areas. This paper presents a method for bandwidth allocation for vehicle video applications within the landscape of vehicle-to-everything (V2X) networks. By utilizing a millimeter-wave (mmWave), terahertz (THz) frequency band, and cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) wireless communication technologies, we provide vehicle passengers with the necessary bandwidth resources. Additionally, we address bandwidth contention issues in congested road segments by incorporating communication methods tailored to the characteristics of vehicular environments. By classifying users and adjusting according to the unique requirements of various multimedia applications, we ensure that real-time applications receive adequate bandwidth. Simulation experiments validate the proposed method's effectiveness in managing bandwidth allocation for in-vehicle video applications within V2X networks. It increases the available bandwidth during peak hours by 32%, demonstrating its ability to reduce network congestion and ensure smooth playback of various video application types. [ABSTRACT FROM AUTHOR]

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.

Recent research indicated that students' ability to construct evidence-based explanations in classrooms through scientific inquiry is critical to successful science education. Structured argumentation support environments have been built and used in scientific discourse in the literature. To the best of our knowledge, no research work in the literature addressed the issue of automatically assessing the student's argumentation quality, and the teaching load of the teacher that used the online argumentation support environments is not alleviated. In this work, an intelligent argumentation assessment system based on machine learning techniques for computer supported cooperative learning is proposed. Learners' arguments on discussion board were examined by using argumentation element sequence to detect whether the learners address the expected discussion issues and to determine the argumentation skill level achieved by the learner. Learners are first assigned to heterogeneous groups based on their responses to the learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. A feedback rule construction mechanism is used to issue feedback messages to the learners in case the argumentation assessment system detects that the learners go in a biased direction. The Moodle, an open source software e-learning platform, was used to establish the cooperative learning environment for this study. The experimental results exhibit that the proposed work is effective in classifying and improving student's argumentation level and assisting the students in learning the core concepts taught at a natural science course on the elementary school level.

With rapid advances in the development of information technology, information access has become central to life in the 21st century. In recent years, the development of useful learning-assistance systems has become a popular topic in literature. Learners can benefit from guidance provided by a tool that assists learning when a student has trouble using an e-learning platform. An effective learning-assistance tool can reduce teacher workload and increase the time spent giving individual guidance to learners who fall behind. However, few machine-learning techniques have been used as learning-assistance tools in literature to determine learner status or provide feedback to learners and teachers. Therefore, this work employs a learning-assistance tool that uses learning-reinforcement techniques to continuously interact with an environment and provides learners with suitable and timely feedback to guide students through difficulties, inspire them, and help them complete assigned tasks. The learning-assistance tool can promote learners' information and communication technology (ICT) literacy and assist learners in overcoming difficulties. Furthermore, teacher workload is significantly reduced because appropriate hints or feedback are automatically delivered to learners without teacher involvement. (Contains 14 tables and 3 figures.)

Recently, a lot of open source e-learning platforms have been offered for free in the Internet. We thus incorporate the intelligent diagnosis and assessment tool into an open software e-learning platform developed for programming language courses, wherein the proposed learning diagnosis assessment tools based on text mining and machine learning techniques are employed to alleviate the loading of the teachers. Experiments were conducted in two introductory-undergraduate programming courses to examine the effectiveness of the proposed diagnosis and assessment tools. The learners' work including the source code and comments were processed by the proposed text mining and machine learning techniques. This system also provides immediate feedback and high-quality evaluation results to guide the learners with poor performance. Our experimental results reveal that the proposed work can effectively assist the low-ability learners. (Contains 6 tables and 13 figures.)

In recent years, designing useful learning diagnosis systems has become a hot research topic in the literature. In order to help teachers easily analyze students' profiles in intelligent tutoring system, it is essential that students' portfolios can be transformed into some useful information to reflect the extent of students' participation in the curriculum activity. It is observed that students' portfolios seldom reflect students' actual studying behaviors in the learning diagnosis systems given in the literature; we thus propose three kinds of learning parameter improvement mechanisms in this research to establish effective parameters that are frequently used in the learning platforms. The proposed learning parameter improvement mechanisms can calculate the students' effective online learning time, extract the portion of a message in discussion section which is strongly related to the learning topics, and detect plagiarism in students' homework, respectively. The derived numeric parameters are then fed into a Support Vector Machine (SVM) classifier to predict each learner's performance in order to verify whether they mirror the student's studying behaviors. The experimental results show that the prediction rate for the SVM classifier can be increased up to 35.7% in average after the inputs to the classifier are "purified" by the learning parameter improvement mechanisms. This splendid achievement reveals that the proposed algorithms indeed produce the effective learning parameters for commonly used e-learning platforms in the literature.

This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to search related learning courseware and discussing what they have learned with their colleagues. Based on the log files that record the learners' past online learning behavior, an intelligent diagnosis system is used to give appropriate learning guidance to assist the learners in improving their study behaviors and grade online class participation for the instructor. The achievement of the learners' final reports can also be predicted by the diagnosis system accurately. Our experimental results reveal that the proposed learning diagnosis system can efficiently help learners to expand their knowledge while surfing in cyberspace Web-based "theme-based learning" model.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2008.12.014 Byline: Chenn-Jung Huang (a), Kai-Wen Hu (a), You-Jia Chen (a), Chun-Hua Chen (a), Yun-Cheng Luo (b) Keywords: Video-on-demand; Heterogeneous network; Quality of service; Fuzzy logic Abstract: In network-aware concept, applications are aware of network conditions and are adaptable to the varying environment to achieve acceptable and predictable performance. Two basic aspects of network-aware applications, network-awareness and network adaptation, have been widely addressed in the literature. In this work, a solution for video on demand service that integrates wireless and wired networks by using the network-aware concepts is proposed to reduce the blocking probability and dropping probability of mobile requests. Fuzzy logic inference system is employed to select appropriate cache relay nodes to cache published video streams and distribute them to different peers through service oriented architecture (SOA). SIP-based control protocol and IMS standard are adopted in this work to ensure the possibility of heterogeneous communication and provide a framework for delivering real-time multimedia services over an IP-based network to ensure interoperability, roaming, and end-to-end session management. The experimental results demonstrate that effectiveness and practicability of the proposed work. Author Affiliation: (a) Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan, ROC (b) Institute of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, ROC Article History: Received 22 June 2008; Revised 19 October 2008; Accepted 19 December 2008 Article Note: (miscellaneous) Responsible Editor: L. Jiang Xie