Search

Programming Languages are core courses in computer science and computing-related disciplines, and there are increasing demands on improving the experiences, motivation and efficiency of programming language teaching and learning. In recent decades, online judge (OJ) systems have been popularly adopted in educational environments to support real-time learning feedback, and provide interactive programming practice. However, most existing OJ systems rely on significant resource for hardware virtualization, and suffer from long development cycles and extensive maintenances. This paper proposed a course-oriented OJ system based on the Docker Container techniques to significantly reduce the cost and maintenance of deployment of existing OJ system. The proposed approach is designed in the form of an experimental system, which is effective in stimulating the students to learn programming language and assist the instant assessment of coursework. Meanwhile, our proposed system can be integrated with major massive open online course (MOOC) systems for assessment. The efficiency and performance of our proposed system has been evaluated and tested by 1460 students in Nanyang Institute of Technology during term time between 2018 and 2019.

Internet of Things (IoT) is on the raise and has become an integral part of our daily activities. Cloud Computing has been serving as an effective platform to IoT application that host data execution at the back-end server at the datacentres. The extent of IoT to mission critical delay intolerant applications require immediate responses from the providers. While the traditional Cloud infrastructure incurs unavoidable process delays, Fog Computing can effectively mitigate such delays to serve latency sensitive applications with reduced energy consumption. However, the success of the Fog architecture relies on effective data management both during transmission and execution. To this end, this paper discusses the advantages of Fog computing over Clouds in addressing energy and time constraints of IoT application, uncovers the challenges faced by data aggregation in Fog computing. An important contribution of this paper is the review on the data aggregation strategies and a recommended hybrid data aggregation scheme for Fog based IoT applications.