Smart Online Exam Proctoring Assist for Cheating Detection

Online exams are the most preferred mode of exams in online learning environment. This mode of exam has been even more prevalent and a necessity in the event of a forced closure of face-to-face teaching such as the recent Covid-19 pandemic. Naturally, conducting online exams poses much greater challenge to preserving academic integrity compared to conducting on-site face-to-face exams. As there is no human proctor for policing the examinee on site, the chances of cheating are high. Various online exam proctoring tools are being used by educational institutes worldwide, which offer different solutions to reduce the chances of cheating. The most common technique followed by these tools is recording of video and audio of the examinee during the whole duration of exam. These videos can be analyzed later by human examiner to detect possible cheating case. However, viewing hours of exam videos for each student can be impractical for a large class and thus detecting cheating would be next to impossible. Although some AI-based tools are being used by some proctoring software to raise flags, they are not always very useful. In this paper we propose a cheating detection technique that analyzes an exam video to extract four types of event data, which are then fed to a pre-trained classification model for detecting cheating activity. We formulate the cheating detection problem as a multivariate time-series classification problem by transforming each video into a multivariate time-series representing the time-varying event data extracted from each frame of the video. We have developed a real dataset of cheating videos and conduct extensive experiments with varying video lengths, different deep learning and traditional machine learning models and feature sets, achieving prediction accuracy as high as 97.7%

NGNaaS:NGNs as cloud computing services

© 2015 IEEE. Next Generation Networks are the future mobile telecommunication networks, capable of provisioning universal access to services (anytime, anywhere, using any device). Cloud computing is an emerging networking paradigm with several inherent advantages including easy introduction of new services, efficient resource use, scalability, and pay-per-use. Cloud computing is ubiquitous in the Internet domain, but its use in the telecommunications context is still in its infancy. Offering next generation networks as cloud computing services will facilitate their deployment and enable deployment and management cost sharing. We envision that many network operators and service providers will soon support this sharing idea, as demonstrated by a number of ongoing agreements and investigation initiatives (e.g. between Rogers and Videotron in Canada, and between Etisalat and DU in the UAE). However, many issues need to be solved before this vision becomes a reality. This position paper presents our vision for offering next generation networks as cloud services, sketches an early business model, identifies the research issues, and presents a roadmap

Towards an open source architecture for multi-operator LTE core networks

© 2016 The deployment of a new mobile telecommunication operator and its operation can lead to very high costs and a long entry time to business market. Sharing LTE network infrastructures and adopting network virtualization technologies are of paramount importance to reduce both capital and operational costs of future mobile networks. However, the number of openly available and realistic implementations to study and enhance such sharing technologies is simply inexistent. This paper proposes an architecture for enabling the sharing of Long Term Evolution (LTE) infrastructures based on Multi-Operator Core Network (MOCN), a well-known specification where different evolved Node B (eNB) base stations are shared among multiple mobile telecommunication operators in order to reduce capital (CAPEX) and operational (OPEX) costs. Technical details of the proposed architecture are described in this contribution. The proposed architecture has been implemented and validated. This contribution constitutes the first open source implementation available of an LTE-Emulator with infrastructure sharing capabilities. It also provides a complete empirical study of the effects of sharing eNBs between different providers. Results show an average overhead around 1.5% when sharing technologies are being utilized whereas the reduction of capital costs is ranging between 50% and 87% for a scenario where 8 telecommunication operators are sharing the infrastructure

A Survey on Personalized TV and NGN Services through Context-Awareness

International audienceThe advances in IPTV (Internet Protocol Television) technology enable a new user-centric and interactive TV model, in which context-awareness is promising in making the user's interaction with the TV dynamic and transparent. Our research interest is how to achieve TV service personalization applying context-awareness to the NGN IPTV architecture. In this article we present the different existing contributions that employ context-awareness to allow interactive services. Some of these contributions directly focus on TV, while others are proposed for specific NGN services. We present a technical analysis for these solutions and give some guidelines for future deployment of personalized IPTV services