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Register a new userTowards Determining the Effect of Age and Educational Level on Cyber-Hygiene
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Added by
Elochukwu Ukwandu Dr
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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As internet related challenges increase such as cyber-attacks, the need for safe practises among users to maintain computer systems health and online security have become imperative, and this is known as cyber-hygiene. Poor cyber-hygiene among internet users is a very critical issue undermining the general acceptance and adoption of internet technology. It has become a global issue and concern in this digital era when virtually all business transactions, learning, communication and many other activities are performed online. Virus attack, poor authentication technique, improper file backups and the use of different social engineering approaches by cyber-attackers to deceive internet users into divulging their confidential information with the intention to attack them have serious negative implications on the industries and organisations, including educational institutions. Moreover, risks associated with these ugly phenomena are likely to be more in developing countries such as Nigeria. Thus, authors of this paper undertook an online pilot study among students and employees of University of Nigeria, Nsukka and a total of 145 responses were received and used for the study. The survey seeks to find out the effect of age and level of education on the cyber hygiene knowledge and behaviour of the respondents, and in addition, the type of devices used and activities they engage in while on the internet. Our findings show wide adoption of internet in institution of higher learning, whereas, significant number of the internet users do not have good cyber hygiene knowledge and behaviour. Hence, our findings can instigate an organised training for students and employees of higher institutions in Nigeria.
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The objectives of this ongoing research are to build Real-Time AI-Powered Educational Dashboard (RAED) as a decision support tool for instructors, and to measure its impact on them while making decisions. Current developments in AI can be combined with the educational dashboards to make them AI-Powered. Thus, AI can help in providing recommendations based on the students performances. AI-Powered educational dashboards can also assist instructors in tracking real-time student activities. In this ongoing research, our aim is to develop the AI component as well as improve the existing design component of the RAED. Further, we will conduct experiments to study its impact on instructors, and understand how much they trust RAED to guide them while making decisions. This paper elaborates on the ongoing research and future direction.
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EAs.LiT is an e-assessment management and analysis software for which contextual requirements and usage scenarios changed over time. Based on these factors and further development activities, the decision was made to adopt a microservice architecture
for EAs.LiT version 2 in order to increase its flexibility to adapt to new and changed circumstances. This architectural style and a few adopted technologies, like RDF as a data format, enabled an eased implementation of various use cases. Thus we consider the microservice architecture productive and recommend it for usage in other educational projects. The specific architecture of EAs.LiT version 2 is presented within this article, targeting to enable other educational projects to adopt it by using our work as a foundation or template.
Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.
Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of available questions, it is important to have an automated way to quantify their properties and intelligently select them for students, enabling effective and personalized learning experiences. In this work, we propose a framework for mining insights from educational questions at scale. We utilize the state-of-the-art Bayesian deep learning method, in particular partial variational auto-encoders (p-VAE), to analyze real students answers to a large collection of questions. Based on p-VAE, we propose two novel metrics that quantify question quality and difficulty, respectively, and a personalized strategy to adaptively select questions for students. We apply our proposed framework to a real-world dataset with tens of thousands of questions and tens of millions of answers from an online education platform. Our framework not only demonstrates promising results in terms of statistical metrics but also obtains highly consistent results with domain experts evaluation.
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