Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers. This article focuses only on plagiarism detection and presents a detailed systematic review of the field of source-code plagiarism detection in academia. This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types. Perspectives on the meaning of source-code plagiarism detection in academia are presented, together with categorisations of the available detection tools and analyses of their effectiveness. While writing the review, some interesting insights have been found about metrics and datasets for quantitative tool comparison and categorisation of detection algorithms. Also, existing obfuscation methods classifications have been expanded together with a new definition of “source-code plagiarism detection in academia.”

The main vision of the Internet of Things (IoT) is to enable seamless connection between physical devices and information systems to improve the lives of people. One of the main obstacles to achieve this vision is the current lack of IoT interoperability. In this article, the authors are giving an overview on how semantics is used in IoT interoperability related research. To do this, they performed a systematic literature review and extracted data from 105 selected primary studies dealing with semantics in IoT interoperability. The authors have analysed the maturity level of this research field and when and where the relevant works were published. This article answers five main research questions about the following issues: what are semantics used for; what types of ontologies exist (which are used to give semantical descriptions); what are the main themes and suggestions for future work in these research articles; and what are other related areas.

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This research aims to develop a knowledge-based system used for calculating course difficulty and producing appropriate learning strategies for students. The system is based on fuzzy reasoning and attempts to contribute to the personalization of the learning process. After the description of the data collection process and the search for regularities in the data, we present the service interface. The knowledge-based system proposed in this paper can yield a number of benefits for both students and institutions: it motivates students to adopt a more practical and personalized approach towards learning and provides an independent learning procedure for both the student and the instructor.