Plagiarism is a growing problem in academia. Academics often use plagiarism detection tools to detect similar source-code files. Once similar files are detected, the academic proceeds with the investigation process which involves identifying the similar source-code fragments within them that could be used as evidence for proving plagiarism. This paper describes PlaGate, a novel tool that can be integrated with existing plagiarism detection tools to improve plagiarism detection performance. The tool also implements a new approach for investigating the similarity between source-code files with a view to gathering evidence for proving plagiarism. Graphical evidence is presented that allows for the investigation of source-code fragments with regards to their contribution toward evidence for proving plagiarism. The graphical evidence indicates the relative importance of the given source-code fragments across files in a corpus. This is done by using the Latent Semantic Analysis information retrieval technique to detect how important they are within the specific files under investigation in relation to other files in the corpus.

A survey using a scenario-based questionnaire format has provided insight into the perceptions of U.K academics who teach programming on computing courses. This survey across various higher education (HE) institutions investigates what academics feel constitutes source-code plagiarism in an undergraduate context. Academics' responses on issues surrounding source-code reuse and acknowledgement are discussed. A general consensus exists among academics that a ldquozero tolerancerdquo plagiarism policy is appropriate; however, some issues concerning source-code reuse and acknowledgement raised controversial responses. This paper discusses the most important findings from the survey and proposes a definition of what can constitute source-code plagiarism from the perspective of U.K. academics who teach programming on computing courses.

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.