Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.     

Scaling up Content Analysis

ABSTRACT

Employing a number of different standalone programs is a prevalent approach among communication scholars who use computational methods to analyze media content. For instance, a researcher might use a specific program or a paid service to scrape some content from the Web, then use another program to process the resulting data, and finally conduct statistical analysis or produce some visualizations in yet another program. This makes it hard to build reproducible workflows, and even harder to build on the work of earlier studies. To improve this situation, we propose and discuss four criteria that a framework for automated content analysis should fulfill: scalability, free and open source, adaptability, and accessibility via multiple interfaces. We also describe how to put these considerations into practice, discuss their feasibility, and point toward future developments.     

The entrepreneurial university and academic discourses: The meta‐synthesis of Higher Education articles

The innovative teaching, knowledge and technology production and societal service activities of academics are largely discussed within the entrepreneurial university discourse in Higher Education journals. Therefore, this research focuses on a meta‐synthesis of Higher Education articles to better understand the concepts of the entrepreneurial university and entrepreneurial academic. After the systematic elimination based on keywords and titles, 25 articles have been selected from the top 10 Higher Education journals in the SCImago Journal Rankings. Content analysis was performed on these articles to highlight the common operational areas in entrepreneurial universities and activities of entrepreneurial academics. The meta‐synthesis shows that, both for universities and academics, entrepreneurship is overwhelmingly characterised by the commercialisation of scholarly activities that enrich institutional income revenue as well as academics’ personal income. However, such an approach can distance universities from their mission of public good; so Higher Education administrators should create mechanisms that would allow both to take place in a balanced way.     

Strength,Weakness, Opportunity,Threat (SWOT) Analysis of the Adaptations to Anatomical Education in the United Kingdom and Republic of Ireland in Response to the Covid-19 Pandemic

The Covid-19 pandemic has driven the fastest changes to higher education across the globe, necessitated by social distancing measures preventing face-to-face teaching. This has led to an almost immediate switch to distance learning by higher education institutions. Anatomy faces some unique challenges. Intrinsically, anatomy is a three-dimensional subject that requires a sound understanding of the relationships between structures, often achieved by the study of human cadaveric material, models, and virtual resources. This study sought to identify the approaches taken in the United Kingdom and Republic of Ireland to deliver anatomical education through online means. Data were collected from 14 different universities in the United Kingdom and Republic of Ireland and compared adopting a thematic analysis approach. Once themes were generated, they were collectively brought together using a strength, weakness, opportunity, threat (SWOT) analysis. Key themes included the opportunity to develop new online resources and the chance to engage in new academic collaborations. Academics frequently mentioned the challenge that time constrains could place on the quality and effectiveness of these resources; especially as in many cases the aim of these resources was to compensate for a lack of exposure to cadaveric exposure. Comparisons of the actions taken by multiple higher education institutions reveal the ways that academics have tried to balance this demand. Discussions will facilitate decisions being made by higher education institutions regarding adapting the curriculum and assessment methods in anatomy.