试论“网球肘”的成因、诊断、预防与治疗

“网球肘”是网球运动中常见的一种伤病。通过研究“网球肘”的成因、诊断、预防与治疗，预防伤病的发生。     

L'experience regionale arabe en matiere d'education aux droits de l'homme dans les situations d'apprentissage informel

This article describes an informal approach to human rights education, which is anchored in reality and based on experience gained throughout the Arab region and more particularly within the Arab Institute for Human Rights. It is argued that the approach of the Institute could be adopted on a larger scale by organisations working in this domain, not only in the Arab region but also in other regions of the world. The article emphasises the essential role of governments and the importance of cooperation between the governmental and non-governmental levels in order to implement this type of human rights education in informal learning situations, and thus to disseminate a universal culture of human rights.     

The Changing Research Data Landscape and the Experiences of Ethics Review Board Chairs: Implications for Library Practice and Partnerships

Academic libraries have to a large extent taken the lead in facilitating new approaches to research data management, but changes to the research data landscape have had an impact on numerous areas of academic work, including ethics review. Using interpretive phenomenological analysis of interviews with chairs of Canadian research ethics boards, this study explores how ethics review boards have experienced changes to data policy and related technologies in order to describe the ethical implications of new approaches to data management and to explore ways in which the library, ethics review boards, and other campus partners might harmonize efforts to support emerging data practices. While ethics review boards in Canada are keenly aware of open data policies, data publishing in practice is still nascent. There is uncertainty about the adoption of changing technologies for research and their impacts on privacy protection. Where responsibility lies for addressing these uncertainties is often unclear. Academic libraries and research ethics boards are well-suited to engage in mutual knowledge transfer and to integrate data management planning and ethics review processes. Institutional-level oversight that includes all campus departments impacted by changes to the research data landscape may facilitate improved communication and reduce role ambiguity.     

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.