Quality flaw prediction in Spanish Wikipedia: A case of study with verifiability flaws

In this work, we present the first quality flaw prediction study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove). Based on the underlying characteristics of each flaw, different state-of-the-art approaches were evaluated. For articles not citing any references, a well-established rule-based approach was evaluated and interesting findings show that some of them suffer from Refimprove flaw instead. Likewise, for articles that need additional citations for verification, the well-known PU learning and one-class classification approaches were evaluated. Besides, new methods were compared and a new feature was also proposed to model this latter flaw. The results showed that new methods such as under-bagged decision trees with sum or majority voting rules, biased-SVM, and centroid-based balanced SVM, perform best in comparison with the ones previously published.     

The turning point in 1985. The history of appraisal and disposal of records in Iceland

The article sets off with a brief summary of the history of appraisal and disposal of records as processed at the National Archives of Iceland (NAI) since its establishment in 1882 to present times. Rules on appraisal and disposal of records introduced in 1900, 1911, and 1916 were ignored and all records were kept. Status quo remained until 1985 when a new National Archives Act was passed. The appraisal and disposal process adopted then is described and explained. It included the publication of instructions on records management as well as forms relating to records management. The influence of acts of law on the appraisal and disposal process is explained. Finally the experience with the arrangement of organised appraisal in Iceland is described.     

The View from Above: Map Reading Skills Project

In South Africa the education system for black children is fraught with problems. These problems are the direct result of apartheid and have been compounded by poverty and illiteracy. In this article the problem of poorly qualified or unqualified teachers is discussed especially with regard to the teaching of map skills to children in their fifth school year. The project team needed to develop affordable materials which could be used in large classes by teachers after a very short period of training. Books and other materials which are available commercially were examined, but for various reasons were found to be unsuitable for local conditions. After determining the extent of the problem, the project team followed a grassroots process of developing a flip chart which teachers learnt to use with confidence and which enabled pupils to master the concepts and skills necessary for understanding a map.     

Multilingual modeling of cross-lingual spelling variants

Technical term translations are important for cross-lingual information retrieval. In many languages, new technical terms have a common origin rendered with different spelling of the underlying sounds, also known as cross-lingual spelling variants (CLSV). To find the best CLSV in a text database index, we contribute a formulation of the problem in a probabilistic framework, and implement this with an instance of the general edit distance using weighted finite-state transducers. Some training data is required when estimating the costs for the general edit distance. We demonstrate that after some basic training our new multilingual model is robust and requires little or no adaptation for covering additional languages, as the model takes advantage of language independent transliteration patterns. We train the model with medical terms in seven languages and test it with terms from varied domains in six languages. Two test languages are not in the training data. Against a large text database index, we achieve 64–78 % precision at the point of 100% recall. This is a relative improvement of 22% on the simple edit distance.     

Teaching Appraisal in an American (U.S.) Archives Program

The author, a professor at Simmons College in Boston, Massachusetts, discusses the use of selected archival literature to nurture an appraisal mindset, particularly within the American context. She then addresses in some detail types of appraisal case studies and the variety of ways that they may be used as teaching tools.     

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.