Likewise, the assessment of competence is distorted, which again can result in undue career benefits for plagiarists.

DOI: This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation on the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection initiatives, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is usually a highly active research field. Over the period we review, the field has seen big innovations regarding the automated detection of strongly obfuscated and therefore hard-to-identify forms of academic plagiarism. These improvements generally originate from better semantic text analysis methods, the investigation of non-textual content features, and also the application of machine learning.

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If the classification accuracy drops significantly, then the suspicious and known documents are likely from the same writer; otherwise, They're likely written by different authors [232]. There is not any consensus within the stylometric features that are most suitable for authorship identification [158]. Table 21 gives an overview of intrinsic detection methods that use machine-learning techniques.

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Those common with earlier versions of mod_rewrite will little question be looking to the RewriteLog and RewriteLogLevel directives.

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The strategy for selecting the query terms from the suspicious document is vital for that success of this strategy. Table nine gives an overview from the strategies for query term selection utilized by papers inside our collection.

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strategy exclusively analyzes the input document, i.e., does not perform comparisons to documents in the reference collection. Intrinsic detection methods utilize a process known as stylometry

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