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8 results for “plagiarism detection”
Software Plagiarism Detection on Intermediate Representation Data Set
<p>This data set contains the necessary files used for the bachelor's thesis Software Plagiarism Detection on Intermediate Representation.</p> <p>This includes the data sets for the tests, the implemented code and scripts for the evaluation as well as referenced work.</p> <p> </p>
PAN Arabic Intrinsic Plagiarism Detection Shared Task Corpus
<p>Evaluation corpus for ARAbic INtrinsic plagiarism detection (InAra Corpus) </p> <p> </p> <p>This corpus has been used in AraPlagDet 2015 shared task </p> <p>More details could be found in : <a href="http://araplagdet.misc-lab.org">https://araplagdet.misc-lab.org/</a> or <a href="http://pan.webis.de/fire15/pan15-web/index.html">https://pan.webis.de/fire15/pan15-web/index.html</a> </p> <p> </p> <p><strong>I. SYNOPSIS </strong></p> <p>InAra corpus comprises 2048 documents; 80% of them contain passages borrowed from other documents to simulate documents that contain plagiarized fragments. The corpus involves 2 parts: Training and test.</p> <p> </p> <p><strong>II. DESCRIPTION </strong></p> <p>Each part of the corpus (training and test) consists mainly of 2 datasets: textual files and XML files. The textual files represent the suspicious documents i.e., the documents that contain artificial plagiarism; and the XML files are the plagiarism annotation i.e. they provide for each plagiarized passage its starting offset in the suspicious document and its length (offset and length are both expressed in characters). A suspicious document file and its plagiarism annotation file share the same name.</p> <p> </p> <p><strong>III. PURPOSE </strong></p> <p>The purpose of InAra corpus is to evaluate automatic plagiarism detection methods, notably methods of the intrinsic approach. This approach consists in uncovering the plagiarized passages on the basis of the writing style inconsistency in a given suspicious document. As opposed to the external approach, the intrinsic approach does not necessitate any comparison of the suspicious document against the potential sources of plagiarism. Hence, InAra corpus is not appropriate for the evaluation of the external plagiarism detection because the source of plagiarism are not provided.</p> <p>It should be noted that some documents in InAra corpus contain religious quotations (e.g., Quran and Hadith). These quotations have a peculiar writing style and then a simple intrinsic plagiarism detection software can consider them as plagiarism. However, quotations are not plagiarism, and they are not annotated in the XML files in InAra. Hence, it is an important feature for the plagiarism detection systems evaluated on InAra to not consider religious quotations as plagiarism cases unless they appear as part of a larger plagiarism case.</p> <p> </p> <p><strong>IV. BUILDING METHODS </strong></p> <p>The documents that compose InAra corpus do not contain actual plagiarism cases. They are rather artificial suspicious documents in which plagiarism was created automatically by a software that takes fragments of text from one or more sources documents and inserts them in another one according to a set of parameters, namely the percentage of plagiarism and the plagiarized passages lengths. This building method is the same used to construct PAN 2009-2011 corpora of plagiarism detection (see <a href="http://pan.webis.de">http://pan.webis.de</a> for more information on PAN competition and its corpora). </p> <p> </p> <p><strong>V. LANGUAGE AND ENCODING </strong></p> <p>All the textual documents of this corpus are written in Arabic language and encoded in UTF-8 without BOM.</p> <p> </p> <p><strong>VI. SOURCES OF TEXTS </strong></p> <p>Texts used to build this corpus, either suspicious documents or the inserted passages, are taken mainly from the open library Arabic Wikisource (http://ar.wikisource.org), one of Wikimedia Foundation projects. A few numbers of documents were taken from other websites, namely: </p> <ul> <li>Create your own country blog: http://diycountry.blogspot.com </li> <li>Corpus of Classical Arabic (KSUCCA): http://ksucorpus.ksu.edu.sa </li> <li>Islamic book web site: http://www.islamicbook.ws </li> </ul> <p> </p> <p><strong>VII. COPYRIGHT AND AVAILABILITY </strong></p> <p>We were very careful to build the corpus with copyright-free texts only, to be able to make it publicly available without any sort of problems with texts owners. </p> <p> </p> <p><strong>VIII. HOW TO CITE THE CORPUS ?</strong></p> <p>If you publish a paper about your experimentations using InAra corpus, please cite the following paper:</p> <ul> <li>Bensalem, I., Boukhalfa, I., Rosso, P., Abouenour, L., Darwish, K., & Chikhi, S.: Overview of the AraPlagDet PAN@FIRE2015 Shared Task on Arabic Plagiarism Detection. In P. Majumder, M. Mitra, M. Agrawal, & P. Mehta (Eds.), Post Proceedings of the Workshops at the 7th Forum for Information Retrieval Evaluation (FIRE 2015), Gandhinagar, India, December 4-6, CEUR proceedings vol. 1587 (pp. 111–122). CEUR-WS.org (2015).</li> </ul> <p>We encourage you to compare your method tested on InAra with the methods of AraPlagDet competition described in the paper above.</p> <p>Additional information on the corpus building are in the papers:</p> <ul> <li>Bensalem, I., Rosso, P., Chikhi, S.: A New Corpus for the Evaluation of Arabic Intrinsic Plagiarism Detection. In: Forner, P., Müller, H., Paredes, R., Rosso, P., and Stein, B. (eds.) CLEF 2013, LNCS, vol. 8138. pp. 53–58. Springer, Heidelberg (2013).</li> <li>Bensalem, I., Rosso, P., Chikhi, S.: Building Arabic Corpora from Wikisource. 10th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA’13),May 27-30 Fes/Ifran, Morocco (2013).IEEE. </li> </ul> <p> </p> <p>You may wish to compare the results of your experiments with the result of the following papers that used InAra corpus:</p> <ul> <li>Bensalem I, Rosso P, Chikhi S (2019) On the use of character n-grams as the only intrinsic evidence of plagiarism. Language Resources and Evaluation 53:363–396. doi: 10.1007/s10579-019-09444-w</li> <li>Mahgoub AY, Magooda A, Rashwan M, et al (2015) RDI System for Intrinsic Plagiarism Detection (RDI_RID), Working Notes for PAN-AraPlagDet at FIRE 2015. In: Majumder P, Mitra M, Agrawal M, Mehta P (eds) Post Proceedings of the Workshops at the 7th Forum for Information Retrieval Evaluation (FIRE 2015), Gandhinagar, India, December 4-6, CEUR proceedings vol. 1587. CEUR-WS.org, pp 129–130</li> </ul> <p> </p> <p><strong>IX. WARNING </strong></p> <p>It should be noted that the Arabic texts may contain quotations from the Quran and the Hadith; and due to the fact that text insertion is automatic and in random positions, it is possible that the plagiarized text is inserted unintentionally between Quranic verses or sentences of a Hadith cited in a document. Hence, the inserted passages may alter the meaning of the original text. For these reasons, this corpus must not be used outside the purpose for which it was built. Examples of the inappropriate use include using the corpus documents as a source of knowledge or distributing them without mentioning that they contain borrowed texts. If you are not interested in plagiarism detection and you are retaining the corpus because it contains books you want to read, then this corpus is not the right source. Please, you should refer to the </p> <p>sources mentioned in Section VI where you can find the original content of the books you are looking for. We emphasize that we are not responsible for the results of any use of this corpus other than the evaluation of the intrinsic plagiarism detection methods. </p> <p> </p> <p><strong>X. CONTACT US</strong></p> <p>We will be happy to hear from you about your experience in using InAra corpus. Please do not hesitate to contact us with the following email address: bens.imene@gmail.com</p> <p> </p> <p>Imene Bensalem¹, Paolo Rosso², Salim Chikhi¹</p> <p>¹MISC Lab. Constantine 2 university, Algeria</p> <p>²PRHLT, Universitat Politècnica de València, Spain </p>
PAN Arabic External Plagiarism Detection Shared Task Corpus
<p>Evaluation Corpus for ARAbic EXternal plagiarism detection (ExAra Corpus) </p> <p> </p> <p>This corpus has been used in AraPlagDet 2015 shared task </p> <p>More details could be found in : <a href="http://araplagdet.misc-lab.org">https://araplagdet.misc-lab.org/</a> or <a href="http://pan.webis.de/fire15/pan15-web/index.html">https://pan.webis.de/fire15/pan15-web/index.html</a></p> <p>If you publish a paper about your experimentations using ExAra corpus, please cite the following paper:</p> <ul> <li>Bensalem, I., Boukhalfa, I., Rosso, P., Abouenour, L., Darwish, K., & Chikhi, S.: Overview of the AraPlagDet PAN@FIRE2015 Shared Task on Arabic Plagiarism Detection. In P. Majumder, M. Mitra, M. Agrawal, & P. Mehta (Eds.), Post Proceedings of the Workshops at the 7th Forum for Information Retrieval Evaluation (FIRE 2015), Gandhinagar, India, December 4-6, CEUR proceedings vol. 1587 (pp. 111–122). CEUR-WS.org (2015).</li> </ul> <p>We encourage you to compare your method tested on ExAra with the methods of AraPlagDet competition described in the paper above.</p> <p> </p> <p><strong>I. SYNOPSIS </strong></p> <p>ExAra corpus comprises 2345 documents; almost half of them (suspecious doucuments) contain passages borrowed from the other half (source docucments) to simulate documents that contain plagiarized fragments. The corpus involves 2 parts: Training and test.</p> <p> </p> <p><strong>II. DESCRIPTION </strong></p> <p>Each part of the corpus (training and test) consists mainly of 3 datasets: 2 sets of textual files and 1 set of XML files. The 2 sets of the textual files are the suspicious documents (i.e. the documents that contain artificial plagiarism) and the source documents (i.e., the documents from which the suspicious passages have been plagiarised). The 3rd set of documents contains XML files, which are the plagiarism annotation, i.e., they provide for each plagiarized passage its starting offset and its length in both the suspicious and source documents (offset and length were both expressed in characters). A suspicious document file (.txt) and its plagiarism annotation file (.xml) share the same name.</p> <p> </p> <p><strong>III. PURPOSE </strong></p> <p>The purpose of ExAra corpus is to evaluate automatic plagiarism detection methods, notably methods of the External approach. This approach consists in uncovering the plagiarized passages on the basis of their similarity with passages in the source documents.</p> <p>It should be noted that some suspicious documents in ExAra corpus contain religious quotations (e.g., Quran and Hadith) and common phrases. Some of them appear also in some source documents, and hence a simple plagiarism detection software can consider them as plagiarism. However, quotations and common phrases are legitimate text reuse cases and are not annotated in the XML files in ExAra. Therefore, it is an important feature for the plagiarism detection systems evaluated on ExAra to not consider religious quotations and common phrases as plagiarism cases unless they appear as part of a larger plagiarism case.</p> <p> </p> <p><strong>IV. BUILDING METHODS </strong></p> <p>The documents that compose ExAra corpus do not contain actual plagiarism cases, they are rather artificial suspicious documents in which plagiarism was created automatically by a software that takes fragments of text from one or more sources documents and inserts them in another one according to a set of parameters, namely the percentage of plagiarism and the lengths of the plagiarized passages. Some of the plagiarised fragments are obfuscated manually or automatically before inserting them in the suspicious documents.</p> <p>This building method is the same used to construct PAN 2009-2011 corpora of plagiarism detection (see http://pan.webis.de for more information on PAN competition and its corpora). </p> <p> </p> <p><strong>V. LANGUAGE AND ENCODING </strong></p> <p>All the textual documents of this corpus are written in Arabic language and encoded in UTF-8 without BOM.</p> <p> </p> <p><strong>VI. HOW TO CITE THE CORPUS ?</strong></p> <p>If you publish a paper about your experimentations using ExAra corpus, please cite the following paper:</p> <ul> <li>Bensalem, I., Boukhalfa, I., Rosso, P., Abouenour, L., Darwish, K., & Chikhi, S.: Overview of the AraPlagDet PAN@FIRE2015 Shared Task on Arabic Plagiarism Detection. In P. Majumder, M. Mitra, M. Agrawal, & P. Mehta (Eds.), Post Proceedings of the Workshops at the 7th Forum for Information Retrieval Evaluation (FIRE 2015), Gandhinagar, India, December 4-6, CEUR proceedings vol. 1587 (pp. 111–122). CEUR-WS.org (2015).</li> </ul> <p>We encourage you to compare your method tested on ExAra with the methods of AraPlagDet competition described in the paper above.</p> <p> </p> <p><strong>VII. WARNING </strong></p> <p>It should be noted that the Arabic texts may contain quotations from the Quran and the Hadith; and due to the fact that text insertion is automatic and in random positions, it is possible that the plagiarized text is inserted unintentionally between Quranic verses or sentences of a Hadith cited in a document. Hence, the inserted passages may alter the meaning of the original text. For these reasons, this corpus must not be used outside the purpose for which it was built. Examples of the inappropriate use include using the corpus documents as a source of knowledge or distributing them without mentioning that they contain borrowed texts. If you are not interested in plagiarism detection, and you are retaining the corpus because it contains articles you want to read, then this corpus is not the right source. Please, you should refer to the sources mentioned in (Bensalem et al. 2015) (i.e.,the paper above) where you can find the original content of the articles you are looking for.</p> <p>We emphasize that we are not responsible for the results of any use of this corpus other than the evaluation of the external plagiarism detection methods. </p> <p> </p> <p><strong>VIII. CONTACT US</strong></p> <p>We will be happy to hear from you about your experience in using ExAra corpus. Please do not hesitate to contact us with the following email address: bens.imene@gmail.com</p> <p> </p> <p>Imene Bensalem¹, Imene Boukhalfa¹, Paolo Rosso², Salim Chikhi¹</p> <p>¹MISC Lab. Constantine 2 university, Algeria</p> <p>²PRHLT, Universitat Politècnica de València, Spain </p>
Detecting Cross-Language Plagiarism using Open Knowledge Graphs
<p>Corresponding authors: <a href="mailto:meuschke@uni-wuppertal.de?subject=Inquiry%20about%20CL-OSA%20dataset">Norman Meuschke</a>, <a href="mailto:ruas@uni-wuppertal.de?subject=Inquiry%20about%20CL-OSA%20dataset">Terry Ruas</a><br> Venue: 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2021)<br> at the ACM/IEEE Joint Conference on Digital Libraries 2021 (JCDL2021)</p> <p>==========================================================================</p> <p><strong>Source code: <a href="https://github.com/ag-gipp/cl-osa">https://github.com/ag-gipp/cl-osa</a> </strong></p> <p>==========================================================================</p> <p><strong>Dataset Details</strong></p> <p><em><a href="https://jipsti.jst.go.jp/aspec/">ASPEC</a></em>. The Asian Scientific Paper Excerpt Corpus comprises excepts of scientific papers in Japanese that have been manually translated to English and Chinese. We use both subsets of the ASPEC corpus. </p> <ul> <li><em>ASPEC-JC</em><strong> </strong>contains abstracts and paragraphs from the main text of research papers that were translated manually from Japanese to Chinese.</li> <li><em>ASPEC-JE</em> contains abstracts of approx. two million research papers that were translated manually from Japanese to English. </li> </ul> <p><em><a href="https://ec.europa.eu/jrc/en/language-technologies/jrc-acquis">JRC-Acquis</a></em>. The corpus consists of legislative texts in 22 languages, which the European Union's Joint Research Centre (JRC) selected from the cumulative body of EU laws (the so called Acquis communautaire). We sampled our test cases from the 10,000 document pairs in the English-French subset of the corpus.</p> <p><em><a href="https://www.statmt.org/europarl/">Europarl</a></em>. The corpus contains transcripts of European Parliament proceedings in 21 European languages. We exclusively sampled test cases from the 9,443 document pairs in the English-French subset of the corpus.</p> <p><em><a href="https://zenodo.org/record/3250095#.YOr25jqxVH4">PAN-PC-11</a></em>. The corpus contains instances of simulated monolingual and cross-language plagiarism that were used for evaluating plagiarism detection methods as part of the workshop series <a href="https://pan.webis.de/">Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection (PAN)</a>. Most of the 26,939 documents in the corpus were created by extracting text from openly available books. The documents are partially interspersed with instances of simulated plagiarism that were created and obfuscated automatically or by crowdsourced workers. We exclusively sampled test cases from the 2,921 Spanish-English aligned document pairs in the corpus, for which simulated plagiarism instances were either machine-generated or created manually by crowdsourced workers.</p> <ul> </ul> <p>==========================================================================</p> <p><strong>File Structure</strong></p> <p><strong>[corpus_documents] folder</strong>: Corpora of translation-aligned documents used in our experiments composed of: </p> <ul> <li>aspec: Japanese and English</li> <li>aspecx: Japanese and Chinese </li> <li>jrc: English and French </li> <li>europarl: English and French</li> <li>pan: English and Spanish</li> </ul> <p>Each sub-corpus consists of 4,000 translation-aligned files (2,000 per language); the entire corpus has thus 20,000 files.<br> Each set of translation-aligned documents was randomly selected from the original datasets (details in the paper).<br> The Japanese files in aspec and aspecx do not necessarily overlap even though they are from the same dataset.</p> <p><br> <strong>[vectors_documents] folder</strong>: Average vector representation of the documents in the datasets from two pre-trained models:</p> <ul> <li>Universal Sentence Encoder - Multilingual (USE-ML)</li> <li>ConceptNet Numberbatch</li> </ul> <p> </p> <p><strong>Naming convention</strong>: <model>_<dataset>_<language>;</p> <ul> <li>Example: cn_jrc_es: <ul> <li>model: ConceptNet Numberbatch</li> <li>corpus: JRC-Acquis</li> <li>language: Spanish</li> </ul> </li> </ul> <ul> <li>Labels: <ul> <li><model>:<br> cn - ConceptNet Numberbatch<br> um - USE-ML<br> </li> <li><dataset><br> aspec - ASPEC (Asian Scientific Paper Excerpt Corpus) - English and Japanese<br> aspecx - ASPEC (Asian Scientific Paper Excerpt Corpus) - Japanese and Chinese<br> jrc - JRC-Acquis<br> europarl - Europarl<br> pan - PAN-PC-11<br> </li> <li><language><br> en - English<br> es - Spanish<br> fr - French<br> ja - Japanese<br> zh - Chinese</li> </ul> </li> </ul>
Replication Package for "Mitigating Automated Obfuscation Attacks on Software Plagiarism Detection Systems"
<p>This is the replication package for the doctoral dissertation titled "<em>Mitigating Automated Obfuscation Attacks on Software Plagiarism Detection Systems</em>".</p> <p>The contributions of the dissertation were also integrated into the source code plagiarism detection system <a href="https://github.com/jplag/JPlag/">JPlag</a> to ensure they are widely accessible.</p> <p><strong>Contents Overview:</strong></p> <p>- <strong>Datasets</strong>: the artifacts of the evaluation datasets.<br>- <strong>Raw Results</strong>: the measured results of our evaluation.<br>- <strong>Evaluation Scripts</strong>: the evaluation code for plotting and statistical tests.<br>- <strong>Implementation</strong>: the source code of the JPlag-based implementation and the prebuilt application as a JAR file.<br>- <strong>Other</strong>: additional plots.</p>
Reproduction Package for "Preventing Refactoring Attacks on Software Plagiarism Detection through Graph-Based Structural Normalization"
<p>This repository stores all data used in the evaluation of the master's thesis "Preventing Refactoring Attacks on Software Plagiarism Detection through Graph-Based Structural Normalization". It ensures the continuous reproducibility of the results of the thesis.</p> <p>Content:</p> <ul> <li>JPlag v5.1.0 including the Java CPG frontend <ul> <li>Code base</li> <li>Runnable JAR</li> </ul> </li> <li>Data sets used for evaluation</li> <li>Evaluation results</li> <li>R script used to process the results</li> <li>Graphics and tables generated from the results</li> </ul> <p>The data sets were generated by Nils Niehues and Moritz Brödel and were originally published here:</p> <ul> <li><a href="../records/10430322">Supplementary Material for "Detecting Automatic Software Plagiarism via Token Sequence Normalization" (zenodo.org)</a></li> <li><a href="../records/10149536">Reproduction package for: Intelligent Match Merging to Prevent Obfuscation Attacks on Software Plagiarism Detectors (zenodo.org)</a></li> </ul> <p>The data sets are based partly on PROGPedia, available here:</p> <ul> <li><a href="../records/7449056">PROGpedia (zenodo.org)</a></li> </ul> <p>Visit <a title="State-of-the-Art Software Plagiarism & Collusion Detection" href="jplag.github.io/JPlag/" target="_blank" rel="noopener">JPlag</a> on GitHub for the current version.<br>See the thesis document for more information.</p> <p>Read about similar publications about Plagiarism Detection <a title="JPlag" href="https://jplag.github.io/MinimalLandingPage/" target="_blank" rel="noopener">here</a>.</p>
Supplementary Material for "Detecting Automatic Software Plagiarism via Token Sequence Normalization"
<p>This repository contains additional material supporting the paper titled "Detecting Automatic Software Plagiarism via Token Sequence Normalization", presented at ICSE 2024 (research track).</p> <div> <div> <div> <p>The paper presents a defense mechanism against automated plagiarism generators utilizing program dependence graphs and demonstrates its effectiveness in countering insertion-based and reordering-based obfuscation attacks.</p> </div> </div> </div> <p>The defense mechanism was also integrated into the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a>, thus providing a widely accessible solution.</p> <p><strong>Contents Overview:</strong></p> <ul> <li> <p><strong>Datasets:</strong> Two datasets from the <a title="PROGpedia Repository" href="../record/7449056">PROGpedia</a> collection and two internal datasets. For the latter, only the metadata is available due to the sensitive nature of the data.</p> </li> <li> <p><strong>Plagiarized Submissions:</strong> Generated plagiarism instances illustrating various obfuscation methods such as insertion, reordering, and insert-after-reordering.</p> </li> <li> <p><strong>Evaluation Data:</strong> JSON files detailing calculated similarities and runtime measurements for all datasets.</p> </li> <li> <p><strong>Source Code:</strong> The implementation of our defense mechanism based on the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a> (v4.0.0). Note that JPlag is licensed under the GPL-3.0 license.</p> </li> <li> <p><strong>Evaluation Code:</strong> Python code for runtime measurements.</p> </li> <li> <p><strong>Interactive Plots:</strong> HTML visualizations of the paper's plots, offering dynamic insights into the research findings. particularly focusing on the detection of automatic software plagiarism through token sequence normalization.</p> </li> <li><strong>Demo:</strong> A packaged JAR of our implementation alongside an instruction on how to execute it.</li> </ul>
Supplementary Material for "Automated Detection of AI-Obfuscated Plagiarism in Modeling Assignments"
<p>This repository contains additional material supporting the paper titled "Automated Detection of AI-Obfuscated Plagiarism in Modeling Assignments", presented at ICSE 2024 (SEET track).</p> <p>The paper presents a token-based approach for detecting modeling plagiarism. It leverages a novel normalization technique to achieve resilience against common obfuscation attacks.</p> <p>The approach was also integrated into the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a>, thus providing a widely accessible solution.</p> <p><strong>Contents Overview:</strong></p> <ul> <li><strong>Source Code:</strong> The implementation of our approach (contribution 1) based on the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a> (v4.0.0). Note that JPlag is licensed under the GPL-3.0 license.</li> <li><strong>ChatGPT Study:</strong> An exploration of how ChatGPT can be exploited for cheating in modeling assignments.</li> <li><strong>Datasets:</strong> The three datasets of our evaluation based on EMF modeling assignments.</li> <li><strong>Raw Results:</strong> All raw data of our evaluation results, as used in our plots and tables.</li> <li><strong>Demo:</strong> A packaged JAR of our approach's implementation alongside an instruction on how to use it.</li> </ul>
ScienceDex guides
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