{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T19:58:20Z","timestamp":1773950300897,"version":"3.50.1"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030969561","type":"print"},{"value":"9783030969578","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-96957-8_34","type":"book-chapter","created":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T14:04:35Z","timestamp":1645538675000},"page":"393-413","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Identifying Machine-Paraphrased Plagiarism"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2116-9767","authenticated-orcid":false,"given":"Jan Philip","family":"Wahle","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9440-780X","authenticated-orcid":false,"given":"Terry","family":"Ruas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8412-5553","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Folt\u00fdnek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4648-8198","authenticated-orcid":false,"given":"Norman","family":"Meuschke","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6522-3019","authenticated-orcid":false,"given":"Bela","family":"Gipp","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"issue":"1","key":"34_CR1","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s41239-021-00277-8","volume":"18","author":"F Alvi","year":"2021","unstructured":"Alvi, F., Stevenson, M., Clough, P.: Paraphrase type identification for plagiarism detection using contexts and word embeddings. Int. J. Educ. Technol. High. Educ. 18(1), 42 (2021)","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, pp. 3613\u20133618. Association for Computational Linguistics (2019). 10\/ggcgtm","DOI":"10.18653\/v1\/D19-1371"},{"key":"34_CR3","unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: Longformer: the long-document transformer. arXiv:2004.05150 [cs], April 2020"},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135\u2013146 (2017). 10\/gfw9cs","DOI":"10.1162\/tacl_a_00051"},{"key":"34_CR5","unstructured":"Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators. arXiv:2003.10555 [cs], March 2020"},{"key":"34_CR6","doi-asserted-by":"publisher","unstructured":"Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. In: Proceedings Conference on Empirical Methods in Natural Language Processing (2017). https:\/\/doi.org\/10.18653\/v1\/d17-1070","DOI":"10.18653\/v1\/d17-1070"},{"key":"34_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)"},{"key":"34_CR8","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 [cs], May 2019"},{"key":"34_CR9","unstructured":"Dey, K., Shrivastava, R., Kaushik, S.: A paraphrase and semantic similarity detection system for user generated short-text content on microblogs. In: Proceedings International Conference on Computational Linguistics (COLING), pp. 2880\u20132890 (2016)"},{"key":"34_CR10","unstructured":"Dolan, W.B., Brockett, C.: Automatically constructing a corpus of sentential paraphrases. In: Proceedings of the Third International Workshop on Paraphrasing (IWP 2005) (2005)"},{"issue":"1","key":"34_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41239-020-00192-4","volume":"17","author":"T Folt\u00fdnek","year":"2020","unstructured":"Folt\u00fdnek, T., et al.: Testing of support tools for plagiarism detection. Int. J. Educ. Technol. High. Educ. 17(1), 1\u201331 (2020). https:\/\/doi.org\/10.1186\/s41239-020-00192-4","journal-title":"Int. J. Educ. Technol. High. Educ."},{"issue":"6","key":"34_CR12","doi-asserted-by":"publisher","first-page":"112:1","DOI":"10.1145\/3345317","volume":"52","author":"T Folt\u00fdnek","year":"2019","unstructured":"Folt\u00fdnek, T., Meuschke, N., Gipp, B.: Academic plagiarism detection: a systematic literature review. ACM Comput. Surv. 52(6), 112:1-112:42 (2019). https:\/\/doi.org\/10.1145\/3345317","journal-title":"ACM Comput. Surv."},{"key":"34_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1007\/978-3-030-43687-2_68","volume-title":"Sustainable Digital Communities","author":"T Folt\u00fdnek","year":"2020","unstructured":"Folt\u00fdnek, T., et al.: Detecting machine-obfuscated plagiarism. In: Sundqvist, A., Berget, G., Nolin, J., Skjerdingstad, K.I. (eds.) iConference 2020. LNCS, vol. 12051, pp. 816\u2013827. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43687-2_68"},{"issue":"14","key":"34_CR14","doi-asserted-by":"publisher","first-page":"10593","DOI":"10.1007\/s00521-019-04594-y","volume":"32","author":"E Gharavi","year":"2019","unstructured":"Gharavi, E., Veisi, H., Rosso, P.: Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: no training phase. Neural Comput. Appl. 32(14), 10593\u201310607 (2019). https:\/\/doi.org\/10.1007\/s00521-019-04594-y","journal-title":"Neural Comput. Appl."},{"key":"34_CR15","unstructured":"Gutmann, M., Hyv\u00e4rinen, A.: Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS). JMLR W&CP, vol. 9, pp. 297\u2013304 (2010)"},{"key":"34_CR16","doi-asserted-by":"publisher","unstructured":"Hunt, E., et al.: Machine learning models for paraphrase identification and its applications on plagiarism detection. In: Proceedings 10th IEEE International Conference on Big Knowledge, pp. 97\u2013104 (2019). https:\/\/doi.org\/10.1109\/ICBK.2019.00021","DOI":"10.1109\/ICBK.2019.00021"},{"key":"34_CR17","unstructured":"Iyer, S., Dandekar, N., Csernai, K.: First quora dataset release: Question pairs (2017). https:\/\/data.quora.com\/First-Quora-Dataset-Release-Question-Pairs"},{"key":"34_CR18","doi-asserted-by":"publisher","unstructured":"Lan, W., Qiu, S., He, H., Xu, W.: A continuously growing dataset of sentential paraphrases. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp. 1224\u20131234. Association for Computational Linguistics (2017). https:\/\/doi.org\/10.18653\/v1\/D17-1126","DOI":"10.18653\/v1\/D17-1126"},{"key":"34_CR19","unstructured":"Lan, W., Xu, W.: Neural network models for paraphrase identification, semantic textual similarity, natural language inference, and question answering. arXiv:1806.04330 [cs], August 2018"},{"key":"34_CR20","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: a lite BERT for self-supervised learning of language representations. arXiv:1909.11942 [cs], September 2019"},{"key":"34_CR21","doi-asserted-by":"publisher","unstructured":"Lau, J.H., Baldwin, T.: An empirical evaluation of doc2vec with practical insights into document embedding generation. In: Proceedings Workshop on Representation Learning for NLP (2016). https:\/\/doi.org\/10.18653\/v1\/w16-1609","DOI":"10.18653\/v1\/w16-1609"},{"key":"34_CR22","unstructured":"Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings 31st International Conference on Machine Learning, vol. 32, pp. 1188\u20131196 (2014)"},{"key":"34_CR23","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv:1910.13461 [cs], October 2019","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"34_CR24","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv:1907.11692 [cs], July 2019"},{"key":"34_CR25","doi-asserted-by":"publisher","unstructured":"Meuschke, N.: Analyzing non-textual content elements to detect academic plagiarism. Doctoral thesis, University of Konstanz, Department of Computer and Information Science, Konstanz, Germany (2021). https:\/\/doi.org\/10.5281\/zenodo.4913345","DOI":"10.5281\/zenodo.4913345"},{"key":"34_CR26","doi-asserted-by":"publisher","unstructured":"Meuschke, N., Gondek, C., Seebacher, D., Breitinger, C., Keim, D., Gipp, B.: An adaptive image-based plagiarism detection approach. In: Proceedings 18th ACM\/IEEE Joint Conference on Digital Libraries, pp. 131\u2013140 (2018). https:\/\/doi.org\/10.1145\/3197026.3197042","DOI":"10.1145\/3197026.3197042"},{"key":"34_CR27","doi-asserted-by":"publisher","unstructured":"Meuschke, N., Stange, V., Schubotz, M., Gipp, B.: HyPlag: a hybrid approach to academic plagiarism detection. In: Proceedings 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1321\u20131324 (2018). https:\/\/doi.org\/10.1145\/3209978.3210177","DOI":"10.1145\/3209978.3210177"},{"key":"34_CR28","doi-asserted-by":"publisher","unstructured":"Meuschke, N., Stange, V., Schubotz, M., Kramer, M., Gipp, B.: Improving academic plagiarism detection for STEM documents by analyzing mathematical content and citations. In: Proceedings ACM\/IEEE Joint Conference on Digital Libraries, pp. 120\u2013129 (2019). https:\/\/doi.org\/10.1109\/JCDL.2019.00026","DOI":"10.1109\/JCDL.2019.00026"},{"key":"34_CR29","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. arXiv:1310.4546 [cs, stat], October 2013"},{"key":"34_CR30","unstructured":"Napoles, C., Gormley, M., Van Durme, B.: Annotated gigaword. In: Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX), Montr\u00e9al, Canada, pp. 95\u2013100. Association for Computational Linguistics, June 2012"},{"key":"34_CR31","doi-asserted-by":"publisher","unstructured":"Ostendorff, M., Ash, E., Ruas, T., Gipp, B., Moreno-Schneider, J., Rehm, G.: Evaluating document representations for content-based legal literature recommendations. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, S\u00e3o Paulo Brazil, pp. 109\u2013118. ACM, June 2021. https:\/\/doi.org\/10.1145\/3462757.3466073. https:\/\/arxiv.org\/pdf\/2104.13841.pdf","DOI":"10.1145\/3462757.3466073"},{"key":"34_CR32","doi-asserted-by":"publisher","unstructured":"Ostendorff, M., Ruas, T., Blume, T., Gipp, B., Rehm, G.: Aspect-based document similarity for research papers. In: Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain (Online), pp. 6194\u20136206. International Committee on Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.545. https:\/\/aclanthology.org\/2020.coling-main.545.pdf","DOI":"10.18653\/v1\/2020.coling-main.545"},{"key":"34_CR33","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings Conference on Empirical Methods in Natural Language Processing, vol. 14, pp. 1532\u20131543 (2014). 10\/gfshwg","DOI":"10.3115\/v1\/D14-1162"},{"key":"34_CR34","unstructured":"Perone, C.S., Silveira, R., Paula, T.S.: Evaluation of sentence embeddings in downstream and linguistic probing tasks. arXiv:1806.06259 (2018)"},{"key":"34_CR35","doi-asserted-by":"publisher","unstructured":"Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana, pp. 2227\u20132237. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/n18-1202","DOI":"10.18653\/v1\/n18-1202"},{"issue":"1","key":"34_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40979-018-0036-7","volume":"14","author":"FM Prentice","year":"2018","unstructured":"Prentice, F.M., Kinden, C.E.: Paraphrasing tools, language translation tools and plagiarism: an exploratory study. Int. J. Educ. Integr. 14(1), 1\u201316 (2018). https:\/\/doi.org\/10.1007\/s40979-018-0036-7","journal-title":"Int. J. Educ. Integr."},{"key":"34_CR37","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)"},{"issue":"1","key":"34_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40979-016-0013-y","volume":"13","author":"AM Rogerson","year":"2017","unstructured":"Rogerson, A.M., McCarthy, G.: Using Internet based paraphrasing tools: original work, patchwriting or facilitated plagiarism? Int. J. Educ. Integr. 13(1), 1\u201315 (2017). https:\/\/doi.org\/10.1007\/s40979-016-0013-y","journal-title":"Int. J. Educ. Integr."},{"key":"34_CR39","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ins.2020.04.048","volume":"532","author":"T Ruas","year":"2020","unstructured":"Ruas, T., Ferreira, C.H.P., Grosky, W., de Fran\u00e7a, F.O., de Medeiros, D.M.R.: Enhanced word embeddings using multi-semantic representation through lexical chains. Inf. Sci. 532, 16\u201332 (2020). https:\/\/doi.org\/10.1016\/j.ins.2020.04.048","journal-title":"Inf. Sci."},{"key":"34_CR40","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.eswa.2019.06.026","volume":"136","author":"T Ruas","year":"2019","unstructured":"Ruas, T., Grosky, W., Aizawa, A.: Multi-sense embeddings through a word sense disambiguation process. Expert Syst. Appl. 136, 288\u2013303 (2019). https:\/\/doi.org\/10.1016\/j.eswa.2019.06.026","journal-title":"Expert Syst. Appl."},{"key":"34_CR41","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108 [cs], October 2019"},{"key":"34_CR42","doi-asserted-by":"crossref","unstructured":"Spinde, T., Plank, M., Krieger, J.D., Ruas, T., Gipp, B., Aizawa, A.: Neural media bias detection using distant supervision with BABE - bias annotations by experts. In: Findings of the Association for Computational Linguistics: EMNLP 2021. Dominican Republic, November 2021. tex.pubstate: published tex.tppubtype: inproceedings","DOI":"10.18653\/v1\/2021.findings-emnlp.101"},{"key":"34_CR43","unstructured":"Subramanian, S., Trischler, A., Bengio, Y., Pal, C.J.: Learning general purpose distributed sentence representations via large scale multi-task learning. arXiv:1804.00079 [cs], March 2018"},{"key":"34_CR44","unstructured":"Trinh, T.H., Le, Q.V.: A simple method for commonsense reasoning. arXiv:1806.02847 [cs] (2019)"},{"key":"34_CR45","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 5998\u20136008. Curran Associates, Inc. (2017). https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"34_CR46","doi-asserted-by":"crossref","unstructured":"Wahle, J.P., Ashok, N., Ruas, T., Meuschke, N., Ghosal, T., Gipp, B.: Testing the generalization of neural language models for COVID-19 misinformation detection. In: Proceedings of the iConference, February 2022","DOI":"10.1007\/978-3-030-96957-8_33"},{"key":"34_CR47","doi-asserted-by":"crossref","unstructured":"Wahle, J.P., Ruas, T., Meuschke, N., Gipp, B.: Are neural language models good plagiarists? A benchmark for neural paraphrase detection. In: Proceedings of the ACM\/IEEE Joint Conference on Digital Libraries (JCDL), Washington, USA. IEEE, September 2021","DOI":"10.1109\/JCDL52503.2021.00065"},{"key":"34_CR48","doi-asserted-by":"crossref","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. arXiv:1804.07461 [cs], February 2019","DOI":"10.18653\/v1\/W18-5446"},{"key":"34_CR49","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-019-00893-5","author":"D Weber-Wulff","year":"2019","unstructured":"Weber-Wulff, D.: Plagiarism detectors are a crutch, and a problem. Nature (2019). https:\/\/doi.org\/10.1038\/d41586-019-00893-5","journal-title":"Nature"},{"key":"34_CR50","unstructured":"Xu, W.: Data-drive approaches for paraphrasing across language variations. Ph.D. thesis, Department of Computer Science, New York University (2014). http:\/\/www.cis.upenn.edu\/~xwe\/files\/thesis-wei.pdf"},{"key":"34_CR51","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. arXiv:1906.08237 [cs], June 2019"},{"key":"34_CR52","unstructured":"Zellers, R., et al.: Defending against neural fake news. arXiv:1905.12616 [cs] (2019)"},{"key":"34_CR53","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Wang, D.Y., Voelker, G.M.: DSpin: detecting automatically spun content on the web. In: Proceedings Network and Distributed System Security (NDSS) Symposium, pp. 23\u201326 (2014). https:\/\/doi.org\/10.14722\/ndss.2014.23004","DOI":"10.14722\/ndss.2014.23004"},{"key":"34_CR54","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Aligning books and movies: towards story-like visual explanations by watching movies and reading books. In: The IEEE International Conference on Computer Vision (ICCV), December 2015","DOI":"10.1109\/ICCV.2015.11"}],"container-title":["Lecture Notes in Computer Science","Information for a Better World: Shaping the Global Future"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-96957-8_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:09:55Z","timestamp":1645661395000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-96957-8_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030969561","9783030969578"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-96957-8_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"iConference","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 February 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 March 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconference2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ischools.org\/iConference","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ConfTool","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"147","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}