An improved extrinsic monolingual plagiarism detection approach of the Bengali text

Plagiarism is an act of literature fraud, which is presenting others’ work or ideas without giving credit to the original work. All published and unpublished written documents are under the cover of this definition. Plagiarism, which increased significantly over the last few years, is a concerning issue for students, academicians, and professionals. Due to this, there are several plagiarism detection tools or software available to detect plagiarism in different languages. Unfortunately, negligible work has been done and no plagiarism detection software available in the Bengali language where Bengali is one of the most spoken languages in the world. In this paper, we have proposed a plagiarism detection tool for the Bengali language that mainly focuses on the educational and newspaper domain. We have collected 82 textbooks from the National Curriculum of Textbooks (NCTB), Bangladesh, scrapped all articles from 12 reputed newspapers and compiled our corpus with more than 10 million sentences. The proposed method on Bengali text corpus shows an accuracy rate of 97.31

Automated scholarly paper review: Technologies and challenges

Peer review is a widely accepted mechanism for research evaluation, playing a pivotal role in scholarly publishing. However, criticisms have long been leveled on this mechanism, mostly because of its inefficiency and subjectivity. Recent years have seen the application of artificial intelligence (AI) in assisting the peer review process. Nonetheless, with the involvement of humans, such limitations remain inevitable. In this review paper, we propose the concept and pipeline of automated scholarly paper review (ASPR) and review the relevant literature and technologies of achieving a full-scale computerized review process. On the basis of the review and discussion, we conclude that there is already corresponding research and implementation at each stage of ASPR. We further look into the challenges in ASPR with the existing technologies. The major difficulties lie in imperfect document parsing and representation, inadequate data, defective human-computer interaction and flawed deep logical reasoning. Moreover, we discuss the possible moral & ethical issues and point out the future directions of ASPR. In the foreseeable future, ASPR and peer review will coexist in a reinforcing manner before ASPR is able to fully undertake the reviewing workload from humans

Overview of PAN 2018. Author identification, author profiling, and author obfuscation

[EN] PAN 2018 explores several authorship analysis tasks enabling a systematic comparison of competitive approaches and advancing research in digital text forensics.More specifically, this edition of PAN introduces a shared task in cross-domain authorship attribution, where texts of known and unknown authorship belong to distinct domains, and another task in style change detection that distinguishes between single author and multi-author texts. In addition, a shared task in multimodal author profiling examines, for the first time, a combination of information from both texts and images posted by social media users to estimate their gender. Finally, the author obfuscation task studies how a text by a certain author can be paraphrased so that existing author identification tools are confused and cannot recognize the similarity with other texts of the same author. New corpora have been built to support these shared tasks. A relatively large number of software submissions (41 in total) was received and evaluated. Best paradigms are highlighted while baselines indicate the pros and cons of submitted approaches.The work at the Universitat Polit`ecnica de Val`encia was funded by the MINECO research project SomEMBED (TIN2015-71147-C2-1-P)Stamatatos, E.; Rangel-Pardo, FM.; Tschuggnall, M.; Stein, B.; Kestemont, M.; Rosso, P.; Potthast, M. (2018). Overview of PAN 2018. Author identification, author profiling, and author obfuscation. Lecture Notes in Computer Science. 11018:267-285. https://doi.org/10.1007/978-3-319-98932-7_25S26728511018Argamon, S., Juola, P.: Overview of the international authorship identification competition at PAN-2011. In: Petras, V., Forner, P., Clough, P. (eds.) Notebook Papers of CLEF 2011 Labs and Workshops, 19–22 September 2011, Amsterdam, Netherlands, September 2011. http://www.clef-initiative.eu/publication/working-notesBird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Sebastopol (2009)Bogdanova, D., Lazaridou, A.: Cross-language authorship attribution. In: Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014, pp. 2015–2020 (2014)Choi, F.Y.: Advances in domain independent linear text segmentation. In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference (NAACL), pp. 26–33. Association for Computational Linguistics, Seattle, April 2000Custódio, J.E., Paraboni, I.: EACH-USP ensemble cross-domain authorship attribution. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedDaneshvar, S.: Gender identification in Twitter using n-grams and LSA. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedDaniel Karaś, M.S., Sobecki, P.: OPI-JSA at CLEF 2017: author clustering and style breach detection. In: Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings. CLEF and CEUR-WS.org, September 2017Giannella, C.: An improved algorithm for unsupervised decomposition of a multi-author document. The MITRE Corporation. Technical Papers, February 2014Glover, A., Hirst, G.: Detecting stylistic inconsistencies in collaborative writing. In: Sharples, M., van der Geest, T. (eds.) The New Writing Environment, pp. 147–168. Springer, London (1996). https://doi.org/10.1007/978-1-4471-1482-6_12Hagen, M., Potthast, M., Stein, B.: Overview of the author obfuscation task at PAN 2017: safety evaluation revisited. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Hagen, M., Potthast, M., Stein, B.: Overview of the author obfuscation task at PAN 2018. In: Working Notes Papers of the CLEF 2018 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org (2018)Hellekson, K., Busse, K. (eds.): The Fan Fiction Studies Reader. University of Iowa Press, Iowa City (2014)Juola, P.: An overview of the traditional authorship attribution subtask. In: Forner, P., Karlgren, J., Womser-Hacker, C. (eds.) CLEF 2012 Evaluation Labs and Workshop - Working Notes Papers, 17–20 September 2012, Rome, Italy, September 2012. http://www.clef-initiative.eu/publication/working-notesJuola, P.: The rowling case: a proposed standard analytic protocol for authorship questions. Digital Sch. Humanit. 30(suppl–1), i100–i113 (2015)Kestemont, M., Luyckx, K., Daelemans, W., Crombez, T.: Cross-genre authorship verification using unmasking. Engl. Stud. 93(3), 340–356 (2012)Kestemont, M., et al.: Overview of the author identification task at PAN-2018: cross-domain authorship attribution and style change detection. In: Working Notes Papers of the CLEF 2018 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org (2018)Koppel, M., Schler, J., Bonchek-Dokow, E.: Measuring differentiability: unmasking pseudonymous authors. J. Mach. Learn. Res. 8, 1261–1276 (2007)Overdorf, R., Greenstadt, R.: Blogs, Twitter feeds, and reddit comments: cross-domain authorship attribution. Proc. Priv. Enhanc. Technol. 2016(3), 155–171 (2016)Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: Notebook Papers of the 5th Evaluation Lab on Uncovering Plagiarism, Authorship and Social Software Misuse (PAN), Amsterdam, The Netherlands, September 2011Potthast, M., Hagen, M., Stein, B.: Author obfuscation: attacking the state of the art in authorship verification. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2016. http://ceur-ws.org/Vol-1609/Potthast, M., Hagen, M., Völske, M., Stein, B.: Crowdsourcing interaction logs to understand text reuse from the web. In: Fung, P., Poesio, M. (eds.) Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), pp. 1212–1221. Association for Computational Linguistics, August 2013. http://www.aclweb.org/anthology/P13-1119Rangel, F., Celli, F., Rosso, P., Potthast, M., Stein, B., Daelemans, W.: Overview of the 3rd author profiling task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., San Juan, E. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, Toulouse, France, pp. 8–11. CEUR-WS.org, September 2015Rangel, F., et al.: Overview of the 2nd author profiling task at PAN 2014. In: Cappellato, L., Ferro, N., Halvey, M., Kraaij, W. (eds.) CLEF 2014 Evaluation Labs and Workshop - Working Notes Papers, Sheffield, UK, pp. 15–18. CEUR-WS.org, September 2014Rangel, F., Rosso, P., G’omez, M.M., Potthast, M., Stein, B.: Overview of the 6th author profiling task at pan 2018: multimodal gender identification in Twitter. In: CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org (2017)Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013. In: Forner, P., Navigli, R., Tufis, D. (eds.) CLEF 2013 Evaluation Labs and Workshop - Working Notes Papers, 23–26 September 2013, Valencia, Spain, September 2013Rangel, F., Rosso, P., Potthast, M., Stein, B.: Overview of the 5th author profiling task at PAN 2017: gender and language variety identification in Twitter. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Rangel, F., Rosso, P., Verhoeven, B., Daelemans, W., Potthast, M., Stein, B.: Overview of the 4th author profiling task at PAN 2016: cross-genre evaluations. In: Balog, K., Cappellato, L., Ferro, N., Macdonald, C. (eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, September 2016Safin, K., Kuznetsova, R.: Style breach detection with neural sentence embeddings. In: Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Sapkota, U., Bethard, S., Montes, M., Solorio, T.: Not all character n-grams are created equal: a study in authorship attribution. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93–102 (2015)Sapkota, U., Solorio, T., Montes, M., Bethard, S., Rosso, P.: Cross-topic authorship attribution: will out-of-topic data help? In: Proceedings of the 25th International Conference on Computational Linguistics. Technical Papers, pp. 1228–1237 (2014)Stamatatos, E.: Intrinsic plagiarism detection using character $$n$$n-gram Profiles. In: Stein, B., Rosso, P., Stamatatos, E., Koppel, M., Agirre, E. (eds.) SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN 2009), pp. 38–46. Universidad Politécnica de Valencia and CEUR-WS.org, September 2009. http://ceur-ws.org/Vol-502Stamatatos, E.: On the robustness of authorship attribution based on character n-gram features. J. Law Policy 21, 421–439 (2013)Stamatatos, E.: Authorship attribution using text distortion. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 1138–1149. Association for Computational Linguistics (2017)Stamatatos, E., et al.: Overview of the author identification task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., San Juan, E. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, 8–11 September 2015, Toulouse, France. CEUR-WS.org, September 2015Stamatatos, E., et al.: Clustering by authorship within and across documents. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2016. http://ceur-ws.org/Vol-1609/Takahashi, T., Tahara, T., Nagatani, K., Miura, Y., Taniguchi, T., Ohkuma, T.: Text and image synergy with feature cross technique for gender identification. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedTellez, E.S., Miranda-Jiménez, S., Moctezuma, D., Graff, M., Salgado, V., Ortiz-Bejar, J.: Gender identification through multi-modal tweet analysis using microtc and bag of visual words. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedTschuggnall, M., Specht, G.: Automatic decomposition of multi-author documents using grammar analysis. In: Proceedings of the 26th GI-Workshop on Grundlagen von Datenbanken. CEUR-WS, Bozen, October 2014Tschuggnall, M., et al.: Overview of the author identification task at PAN-2017: style breach detection and author clustering. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, vol. 1866. CLEF and CEUR-WS.org, September 2017. http://ceur-ws.org/Vol-1866

Ohjelmointitehtävien klusterointi tarkistuksen ja tutkimisen tehostamiseksi

Programming courses often receive large quantities of program code submissions to exercises which, due to their large number, are graded and students provided feedback automatically. Teachers might never review these submissions therefore losing a valuable source of insight into student programming patterns. This thesis researches how these submissions could be reviewed efficiently using a software system, and a prototype, CodeClusters, was developed as an additional contribution of this thesis. CodeClusters' design goals are to allow the exploration of the submissions and specifically finding higher-level patterns that could be used to provide feedback to students. Its main features are full-text search and n-grams similarity detection model that can be used to cluster the submissions. Design science research is applied to evaluate CodeClusters' design and to guide the next iteration of the artifact and qualitative analysis, namely thematic synthesis, to evaluate the problem context as well as the ideas of using software for reviewing and providing clustered feedback. The used study method was interviews conducted with teachers who had experience teaching programming courses. Teachers were intrigued by the ability to review submitted student code and to provide more tailored feedback to students. The system, while still a prototype, is considered worthwhile to experiment on programming courses. A tool for analyzing and exploring submissions seems important to enable teachers to better understand how students have solved the exercises. Providing additional feedback can be beneficial to students, yet the feedback should be valuable and the students incentivized to read it

Meta-survey on outlier and anomaly detection

The impact of outliers and anomalies on model estimation and data processing is of paramount importance, as evidenced by the extensive body of research spanning various fields over several decades: thousands of research papers have been published on the subject. As a consequence, numerous reviews, surveys, and textbooks have sought to summarize the existing literature, encompassing a wide range of methods from both the statistical and data mining communities. While these endeavors to organize and summarize the research are invaluable, they face inherent challenges due to the pervasive nature of outliers and anomalies in all data-intensive applications, irrespective of the specific application field or scientific discipline. As a result, the resulting collection of papers remains voluminous and somewhat heterogeneous. To address the need for knowledge organization in this domain, this paper implements the first systematic meta-survey of general surveys and reviews on outlier and anomaly detection. Employing a classical systematic survey approach, the study collects nearly 500 papers using two specialized scientific search engines. From this comprehensive collection, a subset of 56 papers that claim to be general surveys on outlier detection is selected using a snowball search technique to enhance field coverage. A meticulous quality assessment phase further refines the selection to a subset of 25 high-quality general surveys. Using this curated collection, the paper investigates the evolution of the outlier detection field over a 20-year period, revealing emerging themes and methods. Furthermore, an analysis of the surveys sheds light on the survey writing practices adopted by scholars from different communities who have contributed to this field. Finally, the paper delves into several topics where consensus has emerged from the literature. These include taxonomies of outlier types, challenges posed by high-dimensional data, the importance of anomaly scores, the impact of learning conditions, difficulties in benchmarking, and the significance of neural networks. Non-consensual aspects are also discussed, particularly the distinction between local and global outliers and the challenges in organizing detection methods into meaningful taxonomies

Improving Word Association Measures in Repetitive Corpora with Context Similarity Weighting

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Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

AI in Education

Artificial intelligence (AI) is changing the world as we know it. Recent advances are enabling people, companies, and governments to envision and experiment with new methods of interacting with computers and modifying how virtual and physical processes are carried out. One of the fields in which this transformation is taking place is education. After years of witnessing the incorporation of technological innovations into learning/teaching processes, we can currently observe many new research works involving AI. Moreover, there has been increasing interest in this research area after the COVID-19 pandemic, driven toward fostering digital education. Among recent research in this field, AI applications have been applied to enhance educational experiences, studies have considered the interaction between AI and humans while learning, analyses of educational data have been conducted, including using machine learning techniques, and proposals have been presented for new paradigms mediated by intelligent agents. This book, entitled “AI in Education”, aims to highlight recent research in the field of AI and education. The included works discuss new advances in methods, applications, and procedures to enhance educational processes via artificial intelligence and its subfields (machine learning, neural networks, deep learning, cognitive computing, natural language processing, computer vision, etc.)

A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces

Mutemi, A., & Bacao, F. (2023). A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces. Scientific Reports, 13(1), 1-16. [12499]. https://doi.org/10.1038/s41598-023-38304-5Organized retail crime (ORC) is a significant issue for retailers, marketplace platforms, and consumers. Its prevalence and influence have increased fast in lockstep with the expansion of online commerce, digital devices, and communication platforms. Today, it is a costly affair, wreaking havoc on enterprises’ overall revenues and continually jeopardizing community security. These negative consequences are set to rocket to unprecedented heights as more people and devices connect to the Internet. Detecting and responding to these terrible acts as early as possible is critical for protecting consumers and businesses while also keeping an eye on rising patterns and fraud. The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set. We achieve these results based on a select set of 45 features out of 58.publishersversionpublishe