PAN@FIRE: Overview of the cross-language !ndian Text re-use detection competition

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40087-2_6The development of models for automatic detection of text re-use and plagiarism across languages has received increasing attention in recent years. However, the lack of an evaluation framework composed of annotated datasets has caused these efforts to be isolated. In this paper we present the CL!TR 2011 corpus, the first manually created corpus for the analysis of cross-language text re-use between English and Hindi. The corpus was used during the Cross-Language !ndian Text Re-Use Detection Competition. Here we overview the approaches applied the contestants and evaluate their quality when detecting a re-used text together with its source.This research work is partially funded by the WIQ-EI (IRSES grant n. 269180)and ACCURAT (grant n. 248347) projects, and the Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 246016 from the European Union. The first author was partially funded by the CONACyT-Mexico 192021 grant and currently works under the ERCIM “Alain Bensoussan” Fellowship Programme. The research of the second author is in the framework of the VLC/Campus Microcluster on Multimodal Interaction in Intelligent Systems and partially funded by the MICINN research project TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (plan I+D+i). The research from AU-KBC Centre is supported by the Cross Lingual Information Access (CLIA) Phase II Project.Barrón Cedeño, LA.; Rosso ., P.; Sobha, LD.; Clough ., P.; Stevenson ., M. (2013). PAN@FIRE: Overview of the cross-language !ndian Text re-use detection competition. En Multilingual Information Access in South Asian Languages. Springer Verlag (Germany). 7536:59-70. https://doi.org/10.1007/978-3-642-40087-2_6S59707536Addanki, K., Wu, D.: An Evaluation of MT Alignment Baseline Approaches upon Cross-Lingual Plagiarism Detection. In: FIRE [12]Aggarwal, N., Asooja, K., Buitelaar, P.: Cross Lingual Text Reuse Detection Using Machine Translation & Similarity Measures. In: FIRE [12]Alegria, I., Forcada, M., Sarasola, K. (eds.): Proceedings of the SEPLN 2009 Workshop on Information Retrieval and Information Extraction for Less Resourced Languages. University of the Basque Country, Donostia, Donostia (2009)Barrón-Cedeño, A., Rosso, P., Pinto, D., Juan, A.: On Cross-Lingual Plagiarism Analysis Using a Statistical Model. In: Stein, B., Stamatatos, E., Koppel, M. (eds.) ECAI 2008 Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN 2008), vol. 377, pp. 9–13. CEUR-WS.org, Patras (2008), http://ceur-ws.org/Vol-377Bendersky, M., Croft, W.: Finding Text Reuse on the Web. In: Baeza-Yates, R., Boldi, P., Ribeiro-Neto, B., Cambazoglu, B. (eds.) Proceedings of the Second ACM International Conference on Web Search and Web Data Mining, pp. 262–271. ACM, Barcelona (2009)Ceska, Z., Toman, M., Jezek, K.: Multilingual Plagiarism Detection. In: Proceedings of the 13th International Conference on Artificial Intelligence (ICAI 2008), pp. 83–92. Springer, Varna (2008)Clough, P.: Plagiarism in Natural and Programming Languages: an Overview of Current Tools and Technologies. Research Memoranda: CS-00-05, Department of Computer Science. University of Sheffield, UK (2000)Clough, P.: Old and new challenges in automatic plagiarism detection. National UK Plagiarism Advisory Service (2003), http://ir.shef.ac.uk/cloughie/papers/pasplagiarism.pdfClough, P., Gaizauskas, R.: Corpora and Text Re-Use. In: Lüdeling, A., Kytö, M., McEnery, T. (eds.) Handbook of Corpus Linguistics. Handbooks of Linguistics and Communication Science, pp. 1249–1271. Mouton de Gruyter (2009)Clough, P., Stevenson, M.: Developing a Corpus of Plagiarised Examples. Language Resources and Evaluation 45(1), 5–24 (2011)Comas, R., Sureda, J.: Academic Cyberplagiarism: Tracing the Causes to Reach Solutions. In: Comas, R., Sureda, J. (eds.) Academic Cyberplagiarism [online dossier], Digithum. Iss, vol. 10, pp. 1–6. UOC (2008), http://bit.ly/cyberplagiarism_csMajumder, P., Mitra, M., Bhattacharyya, P., Subramaniam, L., Contractor, D., Rosso, P. (eds.): FIRE 2010 and 2011. LNCS, vol. 7536. Springer, Heidelberg (2013)Gale, W., Church, K.: A Program for Aligning Sentences in Bilingual Corpora. Computational Linguistics 19, 75–102 (1993)Ghosh, A., Bhaskar, P., Pal, S., Bandyopadhyay, S.: Rule Based Plagiarism Detection using Information Retrieval. In: Petras, et al. [24]Gupta, P., Singhal, K.: Mapping Hindi-English Text Re-use Document Pairs. In: FIRE [12]Head, A.: How today’s college students use Wikipedia for course-related research. First Monday 15(3) (March 2010), http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2830/2476IEEE: A Plagiarism FAQ (2008), http://bit.ly/ieee_plagiarism (published: 2008; accessed March 3, 2010)Kulathuramaiyer, N., Maurer, H.: Coping With the Copy-Paste-Syndrome. In: Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2007 (E-Learn 2007), pp. 1072–1079. AACE, Quebec City (2007)Lee, C., Wu, C., Yang, H.: A Platform Framework for Cross-lingual Text Relatedness Evaluation and Plagiarism Detection. In: Proceedings of the 3rd International Conference on Innovative Computing Information (ICICIC 2008). IEEE Computer Society (2008)Martínez, I.: Wikipedia Usage by Mexican Students. The Constant Usage of Copy and Paste. In: Wikimania 2009, Buenos Aires, Argentina (2009), http://wikimania2009.wikimedia.orgMaurer, H., Kappe, F., Zaka, B.: Plagiarism - a survey. Journal of Universal Computer Science 12(8), 1050–1084 (2006)Palkovskii, Y., Belov, A.: Exploring Cross Lingual Plagiarism Detection in Hindi-English with n-gram Fingerprinting and VSM based Similarity Detection. In: FIRE [12]Palkovskii, Y., Belov, A., Muzika, I.: Using WordNet-based Semantic Similarity Measurement in External Plagiarism Detection - Notebook for PAN at CLEF 2011. In: Petras, et al. [24]Petras, V., Forner, P., Clough, P. (eds.): Notebook Papers of CLEF 2011 LABs and Workshops, Amsterdam, The Netherlands (September 2011)Potthast, M., Stein, B., Eiselt, A., Barrón-Cedeño, A., Rosso, P.: Overview of the 1st international competition on plagiarism detection. 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), vol. 502, pp. 1–9. CEUR-WS.org, San Sebastian (2009), http://ceur-ws.org/Vol-502Potthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: Cross-Language Plagiarism Detection. Language Resources and Evaluation (LRE), Special Issue on Plagiarism and Authorship Analysis 45(1), 1–18 (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd International Competition on Plagiarism Detection. In: Petras, et al. [24]Potthast, M., Stein, B., Barrón-Cedeño, A., Rosso, P.: An Evaluation Framework for Plagiarism Detection. In: Huang, C.R., Jurafsky, D. (eds.) Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), pp. 997–1005. COLING 2010 Organizing Committee, Beijing (2010)Potthast, M., Barrón-Cedeño, A., Eiselt, A., Stein, B., Rosso, P.: Overview of the 2nd International Competition on Plagiarism Detection. In: Braschler, M., Harman, D. (eds.) Notebook Papers of CLEF 2010 LABs and Workshops, Padua, Italy (September 2010)Rambhoopal, K., Varma, V.: Cross-Lingual Text Reuse Detection Based On Keyphrase Extraction and Similarity Measures. In: FIRE [12]Weber, S.: Das Google-Copy-Paste-Syndrom. Wie Netzplagiate Ausbildung und Wissen gefahrden. Telepolis (2007

A Decade of Shared Tasks in Digital Text Forensics at PAN

[EN] Digital text forensics aims at examining the originality and credibility of information in electronic documents and, in this regard, to extract and analyze information about the authors of these documents. The research field has been substantially developed during the last decade. PAN is a series of shared tasks that started in 2009 and significantly contributed to attract the attention of the research community in well-defined digital text forensics tasks. Several benchmark datasets have been developed to assess the state-of-the-art performance in a wide range of tasks. In this paper, we present the evolution of both the examined tasks and the developed datasets during the last decade. We also briefly introduce the upcoming PAN 2019 shared tasks.We are indebted to many colleagues and friends who contributed greatly to PAN's tasks: Maik Anderka, Shlomo Argamon, Alberto Barrón-Cedeño, Fabio Celli, Fabio Crestani, Walter Daelemans, Andreas Eiselt, Tim Gollub, Parth Gupta, Matthias Hagen, Teresa Holfeld, Patrick Juola, Giacomo Inches, Mike Kestemont, Moshe Koppel, Manuel Montes-y-Gómez, Aurelio Lopez-Lopez, Francisco Rangel, Miguel Angel Sánchez-Pérez, Günther Specht, Michael Tschuggnall, and Ben Verhoeven. Our special thanks go to PAN¿s sponsors throughout the years and not least to the hundreds of participants.Potthast, M.; Rosso, P.; Stamatatos, E.; Stein, B. (2019). A Decade of Shared Tasks in Digital Text Forensics at PAN. Lecture Notes in Computer Science. 11438:291-300. https://doi.org/10.1007/978-3-030-15719-7_39S2913001143

Reaction pathways during oxidation of cereal β-glucans

Oxidation of cereal beta-glucans may affect their stability in food products. Generally, polysaccharides oxidise via different pathways leading to chain cleavage or formation of oxidised groups within the polymer chain. In this study, oxidation pathways of oat and barley beta-glucans were assessed with different concentrations of hydrogen peroxide (H2O2) or ascorbic acid (Asc) with ferrous iron (Fe2+) as a catalyst. Degradation of beta-glucans was evaluated using high performance size exclusion chromatography and formation of carbonyl groups using carbazole-9-carbonyloxyamine labelling. Furthermore, oxidative degradation of glucosyl residues was studied. Based on the results, the oxidation with Asc mainly resulted in glycosidic bond cleavage. With H2O2, both glycosidic bond cleavage and formation of carbonyl groups within the beta-glucan chain was found. Moreover, H2O2 oxidation led to production of formic acid, which was proposed to result from Ruff degradation where oxidised glucose (gluconic acid) is decarboxylated to form arabinose. (C) 2016 Elsevier Ltd. All rights reserved.Peer reviewe

Intersubject Regularity in the Intrinsic Shape of Human V1

Previous studies have reported considerable intersubject variability in the three-dimensional geometry of the human primary visual cortex (V1). Here we demonstrate that much of this variability is due to extrinsic geometric features of the cortical folds, and that the intrinsic shape of V1 is similar across individuals. V1 was imaged in ten ex vivo human hemispheres using high-resolution (200 μm) structural magnetic resonance imaging at high field strength (7 T). Manual tracings of the stria of Gennari were used to construct a surface representation, which was computationally flattened into the plane with minimal metric distortion. The instrinsic shape of V1 was determined from the boundary of the planar representation of the stria. An ellipse provided a simple parametric shape model that was a good approximation to the boundary of flattened V1. The aspect ration of the best-fitting ellipse was found to be consistent across subject, with a mean of 1.85 and standard deviation of 0.12. Optimal rigid alignment of size-normalized V1 produced greater overlap than that achieved by previous studies using different registration methods. A shape analysis of published macaque data indicated that the intrinsic shape of macaque V1 is also stereotyped, and similar to the human V1 shape. Previoud measurements of the functional boundary of V1 in human and macaque are in close agreement with these results

Similarity-aware deep attentive model for clickbait detection

© Springer Nature Switzerland AG 2019. Clickbait is a type of web content advertisements designed to entice readers into clicking accompanying links. Usually, such links will lead to articles that are either misleading or non-informative, making the detection of clickbait essential for our daily lives. Automated clickbait detection is a relatively new research topic. Most recent work handles the clickbait detection problem with deep learning approaches to extract features from the meta-data of content. However, little attention has been paid to the relationship between the misleading titles and the target content, which we found to be an important clue for enhancing clickbait detection. In this work, we propose a deep similarity-aware attentive model to capture and represent such similarities with better expressiveness. In particular, we present the ways of either using similarity only or integrating it with other available quality features for the clickbait detection. We evaluate our model on two benchmark datasets, and the experimental results demonstrate the effectiveness of our approach by outperforming a series of competitive state-of-the-arts and baseline methods

Monte Carlo Procedure for Protein Design

A new method for sequence optimization in protein models is presented. The approach, which has inherited its basic philosophy from recent work by Deutsch and Kurosky [Phys. Rev. Lett. 76, 323 (1996)] by maximizing conditional probabilities rather than minimizing energy functions, is based upon a novel and very efficient multisequence Monte Carlo scheme. By construction, the method ensures that the designed sequences represent good folders thermodynamically. A bootstrap procedure for the sequence space search is devised making very large chains feasible. The algorithm is successfully explored on the two-dimensional HP model with chain lengths N=16, 18 and 32.Comment: 7 pages LaTeX, 4 Postscript figures; minor change

Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale

An ensemble data assimilation system for 3D radar reflectivity data is introduced for the convection-permitting numerical weather prediction model of the COnsortium for Small-scale MOdelling (COSMO) based on the Kilometre-scale ENsemble Data Assimilation system (KENDA), developed by Deutscher Wetterdienst and its partners. KENDA provides a state-of-the-art ensemble data assimilation system on the convective scale for operational data assimilation and forecasting based on the Local Ensemble Transform Kalman Filter (LETKF). In this study, the Efficient Modular VOlume RADar Operator is applied for the assimilation of radar reflectivity data to improve short-term predictions of precipitation. Both deterministic and ensemble forecasts have been carried out. A case-study shows that the assimilation of 3D radar reflectivity data clearly improves precipitation location in the analysis and significantly improves forecasts for lead times up to 4 h, as quantified by the Brier Score and the Continuous Ranked Probability Score. The influence of different update rates on the noise in terms of surface pressure tendencies and on the forecast quality in general is investigated. The results suggest that, while high update rates produce better analyses, forecasts with lead times of above 1 h benefit from less frequent updates. For a period of seven consecutive days, assimilation of radar reflectivity based on the LETKF is compared to that of DWD's current operational radar assimilation scheme based on latent heat nudging (LHN). It is found that the LETKF competes with LHN, although it is still in an experimental phase

Mathematical Modelling of Optical Coherence Tomography

In this chapter a general mathematical model of Optical Coherence Tomography (OCT) is presented on the basis of the electromagnetic theory. OCT produces high resolution images of the inner structure of biological tissues. Images are obtained by measuring the time delay and the intensity of the backscattered light from the sample considering also the coherence properties of light. The scattering problem is considered for a weakly scattering medium located far enough from the detector. The inverse problem is to reconstruct the susceptibility of the medium given the measurements for different positions of the mirror. Different approaches are addressed depending on the different assumptions made about the optical properties of the sample. This procedure is applied to a full field OCT system and an extension to standard (time and frequency domain) OCT is briefly presented.Comment: 28 pages, 5 figures, book chapte

Cross-Language Plagiarism Detection

Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on "exact" translations but does not generalize well.This work was partially supported by the TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 project and the CONACyT-Mexico 192021 grant.Potthast, M.; Barrón Cedeño, LA.; Stein, B.; Rosso, P. (2011). Cross-Language Plagiarism Detection. 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