On the detection of SOurce COde re-use

© {Owner/Author | ACM} {2014}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation, http://dx.doi.org/10.1145/2824864.2824878"This paper summarizes the goals, organization and results of the first SOCO competitive evaluation campaign for systems that automatically detect the source code re-use phenomenon. The detection of source code re-use is an important research field for both software industry and academia fields. Accordingly, PAN@FIRE track, named SOurce COde Re-use (SOCO) focused on the detection of re-used source codes in C/C++ and Java programming languages. Participant systems were asked to annotate several source codes whether or not they represent cases of source code re-use. In total five teams submitted 17 runs. The training set consisted of annotations made by several experts, a feature which turns the SOCO 2014 collection in a useful data set for future evaluations and, at the same time, it establishes a standard evaluation framework for future research works on the posed shared task.PAN@FIRE (SOCO) has been organised in the framework of WIQ-EI (EC IRSES grantn. 269180) and DIANA-APPLICATIONS (TIN2012-38603-C02- 01) research projects. The work of the last author was supported by CONACyT Mexico Project Grant CB-2010/153315, and SEP-PROMEP UAM-PTC-380/48510349.Flores Sáez, E.; Rosso, P.; Moreno Boronat, LA.; Villatoro-Tello, E. (2014). On the detection of SOurce COde re-use. En FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation. ACM. 21-30. https://doi.org/10.1145/2824864.2824878S2130C. Arwin and S. Tahaghoghi. Plagiarism detection across programming languages. Proceedings of the 29th Australian Computer Science Conference, Australian Computer Society, 48:277--286, 2006.N. Baer and R. Zeidman. Measuring whitespace pattern sequence as an indication of plagiarism. Journal of Software Engineering and Applications, 5(4):249--254, 2012.M. Chilowicz, E. Duris, and G. Roussel. Syntax tree fingerprinting for source code similarity detection. In Program Comprehension, 2009. ICPC '09. IEEE 17th International Conference on, pages 243--247, 2009.D. Chuda, P. Navrat, B. Kovacova, and P. Humay. The issue of (software) plagiarism: A student view. Education, IEEE Transactions on, 55(1):22--28, 2012.G. Cosma and M. Joy. Evaluating the performance of lsa for source-code plagiarism detection. Informatica, 36(4):409--424, 2013.B. Cui, J. Li, T. Guo, J. Wang, and D. Ma. Code comparison system based on abstract syntax tree. In Broadband Network and Multimedia Technology (IC-BNMT), 3rd IEEE International Conference on, pages 668--673, Oct 2010.J. A. W. Faidhi and S. K. Robinson. An empirical approach for detecting program similarity and plagiarism within a university programming environment. Comput. Educ., 11(1):11--19, Jan. 1987.Fire, editor. FIRE 2014 Working Notes. Sixth International Workshop of the Forum for Information Retrieval Evaluation, Bangalore, India, 5--7 December, 2014.J. L. Fleiss. Measuring nominal scale agreement among many raters. Psychological bulletin, 76(5):378, 1971.E. Flores, A. Barrón-Cedeño, L. Moreno, and P. Rosso. Uncovering source code reuse in large-scale academic environments. Computer Applications in Engineering Education, pages n/a--n/a, 2014.E. Flores, A. Barrón-Cedeño, P. Rosso, and L. Moreno. DeSoCoRe: Detecting source code re-use across programming languages. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session, NAACL-HLT, pages 1--4. Association for Computational Linguistics, 2012.E. Flores, A. Barrón-Cedeño, P. Rosso, and L. Moreno. Towards the Detection of Cross-Language Source Code Reuse. Proceedings of 16th International Conference on Applications of Natural Language to Information Systems, NLDB-2011, Springer-Verlag, LNCS(6716), pages 250--253, 2011.E. Flores, M. Ibarra-Romero, L. Moreno, G. Sidorov, and P. Rosso. Modelos de recuperación de información basados en n-gramas aplicados a la reutilización de código fuente. In Proc. 3rd Spanish Conf. on Information Retrieval, pages 185--188, 2014.D. Ganguly and G. J. Jones. Dcu@ fire-2014: an information retrieval approach for source code plagiarism detection. In Fire [8].R. García-Hernández and Y. Lendeneva. Identification of similar source codes based on longest common substrings. In Fire [8].M. Joy and M. Luck. Plagiarism in programming assignments. Education, IEEE Transactions on, 42(2):129--133, May 1999.A. Marcus, A. Sergeyev, V. Rajlich, and J. Maletic. 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Implementing contextual biasing in GPU decoder for online ASR

GPU decoding significantly accelerates the output of ASR predictions. While GPUs are already being used for online ASR decoding, post-processing and rescoring on GPUs have not been properly investigated yet. Rescoring with available contextual information can considerably improve ASR predictions. Previous studies have proven the viability of lattice rescoring in decoding and biasing language model (LM) weights in offline and online CPU scenarios. In real-time GPU decoding, partial recognition hypotheses are produced without lattice generation, which makes the implementation of biasing more complex. The paper proposes and describes an approach to integrate contextual biasing in real-time GPU decoding while exploiting the standard Kaldi GPU decoder. Besides the biasing of partial ASR predictions, our approach also permits dynamic context switching allowing a flexible rescoring per each speech segment directly on GPU. The code is publicly released and tested with open-sourced test sets.Comment: Accepted to Interspeech 202

PAN@FIRE: Overview of SOCO Track on the Detection of SOurce COde Re-use

© Owner/Author This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM, In Proceedings of the Forum for Information Retrieval Evaluation. FIRE/ 14. http://dx.doi.org/10.1145/2824864.2824878This paper summarizes the goals, organization and results of the first SOCO competitive evaluation campaign for systems that automatically detect the source code re-use phenomenon. The detection of source code re-use is an important research field for both software industry and academia fields. Accordingly, PAN@FIRE task, named SOurce COde Re-use (SOCO); focused on the detection of re-used source codes in C/C++ and Java programming languages. Participant systems were asked to annotate several source codes whether or not they represent cases of source code re-use. In total three teams participated and submitted 13 runs. The training set consisted of annotations made by several experts, a feature which turns the SOCO 2014 collection in a useful data set for future evaluations and, at the same time, it establishes a standard evaluation framework for future research works.PAN@FIRE (SOCO) has been organised in the framework of WIQ-EI (ECIRSES grant n. 269180) and DIANA-APPLICATIONS (TIN2012-38603-C02-01) research projects. The work of the last author was supported by CONACyT Mexico Project Grant CB-2010/153315, and SEP-PROMEP UAM-PTC-380/48510349.Flores Sáez, E.; Rosso, P.; Moreno Boronat, LA.; Villatoro-Tello, E. (2014). PAN@FIRE: Overview of SOCO Track on the Detection of SOurce COde Re-use. ACM. http://hdl.handle.net/10251/66414

Paraphrase Plagiarism Identifcation with Character-level Features

[EN] Several methods have been proposed for determining plagiarism between pairs of sentences, passages or even full documents. However, the majority of these methods fail to reliably detect paraphrase plagiarism due to the high complexity of the task, even for human beings. Paraphrase plagiarism identi cation consists in automatically recognizing document fragments that contain re-used text, which is intentionally hidden by means of some rewording practices such as semantic equivalences, discursive changes, and morphological or lexical substitutions. Our main hypothesis establishes that the original author's writing style ngerprint prevails in the plagiarized text even when paraphrases occur. Thus, in this paper we propose a novel text representation scheme that gathers both content and style characteristics of texts, represented by means of character-level features. As an additional contribution, we describe the methodology followed for the construction of an appropriate corpus for the task of paraphrase plagiarism identi cation, which represents a new valuable resource to the NLP community for future research work in this field.This work is the result of the collaboration in the framework of the CONACYT Thematic Networks program (RedTTL Language Technologies Network) and the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie action. The first author was supported by CONACYT (Scholarship 258345/224483). The second, third, and sixth authors were partially supported by CONACyT (Project Grants 258588 and 2410). The work of the fourth author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the Grant ALMAMATER (PrometeoII/2014/030).Sánchez-Vega, F.; Villatoro-Tello, E.; Montes-Y-Gómez, M.; Rosso, P.; Stamatatos, E.; Villaseñor-Pineda, L. (2019). Paraphrase Plagiarism Identifcation with Character-level Features. Pattern Analysis and Applications. 22(2):669-681. https://doi.org/10.1007/s10044-017-0674-zS66968122

Determining and Characterizing the Reused Text for Plagiarism Detection

An important task in plagiarism detection is determining and measuring similar text portions between a given pair of documents. One of the main difficulties of this task resides on the fact that reused text is commonly modified with the aim of covering or camouflaging the plagiarism. Another difficulty is that not all similar text fragments are examples of plagiarism, since thematic coincidences also tend to produce portions of similar text. In order to tackle these problems, we propose a novel method for detecting likely portions of reused text. This method is able to detect common actions performed by plagiarists such as word deletion, insertion and transposition, allowing to obtain plausible portions of reused text. We also propose representing the identified reused text by means of a set of features that denote its degree of plagiarism, relevance and fragmentation. This new representation aims to facilitate the recognition of plagiarism by considering diverse characteristics of the reused text during the classification phase. Experimental results employing a supervised classification strategy showed that the proposed method is able to outperform traditionally used approaches. 2012 Elsevier Ltd. All rights reserved.This work was done under partial support of CONACyT project Grants: 134186, and Scholarships: 258345/224483. This work is the result of the collaboration in the framework of the WIQEI IRSES project (Grant No. 269180) within the FP 7 Marie Curie. The work of the last author was in the framework of the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Sánchez-Vega, F.; Villatoro-Tello, E.; Montes-Y-Gómez, M.; Villaseñor-Pineda; Luis; Rosso, P. (2013). Determining and Characterizing the Reused Text for Plagiarism Detection. Expert Systems with Applications. 40(5):1804-1813. https://doi.org/10.1016/j.eswa.2012.09.021S1804181340

A Ranking Approach based on Example Texts for Geographic Information Retrieval

Abstract. This paper focuses on the problem of ranking documents for Geographic Information Retrieval. It aims to demonstrate that by using some query-related example texts it is possible to improve the final ranking of the retrieved documents. Experimental results indicated that our approach could improve the MAP of some sets of retrieved documents using only two example texts