This thesis is dealing with programming languages C and C++, various methods writing their constructions and development of application which detects very similar programs written in these languages. The application is intended to control plagiarism in school projects in which students have to create a program in C or C++. The application can check short programs as well as large programs divided into several modules.

Supporting graduate students as authors is one of the many services we provide at the University Library, University of Saskatchewan (USask). Graduate students often submit articles to journals based on content from their electronic theses or dissertations (ETDs). Recently, we have noticed an increase in the number of such article submissions being flagged for possible rejection on “plagiarism” or “prior publication” grounds. We suspect this may be because plagiarism detection software is increasingly being integrated into publishers’ article submission systems. This software is triggered by the existence of the student’s open access (OA) ETD in our institutional repository. This happens despite OA ETD inclusion in repositories being a common practice and despite journal policies often allowing submission of articles based on ETDs. We review common practices and guidelines around publishing of ETD content, two recent cases of journals initially rejecting such submissions by graduate student authors of our institution, and our reflections on this issue and how to address it.

Made available in DSpace on 2016-03-15T19:37:29Z (GMT). No. of bitstreams: 1 Evandro Luquini.pdf: 2663730 bytes, checksum: 45c13b956053ea1328342984d7ecf01c (MD5) Previous issue date: 2010-08-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The broad topic of this research is related to computer supported learning and teaching environment. In particular due to the high society´s demand for human resources capable of building and maintaining computer systems the specific topic of this research is focused on computer tools to support programming learning and teaching using techniques from social network and plagiarism detection. Currently the teachers involved with programming teaching have a set of tools to detect plagiarism of source code in the exercises and assessments made by their students. Although these tools are useful as disciplinary measures they do not allow teachers to think about plagiarism from a social perspective. This paper develops and exploits the assumption that plagiarism in the context of programming teaching does not happen in isolation from the social network formed by students in the classroom. From this formulation is suggested that the plagiarism detection algorithms and the students source code presented during the course are sufficient to model indirectly the social network established by the group of students. An exploratory experiment was conducted to evaluate this hypothesis and an intervention procedure was proposed. This procedure was inspired by immunization techniques from social networks and it has the goal to improve learning. / Este trabalho está inserido no contexto das pesquisas que procuram desenvolver ambientes computacionais que apóiam efetivamente os processos de ensino aprendizagem. Em especial, devido à grande demanda da sociedade por recursos humanos capazes de construir e manter sistemas computacionais. O tema específico desta pesquisa concentra-se na aplicação de técnicas de redes sociais e algoritmos de detecção de plágio à questão da aprendizagem de programação. Atualmente os professores envolvidos com o ensino de programação possuem um conjunto de ferramentas capazes de detectar o plágio de código-fonte nos exercícios e avaliações realizados por seus alunos. Apesar de estas ferramentas serem úteis como instrumentos disciplinadores, elas não permitem ao docente refletir sobre a natureza social do plágio. Este trabalho desenvolve e instrumentaliza a hipótese de que o plágio, no contexto do ensino de programação, não acontece isoladamente da rede social formada pelos alunos em sala de aula. Em decorrência desta formulação propõe-se que os algoritmos de detecção de plágio e os códigos fonte apresentados durante uma disciplina serão suficientes para modelar indiretamente a rede social estabelecida pelo grupo de alunos. Um experimento exploratório foi conduzido para avaliar esta hipótese e um procedimento para intervenção inspirado nas técnicas de imunização de redes sociais foi proposto com o intuito de aumentar a eficácia da aprendizagem.

The thesis describes the design and implementation of a plagiarism tool for programming languages C, Python and PHP. It describes techniques that are used to cover a plagiarism. The aim of this work is to create a tool for detection and visualization of plagiarisms covered up using these techniques. The tool performs detection by transforming input projects into an abstract syntactic tree, which is obtained by lexical and syntactic analysis. These trees will be compared by a proposed algorithm that uses node and subtree valuation using the {hash} function. The found parts of the code that could potentially lead to plagiarism are visualized in the form of a subtree of an abstract syntactic tree that represents the parts of the code found by the tool. Further, the work  describes testing of this tool on identified plagiarism techniques and specifies which of them it can eliminate. In its conclusion, the work describes the possible further development of the tool.

No description available.

Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human languages. One of its most challenging aspects involves enabling computers to derive meaning from human natural language. To do so, several meaning or context representations have been proposed with competitive performance. However, these representations still have room for improvement when working in a cross-domain or cross-language scenario. In this thesis we study the use of knowledge graphs as a cross-domain and cross-language representation of text and its meaning. A knowledge graph is a graph that expands and relates the original concepts belonging to a set of words. We obtain its characteristics using a wide-coverage multilingual semantic network as knowledge base. This allows to have a language coverage of hundreds of languages and millions human-general and -specific concepts. As starting point of our research we employ knowledge graph-based features - along with other traditional ones and meta-learning - for the NLP task of single- and cross-domain polarity classification. The analysis and conclusions of that work provide evidence that knowledge graphs capture meaning in a domain-independent way. The next part of our research takes advantage of the multilingual semantic network and focuses on cross-language Information Retrieval (IR) tasks. First, we propose a fully knowledge graph-based model of similarity analysis for cross-language plagiarism detection. Next, we improve that model to cover out-of-vocabulary words and verbal tenses and apply it to cross-language document retrieval, categorisation, and plagiarism detection. Finally, we study the use of knowledge graphs for the NLP tasks of community questions answering, native language identification, and language variety identification. The contributions of this thesis manifest the potential of knowledge graphs as a cross-domain and cross-language representation of text and its meaning for NLP and IR tasks. These contributions have been published in several international conferences and journals. / El Procesamiento del Lenguaje Natural (PLN) es un campo de la informática, la inteligencia artificial y la lingüística computacional centrado en las interacciones entre las máquinas y el lenguaje de los humanos. Uno de sus mayores desafíos implica capacitar a las máquinas para inferir el significado del lenguaje natural humano. Con este propósito, diversas representaciones del significado y el contexto han sido propuestas obteniendo un rendimiento competitivo. Sin embargo, estas representaciones todavía tienen un margen de mejora en escenarios transdominios y translingües. En esta tesis estudiamos el uso de grafos de conocimiento como una representación transdominio y translingüe del texto y su significado. Un grafo de conocimiento es un grafo que expande y relaciona los conceptos originales pertenecientes a un conjunto de palabras. Sus propiedades se consiguen gracias al uso como base de conocimiento de una red semántica multilingüe de amplia cobertura. Esto permite tener una cobertura de cientos de lenguajes y millones de conceptos generales y específicos del ser humano. Como punto de partida de nuestra investigación empleamos características basadas en grafos de conocimiento - junto con otras tradicionales y meta-aprendizaje - para la tarea de PLN de clasificación de la polaridad mono- y transdominio. El análisis y conclusiones de ese trabajo muestra evidencias de que los grafos de conocimiento capturan el significado de una forma independiente del dominio. La siguiente parte de nuestra investigación aprovecha la capacidad de la red semántica multilingüe y se centra en tareas de Recuperación de Información (RI). Primero proponemos un modelo de análisis de similitud completamente basado en grafos de conocimiento para detección de plagio translingüe. A continuación, mejoramos ese modelo para cubrir palabras fuera de vocabulario y tiempos verbales, y lo aplicamos a las tareas translingües de recuperación de documentos, clasificación, y detección de plagio. Por último, estudiamos el uso de grafos de conocimiento para las tareas de PLN de respuesta de preguntas en comunidades, identificación del lenguaje nativo, y identificación de la variedad del lenguaje. Las contribuciones de esta tesis ponen de manifiesto el potencial de los grafos de conocimiento como representación transdominio y translingüe del texto y su significado en tareas de PLN y RI. Estas contribuciones han sido publicadas en diversas revistas y conferencias internacionales. / El Processament del Llenguatge Natural (PLN) és un camp de la informàtica, la intel·ligència artificial i la lingüística computacional centrat en les interaccions entre les màquines i el llenguatge dels humans. Un dels seus majors reptes implica capacitar les màquines per inferir el significat del llenguatge natural humà. Amb aquest propòsit, diverses representacions del significat i el context han estat proposades obtenint un rendiment competitiu. No obstant això, aquestes representacions encara tenen un marge de millora en escenaris trans-dominis i trans-llenguatges. En aquesta tesi estudiem l'ús de grafs de coneixement com una representació trans-domini i trans-llenguatge del text i el seu significat. Un graf de coneixement és un graf que expandeix i relaciona els conceptes originals pertanyents a un conjunt de paraules. Les seves propietats s'aconsegueixen gràcies a l'ús com a base de coneixement d'una xarxa semàntica multilingüe d'àmplia cobertura. Això permet tenir una cobertura de centenars de llenguatges i milions de conceptes generals i específics de l'ésser humà. Com a punt de partida de la nostra investigació emprem característiques basades en grafs de coneixement - juntament amb altres tradicionals i meta-aprenentatge - per a la tasca de PLN de classificació de la polaritat mono- i trans-domini. L'anàlisi i conclusions d'aquest treball mostra evidències que els grafs de coneixement capturen el significat d'una forma independent del domini. La següent part de la nostra investigació aprofita la capacitat\hyphenation{ca-pa-ci-tat} de la xarxa semàntica multilingüe i se centra en tasques de recuperació d'informació (RI). Primer proposem un model d'anàlisi de similitud completament basat en grafs de coneixement per a detecció de plagi trans-llenguatge. A continuació, vam millorar aquest model per cobrir paraules fora de vocabulari i temps verbals, i ho apliquem a les tasques trans-llenguatges de recuperació de documents, classificació, i detecció de plagi. Finalment, estudiem l'ús de grafs de coneixement per a les tasques de PLN de resposta de preguntes en comunitats, identificació del llenguatge natiu, i identificació de la varietat del llenguatge. Les contribucions d'aquesta tesi posen de manifest el potencial dels grafs de coneixement com a representació trans-domini i trans-llenguatge del text i el seu significat en tasques de PLN i RI. Aquestes contribucions han estat publicades en diverses revistes i conferències internacionals. / Franco Salvador, M. (2017). A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/84285 / TESIS