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

Integrating State-of-the-art NLP Tools into Existing Methods to Address Current Challenges in Plagiarism Detection

Paraphrase plagiarism occurs when text is deliberately obfuscated to evade detection, deliberate alteration increases the complexity of plagiarism and the difficulty in detecting paraphrase plagiarism. In paraphrase plagiarism, copied texts often contain little or no matching words, and conventional plagiarism detectors, most of which are designed to detect matching stings are ineffective under such condition. The problem of plagiarism detection has been widely researched in recent years with significant progress made particularly in the platform of Pan@Clef competition on plagiarism detection. However further research is required specifically in the area of paraphrase and translation (obfuscation) plagiarism detection as studies show that the state-of-the-art is unsatisfactory. A rational solution to the problem is to apply models that detect plagiarism using semantic features in texts, rather than matching strings. Deep contextualised learning models (DCLMs) have the ability to learn deep textual features that can be used to compare text for semantic similarity. They have been remarkably effective in many natural language processing (NLP) tasks, but have not yet been tested in paraphrase plagiarism detection. The second problem facing conventional plagiarism detection is translation plagiarism, which occurs when copied text is translated to a different language and sometimes paraphrased and used without acknowledging the original sources. The most common method used for detecting cross-lingual plagiarism (CLP) require internet translation services, which is limiting to the detection process in many ways. A rational solution to the problem is to use detection models that do not utilise internet translation services. In this thesis we addressed these ongoing challenges facing conventional plagiarism detection by applying some of the most advanced methods in NLP, which includes contextualised and non-contextualised deep learning models. To address the problem of paraphrased plagiarism, we proposed a novel paraphrase plagiarism detector that integrates deep contextualised learning (DCL) into a generic plagiarism detection framework. Evaluation results revealed that our proposed paraphrase detector outperformed a state-of-art model, and a number of standard baselines in the task of paraphrase plagiarism detection. With respect to CLP detection, we propose a novel multilingual translation model (MTM) based on the Word2Vec (word embedding) model that can effectively translate text across a number of languages, it is independent of the internet and performs comparably, and in many cases better than a common cross-lingual plagiarism detection model that rely on online machine translator. The MTM does not require parallel or comparable corpora, it is therefore designed to resolve the problem of CLPD in low resource languages. The solutions provided in this research advance the state-of-the-art and contribute to the existing body of knowledge in plagiarism detection, and would also have a positive impact on academic integrity that has been under threat for a while by plagiarism

A study on plagiarism detection and plagiarism direction identification using natural language processing techniques

Ever since we entered the digital communication era, the ease of information sharing through the internet has encouraged online literature searching. With this comes the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, more attention has been directed towards automatic plagiarism detection. This is a computational approach which assists humans in judging whether pieces of texts are plagiarised. However, most existing plagiarism detection approaches are limited to super cial, brute-force stringmatching techniques. If the text has undergone substantial semantic and syntactic changes, string-matching approaches do not perform well. In order to identify such changes, linguistic techniques which are able to perform a deeper analysis of the text are needed. To date, very limited research has been conducted on the topic of utilising linguistic techniques in plagiarism detection. This thesis provides novel perspectives on plagiarism detection and plagiarism direction identi cation tasks. The hypothesis is that original texts and rewritten texts exhibit signi cant but measurable di erences, and that these di erences can be captured through statistical and linguistic indicators. To investigate this hypothesis, four main research objectives are de ned. First, a novel framework for plagiarism detection is proposed. It involves the use of Natural Language Processing techniques, rather than only relying on the vii traditional string-matching approaches. The objective is to investigate and evaluate the in uence of text pre-processing, and statistical, shallow and deep linguistic techniques using a corpus-based approach. This is achieved by evaluating the techniques in two main experimental settings. Second, the role of machine learning in this novel framework is investigated. The objective is to determine whether the application of machine learning in the plagiarism detection task is helpful. This is achieved by comparing a thresholdsetting approach against a supervised machine learning classi er. Third, the prospect of applying the proposed framework in a large-scale scenario is explored. The objective is to investigate the scalability of the proposed framework and algorithms. This is achieved by experimenting with a large-scale corpus in three stages. The rst two stages are based on longer text lengths and the nal stage is based on segments of texts. Finally, the plagiarism direction identi cation problem is explored as supervised machine learning classi cation and ranking tasks. Statistical and linguistic features are investigated individually or in various combinations. The objective is to introduce a new perspective on the traditional brute-force pair-wise comparison of texts. Instead of comparing original texts against rewritten texts, features are drawn based on traits of texts to build a pattern for original and rewritten texts. Thus, the classi cation or ranking task is to t a piece of text into a pattern. The framework is tested by empirical experiments, and the results from initial experiments show that deep linguistic analysis contributes to solving the problems we address in this thesis. Further experiments show that combining shallow and viii deep techniques helps improve the classi cation of plagiarised texts by reducing the number of false negatives. In addition, the experiment on plagiarism direction detection shows that rewritten texts can be identi ed by statistical and linguistic traits. The conclusions of this study o er ideas for further research directions and potential applications to tackle the challenges that lie ahead in detecting text reuse.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism

Barrón Cedeño, LA. (2012). On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16012Palanci

Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Peer reviewe