Revisiting the challenges and surveys in text similarity matching and detection methods

The massive amount of information from the internet has revolutionized the field of natural language processing. One of the challenges was estimating the similarity between texts. This has been an open research problem although various studies have proposed new methods over the years. This paper surveyed and traced the primary studies in the field of text similarity. The aim was to give a broad overview of existing issues, applications, and methods of text similarity research. This paper identified four issues and several applications of text similarity matching. It classified current studies based on intrinsic, extrinsic, and hybrid approaches. Then, we identified the methods and classified them into lexical-similarity, syntactic-similarity, semantic-similarity, structural-similarity, and hybrid. Furthermore, this study also analyzed and discussed method improvement, current limitations, and open challenges on this topic for future research directions

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Several computing courses allow students to choose which programming language they want to use for completing a programming task. This can lead to cross-language code plagiarism and collusion, in which the copied code file is rewritten in another programming language. In response to that, this paper proposes a detection technique which is able to accurately compare code files written in various programming languages, but with limited effort in accommodating such languages at development stage. The only language-dependent feature used in the technique is source code tokeniser and no code conversion is applied. The impact of coincidental similarity is reduced by applying a TF-IDF inspired weighting, in which rare matches are prioritised. Our evaluation shows that the technique outperforms common techniques in academia for handling language conversion disguises. Further, it is comparable to those techniques when dealing with conventional disguises

Enhancing Plagiarism Detection: The Role of Artificial Intelligence in Upholding Academic Integrity

Plagiarism poses a significant threat to academic integrity, requiring effective measures for its detection and prevention. This paper explores the efficacy of plagiarism detection tools in upholding academic integrity, with a specific focus on the use of artificial intelligence (AI) technologies. The abstract introduces the concept of plagiarism and its impact on scholarly work. It highlights the importance of reliable and accurate plagiarism detection methods and emphasizes the role of AI in enhancing the effectiveness of such tools. The abstract briefly outlines the main points covered in the paper, including the use of AI techniques such as text matching algorithms and natural language processing, the application of machine learning in plagiarism detection, and the challenges and advancements in cross-language detection. The abstract concludes by emphasizing the importance of promoting ethical scholarship and academic integrity in educational institution

An approach to source-code plagiarism detection investigation using latent semantic analysis

This thesis looks at three aspects of source-code plagiarism. The first aspect of the thesis is concerned with creating a definition of source-code plagiarism; the second aspect is concerned with describing the findings gathered from investigating the Latent Semantic Analysis information retrieval algorithm for source-code similarity detection; and the final aspect of the thesis is concerned with the proposal and evaluation of a new algorithm that combines Latent Semantic Analysis with plagiarism detection tools. A recent review of the literature revealed that there is no commonly agreed definition of what constitutes source-code plagiarism in the context of student assignments. This thesis first analyses the findings from a survey carried out to gather an insight into the perspectives of UK Higher Education academics who teach programming on computing courses. Based on the survey findings, a detailed definition of source-code plagiarism is proposed. Secondly, the thesis investigates the application of an information retrieval technique, Latent Semantic Analysis, to derive semantic information from source-code files. Various parameters drive the effectiveness of Latent Semantic Analysis. The performance of Latent Semantic Analysis using various parameter settings and its effectiveness in retrieving similar source-code files when optimising those parameters are evaluated. Finally, an algorithm for combining Latent Semantic Analysis with plagiarism detection tools is proposed and a tool is created and evaluated. The proposed tool, PlaGate, is a hybrid model that allows for the integration of Latent Semantic Analysis with plagiarism detection tools in order to enhance plagiarism detection. In addition, PlaGate has a facility for investigating the importance of source-code fragments with regards to their contribution towards proving plagiarism. PlaGate provides graphical output that indicates the clusters of suspicious files and source-code fragments

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 Machine Learning Approach for Plagiarism Detection

Plagiarism detection is gaining increasing importance due to requirements for integrity in education. The existing research has investigated the problem of plagrarim detection with a varying degree of success. The literature revealed that there are two main methods for detecting plagiarism, namely extrinsic and intrinsic. This thesis has developed two novel approaches to address both of these methods. Firstly a novel extrinsic method for detecting plagiarism is proposed. The method is based on four well-known techniques namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The LSA application was fine-tuned to take in the stylometric features (most common words) in order to characterise the document authorship as described in chapter 4. The results revealed that LSA based stylometry has outperformed the traditional LSA application. Support vector machine based algorithms were used to perform the classification procedure in order to predict which author has written a particular book being tested. The proposed method has successfully addressed the limitations of semantic characteristics and identified the document source by assigning the book being tested to the right author in most cases. Secondly, the intrinsic detection method has relied on the use of the statistical properties of the most common words. LSA was applied in this method to a group of most common words (MCWs) to extract their usage patterns based on the transitivity property of LSA. The feature sets of the intrinsic model were based on the frequency of the most common words, their relative frequencies in series, and the deviation of these frequencies across all books for a particular author. The Intrinsic method aims to generate a model of author “style” by revealing a set of certain features of authorship. The model’s generation procedure focuses on just one author as an attempt to summarise aspects of an author’s style in a definitive and clear-cut manner. The thesis has also proposed a novel experimental methodology for testing the performance of both extrinsic and intrinsic methods for plagiarism detection. This methodology relies upon the CEN (Corpus of English Novels) training dataset, but divides that dataset up into training and test datasets in a novel manner. Both approaches have been evaluated using the well-known leave-one-out-cross-validation method. Results indicated that by integrating deep analysis (LSA) and Stylometric analysis, hidden changes can be identified whether or not a reference collection exists

Exploring Automated Code Evaluation Systems and Resources for Code Analysis: A Comprehensive Survey

The automated code evaluation system (AES) is mainly designed to reliably assess user-submitted code. Due to their extensive range of applications and the accumulation of valuable resources, AESs are becoming increasingly popular. Research on the application of AES and their real-world resource exploration for diverse coding tasks is still lacking. In this study, we conducted a comprehensive survey on AESs and their resources. This survey explores the application areas of AESs, available resources, and resource utilization for coding tasks. AESs are categorized into programming contests, programming learning and education, recruitment, online compilers, and additional modules, depending on their application. We explore the available datasets and other resources of these systems for research, analysis, and coding tasks. Moreover, we provide an overview of machine learning-driven coding tasks, such as bug detection, code review, comprehension, refactoring, search, representation, and repair. These tasks are performed using real-life datasets. In addition, we briefly discuss the Aizu Online Judge platform as a real example of an AES from the perspectives of system design (hardware and software), operation (competition and education), and research. This is due to the scalability of the AOJ platform (programming education, competitions, and practice), open internal features (hardware and software), attention from the research community, open source data (e.g., solution codes and submission documents), and transparency. We also analyze the overall performance of this system and the perceived challenges over the years

Making Presentation Math Computable

This Open-Access-book addresses the issue of translating mathematical expressions from LaTeX to the syntax of Computer Algebra Systems (CAS). Over the past decades, especially in the domain of Sciences, Technology, Engineering, and Mathematics (STEM), LaTeX has become the de-facto standard to typeset mathematical formulae in publications. Since scientists are generally required to publish their work, LaTeX has become an integral part of today's publishing workflow. On the other hand, modern research increasingly relies on CAS to simplify, manipulate, compute, and visualize mathematics. However, existing LaTeX import functions in CAS are limited to simple arithmetic expressions and are, therefore, insufficient for most use cases. Consequently, the workflow of experimenting and publishing in the Sciences often includes time-consuming and error-prone manual conversions between presentational LaTeX and computational CAS formats. To address the lack of a reliable and comprehensive translation tool between LaTeX and CAS, this thesis makes the following three contributions. First, it provides an approach to semantically enhance LaTeX expressions with sufficient semantic information for translations into CAS syntaxes. Second, it demonstrates the first context-aware LaTeX to CAS translation framework LaCASt. Third, the thesis provides a novel approach to evaluate the performance for LaTeX to CAS translations on large-scaled datasets with an automatic verification of equations in digital mathematical libraries. This is an open access book