Georgina Cosma

An Approach to Source-Code Plagiarism Detection and Investigation Using Latent Semantic Analysis

Plagiarism is a growing problem in academia. Academics often use plagiarism detection tools to detect similar source-code files. Once similar files are detected, the academic proceeds with the investigation process which involves identifying the similar source-code fragments within them that could be used as evidence for proving plagiarism. This paper describes PlaGate, a novel tool that can be integrated with existing plagiarism detection tools to improve plagiarism detection performance. The tool also implements a new approach for investigating the similarity between source-code files with a view to gathering evidence for proving plagiarism. Graphical evidence is presented that allows for the investigation of source-code fragments with regards to their contribution toward evidence for proving plagiarism. The graphical evidence indicates the relative importance of the given source-code fragments across files in a corpus. This is done by using the Latent Semantic Analysis information retrieval technique to detect how important they are within the specific files under investigation in relation to other files in the corpus.

Automatic student plagiarism detection : future perspectives

The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to evaluation and comparison of plagiarism detection tools revealed that these contain limitations in detecting complex forms of plagiarism such as extensive paraphrasing and use of technical tricks, such as replacing original characters with similar-looking characters from foreign alphabets. This article investigates limitations in automatic detection of student plagiarism and proposes ways on how these issues could be tackled in future systems by applying various natural language processing and information retrieval technologies. A classification of types of plagiarism is presented, and an analysis is provided of the most promising technologies that have the potential of dealing with the limitations of current state-of-the-art systems. Furthermore, the article concludes with a discussion on legal and ethical issues related to the use of plagiarism detection software. The article, hence, provides a “roadmap” for developing the next generation of plagiarism detection systems.

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Focus is on computational intelligence methods in prostate cancer predictive modeling.We survey metaheuristic optimisation methods.We review machine learning methods.We consider cancer data of different modalities.We discuss recent advances, challenges and provide future directions. Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.

Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582).

Source-code plagiarism in universities: a comparative study of student perspectives in China and the UK

There has been much research and discussion relating to variations in plagiaristic activity observed in students from different demographic backgrounds. Differences in behaviour have been noted in many studies, although the underlying reasons are still a matter of debate. Existing work focuses mainly on textual plagiarism, and most often derives results by studying (small) groups of overseas students studying in a Western context. This study investigates understanding of source-code plagiarism (i.e. plagiarism of computer programmes) amongst university students in China. The survey instrument was a Chinese translation of a survey previously administered in English in the UK. This paper reports the results of the exploratory survey conducted in China, and compares these results to those from a parallel survey conducted in the UK. The results show that there is a significant difference in understanding between the respondents from the two surveys, and suggest topics which a future and more comprehensive study may focus on.

Style Analysis for Source Code Plagiarism Detection — An Analysis of a Dataset of Student Coursework

Plagiarism has become an increasing problem in higher education in recent years. Coding style can be used to detect source code plagiarism that involves writing and deciding the structure of the code which does not affect the logic of a program, thus offering a way to differentiate between different code authors. This paper focuses to identify whether a data set consisting of student programming assignments is rich enough to apply coding style metrics to detect similarities between code sequences, and we use the BlackBox dataset as a case study.

Our people has got to come to terms with that: changing perceptions of the digital rectal examination as a barrier to prostate cancer diagnosis in African-Caribbean men

African‐Caribbean men in the United Kingdom in comparison with other ethnicities have the highest incidence rate of prostate cancer. Psychosocial aspects related to screening and presentation impact on mens behavior, with previous studies indicating a range of barriers. This study explores one such barrier, the digital rectal examination (DRE), due to its prominence within UK African‐Caribbean mens accounts.

Breast Cancer Diagnosis Using a Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information

Feature Selection is the process of selecting a subset of relevant features (i.e. predictors) for use in the construction of predictive models. This paper proposes a hybrid feature selection approach to breast cancer diagnosis which combines a Genetic Algorithm (GA) with Mutual Information (MI) for selecting the best combination of cancer predictors, with maximal discriminative capability. The selected features are then input into a classifier to predict whether a patient has breast cancer. Using a publicly available breast cancer dataset, experiments were performed to evaluate the performance of the Genetic Algorithm based on the Mutual Information approach with two different machine learning classifiers, namely the k-Nearest Neighbor (K-NN), and Support vector machine (SVM), each tuned using different distance measures and kernel functions, respectively. The results revealed that the proposed hybrid approach is highly accurate for predicting breast cancer, and it is very promising for predicting other cancers using clinical data.

A hybrid computational intelligence approach for efficiently evaluating customer sentiments in E-commerce reviews

The Internet has opened new interesting scenarios in the fields of e-commerce, marketing and on-line transactions. In particular, thanks to mobile technologies, customers can make purchases in a faster and cheaper way than visiting stores, and business companies can increase their sales volume due to a world-wide visibility. Moreover, online trading systems allow customers to gather all the required information about product quality and characteristics, via customer reviews, and make an informed purchase. Due to the fact that these reviews are used to determine the extent of customers acceptance and satisfaction of a product or service, they can affect the future selling performance and market share of a company because they can also be used by companies to determine the success of a product, and predict its demand. As a consequence, tools for efficiently classifying textual customer reviews are becoming a key component of each e-commerce development framework to enable business companies to define the most suitable selling strategies and improve their market capabilities. This paper introduces an innovative framework for efficiently analysing customer sentiments in textual reviews in order to compute their corresponding numerical rating to allow companies to better plan their future business activities. The proposed approach addresses different issues involved in this significant task: the dimension and imprecision of ratings data. As shown in experimental results, the proposed hybrid approach yields better learning performance than other state of the art rating predictors.

A New Approach to Ontology-Based Semantic Modelling for Opinion Mining

With the fast growth of World Wide Web 2.0, a great number of opinions about a variety of products have been published in blogs, forums, and social networks. Opinion mining tools are needed to enable users to efficiently process a large number of reviews found online, in order to determine the underlying opinions. This paper presents a new methodology for semantic modelling of the domain knowledge for opinion mining. In particular, the new methodology focuses on modelling the domain knowledge in such a way that it can be translated to a formal ontology, which can then be automatically enriched with ground facts obtained from public Linked Open Data resources. The methodology also considers procedures to link between the formal ontology and Natural Language Processing. Our approach successfully enriches the ontology with the relevant ground facts. This ontology can then be used to perform a variety of data mining tasks including sentiment analysis and information retrieval.