7,435 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

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    Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.Comment: 49 pages, 10 figures, 6 table

    Graph Neural Networks For Mapping Variables Between Programs -- Extended Version

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    Automated program analysis is a pivotal research domain in many areas of Computer Science -- Formal Methods and Artificial Intelligence, in particular. Due to the undecidability of the problem of program equivalence, comparing two programs is highly challenging. Typically, in order to compare two programs, a relation between both programs' sets of variables is required. Thus, mapping variables between two programs is useful for a panoply of tasks such as program equivalence, program analysis, program repair, and clone detection. In this work, we propose using graph neural networks (GNNs) to map the set of variables between two programs based on both programs' abstract syntax trees (ASTs). To demonstrate the strength of variable mappings, we present three use-cases of these mappings on the task of program repair to fix well-studied and recurrent bugs among novice programmers in introductory programming assignments (IPAs). Experimental results on a dataset of 4166 pairs of incorrect/correct programs show that our approach correctly maps 83% of the evaluation dataset. Moreover, our experiments show that the current state-of-the-art on program repair, greatly dependent on the programs' structure, can only repair about 72% of the incorrect programs. In contrast, our approach, which is solely based on variable mappings, can repair around 88.5%.Comment: Extended version of "Graph Neural Networks For Mapping Variables Between Programs", paper accepted at ECAI 2023. Github: https://github.com/pmorvalho/ecai23-GNNs-for-mapping-variables-between-programs. 11 pages, 5 figures, 4 tables and 3 listing

    Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study

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    Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits

    Boundary Spanner Corruption in Business Relationships

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    Boundary spanner corruption—voluntary collaborative behaviour between individuals representing different organisations that violates their organisations’ norms—is a serious problem in business relationships. Drawing on insights from the literatures on general corruption perspectives, the dark side of business relationships and deviance in sales and service organisations, this dissertation identifies boundary spanner corruption as a potential dark side complication inherent in close business relationships It builds research questions from these literature streams and proposes a research structure based upon commonly used methods in corruption research to address this new concept. In the first study, using an exploratory survey of boundary spanner practitioners, the dissertation finds that the nature of boundary spanner corruption is broad and encompasses severe and non-severe types. The survey also finds that these deviance types are prevalent in a widespread of geographies and industries. This prevalence is particularly noticeable for less-severe corruption types, which may be an under-researched phenomenon in general corruption research. The consequences of boundary spanner corruption can be serious for both individuals and organisations. Indeed, even less-severe types can generate long-term negative consequences. A second interview-based study found that multi-level trust factors could also motivate the emergence of boundary spanner corruption. This was integrated into a theoretical model that illustrates how trust at the interpersonal, intraorganisational, and interorganisational levels enables corrupt behaviours by allowing deviance-inducing factors stemming from the task environment or from the individual boundary spanner to manifest in boundary spanner corruption. Interpersonal trust between representatives of different organisations, interorganisational trust between these organisations, and intraorganisational agency trust of management in their representatives foster the development of a boundary-spanning social cocoon—a mechanism that can inculcate deviant norms leading to corrupt behaviour. This conceptualisation and model of boundary spanner corruption highlights intriguing directions for future research to support practitioners engaged in a difficult problem in business relationships

    Creating a Dataset for High-Performance Computing Code Translation: A Bridge Between HPC Fortran and C++

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    In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code. To ensure reliability and applicability, the dataset is initially refined using a meticulous code similarity test. The effectiveness of our dataset is assessed using both quantitative (CodeBLEU) and qualitative (human evaluation) methods. We demonstrate how this dataset can significantly improve the translation capabilities of large-scale language models, with improvements of Ă—5.1\mathbf{\times 5.1} for models with no prior coding knowledge and Ă—9.9\mathbf{\times 9.9} for models with some coding familiarity. Our work highlights the potential of this dataset to advance the field of code translation for high-performance computing. The dataset is available at https://github.com/bin123apple/Fortran-CPP-HPC-code-translation-datase

    Big Data Analytics and Auditing: A Review and Synthesis of Literature

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    The use of data analytics in auditing is increasingly growing. The application of common data analytics to audit engagements appears to be lagging behind other areas of practice, even though data analytics is thought to represent the future of audit, and there are still few publications that have examined this influence. This article reviews data analytics in audits and its potential for future audit engagements to describe the evolution of this research trend and picture its future growth directions. Future audit research potential and difficulties are also discussed. Data analytics application in auditing has enormous potential for refining audit quality, decreasing errors, increasing process transparency, and enhancing stakeholders’ confidence. We conducted a systematic literature review using the PRISMA approach. A total of 100 articles published in English from January 2011 to November 2021 were identified through a systematic search of reputed databases, including Web of Science and Scopus and many others. Our analysis reveals that data analytics is a promising domain for the auditing practice as it improves audit efficiency and promotes audit work digital transformation. While reviewing the most pertinent literature in the context of data analytics in auditing, this study offers insights on potential new directions and waning views on big data analytics in auditing. Doi: 10.28991/ESJ-2023-07-02-023 Full Text: PD

    Monotheism and the Suffering of Animals in Nature

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    This is the Submitted Manuscript Under Review. The final version is available from Cambridge University Press via the DOI in this recordThis Element concerns itself with a particular aspect of the problem posed to monotheistic religious thought by suffering, namely the suffering of non-human creatures in nature. It makes some comparisons between Judaism, Christianity, and Islam, and then explores the problem in depth within Christian thought. After clarification of the nature of the problem, the Element considers a range of possible responses, including those based on a fall-event, those based on freedom of process, and those hypothesising a constraint on the possibilities for God as creator. Proposals based on the motif of self-emptying are evaluated. Two other aspects of the question concern God's providential relationship to the evolving creation, and the possibility of resurrection lives for animals. After consideration of the possibility of combining different explanations, the Element ends its discussion by looking at two innovative proposals at the cutting-edge of the debate
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