Saudi Cultural Missions Theses & Dissertations
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Item Restricted Artificial Intelligence for Automatic Attachment Assessment in School-Age Children: An Approach Based on Language and Paralanguage.(Saudi Digital Library, 2025-06-17) Buker, Areej; Vinciarelli, AlessandroAttachment is a psychological construct that provides a framework for understanding how individuals perceive and interpret social interactions, navigate relational dynamics, and experience and regulate their emotional states, particularly under conditions of stress. An attachment style begins to develop within the first few months of life, shaped by a child’s interactions with their primary caregivers. Consistent and nurturing care promotes the development of a secure attachment style, whereas inconsistent or inadequate caregiving often gives rise to insecure attachment patterns. Insecure attachment is linked to a range of challenges, including behavioural issues such as antisocial tendencies; mental health difficulties like anxiety, emotional dysregulation, and body image concerns; and heightened risks of physical health problems, including sleep disturbances. Early recognition and intervention for insecure attachment increases the likelihood of reshaping maladaptive patterns into secure ones, potentially reducing attachment-related challenges. Automated approaches for attachment recognition offer significant benefits, including consistent delivery of assessments, such as the MCAST, and broader accessibility to a wider population. While there are a few available systems for delivering attachment tests (e.g., CMCAST and SAM), the limited studies focused on developing automated classifiers to analyse the collected data have shown a suboptimal performance. These classifiers often struggle to recognise insecure attachment, achieving a maximum Accuracy of only 62.7%. Furthermore, these studies fail to offer insights into the reasoning behind their classifications, missing an opportunity to advance the understanding of attachment in early to middle childhood. This developmental stage—characterised by significant changes that include the expansion of social circles and the internalisation of emotional representations—has historically received less attention in a field predominantly focused on studying attachment markers in infants and adults. This thesis focuses on two primary objectives: enhancing the automated classification of attachment styles in children, particularly insecure attachment, and identifying markers associated with these styles. The study employs two modalities—language and paralanguage— along with emotions derived from both modalities. These modalities are utilised within a unimodal and a multimodal framework. Among all classifiers developed using the same dataset, the language-based unimodal approach demonstrated the highest effectiveness, achieving exceptional performance in recognising insecure attachment with an Accuracy of 82.2%, all while relying on relatively simple methodologies. Furthermore, this research identified linguistic, acoustic, and emotional markers of attachment, offering valuable insights into attachment representations in children.11 0Item Restricted Detecting Flaky Tests Without Rerunning Tests(George Mason University, 2024-07-26) Alshammari, Abdulrahman Turqi; Lam, Wing; Ammann, PaulA critical component of modern software development practices, particularly continuous integration (CI), is the halt of development activities in response to test failures which requires further investigation and debugging. As software changes, regression testing becomes vital to verify that new code does not affect existing functionality. However, this process is often delayed by the presence of flaky tests—those that yield inconsistent results on the same codebase, alternating between pass and fail. Test flakiness introduces challenges to the trust in testing outcomes and undermines the reliability of the CI process. The typical approach to identifying flaky tests has involved executing them multiple times; if a test yields both passing and failing results without any modifications to the codebase, it is flaky, as discussed by Luo et al. in their empirical study. This approach, while straightforward, can be resource-intensive and time-consuming, resulting in considerable overhead costs for development teams. Moreover, this technique might not consistently reveal flakiness in tests that exhibit varied behavior across varying execution environments. Given these challenges, the research community has been actively seeking more efficient and reliable alternatives to the repetitive execution of tests for flakiness detection. These explorations aim to uncover methods that can accurately detect flaky tests without the need for multiple reruns, thereby reducing the time and resources required for testing. This dissertation addresses three principal dimensions of test flakiness. First, it presents a machine learning classifier designed to detect which tests are flaky, based on previously detected flaky tests. Second, the dissertation proposes three de-duplication-based approaches to assist developers in determining whether a flaky test failure is flaky or not. Third, it highlights the impact of test flakiness on other testing activities (particularly mutation testing) and discusses how to mitigate the effects of test flakiness on mutation testing. This dissertation explores the detection of test flakiness by conducting an empirical study on the limitations of rerunning tests as a method for identifying flaky tests, which results in a large dataset of flaky tests. This dataset is then utilized to develop FlakeFlagger, a machine learning classifier, which is designed to automatically predict the likelihood of a test being flaky through static and dynamic analysis. The objective is to leverage FlakeFlagger to identify flaky tests without the need for reruns by detecting patterns and symptoms common among previously identified flaky tests. In addressing the challenge of detecting whether a failure is due to flakiness, this dissertation demonstrates how developers can better manage flaky tests within their test suites. The dissertation proposes three deduplication-based methods to help developers determine whether a specific failure is genuinely flaky or not. Furthermore, the dissertation discusses the effects of test flakiness on mutation testing, a critical activity for assessing the quality of test suites. It includes an extensive rerun experiment on the mutation analysis of flaky tests identified earlier in the study. This is to highlight the significant impact of flaky tests on the validity of the mutation testing.29 0Item Restricted Developing AI-Powered Support for Improving Software Quality(University of Wollongong, 2024-01-12) Alhefdhi, Abdulaziz Hasan M.; Dam, Hoa Khanh; Ghose, AdityaThe modern scene of software development experiences an exponential growth in the number of software projects, applications and code-bases. As software increases substantially in both size and complexity, software engineers face significant challenges in developing and maintaining high-quality software applications. Therefore, support in the form of automated techniques and tools is much needed to accelerate development productivity and improve software quality. The rise of Artificial Intelligence (AI) has the potential to bring such support and significantly transform the practices of software development. This thesis explores the use of AI in developing automated support for improving three aspects of software quality: software documentation, technical debt and software defects. We leverage a large amount of data from software projects and repositories to provide actionable insights and reliable support. Using cutting-edge machine/deep learning technologies, we develop a novel suite of automated techniques and models for pseudo-code documentation generation, technical debt identification, description and repayment, and patch generation for software defects. We conducted several intensive empirical evaluations which show the high effectiveness of our approach.40 0Item Restricted Unsupervised Semantic Change Detection in Arabic(Queen Mary University of London, 2023-10-23) Sindi, Kenan; Dubossarsky, HaimThis study employs pretrained BERT models— AraBERT, CAMeLBERT (CA), and CAMeLBERT (MSA)—to investigate semantic change in Arabic across distinct time periods. Analyzing word embeddings and cosine distance scores reveals variations in capturing semantic shifts. The research highlights the significance of training data quality and diversity, while acknowledging limitations in data scope. The project's outcome—a list of most stable and changed words—contributes to Arabic NLP by shedding light on semantic change detection, suggesting potential model selection strategies and areas for future exploration.97 0Item Restricted Comparative Analysis of Lossless Data Compression Algorithms for Textual Data(University of Glasgow, 2023-12-15) Mahfouz, Maha; Manlove, DavidThis dissertation presents a comprehensive exploration and comparative assessment of key lossless data compression algorithms, specifically Huffman, Lempel-Ziv-Welch (LZW), and Run-Length Encoding (RLE). The study extends to innovative combined functions, integrating Huffman with RLE, LZW with RLE, LZW with Burrows-Wheeler Transform (BWT), LZW with Trie data structure, and a fusion of LZW, BWT, and RLE. Focused primarily on textual data, the research provides a detailed comparative analysis of these algorithms and their hybrid forms. A key component of this study is the development and implementation of a Command Line Interface (CLI) that facilitates the application and evaluation of these compression techniques and also integrates GPT2 as a text generator. The inclusion of GPT2 adds value to the research by allowing the generation of varied textual data, which are then processed through compression algorithms. It offers a dynamic environment for comprehensive performance analysis while enhancing the practical application of algorithms. As part of the dissertation, systematic experiments and comparisons evaluate individual and combined algorithms for data compression. The findings reveal the algorithms' strengths, limitations, and suitability for different types of text data in modern digital contexts.39 0Item Restricted Pattern Recognition & Predictive Analysis of Cardiovascular Diseases: A Machine Learning Approach(Saudi Digital Library, 2023-11-23) Alseraihi, Faisal Fahad; Naich, AmmarCardiovascular disease (CVD) is a predominant global health concern, with its impact becoming increasingly pronounced in low- and middle- income countries due to challenges like limited healthcare access, inadequate public awareness, and lifestyle-related risks. Addressing CVD's multifactorial origins, which span genetic, environmental, and behavioral domains, requires advanced diagnostic techniques. This research leverages the UCI Heart Disease dataset to develop a deep learning predictive model for CVD, incorporating 14 vital heart health parameters. The models performance is critically assessed against conventional machine learning approaches, shedding light on its efficiency and areas of refinement. Utilizing sophisticated Neural Network structures, this study strives to enhance predictive health analytics, aiming for timely CVD identification and intervention. As the integration of machine learning into healthcare deepens, it's crucial to ensure that these tools are robust, thoroughly evaluated, and augment clinical insights to reduce misdiagnosis risks.68 0