SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted EMPIRICAL EXPLORATION OF SOFTWARE TESTING(New Jersey Institute of Technology, 2024-04-18) Alblwi, Samia; Mili, Ali; Oria, VincentDespite several advances in software engineering research and development, the quality of software products remains a considerable challenge. For all its theoretical limitations, software testing remains the main method used in practice to control, enhance, and certify software quality. This doctoral work comprises several empirical studies aimed at analyzing and assessing common software testing approaches, methods, and assumptions. In particular, the concept of mutant subsumption is generalized by taking into account the possibility for a base program and its mutants to diverge for some inputs, demonstrating the impact of this generalization on how subsumption is defined. The problem of mutant set minimization is revisited and recast as an optimization problem by specifying under what condition the objective function is optimized. Empirical evidence shows that the mutation coverage of a test suite depends broadly on the mutant generation operators used with the same tool and varies even more broadly across tools. The effectiveness of a test suite is defined by its ability to reveal program failures, and the extent to which traditional syntactic coverage metrics correlate with this measure of effectiveness is considered.11 0Item Restricted Investigating Factors Influencing Blockchain Adoption in Saudi Healthcare Data Management(Florida Institute of Technology, 2024-05-15) Alkhalifah, Noura; Slhoub, KhaledBlockchain technology can potentially address security and privacy issues concerning the collection, storage, and sharing of healthcare data. However, its adoption within the healthcare sector is nascent in Saudi Arabia. This underutilization prompted our investigation into the determinants influencing blockchain adoption, intending to fully empower the Saudi healthcare sector to leverage blockchain capabilities. To achieve this, an extensive literature review was conducted to identify the pivotal factors encompassing technology, organization, and environment (TOE) that affect the successful implementation of blockchain technologies in managing healthcare data within the Saudi context. Utilizing the TOE framework, this study formulated three hypotheses concerning the adoption of blockchain technology. Subsequently, a quantitative analysis was undertaken through an online survey distributed among healthcare organizations in Saudi Arabia. We obtained responses from 129 valid ques- tionnaires and employed a partial least squares structural equation model (PLS-SEM) for analysis and hypothesis testing. The results show that technological and organizational factors significantly influence the adoption of blockchains, whereas environmental factors have no significance. This study contributes significantly to bridging a critical gap in the academic literature by clarifying the factors influencing blockchain adoption in healthcare data management in Saudi Arabia. Our findings serve as valuable guidelines for decision-makers contemplating the adoption of blockchain technology in healthcare data management, thus facilitating the effective navigation of associated challenges.22 0Item Restricted Deep Learning-Based Digital Human Modeling And Applications(Saudi Digital Library, 2023-12-14) Ali, Ayman; Wang, PuRecent advancements in the domain of deep learning models have engendered remarkable progress across numerous computer vision tasks. Notably, there has been a burgeoning interest in the field of recovering three-dimensional (3D) human models from monocular images in recent years. This heightened interest can be attributed to the extensive practical applications that necessitate the utilization of 3D human models, including but not limited to gaming, human-computer interaction, virtual systems, and digital twin. The focus of this dissertation is to conceptualize and develop a suite of deep learning-based models with the primary objective of enabling the expeditious and high-fidelity digitalization of human subjects. This endeavor further aims to facilitate a multitude of downstream applications that leverage digital 3D human models. The endeavor to estimate a three-dimensional (3D) human mesh from a monocular image necessitates the application of intricate deep-learning models for enhanced feature extraction, albeit at the expense of heightened computational requirements. As an alternative approach, researchers have explored the utilization of a skeleton-based modality, which represents a lightweight abstraction of human pose, aimed at mitigating the computational intensity. However, this approach entails the omission of significant visual cues, particularly shape information, which cannot be entirely derived from the 3D skeletal representation alone. To harness the advantages of both paradigms, a hybrid methodology that integrates the benefits of 3D human mesh and skeletal information offers a promising avenue. Over the past decade, substantial strides have been made in the estimation of two-dimensional (2D) joint coordinates derived from monocular images. Simultaneously, the application of Convolutional Neural Networks (CNNs) for the extraction of intricate visual features from images has demonstrated its prowess in feature extraction. This progress serves as a compelling impetus for our investigation into a hybrid architectural framework that combines CNNs with a lightweight graph transformer-based approach. This innovative architecture is designed to elevate the 2D joint pose to a comprehensive 3D representation and recover essential visual cues essential for the precise estimation of pose and shape parameters. While SOTA results in 3D Human Pose Estimation (HPE) are important, they do not guarantee the accuracy and plausibility required for biomechanical analysis. Our innovative two-stage deep learning model is designed to efficiently estimate 3D human poses and associated kinematic attributes from monocular videos, with a primary focus on mobile device deployment. The paramount significance of this contribution lies in its ability to provide not only accurate 3D pose estimations but also biomechanically plausible results. This plausibility is essential for achieving accurate biomechanical analyses, thereby advancing various applications, including motion tracking, gesture recognition, and ergonomic assessments. Our work significantly contributes to enhancing our understanding of human movement and its interaction with the environment, ultimately impacting a wide range of biomechanics-related studies and applications. In the realm of human movement analysis, one prominent downstream task is the recognition of human actions based on skeletal data, known as Skeleton-based Human Action Recognition (HAR). This domain has garnered substantial attention within the computer vision community, primarily due to its distinctive attributes, such as computational efficiency, the innate representational power of features, and robustness to variations in illumination. In this context, our research demonstrates that, by representing 3D pose sequences as RGB images, conventional Convolutional Neural Network (CNN) architectures, exemplified by ResNet-50, when complemented by as tute training strategies and diverse augmentation techniques, can attain State-of-the-Art (SOTA) accuracy levels, surpassing the widely adopted Graph Neural Network models. The domain of radar-based sensing, rooted in the transmission and reception of radio waves, offers a non-intrusive and versatile means to monitor human movements, gestures, and vital signs. However, despite its vast potential, the lack of comprehensive radar datasets has hindered the broader implementation of deep learning in radar-based human sensing. In response, the application of synthetic data in deep learning training emerges as a crucial advantage. Synthetic datasets provide an expansive and practically limitless resource, enabling models to adapt and generalize proficiently by exposing them to diverse scenarios, transcending the limitations of real-world data. As part of this research’s trajectory, a novel computational framework known as "virtual radar" is introduced, leveraging 3D pose-driven physics-informed principles. This paradigm allows for the generation of high-fidelity synthetic radar data by merging 3D human models and the principles of Physical Optics (PO) approximation for radar cross-section modeling. The introduction of virtual radar marks a groundbreaking path towards establishing foundational models focused on the nuanced understanding of human behavior through privacy-preserving radar-based methodologies.16 0Item Restricted Instructional Strategies for Teaching Computational Thinking in Secondary School Computer Science Introductory Courses(2023-05) Alghamdi, Khadijah Ali; Leftwich, Anne OttenbreitThere is a consistent call for teaching computational thinking (CT) in computer science (CS) courses and a massive body of CT research that emphasizes its importance in improving students’ computational skills. However, it is not clear how CT has been included and implemented in CS courses. This study explored how prevalent CT practices (abstraction, algorithms, pattern recognition, and decomposition) are in AP Computer Science Principles (AP CSP) courses and how they are implemented and taught. The study also examined teachers’ experiences and needs in teaching CT practices. I employed a mixed method multiple-case study design to explore the implementation of CT practices. Five AP CSP courses were analyzed (Code.org, Mobile CSP, BJC, Microsoft MakeCode, and UTeach). Also, five CS teachers who had at least one year of experience in teaching one of these courses were interviewed. For courses, frequencies and percentages of lessons that included CT practices were determined, and for quantitative data, thematic analysis procedures and constant comparative analysis procedures were employed. The results showed that abstraction was the most included, while decomposition was minimally included. In addition, this study pointed out different instructional strategies to teach abstraction and algorithms in an engaging environment. Teaching pattern recognition was mainly included in activities designed mostly to teach abstraction. However, decomposition was included mainly as a scaffolding strategy. The teachers reported improvement in their students’ understanding of abstraction. However, the teachers mentioned some challenges with teaching CT practices, such as student aversion to incorporate some CT practices in their coding, abstraction and algorithms in particular. Also, teachers mentioned that abstraction and decomposition are difficult for students. The teachers also reported that pattern recognition is not an easy CT practice and that students struggled to recognize patterns in their code. In addition, teachers agreed on the need for instructional strategies to teach pattern recognition and decomposition. It is recommended to explicitly teach pattern recognition and decomposition like what has been done with abstraction and algorithms. Teaching all four CT practices explicitly and as one entity is recommended. Also, showing the relationships between all CT practices as CT practices exist and how they can be used to solve problems is also recommended. More studies to focus on only one CT practice at a time or to examine CT practices in AP CSA curricula could be conducted in the future.46 0