Digital Commons Network^™

Analysis Of The Vicious Circle Of Education And Its Impact On Human Development In Iraq, Walaa Ibrahim Hussein, Lawrence Yahiya Saleh Al- Kubaisi

Journal of Economics and Administrative Sciences

This research focuses on the complex relationship between education quality and human development in Iraq for 2005 to 2023, emphasizing how the poor conditions in education set in place a vicious circle restricting national progress. Utilizing both inductive and deductive realms, official data of the Ministries of Education and Planning were analyzed, along with comparative international experiences. Indicators analyzed include school density, student-teacher ratio, lack of infrastructure, and limitations of vocational training for primary, secondary, and vocational educational levels. The results show a constant mismatch between students' population growth and resource allocation to education, thus summarizing an overcrowded school environment, …

Learning Spatio-Temporal Dynamics For Trajectory Recovery Via Time-Aware Transformer, Tian Sun, Yuqi Chen, Baihua Zheng, Weiwei Sun

Research Collection School Of Computing and Information Systems

In real-world applications, GPS trajectories often suffer from low sampling rates, with large and irregular intervals between consecutive GPS points. This sparse characteristic presents challenges for their direct use in GPS-based systems. This paper addresses the task of map-constrained trajectory recovery, aiming to enhance trajectory sampling rates of GPS trajectories. Previous studies commonly adopt a sequence-to-sequence framework, where an encoder captures the trajectory patterns and a decoder reconstructs the target trajectory. Within this framework, effectively representing the road network and extracting relevant trajectory features are crucial for overall performance. Despite advancements in these models, they fail to fully leverage the …

Potential And Pitfalls Of Romantic Artificial Intelligence (Ai) Companions: A Systematic Review, Qi Hui Jerlyn Ho, Meilan Hu, Tracy Xi Chen, Andree Hartanto

Research Collection School of Social Sciences

As Artificial Intelligence (AI) becomes more integrated into daily life, individuals have increasingly turned to AIdriven systems for emotional support, companionship, and even romantic relationships. These relationships can be both beneficial and detrimental. Given the need for a comprehensive understanding of this phenomenon, this systematic review uses Sternberg’s Triangular Theory of Love to provide a holistic summary of its key potentials and pitfalls. A total of 23 articles were identified from the following databases: EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus, and Web of Science. Results highlighted the key potentials of being in a romantic relationship with AI companions as: the …

Specialization Or Diversification? Creators’ Strategies On User-Generated Content Platforms, Ziwei Ye

Theses and Dissertations in Business Administration

Recent advancements in digital platforms have reshaped content creation and distribution. User-generated content (UGC), created and shared by internet users, is transforming entertainment, communication, and information sharing. The rise of UGC has fueled the growth of the "creator economy"—an ecosystem of creators, users, and advertisers facilitated by platforms such as YouTube and TikTok. While prior research has primarily explored how UGC platforms incentivize content quantity and quality, this study advances the literature by examining how creators' content strategies influence consumer attention and how platform mechanisms shape this relationship, offering new insights into the interplay between creator behavior and platform design. …

Thriving In The Age Of Ai: Navigating Ai Identity Threat Through Ai Job Crafting, Yuming He

Theses and Dissertations in Business Administration

As artificial intelligence (AI) technologies like GenAI tools increasingly reshape the workplace, employees increasingly face threats to their work identity. Grounded in the identity threat response model and job crafting theory, this study investigates how AI identity threat influences employee AI job crafting behaviors and how these behaviors, in turn, affect vitality and learning. Using survey data from 521 full-time employees who actively engage with AI tools, the results indicate that AI identity threat stimulates both AI approach job crafting and AI avoidance job crafting. AI approach crafting enhances both vitality and learning, while AI avoidance crafting only supports vitality. …

Residential Factors Associated With Mental Health In United States Veterans, Air Force Military, And Air Force Employees, Andrew J. Hoisington, Christopher A. Stamper, Molly Penzenik, Meredith Reitter, Elizabeth J. Kovacs, Nazanin H. Bahraini, Lisa A. Brenner

Faculty Publications

Individuals in Westernized countries spend most of their time indoors. However, exploration of residential building factors that may influence occupants’ mental health is limited in scientific literature. The purpose of this study was to explore investigator's perceived areas of importance in residences to mental health via survey methods. To that end, we administered the Housing, Occupancy, Materials, and Environment (HOME) survey to assess factors that may influence mental health to those working in the United States (US) Air Force (n = 230) or past military members, US Veterans (n = 180). Self-reported mental health surveys were also administered to the …

War, Wounds, And Strategy: Patient Movement Lessons From The World Wars For Great Power Competition, Phillip R. Jenkins

Faculty Publications

This thesis examines the evolution of the U.S. military's patient movement system during World War I and World War II to evaluate how well it may perform under the conditions of future large-scale combat operations. It asks whether the United States can move, treat, and sustain wounded personnel at the scale and pace required to preserve combat power in a prolonged, high-intensity conflict. Using detailed case studies of the Meuse-Argonne Offensive and the Battle of the Bulge, the analysis focuses on how transportation platforms, organizational structure, and standard operating procedures (SOPs) shaped patient movement under conditions of attrition, disruption, and …

Does Third Party Litigation Funding Need Regulations On Consumer Protection, Ronen Avraham, Eric Schuller, Anthony J. Sebok

Articles

Transcript of a roundtable discussion during the Third-Party Litigation Symposium at the S.J. Quinney College of Law.

The Effect Of Uniaxial Compressive And Tensile Strains On The Structural, Dynamical, Electronic, And Optical Properties Of Zrcl2 Monolayer: Ab-Initio Calculations, Hind Alqurashi, Bothina Hamad, M. O. Manasreh

Physics Faculty Publications and Presentations

Recently, the two-dimensional material zirconium dihalide (ZrCl2) has received a significant attention for prospective device applications due to its unique electronic, mechanical, magnetic, and topological properties. This work reports theoretical predictions for the structural, dynamical, electronic, and optical properties of ZrCl2 under uniaxial compressive and tensile strains using density functional theory (DFT). The band gap structures were found to be highly sensitive to the uniaxial compressive and tensile strains of ZrCl2 monolayer (ML). The unstrained ZrCl2 ML has a semiconducting behavior with an indirect band gap of 1.19 eV. Under the uniaxial compressive tensile stress (epsilon x) of- 6%,- 4%,- …

Nsf Eec: Establishing Utrgv’S Center For Broadening Participation In Engineering: Engage, Educate, Enrich, Ala Qubbaj, Laura Benitez, Noe Vargas Hernandez, Constantine Tarawneh, Arturo A. Fuentes, Nazmul Islam, Edna Orozco-Leonhardt, Thuy Vu, Angela M. Chapman

Mechanical Engineering Faculty Publications

Hispanics are one of the fastest growing populations in the US, yet they are underrepresented in engineering. University of Texas Rio Grande Valley (UTRGV), a major Hispanic Serving Institution (HSI) with a student population over 95% Hispanic, is well-positioned to address this disparity. UTRGV established a Center for Broadening Participation in Engineering: Engage, Educate, Enrich (CBPE-E3) to enhance Hispanic participation in engineering from early awareness through professional employment. The CBPE -E3 aims to increase enrollment, retention, and advancement rates of Hispanic students in higher education engineering, especially Latinas facing intersectional barriers of race and gender. The CBPE -E3 envisions becoming …

Predictors Of Nursing Students' Stress, Anxiety, And Depression During The Covid-19 Pandemic In A Hispanic-Serving University In South Texas: A Cross-Sectional Study, Maria I. Diaz, Eleftherios Gkioulekas, Nancy Nadeau

School of Mathematical & Statistical Sciences Faculty Publications

Background: In nursing education, there have been several studies on the impact of the COVID-19 pandemic on the ability of nursing students to cope while in nursing school.

Purpose statement: The goal of this study is to assess undergraduate nursing students' support mechanisms as predictors of stress, anxiety, and depression during the COVID-19 pandemic within a Hispanic-serving institution in South Texas.

Methods: Across-sectional design was used in this study. An online survey using self-reported questionnaires was used to gather data from an undergraduate nursing student cohort during the Fall 2021 semester. Linear regression was used to identify the predictors of …

“What Makes It Eigen-Esque-Ish?”: A Form-Function Analysis Of The Development Of Eigentheory Concepts In A Quantum Mechanics Course, Megan Wawro, Kaitlyn Stephens Serbin

School of Mathematical & Statistical Sciences Faculty Publications

Eigentheory concepts are central in mathematics and physics; they serve multiple functions, such as symbolizing physical phenomena and facilitating mathematical computations. Words and meanings associated with eigentheory develop and vary over time, as do their associated symbols. In this study, we investigate how “eigen” develops over time in one quantum mechanics course by analyzing form-function relations (Saxe, 1999) for eigentheory concepts over 22 class sessions. We share results concerning our microgenetic and ontogenetic analyses of the creation of form-function relations and their shifts over time by characterizing the continuity and discontinuity of the various functions and forms associated with concepts …

High Moment And Pathwise Error Estimates For Fully Discrete Mixed Finite Element Approximations Of The Stochastic Stokes Equations With Multiplicative Noise, Liet Vo

School of Mathematical & Statistical Sciences Faculty Publications

This paper is concerned with high moment and pathwise error estimates for both velocity and pressure approximations of the Euler–Maruyama scheme for time discretization and its fully discrete mixed finite element discretization. Optimal rates of convergence are established for all pth moment errors for p ≥ 2 using a novel doubling of moments technique. The almost optimal rates of convergence are then obtained using Kolmogorov’s theorem based on the high moment error estimates. Unlike for the velocity error estimate, the high moment and pathwise error estimates for the pressure approximation are proved in a time-averaged norm. In addition, the …

On The Sensitivity Of Short Design Waves For Semi-Submersible Wind Platforms, S.A. Brown, T. Tosdevin, M. Hann, D.M. Greaves

School of Engineering, Computing and Mathematics

Determining ultimate loads and platform motions is essential for both survivability and cost-effectiveness in the development of floating offshore wind. Current design standards utilise time-consuming methodologies that rely on irregular sea state simulations to determine design loads. Design waves offer the potential to accelerate the design process through the simulation of shorter wave profiles that specifically target extreme responses. However, it is not well understood how reliable design waves are for floating devices. Using a mid-fidelity numerical tool, this paper explores the characteristic loads produced by four design wave methods for a semi-submersible floating wind device over a wide range …

Analytical Modelling Of Chloride Diffusion In Circular Section Concrete Columns With Binding Effects, Zaiwei Li, Long Yuan Li

School of Engineering, Computing and Mathematics

– Reinforced concrete structures in marine environments face significant durability challenges due to chloride-induced corrosion of the steel reinforcement. Understanding and modelling chloride ingress are critical for the prediction of the service life of these structures. This study presents an analytical model for chloride diffusion in circular section concrete columns, addressing a critical gap in existing research by incorporating the effects of chloride binding. The model employs a bilinear chloride binding isotherm to derive an analytical solution in cylindrical coordinates, which captures the nonlinear interaction between free and bound chlorides. Validation is performed by comparing the model's predictions with numerical …

Ccp-Wsi Blind Test Series 5: Numerical Investigation Of Isothermal Sloshing In A Circular Tank Using Openfoam, Scott Brown, Vivek Francis, Stuart Colville, Deborah Greaves, Ignacio Pregnan Johanessen

School of Engineering, Computing and Mathematics

Sloshing dynamics in partially filled tanks is a critical concern across various engineering disciplines, including maritime, aerospace, automotive, and industrial applications. This work concerns a series of sloshing test cases using computational fluid dynamics and represents an individual contribution to the CCP-WSI Blind Test Series 5, in which the submitted results are compared against both physical and alternative numerical solutions. Free surface and centre of gravity measurements are presented and the sensitivity to crucial numerical techniques such as turbulence modelling are assessed. Results suggest that capturing the onset of excitation is particularly sensitive to the choice of turbulence model and …

Developing Human-Autonomy Teaming Strategies For Maritime Cyber Security Resilience In Uncrewed Autonomous And Remote Surface Vessel Operations, Juan Palbar Misas, Kimberly Tam, Kevin Jones

School of Engineering, Computing and Mathematics

The development of new technologies and digital capabilities for Uncrewed Autonomous and Remote Surface Vessel Operations (UARSVO) is driven by various industry stakeholders. This evolution impacts the maritime industry's human role, transforming from Human-Autonomy Hybrid (HAH) to Human-Autonomy Teaming (HAT). Human-Autonomy Collaboration (HAC) is vital for maritime safety, security, and sustainability, particularly in light of increasing cyber incidents in remote operations, which necessitates greater cyber resilience due to technology at sea and ashore. This paper aims to provide a holistic socio-technical approach to investigate and present an overview of the current state-of-the-art research, focusing on the human perspective in maritime …

On-Demand Heterogeneous Drone Delivery Problem, Xupeng Wen, Zhiguang Cao, Shu Xu, Dapeng Ren, Guohua Wu, Yaoxin Wu

Research Collection School Of Computing and Information Systems

In the on-demand problem domain, actual demand frequently deviates from the expected demand. This paper intricately delves into the exploration of on-demand heterogeneous multi-drone routing problem (ODHDRP), in which a transport drone carries multiple terminal drones to subregions in the first echelon, and the terminal drones deliver parcels during a flight trip to customers with demands in subregions to maintain economies of scale in the second echelon. We formulate the customer demands using a normal distribution, and exploit a reliability model of customer demands with chance constraints. To solve the ODHDRP efficiently, we propose a hybrid iterative optimisation heuristic (HIOH) …

Outperforming The Best With Minimal Effort: Algorithm Selection For Constrained Multi-Objective Optimization, Mustafa Misir, Aldy Gunawan

Research Collection School Of Computing and Information Systems

The present study performs algorithm selection on a suite of optimization algorithms targeting the constrained multi-objective optimization problems. The idea is to utilize the existing, relevant algorithmic experience in the literature to deliver an improved solver with limited effort. The reason being that algorithm development, in general, is a challenging and time-consuming process, especially with the goal of outperforming the existing methods from varying perspectives such as performance, speed, and robustness. Concerning the multi-objective optimization problems, the required development efforts happen to be even harder than addressing the single-objective ones. Furthermore, referring to the fact that the number of candidate …

De-Duplicating Silent Compiler Bugs Via Deep Semantic Representation, Junjie Chen, Xingyu Fan, Chen Yang, Shuang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

The compiler bug duplication problem (where many test failures are caused by the same compiler bug) can lead to huge waste of time and resource in diagnosing test failures produced by compiler testing. It is particularly challenging with regard to the silent compiler bugs that do not produce any error messages. To address this problem, multiple white-box techniques were proposed, but they are inapplicable in many practical scenarios. Black-box techniques are more practical, but the existing ones are less effective as they often rely on irrelevant syntactic information. To bridge this gap, we propose a novel black-box technique (BLADE), which …

A Comprehensive Study Of Oop-Related Bugs In C++ Compilers, Bo Wang, Chong Chen, Junjie Chen, Bowen Xu, Chen Ye, Youfang Lin, Guoliang Dong, Jun Sun

Research Collection School Of Computing and Information Systems

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from …

Moditector: Module-Directed Testing For Autonomous Driving Systems, Renzhi Wang, Mingfei Cheng, Xiaofei Xie, Yuan Zhou, Lei Ma

Research Collection School Of Computing and Information Systems

Testing Autonomous Driving Systems (ADSs) is crucial for ensuring their safety, reliability, and performance. Despite numerous testing methods available that can generate diverse and challenging scenarios to uncover potential vulnerabilities, these methods often treat ADS as a black-box, primarily focusing on identifying system-level failures like collisions or near-misses without pinpointing the specific modules responsible for these failures. This lack of root causes understanding for the failures hinders effective debugging and subsequent system repair. Furthermore, current approaches often fall short in generating violations that adequately test the individual modules of an ADS from a system-level perspective, such as perception, prediction, planning, …

Demystifying Memorization In Llm-Based Program Repair Via A General Hypothesis Testing Framework, Jiaolong Kong, Xiaofei Xie, Shangqing Liu

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have achieved remarkable success in various applications, particularly in code-related tasks such as code generation and program repair, setting new performance benchmarks. However, the extensive use of large training corpora raises concerns about whether these achievements stem from genuine understanding or mere memorization of training data—a question often overlooked in current research. This paper aims to study the memorization issue within LLM-based program repair by investigating whether the correct patches generated by LLMs are the result of memorization. The key challenge lies in the absence of ground truth for confirming memorization, leading to various ad-hoc methods …

Hd-Epic: A Highly-Detailed Egocentric Video Dataset, Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha, Omar Emara, Sam Pollard, Kranti Kumar Parida, Kaiting Liu, Prajwal Gatti, Siddhant Bansal, Kevin Flanagan, Jacob Chalk, Zhifan Zhu, Rhodri Guerrier, Fahd Abdelazim, Bin Zhu, Davide Moltisanti, Michael Wray, Hazel Doughty, Dima Damen

Research Collection School Of Computing and Information Systems

We present a validation dataset of newly-collected kitchenbased egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations. Importantly, all annotations are grounded in 3D through digital twinning of the scene, fixtures, object locations, and primed with gaze. Footage is collected from unscripted recordings in diverse home environments, making HDEPIC the first dataset collected in-the-wild but with detailed annotations matching those in controlled lab environments. We show the potential of our highly-detailed annotations through a challenging VQA benchmark of 26K questions assessing the capability to …

Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu

Research Collection School Of Computing and Information Systems

Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision …

Collaborative Tree Search For Enhancing Embodied Multi-Agent Collaboration, Lizheng Zu, Lin Lin, Song Fu, Na Zhao, Pan Zhou

Research Collection School Of Computing and Information Systems

Embodied agents based on large language models (LLMs) face significant challenges in collaborative tasks, requiring effective communication and reasonable division of labor to ensure efficient and correct task completion. Previous approaches with simple communication patterns carry erroneous or incoherent agent actions, which can lead to additional risks. To address these problems, we propose Cooperative Tree Search (CoTS), a framework designed to significantly improve collaborative planning and task execution efficiency among embodied agents. CoTS guides multi-agents to discuss long-term strategic plans within a modified Monte Carlo tree, searching along LLMdriven reward functions to provide a more thoughtful and promising approach to …

Modfinity: Unsupervised Domain Adaptation With Multimodal Information Flow Intertwining, Shanglin Liu, Jianming Lv, Jingdan Kang, Huaidong Zhang, Zequan Liang, Shengfeng He

Research Collection School Of Computing and Information Systems

Multimodal unsupervised domain adaptation leverages unlabeled data in the target domain to enhance multimodal systems continuously. While current state-of-the-art methods encourage interaction between sub-models of different modalities through pseudo-labeling and feature-level exchange, varying sample quality across modalities can lead to the propagation of inaccurate information, resulting in error accumulation. To address this, we propose Modal-Affinity Multimodal Domain Adaptation (MODfinity), a method that dynamically manages multimodal information flow through fine-grained control over teacher model selection, guiding information intertwining at both feature and label levels. By treating labels as an independent modality, MODfinity enables balanced performance assessment across modalities, employing a novel …

Towards Uncertainty Aware Task Delegation And Human-Ai Collaborative Decision-Making, Min Hun Lee, Martyn Zhe Yu Tok

Research Collection School Of Computing and Information Systems

Despite the growing promise of artificial intelligence (AI) in supporting decision-making across domains, fostering appropriate human reliance on AI remains a critical challenge. In this paper, we investigate the utility of exploring distance-based uncertainty scores for task delegation to AI and describe how these scores can be visualized through embedding representations for human-AI decision-making. After developing an AI-based system for physical stroke rehabilitation assessment, we conducted a study with 19 health professionals and 10 students in medicine/health to understand the effect of exploring distance-based uncertainty scores on users’ reliance on AI. Our findings showed that distance-based uncertainty scores outperformed traditional …

A Knowledge Enhanced Large Language Model For Bug Localization, Yue Li, Bohan Liu, Ting Zhang, Zhiqi Wang, David Lo, Lanxin Yang, Jun Lyu, He Zhang

Research Collection School Of Computing and Information Systems

A significant number of bug reports are generated every day as software systems continue to develop. Large Language Models (LLMs) have been used to correlate bug reports with source code to locate bugs automatically. The existing research has shown that LLMs are effective for bug localization and can increase software development efficiency. However, these studies still have two limitations. First, these models fail to capture context information about bug reports and source code. Second, these models are unable to understand the domain-specific expertise inherent to particular projects, such as version information in projects that are composed of alphanumeric characters without …

Human-Computer Interaction And Artificial Intelligence For Ageing Population, Keng Siau, Hailiang Wang, Fiona Fui-Hoon Nah, Runyu Wang, Ruitong Che, Can Liu

Research Collection School Of Computing and Information Systems

As the global population ages rapidly, the field of human-computer interaction (HCI) is in urgent need of innovation, redesign, and reengineering to meet the evolving needs of older adults. The older demographic faces a range of challenges—including physical limitations, cognitive decline, reduced social in-tegration, and varying levels of technological literacy—that can hinder effective engagement with digital technologies. In response to these challenges, research-ers and designers are using inclusive and adaptive approaches to enhance acces-sibility, usability, and emotional well-being. This paper reviews key design prin-ciples in HCI for the ageing population and discusses how artificial intelligence (AI) tools, such as voice …