Computer Sciences Commons^™

Towards Generalizable Representation Learning Across Domains, Hossein Kashiani

All Dissertations

Despite remarkable progress in deep learning, a major challenge remains: machine learning models often struggle to generalize to unseen domains under distribution shift. In real-world settings, data often differ from training conditions due to changes in lighting, sensor type, image resolution, and style. These differences can significantly degrade performance, highlighting the need for representations that are both robust and generalizable. This thesis addresses this challenge by developing a set of frameworks for generalization across domains in anomaly detection, deepfake detection, and vision-language image recognition. For anomaly detection, this thesis introduces ROADS, a robust prompt-driven framework for multi-class unified anomaly detection. …

Adversarial Heterogeneous Agent Learning For Robotic Systems: A Framework For Coordinated Competitive Behaviors, Christopher T. Allred

All Graduate Theses and Dissertations, Fall 2023 to Present

Autonomous robot teams must move reliably, explain their actions, and work together—even in changing or adversarial settings. We present a structured, three-stage pathway that builds coordinated team behavior from strong single-robot skills. First, we develop robust legged-robot locomotion and interpretability using only internal actuator signals. From these proprioceptive cues, robots learn to classify terrain and predict short-term power use, enabling energy-aware movement without external sensors. We further analyze learned behaviors with motif discovery to reveal recurring sensor–action patterns, which clarify how agility emerges and guide reward design. Second, we compose these skills into heterogeneous teamwork using centralized training with decentralized …

Large Language Models For Introductory Computer Science Education: Content Generation, Intelligent Tutoring, And Learner Modeling, Muhammad Fawad Akbar Khan

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation studies how artificial intelligence, especially large language models such as GPT, can help students learn introductory computer programming when the models are used inside a carefully designed learning system. Instead of focusing on AI as a standalone tool, the dissertation follows a connected story: generating learning resources, building a tutoring platform, running a user study, and then analyzing how students behave while they program.

The work first uses prompt engineering to create a large collection of 11,700 Python exercises aligned with introductory computer science topics. Students and instructors then evaluate these exercises to check whether they are clear, …

A Survey On Digital Reading Materials And Personal Study Of Christian Religious Texts, Teancum Price

All Graduate Theses and Dissertations, Fall 2023 to Present

Religion, including reading from religious texts such as scriptures, are a part of the daily lives of many people. Modern technology has influenced the way that this religious reading takes place, but its effects have not yet been studied. Existing research of the effects of technology on reading focus on topics such as reading comprehension, but the study of religious texts is often focused on achieving a religious experience, so the existing research does not capture the whole scope of these changes. In our study, we surveyed two universities (Utah State University and Abilene Christian University) to ask individuals how …

Sevoauth: Secure Voiceprint Authentication With Hash-Based Feature Transformation, Rui Zhang, Zheng Yan, Robert H. Deng

Research Collection School Of Computing and Information Systems

While voiceprint authentication offers convenient user authentication and access control through voice feature recognition, a critical research gap remains: existing voiceprint authentication systems fail to simultaneously achieve sound security against replay, spoofing, and adversarial attacks, preserve voice privacy leakage, and satisfy usability demand. Previous efforts have struggled to balance these issues comprehensively. To bridge this gap, we present SeVoAuth, a cloud-based Voiceprint Authentication as a Service (VAaaS) system designed to provide privacy preservation, robust security, and enhanced usability. SeVoAuth stores a synthesized voiceprint of a user in the cloud during user registration, thereby safeguarding the privacy of the real voiceprint …

Mease: Multi-Agent Episodic Action Sequence Explanation, Phyo Wai Khaing, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning (MARL) achieves remarkable performance in complex coordination tasks, yet interpreting the emergent behaviors of trained agents remains a fundamental challenge. Most current explainability methods focus on individual agent decisions, overlooking the critical interplay of joint strategiesand temporal coordination patterns that define successful multi-agent policies. We present MEASE (Multi-agent Episodic Action Sequence Explanation), a novel explainable MARL (XMARL) framework that explains trained MARL policies as human-interpretable emergent cooperative joint behaviors. MEASE employs a cognition-inspired episodic memory model to learn spatio-temporal multi-agent interaction patterns, coupled with abstraction algorithms that identify significant cooperative agent behaviors. We evaluate MEASE on diverse …

Synthesis And Evaluation Of Long-Term History-Aware Medical Dialogue, Hebin Hu, Renke Dai, Ah-Hwee Tan, Yilin Kang

Research Collection School Of Computing and Information Systems

An effective healthcare agent must be able to recall and reason over a patient’s longitudinal medical history. However, the absence of datasets with realistic long-term dialogue timelines limits systematic evaluation. Real clinical text is constrained by privacy and ethics, while existing benchmarks focus on isolated interactions, failing to capture cross-session reasoning. We introduce a framework for synthesizing high-quality, long-term medical dialogues with LLMs. Our approach entails a knowledge-guided decomposition into three stages: constructing synthetic patient profiles with diverse disease and complication trajectories, generating multiturn dialogues per encounter, and integrating them into a coherent longitudinal history dataset, MediLongChat. We establish three …

Collaborative Practices And Tool Utilization In Software Development Projects: A Student Perspective, Yi Meng Lau, Muhammad Syahmi Bin Abbas, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Software development is a collaborative activity that depends on effective teamwork, shared understanding, and coordinated use of development practices and tools. While these aspects are well studied in professional environments, they are less frequently examined within software engineering education. This study investigates how students collaborate in group projects, focusing on collaborative practices, tool usage, and their perceptions of software quality. We conducted a quantitative post-project survey with 143 second-year undergraduate students enrolled in a software development course. The results show that students actively share information and often establish team norms to support coordination and collaboration. However, students face challenges in …

Causality-Driven Test Case Minimisation For Cyber-Physical Systems, Michael Foster, Christopher M. Poskitt, Nicholas R. Latimer, Neil Walkinshaw, Richard Somers, Robert M. Hierons

Research Collection School Of Computing and Information Systems

Cyber-physical systems allow digital control systems to interact with the physical world using sensors and actuators. They are increasingly being used to automate critical infrastructure, where software faults can have dire consequences. Due to the complex nature and unpredictability of these systems, their resilience is often tested using a technique called fuzzing, which generates quasi-random sequences of sensor and actuator manipulations with the goal of forcing a system into unsafe states. However, there is currently no way of determining which manipulations of a test case cause a failure without systematically removing each one and re-running the test, which can be …

Detecting Doubt In Reflective Learning: A Learning Analytics Study With Large And Small Language Models, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Reflective learning enhances understanding, especially when instructors promptly address difficulties raised in student reflections. Automated doubt detection can reduce time for instructors, yet existing classification approaches take substantial time for manual annotation and model training. This paper investigates whether large and small language models (LLMs, SLMs) can automate doubt detection without time-consuming training. Using a dataset of anonymized student reflections, we evaluate zeroshot, few-shot prompting, and multi-step reasoning against prior supervised classification baselines. We show that LLMs (GPT-4o, Claude-4, Gemini-2.5) surpass earlier F1 scores without prompting, while prompting further improves their performance. However, using proprietary LLMs can raise cost and …

Gencode: A Generic Data Augmentation Framework For Boosting Deep Learning-Based Code Understanding, Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code data that automatically helps developers prepare training data lacks study in this field. In this paper, we introduce a generic data augmentation framework, GenCode, to enhance the training of code understanding models. Simply speaking, GenCode follows a generation-and-selection paradigm to prepare useful training code data. Specifically, it employs code augmentation techniques to generate new code candidates first and then identifies important ones as the training data by influence scores. To evaluate the effectiveness of GenCode, we …

Understanding Critical Thinking In Generative Artificial Intelligence Use: Development, Validation, And Correlates Of The Critical Thinking In Ai Use Scale, Gabriel R. Lau, Wei Yan Low, Louis Tay, Ysabel Thereze Ang Guevarra, Dragon Gašević, Andree Hartanto

Research Collection School of Social Sciences

Generative AI tools are increasingly embedded in everyday work and learning, yet their fluency, opacity, and propensity to hallucinate mean that users must critically evaluate AI outputs rather than accept them at face value. The present research conceptualises critical thinking in AI use as a dispositional tendency to verify the source and content of AI-generated information, to understand how models work and where they fail, and to reflect on the broader implications of relying on AI. Across six studies ( N = 1341), we developed and validated the 13-item critical thinking in AI use scale and mapped its nomological network. …

A Generative Ai Method For Minority Class Handling In Anomaly Detection With Drift And Explainability Analysis, Kelvin J. Mwiga, Mussa A. Dida, Ahmad Mohsin, Iqbal H. Sarker

Research outputs 2022 to 2026

Artificial Intelligence, particularly machine learning (ML) algorithms, plays a crucial role in detecting cyberattacks, including anomalies and intrusions. However, machine learning models trained on imbalanced cybersecurity datasets often struggle to accurately detect minority data instances and potential threats, thereby weakening overall system security. Despite extensive research, a persistent challenge is the inadequate explanation for model predictions concerning minority data classes. This study aims to address these limitations by developing a generative AI-based approach to manage minority classes in anomaly detection, incorporating concept drift handling and explainability analysis. We introduce an over-sampling technique, CGGReaT, designed to enhance the presence of minority …

A Survey Of Privacy-Preserving Federated Learning For Intrusion Detection Systems, Thomas Bunko, Michael N. Johnstone, Wencheng Yang, Ben A. Scott

Research outputs 2022 to 2026

Intrusion detection systems (IDS) monitor and detect malicious activity and unauthorized access that may compromise systems. Traditional IDS approaches send data to a central server for analysis, raising privacy concerns as data owners lose control over security. Federated Learning (FL) offers a privacy-preserving alternative by allowing local devices to process their data and generate models without sharing raw data. These local models are aggregated centrally to form a comprehensive model with performance comparable to centralized systems. This paper reviews FL-based IDS research, and is the first review paper to focus on privacy-preserving techniques collectively known as privacy-preserving Federated Learning (PPFL) …

A Multi-Stage Framework With Taxonomy-Guided Reasoning For Occupation Classification Using Large Language Models, Palakorn Achananuparp, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Automatically annotating job data with standardized occupations from taxonomies, known as occupation classification, is crucial for labor market analysis. However, this task is often hindered by data scarcity and the challenges of manual annotations. While large language models (LLMs) hold promise due to their extensive world knowledge and in-context learning capabilities, their effectiveness depends on their knowledge of occupational taxonomies, which remains unclear. In this study, we assess the ability of LLMs to generate precise taxonomic entities from taxonomy, highlighting their limitations, especially for smaller models. To address these challenges, we propose a multi-stage framework consisting of inference, retrieval, and …

Llmaide: Language-Assisted Neural Solver For Vehicle Routing Problems, Manuj Malik, Jianan Zhou, Yan Jin, Zhiguang Cao

Research Collection School Of Computing and Information Systems

We propose a novel approach to Vehicle Routing Problems (VRPs) that integrates the strengths of neural spatial embeddings with the semantic understanding of large language models (LLMs). While traditional neural methods excel in structured routing, they typically ''feel'' constraints through rigid mechanisms like feasibility masking, limiting their expressiveness. Conversely, LLMs demonstrate robust semantic capabilities but lack the spatial reasoning essential for precise routing optimization. To address these gaps, we introduce a hierarchical, multi-scale fusion architecture that integrates LLM-derived semantics with spatial routing features. Our contributions include: (1) a scale-aware decomposition aligning LLM features with spatial representations, (2) a bidirectional cross-modal …

Dragon: Llm-Driven Decomposition And Reconstruction Agents For Large-Scale Combinatorial Optimization, Shengkai Chen, Zhiguang Cao, Jianan Zhou, Yaoxin Wu, Senthilnath Jayavelu, Zhuoyi Lin, Xiaoli Li, Shili Xiang

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently shown promise in addressing combinatorial optimization problems (COPs) through prompt-based strategies. However, their scalability and generalization remain limited, and their effectiveness diminishes as problem size increases, particularly in routing problems involving more than 30 nodes. We propose DRAGON, which stands for Decomposition and Reconstruction Agents Guided OptimizatioN, a novel framework that combines the strengths of metaheuristic design and LLM reasoning. DRAGON autonomously identifies regions in initial solution with high optimization potential and strategically decompose large-scale COPs into manageable subproblems. Each subproblem is then reformulated as a concise, localized optimization task and solved through targeted …

Pyvrp+: Llm-Driven Metacognitive Heuristic Evolution For Hybrid Genetic Search In Vehicle Routing Problems, Manuj Malik, Jianan Zhou, Shashank Reddy Chirra, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Designing high-performing metaheuristics for NP-hard combinatorial optimization problems, such as the Vehicle Routing Problem (VRP), remains a significant challenge, often requiring extensive domain expertise and manual tuning. Recent advances have demonstrated the potential of large language models (LLMs) to automate this process through evolutionary search. However, existing methods are largely reactive, relying on immediate performance feedback to guide what are essentially black-box code mutations. Our work departs from this paradigm by introducing Metacognitive Evolutionary Programming (MEP), a framework that elevates the LLM to a strategic discovery agent. Instead of merely reacting to performance scores, MEP compels the LLM to engage …

Multi-Level Graph Partition Via Hierarchical Learning For Large-Scale Vehicle Routing Problems, Yuxin Pan, Ruohong Liu, Yize Chen, Zhiguang Cao, Fangzhen Lin

Research Collection School Of Computing and Information Systems

Vehicle Routing Problems (VRPs) involve multi-agent route optimization, with the objective of targeting optimal routes for a fleet of vehicles to serve a set of customers. Existing neural solvers based on the divide-and-conquer approach for VRPs in general, and capacitated VRP (CVRP) in particular, integrate the global partition of an instance with the local construction for each resulting subinstance to enhance generalization. However, during the global partition phase, misclusterings within subgraphs have a tendency to progressively compound throughout the multi-step decoding process of the learning-based partition policy. This suboptimal behavior of the partition policy, in turn, may lead to a …

Advancing Context-Aware Detection Of Socially Harmful Discourse Using Transformer-Based Models, Santosh Chapagain

All Graduate Theses and Dissertations, Fall 2023 to Present

Social media platforms are a central part of modern communication, shaping how people share ideas, build communities, and discuss social issues. While these spaces can support connection and self expression, they also enable the spread of harmful language such as hate speech. At the same time, social media is an important place where members of marginalized communities, including sexual and gender minorities, express stress, discrimination, and emotional challenges in ways that are often indirect and context dependent.

This research examines whether modern artificial intelligence systems can better identify harmful language and expressions of minority stress in online posts. The study …

How Novices Write Code: Discovering Best Practices, Matt Rau

All Graduate Theses and Dissertations, Fall 2023 to Present

Learning to program is a difficult endeavor, leading to chronically high failure rates in introductory programming courses. One thing that makes teaching programming difficult is that we don’t fully understand what problem solving habits and writing strategies separate successful programmers from struggling ones. Knowing how to teach these habits to a new programmer is an equally difficult challenge,

Studying the way people write code has proved difficult. Until recently, there was very little relevant publicly available data, and no good ways to analyze student programming behavior at a large scale. In this thesis, I address both issues. I publish a …

St-Fmformer: An Autoregressive Generation Framework For Scientific Ensemble Data Predictions, Md Robiul Islam

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding how physical systems change over time is important in areas such as weather prediction, fluid dynamics, and environmental science. However, accurately predicting future behavior is difficult because these systems are complex and constantly evolving. 

This research develops a deep learning approach to predict how such systems evolve over time. The model learns patterns from past observations and uses them to generate future states step by step. This provides a faster alternative to traditional simulation methods while maintaining strong predictive performance. 

The proposed method focuses on improving the consistency of predictions over time and is designed to work across different …

An Alternative Representation For Temporal Json, Bishal Sarkar

All Graduate Theses and Dissertations, Fall 2023 to Present

JavaScript Object Notation (JSON) is a common format for representing and exchanging data on the web. Most systems only keep the current version of a JSON document, even though, in many situations, it is also important to know how that document changed over time. For example, an application might need to answer questions such as “What did this record look like last week?” or “How has this list grown over the past year?” A simple way to keep this history is to store a full copy of the document every time it changes, but this quickly becomes wasteful, most of …

Post-Vote Tampering In Nigerian Elections And The Role Of Blockchain-Enabled Electoral Systems, Ransome Chukwubuikem Enechukwu

Electronic Theses and Dissertations

Post-vote tampering during the collation and transmission of election results remains a persistent challenge in Nigerian elections, enabling manipulation of already-cast votes and weakening public trust in electoral outcomes. Existing technological interventions, including biometric voter accreditation and digital result transmission systems, improve voter authentication but do not adequately secure the post-vote result collation process. This thesis proposes a blockchain-enabled framework designed to protect the integrity of election results during the collation and transmission stages. Using a Design Science Research methodology, the study develops a permissioned blockchain framework based on Hyperledger Fabric that records polling-unit results as immutable ledger entries and …

Empowering Sustainable Household Waste Management Through Rubbin: App-Based Transactions Using Google Maps Api And Qr Code Recognition, H.A. Danang Rimbawa, Muhammad Abditya Arghanie, Muhammad Rey Renoult, Dea Dwi Ananda

Smart City

The Digitization of environmentally friendly technology must be applied to advance smart cities. Waste management carried out conventionally often causes irregularities in the classification data collection, difficulties in accessing information related to garbage collection schedules, and a lack of transaction history information, which causes a decrease in the quality of waste collection. The development of functional application features is urgently needed as a container for proper waste management. The Rubbin App has been created, which implements Google Maps API for route optimization and waste mapping, QR code recognition using Enhanced Adaptive Median Filter, and private chat for clients and collectors …

Dementia Detection In Low-Resource Languages: Evaluating Translation-Assisted Transfer Learning For Multilingual Clinical Assessment, Kylar A. Deloach

Honors Theses

Alzheimer's disease (AD) is a growing global health concern, with millions of people affected worldwide and cases expected to rise significantly in the coming decades. Early detection is critical for patient treatment and care, and recent advances in natural language processing (NLP) have shown promise in identifying linguistic markers associated with AD. However, most existing work has focused on English, leaving speakers of other languages with limited access to such tools. This study investigates how effective AD detection models trained on English data are at transferring to Greek, a low-resource language with limited dementia-related speech data available. We propose a …

It's Not Nde Without U And X: Preparing For Change With Inclusive Research, Sanjeet Mann, Heather L. Cribbs

Library Faculty Publications & Presentations

The upcoming Next Discovery Experience (NDE) introduces major changes to how users search, interpret information, and navigate Primo. Preparing for NDE is an opportunity to center the diverse students and faculty who rely on discovery systems every day, ensuring that their lived experiences, accessibility needs, and research practices guide interface design, configuration, and communication. This session presents a consortial approach to NDE readiness that positions students and community members as partners in the development process. We describe strategies for creating ethical and rigorous UX research workflows that include IRB approval, purposeful recruitment, accessible study design, and clear documentation on the …

Enhanced Parametric Approach For Solving Interval-Valued Trapezoidal Neutrosophic Linear Fractional Programming Problems, Hamiden Abd El- Wahed Khalifa H.A.Khalifa, Moodi Abdulrahman Abdullah Al-Rajeh, Sultan S. Alodhaibi

Neutrosophic Systems with Applications

Neutrosophic sets (NSs) generalize the classical versions, by providing a flexible framework capable of representing incomplete, inconsistent, and unclassified data that frequently arises in practical decision frameworks. In this study, a linear fractional programming (LFP) problem with uncertain parameters is investigated. All coefficients in the objective function (OF) as well as the left- and right-hand sides of the constraints are represented using fully trapezoidal neutrosophic numbers (NNs). By employing a suitable score function, the proposed neutrosophic LFP model is transformed into an equivalent scalar LFP problem. Subsequently, a parametric solution procedure is established to regulate the neutrosophic optimum solution. This …

Gauss Elimination Method For Solving The System Of Neutrosophic Linear Equations, Elsayed Badr, Shokry Nada, Saeed Ali, Aya Rabie

Neutrosophic Systems with Applications

This paper proposes a unified computational framework for solving linear systems under trapezoidal Neutrosophic uncertainty. The system is formulated as A( I )x = b( I ), where both the coefficient matrix and the right-hand side vector incorporate an indeterminacy parameter I, expressed as A( I ) = A₀ + IA₁ and b( I ) = b₀ + Ib₁. A decomposition strategy is developed to separate the model into deterministic and indeterminacy components, yielding a solution of the form x( I ) = x₀ + Ix₁. The deterministic component is obtained via …

Ai Literacy: An Annotated Oer Bibliography, Houy Yvonne

UNLV Best Teaching Practices Expo

"Scalable, discipline-agnostic AI literacy instruction can be implemented incrementally without requiring full course redesign in higher education, supporting both technical understanding and critical engagement with the social and ethical dimensions of AI: The curated list of open educational resources (OER) on this poster enable a flexible, modular approach to teaching foundational AI literacy. The annotated list includes self-paced, hands-on projects with complementary educator-guided activities and discussion to support conceptual understanding of machine learning, training data, and algorithmic bias, drawing on OER such as Code.org’s AI curriculum, MIT RAISE’s Day of AI, and the multi-lingual Elements of AI course. Many resources …