Computer Sciences Commons^™

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 …

Datapeer: A Hybrid Interaction Model Web Application Integrating Llms For Human–Llm Collaboration In Data Analysis And Exploration, William I. Bumcum

Honors Theses

DataPeer is a web application that combines open-ended high agency (OHA) and structured low-agency (SLA) interaction paradigms to support human-LLM collaboration in exploratory data analysis. DataPeer integrates a large language model (LLM) with a React frontend and FastAPI backend, allowing both qualitative and quantitative analysis of user-uploaded CSV datasets through a chat-based interface. Users can attach datasets and provide natural language queries, while the LLM provides data analysis to the user. This thesis investigates the integration of LLMs into data analysis workflows and addresses gaps in interaction design and user agency in LLM-driven data tools by offering a responsive interface …

Modeling Causal Interactions Across Brain Functional Systems For Population-Specific Disease Analysis, Alissen Moreno

Theses and Dissertations

Functional brain connectivity provides critical insight into neural mechanisms underlying neurodegenerative and affective disorders. Traditional neuroimaging studies rely on undirected or region-specific connectivity measures developed predominantly using racially homogeneous cohorts, limiting fairness and generalizability across diverse populations.

We propose a population-aware framework modeling directed causal interactions across large-scale brain functional subnetworks. Resting-state fMRI data from the HABS-HD cohort were used to construct subject-level causal connectivity matrices via ICA-LiNGAM, aggregated into interpretable system-level hyper-connectomes representing interactions among eleven canonical brain subsystems. These features trained nonlinear models for Alzheimer’s disease stage classification and trait worry prediction.

Results demonstrate that MLP models outperform …

Adaptive Artificial Potential Field Guidance And Control For Autonomous Docking With Uncooperative And Unknown Spacecraft, Steven Holmberg

Theses and Dissertations

The increasing demand for on-orbit servicing (OOS), active debris removal (ADR), and space domain awareness (SDA) missions has increased the need for autonomous spacecraft rendezvous and proximity operations (RPO) with uncooperative and unknown targets. Traditional guidance and control methods are typically designed for cooperative systems with known geometry and state information. This work builds on previous research to develop and evaluate an artificial potential field (APF)-based control framework capable of autonomous operation with minimal prior target knowledge and applicability to both relatively static and tumbling spacecraft.

The proposed APF formulation incorporates established safety constructs from cooperative docking systems, including an …

Moodify: A Mood-Based Music Recommendation System, Meghana Kagitha

Theses and Dissertations

Music has long been recognised as a powerful tool for emotional regulation, yet existing music streaming platforms often fail to align song recommendations with a user's current emotional state. Moodify is a mood-based music recommendation system designed to bridge this gap by delivering personalised playlists that reflect how a user feels in real time.

This project presents the design, development, and evaluation of Moodify, a mobile application that leverages the Circumplex Model of Emotion to capture user mood through an intuitive two-dimensional valence-arousal interface. Rather than relying on text input or manual search, users plot their emotional state directly onto …

Agentcite: Trustworthy Ai Agents For Information Verification Across Referenced Documents, Eymen Yigit

Master's Theses

This thesis presents AgentCite, a multi-agent framework for automated verification of referenced numerical data in research documents. The framework employs a hybrid design in which LLM-based agents handle document understanding and evidence retrieval, while deterministic components manage structured parsing and value comparison, improving consistency and reproducibility while reducing token consumption and execution time compared to fully agentic approaches.

AgentCite consists of three autonomous agents: a Negotiator, a Main Document Agent, and a Source Documents Agent, coordinated through a fixed tool-call pipeline. The Negotiator orchestrates verification by extracting tabular data from the main document, retrieving evidence from per-source vector stores, and …

Probing Representational Emergence In Large Language Models, Shawn Ismail

Master's Theses

This thesis investigates whether abrupt behavioral gains in large language models under scaling are accompanied by systematic changes in internal representations. It combines a behavioral screen of 65 tasks per family with targeted layerwise probing across eight decoder-only, open-weight model families. Behavioral emergence is defined for each family-task trajectory using an empirical jump detector, with segmented regression retained only as a diagnostic. The representational follow-up analyzes 27 selected MMLU subtasks shared across all families, spanning 37 checkpoints and 216 family-task units.

For each follow-up checkpoint, frozen linear probes are trained on every layer's hidden states to measure how much task-relevant …

Federated Neuromorphic Intelligence: Advancing Robustness, Efficiency, And Continual Adaptation In Edge Environments, Manh V. Nguyen

Dissertations

This dissertation investigates how spiking neural networks (SNNs) can improve federated edge intelligence by advancing three interconnected goals: communication efficiency, adversarial robustness, and continual adaptation. As edge computing deployments expand across Internet of Things (IoT), sensing, and privacy-sensitive applications, conventional federated learning approaches built around artificial neural networks (ANNs) face growing limitations in power consumption, bandwidth demand, and resilience to real-world uncertainty. SNNs offer an alternative computational paradigm based on event-driven, sparse, and temporally structured processing that is naturally suited to constrained edge environments. However, their behavior in practical federated settings remains insufficiently understood.

To address this gap, this dissertation …

A Webcam Eye Tracking Infrastructure For Software Engineering Tasks, Zachary M. Kozak

School of Computing: Dissertations, Theses, and Student Research

Performing eye tracking utilizing commodity webcams has been explored for over a decade, but limited camera quality and sensitivity to head movements have hindered its adoption in research settings. Recent advances in consumer-grade webcams and machine learning methods present an opportunity to improve the accuracy of webcam eye tracking and extend the feasibility of studies beyond controlled laboratory environments.

Current popular webcam eye tracking methods restrict implementations to the browser and rely on continuous user interactions for calibration, limiting the kinds of studies that can be conducted. This thesis presents a feature-based gaze prediction system that incorporates eye geometry and …

Leveraging Code Embeddings To Identify And Address Blind Spots In Benchmark Creation, Charles Moloney

School of Computing: Dissertations, Theses, and Student Research

Formal software verification remains critical for early vulnerability detection, yet benchmarking these tools is costly and often reliant on centralized datasets such as SV-COMP. While such repositories enable standardized evaluation, they introduce risks of overfitting and bias, particularly due to first-party benchmark contributions. To address these limitations, we extend ARG-V, our tool for generating SV-COMP-compatible benchmarks from real-world Java code, with a novel approach of using code embedding techniques to selectively sample from mined code. By leveraging Nomic Embed Code and a cosine-based Minimum Hyperspherical Energy (MHE) objective, we systematically select and transform benchmarks from scraped GitHub code that …

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 …

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 …

Market Reactions To Deceptive Language In Fake News: Implications From Language Expectancy Theory And Transfer Learning, Ka Chung Ng, Ping Fan Ke, Ping Fan, Mike So, Tam, Kar Yan

Research Collection School Of Computing and Information Systems

The advent of generative artificial intelligence (AI) has heightened the proliferation of fake news. A key challenge is the limited real-world data to investigate the societal impact of fake news produced by generative AI. In this paper, we examine stock market reactions to financial news articles that exhibit stylometric similarity to human-crafted and AI-crafted fake financial news. Grounded in language expectancy theory, we employ a style-based transfer learning model, pre-trained to recognizing deceptive language employed in various types of fake news intricacies. We then apply this model to a comprehensive dataset of financial news, assigning a “veracity style score” to …

Fighting Against Recruitment Scams: Theory-Driven Supervised Learning And Empirical Analysis For Digital Fraudulent Recruitment Posting Behavior, Tom (Tianteng) Wang, David (Jingjun) Xu, Keng Siau, Zhongju (John) Zhang

Research Collection School Of Computing and Information Systems

The number of recruitment postings on digital recruitment hiring platforms has increased since the COVID-19 pandemic. However, the weak surveillance and operations of these platforms, combined with the fact that most job seekers have relatively low vigilance and a strong desire for recruitment offers, enable scammers to easily deceive job seekers for their money and confidential information. In this work, we combine prevailing text mining techniques (i.e., ChatGPT with prompting engineering and supervised machine learning) with interpersonal deception theory (IDT) from social science to design an interpretable IT system to predict fraudulent recruitment postings on digital recruitment-hiring platforms. We compare …

Enhancing Action And Ingredient Modeling For Semantically Grounded Recipe Generation, Guoshan Liu, Bin Zhu, Yian Li, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in Multimodal Large Language Models (MLMMs) have enabled recipe generation from food images, yet outputs often contain semantically incorrect actions or ingredients despite high lexical scores (e.g., BLEU, ROUGE). To address this gap, we propose a semantically grounded framework that predicts and validates actions and ingredients as internal context for instruction generation. Our two-stage pipeline combines supervised fine-tuning (SFT) with reinforcement fine-tuning (RFT): SFT builds foundational accuracy using an Action-Reasoning dataset and ingredient corpus, while RFT employs frequency-aware rewards to improve long-tail action prediction and ingredient generalization. A Semantic Confidence Scoring and Rectification (SCSR) module further filters and …

Selective Concolic Testing, Guofeng Zhang, Zhenbang Chen, Ziqi Shuai, Jun Sun, Weijiang Hong, Yufeng Zhang, Ji Wang, Yang Liu

Research Collection School Of Computing and Information Systems

The principled combination of symbolic execution and random testing lacks a formal foundation, especially in deciding which inputs to symbolize. We propose selective concolic testing, a cost-aware framework that formulates this choice as an optimized policy problem of a MDP (Markov Decision Process). We model program exploration over a finite control-flow graph, where MDP states represent covered statements, actions partition path constraints into symbolic and random fragments, rewards reflect coverage gain, and costs account for SMT solving effort and sampling inefficiency. Our framework yields the first formal characterization of selective symbolization as policy synthesis in a probabilistic system. We prove …

Conditioning Hierarchical Diffusion Transformers On Rf Circuit Parameters, Ethan Morton

McKelvey School of Engineering Graduate Student Theses & Dissertations

As modern ML techniques have become increasingly advanced, they have begun to be integrated into wireless RF systems for classification, identification, and spectrum management. Deep Neural Networks (DNNs) enable RF system operators and designers to design more flexible systems with greater robustness to errors and attacks. However, neural networks require significant amounts of properly annotated data to train. Current data labeling methods lack the ability to obtain reliable true labels for circuit properties such as carrier frequency offset (CFO), power amplifier (PA) non-linearity, and in-phase/quadrature (IQ) imbalance. This thesis investigates the efficacy of a novel architecture, RF-Diffusion, for generating high-quality …

Hdra-Fusion: Hybrid Detection With Routed Architecture For Manipulation-Aware Ai Face Forgery Detection, Omar Ebeid

Theses and Dissertations

With the rapid advancements in artificial intelligence-based image generation and manipulation tools, it is extremely difficult to detect if an image is genuine or artificially crafted. Despite extensive research in this area, existing image detection systems suffer from three major problems: suboptimal cross-dataset generalization due to shortcut learning of dataset-specific patterns, unreliable probability estimates due to domain shift, particularly in cross-manipulation evaluation settings, and an inability to detect images manipulated by multiple types of manipulations within a single detection framework. To address these limitations, we propose HDRA-Fusion (Hybrid Detection with Routed Architecture), a framework built on the conclusion that different …

Enhancing Control Charting Schemes And Exploring New Assessment Metrics To Advance Quality Control And Cyber-Attack Detection In Manufacturing, Ahmad Al Majali

Dissertations

The increasing integration of digital technologies and industrial control systems in modern manufacturing has introduced new cybersecurity vulnerabilities within cyber–physical production environments. Malicious actors can exploit these vulnerabilities to induce subtle process deviations that degrade product quality while remaining undetected by conventional statistical monitoring tools. Such attacks can be deliberately engineered to manipulate process behavior through transient shifts that vary in magnitude, duration, and frequency. Despite extensive research on transient shifts caused by assignable causes in Statistical Process Control (SPC), limited attention has been given to intelligently designed cyber–physical attacks that exploit the structural characteristics and limitations of control charting …

Student Programming Behavior With And Without Phone Notification Suppression, Gavin T. Eddington

All Graduate Theses and Dissertations, Fall 2023 to Present

Many students work on programming assignments while receiving notifications from their phones, such as text messages or social media alerts. These notifications can interrupt focus and make it harder to stay engaged with a task. This study examines whether silencing phone notifications helps students stay more focused while programming. 

We collected data from students in an introductory computer science course while they worked on programming assignments. Students completed some assignments with notifications silenced and others without. We measured their activity using software that records typing behavior and identifies when students take long pauses, which can indicate interruptions or loss of …

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, …

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 …

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) …

Augmented Reality In Fashion Retail: A Walmart Unlimited Study, Chloe A. Mcpherson

Apparel Merchandising and Product Development Undergraduate Honors Theses

As technology continues to evolve, augmented reality (AR) has become increasingly common within the retail and fashion industries. This study explored Gen Z consumers’ perceptions of immersive AR shopping experiences through Walmart Unlimited, an interactive digital shopping platform. The purpose of this research was to better understand how younger consumers respond to AR-enhanced shopping environments and whether these technologies influence attitudes toward convenience, engagement, and sustainability in retail.

A quantitative research design was used for this study. Participants completed the Walmart Unlimited shopping experience and then responded to a Qualtrics survey measuring areas such as immersion, satisfaction, ease of use, …

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 …

Counterfactual Explanations For Time Series Through Local Pattern Mining And Generative Models, Omar Bahri

All Graduate Theses and Dissertations, Fall 2023 to Present

Machine learning systems increasingly make decisions that affect people’s lives, from medical diagnoses to loan approvals. When these systems analyze time series data—sequences of measurements collected over time, such as heart rhythms or sensor readings—users need to understand why a particular prediction was made. Counterfactual explanations address this need by answering “what-if” questions: what would need to change in the input for the system to make a different prediction? 

This dissertation develops methods for generating counterfactual explanations specifically designed for time series data. Time series often contain distinctive local patterns—short subsequences that distinguish one class from another. For example, 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 …

Visualizing Probabilistic Model Checking: An Interactive Framework For Exploring Ctmc Models, Ishara Mawelle Kankanamge

All Graduate Theses and Dissertations, Fall 2023 to Present

Probabilistic model checking is a critical method for analyzing systems characterized by uncertainty, such as communication protocols, randomized algorithms, and biochemical networks. While formal verification tools provide precise numerical data about these systems, interpreting these results is often limited by large state space, high-dimensional state space and time dependent evolution. Current tools typically output raw numerical data, offering limited support for intuitively understanding the time-dependent behavior of a model. This research presents an interactive visualization framework designed to bridge the gap between complex numerical analysis and human intuition. The framework integrates coordinated visual interfaces, including lower-dimensional state-space projections and synchronized …