Digital Commons Network^™

Visual Analytics For Interpretable Quantum Computing, Shaolun Ruan

Dissertations and Theses Collection (Open Access)

Quantum computing has entered a stage of increasing practicality. Many quantum hardware vendors such as IBM, Rigetti, Honeywell, and IonQ now enable experiments on real devices in the Noisy Intermediate-Scale Quantum (NISQ) era. These platforms show computational advantages in domains such as optimization, machine learning, and materials science. However, they remain limited by hardware noise and the absence of human-interpretable information. Existing visual metaphors, such as the Bloch Sphere for single-qubit states or circuit schematics for algorithm design, struggle to convey multi-qubit entanglement or measurement probabilities in ways accessible to human reasoning. Likewise, the rise of variational quantum circuits and …

Constrained Reinforcement Learning: From Single-Agent Safety To Multi-Agent Coordination, Hao Jiang

Dissertations and Theses Collection (Open Access)

Real-world decision-making systems such as autonomous driving and largescale ride-pooling must operate under strict safety and resource constraints. Traditional Reinforcement Learning (RL) methods, while powerful in simulation, often fail to guarantee such constraints, limiting their real-world deployment. The fundamental challenge lies in integrating constraint satisfaction with long-term reward optimization, especially when outcomes are stochastic and interdependent across multiple agents.

This dissertation advances the field of Constrained Reinforcement Learning (CRL) from both single-agent safety and multi-agent coordination perspectives. In the single-agent setting, we introduce a Reward Penalty framework that augments the state space with cumulative cost and penalizes only trajectories that …

Improving Pid Control With Bayesian Optimization For Adversarially Robust Federated Learning, Adrian Pena, Sergei Chuprov, Raman Zatsarenko, Leon Reznik

Computer Science Faculty Publications

Federated Learning (FL) often suffers from unstable convergence and reduced robustness under non-IID data and malicious attacks. In this paper, we present FedPIDAvg_tuned, a control-theoretic aggregation framework for improving stability and adversarial robustness in FL. In particular, our approach combines a server-side Proportional–Integral–Derivative (PID) controller with Bayesian optimization to tune controller gains for different data and attack conditions. The PID controller regulates global model updates through feedback on loss dynamics, providing adaptive scaling that improves stability and convergence. The tuned gains are applied within a trust-weighted trimmed-mean mechanism to remove adversarial or outlier updates. Using the Flower framework, we evaluate …

Redefining Educational Technology: A Critical Collaborative Inquiry, Aras Bozkurt, Helen Crompton, Robert Farrow, Agnes Kukulska-Hulme, Jon Dron, Richard West, Agnieszka (Aga) Palalas, Matth Bower, Junhong Xiao, Ahmed Tlili, Danah Henriksen, Angelica Pazurek, Henk Huijser, Thomas K. F. Chiu, Petar Jandrić, Katy Jordan, John Curry, Royce Kimmons, Mutlu Cukurova, Thomas Reeves, Gwo-Jen Hwang, Peter Shea, Jason Lodge, Martin Weller, Davy Ng, Tutaleni Iita Asino

STEMPS Faculty Publications

Educational technologists have not settled on a fixed definition of the field and likely never will. However, attempting to define the field helps to understand the epistemological meanings that shape what the field sees, values, and considers worth pursuing. Through a critical historical review spanning over a century, alongside theoretical engagement with the concepts of entanglement and distributed agency, this paper identifies three key insufficiencies in current educational technology frameworks. These are the persistence of an instrumental-facilitative paradigm that treats technology as a resource deployed by human agents; the theoretical dissolution of the pedagogy-technology dichotomy that existing definitions have not …

Monitoring, Oversight, And Learning In Medical Ai, W. Nicholson Price Ii

Articles

When medical AI errs, it often goes unnoticed. If there’s a specific patient injury, and the link to AI is obvious, that problem might be reported to the Food and Drug Administration (FDA), but not always. And many other types of problems, like worse performance on specific groups or ineffective integration into health system workflows, simply don’t fall within the contours of regularized reporting. Even if they are noticed by the health system—far from a given—there’s no obvious way to share that information more broadly. Against this backdrop, there are justified calls for better oversight and reporting. But there’s the …

Multimodal Emotion Detection System, Shubhankar Sameer Munshi

Master's Projects

Trying to understand emotion from speech is a problem that is present in human computer interaction. Nevertheless, there are still some shortcomings in current SER methods. Text-based systems may miss vital vocal cues, such as sarcasm, tone changes, and delivery. On the other hand, purely audio-based systems are prone to noise and unstable acoustic features. The combination of linguistic and acoustic features in multimodal approaches partially solves this problem, but many existing approaches use inflexible multimodal fusion techniques that cannot adjust their behaviors according to the quality of input signals. In this work, we propose a multimodal approach based on …

Modeling Sealed Deck Construction In Collectible Card Games Using Learning-To-Rank Approach, Michael Dinh Nguyen

Master's Projects

Artificial intelligence has demonstrated strong performance in complex decision-making domains such as chess and Go, motivating research into its application for games with even richer rules and combinatorial complexity. In collectible card games like Magic: The Gathering, deck construction from a constrained card pool is a critical and challenging task that requires evaluating card strength, synergy, and resource balance. This project explores whether machine learning, specifically learning-to-rank (LTR), can effectively model these human decision processes to construct competitive decks in a sealed format. The results found here can also be applicable to other areas of note, such as sports drafting …

Cogram: A Computational Pipeline For Genome Assembly And Reconstruction Using Graph Neural Networks, William Coggins

College of Graduate Studies: Theses & Dissertations

Genome assembly — the reconstruction of a complete DNA sequence from short, overlapping reads — remains a fundamental challenge in computational biology. A central difficulty is distinguishing true genomic overlaps from spurious connections arising from repetitive sequences, a task that traditional assemblers address through hand-tuned heuristic rules applied to de Bruijn or overlap graphs. This thesis introduces COGRAM (Coggins–Ramasamy Assembly Method), a genome assembly pipeline that reframes sequence reconstruction as an edge classification task on a k-mer overlap graph, replacing heuristic graph cleaning with a learned model.

COGRAM constructs a directed overlap graph from raw sequencing reads using a k-mer …

The Devil Is In The Det[Ai]Ls: Ai Agents, Ghost Students, And The Crisis Of Verified Presence In An Agentic Ai World, Aras Bozkurt, Helen Crompton, Caroline Fell Kurban

STEMPS Faculty Publications

The transition from reactive Generative Artificial Intelligence (GenAI) to agentic AI systems marks a categorical shift in digital education, moving beyond simple content generation to goal-oriented, autonomous execution. This paper explores the emergence of the “ghost student”: a digital surrogate created by the coupling of Large Language Models (the “mind”) and agentic AI browsers (the “body”). These entities are capable of navigating Learning Management Systems (LMS), engaging with content, and completing assessments with human-like mimicry, often rendering the actual learner’s presence optional. We argue that this phenomenon creates a verification gap that traditional proctoring and detection tools are structurally unable …

Instructional Designers' Reflections On Generative Ai Use For Scenario-Based And Performance-Centered Learning: A Collective Autoethnography, Nour El Houda Maache, Dan V. Dao, Jiyoon Jung, Jayanth Nadheri, Nari Kim, Chikezie Ozuzu, Xinyue Ren

STEMPS Faculty Publications

This study examines how instructional designer-instructors (IDIs) use and evaluate generative artificial intelligence (GenAI) when designing scenario-based and performance-centered authentic learning in higher education. Using a collective autoethnography (CAE) approach, the study draws on semi-structured interviews and reflective narratives from five IDIs with varied professional experience. Findings indicate that GenAI enhanced design capacity by accelerating scenario development, translating complex content, and supporting scenario-based and performance-based task construction. At the same time, participants reported limitations related to contextual misalignment, output unreliability, and the cognitive demands of prompt refinement. Across cases, effective integration depended on sustained human oversight, disciplinary judgment, and ethical …

Chatgpt In Secondary English Language Learning: Educators' And Students' Literacy, Perceptions, And Experiences, Victoria Brenes, Sierra Outerbridge, Xinyue Ren

STEMPS Faculty Publications

The increasing use of generative artificial intelligence (GenAI) has shown the potential of transforming teaching and learning practices in various educational settings, such as in English language learning (ELL). As English language learners (ELLs) often experience many challenges and barriers in schools in the United States, it is urgent to leverage the educational affordances of GenAI in fostering the effectiveness of ELL. Given the limited research investigating GenAI adoption, especially ChatGPT literacy within K-12 ELL, this convergent mixed methods research aims to investigate students' and teachers’ perceptions of using ChatGPT and their ChatGPT literacy in secondary ELL contexts. We will …

Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis, Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick, Michel Audette

Engineering Technology Faculty Publications

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs’ consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs’ responses using a convex hull approach on a healthcare application for visual question answering (VQA). For any VLM, temperature refers to a sampling parameter used in probabilistic generation, which controls the randomness of the model’s output. The LLM-CXR model is selected as the medical …

A Methodological Framework For Modernizing Engineering Course Content With Large Language Models, Katherine Smith, Dalya Ismael, Otilia Popescu, Murat Kuzlu, Adel El-Shahat, Vukica M. Jovanovic

Engineering Technology Faculty Publications

The rapid evolution of technology presents challenges for engineering educators. While the core engineering methods often remain relevant over time, course materials rapidly become outdated in presentation and pedagogical approach. This paper presents a methodological framework for using large-language models (LLMs) to modernize engineering course content with a case study in an advanced technical analysis course.

The methodology follows a phased approach that is designed to be repeatable and verify the accuracy and completeness of course content. During the first phase, an LLM is used to map outdated text-heavy content to a modern format using a LaTeX template. The second …

When Helpfulness Becomes Harmful: Jailbreaking Llms For Malicious Code Generation, Noelle Capodieci

Electronic Theses & Dissertations (2024 - present)

Generative AI (GenAI) and Large Language Models (LLMs) have made large strides in coding task capabilities, with many software developers integrating agentic engineering into their workflow. While GenAI has largely benefited professional software engineers who can automate their work, it has also created room for those with little coding expertise to also create fully fledged programs and applications. It is commonly noted that GenAI is trained with two major goals in mind: to be as helpful as possible, and be as harmless as possible. There exist moments where helpfulness may be prioritized over harmlessness when these goals conflict. LLMs may …

Towards Sample-Efficient Deep Reinforcement Learning, Guang Yang

Theses and Dissertations

Deep reinforcement learning (DRL), combining reinforcement learning and high-performance function approximations such as deep neural networks (DNN), is a powerful approach to solving complex sequential decision-making problems. However, due to the complex solution space of the sequential decision-making problems and the inefficient design of the DRL algorithms, DRL algorithms usually require a prohibitively large number of data samples to train effective strategies. Consequently, it is difficult to apply these DRL algorithms to complex real-world problems that require high costs to collect a large volume of data samples. This dissertation proposes new mechanisms to address this sample inefficiency issue, realizing sample-efficient …

Advancing Cybersecurity Through Userland Memory Forensics: From Runtime Analysis To Security Applications, Hala Ali

Theses and Dissertations

Memory forensics has become a crucial component of digital investigations, particularly for detecting malware operating solely in system memory. As operating system vendors implemented kernel access restrictions, malware authors shifted to userland malware. However, existing memory forensics techniques have largely focused on kernel-level analysis, leaving userland runtimes insufficiently covered. This dissertation addresses this gap by expanding memory analysis capabilities across two distinct paradigms: interpreted and compiled runtimes. The first phase targets the Python runtime, developing automated recovery techniques that enable several security applications. For malware detection, these techniques extract critical forensic artifacts such as encryption keys and command-and-control configurations. For …

Proof Of Recovery: A Model To Enhance Data Integrity In Data Management Systems, Gustaf Barkstrom

Theses and Dissertations

Businesses lose millions of dollars every year when they can’t restore data from backups. Research shows that Disaster Recovery Plan (DRP) testing is not conducted frequently enough, nor are records maintained that demonstrate full data recovery from backups. This work introduces a design science artifact called PRTOK that aims to increase DRP testing. The design science artifact is a software solution that integrates with Data Management Systems (DMS)

such as iRODS and DSpace, and can work with formats such as HDF5 and BagIt. Proof-of- recovery records, or tokens, are recorded in a replicated, resilient, and indelible proof-of- authority blockchain data …

Measuring The Efficiency Of The Arts And Culture Sector Relative To Gdp: A State-Level Analysis Of The United States, Ameerkhan Jaffar Khan Khader Khan

Selected Full-Text Master Theses 2021-

Arts and cultural production contributed $1.17 trillion to United States gross domestic product in 2023, 4.2% of the national total, but that contribution is spread very unevenly across states, and the official statistics describe how large the sector is rather than how efficiently it operates (Bureau of Economic Analysis, 2024). This study asks how efficiently each state convert growth in arts and culture employment into growth in value added, how many inputs the efficiency model can carry before it stops distinguishing 51 observations, and whether observable state characteristics explain the differences found. The analysis uses 2023 state-level data for all …

Optimizing Markov Chain Monte Carlo Algorithms For Fairness, Scalability, And Interpretability In Electoral Redistricting, Madhukara Kekulandara

Open Access Dissertations

Gerrymandering undermines democratic representation by manipulating electoral district boundaries to dilute the political influence of targeted communities. While Markov Chain Monte Carlo (MCMC) based redistricting algorithms have become a standard computational tool for detecting partisan gerrymandering, their effectiveness in addressing racial gerrymandering and their scalability on modern computing systems remain limited. This dissertation advances the theory and practice of algorithmic redistricting by optimizing MCMC-based approaches across three dimensions: racial fairness, computational efficiency, and interpretability.

First, this work introduces the Partial Map MCMC algorithm, a novel redistricting method designed specifically to address racial gerrymandering under the legal framework of Section 2 …

Mg-Spair: Multi-Grade Sparse-Guided Implicit Representation For Training-Data-Free Image Restoration, Jianmin Liao, Lei Huang, Ronglong Fang, Ashley Prater-Bennette, Lixin Shen, Yuesheng Xu

Mathematics & Statistics Faculty Publications

MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building on implicit neural representations (INRs), we introduce a multi-grade residual hierarchy that progressively refines the reconstruction from low to high spatial frequencies across grades, improving representational fidelity and mitigating spectral limitations. To stabilize reconstruction optimization and suppress INR-induced artifacts, we further propose an explicit sparse proximal regularization (e.g., ℓ0 type) applied directly in the high-resolution image domain, which discourages spurious high-frequency patterns while preserving sharp structures. The resulting optimization is solved efficiently via a …

Machine Learning-Based Intrusion Detection System For Iot Networks Using The Rt-Iot 2022 Dataset, Bukunmi Ebenezer Afolabi

Theses, Dissertations and Capstones

The rapid expansion of the Internet of Things (IoT) has transformed modern computing by enabling seamless connectivity among heterogeneous devices across diverse application domains. However, this increased interconnectivity has significantly enlarged the attack surface of IoT networks, exposing them to a wide range of sophisticated cyber threats. Conventional security mechanisms often lack the capability to detect emerging attacks in real time, thereby necessitating the development of intelligent Intrusion Detection Systems (IDS) capable of accurately identifying malicious network activities. This study developed and evaluated a machine learning-based intrusion detection framework for multiclass IoT attack detection using the RT-IoT2022 dataset. The dataset …

Artificial Intelligence, Fundamental Motives, And Evolutionary Mismatch, Amy J. Lim, Jose. C. Yong, Edison Sora Tan

Research Collection School of Social Sciences

In recent years, the intersection of artificial intelligence (AI) and psychology has garnered unprecedented attention, particularly following the advent of generative AI tools in 2022. These tools, capable of producing human-like text, images, and even deepening our understanding of cognitive processes, have not only captured the public imagination but also sparked new concerns and debates within the psychological community. While AI has been a subject of research for decades, the emergence of its generative capabilities has truly thrust AI into the spotlight. This article explores how these advancements are reshaping our understanding of human cognition and behavior, as well as …

Critical Success Factors For An Effective Security Risk Management Program: An Exploratory Case Study, Jason A. Williams, Humayun Zafar, Saurabh Gupta

Faculty Articles

This paper evaluates the perceived effectiveness of the security risk management (SRM) programs at a Fortune 500 firm. Layers of management and staff participated in the study. Perceived effectiveness of their SRM programs was based on nine critical success factors (CSFs). Interviews confirmed six initial CSFs (Executive Management Support, Organizational Maturity, Open Communication, Risk Management Stakeholders, Team Member Empowerment, and Holistic View of an Organization) that were extracted from the literature. They were confirmed and synthesized with three additional CSFs (Security Maintenance, Corporate Security Strategy, and Human Resource Development). Implications for SRM are discussed.

Benchmarking And Advancing Heterogeneous Data Reasoning With Large Language Models, Yebowen Hu

Graduate Studies Theses and Dissertations 2026

Large language models (LLMs) increasingly face complex real-world tasks requiring complex reasoning over heterogeneous inputs, including text, numbers, and structured data. This dissertation first investigates the capability boundaries of LLMs under such scenarios. Through the MeetingBank and SportsMetrics benchmarks, we show that while LLMs exhibit linguistic fluency, they struggle with factual accuracy, information density, and quantitative cross-referencing in meeting summarization and sports analysis. Using DecipherPref, a pair-wise evaluation framework grounded in the Bradley-Terry-Luce model, we further probe LLMs' inherent preferences for information-rich and lengthy inputs, and we identify critical deficiencies in financial decision-making through DeFine, where models lack precise insight …

Hallucination Detection In Scientific Writing: Quantification, Trend Analysis And Benchmarking, Adiba Ibnat Hossain

Graduate Research Theses & Dissertations

The landscape of scientific communication has undergone a significant transformation with the emergence and widespread adoption of Large Language Models (LLMs). LLMs have enhanced productivity and creativity in scientific writing, raising serious questions about the faithfulness and reliability of the generated content. A common problem in LLMs is hallucination, which occurs when models generate fluent but inaccurate or inconsistent content. When hallucinations enter scientific writing, the intended meaning may be distorted, coherence disrupted, and the study’s overall integrity threatened.

This thesis investigates hallucinations in scientific writing from both an analytical and dataset-driven approach. First, it offers a comprehensive analysis of …

Cybercrime, Vulnerability And Digital Guardianship: Opportunity Structures And Prevention In A Changing Online Landscape, Mike Toro-Alvarez, Amy Lim

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Analyzing Modern Scam Typologies: From Pig-Butchering And Nigerian Advance-Fee Fraud To Crypto Airdrop Schemes, Katalin Parti, Sinyong Choi, Thomas Dearden

International Journal of Cybersecurity Intelligence & Cybercrime

Rapid advancements in digital infrastructure and decentralized networks have fundamentally altered the nature of contemporary cybercrime, making comprehensive empirical and technical analysis more crucial than ever. To address these challenges, this editorial summarizes the research contributions featured in this issue of the International Jour nal of Cybersecurity Intelligence and Cybercrime. The included papers examine the structural on-chain dynamics of sanctioned pig-butchering operations, the representational production and AI-driven evolution of the “Nigerian scam” label, the critical transaction-authorization factors driving losses in crypto airdrop schemes, and the optimization of sentence-transformer models for automated Host Intrusion Detection System (HIDS) alert enrichment. Together, these …

Three-Tier On-Chain Transaction Architecture In A Sanctions-Linked Pig-Butchering Network: A Blockchain-Forensics Case Study, Matthew Stern, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

n October 2025, the U.S. Department of the Treasury’s Office of Foreign Assets Control (OFAC) sanctioned 29 Bitcoin addresses asso ciated with the Prince Group and Chen Zhi, providing an opportunity to examine the internal on-chain structure of a sanctions-linked pig-butchering network. This blockchain-forensics case study analyzes the group of 29 addresses using blockchain tracing, cross-plat form attribution checks across multiple commercial analytics platforms, exposure screening, and thematic analysis of transaction be havior. Because the case rests on OFAC designations and DOJ allegations, the traced flows are interpreted as patterns consistent with suspected laundering rather than adjudicated crimes; no fiat …

Attribution Post ‘Aura’ Loss: A Qualitative Discourse Analysis Of The Nigerian Scam Label And Fraud Attribution, Emaediong Akpan

International Journal of Cybersecurity Intelligence & Cybercrime

In this essay, I employ qualitative discourse analysis of eleven purposively selected literature to examine how “Nigerian scam” became an established yet detached signifier of advance-fee fraud in global discourse. I do not treat this association as a reflection of offender identity or national inclination. Instead, the analysis traces how attribution is produced through representational repetition, institu tional uptake, and the circulation of familiar narrative cues. Using qualitative discourse analysis and an interpretive approach, this paper distinguishes among the socio-technical conditions that enable fraud, the institutional processes that stabilize attribution, and the semiotic dynamics through which national categories acquire durability. …

A Mixed-Methods Study Of K–12 Educators’ Perceptions And Use Of Artificial Intelligence Across Multiple Settings: Personal, Professional, And Classroom, Christopher M. Casal

Selected Full Text Dissertations, 2011-

How U.S. K-12 educators perceive and use artificial intelligence (AI) is an emerging field of research as most existing research originates from Europe and Asia, often focused on higher education. This mixed-methods study examines U.S. K-12 educators’ perceptions of AI across three settings; personal, professional, and direct instruction, along with factors influencing acceptance, or resistance, and strategies to support integration. An anonymous survey, administered in the Spring of 2026, yielded 79 responses, primarily from suburban public school educators in the New York City vicinity. Quantitative data were analyzed, and open-ended responses were thematically coded. One of the most notable findings …