Digital Commons Network^™

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 …

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 …

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 …

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. …

Advancing Food Equity Through Explainable Ai (Xai): Identifying Place-Based Factors And Conditions Of Food Security, Leslie Hoglund, Hyoshin Park

Health Behavior, Policy & Management Faculty Publications

Food behaviors, food security, and their association with socioeconomic factors constitute a critical area of study with implications for public health, economic stability, and social equity. Understanding these relationships are essential for developing effective policies and interventions that promote sustainable, healthy food systems and greater food equity. This paper employs explainable artificial intelligence (XAI) methods to identify key features influencing household food behaviors. The insights gained from the XAI analysis are further utilized in conjunction with inverse reinforcement learning (IRL) to examine expert behaviors related to eating habits satisfaction. The XAI results reveal that household health conditions, spending patterns, and …

Machine Learning: Thematic Feature Grouping, And The Magnificent Seven: A Forecasting Analysis, Mirarmia Jalali, Mohammad Najand, Andrew Cohen

Finance Faculty Publications

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, …

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.

The Future Of Monetary Federalism: Rethinking Supremacy In The Stablecoin Era, Richard H. Fair

American University Business Law Review

[INTRODUCTION] In the summer of 2023, the State of Wyoming enacted a law authorizing its state treasurer to issue a blockchain-based, state-backed digital stablecoin known as the Wyoming Stable Token (“WYST”). Two years later, Congress passed the Guiding and Establishing National Innovation for U.S. Stablecoins Act (GENIUS Act, GENIUS, or the Act), moving to establish a comprehensive federal regulatory regime for stablecoins. These dueling initiatives have sparked more than regulatory confusion; they have set the stage for a structural clash between state financial innovation and federal monetary supremacy. At the heart of this confrontation lies a question that the Constitution …

A Proposed Tort To Address The Negligent Enablement Of Cloud Data Breaches, Michael L. Rustad

American University Business Law Review

[INTRODUCTION] The term “cloud computing” means the remote storage of software applications, tools, and data accessed through the internet. Cloud customers enter into subscription agreements with providers who give 24/7, on-demand, as-needed access to software, storage, and networking services owned and managed by providers through a web browser. “Many businesses are transitioning to the cloud for data storage, remote work, and collaboration.” Cloud providers operate their software as a software-as-a-service (“SaaS”) model, under which customers pay a subscription fee to access the software. Netflix and Amazon Prime Video are examples of subscription services that deliver television programs and videos through …

Lost In The Language: Data Breaches And The Strategic Fog Of Risk Disclosures, Ling Tuo, Shipeng Han

Accounting Faculty Publications

This study examines whether firms strategically adjust the readability of Item 1A (“Risk Factors”) disclosures following data breaches. Using U.S. firm-year observations from 2006 to 2023, we find that data breaches are associated with a significant decline in Item 1A readability. This decline is not accompanied by a meaningful increase in informational content; instead, post-breach disclosures exhibit higher syntactic complexity, more positive tone, and lower textual similarity to prior and industry peers' filings, consistent with strategic obfuscation rather than transparent reporting. The readability decline is amplified among firms facing higher litigation risk but attenuated among firms with stronger reputations for …

Lessons On Generative Artificial Intelligence From The American Association Of Dental Editors And Journalists (Aadej), Christopher J. Smiley

Journal of the American College of Dentists

The American Association of Dental Editors and Journalists (AADEJ) recently released "Guidance for Authors, Editors and Publishers on the Use of Generative AI". Developed by an 11-member stakeholder panel, this guidance paper serves a dual purpose: It provides practical strategies for mitigating the risks that generative artificial intelligence (GAI) poses to professional writing and explains how GAI creates these risks. Understanding both the underlying vulnerabilities of GAI and how to address them is critical for users at all levels, from authors and reviewers to editors, publishers, and general users, to maintain the validity and reliability of their written work. This …

Provoking Generative Ai Futures: Merging Theory And Praxis, Regina M. Luttrell Ph.D., Nick Bowman

Media Studies - All Scholarship

An accessible exploration of the myriad applications and challenges of generative AI for media and communication students, scholars, and practitioners alike.

The latest emergence of increasingly low-cost and scalable AI technologies presents a point of both celebration and concern for the contemporary media and information ecosystem. To this end, this edited volume gathers media and communications scholars and practitioners to engage in discussions and exchange ideas about current trends and developments in the field. Questions this volume asks include: What are the essentials of generative AI from a media and communication perspective? How has generative AI influenced research and scholarship? …

Michael Scott Is Not A Juror: The Limits Of Ai In Simulating Human Judgment, Sean Harrington, Hayley Stillwell

Faculty Articles

Can AI replace human jurors? More specifically, can large language models predict how jurors interpret evidence and reach decisions based on legally salient facts and demographic characteristics? As legal scholars and practitioners increasingly explore AI-generated jury simulations, this Article offers the first empirical test of whether models like GPT-4, Claude, and Gemini can faithfully replicate juror reasoning. The answer, for now, is no. Across a series of mock trial scenarios involving redacted confessions, GPT- 4, Claude, and Gemini repeatedly failed to replicate how real jurors interpret evidence or exercise judgment. Their errors were not random, but systematic. Hidden prompts, built-in …

Fluid Agency In Ai Systems: A Case For Functional Equivalence In Copyright, Patent, And Tort, Anirban Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

Modern Artificial Intelligence (AI) systems exhibit fluid agency in multi-step workflows: lacking human-like consciousness or culpability, yet they display behavior that is (i) stochastic (probabilistic and path‑dependent), (ii) dynamic (co‑evolving with user interaction), and (iii) adaptive (able to reorient across contexts). These properties generate valuable outputs but collapse attribution, irreducibly entangling human and machine inputs. Doctrines that assume traceable provenance—authorship, inventorship, and liability—fracture under this unmappability, yielding ownership gaps and moral “crumple zones.”This Article argues that only functional equivalence stabilizes doctrine under unmappability: Where provenance is indeterminate, legal frameworks should treat human and AI contributions as equivalent for allocating rights …

Key-Point-Based Sign Language Translation With Multi-Stream Motion Modeling And Cross-Lingual Transfer, Alex Muchiri Kagozi

Dissertations and Theses

Automatic sign language translation remains challenging because models must recover linguistic structure from complex visual motion while relying on limited annotated data. This thesis investigates whether compact skeletal key-points can serve as an effective alternative to RGB video for continuous sign language recognition and translation, motivated by the privacy, computational efficiency, and transferability of skeletal representations. To address this problem, a segmented key-point-based framework is evaluated in continuous sign language recognition (Sign2Gloss), gloss-mediated translation (Sign2Gloss2Text), gloss-free translation (Sign2Text), and transfer learning within a common modeling pipeline. The proposed framework is a motion-aware multi-stream transformer encoder. Rather than processing the signer …

Real-Time Immersive Wildfire Simulation: A Game Engine Approach, Jake Bova

Graduate Student Theses, Dissertations, & Professional Papers

Wildfires are growing in frequency, size, and severity, placing escalating demands on firefighting resources and putting lives, property, and ecosystems at risk. Accurate prediction of fire behavior is critical to suppression planning, evacuation decisions, and firefighter training, yet the operational models that fire managers rely upon face a persistent tradeoff. High-fidelity physics-based models capture fire–atmosphere interactions faithfully but are computationally expensive, often running far slower than real time on CPU hardware, while faster empirical models sacrifice the dynamic coupling between a fire and the wind field it generates. Furthermore, most existing tools produce abstract, two-dimensional outputs that offer limited visual …

Rrto: A High-Performance Transparent Offloading System For Model Inference In Mobile Edge Computing, Zekai Sun, Xiuxian Guan, Zheng Lin, Yuhao Qing, Haoze Song, Zihan Fang, Zhe Chen, Fangming Liu, Heming Cui, Wei Ni, Jun Luo

Research outputs 2022 to 2026

Deploying Machine Learning (ML) applications on resource-constrained mobile devices remains challenging due to limited computational resources and poor platform compatibility. While Mobile Edge Computing (MEC) offers an offloading-based inference paradigm using GPU servers, existing approaches can be divided into non-transparent and transparent methods, with the former necessitating source-code modifications. Non-transparent offloading achieves high performance but requires intrusive code changes, limiting compatibility with diverse applications. Transparent offloading, in contrast, offers wide compatibility but introduces two challenges: performance degradation under MEC's low and fluctuating bandwidth, where per-operator remote procedure calls (RPCs) inflate end-to-end latency and energy consumption; and black-box logic reconstruction, which …

Optimizing Maintenance Routes For Highway Infrastructure Using Leader-Follower Autonomous Vehicles, Qing Tang, Chenxi Chen, Xianbiao Hu, Yuxin Ding, Tianjia Yang

Civil & Environmental Engineering Faculty Publications

The Autonomous Truck Mounted Attenuator (ATMA), a leader–follower style connected and automated vehicle system, enhances safety during transportation infrastructure maintenance in work zones. However, the significantly lower speed of ATMA, compared to regular vehicles, causes moving bottlenecks that reduce roadway capacity and prolong queuing, leading to further delays. Different ATMA routes lead to varying patterns of time-dependent capacity drop, affecting the user equilibrium traffic assignment and resulting in differing system costs. This study aims to optimize ATMA routing within a network to minimize the system cost associated with its slow-moving operation. To this end, a queuing-based traffic assignment approach is …

When Slides Replace Thinking: How Powerpoint Culture Undermines Conceptual Learning In Engineering Education, Shahin Nayyeri Amiri, Hamid Eisazadeh

Civil & Environmental Engineering Faculty Publications

Engineering education has gradually shifted from deep conceptual teaching to a culture dominated by presentation slides. In many universities today, core courses such as Statics, Dynamics, and Mechanics of Materials are taught through PowerPoint files rather than through reasoning, derivation, and problem solving. While slide-based instruction may appear efficient, it encourages passive learning and has severely weakened students’ ability to think independently and make sound engineering judgments. Traditionally, these fundamental courses were taught by seasoned instructors who had mastered the subjects and took genuine pride in explaining complex ideas with clarity and passion. They guided students through the logic behind …

Airaclex: Automated Detection Of Price Oracle Manipulations Via Llm-Driven Knowledge Mining And Prompt Generation, Bo Gao, Yuan Wang, Qingsong Wei, Yong Liu, Rick Siow Mong Goh, David Lo

Research Collection School Of Computing and Information Systems

Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure and fair transactions. However, poorly integrated oracles remain susceptible to manipulation, enabling attackers to exploit smart contract logic for unfair asset valuation and financial gain. While many such vulnerabilities are only detected after deployment, smart contracts are typically immutable once deployed, making post-hoc fixes costly or infeasible. This highlights the critical need for detecting oracle manipulation risks before deployment. In this paper, we propose AiRacleX, a novel LLM-driven framework that enables pre-deployment detection of price oracle manipulation vulnerabilities by leveraging the complementary strengths of multiple large language models …

Evops: Evolutionary Patch Selection In The Embedding Space Of Whole Slide Images, Saya Hashemian

Theses and Dissertations (Comprehensive)

Computational pathology increasingly relies on the analysis of Whole-Slide Images (WSIs), which capture tissue specimens at gigapixel resolution. Because a single slide is far too large to process directly, the

prevailing paradigm decomposes each WSI into thousands of small patches and encodes them as high- dimensional feature embeddings using deep learning backbones. While effective, this paradigm carries a

substantial cost: the resulting collections of patch embeddings are computationally expensive to store and process, and they are frequently dominated by redundant, homogeneous, or otherwise uninformative tissue regions that dilute the diagnostic signal. Existing patch selection methods largely depend on heuristic or …

Geovig And Purevig: Geometry-Aware Architectures For Efficient Computer Vision, Omar Ismail

Theses and Dissertations (Comprehensive)

Deploying deep learning models for medical image analysis on mobile devices requires a balance between inference latency, memory footprint, and delineating anatomical boundaries with high accuracy. While Convolutional Neural Networks (CNNs) and mobile Vision Transformers (ViTs) offer efficiency, they often struggle to model the irregular, non-local geometric structures inherent in biological tissues without incurring prohibitive computational costs. In this thesis, we introduce GeoViG (Geometric Vision Graph), an architecture that bridges the gap between efficient grid-based processing and explicit Geometric Deep Learning. GeoViG introduces a novel transition from high-resolution pixel grids to low-resolution dynamic graphs via a SpreadEdgePool operator, a geometry-aware …

Error-Driven Density Control For Compact Gaussian Splatting Under Sparse Supervision, Abdelrhman Elrawy

Theses and Dissertations (Comprehensive)

This thesis studies efficiency and stability challenges in Gaussian-splatting-based reconstruction under sparse supervision. In few-shot novel view synthesis, standard 3D Gaussian Splatting (3DGS) can overfit the limited training views and grow an unnecessarily large number of primitives due to limitations in its Adaptive Density Control (ADC) mechanism. This thesis introduces an error-driven reformulation of ADC that triggers densification using opacity gradients as a lightweight proxy for rendering error, and shows that such aggressive densification must be paired with delayed and conservative pruning to prevent destructive create--destroy cycles. When combined with depth-based geometric regularization, the resulting framework produces substantially more compact …

Consensus And Controversies Of International Guidelines For The Diagnosis, Surveillance, And Management Of Fetal Growth Restriction: An Updated Comparison, Daniele Diane Mascio, Suneet P. Chauhan, Tullio Ghi, Asma Khalil, Juliana G. Martins, Sara Sorrenti, Tamara Stampalija, Fabrizio Zullo, Francesc Figueras

Department of Obstetrics & Gynecology Faculty Publications

OBJECTIVE: To compare areas of consensus and disagreements across contemporary international and national guidelines on the diagnosis, surveillance, and management of fetal growth restriction (FGR).

DATA SOURCES: Electronic searches of MEDLINE from database inception up to March 2026 using MeSH terms and keywords related to FGR and guidelines. STUDY ELIGIBILITY CRITERIA: Critical, structured comparison of national or international guidelines on FGR published since 2010. Final inclusion required unanimous agreement from all authors.

STUDY APPRAISAL AND SYNTHESIS METHODS: Pre-specified extraction across domains: definition; prediction/prevention; surveillance tools and frequency; delivery timing and mode; and labor induction methods. Dual data …

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 …

Structured Decomposition And Efficient Adaptation For Scientific Data Movement: From Transfer Concurrency To Genomic Data Acquisition, Rasman Mubtasim Swargo

Masters Theses

"The end-to-end performance of scientific data movement is increasingly limited not by raw network capacity but by whichever resource along the path is momentarily slowest, a bottleneck that no single static configuration can track. This thesis argues that the right response is to decompose each problem along the resource or workflow axis where bottlenecks vary, adapt only the genuinely dynamic control variables at runtime, and make that adaptation efficient through utility-guided or offline-learned control rather than expensive online search. The thesis develops this principle through four systems of widening scope. AutoMDT decomposes a transfer by operation, assigning separate concurrency levels …