Digital Commons Network^™

Comparative Economic Impact Modelling Of Floods Vs Drought On A Global Scale, Diogo Lemos Aguiar, Paulo Henrique Oliveira Machado

ICT

Climate change is generating unprecedented economic losses through extreme weather events, yet comparative predictive analysis between different hazard types remains limited. This report presents a Machine Learning–Based Comparative Economic Impact Modelling System designed to predict and compare the economic damages caused by floods and droughts on a global scale. The system integrates historical disaster records from the EM-DAT International Disaster Database with country-level socioeconomic indicators from the World Bank's World Development Indicators, applying supervised regression modelling to a unified dataset of 1,301 disaster events across 133 countries from 2000 to 2025. XGBoost and a Multi-Layer Perceptron Neural Network were evaluated …

Predicting Co₂ Emissions From Cattle-Driven Deforestation In The Amazon Rainforest Using Machine Learning, Daniel Ambrosio, Suelen Rocha

ICT

Deforestation has become a major environmental issue worldwide, especially in the Amazon Rainforest, which is known as one of the world’s largest carbon sinks. This region has been significantly impacted by cattle farming, contributing to increased CO₂ emissions and land degradation. As Brazil is one of the world’s largest beef producers and exporters, the environmental impact of livestock production has attracted increasing attention from organisations, governments, and sustainability analysts.

This project aims to analyse the relationship between cattle farming, deforestation, and CO₂ emissions in the Amazon using Machine Learning. Using the CRISP-DM framework, the project explored each stage of the …

Flair- Flow-Level Anomaly Intrustion Recognition, Joseph Dumond

2026 Research Poster Competition

Industrial control systems (ICS) and industrial Internet of Things (IIoT) networks support critical infrastructure such as power generation, water treatment, and manufacturing. As these systems become more connected, they are increasingly exposed to cyber threats. However, many existing intrusion detection approaches rely on detailed packet inspection or supervised machine learning techniques that require labeled attack data and extensive tuning, making them difficult to deploy in real industrial environments.ICS networks typically generate highly regular and predictable communication patterns due to periodic control logic and deterministic device behavior. Because of this structure, abnormal or malicious activity often appears as a disruption to …

Solidfit: A Decentralized Mobile Health-Tracking App Using Personal Online Data Stores (Pods), Evan Meyers

2026 Research Poster Competition

Personal fitness data is increasingly collected through mobile applications, yet most current systems store this information in centralized platforms that limit user control, long-term ownership, and data portability. This project addresses the question of whether a decentralized data model can be used to support everyday fitness tracking while preserving user autonomy and privacy. As individuals become more aware of how their health data is collected and shared, there is a growing need for systems that allow users to retain ownership of their personal information without sacrificing usability or functionality.This research presents SolidFit, an Android-based fitness tracking application designed around the …

Seeing The Invisible Load: Xr+ Multimodal Sensing For Cognitive Ergonomics In Industrial Training, Jessica M. Johnson, Andwele Grant

Virginia Digital Maritime Center (VDMC) Faculty Publications

Extended reality (XR) technologies are increasingly positioned as disruptive Industry 5.0 tools for human-centric industrial training and intelligent human–system integration. Coupled with multimodal sensing (eye tracking, EEG, HRV, GSR, and other physiological signals), XR environments promise to make otherwise invisible cognitive demands observable, especially for novice trainees entering complex industrial settings. Yet the evidence base is fragmented: (1) there is no quantitative synthesis of the cognitive ergonomics benefits of XR plus sensing; (2) little is known about which XR–sensor configurations yield the strongest effects; (3) prior reviews rarely focus on industrial and manufacturing tasks; (4) multimodal signals are used predominantly …

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 …

Artificial Elections: How Artificial Intelligence Can Affect Elections And Decide Our Future, Ryan D. Granger

Honors Undergraduate Theses

This thesis examines the growing role of artificial intelligence (AI) in democratic elections, highlighting both its transformative potential and its associated risks. Drawing on a qualitative analysis of existing literature, the study explores how AI is increasingly integrated into political campaigns, election administration, and voter engagement. Key benefits include enhanced data analysis, personalized political messaging, and improved efficiency in campaign operations. AI also supports real-time fact-checking and more accurate vote tabulation, which can strengthen transparency and trust in electoral processes. However, the thesis emphasizes that these advantages are accompanied by significant challenges. AI technologies enable the rapid creation and dissemination …

To Print A Remake: An Analysis Of Hollywood Remakes And Their Cultural Value, Connor F. Seaton

Honors Undergraduate Theses

In recent years, audiences and movie critics have expressed concern that Hollywood’s growing reliance on remakes, sequels, franchises, and similar adaptations has led to a broader worry that originality is fading from modern cinema and that the industry is instead focused on using adaptations to maximize profits. Although adaptation is often seen as a commercially driven framework for reproducing existing intellectual property in a new media format, this thesis argues that it should be recognized as an autonomous cultural category with its own artistic, historical, and social significance and merit. By analyzing adaptation scholarship and reviewing its complex historical development, …

Data Defines Success: Algorithm For Dataset Quality Assessment In Deep Learning For Malware Detection, Matei Ionescu

Theses and Dissertations

The field of artificial intelligence is based upon the premise of constructing architectures through which to propagate training data. However, the majority of existing research literature is focused on architecture. While necessary, the attention devoted to the architecture should not so precipitously exceed that of the data. It should be noted that this disparity is not without reasonable cause. Data quality is often exceedingly difficult to verify due to particularities of the field or subfield; LLM repositories of text are distinct from image recognition pictures of dog breeds which are distinct from EEG waveforms of human brains which are distinct …

ℵ-Ipomdp: Mitigating Deception In A Cognitive Hierarchy With Off-Policy Counterfactual Anomaly Detection, Nitay Alon, Joseph M. Barnby, Stefan Sarkadi, Lion Schulz, Jeffrey S. Rosenschein, Peter Dayan

Research outputs 2022 to 2026

Social agents with finitely nested opponent models are vulnerable to manipulation by agents with deeper recursive capabilities. This imbalance, rooted in logic and the theory of recursive modelling frameworks, cannot be solved directly. We propose a computational framework called ℵ-IPOMDP, which augments the Bayesian inference of model-based RL agents with an anomaly detection algorithm and an out-of-belief policy. Our mechanism allows agents to realize that they are being deceived, even if they cannot understand how, and to deter opponents via a credible threat. We test this framework in both a mixed-motive and a zero-sum game. Our results demonstrate the ℵ-mechanism’s …

Modeling The Impact Of Projected Water Demand On Groundwater Flow And Transport In The Mahomet Aquifer, Christabel Chinonye Abugu

Theses and Dissertations

The Mahomet Aquifer is a vital groundwater resource in east-central Illinois, supporting municipal, agricultural, and industrial water use. With projected changes in water demand driven by population growth and future socioeconomic conditions, assessing the long-term response of the aquifer is critical for sustainable groundwater management. Increased pumping to meet future demand may contribute to groundwater-level decline, intensified cones of depression, and altered groundwater-flow paths relevant to contaminant migration. This study addressed two research questions: (1) how projected changes in groundwater demand affect groundwater levels in the Mahomet Aquifer, and (2) how projected demand influences groundwater-flow paths relevant to contaminant migration. …

Cost-Aware Predictive Routing For Vision-Language Models, Sahil Sait Naveed

Master's Projects

Vision-language models (VLMs) are increasingly used for multimodal tasks, but their inference costs vary widely across model tiers. This work presents a predictive routing framework that assigns each query to the most cost-effective VLM by using multimodal embeddings, clustering, and per-cluster model error estimates. Evaluated on six commercial VLMs, the selected router reduces average cost by about 49 percent relative to the quality-first setting on both validation and test. This comes with accuracy losses of 0.4 percentage points on validation and 0.9 on held-out test data. It also outperforms K-NN and ZeroRouter routing baselines while providing lowercost operating points than …

Challenges And Applications Of Fine-Grained Temporal Action Understanding: Modeling Temporal Granularity And Data Efficiency, Halil I. Helvaci

Theses and Dissertations--Electrical and Computer Engineering

Fine-grained Temporal Action Segmentation (TAS) has become a cornerstone of video understanding, offering dense frame-level predictions essential for clinical assessment, surgical skill evaluation, and human-computer interaction. While TAS methods have delivered strong results on coarse-grained benchmarks, two fundamental challenges persist: (1) global attention mechanisms dilute boundary information critical for subsecond precision, a phenomenon we term the temporal granularity bottleneck, and (2) dense frame-level annotation remains prohibitively expensive, with most datasets requiring exhaustive labeling of lengthy untrimmed videos. These challenges are particularly pronounced in medical domains, where sub-second primitives define clinical outcomes while expert annotation remains scarce. In this dissertation, we …

Generative Ai Peer Tutoring To Support Peer-Reviewed Source Identification And Evaluation, Rae Mair, Michelle Kelley, Taylar Wenzel, Andrea C. Borowczak, Yuqing Li, Mike Borowczak

Faculty Scholarship and Creative Works

This chapter explores the design and evaluation of a generative artificial intelligence peer tutor prompt to support college students in identifying and evaluating peer-reviewed sources for academic research. Grounded in literature on peer tutoring, Socratic dialogue, and AI-supported learning, the authors describe an iterative prompt engineering process designed to transform large language models (LLMs) into Socratic-style peer tutors capable of scaffolding student reasoning without completing tasks for them. Five guiding criteria for an effective peer tutor shaped development and evaluation: cognitive congruence, step-by-step guidance, avoiding giving answers, adaptability to student level, metacognitive transparency, and following assignment directions. Across multiple human-centered …

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 …

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 …

Ai-Powered Lawyering: Ai Reasoning Models, Retrieval Augmented Generation, And The Future Of Legal Practice, Daniel Schwarcz, Sam Manning, J. J. Prescott, Patrick Barry, David R. Cleveland, Beverly Rich

Articles

Generative AI is set to transform the legal profession, though its most promising uses and ultimate effects are still unclear. While AI models like GPT-4 improve efficiency, they can also “hallucinate” and may undermine legal judgment, particularly in complex tasks typically handled by skilled lawyers. This article examines two emerging AI innovations that may mitigate these concerns: Retrieval Augmented Generation (RAG), which grounds AI-powered analysis in legal sources, and AI reasoning models, which structure complex reasoning before generating output. We conduct the first randomized controlled trial assessing these technologies, assigning upper-level law students to complete legal tasks using a RAG-powered …

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 …

A Novel Federated Llm Framework For Distributed Traffic Modelling In Intelligent Transportation Systems, Seerat Kaur

Theses and Dissertations (Comprehensive)

Intelligent transportation systems (ITS) depend on accurate traffic prediction to support congestion management, infrastructure planning, and real-time operational decisions. Despite substantial progress in data-driven forecasting, several challenges continue to limit practical deployment: traffic data is distributed across independent regional authorities, making centralized aggregation infeasible, standard federated aggregation strategies ignore traffic-specific characteristics that meaningfully affect model quality, and existing models produce only numerical outputs without interpretable reasoning that urban planners can act upon. This thesis addresses these challenges through four contributions that collectively advance privacy-preserving, explainable, and scalable traffic forecasting.

The first contribution provides a systematic review of 129 peer-reviewed publications, …

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 …

Polysemicolon; Novice Programmers And Java Keywords, Briana C. Bettin

Michigan Tech Publications

Most industrial programming languages leverage the English language for reserved keywords – words which a program compiler recognizes as specific execution commands. The divide between expert and novice programmers showcases an intriguing middle-ground by which the polysemy of many keywords becomes revealed. This essay explores a sampling of the multitude of keyword interpretations that a novice programmer may derive from the Java language’s syntactic style and keywords specifically, and how the polysemy of both “English” and “code” meanings to these terms affects the novice-expert programmer transition.The transition from novice to expert, and the mapping of the career of metaphor to …

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 …

Artificial Intelligence (Ai) In Educating Next Generation Of Engineering Technology Students, Adel El-Shahat, Murat Kuzlu, Vukica M. Jovanovic, Katherine Smith, Abdullah Al Mamun, Otilia Popescu

Engineering Technology Faculty Publications

Artificial Intelligence (AI) is transforming education, particularly for electrical engineering technology (EET) students, by presenting adaptive learning, immediate responses, and unconventional tools. Therefore, this paper proposes investigating modern learning to employ AI in educating future electrical engineering technology students. Firstly, the paper explores how to shape AI knowledge for EET students, supplying them with hands-on skills in AI tasks, clarifying coding, data analysis, and AI ethical usage. Then, as educators, what are the efficient AI tools to utilize in teaching, such as tailored tutoring, automated code assessment, AI-driven design/simulation, lecture dictation, and smart content creation? Key tools, for instance, Google …

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 …

Can We Read Ai’S Mind? A Quest For Transparency, Santhosh Kumar Ravindran, Estera Kot, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Although Artificial Intelligence (AI) systems are playing an increasing role in critical domains such as healthcare, finance, and autonomous systems, their decision-making processes remain largely opaque. This paper examines the challenges of AI transparency, addressing the “black box” problem using Explainable AI (XAI) techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It also examines the ethical, regulatory, and societal implications of AI opacity and proposes a Comprehensive AI Observability (CAO) Framework that integrates deep explainability, provenance tracking, and real-time monitoring to enhance AI accountability. By bridging technical solutions with governance structures, this research emphasizes the …

Design Principles For Customer-Engaging Digital Service Systems: An Action Research Study, Keng Leng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Towards Secure Program Partitioning For Smart Contracts With Llm’S In-Context Learning, Ye Liu, Yuqing Niu, Chengyan Ma, Ruidong Han, Wei Ma, Yi Li, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present PARTITIONGPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into critical (privileged) and normal codebases, guided by a few annotated sensitive data variables. We evaluated PARTITIONGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PARTITIONGPT successfully generates …

Llamoco: Instruction Tuning Of Large Language Models For Optimization Code Generation, Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo, Jiacheng Chen, Yining Ma, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, combining the strength of large language models (LLMs) and Evolutionary Computation (EC) has shown promising results for addressing optimization problems. It typically involves either iterative next-step solution seeking or directly prompting LLMs to generate critical optimization codes. However, these methods often suffer from low computational efficiency, high sensitivity to prompt design, and a lack of domain-specific knowledge. We introduce LLaMoCo, the first instruction-tuning framework designed to adapt LLMs for solving optimization problems in a code-to-code manner. LLaMoCo features a comprehensive instruction set that includes code-style problem descriptions as input prompts and robust optimization codes from expert EC optimizers as …

Energy-Efficient Security For Narrowband Iot Using Blockchain And Ep-Cumac, Hafizullah Kakar

UNF Graduate Theses and Dissertations

The Narrowband Internet of Things (NB-IoT) continues to expand but faces challenges such as cryptographic overhead and energy consumption. Security frameworks such as blockchain and Energy-Performance Cumulative Message Authentication Codes (EP-CuMAC) rely heavily on SHA-256, which is not optimized for energy-limited devices.

This work unifies two complementary approaches, a hybrid blockchain-based NB-IoT framework and an EP-CuMAC-based framework, by engineering their cryptographic core with an Energy Complexity Model-optimized SHA-256 (ECM-SHA256). ECM applies parallel memory-bank mapping and block-level access optimization to reduce redundant power usage while preserving algorithmic integrity.

Experimental evaluation on identical Intel DDR3 systems using pyRAPL shows energy savings of …