Digital Commons Network^™

The Making Of An Extremist: How Do We Become Someone Else’S Nightmare?, María Paula Morales

The Confluence

This article explores how ordinary people can be pulled into extremist movements and what psychological forces drive that process. It looks at three perspectives: social identity theory, which explains how group belonging shapes behavior, identity development, which shows how people searching for meaning may find it in extremist causes; and social neuroscience, which connects radicalization to brain activity linked to fear, loyalty, and moral judgement. Together, these approaches show that radicalization is not simply about ideology but about identity, emotion, and belonging. By understanding these dynamics, we can find better ways to prevent extremism and promote healthier, more inclusive communities.

Llms In Compiler Construction, Raffi Khatchadourian

Open Educational Resources

These lecture slides survey the use of large language models (LLMs) in compiler construction for a graduate compiler course (CSc 81010). They situate LLMs across the compiler pipeline and examine representative work: foundation models trained on LLVM IR and assembly (Meta's LLM Compiler), LLM-driven code optimization, binary decompilation (LLM4Decompile), and LLM-assisted automated refactoring—alongside the challenges of applying probabilistic models to tasks that demand correctness. The slides are a self-contained HTML (W3C Slidy) deck with editable Pandoc Markdown source. Part of a two-session unit on advanced compiler topics; see also "Deep Learning Compilers."

Uncovering The Impact Of Youtube's Hidden Algorithm On Its Users, Oscar Perez

COD Library Student Research and Award Symposium

YouTube is a well-known platform that offers users endless hours of news, entertainment, and education. This research seeks to understand how the algorithm functions and uncover the effects of allowing a system to curate content for viewers. The research combines academic sources with fieldwork to understand the impact of YouTube's algorithm.

Faculty Sponsor:  Professor Jacqueline McGrath

Shaped By The Feed: Big Data, Identity And Power, Ana Paula Andrada

COD Library Student Research and Award Symposium

I explored how big data and algorithms shape the way we think and interact at an individual and societal level. This started from things I kept noticing in everyday life. Through my research, I learned how the personalization and predictability in feeds can reinforce beliefs, limit our critical thinking, deepen polarization and threaten our freedom.

Faculty Sponsor:  Professor Aleisha Balestri

Energy-Efficiency Optimization And Comparison For Irs-Assisted Bidirectional Relay And Direct Transmissions, Caixia Cai, Jiayao Zhang, Fuli Zhong, Han Hai, Yayu Yang, Sunil Chinnadurai, Anwer Al-Dulaimi

All Works

Intelligent reflecting surface (IRS) has emerged as a promising technique for achieving high-transmission rate and low-power consumption transmission to meet the requirements of future beyond 5G and 6G communication. In this paper, we optimize and compare the energy-efficiency (EE) of IRS-assisted bidirectional relay transmission (BRT) and bidirectional direct transmission (BDT). In specific, we firstly consider and give the IRS-assisted BRT and BDT models. Then, we give the analyses of signal transmission models and EE for both IRS-assisted BRT and BDT. In addition, to optimize the EE, we give the joint optimization problems for both IRS-assisted BRT and BDT, which incorporate …

Dynamic Trust Calibration, Bruno Miranda Henrique

Dartmouth College Ph.D Dissertations

Trust calibration between humans and Artificial Intelligence (AI) is crucial for optimal decision-making in collaborative settings. Excessive trust can lead users to accept AI-generated outputs without question, overlooking critical flaws, while insufficient trust may result in disregarding valuable insights from AI systems, hindering performance. Despite its importance, there is currently no definitive and objective method for measuring trust calibration between humans and AI. Current approaches lack standardization and consistent metrics that can be broadly applied across various contexts, and they don’t distinguish between the formation of opinions and subsequent human decisions. This thesis brings a novel and objective method for …

Stridevision: Automated Detection Of Running Form Deviations From 2d Pose Estimation And Machine Learning, Paulina Eguibar Ortega

Honors Theses

Running gait analysis plays a critical role in injury prevention and performance optimization, however, existing approaches often rely on specialized laboratory equipment or wearable sensors with limited interpretability. Recent advances in computer vision, particularly 2D human pose estimation, enable markerless motion analysis from standard video. However, progress remains constrained by the lack of publicly available datasets designed for running form analysis.

In this work, we introduce a preliminary dataset and benchmark for stride-level running gait analysis. The dataset consists of 73 treadmill running videos from 15 participants with varying experience levels, annotated with over 4,600 stride-level labels across multiple biomechanical …

Development And Launch Of Abn: An Adventist Freelance Web Application, Abishur Moses-Pakkianathan

MS in Computer Science Project Reports

The Seventh-day Adventist community often relies on personal networks and word-of-mouth for Adventist business services. Many of these businesses meet and provide services to their clients primarily through church connections and community recommendations. As the community and businesses grow, traditional networking methods become increasingly difficult to maintain. General marketplaces lack the trust framework shared by Adventists, and faith-based directories are often static or outdated without booking capabilities. Our solution provides a reliable and modern platform for Adventists to create service listings, receive and manage bookings, and connect with faith-aligned clients.

How Ai Governance Differs Between Regime Type, Jackson T. Guillou

Honors Theses

This thesis will examine how artificial intelligence (AI) regulation and development differ across political regime types, arguing that governance outcomes are fundamentally shaped by institutional political structures. This thesis will draw on comparative frameworks analyzing democratic and authoritarian systems. This thesis will also include case studies of the European Union, the United States of America, China, and Russia. The study finds that democratic regimes tend to emphasize transparency, accountability, and rights-based regulation of AI. This often results in slow regulation of AI, but it is more ethically legitimate and constrained. In contrast, authoritarian regimes prioritize centralized control, strategic coordination, and …

High-Frequency Vr-Native Eye Tracking: From Data Collection To Machine Learning Models, Meherun Nesa Shraboni, Aryabrata Basu

Research and Creative Works Expo

No abstract provided.

Spaceforge: Spatial Reconstruction For Signal Simulations, Compton Ross

Honors Theses

Signal simulation environments require accurate three dimensional representations of physical spaces, yet current methods for generating these representations, including Light Detection and Ranging (LiDAR) scanning, manual 3D modeling, and commercial photogrammetry, are both costly and time intensive. SpaceForge addresses this gap with a prompt guided pipeline that takes an ordinary indoor photograph and a configurable set of simulation relevant object categories as input and produces a voxelized 3D scene compatible with downstream signal simulation workflows. The pipeline proceeds through five major stages: open set object detection and segmentation, object level preprocessing, single image 3D mesh reconstruction, heuristic pose estimation and …

Developing A Comprehensive Resource Guide For Caregivers Within The Area Agency On Aging, Aiden R. Murphy

Public Health Capstone Projects

This project developed a new universal caregiver resource guide for caregivers within the Area Agency on Aging, in order to improve resource navigation and workflow efficiency. Resources were collected, verified, and organized into a new, streamlined guide via the ARIA chatbot, covering multiple needs. Caregiver resources were collected and verified by the capstone student and mentor, Michael Kroeker, and organized into a centralized knowledge base within the ARIA chatbot. A mixed methods evaluation was conducted utilizing a 5-point Likert scale with three quantitative questions and one open-ended qualitative question. The data was given to the SeniorLine staff, who wanted to …

Largest 2-Regular Subgraphs In Complete S-Partite Graphs, Yiyang Jiang

McKelvey School of Engineering Graduate Student Theses & Dissertations

In this thesis, we focus on the class of complete $S$-partite graphs, for $S$ an undirected graph possibly with self-loops, and address the problem of finding largest $2$-regular subgraphs of these graphs, which can be formulated as an integer linear program. Roughly speaking, a complete $S$-partite graph is obtained by replacing every single node of $S$ with a number of nodes, preserving the edge/non-edge relations of $S$. Our motivation in studying largest $2$-regular subgraphs is rooted in the structural systems theory, particularly in the problem of finding largest subnetworks that can sustain controllability or asymptotic stability of the corresponding subsystems. …

Predicting And Decoding Allosteric Binding Sites Using Protein Language Models And Structure-Based Machine Learning: An Energy Landscape-Guided Explainable Ai Framework, Kamila Riedlová, Vít Skrhák, William G. Gatlin, Max Ludwick, Lucas Turano, Marian Novotný, David Hoksza, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Computational prediction of allosteric binding sites in protein structures remains a persistent challenge, as these regulatory pockets evade detection by both sequence-based and structure-based algorithms. Both computational and physical origins of this predictive asymmetry remain insufficiently understood. In this study, we systematically examine the determinants of binding site predictability using a dual framework that integrates a fine-tuned protein language model and the structure-based method P2Rank as complementary tools probing a diverse data set of 453 human kinases, together with a physics-based interpretability layer derived from energy landscape frustration analysis. Both predictors exhibit a sharp and reproducible dichotomy on protein kinases, …

Online Legislation: Developments And Trends In Data Privacy And Software Development, Garrett J. Splinter

Honors Program: Senior Projects (Public)

The increasingly relevant interaction between the law and data privacy, as well as compliance requirements enforced by the United States, is currently in a state of transition. Various states, such as California, Colorado, Connecticut, Nebraska, and many others, all have passed legislation with varied requirements and definitions that make for a challenging framework in which software developers operate, as the universal nature of the internet renders their compliance with current legislation a challenge. Enforcement also tends to be relatively relaxed in most modern examples, though it has escalated after 2020, and this trend may continue in the future, lending credence …

Towards Improving The Performance Of The Adcirc Storm Surge Modeling Software, Nick Weldner, Tim Stitt, Jijun Tang

Caravel Undergraduate Research Journal

Accurately predicting storms and hurricanes is critical to saving lives and reducing economic loss. Therefore, it is necessary to use the most efficient software and hardware technology available in order to improve the performance and fidelity of these predictive mathematical models. For over ten years, the Computational Hydraulics Lab (CHL) at the University of Notre Dame has been involved in developing the high-resolution ADvanced CIRCulation (ADCIRC) storm surge model to predict storm surges in coastal areas. The objective of the work reported here was to port a novel adaption of the parallel ADCIRC code to the state-of-the-art Intel Xeon Phi …

Faircarenlp: An Ai-Driven Patient Review Analyzer For Healthcare, Sayyed Mohammad Pourya Momtaz Esfahani, Davey Seeman, Christoffer Dharma, Mohammad Noaeen, Shion Guha, Zahra Shakeri

Health Services and Informatics Research

Objective

To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to improve the efficiency, fairness, and accuracy of healthcare feedback analysis.

Materials and methods

We designed a multi-component pipeline incorporating sentiment analysis, key theme extraction, clinical Named Entity Recognition (NER), and fairness modules. Bias mitigation was addressed through the integration of three complementary approaches: adversarial debiasing, Hard Debiasing, and Iterative Null-space Projection (INLP). Multiple BERT-based models (DistilBERT, BioBERT, RoBERTa-base, BERT-base-uncased) were trained and evaluated under varying hyperparameters and fairness/adversarial loss configurations. Model performance was assessed using accuracy, F1, recall, …

Automated Analysis Of Radiation Oncology Incident Reports Using Large Language Models, Nathan A. Dobranski

LSU Master's Theses

Patient safety incident reporting in radiation oncology requires expert analysis that is time-intensive and subject to variability. This thesis presents the development and technical validation of a locally deployed large language model (LLM) system for automated incident report analysis across multiple cancer centers. The system was designed for automated summarization and taxonomy assignment of Radiation Oncology Incident Learning System (RO-ILS) reports, operating entirely on local infrastructure to preserve patient privacy. A two-round, multi-rater evaluation methodology was employed, incorporating 600 total expert evaluations from two academic cancer centers. Round 1 established baseline performance using Mistral 7B and Mixtral 8x7B models with …

A Survey On Knowledge-Enhanced Healthcare Question Answering Systems, Junnan Su, Pu Han, Jianxiang Wei

Journal of Scientific Information Research

[Purpose/significance] This paper aims to review the research progress and applications of knowledge enhancement techniques in healthcare question answering systems, in response to the limitations of traditional systems in knowledge representation and reasoning, as well as challenges faced by current large language model-based systems, such as insufficient domain knowledge, privacy concerns, and hallucination. The review provides a systematic reference for improving the precision and knowledge reliability of such systems. [Process/method] Focusing on knowledge enhancement strategies, this paper firstly outlines their fundamental concepts and overall framework. The strategies are then categorized into explicit and implicit types, with an analysis of their …

Research On Smart Intelligence Service System Of National Defense Science And Technology In Complex Information Environment, Yang Yang, Keping Wang, Huawei Sun

Journal of Scientific Information Research

[Purpose/significance] National defense science and technology intelligence serves as a powerful guarantee for promoting national defense science and techndogy innovation and development. Exploring optimization paths for defense science and technology intelligence service systems holds practical significance in developing new quality combat capabilities and safeguarding national security and stability. [Method/process] Through literature review and summarization, this study clarifies the impact of complex information environments on intelligent defense science and technology intelligence services, as well as the core tasks of intelligent intelligence services. Based on activity theory, the study deconstructs system elements and employs systems engineering principles to construct an intelligent defense …

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 …

Designing Enhanced Nonlinearity In Plasmonic Devices With Epsilon-Near-Zero Films, Kevin Tran Le

Electrical Engineering and Computer Science (MS) Theses

The growing demand for energy-efficient optical information processing motivates compact nonlinear photonic devices that can operate at low power. Silicon photonics is a mature platform for linear optical functions, but nonlinear operation remains challenging because of its weak Kerr response, two-photon absorption at telecommunication wavelengths, and limited compatibility with deeply subwavelength plasmonic confinement. This thesis computationally investigates epsilon-near-zero thin films integrated into plasmonic waveguide architectures as a route toward stronger light–matter interaction in compact nonlinear devices.

Two waveguide geometries are examined: a hybrid metal-insulator-metal plasmonic slab waveguide incorporating an ultrathin indium tin oxide epsilon-near-zero layer (5–50 nm), and a dielectric-loaded …

Perspectives On Interpretability For Neural Text Representations, Jia Peng Lim

Dissertations and Theses Collection (Open Access)

In this dissertation, we investigate interpretability in the three elements of learning neural text representations: inputs, passed into models, to produce probabilistic outputs. We emphasise perspectives as we present alternative novel methods to mine and organise meaning in this work.

Models. We initiate our investigation by examining Neural Topic Models (NTM), proposing an alternate angle of interpreting its word-topic distribution, producing better topic representations for interpretation. Our method maps the problem of finding these better interpretations to classical NP-hard graph problems, enabling examination of topic distributions in a composite manner. Next, we apply our previous findings to extract interpretations from …

Pyspqr: A Python Package For Density Estimation Using Deep Learning, Cameron Eddy, Reetam Majumder

Electrical Engineering and Computer Science Undergraduate Honors Theses

Splines are used for representing complex functions. In statistics, splines can be used for distributional shapes that are difficult to model by traditional parametric approaches. Ramsay (1) uses M-Spline bases to estimate continuous distributions. Semi-Parametric Quantile Regression (SPQR), developed by Xu and Reich (2), models conditional distributions where a neural network is used to estimate the basis function weights that depend on covariates. (3) implements a package for SPQR in R. We build on this by implementing a version of SPQR in Python with PyTorch. By using PyTorch, we can use more sophisticated deep learning architectures than those available in …

Evaluating The Use Of Extended Reality Technology To Improve Marching Band Conducting Patterns, Nathan R. Fuhrman

Electrical Engineering and Computer Science Undergraduate Honors Theses

Conducting pattern consistency is an essential skill for marching band drum

majors, yet developing this consistency through individual practice remains diffi-

cult without real-time feedback. This thesis investigates if the use of extended

reality technologies can be used to enhance the conducting skills of novice drum

majors. Using the Meta Quest 3’s passthrough capability, the system overlays vi-

sual feedback elements — including a 3D pattern guide, path visualization, tempo

cues, and a real-time score — onto the user’s physical environment. A within-

subjects study with seven participants evaluated eight combinations of three binary

feedback variables: pattern guide visibility, tempo …

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) …

Volume 17, Christian O’Neill, Kyara Greene, Savva Sidorov, Laura Bisaillon, Luke Clemmer, Hannah Gordon, Kitt Benson, Taylor Blount, Rachel Danzitz, Nicholas Duellman, Chase Gionis, Hima Fernando, Seth Franzyshen, Onyx Gonzalez, Bryan Lin, Samantha Start, Ysabel Wells, Maggie Duncan

Incite: The Journal of Undergraduate Scholarship

Introduction Dr. Amorette Barber, Director, Office of Student Research

From the Editor Dr. Hannah Dudley-Shotwell

Cover Artist’s Statement Maggie Duncan

On Mentoring Dr. Yulia Uryadova

Ukrainian Resistance in the Face of Russification: Nestor Makhno and Anarchism

by Christian O’Neill

Life Vest by Kyara Greene

Isolation and 16S rRNA Identification of Bacteria from Fire Department Connection Pipe by Savva Sidorov

The Effectiveness of Planned Exercise in Reducing ADHD Symptoms in Children by Laura Bisaillon & Luke Clemmer

Linguistic Analysis on Confidence and Communication Strategies with Disparities Between Sign Fluency and Hearing Impairment by Hannah Gordon

Freedmen in Indian Territory by Kitt …

Analyzing The Evolution Of Science: Topological Cycles And Community Detection In Knowledge Networks, Frances C. Mcconnell

Mathematics, Statistics, and Computer Science Honors Projects

How scientific knowledge grows and organizes itself is a central question in the study of science. This thesis uses tools from topology and network science to detect and characterize knowledge gaps—places in a field’s literature where related concepts do not co-occur. We develop a metric to quantify the degree of interdisciplinarity of each gap, using the community structure of the underlying network as a proxy for subfields. Across a wide range of fields, gaps reliably span multiple subfields and evolve in recognizable temporal patterns, highlighting new insights into how scientific fields are structured and their stage of development.

Search For Slow-Moving Magnetic Monopoles With An Improved High-Energy Event Removal Algorithm, Reeshi N. Gihosal

Honors Theses

Fermilab’s NOvA (NuMI Off-axis 𝑣𝑒 Appearance) experiment focuses on understanding the behavior of neutrinos and how they affect the cosmos. A sub-focus of the NOvA experiment is the search for magnetic monopoles. These elusive particles have not yet been observed in nature, leaving their behavior to be mysterious. The Far Detector, located in Ash River, MN, is integral in the search for these particles. This project is on simulated magnetic monopoles with speeds thousandths the speed of light, with focus on the role of slicing algorithms in event reconstruction using the NOvA experiment’s reconstruction algorithm. Using sample data, analysis occurred …

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 …