Digital Commons Network^™

Generating Negotiations For Iago, Kylee R. Weener

Honors Undergraduate Theses

Negotiation is a complex field that can benefit from introducing artificial intelligence (AI); doing so would benefit researchers as they try to deepen their understanding of human-human and human-agent negotiation. Investigating how large language models (LLMs) can generate negotiation dialogue with emotional context would bring agents closer to acting more human. This study explores how fine-tuning and prompt engineering can achieve this goal and the possibilities for an AI that fills these criteria to be included in the Interactive Arbitration Guide Online platform (IAGO). Doing so will make the negotiation interactions in IAGO feel more complex and natural, allowing researchers …

Analysis Of Early Interventions To Retain Underrepresented Students In Computer Science, Michael Conti

Open Access Dissertations

Computer science, like many STEM disciplines, faces persistent challenges in recruiting and retaining women and individuals from racially and ethnically minoritized backgrounds. This study examines whether targeted interventions can produce sustained improvements in academic performance and sense of belonging among these underrepresented groups. By analyzing longitudinal data, this research aims to evaluate the effectiveness of these interventions in promoting equity and persistence in computer science education.

Predicting Biomechanical Risk Factors For Division - I Women’S Basketball Athletes, Aayushi Shah, Vanaja Agarwal, Dhairya Shah, Harman Jani, Sristi Sharma, Kaya Tolga, Christopher Taber, Mehul Raval

School of Computer Science & Engineering Faculty Publications

Collegiate basketball is characterized by high-impact movements such as jump landings, making athletes more susceptible to injuries. Critical biomechanical factors like knee flexion, lateral trunk flexion, and foot landing asymmetry are strongly associated with injury risk. This study aims to predict six biomechanical risk factors in the landing error scoring system (LESS). The dataset comprises 8600 video frames of counter-movement jumps (CMJs) from 17 NCAA Division I female basketball athletes, recorded from frontal and lateral perspectives and annotated using a customized error annotation algorithm. The study uses the You Only Look Once (YOLOv5nu) model to analyze the basketball athletes’ CMJ …

Navigating Together: The Conav Testbed And Framework For Benchmarking Cooperative Localization, Rohith Boyinine, Jayanth Ammapalli, Anusna Chakraborty, Rajnikant Sharma, Kevin Brink, Clark N. Taylor

Faculty Publications

This paper presents CoNaV, a comprehensive framework for creating a multi-vehicle cooperative localization (CL) testbed designed to support the benchmarking, development, and deployment of cooperative navigation algorithms. Given the essential role of CL in improving localization accuracy for both defense and civilian applications, CoNaV provides a robust environment for rigorously validating algorithms under real-world conditions. By establishing a benchmark for CL algorithms, CoNaV lays a foundation for advancing research into more sophisticated and distributed CL solutions. This framework highlights the potential of cooperative navigation to enhance multi-vehicle operations and offers a scalable, practical approach for future developments in CL technology.

Knowledge And Ontology Enhanced Approach To Natural Language Understanding (Koe-Nlu) In Computational Social Media And Healthcare, Naga Usha Gayathri Lokala

Theses and Dissertations

Natural Language Understanding (NLU) faces both opportunities and challenges as the amount of social media and healthcare data grows. This is particularly evident in context-sensitive applications such as evaluating cognitive health, identifying mental health symptoms, and monitoring drug abuse. Even though traditional NLU models work well for processing language in a wide range of areas, they often lack the ability to understand language in a specific domain, reason in context, and incorporate structured external knowledge. This dissertation talks about the Knowledge and Ontology Enhanced Approach to Natural Language Understanding (KOE-NLU), a new framework that is meant to make NLU systems …

Relationship Extraction Using Retrieval Augmented Generation For Biomedical Dataset, Jannat Jannat

Theses and Dissertations

With the increasing number of structured and unstructured data, obtaining reliable information effectively has become crucial. In the biomedical domain, extracting information from the scientific papers is crucial in order to stay up-to-date with accurate information, given the increased pace by which new research studies are published. This work focuses on identifying relationships between entities that are extracted from the abstracts and titles of biomedical research papers. In this work, we developed a Retrieval Augmented Generation (RAG) based system to automatically identify relations between biomedical entities. We evaluate multiple open source Large Language Models (LLMs) and the number of examples …

Application Of Machine Learning And Large Language Models In Healthcare For Data Prediction And Summarization, Chiazam Chisom Izuchukwu

College of Graduate Studies: Theses & Dissertations

This study aims to examine the use of machine learning (ML) and large language models (LLMs) in healthcare to enhance disease prediction, clinical decision-making, and information management. Five supervised ML models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT), and Naïve Bayes (NB)—on three different computing platforms—Google Colab, Databricks, and Snowflake—were employed for disease classification. Data preprocessing included treating missing values, encoding categorical variables utilizing one-hot-encoding, feature scaling when needed, and tackling class imbalance with Synthetic Minority Over-sampling Technique (SMOTE) before an 80-20 train-test separation. Models were created with Scikit-learn (Google Collab), Spark MLlib (Databricks), and …

Machine Learning Methods For Intrusion Detection And Response In Network Security, Ayomide Oyemaja

College of Graduate Studies: Theses & Dissertations

Intrusion Detection Systems (IDS) play a crucial role in computer network security by identifying malicious activities and potential cyberattacks. This thesis combines machine learning and cybersecurity by applying Reinforcement Learning (RL) in intrusion detection and response using the NSL-KDD dataset.

We designed and implemented a Q-learning framework where an agent learns to classify network traffic over time by interacting with the environment and receiving rewards based on detection accuracy. We also look at the importance of feature selection and classification techniques and how effective they are in improving model performance, reducing the complexity of computation, and producing more desirable results. …

Performance Of Standard Medical Mllms On Ecg Image Data, Prisha Anil

Masters Theses

This work presents a structured benchmarking study of multimodal large language models (MLLMs) applied to electrocardiogram (ECG) interpretation tasks. We evaluate three representative architectures: MedGemma, HuatuoGPT-Vision, and LLaVA-Med, across progressive experimental stages involving text-only structured prompt normalization, text–image fusion with ECG plots, and full multimodal fusion incorporating time-series signals. A standardized five-section cardiology prompt was designed to enforce consistent output structure and SCP-code alignment, enabling reproducible metric computation across models. Quantitative evaluation using BERTScore, token-level F1, and diagnostic accuracy demonstrates that HuatuoGPT-Vision achieves the highest semantic and diagnostic alignment, while MedGemma exhibits superior formatting stability and reproducibility. In contrast, LLaVA-Med …

Cognitive Map Generation For Vision And Language Navigation, Alexander Sandoval Mesa

Dissertations and Theses

Visual-Language Navigation (VLN) presents significant challenges for autonomous agents, such as robots and virtual assistants, particularly in complex, dynamic environments where the seamless integration of visual perception and natural language understanding is critical. Traditional VLN systems often struggle with effectively aligning language instructions and visual scene understanding, limiting their adaptability and navigation efficiency.

This thesis proposes a novel Cognitive Map-based framework that addresses these challenges by transforming natural language navigation instructions into structured graph representations. The Cognitive Map consists of nodes representing waypoints, landmarks, decision points, and edges encoding spatial relationships and navigational actions. These maps are generated using Large …

Pointer Land Game App With Unity, Isaac S. Mullison

Senior Honors Theses and Projects

In this project, I implemented my game concept, Pointer Land, using the Unity game engine. I have implemented the game in the past using other frameworks (such as Flutter), but I thought that doing this using Unity would help me gain two major types of experience: using cross-platform software frameworks in general and using Unity. I found this project to be helpful in doing that. I have found that, with every software framework that I learn, there are new concepts associated with the framework. For Unity, I quickly figured out that a lot of the scripting I was doing involved …

Large Scale Machine Learning Over Knowledge Graphs, Bedirhan Gergin

Electronic Theses & Dissertations (2024 - present)

Knowledge graphs (KGs) have become popular across various fields, providing convenient access to web-based knowledge while storing and formalizing domain-specific information. By analyzing KGs, patterns, connections, and dependencies can be identified across different data sources, enabling the inference of new knowledge from given facts. As the use of KGs expands, the size of modern KGs has grown significantly, making them impossible to process within the main memory of a single computer. Distributed computing offers a viable solution to this challenge by leveraging the combined capabilities of multiple servers within a cluster. This thesis explores how distributed computing can be effectively …

Copyright And Artificial Intelligence, Part 2: Copyrightability

Copyright, Fair Use, Scholarly Communication, etc.

This report by the United States Copyright Office addresses the legal and policy issues related to artificial intelligence (AI) and copyright as outlined in the Office’s August 2023 Notice of Inquiry (NOI).

The report will be published in several parts each one addressing a different topic. This part addresses the copyrightability of works created using generative AI. The first part, published in 2024, addresses the topic of digital replicas—the use of digital technology to realistically replicate an individual’s voice or appearance. A subsequent part will turn to the training of AI models on copyrighted works, licensing considerations, and allocation of …

Learning From Non-Stationary Data Streams, Gabriel Jonas Aguiar

Theses and Dissertations

The rapid growth of data from sources such as mobile applications, sensors, and network monitoring has increased the need for machine learning algorithms capable of handling non-stationary data streams. However, learning from such streams presents significant challenges due to their evolving nature and the presence of concept drift. One of the most complex issues is learning from imbalanced data streams, where shifting data distributions, combined with feature space drifts, complicate continuous adaptation. These challenges become even more pronounced in multi-class scenarios, which are common in real-world applications. Detecting concept drift in such contexts is particularly demanding, as it requires tracking …

Enhancing Cybersecurity Through Autonomous Knowledge Graph Construction By Integrating Heterogeneous Data Sources, Hatoon Alharbi, Ali Hur, Hasan Alkahtani, Hafiz Farooq Ahmad

Research outputs 2022 to 2026

Cybersecurity plays a critical role in today’s modern human society, and leveraging knowledge graphs can enhance cybersecurity and privacy in the cyberspace. By harnessing the heterogeneous and vast amount of information on potential attacks, organizations can improve their ability to proactively detect and mitigate any threat or damage to their online valuable resources. Integrating critical cyberattack information into a knowledge graph offers a significant boost to cybersecurity, safeguarding cyberspace from malicious activities. This information can be obtained from structured and unstructured data, with a particular focus on extracting valuable insights from unstructured text through natural language processing (NLP). By storing …

A Universal Implementation Of Radiative Effects In Neutrino Event Generators, Júlia Tena-Vidal, Adi Ashkkenazi, Lawrence B. Weinstein, Peter Blunden, Steven Dytman, Noah Steinberg

Physics Faculty Publications

Due to the similarities between electron-nucleus (eA) and neutrino-nucleus scattering (νA), eA data can contribute key information to improve cross-section modeling in eA and hence in νA event generators. However, to compare data and generated events, either the data must be radiatively corrected or radiative effects need to be included in the event generators. We implemented a universal radiative corrections program that can be used with all reaction mechanisms and any eA event generator. Our program includes real photon radiation by the incident and scattered electrons, and virtual photon exchange and photon vacuum polarization diagrams. It …

Including Individuals' Sense Of Self In Digital Information Privacy, Peter N. Meso, Solomon Negash, Humayun Zafar, Gurpreet Dhillon

Faculty Articles

The nature of contemporary digital ecosystems causes concerns that affect the person, the individual-self, an integral part of an individual’s information privacy calculus and hence a component of individuals’ Information Privacy Concerns (IPC). Yet, prior IPC models overlook self-focused concerns. This study articulates two constructs, termed “loss of autonomy” (i.e., autonomy) and “control over profiling” (i.e., profiling), that reflect individuals’ self-focused privacy concerns. Combining these new constructs with conventional IPC constructs that capture data-focused and device-focused concerns yields an IPC model made up of three dimensions: self-focused concerns, data-focused concerns, and device-focused concerns. The authors first develop instrument items for …

Automated Generation Of Malware Metadata Signatures, Joel Schott

Masters Theses

In advanced, targeted malware attacks, the custom software tools used to package and send malicious files and messages can lead to distinctive metadata values that facilitate creation of a malware metadata signature. Manual creation of these signatures requires expert domain knowledge and is time-consuming and error-prone. Our goal is to automate this process. We created several methods of automatically generating malware metadata signatures for ZIP files and emails. We evaluated these methods by comparing signatures generated with these methods to existing expert-created signatures. We found automated methods for ZIP files and emails that are capable of generating metadata signatures that …

Augmentation Of Quality Of Service, Security, And Trust In Edge-Enhanced Iot Networks Leveraging Blockchain, Kyle Matthew Whitlatch

Masters Theses

The meteoric rise of the Internet of Things (IoT) has led to multiple architectural schemas to handle the data these devices create. Edge-enhancement is a technique where groups of IoT report to a median layer to aggregate the data before relaying to the endpoint. These edges also open opportunities to perform more operations to ensure devices are behaving properly before committing the data to long term storage. By interconnecting these edges with a technology like blockchain, it is possible to have an interconnected and responsive system to ensure the Quality of Service (QoS) of the IoT devices within the architecture …

Constraint Programming For Optimized Degree Paths, Mitchell Lee Skaggs

Masters Theses

This work presents a degree planning tool developed as part of the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS) project which generates complete, valid, and personalized degree paths at any point from admission to graduation. This eliminates tedious calculation and double-checking, allowing advisors to focus on a student’s long-term plans and students to proactively explore potential degree paths. The original research contribution of this work is the use of a unified model for academic requirements to automatically translate complex, real-world curricula into a constraint programming model that can be quickly optimized based on personalized student criteria.

Automatically translating …

Leveraging Synthetic Data For Efficient Training Of Ai Models For Real-World Object Detection, Reinaldo A. Moraga

Graduate Research Theses & Dissertations

Modern computer vision (CV) systems largely depend on real-world data for training, which is costly in terms of time, materials, and resources. As industries push toward automation and Artificial Intelligence (AI) -driven solutions, the need for enabling more efficient model training is growing. The primary aim of this work is to explore a framework tailored for industrial applications that uses synthetic images generated from 3D models to train a CV model capable of real-world object detection. This approach seeks to reduce the time, cost, and resources typically required for training AI models with real-world data. This work presents a method …

A Study Of Pattern Of Cybercrime Abuse Of Individual Internet Users In Umuahia North Lga, Abia State Of South-Eastern Nigeria, Ogochukwu Favour Nzeakor, Rita Ngozi Okafor, Chibuike Ndubuisi Nwoke

International Journal of Cybersecurity Intelligence & Cybercrime

Although a number of studies exist on cybercrime and its abuses, little is known about the pattern of cybercrime abuses individual Internet users experience in Nigeria, especially the south eastern region. Using data collected via various methods, this study examines the pattern of cybercrime abuses of individual Internet users in Umuahia, Abia State, of South Eastern Nigeria. The result of the analysis of 1,067 samples drawn from 223,134 Internet users in Umuahia North LGA of Abia Sate showed that: while most users are victims of stolen ICT-gadgets (19%), fraud related offences (17%), and hacking (15%); they rarely fall victims of …

Automated Video Colorization Techniques For Enhanced Visual Realism And Computational Efficiency, Zahoor M. Aydam, Nidhal K. El Abbadi

Mesopotamian Journal of Computer Science

Automatic video colorization remains a challenging computer vision task, particularly when ensuring semantic accuracy and temporal coherence across dynamic, multi-scene content. Existing methods often rely on a single fixed reference image, which fails to adapt to abrupt scene changes or variations in lighting and texture. This study presents a hybrid deep learning framework that dynamically selects multiple reference images per scene using adaptive thresholds derived from the Structural Similarity Index Measure (SSIM) and deep features extracted via a ResNet50 backbone with Generalized Mean Pooling (GeM). The framework integrates three specialized modules pre-processing, reference image processing, and attention-based colorization—operating in the …

Is It Getting Better? An Evaluation Of Two Successive Generations Of Chatgpt In Answering Specialized Vascular Surgery Questions, Dongjin Suh, Quang Le, Leana Dogbe, Kedar Lavingia, Michael Amendola

Department Surgery Faculty Publications

Purpose: Large language models (LLMs) can generate clinically relevant text; however, their performance in highly specialized medical domains remains uncertain. This study evaluated ChatGPT-3.5 and ChatGPT-4 (OpenAI) using vascular surgery board–style questions from the Vascular Education and Self-Assessment Program, version 4 (VESAP4) and compared the two public model versions (June and November 2023).

Materials and Methods: All non-image VESAP4 questions (n=384) were presented independently three times to each model version (ChatGPT-3.5 June/November; ChatGPT-4, June/November). Outcomes included accuracy (proportion correct), consistency (same option letter across all three attempts and “consistently correct”), explanation length (word count), and modes of failure classified for …

Generalizing Classification Of Pilot Workload: Transfer Learning Versus A Jepa-Inspired Transformer Architecture, Naim Barnett, Shivani Nagrecha, Morgan Glover, Clayton Harper, Justin Wilson, James Maher, Eric C. Larson

International Journal of Aviation, Aeronautics, and Aerospace

Within the context of learning, there poses difficulty when objectively measuring human performance. In this work, we investigate the evaluation of human performance via its relation to the individual's mental capacity by classification of cognitive load within the domain of aviation. By utilizing a mixed virtual and physical flight simulation environment in conjunction with biometric sensing, we create and evaluate the predictive capabilities of a Joint-Embedding Predictive Architecture (JEPA) and compare the architecture and results to traditional methods for transfer learning and domain adaptation. We find that our JEPA inspired architecture can achieve more than 70% accuracy of cognitive workload, …

Afit Generative Ai Teaching Guidebook, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

AFIT is proud to highlight the Generative AI Teaching Guidebook, a resource designed to provide military educators with practical insights, strategies, and use cases for integrating Generative AI (Gen AI) into their teaching practices. Developed through a collaborative effort involving AFIT faculty across various departments within the Graduate School of Engineering and Management and the School of Systems and Logistics, this digital resource serves as a starting point for educators exploring how to leverage Gen AI in their classrooms. It offers accessible examples and best practices, ensuring utility for instructors of all technical backgrounds. The guidebook provides a comprehensive overview …

Afit Generative Ai Teaching Guidebook Synopsis, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

The main objective of this work was to bring together various perspectives on how to envision incorporating Gen AI capabilities into the learning environment and identify some best practices for their implementation. Any instructor who is interested in these capabilities but does not necessarily have a technical background can find pragmatic use of the examples provided. While the examples have a wide range of applicability, they are meant to serve as a starting point for educators to explore what would be beneficial to their educational environment, from traditional classroom settings to online continuing education courses.

Ml Model To Better Identify Instances Of Bullying Faced By Members Of The Lgbtq+ Community, Arslan Bisharat

Master's Theses

Cyberbullying poses a significant threat to online communities, with the LGBTQ+ community facing disproportionately higher rates of harassment. While existing cyberbullying detection systems have made progress in identifying general instances of online harassment, they often fail to capture the nuanced and context-dependent nature of LGBTQ+-targeted bullying. This thesis presents a novel approach to this challenge by developing SpectrumNet, an LGBTQ+-centric transformer-based model for cyberbullying detection. Our research was conducted in two phases. In Phase 1, we evaluated the effectiveness of pre-trained transformer models (RoBERTa, BERT, and GPT-2) in identifying LGBTQ+-related cyberbullying. Building on these findings, Phase 2 introduced SpectrumNet which …

Securing Secrets: Exploring The Aes Encryption And Key Security Capabilities Of Chatgpt, Kayla Taylor

Student Works

The development and increasing accessibility of generative artificial intelligence (AI) tools and large language models (LLMs) have allowed cryptographers to explore a variety of cryptanalysis problems in dynamic and interactive ways. Prompt engineering, the process by which input text is tested and refined to elicit a desired response from LLMs, is a nascent area of research that remains largely unexplored in many contexts, including cryptography. This study will explore the potential applications and limitations of prompt engineering in the context of Advanced Encryption Standard (AES) encryption and key security with OpenAI’s ChatGPT (GPT-4o) through two main objectives: First, given a …

Towards Human Explainable Digital Forensics: Generating Human Interpretable Evidence For Semantic Understanding In Manipulated Images And Text, Yuwei Chen

Electronic Theses & Dissertations (2024 - present)

Detecting and characterizing manipulations in digital media continues to pose a significant challenge within the field of digital forensics. Despite notable advancements, the discipline often remains in a reactive stance against emerging threats. Current state-of-the-art methods, typically evaluated within academic settings, fails to mirror the complexities of real-world disinformation scenarios. These methods generally prioritize high performance based on quantitative metrics, yet they demonstrate a considerable dependency on training data and lack adaptability to new novel attack signatures. With the rapid evolution of attack methodologies, the dependency on highly accurate models that do not generalize or adapt well to unseen threats …