Digital Commons Network^™

Implementing Rsa Accumulators For Asynchronous And Permissionless Reliable Broadcasting, Eric Webb

CCAC Theses and Dissertations

Asynchronous consensus protocols are essential for decentralized and trustless environments such as decentralized finance (DeFi), supply chain management, and voting systems. These protocols eliminate centralized authority and timing assumptions while improving resilience and security. However, as network sizes increase the communication overhead in these systems becomes a bottleneck that limits scalability and efficiency. One notable example is the Aleph protocol, which stands out as one of the first consensus protocols to be both asynchronous and permissionless while providing Byzantine Fault Tolerance. Unlike many existing asynchronous consensus mechanisms, Aleph is permissionless in nature and does not rely on a trusted dealer …

Environment And Intention Awareness For Navigation And Collaboration, Bhaskar Chandra Trivedi

Computer Science and Engineering Dissertations - Archive

Unmanned Aerial Systems (UAS) have become increasingly popular as versatile platforms for tasks such as surveillance, inspection, delivery, and maintenance. In many applications, UAS operate in environments frequented by people or containing sensitive infrastructure, which introduces physical risks in case of vehicle failure, as well as psychological and privacy concerns that may limit their acceptability. Ensuring safe and efficient operation thus requires that UAS consider these risks when planning navigation strategies. While prior information, such as city maps and building layouts, can partially inform risk assessment, such data is often incomplete, necessitating real-time augmentation of risk maps using sensor information. …

Scalable, Secure, And Adaptable Perception Systems Through Adversarial Analysis And Federated Fine-Tuning, Arkajyoti Mitra

Computer Science and Engineering Dissertations - Archive

Perception systems are fundamental to intelligent machines, enabling them to sense, understand, and interpret complex environments. However, as perception increasingly underpins critical applications such as autonomous vehicles, IoT healthcare devices, and smart trading platforms, challenges related to security, scalability, and environmental understanding have become more pressing. This work addresses three core research questions: (1) How can we identify, analyze, and mitigate adversarial vulnerabilities in perception systems to ensure reliable operation under adversarial conditions? (2.1) How can AVPS models be efficiently scaled and fine-tuned across decentralized and resource-constrained environments while preserving privacy and performance? (2.2) How can we scale generative models …

Parallel Algorithms For Large Scale Dynamic Graph Analysis, Arindam Khanda

Doctoral Dissertations

A complex system of interacting entities in contemporary scenarios, be it biological, technological, or social, can be represented using graphs. Dynamic graphs, unlike their static counterparts, are ones in which the underlying topology changes over time. These networks act as a model for numerous systems, from transportation to social interactions, capturing the ever-evolving nature of real-world phenomena. However, the inherent temporality of these networks presents a unique set of challenges and the traditional static graph algorithms often fall short in efficiency and applicability. In our research, we delve into the complexities presented by large dynamic networks and suggest various methodologies …

Insights In Cybersecurity Of A Smart Campus - A Review, Mircea Ţălu

Journal of Cybersecurity Education, Research and Practice

The profound impact of the Internet of Things (IoT) on various fronts, is driven by technological advancements, the ubiquitous spread of information, and the emergence of transformative events. IoT presents a diverse array of possibilities within university environments, fostering a more connected and enhanced educational experience. This research undertakes a comprehensive review of existing literature to provide context to the IoT and underscore its crucial significance in the realm of smart campuses. Additionally, the paper explores the intricate connections between IoT and key concepts such as cybersecurity and wireless sensor networks to present a holistic perspective. It delves into the …

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 …

Enhancing The Accuracy Of Image Classification For Degenerative Brain Diseases With Cnn Ensemble Models Using Mel-Spectrograms, Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

Mathematics & Statistics Faculty Publications

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice …

Perceptions Of Artificial Intelligence Use To Enhance Feedback For Preservice Teachers During Field Experiences, Betsy Schamber

Dissertations and Theses

University supervisors (USs) play a key role in providing feedback for preservice teachers (PSTs). Although artificial intelligence (AI)’s use in providing feedback in educational settings had been explored, its use for feedback for PSTs’ field experience remained unknown. The first case study herein explores PSTs’ perceptions of AI-assisted feedback for field experiences. Findings highlighted PSTs’ perceptions of AI as a catalyst for new idea generation. While AI provided a starting point, the ending output still needed to reflect PSTs’ personalities. Similarly, PSTs valued the human element of feedback, noting how USs’ lived experiences provided an added value. In K–12 classrooms, …

Embedding With Large Language Models For Classification Of Hipaa Safeguard Compliance Rules, Md Abdur Rahman, Md Abdul Barek, Abm Kamrul Islam Riad, Md Mostafizur Rahman, Bajlur Rashid, Md Raihan Mia, Hossain Shahriar, Guillermo Francia, Fan Wu, Alfredo Cuzzocrea, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to …

From Code To Motion: Adventures With Wall-A Robot, Simon Sarah Mampouya-Balende

A with Honors Projects

This essay is about the author's experience working on a robot and programming, and what they learned from the project.

Artificial Intelligence In Society: Transformations And Ethical Paradigm, Yashoda Urmilla Itwaru

Selected Full-Text Master Theses 2021-

The recent advancement and adoption of AI (Artificial Intelligence) make it vital to study the effects it has on individuals and communities. Adopting AI into daily life changes interactions such as the decision-making process and task performance. AI algorithms can shape a user’s online experiences, influence their preferences, beliefs, and behavior. However, people raised concerns regarding privacy, autonomy, and manipulation. For these reasons, debates regarding the ethical aspects of AI arose.

AI provides society with more tools to complete tasks. AI can automate data intensive jobs and physical tasks. This brings a sense of job insecurity, which affects the mental …

Edge-Enhanced Yolo V8 Architecture For Accurate Kl Assessment In Knee Osteoarthritis Imaging, Meghana Arikilla

Selected Full-Text Master Theses 2021-

Knee Osteoarthritis (KOA) is a degenerative joint condition characterized by the progressive narrowing of joint space and structural deterioration. The structural degradation of the joint space is evaluated using the Kellgren–Lawrence (KL) grading system, and accurate classification across all grades, specifically in the early stages, remains a challenge owing to subtle radiographic differences. This study presents an automated KL-grade classification framework that integrates joint edge enhancement with deep learning to improve KOA grading using radiographic images.

Edge detection filters, namely Sobel, Scharr, and Canny, were applied to X-ray images to enhance the joint space boundaries and osteoarthritic features. These preprocessed …

Exploiting Artificial Intelligence And Optimization For Smart Agriculture, Jackson K. Butcher

Theses and Dissertations--Computer Science

Dynamic integration of Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) has become a vital component for unlocking the potential of smart agriculture. Currently, limitations such as limited computational resources, poor network connectivity, and rigid treatment strategies stifle optimal agricultural outcomes. This creates a challenge of leveraging the capabilities of modern artificial intelligence to combat the natural and artificial constraints of the smart agriculture environment. The primary contribution of this thesis is the development of frameworks to alleviate the overhead data and computational demand for AI within smart agriculture settings. The first framework, iCrop+, utilizes TinyML and LoRa to guarantee high-precision …

Design And Implementation Of A Low-Cost Raspberry Pi And Ai-Based Intrusion Detection System For Surveillance, Metrine Nyaboke Osiemo

All Graduate Theses, Dissertations, and Other Capstone Projects

As security concerns continue to rise, there is a growing demand for affordable and intelligent surveillance solutions to ensure safety in homes, businesses, and other environments. Many individuals are embracing AI-driven technologies such as Closed-Circuit Television (CCTV), smart doorbells, and automated security systems to protect their properties. This project presents a design and implementation of a cost-effective AI-powered intrusion detection system utilizing Raspberry Pi 5 for home surveillance, with adaptability for broader applications. The system integrates a camera module and an LCD screen running on a Linux-based platform, with Python, and OpenCV as key software components. It employs dlib’s deep …

Teaching Object-Oriented Design Through Interactive Uml: A Dual-Approach Framework For Code-Based Generation And Direct Diagram Manipulation, Moses Kayuni

Masters Theses & Specialist Projects

This thesis introduces an interactive educational framework for teaching object-oriented design through UML class diagrams. The framework implements a dual-approach methodology: code-based generation, where students write Java code that automatically transforms into UML diagrams, and direct diagram manipulation, where students build diagrams by interacting with highlighted terms in problem descriptions. This approach addresses common challenges in teaching UML, including cognitive load difficulties, visualization problems, and the disconnect between code implementation and visual design. Built on the Mermaid diagramming framework, the system features a React frontend for diagram creation and manipulation, and a Flask backend that handles some code parsing and …

Teaming With Technology: Adaptive Automation In Joint Cognitive Systems For Industry 5.0, Jessica Johnson

Virginia Digital Maritime Center (VDMC) Faculty Publications

Adaptive automation enables dynamic reallocation of functions between people and autonomous agents to improve performance in complex work. This paper presents a meta-analysis of experimental and quasi-experimental studies (2000-2025) on joint cognitive systems in industrially relevant contexts, quantifying effects on task performance, safety/failure management, workload, trust, and learning. Across studies, adaptive automation reliably reduces operator workload and shows moderate gains in task performance and safety, with healthier trust dynamics when adaptations are triggered by human-state or event cues, made transparent to the user, and remain rapidly overridable. Risks emerge when performance-triggered switching is opaque or poorly timed, which can erode …

Credit Card Fraud Detection Via Model Retraining And Fine-Tuning, Anamol Khadka

Computer Science and Engineering Student Research - Archive

Credit card fraud detection is a critical task in financial systems, especially given the rarity and evolving nature of the fraudulent behavior. The highly imbalanced class levels of the fraudulent and non-fraudulent transactions make it a challenging classification problem to solve. This study investigates the effectiveness of machine learning models: Logistic Regression, XGBoost, and Multi-Layer Perceptron (Neural Network), evaluated under temporal retraining and fine-tuning scenarios using a publicly available, highly imbalanced dataset of European credit card transactions. The dataset includes 284,807 transactions, of which only 492 (0.172%) are labeled as fraudulent, making it a well-known example of an imbalanced classification …

Comparative Evaluation Of Traditional Machine Learning And Deep Cnn Models For Static Hand Gesture Recognition, Anamol Khadka, Prit Desai

Computer Science and Engineering Student Research - Archive

Hand gesture recognition plays a vital role in facilitating natural and intuitive human-computer interaction, with applications ranging from sign language translation to touchless control systems. This study presents a comparative evaluation of traditional machine learning models and a deep convolutional neural network (CNN) for static hand gesture classification. The experimental dataset comprises 24,000 training images and 6,000 testing images, spanning 20 gesture classes. Traditional models, including k-Nearest Neighbors (KNN) and Support Vector Machines (SVM), utilize handcrafted features such as convex hull, convexity defects, and Hu moments. In contrast, the deep learning approach fine-tunes a ResNet18 architecture to learn features directly …

Smart Irrigation System Using Iot And Lstm For Optimal Water Management, Farley Y. Ruiz

Electrical Engineering Theses - Archive

This thesis presents the design and implementation of a smart irrigation system that combines Internet of Things hardware with a Long Short-Term Memory (LSTM) neural network for predictive soil moisture management. The goal is an affordable and reliable solution that uses real-time sensor data and environmental data to schedule irrigation before the substrate moisture drops below its target range. The system integrates soil moisture, temperature, humidity, and sensors on an Arduino Nano that communicates wirelessly with a Raspberry Pi. The Raspberry Pi runs a Python/Flask backend that collects and processes data, executes the LSTM model, and serves a secure web …

Understanding Misinformation On Social Media Through Truthfulness Stance, Zhengyuan Zhu

Computer Science and Engineering Dissertations - Archive

Misinformation on social media has become a pervasive issue that profoundly influences public opinion and decision-making. As false or misleading claims circulate widely online, there is a critical need for analytical tools to understand how people react to such claims. This dissertation introduces the concept of truthfulness stance as a key lens for social sensing. In essence, truthfulness stance assesses whether a textual utterance believes a factual claim to be true, false, or expresses a neutral stance or no stance toward the claim. Leveraging stance in this manner fills an important gap in misinformation research: it enables us to gauge …

Integrating Data Management Plans Into The Unified Architecture Framework Standards Views, Cansu Yalim, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

System Architecting translates an operational concept into a model of the system to be realized. There is a need for a Data Management Plan (DMP) to be included in the overall system engineering process with the advent of Digital Engineering. Data longevity, accessibility, and integrity can all be improved throughout the system's lifecycle by a well-defined DMP. System engineers use an architecture framework to arrange the system data into several sets of viewpoints. Incorporating a DMP at this point specifies the procedures for gathering, storing, retrieving, and maintaining data to ensure that all interested parties have access to current, correct …

Detection Of Data Leakage And Disruption Of Covert Timing Channel In Secure Drone Communication Using Machine And Deep Learning, Jonathan Walatkiewicz

Master's Theses and Doctoral Dissertations

The utilization of recreational drones has experienced a substantial increase in both the United States and globally. However, it is noteworthy that most drones, classified as Internet of Things devices, are produced with a limited security lifecycle. This study's findings are of paramount importance, as traditional computing exploits can be applied to drones, designating them as high- value targets. This study examines the detectability and disruptability of covert timing channel traffic in secure drones. The investigation aims to ascertain the effects of multiple interarrival times, distances ranging from 1 to 330 feet, various detection algorithms, and stream sizes between 32-bit …

Deep Learning-Based Ensemble Two-Step Classification Of Medical Images Using Cnn Architectures And Ensemble Methods, Noreliz Alorico

Master's Theses or Doctor of Nursing Practice

Breast cancer remains one of the most common cancers amongst women globally. Early detection is crucial for improving survival rates. While mammography is widely used and an effective imaging technique, it can sometimes yield false positive or false negatives. Mammogram interpretation is highly operator-dependent, introducing variability and the potential for diagnostic errors. Additionally, mammographic images have limitations, such as low contrast in breast tissue and overlapping structures that can obscure lesions or mimic abnormalities. These limitations can lead to unnecessary biopsies or delayed diagnosis. These challenges highlight the needs for advanced and data driven diagnostic tools to support and enhance …

A Study Of User Experiences Of Pediatric Physicians With Electronic Health Record Systems: Encounters With Task Complexity And Efficiency Of User Task Flows, Roseanne Alhindi

CCAC Theses and Dissertations

way patient information is stored, managed, and accessed. This transition to Electronic Health Record (EHR) systems has enhanced the efficiency and accuracy of healthcare delivery by enabling quick access to patient records, reduction of errors, and facilitation of coordination among healthcare providers. In the EHR system, diverse tasks are performed for clinical processes and patient care. These tasks can be considered simple or complex, ranging from documenting patient visits and updating medical histories to ordering tests and managing prescriptions. Although EHR systems have become more prevalent in their use, there are noted challenges associated with the design of the system …

Multi-Objective Bike Routing Problem: A Survey And Comparative Evaluation, Dominic Peter Macisaac

EWU Masters Thesis Collection

Multi-objective routing has been studied for over forty years, yet its application to the bike routing problem is a relatively recent development. The Multi-Objective Bike Routing Problem (MOBRP) seeks to optimize a set of bike routes from a single source to a single destination, given multiple cost criteria. This paper makes two primary contributions. First, it consolidates existing research on the MOBRP, including the criteria chosen and associated cost functions, the search algorithms used, and the testing setups and metrics used for evaluation. Second, it implements the most promising search algorithms and evaluates these approaches through comprehensive cross-testing, a topic …

Computer Science For Middle School (Cs4ms), Melani Loney, Lisa Steffian, Tancy J. Vandecar-Burdin

Center for Educational Partnerships Publications

This session will discuss the project Computer Science for Middle School and the associated findings. This Virginia Department of Education funded project provides computer science Professional Development opportunities for in-service teachers, located in Southeastern Virginia, who instruct students in middle school grades 6- 8. Participating teachers learn to integrate computer science standards into core and elective content through online, asynchronous microcredentials and in-person Summer Workshops. The presentation will include project findings and next steps including teacher efficacy for implementing the computer science standards before and after completing the microcredentials and their students’ attitudes about computer science.

Intergenerational Classification Of Reddit Comments Based On Slang And Emoji Usage, James T. Dracup

West Chester University Master’s Theses

The rapid evolution of language, driven by technological advancements, has created notable cultural gaps between generations, particularly in how they communicate. This gap is most apparent in the growing use of slang and emojis among younger generations. This study aims to explore whether Reddit comments can be classified by generation based on the usage of slang and emojis, the frequency of their use across generations, and how such features (slang and emojis) might influence the meaning of traditional language. Using Reddit’s API, we collected comments from four generational subreddits and applied various machine learning models, Naïve Bayes, Neural Networks, and …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Information Technology & Decision Sciences Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

Robust Palm Print For Mobile Authentication System, Son Nguyen

Chulalongkorn University Theses and Dissertations (Chula ETD)

Smartphones are gateways to financial, health, and personal data; consequently, mobile authentication must be accurate, fast, privacy-preserving, and scalable. This thesis presents an end-to-end palmprint authentication framework that addresses a practical trilemma: label-efficient learning, on-device efficiency, and cloud-scale identification. We pretrain a ResNet-18 encoder with self-supervised contrastive learning on unlabeled palm images, distill its representation to a lightweight MobileNetV3 student for real-time inference on phones, and support both 1:1 on-device verification and 1:N cloud identification using FAISS/HNSW. On public datasets, the system attains 99.2% accuracy, a 0.15% equal-error rate (EER), and ~87 ms end-to-end latency on iPhone-class hardware. FAISS scales …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Psychology Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …