Computer Engineering Commons^™

Between Blockchain And Black Markets: South Africa's Legal Readiness For Crypto-Driven Cyberfraud, Sagwadi Mabunda, Yassin Chande

Communications of the IIMA

This paper examines whether the proliferation of cryptocurrency-facilitated fraud warrants a reclassification of the terrestrial crime of fraud into the distinct statutory offence of cyberfraud under South African law. Engaging with established fraud typologies — exit scams, Initial Coin Offering (ICO) scams, Ponzi schemes, pump-and-dump schemes, and market manipulation — the article tests their definitional fit against both the common law of fraud and section 8 of the Cybercrimes Act 19 of 2020. Through a hypothetical composite scenario combining multiple fraud typologies, the article demonstrates that whilst cryptocurrency significantly amplifies the reach and complexity of fraudulent schemes, it functions primarily …

Harmonicthreads: A Formative Evaluation Of A Fabric-Based Digital Musical Instrument Toward Inclusive Music-Making, Ellie Nguyen, Franceli L. Cibrian

Engineering Faculty Articles and Research

Background:

Inclusive music-making requires instruments that support varied bodies, abilities, musical backgrounds, and forms of participation. Digital musical instruments provide diverse approaches to sound creation, and fabric-based interfaces offer an alternative interaction modality that may support participation for some users and contexts. Their tactile and deformable properties enable forms of interaction that differ from conventional rigid or screen-based controllers and may offer inclusive possibilities in particular settings.

Objective:

This paper presents HarmonicThreads as a formative interaction-design case of a fabric-based digital musical instrument. The prototype explores how tactile cues, fabric deformation, projected visual feedback, and assisted accompaniment can support low-barrier …

Ai Cyber First Responders: Bottleneck Analysis Of Heterogeneous Cpu–Gpu Pipelines For Security Operations Center Triage, Christine Pierce

Harrisburg University Other Works

Abstract — Modern Security Operations Centers (SOCs) must continuously process massive volumes of heterogeneous security telemetry while meeting stringent throughput, latency, and operational continuity requirements. Although transformer-based artificial intelligence has significantly improved threat detection and alert prioritization, most cybersecurity research evaluates model accuracy rather than the end-to-end behavior of AI-enabled operational pipelines. Consequently, relatively little is known about how heterogeneous CPU–GPU coordination, scheduling overhead, memory movement, and synchronization collectively influence operational SOC performance. This paper presents the AI Cyber First Responder, a heterogeneous SOC triage architecture that integrates GPU-accelerated transformer inference with CPU-based doctrine-driven reasoning to investigate end-to-end pipeline behavior …

Hyperchaotic Noise Generation For Adversarial Encryption In Privacy-Preserving Image Classification, Neeraja Beesetti

Master's Theses

Artificial intelligence systems make useful predictions by taking in data and returning a classification, recommendation, or decision. Obtaining that prediction, however, requires sharing the data first. This creates a fundamental privacy challenge in machine learning: users must expose their data to receive a valuable prediction. Machine learning systems increasingly rely on cloud-based image classification for this reason, transmitting images from edge devices to remote servers rather than running large models locally. This creates a conflict between the accuracy a classifier requires and the privacy a data owner wants. Traditional encryption destroys the image structure on which a classifier depends, while …

Developing A Natural Language Interface For Knowledge Graphs, Ruth Assefa, Sarah Mendoza, Luke Voinov, Oyku Serap Ogut, Nurcan Yuruk

SMU Journal of Undergraduate Research

This paper proposes to solve the challenge of making databases more user-friendly by interfacing them with OpenAI's ChatGPT-3.5 model. We implemented this solution to assist researchers in easily finding others with similar research interests. Our study involves 184 researchers from 14 departments at Southern Methodist University (SMU). We collected researchers' areas of expertise and biographies and stored them in a Neo4j graph database. We used OpenAI's embedding models to create vector representations of the collected data, allowing for accurate similarity assessments via Neo4j's built-in algorithms. By integrating this system with LangChain, we enabled natural language queries. The results demonstrated high …

Case Study: Feasibility Of Creating A Simulated Mobile Data Center For Cross-Disciplinary Academic Programs, Stanley Mierzwa, Christoper J. Schultz, Iassen Christov, Michael Fagioli, Thomas Ikeda, Reinaldo Jaramillo, Giolian Sanagustin

Center for Cybersecurity

This case study examines the potential to envision, create, and deploy a simulated mobile micro data center solution that can be easily replicated and transported between locations and educational settings. The coined term for this solution is the Mobile AI-Centered Data Center (Mobile ACDC), which provides students with a platform to construct, in a hands-on fashion, such a solution and navigate the product to gain greater competencies and understanding of the components found in a data center. Instructor and student feedback assessments from the pilot classroom modules and laboratory experiential learning activities indicate that such a solution helps to improve …

Assessing Computer Vision Based Conflict Detection In Uas Traffic Monitoring Under Secure Communication Constraints, Fadjimata Issoufou Anaroua

Doctoral Dissertations and Master's Theses

The rapid growth of Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) is creating a new low-altitude airspace ecosystem where drones, air taxis, service suppliers, communication networks, sensors, and ground-based monitoring systems must work together safely. Within this ecosystem, UAS Traffic Management (UTM) is expected to provide a digital framework for coordinating operations beyond traditional air traffic control. However, reliable integration also requires resilient monitoring methods that can detect non-cooperative aircraft, protect communication links, and maintain timely situational awareness under real-world constraints.

This dissertation examines how computer vision can support cooperative monitoring systems such as Remote ID and ADS-B …

Design And Implementation Of Cyber Risk Management In The Indonesian Aviation Sector Based On Nist Csf And Iso/Iec 27002:2022, Annisa Aulia Budianti Qurota'aini, Ira Rosianal Hikmah, Yulial Hikmah

Jurnal Vokasi Indonesia

As technology usage increases, cyberspace in Indonesia has developed significantly, including in Vital Information Infrastructure (VII) sectors such as aviation. However, this advancement introduces potential cyber threats that can disrupt operations. This research aims to design a systematic cyber risk management framework for a navigation authority within the Indonesian aviation sector using the National Institute of Standards and Technology Cybersecurity Framework (NIST CSF) as the primary standard. This research also utilizes national aviation regulations, Center for Internet Security Controls (CIS Controls) v8.0, and ISO/IEC 27002:2022 as supporting frameworks. The research employs a qualitative descriptive approach, gathering data through field observations, …

A Cross-Dataset Vision Transformer Study For Brain Tumor Mri Image Classification, Sharon Kawira Mungania

Masters Theses

Brain tumor MRI classification is an important medical-imaging task because MRI scans contain complex anatomical patterns that can be time consuming to interpret manually. This study evaluates whether a pre-trained Vision Transformer can classify brain tumor MRI images consistently across datasets with different class structures. Three publicly available Kaggle datasets were used: Nickparvar, Br35H, and Figshare. Nickparvar and Figshare were treated as multi-class classification tasks, while Br35H was treated as a binary tumor/no-tumor task. Images were converted to three-channel format, resized to 384 × 384 pixels, normalized using ImageNet statistics, and augmented during training. The selected model was ViT-Base Patch …

Trustworthy Intelligence Fusion For Search And Rescue: Designing Grounded And Secure Multi-Agent Ai For Mission Decision Support, Yayun Tan

Master's Theses

Search and rescue (SAR) operations require teams to integrate uncertain information under severe time pressure. Large language model (LLM)-based multi-agent systems (MAS) can support decision-making, but they also risk producing hallucinated outputs and processing unreliable or malicious data. This thesis addresses these risks through three linked studies. First, it proposes a modular eight-agent SAR MAS architecture that reflects the structure of real SAR missions by assigning specialized roles to different agents. Second, it introduces a post-hoc probabilistic verification framework that checks LLM agent outputs against a probabilistic knowledge graph built from historical SAR incidents.  Third, the thesis examines indirect adversarial …

Reproducing And Evaluating Charger Surfing: Robustness Of Smartphone Charging-Line Side-Channels, Colby M. Watts

Master's Theses

Smartphones are frequently connected to external, untrusted charging hardware, creating opportunities for side-channel attacks that do not require malware or direct access to device data. Charger Surfing, a recently proposed charging-line power analysis side-channel attack, reported high accuracy in inferring touchscreen input from voltage measurements collected from a smartphone’s charging cable; however, the reproducibility and robustness of these results under different conditions remain unclear. This thesis presents an independent replication and evaluation of Charger Surfing, including the development of an end-to-end data collection pipeline consisting of a modified charging cable, oscilloscope-based recordings, custom Android app, automated trace processing, and convolutional …

Stock Market Analysis And Volatility Behavior During The Covid-19 Pandemic, Manoj Venkatachalaiah, Soon Leong Yeap, Salman Ahmed Lnu, Sangwhan Cha

Harrisburg University Other Works

This report outlines the structural design, cloud implementation, and analytical findings of a scalable Big Data architecture deployed on Google Cloud Platform (GCP). The primary objective is to investigate the macroeconomic and microeconomic disruption caused by the COVID-19 pandemic on global equities, focusing on two dominant digital business models: online retail/cloud computing (Amazon, Inc. - AMZN) and subscription-based digital streaming entertainment (Netflix, Inc. - NFLX). Through a serverless orchestration pipeline leveraging GCP Cloud Run, automated workflows fetched and blended high-velocity epidemiological metrics alongside daily financial asset layers. Data transformations and parallel analytical calculations were executed utilizing Apache Beam pipelines inside …

Polysaber: A Custom Reactive Lightsaber Soundboard, Pedro B. Medeiros

Computer Engineering

The PolySaber project was developed as a custom reactive lightsaber control system built as a fully custom PCB design. The purpose of the project was to create a lower-cost and more customizable alternative to commercially available lightsaber soundboards while simultaneously providing hands-on experience in PCB design, embedded systems development, and hardware integration. Commercial lightsaber soundboards are expensive, proprietary, and difficult for hobbyists to customize. The PolySaber project addresses this by creating a modifiable hardware platform built around the ESP32 microcontroller. The system supports programmable firmware, RGB NeoPixel blade control, motion sensing, reactive swing and clash effects, onboard audio amplification, and …

Design And Parametric Study Of A Mems-Based Reservoir Computer For Reinforcement Learning, Andrew P. Carr

Master's Theses

Single-node reservoir computing (RC) is a hardware-efficient approach to machine learning, leveraging the dynamics of physical systems. In this work, two reinforcement learning algorithms, Q-learning and Proximal Policy Optimization (PPO), are applied to a simulated micro-electro-mechanical system (MEMS)-based reservoir computer to solve both discrete and continuous control tasks. MEMS-based reservoirs are low-power, compact, and their natural frequencies (kHz to MHz) pair well with real-time control loops. To explore the relationship between reservoir dynamics and learning performance, a parametric study is conducted on two reservoir hyperparameters, reservoir size and neuron separation, using CartPole-v1 and MountainCar-v0. The RC successfully learns multiple tasks …

Omama-Db: The Oregon-Massachusetts Mammography Database, Avanih Kanamarlapudi

Graduate Masters Theses

Public datasets for training AI models in breast cancer screening are limited in size and quality, making it difficult to develop reliable systems. We introduce OMAMA-DB, an extensive publicly available collection of 2D mammograms and 3D tomosynthesis volumes. Starting from 967,991 images, we created a curated set of 231,080 images us ing a multi-stage filtering process that removes missing labels, uncommon dimensions, rare scanner types, duplicate studies, and invalid DICOM files. All 2D images then undergo additional outlier detection using histogram filtering and a variational autoen coder to remove low-quality outliers. OMAMA-DB includes pathology-based cancer labels and automated lesion annotations …

Evaluating Soil Health And Crop Yield In Louisiana Agricultural Systems: Impacts Of Best Management Practices And Prediction Models, Hector J. Mendoza Lagos

LSU Doctoral Dissertations

The adoption of conservation management practices is critical for improving soil health, enhancing nutrient use efficiency, and sustaining crop productivity in row crop systems in Louisiana. This study evaluated the role of conservation agronomic practices, soil biochemical indicators, and machine learning predictive models to improve soil nutrient dynamics, soil health indicators, microbial communities (MC), and crop productivity on a corn (Zea mays L.) research plot scale and in a commercial forty-hectare cotton (Gassypium hirsutum L.)-corn-soybean (Glycine max L.) rotation system in northeast Louisiana. The objectives of the study were to evaluate soil nutrient dynamics and MCs under …

Decision Making At Triage Classification Using Svm With Smote Technique, Mehanas Shahul, Pushpalatha Kp

Northeast Journal of Complex Systems (NEJCS)

The efficient functioning of triage gates in overcrowded emergency departments (EDs) occurs in the context of the complex adaptive system (CAS) framework, where diverse system elements – patients, medical personnel, resources, patients’ inflow patterns, and patients themselves – simultaneously and dynamically influence the decision process. This study addresses the automated incorporation of machine learning triage algorithms as part of the system triage process to support automated classified risk-level recognition based on a limited set of vital signs. Patients are dynamically subsumed under high and low-risk categories enhanced by sensitivity, which enables optimal diagnosis and triage response to the critical clinician …

Real-Time Fraud Detection, Haidi Aly Fahmy, Abdelhadi Nait-Zerrad, Shiva Krishana Reddy Ravuula, Sangwhan Cha

Harrisburg University Other Works

Financial fraud detection is a high-volume, high-velocity analytics problem. Traditional rule-based systems are often easy to deploy, but they are limited by static thresholds, delayed response, high false-positive rates, and weak explainability. This report presents a formalized end-to-end Big Data architecture for real-time fraud and anomaly detection in financial transaction streams.

The proposed architecture ingests transaction events through AWS Kinesis, enriches them through an Apache Flink stream-processing layer, scores them with an XGBoost classifier, explains model outputs using SHAP, and converts structured evidence into human-readable summaries through a controlled GPT explanation layer. Results are persisted through a hybrid storage strategy …

Predictive Natural Language Metrics Of Alzheimer's Disease And Cognitive Decline Trend Analysis, Zerui Ma

Computer Science and Engineering Theses and Dissertations

Inspired by Dr. David Snowden's Nun Study, which linked early-life Propositional Idea Density (PID) to later-life Alzheimer's disease, this thesis investigates two questions: whether fine-tuned Transformer-based large language models (LLM) can detect cognitive decline from patient speech transcripts with meaningful feature attribution, and whether longitudinal PID trends are observable across large-scale internet and academic text corpora. We evaluate dementia prediction on the DementiaBank Pitt Corpus and conduct an exploratory longitudinal PID analysis across seven diverse datasets spanning up to 29 years and over 12.6 million documents. This work suggests that linguistic ability metrics, traditional PID metrics and novel LLM-based analysis, …

Machine Learning And Formal Methods In Quantum Chemistry: Theory And Application, Ishna Satyarth

Computer Science and Engineering Theses and Dissertations

In recent years, the progress in inter-disciplinary application of machine learning and artificial intelligence (ML/AI) have truly transformed various fields, from weather forecasting and drug development to medical diagnostics, energy, and sustainability. Computational chemistry uses computational tools to model, predict, analyze, and explain chemical phenomena, while the Quantum chemistry specifically uses techniques based on quantum mechanics (as opposed to classical mechanics or empirical models). Quantum chemistry or Computational chemistry has also observed a momentum in application of ML techniques over the past decade significantly accelerating results and providing valuable insights into vast datasets, often surpassing traditional methods.

This dissertation explores …

Native Wayland Compositing On Apple Ecosystems: Assessing The Feasibility Of “Wawona” Compositor, Alex Spaulding

2026 Symposium

The Wayland display protocol is the modern standard for Linux window management, emphasizing security, performance, and simplicity. Expanding this ecosystem to macOS, iOS, and Android introduces technical hurdles due to proprietary windowing systems and divergent hardware APIs. This research evaluates the feasibility of developing a native Wayland Compositor for Apple and Android, given the closed nature of these ecosystems.

“Wawona” bridges this gap by architecting a native Wayland Compositor capable of executing unmodified Linux applications. The methodology involves implementing the Wayland protocol stack into native abstractions leveraging Metal, Android’s graphics pipeline, and CoreAnimation.

Evaluating Team Formation Strategies With Algorithms, Ethan E. Lopez, Andy Vo

Student Scholar Symposium Abstracts and Posters

Effective teamwork is essential for collaborative learning, yet forming compatible student groups remains a persistent challenge. This study explores how algorithmic team-building methods influence students’ social and academic compatibility overtime. To support this investigation, we designed a visual survey system called “The Disco Ball." Evaluating work ethics, lifestyles, behaviors, and communication styles, students were initially assigned to project teams using one of three conditions: (1) random assignment (control), (2) a “most matches” condition maximizing similarity between students, and (3) a “diverse communication styles” condition designed to balance differing interaction preferences. After an initial collaboration period, students were given the opportunity …

Effectiveness Of Quiz-Based Interventions In Virtual Reality Lectures On Learning, Mason T. Prather

Master's Theses

This thesis examined whether quiz-based interventions affect learning during an immersive virtual reality (VR) lecture and whether quiz timing strategy matters when prompts are delivered on a fixed timer or through gaze/Region of Interest (ROI)-based attention timing. The study used an eye-tracking virtual reality headset and a game engine-based classroom application. The final completed cohort included 29 participants assigned to three between-subjects conditions: No Intervention (n = 10), Timer-Based Intervention (n = 9), and Attention-Based Intervention (n = 10). All participants viewed the same virtual reality lecture and completed the same 15-item post-lecture assessment. Mean learning scores were 44.00% for …

Parallelism In Java, Matt Vierow

Departmental Honors & Graduate Capstone Projects

Parallelism and multithreading have become an important facet of programming in recent years. Much work has been done in programming languages such as C and C++ with libraries such as OpenMP. However, less effort has been made to create parallelism tools in higher level languages such as Java. This is important because most programmers learn the basics in languages such as Python and Java that lack parallel tools like OpenMP. This means they must directly manage threads which requires knowledge of object-oriented programming and design.

This project sought to incorporate some elements of OpenMP into Java to allow newer programmers …

Robust Hardware Trojan Detection Leveraging Dual‑Domain Features And Stacked Ensemble Learning, Sefatun-Noor Puspa

All Theses

In Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). These ICs are vulnerable to malicious hardware attacks, with hardware Trojans being one of the stealthiest threats. Trojans are malicious implants in the circuitry, which are often inserted during design or fabrication stages. This stealthy addition remains dormant until triggered and might cause functional disruptions or sensitive information leakage once triggered. Traditional IC validation methods, such as functional testing and logic analysis, usually fail to capture these subtle anomalies because hardware Trojans are intentionally designed to mimic normal circuit behavior. They often remain dormant under …

Hdra-Fusion: Hybrid Detection With Routed Architecture For Manipulation-Aware Ai Face Forgery Detection, Omar Ebeid

Theses and Dissertations

With the rapid advancements in artificial intelligence-based image generation and manipulation tools, it is extremely difficult to detect if an image is genuine or artificially crafted. Despite extensive research in this area, existing image detection systems suffer from three major problems: suboptimal cross-dataset generalization due to shortcut learning of dataset-specific patterns, unreliable probability estimates due to domain shift, particularly in cross-manipulation evaluation settings, and an inability to detect images manipulated by multiple types of manipulations within a single detection framework. To address these limitations, we propose HDRA-Fusion (Hybrid Detection with Routed Architecture), a framework built on the conclusion that different …

A Machine Learning-Based Apogee Prediction Methodology For Experimental Student Rockets, Price Hamilton Drawdy

Senior Honors Theses

The ability to predict the maximum altitude of a rocket (apogee) in real-time is incredibly useful for collegiate-level competition rockets. This project creates a machine learning-based real-time apogee prediction methodology. Three model types were tested: linear regression, random forest, and a 3-layer multi-layer perceptron (MLP) neural network. These models were trained on a large dataset of simulated flights. All models performed well on simulated test flights, with the linear regression model showing most promise for use on edge compute. More development and real-world testing are necessary to determine how applicable this method is for real-time operation. Nevertheless, this methodology provides …

Generative Ai Of Breast Cancer Progression In Gene Expression Space, Xusheng Ai

All Dissertations

Breast cancer is one of the most common and deadly cancers in the world. Although doctors can use gene activity data to better understand different types of breast cancer, it is still difficult to identify the most important genes and to track how the disease changes over time. This is partly because gene data are very large and complex.

This dissertation develops a computer-based framework to study breast cancer using gene expression data. The work focuses on three goals: creating realistic synthetic gene data, identifying important genes linked to cancer, and modeling how breast cancer changes from normal tissue to …

Culturally Inclusive Usability Engineering (Ciue): A Framework For Evaluating Cultural Inclusion In Study Abroad Platforms, Louis Muhammad

Master's Theses

Study abroad programs provide students with opportunities to develop global com- petence and intercultural understanding. However, the digital platforms that support these programs often fail to communicate information that reflects the cultural and faith-based needs of diverse student populations. While prior research in Human-Computer Interac- tion (HCI) and usability engineering has explored cross-cultural and accessible design, cultural and religious considerations are rarely integrated into usability engineering pro- cesses. This thesis introduces the Culturally Inclusive Usability Engineering (CIUE) frame- work, which extends traditional usability engineering by integrating culturally informed con- siderations into the system development lifecycle. CIUE begins by identifying the …

Conceptual Model For Protecting Personal Data By Depersonalization In Information Systems: Principles, Components, And Life Cycle, Zarina Ildarovna Azizova

Chemical Technology, Control and Management

This article presents a systematic approach to personal data protection through depersonalization in the context of regulatory pressure and growing cyber threats. It proposes a comprehensive conceptual model that formalizes the de-identification process as a manageable sequence of steps, from attribute classification and method selection to mandatory verification of the result. The article also provides a comparative analysis of existing depersonalization methods in terms of their applicability within the proposed model. The model serves as a basis for the development of specific algorithms, as demonstrated by the example of a data shuffling approach.