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Articles 61 - 90 of 4315
Full-Text Articles in Computer Sciences
A Study Of Visualized Diagnostics In Early Stage Digital Twin Implementation Of An Industrial Control System, Michael R. Kinzel
A Study Of Visualized Diagnostics In Early Stage Digital Twin Implementation Of An Industrial Control System, Michael R. Kinzel
All-Inclusive List of Electronic Theses and Dissertations
Industrial Control Systems (ICS) are used for process control in almost all industries. An ICS combines Operational Technologies (OT) with Information Technologies (IT) to allow human supervision of a process through surveillance of process variables and manipulation of controlling elements such as valves to maintain stable process conditions. ICSs have been in-service for several decades and may remain operational past their technological service life. Organizational personnel interact with the ICS through visual displays that both indicate the process variables and also the controlling elements. The Human Machine Interface (HMI) allows visibility of the process and the ability to manipulate controlling …
Autonomous Deficiency Detection And Vision-Language Summarization For Underground Infrastructure On Embedded Edge Systems, Johny Lopez
LSU New Orleans Theses and Dissertations
Aging underground infrastructure poses significant risks to public health and environmental safety, yet structural condition assessment remains bottlenecked by labor-intensive manual CCTV inspections. This thesis proposes a comprehensive algorithmic framework enabling fully autonomous, real-time deficiency detection, geometric assessment, and natural language reporting on resource- constrained edge computing platforms. Three core components address this challenge. First, RAPID-SCAN, a novel semantic segmentation architecture utilizing a Dynamic Feature Pyramid Network and Channel-Spatial Attention, achieves real-time, pixel-precise defect localization with dramatically reduced parameters. Second, an Edge-Optimized Vision-Language Model pipeline employing LoRA and 4-bit QLoRA quantization compresses Phi-3.5 for local deployment, en- abling autonomous technical …
Post-Quantum Cryptography Encryption Implementation For Messaging App, Callum S. Ward
Post-Quantum Cryptography Encryption Implementation For Messaging App, Callum S. Ward
Theses/Capstones/Creative Projects
This paper and complementary capstone project aim to explore the state of post-quantum cryptography today by defining the algorithms with which quantum computers can decipher modern asymmetric cryptographic algorithms in exponentially accelerated time, exploring national standards body NIST’s recommendations to circumvent these weaknesses with post-quantum solutions, and implementing recommended algorithms in my group’s project for the UNO Computer Science Capstone course, LockTalk. After having decided on ML-KEM for quantum-resistant asymmetric key transfer and AES-256 for symmetric message encryption and decryption, I was able to cryptographically encode messages to obscure their plaintext values from communication interceptions without any discernible increase in …
Gencode: A Generic Data Augmentation Framework For Boosting Deep Learning-Based Code Understanding, Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon, Jianjun Zhao
Gencode: A Generic Data Augmentation Framework For Boosting Deep Learning-Based Code Understanding, Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon, Jianjun Zhao
Research Collection School Of Computing and Information Systems
Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code data that automatically helps developers prepare training data lacks study in this field. In this paper, we introduce a generic data augmentation framework, GenCode, to enhance the training of code understanding models. Simply speaking, GenCode follows a generation-and-selection paradigm to prepare useful training code data. Specifically, it employs code augmentation techniques to generate new code candidates first and then identifies important ones as the training data by influence scores. To evaluate the effectiveness of GenCode, we …
Know Thy Enemy: Building A Command-And-Control Solution For Adversarial Emulation, Caleb J. Chen
Know Thy Enemy: Building A Command-And-Control Solution For Adversarial Emulation, Caleb J. Chen
Senior Honors Theses
Command and Control (C2) is a critical part of any cyberattack. It serves many purposes, including Distributed Denial of Service (DDoS) attacks, data exfiltration, and malware deployment. Consequently, C2 frameworks play an important part in red team engagements and adversary emulation. However, many adversary emulation solutions focus on comprehensive testing through sequential technique execution instead of realistic chained and automated attacks. The proposed solution is Centurion, an open-source C2 framework that integrates MITRE's ATT&CK framework and several cybersecurity tools into modular playbooks for effective threat emulation. This paper provides background by defining key terms and concepts before delving into a …
Open Source Software Development Tool Installation: Challenges And Strategies For Novice Developers, Larissa Salerno, Christoph Treude, Patanamon Thongtanunam
Open Source Software Development Tool Installation: Challenges And Strategies For Novice Developers, Larissa Salerno, Christoph Treude, Patanamon Thongtanunam
Research Collection School Of Computing and Information Systems
As the world of technology advances, so do the tools that software developers use to create new programs. In recent years, software development tools have become more popular, allowing developers to work more efficiently and produce higher-quality software. Still, installing such tools can be challenging for novice developers at the early stage of their careers, as they may face issues such as compatibility problems (e.g., with operating systems) and unclear instructions. Therefore, this work aims to investigate the challenges novice developers face when installing software development tools and the strategies they employ to overcome them. To investigate these, we conducted …
Securing Cloud-Native Systems: From Vulnerability Analysis To External And Insider Threat Detection, Jiongchi Yu
Securing Cloud-Native Systems: From Vulnerability Analysis To External And Insider Threat Detection, Jiongchi Yu
Dissertations and Theses Collection (Open Access)
Cloud-native systems have become the backbone of modern software infrastructure. However, their dynamic resource orchestration and complex configurability introduce a large attack surface and intricate security challenges. Adversaries can externally exploit vulnerabilities in cloud components or perform insider movement within cloud environments to launch attacks. As these systems increasingly support critical services, security breaches can lead to severe operational and economic consequences.
Despite extensive efforts in vulnerability detection and attack monitoring, existing approaches struggle to remain effective in cloud-native environments characterized by rapid evolution and inherent heterogeneity. In particular, they exhibit three fundamental limitations: (1) Insufficient understanding of defect patterns …
Natural Adversaries: Fuzzing Autonomous Vehicles With Realistic Roadside Object Placements, Yang Sun, Haoyu Wang, Christopher M. Poskitt, Jun Sun
Natural Adversaries: Fuzzing Autonomous Vehicles With Realistic Roadside Object Placements, Yang Sun, Haoyu Wang, Christopher M. Poskitt, Jun Sun
Research Collection School Of Computing and Information Systems
The emergence of Autonomous Vehicles (AVs) has spurred research into testing the resilience of their perception systems, i.e., ensuring that they are not susceptible to critical misjudgements. It is important that these systems are tested not only with respect to other vehicles on the road, but also with respect to objects placed on the roadside. Trash bins, billboards, and greenery are examples of such objects, typically positioned according to guidelines developed for the human visual system, which may not align perfectly with the needs of AVs. Existing tests, however, usually focus on adversarial objects with conspicuous shapes or patches, which …
Post-Vote Tampering In Nigerian Elections And The Role Of Blockchain-Enabled Electoral Systems, Ransome Chukwubuikem Enechukwu
Post-Vote Tampering In Nigerian Elections And The Role Of Blockchain-Enabled Electoral Systems, Ransome Chukwubuikem Enechukwu
Electronic Theses and Dissertations
Post-vote tampering during the collation and transmission of election results remains a persistent challenge in Nigerian elections, enabling manipulation of already-cast votes and weakening public trust in electoral outcomes. Existing technological interventions, including biometric voter accreditation and digital result transmission systems, improve voter authentication but do not adequately secure the post-vote result collation process. This thesis proposes a blockchain-enabled framework designed to protect the integrity of election results during the collation and transmission stages. Using a Design Science Research methodology, the study develops a permissioned blockchain framework based on Hyperledger Fabric that records polling-unit results as immutable ledger entries and …
Selective Concolic Testing, Guofeng Zhang, Zhenbang Chen, Ziqi Shuai, Jun Sun, Weijiang Hong, Yufeng Zhang, Ji Wang, Yang Liu
Selective Concolic Testing, Guofeng Zhang, Zhenbang Chen, Ziqi Shuai, Jun Sun, Weijiang Hong, Yufeng Zhang, Ji Wang, Yang Liu
Research Collection School Of Computing and Information Systems
The principled combination of symbolic execution and random testing lacks a formal foundation, especially in deciding which inputs to symbolize. We propose selective concolic testing, a cost-aware framework that formulates this choice as an optimized policy problem of a MDP (Markov Decision Process). We model program exploration over a finite control-flow graph, where MDP states represent covered statements, actions partition path constraints into symbolic and random fragments, rewards reflect coverage gain, and costs account for SMT solving effort and sampling inefficiency. Our framework yields the first formal characterization of selective symbolization as policy synthesis in a probabilistic system. We prove …
Automated, Modular, Agentless Adversarial Emulation In Cloud Environments For Higher Education And Student Training, Doc Harley
Senior Honors Theses
Currently, the leading technologies in the market of adversarial emulation are MITRE Caldera, Atomic Red Team by IBM, and multiple proprietary products that come with support packages for different vendors like AttackIQ, Cymulate, SafeBreach, and many more. While it is clear that much work has been done in the broad category of adversarial emulation, when it comes to open source solutions, there are no agentless options with built in automation and modularity that have good support for cloud environments. Agentless adversarial emulation provides a unique advantage in that it can be both simpler and a better representation of the true …
Reminders App Execution, Alexander Gardner
Reminders App Execution, Alexander Gardner
Harrisburg University Other Works
This poster highlights the creation of an Android mobile application for reminders. This app is titled It's Time, Remind! Developed with the Flutter SDK and SQLite for the local database. Firebase Authentication was utilized for user account registration, login/ logout, and profile management. Users can additionally choose to use the app without an account. Users are able to view, create, and delete reminders and notes. Reminders also notify at specified times.
Spatial Computing With The Apple Vision Pro In Minimally Invasive Procedure Simulation: A Randomized Crossover Feasibility Study, Sydney Cooper, Aaron Kyle Jones, Rahul Anil Sheth, Koustav Pal, Bruno Odisio, Mark Blaylock, Shelita Kimble, Justin Bird, David Rice, Daniel Shoenthal, Emil Patel, Vipin Kamath, Sanjay Gupta, Jeffrey Siewerdsen, Joshua Kuban
Spatial Computing With The Apple Vision Pro In Minimally Invasive Procedure Simulation: A Randomized Crossover Feasibility Study, Sydney Cooper, Aaron Kyle Jones, Rahul Anil Sheth, Koustav Pal, Bruno Odisio, Mark Blaylock, Shelita Kimble, Justin Bird, David Rice, Daniel Shoenthal, Emil Patel, Vipin Kamath, Sanjay Gupta, Jeffrey Siewerdsen, Joshua Kuban
Advances in Cancer Education and Quality Improvement
Purpose: This study aimed to evaluate the feasibility of wearing the Apple Vision Pro (AVP), a mixed-reality headset that integrates augmented and virtual reality, while performing minimally invasive procedures. While studies have demonstrated that spatial computing technology can improve surgical precision and reduce the risks of surgical complications, to our knowledge, no studies have specifically addressed the impact of the AVP on task performance during simulated image-guided procedures.
Materials and Methods: Thirteen diagnostic and interventional radiology residents performed image-guided central venous catheter placement, thoracentesis, and paracentesis on simulation models. Each participant completed a non-timed practice followed by the procedures once …
A Backend Database Architecture For Persistent Epilepsy Classification Records, Attiksh A. Panda, Deep Desai, Artem Zabarov, Katrina D. Prantzalos, Satya S. Sahoo, Shuai Xu
A Backend Database Architecture For Persistent Epilepsy Classification Records, Attiksh A. Panda, Deep Desai, Artem Zabarov, Katrina D. Prantzalos, Satya S. Sahoo, Shuai Xu
Student Scholarship
Epilepsy affects over five million people globally each year, yet consistent clinical diagnosis remains a persistent challenge due to the lack of standardized classification workflows across medical institutions. The Four-Dimensional Epilepsy Classification (4D-EC) framework, developed by Lüders et al., provides a comprehensive structure for characterizing paroxysmal events across four dimensions: seizure semiology, epileptogenic zone, etiology, and comorbidities. Despite its clinical and educational value, no dedicated informatics platform existed to support its routine use until recently, limiting widespread adoption among clinicians and trainees. This project addresses that gap by implementing a full-stack web application that operationalizes the 4D-EC framework for clinical …
The Texture Of A Threat: Adversarial Training, Cnns, And Obfuscated Malware Detection, Kaelyn Haynie
The Texture Of A Threat: Adversarial Training, Cnns, And Obfuscated Malware Detection, Kaelyn Haynie
Senior Honors Theses
Accurately detecting malicious programs is an expanding field of research for machine learning (ML), with a novel approach incorporating a bytecode-to-image pipeline that produces images representative of software. These images are provided to convolutional neural networks (CNNs) to be examined for malicious pattern indicators. However, CNNs struggle to generalize these patterns effectively while still being robust against adversarial data, an issue which this research addresses with adversarial training. In this paper, three unique CNN architectures (a DBFS-MC-inspired baseline, MIRACLE, and PSP-CNN) are trained for binary classification with 15,000 benign and malicious software samples encoded into images for Android, Windows, and …
Umm (Ultimate Memory Manager): A Note–Taking App Built From Scratch, Caleb M. Early
Umm (Ultimate Memory Manager): A Note–Taking App Built From Scratch, Caleb M. Early
ASPIRE 2026
For my honors project, I’m building UMM, an advanced note-taking application built entirely from scratch. I’m someone who takes many notes and primarily uses OneNote and Notion, but over time have found myself wanting something faster and more flexible than any note-taking application I could find. UMM is my attempt to create the note-taking app I wish I had, as well as learn how to create such a program.
Instead of relying on pre-made code, I built my own systems for how documents are structured, edited, saved, as well as how the cursor moves, text is formatted, and how selections …
Developing Machine Learning Algorithms For Highly Imbalanced Neonatal Disorder Data, Ali Nawaz
Developing Machine Learning Algorithms For Highly Imbalanced Neonatal Disorder Data, Ali Nawaz
Thesis/ Dissertation Defenses
Neonatal disorders such as low birth weight, very low birth weight, extremely low birth weight, preterm birth, and very preterm birth increase the likelihood of high neonatal morbidity or mortality and call for early identification. However, the rarity of occurrence of these conditions in the clinical datasets has resulted in a severe class imbalance, raising questions about the application of binary classification models to them. Therefore, this thesis proposes a sequential methodological framework for neonatal disorder detection under different assumptions related to the availability of labels. Initially, binary classification experiments are conducted to analyze the behaviour of commonly used classification …
Tavern Table: A Peer-To-Peer Online Platform For Collaborative Dungeons & Dragons Campaigns, Dustin Needham, Kristen Avery, Aidan Martin, Seth Poindexter
Tavern Table: A Peer-To-Peer Online Platform For Collaborative Dungeons & Dragons Campaigns, Dustin Needham, Kristen Avery, Aidan Martin, Seth Poindexter
ATU Scholars Symposium
Designed to address the lack of free, accessible, and feature-complete online Dungeons & Dragons gameplay platforms, Tavern Table gives users the ability to create or participate in Dungeons & Dragons campaigns online via peer-to-peer multiplayer. This platform is targeted primarily for two sets of users: Dungeon Masters (the game masters), who will be creating and hosting campaigns for players to participate in, and the players participating in said campaigns. We chose the Unity Real-Time Development Platform to develop Tavern Table as it was a free and effective platform that supported 2D game development as well as peer-to-peer multiplayer. To create …
Simple D&D: A Web-Based Character Creation System For Dungeons & Dragons, Brennan M. Bondio, Dalton N. Tate, Landon F. Boyd
Simple D&D: A Web-Based Character Creation System For Dungeons & Dragons, Brennan M. Bondio, Dalton N. Tate, Landon F. Boyd
ATU Scholars Symposium
Dungeons & Dragons, published by Wizards of the Coast, is a widely played tabletop role-playing game that requires players to create detailed characters governed by structured rule systems. Character creation involves managing interdependent attributes, calculations, and constraints that can be difficult for new players and time-consuming even for experienced participants. These complexities create a barrier to entry and reduce efficiency during gameplay preparation.
This project addresses that challenge through the development of Simple D&D, a web-based character creation system designed to streamline and automate rule-driven character configuration. The system aims to reduce manual calculation errors and setup time while maintaining …
Digitizing Transportation Operations At Safe Haven, Luke S. Garrett, William B. Turk, Clay A. Curtis, Aiden H. Behler
Digitizing Transportation Operations At Safe Haven, Luke S. Garrett, William B. Turk, Clay A. Curtis, Aiden H. Behler
ATU Scholars Symposium
Safe Haven’s transportation department currently relies on a paper-based documentation process that requires physical transfer of records between buildings and repeated manual uploading of documents into storage systems. This workflow creates delays, redundant administrative tasks, and increased risk of misplaced or inconsistent records. Drivers, transportation coordinators, reviewers, and clients all interact with this process, making efficiency and data accuracy critical to daily operations.
This project develops a web-based transportation scheduling system designed to digitize documentation workflows and automate many of the repetitive tasks. The system replaces physical records with digital data management, reducing unnecessary manual handling and improving information accessibility …
Moneyup: A Predictive Financial Management System For College Students, Isaiah J. Adams, Malaya E. Wilburd, Dan V. Le, Joshua P. Golden
Moneyup: A Predictive Financial Management System For College Students, Isaiah J. Adams, Malaya E. Wilburd, Dan V. Le, Joshua P. Golden
ATU Scholars Symposium
College students often lack accessible tools that combine real-time financial tracking, mobile accessibility, predictive analytics, and secure system design, leaving many without structured insight into their spending behavior. MoneyUP is a full-stack financial management platform developed to address these challenges through a secure, data-driven budgeting system deployed as both a web application and a cross-platform Flutter mobile application. The system integrates the Plaid API in its Sandbox environment to synchronize simulated banking data for secure testing without exposing live financial credentials. Transaction data is processed and stored using Supabase with a relational PostgreSQL database structured to enforce normalization, referential integrity, …
Llm-Based Stock Sentiment And Market Intelligence Platform, Joshua Thrower, Andrew Pinkerton, Ian Duggan, Wyatt Lester
Llm-Based Stock Sentiment And Market Intelligence Platform, Joshua Thrower, Andrew Pinkerton, Ian Duggan, Wyatt Lester
ATU Scholars Symposium
Financial markets increasingly react to social media discourse, yet investors lack tools to translate this unstructured commentary into measurable indicators. Platforms such as YouTube host extensive discussions about publicly traded equities, but extracting reliable sentiment trends from high-volume, noisy comment streams remains technically challenging. This project develops a stock sentiment and market intelligence platform that transforms YouTube comment data into aggregated sentiment indicators aligned to specific equities. Comments are mapped to equities using ticker specific keyword identification combined with contextual filtering to reduce false associations from ambiguous or off-topic mentions. The system assigns numerical sentiment scores to individual comments and …
Department Portfolio Web App*, Phillip Suvacarov, Tommy Aitchison
Department Portfolio Web App*, Phillip Suvacarov, Tommy Aitchison
Campus Research Month
Southern Adventist University’s School of Computing produces numerous course projects, capstones, and research papers each year, yet there is no centralized, public showcase for this work. Our system provides a structured submission workflow for current and former students, faculty approval to ensure academic quality, and moderated commenting and likes to encourage constructive engagement. We outline the content model, role-based access control, and review queue, and describe search, tagging, and media support (including PDFs, images, and code links). By making student work visible beyond the classroom, the portfolio supports recruitment, alumni relations, and employer outreach while strengthening the School’s scholarly community.
Sau Physical Activity Website (Paw)*, Lisbette Sanchez, Alberto Elizondo, Caleb Tenold, Evelyn Shtereva, Siegwart Mayr
Sau Physical Activity Website (Paw)*, Lisbette Sanchez, Alberto Elizondo, Caleb Tenold, Evelyn Shtereva, Siegwart Mayr
Campus Research Month
Problem: Southern’s Physical Activity Website (PAW) used to track physical activity from students and faculty was no longer functional. Aside from unsupported API versions, there was also an issue with authorization and role assignments.
Solution: Southern’s Center for Innovation and Research in Computing (CIRC) adopted the assignment of restructuring a new web application to track physical activity. This project focuses on building a secure and scalable architecture that connects user devices, third-party fitness APIs, and a centralized database to support activity tracking, workout analysis, and fitness history. By combining modern web development practices with API integration and backend data processing, …
Towards Physics-Informed Neural Networks For Simulating Multiphase Geothermal Convection*, Daniel C. Patton, Andrew Harrison Eno
Towards Physics-Informed Neural Networks For Simulating Multiphase Geothermal Convection*, Daniel C. Patton, Andrew Harrison Eno
Campus Research Month
Water and steam flow through porous rock, transferring heat via conduction and buoyancy-driven convection caused by density differences. Traditional numerical methods (finite-volume/finite-element) model this well but can become memory-intensive and unstable for long, high-detail simulations. This work demonstrates a Physics-Informed Neural Network (PINN) using a finite-difference approach within the NVIDIA PhysicsNeMo framework to simulate magma chambers in 2D. Tested on the Rio Pisco pluton in Peru, results are compared with the USGS HYDROTHERM model. PINNs learn from physical laws, offering accurate, flexible solutions with less data and development effort.
Chat Component For Php Web Apps*, Linton W. Feitosa, Caleb N. Hoffman
Chat Component For Php Web Apps*, Linton W. Feitosa, Caleb N. Hoffman
Campus Research Month
Chat rooms are a common feature in much of today's software. While many web applications could benefit from them, developing individual chat implementations can be repetitive and time consuming. Furthermore, publicly available and integrable chat components are difficult to find.
We created a general-use chat component for PHP web applications using the Yii2 framework. Our component features chat rooms, asynchronous messaging, and contact management. It is widely applicable, customizable, documented, and can be easily extended by future developers.
Code Visualizer: An Interactive Code Visualization Tool, Tadd Trumbull
Code Visualizer: An Interactive Code Visualization Tool, Tadd Trumbull
Campus Research Month
Code Visualizer is a web-based, interactive algorithm visualization tool designed to help introductory computer science students develop a deeper understanding of array searching and sorting algorithms. Code Visualizer presents a step-by-step simulation environment built on a restricted Python subset, which allows students to observe array traversal, index manipulation, and algorithmic operations in real time. The tool features two learning modes: View Mode and Predict Mode. The underlying architecture utilizes a behavioral software design pattern called command pattern.
Match-A-Fit, Brianna Mendoza, Adan Diaz De Leon, Pedro Jacobo, Juan Marco Saca Dada
Match-A-Fit, Brianna Mendoza, Adan Diaz De Leon, Pedro Jacobo, Juan Marco Saca Dada
Posters - 2026
Welcome to Match-a-Fit! Match-a-Fit is an iOS application that allows the user to create a digital closet by uploading images of their clothing items. With AI, the program can generate outfits based on the digital closet, the time, and the occasion. Match-a-Fit’s purpose is designed to help users who struggle to get ready, run out of time, or can’t decide on an outfit, by easily generating outfit options based on the occasion.
Maddenlite, Sergio Pena
Maddenlite, Sergio Pena
Presentations - 2026
Problem •“What If” scenarios impossible to test accurately •Commercial games rely on arcade physics •Spreadsheets lack visual engagement
Motivation •Passion for football analytics •Desire to simulate cross-era matchups •Apply math models to real-world sports data
Solution •Python based simulation engine using historical play-by-play data •Simulates outcomes based on probability
A.I.R.E., Laurene Robinson
A.I.R.E., Laurene Robinson
Presentations - 2026
•Cybersecurity analysts rely on reverse engineering to understand suspicious software. •Ghidra can surface decompiled code, but it does not fully explain function purpose, behavioral meaning, or analyst priority. •When symbols are stripped and context is weak, analysts must still reconstruct intent manually from low-level output. •That process is Time-consuming , complex and , operationally costly