Computer Sciences Commons^™

Monero: Powering Anonymous Digital Currency Transactions, Jake Braddy

Theses/Capstones/Creative Projects

Cryptocurrencies rely on a distributed public ledger (record of transactions) in order to perform their intended functions. However, the public’s ability to audit the network is both its greatest strength and greatest weakness: Anyone can see what address sent currency, and to whom the currency was sent. If cryptocurrency is ever going to take some of the responsibility of fiat currency, then there needs to be a certain level of confidentiality. Thus far, Monero has come out on top as the preferred currency for embodying the ideas of privacy and confidentiality. Through numerous cryptographic procedures, Monero is able to obfuscate …

Navigate The World Of Rfid: Diversity, Capabilities, And Constraints Of Readers And Tags, Rachana Pandey

2024 Spring Honors Capstone Projects - Archive

Radio-Frequency Identification (RFID) technology, a method for storing and retrieving data through electromagnetic transmission to an RFID tag, is revolutionizing inventory and asset management in various sectors, including healthcare. This research explores the applications of RFID in a medical setting. It assesses various RFID readers and tags, focusing on their functional capabilities, ranges, and limitations within a medical environment. Employing a comprehensive approach, the study integrates an extensive literature review, comparative analysis, and empirical data from both experimental simulations and real-world healthcare scenarios. The aim is to identify RFID solutions that optimize surgical equipment management, thereby enhancing both operational efficiency …

Robust And Trustworthy Deep Learning: Attacks, Defenses And Designs, Bingyin Zhao

All Dissertations

Deep neural networks (DNNs) have achieved unprecedented success in many fields. However, robustness and trustworthiness have become emerging concerns since DNNs are vulnerable to various attacks and susceptible to data distributional shifts. Attacks such as data poisoning and out-of-distribution scenarios such as natural corruption significantly undermine the performance and robustness of DNNs in model training and inference and impose uncertainty and insecurity on the deployment in real-world applications. Thus, it is crucial to investigate threats and challenges against deep neural networks, develop corresponding countermeasures, and dig into design tactics to secure their safety and reliability. The works investigated in this …

Proof-Of-Concept For Converging Beam Small Animal Irradiator, Benjamin Insley

Dissertations and Theses (Open Access)

The Monte Carlo particle simulator TOPAS, the multiphysics solver COMSOL., and

several analytical radiation transport methods were employed to perform an in-depth proof-ofconcept

for a high dose rate, high precision converging beam small animal irradiation platform.

In the first aim of this work, a novel carbon nanotube-based compact X-ray tube optimized for

high output and high directionality was designed and characterized. In the second aim, an

optimization algorithm was developed to customize a collimator geometry for this unique Xray

source to simultaneously maximize the irradiator’s intensity and precision. Then, a full

converging beam irradiator apparatus was fit with a multitude …

Many Direct-To-Consumer Canine Genetic Tests Can Identify The Breed Of Purebred Dogs, Halie M. Rando, Kiley Graim, Greg Hampikian, Casey S. Greene

Computer Science: Faculty Publications

OBJECTIVE To compare pedigree documentation and genetic test results to evaluate whether user-provided photographs influence the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs.

ANIMALS 12 registered purebred pet dogs representing 12 different breeds. METHODS Each dog owner submitted 6 buccal swabs, 1 to each of 6 DTC genetic testing companies. Experimenters registered each sample per manufacturer instructions. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. DNA analysis and breed ancestry prediction were conducted by each company. The effect of …

A Study Of Cybersecurity Landscape In The United States: Trend, Regional Variations, And Socioeconomic Factors, Trang Thi Thu Horn

All-Inclusive List of Electronic Theses and Dissertations

The Internet has become an essential part of our daily lives. Cyberspace has emerged significantly with an enormous number of users, creating a lucrative hunting field for cybercriminals. The exponential growth of internet users and online activities, along with the increased sophistication of cybercrimes, is raising significant global concerns for individuals, organizations, and governments. This research aims to explore the cybersecurity landscape in the United States, investigate regional variations, and the potential impact of the COVID-19 pandemic on the number of cybercrimes. The study utilizes a mixed research method approach, including historical analysis and a Delphi study. Historical analysis studied …

Learning, Optimizing, And Simulating Fermions With Quantum Computers, Andrew Zhao

Physics & Astronomy ETDs

Fermions are fundamental particles which obey seemingly bizarre quantum-mechanical principles, yet constitute all the ordinary matter that we inhabit. As such, their study is heavily motivated from both fundamental and practical incentives. In this dissertation, we will explore how the tools of quantum information and computation can assist us on both of these fronts. We primarily do so through the task of partial state learning: tomographic protocols for acquiring a reduced, but sufficient, classical description of a quantum system. Developing fast methods for partial tomography addresses a critical bottleneck in quantum simulation algorithms, which is a particularly pressing issue for …

Forya.Ai Business Plan, Landen Usher

Finance Undergraduate Honors Theses

This thesis presents a comprehensive business plan for Forya.AI, an innovative artificial intelligence-powered platform designed to streamline social media content creation for churches. The platform distinguishes itself by automating the generation of engaging content, leveraging church-provided images to produce visually appealing posts accompanied by contextually relevant captions. By integrating advanced AI algorithms, Forya.AI analyzes uploaded images, identifies thematic elements, and crafts captions that resonate with the spiritual and community values of the church. This process not only enhances the digital presence of religious organizations but also significantly reduces the time and financial resources traditionally required for content development. The business …

Lecture-Style Tutorial: Towards Graph Foundation Models, Chuan Shi, Cheng Yang, Yuan Fang, Lichao Sun, Philip Yu

Research Collection School Of Computing and Information Systems

Emerging as fundamental building blocks for diverse artificial intelligence applications, foundation models have achieved notable success across natural language processing and many other domains. Concurrently, graph machine learning has gradually evolved from shallow methods to deep models to leverage the abundant graph-structured data that constitute an important pillar in the data ecosystem for artificial intelligence. Naturally, the emergence and homogenization capabilities of foundation models have piqued the interest of graph machine learning researchers. This has sparked discussions about developing a next-generation graph learning paradigm, one that is pre-trained on broad graph data and can be adapted to a wide range …

An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient …

Vascular Brain Digital Twins For Medical Training And Education In Metaverse, Shamma Khaled Alghafri

Theses

This study addresses the need for innovative educational tools in the field of anatomy, specifically focusing on brain anatomy. The objective is to develop a virtual reality application and a 3D visualization that offer immersive and interactive learning experiences for students. The VR application, developed using Unity and designed for the Oculus Quest 2 headset, creates an immersive virtual laboratory environment. This environment includes interactive elements such as a table with buttons for displaying brain models and a whiteboard for user interaction. Users can manipulate and explore different brain structures, enhancing their understanding of complex anatomical features. Additionally, we integrated …

The Role Of Student Motivation In Integrating Ai Into Web Design Education: A Longitudinal Study, Jason Lively, James Hutson

Faculty Scholarship

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, …

Formalization Of A Security Framework Design For A Health Prescription Assistant In An Internet Of Things System, Thomas Rolando Mellema

Electronic Theses and Dissertations

Security system design flaws will create greater risks and repercussions as the systems being secured further integrate into our daily life. One such application example is incorporating the powerful potential of the concept of the Internet of Things (IoT) into software services engineered for improving the practices of monitoring and prescribing effective healthcare to patients. A study was performed in this application area in order to specify a security system design for a Health Prescription Assistant (HPA) that operated with medical IoT (mIoT) devices in a healthcare environment. Although the efficiency of this system was measured, little was presented to …

In-Between Frame Generation For 2d Animation Using Generative Adversarial Networks, Francisco Arriaga Pazos

Open Access Theses & Dissertations

Traditional 2D animation remains a largely manual process where each frame in a video is hand-drawn, as no robust algorithmic solutions exist to assist in this process. This project introduces a system that generates intermediate frames in an uncolored 2D animated video sequence using Generative Adversarial Networks (GAN), a deep learning approach widely used for tasks within the creative realm. We treat the task as a frame interpolation problem, and show that adding a GAN dynamic to a system significantly improves the perceptual fidelity of the generated images, as measured by perceptual oriented metrics that aim to capture human judgment …

Asteroidal Sets And Dominating Targets In Graphs, Oleksiy Al-Saadi

School of Computing: Dissertations, Theses, and Student Research

The focus of this PhD thesis is on various distance and domination properties in graphs. In particular, we prove strong results about the interactions between asteroidal sets and dominating targets. Our results add to or extend a plethora of results on these properties within the literature. We define the class of strict dominating pair graphs and show structural and algorithmic properties of this class. Notably, we prove that such graphs have diameter 3, 4, or contain an asteroidal quadruple. Then, we design an algorithm to to efficiently recognize chordal hereditary dominating pair graphs. We provide new results that describe the …

Data Recovery Beyond The Obvious Using Digital Forensic Techniques, Smit Chandrakant Nayak

Theses, Dissertations and Culminating Projects

Advancement in drone technology, particularly for smaller drones, are creating new research fields and potential applications, particularly with regard to smaller drones. On the other hand, these enhancements bring forth additional hurdles in terms of adaptability, homogeneity, and safety. The purpose of this study is to investigate the science and technology behind drones, as well as their applications, the many ways in which citizens implement them, and the risks, precautions, and privacy problems that are associated with their utilization. This article discusses the existing literature, as well as the available solutions for drone cybersecurity, the security challenges related with drones …

Stock Market Predictions With Machine Learning ., Daniel Bezerra Martellini

ICT

The focus of this project is developing a tool that can be used in conjunction with other methods to help an investor and/or financial analyst in making an informed decision when making an investment choice taking into consideration stock data.

The project has three main goals which are to try predicting buy and sell signals with the use of a classification model, and to predict the approximate value for next day’s closing price of a stock of our choice with the use of a regression model.

My last goal with this project is to create models easy to use and …

Advancing Learning Models For High-Dimensional Data: From Molecular Modeling To Motion Planning, Tuan Nguyen Anh Tran

Legacy Theses & Dissertations (2009 - 2024)

Processing and analyzing high-dimensional data, particularly in domains like protein research and robotics, introduces unique challenges. In protein analysis, high-dimensional data often arises from complex molecular structures and their interactions. Analyzing protein data involves intricate computational models and algorithms that must deal with the large size of molecular datasets and the need to extract biologically relevant information. This makes it challenging to discern significant protein structure-activity relationships, understand complex biological interactions, or predict protein behavior accurately. Similarly, in robotics, high-dimensional data is prevalent when dealing with sensory inputs, the dimensionality of the robot, or the complexity of the operational environments. …

Investigating User Awareness Of Privacy And Security Concerns In The Iot Era, Jack Ruffner

ALL - Honors Theses

The Internet of Things (IoT) has had a significant impact on the way we view and interact with technology. This is especially prevalent in the areas of smart homes, smart tech, and mobile devices. However, despite the advantageous functions of IoT devices, they are accompanied by numerous security concerns that enable several severe privacy concerns. Many studies and informative articles present ideas that explain and prove the presence of the various risks associated with IoT devices and the need to address them. This thesis paper aims to explore the relationship between IoT device usage and security and privacy risks as …

Cctfv2: Modeling Cyber Competitions, Basheer Qolomany, Tristan J. Calay, Liaquat Hossain, Aos Mulahuwaish, Jacques Bou Abdo

School of Computing Faculty Scholarship and Creative Works

Cyber competitions are usually team activities, where team performance not only depends on the members’ abilities but also on team collaboration. This seems intuitive, especially given that team formation is a well-studied discipline in competitive sports and project management, but unfortunately, team performance and team formation strategies are rarely studied in the context of cybersecurity and cyber competitions. Since cyber competitions are becoming more prevalent and organized, this gap becomes an opportunity to formalize the study of team performance in the context of cyber competitions. This work follows a cross-validating two-approach methodology. The first is the computational modeling of cyber …

Dynamic Storytelling Algorithms Using Contextual Aspects Of A Large Language Model, Alireza Pasha Nouri

Open Access Theses & Dissertations

Storytelling is a set of algorithms used to create narratives by connecting documents in a sequencethat accurately reflects the evolution of events and entities within a particular topic or theme. Early storytelling algorithms face challenges in encoding the progression and interconnections of information between consecutive texts, given that the conventional approaches rely primarily on connecting document pairs based on content overlap. They often neglect critical linguistic features, such as word contexts, semantics, the roles words play across different documents, and attention to the historical contexts of the underlying documents. Many existing storytelling models frequently produce story chains that, while connected …

Automated Cinematographer For Vr Viewing Experiences, Zihan Wu

Dartmouth College Master’s Theses

As the virtual reality (VR) industry continues to evolve, the question of how to effectively capture VR experiences for an audience remains a challenge. The predominant method of showcasing VR applications through first-person recordings lacks cinematic interest, failing to capture other viewpoints and the essence of the moment. Meanwhile, manually setting up cameras and editing videos requires technical expertise on behalf of the user. In this paper, we propose the use of machine learning (ML) to automatically select the most compelling predefined viewpoint in a VR environment, at any given moment. Our models, trained on actor motion and voice volume, …

White Cell Support Application For Expo Ops Tactical Wargame System, Zackery Joseph Milder

Theses

The purpose of this project was to create a support application for a tabletop wargame currently used for training and scenario simulation by the United States Marine Corps. The EXPO OPS Companion is meant to enhance the capabilities of the White Cell/table director, the unbiased third party responsible for running adjudication for the EXPO OPS Tactical Wargames System. EXPO OPS TWS is “…a table top wargame covering contemporary and future conflict at the platoon, company and battalion level. It is a wargame toolkit that enables wargaming scenarios in the 2020 to 2030 timeframe. The design centers on plans and decisions …

The Impact Of Ai On Ux: Challenges And Opportunities, Susan Stephanie Wells

Theses

Integrating artificial intelligence (AI) in user experience (UX) design is reshaping the field of UX, offering new opportunities and challenges for designers. This thesis project explores the multifaceted relationship between AI and UX design, focusing on the challenges, opportunities, and skills demanded of UX designers in the age of AI. Through a review of academic research and real-world experiences, this project studies the impact of AI on web design processes, UX testing, and data analysis. Key findings highlight the transformative potential of AI in enhancing user experiences, from suggesting website structures to facilitating UX testing and data analysis.

Comparative analysis …

Enhancing Monthly Streamflow Prediction Using Meteorological Factors And Machine Learning Models In The Upper Colorado River Basin, Saichand Thota, Ayman Nassar, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh

Computer Science Student Research

Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using …

Magdm Model Using Single-Valued Neutrosophic Credibility Matrix Energy And Its Decision-Making Application, Jun Ye, Rui Yong, Wanlu Du

Neutrosophic Systems with Applications

This paper aims to develop a MAGDM model using single-valued neutrosophic credibility matrix (SVNCM) energy in a SVNCM scenario. To do it, first, SVNCM energy and its score function are presented as a conceptual extension of existing single-valued neutrosophic matrix (SVNM) energy. Then, a MAGDM model is developed in terms of SVNCM energy and its score function in a SVNCM scenario and also its decision algorithm is provided to solve MAGDM problems with SVNCMs. Finally, the developed MAGDM model is applied in the school site selection problem as an actual example, then the comparative investigation of the decision results in …

Comparative Predictive Analysis Of Stock Performance In The Tech Sector, Asaad Sendi

LSU New Orleans Theses and Dissertations

This study compares the performance of deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, in predicting stock prices across five companies (AAPL, CSCO, META, MSFT, and TSLA) from July 2019 to July 2023. Key findings reveal that GRU models generally exhibit the lowest Mean Absolute Error (MAE), indicating higher precision, particularly notable for CSCO with a remarkably low MAE. While LSTM models often show slightly higher MAE values, they outperform Transformer models in capturing broader trends and variance in stock prices, as evidenced by higher R-squared (R2) values. Transformer models generally exhibit higher MAE …

Choreographing The Rhythms Of Observation: Dynamics For Ranged Observer Bipartite-Unipartite Spatiotemporal (Robust) Networks, Edward A. Holmberg Iv

LSU New Orleans Theses and Dissertations

Existing network analysis methods struggle to optimize observer placements in dynamic environments with limited visibility. This dissertation introduces the novel ROBUST (Ranged Observer Bipartite-Unipartite SpatioTemporal) framework, offering a significant advancement in modeling, analyzing, and optimizing observer networks within complex spatiotemporal domains. ROBUST leverages a unique bipartite-unipartite approach, distinguishing between observer and observable entities while incorporating spatial constraints and temporal dynamics.

This research extends spatiotemporal network theory by introducing novel graph-based measures, including myopic degree, spatial closeness centrality, and edge length proportion. These measures, coupled with advanced clustering techniques like Proximal Recurrence, provide insights into network structure, resilience, and the effectiveness …

Super Mario Evolution By The Augmentation Of Topology, Russell A. Autin

LSU New Orleans Theses and Dissertations

This paper describes the creation and development of an implementation of the NeuroEvolution of Augmenting Topologies (NEAT) architecture to train an agent to play Super Mario Brothers. Building off of a basic implementation of NEAT, this thesis project shows the process of refining the fitness calculation that ranks the networks in the population and also defines the creation and application of a dataset to train the agent. The use of a dataset to train an agent is a novel idea in the world of reinforcement learning because, generally, reinforcement learning trains an agent to complete a singular task like the …

The Pawn System: How Procedurally Adaptive Webbed Narratives Create Stories, Steven T. Bordelon

LSU New Orleans Theses and Dissertations

This thesis describes the design, implementation, and testing of a novel procedural narrative system called the Procedurally Adaptive Webbed Narrative (PAWN) system. PAWN procedurally generates characters and, responding to choices made by the player, produces more responsive characters and relationships involving the player and these narrative agents. Initially, this thesis discusses other interactive narrative types that exist, such as emergent or event-driven narratives, along with their strengths and weaknesses. It then examines each aspect of PAWN, starting with initial actor generation, then moving to the capturing of game events and translating them into logical objects called Occurrences. These Occurrences are …