Other Computer Sciences Commons^™

Improving The Robustness Of Neural Networks To Adversarial Patch Attacks Using Masking And Attribution Analysis, Atandra Mahalder

Honors Undergraduate Theses

Computer vision algorithms, including image classifiers and object detectors, play a pivotal role in various cyber-physical systems, spanning from facial recognition to self-driving vehicles and security surveillance. However, the emergence of real-world adversarial patches, which can be as simple as stickers, poses a significant threat to the reliability of AI models utilized within these systems. To address this challenge, several defense mechanisms such as PatchGuard, Minority Report, and (De)Randomized Smoothing have been proposed to enhance the resilience of AI models against such attacks. In this thesis, we introduce a novel framework that integrates masking with attribution analysis to robustify AI …

Machine-Learning-Assisted Design Of Deep Eutectic Solvents Based On Uncovered Hydrogen Bond Patterns, Usman Lame Abbas, Yuxuan Zhang, Joseph Tapia, Md Selim, Jin Chen, Jian Shi, Qing Shao

Markey Cancer Center Faculty Publications

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportuni- ties. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the …

Investigating Efficiency Of Free-For-All Models In A Matchmaking Context, Emil Gensby, Bryan S. Weber, Anders H. Christiansen

Publications and Research

We explore several popular (and unpopular) systems for matchmaking and ranking in free-for-all (FFA) environments. The commonplace existing methods involve the reinterpretation of established two-player ranking systems (ie. Elo/Glicko) and decomposing multiplayer games into a set of multiple one-vs-one pairings. This decomposition, while commonplace, is not part of the intended use-case of these two-player ranking systems. We are the first to formally explore this ad-hoc usage and reassuringly find evidence that it converges to correct values. Second, we identify a method that appears to dominate what appears to be the most common publicly used method. At the same time, this …

Bias-Aware Gaze Uniformity Assessment In Group Images, Omkar Kulkarni

Electronic Theses & Dissertations (2024 - present)

Today, more than 5 billion photos are captured every day, with smartphones generating over 94\% of these images. However, despite advancements in technology, achieving aesthetically pleasing group photos remains challenging, especially when it comes to aligning the direction of everyone’s gaze. While current methods focus on facial features, they often fail to ensure consistent gaze direction. The introduction of the iPhone's Live mode, which captures a 1.5-second video snippet along with still images, complicates the selection of the best key photo due to its subjective nature and a lack of publicly available data, especially during the pandemic.

To address these …

Towards A Concurrency Platform For Scalable Multi-Axial Real-Time Hybrid Simulation, Marion Sudvarg, Oren Bell, Tyler Martin, Benjamin Standaert, Tao Zhang, Sun Beom Kwon, Chris Gill, Arun Prakash

Computer Science Faculty Research & Creative Works

Multi-axial real-time hybrid simulation (maRTHS) uses multiple hydraulic actuators to apply loads and deform experimental substructures, enacting both translational and rotational motion. This allows for an increased level of realism in seismic testing. However, this also demands the implementation of multiple-input, multiple-output control strategies with complex nonlinear behaviors. To realize true real-time hybrid simulation at the necessary sub-millisecond timescales, computational platforms will need to support these complexities at scale, while still providing deadline assurance. This paper presents initial work towards supporting (and is influenced by the need for) envisioned larger-scale future experiments based on the current maRTHS benchmark: it discusses …

Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts, Thomas Pany, Dennis Akos, Javier Arribas, M. Zahidul H. Bhuiyan, Pau Closas, Fabio Dovis, Ignacio Fernandez-Hernandez, Carles Fernandez-Prades, Sanjeev Gunawardena, Todd Humphreys, Zaher M. Kassas, Jose A. Lopez Salcedo, Mario Nicola, Mario L. Psiaki, Alexander Rugamer, Yong-Jin Song, Jong-Hoon Won

Faculty Publications

Taking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR development. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on …

Multi-Activity Student Knowledge And Behavior Modeling Via Transfer Learning, Siqian Zhao

Electronic Theses & Dissertations (2024 - present)

Online education systems have grown in popularity over the past few years, providing abundant opportunities for students to learn. As the number of students using these systems grows, it promotes the development of the Educational Data Mining (EDM) field, which leverages statistical, machine learning, and data mining technologies to explore large-scale educational data and develop methods to better understand student learning.

In this dissertation, we investigate two essential topics in EDM: Student Knowledge Tracing (KT) and Behavior Modeling (BM). KT aims to quantify and model student knowledge gained from learning activities, while BM focuses on tasks such as modeling student …

A Self-Supervised Knowledge Distillation Approach To Anomaly Detection In X-Ray Imagery, Kaden Quinn Mceldowney

Graduate Theses, Dissertations, and Problem Reports (ETD)

Many cargo containers enter the United States every day by truck, rail, and sea. As a result of the large number of cargo containers entering the United States, not all of them can be thoroughly inspected. Most of these containers contain properly documented and legal cargo, but some people take advantage of this situation by hiding illicit items in the cargo containers such as drugs. To more efficiently and thoroughly inspect cargo containers, Customs and Border Protection (CBP) uses X-ray imaging machines to obtain images that reveal the interior of cargo containers. These X-ray images must be inspected to ensure …

Computational Study Of The Effect Of Geometry On Molecular Interactions, Sarika Kumar

Computer Science ETDs

The specificity and predictability of DNA make it an excellent programmable material and have allowed bio-programmers to build sophisticated molecular circuits. These molecular devices should be precise, correct, and function as intended. In order to implement these circuits, the challenge is to build a robust, reliable, and scalable logic circuit with ideally minimum unwanted signal release. Performing experiments are expensive and time-consuming, so modeling and analyzing these bio-molecular systems become crucial in designing molecular circuits. This dissertation aimed to develop algorithms and build computational tools for automated analysis of molecular circuits that incorporate the molecular geometry of nanostructures. Molecular circuits …

Ascot App, Milla Penelope Markovic

Honors Thesis

The Ascot App is a research tool for acquiring and analyzing data. The app comprises of both mobile and web platforms, each serving a unique purpose. The mobile side allows users to input data through the app’s form, which is uploaded to a database for further processing and analysis. The web app, which is still under development as of April of 2024, allows users to manage their research project and download data in the form of a parsed CSV. These components ensure a seamless process for research teams to record data with persistence and security while allowing for analysis.

Ascot …

Movie Recommendation System Using Content Based Filtering, Sribhashyam Rakesh

Al-Bahir

The movie recommendation system plays a crucial role in assisting movie enthusiasts in finding movies that match their interests, saving them from the overwhelming task of sifting through countless options. In this paper, we present a content-grounded movie recommendation system that leverages an attribute-based approach to offer personalized movie suggestions to users. The proposed method focuses on attributes such as cast, keywords, crew, and genres of movies to predict users' preferences accurately. Through extensive evaluation, our content-grounded recommendation system demonstrated significant improvements in performance compared to conventional methods. The precision and recall scores increased by an average of 20% and …

Overcoming Foreign Language Anxiety In An Emotionally Intelligent Tutoring System, Daneih Ismail

College of Computing and Digital Media Dissertations

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning.

Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct …

Adaptable Object And Animation System For Game Development, Isaiah Turner

Masters Theses & Specialist Projects

In contemporary times, video games have swiftly evolved into a prominent medium, excelling in both entertainment and narrative delivery, positioning themselves as significant rivals to traditional forms such as film and theater. The burgeoning popularity of gaming has led to a surge in aspiring game developers seeking to craft their own creations, driven by both commercial aspirations and personal passion. However, a common challenge faced by these individuals involves the considerable time investment required to acquire essential skills and establish a foundational framework for their projects. Accessible game development engines that offer a diverse range of fundamental features play a …

Rising Threat - Deepfakes And National Security In The Age Of Digital Deception, Dougo Kone-Sow

Cybersecurity Undergraduate Research Showcase

This paper delves into the intricate landscape of deepfakes, exploring their genesis, capabilities, and far-reaching implications. The rise of deepfake technology presents an unprecedented threat to American national security, propagating disinformation and manipulation across various media formats. Notably, deepfakes have evolved from a historical backdrop of disinformation campaigns, merging with the advancements of artificial intelligence (AI) and machine learning to craft convincing but false multimedia content.

Examining the capabilities of deepfakes reveals their potential for misuse, evidenced by instances targeting individuals, companies, and even influencing political events like the 2020 U.S. elections. The paper highlights the direct threats posed by …

Privacy Concerns And Proposed Solutions With Iot In Wearable Technology, Hyacinth Abad

Cybersecurity Undergraduate Research Showcase

This paper examines the dynamic relationship between IoT cybersecurity and privacy concerns associated with wearable devices. IoT, with its exponential growth, presents both opportunities and challenges in terms of accessibility, integrity, availability, scalability, confidentiality, and interoperability. Cybersecurity concerns arise as diverse attack surfaces exploit vulnerabilities in IoT systems, necessitating robust defenses. In the field of wearable technology, these devices offer benefits like health data tracking and real-time communication. However, the adoption of these devices raises privacy concerns. The paper explores proposed solutions, including mechanisms for user-controlled data collection, the implementation of Virtual Trip Line (VTL) and virtual wall approaches, and …

Ensuring Non-Repudiation In Long-Distance Constrained Devices, Ethan Blum

Honors Theses

Satellite communication is essential for the exploration and study of space. Satellites allow communications with many devices and systems residing in space and on the surface of celestial bodies from ground stations on Earth. However, with the rise of Ground Station as a Service (GsaaS), the ability to efficiently send action commands to distant satellites must ensure non-repudiation such that an attacker is unable to send malicious commands to distant satellites. Distant satellites are also constrained devices and rely on limited power, meaning security on these devices is minimal. Therefore, this study attempted to propose a novel algorithm to allow …

Cloud Computing In The World Of Generative Ai, Yassine Chahid, Patrick Slattery

Publications and Research

The purpose of this research is to evaluate the progress of cloud computation and generative artificial intelligence, and how the improvements in these respective technologies could be combined for future uses. Cloud computation and generative AI have rapidly developed in their capabilities. By analyzing the ways in which cloud computing and generative AI have been implemented thus far, a better understanding can be established regarding how they may influence current tech solutions within the information technology sector and beyond. The first portion of research consisted of researching the current capabilities of the two technologies respectively. By reviewing relevant publications and …

Deficiency Of Acute-Phase Serum Amyloid A Exacerbates Sepsis-Induced Mortality And Lung Injury In Mice, Ailing Ji, Andrea C. Trumbauer, Victoria P. Noffsinger, Luke W. Meredith, Brittany Dong, Qian Wang, Ling Guo, Xiangan Li, Frederick C. De Beer, Nancy R. Webb, Lisa R. Tannock, Marlene E. Starr, Christopher M. Waters, Preetha Shridas

Saha Cardiovascular Research Center Faculty Publications

Serum amyloid A (SAA) is a family of proteins, the plasma levels of which may increase >1000-fold in acute inflammatory states. We investigated the role of SAA in sepsis using mice deficient in all three acute-phase SAA isoforms (SAA-TKO). SAA deficiency significantly increased mortality rates in the three experimental sepsis mouse models: cecal ligation and puncture (CLP), cecal slurry (CS) injection, and lipopolysaccharide (LPS) treatments. SAA-TKO mice had exacerbated lung pathology compared to wild-type (WT) mice after CLP. A bulk RNA sequencing performed on lung tissues excised 24 h after CLP indicated significant enrichment in the expression of genes associated …

Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources, Duncan Arnfield

Electronic Theses and Dissertations

This work examined the relative effectiveness of multilayer perceptron, random forest, and multinomial naïve Bayes classifiers, trained using bag of words and term frequency-inverse dense frequency transformations of documents in the Fake News Corpus and Fake and Real News Dataset. The goal of this work was to help meet the formidable challenges posed by proliferation of fake news to society, including the erosion of public trust, disruption of social harmony, and endangerment of lives. This training included the use of context-categorized fake news in an effort to enhance the tools’ effectiveness. It was found that term frequency-inverse dense frequency provided …

Explainable Artificial Intelligence: Approaching It From The Lowest Level, Ralf P. Riedel

Graduate Theses and Dissertations (2019 - present)

The increasing complexity of artificial intelligence models has given rise to extensive work toward understanding the inner workings of neural networks. Much of that work, however, has focused on manipulating input data feeding the network to assess their affects on network output or pruning model components after the often-extensive time-consuming training. It is postulated in this study that understanding of neural network can benefit from model structure simplification. In turn, it is shown that model simplification can benefit from investigating network node, the most fundamental unit of neural networks, evolving trends during training. Whereas studies on simplification of model structure …

Remote Side-Channel Disassembly On Field-Programmable Gate Arrays, Brandon R. Baggett

Graduate Theses and Dissertations (2019 - present)

Over the last two decades, side-channel vulnerabilities have shown to be a major threat to embedded devices. Most side-channel research has developed our understanding of the vulnerabilities to cryptographic devices due to their implementation and how we can protect them. However, side-channel leakage can yield useful information about many other processes that run on the device. One promising area that has received little attention is the side-channel leakage due to the execution of assembly instructions. There has been some work in this area that has demonstrated the idea’s potential, but so far, this research has assumed the adversary has physical …

Hypothyroid Disease Analysis By Using Machine Learning, Sanjana Seelam

Electronic Theses, Projects, and Dissertations

Thyroid illness frequently manifests as hypothyroidism. It is evident that people with hypothyroidism are primarily female. Because the majority of people are unaware of the illness, it is quickly becoming more serious. It is crucial to catch it early on so that medical professionals can treat it more effectively and prevent it from getting worse. Machine learning illness prediction is a challenging task. Disease prediction is aided greatly by machine learning. Once more, unique feature selection strategies have made the process of disease assumption and prediction easier. To properly monitor and cure this illness, accurate detection is essential. In order …

Review Classification Using Natural Language Processing And Deep Learning, Brian Nazareth

Electronic Theses, Projects, and Dissertations

Sentiment Analysis is an ongoing research in the field of Natural Language Processing (NLP). In this project, I will evaluate my testing against an Amazon Reviews Dataset, which contains more than 100 thousand reviews from customers. This project classifies the reviews using three methods – using a sentiment score by comparing the words of the reviews based on every positive and negative word that appears in the text with the Opinion Lexicon dataset, by considering the text’s variating sentiment polarity scores with a Python library called TextBlob, and with the help of neural network training. I have created a neural …

Performative Mixing For Immersive Audio, Brian A. Elizondo

LSU Doctoral Dissertations

Immersive multichannel audio can be produced with specialized setups of loudspeakers, often surrounding the audience. These setups can feature as few as four loudspeakers or more than 300. Performative mixing in these environments requires a bespoke solution offering intuitive gestural control. Beyond the usual faders for gain control, advancements in multichannel sound demand interfaces capable of quickly positioning sounds between channels. The Quad Cartesian Positioner is such a solution in the form of a Eurorack module for surround mixing for use in live or studio performances.

Diffusion/mixing methods for live multichannel immersive music often rely on the repurposing of hardware …

Closing The Gap: Leveraging Aes-Ni To Balance Adversarial Advantage And Honest User Performance In Argon2i, Nicholas Harrell, Nathaniel Krakauer

CERIAS Technical Reports

The challenge of providing data privacy and integrity while maintaining efficient performance for honest users is a persistent concern in cryptography. Attackers exploit advances in parallel hardware and custom circuit hardware to gain an advantage over regular users. One such method is the use of Application-Specific Integrated Circuits (ASICs) to optimize key derivation function (KDF) algorithms, giving adversaries a significant advantage in password guessing and recovery attacks. Other examples include using graphical processing units (GPUs) and field programmable gate arrays (FPGAs). We propose a focused approach to close the gap between adversarial advantage and honest user performance by leveraging the …

Emerging Sensing, Imaging, And Computational Technologies To Scale Nano-To Macroscale Rhizosphere Dynamics – Review And Research Perspectives, Amir H. Ahkami, Odeta Qafoku, Tiina Roose, Quanbing Mou, Yi Lu, Zoe G. Cardon, Yuxin Wu, Chunwei Chou, Joshua B. Fisher, Tamas Varga, Pubudu Handakumbura, Jayde A. Aufrecht, Arunima Bhattacharjee, James J. Moran

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The soil region influenced by plant roots, i.e., the rhizosphere, is one of the most complex biological habitats on Earth and significantly impacts global carbon flow and transformation. Understanding the structure and function of the rhizosphere is critically important for maintaining sustainable plant ecosystem services, designing engineered ecosystems for long-term soil carbon storage, and mitigating the effects of climate change. However, studying the biological and ecological processes and interactions in the rhizosphere requires advanced integrated technologies capable of decoding such a complex system at different scales. Here, we review how emerging approaches in sensing, imaging, and computational modeling …

Localization Of Gamma-Ray Bursts In A Balloon-Borne Telescope, Ye Htet, Marion Sudvarg, Jeremy Buhler, Roger Chamberlain, James Buckley

Computer Science Faculty Research & Creative Works

Multi-messenger astrophysics combines observations from multiple instruments to study transient astrophysical phenomena, many occurring at seconds-level timescales. To identify and precisely localize these events in the sky, current systems often search through extensive sensor data, requiring resource-intensive computation to achieve results on the timescale of the events themselves. We seek to reduce computational requirements so as to perform real-time event localization with limited computational resources suitable for an orbital platform. This work studies the performance of a computational pipeline for real-time gamma-ray burst (GRB) detection and localization aboard the Antarctic Demonstrator for the Advanced Particle-astro-physics Telescope (ADAPT), a balloon-borne prototype …

Enhancing Search Engine Results: A Comparative Study Of Graph And Timeline Visualizations For Semantic And Temporal Relationship Discovery, Muhammad Shahiq Qureshi

Electronic Theses and Dissertations

In today’s digital age, search engines have become indispensable tools for finding information among the corpus of billions of webpages. The standard that most search engines follow is to display search results in a list-based format arranged according to a ranking algorithm. Although this format is good for presenting the most relevant results to users, it fails to represent the underlying relations between different results. These relations, among others, can generally be of either a temporal or semantic nature. A user who wants to explore the results that are connected by those relations would have to make a manual effort …

Designing Secure Mental Healthcare Chatbots For Older Adults, Aishwarya Surani

Electronic Theses and Dissertations

The landscape of mental health support has evolved as a result of the rising demand for digital mental healthcare services. Users now have an opportunity to seek mental health support online due to the growth of digital platforms. For those looking for mental health treatments, chatbots have evolved as user-friendly, accessible platforms that provide remote access and convenience. However, for chatbots to be effective, users must divulge personal and sensitive information, such as demographics, insurance information, and a history of mental illness. While chatbots offer services to a variety of demographic users, older adults face unique challenges related to usability, …

Optimizing E-Payment Applications For Older Adults: User-Centered Solutions To Improve Security, Privacy, Usability, And Accessibility, Urvashi Kishnani

Electronic Theses and Dissertations

In an increasingly digital world, older adults are rapidly becoming a vital demographic in the realm of electronic financial transactions. It is imperative to address their unique needs and challenges to ensure their financial well-being. Older adults can be more vulnerable to various online threats, making security and privacy paramount. As they adapt to the digital age, understanding their specific privacy concerns and preferences is crucial for creating trustworthy e-payment systems. Moreover, enhancing the usability of e-payment applications for older adults promotes financial independence and inclusion, contributing to their overall quality of life. By focusing on these critical dimensions, we …