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Articles 1 - 21 of 21
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Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury
Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury
Graduate Theses and Dissertations
The transportation sector stands as a significant contributor to greenhouse gas emissions in the United States, with its environmental impact steadily escalating over the past few decades. This has prompted government agencies to facilitate the adoption and usage of low-carbon transportation (LCT) options as alternatives to fossil-fuel-powered transportation. LCTs include modes of transportation that minimize the overall carbon footprint of the transportation sector by relying on energy sources that are environmentally sustainable. These sustainable transportation options have also garnered significant interest in the transportation research community. For government agencies and researchers alike, a comprehensive understanding of the adoption and usage …
Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia
Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia
Graduate Theses and Dissertations
This thesis introduces a novel approach to Photovoltaic (PV) installation segmentation by proposing a new architecture to understand and identify PV modules from overhead imagery. Pivotal to this concept is the creation of a new Transformer-based network, S3Former, which focuses on small object characterization and modelling intra- and inter- object differentiation inside an image. Accurate mapping of PV installations is pivotal for understanding their adoption and guiding energy policy decisions. Drawing insights from current Deep Learning methodologies for image segmentation and building upon State-of-the-Art (SOTA) techniques in solar cell mapping, this work puts forth S3Former with the following enhancements: 1. …
Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu
Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu
Graduate Theses and Dissertations
With the development of artificial intelligence, automated decision-making systems are increasingly integrated into various applications, such as hiring, loans, education, recommendation systems, and more. These machine learning algorithms are expected to facilitate faster, more accurate, and impartial decision-making compared to human judgments. Nevertheless, these expectations are not always met in practice due to biased training data, leading to discriminatory outcomes. In contemporary society, countering discrimination has become a consensus among people, leading the EU and the US to enact laws and regulations that prohibit discrimination based on factors such as gender, age, race, and religion. Consequently, addressing algorithmic discrimination has …
Data-Centric Image Super-Resolution In Magnetic Resonance Imaging: Challenges And Opportunities, Mamata Shrestha
Data-Centric Image Super-Resolution In Magnetic Resonance Imaging: Challenges And Opportunities, Mamata Shrestha
Graduate Theses and Dissertations
Super-resolution has emerged as a crucial research topic in the field of Magnetic Resonance Imaging (MRI) where it plays an important role in understanding and analysis of complex, qualitative, and quantitative characteristics of tissues at high resolutions. Deep learning techniques have been successful in achieving state-of-the-art results for super-resolution. These deep learning-based methods heavily rely on a substantial amount of data. Additionally, they require a pair of low-resolution and high-resolution images for supervised training which is often unavailable. Particularly in MRI super-resolution, it is often impossible to have low-resolution and high-resolution training image pairs. To overcome this, existing methods for …
Developing Detection And Mapping Of Roads Within Various Forms Of Media Using Opencv, Jordan C. Lyle
Developing Detection And Mapping Of Roads Within Various Forms Of Media Using Opencv, Jordan C. Lyle
Computer Science and Computer Engineering Undergraduate Honors Theses
OpenCV, and Computer Vision in general, has been a Computer Science topic that has interested me for a long time while completing my Bachelor’s degree at the University of Arkansas. As a result of this, I ended up choosing to utilize OpenCV in order to complete the task of detecting road-lines and mapping roads when given a wide variety of images. The purpose of my Honors research and this thesis is to detail the process of creating an algorithm to detect the road-lines such that the results are effective and instantaneous, as well as detail how Computer Vision can be …
Towards Multi-Modal Explainable Video Understanding, Kashu Yamazaki
Towards Multi-Modal Explainable Video Understanding, Kashu Yamazaki
Graduate Theses and Dissertations
This thesis presents a novel approach to video understanding by emulating human perceptual processes and creating an explainable and coherent storytelling representation of video content. Central to this approach is the development of a Visual-Linguistic (VL) feature for an interpretable video representation and the creation of a Transformer-in-Transformer (TinT) decoder for modeling intra- and inter-event coherence in a video. Drawing inspiration from the way humans comprehend scenes by breaking them down into visual and non-visual components, the proposed VL feature models a scene through three distinct modalities. These include: (i) a global visual environment, providing a broad contextual understanding of …
Culture In Computing: The Importance Of Developing Gender-Inclusive Software, Creighton France
Culture In Computing: The Importance Of Developing Gender-Inclusive Software, Creighton France
Computer Science and Computer Engineering Undergraduate Honors Theses
The field of computing as we know it today exists because of the contributions of numerous female mathematicians, computer scientists, and programmers. While working with hardware was viewed as “a man’s job” during the mid-20th century, computing and programming was viewed as a noble and high-paying field for women to occupy. However, as time has progressed, the U.S. has seen a decrease in the number of women pursuing computer science. The idea that computing is a masculine discipline is common in the U.S. today for reasons such as male-centered marketing of electronics and gadgets, an inaccurate representation of what it …
Open Source Intelligence For Cybersecurity Events Via Twitter Data, Dakota Dale
Open Source Intelligence For Cybersecurity Events Via Twitter Data, Dakota Dale
Graduate Theses and Dissertations
Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain …
Linux Malware Obfuscation, Brian Roden
Linux Malware Obfuscation, Brian Roden
Computer Science and Computer Engineering Undergraduate Honors Theses
Many forms of malicious software use techniques and tools that make it harder for their functionality to be parsed, both by antivirus software and reverse-engineering methods. Historically, the vast majority of malware has been written for the Windows operating system due to its large user base. As such, most efforts made for malware detection and analysis have been performed on that platform. However, in recent years, we have seen an increase in malware targeting servers running Linux and other Unix-like operating systems resulting in more emphasis of malware research on these platforms. In this work, several obfuscation techniques for Linux …
Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman
Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman
Graduate Theses and Dissertations
Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that …
Achieving Causal Fairness In Recommendation, Wen Huang
Achieving Causal Fairness In Recommendation, Wen Huang
Graduate Theses and Dissertations
Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed …
Geochemical Analysis And Numerical Modeling Of Central And East Tennessee Mississippi Valley-Type Ore Districts: Constraints On Ore Genesis, Jackson Price Copeland
Geochemical Analysis And Numerical Modeling Of Central And East Tennessee Mississippi Valley-Type Ore Districts: Constraints On Ore Genesis, Jackson Price Copeland
Geosciences Undergraduate Honors Theses
A simple two-way stochastic mixing model is presented for analysis of the lead (Pb) isotope compositions of the North American Mississippi Valley-Type (MVT) districts of East Tennessee, Central Tennessee, and Central Kentucky. Four distinct mixing scenarios were run to critically evaluate the stochastic model and examine different hypotheses regarding the genesis of Central Tennessee and Central Kentucky MVT deposits. Additionally, Pb isotope analysis was conducted on sphalerite samples from the Central and East Tennessee MVT districts. Model and sampling results suggest that Central Tennessee and Central Kentucky ores likely formed by mixing of three fluids. In contrast to conclusions from …
Developing A Multi-Platform Application To Facilitate Internal Campus Hiring, Carissa Patton
Developing A Multi-Platform Application To Facilitate Internal Campus Hiring, Carissa Patton
Computer Science and Computer Engineering Undergraduate Honors Theses
Undergraduate research has proven to be highly beneficial to students, yet there are many students who do not know how to get involved or who are too timid to approach professors to inquire about potential research opportunities. Our hypothesis is that a cross-platform application has the potential to bridge the gap and help more students get involved in undergraduate research by providing them information about open positions and the faculty or staff members who are mentoring the projects. The key focus of this thesis is to develop an application that provides details about participating faculty or staff including their research …
Realtime In-Network Cyberattack Detection In Power Grid Systems Using A Programmable Network, Luke Waind
Realtime In-Network Cyberattack Detection In Power Grid Systems Using A Programmable Network, Luke Waind
Computer Science and Computer Engineering Undergraduate Honors Theses
Power grid communication networks are important systems to detect intrusions from an attacker due to them being necessary to maintain critical infrastructure. This thesis applies recent advancements in P4 technology to detect cyberattacks in SCADA systems. In previous work, a list has been compiled of potential attacks that exploit one of the most common protocols in SCADA systems, DNP3. Solutions for detecting these attacks can be categorized by the broad methods that they use. The two methods that are focused on are single-packet inspection and multiple-packet inspection. For each of these, a specific attack is chosen and a detection algorithm …
Reverse Engineering Post-Quantum Cryptography Schemes To Find Rowhammer Exploits, Sam Lefforge
Reverse Engineering Post-Quantum Cryptography Schemes To Find Rowhammer Exploits, Sam Lefforge
Computer Science and Computer Engineering Undergraduate Honors Theses
Post-quantum cryptography is a necessary countermeasure to protect against attacks from quantum computer. However, the post-quantum cryptography schemes are potentially vulnerable to side channel attacks. One such method of attacking involves creating bit-flips in victim memory through a process called Rowhammer. These attacks can vary in nature, but can involve rowhammering bits to raise the encryption scheme's decryption failure rate, or modifying the scheme's random seed. With a high enough decryption failure rate, it becomes feasible to generate sufficient information about the secret key to perform a key recovery attack. This thesis proposed two attacks on proposed post-quantum cryptography algorithms, …
Analysis Of A Federated Learning Framework For Heterogeneous Medical Image Data: Privacy And Performance Perspective, Julia Brixey
Analysis Of A Federated Learning Framework For Heterogeneous Medical Image Data: Privacy And Performance Perspective, Julia Brixey
Computer Science and Computer Engineering Undergraduate Honors Theses
The massive amount of data available in our modern world and the increase of computational efficiency and power have allowed for great advancements in several fields such as computer vision, image processing, and natural languages. At the center of these advancements lies a data-centric learning approach termed deep learning. However, in the medical field, the application of deep learning comes with many challenges. Some of the fundamental challenges are the lack of massive training datasets, unbalanced and heterogenous data between health applications and health centers, security and privacy concerns, and the high cost of wrong inference and prediction. One of …
Characterization Of 2d Quantum Materials Using Ai And Large-Scale Quantum Data Collection, Apoorva Bisht
Characterization Of 2d Quantum Materials Using Ai And Large-Scale Quantum Data Collection, Apoorva Bisht
Computer Science and Computer Engineering Undergraduate Honors Theses
2D materials like hexagonal boron nitride, graphene, and tungsten diselenide are widely utilized for studying their unique mechanical and opto-electronic properties to exploit them to make transistors and fabricating a variety of other devices. All these applications require that the 2D materials used be of specific uniform thickness. Until very recently, this process has been largely manual and tedious. However, few applications exploit the characteristic color-to-thickness correspondence of these near-transparent materials. To continue this effort, in this work we create a large-scale dataset for three different materials (hBN, graphene, and WSe$_2$) to train and test an image segmentation model along …
Universal Computation Using Self-Assembling, Crisscross Dna Slats, Jackson S. Bullard
Universal Computation Using Self-Assembling, Crisscross Dna Slats, Jackson S. Bullard
Computer Science and Computer Engineering Undergraduate Honors Theses
I first give a brief introduction to formal models of computation. I then present three different approaches for computation in the aTAM. I later detail generating systems of crisscross slats given an arbitrary algorithm encoded in the form of a Turing machine. Crisscross slats show potential due to their high levels of cooperativity, so it is hoped that implementations utilizing slats are more robust to various growth errors compared to the aTAM. Finally, my software converts arbitrary crisscross slat systems into various physical representations that assist in analyzing their potential to be realized in experiments.
Chicken Keypoint Estimation, Rohit Kala
Chicken Keypoint Estimation, Rohit Kala
Computer Science and Computer Engineering Undergraduate Honors Theses
Poultry is an important food source across the world. To facilitate the growth of the global population, we must also improve methods to oversee poultry with new and emerging technologies to improve the efficiency of poultry farms as well as the welfare of the birds. The technology we explore is Deep Learning methods and Computer Vision to help automate chicken monitoring using technologies such as Mask R-CNN to detect the posture of the chicken from an RGB camera. We use Meta Research's Detectron 2 to implement the Mask R-CNN model to train on our dataset created on videos of chickens …
A Survey And Comparative Study On Vulnerability Scanning Tools, Cassidy Khounborine
A Survey And Comparative Study On Vulnerability Scanning Tools, Cassidy Khounborine
Computer Science and Computer Engineering Undergraduate Honors Theses
Vulnerability scanners are a tool used by many organizations and developers as part of their vulnerability management. These scanners aid in the security of applications, databases, networks, etc. There are many different options available for vulnerability scanners that vary in the analysis method they encompass or target for which they scan, among many other features. This thesis explores the different types of scanners available and aims to ease the burden of selecting the ideal vulnerability scanner for one’s needs by conducting a survey and comparative analysis of vulnerability scanners. Before diving into the vulnerability scanners available, background information is provided …
Critical Infrastructure Workforce Development Pods For Teaching Cybersecurity Using Netlab+, Gideon Sutterfield
Critical Infrastructure Workforce Development Pods For Teaching Cybersecurity Using Netlab+, Gideon Sutterfield
Computer Science and Computer Engineering Undergraduate Honors Theses
As digital automation for Industrial Control Systems has grown, so has its
vulnerability to cyberattacks. The world of industry has responded effectively to this, but the world of academia is still lagging as its emphasis is still almost entirely on information technology. Considering this, we created a workforce development pod that serves as a hands-on learning module for teaching students key cybersecurity ideas surrounding operational technology using the NETLAB+ platform. A pod serves as the virtual environment where the learning exercise takes place. This project’s implementation involved the creation of a segmented network within the pod where a student starts …