Computer Sciences Commons^™

Phenotyping Cotton Compactness Using Machine Learning And Uas Multispectral Imagery, Joshua Carl Waldbieser

Theses and Dissertations

Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.

A Conceptual Decentralized Identity Solution For State Government, Martin Duclos

Theses and Dissertations

In recent years, state governments, exemplified by Mississippi, have significantly expanded their online service offerings to reduce costs and improve efficiency. However, this shift has led to challenges in managing digital identities effectively, with multiple fragmented solutions in use. This paper proposes a Self-Sovereign Identity (SSI) framework based on distributed ledger technology. SSI grants individuals control over their digital identities, enhancing privacy and security without relying on a centralized authority. The contributions of this research include increased efficiency, improved privacy and security, enhanced user satisfaction, and reduced costs in state government digital identity management. The paper provides background on digital …

Study Of Augmentations On Historical Manuscripts Using Trocr, Erez Meoded

Theses and Dissertations

Historical manuscripts are an essential source of original content. For many reasons, it is hard to recognize these manuscripts as text. This thesis used a state-of-the-art Handwritten Text Recognizer, TrOCR, to recognize a 16^th-century manuscript. TrOCR uses a vision transformer to encode the input images and a language transformer to decode them back to text. We showed that carefully preprocessed images and designed augmentations can improve the performance of TrOCR. We suggest an ensemble of augmented models to achieve an even better performance.

Designing An Artificial Immune Inspired Intrusion Detection System, William Hosier Anderson

Theses and Dissertations

The domain of Intrusion Detection Systems (IDS) has witnessed growing interest in recent years due to the escalating threats posed by cyberattacks. As Internet of Things (IoT) becomes increasingly integrated into our every day lives, we widen our attack surface and expose more of our personal lives to risk. In the same way the Human Immune System (HIS) safeguards our physical self, a similar solution is needed to safeguard our digital self. This thesis presents the Artificial Immune inspired Intrusion Detection System (AIS-IDS), an IDS modeled after the HIS. This thesis proposes an architecture for AIS-IDS, instantiates an AIS-IDS model …

Big Data Applications And Challenges In Giscience (Case Studies: Natural Disaster And Public Health Crisis Management), Amir Masoud Forati

Theses and Dissertations

This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis.The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic …

Statically Scheduling Circular Remote Attribute Grammars, Seyedamirhossein Hesamian

Theses and Dissertations

Classical attribute grammars invented by Knuth have been the subject of extensive study. Over the years there have been various extensions introduced, each with the goal of making attribute grammar more useful for applications such as program analysis. The first extension described here is circular attribute grammar by Farrow. It is followed by remote attribute grammar, which was introduced separately by Boyland and Hedin. More recently, Hedin introduced circular remote attribute grammars and a proof of concept implementation with demand evaluation. Remote attribute grammars make it possible for semantic rules to access attributes of nodes that are not local, and …

Private Ethereum Blockchain Implementation And Its Security Features For Smart Home Iot, Hasibul Grande Alam

Theses and Dissertations

The security and privacy of IoT devices have become primary concerns as smart home networks are connected to the internet. Ethereum blockchain can be a solution to mitigate or prevent attacks – sniffing attacks, malware attacks, Eavesdropping, and Distributed Denial of Services (DDoS) attacks. Deploying Ethereum in resource constraint IoT devices is challenging due to resultant energy consumption, computational overhead, and delay. We adopted smart home as a case study to examine our methodology as a model for general IoT applications. This thesis work presents the implementation of private Ethereum blockchain that is optimized and installable on smart home IoT. …

Intellibeehive, Christian Ivan Narcia-Macias

Theses and Dissertations

Utilizing computer vision and the latest technological advancements, in this study, we developed a honey bee monitoring system that aims to enhance our understanding of Colony Collapse Disorder, honey bee behavior, population decline, and overall hive health. The system is positioned at the hive entrance providing real-time data, enabling beekeepers to closely monitor the hive's activity and health through an account-based website. Using machine learning, our monitoring system can accurately track honey bees, monitor pollen-gathering activity, and detect Varroa mites, all without causing any disruption to the honey bees. Moreover, we have ensured that the development of this monitoring system …

Use Of Digital Twins To Mitigate Communication Failures In Microgrids, Andrew Eggebeen

Theses and Dissertations

This work investigates digital twin (DT) applications for electric power system (EPS) resilience. A novel DT architecture is proposed consisting of a physical twin, a virtual twin, an intelligent agent, and data communications. Requirements for the virtual twin are identified. Guidelines are provided for generating, capturing, and storing data to train the intelligent agent. The relationship between the DT development process and an existing controller hardware-in-the-loop (CHIL) process is discussed. To demonstrate the proposed DT architecture and development process, a DT for a battery energy storage system (BESS) is created based on the simulation of an industrial nanogrid. The creation …

A Design Strategy To Improve Machine Learning Resiliency Of Physically Unclonable Functions Using Modulus Process, Yuqiu Jiang

Theses and Dissertations

Physically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks. To improve the resilience of a system to general ML attacks instead of a specific ML technique, a common solution is to improve the complexity of the system. Structures, such as XOR-PUFs, can significantly increase the nonlinearity …

Automated Human Activity Recognition From Controlled Environment Videos, Pranay Mandadapu

Theses and Dissertations

This thesis explores deep learning methods for Human Activity Recognition (HAR) from videos to automate the annotation of human activities in videos. The research is particularly relevant for continuous monitoring in healthcare settings such as nursing homes and hospitals. The innovative part of the approach lies in using YOLO models to first detect humans in video frames and then isolating them from the rest of the image for activity recognition which leads to an improvement in accuracy. The study employs pre-trained deep residual networks, such as ResNet50, ResNet152-V2, and Inception-ResNetV2, which were found to work better than custom CNN-based models. …

Enhancing Time Series Hashing Performance Via Deep Orthogonal Hashing, Mahmudul Hasan Robin

Theses and Dissertations

Deep hashing has been widely used for efficient retrieval and classification of high-dimensional data like images and text. However, its application to time series data is still challenging due to the data’s temporal nature. To tackle this issue, a new deep hashing method has been proposed that generates efficient hash codes and enhances the time series hashing performance using a ResNet model with Orthohash (Cosine Similarity Loss). The proposed method uses one loss architecture while using ResNet model for efficient hashing. It uses the Character Trajectories dataset to extract discriminative features from the time series data. These features are then …

Robust And Uncertainty-Aware Image Classification Using Bayesian Vision Transformer Model, Fazlur Rahman Bin Karim

Theses and Dissertations

Transformer Neural Networks have emerged as the predominant architecture for addressing a wide range of Natural Language Processing (NLP) applications such as machine translation, speech recognition, sentiment analysis, text anomaly detection, etc. This noteworthy achievement of Transformer Neural Networks in the NLP field has sparked a growing interest in integrating and utilizing Transformer models in computer vision tasks. The Vision Transformer (ViT) model efficiently captures long-range dependencies by employing a self-attention mechanism to transform different image data into meaningful, significant representations. Recently, the Vision Transformer (ViT) has exhibited incredible performance in solving image classification problems by utilizing ViT models, thereby …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

Anxiety In International Graduate Students With U.S. Education, Aeshah Zarraa

Theses and Dissertations

Anxiety in international graduate students is a significant concern for most students planning to move out of their home country to pursue higher education in the United States. The primary purpose of this research is to identify the prime causes of anxiety triggers in the targetted Graduate students, thereby determining a step-wise methodology development to address the causes. The study initially identified a set of graduate students who were voluntarily willing to collaborate and address their concerns anonymously to identify the significant issues faced by current students and alumni of the Florida Institute of Technology, Melbourne, Florida. Several questions were …

Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya

Theses and Dissertations

High-performance reconfigurable computers (HPRCs) make use of Field-Programmable Gate Arrays (FPGAs) for efficient emulation of quantum algorithms. Generally, algorithm-specific architectures are implemented on the FPGAs and there is very little flexibility. Moreover, mapping a quantum algorithm onto its equivalent FPGA emulation architecture is challenging. In this work, we present an automation framework for converting quantum circuits to their equivalent FPGA emulation architectures. The framework processes quantum circuits represented in Quantum Assembly Language (QASM) and derives high-level descriptions of the hardware emulation architectures for High-Level Synthesis (HLS) on HPRCs. The framework generates the code for a heterogeneous architecture consisting of a …

Enhancing Inter-Document Similarity Using Sub Max, Richard Imorobebh Igbiriki

Theses and Dissertations

Document similarity, a core theme in Information Retrieval (IR), is a machine learning (ML) task associated with natural language processing (NLP). It is a measure of the distance between two documents given a set of rules. For the purpose of this thesis, two documents are similar if they are semantically alike, and describe similar concepts. While document similarity can be applied to multiple tasks, we focus our work on the accuracy of models in detecting referenced papers as similar documents using their sub max similarity. Multiple approaches have been used to determine the similarity of documents in regards to literature …

Smart Homes And You: Iot Device Data Risks In An Ever-Changing World, Autumn Person

Theses and Dissertations

Social media applications are increasingly seen as a national security threat and a cause for concern because they can be used to create user profiles on government personnel and on US citizens. These profiles could be used for big data and artificial intelligence purposes of interest to foreign governments. With the rise of big data and AI being used, foreign governments could use this data for a variety of purposes that can affect normal everyday citizens, not just high value personnel. IoT (Internet of Things) devices that the population uses everyday can also pose the same threat. These devices can …

Docker Technology For Small Scenario-Based Excercises In Cybersecurity, Zeinab Ahmed

Theses and Dissertations

This study aims to better prepare students for cybersecurity roles by providing practical tools that bridge the gap between theory and real-world applications. We investigate the role of small scenario-based exercises for students’ understanding of cybersecurity concepts. In particular, we assess the use of Docker technology to deliver training that includes a simple small scenario on html code injection. The effectiveness of scenario-based learning has long been defined and by using SBL, we are going to create hands-on activity that involves the fundamental topics in cybersecurity using Docker technology, allowing students to see the exploitation of the vulnerabilities and defense …

Computerized Psychological Testing: Designing And Developing An Efficient Test Suite Using Hci And Reinforcement Learning Techniques, William Henry Hoskins

Theses and Dissertations

In this work we discuss the design and development of the Carolina Automated Reading Evaluation (CARE), created to facilitate the finding of deficits in the reading ability of children from four to nine years of age. Designed to automate the process of screening for reading deficits, the CARE is an interactive computer-based tool that helps eliminate the need for one-on-one evaluations of pupils to detect dyslexia and other reading deficits and facilitates the creation of new reading tests within the platform. While other tests collect specific data points in order to determine whether a pupil has dyslexia, they typically focus …

Robust Underwater State Estimation And Mapping, Bharat Joshi

Theses and Dissertations

The ocean covers two-thirds of Earth, which is relatively unexplored compared to the landmass. Mapping underwater structures is essential for both archaeological and conservation purposes. This dissertation focuses on employing a robot team to map underwater structures using vision-based simultaneous localization and mapping (SLAM). The overarching goal of this research is to create a team of autonomous robots to map large underwater structures in a coordinated fashion. This requires maintaining an accurate robust pose estimate of oneself and knowing the relative pose of the other robots in the team. However, the GPS-denied and communication-constrained underwater environment, along with low visibility, …

Machine Learning And Causality For Interpretable And Automated Decision Making, Maria Lentini

Theses and Dissertations

This abstract explores two key areas in decision science: automated and interpretable decision making. In the first part, we address challenges related to sparse user interaction data and high item turnover rates in recommender systems. We introduce a novel algorithm called Multi-View Interactive Collaborative Filtering (MV-ICTR) that integrates user-item ratings and contextual information, improving performance, particularly for cold-start scenarios. In the second part, we focus on Student Prescription Trees (SPTs), which are interpretable decision trees. These trees use a black box "teacher" model to predict counterfactuals based on observed covariates. We experiment with a Bayesian hierarchical binomial regression model as …

Identity Management Pki System Using Blockchain, Mohamed Abdel Fattah Abdel Baki Awad

Theses and Dissertations

Identity management is one of the most important topics in the security field. Public Key Infrastructure (PKI) is the most commonly used approach in legally identity management systems. PKI systems have many centralized services that might affect the availability and trustworthiness of the system. Issues related to certificate verification methods such as Certificate Revocation List (CRL) and Online Certificate Status Protocol (OCSP) can be avoided if blockchain is used. Blockchain is a decentralized trusted system where data can only be appended to a public ledger. Edits are not allowed in blockchain. Blockchain consists of several nodes all of them have …

Methods Of Evaluating Quantum Phase Estimation Circuit Output, Charles A. Woodrum

Theses and Dissertations

The quantum phase estimation (QPE) algorithm is one of the most important quantum computing algorithms that has been developed. The QPE algorithm estimates the phase or phases of the eigenvalue or eigenvalues of a unitary operator. It is a critical step for applications like Shor’s algorithm for factoring and the HHL algorithm for solving linear systems of equations, but it remains difficult to implement on current quantum computers due to small numbers of logical qubits and high error rates. This investigation derives a more accurate estimation of the phase of a unitary operator than would otherwise be attained with the …

Advances In Quaternion-Valued Neural Networks, Jeremiah P. Bill

Theses and Dissertations

This dissertation investigates the construction, optimization, and application of quaternion neural networks (QNNs) to Department of Defense (DoD) related problem sets. QNNs are a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. This work provides a critical evaluation of the myriad different quaternion backpropagation derivations that exist in the literature, testing the performance of each on a range of regression problem sets. The optimization dynamics of QNNs are explored, presenting visualizations of QNN loss surfaces and a novel method for assessing the “smoothness” of these loss surfaces. Finally, this dissertation presents …

Expanding One-Dimensional Game Theory-Based Group Decision Models: Extension To N-Dimension And Integration Of Distributed Position Function, Mirhossein Mousavi Karimi

Theses and Dissertations

This dissertation aims to expand the current one-dimensional game theory based model to a multidimensional model for multi-actor predictive analytics and generalize the concept of position to address problems where actors’ positions are distributed over a position spectrum. The one-dimensional models are used for the problems where actors are interacting in a single issue space only. This is less than an ideal assumption since, in most cases, players’ strategies may depend on the dynamics of multiple issues when dealing with other players. In this research, the one-dimensional model is expanded to N-Dimensional model by considering different positions, and separate salience …

Signings Of Graphs And Sign-Symmetric Signed Graphs, Ahmad Asiri

Theses and Dissertations

In this dissertation, we investigate various aspects of signed graphs, with a particular focus on signings and sign-symmetric signed graphs. We begin by examining the complete graph on six vertices with one edge deleted ($K_6$\textbackslash e) and explore the different ways of signing this graph up to switching isomorphism. We determine the frustration index (number) of these signings and investigate the existence of sign-symmetric signed graphs. We then extend our study to the $K_6$\textbackslash 2e graph and the McGee graph with exactly two negative edges. We investigate the distinct ways of signing these graphs up to switching isomorphism and demonstrate …

Visual And Spatial Audio Mismatching In Virtual Environments, Zachary Lawrence Garris

Theses and Dissertations

This paper explores how vision affects spatial audio perception in virtual reality. We created four virtual environments with different reverb and room sizes, and recorded binaural clicks in each one. We conducted two experiments: one where participants judged the audio-visual match, and another where they pointed to the click direction. We found that vision influences spatial audio perception and that congruent audio-visual cues improve accuracy. We suggest some implications for virtual reality design and evaluation.

Ide-Based Learning Analytics For Assessing Introductory Programming Skill, Phyllis J. Beck

Theses and Dissertations

Providing a sufficient level of personalized feedback on students' current level of strategic knowledge within the context of the natural programming environment through IDE-based learning analytics would transform learning outcomes for introductory programming students. However, providing sufficient insight into the programming process was previously inaccessible due to the need for more complex and scalable data collection methods and metrics with a wider variety for understanding programming metacognition and the full programming process.

This research developed a custom-built web-based IDE and event compression system to investigate two of the five components of a five-dimensional model of cognition for programming skill estimation …

Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney

Theses and Dissertations

Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …