Physical Sciences and Mathematics Commons^™

Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification, Donn E. Bartram

Graduate Theses, Dissertations, and Problem Reports

Gait patterns are a class of biometric information pertaining to the way a person moves and poses. Gait information is unique to each person and can be used to identify and reidentify people. Historically, this task has been achieved through the use of multiple ground-based imaging sensors. However, as Unmanned Aerial Vehicles (UAVs) advance, they present the opportunity to evolve the process of persons identification and re-identification. Collecting human gait data using UAVs at distances ranging from 20m to 500m and altitudes ranging from 0m to 120m is a challenging task. The current biometric data collection methods, primarily designed for …

Inpainting Program, Owen Culmer

Williams Honors College, Honors Research Projects

Image processing is a quickly developing field of computer science, especially with the growth of computer vision and generative artificial intelligence. For my project, I focused on object removal, where a targeted object or area can be removed from a digital image. I first accomplished this by implementing a seam carving algorithm, where rows or columns of pixels called seams are removed from an image, that prioritizes the removal of seams that include targeted areas. I next explored a digital inpainting algorithm that would replace targeted areas, rather than removing them, in order to maintain the exact image dimensions and …

Managing Inventory With A Database, David Bartlett

Williams Honors College, Honors Research Projects

Large commercial companies often use warehouses to store and organize their product inventory. However, manually keeping track of inventory through physical means can be a tedious process and is at risk for a variety of potential issues. It is very easy for records to be inaccurate or duplicated, especially if large reorganizations are undertaken, as this can cause issues such as duplicate product ID numbers. Therefore, it was decided that an inventory management system utilizing a SQL database should be created. The system needed to have capabilities including allowing the entry of product information, the ability to search database records …

Robot-Based 3d Printing, Aaron Hoffman

Williams Honors College, Honors Research Projects

Details of a large-format 3D printer created to print experimental materials, test multi-axis print techniques, and quickly print large objects. The printer consists of a 7-axis robotic arm and pellet extruder, which are controlled by a PC. Experimental materials such as recycled polymers or carbon-fiber reinforced materials can be easily tested with the pellet format of the extruder. The printer can perform different printing techniques and can be used to experiment with material properties when using these techniques with different polymers. The print surface is around 5 times larger than the average commercial 3D printer, and the robotic arm provides …

A Comparison Of Lexical Tokenization Methods, Nathan Culmer

Williams Honors College, Honors Research Projects

The purpose of this project was to compare tokenization methods, or methods of breaking up a text into meaningful parts for use in natural language processing. The effectiveness of several commonly used tokenization methods were investigated, including morpheme tokenization, which takes into account the linguistic features of the language. In addition, I proposed and implemented a new technique to consider the capitalization pattern of a word in the tokenization process, in order to allow this process to include more natural language features. The effectiveness of these methods was compared by using them in a sentiment analysis model for various datasets, …

Attitudes And Perceptions Towards Privacy And Surveillance In Australia, Aleatha J. Shanley

Theses: Doctorates and Masters

Understanding attitudes towards privacy and surveillance technologies used to enhance security objectives is a complex, but crucial aspect for policy makers to consider. Historically, terrorism-related incidents justified the uptake of surveillance practices. More recently however, biosecurity concerns have motivated nation-states to adopt more intrusive surveillance measures. There is a growing body of literature that supports the public’s desire to maintain privacy despite fears of biological or physical threats.

This research set out to explore attitudes towards privacy and surveillance in an Australian context. Throughout the course of this endeavour, the COVID-19 pandemic emerged bringing with it a variety of track …

Effective Drag Coefficient Prediction On Single-View 2d Images Of Snowflakes, Cameron Hudson

Graduate College Dissertations and Theses

The drag coefficient of snowflakes is an crucial particle descriptor that can quantify the relationships with the mass, shape, size, and fall speed of snowflake particles. Previous studies has relied on estimating and improving empirical correlations for the drag coefficient of particles, utilizing 3D images from the Multi-Angled Snowflake Camera Database (MASCDB) to estimate snowflake properties such as mass, geometry, shape classification, and rimming degree. However, predictions of the drag coefficient with single-view 2D images of snowflakes has proven to be a challenging problem, primarily due to the lack of data and time-consuming, expensive methods used to estimate snowflake shape …

Model-Based Deep Autoencoders For Clustering Single-Cell Rna Sequencing Data With Side Information, Xiang Lin

Dissertations

Clustering analysis has been conducted extensively in single-cell RNA sequencing (scRNA-seq) studies. scRNA-seq can profile tens of thousands of genes' activities within a single cell. Thousands or tens of thousands of cells can be captured simultaneously in a typical scRNA-seq experiment. Biologists would like to cluster these cells for exploring and elucidating cell types or subtypes. Numerous methods have been designed for clustering scRNA-seq data. Yet, single-cell technologies develop so fast in the past few years that those existing methods do not catch up with these rapid changes and fail to fully fulfil their potential. For instance, besides profiling transcription …

Mitigating The Shortcomings Of Language Models: Strategies For Handling Memorization & Adversarial Attacks, Aly Kassem

Electronic Theses and Dissertations

Deep learning models have recently achieved remarkable progress in Natural Language Processing (NLP), specifically in classification, question-answering, and machine translation. However, NLP models face challenges related to security and privacy. Security-wise, even small perturbations in the input can significantly impact a model's prediction. This highlights the importance of generating natural adversarial attacks to analyze the weaknesses of NLP models and bolster their robustness through adversarial training (AT). Conversely, Large Language Models (LLMs) are trained on vast amounts of data, which may include sensitive information. If exposed, this poses a risk to personal privacy. LLMs can memorize portions of their training …

Advanced Deep Learning Multivariate Multi-Time Series Framework For A Novel Covid-19 Dataset, Swastik Bagga

Electronic Theses and Dissertations

This thesis introduces an innovative framework aimed at addressing the complexities of predicting outcomes in multivariate multi time series datasets in regression analysis. By applying this framework to a novel COVID-19 dataset, it enhances predictive analytics by providing accurate forecasts for epidemic trends at regional or provincial levels, going beyond national-level analysis. The framework incorporates advanced data preprocessing, feature selection, engineering, encoding, and model architecture, effectively capturing intricate variable interactions and temporal dependencies. This makes it a powerful tool for tackling multivariate multi time series regression challenges, offering valuable insights for informed decision-making. Predicting outcomes in such datasets is challenging …

Enhancing Urban Life: A Policy-Based Autonomic Smart City Management System For Efficient, Sustainable, And Self-Adaptive Urban Environments, Elham Okhovat

Electronic Thesis and Dissertation Repository

This thesis proposes the concept of the Policy-based Autonomic Smart City Management System, an innovative framework designed to comprehensively manage diverse aspects of urban environments, ranging from environmental conditions such as temperature and air quality to the infrastructure which comprises multiple layers of infrastructure, from sensors and devices to advanced IoT platforms and applications. Efficient management requires continuous monitoring of devices and infrastructure, data analysis, and real-time resource assessment to ensure seamless city operations and improve residents' quality of life. Automating data monitoring is essential due to the vast array of hardware and data exchanges, and round-the-clock monitoring is critical. …

Smart Applications And Resource Management In Internet Of Things, Zeinab Akhavan

Computer Science ETDs

Internet of Things (IoT) technologies are currently the principal solutions driving smart cities. These new technologies such as Cyber Physical Systems, 5G and data analytic have emerged to address various cities' infrastructure issues ranging from transportation and energy management to healthcare systems. An IoT setting primarily consists of a wide range of users and devices as a massive network interacting with different layers of the city infrastructure resulting in generating sheer volume of data to enable smart city services. The goal of smart city services is to create value for the entire ecosystem, whether this is health, education, transportation, energy, …

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 …

Roadside Lidar Data Processing For Intelligent Transportation System, Md Parvez Mollah

Computer Science ETDs

Roadside LiDAR (Light Detection and Ranging) sensors are recently being explored for Intelligent Transportation System aiming at safer and faster traffic management and vehicular operations. However, massive data volume, occlusion, and limited viewing angles are significant obstacles to the widespread use of roadside LiDARs. In this dissertation, we address three major challenges to enable applications of Intelligent Transportation System through roadside LiDAR data: (i) real-time transmission of the massive point-cloud data from the roadside LiDAR devices to the cloud using 5G network, (ii) mitigating sensor occlusion problem to increase coverage and detect events occurred in occluded regions of a sensor, …

Context-Driven Behavior: Improved Contextual Reasoning For Context-Aware Agents, Christian L. Wilson

Electronic Theses and Dissertations

Over the last three decades, a considerable amount of research has been dedicated to improving an artificial agent's ability to recognize and deal effectively with context. In this paper, I discuss a framework for a novel form of contextual reasoning. Unlike existing contextual reasoning frameworks, which allow an agent to apply its contextual knowledge after it is operating in an instance of a known context, the model I discuss allows an agent to reason about context proactively. With a proactive model, an agent forecasts the future contexts it will encounter, then takes steps to ensure its behaviors are appropriate for …

Learning Mortality Risk For Covid-19 Using Machine Learning And Statistical Methods, Shaoshi Zhang

Electronic Thesis and Dissertation Repository

This research investigates the mortality risk of COVID-19 patients across different variant waves, using the data from Centers for Disease Control and Prevention (CDC) websites. By analyzing the available data, including patient medical records, vaccination rates, and hospital capacities, we aim to discern patterns and factors associated with COVID-19-related deaths.

To explore features linked to COVID-19 mortality, we employ different techniques such as Filter, Wrapper, and Embedded methods for feature selection. Furthermore, we apply various machine learning methods, including support vector machines, decision trees, random forests, logistic regression, K-nearest neighbours, na¨ıve Bayes methods, and artificial neural networks, to uncover underlying …

Probing And Enhancing The Reliance Of Transformer Models On Poetic Information, Almas Abdibayev

Dartmouth College Ph.D Dissertations

Transformer models have achieved remarkable success in the widest variety of domains, spanning not just a multitude of tasks within natural language processing, but also those in computer vision, speech, and reinforcement learning. The key to this success is largely attributed to the self-attention mechanism, particularly its ability to scale in performance as it grows in the number of parameters. Extensive effort has been underway to study the major linguistic properties learned by these models during the course of their pretraining. However, the role of certain finer linguistic phenomena present in language and their utilization by Transformers has not been …

Cta’S ‘L’ System Visualization And Animation, Julia Finegan

Honors Capstones

The Chicago Transit Authority (CTA) is a vital public transportation system for the city of Chicago and the surrounding suburbs, and all of its ‘L’ train data was recorded from March 2022 to February 2023 for this research. The main goal of this project was to create interactive/animated charts, graphs, and/or transit maps to present this raw data in a meaningful form that could help future researchers learn more about the CTA system, its patterns, and/or its unexplained inconsistencies/irregularities. A simple animation of the ‘L’ trains running within a specified time frame was created with the Python libraries Pandas, Shapely, …

Cm-Ii Meditation As An Intervention To Reduce Stress And Improve Attention: A Study Of Ml Detection, Spectral Analysis, And Hrv Metrics, Sreekanth Gopi

Master of Science in Computer Science Theses

Students frequently face heightened stress due to academic and social pressures, particularly in de- manding fields like computer science and engineering. These challenges are often associated with serious mental health issues, including ADHD (Attention Deficit Hyperactivity Disorder), depression, and an increased risk of suicide. The average student attention span has notably decreased from 21⁄2 minutes to just 47 seconds, and now it typically takes about 25 minutes to switch attention to a new task (Mark, 2023). Research findings suggest that over 95% of individuals who die by suicide have been diagnosed with depression (Shahtahmasebi, 2013), and almost 20% of students …

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.

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 …

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.

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 …

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

High-Performance Computing In Covariant Loop Quantum Gravity, Pietropaolo Frisoni

Electronic Thesis and Dissertation Repository

This Ph.D. thesis presents a compilation of the scientific papers I published over the last three years during my Ph.D. in loop quantum gravity (LQG). First, we comprehensively introduce spinfoam calculations with a practical pedagogical paper. We highlight LQG's unique features and mathematical formalism and emphasize the computational complexities associated with its calculations. The subsequent articles delve into specific aspects of employing high-performance computing (HPC) in LQG research. We discuss the results obtained by applying numerical methods to studying spinfoams' infrared divergences, or ``bubbles''. This research direction is crucial to define the continuum limit of LQG properly. We investigate the …

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 …

Random Variable Spaces: Mathematical Properties And An Extension To Programming Computable Functions, Mohammed Kurd-Misto

Computational and Data Sciences (PhD) Dissertations

This dissertation aims to extend the boundaries of Programming Computable Functions (PCF) by introducing a novel collection of categories referred to as Random Variable Spaces. Originating as a generalization of Quasi-Borel Spaces, Random Variable Spaces are rigorously defined as categories where objects are sets paired with a collection of random variables from an underlying measurable space. These spaces offer a theoretical foundation for extending PCF to natively handle stochastic elements.

The dissertation is structured into seven chapters that provide a multi-disciplinary background, from PCF and Measure Theory to Category Theory with special attention to Monads and the Giry Monad. The …

Generalized Differentiable Neural Architecture Search With Performance And Stability Improvements, Emily J. Herron

Doctoral Dissertations

This work introduces improvements to the stability and generalizability of Cyclic DARTS (CDARTS). CDARTS is a Differentiable Architecture Search (DARTS)-based approach to neural architecture search (NAS) that uses a cyclic feedback mechanism to train search and evaluation networks concurrently, thereby optimizing the search process by enforcing that the networks produce similar outputs. However, the dissimilarity between the loss functions used by the evaluation networks during the search and retraining phases results in a search-phase evaluation network, a sub-optimal proxy for the final evaluation network utilized during retraining. ICDARTS, a revised algorithm that reformulates the search phase loss functions to ensure …

Towards Safer Code Reuse: Investigating And Mitigating Security Vulnerabilities And License Violations In Copy-Based Reuse Scenarios, David Reid

Doctoral Dissertations

Background: A key benefit of open source software is the ability to copy code to reuse in other projects. Code reuse provides benefits such as faster development time, lower cost, and improved quality. There are several ways to reuse open source software in new projects including copy-based reuse, library reuse, and the use of package managers. This work specifically looks at copy-based code reuse.

Motivation: Code reuse has many benefits, but also has inherent risks, including security and legal risks. The reused code may contain security vulnerabilities, license violations, or other issues. Security vulnerabilities may persist in projects that copy …

Towards Expressive And Versatile Visualization-As-A-Service (Vaas), Tanner C. Hobson

Doctoral Dissertations

The rapid growth of data in scientific visualization has posed significant challenges to the scalability and availability of interactive visualization tools. These challenges can be largely attributed to the limitations of traditional monolithic applications in handling large datasets and accommodating multiple users or devices. To address these issues, the Visualization-as-a-Service (VaaS) architecture has emerged as a promising solution. VaaS leverages cloud-based visualization capabilities to provide on-demand and cost-effective interactive visualization. Existing VaaS has been simplistic by design with focuses on task-parallelism with single-user-per-device tasks for predetermined visualizations. This dissertation aims to extend the capabilities of VaaS by exploring data-parallel visualization …