Human And Technical Factors In The Adoption Of Quantum Cryptographic Algorithms, 2023 Rose-Hulman Institute of Technology

#### Human And Technical Factors In The Adoption Of Quantum Cryptographic Algorithms, Alyssa Pinkston

*Mathematical Sciences Technical Reports (MSTR)*

The purpose of this research is to understand what factors would cause users to choose quantum key distribution (QKD) over other methods of cryptography. An Advanced Encryption Standard (AES) key can be exchanged through communication using the Rivest, Shamir, Adleman (RSA) cryptographic algorithm, QKD, or post-quantum cryptography (PQC). QKD relies on quantum physics where RSA and PQC use complex mathematics to encrypt data. The BB84 quantum cryptographic protocol involves communication over a quantum channel and a public channel. The quantum channel can be technically attacked by beamsplitting or intercept/resend. QKD, like other forms of cryptography, is vulnerable to social attacks …

Algorithmic Bias: Causes And Effects On Marginalized Communities, 2023 University of San Diego

#### Algorithmic Bias: Causes And Effects On Marginalized Communities, Katrina M. Baha

*Undergraduate Honors Theses*

Individuals from marginalized backgrounds face different healthcare outcomes due to algorithmic bias in the technological healthcare industry. Algorithmic biases, which are the biases that arise from the set of steps used to solve or analyze a problem, are evident when people from marginalized communities use healthcare technology. For example, many pulse oximeters, which are the medical devices used to measure oxygen saturation in the blood, are not able to accurately read people who have darker skin tones. Thus, people with darker skin tones are not able to receive proper health care due to their pulse oximetry data being inaccurate. This …

Feature Selection From Clinical Surveys Using Semantic Textual Similarity, 2023 Washington University in St. Louis

#### Feature Selection From Clinical Surveys Using Semantic Textual Similarity, Benjamin Warner

*McKelvey School of Engineering Theses & Dissertations*

Survey data collected from human subjects can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features to learn upon. A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome. The relationships between feature names …

Visualized Algorithm Engineering On Two Graph Partitioning Problems, 2023 Southern Methodist University

#### Visualized Algorithm Engineering On Two Graph Partitioning Problems, Zizhen Chen

*Computer Science and Engineering Theses and Dissertations*

Concepts of graph theory are frequently used by computer scientists as abstractions when modeling a problem. Partitioning a graph (or a network) into smaller parts is one of the fundamental algorithmic operations that plays a key role in classifying and clustering. Since the early 1970s, graph partitioning rapidly expanded for applications in wide areas. It applies in both engineering applications, as well as research. Current technology generates massive data (“Big Data”) from business interactions and social exchanges, so high-performance algorithms of partitioning graphs are a critical need.

This dissertation presents engineering models for two graph partitioning problems arising from completely …

Exploration Of Feature Selection Techniques In Machine Learning Models On Hptlc Images For Rule Extraction, 2023 University of Mississippi

#### Exploration Of Feature Selection Techniques In Machine Learning Models On Hptlc Images For Rule Extraction, Bozidar-Brannan Kovachev

*Honors Theses*

Research related to Biology often utilizes machine learning models that are ultimately uninterpretable by the researcher. It would be helpful if researchers could leverage the same computing power but instead gain specific insight into decision-making to gain a deeper understanding of their domain knowledge. This paper seeks to select features and derive rules from a machine learning classification problem in biochemistry. The specific point of interest is five species of *Glycyrrhiza*, or Licorice, and the ability to classify them using High-Performance Thin Layer Chromatography (HPTLC) images. These images were taken using HPTLC methods under varying conditions to provide eight …

Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, 2023 Mississippi State University

#### Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest

*Theses and Dissertations*

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.

Procedural Level Generation For A Top-Down Roguelike Game, 2023 Loyola Marymount University

#### Procedural Level Generation For A Top-Down Roguelike Game, Kieran Ahn, Tyler Edmiston

*Honors Thesis*

In this file, I present a sequence of algorithms that handle procedural level generation for the game Fragment, a game designed for CMSI 4071 and CMSI 4071 in collaboration with students from the LMU Animation department. I use algorithms inspired by graph theory and implementing best practices to the best of my ability. The full level generation sequence is comprised of four algorithms: the terrain generation, boss room placement, player spawn point selection, and enemy population. The terrain generation algorithm takes advantage of tree traversal methods to create a connected graph of walkable tiles. The boss room placement algorithm randomly …

Areas Of Same Cardinal Direction, 2023 The University of Maine

#### Areas Of Same Cardinal Direction, Periyandy Thunendran

*Electronic Theses and Dissertations*

Cardinal directions, such as North, East, South, and West, are the foundation for qualitative spatial reasoning, a common field of GIS, Artificial Intelligence, and cognitive science. Such cardinal directions capture the relative spatial direction relation between a reference object and a target object, therefore, they are important search criteria in spatial databases. The projection-based model for such direction relations has been well investigated for point-like objects, yielding a relation algebra with strong inference power. The Direction Relation Matrix defines the simple region-to-region direction relations by approximating the reference object to a minimum bounding rectangle. Models that capture the direction between …

Partitions Of R^N With Maximal Seclusion And Their Applications To Reproducible Computation, 2023 University of Nebraska-Lincoln

#### Partitions Of R^N With Maximal Seclusion And Their Applications To Reproducible Computation, Jason Vander Woude

*Department of Mathematics: Dissertations, Theses, and Student Research*

We introduce and investigate a natural problem regarding unit cube tilings/partitions of Euclidean space and also consider broad generalizations of this problem. The problem fits well within a historical context of similar problems and also has applications to the study of reproducibility in randomized computation.

Given $k\in\mathbb{N}$ and $\epsilon\in(0,\infty)$, we define a $(k,\epsilon)$-secluded unit cube partition of $\mathbb{R}^{d}$ to be a unit cube partition of $\mathbb{R}^{d}$ such that for every point $\vec{p}\in\R^d$, the closed $\ell_{\infty}$ $\epsilon$-ball around $\vec{p}$ intersects at most $k$ cubes. The problem is to construct such partitions for each dimension $d$ with the primary goal of minimizing …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, 2023 Clemson University

#### Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

*All Dissertations*

Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …

Immersive Learning Environments For Computer Science Education, 2023 East Tennessee State University

#### Immersive Learning Environments For Computer Science Education, Dillon Buchanan

*Electronic Theses and Dissertations*

This master's thesis explores the effectiveness of an educational intervention using an interactive notebook to support and supplement instruction in a foundational-level programming course. A quantitative, quasi-experimental group comparison method was employed, where students were placed into either a control or a treatment group. Data was collected from assignment and final grades, as well as self-reported time spent using the notebook. Independent t-tests and correlation were used for data analysis. Results were inconclusive but did indicate that the intervention had a possible effect. Further studies may explore better efficacy, implementation, and satisfaction of interactive notebooks across a larger population and …

Achieving Causal Fairness In Recommendation, 2023 University of Arkansas-Fayetteville

#### 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 …

Universal Computation Using Self-Assembling, Crisscross Dna Slats, 2023 University of Arkansas, Fayetteville

#### 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.

Explicit Constructions Of Canonical And Absolute Minimal Degree Lifts Of Twisted Edwards Curves, 2023 University of Tennessee, Knoxville

#### Explicit Constructions Of Canonical And Absolute Minimal Degree Lifts Of Twisted Edwards Curves, William Coleman Bitting Iv

*Doctoral Dissertations*

Twisted Edwards Curves are a representation of Elliptic Curves given by the solutions of bx^2 + y^2 = 1 + ax^2y^2. Due to their simple and unified formulas for adding distinct points and doubling, Twisted Edwards Curves have found extensive applications in fields such as cryptography. In this thesis, we study the Canonical Liftings of Twisted Edwards Curves and the associated lift of points Elliptic Teichmu ̈ller Lift. The coordinate functions of the latter are proved to be polynomials, and their degrees and derivatives are computed. Moreover, an algorithm is described for explicit computations, and some properties of the general …

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, 2023 University of Tennessee, Knoxville

#### A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

*Masters Theses*

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …

Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, 2023 Singapore Management University

#### Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo

*Research Collection School Of Computing and Information Systems*

Stack Overflow is a popular platform for developers to seek solutions to programming-related problems. However, prior studies identified that developers may suffer from the redundant, useless, and incomplete information retrieved by the Stack Overflow search engine. To help developers better utilize the Stack Overflow knowledge, researchers proposed tools to summarize answers to a Stack Overflow question. However, existing tools use hand-craft features to assess the usefulness of each answer sentence and fail to remove semantically redundant information in the result. Besides, existing tools only focus on a certain programming language and cannot retrieve up-to-date new posted knowledge from Stack Overflow. …

Analysis Of Honeypots In Detecting Tactics, Techniques, And Procedure (Ttp) Changes In Threat Actors Based On Source Ip Address, 2023 Kennesaw State University

#### Analysis Of Honeypots In Detecting Tactics, Techniques, And Procedure (Ttp) Changes In Threat Actors Based On Source Ip Address, Carson Reynolds, Andy Green

*Symposium of Student Scholars*

The financial and national security impacts of cybercrime globally are well documented. According to the 2020 FBI Internet Crime Report, financially motivated threat actors committed 86% of reported breaches, resulting in a total loss of approximately $4.1 billion in the United States alone. In order to combat this, our research seeks to determine if threat actors change their tactics, techniques, and procedures (TTPs) based on the geolocation of their target’s IP address. We will construct a honeypot network distributed across multiple continents to collect attack data from geographically separate locations concurrently to answer this research question. We will configure the …

An Algorithm For Finding Data Dependencies In An Event Graph, 2023 Old Dominion University

#### An Algorithm For Finding Data Dependencies In An Event Graph, Erik J. Jensen

*Modeling, Simulation and Visualization Student Capstone Conference*

This work presents an algorithm for finding data dependencies in a discrete-event simulation system, from the event graph of the system. The algorithm can be used within a parallel discrete-event simulation. Also presented is an experimental system and event graph, which is used for testing the algorithm. Results indicate that the algorithm can provide information about which vertices in the experimental event graph can affect other vertices, and the minimum amount of time in which this interference can occur.

U-Net Based Multiclass Semantic Segmentation For Natural Disaster Based Satellite Imagery, 2023 Old Dominion University

#### U-Net Based Multiclass Semantic Segmentation For Natural Disaster Based Satellite Imagery, Nishat Ara Nipa

*Modeling, Simulation and Visualization Student Capstone Conference*

Satellite image analysis of natural disasters is critical for effective emergency response, relief planning, and disaster prevention. Semantic segmentation is believed to be on of the best techniques to capture pixelwise information in computer vision. In this work we will be using a U-Net architecture to do a three class semantic segmentation for the Xview2 dataset to capture the level of damage caused by different natural disaster which is beyond the visual scope of human eyes.

Enhancing Pedestrian-Autonomous Vehicle Safety In Low Visibility Scenarios: A Comprehensive Simulation Method, 2023 Old Dominion University

#### Enhancing Pedestrian-Autonomous Vehicle Safety In Low Visibility Scenarios: A Comprehensive Simulation Method, Zizheng Yan, Yang Liu, Hong Yang

*Modeling, Simulation and Visualization Student Capstone Conference*

Self-driving cars raise safety concerns, particularly regarding pedestrian interactions. Current research lacks a systematic understanding of these interactions in diverse scenarios. Autonomous Vehicle (AV) performance can vary due to perception accuracy, algorithm reliability, and environmental dynamics. This study examines AV-pedestrian safety issues, focusing on low visibility conditions, using a co-simulation framework combining virtual reality and an autonomous driving simulator. 40 experiments were conducted, extracting surrogate safety measures (SSMs) from AV and pedestrian trajectories. The results indicate that low visibility can impair AV performance, increasing conflict risks for pedestrians. AV algorithms may require further enhancements and validations for consistent safety performance …