Computer Sciences Commons^™

Exploring Improvements To Space-Bounded Derandomization From Better Pseudorandom Generators, Boxian Wang

Computer Science Senior Theses

Saks and Zhou used Nisan’s PRG in a recursive manner to obtain BPL ⊆ L^(3/2). We describe how this framework could be generalized to use arbitrary PRGs following Armoni’s sampler idea. We then give a theorem relating the seed length of a better PRG to the implied improvements in derandomizing BPL. Recently, Hoza used Armoni’s PRG in the Saks-Zhou framework to obtain an even better derandomization. We describe the construction of Armoni’s PRG and conjecture that by using basic components other than extractors, parameters in that construction could be improved. Under some assumptions, we calculate the extent to which such …

Interpreting Business Strategy And Market Dynamics: A Multi-Method Ai Approach, Lobna Jbeniani

Computer Science Senior Theses

This research paper presents an integrated approach that combines Long Short-Term Memory (LSTM), Q-Learning, Monte Carlo methods, and Text-to-Text Transfer Transformer (T5) to analyze and evaluate the business strategies of public companies. Leveraging a large and diverse dataset sourced from multiple reliable sources, the study examines corporate strategies and their impact on market dynamics. LSTM and Q-Learning are employed to process sequential data, enabling informed decision-making in simulated market environments and providing insights into potential outcomes of different strategies. The Monte Carlo method manages uncertainty, allowing for a comprehensive analysis of risks and rewards associated with specific strategies. T5 interprets …

Unmasking Bias: Investigating Strategies For Minimizing Discrimination In Ai Models, Julia L. Martin

Computer Science Senior Theses

Artificial Intelligence (AI) models are increasingly used as predictive tools with real-world applications occurring in diverse fields ranging from the healthcare industry to the criminal justice system. While AI often offers efficient and relatively effective solutions, there are growing concerns regarding AI’s role in decision-making processes due to potential biases embedded in these models. In many cases, bias in AI models can produce unfair outcomes, perpetuate social inequities, and undermine the trustworthiness of AI systems. This thesis explores this problem and spotlights certain biased models that are currently utilized in real-world situations. One such example is a highly biased AI …

An Algorithmic Approach To Jazz Guitar Voice-Leading Chord Fingerings, Matthew B. Keating

Computer Science Senior Theses

A problem in guitar practice is choosing chord voicings that fit together in sequence, a process known as voice leading. In jazz, a guitarist follows voice leading by maintaining stepwise or limited motion for smoother harmony. The main avenues to learn jazz guitar voice leading theory are through a guitar instructor or chord books. To our knowledge, no computational method of generating voice-leading given chord labels exists. First, we demonstrate the complexity of this problem by presenting a graph search algorithm to optimize for a simplified version of voice leading. Then, we present a novel approach to algorithmically derive tablature …

Connecting Linguistic Expressions And Pain Relief Through Transformer Model Construction And Analysis, Sarah M. Chacko

Computer Science Senior Theses

Chronic pain is a widespread problem that significantly impacts quality of life. Overprescription and abuse of pain medication continues to be a major public health issue and can further burden patients due to a fragmented health care system. Previous research has suggested a possible psychological basis to pain and the potential for safer, non-pharmacological alternatives for pain relief. This project leverages language models to study chronic pain development and relief through psychological treatments, which will be assessed through responses to post-treatment interviews. A transformer-based natural language processing model is employed to identify connections between language expressions and pain on a …

Investigating English-Language Dialect-Adjusted Models, Samiha Datta

Computer Science Senior Theses

This thesis describes several approaches to better understand how large language models interpret different dialects of the English language. Our goal is to consider multiple contexts of textual data and to analyze how English-language dialects are realized in them, as well as how a variety of machine learning techniques handle these differences. We focus on two genres of text data: news and social media. In the news context, we establish a dataset covering news articles from five countries and four US states and consider language modeling analysis, topic and sentiment distributions, and manual analysis before performing nine experiments and evaluating …

Data-Optimized Spatial Field Predictions For Robotic Adaptive Sampling: A Gaussian Process Approach, Zachary Nathan

Computer Science Senior Theses

We introduce a framework that combines Gaussian Process models, robotic sensor measurements, and sampling data to predict spatial fields. In this context, a spatial field refers to the distribution of a variable throughout a specific area, such as temperature or pH variations over the surface of a lake. Whereas existing methods tend to analyze only the particular field(s) of interest, our approach optimizes predictions through the effective use of all available data. We validated our framework on several datasets, showing that errors can decline by up to two-thirds through the inclusion of additional colocated measurements. In support of adaptive sampling, …

Utilizing Natural Language Processing For Automated Clinical Text Review: Identification Of Care Preference Documentation In Patients’ Discharge Summaries, Saksham Arora

Computer Science Senior Theses

Improving patient-centered care necessitates accurate documentation of care preferences, a crucial aspect often underrepresented in administrative data. Most studies apply care documentation to specific patient populations, rather than more appropriately broad population of `seriously ill' patients. This paper addresses this gap by leveraging transformer-based machine learning models, exhibiting an improvement over traditional keyword-based search methods in identifying care preference documentation.

In order to capture a broad spectrum of seriously ill patients, we matched decedent patients to non-decedent counterparts by utilizing a propensity score matching, accounting for important variables like age, gender, primary diagnoses and commodities. We trained and fine-tuned Bio_ClinicalBERT …

Deep Learning For Skin Photoaging, Gokul Srinivasan

Computer Science Senior Theses

Skin photoaging is the premature aging of skin that results from ultraviolet light exposure. It is a major risk factor for the development of skin cancer, among other malignant skin pathologies. Accordingly, understanding its etiology is important for both preventative and reparative clinical action. In this study, skin samples obtained from patients with ranging solar elastosis grades – a proxy for skin photoaging – were sequenced using next-generation sequencing techniques to further understand the genomic, epigenomic, and histological signs and signals of skin photoaging. The results of this study suggest that tissues with severe photoaging exhibit increases in the frequency …

Georcf-Gn: Geography-Aware State Prediction In Dynamic Networks, Barkin Cavdaroglu

Computer Science Senior Theses

No abstract provided.

Neutrosophic Mcdm Methodology For Risk Assessment Of Autonomous Underwater Vehicles, Shimaa S. Mohamed, Ahmed Abdel-Monem, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

Due to its usefulness in several industries and the military, researchers have concentrated on developing autonomous underwater vehicles (AUVs). However, AUV navigation continues to be a difficult challenge to solve owing to the variety of underwater settings. The usage of AUVs, or autonomous underwater vehicles, is not without dangers like malfunction, ecological risks, loss of communications, cybersecurity risks, collisions, and others. There are many criteria to assess these risks technical, operational, economic, and regulatory. So, the methods of multi-criteria decision-making (MCDM) is used to deal with these various criteria. The analytical hierarchy process (AHP) method is an MCDM methodology, that …

A Survey On Online Matching And Ad Allocation, Ryan Lee

Theses

One of the classical problems in graph theory is matching. Given an undirected graph, find a matching which is a set of edges without common vertices. In 1990s, Richard Karp, Umesh Vazirani, and Vijay Vazirani would be the first computer scientists to use matchings for online algorithms [8]. In our domain, an online algorithm operates in the online setting where a bipartite graph is given. On one side of the graph there is a set of advertisers and on the other side we have a set of impressions. During the online phase, multiple impressions will arrive and the objective of …

Mining Themes In Clinical Notes To Identify Phenotypes And To Predict Length Of Stay In Patients Admitted With Heart Failure, Ankita Agarwal, Tanvi Banerjee, William Romine, Krishnaprasad Thirunarayan, Lingwei Chen, Mia Cajita

Computer Science and Engineering Faculty Publications

Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and to group patients based on their similar characteristics which could also help in predicting patient outcomes like length of stay. These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients …

Rapid Assessment Of Fish Freshness For Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy And Fusion-Based Artificial Intelligence, Hossein Kashani Zadeh, Mike Hardy, Mitchell Sueker, Yicong Li, Angelis Tzouchas, Nicholas Mackinnon, Gregory Bearman, Simon A Haughey, Alireza Akhbardeh, Insuck Baek, Chansong Hwang, Jianwei Qin, Amanda M Tabb, Rosalee S Hellberg, Shereen Ismail, Hassan Reza, Fartash Vasefi, Moon Kim, Kouhyar Tavakolian, Christopher T Elliott

Faculty, Staff and Student Publications

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and …

"Church On My Couch": Predicting The Future Impact Of Online Ministry Based On The Impact During Covid-19, Samukeliso Mabarani, Sikhumbuzo Dube

Adventist Human-Subject Researchers Association

With “everything from home” as the new norm, “how does the use of digital platforms impact Adventist education, community engagement, and spiritual outreach?” Using a quantitative approach, we draw insights from online ministry during Covid-19 and use the insights to predict the future impact of online ministry statistically.

Efficient Convoy Routing And Bridge Load Optimization User Interface, Brandon Lacy, Will Heller, Yonas Kassa, Brian Ricks, Robin Gandhi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

Hacker, Influencer, Counter-Culture Spy: Cyberspace Actors’ Models Of Misinformation And Counter-Operations, Benjamin Kessell

College of Computing and Digital Media Dissertations

As misinformation continues to spread on social media, its residents have begun to fight back, independent of any platform. This organic resistance to the diffusion of misinformation is a clearly observable phenomenon with roots in Anonymous’ distributed campaigns from the 2010s outwards. Hacker and information security communities are acting in defense of some of their favorite spaces, most notably, Twitter. Security researchers of all stripes use it for sharing indicators of compromise but, as the diffusion of misinformation becomes more problematic it becomes more difficult to find signals in the noise.

These actors’ response to the issues at hand is …

A Machine Learning Model To Predict The Asd Traits And Challenging Behaviors In Children, Biswaranjan Senapati

Theses and Dissertations

A neurological disorder, along with several behavioral issues, may be to blame for a child's subpar performance in the academic journey (such as anxiety, depression, learning disorders, and irritability). These symptoms can be used to diagnose children with ASD, and supervised machine learning models can help differentiate between ASD traits and other behavioral traits in children, so that a correct diagnosis can be made. Autism usually shows up in a child within the first two years of life, but it can happen at any time. This research aims to create a model for predicting ASD traits and challenging behaviors in …

How To Select Simple-Yet-Accurate Model Of Bridge Maintenance?, Akshay Kale, Yonas Kassa, Brian Ricks, Robin Gandhi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

Developing Architecture For A Routing System Using Bridge Data And Adversary Avoidance, Will Heller, Brian Ricks, Yonas Kassa, Brandon Lacy, Rahul Kamar Nethakani

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

Building Explainable Machine Learning Lifecycle: Model Training, Selection, And Deployment With Explainability, Vidit Singh, Yonas Kassa, Brian Ricks, Robin Gandhi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

Progress In A New Visualization Strategy For Ml Models, Alex Wissing, Brian Ricks, Robin Gandhi, Yonas Kassa, Akshay Kale

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

Covid-19 In Casinos: Analysis Of Covid-19 Contamination And Spread With Economic Impact Assessment, Anastasia (Stasi) D. Baran, Jason D. Fiege

International Conference on Gambling & Risk Taking

Abstract:

The COVID-19 pandemic caused tremendous disruption for casinos, with the virus causing various lengths of shutdowns, capacity restrictions, and social distancing strategies such as machine removals or section closures. Although most of the world has now eased off these measures, it is important to review lessons learned to understand, and better prepare for similar circumstances in the future. We present Monte Carlo slot floor simulation software customized to simulate players spreading COVID-19 on the slot floor. We simulate the amount of touch surface contamination; the number of potential surface contact exposure events per day, and a proximity exposures statistic …

Statistical Methods To Generate Artificial Slot Floor Data For The Advancement Of Casino Related Research, Courtney Bonner, Anastasia (Stasi) D. Baran, Jason D. Fiege, Saman Muthukumarana

International Conference on Gambling & Risk Taking

Abstract:

A common difficulty when researching gambling topics is the availability of high-quality data sets for development and testing. Due to the high level of secrecy within the gambling industry, if data is obtained for research purposes it is often prohibitively obfuscated, incomplete, or aggregated. Although these data have allowed for advancement in academic work, it leaves both the researchers and readers left wondering about what would be possible if more detailed data sets were available. To mitigate the paucity of data available to researchers, we present a Markov chain-based statistical process for producing artificial event data for a simulated …

Energy Efficiency And Material Cost Savings By Evolution Of Solar Panels Used In Photovoltaic Systems Under Neutrosophic Model, Ahmed Sleem, Ibrahim Elhenawy

Neutrosophic Systems with Applications

Traditional applications of solar panels have been limited to smaller-scale energy production, such as that required by single houses or apartment complexes. Researchers from all around the globe have been working together to develop creative, efficient goods, increase the energy efficiency of solar panels, and build new, ground-breaking practices using photovoltaic system design. Solar photovoltaic (PV) system planning demands a strategic decision-making approach to socioeconomic growth in many nations due to the rising understanding of the financial, social, and ecological aspects. The primary goal of this study is to provide a novel, adaptable method of Multi-Criteria Decision Making (MCDM) for …

Energy Efficiency And Material Cost Savings By Evolution Of Solar Panels Used In Photovoltaic Systems Under Neutrosophic Model, Ahmed Sleem, Ibrahim Elhenawy

Neutrosophic Systems with Applications

Traditional applications of solar panels have been limited to smaller-scale energy production, such as that required by single houses or apartment complexes. Researchers from all around the globe have been working together to develop creative, efficient goods, increase the energy efficiency of solar panels, and build new, ground-breaking practices using photovoltaic system design. Solar photovoltaic (PV) system planning demands a strategic decision-making approach to socioeconomic growth in many nations due to the rising understanding of the financial, social, and ecological aspects. The primary goal of this study is to provide a novel, adaptable method of Multi-Criteria Decision Making (MCDM) for …

Predicting Flux Data For Exoplanet Detection., Ronald Kroening

Honors College Theses

This paper will focus on utilizing five different methods of machine learning models to properly classify celestial bodies orbiting a star as an exoplanet or a false positive. We will be utilizing a recurrent neural network (RNN), a logistical regression model (LR), and a Random Forest Classifier (RF). The focus of the data set was to improve access to balanced data in the form of extracted features and time series graphs, as well as looking into potential solutions for previous shortcomings outlined in prior work, specifically relating to logistical regression models. Training data was assembled from Astronet, a pipeline that …

The Locals Casino As A Social Network – Can An Interconnected Community Of Players Detect Differences In Hold?, Jason D. Fiege, Anastasia (Stasi) D. Baran

International Conference on Gambling & Risk Taking

Abstract

It is difficult for individual players to detect differences in theoretical hold between slot machines without playing an unrealistically large number of games. This difficulty occurs because the fractional loss incurred by a player converges only slowly to the theoretical hold in the presence of volatility designed into slot pay tables. Nevertheless, many operators believe that players can detect changes in hold or differences compared to competition, especially in a locals casino market, and therefore resist increasing holds. Instead of investigating whether individual players can detect differences in hold, we ask whether a population of casino regulars who share …

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 …

Handling Realistic Label Noise In Bert Text Classification, Maha Tufail Agro, Hanan Al Darmaki

Natural Language Processing Faculty Publications

Label noise refers to errors in training labels caused by cheap data annotation methods, such as web scraping or crowd-sourcing, which can be detrimental to the performance of supervised classifiers. Several methods have been proposed to counteract the effect of random label noise in supervised classification, and some studies have shown that BERT is already robust against high rates of randomly injected label noise. However, real label noise is not random; rather, it is often correlated with input features or other annotator-specific factors. In this paper, we evaluate BERT in the presence of two types of realistic label noise: feature-dependent …