Physical Sciences and Mathematics Commons^™

Temporal Json Keyword Search, Curtis Dyreson, Amani Shatnawi, Sourav S. Bhowmick, Vishal Sharma

Computer Science Faculty and Staff Publications

JSON keyword search searches the current versions of documents in a collection. However, JSON documents change over time due to edits. Some applications, such as data forensics and auditing, need to search past versions of documents and for changes to documents. This paper introduces a system called Temporal JSON Keyword Search (Tjks) for search in a collection of JSON documents that vary over time. Tjks lets users control which temporal slice, or part of the history, can be searched using a temporal search semantics; we support both of the major temporal semantics: sequenced and nonsequenced search. This paper …

Anion Binding And Sensing Using Cs124-Sensitized Luminescent Terbium Complexes, Alessandro Rizzi, Minhee Lee, Wade Grabow, Olivia Brooks, Helena Nguyen, Neal Yakelis, Clarisse Vanderfeltz

Honors Projects

Two terbium complexes with varying degrees of intramolecular coordination, Tb:DO2A-Cs124 and Tb:DOTA-Cs124, were prepared. Their capacity to detect biologically and environmentally relevant anions through their luminescence changes was investigated. Tb:DOTA-Cs124 demonstrated exceptional selectivity as a sensor for nitrite, while Tb:DO2A-Cs124 detects nitrite, phosphates, and a range of carboxylate-containing anions.

A Brief Introduction To General Topology, Richard P. Millspaugh

Open Educational Resources

The material in this text is intended to be accessible to undergraduates who have had an introduction to elementary set theory and proof techniques. It includes sufficient material from general topology to prove the two main topological results found in a standard first semester calculus course: the Intermediate Value Theorem and the Extreme Value Theorem. This material can be found in Chapters 2 through 6 and makes up the bulk of the text. Rather than approaching these topics through use of the standard euclidean metric, it defines the standard topology on R in terms of the usual order on R. …

Risk Factors For Pediatric Ischemic Stroke And Intracranial Hemorrhage: A National Electronic Health Record Based Study, Stuart Fraser, Samantha M Levy, Amee Moreno, Gen Zhu, Sean Savitz, Alicia Zha, Hulin Wu

Faculty, Staff and Student Publications

BACKGROUND: Stroke is an important cause of morbidity in pediatrics. Large studies are needed to better understand the epidemiology, pathogenesis and risk factors associated with pediatric stroke. Large administrative datasets can provide information on risk factors in perinatal and childhood stroke at low cost. The aim of this hypothesis-generating study was to use a large administrative dataset to assess for prevalence and odds-ratios of rare exposures associated with pediatric stroke.

METHODS: The data for patients aged 0-18 with a diagnosis of either ischemic stroke or intracranial hemorrhage were extracted from the Cerner Health Facts EMR Database from 2000 to 2018. …

Exploring The Tradeoff Between Data Privacy And Utility With A Clinical Data Analysis Use Case, Eunyoung Im, Hyeoneui Kim, Hyungbok Lee, Xiaoqian Jiang, Ju Han Kim

Faculty, Staff and Student Publications

BACKGROUND: Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility.

METHODS: Predictive modeling of emergency department length of stay was used as …

Measuring Confidentiality With Multiple Observables, John J. Utley

Computer Science Senior Theses

Measuring the confidentiality of programs that need to interact with the outside world can prevent leakages and is important to protect against dangerous attacks. However, information propagation is difficult to follow through a large program with implicit information flow, tricky loops, and complicated instructions. Previous works have tackled this problem in several ways but often measure leakage a program has on average rather than the leakage produced by a set of particularly compromising interactions. We introduce new methods that target a specific set of observables revealed throughout execution to cut down on the resources needed for analysis. Our implementation examines …

Foam Fraction Flow: Using Mls-Mpm With Volume Fractions To Simulate Low-Density Dry Foam, Alexander Neville

Theses and Dissertations

Foaming bubbles are a common element seen throughout both nature and man-made civilization. All types of bubbles behave variably. For example, foams float in water, splash out from buckets, and are squeezed out of hand soap dispensers. Due to their complexity and variance in behavior, they can be expensive to simulate and difficult to portray realistically. When simulating dense concentrations of bubbles with little free-flowing water, such as soap films, three variables need to be accounted for: how the fluid will move, how the bubbles will be sized, and how the bubbles will behave when they come into contact with …

Customization Of Large Language Models For Causal Inference And Data Quality, Xingqiao Wang

Theses and Dissertations

In the burgeoning fields of artificial intelligence (AI) and natural language processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for understanding complex textual data. This dissertation focuses on the novel customization of LLMs for enhancing causal inference in pharmacovigilance and improving entity matching for data quality—two critical challenges in healthcare analytics and data management. Through an in-depth exploration of encoder and decoder LLMs, this study illustrates how domain-specific customization can significantly advance the processing and interpretation of textual information. For pharmacovigilance, it demonstrates how tailored LLMs can extract causal relationships from adverse event reports, offering a new …

Exploring Applications Of Ai In Developer-Side Web Accessibility Practices, Maria H. Cristoforo

Computer Science Senior Theses

No abstract provided.

Singleadv: Single-Class Target-Specific Attack Against Interpretable Deep Learning Systems, Eldor Abdukhamidov, Mohammed Abuhamad, George K. Thiruvathukal, Hyoungshick Kim, Tamer Abuhmed

Computer Science: Faculty Publications and Other Works

In this paper, we present a novel Single-class target-specific Adversarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the target model into confusing a specific category of objects with a target category while ensuring highly relevant and accurate interpretations. The universal perturbation is stochastically and iteratively optimized by minimizing the adversarial loss that is designed to consider both the classifier and interpreter costs in targeted and non-targeted categories. In this optimization framework, ruled by the first- and second-moment estimations, the desired loss surface promotes high confidence and interpretation score of adversarial samples. By …

Unveiling The Metaverse: A Survey Of User Perceptions And The Impact Of Usability, Social Influence And Interoperability, Mousa Al-Kfairy, Ayham Alomari, Mahmood Al-Bashayreh, Omar Alfandi, Mohammad Tubishat

All Works

This review explores the Metaverse, focusing on user perceptions and emphasizing the critical aspects of usability, social influence, and interoperability within this emerging digital ecosystem. By integrating various academic perspectives, this analysis highlights the Metaverse's significant impact across various sectors, emphasizing its potential to reshape digital interaction paradigms. The investigation reveals usability as a cornerstone for user engagement, demonstrating how social dynamics profoundly influence user behaviors and choices within virtual environments. Furthermore, the study outlines interoperability as a paramount challenge, advocating for establishing unified protocols and technologies to facilitate seamless experiences across disparate Metaverse platforms. It advocates for the adoption …

Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins

SMU Data Science Review

This paper presents a comprehensive study examining the real estate market potential in the dynamic urban landscapes of Frisco and Plano, Texas. Combining traditional real estate analysis with cutting-edge machine learning techniques, the study aims to predict home prices and assess investment feasibility. Leveraging these findings, the study proposes a strategic focus on predictive modeling and investment potential identification, emphasizing the continual refinement of machine learning models with updated data to accurately forecast changes in the real estate market. By harnessing the predictive power of these models, investors can identify high-growth areas and optimize their investment decisions, thus capitalizing on …

A Symbolic Approach To Nonlinear Time Series Analysis, Ranjan Karki, Nibhrat Lohia, Michael B. Schulte

SMU Data Science Review

Current nonlinear time series methods such as neural networks forecast well. However, they act as a black box and are difficult to interpret, leaving the researchers and the audience with little insight into why the forecasts are the way they are. There is a need for a method that forecasts accurately while also being easy to interpret. This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression. The final method relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the …

Intelligent Solutions For Retroactive Anomaly Detection And Resolution With Log File Systems, Derek G. Rogers, Chanvo Nguyen, Abhay Sharma

SMU Data Science Review

This paper explores the intricate challenges log files pose from data science and machine learning perspectives. Drawing inspiration from existing methods, LAnoBERT, PULL, LLMs, and the breadth of recent research, this paper aims to push the boundaries of machine learning for log file systems. Our study comprehensively examines the unique challenges presented in our problem setup, delineates the limitations of existing methods, and introduces innovative solutions. These contributions are organized to offer valuable insights, predictions, and actionable recommendations tailored for Microsoft's engineers working on log data analysis.

Baseball Decision-Making: Optimizing At-Bat Simulations, Varun Gopal, Krithika Kondakindi, Nibhrat Lohia, Morgan Williams

SMU Data Science Review

Pitch selection in baseball plays a crucial role, involving pitchers, catchers, and batters working together. This practice, dating back to early baseball, has seen teams try various methods to gain an advantage. This research aims to use reinforcement learning and pitch-by-pitch Statcast data to improve batting strategies. It also builds on previous statistical work (sabermetrics) to make better choices in pitch selection and plate discipline. The dataset used, including over 700,000 pitches for each full season and 200,000 pitches for the COVID-shortened 2020 season, encompasses a wealth of crucial metrics including pitch release point, velocity, and launch angle. This study …

Reevaluating Texas Energy Market Forecasts In The Wake Of Recent Extreme Weather Events, Robert A. Derner, Richard W. Butler Ii, Alexandria Neff, Adam R. Ruthford

SMU Data Science Review

This paper provides updated forecasts of energy demand in Texas and recognizes the impact of sustainable energy. It is important that the forecasts of the adoption of sustainable energy are reexamined after Winter Storm Uri crippled the Texas power grid and left many without power. This storm highlighted the issues the Texas power grid had and has continued to struggle with in supplying the state with energy. This paper will offer an overview of the relevant literature on the adoption of sustainable energy and relevant events that have occurred in the state of Texas that will give the reader the …

Multi-Class Emotion Classification With Xgboost Model Using Wearable Eeg Headband Data, James Khamthung, Nibhrat Lohia, Seement Srivastava

SMU Data Science Review

Electroencephalography (EEG) or brainwave signals serve as a valuable source for discerning human activities, thoughts, and emotions. This study explores the efficacy of EXtreme Gradient Boosting (XGBoost) models in sentiment classification using EEG signals, specifically those captured by the MUSE EEG headband. The MUSE device, equipped with four EEG electrodes (TP9, AF7, AF8, TP10), offers a cost-effective alternative to traditional EEG setups, which often utilize over 60 channels in laboratory-grade settings. Leveraging a dataset from previous MUSE research (Bird, J. et al., 2019), emotional states (positive, neutral, and negative) were observed in a male and a female participant, each for …

Building Effective Large Language Model Agents, Sydney Holder, Shreyash Taywade

SMU Data Science Review

The advancement of large language models (LLMs) has significantly expanded the influence of artificial intelligence across various sectors. This paper explores building LLM agents to power applications and examines what is necessary to build an efficient and helpful AI assistant. The research investigates the core components necessary to create specialized agents, facilitate collaboration in problem-solving, and improve human task performance. The development and application of tools designed to augment the capabilities of LLM agents are also explored. The paper addresses the potential risks of the unknowns, such as hallucinations, which can compromise the success of agent-based solutions within LLM applications. …

Game Recommendation Analysis Using Steam Profiles And Reviews, Robert Blue, Luis Garcia, Jacob Turner

SMU Data Science Review

Smaller game studios are at a disadvantage when it comes to getting their product noticed by users. This study aims to provide insights on how recommendation engines work so that these smaller studios can have their games noticed on Steam. Steam is one of the largest video game distribution services and they have a recommendation engine which promotes games to its user base. This study utilized user information such as number of games played, the type of games, and the hours played and created recommendation engines to identify the qualities in the game that are driving recommendations.

Leveraging Transformer Models For Genre Classification, Andreea C. Craus, Ben Berger, Yves Hughes, Hayley Horn

SMU Data Science Review

As the digital music landscape continues to expand, the need for effective methods to understand and contextualize the diverse genres of lyrical content becomes increasingly critical. This research focuses on the application of transformer models in the domain of music analysis, specifically in the task of lyric genre classification. By leveraging the advanced capabilities of transformer architectures, this project aims to capture intricate linguistic nuances within song lyrics, thereby enhancing the accuracy and efficiency of genre classification. The relevance of this project lies in its potential to contribute to the development of automated systems for music recommendation and genre-based playlist …

Investigating Bias In Mortgage-Rate Machine Learning Models, Will Kalikman

Computer Science Senior Theses

Banks and fintech lenders increasingly rely on computer-aided models in lending decisions. Traditional models were interpretable: decisions were based on observable factors, such as whether a borrower's credit score was above a threshold value, and explainable in terms of combinations of these factors. In contrast, modern machine learning models are opaque and non-interpretable. Their opaqueness and reliance on historical data that is the artifact of past racial discrimination means these new models risk embedding and exacerbating such discrimination, even if lenders do not intend to discriminate. We calibrate two random forest classifiers using publicly available HMDA loan data and publicly …

Engineering Thermodynamics, Paul J. Marchese

Open Educational Resources

This collection of assignments is designed for an introductory thermodynamics class. It includes comprehensive readings that cover the fundamental concepts, problem sets to reinforce learning through practical application, and YouTube videos that provide detailed explanations and visual demonstrations of the material. These resources can be utilized in a regular classroom setting or for independent study, offering flexibility to accommodate different learning environments and preferences.

Visible-Light Photocatalytic C-H Amination Of Arenes Utilizing Acridine-Lewis Acid Complexes, Matthew R. Lasky, En Chih Liu, Matthew S. Remy, Melanie S. Sanford

Chemistry Faculty Research & Creative Works

This report describes the development of a visible-light photocatalytic system for C(sp²)-H amination that leverages in situ-generated photocatalysts. We demonstrate that the combination of acridine derivatives and Lewis acids form potent photooxidants that promote the C-H amination of electronically diverse arenes upon irradiation with visible-light (440 nm). A first-generation photocatalyst composed of Sc (OTf)₃ and acridine effects the C-H amination of substrates with oxidation potentials ≤ +2.5 V vs SCE with pyrazole, triazole, and pyridine nucleophiles. Furthermore, the simplicity and modularity of this system enable variation of both Lewis acid and acridine to tune reactivity. This enabled …

Detecting Drifts In Data Streams Using Kullback-Leibler (Kl) Divergence Measure For Data Engineering Applications, Jeomoan Francis Kurian, Mohamed Allali

Engineering Faculty Articles and Research

The exponential growth of data coupled with the widespread application of artificial intelligence(AI) presents organizations with challenges in upholding data accuracy, especially within data engineering functions. While the Extraction, Transformation, and Loading process addresses error-free data ingestion, validating the content within data streams remains a challenge. Prompt detection and remediation of data issues are crucial, especially in automated analytical environments driven by AI. To address these issues, this study focuses on detecting drifts in data distributions and divergence within data fields processed from different sample populations. Using a hypothetical banking scenario, we illustrate the impact of data drift on automated …

Collisional Damping In Plasmonic Wakefield Accelerators, Maxime Pindrys

Honors Scholar Theses

We investigate the previously proposed role of collisional damping in plasmonic wakefield accelerators. Wakefields driven in a doped semi-conductor will exist in differing regimes dependent on the driving beam’s intensity. At low intensities, the mobility of free carrier electrons is limited. Here, wakefields will be small if the mean free path of an electron is short compared to the quiver amplitude of a free electron. After reaching a threshold intensity, conduction electrons in the semiconductor will be driven to such high speeds that their coulomb cross section will drop sharply. Here the resulting wakes will resemble those in a hollow …

Automatic Measurement Of Dialogue Engagingness In Multilingual Settings, Amila Ferron

Dissertations and Theses

Expansive use of large language models (LLMs) as dialogue systems brings increased importance to the evaluation of the responses they generate. Although evaluation of qualities such as coherence and fluency are readily possible with well-established automatic metrics, engagingness is often measured with human evaluation -- a process that can be costly and slows the pace of development. Existing automatic metrics for engagingness have low to moderate correlation with human annotations, evaluate the response without the conversation history, are complicated to implement, or are designed for a specific dataset. Moreover, they have been tested exclusively on English conversations. Given that dialogue …

Molecular-Level Studies Of Nanopatterned Biomolecules With Atomic Force Microscopy, Ashley R. Walker

LSU Doctoral Dissertations

Atomic force microscopy (AFM) is an analytical technique in which a tipped probe is gently scanned across the surface in a raster pattern to generate digital images of a sample at the nanoscale. The AFM instrument has three general operational modes, which are contact, non-contact and tapping-mode, that can be used to examine materials at the atomic level. Single-molecular details of biological molecules and other soft organic materials can be captured with minimal denaturation in either ambient or liquid environments when using tapping-mode AFM. In tapping-mode, the probe is driven to oscillate vertically while the tip is scanned across the …

Emotional Regulation On Modulating Associations Between Depression And Physical Activity As Characterized Via Deep Learning, Franklin Ye Ruan

Computer Science Senior Theses

Emotional regulation and physical activity are known to be associated with depression; however, a deeper understanding of how emotional regulation may strengthen or weaken the bonds between depressive symptoms and physical activity may aid clinicians and researchers in developing cognitive behavioral therapy (CBT) for those adversely affected by depression. As part of the Tracking Depression Study, this analysis uses data collected from 306 participants diagnosed with Major Depressive Disorder. To study their behavior, we analyze actigraphy data, or longitudinal physical activity intensity data, as it relates to depression severity, quantified by the daily PHQ-9 questionnaires. We study these associations through …

Data-Driven Computing Methods For Nonlinear Physics Systems With Geometric Constraints, Yunjin Tong

Computer Science Senior Theses

In a landscape where scientific discovery is increasingly driven by data, the integration of machine learning (ML) with traditional scientific methodologies has emerged as a transformative approach. This paper introduces a novel, data-driven framework that synergizes physics-based priors with advanced ML techniques to address the computational and practical limitations inherent in first-principle-based methods and brute-force machine learning methods. Our framework showcases four algorithms, each embedding a specific physics-based prior tailored to a particular class of nonlinear systems, including separable and nonseparable Hamiltonian systems, hyperbolic partial differential equations, and incompressible fluid dynamics. The intrinsic incorporation of physical laws preserves the system's …

Connection-Saving Gate Assignment: A Computational Approach, Rob Mailley

Computer Science Senior Theses

The growth of the commercial aviation industry has yielded many interesting problems in the field of Operations Research, many of which are now able to be solved as both technology and mathematical optimization improve. A particularly interesting problem in airport operations re- search is the Aircraft Gate Assignment Problem (AGAP), which seeks to create a feasible match- ing between planes and flights at an airport. This problem is well-suited to modeling with Integer Programming, and has attracted research since the 1970s. Researchers of the AGAP have considered many different objectives, ranging from airline-focused objectives to more passenger-focused objective functions. In …