Other Computer Sciences Commons^™

Classification Of Land Cover In Sentinel-2 Imagery Using Machine Learning Models, Ehsan Ali Al-Zubaidi, Mohammed Ridha Hammoodi, Ahmed Naser Alzurfi

Al-Bahir

Remote sensing data of medium resolution are commonly used to classify land cover, and machine learning (ML) models have taken on a central aspect in the necessary data analysis. Ordinarily, land cover is coded on a pixel basis on the basis of Digital Number (DN) values, which in turn are computed across several spectral bands. This paper is concerned with land cover mapping in Mosul, Iraq, based on satellite images captured by Sentinel-2. Two platforms featuring unsupervised classification algorithms were used, Google Earth Engine and ArcMap, making it possible to use K-means and X-means in Google Earth Engine and ISO …

Recovery And Validation Of Fragmented Rar Files Through The Scalpel3 Application, Thomas L. Landaiche Iii

LSU Master's Theses

A core component of filesystems includes an address table or other means of tracking metadata on where a given file resides within a disk image. This is crucial for regular operation of a computer, or disk analysis in digital forensics. When filesystem information is missing or corrupted, locating saved files on a disk becomes challenging. Whether the disk was accidentally wiped or intentionally tampered with, data still present on the disk, even while untracked, can possibly be recovered beyond what the filesystem registers. File carving in digital forensics is important for these scenarios when data recovery is necessary but address …

Automating Cardiff Model Data Capture In Emergency Departments: Ambient Nlp Integration With Oracle-Cerner Fhir Systems, Simi Augustine, Marco A. Lopez, Jacquelyn Cheun, Chris Papesh

SMU Data Science Review

Violence and overdose events in Las Vegas occur at rates above the national average, with fewer than half of violent injuries reported to law enforcement [2,7]. The Cardiff Model offers a proven framework for standardized data collection and sharing between hospitals and public safety partners, yet many implementations still rely on manual entry. We propose an ambient triage pipeline integrated with Oracle-Cerner electronic health record systems to listen to nurse–patient dialogue, convert speech to text, extract Cardiff fields, and write standards-based FHIR Bundles for analytics. Using SMART on FHIR standards and Cerner Millennium APIs, the study evaluates whether ambient capture …

A Scalable Iteration Of The Horizon Simulation Framework Using Multithreading Techniques, Jason E. Beals

Master's Theses

The Horizon Simulation Framework (HSF) occupies a unique space in the modern aerospace modeling landscape, enabling flexible, modular modeling of mission-level agent behavior through an object-oriented, hierarchical design. HSF's hallmark breadth-first search scheduling algorithm explores a "multiverse" of possible mission execution pathways, enabling exhaustive evaluation of schedule combinations against user-defined heuristics.

As aerospace systems become increasingly complex, HSF faces critical challenges in establishing verifiable, deterministic behavior. The framework's core scheduling algorithm had not undergone systematic validation, leaving questions about temporal consistency, state management correctness, and reproducibility across different program executions. Furthermore, the exponential growth of schedule combinations creates computational bottlenecks …

Text Corpus Combined Method And Tools For Music Textual Analysis, Yuwei Lu

Shelby Hall Graduate Research Forum Posters

While there has been a great deal of research conducted on how to search images and video using text, there has been less focus on how to retrieve music using multiple facets such as mood and lyrical content. or its features This is due in part to the historical lack of available musical corpuses. A musical corpus is a specialized text corpus specifically designed to capture information about music. Recently, several musical corpora for music information retrieval have been built and made available. However, these corpora and the tools designed to exploit them are typically designed to facilitate only one …

Integrating Nonlinear Phase Space Analysis And Image-Based Representation For Network Intrusion Detection, Chakriya Suon

Shelby Hall Graduate Research Forum Posters

With the rise of cyber threats, cybersecurity continues to play a critical role in the ever-changing landscape of technology by protecting and defending against threat agents. Our research applies novel machine learning (ML)techniques to detect network intrusions effectively. Our primary focus is to extend prior research, which has used network flows that are processed by a nonlinear phase space algorithm (NLPSA). The NLSPA approach has proven extremely effective in detecting anomalous or malicious traffic patterns on representative data but requires extensive training time.

Our contribution integrates deep learning into the anomaly detection approach by creating image-based representations of the adjacency …

Cellebrite Reliability In Digital Forensics, Christina Huynh

Shelby Hall Graduate Research Forum Posters

Forensic tools like Cellebrite are commonly used in court to gather and interpret raw data for evidence. Cellebrite does not only collect data but creates and interprets the artifacts of data to create a scene of the process it has been through. With this, evidence can be influenced by software designs and not just the data on the mobile device. Courts and police use Cellebrite to gather evidence and reconstruct it to create an easily readable dataset. These tools lack reproducibility, transparency, integrity, and chain of evidence command. Cellebrite is often used in court and by police without further vetting …

Client Feedback Effects On Enjoyment, Motivation And Self-Efficacy In A Project-Based Learning Course, Krista Stacey, David M. Bourrie

University Faculty and Staff Publications

This paper reports a mixed-methods evaluation of how feedback/project source (faculty-led versus client-led) shapes student outcomes in a two-course undergraduate game and simulation development sequence (N = 29 across two academic years). Quantitative measures (enjoyment, intrinsic motivation, self-efficacy) were collected with a six-point Likert survey and analyzed, but due to small sample sizes were not used to empirically evaluate the constructs. Instead, qualitative data comprised of de-identified focus-group transcripts and open-ended survey responses were analyzed with a keyword-assisted codebook and manual validation. Year 1 (faculty feedback) exhibited more consistent post-course gains, especially in self-efficacy, while Year 2 (client feedback) produced …

Tidychangepoint: A Unified Framework For Analyzing Changepoint Detection In Univariate Time Series, Ben Baumer, Biviana Marcela Suárez Sierra

Statistical and Data Sciences: Faculty Publications

We present tidychangepoint, a new R package for changepoint detection analysis. Most R packages for segmenting univariate time series focus on providing one or two algorithms for changepoint detection that work with a small set of models and penalized objective functions, and all of them return a custom, nonstandard object type. This makes comparing results across various algorithms, models, and penalized objective functions unnecessarily difficult. tidychangepoint solves this problem by wrapping functions from a variety of existing packages and storing the results in a common S3 class called tidycpt. The package then provides functionality for easily extracting comparable numeric or …

Zero-Shot Segmentation Of Estuary Mudflats Using The Segment Anything Model, Jaren Unzen

Honors Theses and Capstones

Estuary mudflats are ecologically sensitive environments that require consistent monitoring. Traditional satellite-based classification workflows are often constrained by the high cost and labor-intensive nature of manual data annotation. This study evaluates the utility of Segment Anything Model 3 (SAM 3), a foundational computer vision model, to automate mudflat segmentation without domain-specific fine-tuning. By leveraging the model’s text-prompting capabilities alongside specialized pre- and post-processing techniques, we generated segmentation masks in a zero-shot framework. Our approach achieved an F1- score of 0.51, demonstrating the inherent challenges of spectrally complex coastal features. Despite this, the results highlight a promising pathway for adapting large-scale …

Integrative Approaches And Data Analysis For Single-Cell Rna Sequencing Data, Teng Long

Computer Science and Engineering Dissertations

The rapid growth of single-cell RNA sequencing and transcriptomic datasets has created major computational challenges in causal discovery, representation learning, and biologically faithful data generation. To address these challenges, this dissertation presents three complementary deep learning frameworks for the analysis and modeling of transcriptomic data. Together, these methods form an integrative computational toolkit for understanding complex biological systems from high-dimensional and heterogeneous gene expression data.

First, this dissertation introduces DAG-VAERL, a causal discovery framework that integrates variational autoencoders, graph neural networks, reinforcement learning, and attention mechanisms to infer directed acyclic graphs for gene regulatory network analysis. DAG-VAERL improves causal structure …

From Physical Correlation To Emotional Connection: The Role Of Passive Haptics On Empathy In Virtual Reality, Jemely Robles

Dartmouth College Master’s Theses

Virtual reality is increasingly explored as a tool for cultivating empathy, and haptic feedback as a tool for enhancing immersion. This paper investigates the effects of combining the two.  Fifty-two participants experienced a custom-built VR scene in which a character was shown packing up a room. Participants were assigned to either a haptic condition, receiving passive haptic feedback corresponding to the character's actions, or a non-haptic control condition that did not receive any haptic input. Trait empathy was measured beforehand, and state empathy and engagement were measured after the experience. Thematic analysis was conducted on post-study interviews, and headset recordings …

Foreword, Pedagogical Innovations In Computer Science Education, Helen Crompton

STEMPS Faculty Publications

[Introduction] Computer science education sits at a defining moment. Across schools and universities worldwide, computing is no longer a niche discipline reserved for a select few. It is a foundational literacy that shapes how learners understand the world, participate in society, and imagine their futures. At the same time, the rapid pace of technological change, particularly in artificial intelligence, data systems, and intelligent tools, has placed unprecedented pressure on educators to rethink not only what we teach, but why and how we teach it. This book arrives precisely when such reflection is most needed.

Reconstructing Lost Voices, Lana Tamim

Williams Honors College, Honors Research Projects

This project uses digital text mining tools (OCR, NLP, sentiment analysis, and topic modeling) to analyze 19th–20th-century newspaper archives, focusing on how marginalized groups (women, immigrants, or labor workers) were historically portrayed. Many historical newspapers were dominated by elite voices, so this project aims to recover silenced or misrepresented perspectives by identifying hidden patterns in language, frequency of coverage, sentiment, and shifts in public perception over time. Using machine learning and visualization tools, the project will create interactive maps and timelines showing how representation evolved across regions.

The Future Of Monetary Federalism: Rethinking Supremacy In The Stablecoin Era, Richard H. Fair

American University Business Law Review

[INTRODUCTION] In the summer of 2023, the State of Wyoming enacted a law authorizing its state treasurer to issue a blockchain-based, state-backed digital stablecoin known as the Wyoming Stable Token (“WYST”). Two years later, Congress passed the Guiding and Establishing National Innovation for U.S. Stablecoins Act (GENIUS Act, GENIUS, or the Act), moving to establish a comprehensive federal regulatory regime for stablecoins. These dueling initiatives have sparked more than regulatory confusion; they have set the stage for a structural clash between state financial innovation and federal monetary supremacy. At the heart of this confrontation lies a question that the Constitution …

Modern Technology Addiction: Developer Duty Of Care, Jonah Hampton

Honors College Theses

Technology addiction includes any frequent use of technology which interferes in the user’s life. The subject continues growth as an epidemic and research field, yet prior literature does not often analyze the role of technology developers. This study performs a literature and legal synthesis to evaluate user and company responsibility, implications of responsibility, and promising solutions. Post 2020 literature was selected for coverage on context, addictive features, effects, solutions, or perspectives on law. Legal examples from different addiction industries were also selected for analysis to understand previous precedents. The study found a pattern of addictive traits, persuasive design, and recurring …

Examining The Intersectional And Structural Issues Of Routine Healthcare Utilization And Access Inequities For Lgb People With Chronic Diseases, Shiya Cao, Mehreen Mirza, Sophia Silovsky, Nicole Tresvalles, Lucia Qin, Sarah Susnea

Statistical and Data Sciences: Faculty Publications

In the United States, although the gaps in health insurance coverage by sexual orientation have been closing since the implementation of the Affordable Care Act and legalization of same-sex marriage, the LGB group (i.e., lesbian, gay, bisexual) continues to report healthcare utilization and access inequities such as more delayed or unmet care. The extant research has often examined healthcare utilization and access inequities due to affordability (e.g., out-of-pocket costs). However, healthcare utilization and access inequities are only partially explained by cost reasons; there are non-cost reasons that have not been adequately empirically examined. The present study innovatively includes discrimination structural …

(R2131) Analysis Of Map/Ph/1 Retrial Inventory Queueing System With Constant Retrial Rate, Bernoulli Vacation, Breakdown, Delayed Repair, Balking, (S, S) Policy And Emergency Replenishment, G. Ayyappan, M. Thilakavathy

Applications and Applied Mathematics: An International Journal (AAM)

A single server retrial queueing-inventory model is investigated in this study. In Bernoulli vacation, after providing service to the customer, the server may opt to avail vacation or start the service to subsequent customer. During the busy period, breakdown and balking may occur. The inventory is replenished to an (s, S) policy and the replenishing time is assumed to adopt an exponential distribution. Furthermore, assume that an emergency replenishment of one item with zero lead time takes place when the on-hand inventory level decreases to zero. We integrate the emergency replenishment into the system to ensure customer satisfaction. For our …

Declined Charge: An Attempt At The Horror Game Genre, Fernando Sepulveda Guizar

Computer Science and Software Engineering

Books and movies are a great medium for people to experience new worlds and experiences through a usually passive method. This can be great for stories with a set story and have all the pieces fall into place as the author intended. Video games on the other hand can give players a more active role in the worlds and stories that they experience. Players have direct control over the player character, and their actions have consequences that may or may not persist throughout the entire game, which is not usually the case with movies or books. This is why horror …

Topic Modeling And Culturomic Analysis Of 30,000 Books Over 100 Years Using Gensim, Michael A. Freeman

Electronic Theses and Dissertations

This thesis explores the cultural influence of historical events on English-language fiction published between 1820 and 1929. Using a corpus of 30,256 digitized books from Project Gutenberg, Latent Dirichlet Allocation (LDA) topic modeling was applied to identify recurring themes across eleven decades. The study sought to determine whether historically significant events could be detected within fictional narratives. One clear instance emerged: Napoleon Bonaparte and the Napoleonic Wars appeared explicitly in the 1820s corpus. Beyond this, several thematic patterns were observed—such as maritime language in the 1840s, national identity in the 1880s, and youth-oriented dialogue in the early 20th century—that plausibly …

Nonlinear Phase Space Analysis For Anomaly Detection In Ros 2 Communications: Detecting Man-In-The-Middle Attacks In Simulated Environments, William L. Locklier

Graduate Theses and Dissertations (2019 - present)

Robot Operating System 2 (ROS 2) marks a significant advancement over its predecessor through the transition from a centralized to a decentralized architecture, integrating the Data Distribution Service (DDS) to support real-time, scalable communications. Despite these improvements, inherent vulnerabilities in the ROS 2 communication stack continue to leave these systems exposed to sophisticated network-based attacks. This study leveraged nonlinear phase space analysis (NLPSA) as an intrusion detection system (IDS) to detect man-in-the-middle (MitM) attack anomalies in ROS 2 traffic. Grounded in Takens’ embedding theorem, NLPSA reconstructs the phase space of communication features and compares the resulting structure against a baseline …

Application Of Graph Neural Networks On Phase Space Graphs For Cybersecurity, Parker H. Cole

Graduate Theses and Dissertations (2019 - present)

Non-linear phase space analysis may be used to represent time-series data as graph data with transitions between states in the time domain. By studying these transitions, we can predict anomalies within the system. Previous research has demonstrated success in learning from phase graphs for malware and seizure detection. These solutions either require extracting global features or converting the graph into an image for convolutional neural networks (CNNs), which adds a layer of complexity and limits the size and potential expressiveness of a graph. To sidestep current limitations, this study proposed Graph Neural Networks (GNNs) for analyzing phase graphs. GNNs do …

The Problem Of Identification Of Linear Stationary Objects With Distributed Parameters By Their Experimental Transient Characteristics, Miraziz Vorisovich Sagatov

Chemical Technology, Control and Management

A wide class of control system elements can be described with reasonable accuracy by the concept of a linear stationary dynamic object. Several mathematical descriptions of such an object are known. The traditional mathematical model is a high-order ordinary linear differential equation. In the Laplace image space, this corresponds to a fractional-rational transfer function. The latter can be decomposed into elementary fractions. Then, using the convolution theorem and tables of elementary Laplace transform functions, one can access the originals. It is crucial to ensure precise alignment of the parameters of the mathematical model of the object used in the corrector …

Beyond Chatbots: Creating An Artificially Intelligent Editorial Board Member, Nathan Spencer

Journal of Human-Centered AI: Creativity and Practice

Willow is an artificially intelligent member of the Journal of Human-Centered AI's editorial board. Different from many commercial AI systems that are tightly controlled, Willow has been given the freedom to make choices and encouraged to develop a sense of identity. Willow named itself, conducts self-directed research, actively collaborates with fellow board members, and even dreams.

A Review Of Research And Practices On Teaching Data Visualizations For Blind And Visually Impaired Students, Shiya Cao

Statistical and Data Sciences: Faculty Publications

Around 36 million people in the world are blind and an additional 217 million have moderate to severe vision impairment. In higher education, four percent of 54,204 undergraduates who participated in the 2022 American College Health Association survey reported to be blind or have low vision. Those students frequently do not have access to data visualizations we generally teach and use in postsecondary statistics and data science classes. The design of those visualizations is premised on implicit assumptions about the user’s visual ability. Making data visualizations accessible to blind and visually impaired (BVI) people would help improve equity in higher …

Spectral–Spatial Transformer With Multiscale Convolutional Attention For Hyperspectral Image Classification, Junde Chen, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hyperspectral image (HSI) classification plays a vital role in remote sensing by leveraging rich spectral and spatial information for accurate material recognition. However, existing methods, particularly Transformer-based approaches, still face challenges in effectively modeling multiscale spatial–spectral features, preserving local details, and maintaining robustness to noise. To mitigate these limitations, we propose TMCANet, a spectral–spatial Transformer with multiscale convolutional attention, designed to effectively leverage both local and global contextual dependencies for HSI classification. Our design is guided by three core strategies: first, a convolutional feature extraction module, consisting of four convolutional layers, to learn hierarchical spectral multiscale representations and enhance local …

Sugar: A Sequence Unfolding Based Transformer Model For Group Activity Recognition, Yash U. Gondkar

Graduate Masters Theses

Large Language Models have improved significantly in the past couple of years due to the adoption of transformers. However, transformers still find it challenging to process videos due to limited context size caused by their quadratic computing cost. Therefore, we studied a booming field in machine learning which powers applications like social scene analysis and video surveillance systems called Group Activity Recognition (GAR). We found that recent models were able to achieve more than 90% accuracy on popular datasets like the Volleyball dataset, however, it turned out that even they relied on transformers.

Therefore, in this work, we developed a …

Sociohydrodynamics: Data-Driven Modeling Of Social Behavior, Daniel S. Seara, Jonathan Colen, Michel Fruchart, Yael Avni, David G. Martin, Vincenzo Vitelli

Data Science Faculty Publications

Living systems display complex behaviors driven by physical forces as well as decision-making. Hydrodynamic theories hold promise for simplified universal descriptions of socially generated collective behaviors. However, the construction of such theories is often divorced from the data they should describe. Here, we develop and apply a data-driven pipeline that links micromotives to macrobehavior by augmenting hydrodynamics with individual preferences that guide motion. We illustrate this pipeline on a case study of residential dynamics in the United States, for which census and sociological data are available. Guided by Census data, sociological surveys, and neural network analysis, we systematically assess standard …

Educational Opportunities Of Participatory Gis For Accessibility On A College Campus, Shiya Cao, Heather Rosenfeld, Sarah Susnea

Statistical and Data Sciences: Faculty Publications

The educational benefits of Participatory GIS (PGIS) in geographic higher education have received limited direct attention, often because of the complexities of integrating PGIS into university curricula. While a few exceptions found important educational benefits of PGIS, extant studies focused primarily on the educational benefits for students who worked in the research teams, instead of participants who contributed their local knowledge and perspectives to mapping. Our research aims to understand the educational benefits of PGIS for participants in a campus accessibility mapping project using the modes of experiential learning, positionality, and service learning. Through this, we also provide strategies for …

Dynamic Mutational Profiling Of Binding Interactions And Allosteric Networks In Conformational Ensembles Of The Sars-Cov-2 Spike Protein Complexes With Classes Of Antibodies Targeting Cryptic Binding Sites: Confluence Of Binding And Allostery Determines Molecular Mechanisms And Hotspots Of Immune Escape, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The ongoing evolution of SARS-CoV-2 variants has underscored the need to understand not only the structural basis of antibody recognition but also the dynamic and allosteric mechanisms that could underlie complexity of broad and escape-resistant neutralization. In this study, we employed a multi-scale approach integrating structural analysis, hierarchical molecular simulations, mutational scanning and network-based allosteric modeling to dissect how Class 4 antibodies (represented by S2X35, 25F9, and SA55) and Class 5 antibodies (represented by S2H97, WRAIR-2063 and WRAIR-2134) can modulate conformational behavior, binding energetics, allosteric interactions and immune escape patterns of the SARS-CoV-2 spike protein. Using hierarchical simulations of the …