Computer Sciences Commons^™

Computer Organization And Assembly Language Programming, Chenxi Wang Phd, Muhammad Rashed Phd

Mavs Open Press Open Educational Resources

Computer Organization and Assembly Language Programming is an open textbook written for CSE 2312 students at The University of Texas at Arlington and for anyone who wants to see clearly how high-level code becomes machine operations. The book takes the position that assembly is not a historical curiosity but a working tool: it is where system programming, embedded development, performance tuning, and real debugging skill begin.

Across sixteen chapters, this textbook builds from number systems and base conversion through ALU operations, status flags, and shift operations, then into ARMv7 assembly syntax, the load and store architecture, endianness, addressing modes, branch …

Synthetic-Chicken-Fillets, Chirantan Sen Mukherjee, Seung-Chul Yoon, William J. Beksi

Agriculture

This Synthetic-Chicken-Fillets dataset contains 1,000 synthetic 3D meshes designed to capture the natural variance and size diversity of real broiler fillets. The collection was developed to test automated woody breast detection algorithms within a physics-based simulation environment. We utilized a seed dataset of 2D depth maps derived from 40 real-world RGBD point cloud scans. These real depth maps were fed into a few-shot transfer learning pipeline using a generative adversarial network architecture. The resulting generated depth maps were reconstructed back into 3D meshes. The length and thickness of each mesh were randomly scaled based on physical measurements of real broiler …

3dcotton, Md Ahmed Al Muzaddid, William J. Beksi

Agriculture - Archive

3DCotton is an image dataset consisting of 8 cotton plants recorded at the Texas A&M University Research Farm. The images were captured using an Apple iPhone at a resolution of 1040x1920 pixels. Approximately 150 images per plant were taken from a distance of 1 m by recording multiple viewpoints. These images can be utilized for developing 3D reconstruction methods.

The Waldo Dataset, Mary E. Koone, Rosie Kallie, Vassilis Athisos, Laurel S. Stvan

Computer Science and Engineering Datasets - Archive

Distinct from the task of predicting the author of a document (authorship attribution), we focus on addressing the issue of how to estimate the similarity between the written language styles of authors. To do so, we present a dataset of metadata derived by asking human annotators, who were presented with three documents, to identify which two were written by the same author and which was written by a different author. The dataset has over 400 such annotations, creating a companion to the Amazon Web Services (AWS) customer review dataset, laying the groundwork for crowdsourcing applications to other natural language processing …

Ueof, Nick Truong, Pritam P. Karkomar, William J. Beksi

Event-Based Vision - Archive

UEOF is the first synthetic underwater event-based optical flow dataset derived from physically-based ray-traced RGBD sequences. It was constructed using a modern video-to-event pipeline applied to rendered underwater videos. It consists of realistic event data streams with dense ground-truth flow, depth, and camera motion. The dataset is composed of 12 minutes and 51 seconds of data across 13,714 RGB frames. This results in a total of 4.94 billion events across all scenes. UEOF exhibits a high dynamic range of motion with a mean flow magnitude of 6.1 px and a median of 3.6 px. The motion distribution is heavy-tailed. While …

Universal Sound Separation: Distance-Aware Mixture Simulation, Co-Occurrence Conditioning, And Chain-Of-Inference, Wonjun Park

Computer Science and Engineering Theses - Archive

Universal Sound Separation (USS) -- the task of disentangling arbitrary sound sources from a single-channel acoustic mixture -- remains an open challenge due to the ill-posed nature of the problem and the distributional gap between synthetic training data and real-world recordings. This thesis addresses three distinct bottlenecks in the USS pipeline: training data realism, inference strategy, and conditioning richness. We first present two knowledge-guided approaches to sound source separation. The first is a distance-aware mixing strategy that leverages Large Language Models (LLMs) to assign plausible loudness relationships between audio sources during training data synthesis. By querying an LLM about the …

Ai For Life Sciences: From Geometric Protein Modeling To Multimodal Drug Design, Feng Jiang

Computer Science and Engineering Dissertations

Predicting biomolecular interactions, from immune recognition to drug–target binding, is a central problem in the life sciences and computational drug discovery. Deep learning has advanced this area, yet three challenges persist: the topology of large, highly imbalanced interaction networks; structural noise in computationally predicted protein models; and the integration of multimodal information such as functional text and taxonomic annotations. This dissertation develops a coherent set of models spanning immune complex prediction and small-molecule drug design: graph learning that addresses network topology and severe class imbalance; a noise-tolerant method that fuses predicted structures with evolutionary sequence features; and multimodal representation learning …

Towards Multimodal Guideline-Aligned Agentic Systems, Wenliang Zhong

Computer Science and Engineering Dissertations - Archive

I present my work on building multimodal guideline-aligned agentic systems designed to enable AI agents to solve complex real-world tasks. My research addresses two critical perspectives: (1) Instruction-Aware Embedding Models for flexible and universal embedding tasks, and (2) Guideline-Driven LLM Agents that leverage domain-specific guidelines to perform expert-level tasks. These components address embedding and generation tasks, respectively, and lay the foundation for a hybrid agent capable of tackling challenging real-world applications.

From the embedding perspective, I first address the instruction-following capabilities of embedding models. While Large Language Models (LLMs) excel at instruction following, they are primarily designed for generation rather …

Multimodal Deep Learning For Biological Data Understanding, Saiyang Na

Computer Science and Engineering Dissertations

This dissertation presents three contributions to multimodal deep learning for biological data understanding, addressing the fundamental challenge of cross-modal alignment from two complementary perspectives: designing effective multimodal fusion methods for specific biomedical applications, and proposing a general framework for higher-order multimodal alignment that captures hierarchical structure in data.

First, we develop Cmai, a deep learning framework for B cell receptor (BCR) to antigen binding prediction that aligns BCR sequence information with antigen three-dimensional structures using contrastive learning. Cmai achieves an average AUROC of 0.907 across 17 antigens and 5 independent cohorts, and demonstrates clinical utility in predicting immune checkpoint inhibitor …

Systematic Approaches To Characterizing Vulnerabilities And Enhancing Robustness Of Text And Vision-Language Models, Poojitha Thota

Computer Science and Engineering Dissertations

The proliferation of artificial intelligence (AI) across critical domains, including news summarization, privacy-policy analysis, and medical decision support, has raised growing concerns about the security and robustness of these systems against adversarial manipulation. This dissertation investigates adversarial robustness in generative AI by addressing three key research goals: (1) characterizing adversarial vulnerabilities across generative models, (2) developing systematic defenses to improve the robustness of generative models, and (3) designing deployment-time safeguards for securing LLM interactions.

Towards the first goal, we characterize adversarial vulnerabilities across text-based and multimodal systems. In abstractive text summarization, we show that inference-time perturbations can exploit lead bias …

Trustworthy Multimodal Ai For Medical Imaging: Enhancing Diagnosis, Reasoning, And Human-Agent Interaction In Extended Reality, Jai Prakash Veerla

Computer Science and Engineering Dissertations

The transition from traditional microscopy to digital pathology has digitized diagnostic data, yet clinical workflows remain constrained by two-dimensional screens and passive, opaque analysis tools that fail to capture the spatial complexity of biological systems. While Foundation Models now promise to reason across histology and genomics, a critical disconnect persists between the richness of this data and the limited cognitive bandwidth of clinicians, who currently lack the immersive interfaces and trustworthy agents necessary to utilize it effectively. This dissertation presents a unified framework for "Embodied Agentic AI," establishing a pipeline that augments physician capabilities through immersive visualization, robust security, and …

Artificial Intelligence Adoption In The Workplace. An Exploration Of Augmentation, Oyinkansola O. Sodiya

Management Dissertations

As collaborative work with artificial intelligence (AI augmentation) gains interest, it is crucial to investigate factors that affect how employees perceive and use AI tools at work. Drawing on task-technology fit and technology adoption theories, this dissertation examines the ways in which task dimensions, organizational contexts, and individual differences affect the perceived usefulness of working with AI tools. This dissertation demonstrates that task-technology fit is fundamental. Employees in jobs with high information processing demands are likely to positively perceive the usefulness of AI augmentation relative to employees in jobs with high interpersonal demands. Employees with more proactive personalities perceive greater …

Toward Interpretable Multi-Omics Multimodal Biomedical Artificial Intelligence, Yanjun Lyu

Computer Science and Engineering Dissertations

The complexity of human disease arises from biological processes that unfold across multiple scales, from molecular variation through cellular function, tissue organisation, brain phenotypes, each of which is associated with distinct measurement modalities, regularities, and characteristic. Contemporary biomedical artificial intelligence has brought the opportunity to reveal the complexity with in; however, its methodological default, in which models are trained on most readily available modality, does not adequately engage with the multi-scale connected structure by which biological meaning is constituted. The research area of multi-omics and multi-modal AI for biomedicine remains at an early exploratory stage, and the work presented in …

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 …

Reliable And Label-Efficient Learning For Open-World Visual Perception And Robot Learning Under Uncertainty, Zongyao Lyu

Computer Science and Engineering Dissertations

Modern learning systems deployed in open-world environments must make reliable decisions despite predictive uncertainty, previously unseen classes, limited annotations, and distribution shifts. This dissertation develops methods for reliable and label-efficient learning in visual perception and robot control.

First, this work studies uncertainty in object detection by representing semantic and spatial predictions probabilistically. A deep-ensemble framework aggregates detections into class-probability distributions and probabilistic bounding boxes, while a subsequent extension combines deep ensembles with Monte Carlo dropout to further investigate predictive uncertainty. Second, this dissertation addresses open-set recognition, where classes absent during training may appear at inference time. An empirical study shows …

Improving User Retention And Learning Through Interactive Tutorial Systems, Prakhyat Chaube

2025 Spring Honors Capstone Projects - Archive

The onboarding experience in software applications is crucial for user engagement and retention. Traditional static tutorials often fail to provide adaptive, role-specific learning, leading to user frustration and drop-off. This project introduces an interactive tutorial system tailored for students and tutors using the CSE Student Success Center App at the University of Texas at Arlington. Designed to enhance usability and accessibility, the system personalizes onboarding experiences through guided, role-based learning paths and real-time feedback. By streamlining the learning curve, the tutorial system fosters greater user confidence and engagement, ensuring a more intuitive transition into the application. User evaluations indicate a …

Cuegen: Customizing Sensor Captions For Neon Bending Tutorials, Gunnika Kapoor

2025 Spring Honors Capstone Projects - Archive

Methods of knowledge transfer that rely primarily on visual and/or auditory formats do not effectively convey context-specific or implicit skills, known as tacit skills. This limits knowledge transfer. In this work, the use of customizable pitch captions and spatial audio vibration captions is proposed to aid in conveying this tacit knowledge for neon glass bending video tutorials. Such a system is designed to provide users with greater control and support, which may maximize the information they obtain from, improve the autonomy they have with, and experience they have with a learning tool. As such, a system interface was developed that …

Controlling A Mobile Inverted Pendulum And Optimizing Leaning Angle To Apply Force Using Reinforcement Learning, Aryan Mediratta

2025 Spring Honors Capstone Projects - Archive

Reinforcement Learning is a Machine Learning paradigm that involves simulating learning through rewards and penalties in intelligent systems. This technique is often employed in robotics when traditional control methods are insufficient or when human intuition does not provide a good solution on how to control robot systems, This project involves training a Segway-style Mobile Inverted Pendulum (MIP) robot to balance and push a box forward. The BeagleBone Blue board is used that includes a built-in Inertial Measurement Unit (IMU) and encoder ports. These sensors enable the system to measure its current state. The goal is to find the optimal leaning …

Tutortech: A Web App For A Smarter And More Efficient Tutoring System, Smarika Pathak

2025 Spring Honors Capstone Projects - Archive

The Computer Science and Engineering (CSE) department faces challenges with managing its tutoring services, especially tracking attendance, booking sessions, and overall management of the tutoring system - all of which severely limits the ability for tutors to connect and engage with students. To help overcome these issues, TutorTech, a web-based application that provides improved management of the tutoring system and supports more engaging learning experiences between students and tutors was designed. Through this project, the aim was to optimize the TutorTech search capabilities - assisting students to find tutors based on skills, while also considering the effect of user interface …

Generating Motivational Messages For Behavior Change: Encouraging Users To Be More Physically Active, Hananeel Pankaj

2025 Spring Honors Capstone Projects - Archive

High levels of sedentary lifestyles can cause adverse effects in individuals’ health. This has prompted researchers to analyze ways to increase physical activity, including the use of Large Language Models (LLMs) to generate motivational messages. While research has found LLMs to be feasible for this task, the findings are limited in availability and scope given that the research focuses on a conversational, chatbot setting—which is not ideal in the real world. This research assesses OpenAI’s GPT-4o mini’s (one of several models powering ChatGPT) ability to tailor messages towards a user. This is done by passing user health data to the …

Navigation Of Unmanned Aerial Vehicle Using Computer Vision In Raytheon Drone Competition, Joseph R. Pavlik Iii

2025 Spring Honors Capstone Projects - Archive

A major problem with using GPS to navigate an unmanned aerial vehicle is that GPS signals do not accurately work while inside a building. This work presents the usage of the Simultaneous Localization and Mapping library, ORB-SLAM2, in C++ to solve this issue. By using the camera attached to the unmanned aerial vehicle, a map of the area covered by the drone will be created, and landmarks in area will be utilized to navigate throughout the interior of the building without the GPS. Based on previous studies, this navigation method should be viable. Preliminary tests show that this method will …

Modeling Trust And Deception In Multi-Agent Reinforcement Learning Using The Werewolf Game, Pathikkumar Dharmeshbhai Patel

Computer Science and Engineering Theses - Archive

This thesis explores the emergence of trust, deception, and adaptive strategy in multi-agent reinforcement learning (MARL) environments using the social deduction game Werewolf as a simulation framework. In this environment, agents operate with hidden roles, incomplete information, and the need to reason about others’ intentions- mirroring the complexities of real-world social interactions. We present and evaluate two agent architectures: Agent vA, a symbolic, heuristic-based agent with probabilistic trust modeling and scalable memory structures; and Agent vB, a modular Q-learning agent that learns phase-specific policies through reinforcement. Agent vA relies on symbolic reasoning, bounded belief updates, and generalizable heuristics, while Agent …

Rearchitecting Aerial Omniverse Digital Twin For Script-Driven And Scalable Simulations, Sarath Chandra Viswanadh Nagadevara

Computer Science and Engineering Theses - Archive

The Nvidia Aerial Omniverse Digital Twin (AODT) platform provides a comprehensive framework for modeling radio network behavior under varying user mobility scenarios for 5G, 6G, and beyond. However, the current AODT implementation relies on a graphical user interface (GUI) to manually configure simulation parameters and initiate runs, which limits its capability to support automated, large-scale simulations that are needed by future cellular networks. In this study, we re-architect AODT to enable automation and control through scripts by decoupling its original simulation backend engine from the GUI. We adopt a client-server architecture, where the server runs the simulation backend engine controlled …

Centrality Algorithms For Weighted Homogeneous Multilayer Networks, Ayomide Ayowole-Obi

Computer Science and Engineering Theses - Archive

Applications need to be modeled for analyses. With the availability of many alternate data Models, choosing one needs to be done carefully by considering the complexity of data to be modeled and its analyses requirements. Social networks and other newer applications are primarily relationship-oriented and also have multiple types of entities and relationships. Although simple and attributed graphs have been used historically for modeling these applications, recently, multilayer networks (or MLNs) have been shown to be more effective in preserving the semantics of the application better and further provide flexibility of analyses. However, choice of MLN as a data model …

Technology-Facilitated Abuse (Tfa): Analyzing Trends, Tactics, And Victim Responses On Reddit, Solomon G. Dandekar

Computer Science and Engineering Theses - Archive

The increasing integration of technology into daily life has provided numerous benefits but also significant risks, particularly when exploited by malicious actors in cases of technology facilitated abuse (TFA). Per- petrators can misuse technology to monitor, control, and intimidate their partners, random strangers, etc. exacerbating cycles of abuse. From location tracking and cellphone surveillance to smart device manipula- tion, spyware, and doxing, digital tools have become powerful instruments for coercion and control. This research project investigates the role of technology in stalking and harassment by analyzing discussions on a relevant subreddit where victims share their experiences, strategies for coping, and …

A Deep Reinforcement Learning Framework For Sequential Art Creation, Asmin Pothula

Computer Science and Engineering Theses - Archive

Most computational art systems rely on generative models that produce a complete artwork in a single pass, without capturing the gradual, decision-driven process through which human artists construct visual pieces. Prior research in sequential, stroke-based image generation, including differentiable neural painters and model-based reinforcement learning agents, has explored step-by-step creation, but these systems typically aim to reconstruct the input image within the same visual representation space, closely matching brushstrokes, textures, or colors to the target. In contrast, this thesis investigates sequential art creation in a different artistic representation, where the final artwork does not share the same visual form as …

Scalable Approaches Towards Characterizing And Mitigating Emerging Phishing Scams, Sayak Saha Roy

Computer Science and Engineering Dissertations - Archive

Phishing scams are among the most dangerous and persistent forms of cybercrime, leveraging social engineering to exploit human behavior and obtain sensitive information, leading to widespread identity theft and data breaches. In the past year, these attacks have resulted in financial losses exceeding $10 billion in the United States alone. As phishing scams continue to evolve, they have not only expanded in scale but also grown in sophistication, spreading rapidly across social media and employing adversarial techniques to evade detection by anti-scam tools. The situation is further exacerbated by the availability of advanced phishing kits, and more recently, generative AI, …

Rotary Operations Management & Automation Platform (Romap): Modernizing Attendance Tracking And Data Submission For Rotary Clubs, Zaineel Mithani

2025 Fall Honors Capstones Projects - Archive

As Technical Lead of the Rotary Operations Management & Automation Platform (ROMAP), my Honors contribution focused on developing a Bluetooth Low Energy proximity-based attendance system enabling automatic, hands-free member check-ins. I researched and selected beacon hardware, designed RSSI-based distance calculation algorithms, and implemented platform-specific background processing for iOS and Android, achieving 97% detection accuracy. Beyond this Honors component, I architected the complete backend infrastructure including a Node.js API with 20+ endpoints, PostgreSQL database with Prisma ORM, and JWT authentication. I also developed a novel GPT-4 Vision automation system that intelligently populates web forms through computer vision, achieving 95% success rate …

Environment And Intention Awareness For Navigation And Collaboration, Bhaskar Chandra Trivedi

Computer Science and Engineering Dissertations - Archive

Unmanned Aerial Systems (UAS) have become increasingly popular as versatile platforms for tasks such as surveillance, inspection, delivery, and maintenance. In many applications, UAS operate in environments frequented by people or containing sensitive infrastructure, which introduces physical risks in case of vehicle failure, as well as psychological and privacy concerns that may limit their acceptability. Ensuring safe and efficient operation thus requires that UAS consider these risks when planning navigation strategies. While prior information, such as city maps and building layouts, can partially inform risk assessment, such data is often incomplete, necessitating real-time augmentation of risk maps using sensor information. …

Scalable, Secure, And Adaptable Perception Systems Through Adversarial Analysis And Federated Fine-Tuning, Arkajyoti Mitra

Computer Science and Engineering Dissertations - Archive

Perception systems are fundamental to intelligent machines, enabling them to sense, understand, and interpret complex environments. However, as perception increasingly underpins critical applications such as autonomous vehicles, IoT healthcare devices, and smart trading platforms, challenges related to security, scalability, and environmental understanding have become more pressing. This work addresses three core research questions: (1) How can we identify, analyze, and mitigate adversarial vulnerabilities in perception systems to ensure reliable operation under adversarial conditions? (2.1) How can AVPS models be efficiently scaled and fine-tuned across decentralized and resource-constrained environments while preserving privacy and performance? (2.2) How can we scale generative models …