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Articles 1861 - 1890 of 27694
Full-Text Articles in Entire DC Network
Reverse Engineering Of Binary Programs Using Graph Attention Networks, Sai Nikhila Kanigiri
Reverse Engineering Of Binary Programs Using Graph Attention Networks, Sai Nikhila Kanigiri
Master's Theses
Understanding the functionality and behavior of binary code is essential for many software engineering tasks, including malware analysis, vulnerability detection, and program optimization. However, automating this process is challenging due to the complexity of machine code and the significant manual effort required from experienced software engineers. In this paper, we present BinGAT (Reverse Engineering of Binary Programs using Graph Attention Networks), a method for classifying binary programs into algorithmic categories using Graph Attention Neural Networks (GNNs) based on their Control-Flow Graphs (CFGs). Given a binary program, BinGAT extracts its CFG through static analysis and transforms the assembly instructions within each …
Computational Design Of Potent Sirna For Braf Oncogene Silencing For Enhancing Cancer Therapy, Muhammad Hermawan Widyananda, Ricadonna Raissa
Computational Design Of Potent Sirna For Braf Oncogene Silencing For Enhancing Cancer Therapy, Muhammad Hermawan Widyananda, Ricadonna Raissa
Karbala International Journal of Modern Science
The discovery of oncogenic BRAF mutations has prompted the development of inhibitors, yet resistance remains widespread. A more effective strategy involves targeting BRAF mRNA with siRNA to overcome resistance to BRAF inhibitors. This study aims to design potent siRNA for BRAF oncogene silencing using a computational approach. The full coding sequence of BRAF was retrieved from the NCBI database and potential siRNAs were predicted using the Ui-Tei, Reynolds, and Amarzguioui rules. Identified siRNAs were further analyzed using various prediction systems and parameters, including their interaction with the hAgo2 protein. The results identified that seven siRNAs (siRNA 23, siRNA 24, siRNA …
Algorithmically Optimal Outer Measures, J. H. Lutz, Neil Lutz
Algorithmically Optimal Outer Measures, J. H. Lutz, Neil Lutz
Computer Science Faculty Works
We investigate the relationship between algorithmic fractal dimensions and the classical local fractal dimensions of outer measures in Euclidean spaces. We introduce global and local optimality conditions for lower semicomputable outer measures. We prove that globally optimal outer measures exist. Our main theorem states that the classical local fractal dimensions of any locally optimal outer measure coincide exactly with the algorithmic fractal dimensions. Our proof uses an especially convenient locally optimal outer measure κ defined in terms of Kolmogorov complexity. We discuss implications for point-to-set principles.
Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad
Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad
Student Scholar Symposium Abstracts and Posters
Developing an affordable and STEM learning-focused Braille display addresses a significant disparity in the market for Braille displays, where most fail to provide a cost-effective, accessible, and education-oriented solution. This research aims to bridge this gap through innovative hardware and software development, offering a comprehensive learning experience to elementary school children (K-6) who are blind/visually impaired. The hardware features a piezo-electric tactile display that displays up to six Braille characters at once or a shape in an 8x8 pin array configuration. The educational software includes a user-friendly website packed with engaging STEM activities specifically designed for blind/visually impaired children. The …
Praxly: An Online Ide For The Praxis Cs Test Pseudocode, Benjamin Saupp
Praxly: An Online Ide For The Praxis Cs Test Pseudocode, Benjamin Saupp
James Madison Undergraduate Research Journal (JMURJ)
No abstract provided.
Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure, Matthew Willoughby
Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure, Matthew Willoughby
Doctoral Dissertations and Master's Theses
This study presents a reinforcement learning (RL) approach for reestablishing communication with deep-space satellites under unknown attitude determination and control system (ADCS) failures. When traditional fault-tolerant control methods cannot restore signal, the proposed RL controller acts as a last-resort measure by autonomously reorienting the satellite’s antenna toward Earth while charging the battery via solar panels. A generic reward function, designed for the RL-based method, enables the controller to adapt to diverse failure scenarios, including severe actuator noise, misalignment, and complete actuator failure. Simulations are conducted in the Basilisk environment and trained with the tonic framework and demonstrate ranging capabilities of …
Improving The Reproducibility Of Deep Learning Software: An Initial Investigation Through A Case Study Analysis, Nikita Ravi, Abhinav Goel, James C. Davis, George K. Thiruvathukal
Improving The Reproducibility Of Deep Learning Software: An Initial Investigation Through A Case Study Analysis, Nikita Ravi, Abhinav Goel, James C. Davis, George K. Thiruvathukal
Computer Science: Faculty Publications and Other Works
The field of deep learning has witnessed significant breakthroughs, spanning various applications, and fundamentally transforming current software capabilities. However, alongside these advancements, there have been increasing concerns about reproducing the results of these deep learning methods. This is significant because reproducibility is the foundation of reliability and validity in software development, particularly in the rapidly evolving domain of deep learning. The difficulty of reproducibility may arise due to several reasons, including having differences from the original execution environment, incompatible software libraries, proprietary data and source code, lack of transparency, and the stochastic nature in some software. A study conducted by …
Real-Time System Availability For Cyber-Physical Systems, Jinwen Wang
Real-Time System Availability For Cyber-Physical Systems, Jinwen Wang
McKelvey School of Engineering Graduate Student Theses & Dissertations
Cyber-physical systems (CPSs), such as autonomous vehicles, are increasingly being deployed. The sensing, control, and actuation loop in CPSs must complete within strict timing constraints. Missing a real-time deadline can lead to catastrophic consequences, as CPSs continuously interact with the physical world. This highlights the importance of real-time system availability (i.e., timely execution) in CPS tasks, going beyond traditional security goals that primarily focus on confidentiality and integrity. From a security perspective, two factors affect real-time system availability. First, attackers with access to hardware resources in CPSs may disrupt the execution timing of real-time tasks. Second, the deployment of security …
Improving User Retention And Learning Through Interactive Tutorial Systems, Prakhyat Chaube
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 …
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …
Noninvasive Assessment Of The Tumor Using Cfdna, Irfan Alahi
Noninvasive Assessment Of The Tumor Using Cfdna, Irfan Alahi
McKelvey School of Engineering Graduate Student Theses & Dissertations
Next-generation high-throughput sequencing, which is increasingly generating vast amounts of genomic data, offers opportunities for a deeper understanding of the multifaceted nature of cancer and, hence, better patient care. However, the inherently complex and heterogeneous nature of cancer and the significant challenges in the generated data demand advanced data-driven frameworks to decode the molecular underpinnings of cancer. Moreover, the undeniable need for non-invasive approaches presents additional technical challenges in this domain. This dissertation proposes novel frameworks addressing three key challenges in computational oncology. The first study of this dissertation develops a data-driven algorithm to identify stemness signatures in metastatic castration-resistant …
Voice Interaction With Conversational Ai Could Facilitate Thoughtful Reflection And Substantive Revision In Writing, Jiho Kim, Philippe Laban, Xiang 'Anthony' Chen, Kenneth C. Arnold
Voice Interaction With Conversational Ai Could Facilitate Thoughtful Reflection And Substantive Revision In Writing, Jiho Kim, Philippe Laban, Xiang 'Anthony' Chen, Kenneth C. Arnold
University Faculty Publications and Creative Works
Writing well requires not only expressing ideas but also refining them through revision, a process facilitated by reflection. Prior research suggests that feedback delivered through dialogues, such as those in writing center tutoring sessions, can help writers reflect more thoughtfully on their work compared to static feedback. Recent advancements in multi-modal large language models (LLMs) now offer new possibilities for supporting interactive and expressive voice-based reflection in writing. In particular, we propose that LLM-generated static feedback can be repurposed as conversation starters, allowing writers to seek clarification, request examples, and ask follow-up questions, thereby fostering deeper reflection on their writing. …
Intuiting Interaction: Meta-Reasoning And Meta-Learning As Foundations For Intelligent User Interfaces, Jeffrey Hsu
Intuiting Interaction: Meta-Reasoning And Meta-Learning As Foundations For Intelligent User Interfaces, Jeffrey Hsu
Theses and Dissertations
This research presents MARCO—a cognitive framework for Intelligent User Interfaces that uses meta-reasoning for context-aware adaptation across diverse tasks. It integrates multiple reasoning modules coordinated by a Meta-Cognitive Unit that selects strategies based on evolving demands. Evaluations show MARCO outperforms baselines in reasoning accuracy and computational efficiency.
Optimizing Small Ai Models For Biomedical Tasks Through Efficient Knowledge Transfer From Large Domain Models, Girish Sundaram
Optimizing Small Ai Models For Biomedical Tasks Through Efficient Knowledge Transfer From Large Domain Models, Girish Sundaram
Theses and Dissertations
The PICO (Population, Intervention, Comparison, Outcome) framework is a widely adopted methodology for structuring clinical research questions and extracting relevant information from unstructured medical texts. However, traditional approaches for PICO classification demand computationally expensive domain-specific language models, such as BioBERT and ClinicalBERT, which require extensive training and large annotated datasets. This dissertation introduces Distilled Rapid Embedding Transfer (DRET), a novel knowledge transfer method designed to enable resource-constrained domain adaptation. DRET aims to efficiently transfer biomedical domain knowledge from large, specialized models to a compact, general-purpose model, DistilBERT, thereby enhancing its ability to perform domain-specific tasks without access to the original …
Abel Inversion Comparison Of Geant4 Simulation And Ozone Production Using Cavity Ringdown Spectroscopy In Nitrogen/Oxygen Mixtures In The Presence Of Alpha Radiation, Sidney John Gautrau
Abel Inversion Comparison Of Geant4 Simulation And Ozone Production Using Cavity Ringdown Spectroscopy In Nitrogen/Oxygen Mixtures In The Presence Of Alpha Radiation, Sidney John Gautrau
Dissertations
The effects of radioactive materials on atmospheric gases have been a topic of interest for years. Radioactive materials ionize the surrounding air, and subsequent reactions lead to molecules such as ozone and nitrogen oxides. The presence of these species above background levels can be used as a marker for radioactive materials which has desirable defense applications like remote detection of radioactive materials. The molecules created in the presence of radioactive materials have been quantified in literature using G-values, which is the number of molecules of a product produced per 100 eV of deposited energy. In this work, Cavity Ringdown Spectroscopy …
Shipnavisim: Data-Driven Simulation For Real-World Maritime Navigation, Quang Anh Pham, Janaka Chathuranga Brahmanage, Akshat Kumar
Shipnavisim: Data-Driven Simulation For Real-World Maritime Navigation, Quang Anh Pham, Janaka Chathuranga Brahmanage, Akshat Kumar
Research Collection School Of Computing and Information Systems
Maritime traffic management in busy ports faces growing challenges due to increased vessel traffic and complex waterway interactions. Strategies such as e-navigation by the International Maritime Organization aim to enhance navigation safety through traffic digitization. Maritime traffic simulation is essential for these systems, offering a virtual environment to model, analyze, and optimize traffic flows. Unlike road traffic, there are few simulators for maritime traffic, and they often lack realism and multi-ship interactions. In this paper, we (a) present ShipNaviSim, a data-driven maritime traffic simulator that utilizes a large-scale dataset over 2 years and electronic navigation charts to model vessel movements …
Unlocking The Planning Capabilities Of Llms Through Maximum Diversity Fine-Tuning, Wenjun Li, Changyu Chen, Pradeep Varakantham
Unlocking The Planning Capabilities Of Llms Through Maximum Diversity Fine-Tuning, Wenjun Li, Changyu Chen, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Large language models (LLMs) have demonstrated impressive task-solving capabilities through prompting techniques and system designs, including solving planning tasks (e.g., math proofs, basic travel planning) when sufficient data is available online and used during pre-training. However, for planning tasks with limited prior data (e.g., blocks world, advanced travel planning), the performance of LLMs, including proprietary models like GPT and Gemini, is poor. This paper investigates the impact of fine-tuning on the planning capabilities of LLMs, revealing that LLMs can achieve strong performance in planning through substantial (tens of thousands of specific examples) fine-tuning. Yet, this process incurs high economic, time, …
Eduqate: Generating Adaptive Curricula Through Rmabs In Education Settings, Sidney Tio, Dexun Li, Pradeep Varakantham
Eduqate: Generating Adaptive Curricula Through Rmabs In Education Settings, Sidney Tio, Dexun Li, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
There has been significant interest in the development of personalized and adaptive educational tools that cater to a student's individual learning progress. A crucial aspect in developing such tools is in exploring how mastery can be achieved across a diverse yet related range of content in an efficient manner. While Reinforcement Learning and Multi-armed Bandits have shown promise in educational settings, existing works often assume the independence of learning content, neglecting the prevalent interdependencies between such content. In response, we introduce Education Network Restless Multi-armed Bandits (EdNetRMABs), utilizing a network to represent the relationships between interdependent arms. Subsequently, we propose …
Compiling Haskell Into Lean: A Common Abstract Syntax For Haskell And Interactive Theorem Provers, Talitha Holcombe
Compiling Haskell Into Lean: A Common Abstract Syntax For Haskell And Interactive Theorem Provers, Talitha Holcombe
Electrical Engineering and Computer Science (MS) Theses
In this work, we introduce a program conversion tool, HS-TO-LEAN, that uses GHC's ghc-lib-parser API to translate Haskell programs into Lean code, which is then validated by the Lean compiler. The repo can be found at https://github.com/holcombet/hs-to-lean/tree/main. The result is a successful compilation of a fragment of Haskell into correct and executable Lean code that users can prove theorems about. We conducted a case study using a heap sort algorithm to support our claim that HS-TO-LEAN produces verifiable Lean code. Our approach is inspired by recent advances in formal verification of Haskell programs in Coq, and we currently restrict our …
Current Wind Simulation Techniques, Keegan J. Sims
Current Wind Simulation Techniques, Keegan J. Sims
Theses/Capstones/Creative Projects
Not much exists in the realm of wind simulation. For what does exist involves tornadoes, and even then that is stretched far and few between. Due to this we have a lot of room to explore, and figure things out. How do we simulate real time wind? My capstone project involves a simple wind algorithm, how does it compare to what does currently exist? This paper covers the papers about wind and how it it interacts with objects and itself.
Cuegen: Customizing Sensor Captions For Neon Bending Tutorials, Gunnika Kapoor
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
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 …
Generating Motivational Messages For Behavior Change: Encouraging Users To Be More Physically Active, Hananeel Pankaj
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
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 …
Usage Of Natural Language Processing And Deep-Learning Techniques On Thematic Apperception Tests To Predict Big Five Personality Traits, Blayten Jones
Usage Of Natural Language Processing And Deep-Learning Techniques On Thematic Apperception Tests To Predict Big Five Personality Traits, Blayten Jones
Electrical Engineering and Computer Science Undergraduate Honors Theses
The usage of personality as a method of behavioral prediction and outcomes of success has grown considerably over the last few decades. This project explores predicting user personality profiles via the Big Five personality index through the integration of advanced natural language processing techniques as well as neural networks. Using a dataset provided by Dr. James W. Pennebaker, participants analyze an image—formally referred to as a thematic apperception test—and write a thorough paragraph describing the details. This free-form text, along with their personality test results, is captured in a structured dataset. Many deep-learning and machine learning models have been used …
A Novel Approach To Attention-Based Models In Image Completion: Weighted Spatial-Attention Using Radial Distance, Tyler D. Kuper
A Novel Approach To Attention-Based Models In Image Completion: Weighted Spatial-Attention Using Radial Distance, Tyler D. Kuper
Electrical Engineering and Computer Science Undergraduate Honors Theses
Humans infer missing visual information by focusing on spatial relationships in the context of their surroundings. Machine learning aims to replicate this skill through image completion, a fundamental task in current computer vision research. While advances in self-attention layers have recently enhanced generative machine learning models for text, these mechanisms still currently lack the capability to handle sparse image completion efficiently. We introduce a distance-based attention mechanism that uses radial-based weights to efficiently reconstruct an image. We compare this attention mechanism with self-attention and a fully connected network on an image completion task using the MNIST dataset. Our results show …
Fairness In Recommender Systems: Balancing Bias In Academic Paper Selection, Zachary Bergin
Fairness In Recommender Systems: Balancing Bias In Academic Paper Selection, Zachary Bergin
Electrical Engineering and Computer Science Undergraduate Honors Theses
Bias in academic paper selection remains a consistent issue, even within processes designed to promote fairness, such as double-blind peer review, bias stays persistent. In this paper we investigate demographic bias while particularly focusing on racial bias in the process of selecting academic papers and explore the impact of fairness aware recommender systems on the demographic parity. To build an effective system our focus is on the Special Interest Group on Computer Human Interaction (SIGCHI) a pillar in the community, we develop a neural network-based recommender system that uses real demographic data collected by other systems withing the context of …
Leveraging P4 Programmable Switches For Resilient Operation And Design Of Phasor Measurement Unit Networks, Eva Casto
Electrical Engineering and Computer Science Undergraduate Honors Theses
The power grid utilizes a device called the phasor measurement unit (PMU), allowing power system administrators to remotely monitor and manage the state of the grid in Wide Area Monitoring Systems (WAMS). The advantages of PMUs – such as fine-grained, time-synchronized measurements and efficient, decentralized monitoring – are what make them key devices in the power grid. However, PMU technology also comes with new threats of the digital age, like malfunctions and cyberattacks, which can result in missing and faulty measurements that compromise power grid observability. P4 programmable networks can be used to detect faulty PMU data in a decentralized, …