Computer Sciences Commons^™

Exploring Educational Affordances Of Interactive Murals, Alyshia Bustos

Computer Science ETDs

Interactive murals are a new technology that blend traditional mural painting and embedded electronics. My work contrasts traditional STEAM projects by introducing youth to programming, building electronics, and interaction design principles within the context of designing and constructing interactive murals. Accordingly my dissertation addresses the following: 1) How can we integrate traditional mural practices and ubiquitous computing in an interactive mural, 2) How do interactive murals support youth in learning how to program and build electronics, and 3) How can we design learning activities that support youth as interaction designers?

To address my research questions, I first conducted technical tests …

Physics Embedded Neural Network: A Novel Data-Free Numerical Method For Solving Computational Physics Problems, Pawan Gaire

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A novel approach for solving partial differential equations (PDEs) using neural networks for scientific computing is introduced. The proposed approach, referred to as physics-embedded neural network (PENN), features a unique architecture that incorporates the PDE and boundary conditions information directly within the final fully-connected layer of the feed-forward neural network (NN). The key aspect of PENN is the parallel numerical embedding of a differential equation associated with physical problems within the activation function of the network’s final layer. This integration leads to a new class of computational solvers competitive with classical methods like the Finite Element Method (FEM) and capable …

Advancing Snow Water Equivalent Monitoring With Machine Learning And L-Band Interferometric Synthetic Aperture Radar (Insar) Data, Ibrahim Olalekan Alabi

Boise State University Theses and Dissertations

Seasonal snow is a critical freshwater resource for an estimated 2 billion people worldwide. Yet, accurately measuring the amount of water sitting in a snowpack, referred to as snow water equivalent (SWE), over large, often mountainous regions has posed a long-standing challenge. Ground-based measurements of SWE are precise but sparse, while remote sensing techniques like passive microwave sensors struggle with coarse resolution and signal saturation in deep snow. Due to the challenges of direct SWE measurement, snow depth has emerged as an alternative pathway to SWE estimation. SWE is strongly correlated with snow depth, and by leveraging this relationship, we …

5g Network Slice Vulnerabilities And Exploit Chaining Through Enablers, John Breeden

Electronic Theses and Dissertations

Network slicing provides fundamental support for the enhanced features of 5G. Network slicing enablers, such as software-defined networking and network function virtualization facilitate the separation of the physical distributed infrastructures and the functions that create isolated slices. With this framework, the network slice is no longer under the control of a single entity. Multiple infrastructure providers share responsibility for the slice. The 5G architecture derives from multiple services, distributed over great distances, and managed by multiple parties. Each enabler and provider adds vulnerability to network slicing. We examine the interweaving of these enablers to identify vulnerabilities, discuss potential mitigation, and …

Radar Precursors To Severe Weather Reports In Left-Moving Supercells, Eric A. Carothers

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

While much research has examined dual-polarimetric signatures of right-moving supercells, very little has been done with left-moving supercells. Given that left-moving supercells are thought to be disproportionate producers of large hail, understanding their internal dynamics is vitally important. This study examines differences and trends in the dual-polarimetric signatures of left-moving supercells to identify precursors to severe weather reports. A dataset of left-moving supercells associated with severe weather reports was created. These storms are processed with an automated analysis algorithm that identifies and quantifies the polarimetric signatures in each storm. A method for analysis of differences and trends in their dual-polarization …

Understanding Automation From A Computer Science Perspective, Matthew Donsig

Honors Program: Senior Projects (Public)

This thesis looks into automation and analyzes its benefits and problems. It begins with an explanation of a capstone project, automating the UNL State Museum’s reservation system. Problems of automation are presented in unsuccessful attempts and some pitfalls of automation. Next this thesis turns to an examination of artificial intelligence in automation. Along with that, we look at bias in automation and how people can bias automated tools or be biased by them. Turning successful examples of automation and then automation in manufacturing shows its benefits. Automation creates new jobs or changes work as much as it eliminates positions. At …

Towards Supporting Children's Metacomprehension During Web Search, Christine Dianne Pinney

Boise State University Theses and Dissertations

As children interact online with search engine result pages (SERPs), children’s comprehension monitoring skills are put to work. Children are known to struggle with utilizing online information as the text content can be misaligned with their reading abilities. Comprehension monitoring skills allow people to assess the comprehensibility of text and is measured by how well comprehension predictions align with performance on comprehension tests. Previous research has shown that augmenting standard SERPs with readability visual cues can be helpful for children searching online. While comprehension predictions are traditionally collected after reading, SERP information provides the opportunity to measure children's perceived comprehensibility …

Cyber Resiliency Framework And Mechanisms For Software Defined Tactical Networks, Anthony Castanares

Open Access Theses & Dissertations

Traditional tactical networks fail to achieve cyber resiliency for many reasons, but the most prevalent causes include flat designs, the absence of cyber detection capabilities at the lowest level, and immutable resource allocations after instantiation. These design choices allow network threats direct visibility to each device on the network, and the lack of detection allows infections to proliferate. Furthermore, tactical battlefield networks are difficult to secure because of the lack of persistent oversight by an intelligent agent that can exercise control over the network's topology or resources in real-time. However, advances in software-defined networking (SDN) provide an opportunity to address …

Generative Ai For 3d Printed Antenna Design, Jennifer Ann Chavez

Open Access Theses & Dissertations

This research explores the integration of generative artificial intelligence (AI) with a physics-informed particle swarm optimizer (PSO) to develop 3D printable microstrip patch antennas. A neural network was trained on a dataset of microstrip patch antenna geometries and their corresponding performance metrics: return loss and gain. The PSO used a fitness function prioritizing low return loss in potential antennas, eventually yielding novel antenna geometries with parasitic components. 3D printing constraints were also hard coded into the framework, thus preventing any geometries being generated that cannot be fabricated. When simulated using Ansys HFSS, the AI generated microstrip patch antennas exceeded the …

Hidden Layer Reshaping On Convolutional Neural Network, Alan Delgado

Open Access Theses & Dissertations

Artificial Intelligence (AI) technologies have become really popular in recent years. From ChatGPT to Tesla cars, many applications can benefit from these type of technologies. Automotive, healthcare, biomedical, cybersecurity, finances, and retail are some of the fields that take advantage of it. It has been seen that AI can solve complex problems, but there is still work to be done to optimize it. A deep learning neural network (DLNN) tries to simulate how a human brain operates. These DLNNs are made up of artificial neurons which are connected by weight that are modified when the network is trained. These networks …

From Text To Utility: Distance-Aware Contrastive Learning For Detection-Ready And Shareable Malware Descriptions, Ivan Alejandro Montoya Sanchez

Open Access Theses & Dissertations

The rapid rise of sophisticated malware variants poses significant challenges for cybersecurity analysts, particularly due to the scarcity of data on newly emerging threats. Due to privacy, legal, and operational constraints, malware samples are often not shareable; instead, organizations publish cyber threat intelligence (CTI) in natural language. However, these reports are typically unstructured and inconsistent, limiting their utility in machine learning (ML) models. This thesis explores whether high-fidelity, shareable threat intelligence can be automatically generated from structured malware behaviors to supplement ML models when direct access to malware samples is limited. Two central questions are addressed:(i) How can descriptions be …

Enhancing Security And Resiliency In Operational Technology Environments Through Network Slicing And Federated Learning, Brian Giovanni Rodiles Delgado

Open Access Theses & Dissertations

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation.

The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, …

Machine Learning And Time Series Forecasting For Hydropower Predictions, Jose Reynaldo Vega

Open Access Theses & Dissertations

Recent advancements in machine learning have led to the design of many neural network architectures aimed at solving real-world problems. Each network works to make predictions by finding patterns in the provided data. One common application is time series forecasting, where a model predicts future events based on historical time series data. Time series forecasting is used in a variety of fields, one example being in predicting water releases of Hybrid Floating Photovoltaic-Hydropower (HFPVH) systems. As global population growth drives an increase in energy demand, the need for resilient and sustainable energy generation has become urgent. HFPVH systems have emerged …

Alphamissense Predictions And Clinvar Annotations: A Deep Learning Approach To Uveal Melanoma, David J. Taylor Gonzalez, Mak B. Djulbegovic, Meghan Sharma, Michael Antonietti, Colin K. Kim, Vladimir N. Uversky, Carol L. Karp, Carol L. Shields, Matthew W. Wilson

Wills Eye Hospital Papers

OBJECTIVE: Uveal melanoma (UM) poses significant diagnostic and prognostic challenges due to its variable genetic landscape. We explore the use of a novel deep learning tool to assess the functional impact of genetic mutations in UM.

DESIGN: A cross-sectional bioinformatics exploratory data analysis of genetic mutations from UM cases.

SUBJECTS: Genetic data from patients diagnosed with UM were analyzed, explicitly focusing on missense mutations sourced from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

METHODS: We identified missense mutations frequently observed in UM using the COSMIC database, assessed their potential pathogenicity using AlphaMissense, and visualized mutations using AlphaFold. Clinical …

Augsso: Secure Threshold Single-Sign-On Authentication With Popular Password Collection, Changsong Jiang, Chunxiang Xu, Guomin Yang

Research Collection School Of Computing and Information Systems

Single-sign-on authentication is widely deployed in mobile systems, which allows an identity server to authenticate a mobile user and issue her/him with a token, such that the user can access diverse mobile services. To address the single-point-offailure problem, threshold single-sign-on authentication (PbTA) is a feasible solution, where multiple identity servers perform user authentication and token issuance in a threshold way. However, existing PbTA schemes confront critical drawbacks. Specifically, these schemes are vulnerable to perpetual secret leakage attacks (PSLA): an adversary perpetually compromises secrets of identity servers (e.g., secret key shares or credentials) to break security. Besides, they fail to achieve …

Dissecting Global Search: A Simple Yet Effective Method To Boost Individual Discrimination Testing And Repair, Lili Quan, Tianlin Li, Xiaofei Xie, Zhenpeng Chen, Sen Chen, Lingxiao Jiang, Xiaohong Li

Research Collection School Of Computing and Information Systems

Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns. Among these unfair behaviors, individual discrimination-examining inequalities between instance pairs with identical profiles differing only in sensitive attributes such as gender, race, and age-is extremely socially impactful. Existing methods have made significant and commendable efforts in testing individual discrimination before deployment. However, their efficiency and effectiveness remain limited, particularly when evaluating relatively fairer models. It remains unclear which phase of the existing testing framework (global or local) is the primary bottleneck limiting performance. Facing the above issues, we first identify …

Ai Model For Predicting Asthma Prognosis In Children, Elham Sagheb, Chung-Il Wi, Katherine S King, Bhavani Singh Agnikula Kshatriya, Euijung Ryu, Hongfang Liu, Miguel A Park, Hee Yun Seol, Shauna M Overgaard, Deepak K Sharma, Young J Juhn, Sunghwan Sohn

Faculty, Staff and Student Publications

BACKGROUND: Childhood asthma often continues into adulthood, but some children experience remission. Utilizing electronic health records (EHRs) to predict asthma prognosis can aid health care providers and patients in developing effective prioritized care plans.

OBJECTIVE: We aimed to develop artificial intelligence (AI) models using various clinical variables extracted from EHRs to predict childhood asthma prognosis (remission vs no remission) in different age groups.

METHODS: We developed AI models utilizing patients' EHRs during the first 6, 9, or 12 years of their lives to predict their asthma prognosis status at ages 6 to 9, 9 to 12, or 12 to 15 …

Global Sporadic-E Prediction And Climatology Using Deep Learning, J. A. Ellis, Daniel J. Emmons, M. B. Cohen

Faculty Publications

Sporadic-E (E_s) is an ionospheric phenomenon defined by strong layers of plasma which may interfere with radio wave propagation. In this work, we develop deep learning models to improve the understanding of E_s, including the presence, intensity and height of the layers. We developed three separate models. The first, building off earlier work in (J. A. Ellis et al., 2024, link in AFIT Scholar, 10.1029/2023sw003669), includes only the main features from radio occultation (RO) measurements. The second adds to that time, date, location, geomagnetic and solar indices, solar winds, x-ray flux, weather and lightning. A …

Exploring The Biasing Effects Of Gender On Personality Disorder Diagnoses Formulated By Artificial Intelligence, Zoe Colclough

Student Theses

Gender bias is prevalent in personality disorder assessments, and while artificial intelligence has been posited as a solution to improve diagnostic objectivity and accuracy, the potential for such technologies to propagate human gender bias in mental health contexts remains underexplored. This study investigated the influences of gender bias on the diagnostic performance of ChatGPT-4o for personality disorders using three factorial research designs, which involved experimentally manipulating patient gender in a combined sample of 360 vignettes and case studies. Vignettes were synthesized through a novel artificial intelligence-assisted methodology established for this research, and case studies were identified from the literature. Significant …

Advancing Precision And Autonomy In Agriculture And Medical Imaging Through Ai And Computer Visions, Tan-Hanh Pham

Theses and Dissertations

Deep learning has revolutionized numerous fields by enhancing precision, automation, and decision-making capabilities. This dissertation explores its applications in agriculture and medical image processing, introducing novel methodologies to improve accuracy and efficiency in these domains. These fields hold critical societal importance -- agriculture underpins global food security and sustainability, while medical imaging drives advancements in diagnostics and personalized healthcare, both benefiting significantly from data-driven innovations. In agriculture, deep learning is applied to precision spray systems through droplet analysis. Specifically, a generative model is designed to create synthetic droplet images, addressing the challenge of limited training samples, which are expensive …

Stability Analysis Of Turbulent Fluid Flow, Adam D. Schroeder

Mathematics, Statistics, and Computer Science Honors Projects

Hydrodynamic stability refers to the study of when and how laminar flows transition to turbulence. This includes investigations of the mechanisms of transition, as well as the classification of known flow configurations as either stable or unstable and the identification of critical values of flow parameters at which this bifurcation occurs. In this thesis, we introduce the mathematical theory behind continuum mechanics and fluid dynamics as well as some tools from the study of dynamical systems. We apply these concepts to the linear stability analysis of zero pressure gradient flat plate flow via numerical simulations in OpenFOAM, discussing both the …

Generalizable Skill Learning In Robotic Agents Using Transformer Models, Erik Enriquez

Theses and Dissertations

This work explores the application of Transformer models to robotic skill learning, aiming to enhance generalization across various physical tasks and environments with continuous control. Despite their success in other domains, our experiments reveal that the utility of Transformers in robotics heavily depends on pretraining strategies. Specifically, Transformers pretrained on reinforcement learning tasks generalized effectively, while those trained with task-agnostic masking strategies did not. These findings challenge assumptions about the universality of Transformer-based methods and underscore the importance of domain-aligned pretraining for developing versatile robotic agents.

Hdwsa2: A Secure Hierarchical Deterministic Wallet Supporting Stealth Address And Signature Aggregation, Xin Yin, Zhen Liu, Guomin Yang, Guoxing Chen, Haojin Zhu

Research Collection School Of Computing and Information Systems

Hierarchical Deterministic Wallet (HDW) and Stealth Address (SA) are widely used in cryptocurrency communities due to their functionality and security. In the preliminary version of this work (ESORICS 2022), we formally define the syntax and security models of Hierarchical Deterministic Wallet supporting Stealth Address (HDWSA), capturing the functionality and security requirements imposed by the practice in cryptocurrency. We propose a concrete HDWSA construction and prove its security in the random oracle model. Note that when applied in blockchain, in practice, signature aggregation could reduce the cost of computation, storage, and communication dramatically. In this full version, we develop HDWSA definition …

Reverse Modeling In Large Language Models, Sicheng Yu, Yuanchen Xu, Cunxiao Du, Yanying Zhou, Minghui Qiu, Qianru Sun, Hao Zhang, Jiawei Wu

Research Collection School Of Computing and Information Systems

Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse modeling, specifically with reversed text inputs. We found that publicly available pre-trained LLMs cannot understand such inputs. However, LLMs trained from scratch with both forward and reverse texts can understand them equally well during inference. Our case study shows that different-content texts result in different losses if input (to LLMs) in different directions---some get lower losses for forward while some for reverse. This leads us …

Fixdrive: Automatically Repairing Autonomous Vehicle Driving Behaviour For $0.08 Per Violation, Yang Sun, Christopher M. Poskitt, Kun Wang, Jun Sun

Research Collection School Of Computing and Information Systems

Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV's planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose FixDrive, a framework that analyses driving records from near-misses or law …

Decictor: Towards Evaluating The Robustness Of Decision-Making In Autonomous Driving Systems, Mingfei Cheng, Xiaofei Xie, Yuan Zhou, Junjie Wang, Guozhu Meng, Kairui Yang

Research Collection School Of Computing and Information Systems

Autonomous Driving System (ADS) testing is crucial in ADS development, with the current primary focus being on safety. However, the evaluation of non-safety-critical performance, particularly the ADS's ability to make optimal decisions and produce optimal paths for autonomous vehicles (AVs), is also vital to ensure the intelligence and reduce risks of AVs. Currently, there is little work dedicated to assessing the robustness of ADSs' path-planning decisions (PPDs), i.e., whether an ADS can maintain the optimal PPD after an insignificant change in the environment. The key challenges include the lack of clear oracles for assessing PPD optimality and the difficulty in …

Hello Again! Llm-Powered Personalized Agent For Long-Term Dialogue, Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus on brief single-session interactions, neglecting the real-world demands for long-term companionship and personalized interactions with chatbots. Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses. Recent progress in the human-like cognitive and reasoning capabilities of LLMs suggests that LLM-based agents could significantly enhance automated perception, decision-making, and problem-solving. In response to this potential, we introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent), which incorporates three independently …

Advancing Multi-Agent Robotics Simulations Through Heterogeneous Reinforcement Learning In Isaaclab, Jacob R. Haight

All Graduate Theses and Dissertations, Fall 2023 to Present

Robots increasingly operate in collaborative teams across domains such as search-and- rescue, warehouse automation, and autonomous driving—scenarios that demand advanced coordination strategies enabled by multi-agent reinforcement learning (MARL). However, existing simulation frameworks often struggle to balance realism, speed, and scalability, especially when supporting diverse, heterogeneous robot teams. This research extends Isaac Lab, a high-performance robotics simulator, by integrating heterogeneous-agent reinforcement learning (HARL) capabilities. The result is a flexible and GPU-accelerated platform for training both homogeneous and heterogeneous robot teams in complex, physics-based environments. These enhancements significantly narrow the gap between simulation and real-world deployment for multi-robot systems.

Ml Playground: Data Modification/Preprocessing And Model Simulation Tool, Marco D. Cerrato

Electronic Theses, Projects, and Dissertations

There is a heavy reliance on programming when it comes to learning machine learning (ML). This often creates barriers for students and newcomers unfamiliar with coding. While the lessons you learn in the classroom provide essential foundational understanding, some technical or practical aspects of ML—such as data preprocessing, feature engineering, and model tuning—are best learned through hands-on interaction. ML Playground was developed to act as a proof-of-concept application to address this gap by offering a browser-based, graphical user interface that lets users engage with core ML workflows without writing code. Designed with educational accessibility in mind, the application allows users …

Understanding The Impact Of Ransomware On Biotechnology, Tswvyim Vang

Electronic Theses, Projects, and Dissertations

Biotechnology encompasses the use of research on both biology and technology to create products that can advance in areas such as healthcare and agriculture. Because of the technology and products that arise from the use of biotechnology, the industry is especially targeted by cyber-attacks. A prominent type of cyber-attack that is utilized by cyber criminals on biotechnology is ransomware. The goal of this project is to determine the impact of ransomware on biotechnology. The research questions posed are: Q1) What are real examples of ransomware attacks that have occurred on biotechnology companies and what patterns can be identified by these …