Digital Commons Network^™

Measurement Of Luminous Intensity Distribution For Film And Television Led Light Sources And Its Simulation Research In Game Engines, Jingyi Suo, Baihong Lu, Che Qu

Journal of System Simulation

Abstract: To address the issues of mismatched photometric characteristics between light sources in virtual environments and real-world lighting during film and television lighting design and lighting preview using game engines, a testing solution for measuring the luminous intensity distribution for film and television LED light sources was proposed, building upon existing luminaire light intensity distribution testing systems. Based on the obtained data, a light source calibration process was constructed in the UE5 to correctly simulate the photometric characteristics of light sources in the virtual environment. Simulation results have shown that the calibration process can accurately and efficiently reproduce the …

Real-Time Production Of High-Resolution, Gap-Free, 3-Hourly Aod Over South Korea: A Machine Learning Approach Using Model Forecasts, Satellite Products, And Air Quality Data, Seoyeon Kim, Youjeong Youn, Menas Kafatos, Jaejin Kim, Wonsik Choi, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) AOD retrieval system for South Korea. The system integrates Copernicus Atmosphere Monitoring Service (CAMS) forecasts, high-resolution meteorological fields, and ground-based air quality observations within a machine learning framework. Three models with varying training periods were systematically evaluated using cross-validation and independent validation with 2024 Aerosol Robotic Network (AERONET) data. The optimal model, trained on 2015–2023 data, achieved …

Robust Security Assurance In Cyber-Physical Systems: From Attack Diagnosis To Attack Resilience, Zifan Wang

Dissertations - ALL

Cyber-Physical Systems (CPS) have become integral to critical infrastructure, from autonomous vehicles to industrial control systems. However, their increased connectivity and sophistication introduce new vulnerabilities, making security a paramount concern. This dissertation presents a comprehensive pipeline for enhancing CPS security, focusing on accurate diagnosis of trustworthy time frames and subsystems, followed by system resilience measures to restore safe states. The foundation of system resilience lies in an advanced checkpointing protocol for real-time multi-process systems. A novel three-step approach uniquely addresses both logical and timing correctness, which are crucial for modern CPS applications. By partitioning processes into directed acyclic graphs, implementing …

Model Inference And Sparse Network Analysis: Machine Learning In The Gene Regulatory Network Framework, Youchuan Wang

Dissertations - ALL

This thesis explores sparse network inference from high-dimensional, noisy, and underdetermined data—a fundamental challenge in many scientific domains. We focus on the development of evolutionary computation methods for discovering underlying network structures that are both interpretable and biologically plausible. Our methods are applied to the domain of Gene Regulatory Network (GRN) inference, where sparsity, indirect interactions, and limited observations pose significant hurdles. The framework models both steady-state and time-series gene expression data, with particular emphasis on biological sparsity and regulatory dynamics. We approach the problem from three perspectives: (1) edge-level analysis using transitive reduction to distinguish direct from indirect regulation; …

Balancing Robustness And Practicality In Model Compression, Personalization, And Healthcare Interventions, Sawinder Kaur

Dissertations - ALL

Advances in machine learning have enabled significant progress in generating robust solutions. However, designing solutions that balance robustness while meeting the practical constraints of diverse applications, such as limited resources and data, remains a challenge. We explored three key aspects of this interplay: verified robust compressed neural networks, context-wise robust personalization, and reliable counterfactual interventions for healthcare. First, we introduce VeriCompress, a novel framework to streamline the synthesis of compressed neural networks with formal guarantees of adversarial robustness. This enables the deployment of reliable and efficient models in resource-constrained environments, such as smartphones. Second, we developed CRoP (Context-wise Robust Static …

Adaptive Generation In Evolutionary Robotics: From Adversarial Objects To Guided Optimization, Unknown Akshay

Dissertations - ALL

Evolution-inspired algorithms have proven effective for complex optimization problems butsuffer from computational inefficiency due to their reliance on random variation operators. This is problematic in domains where fitness evaluation depends on expensive procedures such as training a neural network or running a robot, either in simulation or on hardware. This dissertation presents novel approaches for evolutionary robotics that replace stochastic evolutionary operations with learned, adaptive strategies using reinforcement learning (RL), significantly improving search efficiency while maintaining population diversity.The first contribution is a voxel-based evolutionary framework for generating adversarial objects that challenge robotic grasping systems. By evolving objects with controlled similarity …

Multipacking On Graphs And Euclidean Metric Space, Sk Samim Islam

Doctoral Theses

A multipacking in an undirected graph G = (V,E) is a set M ⊆ V such that for every vertex v ∈ V and for every integer r ≥ 1, the ball of radius r around v contains at most r vertices of M, that is, there are at most r vertices in M at a distance at most r from v in G. The multipacking number of G is the maximum cardinality of a multipacking of G and is denoted by mp(G). The MULTIPACKING problem asks whether a graph contains a multipacking of size at least k. For more …

Fine-Tuning Llama2 For Summarizing Discharge Notes: Evaluating The Role Of Highlighted Information, Mahshad Koohi Habibi Dehkordi, Yehoshua Perl, Fadi P. Deek, Hao Liu

School of Computing Faculty Scholarship and Creative Works

This study investigates whether incorporating highlighted information in discharge notes improves the quality of the summaries generated by Large Language Models (LLMs). Specifically, it evaluates the effect of using highlighted versus unhighlighted inputs for fine-tuning LLaMA2-13B model for summarization tasks. We fine-tuned LlaMA2-13B in two variants using MIMIC-IV-Ext-BHC dataset: one variant fine-tuned with the highlighted discharge notes (H-LLaMA), and the other on the same set of notes without highlighting (U-LLaMA). Highlighting was performed automatically using a Cardiology Interface Terminology (CIT) presented in our previous work. H-LLaMA and U-LLaMA were evaluated on a randomly selected test set of 100 discharge notes …

Mapping Sustainability To Cybernetic-Generative Artificial Intelligence-Based Education: An Innovative Neutrosophic Orbifold-Lattice Methodology, Mona Mohamed, Ahmed A. Metwaly

Neutrosophic Systems with Applications

The lightning-fast development of artificial intelligence (AI), notably generative artificial intelligence (Gen AI) technologies, has intrigued multiple disciplines, particularly education. In this context, Large Language Models (LLMs) serve as beneficial cognitive resources that address knowledge deficits and provide tailored educational support for learners and staff. For learners, LLMs are regarded as knowledgeable educators who offer prompt, focused responses and rationales to tricky queries. Whereby LLMs for staff, Strength enhancer, automating tedious tasks, and creating intelligent resources.Gen AI’s rapid growth offers enormous obstacles for educational systems, compelling them to discover solutions for these obstacles.

Conceptually, cybernetics is leveraged for bridging the …

Chaos Engineering In Multi-Gigahertz Solid-State Lasers: A Novel Approach To Optoelectronic Control, Mikhail V. Gorbunkov, Yulia Ya. Maslova, Yulia A. Sinichkina

Karbala International Journal of Modern Science

This paper presents a comprehensive study of a multi-gigahertz chaotic generator of light pulses based on solid-state laser sources, including fiber lasers, governed by carefully designed positive and negative feedback loops. It harnesses the inherent nonlinear dynamics within a solid-state laser controlled by a combination of two inertial feedback loops, enabling the realization of complex chaotic behavior, including the logistic map scenario, under moderate amplification conditions. The laser system dynamics are rigorously investigated through theoretical modeling, employing a nonlinear map approach, and high-resolution picosecond simulations. The results of our numerical simulations highlight the efficacy of fast electro-optical feedback system with …

Application Of Reinforcement Learning To Precision Aerial Delivery System Control In Adverse Wind Conditions, Radman Zarbock

The Journal of Purdue Undergraduate Research

Precision aerial delivery systems (PADS) are a subset of airdropped parachute-leveraging package delivery systems that use autonomous guidance, navigation, and control (GNC) to reach targets with high degrees of accuracy. This technology emerged in the 1990s, and strides have been made since to improve the reliability of traditional physics-based controllers that guide PADS. However, these algorithms still struggle to deliver acceptable performance results when PADS are subjected to austere operating environments, such as those with unpredictable wind. Building on a foundational study in 2022 that used artificial intelligence (AI) and machine learning to improve PADS GNC performance, this study aims …

Visualizing And Evaluating Binary Classifier Performance With Contingency Space, Colin D. Kehoe, Azim Ahmadzadeh

Undergraduate Research Symposium

Traditional metrics for evaluating binary classifiers, such as Accuracy, F1 Score, and True Skill Statistic (TSS), often obscure the underlying tradeoffs between true positive and true negative performance—particularly in imbalanced or high-stakes domains. This poster introduces the Contingency Space, a two-dimensional representation of classifier behavior defined by true positive rate (TPR) and true negative rate (TNR). Within this space, scalar performance metrics become geometric surfaces, revealing how scores vary across the entire landscape of possible classifier outputs.

We present a Python package that implements this framework, enabling users to map model predictions into the Contingency Space, visualize metric surfaces …

Pixel-Perfect Segmentation Of Solar Filaments, Jamie Harris

Undergraduate Research Symposium

The observation and classification of solar filaments has a drastic impact on the ability to predict solar-magnetic weather phenomena that threatens to put both satellite infrastructure and astronauts at risk. Using the Hɑ filter provided by the Global Oscillations Network Group (GONG), a network of six telescopes around the world dedicated to 24/7 surveillance of the sun, we are able to get images that clearly and prominently display filament activity. With the vast amount of images the GONG takes, it is not possible to manually analyze every image. Using the U-Net model for computer vision, we were able to train …

Gauss’S Method For Orbital Determination, Milagros Tamara Giraldo

Honors Program Theses and Projects

Accurately predicting the orbital trajectory of celestial objects is essential for precise spacecraft navigation, planning planetary missions, avoiding potential collisions with space debris, and studying the long-term stability of planetary systems. Gauss’s method for orbital determination provides a way to predict the path of a celestial body accurately using only a small number of observations. In this project, we create an implementation that is not just a tool for executing Gauss’s method, but also an opportunity to study the formulation of the method itself. It allows for a practical and detailed examination of how different inputs, assumptions, and numerical choices …

Tests Without Borders: A Global Approach To Measuring Visualization Literacy, Olivia A. Guess

McKelvey School of Engineering Graduate Student Theses & Dissertations

Visualization literacy assessments shape how we understand people's ability to interpret data, yet most existing instruments embed Western datasets and assumptions that limit their relevance for global audiences. This thesis argues that because data is personal, assessments must also be culturally grounded. We introduce a unified framework for adapting the Mini-VLAT into 22 regionally responsive short-form assessments, each retaining the structure of the original test while incorporating datasets and scenarios tailored to specific regions around the world. To demonstrate how such adaptations can be customized and validated, we present a detailed case study of a Ghana-adapted Mini-VLAT, developed in collaboration …

Integrating Post-Quantum Cryptography Into A Cdn-Style Pki: Tls And Quic Performance Across Pqc Suites, Katrina Mary Parsom

UNLV Theses, Dissertations, Professional Papers, and Capstones

Post-quantum cryptography (PQC) adds larger certificates and extra workload to the Transport Layer Security (TLS) handshake. In order to analyze the effect of that change on content delivery, a controlled testbed with conditions replicating a Content Delivery Network (CDN) is used. The system uses a standard X.509 certificate chain over TLS 1.3. It compares RSA with classical key exchange, RSA with hybrid key encapsulation mechanism (KEM), and Module Lattice-based Digital Signature Algorithm (ML-DSA) with hybrid KEM. The test harness generates and provides certificates and establishes TLS parameters. The test harness also supports mixed web workloads and records key metrics, including …

Ankimedbench: Evaluating Hierarchical Medical Knowledge In Language Model Embeddings, Neel Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Despite achieving over 90% accuracy on medical benchmarks, recent studies show physicians cannot effectively leverage language models to improve clinical reasoning. Current benchmarks test isolated factual recall, but clinical practice requires hierarchical navigation through diagnostic categories—starting broad and narrowing systematically from chest pain to cardiovascular pathology to myocardial infarction to specific STEMI types. Existing evaluations cannot measure whether models preserve this taxonomic structure essential for clinical reasoning.

We introduce AnkiMedBench, built from 16,512 medical flashcards used by students preparing for licensing exams. Cards are organized across six hierarchy levels spanning 16 broad medical specialties to 672 specific diseases and conditions. …

Advancing Cybersecurity Practice: Explainable Machine Learning For Network Intrusion Detection, Adam Grabowski, Shengjie Xu

Journal of Cybersecurity Education, Research and Practice

This research investigates explainable artificial intelligence (XAI) integration within machine learning (ML)-based intrusion detection systems (IDS), focusing on distinguishing malicious from benign network activities. We employed Random Forest and XGBoost models evaluated on widely recognized datasets, including NSL-KDD and UNSW-NB15, using both binary and multi-class classification tasks. The objective was to enhance cybersecurity operations through improved model transparency and interpretability. By integrating SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), the study offers comprehensive global and local insights into model decision-making processes. Results demonstrate SHAP's effectiveness in providing a broad, dataset-wide understanding of feature interactions and importance, while …

Developing An Ai-Assisted Grading System Using Large Language Models, Andrei Modiga

MS in Computer Science Project Reports

We present a grading system that accelerates evaluation of open-ended student work across scanned and digital workflows. The system crops answer regions from PDFs, assigns submissions via OCR on identity regions only, and groups answers by visual semantics using a vision LLM. Instructors review and edit groups, apply rubric items once per group, and export grades from an on-screen table. The solution integrates Ghostscript rasterization, PdfPig page orchestration, SkiaSharp region extraction, Tesseract identity OCR, and GPT-4o Vision for grouping. We detail the architecture, token-budgeted batching strategy, and persistence design, then describe testing results for grouping quality, time-on-task, and usability. The …

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 …

Flooding Behavior Near The Us/Canada Border: Complications And Approaches, Maria T. Dodson

Honors College Theses

Flood forecasting remains a major challenge due to the nonlinear nature of hydrological systems and uncertainties in environmental data. This study aimed to address the prevalent challenges that arise from forecasting flooding behavior. To address the inherent complexity of hydrological forecasting, a machine learning framework was developed and trained on major contributing factors. To achieve an optimal balance between computational efficiency and predictive performance, a Gated Recurrent Unit (GRU) was selected as the optimal machine learning model. As the chosen dataset, North American Land Data Assimilation System Phase 2 (NLDAS2), is known to have inaccuracies in the important feature Relative …

Application Of Augmented Reality Technology As A Dietary Monitoring And Control Measure Among Adults: A Systematic Review, Gabrielle Victoria Gonzalez, Bingjing Mao, Ruxin Wang, Wen Liu, Chen Wang, Tung Sung Tseng

School of Public Health Faculty Publications

Background/Objectives: Traditional dietary monitoring methods such as 24 h recalls rely on self-report, leading to recall bias and underreporting. Similarly, dietary control approaches, including portion control and calorie restriction, depend on user accuracy and consistency. Augmented reality (AR) offers a promising alternative for improving dietary monitoring and control by enhancing engagement, feedback accuracy, and user learning. This systematic review aimed to examine how AR technologies are implemented to support dietary monitoring and control and to evaluate their usability and effectiveness among adults. Methods: A systematic search of PubMed, CINAHL, and Embase identified studies published between 2000 and 2025 that evaluated …

Using Quantum Annealing For Sampling And Pattern Generation In Generative Machine Learning And Catastrophic Forgetting Mitigation, Abdelmoula El Yazizi

Theses and Dissertations

The first goal of this dissertation was to understand the reasons for the absence in previous investigations of significant and consistent improvements in the trainability of Restricted Boltzmann Machines (RBM) when the Quantum Annealer (QA) was used for sampling from the RBM probability distribution. The second goal was to address the shortcomings of those previous investigations, explore possibilities of improving RBM training, and identify other machine learning applications that could benefit from sampling or from generating patterns by the QA. The first part of this dissertation focused on a Local-Valley (LV) centered approach to assessing the quality of sampling. QA-based …

Impacts Of Upgrading Gcc On The Srtuner Compiler Autotuning Algorithm, Jonathan D. Butterfield

Theses and Dissertations

An often-overlooked research gap is the applicability of research over time. The impact of updating the GNU Compiler Collection version was investigated for the SRTuner compiler optimization research. Versions 7 through 14 were tested using documented flags and recommended flag dependencies. Quality controls measured and enforced consistency across test runs and benchmarks. The performance of SRTuner was compared to a random control while the impact of enforcing flag dependencies was evaluated. The most influential flags were investigated by turning them off in isolation, but results were mixed. It was determined that enforcing flag dependencies resulted in improved performance, but the …

Polyminhash: Efficient Area-Based Minhashing Of Polygons For Approximate Nearest Neighbor Search, Alima Subedi, Sankalpa Pokharel, Satish Puri

Computer Science Faculty Research & Creative Works

Similarity searches are a critical task in data mining. As datasets grow larger, exact nearest neighbor searches quickly become unfeasible, leading to the adoption of approximate nearest neighbor (ANN) searches. ANN has been studied for text data, images, and trajectories. However, there has been little effort to develop ANN systems for polygons in spatial database systems and geographic information systems. We present PolyMinHash, a system for approximate polygon similarity search that adapts MinHashing into a novel 2D polygon-hashing scheme to generate short, similarity-preserving signatures of input polygons. Minhash is generated by counting the number of randomly sampled points needed before …

A Trigger For The Autonomous Decommissioning Of Smart Devices, Ravindra Mangar, Jared Chandler, Jingyu Qian, Carl A. Gunter, Timothy J. Pierson, David Kotz

Dartmouth Scholarship

Smart devices are ubiquitous in modern environments, yet their decommissioning phase remains poorly studied and often overlooked in system design. We define secure decommissioning as the process by which a smart device securely disconnects from its environment and makes sensitive data inaccessible. If not decommissioned, devices may retain sensitive information – such as security credentials or user-behavior data that could be recovered by an adversary. Unfortunately, some users may forget to decommission a device when they dispose or sell it, and cannot decommission a device that is lost or stolen. This paper investigates a trigger mechanism for individual wireless smart …

Causal Structure Discovery For Explaining Important Features In Machine Learning, Yina Hou

Tennessee State University Alumni Theses and Dissertations

Understanding causal relationships between input variables and outcomes is critical for scientific advancement. The famous adage in medicine, "correlation does not imply causation," holds profound significance for understanding disease etiology. Although explainable machine learning (ML) has shown major advances in predictive modeling, it remains unclear how ML-derived important variables relate to causal variables. This thesis investigates the association between causal variables and those identified as correlated and important for ML-based prediction to bridge a critical knowledge gap in data science. Specifically, the thesis explores two research questions: when and to what extent (1) statistically correlated variables are also causal? and …

A Fault Tolerant Honeybee Behaviour Load Balancing Algorithm, Charmaine Adegbite

Tennessee State University Alumni Theses and Dissertations

Cloud computing enables flexible, scalable access to virtualised infrastructure but remains challenged by maintaining performance in fault-prone environments. Faults such as virtual machine failures can degrade task allocation and system reliability. However, traditional load balancing algorithms and bio-inspired strategies, including the Honeybee Behaviour Load Balancing algorithm, assume fault-free conditions. This thesis introduces a Fault-Tolerant Honeybee Behaviour Load Balancing algorithm that enhances the original Honeybee Behaviour Load Balancing algorithm by integrating fault detection. The proposed algorithm monitors the health of VMs and dynamically reallocates workloads when faults are detected which improves stability and reliability. The proposed fault tolerant algorithm was implemented …

Parameter Informed Reinforcement Learning For Vehicle System Identification, Nathan Schaff

Doctoral Dissertations and Master's Theses

Accurate system identification is essential for modeling and controlling vehicle dynamics. This dissertation explores the application of Parameter Informed Reinforcement Learning (PIRL) as a novel approach to system identification (SYSID). PIRL integrates prior system knowledge, such as physical parameters, into reinforcement learning (RL) frameworks to improve estimation accuracy. The study begins with an overview of traditional SYSID methods and then introduces PIRL as a modification of standard RL. The research applies PIRL to short-period aircraft dynamics, demonstrating its effectiveness in both offline and online learning frameworks. The dissertation then further explores PIRL’s utility in an indirect model reference adaptive control …

Challenges In Engineering Machine Learning (Software) Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides on the software engineering challenges unique to machine learning systems, for an undergraduate software engineering course. After contrasting traditional programming with machine learning, the deck examines why the usual tools for managing complexity—abstraction, reuse, and composition—are harder to apply to ML, given the lack of clear specifications and modularity. It covers concept drift, feedback loops (illustrated with crime-prediction and recommendation examples), and the accumulation of technical debt in ML systems, including the role and pitfalls of notebooks in moving from experimentation to production. Based on "Machine Learning in Production/AI Engineering" by Christian Kaestner and Eunsuk Kang (Carnegie Mellon …