Praxly: An Online Ide For The Praxis Cs Test Pseudocode,
2025
James Madison University
Praxly: An Online Ide For The Praxis Cs Test Pseudocode, Benjamin Saupp
James Madison Undergraduate Research Journal (JMURJ)
No abstract provided.
Intuiting Interaction: Meta-Reasoning And Meta-Learning As Foundations For Intelligent User Interfaces,
2025
CUNY Hunter College
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.
Current Wind Simulation Techniques,
2025
University of Nebraska at Omaha
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.
Maximal Independent Set Algorithms Within Procedural Planar Maps: A Large-Scale Evaluation,
2025
Murray State University
Maximal Independent Set Algorithms Within Procedural Planar Maps: A Large-Scale Evaluation, Chaucer Ihrig
Honors College Theses
Analysis of a childhood game has led us to the problem of maximum independent sets in planar graphs. We wrote a graph creation utility using R to generate a random planar map and its dual graph. This utility then finds a graph’s maximal independent set using a variety of six algorithms. We investigate statistical connections between graph structure, colorability, and the maximal independent sets found using these algorithms over an incredibly large and procedurally generated dataset. We find one can always win the coloring game if the resultant graph is two-colorable. The algorithms perform statistically and practically significantly better on …
Comparative Analysis Of Classical And Machine Learning Pathfinding Approaches,
2025
University of South Alabama
Comparative Analysis Of Classical And Machine Learning Pathfinding Approaches, Miguel Gapud
Honors Theses
Pathfinding is an essential task for any autonomous robot. Graph-based classical pathfinding algorithms and machine learning approaches have both been used for this end, but they are often not compared against each other. An implementation of end-to-end (E2E) pathfinding using Proximal Policy Optimization (PPO) and an Alexnet architecture is compared against an implementation of Hybrid A*. A digital twin in Unity3D is used as the testing environment with the Clearpath Dingo as the pathfinding robot. In machine learning, the robot is controlled using PPO through ROS-Noetic with a camera as its sensor. Hybrid A* and its controls are implemented directly …
Advancing Precision And Autonomy In Agriculture And Medical Imaging Through Ai And Computer Visions,
2025
Florida Institute of Technology
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 …
Towards Visual Inertial Navigation With Fixed Tetrahedral Targets,
2025
Florida Institute of Technology
Towards Visual Inertial Navigation With Fixed Tetrahedral Targets, Joao Leonardo Silva Cotta
Theses and Dissertations
This dissertation presents a robust method for 6DoF position estimation under impaired visual conditions utilizing a minimum 4-point Perspective-n-Point (P4P) solver designed for tetrahedral targets. Using SO(3) × R 3 instead of SE(3), the method uses a Lie group-based formulation to discriminate between rotation and translation, thereby enabling computationally efficient, resource-conscious op- optimization while preserving correct geometric behavior. Designed using the contemporary C++17 library ShomerTarget, the solver is analytically formulated and assessed under pragmatic robotic conditions. Particularly in low-light and high-dynamic environments, experiments on embedded systems, UAVs, and NASA’s Astrobee show that the proposed solver attains enhanced accuracy compared to …
Quantifying The Transfer Effectiveness Of An Artificial Intelligence-Based Simulator Pre-Training Program For Student Pilots,
2025
University of North Dakota
Quantifying The Transfer Effectiveness Of An Artificial Intelligence-Based Simulator Pre-Training Program For Student Pilots, Ryan Guthridge
Journal of Aviation/Aerospace Education & Research
Since the airline pilot shortage was initially studied in 2016, the pilot hiring model has been significantly impacted, with airlines hiring qualified pilots at unprecedented rates. The COVID-19 pandemic has slowed this hiring rate, however it is expected that airline hiring will soon increase to a rate higher than initially expected (Bureau of Transportation Statistics, 2022). With this dynamic, certified flight instructors are often the most qualified recruits for airlines, due to the number of hours and experience they have gained in the flight training organization. In turn, certified flight instructors are in short supply for flight training organizations worldwide. …
Graphtreemed: A Hybrid Graph-Tree Rag Architecture For Mission-Critical Medical Applications,
2025
Indian Institute of Technology Kanpur
Graphtreemed: A Hybrid Graph-Tree Rag Architecture For Mission-Critical Medical Applications, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri
Graduate Student Government Association Research Conference
Studies within engineering management indicate that decision-making is often based on the cognitive processing of grouped and pictographic information clusters entangled with high-level pattern recognition. Similarly, graph-based retrieval-augmented generation (RAG) architectures substantially improve diagnostic accuracy and interpretability, while tree-structured systems reduce critical misses through hierarchical reasoning. However, existing solutions often lack a unified framework that seamlessly integrates these two paradigms to address the multifaceted demands of mission-critical healthcare settings. This proposal introduces GraphTreeMed, a novel hybrid RAG architecture designed to harness the complementary strengths of graph-based and tree-based retrieval mechanisms, thereby advancing the safety and efficacy of clinical decision support …
Leveraging Benford’S Law And Machine Learning For Financial Fraud Detection,
2025
William & Mary
Leveraging Benford’S Law And Machine Learning For Financial Fraud Detection, Benjamin R. Fu
Cybersecurity Undergraduate Research Showcase
Financial fraud, particularly credit card fraud, continues to pose substantial challenges to financial institutions due to its increasing frequency and impact on consumer trust. While traditional rule-based methods have provided foundational defenses, their limitations in scalability and adaptability have accelerated the adoption of machine learning (ML) techniques. Concurrently, Benford’s Law—a statistical principle often used in forensic accounting—has demonstrated efficacy in detecting anomalies within naturally occurring numerical datasets. This study explores a hybrid fraud detection approach that integrates Benford’s Law with supervised machine learning algorithms, including Logistic Regression, Random Forest, and k-Nearest Neighbors. Using the publicly available European credit card fraud …
Algorithms For Order Statistics In Farey Sequences: A Computational Study,
2025
University of Nebraska-Lincoln
Algorithms For Order Statistics In Farey Sequences: A Computational Study, Connor Weyers
School of Computing: Dissertations, Theses, and Student Research
Farey sequences are the sets of irreducible fractions in increasing order with denominator less or equal to some integer n. They are a well-known concept in number theory problems and are related to many other concepts in number theory including integer factoring, Fibonacci sequences, and Riemann’s Zeta function. In this paper, we investigate some known algorithms to solve certain problems in Farey sequences from a computational perspective. In particular, we implement established algorithms that have not been previously implemented with the goal of creating a package that can be used more broadly. We also develop a new algorithm for rational …
Open-Set Graph Anomaly Detection Via Normal Structure Regularisation,
2025
Singapore Management University
Open-Set Graph Anomaly Detection Via Normal Structure Regularisation, Qizhou Wang, Guansong Pang, Mahsa Salehi, Xiaokun Xia, Christopher Leckie
Research Collection School Of Computing and Information Systems
This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (referred to as *seen anomalies*) to detect both seen anomalies and *unseen anomalies* (*i.e*., anomalies that cannot be illustrated the training anomalies). Those labelled training data provide crucial prior knowledge about abnormalities for GAD models, enabling substantially reduced detection errors. However, current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes. Further, existing open-set AD models were introduced …
Ai Models By Boodlebox: Purpose-Built Intelligence,
2025
Fort Hays State University
Ai Models By Boodlebox: Purpose-Built Intelligence, Kyle Horn
SACAD: Scholarly Activities
Generative AI has transformed the way we interact with technology, enabling dynamic and intelligent conversations through AI-driven bots. This project explores my experience with BoodleBox, a platform that hosts AI chatbots, offering users access to leading AI models such as ChatGPT, Gemini, DALL·E, and DeepSeek. Through the FHSU Generative AI Initiative, I was granted access to experiment with these models and create my own custom AI bot tailored to specific needs. This poster highlights the process of developing a custom bot, including defining instructions, enforcing rules, and sharing the bot for others to use. Additionally, it discusses the background of …
Robust Spacecraft Autonomy For Deep Space Exploration In Special Euclidean Group Se(3),
2025
Embry-Riddle Aeronautical University
Robust Spacecraft Autonomy For Deep Space Exploration In Special Euclidean Group Se(3), Matthew Wittal
Doctoral Dissertations and Master's Theses
Over the past half-century, humanity has gained extensive experience conducting manned spaceflight near Earth. Arguably, "near Earth" could even include the Moon — the most distant destination humans have reached. However, "near" in this work primarily refers low Earth orbit (LEO). One could argue that we have not truly left Earth since the Apollo, as spacecraft in some LEOs remain subject to atmospheric drag thus emphasizing their continued connection to Earth's immediate environment. Reflecting on this, it becomes clear that humanity has largely remained bound to Earth’s immediate vicinity since the Apollo missions reached the Moon. However, that is set …
Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review,
2025
Georgia Southern University
Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins
Honors College Theses
This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …
Investigating Key Structures In Protective Scenes For Llms,
2025
Portland State University
Investigating Key Structures In Protective Scenes For Llms, Eben M. Weisman
University Honors Theses
This research delves into the realm of "protective scenes" within Large Language Models (LLMs), exploring their impact on bias mitigation, deception, and context preservation. The study investigates the use of roleplay prompting human-like behavior and reasoning in LLMs, focusing on the Character-LLM framework's concept of protective scenes with graduated levels of protection. By combining insights from psychology, cognitive science, and computational analysis, this research aims to develop a framework for understanding how protective scenes influence roleplay performance in LLMs, ultimately contributing to the development of more reliable and ethical AI systems.
Unraveling Genetic Links Between Diabetes And Heart Failure-A Machine Learning Approach,
2025
The University of Texas Rio Grande Valley
Unraveling Genetic Links Between Diabetes And Heart Failure-A Machine Learning Approach, Sunakhi Sahoo, Marzieh Ayati
Research Symposium
Background: Diabetic heart failure (DHF) is defined as a chronic and progressive disease which is associated with both diabetes and heart failure (HF). Even though there have been many developments in the knowledge of these diseases, there is still much to learn about the genetic crossovers between the two. In this study, we identified genes that are associated with diabetic heart failure and heart failure by using gene expression data from patients with DHF, HF, and a control group of patients who died of natural causes. We sought to identify genes that had altered expression levels which could possibly play …
Detecting Sensor Data Manipulation,
2025
University of South Alabama
Detecting Sensor Data Manipulation, Ricky Green, Michael Black
Shelby Hall Graduate Research Forum Posters
The integration of Information Technology (IT) and Operational Technology (OT) have made OT devices vulnerable to threats that have been successfully exploited with devastating results. Many modern techniques for hardening and securing enterprise IT systems are either incompatible with OT components in an Industrial Control System (ICS), reduce the efficiency of processes, or are prohibitively expensive to implement. Research in the area of ICS security focuses on a top-down approach, such as intrusion prevention by securing the perimeter of the network and hardening computer systems. This approach is useful in business IT systems, but full compatibility with OT components in …
Adversarial Attacks And Defense Methods In Robotic Systems,
2025
University of South Alabama
Adversarial Attacks And Defense Methods In Robotic Systems, Thanh D. Le
Shelby Hall Graduate Research Forum Presentations
Presentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama.
Analyzing And Comparing Refinement Indicators For Rbf-Fd Adaptive Algorithms,
2025
Air Force Institute of Technology
Analyzing And Comparing Refinement Indicators For Rbf-Fd Adaptive Algorithms, Anders R. Johnson
Theses and Dissertations
Recent progress has been made in the development of collocation-based iterative algorithms that approximate solutions to PDEs. These algorithms rely on the ability to identify regions within a domain where a finer discretization is required. Such iterative algorithms are beneficial particularly when solution functions have highly localized behavior. This thesis proposes an indicator for node refinement that is constructed by approximating the forward error. This proposed indicator also helps to establish confidence in the accuracy of a given solution estimate. The proposed error estimator is theoretically examined and compared with contemporary refinement indicators. It is shown that an iterative algorithm, …
