Theory and Algorithms Commons^™

Teaching Machines To Deter: Exploring Strategic Deterrence In Ai Models, Will Taylor

Theses/Capstones/Creative Projects

This capstone project investigates whether deterrence can emerge as a meaningful strategy within a zero-sum stochastic game using multi-agent reinforcement learning (MARL). After outlining core concepts in game theory and deterrence, the study models a simplified deterrence environment in which two minimax-Q agents repeatedly interact under uncertainty and adversarial incentives. The agents learn from rewards shaped by escalation costs, unilateral vulnerability, and the stabilizing benefits of restraint. Results show that both agents consistently converge toward a conservative, status-quo strategy, overwhelmingly selecting the Maintain action while avoiding both escalation and restraint in most scenarios. This behavior reflects the risk-averse logic of …

Post-Quantum Cryptography Encryption Implementation For Messaging App, Callum S. Ward

Theses/Capstones/Creative Projects

This paper and complementary capstone project aim to explore the state of post-quantum cryptography today by defining the algorithms with which quantum computers can decipher modern asymmetric cryptographic algorithms in exponentially accelerated time, exploring national standards body NIST’s recommendations to circumvent these weaknesses with post-quantum solutions, and implementing recommended algorithms in my group’s project for the UNO Computer Science Capstone course, LockTalk. After having decided on ML-KEM for quantum-resistant asymmetric key transfer and AES-256 for symmetric message encryption and decryption, I was able to cryptographically encode messages to obscure their plaintext values from communication interceptions without any discernible increase in …

Learning Global Context For Sparse Activity Recognition In Lengthy Recordings With Limited Dataset Size, Zeyu Tang

All Dissertations

This dissertation describes methods to analyze lengthy recordings of data in order to detect sparsely occurring activities. The narrative below describes the progression of research that led to the development of these methods and their generalization into a unified framework. My research started with designing models for dietary monitoring, including detecting meals from day-long recordings and detecting intake gestures from meal-length recordings. Both tasks share some common characteristics: (a) the target event takes only a small portion of data recordings, and (b) there is global context within full-length data recordings that can help a model make better decisions. After finishing …

Dynamic-Query Robustness Of Ann Indexes Under Time-Indexed Drift, Stellamaris Nakacwa, Majid Shaalan

Harrisburg University Other Works

Approximate nearest-neighbor search is a central retrieval primitive in dense question-answering and retrieval-augmented generation systems. Existing ANN evaluation protocols typically measure recall, latency, throughput, and search-effort sensitivity under a fixed-query assumption: a query vector is submitted to an index, approximate neighbors are retrieved, and the result is compared with exact nearest-neighbour ground truth. This assumption is appropriate for conventional vector-search benchmarking, but it is less complete for multi-step, distributed, and agent-controlled retrieval pipelines in which the retrieval-facing query may be refined, recomputed, or displaced across execution steps. This paper introduces a time-driven dynamic query evaluation framework for ANN search. The …

The Texture Of A Threat: Adversarial Training, Cnns, And Obfuscated Malware Detection, Kaelyn Haynie

Senior Honors Theses

Accurately detecting malicious programs is an expanding field of research for machine learning (ML), with a novel approach incorporating a bytecode-to-image pipeline that produces images representative of software. These images are provided to convolutional neural networks (CNNs) to be examined for malicious pattern indicators. However, CNNs struggle to generalize these patterns effectively while still being robust against adversarial data, an issue which this research addresses with adversarial training. In this paper, three unique CNN architectures (a DBFS-MC-inspired baseline, MIRACLE, and PSP-CNN) are trained for binary classification with 15,000 benign and malicious software samples encoded into images for Android, Windows, and …

Unique Combinations Of Packing Integer Squares, Keith M. Dreiling, Austin Leanna, William Mooney

SACAD: Scholarly Activities

This research investigates a function, informally named WAK(x), that describes the number of ways to divide an integer square into integer subsquares counting only the list of parts. Previous research has shown values up to 28, though finding these values is computationally complex and requires a long runtime using computer algorithms. We attempt to find patterns in the values and many aspects of the values, hoping to find a general solution. We are unsure if a solution exists, but we have ideas for how to move forward in finding a solution.

Towards Physics-Informed Neural Networks For Simulating Multiphase Geothermal Convection​*, Daniel C. Patton, Andrew Harrison Eno

Campus Research Month

Water and steam flow through porous rock, transferring heat via conduction and buoyancy-driven convection caused by density differences. Traditional numerical methods (finite-volume/finite-element) model this well but can become memory-intensive and unstable for long, high-detail simulations. This work demonstrates a Physics-Informed Neural Network (PINN) using a finite-difference approach within the NVIDIA PhysicsNeMo framework to simulate magma chambers in 2D. Tested on the Rio Pisco pluton in Peru, results are compared with the USGS HYDROTHERM model. PINNs learn from physical laws, offering accurate, flexible solutions with less data and development effort.

Code Visualizer: An Interactive Code Visualization Tool, Tadd Trumbull

Campus Research Month

Code Visualizer is a web-based, interactive algorithm visualization tool designed to help introductory computer science students develop a deeper understanding of array searching and sorting algorithms. Code Visualizer presents a step-by-step simulation environment built on a restricted Python subset, which allows students to observe array traversal, index manipulation, and algorithmic operations in real time. The tool features two learning modes: View Mode and Predict Mode. The underlying architecture utilizes a behavioral software design pattern called command pattern.

Scenarioxp: A Complete Scenario-Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Doctoral Dissertations and Master's Theses

Today is an age of exciting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

Study Of Output And Behavior Of Llms Using Confidence Framing In Prompt Engineering, Micah Parrilla

Doctoral Dissertations and Master's Theses

While prompt engineering is pivotal for shaping Large Language Model (LLM) outputs, the impact of confidence framing on behavioral calibration remains underexplored. This study investigates the ways in which psychological framing, utilizing techniques such as capability praise, role amplification, and doubt induction, affects linguistic tone, objective accuracy, and internal calibration. A 1,080-trial experimental matrix evaluated six diverse models across factual, logical, coding, and cyber security domains. Analysis using the Kruskal-Wallis H-test revealed highly significant behavioral shifts across all measured dimensions, providing conclusive evidence that the applied frames exert a substantial influence on model performance.

The findings identify a distinct cognitive …

Algorithm Performance In The Search For Hamiltonian Cycles, Chance Davis

Honors Theses

The Hamiltonian cycle problem is ubiquitous in both computer science and graph theory: Given a connected graph, a solution would either confirm the existence of a cycle which visits each vertex only once or its nonexistence. The importance of this problem, as well as its difficulty, is described in the Clay Mathematics Institute’s Millenium Prize Problems and Karp’s 21 NP-complete problems. Despite its “hardness,” solutions to the Hamiltonian cycle problem are desired in logistics, electronic circuit design, and network routing, among other fields. In this work, we benchmark a promising exhaustive enumeration algorithm on various graphs, including ones derived from …

A General Weighting Theory For Ensemble Learning: Beyond Variance Reduction Via Spectral And Geometric Structure, Ernest Fokoue

Articles

Ensemble learning is traditionally justified as a variance-reduction strategy, explaining its strong performance for unstable predictors such as decision trees. This explanation, however, does not account for ensembles constructed from intrinsically stable estimators-including smoothing splines, kernel ridge regression, Gaussian process regression, and other regularized reproducing kernel Hilbert space (RKHS) methods whose variance is already tightly controlled by regularization and spectral shrinkage. This paper develops a general weighting theory for ensemble learning that moves beyond classical variance-reduction arguments. We formalize ensembles as linear operators acting on a hypothesis space and endow the space of weighting sequences with geometric and spectral constraints. …

On Fibonacci Ensembles: An Alternative Approach To Ensemble Learning Inspired By The Timeless Architecture Of The Golden Ratio, Ernest Fokoue

Articles

Nature rarely reveals her secrets bluntly, yet in the Fibonacci sequence she grants us a glimpse of her quiet architecture of growth, harmony, and recursive stability \citep{Koshy2001Fibonacci, Livio2002GoldenRatio}. From spiral galaxies to the unfolding of leaves, this humble sequence reflects a universal grammar of balance. In this work, we introduce \emph{Fibonacci Ensembles}, a mathematically principled yet philosophically inspired framework for ensemble learning that complements and extends classical aggregation schemes such as bagging, boosting, and random forests \citep{Breiman1996Bagging, Breiman2001RandomForests, Friedman2001GBM, Zhou2012Ensemble, HastieTibshiraniFriedman2009ESL}. Two intertwined formulations unfold: (1) the use of normalized Fibonacci weights -- tempered through orthogonalization and Rao--Blackwell optimization -- …

Addressing The Problems Of Data Variations, Quality, And Scarcity In Training Deep Neural Networks, Jian Sun

Electronic Theses and Dissertations

The performance of deep neural networks (DNNs) is strongly influenced by the characteristics and quality of the underlying datasets. This Ph.D. dissertation addresses three pervasive data challenges-imbalance, quality degradation, and scarcity-that commonly hinder the effectiveness of DNNs in computer vision (CV) and natural language processing (NLP) applications.

Class imbalance remains one of the most frequent causes of degraded model generalization. While Focal Loss effectively mitigates inter-class imbalance by assigning higher weights to minority classes, it struggles with intra-class imbalance, particularly in video datasets where longer clips dominate feature representation. To address this, I implement and utilize …

Evolving Solutions For Red Blood Cell Preservation, Ali Alkafaji, Charles A. Elder, Mohammad Zaidi, Kavin Parthiv, Michael A. Menze

The Cardinal Edge

In emergencies such as natural disasters, armed conflicts, or during outer space missions, the availability of transfusable blood can mean the difference between life and death. Red blood cells (RBCs) must be stored at +4 ± 2 °C and have a shelf life of just 42 days, which makes maintaining a stable blood supply during adverse conditions extraordinarily challenging. This challenge was especially apparent during the COVID-19 pandemic when hospitals faced severe blood shortages. Freeze-drying, or lyophilization, offers a promising avenue to extend the shelf life of RBCs for transfusion during crises. However, a significant hurdle in dry preservation is …

A Knowledge Transfer-Based Membrane Evolutionary Algorithm For Solving Large-Scale Sorted Waste Collection Problem With Timeliness, Wenxue Zhang, Boquan Gao, Aldy Gunawan, Yunyun Niu, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The sorted collection of municipal solid waste has emerged as an effective waste management strategy due to varying timeliness requirements across different waste types, giving rise to the critical research challenge of timeliness-based waste collection. While existing algorithms primarily focus on small-scale versions of this problem, solving large-scale timeliness-based waste collection problems remains particularly challenging. To tackle this issue, this paper proposes a knowledge transfer-based membrane evolutionary algorithm. Specifically, the original problem and simplified problem are constructed in different membranes respectively, and the knowledge transfer learning mechanism is incorporated into the membrane evolutionary algorithm, enabling effective information exchange between the …

Demystifying Hardware Formal Verification For Undergraduate Education: A Risc-V Processor Case Study With Coursework Implementation, Riley A. Peters

Master's Theses

Hardware verification engineers apply formal methods to prove that a digital device always behaves according to its specification. This differs from traditional functional verification, in which engineers establish correctness by repeatedly sending test inputs to the device and comparing the outputs against a reference model. With the growing complexity of integrated circuits, the demand for digital verification engineers with formal methods experience has continued to increase. However, California Polytechnic State University: San Luis Obispo's current curriculum lacks dedicated material to prepare students for these roles.

This thesis seeks to address the lack of formal methods material through two efforts. First, …

A Scalable Iteration Of The Horizon Simulation Framework Using Multithreading Techniques, Jason E. Beals

Master's Theses

The Horizon Simulation Framework (HSF) occupies a unique space in the modern aerospace modeling landscape, enabling flexible, modular modeling of mission-level agent behavior through an object-oriented, hierarchical design. HSF's hallmark breadth-first search scheduling algorithm explores a "multiverse" of possible mission execution pathways, enabling exhaustive evaluation of schedule combinations against user-defined heuristics.

As aerospace systems become increasingly complex, HSF faces critical challenges in establishing verifiable, deterministic behavior. The framework's core scheduling algorithm had not undergone systematic validation, leaving questions about temporal consistency, state management correctness, and reproducibility across different program executions. Furthermore, the exponential growth of schedule combinations creates computational bottlenecks …

Cerebral Documents And Algorithmic Sensemaking: Searching For Expressions In Human And Artificial Cognitive Collaborations, Rebekah L. Cowell

Proceedings from the Document Academy

Generative Artificial Intelligences (AIs) and current advanced large language models (LLMs) are algorithmically designed to generate text-based conversations as conversational agents (CAs), by replicating human language and conversational communication. Pairing human cognition with generative computationally coded cognition. We have never been here before: cerebral and artificial information collaborations and processing producing expressions that may or may not become visible as second-hand/secondary source documents.

Sensemaking or sense(un)making is a unique autonomous human drive cognitively, our information processing is sensemaking in action and expressions and articulations are evidence of the sensemaking cycle. Documentation [expressed or articulated through various mediums] are a product …

Light Cone Cancellation For Variational Quantum Eigensolver In Solving Noisy Max-Cut, Xinwei Lee, Xinjian Yan, Ningyi Xie, Yoshiyuki Saito, Leo Kurosawa, Nobuyoshi Asai, Dongsheng Cai, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Variational Quantum Eigensolver (VQE) is a quantum-classical hybrid algorithm used to estimate the ground energy of a given Hamiltonian. It consists of a parameterized quantum circuit, which the parameters are optimized using a classical optimizer. With the increasing need in solving large-scale problems in real-world applications, solving those large problems with fewer qubits and fewer gates becomes essential, so that we reduce the simulation difficulty and mitigate the effect of noise in real quantum hardware. In this study, we applied the Light Cone Cancellation (LCC) method to reduce the number of qubits and gates required in a two-local ansatz. LCC …