Theory and Algorithms Commons^™

Multi-Objective Bike Routing Problem: A Survey And Comparative Evaluation, Dominic Peter Macisaac

EWU Masters Thesis Collection

Multi-objective routing has been studied for over forty years, yet its application to the bike routing problem is a relatively recent development. The Multi-Objective Bike Routing Problem (MOBRP) seeks to optimize a set of bike routes from a single source to a single destination, given multiple cost criteria. This paper makes two primary contributions. First, it consolidates existing research on the MOBRP, including the criteria chosen and associated cost functions, the search algorithms used, and the testing setups and metrics used for evaluation. Second, it implements the most promising search algorithms and evaluates these approaches through comprehensive cross-testing, a topic …

A Time-Domain Boundary Integral Equation For Moving Acoustic Sources In Uniform Flow And Its Solution By An Advanced Time Propagation Approach, Fang Q. Hu, Douglas M. Nark

Mathematics & Statistics Faculty Publications

This paper presents a time-domain boundary integral equation (TDBIE) formulation for predicting acoustic scattering from moving sources in a uniform mean flow. This work is motivated by the increasing need for accurate aeroacoustic modeling of modern aircraft configurations, including VTOL and eVTOL systems with rotating components. A key challenge in time-domain scattering simulations with moving sources is the determination of retarded time for a given observer time, which involves solving an implicit equation at each time step. This can be computationally costly, particularly for numerical solution of the TDBIE where every surface element on the scattering body acts as an …

Studies On Convexity Of Dnf Formulae, Josue A. Ruiz

Electronic Theses & Dissertations (2024 - present)

In this dissertation, we investigate the problem of determining whether a Boolean formula given in disjunctive normal form (DNF) is convex. Although Boolean formulas have various applications, our research focuses on the practical application for rule-based access control policies, where policies are often expressed as a set of Boolean rules. Understanding the structural properties of such formulas is crucial for determining whether a policy can be efficiently represented within a specific access control model.

The main contribution of this research is the conception and analysis of convexity derived from the “gap problem.” In this context, convexity is characterized by the …

Three-Sided Skyline Counting Queries, Suruchi Kushwaha

Dissertations, Master's Theses and Master's Reports

A two-dimensional point p=(p.x,p.y) dominates another point p'=(p'.x,p'.y) if p.x ≥ p'.x and p.y>p'.y or p.x>p'.x and p.y ≥ p'.y. The skyline of a point set P is a subset P' ⊆ P such that every point in P' is not dominated by any other point in P. An orthogonal skyline counting query Q on a set of points P asks for the number of points on the skyline of P ⋂ Q.

In this work we study data structures that support orthogonal skyline counting queries in the special case when the query range is bounded on three …

Analysis Of Bin Packing Variants, Kyle T. Ambrose

UNF Graduate Theses and Dissertations

The Bin Packing problem is a classic and widely studied optimization problem that arises naturally in applications like manufacturing, logistics, and memory allocation, where space and resource constraints are critical. In this thesis, we first demonstrate the NP-completeness of Bin Packing via a reduction from Three-Dimensional Matching, establishing its foundational complexity. We then survey core heuristics for the one-dimensional case and extend our analysis to two and three-dimensional variants, including both offline and online strategies. Special attention is given to stochastic bin packing, where item sizes are modeled as random variables drawn from distributions such as uniform, truncated normal, and …

Schedulability Analysis Of Multi-Phase Limited-Preemption Tasks, Benjamin Standaert

McKelvey School of Engineering Graduate Student Theses & Dissertations

This work addresses hard real-time systems, in which tasks must be scheduled so that they are guaranteed to meet deadlines. In particular, when tasks execute across multiple domains with high preemption costs, the combined cost of these preemptions can cause the system to become unschedulable. The number of preemptions must therefore be bounded to limit the overall task execution time, while ensuring that task blocking times are small enough to allow the system to be schedulable. Prior work introduces the Multi-Phase Secure model, which describes a more exact version of this scenario, and an algorithm to determine schedulability of sporadic …

Optimal Algorithm For Managing On-Campus Student Transportation, Youssef Harrath, Jood Alyusuf, Zeena Ghulam, Muna Aldoseri

Research & Publications

This study analyzed the transportation issues at the University of Bahrain Sakhir campus, where a bus system with an unorganized and fixed number of buses allocated each semester was in place. Data was collected through a survey, onsite observations, and student schedules to estimate the number of buses needed. The study was limited to students who require to move between buildings for academic purposes and not those who choose to ride buses for other reasons. An algorithm was designed to calculate the optimal number of buses for each time slot, and for each day. This solution could improve transportation efficiency, …

Visualization Of Paleocurrents On A Web Application Using Gplates, Anjan Sapkota

MS in Computer Science Theses

Paleocurrents are flow directions derived from features of sedimentary rocks that reveal the direction of the current of wind or water that deposited the sediment. In 2015, Brand et al. created a global database of paleocurrents, which contains over 1,000,000 measurements worldwide: North America, South America, Australia, Great Britain, parts of Western Europe, China, Africa are fairly well represented; Antarctica, Eastern Europe, and Asia are modestly represented and Russia is poorly represented. The contribution of this thesis is a web application that uses the GPlates’ Application Programming Interface (API) to visualize global paleocurrents through time in an interactive way based …

Universal Shape Replication Via Self-Assembly With Signal-Passing Tiles, Andrew Alseth, Daniel Hader, Matthew J. Patitz

Computer Science and Computer Engineering Faculty Publications and Presentations

In this paper, we investigate shape-assembling power of a tile-based model of self-assembly called the Signal-Passing Tile Assembly Model (STAM). In this model, the glues that bind tiles together can be turned on and off by the binding actions of other glues via “signals”. Specifically, the problem we investigate is “shape replication” wherein, given a set of input assemblies of arbitrary shape, a system must construct an arbitrary number of assemblies with the same shapes and, with the exception of size-bounded junk assemblies that result from the process, no others. We provide the first fully universal shape replication result, namely …

Q-Learning In Starclash, Hanani Pankaj

2024 Fall Honors Capstone Projects - Archive

Developers create video games using Artificial Intelligence (AI) agents to provide a challenging opponent in a single-player game. However, studies show that when Reinforcement Learning (RL) agents are used, they outperform the AI agents. This project sought to test how RL agents would perform in StarClash, a video game without RL agents, using Q-Learning. This was done by creating two Q-Learning agents: a Simple agent and an Advanced (more complex) agent. These two agents were tested against each other and a Random AI agent. As expected, the Advanced agent did better than the Simple agent but only performed slightly better, …

Counting Catalan: An Experimental Evaluation Of The Mixing Time For The Triangulation Markov Chain, Roy Gotlieb

Master's Theses

Monte Carlo Markov chains (MCMCs) are used in many areas as a way to model a system’s behavior. By running a probabilistic simulation on a system’s state space, we can estimate properties of the system that could be untenable to directly compute. It is of interest to determine how quickly a Markov chain mixes\textemdash that is, settles into its stationary distribution. One such chain is induced by taking a binary search tree and performing a rotation or flip on one of its edges. We know that this chain eventually settles into the uniform distribution, but the time complexity bounds on …

Adan: Adaptive Nesterov Momentum Algorithm For Faster Optimizing Deep Models, Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that …

Harnessing Collective Structure Knowledge In Data Augmentation For Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are among the most commonly-used GNNs. However, a wealth of structural information of individual nodes and full graphs is often ignored in such process, which restricts the expressive power of GNNs. Various graph data augmentation methods that enable the message passing with richer structure knowledge have been introduced as one main way to tackle this issue, but they are often focused on individual structure features and difficult to scale up with …

Ohss: Optimizing Homomorphic Secret Sharing To Support Fast Matrix Multiplication, Shuguang Zhang, Jianli Bai, Kun Tu, Ziyue Yin, Chan Liu

Research Collection School Of Computing and Information Systems

Homomorphic Secret Sharing (HSS) has evolved as a state-of-the-art methodology for achieving secure two-party computation, synthesizing the advantages of secret sharing and homomorphic encryption. This amalgamation ensures minimal computational and communicational overhead, making it particularly adept at arithmetic operations. However, HSS faces challenges in scalability and efficiency when confronted with extensive matrix operations, including both matrix-vector and matrix-matrix multiplications, which are fundamental in numerous privacy-preserving computations, notably within the realm of privacy-preserving machine learning. In this research, we introduce Optimized Homomorphic Secret Sharing (OHSS), a refined version of HSS, crafted to address these limitations. Our contributions include enhancements to the …

Investigation Of Social Networks Upon Academic Performance And Mental Health, Rachel Izenson

Master's Theses

It has been shown that computing students have a statistically significantly lower overall sense of belongingness compared to other science students. A sense of community is important for many reasons. For example, there are studies that show that a student's sense of belonging correlates with improved academic performance. Our research aims to analyze the sense of belonging among computing students at Cal Poly San Luis Obispo through a network science lens. We surveyed for their sense of belonging, as well as their social network, to understand how friendships impact one's sense of belonging. When student responses were split by gender, …

Decoding Neural Networks: An Information-Theoretic Guide To Interpretability, Error Analysis And Efficiency, Mackenzie J. Meni

Theses and Dissertations

This dissertation addresses critical challenges in neural network design by leveraging entropy-based techniques to improve model efficiency, interpretability, and bias reduction. Focusing on the unique demands of computer vision applications, particularly object detection and classification for real-time systems, this work introduces a series of innovative methods centered on information theory. At the core of these methods is the Probabilistic Explanations of Entropic Knowledge (PEEK) framework, a tool developed to analyze and visualize entropy distributions across feature maps. PEEK offers insights into information flow within neural networks, making it possible to pinpoint layers that contribute meaningfully to decision-making or identify those …

Holistic Correlation Measure For Enhanced Encapsulation Of Trait Heterogeneity And Discovery Of Co-Expression, Zachary Valleroy

Theses

Large-scale, high-dimensional data analyses can be computationally prohibitive due to combinatorial explosion of the search space for finding complex patterns; a viable alternative is network modeling for abstraction and quantifying intrinsic data associations. Prominent network analysis methods furnish frameworks for model synthesis and validation but rely on standard correlation measures impaired by semi-supervised biases, latent heterogeneity, and uneven discretization techniques. Here we investigate a holistic measure for encapsulating data heterogeneity for enhanced efficacy of revealing complex patterns through network analysis. Our unique correlation metric, K-medoids Utility for Duo Original Similarities (Kudos), exhaustively factors real-valued analyte data to compute …

Competitive Conquest: Charting The Climb To Pokémon Supremacy, Robert Dilworth

BCoE Publications

This manuscript presents a comprehensive exploration of optimizing Pokémon gameplay through data-driven methodologies, aimed at enhancing competitive performance in high-stakes environments. In the first section, we introduce a robust Pokémon teambuilding algorithm that leverages statistical analysis of championship-winning compositions. By employing multiple linear regression techniques, we predict team performance based on critical factors such as Base Stat Totals (BSTs) and various coverage types. This integration of data science principles into Pokémon strategy underscores the importance of offensive capabilities over defensive considerations, ultimately contributing to advancements in teambuilding strategies. Our proficiency in R programming facilitated the development of an efficient codebase …

Irl For Restless Multi-Armed Bandits With Applications In Maternal And Child Health, Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe

Research Collection School Of Computing and Information Systems

Public health practitioners often have the goal of monitoring patients and maximizing patients’ time spent in “favorable” or healthy states while being constrained to using limited resources. Restless multi-armed bandits (RMAB) are an effective model to solve this problem as they are helpful to allocate limited resources among many agents under resource constraints, where patients behave differently depending on whether they are intervened on or not. However, RMABs assume the reward function is known. This is unrealistic in many public health settings because patients face unique challenges and it is impossible for a human to know who is most deserving …

Lr-Auth: Towards Practical Implementation Of Implicit User Authentication On Earbuds, Changshuo Hu, Xiao Ma, Xinger Huang, Yiran Shen, Dong Ma

Research Collection School Of Computing and Information Systems

The increasing use of earbuds in applications like immersive entertainment and health monitoring necessitates effective implicit user authentication systems to preserve the privacy of sensitive data and provide personalized experiences. Existing approaches, which leverage physiological cues (e.g., jawbone structure) and behavioral cues (e.g., gait), face challenges such as limited usability, high delay and energy overhead, and significant computational demands, rendering them impractical for resource-constrained earbuds. To address these issues, we present LR-Auth, a lightweight, user-friendly implicit authentication system designed for various earbud usage scenarios. LR-Auth utilizes the modulation of sound frequencies by the user's unique occluded ear canal, generating user-specific …