Other Computer Sciences Commons^™

Adaptive Neh With Constrained Nearest Neighbor Subtours For The Electric Vehicle Routing Problem With Time Windows, Andrew Struthers

All Master's Theses

The development of electric vehicles is currently considered one of the most innovative areas in manufacturing. Largely driven by the desire to reduce greenhouse emissions, electric vehicles are seen as a viable alternative to internal combustion engine cars. Starting from consumer cars, a dedicated effort is being made to translate this into commercial vehicles for freight and delivery. This research introduces a novel adaptive Nawaz, Enscore, Ham (NEH) algorithm with constrained nearest neighbor subtour (NEH-NN). This algorithm is tested on the standard benchmark problems in literature and used as a seed solution for the Genetic Algorithm (GA). The performance and …

A Self-Supervised Knowledge Distillation Approach To Anomaly Detection In X-Ray Imagery, Kaden Quinn Mceldowney

Graduate Theses, Dissertations, and Problem Reports (ETD)

Many cargo containers enter the United States every day by truck, rail, and sea. As a result of the large number of cargo containers entering the United States, not all of them can be thoroughly inspected. Most of these containers contain properly documented and legal cargo, but some people take advantage of this situation by hiding illicit items in the cargo containers such as drugs. To more efficiently and thoroughly inspect cargo containers, Customs and Border Protection (CBP) uses X-ray imaging machines to obtain images that reveal the interior of cargo containers. These X-ray images must be inspected to ensure …

Improving The Robustness Of Neural Networks To Adversarial Patch Attacks Using Masking And Attribution Analysis, Atandra Mahalder

Honors Undergraduate Theses

Computer vision algorithms, including image classifiers and object detectors, play a pivotal role in various cyber-physical systems, spanning from facial recognition to self-driving vehicles and security surveillance. However, the emergence of real-world adversarial patches, which can be as simple as stickers, poses a significant threat to the reliability of AI models utilized within these systems. To address this challenge, several defense mechanisms such as PatchGuard, Minority Report, and (De)Randomized Smoothing have been proposed to enhance the resilience of AI models against such attacks. In this thesis, we introduce a novel framework that integrates masking with attribution analysis to robustify AI …

Weed Seed Wizard Scenario - Dormancy Shift In Barley Grass In Balaklava, South Australia, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity research reports

The Weed Seed Wizard is a national collaborative project that uses paddock management information to predict weed emergence and crop losses now and in the future.

The Weed Seed Wizard is a computer simulation tool that:

• applies to all Australian grain growing areas

• helps growers understand and manage weed seedbanks on their farms

• uses farm management records to simulate how different crop rotations, weed control techniques, irrigation, grazing and harvest management tactics can affect weed numbers, the weed seedbank and yields

• uses farm-specific management and site-specific weather

• is multi-species

See www.dpird.wa.gov.au for further information on Weed Seed Wizard.

This South …

Weed Seed Wizard Case Study - Grower From Western Australia’S Central Wheatbelt, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity research reports

The Weed Seed Wizard is a national collaborative project that uses paddock management information to predict weed emergence and crop losses now and in the future.

The Weed Seed Wizard is a computer simulation tool that:

• applies to all Australian grain growing areas

• helps growers understand and manage weed seedbanks on their farms

• uses farm management records to simulate how different crop rotations, weed control techniques, irrigation, grazing and harvest management tactics can affect weed numbers, the weed seedbank and yields

• uses farm-specific management and site-specific weather

• is multi-species

See www.dpird.wa.gov.au for further information on Weed Seed Wizard.

This case …

Weed Seed Wizard Scenario - Herbicide Resistance In Wild Oats In Wagga Wagga, New South Wales, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity research reports

The Weed Seed Wizard is a national collaborative project that uses paddock management information to predict weed emergence and crop losses now and in the future.

The Weed Seed Wizard is a computer simulation tool that:

• applies to all Australian grain growing areas

• helps growers understand and manage weed seedbanks on their farms

• uses farm management records to simulate how different crop rotations, weed control techniques, irrigation, grazing and harvest management tactics can affect weed numbers, the weed seedbank and yields

• uses farm-specific management and site-specific weather

• is multi-species

See www.dpird.wa.gov.au for further information on Weed Seed Wizard.

This New …

Recovering Access Control Via Disk Forensics On Low-Level Flash Memory, Caleb J. Rother

Dissertations, Master's Theses and Master's Reports

In the history of access control, nearly every system designed has relied on the operating system (OS) to enforce the access control protocols. However, if the OS (and specifically root access) is compromised, there are few if any solutions that can get users back into their system efficiently. In this work, we have proposed a novel approach that allows secure and efficient rollback of file access control after an adversary compromises the OS and corrupts the access control metadata. Our key observation is that the underlying flash memory typically performs out-of-place updates. Taking advantage of this unique feature, we can …

Machine-Learning-Assisted Design Of Deep Eutectic Solvents Based On Uncovered Hydrogen Bond Patterns, Usman Lame Abbas, Yuxuan Zhang, Joseph Tapia, Md Selim, Jin Chen, Jian Shi, Qing Shao

Markey Cancer Center Faculty Publications

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportuni- ties. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the …

Investigating Efficiency Of Free-For-All Models In A Matchmaking Context, Emil Gensby, Bryan S. Weber, Anders H. Christiansen

Publications and Research

We explore several popular (and unpopular) systems for matchmaking and ranking in free-for-all (FFA) environments. The commonplace existing methods involve the reinterpretation of established two-player ranking systems (ie. Elo/Glicko) and decomposing multiplayer games into a set of multiple one-vs-one pairings. This decomposition, while commonplace, is not part of the intended use-case of these two-player ranking systems. We are the first to formally explore this ad-hoc usage and reassuringly find evidence that it converges to correct values. Second, we identify a method that appears to dominate what appears to be the most common publicly used method. At the same time, this …

Multi-Activity Student Knowledge And Behavior Modeling Via Transfer Learning, Siqian Zhao

Electronic Theses & Dissertations (2024 - present)

Online education systems have grown in popularity over the past few years, providing abundant opportunities for students to learn. As the number of students using these systems grows, it promotes the development of the Educational Data Mining (EDM) field, which leverages statistical, machine learning, and data mining technologies to explore large-scale educational data and develop methods to better understand student learning.

In this dissertation, we investigate two essential topics in EDM: Student Knowledge Tracing (KT) and Behavior Modeling (BM). KT aims to quantify and model student knowledge gained from learning activities, while BM focuses on tasks such as modeling student …

Towards A Concurrency Platform For Scalable Multi-Axial Real-Time Hybrid Simulation, Marion Sudvarg, Oren Bell, Tyler Martin, Benjamin Standaert, Tao Zhang, Sun Beom Kwon, Chris Gill, Arun Prakash

Computer Science Faculty Research & Creative Works

Multi-axial real-time hybrid simulation (maRTHS) uses multiple hydraulic actuators to apply loads and deform experimental substructures, enacting both translational and rotational motion. This allows for an increased level of realism in seismic testing. However, this also demands the implementation of multiple-input, multiple-output control strategies with complex nonlinear behaviors. To realize true real-time hybrid simulation at the necessary sub-millisecond timescales, computational platforms will need to support these complexities at scale, while still providing deadline assurance. This paper presents initial work towards supporting (and is influenced by the need for) envisioned larger-scale future experiments based on the current maRTHS benchmark: it discusses …

Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts, Thomas Pany, Dennis Akos, Javier Arribas, M. Zahidul H. Bhuiyan, Pau Closas, Fabio Dovis, Ignacio Fernandez-Hernandez, Carles Fernandez-Prades, Sanjeev Gunawardena, Todd Humphreys, Zaher M. Kassas, Jose A. Lopez Salcedo, Mario Nicola, Mario L. Psiaki, Alexander Rugamer, Yong-Jin Song, Jong-Hoon Won

Faculty Publications

Taking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR development. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on …

A Virtual Time Driven Simulator For Devs Models, Ronald R. Stempien

Dissertations, Master's Theses and Master's Reports

When simulating a system that includes some software component, simulation authors are faced with the problem of how to appropriately model the software within the simulation. While many formal methods for modeling software exist, in some contexts these may not be appropriate or viable for a given simulation. Instead, simulation authors may model a computer within the simulation, and run the software in question “as is” on the modeled machine. In this work, we introduce a theoretical framework to allow for the use of hardware virtualization technologies as a hardware accelerator for CPU models in Discrete Event System Specification (DEVS) …

Bias-Aware Gaze Uniformity Assessment In Group Images, Omkar Kulkarni

Electronic Theses & Dissertations (2024 - present)

Today, more than 5 billion photos are captured every day, with smartphones generating over 94\% of these images. However, despite advancements in technology, achieving aesthetically pleasing group photos remains challenging, especially when it comes to aligning the direction of everyone’s gaze. While current methods focus on facial features, they often fail to ensure consistent gaze direction. The introduction of the iPhone's Live mode, which captures a 1.5-second video snippet along with still images, complicates the selection of the best key photo due to its subjective nature and a lack of publicly available data, especially during the pandemic.

To address these …

Computational Study Of The Effect Of Geometry On Molecular Interactions, Sarika Kumar

Computer Science ETDs

The specificity and predictability of DNA make it an excellent programmable material and have allowed bio-programmers to build sophisticated molecular circuits. These molecular devices should be precise, correct, and function as intended. In order to implement these circuits, the challenge is to build a robust, reliable, and scalable logic circuit with ideally minimum unwanted signal release. Performing experiments are expensive and time-consuming, so modeling and analyzing these bio-molecular systems become crucial in designing molecular circuits. This dissertation aimed to develop algorithms and build computational tools for automated analysis of molecular circuits that incorporate the molecular geometry of nanostructures. Molecular circuits …

Ascot App, Milla Penelope Markovic

Honors Thesis

The Ascot App is a research tool for acquiring and analyzing data. The app comprises of both mobile and web platforms, each serving a unique purpose. The mobile side allows users to input data through the app’s form, which is uploaded to a database for further processing and analysis. The web app, which is still under development as of April of 2024, allows users to manage their research project and download data in the form of a parsed CSV. These components ensure a seamless process for research teams to record data with persistence and security while allowing for analysis.

Ascot …

Movie Recommendation System Using Content Based Filtering, Sribhashyam Rakesh

Al-Bahir

The movie recommendation system plays a crucial role in assisting movie enthusiasts in finding movies that match their interests, saving them from the overwhelming task of sifting through countless options. In this paper, we present a content-grounded movie recommendation system that leverages an attribute-based approach to offer personalized movie suggestions to users. The proposed method focuses on attributes such as cast, keywords, crew, and genres of movies to predict users' preferences accurately. Through extensive evaluation, our content-grounded recommendation system demonstrated significant improvements in performance compared to conventional methods. The precision and recall scores increased by an average of 20% and …

Overcoming Foreign Language Anxiety In An Emotionally Intelligent Tutoring System, Daneih Ismail

College of Computing and Digital Media Dissertations

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning.

Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct …

Adaptable Object And Animation System For Game Development, Isaiah Turner

Masters Theses & Specialist Projects

In contemporary times, video games have swiftly evolved into a prominent medium, excelling in both entertainment and narrative delivery, positioning themselves as significant rivals to traditional forms such as film and theater. The burgeoning popularity of gaming has led to a surge in aspiring game developers seeking to craft their own creations, driven by both commercial aspirations and personal passion. However, a common challenge faced by these individuals involves the considerable time investment required to acquire essential skills and establish a foundational framework for their projects. Accessible game development engines that offer a diverse range of fundamental features play a …

Rising Threat - Deepfakes And National Security In The Age Of Digital Deception, Dougo Kone-Sow

Cybersecurity Undergraduate Research Showcase

This paper delves into the intricate landscape of deepfakes, exploring their genesis, capabilities, and far-reaching implications. The rise of deepfake technology presents an unprecedented threat to American national security, propagating disinformation and manipulation across various media formats. Notably, deepfakes have evolved from a historical backdrop of disinformation campaigns, merging with the advancements of artificial intelligence (AI) and machine learning to craft convincing but false multimedia content.

Examining the capabilities of deepfakes reveals their potential for misuse, evidenced by instances targeting individuals, companies, and even influencing political events like the 2020 U.S. elections. The paper highlights the direct threats posed by …