Physical Sciences and Mathematics Commons^™

Creating A Virtual Hierarchy From A Relational Database, Yucong Mo

All Graduate Theses and Dissertations, Fall 2023 to Present

In data management and modeling, the value of the hierarchical model is that it does not require expensive JOIN operations at runtime; once the hierarchy is built, the relationships among data are embedded in the tree-like hierarchical structure, and thus querying data could be much faster than using a relational database. Today most data is stored in relational databases, but if the data were stored in hierarchies, what would these hierarchies look like? And more importantly, would this transition lead to a more efficient database? This thesis explores these questions by introducing a set of algorithms to convert a relational …

Classification Of Major Solar Flares From Extremely Imbalanced Multivariate Time Series Data Using Minimally Random Convolutional Kernel Transform, Kartik Saini, Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Computer Science Faculty and Staff Publications

Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and are caused by the changes in magnetic field states in active solar regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares, ranging from electronic communication disruption to radiation exposure-based health risks to astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MiniRocket), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations Sequence …

Inferring A Hierarchical Input Type For An Sql Query, Santosh Aryal

All Graduate Theses and Dissertations, Fall 2023 to Present

SQL queries are a common method to retrieve information from databases, much like asking a detailed question and getting a precise answer. Plug-and-play queries simplify the process of querying. In a Plug-and-play SQL query a programmer sketches the shape of the input to the query as a hierarchy. But the programmer could make a mistake in specifying the hierarchy and it takes programmer time and effort to specify the hierarchy. A better solution is to automatically infer the hierarchy from a query. This thesis presents a system to infer a hierarchical input type for an SQL query. We consider two …

A Framework That Explores The Cognitive Load Of Cs1 Assignments Using Pausing Behavior, Joshua O. Urry

All Graduate Theses and Dissertations, Fall 2023 to Present

Pausing behavior in introductory Computer Science (CS1) courses has been related to a student’s performance in the course and could be linked to a student’s cognitive load, or assignment difficulty. Having an objective measure of the cognitive load would be beneficial to course instructors as it would help them design assignments that are not too difficult. Two studies are presented in this work. The first study uses Cognitive Load Theory and Vygotsky’s Zone of Proximal Development as a theoretical framework to analyze pause times between keystrokes to better understand what types of assignments need more educational support than others. The …

Enhancing Monthly Streamflow Prediction Using Meteorological Factors And Machine Learning Models In The Upper Colorado River Basin, Saichand Thota, Ayman Nassar, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh

Computer Science Student Research

Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using …

Achieving Responsible Anomaly Detection, Xiao Han

All Graduate Theses and Dissertations, Fall 2023 to Present

In the digital transformation era, safeguarding online systems against anomalies – unusual patterns indicating potential threats or malfunctions – has become crucial. This dissertation embarks on enhancing the accuracy, explainability, and ethical integrity of anomaly detection systems. By integrating advanced machine learning techniques, it improves anomaly detection performance and incorporates fairness and explainability at its core.

The research tackles performance enhancement in anomaly detection by leveraging few-shot learning, demonstrating how systems can effectively identify anomalies with minimal training data. This approach overcomes data scarcity challenges. Reinforcement learning is employed to iteratively refine models, enhancing decision-making processes. Transfer learning enables the …

A Review Of Student Attitudes Towards Keystroke Logging And Plagiarism Detection In Introductory Computer Science Courses, Caleb Syndergaard

All Graduate Theses and Dissertations, Fall 2023 to Present

The following paper addresses student attitudes towards keystroke logging and plagiarism prevention measures. Specifically, the paper concerns itself with changes made to the “ShowYourWork” plugin, which was implemented to log the keystrokes of students in Utah State University’s introductory Computer Science course, CS1400. Recent work performed by the Edwards Lab provided insights into students’ feelings towards keystroke logging as a measure of deterring plagiarism. As a result of that research, we have concluded that measures need to be taken to enable students to have more control over their data and assist students to feel more comfortable with keystroke logging. This …

Advancing Game Development And Ai Integration: An Extensible Game Engine With Integrated Ai Support For Real-World Deployment And Efficient Model Development, Ryan Anderson

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis introduces Acacia, a game engine with built-in artificial intelligence (AI) capabilities. Acacia allows game developers to effortlessly incorporate Reinforcement Learning (RL) algorithms into their creations. By tagging game elements to convey information about the game state or rewards, developers gain precise control over how RL algorithms interact with their games, mirroring real player behavior or providing full knowledge of the game world.

To showcase Acacia’s versatility, the thesis presents three games across different genres, each demonstrating the engine’s AI plugin. The goal is to establish Acacia as a preferred resource for creating 2D games with RL support without …

Empowering Graphics: A Distributed Rendering Architecture For Inclusive Access To Modern Gpu Capabilities, Taylor Anderson

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern rendering software requires powerful GPUs with the latest hardware features in order to utilize all of the newest rendering techniques. Many users do not have access to this hardware, and rely on remote server farms or reduced performance to achieve usable results. In this thesis, the software is designed and created to allow for a user to share the resources of their computer with another, modeling a split-screen setup like was common in the past, but without requiring users to be in the same location.

By designing the software from the ground up to support this, instead of adding …

Pedestrian Pathing Prediction Using Complex Contextual Behavioral Data In High Foot Traffic Settings, Laurel Bingham

All Graduate Theses and Dissertations, Fall 2023 to Present

Ensuring the safe integration of autonomous vehicles into real-world environments requires a comprehensive understanding of pedestrian behavior. This study addresses the challenge of predicting the movement and crossing intentions of pedestrians, a crucial aspect in the development of fully autonomous vehicles.

The research focuses on leveraging Honda's TITAN dataset, comprising 700 unique clips captured by moving vehicles in high-foot-traffic areas of Tokyo, Japan. Each clip provides detailed contextual information, including human-labeled tags for individuals and vehicles, encompassing attributes such as age, motion status, and communicative actions. Long Short-Term Memory (LSTM) networks were employed and trained on various combinations of contextual …

Generative Ai In Education From The Perspective Of Students, Educators, And Administrators, Aashish Ghimire

All Graduate Theses and Dissertations, Fall 2023 to Present

This research explores how advanced artificial intelligence (AI), like the technology that powers tools such as ChatGPT, is changing the way we teach and learn in schools and universities. Imagine AI helping to summarize thick legal documents into something you can read over a coffee break or helping students learn how to code by offering personalized guidance. We looked into how teachers feel about using these AI tools in their classrooms, what kind of rules schools have about them, and how they can make learning programming easier for students. We found that most teachers are excited about the possibilities but …

Decentralized Unknown Building Exploration By Frontier Incentivization And Voronoi Segmentation In A Communication Restricted Domain, Huzeyfe M. Kocabas

All Graduate Theses and Dissertations, Fall 2023 to Present

Exploring unknown environments using multiple robots poses a complex challenge, particularly in situations where communication between robots is either impossible or limited. Existing exploration techniques exhibit research gaps due to unrealistic communication assumptions or the computational complexities associated with exploration strategies in unfamiliar domains. In our investigation of multi-robot exploration in unknown areas, we employed various exploration and coordination techniques, evaluating their performance in terms of robustness and efficiency across different levels of environmental complexity.

Our research is centered on optimizing the exploration process through strategic agent distribution. We initially address the challenge of city roadway coverage, aiming to minimize …

Combining Empirical And Physics-Based Models For Solar Wind Prediction, Rob Johnson, Soukaina Filali Boubrahimi, Omar Bahri, Shah Muhammad Hamdi

Computer Science Faculty and Staff Publications

Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to …

Exploring Practical Measures As An Approach For Measuring Elementary Students’ Attitudes Towards Computer Science, Umar Shehzad, Mimi M. Recker, Jody E. Clarke-Midura

Publications

This paper presents a novel approach for predicting the outcomes of elementary students’ participation in computer science (CS) instruction by using exit tickets, a type of practical measure, where students provide rapid feedback on their instructional experiences. Such feedback can help teachers to inform ongoing teaching and instructional practices. We fit a Structural Equation Model to examine whether students' perceptions of enjoyment, ease, and connections between mathematics and CS in an integrated lesson predicted their affective outcomes in self-efficacy, interest, and CS identity, collected in a pre- post- survey. We found that practical measures can validly measure student experiences.

Anomaly Detection On Small Wind Turbine Blades Using Deep Learning Algorithms, Bridger Altice, Edwin Nazario, Mason Davis, Mohammad Shekaramiz, Todd K. Moon, Mohammad A. S. Masoum

Electrical and Computer Engineering Faculty Publications

Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and …

Facilitating Mathematics And Computer Science Connections: A Cross-Curricular Approach, Kimberly E. Beck, Jessica F. Shumway, Umar Shehzad, Jody Clarke-Midura, Mimi Recker

Publications

In the United States, school curricula are often created and taught with distinct boundaries between disciplines. This division between curricular areas may serve as a hindrance to students' long-term learning and their ability to generalize. In contrast, cross-curricular pedagogy provides a way for students to think beyond the classroom walls and make important connections across disciplines. The purpose of this paper is a theoretical reflection on our use of Expansive Framing in our design of lessons across learning environments within the school. We provide a narrative account of our early work in using this theoretical framework to co-plan and enact …

Solar Flare Prediction From Extremely Imbalanced Multivariate Time Series Data Using Minimally Random Convolutional Kernel Transform, Kartik Saini

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun's surface, and caused by the changes in magnetic field states in solar active regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares ranging from electronic communication disruption to radiation exposure-based health risks to the astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MINIROCKET), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations SEQuence …

Deep Learning With Effective Hierarchical Attention Mechanisms In Perception Of Autonomous Vehicles, Qiuxiao Chen

All Graduate Theses and Dissertations, Fall 2023 to Present

Autonomous vehicles need to gather and understand information from their surroundings to drive safely. Just like how we look around and understand what's happening on the road, these vehicles need to see and make sense of dynamic objects like other cars, pedestrians, and cyclists, and static objects like crosswalks, road barriers, and stop lines.

In this dissertation, we aim to figure out better ways for computers to understand their surroundings in the 3D object detection task and map segmentation task. The 3D object detection task automatically spots objects in 3D (like cars or cyclists) and the map segmentation task automatically …

Advanced Caching And Streaming For Large Scale Point Cloud Data Visualization On The Web, Pravin Poudel

All Graduate Theses and Dissertations, Fall 2023 to Present

Point clouds are widely used in various applications such as 3D modeling, geospatial analysis, robotics, and more. One of the key advantages of 3D point cloud data is that, unlike other data formats like texture, it is independent of viewing angle, surface type, and parameterization. Since each point in the point cloud is independent of the other, it makes it the most suitable source of data for tasks like object recognition, scene segmentation, and reconstruction. Point clouds are complex and verbose due to the numerous attributes they contain, many of which may not be always necessary for rendering, making retrieving …

Collaborative Task Completion For Simulated Hexapod Robots Using Reinforcement Learning, Tayler Don Baker

All Graduate Theses and Dissertations, Fall 2023 to Present

There is growing interest in developing autonomous systems capable of exhibiting collaborative behaviors. Using methods such as reinforcement learning is another way to train multiple robots for collaborative task completion. This study was able to successfully in simulation train multiple hexapod robots to push a target to a designated goal collaboratively. This required each robot to learn how find the target and push that target to a goal. This work suggests that using reinforcement learning for collaborative task completion for hexapod robots may simplify the complexity of the software and improve the decisions that they make.

Optimal Stopping Of Multi-Robot Exploration For Unknown, Bounded Environments, Trey D. Crowther

All Graduate Theses and Dissertations, Fall 2023 to Present

Limited resources and uncertainty pose a substantial problem for multi-robot exploration of unknown environments. This research paper looks to determine the optimal time to terminate robot exploration while maximizing information gathered. Whilst making this determination, the system's resources and capabilities must be taken into account. To see if our strategy works, we ran many simulations in varying environments. The results of this research are important for real-world uses like robot exploration, search and rescue missions, and automated surveillance. Determining when to stop exploring can help the system save resources, explore faster, and make better decisions.

Analysis Of Student Behavior And Score Prediction In Assistments Online Learning, Aswani Yaramala

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding and analyzing student behavior is paramount in enhancing online learning, and this thesis delves into the subject by presenting an in-depth analysis of student behavior and score prediction in the ASSISTments online learning platform. We used data from the EDM Cup 2023 Kaggle Competition to answer four key questions. First, we explored how students seeking hints and explanations affect their performance in assignments, shedding light on the role of guidance in learning. Second, we looked at the connection between students mastering specific skills and their performance in related assignments, giving insights into the effectiveness of curriculum alignment. Third, we …

Adversarially Reweighted Sequence Anomaly Detection With Limited Log Data, Kevin Vulcano

All Graduate Theses and Dissertations, Fall 2023 to Present

In the realm of safeguarding digital systems, the ability to detect anomalies in log sequences is paramount, with applications spanning cybersecurity, network surveillance, and financial transaction monitoring. This thesis presents AdvSVDD, a sophisticated deep learning model designed for sequence anomaly detection. Built upon the foundation of Deep Support Vector Data Description (Deep SVDD), AdvSVDD stands out by incorporating Adversarial Reweighted Learning (ARL) to enhance its performance, particularly when confronted with limited training data. By leveraging the Deep SVDD technique to map normal log sequences into a hypersphere and harnessing the amplification effects of Adversarial Reweighted Learning, AdvSVDD demonstrates remarkable efficacy …

On The Computability Of Primitive Recursive Functions By Feedforward Artificial Neural Networks, Vladimir A. Kulyukin

Computer Science Faculty and Staff Publications

We show that, for a primitive recursive function h(x, t), where x is a n-tuple of natural numbers and t is a natural number, there exists a feedforward artificial neural network 𝔑(x, t), such that for any n-tuple of natural numbers z and a positive natural number m, the first m + 1 terms of the sequence {h(z, t)} are the same as the terms of the tuple (𝔑(z, 0), ... ,𝔑(z, m)).

Contemporary Art Authentication With Large-Scale Classification, Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho, Zbigniew Ras

Computer Science Faculty and Staff Publications

Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible …

A Novel Fuzzy Relative-Position-Coding Transformer For Breast Cancer Diagnosis Using Ultrasonography, Yanhui Guo, Ruquan Jiang, Xin Gu, Heng-Da Cheng, Harish Garg

Computer Science Faculty and Staff Publications

Breast cancer is a leading cause of death in women worldwide, and early detection is crucial for successful treatment. Computer-aided diagnosis (CAD) systems have been developed to assist doctors in identifying breast cancer on ultrasound images. In this paper, we propose a novel fuzzy relative-position-coding (FRPC) Transformer to classify breast ultrasound (BUS) images for breast cancer diagnosis. The proposed FRPC Transformer utilizes the self-attention mechanism of Transformer networks combined with fuzzy relative-position-coding to capture global and local features of the BUS images. The performance of the proposed method is evaluated on one benchmark dataset and compared with those obtained by …

A Neural-Network-Based Landscape Search Engine: Lse Wisconsin, Matthew Haffner, Matthew Dewitte, Papia F. Rozario, Gustavo A. Ovando-Montejo

Environment and Society Faculty Publications

The task of image retrieval is common in the world of data science and deep learning, but it has received less attention in the field of remote sensing. The authors seek to fill this gap in research through the presentation of a web-based landscape search engine for the US state of Wisconsin. The application allows users to select a location on the map and to find similar locations based on terrain and vegetation characteristics. It utilizes three neural network models—VGG16, ResNet-50, and NasNet—on digital elevation model data, and uses the NDVI mean and standard deviation for comparing vegetation data. The …

Physics-Guided Deep Learning For Solar Wind Modeling At L1 Point, Robert M. Johnson

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Neural networks are adept at finding patterns that are too long and too small for humans to find in data. Usually, this power is used to generate predictions with greater accuracy than most alternative models. However, we can also use this power to understand more about the data we train these networks on. We do this by changing the data that the networks train on and the data they are tested on. This allows us to both control the maximum length of a pattern and to compare data between different groups, in our case, different solar cycles. This thesis is …

Proxy Voting Coordination Mechanisms: Determining How Agents Should Coordinate In A Continuous Preference Space, Michael D. Hegerhorst

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Illness, injury, and other impediments are common occurrences of everyday life. Such impediments prevent or deter voters from participating in important parts of the voting process, especially deliberation, bargaining, and the voting itself. Without participation, the results of the vote may change. There is a need to provide a system in which voters are still able to participate in important voting processes to ensure their vote is represented. We explore ‘proxy voting,’ a system in which voters are able to select another individual, or proxy, to vote on their behalf. By choosing a good proxy, a voter can still …

Comparative Study Of Clustering Techniques On Eye-Tracking In Dynamic 3d Virtual Environments, Scott Johnson

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Eye-tracking has been used for decades to understand how and why an individual focuses on particular objects, areas, and elements of space. A vast body of knowledge exists on how eye-tracking is measured. However, historically, eye-tracking has been predominately studied using 2D environments, with limited work in 3D environments. The purpose of this study is to identify which methods most accurately represent the areas that have captured the participant’s visual attention within a 3D dynamic environment. This will be completed by evaluating different clustering methods of fixations using a customized virtual reality tool that collects eye-tracking data. There exist several …