Digital Commons Network^™

Semantic Segmentation Of Point Cloud Sequences Using Point Transformer V3, Marion Sisk

Master's Theses

Semantic segmentation of point clouds is a basic step for many autonomous systems including automobiles. In autonomous driving systems, LiDAR sensors are frequently used to produce point cloud sequences that allow the system to perceive the environment and navigate safely. Modern machine learning techniques for segmentation have predominately focused on single-scan segmentation, however sequence segmentation has often proven to perform better on common segmentation metrics. Using the popular Semantic KITTI dataset, we show that by providing point cloud sequences to a segmentation pipeline based on Point Transformer v3, we increase the segmentation performance between seven and fifteen percent when compared …

A Study On Ethical Hacking In Cybersecurity Education Within The United States, Jordan Chew

Master's Theses

As the field of computer security continues to grow, it becomes increasingly important to educate the next generation of security professionals. However, much of the current education landscape primarily focuses on teaching defensive skills. Teaching offensive security, otherwise known as ethical hacking, is an important component in the education of all students who hope to contribute to the field of cybersecurity. Doing so requires a careful consideration of what ethical, legal, and practical issues arise from teaching students skills that can be used to cause harm. In this thesis, we first examine the current state of cybersecurity education in the …

Improving Automatic Transcription Using Natural Language Processing, Anna Kiefer

Master's Theses

Digital Democracy is a CalMatters and California Polytechnic State University initia-
tive to promote transparency in state government by increasing access to the Califor-
nia legislature. While Digital Democracy is made up of many resources, one founda-
tional step of the project is obtaining accurate, timely transcripts of California Senate
and Assembly hearings. The information extracted from these transcripts provides
crucial data for subsequent steps in the pipeline. In the context of Digital Democracy,
upleveling is when humans verify, correct, and annotate the transcript results after
the legislative hearings have been automatically transcribed. The upleveling process
is done with the …

Foundations Of Memory Capacity In Models Of Neural Cognition, Chandradeep Chowdhury

Master's Theses

A central problem in neuroscience is to understand how memories are formed as a result of the activities of neurons. Valiant’s neuroidal model attempted to address this question by modeling the brain as a random graph and memories as subgraphs within that graph. However the question of memory capacity within that model has not been explored: how many memories can the brain hold? Valiant introduced the concept of interference between memories as the defining factor for capacity; excessive interference signals the model has reached capacity. Since then, exploration of capacity has been limited, but recent investigations have delved into the …

Smartphone Based Object Detection For Shark Spotting, Darrick W. Oliver

Master's Theses

Given concern over shark attacks in coastal regions, the recent use of unmanned aerial vehicles (UAVs), or drones, has increased to ensure the safety of beachgoers. However, much of city officials' process remains manual, with drone operation and review of footage still playing a significant role. In pursuit of a more automated solution, researchers have turned to the usage of neural networks to perform detection of sharks and other marine life. For on-device solutions, this has historically required assembling individual hardware components to form an embedded system to utilize the machine learning model. This means that the camera, neural processing …

Predicting Location And Training Effectiveness (Plate), Erik Rolf Bruenner

Master's Theses

Abstract Predicting Location and Training Effectiveness (PLATE)
Erik Bruenner

Physical activity and exercise have been shown to have an enormous impact on many areas of human health and can reduce the risk of many chronic diseases. In order to better understand how exercise may affect the body, current kinesiology studies are designed to track human movements over large intervals of time. Procedures used in these studies provide a way for researchers to quantify an individual’s activity level over time, along with tracking various types of activities that individuals may engage in. Movement data of research subjects is often collected through …

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An Empirical Evaluation Of Neural Process Meta-Learners For Financial Forecasting, Kevin G. Patel

Master's Theses

Challenges of financial forecasting, such as a dearth of independent samples and non- stationary underlying process, limit the relevance of conventional machine learning towards financial forecasting. Meta-learning approaches alleviate some of these is- sues by allowing the model to generalize across unrelated or loosely related tasks with few observations per task. The neural process family achieves this by con- ditioning forecasts based on a supplied context set at test time. Despite promise, meta-learning approaches remain underutilized in finance. To our knowledge, ours is the first application of neural processes to realized volatility (RV) forecasting and financial forecasting in general.

We …

A Novel Approach To Extending Music Using Latent Diffusion, Keon Roohparvar, Franz J. Kurfess

Master's Theses

Using deep learning to synthetically generate music is a research domain that has gained more attention from the public in the past few years. A subproblem of music generation is music extension, or the task of taking existing music and extending it. This work proposes the Continuer Pipeline, a novel technique that uses deep learning to take music and extend it in 5 second increments. It does this by treating the musical generation process as an image generation problem; we utilize latent diffusion models (LDMs) to generate spectrograms, which are image representations of music. The Continuer Pipeline is able to …

Deep Learning Recommendations For The Acl2 Interactive Theorem Prover, Robert K. Thompson, Robert K. Thompson

Master's Theses

Due to the difficulty of obtaining formal proofs, there is increasing interest in partially or completely automating proof search in interactive theorem provers. Despite being a theorem prover with an active community and plentiful corpus of 170,000+ theorems, no deep learning system currently exists to help automate theorem proving in ACL2. We have developed a machine learning system that generates recommendations to automatically complete proofs. We show that our system benefits from the copy mechanism introduced in the context of program repair. We make our system directly accessible from within ACL2 and use this interface to evaluate our system in …

Neural Tabula Rasa: Foundations For Realistic Memories And Learning, Patrick R. Perrine

Master's Theses

Understanding how neural systems perform memorization and inductive learning tasks are of key interest in the field of computational neuroscience. Similarly, inductive learning tasks are the focus within the field of machine learning, which has seen rapid growth and innovation utilizing feedforward neural networks. However, there have also been concerns regarding the precipitous nature of such efforts, specifically in the area of deep learning. As a result, we revisit the foundation of the artificial neural network to better incorporate current knowledge of the brain from computational neuroscience. More specifically, a random graph was chosen to model a neural system. This …

Understanding The Impacts Of Topobathymetric Data On Storm Surge Model Predictions, Sydni Crain

Master's Theses

The topobathymetric characteristics of a region are regularly altered by natural and anthropogenic causes. This directly impacts the resulting storm surge during a hurricane. The primary goal of this research was to gain a better understanding of the impact that topography and bathymetry have on storm surge models, particularly the Advanced Circulation (ADCIRC) Model. Hurricane Zeta (2020) and Hurricane Ida (2021) were chosen as case studies; therefore, the Gulf of Mexico (GOM) was chosen as the study site. This research was completed by comparing ADCIRC storm surge results which were based on older, lower-resolution data with results derived from more …

Rattus Norvegicus As A Biological Detector Of Clandestine Remains And The Use Of Ultrasonic Vocalizations As A Locating Mechanism, Gabrielle M. Johnston

Master's Theses

In investigations, locating missing persons and clandestine remains are imperative. One way that first responder and police agencies can search for the remains is by using cadaver dogs as biological detectors. Cadaver dogs are typically used due to their olfactory sensitivity and ability to detect low concentrations of volatile organic compounds produced by biological remains. Cadaver dogs are typically chosen for their stamina, agility, and olfactory sensitivity. However, what is not taken into account often is the size of the animal and the expense of maintaining and training the animal. Cadaver dogs are typically large breeds that cannot fit in …

Trace Dna Detection Using Diamond Dye: A Recovery Technique To Yield More Dna, Leah Davis

Master's Theses

This study aspires to find a new screening approach to trace DNA recovery techniques to yield a higher quantity of trace DNA from larger items of evidence. It takes the path of visualizing trace DNA on items of evidence with potential DNA so analysts can swab a more localized area rather than attempting to recover trace DNA through the general swabbing technique currently used for trace DNA recovery. The first and second parts consisted of observing trace DNA interaction with Diamond Dye on porous and non-porous surfaces.

The third part involved applying the Diamond Dye solution by spraying it onto …

Modeling Daily Fantasy Basketball, Martin Jiang

Master's Theses

Daily fantasy basketball presents interesting problems to researchers due to the extensive amounts of data that needs to be explored when trying to predict player performance. A large amount of this data can be noisy due to the variance within the sport of basketball. Because of this, a high degree of skill is required to consistently win in daily fantasy basketball contests. On any given day, users are challenged to predict how players will perform and create a lineup of the eight best players under fixed salary and positional requirements. In this thesis, we present a tool to assist daily …

Psf Sampling In Fluorescence Image Deconvolution, Eric A. Inman

Master's Theses

All microscope imaging is largely affected by inherent resolution limitations because of out-of-focus light and diffraction effects. The traditional approach to restoring the image resolution is to use a deconvolution algorithm to “invert” the effect of convolving the volume with the point spread function. However, these algorithms fall short in several areas such as noise amplification and stopping criterion. In this paper, we try to reconstruct an explicit volumetric representation of the fluorescence density in the sample and fit a neural network to the target z-stack to properly minimize a reconstruction cost function for an optimal result. Additionally, we do …

Post Pandemic Cyberbiosecurity Threats From Terrorist Groups, Haley D. Dodge

Master's Theses

The research in this thesis explored the research question: Are United States (US) health systems accessible to cyber-bio terrorist attacks post-pandemic, within the context of the emerging discipline of cyberbiosecurity? Key findings of the analysis demonstrated how US health systems are more accessible to cyber-bio terrorist attacks specifically from cyber hacking groups based on the increasing sophistication of their cyber capabilities and the lack of cyber protection for biological systems. The concept of cyberbiosecurity was first introduced in 2018 by researchers exploring the converging threat landscape of the cyber and biology domains. As biology is growing more dependent upon vulnerable …

Predicting Startup Success Using Publicly Available Data, Emily Gavrilenko

Master's Theses

Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction.

To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential …

Wildfire Spread Prediction Using Attention Mechanisms In U-Net, Kamen Haresh Shah, Kamen Haresh Shah

Master's Theses

An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression …

Rasm: Compiling Racket To Webassembly, Grant Matejka

Master's Theses

WebAssembly is an instruction set designed for a stack based virtual machine, with an emphasis on speed, portability and security. As the use cases for WebAssembly grow, so does the desire to target WebAssembly in compilation. In this thesis we present Rasm, a Racket to WebAssembly compiler that compiles a select subset of the top forms of the Racket programming language to WebAssembly. We also present our early findings in our work towards adding a WebAssembly backend to the Chez Scheme compiler that is the backend of Racket. We address initial concerns and roadblocks in adopting a WebAssembly backend and …

Comparing Learned Representations Between Unpruned And Pruned Deep Convolutional Neural Networks, Parker Mitchell

Master's Theses

While deep neural networks have shown impressive performance in computer vision tasks, natural language processing, and other domains, the sizes and inference times of these models can often prevent them from being used on resource-constrained systems. Furthermore, as these networks grow larger in size and complexity, it can become even harder to understand the learned representations of the input data that these networks form through training. These issues of growing network size, increasing complexity and runtime, and ambiguity in the understanding of internal representations serve as guiding points for this work.

In this thesis, we create a neural network that …

Legislative Language For Success, Sanjana Gundala

Master's Theses

Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is "For" and the bill passes or alignment is "Against" and the bill fails). The process of finding what …

Wildfire Risk Assessment Using Convolutional Neural Networks And Modis Climate Data, Sean F. Nesbit

Master's Theses

Wildfires burn millions of acres of land each year leading to the destruction of homes and wildland ecosystems while costing governments billions in funding. As climate change intensifies drought volatility across the Western United States, wildfires are likely to become increasingly severe. Wildfire risk assessment and hazard maps are currently employed by fire services, but can often be outdated. This paper introduces an image-based dataset using climate and wildfire data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). The dataset consists of 32 climate and topographical layers captured across 0.1 deg by 0.1 deg tiled regions in California and Nevada between …

Out-Of-Core Gpu Path Tracing On Large Instanced Scenes Via Geometry Streaming, Jeremy Berchtold

Master's Theses

We present a technique for out-of-core GPU path tracing of arbitrarily large scenes that is compatible with hardware-accelerated ray-tracing. Our technique improves upon previous works by subdividing the scene spatially into streamable chunks that are loaded using a priority system that maximizes ray throughput and minimizes GPU memory usage. This allows for arbitrarily large scaling of scene complexity. Our system required under 19 minutes to render a solid color version of Disney's Moana Island scene (39.3 million instances, 261.1 million unique quads, and 82.4 billion instanced quads at a resolution of 1024x429 and 1024spp on an RTX 5000 (24GB memory …

Artist-Configurable Node-Based Approach To Generate Procedural Brush Stroke Textures For Digital Painting, Keavon Chambers

Master's Theses

Digital painting is the field of software designed to provide artists a virtual medium to emulate the experience and results of physical drawing. Several hardware and software components come together to form a whole workflow, ranging from the physical input devices, to the stroking process, to the texture content authorship. This thesis explores an artist-friendly approach to synthesize the textures that give life to digital brush strokes.

Most painting software provides a limited library of predefined brush textures. They aim to offer styles approximating physical media like paintbrushes, pencils, markers, and airbrushes. Often these are static bitmap textures that are …

Patterns Of Academic Help-Seeking In Undergraduate Computing Students, Augie Doebling

Master's Theses

Knowing when and how to seek academic help is crucial to the success of undergraduate computing students. While individual help-seeking resources have been studied, little is understood about the factors influencing students to use or avoid certain re- sources. Understanding students’ patterns of help-seeking can help identify factors contributing to utilization or avoidance of help resources by different groups, an important step toward improving the quality and accessibility of resources. We present a mixed-methods study investigating the help-seeking behavior of undergraduate computing students. We collected survey data (n = 138) about students’ frequency of using several resources followed by one-on-one …

Randomness Distillation To Improve Key Quality For Context-Based Authentication Schemes, Jackson West

Master's Theses

Context-based authentication is a method for transparently validating another device’slegitimacy to join a network based on location. Devices can pair with one another by continuously harvesting environmental noise to generate a random key with no user involvement. However, there are gaps in our understanding of the theoretical limitations of environmental noise harvesting, making it difficult for researchers to build efficient algorithms for sampling environmental noise and distilling keys from that noise. This work explores the information-theoretic capacity of context-based authentication mechanisms to generate random bit strings from environmental noise sources with known properties. Using only mild assumptions about the source …

Privacy-Aware And Hardware-Based Accleration Authentication Scheme For Internet Of Drones, Tom Henson

Master's Theses

Drones are becoming increasingly present into today’s society through many different means such as outdoor sports, surveillance, delivery of goods etc. With such a rapid increase, a means of control and monitoring is needed as the drones become more interconnected and readily available. Thus, the idea of Internet of drones (IoD) is formed, an infrastructure in place to do those types of things. However, without an authentication system in place anyone could gain access or control to real time data to multiple drones within an area. This is a problem that I choose to tackle using a Field Programmable Gate …

Plant Disease Detection Through Convolutional Neural Networks: A Survey Of Existing Literature, Best Practices, And Implementation, Kevin Label

Master's Theses

In the United States alone, common diseases spread among plants account for billions of dollars lost in crop yield each year. This issue is exacerbated in countries with less infrastructure to defend against crop epidemics, and can lead to famine and forced migration. Farmers can seek the help of plant pathology experts to defend against diseases and detect crop irregularities early on. However, access to experts can be difficult, and even those trained in the field may miss symptoms before it is too late. To assist in early disease detection, a number of papers have been released on the potential …

Millipyde: A Cross-Platform Python Framework For Transparent Gpu Acceleration, James B. Asbury

Master's Theses

The prevalence of general-purpose GPU computing continues to grow and tackle a wider variety of problems that benefit from GPU-acceleration. This acceleration often suffers from a high barrier to entry, however, due to the complexity of software tools that closely map to the underlying GPU hardware, the fast-changing landscape of GPU environments, and the fragmentation of tools and languages that only support specific platforms. Because of this, new solutions will continue to be needed to make GPGPU acceleration more accessible to the developers that can benefit from it. AMD’s new cross-platform development ecosystem ROCm provides promise for developing applications and …

Jited: A Framework For Jit Education In The Classroom, Caleb Watts

Master's Theses

The study of programming languages is a rich field within computer science, incorporating both the abstract theoretical portions of computer science and the platform specific details. Topics studied in programming languages, chiefly compilers or interpreters, are permanent fixtures in programming that students will interact with throughout their career. These systems are, however, considerably complicated, as they must cover a wide range of functionality in order to enable languages to be created and run. The process of educating students thus requires that the demanding workload of creating one of the systems be balanced against the time and resources present in a …