Computer Sciences Commons^™

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Confronting Barriers To Human-Robot Cooperation: Balancing Efficiency And Risk In Machine Behavior, Tim Whiting

Theses and Dissertations

In strategically rich settings in which machines and people do not fully share the same preferences, machines must learn to cooperate and compromise with people to establish mutually successful relationships. However, designing machines that effectively cooperate with people in these settings is difficult due to a variety of technical and psychological challenges. To better understand these challenges, we conducted a series of user studies in which we investigated human-human, robot-robot, and human-robot cooperation in a simple, yet strategically rich, resource-sharing scenario called the Block Dilemma, a game in which players must balance fairness, efficiency, and risk. While both human-human and …

Symbolic Semantic Memory In Transformer Language Models, Robert Kenneth Morain

Theses and Dissertations

This paper demonstrates how transformer language models can be improved by giving them access to relevant structured data extracted from a knowledge base. The knowledge base preparation process and modifications to transformer models are explained. We evaluate these methods on language modeling and question answering tasks. These results show that even simple additional knowledge augmentation leads to a reduction in validation loss by 73%. These methods also significantly outperform common ways of improving language models such as increasing the model size or adding more data.

Outvoice: Bringing Transparency To Healthcare, Autumn Clark

Undergraduate Honors Theses

Industries are not incentivized to price reasonably and spend responsibly if consumers do not have the ability to shop around within that industry, and shopping around is not possible without pricing transparency (knowing how much a good or service costs before purchasing it). But in the healthcare industry, we typically default to whichever clinic or hospital is closest, with no prior knowledge of what costs we can expect to incur at that particular institution. According to a poll published by Harvard University, nine out of ten Americans feel the healthcare industry is too opaque and greater transparency is needed.

We …

Beware Of Ips In Sheep's Clothing: Measurement And Disclosure Of Ip Spoofing Vulnerabilities, Alden Douglas Hilton

Theses and Dissertations

Networks not employing destination-side source address validation (DSAV) expose themselves to a class of pernicious attacks which could be prevented by filtering inbound traffic purporting to originate from within the network. In this work, we survey the pervasiveness of networks vulnerable to infiltration using spoofed addresses internal to the network. We issue recursive Domain Name System (DNS) queries to a large set of known DNS servers world-wide using various spoofed-source addresses. In late 2019, we found that 49% of the autonomous systems we tested lacked DSAV. After a large-scale notification campaign run in late 2020, we repeated our measurements in …

Who Uses Multi-Factor Authentication?, Leah Roberts

Undergraduate Honors Theses

A sample of 47 BYU students were recruited to participate in this study to determine who was using Multi-factor Authentication (MFA) on their online accounts. This study determined that there were many different factors that separated those who used MFA and those who did not. Some of those factors included: time spent on the internet each day, gender, the website itself, and personal privacy behaviors.

Realium: Building The Future Of Real Estate On The Blockchain, Demitri Haddad

Undergraduate Honors Theses

This paper discusses the prospective challenges, limitations and opportunities in the real estate sector for blockchain. It outlines the idea of Realium, a financial technology application that aims to assist in the purchase, sale, and legal compliance of real estate assets. For more information see docs.realium.io

The Communicative Effects Of Anonymity Online: A Natural Language Analysis Of The Faceless, Caleb Johnson

Undergraduate Honors Theses

An ever-increasing number of Americans have an active social media

presence online. As of March 2020, an estimated 79% of Americans were active

monthly users of some sort. Many of these online platforms allow users to

operate anonymously which could potentially lead to shifts in communicative

behavior. I first discuss my compilation process of the Twitter Anonymity

Dataset (TAD), a human-classified dataset of 100,000 Twitter accounts that are

categorized by their level of identifiability to their real-world agent. Next, I

investigate some of the structural differences between the classification levels

and employ a variety of Natural Language Processing models and …

Metalearning By Exploiting Granular Machine Learning Pipeline Metadata, Brandon J. Schoenfeld

Theses and Dissertations

Automatic machine learning (AutoML) systems have been shown to perform better when they use metamodels trained offline. Existing offline metalearning approaches treat ML models as black boxes. However, modern ML models often compose multiple ML algorithms into ML pipelines. We expand previous metalearning work on estimating the performance and ranking of ML models by exploiting the metadata about which ML algorithms are used in a given pipeline. We propose a dynamically assembled neural network with the potential to model arbitrary DAG structures. We compare our proposed metamodel against reasonable baselines that exploit varying amounts of pipeline metadata, including metamodels used …

Trace: A Differentiable Approach To Line-Level Stroke Recovery For Offline Handwritten Text, Taylor Neil Archibald

Theses and Dissertations

Stroke order and velocity are helpful features in the fields of signature verification, handwriting recognition, and handwriting synthesis. Recovering these features from offline handwritten text is a challenging and well-studied problem. We propose a new model called TRACE (Trajectory Recovery by an Adaptively-trained Convolutional Encoder). TRACE is a differentiable approach using a convolutional recurrent neural network (CRNN) to infer temporal stroke information from long lines of offline handwritten text with many characters. TRACE is perhaps the first system to be trained end-to-end on entire lines of text of arbitrary width and does not require the use of dynamic exemplars. Moreover, …

Ansible: Select-To-Edit For Physical Widgets, Benjamin M. Crowder

Theses and Dissertations

Ansible brings select-to-edit functionality to physical widgets. When programming sets of physical widgets, it can be bothersome for a programmer to remember the name of the software object that corresponds to a specific widget. Click-to-edit functionality in GUI programming provides a physical action--moving the mouse to a widget and clicking a button on the mouse--to select a virtual widget. In a similar vein, when programming physical widgets, it is natural to point at a widget and think, "I want to program that one." Ansible allows physical user interface programmers to "click" on a physical widget by making a physical action: …

Specialization: Do Your Job Well Helping Students Who Are Considering A Career In Programming Know How To Invest Their Time., Scott Pulley

Marriott Student Review

The article examines the effects of specialization on the hiring process for undergraduates studying programming whether in information systems or computer science.

Using Logical Specifications For Multi-Objective Reinforcement Learning, Kolby Nottingham

Undergraduate Honors Theses

In the multi-objective reinforcement learning (MORL) paradigm, the relative importance of environment objectives is often unknown prior to training, so agents must learn to specialize their behavior to optimize different combinations of environment objectives that are specified post-training. These are typically linear combinations, so the agent is effectively parameterized by a weight vector that describes how to balance competing environment objectives. However, we show that behaviors can be successfully specified and learned by much more expressive non-linear logical specifications. We test our agent in several environments with various objectives and show that it can generalize to many never-before-seen specifications.

Machine Learning For Effective Parkinson's Disease Diagnosis, Brennon Brimhall

Undergraduate Honors Theses

Parkinson’s Disease is a degenerative neurological condition that affects approximately 10 million people globally. Because there is currently no cure, there is a strong motivation for research into improved and automated diagnostic procedures. Using Random Forests, a computer can effectively learn to diagnose Parkinson’s disease in a patient with high accuracy (94%), precision (95%), and recall (91%) across the data of over 2800 patients. Using similar techniques, I further determine that the most predictive medical tests relate to tremors observed in patients.

Data For The Review Of Gamified Fitness Tracker Apps, Aatish Neupane, Derek Hansen, Anud Sharma, Jerry Alan Fails

ScholarsArchive Data

This is a supplemental dataset to a paper that reviews 103 gamified fitness tracker apps and analyzes the presence and usage of various game elements. This dataset contains the list of those apps that were reviewed. It also contains the coding that represents the presence of difference game elements.

Light-Field Style Transfer, David Marvin Hart

Theses and Dissertations

For many years, light fields have been a unique way of capturing a scene. By using a particular set of optics, a light field camera is able to, in a single moment, take images of the same scene from multiple perspectives. These perspectives can be used to calculate the scene geometry and allow for effects not possible with standard photographs, such as refocus and the creation of novel views.Neural style transfer is the process of training a neural network to render photographs in the style of a particular painting or piece of art. This is a simple process for a …

Cybersecurity Education In Utah High Schools: An Analysis And Strategy For Teacher Adoption, Cariana June Cornel

Theses and Dissertations

The IT Education Specialist for the USBE, Brandon Jacobson, stated:I feel there is a deficiency of and therefore a need to teach Cybersecurity.Cybersecurity is the “activity or process, ability or capability, or state whereby information and communications systems and the information contained therein are protected from and/or defended against damage, unauthorized use or modification, or exploitation” (NICE, 2018). Practicing cybersecurity can increase awareness of cybersecurity issues, such as theft of sensitive information. Current efforts, including but not limited to, cybersecurity camps, competitions, college courses, and conferences, have been created to better prepare cyber citizens nationwide for such cybersecurity occurrences. In …

Semantically Aligned Sentence-Level Embeddings For Agent Autonomy And Natural Language Understanding, Nancy Ellen Fulda

Theses and Dissertations

Many applications of neural linguistic models rely on their use as pre-trained features for downstream tasks such as dialog modeling, machine translation, and question answering. This work presents an alternate paradigm: Rather than treating linguistic embeddings as input features, we treat them as common sense knowledge repositories that can be queried using simple mathematical operations within the embedding space, without the need for additional training. Because current state-of-the-art embedding models were not optimized for this purpose, this work presents a novel embedding model designed and trained specifically for the purpose of "reasoning in the linguistic domain".Our model jointly represents single …

Measuring Influence On Linear Dynamical Networks, Jaekob Chenina

Theses and Dissertations

Influence has been studied across many different domains including sociology, statistics, marketing, network theory, psychology, social media, politics, and web search. In each of these domains, being able to measure and rank various degrees of influence has useful applications. For example, measuring influence in web search allows internet users to discover useful content more quickly. However, many of these algorithms measure influence across networks and graphs that are mathematically static. This project explores influence measurement within the context of linear time invariant (LTI) systems. While dynamical networks do have mathematical models for quantifying influence on a node-to-node basis, to the …

A Shared-Memory Coupled Architecture To Leverage Big Data Frameworks In Prototyping And In-Situ Analytics For Data Intensive Scientific Workflows, Alexander Michael Lemon

Theses and Dissertations

There is a pressing need for creative new data analysis methods whichcan sift through scientific simulation data and produce meaningfulresults. The types of analyses and the amount of data handled by currentmethods are still quite restricted, and new methods could providescientists with a large productivity boost. New methods could be simpleto develop in big data processing systems such as Apache Spark, which isdesigned to process many input files in parallel while treating themlogically as one large dataset. This distributed model, combined withthe large number of analysis libraries created for the platform, makesSpark ideal for processing simulation output.Unfortunately, the filesystem becomes …

Inclusion Of Women In Computer Science, Naomi Johnson, Dr. Kevin Seppi

Journal of Undergraduate Research

Since the 1980’s, the percentage of computer science degrees awarded to women in the United States has fallen dramatically. There are growing numbers of men earning bachelor’s degrees in CS, and the numbers of women are increasing very slowly. For decades, researchers have been studying recruitment and retention of women and other minorities in CS, yet it is still not apparent what departments, professors, or students can do in order to get the numbers of women earning degrees in CS up again.

Deep Synthetic Noise Generation For Rgb-D Data Augmentation, Patrick Douglas Hammond

Theses and Dissertations

Considerable effort has been devoted to finding reliable methods of correcting noisy RGB-D images captured with unreliable depth-sensing technologies. Supervised neural networks have been shown to be capable of RGB-D image correction, but require copious amounts of carefully-corrected ground-truth data to train effectively. Data collection is laborious and time-intensive, especially for large datasets, and generation of ground-truth training data tends to be subject to human error. It might be possible to train an effective method on a relatively smaller dataset using synthetically damaged depth-data as input to the network, but this requires some understanding of the latent noise distribution of …

Multi-Human Management Of A Hub-Based Colony: Efficiency And Robustness In The Cooperative Best M-Of-N Task, John Rolfes Grosh

Theses and Dissertations

Swarm robotics is an emerging field that is expected to provide robust solutions to spatially distributed problems. Human operators will often be required to guide a swarm in the fulfillment of a mission. Occasionally, large tasks may require multiple spatial swarms to cooperate in their completion. We hypothesize that when latency, bandwidth, operator dropout, and communication noise are significant factors, human organizations that promote individual initiative perform more effectively and resiliently than hierarchies in the cooperative best-m-of-n task. Simulations automating the behavior of hub-based swarm robotic agents and groups of human operators are used to evaluate this hypothesis. To make …

Representation And Reconstruction Of Linear, Time-Invariant Networks, Nathan Scott Woodbury

Theses and Dissertations

Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required) and not necessarily strictly proper.The ultimate objective of this dissertation is to develop algorithms capable of …

Deep Learning For Document Image Analysis, Christopher Alan Tensmeyer

Theses and Dissertations

Automatic machine understanding of documents from image inputs enables many applications in modern document workflows, digital archives of historical documents, and general machine intelligence, among others. Together, the techniques for understanding document images comprise the field of Document Image Analysis (DIA). Within DIA, the research community has identified several sub-problems, such as page segmentation and Optical Character Recognition (OCR). As the field has matured, there has been a trend of moving away from heuristic-based methods, designed for particular tasks and domains of documents, and moving towards machine learning methods that learn to solve tasks from examples of input/output pairs. Within …

Moderating Influence As A Design Principle For Human-Swarm Interaction, C Chace Ashcraft

Theses and Dissertations

Robot swarms have recently become of interest in both industry and academia for their potential to perform various difficult or dangerous tasks efficiently. As real robot swarms become more of a possibility, many desire swarms to be controlled or directed by a human, which raises questions regarding how that should be done. Part of the challenge of human-swarm interaction is the difficulty of understanding swarm state and how to drive the swarm to produce emergent behaviors. Human input could inhibit desirable swarm behaviors if their input is poor and has sufficient influence over swarm agents, affecting its overall performance. Thus, …

After Https: Indicating Risk Instead Of Security, Matthew Wayne Holt

Theses and Dissertations

Browser security indicators show warnings when sites load without HTTPS, but more malicious sites are using HTTPS to appear legitimate in browsers and deceive users. We explore a new approach to browser indicators that overcomes several limitations of existing indicators. First, we develop a high-level risk assessment framework to identify risky interactions and evaluate the utility of this approach through a survey. Next, we evaluate potential designs for a new risk indicator to communicate risk rather than security. Finally, we conduct a within-subjects user study to compare the risk indicator to existing security indicators by observing participant behavior and collecting …

Computationally Modeling The Trophic Cascade In Yellowstone National Park, Emily Menden

Undergraduate Honors Theses

Many of the world’s ecosystems are facing species elimination (2). Whether this elimination is intentional or accidental, the consequences need to be understood in order to make better resource management decisions. Computational models can be helpful in making these management decisions. Yellowstone National Park gives ecologists a unique opportunity to study species elimination and reintroduction.

In the 1920s, wolves were extirpated from the Greater Yellowstone Area. The absence of wolves allowed the elk population to increase unbounded by a natural predator. Over the years, Yellowstone management took various measures to control the elk population. In the 1970s, the National Park …

Emergence Of Collective Behaviors In Hub-Based Colonies Using Grammatical Evolution And Behavior Trees, Aadesh Neupane

Theses and Dissertations

Animals such as bees, ants, birds, fish, and others are able to efficiently perform complex coordinated tasks like foraging, nest-selection, flocking and escaping predators without centralized control or coordination. These complex collective behaviors are the result of emergence. Conventionally, mimicking these collective behaviors with robots requires researchers to study actual behaviors, derive mathematical models, and implement these models as algorithms. Since the conventional approach is very time consuming and cumbersome, this thesis uses an emergence-based method for the efficient evolution of collective behaviors. Our method, Grammatical Evolution algorithm for Evolution of Swarm bEhaviors (GEESE), is based on Grammatical Evolution (GE) …

The Security Layer, Mark Thomas O'Neill

Theses and Dissertations

Transport Layer Security (TLS) is a vital component to the security ecosystem and the most popular security protocol used on the Internet today. Despite the strengths of the protocol, numerous vulnerabilities result from its improper use in practice. Some of these vulnerabilities arise from weaknesses in authentication, from the rigidity of the trusted authority system to the complexities of client certificates. Others result from the misuse of TLS by developers, who misuse complicated TLS libraries, improperly validate server certificates, employ outdated cipher suites, or deploy other features insecurely. To make matters worse, system administrators and users are powerless to fix …