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Demystifying The "Social Media Algorithm": The Legacy Of Surveillance Advertising And Platformization, Garrett Crites
Demystifying The "Social Media Algorithm": The Legacy Of Surveillance Advertising And Platformization, Garrett Crites
Honors Projects
Recently, more individuals are becoming aware that they are being served content on social media platforms by automated means. Due to the lack of transparency, a colloquial understanding of the “social media algorithm” has emerged in popular discourse. To shed light on the real–world phenomena that these ideas surround, I look at the rise of surveillance advertising and the platformization of the internet in conjunction with the automated platform operations employed by large social media platforms like Facebook, YouTube, TikTok, and X. In doing so I provide a clearer idea of the colloquial “social media algorithm” to encourage the reader …
A Survey Of Practical Haskell: Parsing, Interpreting, And Testing, Parker Landon
A Survey Of Practical Haskell: Parsing, Interpreting, And Testing, Parker Landon
Honors Projects
Strongly typed pure functional programming languages like Haskell have historically been confined to academia as vehicles for programming language research. While features of functional programming have greatly influenced mainstream programming languages, the imperative programming style remains pervasive in practical software development. This paper illustrates the practical utility of Haskell and pure functional programming by exploring “hson,” a scripting language for processing JSON developed in Haskell. After introducing the relevant features of Haskell to the unfamiliar reader, this paper reveals how hson leverages functional programming to implement parsing, interpreting, and testing. By showcasing how Haskell’s language features enable the creation of …
Analysis Of Green Data Center Efforts And Energy Usage, Dillon J. Goicoechea
Analysis Of Green Data Center Efforts And Energy Usage, Dillon J. Goicoechea
Honors Projects
This paper is an undergraduate level literature review and analysis of research surrounding the Green Data Center phenomenon. Review of work covering energy usage, data usage, usage predictions, and strategies for decreasing energy requirements is the main analysis of this work. The analysis shows that while data centers are becoming greener, the increase in usage of their capacities is negating those efficiency increases. The increase in the energy efficiency of data centers is crucial, however, there must be made efforts to lower computational and data usage to help achieve lower energy usage of data centers.
Helping The Home Cook: How Unsupervised Machine Learning Can Prevent Food Waste, Ryan B. Watson
Helping The Home Cook: How Unsupervised Machine Learning Can Prevent Food Waste, Ryan B. Watson
Honors Projects
A common problem for the home cook is having too much of one food ingredient leftover, then not knowing what to do with it. To alleviate this problem, I propose using an unsupervised machine learning model to recommend recipes based on what ingredients the home cook wants to use. This model is built with FastText and trained on the recipe ingredients in the RecipeNLG dataset. Recipes are recommended based on which recipe ingredient set is most similar to the recipe ingredients provided in the user input. This solution will reduce consumer food waste by giving the home cook the information …
Graph-Based Learning, Jason Gronn
Graph-Based Learning, Jason Gronn
Honors Projects
An educational approach to teaching students based on prerequisite knowledge they may or may not have is presented. This approach represents educational content in the form of a graph, where edges link each topic to the prerequisites of that topic. A proof-of-concept website is created based on this approach, where qualitative results are observed and a number of conclusions are drawn. Some of the findings are that, while it can prevent users from being confused by lacked prior knowledge, the users may instead be confused by the presentation of the graph structure. The work finds that the approach is workable, …
The Social Pot: A Social Media Application, Reid Long
The Social Pot: A Social Media Application, Reid Long
Honors Projects
The Social Pot is a web application that allows a user to post to Instagram and X simultaneously from one place. The user creates a Social Pot Account and from there can set their Instagram username and password within the home page. Once the user attempts to post, it will redirect them to login to X which once successful will make the tweet. Used the API 'instagram-private-api'. User needed to give access to my X Project which in turn gave an Auth token (via X redirect URL). The auth token was then sent to my endpoint in order to get …
Statistically Principled Deep Learning For Sar Image Segmentation, Cassandra Goldberg
Statistically Principled Deep Learning For Sar Image Segmentation, Cassandra Goldberg
Honors Projects
This project explores novel approaches for Synthetic Aperture Radar (SAR) image segmentation that integrate established statistical properties of SAR into deep learning models. First, Perlin Noise and Generalized Gamma distribution sampling methods were utilized to generate a synthetic dataset that effectively captures the statistical attributes of SAR data. Subsequently, deep learning segmentation architectures were developed that utilize average pooling and 1x1 convolutions to perform statistical moment computations. Finally, supervised and unsupervised disparity-based losses were incorporated into model training. The experimental outcomes yielded promising results: the synthetic dataset effectively trained deep learning models for real SAR data segmentation, the statistically-informed architectures …
Creating Project Contrast: A Video Game Exploring Consciousness And Qualia, Pierce Papke
Creating Project Contrast: A Video Game Exploring Consciousness And Qualia, Pierce Papke
Honors Projects
Project Contrast is a video game that explores how the unique traits inherent to video games might engage reflective player responses to qualitative experience. Project Contrast does this through suspension of disbelief, avatar projection, presence, player agency in storytelling, visual perception, functional gameplay, and art. Considering the difficulty in researching qualitative experience due to its subjectivity and circular explanations, I created Project Contrast not to analyze qualia, though that was my original hope. I instead created Project Contrast as an avenue for player self-reflection and learning about qualitative experience. While video games might be just code and art on a …
Using Deep Neural Networks To Classify Astronomical Images, Andrew D. Macpherson
Using Deep Neural Networks To Classify Astronomical Images, Andrew D. Macpherson
Honors Projects
As the quantity of astronomical data available continues to exceed the resources available for analysis, recent advances in artificial intelligence encourage the development of automated classification tools. This paper lays out a framework for constructing a deep neural network capable of classifying individual astronomical images by describing techniques to extract and label these objects from large images.
A Machine Learning Approach To Sector Based Market Efficiency, Angus Zuklie
A Machine Learning Approach To Sector Based Market Efficiency, Angus Zuklie
Honors Projects
In economic circles, there is an idea that the increasing prevalence of algorithmic trading is improving the information efficiency of electronic stock markets. This project sought to test the above theory computationally. If an algorithm can accurately forecast near-term equity prices using historical data, there must be predictive information present in the data. Changes in the predictive accuracy of such algorithms should correlate with increasing or decreasing market efficiency.
By using advanced machine learning approaches, including dense neural networks, LSTM, and CNN models, I modified intra day predictive precision to act as a proxy for market efficiency. Allowing for the …
Computer Simulation Of The Light Absorption Band Of The Jumping Spider Isorhodopsin, Noah Zoldak
Computer Simulation Of The Light Absorption Band Of The Jumping Spider Isorhodopsin, Noah Zoldak
Honors Projects
In order to simulate the photoisomerization of the 9-cis Jumping Spider Isorhodopsin (JSiR-1) it is necessary to first simulate its light-absorption band. Here we report on the absorption band simulated using protein models constructed using the advanced Automatic Rhodopsin Modeling (a-ARM) program. A population of S0 models was created and the corresponding S0 to S1 transitions were determined for each member of the resulting population. The calculation resulted in a Gaussian plot showing that the wavelength of the absorption maximum of 560 nm (a violet color) that is consistent, but red-shifted, with respect the experimentally observed value.
Behaviors For Which Deinonychosaurs Used Their Feet, Alexander King
Behaviors For Which Deinonychosaurs Used Their Feet, Alexander King
Honors Projects
This paper seeks to show for what purpose deinonychosaurs used their feet. Fowler et al., (2011) showed that D. antirrhopus’s feet were closest in function to accipitrids, as they found it was more built for grasping prey than running.
I answered this question by using 2D images of the feet of three modern birds (Buteo jamaicensis, Phasianus colchicus, and Gallus gallus domesticus), one eudromaeosaur (Deinonychus antirrhopus), and one troodontid (Borogovia gracilicrus). I used ImageJ to apply 73 landmarks to each foot, capturing the variation between species in the metatarsals and pedal phalanges. These data were then uploaded to the software …
Read This: A Content Analysis Framework For Book Recommendation Applications, Cypress S. Payne
Read This: A Content Analysis Framework For Book Recommendation Applications, Cypress S. Payne
Honors Projects
Book recommendation applications combine word-of-mouth recommendations with algorithms that can suggest books based on a user’s account activity, creating a robust system for finding new books to read. Current research on recommendation systems is purely quantitative, focusing on the efficacy of the system, and content analyses are only just beginning to be performed on mobile applications. I use previous content analyses on applications as a basis for creating a content analysis framework for book recommendation applications. This framework can be used to analyze what users find important in book recommendation apps and inform app creators about their users’ wants and …
The Quest For New Music: A Recommendation Algorithm For Spotify Users, Ian Curtis
The Quest For New Music: A Recommendation Algorithm For Spotify Users, Ian Curtis
Honors Projects
Music is one of the rare forms of communication that can be understood on a profound level by anyone; it has the power to cause significant emotional effects, to spark inspiration, to ignite change, to spread knowledge, and more, even regardless of song language. A popular subject of research in music pertains to recommendations; determining a song a listener would enjoy is not an easy task. Moreover, certain factors may influence a user's satisfaction with recommended songs and their likelihood to continue using a service. Focusing on the major streaming service Spotify, we build a K-Means clustering algorithm to recommend …
Exploiting Context In Linear Influence Games: Improved Algorithms For Model Selection And Performance Evaluation, Daniel Little
Exploiting Context In Linear Influence Games: Improved Algorithms For Model Selection And Performance Evaluation, Daniel Little
Honors Projects
In the recent past, extensive experimental works have been performed to predict joint voting outcomes in Congress based on a game-theoretic model of voting behavior known as Linear Influence Games. In this thesis, we improve the model selection and evaluation procedure of these past experiments. First, we implement two methods, Nested Cross-Validation with Tuning (Nested CVT) and Bootstrap Bias Corrected Cross-Validation (BBC-CV), to perform model selection and evaluation with less bias than previous methods. While Nested CVT is a commonly used method, it requires learning a large number of models; BBC-CV is a more recent method boasting less computational cost. …
Outlier Detection In Energy Datasets, Stephen Crawford
Outlier Detection In Energy Datasets, Stephen Crawford
Honors Projects
In the past decade, numerous datasets have been released with the explicit goal of furthering non-intrusive load monitoring research (NILM). NILM is an energy measurement strategy that seeks to disaggregate building-scale loads. Disaggregation attempts to turn the energy consumption of a building into its constituent appliances. NILM algorithms require representative real-world measurements which has led institutions to publish and share their own datasets. NILM algorithms are designed, trained, and tested using the data presented in a small number of these NILM datasets. Many of the datasets contain arbitrarily selected devices. Likewise, the datasets themselves report aggregate load information from building(s) …
Space Science And Social Media: Automating Science Communication On Twitter, Maia Williams
Space Science And Social Media: Automating Science Communication On Twitter, Maia Williams
Honors Projects
This project analyzes how social media is used to engage general audiences in astronomy and space science, as well as ways to improve engagement through automation. Tweets from five space science organizations were sampled. The engagement rate for each tweet was calculated from the number of interactions it received. Accounts that tweet more per day had more followers, and accounts with more followers received more interactions. This project also investigated how to build a Twitter bot to automate science communication. Using NASA Application Programming Interfaces, a Twitter bot was written in Python to tweet images taken by the NASA Mars …
Can Parallel Gravitational Search Algorithm Effectively Choose Parameters For Photovoltaic Cell Current Voltage Characteristics?, Alan Kirkpatrick
Can Parallel Gravitational Search Algorithm Effectively Choose Parameters For Photovoltaic Cell Current Voltage Characteristics?, Alan Kirkpatrick
Honors Projects
This study asks the question “Can parallel Gravitational Search Algorithm (GSA) effectively choose parameters for photovoltaic cell current voltage characteristics?” These parameters will be plugged into the Single Diode Model to create the IV curve. It will also investigate Particle Swarm Optimization (PSO) and a population based random search (PBRS) to see if GSA performs the search better and or more quickly than alternative algorithms
Machine Learning In Stock Price Prediction Using Long Short-Term Memory Networks And Gradient Boosted Decision Trees, Carl Samuel Cederborg
Machine Learning In Stock Price Prediction Using Long Short-Term Memory Networks And Gradient Boosted Decision Trees, Carl Samuel Cederborg
Honors Projects
Quantitative analysis has been a staple of the financial world and investing for many years. Recently, machine learning has been applied to this field with varying levels of success. In this paper, two different methods of machine learning (ML) are applied to predicting stock prices. The first utilizes deep learning and Long Short-Term Memory networks (LSTMs), and the second uses ensemble learning in the form of gradient tree boosting. Using closing price as the training data and Root Mean Squared Error (RMSE) as the error metric, experimental results suggest the gradient boosting approach is more viable.
Honors Symposium: ML is …
Using Machine Learning For Detection Of Covid-19, Justin Rickert
Using Machine Learning For Detection Of Covid-19, Justin Rickert
Honors Projects
Currently, the most widely used diagnostic tool for COVID-19 is the RT-PCR nasal swab test recommended by the CDC. However, some studies have shown that chest CT scans have the potential to be more accurate and are also capable of detecting the virus in its earlier stages. Unfortunately, CT results are not instantaneously available as it may be days before a radiologist can review the scan. This delay is one of the factors preventing the widespread use of CT scans for COVID detection. To address the delay, this project investigated Convolutional Neural Networks, an advanced form of machine learning used …
Making The Easy Accessibility Package, Aaron G. Trudeau
Making The Easy Accessibility Package, Aaron G. Trudeau
Honors Projects
The Easy Accessibility Package is a code package for Unity (a game engine bundled with game development software) that is meant to help video game developers quickly and easily make their games accessible to disabled gamers. The two main features I include in the project were remappable controls (changing which button performs which in-game action) and screen reader support (reading on screen text or game status aloud), both of which are vital to making games accessible.
The repository for the project at the time of submission can be found here: https://github.com/trudeaua21/EasyAccessibilityPackage/tree/v0.1-alpha
The up-to-date repository for the project can be found …
Improving Energy Efficiency Through Compiler Optimizations, Jack Beckitt-Marshall
Improving Energy Efficiency Through Compiler Optimizations, Jack Beckitt-Marshall
Honors Projects
Abstract--- Energy efficiency is becoming increasingly important for computation, especially in the context of the current climate crisis. The aim of this experiment was to see if the compiler could reduce energy usage without rewriting programs themselves. The experimental setup consisted of compiling programs using the Clang compiler using a set of compiler flags, and then measuring energy usage and execution time on an AMD Ryzen processor. Three experiments were performed: a random exploration of compiler flags, utilization of SIMD, as well as benchmarking real world applications. It was found that the compiler was able to reduce execution time, especially …
Using Alteryx Designer In Audit, Nolan Asiala
Using Alteryx Designer In Audit, Nolan Asiala
Honors Projects
My senior project was built around data analysis and how it relates to the auditing profession. Initially, I was planning on attending a data analytics competition, but that was canceled due to the events of COVID-19. This project utilized the Alteryx Designer program to demonstrate how it can be used during an audit engagement. By creating a workflow in Alteryx Designer, a report from a client can be cleaned and reformatted into a working dataset. My project includes two Excel files, a Microsoft Word document that serves as a brief introduction to the program, and a video describing the workflow …
Text Anomaly Detection With Arae-Anogan, Tec Yan Yap
Text Anomaly Detection With Arae-Anogan, Tec Yan Yap
Honors Projects
Generative adversarial networks (GANs) are now one of the key techniques for detecting anomalies in images, yielding remarkable results. Applying similar methods to discrete structures, such as text sequences, is still largely an unknown. In this work, we introduce a new GAN-based text anomaly detection method, called ARAE-AnoGAN, that trains an adversarially regularized autoencoder (ARAE) to reconstruct normal sentences and detects anomalies via a combined anomaly score based on the building blocks of ARAE. Finally, we present experimental results demonstrating the effectiveness of ARAE-AnoGAN and other deep learning methods in text anomaly detection.
Word Embedding Driven Concept Detection In Philosophical Corpora, Dylan Hayton-Ruffner
Word Embedding Driven Concept Detection In Philosophical Corpora, Dylan Hayton-Ruffner
Honors Projects
During the course of research, scholars often explore large textual databases for segments of text relevant to their conceptual analyses. This study proposes, develops and evaluates two algorithms for automated concept detection in theoretical corpora: ACS and WMD retrieval. Both novel algorithms are compared to key word retrieval, using a test set from the Digital Ricoeur corpus tagged by scholarly experts. WMD retrieval outperforms key word search on the concept detection task. Thus, WMD retrieval is a promising tool for concept detection and information retrieval systems focused on theoretical corpora.
Improving 3d Printed Prosthetics With Sensors And Motors, Rachel Zarin
Improving 3d Printed Prosthetics With Sensors And Motors, Rachel Zarin
Honors Projects
A 3D printed hand and arm prosthetic was created from the idea of adding bionic elements while keeping the cost low. It was designed based on existing models, desired functions, and materials available. A tilt sensor keeps the hand level, two motors move the wrist in two different directions, a limit switch signals the fingers to open and close, and another motor helps open and close the fingers. All sensors and motors were built on a circuit board, programmed using an Arduino, and powered by a battery. Other supporting materials include metal brackets, screws, guitar strings, elastic bands, small clamps, …
Code4her Spring 2019, Aidan White
Code4her Spring 2019, Aidan White
Honors Projects
CODE4her is a mentorship program for girls in grades 5-8. Participants are paired with a BGSU student who acts as their mentor for the duration of the session. The goal of the organization is to give the girls a welcoming environment where the participants are able to learn about computer science.
Gem-Pso: Particle Swarm Optimization Guided By Enhanced Memory, Kevin Fakai Chen
Gem-Pso: Particle Swarm Optimization Guided By Enhanced Memory, Kevin Fakai Chen
Honors Projects
Particle Swarm Optimization (PSO) is a widely-used nature-inspired optimization technique in which a swarm of virtual particles work together with limited communication to find a global minimum or optimum. PSO has has been successfully applied to a wide variety of practical problems, such as optimization in engineering fields, hybridization with other nature-inspired algorithms, or even general optimization problems. However, PSO suffers from a phenomenon known as premature convergence, in which the algorithm's particles all converge on a local optimum instead of the global optimum, and cannot improve their solution any further. We seek to improve upon the standard Particle Swarm …
Real-Time Object Recognition Using A Multi-Framed Temporal Approach, Corinne Alini
Real-Time Object Recognition Using A Multi-Framed Temporal Approach, Corinne Alini
Honors Projects
Computer Vision involves the extraction of data from images that are analyzed in order to provide information crucial to many modern technologies. Object recognition has proven to be a difficult task and programming reliable object recognition remains elusive. Image processing is computationally intensive and this issue is amplified on mobile platforms with processor restrictions. The real-time constraints demanded by robotic soccer in RoboCup competition serve as an ideal format to test programming that seeks to overcome these challenges. This paper presents a method for ball recognition by analyzing the movement of the ball. Major findings include enhanced ball discrimination by …
Budgeting In Student Life: An Educational Website, Heather Grunden
Budgeting In Student Life: An Educational Website, Heather Grunden
Honors Projects
An applied honors project in the form of a website prototype. The purpose of this website is to introduce college students to the concept of budgeting and to teach them the core steps of creating their own budget, since many existing budgeting applications are pay-to-use, and the free options tend to have little to no instruction.