Computer Sciences Commons^™

App Development For Wearable Sensors, Connor Pittman, Christopher Chappie, Patrick A. Tetreault

Thinking Matters Symposium

The objective of this research project was to create a wearable device that monitors bodily functions for the user to view on their smartphone. Sensor data is processed using the Arduino Nano 33 BLE microcontroller. The sensors used in this project include: proximity, temperature, humidity, heart rate, pressure, and skin impedance. This project takes advantage of the Arduino's Bluetooth low energy (BLE) capabilities so that all the data can be transmitted to a smartphone. This presentation shows the challenges faced during the project and how they were overcome. Some of these challenges include: programming, how heart rate sensors work, and …

Understanding Model Reasoning In Automated Speech Systems: Implementing A Prototype Explanation System Using The Lime Method, Vadim Kudlay

Honors Theses

The field of voice processing has seen great advancements thanks in part to the rise of deep learning. However, the application of these deep learning techniques with an audio input space leads to an interesting result not commonly found when dealing with other input domains. Namely, common techniques for generating auditory adversarial samples using gradient-based optimization have been observed to have extremely low transferability among even the same model structure. This implies an inherent difference in the latent representations of audio samples that may be worth investigating in the pursuit of a more resilient and interpretable voice processing framework. Our …

Quantum Simulation Using High-Performance Computing, Collin Beaudoin, Christian Trefftz, Zachary Kurmas

Masters Theses

Hermitian matrix multiplication is one of the most common actions that is performed on quantum matrices, for example, it is used to apply observables onto a given state vector/density matrix.

ρ→Hρ

Our goal is to create an algorithm to perform the matrix multiplication within the constraints of QuEST [1], a high-performance simulator for quantum circuits. QuEST provides a system-independent platform for implementing and simulating quantum algorithms without the need for access to quantum machines. The current implementation of QuEST supports CUDA, MPI, and OpenMP, which allows programs to run on a wide variety of systems.

Statistical Analysis Of Eclipse Refactoring Bug Reports, Lipika Chandrashekar, Jasmine Howard, Akash Kumar, Eric Lacker, Srilaxmi Paramaiahgari

Computer Science Student Work

Software refactoring is the process of making code changes to a program to improve its structure, design, and implementation, in such a way that it does not change the original program behavior. One example is the “rename” refactoring that changes program elements to new names that are easier to understand. Many software development tools, including Eclipse, offer various built-in refactorings such as rename, move, extract, etc. However, we discovered that over four thousand bugs related to Eclipse refactorings were reported as of January 2021. Many of these bugs get fixed after they are reported on the Eclipse bug report website …

A Deep Analysis And Algorithmic Approach To Solving Complex Fitness Issues In Collegiate Student Athletes, Holly N. Puckett

Honors College Theses

Sports are not simply an entertainment source. For many, it creates a sense of community, support, and trust among both fans and athletes alike. In order to continue the sense of community sports provides, athletes must be properly cared for in order to perform at the highest level possible. Thus, their fitness and health must be monitored continuously. In a professional sense, one can expect individualized attention to athletes daily due to an abundance of funding and resources. However, when looking at college communities and student athletes within them, the number of athletes per athletic trainer increases due to both …

A Review On Virtual Reality Skill Training Applications, Biao Xie, Huimin Liu, Rawan Alghofaili, Yongqi Zhang, Yeling Jiang, Flavio Destri Lobo, Changyang Li, Wanwan Li, Haikun Huang, Mesut Akdere, Christos Mousas, Lap-Fai Yu

Purdue University Libraries Open Access Publishing Fund

This study aimed to discuss the research efforts in developing virtual reality (VR) technology for different training applications. To begin with, we describe how VR training experiences are typically created and delivered using the current software and hardware. We then discuss the challenges and solutions of applying VR training to different application domains, such as first responder training, medical training, military training, workforce training, and education. Furthermore, we discuss the common assessment tests and evaluation methods used to validate VR training effectiveness. We conclude the article by discussing possible future directions to leverage VR technology advances for developing novel training …

A Man Out West Is A Man, Nikola Andric

Undergraduate Research Conference at Missouri S&T

The goal of this research is to analyze who the cowboys were and show how the standard set for the cowboy in novels remained consistent during the period between 1902 and 1953. The presentation allows readers to think of their idea of a cowboy and then compare it with the idea of the cowboy drawn from the books used in the research. It is a great opportunity to find out who the perfect cowboy is, what cowboys' relationships with their horses look like, what is considered good, and what is considered evil during those fifty years. Above all, the most …

Mapping Renewal: How An Unexpected Interdisciplinary Collaboration Transformed A Digital Humanities Project, Elise Tanner, Geoffrey Joseph

Digital Initiatives Symposium

Funded by a National Endowment for Humanities (NEH) Humanities Collections and Reference Resources Foundations Grant, the UA Little Rock Center for Arkansas History and Culture’s “Mapping Renewal” pilot project focused on creating access to and providing spatial context to archival materials related to racial segregation and urban renewal in the city of Little Rock, Arkansas, from 1954-1989. An unplanned interdisciplinary collaboration with the UA Little Rock Arkansas Economic Development Institute (AEDI) has proven to be an invaluable partnership. One team member from each department will demonstrate the Mapping Renewal website and discuss how the collaborative process has changed and shaped …

Small-Scale Wind Power Prediction, Nathan Skelton

Undergraduate Research Conference at Missouri S&T

This study is focused on the development of viable power generation modeling for small scale wind power installations. Armed with a viable model, businesses and individuals would have another option to reliably power small-scale buildings or installations. This study defines small-scale wind power turbines as those installed on buildings, or otherwise operating under 30 meters (~100 feet). Both of these installation environments provide inconsistent wind speeds, among other properties, that most wind power models do not address. Through the development of a sample wind turbine and accompanying weather station, multiple methods of prediction have been implemented with varying degrees of …

Shedding Light On Dark Patterns: A Case Study On Digital Harms, Noreen Y. Whysel

Publications and Research

You’ve been there before. You thought you could trust someone with a secret. You thought it would be safe, but found out later that they blabbed to everyone. Or maybe they didn’t share it, but the way they used it felt manipulative. You gave more than you got and it didn’t feel fair. But now that it’s out there, do you even have control anymore?

Ok. Now imagine that person was your supermarket. Or your bank. Or your boss.

As designers of digital spaces for consumer products and services, how often do we consider the relationship we have with our …

Characterizing Students’ Engineering Design Strategies Using Energy3d, Jasmine Singh, Viranga Perera, Alejandra Magana, Brittany Newell

Discovery Undergraduate Interdisciplinary Research Internship

The goals of this study are to characterize design actions that students performed when solving a design challenge, and to create a machine learning model to help future students make better engineering design choices. We analyze data from an introductory engineering course where students used Energy3D, an open source computer-aided design software, to design a zero-energy home (i.e. a home that consumes no net energy over a period of a year). Student design actions within the software were recorded into text files. Using a sample of over 300 students, we first identify patterns in the data to assess how students …

Gc-52 Ksu Spring Capstone - It Security Solution For Small Business – Group 1, William Simmons, Olajumoke Giwa, Beau Beard, Collin Peters

C-Day Computing Showcase

For this project, we decided to use the nopCommerce open-source eCommerce solution for our simulated small business. Leveraging the pre-configured web server provide to our group, we installed the nopCommerce solution package and built our security program around the web site. Our security program consists of nginx configured web server and load balancer, SSL certificate for encryption, firewall and a secure backend SQL server.Advisors(s): Project Sponsor: Dr. Lei Li Course Professor: Dr. Meng HanTopic(s): SecurityIT 7993

Gr-29 Wrist Intent Recognition For Stroke Rehabilitation, Suman Bharti

C-Day Computing Showcase

Abstract Hand mentor robotic device is beneficial for stroke patients . This is rehabilitation technique used in stroke therapy. It strengthens and improves the range of motion which ultimately improves the quality of life for severely impaired stroke patients. It is easy to use without assistance and most importantly stroke survivors able to use independently. Usage of hand mentor device is quite expensive for stroke patients on hourly basis . Coming up with most efficient deep learning algorithm for sensor data is motivation to cut down the cost and easy availability usage for stroke patients. EMG signal is recorded using …

Uc-14 Teleclinic, Jay P Bhatt, Lucius Burch, Zekai Fei, Bijoy Shah, Hao Zhang

C-Day Computing Showcase

TeleClinic is a telemedicine web application that provides ease of access and a medium for interaction between patients and their respective doctors and administrators. In particular, this web portal includes a chat feature, an area for medical reports, an area for appointment requests, and an area for video recordings. Additionally, TeleClinic meets the requirements prescribed by the Health Insurance Portability and Accountability Act (H.I.P.A.A.) via upholding data privacy and safeguarding medical information. To maximize its overall utility, TeleClinic utilizes the React and React Redux libraries for its front-end and a NoSQL database in Google Firebase for its back-end.Advisors(s): Dr. Ken …

Gr-64 How Could Iot Assist Healthcare System During Covid-19 Or Future Pandemics?, Mohammad Nasajpour Esfahani

C-Day Computing Showcase

The Internet of Things (IoT), a technology built upon sensors and devices, has shown great applicability among various domains, especially healthcare. This pandemic has critically impacted all parts of society including people, health centers, businesses, authorities, etc. Researchers are attempting to adopt different technologies to mitigate this virus faster and save more lives. Regarding the great benefits that Internet of Things (IoT) has brought into different areas within the healthcare domain, this technology has been performing several main tasks including diagnosing, monitoring, tracing, disinfecting, and vaccinating to combat this virus. Our research is conducted of the possible IoT solutions to …

Gc-54 Covid-19 Mortality Prediction Using Machine Learning Techniques, Lindsay Schirato, Kennedy Makina, Dwayne Flanders

C-Day Computing Showcase

In late 2019, SARS-CoV2 also known as COVID-19 was first identified in the city of Wuhan, China. This virus can infect a person and without showing any signs of sickness, can spread of COVID-19 unknowingly. The World Health Organization declared it a global pandemic in March 2020 because of its far-reaching effects in every part of the world. Scientists have been working to leverage technology to prevent spread, detection and vaccine development. With machine learning, models can predict which patient will most likely have a higher mortality rate. Using WEKA, a machine learning tool and a data set based on …

Gr-33 Efficient Yet Robust Privacy Preservation \\For Mpeg-Dash Based Video Streaming, Luke A Cranfill

C-Day Computing Showcase

MPEG-DASH is a video streaming standard that outlines protocols for sending audio and video content from a server to a client over HTTP. However, it creates an opportunity for an adversary to invade users' privacy. While a user is watching a video, information is leaked in the form of meta-data, the size and time that the server sent data to the user. After a fingerprint of this data is created, the adversary can use this to identify whether a target user is watching the corresponding video. Only one defense strategy has been proposed to deal with this problem: differential privacy …

Uc-11 Information Recall For Kids With Autism, Alex J Bechke, Elizabeth Burnside, Haiden Gembinski, Ross Murphy, Ryan Taylor, Henok Demisse

C-Day Computing Showcase

Description: Our project "Information Recall For Kids With Autism" also known as the product name given by the client "Safe Kid" is an app to help children with autism understand basic contact information such as phone numbers, addresses, and names. This app is being created for our client Spectrum Behavioral Associates who specialize in helping kids and young adults who have autism, learning development delays, or other behavioral challenges. Motivation: To teach kids who have autism basic contact information in case of an emergency. Materials and Methods: The app we are creating is a local based app made within Unity. …

Gr-23 Machine Learning Techniques For Malware Network Traffic Detection, Jermaine Cameron

C-Day Computing Showcase

Persistent malware variants are a constant threat to computing infrastructure across all regions and business sectors. Traditional detection systems focus primarily on signature-based analysis but this approach cannot adequately keep pace with the velocity and volume of new malware variants that are continuously deployed onto the internet. Most network traffic detection techniques are focused on analyzing raw packets and have not deterred the surge of persistent malware. Therefore, it is important to develop new research techniques that are focused on optimized metadata from malware network traffic to effectively identify an ever-increasing expanse of malicious software. Recent research efforts by Letteri …

Uc-16 Understanding The Drivers Of Medication Nonadherence In The United States, Roman A Schwieterman, Austin Poole, Austin Kay, Muhammad Usman Mustafa, Issifou Ali

C-Day Computing Showcase

Medication nonadherence is generally defined as a patient’s inability to take their medications correctly as prescribed by their doctors. Medication nonadherence adversely affects patient outcomes and increases healthcare costs. Prior research found that health system-, condition-, patient- (older age is one factor), therapy- and social/economic-related factors have been identified to show effect on non-adherence. Our goal is to analyze the NHIS data to understand the sociodemographic and health causes of medication nonadherence, as well as answer the following questions about our selected topic: What variables are the most relevant drivers of nonadherence? Does the direction and strength of the associations …

Uc-26 Pose Extraction For Real-Time Workout Assist_Capstone Group_W01_Spring Semester, Royce Camp, Zach Christmas, Jonathon Segars, Amanda Mead, Cameron Page

C-Day Computing Showcase

Motion Intelligence Research Project We installed and tested many existing pose extraction technologies in many situations. We provided reports on the different software solutions and decided on a single solution that performed the best. We will extract key-points and track the movements across multiple dimensions. We will demonstrate the X and Y movements of everyone for our chosen software solution in a Business Intelligent tool (PowerBI). Given the current epidemic, we are not going to be able to compare against the professional KinaTrax software on campus. Although, Dr. Xie has given us other software packages to compare our given software …

Uc-31 An Empirical Study Of Thermal Attacks On Edge Platforms, Tyler Holmes, Justin Duchatellier

C-Day Computing Showcase

Cloud-edge systems are vulnerable to thermal attacks as the increased energy consumption may remain undetected, while occurring alongside normal, CPU-intensive applications. The purpose of our research is to study thermal effects on modern edge systems. We also analyze how performance is affected from the increased heat and identify preventative measures. We speculate that due to the technology being a recent innovation, research on cloud-edge devices and thermal attacks is scarce. Other research focuses on server systems rather than edge platforms. In our paper, we use a Raspberry Pi 4 and a CPU-intensive application to represent thermal attacks on cloud-edge systems. …

Uc-36 Using Machine Learning Techniques To Predict Rt-Pcr Results For Covid-19 Patients., Bradley T Durden, Mathew Shulman, Andy Reynolds, Thomas A Phillips, Demontae Moore, Indya Andrews

C-Day Computing Showcase

With the COVID-19 pandemic still a threat, healthcare professionals and medical industries keep searching for better ways to mitigate the spread of COVID-19. While Machine Learning has been applied in many other domains, there is now a high demand for diagnosis systems that utilize Machine Learning techniques in the healthcare domain and in particular combating COVID-19. In this project, we explore the role of Machine Learning models in combating COVID-19, using WEKA as the main tool for analysis.Advisors(s): Dr. Ming Yang - IT 4983 Capstone Professor Dr. Seyedamin Pouriyeh - Project OwnerTopic(s): Data/Data AnalyticsIT 4983

Uc-35 Development Of An Automated Software Packaging Solution For Linux, Robert D Ryan, Sammi Figueroa, Dylan Parker, Blair Hill, Bishwo R Marhatta

C-Day Computing Showcase

The main problem with using Linux software in the science and Bioinformatics community is because Linux has a large number of distributions and dependencies. This hinders researches and science students with the problem of tracking down dependencies for software which could then further break the existing system dependencies. Our team looked to solve these problems by creating a BASH script that could quickly mass package AppImages and contain Linux software with all dependencies. Our team worked through the last ten weeks and researched all components of AppImage and discovered all means to more easily package and have a repeatable process …

Uc-37 Interactive Pdf File Editing For Online Classes, David J Hall, Chris J Stubbs, Justin Masters, Dalton G Parker, Rosendo Lopez

C-Day Computing Showcase

This system aims to create an interactive environment for teachers to view/grade/edit student submission in virtual classes. Objectives for this project are to create independent component or logic model that includes the following functions. This component should be integrated with a .net core application easily. -Upload pdf files to the system and save files to the server; -Record audio online and save audio to the system; also, the audio can be played online; -Upload and play video or video link (YouTube); -Split file. When uploading a PDF file, the system will allow to split or crop the file (partial file …

Uc-39 Journalistic Integrity Vis Artifical Intelligence, Dylan Dalton, Ray Martin, Duy Nquyen, Yesse Quezada, Brian Dominguez

C-Day Computing Showcase

We are developing a web app to recognize and rate political bias in online journalism using artificial intelligence. All human writing inherently contains bias ,however bias is less harmful if it is transparent to the reader because they can now make informed decisions about what they read. We've collected articles and and reactions to them from online sources, and then used Neural Networks trained for natural language processing to determine bias. The project can predict bias labels on a news articles with 82% accuracy.Advisors(s): Reza Meimandi Parizi - course instructor Asher Nuckolls - project ownerTopic(s): Artificial IntelligenceSWE4724

Uc-56 Rendeview, James C Noltimier, Gyasi Igyan, Barrett Rose, Niyi Adekunle, Ashley Lowe

C-Day Computing Showcase

Rendeview is a mobile application designed to allow users to find a physical meeting location equitable for 3+ people, taking into account drive time and traffic conditions.Advisors(s): Dr. Reza PariziTopic(s): Software EngineeringSWE 4724

Uc-7 Software Engineer – Clarity Llc, Amy Mullins

C-Day Computing Showcase

Clarity makes an app called CaptionMate that does closed captions for phone calls.. During the internship, a website was made to visualize metrics that are collected on users such as calls made, minutes used, time active, region, age, theme, font, and platform used. Bar charts are used to show minutes and calls used on days of the week. 100% bar charts are used to show how much a day contributes to the usage of the app; a user contributes to minutes, calls and platform usage; and show calls incoming vs. outgoing. Line graphs were created to show growth in app …

Ur-48 Using Semantic Segmentation In A Convoluted Neural Network For Vocal Localization In Music, Trevor E Stanca, Trinite, Noah

C-Day Computing Showcase

I. PROJECT OVERVIEW A. Research Question In this project, the question was asked: ”Is there an easier way to extract vocals from music?” Many other works are able to extract vocals with Deep Neural Networks using Multitask Learning, which are large and take a long time to train. To rival this, we wish to present a method to identify vocals with a Convolutional U-Network (U-Net) for Semantic Segmentation of audio files. B. Project Description This project differs from other works by identifying vocal locations by converting audio files in Short Time Fourier Transforms(STFT), and treating them as images in the …

Ur-60 Video-To-Video Synthesis With Semantically Segmented Video, Aydan Mufti, Jordan S Hasty

C-Day Computing Showcase

Our project involves studying the usage of generative adversarial networks (GANs) to translate semantically segmented video to photo-realistic video in a process known as video-to-video synthesis. The model is able to learn a mapping from semantically segmented masks to real-life images which depict the corresponding semantic labels. To achieve this, we employ a conditional GAN-based learning method that produces output conditionally based on the source video to be translated. Our model is capable of synthesizing a translated video, given semantically labeled video, that resembles real video by accurately replicating low-frequency details from the source.Advisors(s): Dr. Mohammed AledhariTopic(s): Artificial IntelligenceCS 4732