Computer Sciences Commons^™

Uc-225 Golf Course Pace Management Simulation, Ashton D Miller

C-Day Computing Showcase

The game of golf has been played for centuries so it has seen handfuls of evolutions throughout its time being played. Throughout the games evolution one factor of its existence has hardly ever changed, time. In current day golf standards, time and the management of time is a significant part of not only how well the game as a whole run but how the courses that own venues to play the game operate as well. In golf there is a standard for time known as the pace of play model, where groups that are sent off during the day are …

Uc-242 Ac-10 Ai & Music Processing, Michael J Zboinski, Selam B Kelil, Sterling J Wilson, Michael Egwuatu

C-Day Computing Showcase

There is a broad range of styles and philosophies, for teaching young children how to play music. Some are based on repetition and memorization of songs, and others build up a foundation of musical patterns and motifs. Arguably, the latter style, will better develop the skills needed for improvisation and composition of new music. Inspired by this observation, we aim to improve the ability of (recurrent) neural networks to synthesize music based on a more careful training.

Uc-247 Using Dynamic Difficulty Adjustment (Dda) To Improve Health And Wellness Apps And Programs, Fernando Orfila

C-Day Computing Showcase

Physical inactivity, obesity and Type 2 Diabetes cost the United States’ economy more than $700 billion a year (CDC). Yet, individuals spend $137 billion dollars a year on gym memberships to get in shape and feel better, without attaining results and dropping out. “…63% of new members will abandon activities before the third month, and less than 4% will remain for more than 12 months of continuous activity.” (Sperandei et al). The personal training apps don’t fare better, with 71% of users disengaging within 90 days (Amagai et al). The higher chances of people dropping out are due to "a …

Ur-239 Human-Ai Annotator Tool​, Anh Duong, Habeebah Muse, Chase Castro

C-Day Computing Showcase

The HK-01 Human-AI Annotator Tool is a web-based system developed to facilitate the annotation of Electronic Health Records (EHRs) for mental and behavioral health, using ICD-10 codes. The tool allows multiple expert annotators to tag critical information, making it easier to catalog patient data accurately. Future plans include integrating AI to streamline and scale the annotation process, improving both efficiency and accuracy.

Gmc-146 Legal-Insight - Legal Text Summarizer, Ravi K Rogannagari, Sasi Pavan Khadyoth Gunturu, Lakshmi Narasimha Naidu Sripathi

C-Day Computing Showcase

Many people struggle to fully understand complex legal documents, such as terms of service agreements, contracts, and privacy policies, due to dense jargon, small fonts, and lengthy paragraphs that make critical information difficult to grasp. This lack of clarity can lead individuals to inadvertently agree to terms they might not fully understand or miss important clauses. Recognizing these challenges, we developed LegalInsight to make legal information more accessible and comprehensible. LegalInsight simplifies lengthy legal documents by creating clear and concise summaries, allowing users to easily digest essential information. It also includes an interactive Q&A feature where users can ask specific …

Gmc-158 Evaluating Tcp Protocol Performance In Cloud Environments, Nong Ming

C-Day Computing Showcase

This research investigates the performance of four different TCP algorithms—BBR, Reno, Vegas, and Cubic in a high-latency and congested condition within a cloud-based environment using EC2 instances and Mininet for network simulation. The study aims to evaluate the throughput and congestion window (cwnd) behavior of each algorithm under various network conditions to identify their strengths and weaknesses. By analyzing the performance metrics across different TCP algorithms, we provide insights into their suitability for cloud infrastructure, contributing to optimized network protocol choices for cloud-based applications and services. The results offer valuable guidance for enhancing network performance in dynamic cloud environments.

Improving Students’ Cognitive Abilities In Remote Learning Environment Using Brain Computer Interface And Eye-Tracking, Nuraini Jamil

Thesis/ Dissertation Defenses

Attention and cognitive engagement are crucial factors in remote learning environments, where the absence of physical presence often diminishes learning outcomes. Traditional methods for assessing these cognitive states, such as observation and self-reporting, are limited by subjectivity and inefficiency. Automated solutions, particularly those based on biometric data like EEG and eye-tracking, offer a more accurate and scalable alternative. However, developing robust systems that leverage biometric data in real-time presents significant challenges. These include handling large volumes of complex data, ensuring low-latency processing, and adapting machine learning models to diverse learning environments and individual cognitive states. Additionally, the integration of neurofeedback …

Drone Vs. Drone, Mariah Smith

Cybersecurity Undergraduate Research Showcase

This paper focuses on the problems that drones pose to digital and physical infrastructure, as well as potential solutions to combat these issues. One solution is incorporating drone usage into ethical hacking. These drone-based attacks are affecting not only economic spaces but also seemingly high-security areas such as prison systems. It is only a matter of time before critical infrastructure is targeted. Conversely, by simulating drone attacks, drones equipped with complex hacking tools and sensors can detect unauthorized pathways and infiltrate networks for the greater good. Incorporating these new practices would enhance digital and physical protection. "Drone vs. Drone" highlights …

Improving Data Curation With Spectral Clustering And Shannon Entropy: An Unsupervised Approach Within The Data Washing Machine, Erin Hathorn

Theses and Dissertations

In the ever-expanding landscape of digital technologies, the exponential growth of data presents both challenges and opportunities, demanding innovative approaches to data curation. Effective data curation is pivotal for extracting meaningful insights from vast and complex datasets. This study explores the integration of spectral clustering and Shannon Entropy within the Data Washing Machine (DWM), a novel tool designed to streamline unsupervised data curation processes. The DWM incorporates Shannon Entropy into its clustering process, allowing for adaptive refinement of clustering strategies based on entropy levels observed within data clusters. Spectral clustering, known for its ability to handle complex and non-linearly separable …

Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl

Faculty Publications

Excerpt: Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.

Uc-131 Karah Khronicles, Dion Green, Jake Stipetich, Grace Bowe, Jesse Israel, Vedasri Malatker

C-Day Computing Showcase

Karah is a thief with a heart of gold, you raid enemy camps and dungeons to steal back the money stolen from towns and villages and upgrade enchanted items to deal with dangerous foes. After successfully returning the wealth to the local town, you must then face down and defeat a general of the evil king.

A Dynamical Systems Approach For Modeling Malware Propagating Through A Network And Potential Solutions Towards Mitigating Spread, James Johnson

Cybersecurity Undergraduate Research Showcase

Many people draw close parallels between malware propagating through a network and an epidemic spreading through a population. Epidemics are often modeled by a Susceptible-Infected-Recovered (SIR) model, in which a similar system of equations can model the spread of a virus through a computer network, and can be simplified when making assumptions about the network itself and its fixed number of nodes and edges. In this instance, malware propagating in a network also should reflect the network it is propagating through, in which the dynamical system will factor in the nodes of the network and their properties. The system itself …

Enhancing 3d Knowledge-Based Authentication In Vr By Having Fun, William James Doyle V, Christopher Kreider

Cybersecurity Undergraduate Research Showcase

In the modern era of cyber attacks, and an ever-changing online world, safety and security must constantly evolve to fit the current standards. In virtual reality (VR) simple keyboard-typed passwords are a thing of the past. These passwords are complicated and frustrating to input. Along with the creation of VR, it also opened the door to a new era of password-based authentication. Using these systems' technology, password inputting can be a fun experience. These passwords are not only easier, and less frustrating to input, but as shown in previous studies, they can also be faster while still being secure. In …

First Amendment Roadblock? Regulating The Misuse Of Generative Ai Technologies: Impersonation And Appropriation Of Likeness Without Permission, Muhammad Rabiu

Cybersecurity Undergraduate Research Showcase

This paper initially explores the misuse of generative AI technologies, particularly their role in impersonating or appropriating individuals' likeness without consent. It will then analyze technical and legal mitigation strategies and propose recommendations to address this issue in light of the First Amendment’s constitutional Freedom of Speech provision.

From Classroom To Cloud: Why Cybersecurity Education Is Crucial For Tomorrow's Learners, Kiori Edwards

Cybersecurity Undergraduate Research Showcase

This paper examines the transformative impact of cloud computing on education, highlighting how cloud technologies are reshaping teaching and learning environments, making education more accessible, flexible, and collaborative. It also explores the critical importance of incorporating cybersecurity education into school curricula. As digital devices and cloud-based platforms become integral to students' daily lives, they are increasingly vulnerable to cyber threats. This paper argues that early cybersecurity education, starting at the elementary level, is essential for equipping future generations with the knowledge and skills needed to navigate the digital world safely, protect their personal information, and avoid cybercrime.

Integrating Humanities Into Cybersecurity Education: Enhancing Ethical, Historical, And Sociopolitical Understanding In Technical Training, Joseph Frusci

Journal of Cybersecurity Education, Research and Practice

The increasing complexity of cybersecurity challenges necessitates a holistic educational approach that integrates both technical skills and humanistic perspectives. This article examines the importance of infusing humanities disciplines such as history, ethics, political science, sociology, law, and anthropology—into cybersecurity education. Through a pilot course developed for Staten Island Technical High School, aligned with the New York State K-12 Computer Science and Digital Fluency Standards, students were introduced to an interdisciplinary curriculum that combined technical cybersecurity training with historical analysis, ethical reasoning, and sociopolitical context. The results of pre- and post-course assessments demonstrated significant improvements in critical thinking, ethical decision-making, and …

Dualrep: Knowledge Graph Completion By Utilizing Dual Representation Of Relational Paths And Tail Node Density Insights, Haji Gul, Feras Al-Obeidat, Adnan Amin, Muhammad Wasim, Fernando Moreira

All Works

Knowledge graphs (KGs) possess a vital role in enhancing the semantic comprehension of extensive datasets across many fields. It facilitate activities like recommendation systems, semantic searching, and intelligent data mining. However, lacking information can sometimes limit the usefulness of knowledge graphs (KGs), as the lack of relationships between entities could severely limit their practical application. Most existing approaches for KG completion primarily concentrate on embedding-based methods or just use relational paths, neglecting the valuable structural information offered by node density. This research presents an approach that effectively combines relational paths and the density features of tail nodes to enhance the …

Beyond Human-Centric Models In Cybersecurity Education: A Pilot Posthuman Analysis Of The Nice Workforce Framework For Cybersecurity, Ryan Straight

Journal of Cybersecurity Education, Research and Practice

This study applies a posthuman lens to the National Initiative for Cybersecurity Education (NICE) Workforce Framework, examining two key Work Roles in cybersecurity education. Employing a novel posthuman coding scheme, the associated Tasks, Knowledge, and Skills (TKS) statements were analyzed. Findings reveal significant posthuman elements within the framework while identifying opportunities for further integration. The analysis demonstrates a strong presence of human-technology entanglement and adaptive learning concepts, yet highlights areas where the framework could emphasize system complexity and interconnectedness. This research contributes to ongoing discussions on cybersecurity education in complex technological landscapes, proposing a theoretical framework for integrating posthuman concepts …

Between Copyright And Computer Science: The Law And Ethics Of Generative Ai, Devin R. Desai, Mark Riedl

Northwestern Journal of Technology and Intellectual Property

Copyright and computer science continue to intersect and clash, but they can coexist. The advent of new technologies such as digitization of visual and aural creations, sharing technologies, search engines, social media offerings, and more, challenge copyright-based industries and reopen questions about the reach of copyright law. Breakthroughs in artificial intelligence research, especially Large Language Models that leverage copyrighted material as part of training, are the latest examples of the ongoing tension between copyright and computer science. The exuberance, rush-to-market, and edge problem cases created by a few misguided companies now raises challenges to core legal doctrines and may shift …

Phishing Emails: An Evolving Cyberattack, Brooke Waltz

Cybersecurity Undergraduate Research Showcase

This paper describes the history of phishing attacks and how they turned into cyberattacks, focusing on companies. Over the course of 34 years, phishing has been evolving at an alarming rate, especially with AI now coming into play. As phishing attacks have become more prominent towards companies, there has been an increase in financial loss and data breaches, resulting in a loss of trust in companies. With this loss, companies are trying to find solutions to this problem. Some notable attacks were the RSA breach in 2011, the Texas Energy Company in 2014, and the ILOVEYOU virus in 2000. They …

Machine Learning-Driven Process Analysis And Optimization In Solid-State Welding And Fusion-Based Additive Manufacturing, Radif Uddin Ahmed

Master's Theses

In the modern era of advanced manufacturing, optimizing process parameters is pivotal in ensuring the quality and reliability of sophisticated component fabrication. This study presents a novel, data-driven approach to parameter optimization in two cutting-edge manufacturing techniques: Friction Stir Welding (FSW) and Laser Powder Bed Fusion (LPBF). By leveraging machine learning methodologies, this research addresses the critical challenge of efficiently determining optimal process parameters, a task traditionally relying on time-consuming and resource-intensive trial-and-error methods. This study will lead to a robust data-driven framework for process analysis of more advanced manufacturing techniques like the Additive Friction Stir Deposition (AFSD) process. Friction …

Improving College Students’ Attention Retention With A Brain-Controlled Drone Simulation, Ji Won Bae

USF Tampa Graduate Theses and Dissertations

In contemporary society, individuals are continuously exposed to a plethora of stimuli, which can precipitate distractions and impede cognitive performance in tasks such as professional work and academic studies. This investigation proposes an innovative approach aimed at enhancing attentional focus through the utilization of Brain-Computer Interfaces (BCI). BCIs represent advanced methodologies for deciphering neural activity patterns. Specifically,electroencephalography (EEG), a technique for monitoring the brain's electrical signals, serves as the foundation for BCI applications. EEG analyses reveal distinctive wave patterns indicative of states of concentration, vigilance, and cognitive engagement. Consequently, BCIs hold promise for the real-time assessment of users' attentional states. …

Robotic Multi-Object Grasping From A Pile: Techniques And Algorithms For Enhanced Dexterity, Tianze Chen

USF Tampa Graduate Theses and Dissertations

As robots become increasingly integrated into real-world applications such as warehousing, fulfillment centers, and manufacturing, the need for efficient and adaptable robotic systems grows. One of the key challenges is enabling robots to grasp multiple objects simultaneously, as this significantly boosts the efficiency of tasks like batch picking, sorting, and object transferring, reducing both time and energy consumption. This dissertation presents a comprehensive multi-object grasping (MOG) pipeline that includes pre-grasp selection, end-pose selection, grasping synergy calculation, and a data-driven model for estimating the number of objects being grasped. Central to this work is the development of the Experience Forest structure, …

Developing Robotic Task Planning Methods For Diverse Real-World Challenges, Md Sadman Sakib

USF Tampa Graduate Theses and Dissertations

The deployment of robotic systems across various domains has expanded significantly, with applications ranging from domestic tasks, such as cleaning and cooking, to industrial operations requiring precision and automation. These advancements highlight the critical importance of effective task planning in ensuring that robots can perform tasks safely, efficiently, and autonomously. However, task planning in robotics faces challenges related to generalization, executability, flexibility, and limitations in existing knowledge bases.

This dissertation addresses these challenges through innovative strategies aimed at enhancing robotic task planning and execution. We first focus on adapting to unknown scenarios by utilizing the Functional Object-Oriented Network (FOON) to …

Exploring Factors That Influence Artificial Intelligence Adoption In Banks And Credit Unions, Vijaya S. Tumma

USF Tampa Graduate Theses and Dissertations

The importance of Artificial Intelligence (AI) is exploding in the banking sector, fueled by enhanced productivity, improved efficiencies, and personalized services to the consumers. For credit unions, the adoption of AI technologies presents opportunities and challenges. This research explores the factors influencing AI adoption in the banking sector through the lens of Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This study aims to explore the influence of key aspects of UTAUT model, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on the intention of AI adoption among credit unions, banks, and their …

Multi-Dimensional Visualization Strategies Using Web Scraping Tools: A Word Formation Synthesis Case Study For Russian Verbs Of Sound, John Simmons

Graduate Student Research & Creative Works

No abstract provided.

Coding Connections: Exploring Relationships Between Computer Science Learning And Mathematics Achievement In Secondary Education, Bradley Hayes

USF Tampa Graduate Theses and Dissertations

This dissertation in practice explores the intersection of computer science education, specifically computational thinking and programming, with mathematics achievement among 15-year-old students in selected English-speaking countries. The research addresses a gap in understanding whether skills developed through computer science can positively influence mathematics performance by assessing the extent to which learning in computer science transfers to mathematics.

To achieve this, a quantitative methodology was employed, incorporating pilot study data from a single school and large-scale survey data from the Programme for International Student Assessment (PISA) 2022. The analysis assessed the correlation between regular participation in programming activities and mathematics attainment, …

Balancing Context And Clarity Through Visualizations For Better Decision-Making, Bhavana Doppalapudi

USF Tampa Graduate Theses and Dissertations

In this era of a data driven world, the effective communication of data through visualizations is pivotal for converting complex information into accurate insights. Effective visualizations not only enhance users' comprehension of complex data but also induce trust, leading to better decision-making. This dissertation explores methods to improve trust in visualizations by providing additional context and enhancing their clarity, ensuring appropriate data interpretations and better decisions from users.

The work in the dissertation begins by examining the impact of scatterplots combined with statistical metrics such as accuracy on users' trust and decision-making focused on recommender systems. In addition, we show …

Intersections Of Living And Machine Agencies: Art-Based Models Of Adaptive Conversation With The More-Than-Human World, Carlos Castellanos

Tradition Innovations in Arts, Design, and Media Higher Education

Today’s AI systems are not built to have reciprocal interplay with their environments and thus they demonstrate little interest in emergence, adaptation or developing mutually productive relationships with the natural world. Is a different kind of AI possible? How can artists contribute to its development? In this essay, I will discuss ways in which the arts might help guide us towards a new kind of AI, built upon adaptive conversation (i.e. shared construction of meaning) with nature. I will discuss how can work with AI while also challenging its prevailing ontology, and even suggest alternative ontologies and epistemologies. I will …

Internet Of Medical Things: Integrating Machine Learning And Wearables For Healthcare, Jingwen Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Internet of Medical Things (IoMT), which integrates Internet of Things technologies into healthcare, has become a powerful tool for enhancing health monitoring and predicting clinical outcomes. By leveraging wearable devices, IoMT facilitates continuous, cost-effective, and convenient tracking of patients' health conditions over time. This dissertation applies data-driven methods to address critical clinical challenges involving wearable devices. Specifically, it focuses on three significant clinical problems: (1) indoor contact tracing for healthcare workers using Bluetooth Low Energy (BLE) beacons, (2) predicting surgical outcomes using wearable data, and (3) developing robust models for surgical outcome prediction that account for patient variability using …