Digital Commons Network^™

Uc-226 Real-Time Bus Monitoring Using Kafka, Samuel A Bostian, Michael Rizig, Charlie Mclarty, Brian A Pruitt, Allen Roman

C-Day Computing Showcase

The GCPS Real-Time Bus Monitoring System aims to enhance bus operations for Gwinnett County Public Schools by transitioning from a polling-based system to a real-time Kafka event-streaming architecture. This project processes telemetry data from over 2,000 buses, simulating a scalable, near-instantaneous data flow into an SQL Server database. Key features include real-time data validation, efficient data storage, and containerized deployment for consistency across environments. Using an Agile approach, our team handled evolving requirements from the sponsor, who is new to senior project collaborations. This system enables GCPS to monitor bus locations with reduced latency, enhanced accuracy, and improved resource management, …

Gmr-215 Efficient Sentiment Analysis Using Encoder-Only Transformer, Rohan Jonnalagadda, Srinidhi Kandimalla, Siri Yellu

C-Day Computing Showcase

In the era of social media, sentiment analysis has emerged as a vital instrument for comprehending public opinion, especially on sites like LinkedIn and Twitter. Because user-generated content is informal and noisy, traditional sentiment classification techniques like Naive Bayes and Support Vector Machines sometimes find it difficult to capture context, sarcasm, and long-term interdependence. In order to improve sentiment analysis accuracy for social media datasets with a specific focus on sentiments related to corporate layoffs, this study suggests an encoder-only transformer model. Our method successfully captures intricate phrase patterns and contextual subtleties in textual data by leveraging the self-attention mechanism …

Uc-140 Streamlining School Bus Monitoring: Gcps's Transition To Real-Time Kafka Event Processing​, Sarah Fashinasi, Tyler J Hood, Jeffrey Sanderson, Amali B Mchie, Alexandra Baker

C-Day Computing Showcase

This project develops a prototype real-time bus monitoring system for Gwinnett County Public Schools using simulated Kafka event streaming to replace current API polling methods. The system processes simulated Asset Location and Speed events, mimicking Samsara's Kafka Connector, performing data validation before storing in SQL Server. The containerized solution demonstrates the potential for near real-time visibility into school bus operations.

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.

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.

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 …

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 …

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 …

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 …

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, …

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 …

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 …

Misinformation, Fraud, And Stereotyping: Towards A Typology Of Harm Caused By Deepfakes, Paulina Trifonova , '25, Sukrit Venkatagiri

Computer Science Faculty Works

Scholars, politicians, and journalists have raised alarm over the potential for AI-generated photos, video, and audio—often referred to as deepfakes—to reduce trust in one another and our institutions. Despite these clarion calls, little empirical work exists on how deepfakes are being used to harm individuals outside of non-consensual intimate imagery (NCII). This research provides a preliminary analysis of 50 wide-ranging incidents of deepfake harm. We find that the most common types of harm are relational, systemic, financial, and emotional. Apart from AI-generated NCII, the most prevalent uses of deepfakes to cause harm were instances of mis- and disinformation, fraud, and …

Video Label Refinement And Temporal Localization Using Motion Signal Patterns, Jennifer Piane

College of Computing and Digital Media Dissertations

Performing video analysis for activity recognition presents challenges beyond classification, including obtaining class labels and performing temporal localization. One such challenge is precisely labeling a video with class labels having the exact start and end frames of an activity - a difficult task for a human to perform. Moreover, the task of annotating a video at any level of precision can quickly become tedious, impacting the attentiveness of the annotator and resulting in class label errors. Temporally localizing an activity within a video presents a second challenge. This dissertation investigates novel signal and image processing methods for motion features extracted …

Hacker, Their Actions, And Fear Appeal: A First Look Through The Lens Of Children, Rizu Paudel, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

With the increasing use of computers and smartphones by children, their online safety has become a major concern due to the lack of security awareness. Prior studies pointed to children's poor password habit and vague perceptions on the significance of passwords. While users must be sufficiently motivated to perform a target behavior, a little study to date, focused on understanding how we can encourage children towards strong password creation. As we begin to address this gap, we examined children's perceptions of adversary's actions that instill fear in the context of password compromise. Our semi-structured interviews with 20 children (aged between …

Flowgpt: Exploring Domains, Output Modalities, And Goals Of Community-Generated Ai Chatbots, Xian Li, Yuanning Han, Di Liu, Pengcheng An, Shuo Niu

Computer Science

The advent of Generative AI and Large Language Models has not only enhanced the intelligence of interactive applications but also catalyzed the formation of communities passionate about customizing these AI capabilities. FlowGPT, an emerging platform for sharing AI prompts and use cases, exemplifies this trend, attracting many creators who develop and share chatbots with a broader community. Despite its growing popularity, there remains a significant gap in understanding the types and purposes of the AI tools created and shared by community members. In this study, we delve into FlowGPT and present our preliminary findings on the domain, output modality, and …

Harnessing Llms For Automated Video Content Analysis: An Exploratory Workflow Of Short Videos On Depression, Jiaying (Lizzy) Liu, Yunlong Wang, Yao Lyu, Yiheng Su, Shuo Niu, Orson Xuhai Xu, Yan Zheng

Computer Science

Despite the growing interest in leveraging Large Language Models (LLMs) for content analysis, current studies have primarily focused on text-based content. In the present work, we explored the potential of LLMs in assisting video content analysis by conducting a case study that followed a new workflow of LLM-assisted multimodal content analysis. The workflow encompasses codebook design, prompt engineering, LLM processing, and human evaluation. We strategically crafted annotation prompts to get LLM Annotations in structured form and explanation prompts to generate LLM Explanations for a better understanding of LLM reasoning and transparency. To test LLM's video annotation capabilities, we analyzed 203 …

Real-Time Lidar Slam Algorithm Based On Distribution Optimal Registration, Weigang Li, Chuxiang Yu, Yongqiang Wang, Shaofeng Zou

Journal of System Simulation

Abstract: When scanning the surrounding environment, a lidar will generate some cluttered and sparse point cloud, which will cause excessive distribution fitting errors and correlation distances in the registration process, thus affecting the accuracy of the registration algorithm and the effect of simultaneous localization and mapping (SLAM). To address this problem, a real-time lidar SLAM algorithm based on distribution optimal registration is proposed. An eigenspectrum filter is designed, which takes the normalized minimum eigenvalue as the filtering object to filter out the points that do not match the set distribution in order to reduce the distribution fitting error. Secondly, a …

Slam Dynamic Algorithm Based On Improved Feature Description, Qiang Fu, Xianyun Teng, Yuanfa Ji, Fenghua Ren

Journal of System Simulation

Abstract: The original ORB descriptor algorithm has a low matching accuracy and long matching time, the positioning accuracy and robustness of the SLAM(simultaneous localization and mapping) system are severely disturbed by moving objects in dynamic scenes, and the ORB-SLAM3 system is incapable of constructing dense maps. To address the above problems, this paper proposes an improved ORB-SLAM3 based on the BEBLID descriptor and object detection. A lightweight YOLOv5s dynamic object detection network and dynamic feature removal module are fused with the tracking thread to improve the system's positioning accuracy. Replacing the original feature description algorithm, an improved local image descriptor …

The Unfairness Of Fair Machine Learning: Leveling Down And Strict Egalitarianism By Default, Brent Mittelstadt, Sandra Wachter, Chris Russell

Michigan Technology Law Review

In recent years, fairness in machine learning (ML), artificial intelligence (AI), and algorithmic decision-making systems has emerged as a highly active area of research and development. To date, most measures and methods to mitigate bias and improve fairness in algorithmic systems have been built in isolation from policymaking and civil societal contexts and lack serious engagement with philosophical, political, legal, and economic theories of equality and distributive justice. Many current measures define “fairness” in simple terms to mean narrowing gaps in performance or outcomes between demographic groups while preserving as much of the original system’s accuracy as possible. This oversimplified …

The Implications Of Chatgpt For Legal Services And Society, Andrew Perlman

Michigan Technology Law Review

On November 30, 2022, OpenAI released a chatbot called ChatGPT.1 To demonstrate the chatbot’s sophistication and its potential implications, both for legal services and society more generally, most of this paper was generated in about an hour through prompts within ChatGPT. Only this abstract, the preface, the outline headers, the footnotes, the epilogue, and the prompts were written by a person. ChatGPT generated the rest of the text with no human editing. To be clear, the responses generated by ChatGPT were imperfect and at times problematic, and the use of an AI tool for law-related services raises a host of …

Inferring Tlb Configuration With Performance Tools, Cristian Agredo, Tor J. Langehaug, Scott R. Graham

Faculty Publications

Modern computing systems are primarily designed for maximum performance, which inadvertently introduces vulnerabilities at the micro-architecture level. While cache side-channel analysis has received significant attention, other Central Processing Units (CPUs) components like the Translation Lookaside Buffer (TLB) can also be exploited to leak sensitive information. This paper focuses on the TLB, a micro-architecture component that is vulnerable to side-channel attacks. Despite the coarse granularity at the page level, advancements in tools and techniques have made TLB information leakage feasible. The primary goal of this study is not to demonstrate the potential for information leakage from the TLB but to establish …

Scholar Perspectives On The Impact Of A Scientific Community Program For Neurodivergent Undergraduate Stem Scholars, Dylan Sullivan, Fernando Zavala, Derek Hidalgo, Jacob Stolle, Rebecca Matte, Christin B. Monroe

Journal of Science Education for Students with Disabilities

Despite the adaptive strengths and unique problem-solving skills demonstrated by neurodivergent (ND) individuals, they remain underrepresented in Science, Technology, Engineering and Mathematics (STEM) fields. High unemployment rates among individuals with disabilities emphasize the need for addressing barriers to entry and persistence in the workforce. This study introduces a program designed to enhance opportunities for neurodivergent STEM scholars with financial needs, supported by the National Science Foundation (NSF). The program involves: 1) a weekly cohort course to engage in professional development, 2) use of the Birkman Method® survey to help scholars identify and communicate strengths, fostering self-awareness and growth, and 3) …

Navigating The Ethical Maze: Emerging Technologies And Pedagogies In Higher Education, Harika Rao

Faculty and Staff Publications & Presentations

No abstract provided.

Seshaiyer: Understanding Non-Linear Dynamics Of Interacting Subpopulations And Implicit Human Behavior Using Physics-Informed Neural Networks, Naima Aubry-Romero, Alonso Ogueda-Oliva, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Predicting And Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence In Pancreatic Cancer, Guangbo Yu, Zigeng Zhang, Aydin Eresen, Qiaoming Hou, Farideh Amirrad, Sha Webster, Surya M. Nauli, Vahid Yaghmai, Zhuoli Zhang

Pharmacy Faculty Articles and Research

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent advancements and identifying gaps in the current research, this review aims to provide a comprehensive overview of how AI and immunotherapy can be integrated to develop more personalized and effective treatment strategies. The insights from this review may guide future research efforts …