Digital Commons Network^™

Collaborative Network Traffic Management Strategies Using Distributed Reinforcement Learning And Large Language Models, Saeed Rashed Alkuwaiti

Theses

The focus of this research is to explore collaborative network traffic management strategies using the Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs) approaches. It emphasizes exploring a new tool for addressing network traffic by utilizing Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs). This is achieved by utilizing self-organizing and self-directing techniques to optimize the network performance. Using the NF-TON-IOT dataset, various classifiers such as Random Forest, AdaBoost, C4. 5, Multi-Layer Perceptron (MLP), and SVM with an RBF kernel were tested for traffic classification and intrusion detection. Research recommends that DRL optimizes the complexity of the network …

A Data-Driven Recommendation System For Selecting The Appropriate Mode Of Learning And Instructional Tools Based On Course Characteristics, Ayisha Manzoor

Theses

The rapid transformation of educational delivery methods during the COVID-19 pandemic required institutions to transition between online, hybrid, and offline learning approaches, creating both challenges and opportunities for educators and students. While online and hybrid learning modes ensured continuity, their effectiveness across different course types remained uncertain. This thesis addresses this gap by developing a datadriven recommendation framework that predicts Course Learning Outcome (CLO) achievement scores using regression, and recommends the most appropriate learning mode (online, hybrid, or offline) along with instructional tools based on course characteristics. This study analyzed 100 undergraduate and postgraduate courses from the College of Information …

The Impact Of Ai Usage On Employee Work Outcomes: The Mediating Roles Of Personal Control And Job Insecurity And The Moderating Role Of Ai Trust, Tiantian Wang

Dissertations and Theses Collection (Open Access)

The widespread application of artificial intelligence (AI) technology in the workplace offers significant potential for process optimization andperformance improvement. However, the psychological mechanisms throughwhich AI usage affects employee outcomes remain underexplored. To address this gap, the present study investigated a sample of 170 employees froma media company in China, utilizing a three-wave longitudinal survey design. Specifically, this study examined how AI usage influenced employee creativity and task performance improvement through two mediatingmechanisms: the enhancement of personal control in problem-solving and the elicitation of job insecurity. Furthermore, the moderating role of trust in AI inthe relationship between AI usage and job …

A Study Of Conceptual Primitive Elimination: Embedding Ingest Into Ptrans, Jamie C. Macbeth, Alexis Kilayko

Computer Science: Faculty Publications

In cognitive systems and cognitive linguistics, primitive decomposition systems attempt to explain cognitive phenomena by breaking things down into conceptual building blocks and provide rich and flexible representations for systems. A prime example is the Schank–Minsky Conceptual Dependency Trans-frames system, which maintains a commitment to keeping the number of primitives small and allowing them to be combined in complex ways in representing meaning, knowledge, and dynamic episodic memory. Motivated by the desire to keep the set of primitives small, this paper describes an effort to eliminate the Conceptual Dependency INGEST primitive and reconstitute its uses through combinations of the CD …

Use Of Search Tools In Software Development: A Study Of Microservice-Based Team Projects, Yi Meng Lau, Christian Michael Koh, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Universities are increasingly integrating real-world projects into software engineering curricula to preparestudents for careers involving complex concepts like Microservices Architecture (MSA). Students frequentlystruggle with such concepts within limited class time and turn to various search tools and online resources for additional help. Search tools are also widely used in the software development industry. While search engines, like Google and Yahoo!, can provide quick solutions, they pose the risk of information overload. Large Language Models (LLMs) such as ChatGPT, offer the advantage of delivering more precise answers. Studies have shown that LLMs can comprehend codes, assist in system architectural design, and …

Frame-Voyager: Learning To Query Frames For Video Large Language Models, Sicheng Yu, Chengkai Jin, Huanyu Wang, Zhenghao Chen, Sheng Jin, Zhongrong Zuo, Xiaolei Xu, Zhenbang Sun, Bingni Zhang, Jiawei Wu, Hao Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it impractical to input entire videos. Existing frame selection approaches, such as uniform frame sampling and text-frame retrieval, fail to account for the information density variations in the videos or the complex instructions in the tasks, leading to sub-optimal performance. In this paper, we propose Frame-Voyager that learns to query informative frame combinations, based on the given textual queries in the task. To train Frame-Voyager, we introduce a new data collection and labeling pipeline, by …

Democratic Training Against Universal Adversarial Perturbations, Bing Sun, Jun Sun, Wei Zhao

Research Collection School Of Computing and Information Systems

Despite their advances and success, real-world deep neural networks are known to be vulnerable to adversarial attacks. Universal adversarial perturbation, an inputagnostic attack, poses a serious threat for them to be deployed in security-sensitive systems. In this case, a single universal adversarial perturbation deceives the model on a range of clean inputs without requiring input-specific optimization, which makes it particularly threatening. In this work, we observe that universal adversarial perturbations usually lead to abnormal entropy spectrum in hidden layers, which suggests that the prediction is dominated by a small number of “feature” in such cases (rather than democratically by many …

Towards Understanding Why Fixmatch Generalizes Better Than Supervised Learning, Jingyang Li, Jiachun Pan, Vincent Tan, Kim-Chuan Toh, Pan Zhou

Research Collection School Of Computing and Information Systems

Semi-supervised learning (SSL), exemplified by FixMatch (Sohn et al., 2020), has shown significant generalization advantages over supervised learning (SL), particularly in the context of deep neural networks (DNNs). However, it is still unclear, from a theoretical standpoint, why FixMatch-like SSL algorithms generalize better than SL on DNNs. In this work, we present the first theoretical justification for the enhanced test accuracy observed in FixMatch-like SSL applied to DNNs by taking convolutional neural networks (CNNs) on classification tasks as an example. Our theoretical analysis reveals that the semantic feature learning processes in FixMatch and SL are rather different. In particular, FixMatch …

Capo: Cooperative Plan Optimization For Efficient Embodied Multi-Agent Cooperation, Jie Liu, Pan Zhou, Yingjun Du, Ah-Hwee Tan, Cees Snoek, Jan-Jakob Sonke, Efstratios Gavves

Research Collection School Of Computing and Information Systems

In this work, we address the cooperation problem among large language model (LLM) based embodied agents, where agents must cooperate to achieve a common goal. Previous methods often execute actions extemporaneously and incoherently, without long-term strategic and cooperative planning, leading to redundant steps, failures, and even serious repercussions in complex tasks like search-and-rescue missions where discussion and cooperative plan are crucial. To solve this issue, we propose Cooperative Plan Optimization (CaPo) to enhance the cooperation efficiency of LLM-based embodied agents. Inspired by human cooperation schemes, CaPo improves cooperation efficiency with two phases: 1) meta-plan generation, and 2) progress-adaptive meta-plan and …

Ivyapc: Auditable Generalized Payment Channels, Ming Li, Yuxian Li, Jian Weng, Yingjiu Li, Jiasi Weng, Junzuo Lai, Robert H. Deng

Research Collection School Of Computing and Information Systems

Payment channels are a cornerstone of a scalable blockchain infrastructure that enables transacting parties to lock assets on the blockchain and perform rapid off-chain updates with minimal latency and overhead. These protocols dramatically reduce on-chain interaction and improve throughput, with blockchain consensus only invoked in the event of disputes or final closure. While widely adopted in single-chain settings—such as in the Lightning Network for Bitcoin—existing constructions have several limitations, in particular they suffer from at least one of the following limitations: 1. No cross-chain. They do not enable fast trading of assets that reside on multiple isolated blockchains. 2. Non-optimal …

Can Llms Replace Manual Annotation Of Software Engineering Artifacts?, Toufique Ahmed, Premkumar Devanbu, Christoph Treude, Michael Pradel

Research Collection School Of Computing and Information Systems

Experimental evaluations of software engineering innovations, e.g., tools and processes, often include human-subject studies as a component of a multi-pronged strategy to obtain greater generalizability of the findings. However, human-subject studies in our field are challenging, due to the cost and difficulty of finding and employing suitable subjects, ideally, professional programmers with varying degrees of experience. Meanwhile, large language models (LLMs) have recently started to demonstrate human-level performance in several areas. This paper explores the possibility of substituting costly human subjects with much cheaper LLM queries in evaluations of code and code-related artifacts. We study this idea by applying six …

How Developers Interact With Ai: A Taxonomy Of Human-Ai Collaboration In Software Engineering, Christoph Treude, Marco A. Gerosa

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code …

Dual Operation Aggregation Graph Neural Networks For Solving Flexible Job-Shop Scheduling Problem With Reinforcement Learning, Peng Zhao, You Zhou, Di Wang, Zhiguang Cao, Yubin Xiao, Xuan Wu, Yuanshu Li, Hongjia Liu, Wei Du, Yuan Jiang, Liupu Wang

Research Collection School Of Computing and Information Systems

With the widespread adoption of Internet Protocol (IP) communication technology and web-based platforms, cloud manufacturing has become a significant hallmark of Industry 4.0. Integrating graph algorithms into these web-enabled environments is crucial as they facilitate the representation and analysis of complex relationships in manufacturing processes, enabling efficient decision-making and adaptability in dynamic environments. As a key scheduling problem in cloud manufacturing, the flexible job-shop scheduling problem (FJSP) finds extensive applications in real-world scenarios. However, traditional FJSP-solving methods struggle to meet the efficiency and adaptability demands of cloud manufacturing due to generalization issues and excessive computational time, while reinforcement learning-based methods …

Graph-Assisted Offline-Online Deep Reinforcement Learning For Dynamic Workflow Scheduling, Yifan Yang, Gang Chen, Hui Ma, Cong Zhang, Zhiguang Cao, Mengjie Zhang

Research Collection School Of Computing and Information Systems

Dynamic workflow scheduling (DWS) in cloud computing presents substantial challenges due to heterogeneous machine configurations, unpredictable workflow arrivals/patterns, and constantly evolving environments. However, existing research often assumes homogeneous setups and static conditions, limiting flexibility and adaptability in real-world scenarios. In this paper, we propose a novel Graph assisted Offline-Online Deep Reinforcement Learning (GOODRL) approach to building an effective and efficient scheduling agent for DWS. Our approach features three key innovations: (1) a task-specific graph representation and a Graph Attention Actor Network that enable the agent to dynamically assign focused tasks to heterogeneous machines while explicitly considering the future impact of …

Context-Aware Representation: Jointly Learning Item Features And Selection From Triplets, Rodrigo Alves, Antoine Ledent

Research Collection School Of Computing and Information Systems

In areas of machine learning such as cognitive modeling or recommendation, user feedback is usually context-dependent. For instance, a website might provide a user with a set of recommendations and observe which (if any) of the links were clicked by the user. Similarly, there is growing interest in the so-called “odd-one-out” learning setting, where human participants are provided with a basket of items and asked which is the most dissimilar to the others. In both of those cases, the presence of all the items in the basket can influence the final decision. In this article, we consider a classification task …

A Functional Software Reference Architecture For Llm-Integrated Systems, Alessio Bucaioni, Martin Weyssow, Junda He, Yunbo Lyu, David Lo

Research Collection School Of Computing and Information Systems

The integration of large language models into software systems is transforming capabilities such as natural language understanding, decision-making, and autonomous task execution. However, the absence of a commonly accepted software reference architecture hinders systematic reasoning about their design and quality attributes. This gap makes it challenging to address critical concerns like privacy, security, modularity, and interoperability, which are increasingly important as these systems grow in complexity and societal impact. In this paper, we describe our emerging results for a preliminary functional reference architecture as a conceptual framework to address these challenges and guide the design, evaluation, and evolution of large …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

The Urgency Of Instituting Systemic Cybersecurity Curriculum Within Stem At Secondary Educational Levels In Preparation For Postsecondary Institutions., Robert Spencer

Journal of Cybersecurity Education, Research and Practice

Over the last 20 years, many secondary institutions have made advances developing curriculum defined as “STEM (Science, Engineering and Mathematics)” in order to ensure secondary students are eligible to apply as well excel in technology degree programs at the college and university levels. Although various initiatives exist, there are studies however, which allude to a great possibility that there will be a lack of cybersecurity professionals filling present day and anticipated future positions. Despite a large number of federal and educational enhancements there is need for additional research regarding instituting overall systemic processes and curriculum which supports secondary student transition …

Review Of Algorithms Of Resistance: The Everyday Fight Against Platform Power, John G. Mcnutt

The Journal of Social Encounters

No abstract provided.

Ransomware In Healthcare: Threats, Impacts, And Mitigation Strategies, Mackenzie Dotson, Kasi Gorli, Alberto Coustasse

Management Faculty Research

Excerpt: The growing digitalization of healthcare has exposed hospitals to significant cybersecurity threats, particularly ransomware attacks. The Health Sector Cybersecurity Coordination Center (HC3) reported that as of mid-2024, there were 730 cyber-attacks worldwide against healthcare institutions, with 530 targeting the U.S. (AHA, 2024). Half of these incidents involved ransomware, a type of malware that restricts access to critical data until a ransom is paid (HHS, 2021). Hospitals are attractive targets for cybercriminals due to their essential role in patient care. Cybercriminals exploit vulnerabilities in hospital networks, often causing severe operational and financial damage. Factors such as understaffed IT teams, outdated …

Cyber Threats In Healthcare: The Ransomware Epidemic, Mackenzie Dotson, Kasi Gorli, Alberto Coustasse

Management Faculty Research

In this presentation, we will delve into the growing ransomware crisis in healthcare, examining how these cyber threats disrupt hospital operations, jeopardize patient safety, and impose significant financial burdens. From understanding how ransomware infiltrates hospital systems to exploring real-world case studies, we will uncover the devastating impact of these attacks. Our discussion will also focus on mitigation strategies, cybersecurity best practices, and policy recommendations to safeguard healthcare institutions from future threats.

Integrative Multi-Omics And Clinical Data Analysis For Predicting Recurrence And Survival In Uterine Cancer, Varun Sai Raigir

USF Tampa Graduate Theses and Dissertations

The prediction of uterine cancer recurrence is very important for assisting women in reducing the cancer risks and also for the growing field of personalized medicine. The primary aim of this thesis is to investigate the integration of various omics data alongside clinical and therapeutic information to predict survival in uterine cancer. The combination is very important for understanding the risk factors, including clinical aspects, genetics, and the treatment schedule, in order to prescribe the appropriate way to reduce the risk of recurrence, make clinical interactions easier, and enhance personalized patient care. This study utilizes the publicly accessible TCGA dataset, …

Automated Data Analysis For Concussion Patient Records: A Flutter-Based Desktop Application, Fhaheem Tadamarry

USF Tampa Graduate Theses and Dissertations

Concussions are a prevalent and complex medical condition requiring careful clinical assessment and data-driven insights for effective management. This thesis presents the development of an automated data analysis system for concussion patient records, integrating Flutter-based desktop application development with SQL-driven data processing. The system provides a streamlined, interactive interface for clincians and researchers to upload, visualize, and analyze patient data efficiently.

The proposed solution automates data cleaning, preprocessing, and statistical analysis, ensuring robust and reliable insights into demographic, clinical, and recovery-related factors. Key analyses include sex-based differences injury mechanisms, prior head injury impact, mood disorder correlations, and time-to-treatment variations. The …

Multimodal Ai-Driven Biomarker For Early Detection Of Cancer Cachexia, Sabeen Ahmed

USF Tampa Graduate Theses and Dissertations

Cancer cachexia is a metabolic syndrome characterized by substantial skeletal muscle loss, impacting cancer patients' survival and quality of life. Despite its clinical significance, early detection remains a challenge due to the lack of standardized diagnostic criteria and the reliance on indirect markers. This work presents an AI-driven approach to enhance cachexia detection and monitoring by integrating multiple deep learning methodologies. We explore transformer architectures for time-series analysis to model sequential medical data, enabling disease prediction and progression modeling. To ensure robust and reliable decision-making in clinical settings, we explore Bayesian deep neural networks for uncertainty estimation. Additionally, we introduce …

Robust Spacecraft Autonomy For Deep Space Exploration In Special Euclidean Group Se(3), Matthew Wittal

Doctoral Dissertations and Master's Theses

Over the past half-century, humanity has gained extensive experience conducting manned spaceflight near Earth. Arguably, "near Earth" could even include the Moon — the most distant destination humans have reached. However, "near" in this work primarily refers low Earth orbit (LEO). One could argue that we have not truly left Earth since the Apollo, as spacecraft in some LEOs remain subject to atmospheric drag thus emphasizing their continued connection to Earth's immediate environment. Reflecting on this, it becomes clear that humanity has largely remained bound to Earth’s immediate vicinity since the Apollo missions reached the Moon. However, that is set …

Addressing Equity Issues In Elementary Computer Science Education: Knowns, Unknowns, And Implications For Future Work, Mike Karlin, Yin-Chan Janet Liao, Swati Mehta, Afreen Iqbal, Mahya Minaiy, Minhye Son, Jessica Pandya

Journal of Computer Science Integration

In 2016, a national coalition of stakeholders released the K-12 computer science (CS) framework. In the time since, there has been an increased push at the local, state, and national level to integrate CS knowledge and skills into K-12 education. Despite this push, significant equity issues exist within the field. While growing research has been done on CS equity issues at the high school level, we know these equity gaps often begin to emerge in elementary school where less is known. Therefore, we conducted a systematic literature review to better understand and explore the elementary CS equity research landscape from …

The Present And Future Of Ai: Ethical Issues And Research Opportunities, Ankita Srivastava, Marco Marabelli, Danielle Blanch-Hartigan, Jeffrey Moriarty, Evan Carey

Computer Information Systems Faculty Publications

Healthcare is currently a fast-changing industry with AI and generative AI (GenAI) playing a prominent role in the transformation of clinical as well as managerial practices. Clinical practices involve AI to diagnose diseases and develop new drugs and compounds, while managerial practices concern AI-supporting processes such as billing patients and insurance companies, handling electronic medical records, and supporting remote connections with patients, increasingly using virtual and augmented reality. Yet, all these opportunities offered by AI come with challenges involving potential ethical issues, such as discrimination, bias, lack of accessibility, and privacy issues. In March 2024, we organized a panel with …

The Impact Of Artificial Intelligence On Quality Of Higher Education, Pragati K. Rouniyar

Honors Thesis

Artificial Intelligence (AI) is redefining higher education, captivating scholars with its promise to personalize learning and streamline institutions. However, underneath this assurance exists a network of ethical challenges, disparities in equity, and inquiries regarding academic integrity that require our focus. In pursuit of this goal, this research employs a mixed-methods strategy—through the implementation of surveys and semi-structured interviews—to investigate the transformative effects of AI on higher education, concentrating on its repercussions for teaching techniques, learning results, and institutional processes. This study’s findings indicate that AI can personalize educational experiences to meet individual needs, ease course administrative workload, and assist with …

Context And Interpretability In Affective Computing Applications, Saandeep Aathreya Sidhapur Lakshminarayan

USF Tampa Graduate Theses and Dissertations

Affective Computing (AC) is a subdomain of AI that primarily deals with recognizing and interpreting human emotions. This field inherently intersects with psychological studies, as the comprehension of human emotions and behaviors necessitates an understanding of their underlying cognitive processes. One such concepts that lends itself from psychology is context. Roughly speaking, context in AC is defined as any meta information (e.g., environment) that can be utilized to describe the interaction between a user and a model to solve a particular application (e.g., emotion recognition). This doctoral dissertation comprises of two distinct yet interconnected components (Part I and II), the …

Advanced Techniques In Symmetric Key Cryptanalysis, Debasmita Chakraborty

Doctoral Theses

Symmetric key cryptographic primitives are essential tools used extensively in daily digital interactions. These primitives are mainly designed to provide three key services: ensuring data confidentiality, maintaining data integrity, and verifying the authenticity of data sources. The primary types of symmetric key primitives that deliver these services include block ciphers, stream ciphers, hash functions, message authentication codes, and authenticated encryption with associated data. This thesis mainly explores the security analysis of hash functions, several block ciphers, and stream ciphers using some advanced cryptanalytic techniques. We begin by examining the collision security of a hash function, specifically under the assumption that …