Computer Sciences Commons^™

A Secure And Effective Framework For Key Concept Mining From Educational Content Using Large Language Models, Ashika Sameem Abdul Rasheed

Theses

This thesis examines the use of Large Language Models (LLMs) in education, with a focus on improving performance and implementing strong security measures. The research has two main goals, namely, the development of an effective lecture summarization technique using LLMs and identifying and addressing security vulnerabilities in LLM applications according to OWASP (Open Web Application Security Project) guidelines. For the former goal, we have proposed an effective framework for fine-tuning LLMs using real lecture datasets and compared the performance of different LLMs. For the latter goal, we conducted a thorough review of the application dataflow of the proposed framework and …

Ai-Powered Pedagogy: Revolutionizing Students’ Learning Experiences Through Integration Of Ai Technologies, Amna Awad Alsaedi

Theses

This research examines the integration of Artificial Intelligence (AI) within the educational sector with the aim of enhancing student learning outcomes. AI offers tailored learning experiences, interactive educational content, prompt feedback, and access to diverse learning resources. Nonetheless, challenges including addiction, costliness, privacy infringement, bias, ethical dilemmas, market competition, and moral considerations require resolution. Research particularly delves into the utilization of chatbots, and algorithms designed to simulate human interactions and generate text resembling human speech. Educational applications powered by AI have the potential to heighten student engagement, comprehension, and academic performance by substituting traditional assignments with concise, information-rich lessons. Furthermore, …

Effect Of Virtual Reality Technology On Computer Science/Engineering Based Laboratories Education – A Case Study, Mariam A. Al Nuaimi

Theses

Virtual reality (VR) is becoming increasingly popular and essential in education as institutions strive to incorporate technology in education. In Engineering and Computer Science, students face difficulties in comprehending many abstract complex concepts that are typically studied in Labs. This situation worsens with hardware failure and the lack of pedagogical tools. The main goal of this paper is to investigate the impact of VR environments on learning advanced and complex STEM concepts. We also explore the impact of different human-computer interaction (HCI) techniques including gamification on the learning experience of STEM students. To measure such impact, we developed a VR …

From Biased Data Inputs To Your Discriminatory Diagnosis Outputs: A Review Of Legal Liability For Artificial Intelligence In Healthcare, Amber Bolden

Michigan Technology Law Review

While health disparities in America occur due to non-medical circumstances, certain protected classes experience healthcare disparities due to the biases of medical professionals. Biased diagnoses, both intentional or unintentional, have existed throughout the history of the medical profession. That those biases are becoming data for training algorithms raises concerns as the medical field increasingly incorporates and standardizes artificial and augmented intelligence in patient diagnosis and treatment. Currently unregulated but with lifedetermining potential, artificial intelligence (AI) when used in patient treatment leads to important questions: should the doctor, the provider, or the AI developers be liable, and for what? Section II …

Some Similarity Measures On Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets And Their Applications In Medical Diagnosis, Afshan Qayyum, Noreen Mushtaq, Tanzeela Shaheen, Wajid Ali, Florentin Smarandache

Neutrosophic Systems with Applications

In recent years, the application of fuzzy sets has gained significant attraction in various fields, including medical diagnosis, due to their ability to manage uncertainties and imprecise information. This paper focuses on the comparative analysis of similarity measures within the realm of Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets (GIVIFSESs) and explores their application in the domain of medical diagnosis. Most of the important topics in fuzzy set theory are the similarity measures between the generalizations of fuzzy set theory. Similarity measures are a crucial tool which was used in data science. In this process, we measure how much the …

Left, Then Right Internal Carotid Artery Dissection: A Case Report, Jeffrey M. Kalczynski, John Douds, Michael E. Silverman

SKMC Student Presentations and Publications

INTRODUCTION: We present a unique case of a patient who presented to the emergency department with stroke-like symptoms found to have a spontaneous, left-sided internal carotid artery dissection (ICAD).

CASE REPORT: The patient was treated successfully with thrombectomy and subsequently developed contralateral symptoms caused by a right-sided ICAD. This was managed with a second contra-lateral thrombectomy. The patient's course was complicated by persistent and mild hypotension, postulated to be secondary to bilateral carotid baroreceptor trauma from the dissections.

CONCLUSION: This case highlights the importance of close neurological monitoring for patients, preferably in a neurologic critical care setting, during and after …

Enhancing Post Silicon Visibility Using Language Modelling Techniques, Nathaniel Joseph Fender

USF Tampa Graduate Theses and Dissertations

The debugging phase is a critical time in the development of a new system on chip product. Specifically, the post-silicon validation phase is one of the most important, as it allows engineers to test the behavior of a device in a real world setting. However, the issue of noisy or incomplete data is a frequent issue when attempting to debug an SoC design during this step. This thesis examines the utility of utilizing machine learning models for the purpose of repairing missing data in a system trace. We trained various models using the transformer architecture to identify missing data in …

Predicting Human Chess Moves With Large Language Models, Benjamin Kreiger

USF Tampa Graduate Theses and Dissertations

As artificial intelligence (AI) surpasses human performance in more tasks, the interest in leveraging and collaborating with this technology for greater productivity continues to grow. However, the black-box nature of current AI can make it difficult to interpret and unsuitable to perform tasks that are more complex and require human intuition. This has led to the pursuit of AI systems that can model individual behavior. Chess offers an ideal environment to explore this task due to its complexity, structure, and the abundance of data containing unique human decision-making examples. Given that a chess game can be fully represented with text, …

Please Understand My Disability: An Analysis Of Youtubers’ Discourse On Disability Challenges, Shuo Niu

Computer Science

Video-sharing platforms offer a unique avenue for people with disabilities (PWDs) to highlight their experiences, including the challenges and accessibility barriers they face. While creators with disabilities effectively use these platforms to share their life struggles and advocate for societal changes, the scope of research exploring the nature of the discourse activities related to disability challenges remains limited. Our study addresses this gap by conducting a comprehensive qualitative content analysis of 468 videos posted by YouTubers with a range of disabilities, including vision, speech, mobility, hearing, and cognitive and neural impairments. Our findings reveal a predominant discussion on stigma and …

Efficient Multiplicative-To-Additive Function From Joye-Libert Cryptosystem And Its Application To Threshold Ecdsa, Haiyang Xue, Ho Man Au, Mengling Liu, Yin Kwan Chan, Handong Cui, Xiang Xie, Hon Tsz Yuen, Chengru Zhang

Research Collection School Of Computing and Information Systems

Threshold ECDSA receives interest lately due to its widespread adoption in blockchain applications. A common building block of all leading constructions involves a secure conversion of multiplicative shares into additive ones, which is called the multiplicative-to-additive (MtA) function. MtA dominates the overall complexity of all existing threshold ECDSA constructions. Specifically, O(n2) invocations of MtA are required in the case of n active signers. Hence, improvement of MtA leads directly to significant improvements for all state-of-the-art threshold ECDSA schemes.In this paper, we design a novel MtA by revisiting the Joye-Libert (JL) cryptosystem. Specifically, we revisit JL encryption and propose a JL-based …

A Survey Of Ontology Expansion For Conversational Understanding, Jinggui Liang, Yuxia Wu, Yuan Fang, Hao Fei, Lizi Liao

Research Collection School Of Computing and Information Systems

In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding. It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world …

Bibliography For "3-D Printing Display", Isabella Piechota, Arianna Tillman, Annikah Carpio

Library Displays and Bibliographies

A bibliography created to support a display about 3D printing at the Leatherby Libraries during November 2024-February 2025 at the Leatherby Libraries at Chapman University.

Size And Shape Dependence Of Hydrogen-Induced Phase Transformation And Sorption Hysteresis In Palladium Nanoparticles, Xingsheng Sun, Rong Jin

Chemical and Materials Engineering Faculty Publications

Phase transitions of metals in hydrogen (H) environments are critically import- ant for applications in energy storage, catalysis, and sensing. Nanostructured metallic particles can lead to faster charging and discharging kinetics, increased lifespan, and enhanced catalytic activities. However, establishing a direct causal link between nanoparticle structure and function remains challenging. In this work, we establish a computational framework to explore the atomic config- uration of a metal-hydrogen system when in equilibrium with a H environ- ment. This approach combines Diffusive Molecular Dynamics with an itera- tion strategy, aiming to minimize the system’s free energy and ensure uniform chemical potential across …

Toward A Responsible Future: Recommendations For Ai-Enabled Clinical Decision Support, Steven Labkoff, Bilikis Oladimeji, Joseph Kannry, Anthony Solomonides, Russell Leftwich, Eileen Koski, Amanda L Joseph, Monica Lopez-Gonzalez, Lee A Fleisher, Kimberly Nolen, Sayon Dutta, Deborah R Levy, Amy Price, Paul J Barr, Jonathan D Hron, Baihan Lin, Gyana Srivastava, Nuria Pastor, Unai Sanchez Luque, Tien Thi Thuy Bui, Reva Singh, Tayler Williams, Mark G Weiner, Tristan Naumann, Dean F Sittig, Gretchen Purcell Jackson, Yuri Quintana

Faculty, Staff and Student Publications

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.

OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.

MATERIALS AND METHODS: In May …

Unlocking Potential: Analyzing The Content, Style, Structure, And Interactivity Of Mesonets As Operational Dashboards, Savannah Olivas, Jeannette Sutton, Michele K. Olson

Emergency Preparedness, Homeland Security, and Cybersecurity Faculty Scholarship

Emergency managers need data and information to make life-saving decisions on behalf of the public. Operational dashboards, if designed appropriately, can provide this information in a central location and reduce cognitive demands during decision-making. Mesonet websites can serve as a type of operational dashboard that has the potential to provide the meteorological data necessary for emergency managers to make decisions. In this study, we use quantitative content analysis to examine the content, style, structure, and interactivity of 18 Mesonet websites from across the contiguous United States. We find that Mesonet websites vary in the type and amount of content they …

Safety-Centric Analysis Of Grounding Systems For Substations In Distribution Grids, Fazel Mohammadi, Mahmood Mirhashemi

Electrical & Computer Engineering and Computer Science Faculty Publications

The safety of grounding systems for substations in distribution grids is paramount to ensuring operational reliability, protecting personnel and equipment, and maintaining the stability of distribution grids while complying with regulatory standards. This paper explores essential safety aspects of grounding systems, including fault current handling strategies, the interdependence between protective devices and grounding systems, and maintenance practices. The integration of grounding systems design with overall substation layout and design considerations by focusing on mitigating Ground Potential Rise (GPR) and optimizing bonding techniques, is examined. Additionally, advanced techniques, such as high-frequency grounding and Transient Ground Potential Rise (TGPR) management, are presented …

Effect Of Virtual Reality Technology On Computer Science/Engineering Based Laboratories Education – A Case Study, Saeed Salem Al Shebli

Theses

The rapid growth in mobile applications raises critical concerns about the security of apps and users' privacy, especially in permission control. Mobile apps access sensitive information of users, and the current cybersecurity landscape faces a huge challenge in ensuring the least required permissions are granted. This research focuses on designing an advanced permission recommendation system that couples the strengths of Natural Language Processing (NLP) and Machine Learning (ML) in solving most of the existing gaps in permission management, thus guiding which permissions are mostly needed by Android applications.

The research thus follows a multi-classification approach, integrating state-of-the-art ML techniques with …

Ultra-High Resolution Image Segmentation Via Locality-Aware Context Fusion And Alternating Local Enhancement, Wenxi Liu, Qi Li, Xindai Lin, Weixiang Yang, Shengfeng He, Yuanlong Yu

Research Collection School Of Computing and Information Systems

Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present the alternating local enhancement …

Large Language Models For Software Engineering: A Systematic Literature Review, Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, Haoyu Wang

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a Systematic Literature Review (SLR) on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes. We selected and analyzed 395 research articles from January 2017 to January 2024 to answer four key Research Questions (RQs). In RQ1, we categorize different LLMs …

Strength Lies In Differences! Improving Strategy Planning For Non-Collaborative Dialogues Via Diversified User Simulation, Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system’s objectives. This poses two main challenges for existing dialogue agents: 1) The inability to integrate user-specific characteristics into the strategic planning, and 2) The difficulty of training strategic planners that can be generalized to diverse users. To address these challenges, we propose TRIP to enhance the capability in tailored strategic planning, incorporating a user-aware strategic planning module and a population-based training paradigm. Through experiments on benchmark non-collaborative dialogue tasks, we demonstrate the effectiveness …

Thoughts To Target: Enhance Planning For Target-Driven Conversation, Zhonghua Zheng, Lizi Liao, Yang Deng, Ee-Peng Lim, Minlie Huang, Liqiang Nie

Research Collection School Of Computing and Information Systems

In conversational AI, large-scale models excel in various tasks but struggle with target-driven conversation planning. Current methods, such as chain-of-thought reasoning and tree-search policy learning techniques, either neglect plan rationality or require extensive human simulation procedures. Addressing this, we propose a novel two-stage framework, named EnPL, to improve the LLMs’ capability in planning conversations towards designated targets, including (1) distilling natural language plans from target-driven conversation corpus and (2) generating new plans with demonstration-guided in-context learning. Specifically, we first propose a filter approach to distill a high-quality plan dataset, ConvPlan1. With the aid of corresponding conversational data and support from …

Angels Or Demons: Investigating And Detecting Decentralized Financial Traps On Ethereum Smart Contracts, Jiachi Chen, Jiang Hu, Xin Xia, David Lo, John Grundy, Zhipeng Gao, Ting Chen

Research Collection School Of Computing and Information Systems

Decentralized Finance (DeFi) uses blockchain technologies to transform traditional financial activities into decentralized platforms that run without intermediaries and centralized institutions. Smart contracts are programs that run on the blockchain, and by utilizing smart contracts, developers can more easily develop DeFi applications. Some key features of smart contracts—self-executed and immutability—ensure the trustworthiness, transparency and efficiency of DeFi applications and have led to a fast-growing DeFi market. However, misbehaving developers can add traps or backdoor code snippets to a smart contract, which are hard for contract users to discover. We call these code snippets in a DeFi smart contract as “DeFi …

Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik

Neutrosophic Systems with Applications

Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with …

Product Perspective From Fuzzy To Neutrosophic Graph Extension- A Review Of Literature, G. Vetrivel, M. Mullai, G. Rajchakit

Neutrosophic Systems with Applications

This review article consolidates and tabulates research on the product operations done in fuzzy, intuitionistic fuzzy, and neutrosophic graphs. This article encompasses the previous product discussions on fuzzy graphs and their extensions. This article aims to list the origin, structural properties, applications, etc. done by the researchers and academicians using the product behavior of two graphs on the fuzzified environment. This review provides a clear understanding of enhancements of product approach on graphs from fuzzy to neutrosophic kind.

Ai And Creativity: Effects Of Culture And Task Emotiveness In Human-Ai Collaboration, Choon Ngee Tan

Dissertations and Theses Collection (Open Access)

Creativity is the driving force behind innovation, propelling individuals and societies toward progress by generating novel ideas and groundbreaking solutions. The emergence of generative AI models, exemplified by GPT-3, offers opportunities to enhance human creativity. This paper explores the potential for unprecedented breakthroughs through the synergy between human intuition and AI-driven creativity, providing practical guidance on leveraging AI to amplify creative capacities. Study 1 finds that AI models trained on data from the U.S. and Chinese cultures exhibit cultural norms, values and cognition of those cultures. Study 2 finds that when humans and AI models of the same culture collaborate …

Towards Trustworthy Recommendation Systems: Beyond Collaborative Filtering, Zhongzhou Liu, Zhongzhou

Dissertations and Theses Collection (Open Access)

Recommendation systems have been widely deployed in various scenarios and applications, such as e-commerce, social media, and streaming services. Recommendation systems have significantly influenced how we interact with various items in a wide range of platforms. They help users discover their preferred items and provide efficient and enjoyable experiences. They also help item providers and platforms to quickly find their potential customers, thus increasing the total revenue and user engagement.

The majority of existing recommendation systems merely focus on the matching between users and items, aiming for higher recommendation accuracy. Collaborative filtering is regarded as one of the most successful …

Contactless And Scalable Approaches For Human Health And Performance Sensing, Ngoc Doan Thu Tran

Dissertations and Theses Collection (Open Access)

Human health and performance sensing has been extensively studied, from physiology to mental health and movement analytics. However, typical approaches rely on invasive and contact sensors or require professional practitioners, limiting their scalability. For example, the gold standard for measuring heart rate is through an electrocardiogram (ECG), which requires multiple probes attached to the skin and is impractical for individuals with skin issues. Additionally, it typically needs to be performed in a hospital setting under the supervision of a trained cardiac physiologist. Depression detection often relies on the expertise of psychologists or psychiatrists. However, there is a shortage of these …

Food Computing: Domain Adaptation And Causal Inference, Qing Wang

Dissertations and Theses Collection (Open Access)

This dissertation addresses two challenges in food computing: food recognition and food image-to-recipe retrieval. The main research ideas are: (1) leveraging Large Language Models (LLMs) to augment food image representations to mitigate the combined challenges of domain gaps and data imbalance in fine-grained food recognition; (2) proposing a causal-theory inspired cross-modal representation learning formulation for reducing the bias caused by the emphasis on certain ingredients for cross-modal recipe retrieval; and (3) extending the framework to incorporate multiple confounding factors, particularly ingredients and cooking actions, allows for more comprehensive modeling of the food image-torecipe retrieval problem.

We first explore the challenges …

Application Of The Neutrosophic Poisson Distribution Series On The Harmonic Subclass Of Analytic Functions Using The Salagean Derivative Operator, Adeniyi M. Gbolagade, Ibrahim T. Awolere, Olusola Adeyemo, Abiodun T. Oladipo

Neutrosophic Systems with Applications

The authors explore the innovative application of the Neutrosophic series, particularly the Neutrosophic Poisson Distribution Series (NPDS), to investigate various indeterminacy or uncertainties inherent in the classical univalent harmonic function class. The Neutrosophic Poisson Distribution Series is equipped with a Salangean derivative operator and convoluted with analytic univalent harmonic function class to derive new properties, such as inclusion relation, and coefficient inequalities for star-likeness. The results obtained demonstrate the effectiveness of this approach in capturing the inherent uncertainties and complexities associated with harmonic functions. There are several other areas of importance of our results that can be unlocked by computer …

A Neutrosophic Model For Measuring Evolution, Involution, And Indeterminacy In Species: Integrating Common And Uncommon Traits In Environmental Adaptation, Antonios Paraskevas, Michael Madas

Neutrosophic Systems with Applications

In 2017, Professor F. Smarandache introduced the Neutrosophic Theory of Evolution, Involution, and Indeterminacy (or Neutrality) (NToEIaI). He concluded that every theory of evolution is characterized by a certain degree of truth, indeterminacy, and untruth, as in neutrosophic logic. In this perspective, he raised several open questions on evolution, neutrality, and involution that required further research effort. Very recently, in 2024, Smarandache conducted research, from a soft sciences/philosophical viewpoint, on identifying and studying common parts in uncommon things and uncommon parts in common things emphasizing the complexity and interconnectedness of concepts within the context of neutrosophy. In this article, we …