Digital Commons Network^™

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 …

Human Capital Development : Bridging The Skills Gap In The Maritime Administration Of Namibia, Agnes Matheus

World Maritime University Dissertations

No abstract provided.

Law-Aware Autonomous Driving, Yang Sun

Dissertations and Theses Collection (Open Access)

Autonomous driving systems (ADSs) necessitate comprehensive testing prior to deployment in Autonomous Vehicles (AVs). High-fidelity simulators are crucial for this testing, as they can replicate a wide range of scenarios, including those that are difficult or dangerous to recreate in real-world conditions. While previous approaches have demonstrated that test cases can be generated automatically, they often focus on weak oracles (e.g., reaching the destination without collisions) and fail to assess whether the journey was conducted safely and in compliance with some complex property specifications such as traffic laws. In this dissertation, beyond assessing basic properties like energy consumption and proximity …

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 …

Tackling Toxicity And Harassment In Online Environments Through The Use Of Artificial Intelligence, Heba Saleous

Dissertations

With the increase in popularity of online communities, such as social media platforms, online games, and chatroom servers, there is a need to improve chat and content moderation. Platforms have reported an increase in the prevalence of toxic behavior and hate speech. Meanwhile, moderators are reporting difficulties in keeping up with the amount of data to check as well and the type of content they are exposed to, which further harms their own mental health. The main objective of this work is to address the challenges that exist within online communities with the rising prevalence of hate speech. Additionally, some …

The Digital Renaissance In Education: Adapting Generative Ai In Pre-Service Teacher And Provider Strategies, Jennifer J. Lesh, Jévaughn J. Lancaster

Faculty and Staff Publications & Presentations

Dr. Lesh's second presentation, "The Digital Renaissance in Education: Adapting Generative AI in Pre-Service Teacher and Provider Strategies," offered insights into the transformative role of generative AI in teacher education. Collaborating with Dr. JeVaughn Lancaster virtually, Lesh and Lancaster shared data from a recent study examining teachers' perceptions of AI in academic research. Findings underscored the potential for AI to enhance educational efficiency while also identifying ethical considerations that must be addressed. Lesh and Lancaster advocated for responsible AI training, stressing that generative AI should augment, not replace, educators' expertise and critical thinking.

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 …

Enhancedbert: A Python Software Tailored For Arabic Word Sense Disambiguation, Sanaa Kaddoura, Reem Nassar

All Works

EnhancedBERT is a software framework designed to disambiguate Arabic polysemous terms using advanced natural language processing techniques. It integrates transformer architectures with ensemble methods to achieve high performance in understanding and processing Arabic text. The framework provides a flexible pipeline that can be directly utilized or fine-tuned according to specific needs. EnhancedBERT stands out for its ease of use, leveraging transformer-based models combined with ensemble strategies to provide superior contextual understanding. This contextual awareness makes it an invaluable tool for researchers and practitioners tackling complexities in Arabic language processing.

Badfl: Backdoor Attack Defense In Federated Learning From Local Model Perspective, Haiyan Zhang, Xinghua Li, Mengfan Xu, Ximeng Liu, Tong Wu, Jian Weng, Robert H. Deng

Research Collection School Of Computing and Information Systems

There is substantial attention to federated learning with its ability to train a powerful global model collaboratively while protecting data privacy. Despite its many advantages, federated learning is vulnerable to backdoor attacks, where an adversary injects malicious weights into the global model, making the global model's targeted predictions incorrect. Existing defenses based on identifying and eliminating malicious weights ignore the similarity variation of the local weights during iterations in the malicious model detection and the presence of benign weights in the malicious model during the malicious local weight elimination, resulting in a poor defense and a degradation of global model …

Ask-Before-Plan : Proactive Language Agents For Real-World Planning, Xuan Zhang, Yang Deng, Zifeng Ren, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

The evolution of large language models (LLMs) has enhanced the planning capabilities of language agents in diverse real-world scenarios. Despite these advancements, the potential of LLM-powered agents to comprehend ambiguous user instructions for reasoning and decision-making is still under exploration. In this work, we introduce a new task, Proactive Agent Planning, which requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction, invoke external tools to collect valid information, and generate a plan to fulfill the user's demands. To study this practical problem, we establish a new benchmark dataset, Ask-before-Plan. To tackle the deficiency of LLMs …

Don’T Just Say “I Don’T Know”! Self-Aligning Large Language Models For Responding To Unknown Questions With Explanations, Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. In this work, we propose a novel and scalable self-alignment method to utilize the LLM itself to enhance its response-ability to different types of unknown questions, being capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. Specifically, the Self-Align method first employ …

Selective Annotation Via Data Allocation: These Data Should Be Triaged To Experts For Annotation Rather Than The Model, Chen Huang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Ido Dagan

Research Collection School Of Computing and Information Systems

To obtain high-quality annotations under limited budget, semi-automatic annotation methods are commonly used, where a portion of the data is annotated by experts and a model is then trained to complete the annotations for the remaining data. However, these methods mainly focus on selecting informative data for expert annotations to improve the model predictive ability (i.e., triage-to-human data), while the rest of the data is indiscriminately assigned to model annotation (i.e., triage-to-model data). This may lead to inefficiencies in budget allocation for annotations, as easy data that the model could accurately annotate may be unnecessarily assigned to the expert, and …

Beyond Persuasion : Towards Conversational Recommender System With Credible Explanations, Peixin Qin, Chen Huang, Yang Deng, Wenqiang Lei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

With the aid of large language models, current conversational recommender system (CRS) has gaining strong abilities to persuade users to accept recommended items. While these CRSs are highly persuasive, they can mislead users by incorporating incredible information in their explanations, ultimately damaging the long-term trust between users and the CRS. To address this, we propose a simple yet effective method, called PC-CRS, to enhance the credibility of CRS’s explanations during persuasion. It guides the explanation generation through our proposed credibility-aware persuasive strategies and then gradually refines explanations via post-hoc self-reflection. Experimental results demonstrate the efficacy of PC-CRS in promoting persuasive …

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 …

Consecutive Batch Model Editing With Hook Layers, Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, Wai Lam

Research Collection School Of Computing and Information Systems

As the typical retraining paradigm is unacceptably time- and resource-consuming, researchers are turning to model editing to find an effective way that supports both consecutive and batch scenarios to edit the model behavior directly. Despite all these practical expectations, existing model editing methods fail to realize all of them. Furthermore, the memory demands for such sequential model editing approaches tend to be prohibitive, frequently necessitating an external memory that grows incrementally over time. To cope with these challenges, we propose CoachHooK, a model editing method that simultaneously supports sequential and batch editing. CoachHooK is memory-friendly as it only needs a …

Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series …

Class Name Guided Out-Of-Scope Intent Classification, Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Kumar Sahu, Xiaoli Li

Research Collection School Of Computing and Information Systems

The paper introduces Semantics of Class Labelbased Unsupervised Out of Scope Intent Detection (SCOOS), a novel method aimed at enhancing out-of-scope (OOS) intent classification in task-oriented dialogue systems. Unlike prior approaches that rely solely on indomain (ID) data features, SCOOS leverages semantic cues embedded in class labels to improve classification accuracy. The method entails forming a compact feature space centered around the semantics of class labels by minimizing losses between ID features and class names. SCOOS achieves this by creating a compact feature space centered around class label semantics, achieved through minimizing losses between in-domain (ID) features and class names. …

Lr-Auth: Towards Practical Implementation Of Implicit User Authentication On Earbuds, Changshuo Hu, Xiao Ma, Xinger Huang, Yiran Shen, Dong Ma

Research Collection School Of Computing and Information Systems

The increasing use of earbuds in applications like immersive entertainment and health monitoring necessitates effective implicit user authentication systems to preserve the privacy of sensitive data and provide personalized experiences. Existing approaches, which leverage physiological cues (e.g., jawbone structure) and behavioral cues (e.g., gait), face challenges such as limited usability, high delay and energy overhead, and significant computational demands, rendering them impractical for resource-constrained earbuds. To address these issues, we present LR-Auth, a lightweight, user-friendly implicit authentication system designed for various earbud usage scenarios. LR-Auth utilizes the modulation of sound frequencies by the user's unique occluded ear canal, generating user-specific …

Mm‑Forecast: A Multimodal Approach To Temporal Event Forecasting With Large Language Models, Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event …

Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang

Research Collection School Of Computing and Information Systems

Adversarial attack is a crucial step when evaluating the reliability and robustness of deep neural networks (DNNs) models. Most existing attack approaches apply an end-to-end gradient update strategy to generate adversarial examples for a classification or regression problem. However, few of them consider the non-differentiable DNN models (e.g., coordinate regression model) that prevent end-to-end backpropagation resulting in the failure of gradient calculation. In this paper, we present a new adversarial example generation approach for both untargeted and targeted attacks on coordinate regression models with non-differentiable operations. The novelty of our approach lies in a k-layer penetrating representation, on which we …

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 …

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 …

Optimizing Uncertainty Estimation On Scientific Visualizations Using Learning Models, Clara Almeida, David Guerrero-Pantoja, Erik Pautsch, Silvio Rizzi, George Thiruvathukal, Maria Pantoja

Computer Science: Faculty Publications and Other Works

Scientific visualizations (SciVis) translate numerical and spatial data as images, enabling scientists to better understand the phenomena described by the data and gain insights that may be overlooked by statistical methods alone. In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis. By combining classical numerical techniques with DL methods, we can create models that potentially achieve the accuracies needed for real-world industrial applications and simulations. However, DL models often exhibit undue confidence, particularly when faced with inputs that differ significantly from the data they were trained on, known as out-of-distribution (OOD) inputs. This can …

Side-Channel Analysis Platform For A Hardware Implementation Of Fips 203 (Crystals-Kyber), Mohamed Mossad

USF Tampa Graduate Theses and Dissertations

In 2024, NIST selected the Post-Quantum Cryptography algorithm CRYSTALS-Kyber for standardization as a public-key encryption, key establishment scheme. CRYSTALS-Kyber was standardized under the Federal Information Processing Standard (FIPS), specifically FIPS 203. FIPS standards represent a set of guidelines, developed by NIST, for secure data handling in federal information systems, mandating cryptographic algorithms that protect sensitive information. This highlights the importance of identifying potential vulnerabilities in the algorithm and assessing how CRYSTALS-Kyber implementations react to hardware side channel attacks. Previous research identified several vulnerabilities in implementations of CRYSTALS-Kyber in software, which were addressed in subsequent releases. This thesis focuses on expanding …

Unveiling Scientific Articles From Paper Mills With Provenance Analysis, João Phillipe Cardenuto, Daniel Moreira, Anderson Rocha

Computer Science: Faculty Publications and Other Works

The increasing prevalence of fake publications created by paper mills poses a significant challenge to maintaining scientific integrity. While integrity analysts typically rely on textual and visual clues to identify fake articles, determining which papers merit further investigation can be akin to searching for a needle in a haystack, as these fake publications have non-related authors and are published on non-related venues. To address this challenge, we developed a new methodology for provenance analysis, which automatically tracks and groups suspicious figures and documents. Our approach groups manuscripts from the same paper mill by analyzing their figures and identifying duplicated and …

Unveiling Scientic Articles From Paper Mills With Provenance Analysis, João Phillipe Cardenuto, Daniel Moreira, Anderson Rocha

Computer Science: Faculty Publications and Other Works

The increasing prevalence of fake publications created by paper mills poses a significant challenge to maintaining scientific integrity. While integrity analysts typically rely on textual and visual clues to identify fake articles, determining which papers merit further investigation can be akin to searching for a needle in a haystack, as these fake publications have non-related authors and are published on non-related venues. To address this challenge, we developed a new methodology for provenance analysis, which automatically tracks and groups suspicious figures and documents. Our approach groups manuscripts from the same paper mill by analyzing their figures and identifying duplicated and …

Photoluminescence Intensity Enhancement And Stability In Cdte/Sio2 Quantum Dots Through Water Molecule Adsorption And Trap Passivation, Daniil S. Daibagya, Ivan A. Zakharchuk, Sergei A. Ambrozevich, Mikhail S. Smirnov, Anna V. Osadchenko, Oleg V. Ovchinnikov, Alexandr S. Selyukov

Karbala International Journal of Modern Science

The study of the luminescence photostability for colloidal nanocrystals is an important task since the understanding of the corresponding physical processes advances new electronic devices based on semiconductor nanoparticles as well as other important applications such as biomarkers. In this paper, we provide the first study and comprehensive analysis of the photostability of the luminescent properties for colloidal CdTe/SiO₂ core/shell quantum dots prepared by an aqueous-based method. The quantum dots were exposed to continuous laser radiation during two time intervals with prolonged break in between. The photoluminescence intensity of the quantum dots increased over time under continuous laser irradiation. …

Radiomics-Based Machine Learning With Natural Gradient Boosting For Continuous Survival Prediction In Glioblastoma, Mert Karabacak, Shiv Patil, Zachary C. Gersey, Ricardo J. Komotar, Konstantinos Margetis

SKMC Student Presentations and Publications

(1) Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an aggressive disease course that requires accurate prognosis for individualized treatment planning. This study aims to develop and evaluate a radiomics-based machine learning (ML) model to estimate overall survival (OS) for patients with GBM using pre-treatment multi-parametric magnetic resonance imaging (MRI). (2) Methods: The MRI data of 865 patients with GBM were assessed, comprising 499 patients from the UPENN-GBM dataset and 366 patients from the UCSF-PDGM dataset. A total of 14,598 radiomic features were extracted from T1, T1 with contrast, T2, and FLAIR MRI sequences …

Algorithmic Reason-Giving, Arbitrary And Capricious Review, And The Need For A Clear Normative Baseline, Cameron Averill

University of Cincinnati Law Review

Federal agencies have caught the artificial intelligence (AI) bug. A December 2023 report by the Government Accountability Office found that twenty of twenty-three federal agencies surveyed reported using some form of AI, with about two hundred current use cases for algorithms and about one thousand more in the planning phase. These agencies are using algorithms in all aspects of administration, including rulemaking, adjudication, and enforcement. The risks of AI are well-documented. Previous work has shown that algorithms can be, among other things, biased and prone to error. However, perhaps no problem poses a more serious threat to the use of …

Interpreting The Biological Effects Of Protons As A Function Of Physical Quantity: Linear Energy Transfer Or Microdosimetric Lineal Energy Spectrum?, Fada Guan, Lawrence Bronk, Matthew Kerr, Yuting Li, Leslie A Braby, Mary Sobieski, Xiaochun Wang, Xiaodong Zhang, Clifford Stephan, David R Grosshans, Radhe Mohan

Faculty, Staff and Student Publications

The choice of appropriate physical quantities to characterize the biological effects of ionizing radiation has evolved over time coupled with advances in scientific understanding. The basic hypothesis in radiation dosimetry is that the energy deposited by ionizing radiation initiates all the consequences of exposure in a biological sample (e.g., DNA damage, reproductive cell death). Physical quantities defined to characterize energy deposition have included dose, a measure of the mean energy imparted per unit mass of the target, and linear energy transfer (LET), a measure of the mean energy deposition per unit distance that charged particles traverse in a medium. The …