Digital Commons Network^™

Towards Multimodal Emotional Support Conversation Systems, Yuqi Chu, Lizi Liao, Zhiyuan Zhou, Chong-Wah Ngo, Richang Hong

Research Collection School Of Computing and Information Systems

The integration of conversational artificial intelligence (AI) into mental health care promises a new horizon for therapist-client interactions, aiming to closely emulate the depth and nuance of human conversations. Despite the potential, the current landscape of conversational AI is markedly limited by its reliance on single-modal data, constraining the systems’ ability to empathize and provide effective emotional support. This limitation stems from a paucity of resources that encapsulate the multimodal nature of human communication essential for therapeutic counseling. To address this gap, we introduce the Multimodal Emotional Support Conversation (MESC) dataset, a first-of-its-kind resource enriched with comprehensive annotations across text, …

Privacy-Preserving Ridge Regression Over Encrypted Data Under Multiple Keys, Yanling Li, Junzuo Lai, Meng Sun, Beibei Song, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the increase of private data being collected by data owners, it has been a trend for data owners to store the data on cloud computing platforms. The huge amounts of data in cloud servers bring fresh development opportunities to machine learning, which is applied to build a high-quality machine learning model based on a large training dataset. However, to ensure the privacy of data and facilitate retrieval, data owners often upload encrypted data under their public keys. But it creates new challenges for machine learning to learn a predictive model over these encrypted data under different keys. Most existing …

Map As A By-Product: Collective Landmark Mapping From Imu Data And User-Provided Texts In Situated Tasks, Ryo Yonetani, Kotaro Hara

Research Collection School Of Computing and Information Systems

This paper presents Collective Landmark Mapper, a novel map-as-a-by-product system for generating semantic landmark maps of indoor environments. Consider users engaged in situated tasks that require them to navigate these environments and regularly take notes on their smartphones. Collective Landmark Mapper exploits the smartphone's IMU data and the user's free text input during these tasks to identify a set of landmarks encountered by the user. The identified landmarks are then aggregated across multiple users to generate a unified map representing the positions and semantic information of all landmarks. In developing the proposed system, we focused specifically on retail applications and …

Guiding Multiple Remote Users In Physical Tasks With Language-Driven Robotic Telepresence, Ruyi Li, Jingfei Guo, Xinyi Zhang, Xuji Zhang, Zeqing Li, Jiannan Li, Jiangtao Gong

Research Collection School Of Computing and Information Systems

Remote assistance through robotic telepresence could involve both control and memory challenges, particularly in one expert to multiple workers situation. In this work, we proposed a novelty language-driven interface to facilitate remote collaboration through telepresence robots. Through operations and maintenance expert interviews and a scenario simulation study, we identified key pain points in executing one-expert-multiple-workers remote guidance using the telepresence robot and proposed two design goals, which together consist of five sub-design goals with corresponding features. These features were integrated into a standard telepresence robot, resulting in the development of a Collaborative LLM-based Embodied Assistant Robot, named CLEAR Robot. A …

Implementing Slack-Free Custom Penalty Function For Qubo On Gate-Based Quantum Computers, Xin Wei Lee, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Solving NP-hard constrained combinatorial optimization problems using quantum algorithms remains a challenging yet promising avenue toward quantum advantage. Variational Quantum Algorithms (VQAs), such as the Variational Quantum Eigensolver (VQE), typically require constrained problems to be reformulated as unconstrained ones using penalty methods. A common approach introduces slack variables and quadratic penalties in the QUBO formulation to handle inequality constraints. However, this leads to increased qubit requirements and often distorts the optimization landscape, making it harder to find high-quality feasible solutions. To address these issues, we explore a slack-free formulation that directly encodes inequality constraints using custom penalty functions, specifically the …

Robface: A Test Suite For Efficient Robustness Evaluation Of Face Recognition Systems, Ruihan Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Face recognition is a widely used authentication technology in practice, where robustness is required. It is thus essential to have an efficient and easy-to-use method for evaluating the robustness of (possibly third-party) trained face recognition systems. Existing approaches to evaluating the robustness of face recognition systems are either based on empirical evaluation (e.g., measuring attacking success rate using state-of-the-art attacking methods) or formal analysis (e.g., measuring the Lipschitz constant). While the former demands significant user efforts and expertise, the latter is extremely time-consuming. In pursuit of a comprehensive, efficient, easy-to-use, and scalable estimation of the robustness of face recognition systems, …

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …

The Density Distribution Of The Material Around Betelgeuse, Iman Behbehani

Dissertations, Theses, and Capstone Projects

Betelgeuse is a red supergiant star visible in the constellation Orion. Its windy and highly convective surface results in a complicated mass loss pattern difficult to understand and replicate in simulations. The ejected mass can form a shell around the star we consider the circumstellar material (CSM). In this study, we use ALMA interferometric observations to find the structure of Betelgeuse's CSM, and connect to the mass loss mechanisms that could form it. We measure a bipolar circumstellar structure with a position angle of 42.3$\pm 7.0^\circ$. We observe asymmetries in the form of hot spots in the north east of …

Towards Automated Evolution Of Imperative Deep Learning Programs, Tatiana Castro-Vélez

Dissertations, Theses, and Capstone Projects

Software engineering (SE) is increasingly intersecting with data-centric domains such as machine learning (ML) and deep learning (DL). Similar to bugs in traditional software systems, defects can emerge in ML and DL systems. ML, including DL, systems are now widespread and rely on dynamic models defined by input data. Developers face the challenge of building dependable systems while addressing the demand for scalable software.

Efficiency is essential to support responsiveness with respect to ever-growing datasets. Traditional DL frameworks achieve scalability through deferred execution, enabling symbolic, graph-based deep neural network (DNN) computation. While efficient, this approach is error-prone, cumbersome, and difficult …

Assessing The Effectiveness Of Crawlers And Large Language Models In Detecting Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

In the emerging field of Meta Computing, where data collection and integration are essential components, the threat of adversary hidden link attacks poses a significant challenge to web crawlers. In this paper, we investigate the influence of these attacks on data collection by web crawlers, which famously elude conventional detection techniques using large language models (LLMs). Empirically, we find some vulnerabilities in the current crawler mechanisms and large language model detection, especially in code inspection, and propose enhancements that will help mitigate these weaknesses. Our assessment of real-world web pages reveals the prevalence and impact of adversary hidden link attacks, …

Large-Scale Graph Algorithms And Applications With An Emphasis On Fintech Data, Fuhuan Li

Dissertations

Graph algorithms are essential analytical tools with applications spanning cybersecurity, biology, social media, and increasingly, financial technology (FinTech). The complex and interconnected nature of financial data, particularly in cryptocurrency networks, presents unique opportunities for graph-based analysis in fraud detection and anomaly identification.

This dissertation presents the design and implementation of scalable graph algorithms tailored for large-scale networks, with particular emphasis on FinTech applications. The primary contributions include: (1) novel cover-edge based triangle counting algorithms that significantly reduce computational overhead through breadth-first search preprocessing, achieving substantial speedups over traditional methods and dramatic communication reduction in distributed settings, (2) optimized parallel implementations …

Sugar: A Sequence Unfolding Based Transformer Model For Group Activity Recognition, Yash U. Gondkar

Graduate Masters Theses

Large Language Models have improved significantly in the past couple of years due to the adoption of transformers. However, transformers still find it challenging to process videos due to limited context size caused by their quadratic computing cost. Therefore, we studied a booming field in machine learning which powers applications like social scene analysis and video surveillance systems called Group Activity Recognition (GAR). We found that recent models were able to achieve more than 90% accuracy on popular datasets like the Volleyball dataset, however, it turned out that even they relied on transformers.

Therefore, in this work, we developed a …

Design And Development Of A Standalone Digital Holographic Microscope Employing Phase-Driven Reconstruction And Classification For Biomedical Imaging And Optical Diagnostics, Charlotte Kyeremah

Graduate Doctoral Dissertations

Access to advanced biomedical imaging technologies remains a significant challenge in resource-limited settings, especially for early disease detection and monitoring of diseases such as malaria, HIV, and other blood-borne diseases. Although point-of-care (POC) devices have gained popularity in global health, many rely on antibody-based tests, lateral flow strips, or optical readouts that often lack quantitative capabilities, sensitivity to early infections, or versatility in different diagnostic targets. In addition, these systems are typically dependent on disposable reagents or manual interpretation, which limits their effectiveness in remote areas. Digital Holographic Microscopy (DHM) presents a promising alternative as a label-free imaging method capable …

Can Artificial Intelligence Models Provide Reliable Medical Counselling To Fertility Patients?, Idan Alcalay, Ariel Weissman, Hadas Ganer Herman, Avi Tsafrir, Matan Friedman, Eran Weiner, Raoul Orvieto, Nikolaos P. Polyzos, Michael H. Dahan, Alex Polyakov, Robert Fischer, Sandro C. Esteves, Baris Ata, Jason M. Franasiak, Yossi Mizrachi

Department of Obstetrics and Gynecology Faculty Papers

RESEARCH QUESTION: Can generative artificial intelligence (AI) models provide reliable counselling to fertility patients regarding real-world clinical questions?

DESIGN: In this cross-sectional study, 12 clinical questions were developed to reflect common, real-life dilemmas encountered during fertility workup and treatment. Responses to each question were generated by two experienced fertility specialists, and two AI models - ChatGPT and Gemini. Eight leading internationally recognized fertility experts, blinded to the source of each reply, independently rated all the responses on a scale from 1 (strongly disagree) to 10 (strongly agree). Ratings were compared across all four repliers using non-parametric statistical tests.

RESULTS: The …

Creepy, Invasive, And Exploitative Algorithms: A Cpm Analysis Of Users' Privacy Breakdowns And Recalibration Practices With Social Media Algorithms, Matthew J. A. Craig, Jeffrey T. Child

Human-Machine Communication

Social media content filtering algorithms can both provide desired personalized content and ads for users. However, sometimes these recommendations can resemble individual private information. How might users navigate these experiences to best manage their private information? The present exploratory study utilizes the rules- and systems-based framework of communication privacy management (CPM) theory to explore social media users’ experiences of privacy breakdowns with social media algorithms and investigates what users do in response to said breakdowns. These responses were refined using content analysis and divided into different categories of privacy breakdowns and recalibration strategies. Implications for future research surrounding human-machine communication …

Enhancing Reliable And Energy-Efficient Uav Communications With Ris And Deep Reinforcement Learning, Wasim Ahmad, Umar Islam, Abdulkadhem A. Abdulkadhem, Babar Shah, Fernando Moreira, Ali Abbas

All Works

The rapid growth in wireless communication demands has led to a surge in research on technologies capable of enhancing communication reliability, coverage, and energy efficiency. Among these, uncrewed aerial vehicles (UAV) and reconfigurable intelligent surfaces (RIS) have emerged as promising solutions. Prior research on using deep reinforcement learning (DRL) to integrate RIS with UAV concentrated on enhancing signal quality and coverage, but it ignored the challenges caused by electromagnetic interference (EMI). This article introduces a novel framework addressing the challenges posed by EMI from Gallium nitride (GaN) power amplifiers in RIS-assisted UAV communication systems. By integrating DRL with quadrature phase …

Approximation And Parameterized Algorithms For Covering With Disks Of Two Types Of Radii, Sayan Bandyapadhyay, Eli Mitchell

Computer Science Faculty Publications and Presentations

We study the Discrete Covering with Two Types of Radii problem motivated by its application in wireless networks. In this problem, the goal is to assign either small-range high frequency or large-range low frequency to each access point, maximizing the number of users in high-frequency regions while ensuring that each user is in the range of an access point. Unlike other weighted covering problems, our problem requires satisfying two simultaneous objectives, which calls for novel approaches that leverage the underlying geometry of the problem. In our work, we present two new algorithms: the first is a polynomial-time (2.5 + ϵ)-approximation, …

A Dual-Model Machine Learning Framework For Predictive Maintenance Of Industrial Digital Press Components, Richmond Darko, Emmanuel Adabor

African Conference on Information Systems and Technology

This study presents a novel dual-model predictive maintenance framework designed to improve maintenance scheduling for components in industrial digital presses. The framework integrates two complementary approaches: a Threshold-Based Maintenance Approach (TBMA) for components operating within acceptable usage limits, and an Overdue Severity-Based Maintenance Approach (OSBMA) for those that have exceeded their expected lifespans or show signs of critical degradation. This study uses real-world operational data from a Konica Minolta C6000 press. It applies advanced machine learning models, including Gradient Boosting Machines and Random Forest for classification, and Generalized Additive Models (GAM) for Remaining Useful Life (RUL) prediction. The goal is …

Feasibility Evaluation Of Secure Offline Large Language Models With Retrieval-Augmented Generation For Cpu-Only Inference, Erick Tyndall, Torrey J. Wagner, Colleen Gayheart, Alexandre Some, Brent T. Langhals

Faculty Publications

Recent advances in large language models and retrieval-augmented generation, a method that enhances language models by integrating retrieved external documents, have created opportunities to deploy AI in secure, offline environments. This study explores the feasibility of using locally hosted, open-weight large language models with integrated retrieval-augmented generation capabilities on CPU-only hardware for tasks such as question answering and summarization. The evaluation reflects typical constraints in environments like government offices, where internet access and GPU acceleration may be restricted. Four models were tested using LocalGPT, a privacy-focused retrieval-augmented generation framework, on two consumer-grade systems: a laptop and a workstation. A technical …

Are Estimands Being Correctly Used? A Review Of Uk Research Protocols, T. P. Clark, Richard H. Wicentowski, S. Cro, M. R. Sydes, B. C. Kahan

Computer Science Faculty Works

Background: The use of estimands in clinical trials was formalised with the adoption of the final International Conference on Harmonisation E9 Addendum on Estimands and Sensitivity Analysis in Clinical Trials (ICH E9(R1) Addendum) in November 2019. The declared objective of the ICH E9(R1) Addendum is to bring clarity and transparency to the research question of interest. For this to be achieved, the estimand must be described in accordance with the requirements of the ICH E9(R1) Addendum so that the target treatment effect is clear to all stakeholders. Previous reviews of publications and published protocols have found that few trials explicitly …

Machine Learning And Crime Prevention, Emily Lizewski

Student Theses

Predictive policing uses machine learning to analyze crime patterns and help law enforcement better efficient use their resources. These tools can improve accuracy by highlighting complex trends in large sets of data. While this technology has its advantages, it also raises important ethical and social questions. Within this paper we looks at how predictive policing works, focusing on the machine learning models often used such as decision trees, random forests, gradient boosting, and models that factor in both time and location. It also explores how these tools might unintentionally reinforce biases already present in historical crime data. In reviewing the …

Domain Obedient Deep Learning, Soumadeep Saha

Doctoral Theses

Deep learning, a family of data-driven artificial intelligence techniques, has shown immense promise in a plethora of applications, and it has even outpaced experts in several domains. However, unlike symbolic approaches to learning, these methods fall short when it comes to abiding by and learning from pre-existing established principles. This is a significant deficit for deployment in critical applications such as robotics, medicine, industrial automation, etc. For a decision system to be considered for adoption in such fields, it must demonstrate the ability to adhere to specified constraints, an ability missing in deep learning-based approaches. Exploring this problem serves as …

A Comprehensive Review On Gamification In Neurocybersecurity, Ms. Kritika

Journal of Cybersecurity Education, Research and Practice

The research investigates gamification methods as it applies to the emerging interdisciplinary domain that brings together cybersecurity with neuroscience and psychology. Neurocybersecurity implements neural concepts to protect digital systems from security threats while targeting the human vulnerabilities that present as the strongest points of attack. Several researchers have examined gamification techniques which incorporate game design elements to enhance cybersecurity training outcomes by improving user participation and knowledge retention and user conduct compliance. The research incorporates Self-Determination Theory along with Cognitive Load Theory to explain the design principles for efficient gamified interventions. The implementation of both cognitive performance improvement and neuroplasticity …

Intelligent Motion Tracking: A Low-Cost Surveillance Framework Using Soc Devices And Sensor Fusion, Aung Myat Khaung, Nicholas Michael Stiffler

Research from the Berry Summer Thesis Institute, 2025

This thesis presents the design and implementation of a lightweight surveillance system capable of realtime motion detection, object tracking, and behavioral history reconstruction in controlled environments. The system uses System-on-Chip devices such as Raspberry Pi boards equipped with NOIR cameras, monocular cameras, and break-beam sensors that work together to detect and track single or multiple moving objects like colored balls. The prototype is validated in structured settings with the goal of eventual deployment in more dynamic environments, addressing the challenge of reliably tracking visually similar objects with minimal distinguishing features. The architecture integrates computer vision with sensor fusion by combining …

Performance Analysis Of Computational Methods For Predicting Protein Function In Rare Diseases, Aichetou Mohamed Sidiya, Hanin Alzaher, Razan Almahdi, Tayeb Brahimi

Effat Undergraduate Research Journal

Protein function prediction is crucial for understanding the underlying mechanisms of rare diseases. With the increasing availability of computational methods including machine learning-based approaches, network-based methods, and sequence-based methods, predicting protein functions has become more accessible. However, it is not clear which of these methods performs better or how they compare to each other in terms of accuracy, efficiency, and scalability. In this study, we evaluate several computational methods for predicting protein functions in rare diseases using key performance indicators (KPIs). We analyze the strengths and weaknesses of each method and provide recommendations for researchers and clinicians interested in using …

A Study Of Rural Health Care For Chronic Care Management Using Machine Learning To Determine Costs, Joanna Maria Keeling

Doctoral Dissertations

Management of diabetes or heart disease may be uniquely challenging for older individuals with multiple chronic conditions[1]. Chronic Care Management would target patients living with comorbidities in Louisiana, who are not receiving sufficient healthcare services and help them receive the care they deserve[2]. This study is aimed at comparing the results of rural Louisiana Medicaid recipients with comorbidities to the National Averages. We will also use Machine Learning to predict Churn and Medical Costs. To help us identify these patients with comorbidities, we decided to use NCQA Quality Measures. We identified several measures from NCQA that we wanted to use, …

A Backtracking Algorithm For Determining The Existence Of Regular Graphs Of Specified Girth And Excess, Stetson Ray Bosecker

Doctoral Dissertations

The study of cages focuses on finding (k, g)-graphs of minimal order. This dissertation generalizes the problem of finding cages to the determination of graphs with specified excess, thereby broadening the significance of the results. The (k, g, ε)-graph problem seeks to determine the existence or nonexistence of k-regular graphs with girth g and excess ε = n(G)−M(k, g) (where M(k, g) represents the Moore bound for cage graphs). Motivated by heuristic methods used to determine properties within the study of cages, we present a backtracking algorithm capable of constructing (k, g, ε)-graphs or determining their nonexistence. Chapter 2 provides …

Privacy-Preserving Structure Learning For Geospatial Data Using Information-Theoretic Dependency Measures, Ahmed Mudhish

Doctoral Dissertations

This dissertation proposes a privacy-preserving framework for structure learning in Bayesian networks (BNs) that addresses the challenges of distributed geospatial data face. Geospatial datasets often exhibit region-specific patterns such as sparsity and nonlinear dependencies. These patterns undermine the effectiveness of traditional machine learning models. Additionally, learned BN structures may reveal sensitive relationships in the generated graph by BNs. These relationships pose a significant privacy risk if reverse-engineered. To address these issues, three novel algorithms are introduced. First, the Selective Naïve Bayes with HSIC (SNB-HSIC) algorithm applies a kernel-based dependency measure to filter redundant and irrelevant features in sparse datasets, improving …

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Protecting Networks Against Entry-Point Attacks, Shivani Mishra

Theses - ALL

In many network applications, it is critical to protect sensitive nodes from discovery by malicious crawlers. This thesis addresses the network protection problem from the data protector’s perspective, focusing on strategically deleting edges to hide target nodes from entry-point attacks. Earlier work on this problem proposed node-level scores to identify key edges for deletion. We propose two novel edge-level scoring functions to identify critical edges for removal: the Shortest Path Change Score (SPCS), which quantifies the damage an edge’s removal causes to shortest paths, and the PageRank Edge Flow Score (PEFS), which estimates an edge’s usage in random walks from …