Digital Commons Network^™

Purified Zero-Shot Sketch-Based Image Retrieval, Yang Zhou, Jingru Yang, Jin Wang, Kaixiang Huang, Guodong Lu, Shengfeng He

Research Collection School Of Computing and Information Systems

Sketches, as a new solution in multimedia systems that can replace natural language, are characterized by sparse visual cues such as simple strokes that differ significantly from natural images containing complex elements such as background, foreground, and texture. This misalignment poses substantial challenges for zero-shot sketch-based image retrieval (ZS-SBIR). Prior approaches match sketches to full images and tend to overlook redundant elements in natural images, leading to model distraction and semantic ambiguity. To address this issue, we introduce a distraction-agnostic framework, purified cross-domain matching (PuXIM), which operates on a straightforward principle: masking and matching. We devise a visual-cross-linguistic (VxL) sampler …

Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang

Research Collection School Of Computing and Information Systems

Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can be crushed in a short time (less than 1 year), and ii) these benchmarks may be easily hacked. To handle these issues, we propose the ever-scalingness for building the benchmarks which are scaling over complexity against crushing, instance against hacking and exploitation, oversight for easy verification, and coverage for real-world relevance. This paper presents Nondeterministic Polynomial-time Problem Challenge (NPPC), an ever-scaling reasoning benchmark for LLMs. Specifically, the NPPC has three main …

Do Comments And Expertise Still Matter? An Experiment On Programmers’ Adoption Of Ai-Generated Javascript Code, Changwen Li, Christoph Treude, Ofir Turel

Research Collection School Of Computing and Information Systems

This paper investigates the factors influencing programmers’ adoption of AI-generated JavaScript code recommendations within the context of lightweight, function-level programming tasks. It extends prior research by (1) utilizing objective (as opposed to the typically self-reported) measurements for programmers’ adoption of AI-generated code and (2) examining whether AI-generated comments added to code recommendations and development expertise drive AI-generated code adoption. We tested these potential drivers in an online experiment with 173 programmers. Participants were asked to answer some questions to demonstrate their level of development expertise. Then, they were asked to solve a LeetCode problem without AI support. After attempting to …

A Case Study Of Gender And Online Team Communication In Software Engineering Education, Rita Garcia, Christoph Treude

Research Collection School Of Computing and Information Systems

Collaboration is crucial in Software Engineering (SE), yet factors like gender bias can shape team dynamics and behaviours. This descriptive case study examines an eight-week project involving 39 SE students across eight teams contributing to GitHub projects. Focusing on gender, we used a mixed-methods approach to analyse Slack communications, identifying gender differences in how students respond to initiated communications and comparing how students’ communications influenced other aspects of students’ performance, including learning gains. We found higher help-seeking and leadership behaviours in the all-woman team involved in this case study, while men responded more slowly. Although communication did not directly affect …

Reinforce Trustworthiness In Multimodal Emotional Support System, Huy M. Le, Dat Tien Nguyen, Ngan T. T. Vo, Tuan D. Q. Nguyen, Nguyen Le Binh, Duy Minh Ho Nguyen, Daniel Sonntag, Lizi Liao, Binh T. Nguyen

Research Collection School Of Computing and Information Systems

In today's world, emotional support is increasingly essential, yet it remains challenging for both those seeking help and those offering it. Multimodal approaches to emotional support show great promise by integrating diverse data sources to provide empathetic, contextually relevant responses, fostering more effective interactions. However, current methods have notable limitations, often relying solely on text or converting other data types into text, or providing emotion recognition only, thus overlooking the full potential of multimodal inputs. Moreover, many studies prioritize response generation without accurately identifying critical emotional support elements or ensuring the reliability of outputs. To overcome these issues, we introduce …

Gig Worker Social Referrals On An On-Demand Food Delivery Platform, Hai Wang, Hao Sun, Peter Zhang

Research Collection School Of Computing and Information Systems

Social referral programs are commonly used by online labor platforms to incentivize labor supply by rewarding existing workers for successful referrals. This study investigates the impact of such programs on gig workers' labor supply in online labor platforms using data from an on-demand food delivery platform in Singapore. In particular, we analyze how gig workers' past labor supply and referral behavior influence the generation and value of social referrals. This research offers insights into the mechanisms that drive labor supply dynamics in the gig economy and highlights the effectiveness of social referral programs for shaping worker behavior and enhancing platform …

Leveraging Large Language Models For Career Mobility Analysis: A Study Of Gender, Race, And Job Change Using Us Online Resume Profiles, Palakorn Achananuparp, Ye Xu, Yao Lu, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are associated with upward mobility. This study addresses key research questions of how the job changes affect their upward career mobility, and how the outcomes of upward career mobility differ by gender and race. We address data challenges – such as missing demographic attributes, missing wage data, and noisy occupation labels – through various data processing and Artificial Intelligence (AI) methods. In particular, we develop a large language models (LLMs) based occupation classification method known …

Generalized Visual Relation Detection With Diffusion Models, Kaifeng Gao, Siqi Chen, Hanwang Zhang, Jun Xiao, Yueting Zhuang, Qianru Sun

Research Collection School Of Computing and Information Systems

Visual relation detection (VRD) aims to identify relationships (or interactions) between object pairs in an image. Although recent VRD models have achieved impressive performance, they are all restricted to pre-defined relation categories, while failing to consider the semantic ambiguity characteristic of visual relations. Unlike objects, the appearance of visual relations is always subtle and can be described by multiple predicate words from different perspectives, e.g., “ride” can be depicted as “race” and “sit on”, from the sports and spatial position views, respectively. To this end, we propose to model visual relations as continuous embeddings, and design diffusion models to achieve …

When Helpfulness Becomes Harmful: Jailbreaking Llms For Malicious Code Generation, Noelle Capodieci

Electronic Theses & Dissertations (2024 - present)

Generative AI (GenAI) and Large Language Models (LLMs) have made large strides in coding task capabilities, with many software developers integrating agentic engineering into their workflow. While GenAI has largely benefited professional software engineers who can automate their work, it has also created room for those with little coding expertise to also create fully fledged programs and applications. It is commonly noted that GenAI is trained with two major goals in mind: to be as helpful as possible, and be as harmless as possible. There exist moments where helpfulness may be prioritized over harmlessness when these goals conflict. LLMs may …

Evops: Evolutionary Patch Selection In The Embedding Space Of Whole Slide Images, Saya Hashemian

Theses and Dissertations (Comprehensive)

Computational pathology increasingly relies on the analysis of Whole-Slide Images (WSIs), which capture tissue specimens at gigapixel resolution. Because a single slide is far too large to process directly, the

prevailing paradigm decomposes each WSI into thousands of small patches and encodes them as high- dimensional feature embeddings using deep learning backbones. While effective, this paradigm carries a

substantial cost: the resulting collections of patch embeddings are computationally expensive to store and process, and they are frequently dominated by redundant, homogeneous, or otherwise uninformative tissue regions that dilute the diagnostic signal. Existing patch selection methods largely depend on heuristic or …

Robust Deep Learning One-Class Classification, Shahd Alnofaie

Graduate Studies Theses and Dissertations 2026

One-Class Classification (OCC) focuses on learning the characteristics of normal data and identifying observations that deviate from this learned pattern as anomalies. It is commonly used in applications such as medical diagnosis, cybersecurity, industrial monitoring, and fraud detection, where abnormal examples are often rare or unavailable during training. Classical approaches such as SVDD and LS-SVDD describe normal data using a hypersphere. While effective in some settings, these methods rely on shallow representations and can be sensitive to noise and contaminated observations. To address these limitations, this dissertation introduces a Deep LS-SVDD framework that combines hypersphere-based data description with deep neural …

Making Unsafe Sequences Unexecutable: Formal Protocol Enforcement For Cyber-Physical Systems, Arthur Amorim

Graduate Studies Theses and Dissertations 2026

Cyber-physical systems execute physical actions in response to software commands, making their communication protocols a primary attack surface. A stealthy attack is a sequence of individually valid messages that violates a required ordering, driving the system into an unsafe state without malware or protocol violation. Existing defenses examine messages or physical state in isolation, not protocol level sequences, and cannot prevent them. Preventing them requires enforcement that makes unsafe sequences unexecutable at the communication boundary.

Formal methods offer a principled path to enforcement, but no tool spans specification to safe deployed hardware. Model checking automates proofs but has no certified …

An Empirical Comparison Of K-Nearest-Neighbors And Logistic Regression Classification Models, Jackson Cushing

Graduate Studies Theses and Dissertations 2026

This thesis presents an empirical comparison of two classification methods: Logistic Regression and K Nearest Neighbors (KNN). The primary objective of this research is to evaluate the strengths and limitations of each method when applied to real-world datasets. Several publicly available datasets on diabetes, breast cancer, heart attack risk, and cardiovascular disease, were analyzed. For each dataset, K Nearest Neighbors models were implemented in the same way logistic regression had already been applied. The results demonstrate that while logistic regression offers interpretable parameter estimates and performs well when the underlying predictor and outcome relationship is approximately linear, however KNN can …

Performance Of Numerical Methods Applied To The Black–Scholes Model, Scott Cameron Williams

UNF Graduate Theses and Dissertations

We compare five numerical approaches for approximating solutions to the Black–Scholes partial differential equation for pricing European call options: FTCS, BTCS, Crank– Nicolson, Monte Carlo simulation, and a physics–informed neural network (PINN). These methods span finite difference techniques, probabilistic simulation, and machine learning. Performance is evaluated based on computational efficiency and accuracy relative to the analytical Black–Scholes solution.

Among the methods, Crank–Nicolson and the PINN demonstrated the strongest overall performance. Crank–Nicolson achieved the highest accuracy but exhibited increased runtime as the number of underlying stock price grid points grew. In contrast, the PINN produced slightly less accurate results but with …

Ai-Powered Lawyering: Ai Reasoning Models, Retrieval Augmented Generation, And The Future Of Legal Practice, Daniel Schwarcz, Sam Manning, J. J. Prescott, Patrick Barry, David R. Cleveland, Beverly Rich

Articles

Generative AI is set to transform the legal profession, though its most promising uses and ultimate effects are still unclear. While AI models like GPT-4 improve efficiency, they can also “hallucinate” and may undermine legal judgment, particularly in complex tasks typically handled by skilled lawyers. This article examines two emerging AI innovations that may mitigate these concerns: Retrieval Augmented Generation (RAG), which grounds AI-powered analysis in legal sources, and AI reasoning models, which structure complex reasoning before generating output. We conduct the first randomized controlled trial assessing these technologies, assigning upper-level law students to complete legal tasks using a RAG-powered …

Monitoring, Oversight, And Learning In Medical Ai, W. Nicholson Price Ii

Articles

When medical AI errs, it often goes unnoticed. If there’s a specific patient injury, and the link to AI is obvious, that problem might be reported to the Food and Drug Administration (FDA), but not always. And many other types of problems, like worse performance on specific groups or ineffective integration into health system workflows, simply don’t fall within the contours of regularized reporting. Even if they are noticed by the health system—far from a given—there’s no obvious way to share that information more broadly. Against this backdrop, there are justified calls for better oversight and reporting. But there’s the …

A Comprehensive Survey On Facial Expression Generation: From Gans To Llm-Guided Multimodal Models, Murad Hasan, Rabab Abdelfattah, Kareem Abdelfatah, Mostafa Fouda, Ahmed Sherif

Faculty Publications

Facial expression generation (FEG) has emerged as a vital area in human–computer interaction, virtual avatars, and affective computing, aiming to synthesize natural and expressive facial behaviors across diverse interaction contexts. This survey presents a comprehensive analysis of recent advances in FEG, organized into six key paradigms: speech-driven expression generation, facial reaction generation, face video generation, facial animation, avatar-based generation, and text-driven expression generation. We review a wide range of model architectures, including VQ-VAEs, Generative Adversarial Networks (GANs), 3D Morphable Models (3DMMs), Transformers, and diffusion-based approaches, and compare their performance using commonly adopted evaluation metrics such as Fréchet Distance (FD), Peak …

A Comprehensive Review Of Cyber Security And Current Practices In Global Mining Critical Infrastructure, Abu Barkat Ullah, Wanli Ma, Mohiuddin Ahmed, Bazlur Rashid, Munir Ahmad Saeed, Omer Arshad, Utkarsh Raghav

Research outputs 2022 to 2026

The purpose of the study is to explore the reasons behind the low uptake of Information Security Management Standards (ISMS), Asset Management, and Business Continuity Plans despite increasing cyber threats to the mining sector. Mining companies need to modernize and automate to keep up with the ‘Fourth Industrial Revolution’, driven by disruptive technology, forcing systems and technologies to become more integrated, increasing cyber attack threats. To address this, we conducted a literature review analyzing the mining industry across various regions. The research is based on a qualitative analysis of diversified literature. The results highlighted factors behind the low uptake of …

Real-Time Isolated Asl Recognition: Evaluating Spatial-Temporal Networks And Multimodal Llms, Raga Mouni Batchu

West Chester University Graduate Theses, Dissertations, and Final Projects

This thesis investigates the deployment of high-accuracy Isolated ASL Recognition (ISLR) in resource-constrained edge environments. We train a lightweight Spatio-Temporal Attention Network (SSTAN,∼2.7 M parameters,∼10 MB) on the WLASL-100 benchmark, achieving 75.25% Top-1 and 88.24% Top-5 accuracy with 139 ms CPU-only inference. A systematic comparison against frontier multimodal LLMs (Gemini 3 Flash, Gemini 3.1 Pro, Qwen 3 VL) shows SSTAN outperforms the best LLM baseline by∼1.85×in accuracy while being 22–230×faster and up to 40×cheaper annually. The LLMs’ core limitation is a lack of fine-grained temporal perception; they impose English-language semantic priors rather than learning the articulatory distinctions that define ASL …

Attacks And Detections In Recommender Systems: A Comprehensive Analysis For Models, Progresses, And Trends, Yan Feng, Zhihai Yang, Kexin Li, Jianxin Li, Pinghui Wang, Zhiquan Liu

Research outputs 2022 to 2026

Recommender systems (RSs), as crucial components of online services, can help users efficiently obtain information they may like. In reality, RSs face long-term threats. Attackers manipulate recommendation results by injecting malicious data in order to obtain benefits. At present, research on the security of RSs lacks a comprehensive understanding of attack capabilities. Moreover, existing defense strategies have not yet been systematically associated with attack characteristics. More importantly, existing defense methods rarely focus on real unlabeled data in practical application scenarios for anomaly detection and forensics. Therefore, this survey systematically analyzes the security of RSs and provides new insights. Specifically, we …

ℵ-Ipomdp: Mitigating Deception In A Cognitive Hierarchy With Off-Policy Counterfactual Anomaly Detection, Nitay Alon, Joseph M. Barnby, Stefan Sarkadi, Lion Schulz, Jeffrey S. Rosenschein, Peter Dayan

Research outputs 2022 to 2026

Social agents with finitely nested opponent models are vulnerable to manipulation by agents with deeper recursive capabilities. This imbalance, rooted in logic and the theory of recursive modelling frameworks, cannot be solved directly. We propose a computational framework called ℵ-IPOMDP, which augments the Bayesian inference of model-based RL agents with an anomaly detection algorithm and an out-of-belief policy. Our mechanism allows agents to realize that they are being deceived, even if they cannot understand how, and to deter opponents via a credible threat. We test this framework in both a mixed-motive and a zero-sum game. Our results demonstrate the ℵ-mechanism’s …

Flood Risk Prediction System For Irish River Networks, Fernando Adalberto Naatz Heringer, Hugo Rodrigues De Freitas

ICT

Flooding costs the Irish economy more than 300 million euros annually; however, the hydrometric and meteorological data needed to predict dangerous river rises is already collected and freely available. This project applies machine learning to a decade of hourly observations from OPW Station 09001 on the River Liffey at Leixlip and Met Eireann Casement Aerodrome to build a flood risk prediction system with a 72-hour forecast horizon.

A two-phase modelling pipeline was built following the CRISP-DM framework. Phase 1 compared LSTM and GRU recurrent neural networks on the task of forecasting the river level in metres 72 hours ahead. LSTM …

Machine Learning System For Cereal Yield Prediction In South America., Victor Gabriel Oliveira, Kelvin Henrique Ferreira Dumas

ICT

This project developed a machine learning solution to predict cereal yield in South America using FAOSTAT agricultural data. The objective was to estimate yield in kg/ha for rice, maize, and wheat based on variables such as country, crop type, year, harvested area, producer price, pesticide use, and nutrient indicators.

The project followed the CRISP-DM methodology, covering business understanding, data understanding, data preparation, modelling, evaluation, and deployment. Several regression models were tested, including Random Forest, Gradient Boosting, AdaBoost, XGBoost, KNN, and SVR.

The models were evaluated using MAE, MSE, RMSE, and R² score. The results showed that tree-based ensemble models performed …

A Comparative Analysis Of Supervised Machine Learning Techniques For Predicting It Incident Resolution Time, Ammad Hussain, Hussnain Yaqoob

ICT

IT support teams are facing the persistent challenge in estimating how long an incident will take to resolve. The lack of reliable predictions, planning decisions around staffing, escalation and user communication are largely guesswork. This project set out to address that gap by building a machine learning solution capable of predicting incident resolution time from details available at the point a ticket is logged.

Using the UCI IT Incident Event Log dataset, which contains 141,712 records from a real service management system, the project followed the CRISP-DM framework across six phases from business understanding through to deployment. After thorough data …

A Comprehensive Study Of Sarima And Xgboost Models For Short-Term Traffic Volume Forecasting At A Melbourne Intersection (Victoria), Australia, Douglas Vinicius Dierings, Felipe Fontanive Marques

ICT

Traffic congestion is an increasing challenge in modern cities, impacting transportation efficiency and urban sustainability. This project focuses on short-term traffic volume forecasting, using 15-minute Sydney Coordinated Adaptive Traffic System (SCATS) data collected from the State of Victoria, Australia. The study compares two forecasting approaches: SARIMA, a statistical time-series model, and XGBoost, a decision-tree machine-learning model. Historical traffic data from 2022 to 2024 was analysed to identify traffic patterns, seasonal trends, and peak traffic periods. The models were evaluated for their effectiveness in handling urban traffic behaviour. The results of this study aim to support smarter traffic management, infrastructure planning, …

Characterizing Cyber Intrusions In Critical Infrastructure Networks Using Discrete-Event Simulation, Lawrence M. Dilworth

Dissertations, Master's Theses and Master's Reports

Over the past two decades, cybersecurity compliance frameworks such as the North American Electric Reliability Corporation Critical Infrastructure Protection (CIP) have introduced prescriptive measures for protecting power system networks, emphasizing restricted access, segmentation, and minimizing routable exposure. While effective for baseline cyber hygiene, these approaches do not capture system-level risks or adversarial propagation across interconnected infrastructure. In contrast, Cyber-Informed Engineering (CIE), advanced by Idaho National Laboratory, embeds security in system design by considering threat vectors and physical constraints.

Despite CIP guidance, many deployments rely on IP-routable, bidirectional communication that enables handshaking, allowing adversaries to infer reachable targets. This work presents …

The Excess Path Length Distribution: A Stochastic Model For Sample-Based Path Planners, Chaz B. Cornwall

Dissertations, Master's Theses and Master's Reports

Through random sampling, sample-based path planners enable autonomous agents to quickly find paths without human intervention. However, due to the paths' randomness, sample-based path planners currently require additional verification, partially nullifying agents' ability to act autonomously. I set out to characterize this uncertainty so humans know what to expect from these path planners and know how to alter the path planner to desired specifications. To ensure the results are theoretical as well as practical, I first create a stochastic model of path length uncertainty using the trade-off between sampling time and optimality. By leveraging this model, my proposed algorithm reduces …

Using Automotive Lidar To Reduce The Energy Consumption Of An Ego Autonomous Vehicle, Logan P. Schexnaydre

Dissertations, Master's Theses and Master's Reports

There is significant potential to reduce the energy consumption of the transportation sector through autonomous vehicles. Prior work on autonomous vehicle energy efficiency focuses on the whole system or the control subsystem. Yet, the sensing and processing components, which have direct and indirect effects on net energy use, are less explored. This dissertation fills this gap by modeling and evaluating these effects for lidar sensors, which provide high-resolution spatial data at the cost of high power and processing demands. I apply lidar to the energy-saving tasks of automated vehicle following and road surface profiling. For automated vehicle following, I model …

Real Time Waste Classification Using Deep Learning: Comparing Mobilenetv2 And Resnet 18 With Transfer Learning And Fine Tuning, Abdul Moaiz

ICT

Waste contamination is a major problem all over the world. The Environmental Protection Agency reports that over two thirds of waste found in general household and commercial bins could have been placed in the recycling or organic waste bins instead in Ireland, with food waste and plastics being the most common misplaced items. This project presents a deep learning solution to classify nine categories of waste from camera images in real time with the goal of helping users sort waste correctly at the source. Following the CRISP DM framework, two convolutional neural network architectures were trained and compared, MobilenetV2 and …

Bitcoin Prediction System Usind Machine Learning Techniques, Carolina Azevedo De Castro

ICT

This project investigated the use of machine learning techniques to predict short-term Bitcoin price direction using historical market data obtained from Yahoo Finance. Following the CRISP-DM methodology, the dataset was analysed, prepared, and transformed through feature engineering techniques including moving averages, volatility indicators, return measures and price position metrics. Multiple classification algorithms were evaluated, including Bayesian Classification, K-Nearest Neighbour, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine and XGBoost. Several optimisation strategies were also tested, including feature selection, hyperparameter tuning, feature scaling and class weight balancing. Results showed that predicting short-term Bitcoin price movements remains challenging, with most models …