Artificial Intelligence and Robotics Commons^™

Accelerating Search And Rescue Response: A Simulation Study On The Dynamic Efficiency Of Flocking-Enabled Drone Swarms, Sophia Beckwith, Carys Del Prete

Discovery Day - Daytona Beach

This project explores how imitations observed in animal group behavior, specifically flocking in birds, can be applied to the functionality of autonomous drone systems to aid in search and rescue efforts. The goal is to demonstrate how incorporating code based on the Boids, Vicsck and predictive control linear algebraic mathematical models for drone flight controls and the collective behaviors of flocks will increase the efficiency of drone maneuvers, allowing them to reorganize and fill gaps when one is removed. A MATLAB-based simulation was developed to model the behaviors using research conducted on the symmetric and synchronized behaviors observed from flocks …

Demonstrating Superresolution In Radar Range Estimation Using A Denoising Autoencoder, Robert Czupryniak, Abhishek Chakraborty, Andrew N. Jordan, John C. Howell

Mathematics, Physics, and Computer Science Faculty Articles and Research

We apply machine learning methods to demonstrate radar range superresolution using a denoising autoencoder trained without supervision. Focusing on the estimation of a single physical parameter, the separation between two scatterers in the subwavelength regime, we constrain the network to a one-dimensional bottleneck layer with its size matched to the parameter dimensionality. We find that the bottleneck layer forms a reproducible, monotonic mapping with the true separation, showing that the network learns a low-dimensional representation directly aligned with the underlying physical parameter. We further show that this representation preserves the Fisher information of the signal, indicating that the network recovers …

Ai And The Music Industry: Its Current Status And A Speculative Projection Of Its Evolutionary Trajectory, Rhett D. Morris, Clayton Rosati, Stefan Fritsch

Honors Projects

Music serves as one of society's biggest cultural outlets, allowing millions to share in what used to be a uniquely human form of expression. The commodification of music has built a huge industry full of companies and platforms that have used technology and property laws to shape music's relationship with the public. This study aims to look into the future to see how AI and its implementation could affect the structure of the music industry. To look into the future, this piece establishes two of the most pressing kinds of AI technology for the music industry and looks to contextualize …

Graph Perturbation Analysis For Subgraph Counting, Hanhua Xiao, Yuchen Li, Kyriakos Mouratidis

PhD Student’s Publications Collection

Subgraph counting, which involves determining the frequency of a query graph within a data graph, has numerous applications such as query optimization, fraud detection, and evaluating the expressiveness of graph neural networks. Despite its importance, there has been no systematic study on the impact of adversarial graph perturbations on subgraph counts. In this work, we examine the kSub problem, which aims to identify k edge additions that maximize the count of a query graph. We prove that kSub is intractable due to its NP-hardness, even for constant approximation. To address this, we relax the problem into a top-k selection, termed …

Efficient Test-Time Retrieval Augmented Generation, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but lack external knowledge like RAG methods, and their high costs require balancing overhead with performance gains. To address these issues, we propose an Efficient Test-Time Retrieval-Augmented Generation Framework named ET2RAG to improve the performance of LLMs while maintaining efficiency. Specifically, ET2RAG is a training-free method, that first retrieves the …

Efficient And Universal Watermarking For Llm-Generated Code Detection, Boquan Li, Zirui Fu, Mengdi Zhang, Peixin Zhang, Jun Sun, Xingmei Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plugand- play watermarking approach for AI-generated code detection, named ACW …

Enabling Multi-Task Neural Network Inference On Heterogeneous Edge Devices, Redwanul Islam Arif

Master's Theses

Deep neural networks are increasingly required to run on the devices that generate the data. If such a device must perform more than one task, the standard practice is deploying one model per task, which makes memory grow linearly with task count, which is unacceptable when the entire budget is kilobytes. This thesis asks one question in three settings: how much capability can a network acquire without incurring deployment cost?

The first study takes an ImageNet-pretrained ResNet-18, sweeps the branch point across every residual stage and the classification-head depth across one, ten, and twenty layers, and deploys the resulting multi-head …

A Simulation Assessment Of The 'Law Of One Price', Caleb Wilkins

Computational and Data Sciences (MS) Theses

The ‘law of one price’ is an appealing notion regarding pricing of tradeable commodities that are priced in different currencies. It states that the prices of the same good in different markets should be equal after adjustment for exchange rates and that equality should persist through exchange rate fluctuations.

My research simulates the market conditions that should precipitate the ‘law of one price.’ Data was obtained from the simulated trade between algorithmic artificial intelligence agents that operated under induced boundedly rational market behaviors. Trade took place in two initially separate markets, a high-price market with a higher equilibrium price and …

Ai-Ready Libraries Require Ai-Ready Librarians: Building Organisational Capability For Digital Transformation, Salihin Mohammed Ali

Research Collection Library

Academic libraries worldwide are rapidly experimenting with artificial intelligence (AI) to enhance research, learning, discovery, operations, and user engagement. However, many institutions continue to approach AI adoption primarily through isolated pilots, individual experimentation, or technology-centric initiatives. While these efforts generate innovation, they often struggle to scale sustainably without corresponding organisational capability development. This presentation argues that AI-ready libraries require AI-ready librarians and proposes an organisational capability approach for sustainable AI transformation in academic libraries. Drawing from the development of a library-wide AI strategy plans at Singapore Management University, the presentation explores how AI capability-building can be operationalised across diverse functional …

Sentinel: Evaluating Occlusion-Centered Next-Best-View Selection Using Rgb-Derived Pseudo-Geometry, Paul Nassar

Master's Theses

Three-dimensional cameras provide direct geometric measurements, but their cost, weight, power requirements, and calibration constraints can limit their use in various lightweight or large-scale sensing systems. A potential alternative is to use conventional two-dimensional RGB cameras together with geometric reconstruction models that infer a partial three-dimensional representation from images. This thesis evaluates that possibility for next-best-view (NBV) selection through Sentinel, an occlusion-centered system for static, object-centric scenes with known camera poses and intrinsics. Sentinel converts source RGB observations into pseudo-geometry using monocular depth or point-map predictions, combines those predictions with camera-ray evidence, identifies occluded unknown regions, and selects a candidate …

Continuous Query For Top-K Maximal Sum Intervals Over Streaming Data, Zhongshuai Zhang, Xiaochun Yang, Baihua Zheng, Rui Zhu, Haomin Li, Bin Wang

Research Collection School Of Computing and Information Systems

The continuous identification of top-k maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small k values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, …

Wevit: Weight-Entangled Vision Transformers With Class-Specific Attention For Weakly Supervised Semantic Segmentation, Narges Saeedizadeh, Seyed Mohammad Jafar Jalali, Burhan Khan, Shady Mohamed

Research outputs 2022 to 2026

Weakly Supervised Semantic Segmentation (WSSS) is a challenging task in computer vision, as it relies on limited supervision to generate precise object localization maps, often using Class Activation Maps (CAMs). Traditional methods struggle with balancing localization accuracy and scalability due to their reliance on fixed network architectures and handcrafted strategies. Neural Architecture Search (NAS), despite its proven success in optimizing network designs across tasks, has not yet been explored in WSSS due to the need for efficient weight sharing. To address these limitations, we propose WEViT, a novel framework that integrates NAS with transformers to optimize network architectures and generate …

Counterfactual Explanations For Time Series Classification: From Localized Perturbations To Realistic Generation, Peiyu Li

All Graduate Theses and Dissertations, Fall 2023 to Present

Machine learning models are often used to classify signals collected over time, such as heart rhythms, movement recordings, industrial sensor measurements, and scientific observations. These models can be accurate, but they are often difficult to understand. Users may need to know not only what a model predicted, but also what would have needed to change for the model to reach a different decision.

This dissertation studies counterfactual explanations for time series data. A counterfactual explanation answers a “what-if” question. For example, if a model classifies a signal as one activity instead of another, the explanation shows how the signal would …

The Relativity Of Education: Student Perspectives On Artificial Intelligence In Community College, Ashley Greta Magana

Electronic Theses, Projects, and Dissertations

This hermeneutic phenomenological study examined how diverse community college students experience and make meaning of the integration of generative artificial intelligence (AI) into their educational contexts. Although AI is quickly transforming higher education through automated grading, personalized learning systems, and new models of assessment, the discourse surrounding its implementation remains dominated by administrators, faculty, and institutional stakeholders, while the perspectives of students, specifically community college students who are often historically underrepresented and economically marginalized, are systematically excluded. Most existing research is quantitative and centered on universities, leaving a critical gap in qualitative understanding of the most diverse population in higher …

Predicting Student Belonging In Computing Education: A Multimodal Machine Learning Approach Using Eeg And Survey Data, Hannah Moshtaghi

Master's Theses

Measuring students’ sense of belonging, characterized by feelings of acceptance, inclusion, and encouragement from teachers, remains a significant challenge in computing education. Prior research has associated this multidimensional construct with positive academic outcomes and has identified instructors’ growth- and fixed-mindset messaging as a potential influence. However, belonging is a complex and deeply personal experience that is difficult to capture through direct observation alone. Current measurement methods rely on self-report surveys, which may not capture every aspect of an experience that can also involve emotional and cognitive responses.

This thesis investigates whether combining EEG data recorded during a belonging questionnaire with …

Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain, Promise Ehimen

Dissertations, Theses, and Projects

The increasing adoption of the Internet of Medical Things (IoMT) has improved healthcare delivery through connected medical devices while simultaneously expanding the cybersecurity risks facing healthcare organizations. Although machine learning based intrusion detection systems have demonstrated high detection accuracy, their ability to respond reliably to previously unseen cyberattacks remains uncertain. This study investigated how a Neural Network model and a Logistic Regression model classified novel cyberattacks within the IoMT environment. The Neural Network and Logistic Regression models were both trained and tested using a subset of the CICIoMT2024 benchmark dataset. The Neural Network achieved 99.82% test accuracy and a 0.94 …

Vision Transformers And Convolutional Neural Networks For Land Use Scene Classification, Arun D. Kulkarni

Computer Science Faculty Publications and Presentations

Land use scene classification (LUSC) from remote sensing imagery plays a critical role in environmental monitoring, urban planning, and sustainable resource management. In recent years, deep learning methods have significantly advanced the state-of-the-art, with Convolutional Neural Networks (CNNs) dominating the field because of their strong ability to capture local spatial features. However, the emergence of Vision Transformers (ViTs) has introduced a new paradigm that models long-range dependencies through self attention mechanisms, potentially enabling improved global context understanding. This study presents a comparative assessment of Vision Transformers and CNN-based architectures for remote sensing land use scene classification. Representative CNN models, such …

Ai-Powered Resume Screening, Sang Suh, Numery Zaber

Faculty Publications

Traditional resume screening is manual, slow, and susceptible to bias, and it struggles to keep pace with today’s application volumes. This paper presents a dual-engine, AI-powered resume screening system designed for transparency and reproducibility. The primary (classical) pipeline encodes resumes and job descriptions using Sentence-BERT (SBERT), computes a resume–job match score via cosine similarity, classifies candidates into 25 job categories using XGBoost, and provides model interpretability through SHAP. In parallel, a prompted large language model (LLM) baseline (GPT-4o/4o-mini) outputs a match score and predicted category for comparative analysis. A Streamlit-based interface integrates both engines to support recruiter workflows and human-in-the-loop …

Artificial Intelligence Mechanisms In The Limit Of Crimes And Law Enforcement, Saad Mefleh Alsuwaileh

Journal of Police and Legal Sciences

This study explores the potential of employing technological mechanisms and modern innovations brought about by the Fourth Industrial Revolution, particularly advancements in the field of information technology, in the domains of criminal investigation, crime prevention, and law enforcement. It aims to analyze the impact of these technologies on crime control efforts and the promotion of justice.

The significance of the study lies in highlighting the power of technology in processing and analyzing massive volumes of data with greater speed and accuracy, thereby enhancing the efficiency of criminal investigations and the ability to predict and prevent crimes. The core research question …

Applying Artificial Intelligence Within Decision Support Systems And Its Role In Improving Proactive Thinking And Reducing Security Threats: The Mediating Role Of Data Quality, Hany Shaaban El Anany

Journal of Police and Legal Sciences

The study aimed to identify the impact of applying artificial intelligence within decision support systems in improving the level of proactive thinking and reducing security threats in government institutions in the Arab Republic of Egypt, as well as to examine the mediating role of data quality in this relationship, at a significance level of (α ≤ 0.05). The study sample consisted of (360) participants working in the departments of information technology, decision support, and cybersecurity within government institutions and national authorities that rely on AI-enhanced decision support systems.

The study adopted the descriptive analytical method and used a questionnaire as …