Computer Sciences Commons^™

Runtime Backdoor Detection For Federated Learning Via Representational Dissimilarity Analysis, Xiyue Zhang, Xiaoyong Xue, Xiaoning Du, Xiaofei Xie, Yang Liu, Meng Sun

Research Collection School Of Computing and Information Systems

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify …

Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection Against Multipath Routing, Ziming Zhao, Zhaoxuan Li, Xiaofei Xie, Zhipeng Liu, Tingting Li, Jiongchi Yu, Fan Zhang, Binbin Chen

Research Collection School Of Computing and Information Systems

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multipath routing phenomenon that …

Potential And Pitfalls Of Romantic Artificial Intelligence (Ai) Companions: A Systematic Review, Qi Hui Jerlyn Ho, Meilan Hu, Tracy Xi Chen, Andree Hartanto

Research Collection School of Social Sciences

As Artificial Intelligence (AI) becomes more integrated into daily life, individuals have increasingly turned to AIdriven systems for emotional support, companionship, and even romantic relationships. These relationships can be both beneficial and detrimental. Given the need for a comprehensive understanding of this phenomenon, this systematic review uses Sternberg’s Triangular Theory of Love to provide a holistic summary of its key potentials and pitfalls. A total of 23 articles were identified from the following databases: EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus, and Web of Science. Results highlighted the key potentials of being in a romantic relationship with AI companions as: the …

What References Are Chatgpt, Gemini, Copilot, And Perplexity Providing For Consumer Health Questions?, Ivan Portillo, Scott Johnson, Catherine Johnson

Library Presentations, Posters, and Audiovisual Materials

No abstract provided.

The Weaving Of Machine Learning And Artificial Intelligence Into The Fabric Of Cybersecurity Curriculum: From Degree Plan To Capstone Projects, Mahmoud K. Quweider, Liyu Zhang, Jorge Castillo, Ala Qubbaj

Informatics and Engineering Systems Faculty Publications

As our newly designed degree in Cybersecurity enters its fourth year, students in the program are starting to take courses beyond the basic ones, including senior courses, technical electives, and capstone projects. While Cybersecurity is at the heart of our degree that addresses the national need for cybersecurity specialists, how we approach the education and pedagogy of cybersecurity in the era of Big Data and AI/ML (Artificial Intelligence/Machine Learning) is a question that we are addressing in real-time as techniques and measures and countermeasures of cybersecurity attacks keep evolving and taking advantages of the rapid advancements in computing, memory, storage, …

Chatgpt’S Performance Evaluation In Spreadsheets Modeling To Inform Assessments Redesign, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Background: Increasingly, students are using ChatGPT to assist them in learning and even completing their assessments, raising concerns of academic integrity and loss of critical thinking skills. Many articles suggested educators to redesign assessments which are more “Generative-AI-resistant” and to focus on assessing students on higher order thinking skills. However, there is a lack of articles that attempt to quantify assessments at different cognitive levels to provide empirical study insights on ChatGPT’s performance at different levels, which will affect how educators redesign their assessments.Objectives: Educators need new information on how well ChatGPT performs to redesign future assessments to assess their …

On-Demand Heterogeneous Drone Delivery Problem, Xupeng Wen, Zhiguang Cao, Shu Xu, Dapeng Ren, Guohua Wu, Yaoxin Wu

Research Collection School Of Computing and Information Systems

In the on-demand problem domain, actual demand frequently deviates from the expected demand. This paper intricately delves into the exploration of on-demand heterogeneous multi-drone routing problem (ODHDRP), in which a transport drone carries multiple terminal drones to subregions in the first echelon, and the terminal drones deliver parcels during a flight trip to customers with demands in subregions to maintain economies of scale in the second echelon. We formulate the customer demands using a normal distribution, and exploit a reliability model of customer demands with chance constraints. To solve the ODHDRP efficiently, we propose a hybrid iterative optimisation heuristic (HIOH) …

Cashift: Benchmarking Log-Based Cloud Attack Detection Under Normality Shift, Jiongchi Yu, Xiaofei Xie, Qiang Hu, Bowen Zhang, Ziming Zhao, Yun Lin, Lei Ma, Ruitao Feng, Frank Liau

Research Collection School Of Computing and Information Systems

With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution could differ from the training distribution and highly affect the performance of LAD. Unfortunately, existing works …

Regtrieve: Reducing System-Level Regression Errors For Machine Learning Systems Via Retrieval-Enhanced Ensemble, Junming Cao, Xuwen Xiang, Mingfei Cheng, Bihuan Chen, Xinyan Wang, You Lu, Chaofeng Sha, Xiaofei Xie, Xin Peng

Research Collection School Of Computing and Information Systems

Multiple machine learning (ML) models are often incorporated into real-world ML systems. However, updating an individual model in these ML systems frequently results in regression errors, where the new model performs worse than the old model for some inputs. While model-level regression errors have been widely studied, little is known about how regression errors propagate at system level. To address this gap, we propose RegTrieve, a novel retrieval-enhanced ensemble approach to reduce regression errors at both model and system level. Our evaluation across various model update scenarios shows that RegTrieve reduces system-level regression errors with almost no impact on system …

Unsupervised Recognition Of Unknown Objects For Open-World Object Detection, Ruohuan Fang, Guansong Pang, Wenjun Miao, Xiao Bai, Jin Zheng, Xin Ning

Research Collection School Of Computing and Information Systems

Open-world object detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models detect the unknowns that exhibit similar features to the known objects, but they suffer from a severe label bias problem, i.e., they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this article proposes a novel module, namely reconstruction error-based Weibull (REW) model, that …

Keep The Balance: A Parameter-Efficient Symmetrical Framework For Rgb+X Semantic Segmentation, Jiaxin Cai, Jingze Su, Qi Li, Wenjie Yang, Shu Wang, Tiesong Zhao, Shengfeng He, Wenxi Liu

Research Collection School Of Computing and Information Systems

Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations …

Modfinity: Unsupervised Domain Adaptation With Multimodal Information Flow Intertwining, Shanglin Liu, Jianming Lv, Jingdan Kang, Huaidong Zhang, Zequan Liang, Shengfeng He

Research Collection School Of Computing and Information Systems

Multimodal unsupervised domain adaptation leverages unlabeled data in the target domain to enhance multimodal systems continuously. While current state-of-the-art methods encourage interaction between sub-models of different modalities through pseudo-labeling and feature-level exchange, varying sample quality across modalities can lead to the propagation of inaccurate information, resulting in error accumulation. To address this, we propose Modal-Affinity Multimodal Domain Adaptation (MODfinity), a method that dynamically manages multimodal information flow through fine-grained control over teacher model selection, guiding information intertwining at both feature and label levels. By treating labels as an independent modality, MODfinity enables balanced performance assessment across modalities, employing a novel …

Event-Based Eye Tracking: Event-Based Vision Workshop 2025, Qinyu Chen, Et. Al.

Research Collection School Of Computing and Information Systems

No abstract provided.

Human-Computer Interaction And Artificial Intelligence For Ageing Population, Keng Siau, Hailiang Wang, Fiona Fui-Hoon Nah, Runyu Wang, Ruitong Che, Can Liu

Research Collection School Of Computing and Information Systems

As the global population ages rapidly, the field of human-computer interaction (HCI) is in urgent need of innovation, redesign, and reengineering to meet the evolving needs of older adults. The older demographic faces a range of challenges—including physical limitations, cognitive decline, reduced social in-tegration, and varying levels of technological literacy—that can hinder effective engagement with digital technologies. In response to these challenges, research-ers and designers are using inclusive and adaptive approaches to enhance acces-sibility, usability, and emotional well-being. This paper reviews key design prin-ciples in HCI for the ageing population and discusses how artificial intelligence (AI) tools, such as voice …

Meta-Learning Hyperparameters For Foundation Model Adaptation In Remote-Sensing Imagery, Zichen Tian, Yaoyao Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

Training large foundation models of remote-sensing (RS) images is almost impossible due to the limited and long-tailed data problems. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module …

Property Testing Ai: An Efficient Frontier, Paul Sopher Lintilhac

Dartmouth College Ph.D Dissertations

In this dissertation, we take a step towards addressing the major problem of a lack of standardized and rigorous approaches to testing and evaluation of AI systems. Taking inspiration from both the fields of Property Testing and Property Based Testing (for programs), we develop a novel taxonomy of partially overlapping classes of properties of AI systems, including simple properties, compound properties, higher order properties, data relation properties, and architecture-utility properties. We argue that this taxonomy categorizes a diverse set of AI traits -- including accuracy, fairness, robustness, monotonicity, point-wise and global privacy properties, sensitivity, and more -- according to the …

Minifying Deep Denoising Networks With Knowledge Distillation, Antonio L. Rozzi

Master's Theses

Hearing loss is a prevalent condition, affecting hundreds of millions globally, with a higher incidence among older adults. While hearing aids are the standard treatment, the majority of those who could benefit from hearing aids choose not to wear them, attributing this decision in large part to their inability to perform well in conversations in large groups and in noisy situations. To date, no denoising systems on commercial hearing aids are able to improve speech intelligibility. Recent advances in artificial intelligence research have shown that large deep-learning models can in fact improve speech intelligibility by removing background noise from audio. …

Adversarial Deep Reinforcement Learning For Tank Duel Simulation Using Lidar-Based Observations, Braedan Kennedy

Master's Theses

Previous research has demonstrated that reinforcement learning agents can learn to steer differential-drive robots around obstacles using 2D lidar scans as observations. However, these studies typically treat all range returns as undifferentiated obstacles—objects to avoid—without distinguishing between different object types. This thesis builds upon previous research by introducing an adversarial task in which an agent must interpret raw range readings to both avoid static obstacles and identify, pursue, and engage a hostile target.

To investigate this problem, this thesis introduces TankGame, a novel, lightweight 2D tank duel simulator. Each agent receives a 360° lidar scan, controls its motion via tread …

Opening The Black Box With Regal: A Novel Explainable Ai Approach To Uncover Key Predictors In Search And Rescue Success, Brandon Hyunjun Kim

Master's Theses

The outcome of a search and rescue (SAR) operation is influenced by a complex, non-linear interplay among numerous factors, including geographic context, subject-specific characteristics, and environmental conditions. The high dimensionality and intricate dependencies among these variables pose significant challenges to traditional exploratory modeling approaches, limiting their ability to uncover meaningful patterns and relationships associated with mission success. This study introduces Rules Based Explanations for Generated neighborhoods Around Localized cases (REGAL), a novel adaptation of the Local Interpretable Model-agnostic Explanations (LIME) framework to explain deep multimodal neural networks and what key features it assesses to determine search and rescue success. REGAL …

Efficient Gan-Based Adversarial Example Generation Against Ml-Based Network Intrusion Detection Systems, Darren D. Hartono

Master's Theses

In the realm of network security, Network Intrusion Detection Systems (NIDS) are essential for identifying and mitigating malicious activities targeting networked devices. Traditionally, these systems have relied on signature-based and anomaly-based detection techniques. However, the increasing complexity and adapt- ability of cyber threats have driven the adoption of Machine Learning (ML) ap- proaches in modern NIDS, significantly improving their ability to detect a wider range of attack vectors. Despite these advancements, ML-based NIDS remain vulnerable to adversarial examples—deliberately crafted inputs designed to mislead models and trigger incorrect classifications. Originally identified in the field of computer vision, adversarial examples now pose …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Model-Based Reinforcement Learning And Deep Learning For Power Converter Circuit Design Automation, Shaoze Fan

Dissertations

This dissertation presents a comprehensive automated framework for power converter design, leveraging reinforcement learning (RL) and graph-transformer networks (GTN) to address critical inefficiencies in traditional manual topology optimization. Motivated by the combinatorial increase of circuit design spaces and the computational cost of iterative simulations, this work develops a robust framework for generating energy-efficient topologies requiring rapid and reliable circuit design.

The framework integrates three key components: (1) an upper-confidence-bound-tree-based (UCT-based) RL model for circuit topology space exploration, (2) parallelized UCT algorithms to accelerate exploration processes, (3) a Graph-Transformer-based Network enabling fast circuit performance evaluation. Experimental validation demonstrates the whole framework …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

Enriching Vision Representation By Deep Neural Networks And Self-Supervised Learning, Yucong Shen

Dissertations

Nowadays, more and more interesting computer vision tasks are tackled by deep learning approaches. However, the increasing model complexity imposes significant computational and storage costs. To address this challenge, this dissertation explores efficient deep learning techniques, proposing morphological layer, an efficient feature extraction layer. It achieves competitive image classification accuracy with significantly decreased model parameters. Another attempt at efficient deep learning is a proposed channel pruning approach that compresses deep neural networks by identifying and removing redundant channels using optimal transport theory. This approach achieves significant reductions in model size and computational cost while maintaining or even improving performance across …

From Neural Networks To Large Language Models: Innovations In Financial Ai, Mathematical Reasoning, And Structured Data Representation, Junyi Ye

Dissertations

This dissertation explores the evolution and application of artificial intelligence techniques across three critical domains: financial modeling, mathematical reasoning, and structured data analysis. The dissertation presents seven research projects that chart a progression from specialized neural architectures to sophisticated large language models (LLMs), contributing novel methodologies and frameworks at each stage.

In the financial domain, the research first introduces TS-Mixer, a MLP-based architecture for time-series forecasting that captures both feature relationships and temporal dependencies through a simple yet effective design, outperforming more complex models in S&P500 index prediction. The dissertation then presents DySTAGE, a dynamic graph representation learning framework that …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

Quantum-Enhanced Training Of Large Language Models: A Hybrid Approach, Nan Wu, Fangmin Song, Xiangdong Li

Publications and Research

The training of large language models (LLMs) presents significant computational challenges, particularly regarding efficient convergence. This paper presents a hybrid quantum-classical framework designed to address the significant computational challenges associated with training large language models (LLMs). By integrating quantum computing principles superposition, entanglement, and tunneling with classical deep learning methods, we propose an approach to accelerate convergence, enhance optimization efficiency, and improve model generalization. Specifically, quantum feature mapping is employed to project classical data into high-dimensional Hilbert spaces, facilitating more expressive data representations. Quantum-assisted optimization algorithms, such as Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), efficiently navigate …

Artificial Intelligence Use In Medical Education: Best Practices And Future Directions, Rasheed A. M. Thompson, Yash B. Shah, Francisco Aguirre, Courtney Stewart, Costas D. Lallas, Mihir S. Shah

Department of Urology Faculty Papers

PURPOSEOF REVIEW: This review examines the various ways artificial intelligence (AI) has been utilized in medical education (MedEd)and presents ideas that will ethically and effectively leverage AI in enhancing the learning experience of medical trainees.

RECENT FINDINGS: AI has improved accessibility to learning material in a manner that engages the wider population. It has utility as a reference tool and can assist academic writing by generating outlines, summaries and identifying relevant reference articles. As AI is increasingly integrated into MedEd and practice, its regulation should become a priority to prevent drawbacks to the education of trainees. By involving physicians in …

Who Should Take Responsibility For Artificial Intelligence Actions And Outcomes? Perception Of Auditors As Users Of Ai Systems, Hanh Hoang Le

Doctoral Dissertations

As artificial intelligence (AI) systems become increasingly embedded in auditing processes, questions arise regarding how professional auditors perceive and allocate responsibility for AI-assisted decisions. This study investigates the effects of AI explainability and auditors’ perceived autonomy on perceived responsibility in the context of audit decision-making. Drawing on theories of moral responsibility and professional judgment, the study employs a 2x2 experimental design using hypothetical audit scenarios to manipulate levels of AI explainability and auditors’ autonomy. Hierarchical regression analysis reveals that perceived autonomy statistically significantly increases auditors’ perception of responsibility for AI-assisted decisionmaking, whereas AI explainability is not a significant predictor. Additionally, …

Characterization Of Sars-Cov-2 Replication And Transcription Complexes Via Structural And Evolutionary Approaches, Amelie Ghirardo, Ben Shabatian, Avishai Aghelian, Kyle Tau, Eleonora Gianti

Undergraduate Research

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused around 700M cases and over 7M COVID-19-related deaths recorded worldwide (World Health Organization, March 2025). Aiming to effectively combat this and other disease-causing Coronaviruses (CoV), unprecedented research efforts led to the development of new vaccines and antiviral therapies. Due to emergence of variants of concern (VOCs) with increased transmissibility, immune evasion from vaccination, and potential to resist the available treatments, SARS-CoV-2 continues to represent a major threat to global health. Hence, there is a pressing need to discover new antivirals with broad-spectrum efficacy against multiple SARS-CoV-2 variants and related CoVs. This project …