Digital Commons Network^™

A Bayesian Optimisation With Segmentation Approach To Optimising Liquid Handling Parameters, Estefania Yap, Viet Huynh, Calvin Vong, Peter Vogel, Viv Louzado, Thomas Barnes, Buser Say, Michael Burke, Dana Kulić, Aldeida Aleti

Research outputs 2022 to 2026

The automation of liquid handling has become integral in speeding up pharmaceutical development for faster drug development and more affordable treatments. However, the optimal parameters which define the aspirate and dispense procedures vary between liquids and liquid volumes, limiting transfer accuracy and precision. Even state-of-the-art liquid handling devices offer predefined parameters for only a handful of liquids and volumes, resulting in novel parameter sets being defined via a manual, time-consuming process. In this study, we propose an experimental framework for automating the optimisation of liquid class parameters for arbitrary liquids. Within our framework, we propose an optimisation and segmentation algorithm, …

Iostom: Offline Imitation Learning From Observations Via State Transition Occupancy Matching, Quang Anh Pham, Brahmanage Janaka Chathuranga Thilakarathna, Tien Mai, Akshat Kumar

Research Collection School Of Computing and Information Systems

Offline Learning from Observations (LfO) focuses on enabling agents to imitate expert behavior using datasets that contain only expert state trajectories and separate transition data with suboptimal actions. This setting is both practical and critical in real-world scenarios where direct environment interaction or access to expert action labels is costly, risky, or infeasible. Most existing LfO methods attempt to solve this problem through state or state-action occupancy matching. They typically rely on pretraining a discriminator to differentiate between expert and non-expert states, which could introduce errors and instability—especially when the discriminator is poorly trained. While recent discriminator-free methods have emerged, …

No Experts, No Problem: Avoidance Learning From Bad Demonstrations, Minh Huy Hoang, Tien Mai, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

This paper addresses the problem of learning avoidance behavior within the context of offline imitation learning. In contrast to conventional methodologies that prioritize the replication of expert or near-expert demonstrations, our work investigates a setting where expert (or desirable) data is absent, and the objective is to learn to eschew undesirable actions by leveraging demonstrations of such behavior (i.e., learning from negative examples).To address this challenge, we propose a novel training objective grounded in the maximum entropy principle. We further characterize the fundamental properties of this objective function, reformulating the learning process as a cooperative inverse Q-learning task. Moreover, we …

Design Principles For Customer‑Engaging Digital Service Systems: An Action Research Study, Keng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Enhancing Smart Contract Security Using A Code Representation And Gan Based Methodology, Dileep Kumar Murala, Samia Loucif, K. Vara Prasada Rao, Habib Hamam

All Works

Smart contracts are changing many business areas with blockchain technology, but they still have vulnerabilities that can cause major financial losses. Because deployed smart contracts (SCs) are irreversible once deployed, fixing these vulnerabilities before deployment is critical. This research introduces a new method that combines code embedding with Generative Adversarial Networks (GANs) to find integer overflow vulnerabilities in smart contracts. Using Abstract Syntax Trees, we can vectorize the source code of smart contracts while keeping all of the important contract characteristics and going beyond what can be achieved with conventional textual or structural analysis. Synthesizing contract vector data using GANs …

Llm-Driven Semantic Explanations For Soil Moisture Prediction Models, Bamory Ahmed Toru Koné, Khouloud Boukadi, Rima Grati, Emna Ben Abdallah, Massimo Mecella

All Works

Efficient soil moisture prediction is crucial for sustainable agricultural practices, especially in the face of climate change and increasing water scarcity. However, the adoption of machine learning (ML) models in this context is frequently limited by their lack of interpretability, particularly among non-expert users such as farmers. This study proposes a novel approach to soil moisture prediction that combines high predictive performance with enhanced explainability. We propose a framework that leverages large language models (LLMs) to generate textual explanations based on a proposed irrigation and soil moisture ontology, thus making the model's predictions more understandable to farmers. The ontology formalizes …

Reinforcement Learning Based Intelligent Optimisation For Bin Packing Problems: A Review, Nadia Dahmani, Amril Nazir, Ikbal Taleb, Syed M.Salman Bukhari

All Works

The convergence of Reinforcement Learning (RL) and Bin Packing Problems (BPP) is a critical field of study that has profound ramifications in logistics, manufacturing, computer, and retail industries. This paper thoroughly examines the progression from simple rule-based tactics to advanced Deep Reinforcement Learning (DRL) techniques in solving BPPs. By conducting a thorough review of 231 papers conducted between 2019 and 2024, we address and provide answers to important research inquiries, such as “To what extent has academic research explored the use of RL for BPP during this time frame?” and “Which specific areas of application and methodologies have been predominantly …

Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan

All Works

Artificial Intelligence (AI) has become a critical tool in modern precision agriculture, particularly in the detection of plant diseases and pests. This study provides a comprehensive review of current AI methodologies applied to crop disease detection, with a focus on machine learning models, dataset availability, and performance metrics. Our findings indicate that Convolutional Neural Networks (CNNs) are the most widely used and cost-effective approach, while Vision Transformers (ViTs) exhibit superior accuracy but require significantly higher computational resources. We identify key research gaps, including the geographic bias in dataset origins, the trade-off between data quality and quantity, and the limited exploration …

Artificial Intelligence In Waste Management Systems: Applications, Challenges, And Prospects, Imane Belyamani

All Works

Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8 % by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based …

Embedding-Driven Dual-Branch Approach For Accurate Breast Tumor Cellularity Classification, Hossam Magdy Balaha, Ali Mahmoud, Khadiga M. Ali, Mohammed Ghazal, Norah Saleh Alghamdi, Ashraf Khalil, Ayman El-Baz

All Works

This study proposes a dual-branch framework for precise classification of breast tumor cellularity via histopathological images where it integrates two distinct branches: the Embedding Extraction Branch (embedding-driven) and the Vision Classification Branch (vision-based). The Embedding Extraction Branch uses the Virchow2 transformation to generate dense, structured embeddings, whereas the Vision Classification Branch employs Nomic AI Embedded Vision v1.5 to process image patches and produce classification logits. Both branches’ outputs are combined to form the final classification. The framework also suggests Knowledge Block with fully connected layers, batch normalization, and dropout to improve feature extraction and reduce overfitting. The proposed approach reports …

Privacy, Identity, And Fairness: Unpacking Ethical Influences On Metaverse Adoption In University Learning, Mousa Al-Kfairy, Meera Alalawi, Saed Alrabaee, Omar Alfandi

All Works

As immersive technologies like the Metaverse continue to reshape higher education, it becomes increasingly vital to examine the ethical dimensions shaping student engagement with these platforms. This study investigates how university students perceive privacy, digital identity, informed consent, and algorithmic fairness in Metaverse-based classrooms, and how these perceptions influence their trust and behavioral intention to adopt the technology. A quantitative survey was conducted with 310 university students, all of whom had prior exposure to virtual learning platforms. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0, the study found that Metaverse Ethical Dimensions (MED) significantly influence both Trusting …

Precision-Weighted Federated Learning, Jonatan Reyes, Lisa Di Jorio, Cecile Low-Kam, Marta Kersten-Oertel

Computer Science Faculty Publications

Federated learning (FL) using the federated averaging (FedAvg) algorithm has shown great advantages for large-scale applications that rely on collaborative learning, especially when the training data is either unbalanced or inaccessible due to privacy constraints. We hypothesize that FedAvg underestimates the full extent of heterogeneity of data when the aggregation is performed. We propose Precision-Weighted Federated Learning (PW) a novel algorithm that takes into account the second raw moment (uncentered variance) of the stochastic gradient when computing the weighted average of the parameters of independent models trained in a FL setting. With PW, we address the communication and statistical challenges …

Bias Testing And Mitigation In Llm-Based Code Generation, Dong Huang, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models but are underexplored in the literature. This paper presents a novel bias testing framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive empirical study on the biases in code generated by five widely studied LLMs (i.e., …

General Test-Time Backdoor Detection In Split Neural Network-Based Vertical Federated Learning, Shunjie Yuan, Xinghua Li, Xuelin Cao, Haiyan Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

As a new distributed machine learning framework, vertical federated learning (VFL) has been widely applied in the industry. However, recent studies have demonstrated that VFL faces serious challenges from backdoor attacks, which significantly hinder its further development. Although a few studies have focused on defending against VFL backdoor attacks, these defenses either do not consider the latest attack methods or show limited effectiveness. Moreover, most existing backdoor defense efforts primarily focus on backdoor attacks in horizontal federated learning (HFL) and centralized learning. Due to the unique architecture of VFL models, these methods cannot be directly applied to backdoor defense in …

Genscore: Agent-Based Short-Answer Question Generation And Scoring In Software Engineering Courses, Nguyen Binh Duong Ta, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Short-answer questions are commonly used in educational assessments, as they are often viewed as a more effective way than multiple-choice questions to determine whether students have achieved the intended learning outcomes. However, manually creating appropriate questions targeting different cognitive levels such as those defined by the Bloom’s Taxonomy, and grading text answers from students are not trivial tasks for instructors. Existing work on auto-question generation and scoring in computing education typically targets coding-based questions. However, in software engineering courses, assessments can extend beyond coding to understanding of processes, DevOps methodologies, system design, etc. This work aims to address the dual …

Large Language Models (Llms) For Clinical Note Generation: International Classification Of Disease (Icd) Code, Knowledge Graph (Kg) And Prompt Evaluation, Ivan P. Makohon

Computer Science Theses & Dissertations

In the past decade, a surge in the amount of electronic health record (EHR) data in the United States occurred, driven by a favorable policy environment created by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 and the 21st Century Cures Act of 2016. Clinical notes for patients’ assessments, diagnoses, and treatments are captured in these EHRs in free-form text by physicians, who spend a considerable amount of time entering them. Manually writing these notes is time-consuming, increasing patient waiting times and potentially delaying diagnoses. Large language models (LLMs), such as GPT-4o, possess the ability …

Multi-Task Vehicle Routing Solver Via Mixture Of Specialized Experts Under State-Decomposable Mdp, Yuxin Pan, Zhiguang Cao, Chengyang Gu, Liu Liu, Peilin Zhao, Yize Chen, Fangzhen Lin

Research Collection School Of Computing and Information Systems

Existing neural methods for multi-task vehicle routing problems (VRPs) typically learn unified solvers to handle multiple constraints simultaneously. However, they often underutilize the compositional structure of VRP variants, each derivable from a common set of basis VRP variants. This critical oversight causes unified solvers to miss out the potential benefits of basis solvers, each specialized for a basis VRP variant. To overcome this limitation, we propose a framework that enables unified solvers to perceive the shared-component nature across VRP variants by proactively reusing basis solvers, while mitigating the exponential growth of trained neural solvers. Specifically, we introduce a State-Decomposable MDP …

Mando-Llm: Heterogeneous Graph Transformers With Large Language Models For Smart Contract Vulnerability Detection, Nhat Minh Nguyen, Huu Hoang Nguyen, Long Le Thanh, Zahra Ahmadi, Thanh Nam Doan, Daoyuan Wu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Detecting vulnerabilities in smart contracts is vital for the security and reliability of decentralized apps. To facilitate vulnerability detection, contract codes, including bug patterns, are represented as heterogeneous graphs with various nodes and edges, like control-flow and function-call graphs. However, existing graph learning techniques struggle with large, complex graphs. This paper presents MANDO-LLM, a novel framework that combines heterogeneous graph transformers (HGTs) with large language models (LLMs) for detecting vulnerabilities in smart contracts represented as heterogeneous contract graphs built upon control-flow and call graphs. MANDO-LLM uses LLMs to capture code features from control-flow and call data, customizes HGTs to learn …

Stableguard: Towards Unified Copyright Protection And Tamper Localization In Latent Diffusion Models, Haoxin Yang, Bangzhen Liu, Xuemiao Xu, Cheng Xu, Yuyang Yu, Zikai Huang, Yi Wang, Shengfeng He

Research Collection School Of Computing and Information Systems

The advancement of diffusion models has enhanced the realism of AI-generated content but also raised concerns about misuse, necessitating robust copyright protection and tampering localization. Although recent methods have made progress toward unified solutions, their reliance on post hoc processing introduces considerable application inconvenience and compromises forensic reliability. We propose StableGuard, a novel framework that seamlessly integrates a binary watermark into the diffusion generation process, ensuring copyright protection and tampering localization in Latent Diffusion Models through an end-to-end design. We develop a Multiplexing Watermark VAE (MPW-VAE) by equipping a pretrained Variational Autoencoder (VAE) with a lightweight latent residual-based adapter, enabling …

Rising From Ashes: Generalized Federated Learning Via Dynamic Parameter Reset, Jiahao Wu, Ming Hu, Yanxin Yang, Xiaofei Xie, Zekai Chen, Chenyu Song, Mingsong Chen

Research Collection School Of Computing and Information Systems

Although Federated Learning (FL) is promising for privacy-preserving collaborative model training, it suffers from low inference performance due to heterogeneous client data. Due to heterogeneous data across clients, FL training easily learns client-specific overfitting features. Existing FL methods adopt coarsegrained averaging, which can easily cause the global model to get stuck in local optima, leading to poor generalization. Specifically, this paper presents a novel FL framework, FedPhoenix, to address this issue. It stochastically resets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific …

Sciteuq: Toward Uncertainty-Aware Complex Scientific Table Data Extraction And Understanding, Kehinde Ajayi

Computer Science Theses & Dissertations

Scientific tables report critical research insights, data, and findings for scientific progress. Because Portable Document Format (PDF) is the de facto standard format for scientific paper publishing, there has been an emerging need for an automatic method to extract data from PDF files. A significant fraction of scientific tables exhibit complex structure and content, making it challenging for machine learning tools to accurately extract the content directly from PDF files. Despite the advancements in Table Structure Recognition (TSR), automated extraction of data from complex scientific tables remains a challenge due to variations in table structures and contents. In this dissertation, …

Human Identification And Action Recognition Using Small Data And Deep Domain Adaptation, Alexander M. Glandon

Electrical & Computer Engineering Theses & Dissertations

Human identification and human action recognition problems are two important research areas for real-world security and surveillance applications. In both human identification and action recognition, it is necessary to operate by collecting small datasets in the field, possibly in a short time window of observation. This dissertation studies and develops computational modeling and high-performance machine learning (ML) and deep learning (DL) models for human identification and human action recognition using small amounts of data. These methods and computational models may be useful for different security and surveillance applications.

This dissertation on human recognition develops a ML computational model to estimate …

Toward Personalizing Quantum Computing Education: An Evolutionary Llm-Powered Approach, Iizalaarab Elhaimeur

Computer Science Theses & Dissertations

Quantum computing education faces significant challenges due to its complexity and the limitations of current tools. This thesis introduces a novel Intelligent Teaching Assistant for quantum computing education and details its evolutionary design process. The system combines a knowledge-graph-augmented architecture with two specialized LLM agents: a Teaching Agent for dynamic interaction and a Lesson Planning Agent for lesson generation. The system is designed to adapt to individual student needs, with interactions meticulously tracked and stored in a knowledge graph. This graph represents student actions, learning resources, and their relationships, aiming to enable reasoning about effective learning pathways. We describe the …

On The Scalability Of Anisotropic Mesh Adaptation On Distributed And Shared Memory Architectures For Numerical Approximations, Kevin Mark Garner Jr.

Computer Science Theses & Dissertations

Mesh generation is a critical component in numerical approximations of Partial Differential Equations (PDEs). One such example includes Computational Fluid Dynamics (CFD), as CFD simulations in turn are crucial for applications in many industries, such as personalized healthcare and the design of aerospace vehicles. Generating high quality meshes for large-scale CFD problems presents a significant bottleneck in the CFD workflow. This dissertation proposes “fast,” parallel 3D mesh generation methodologies that are designed to leverage the concurrency offered by emerging High-Performance Computing (HPC) architectures. First, a distributed memory method is presented that integrates a sequential state-of-the-art isotropic, advancing front local reconnection-based …

Early Conceptual Sketches Of Blended Reality And The Precursor To The Bbs Quad (2022), David Smith

Publications and Research

This document contains two original hand-drawn conceptual sketches created in early 2022, representing the earliest visual formulations of what would later evolve into the Balanced Blended Space (BBS) framework. The drawings predate my first conversations with ChatGPT and were produced as part of my independent sabbatical research into blended environments, mediated performance, and human–machine interaction.

The first drawing examines human–computational mediation, perception, and internal mapping. The second sketch—later referred to informally as the “BBS Quad”—extends this idea by reconciling cognition–computation symmetry with physical–virtual spatial relationships. Published together, these images document the conceptual foundations of the BBS framework prior to its …

Auction Consensus Algorithm With Loss Mechanism For Decentralized Task Allocation, Jose Rodriguez, Wenjie Dong, Constantine Tarawneh, Qi Lu

Electrical and Computer Engineering Faculty Publications

This paper presents an Auction-Consensus Algorithm with a Loss Mechanism (ACALM), a decentralized task allocation method for multi-robot systems that enhances the existing Consensus-Based Auction Algorithm (CBAA) by incorporating a novel loss propagation mechanism. In contrast to purely greedy bidding strategies, it enables agents to dynamically update task priorities based on the accumulated loss from previously unsuccessful bids. This extended work reduces globally inefficient allocations caused by early suboptimal decisions. The proposed approach is evaluated through large-scale simulations in thousands of randomized scenarios and swarm sizes ranging from 5 to 120 robots. Compared to existing CBAA and GCAA algorithms, ACALM …

A Software Framework For Translating Onnx Models Onto The Lace-C3a Hardware, Shawn Jones

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern computers are powerful, but they are not always efficient enough for small, low power systems like those used on satellites and scientific instruments. To solve this problem, engineers often turn to FPGAs—reconfigurable computer chips that can be customized to run specific tasks much faster and with far less energy than ordinary processors. However, finding the best possible design for an FPGA program is extremely difficult because there are millions of ways a design could be built, and only a small fraction of them actually perform well.

This thesis presents SNOW, a new framework that helps automate the search for …

Adaptive Von Mises–Fisher Likelihood Loss For Supervised Deep Time Series Hashing, Juan Manuel Perez, Kevin Garcia, Brooklyn Berry, Dongjin Song, Yifeng Gao

Computer Science Faculty Publications

Indexing time series by creating compact binary representations is a fundamental task in time series data mining. Recently, deep learning-based hashing methods have proven effective for indexing time series based on semantic meaning rather than just raw similarity. The purpose of deep hashing is to map samples with the same semantic meaning to identical binary hash codes, enabling more efficient search and retrieval. Unlike other supervised representation learning methods, supervised deep hashing requires a discretization step to convert real-valued representations into binary codes, but this can induce significant information loss. In this paper, we propose a von Mises–Fisher (vMF) hashing …

Multi-Agent Robotaxi Dispatch Coordination In A Real-World Simulation – Optimizing Rider Assignment, Rebalancing, And Charging Using Battery-Dependent Rewards And Welfare Maximization, Paden Thompson

All Graduate Theses and Dissertations, Fall 2023 to Present

We propose an approach to coordinate a robotaxi fleet for an autonomous ride-hail service. This is a service similar to a traditional ride-hailing service (Uber, Lyft), where customers request a ride and are then picked up in a car and dropped off in a new location; except, driverless vehicles called robotaxis are used to transport the customers.

Our approach teaches helpful coordination strategies to a robotaxi fleet while taking into account the individual battery level of the robotaxis. Each robotaxi acts as an individual agent in our simulation and can choose to pick up a rider, reposition to a new …

Data-Driven Streamflow Forecasting In The Upper Colorado River Basin Using Spatio-Temporal Graph Networks, Akhila Akkala

All Graduate Theses and Dissertations, Fall 2023 to Present

Forecasting river flow is essential for managing water supplies, reducing flood risk, and supporting healthy ecosystems. In the Upper Colorado River Basin, much of the yearly water comes from melting snow. However, many traditional models struggle to capture how snowpack and river flow interact, especially across such a large and complex region.

This study uses a modern machine learning approach called a Spatio-Temporal Graph Neural Network (STGNN) to improve streamflow prediction. The model uses Snow Water Equivalent (SWE)—a measure of how much water is stored in the snowpack—along with river flow data. By treating each river gauge as part of …