Computer Sciences Commons^™

Comparison Of Evolutionary Algorithms: A Case Study On The Multi-Objective Carbon-Aware Mine Planning, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The NP-hard precedence-constrained production scheduling problem (PCPSP) for mine planning chooses the ordered removal of materials from the mine pit and the next processing steps based on resource, geological, and geometrical constraints. Traditionally, it prioritizes the net present value (NPV) of profits across the lifespan of the mine. Yet, the growing shift in environmental concerns also requires shifts to more carbon-aware practices. In this paper, we use the enhanced multi-objective version of the generic PCPSP formulation by adding the NPV of carbon costs as another objective. We then compare how the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto …

Genixer : Empowering Multimodal Large Language Models As A Powerful Data Generator, Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou

Research Collection School Of Computing and Information Systems

Multimodal Large Language Models (MLLMs) demonstrate exceptional problem-solving capabilities, but few research studies aim to gauge the ability to generate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce Genixer, a comprehensive data generation pipeline consisting of four key steps: (i) instruction data collection, (ii) instruction template design, (iii) empowering MLLMs, and (iv) data generation and filtering. Additionally, we outline two modes of data generation: task-agnostic and task-specific, enabling controllable output. We demonstrate that a synthetic VQA-like dataset trained with LLaVA1.5 enhances performance on 10 …

A Two-Stage Matheuristic For The Home Healthcare Routing And Scheduling Problem With Perishable Products, Aldy Gunawan, Nabila Yuraisyah Salsabila, Vincent F. Yu, Pham Kien Minh Nguyen

Research Collection School Of Computing and Information Systems

This study proposes a home healthcare routing and scheduling problem, where perishable products such as medicines, vaccines, or meals must be provided for some patients’ treatments. This problem is formulated as a mixed integer linear programming (MILP). A two-stage matheuristic is then developed as the solution approach. The first stage is a local search to solve the nurse routing problem, and the second stage is run as the relaxed MILP to solve the scheduling problem. The matheuristic is tested on newly generated instances and compared with the results of CPLEX. The proposed matheuristic is able to obtain CPLEX solutions within …

Robust Image Classification System Via Cloud Computing, Aligned Multimodal Embeddings, Centroids And Neighbours, Wei Lun Koh, Boon Yong Koh, Bing Tian Dai

Research Collection School Of Computing and Information Systems

We propose a framework for a cloud-based application of an image classification system that is highly accessible, maintains data confidentiality, and robust to incorrect training labels. The end-to-end system is implemented using Amazon Web Services (AWS), with a detailed guide provided for replication, enhancing the ways which researchers can collaborate with a community of users for mutual benefits. A front-end web application allows users across the world to securely log in, contribute labelled training images conveniently via a drag-and-drop approach, and use that same application to query an up-to-date model that has knowledge of images from the community of users. …

Evaluating Szz Implementations : An Empirical Study On The Linux Kernel, Yunbo Lyu, Hong Jin Kang, Ratnadira Widyasari, Julia Lawall, David Lo

Research Collection School Of Computing and Information Systems

The SZZ algorithm is used to connect bug-fixing commits to the earlier commits that introduced bugs. This algorithm has many applications and many variants have been devised. However, there are some types of commits that cannot be traced by the SZZ algorithm, referred to as “ghost commits”. The evaluation of how these ghost commits impact the SZZ implementations remains limited. Moreover, these implementations have been evaluated on datasets created by software engineering researchers from information in bug trackers and version controlled histories. Since Oct 2013, the Linux kernel developers have started labelling bug-fixing patches with the commit identifiers of the …

Self-Supervised Spatial-Temporal Normality Learning For Time Series Anomaly Detection, Yutong Chen, Hongzuo Xu, Guansong Pang, Hezhe Qiao, Yuan Zhou, Mingsheng Shang

Research Collection School Of Computing and Information Systems

Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a Distance prediction-based Spatial Normality learning …

Enhancing Stance Classification On Social Media Using Quantified Moral Foundations, Hong Zhang, Quoc-Nam Nguyen, Prasanta Bhattacharya, Wei Gao, Liang Ze Wong, Brandon Siyuan Loh, Joseph J. P. Simons, Jisun An

Research Collection School Of Computing and Information Systems

This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals’ moral foundations. These theoretically-derived dimensions aim to provide an interpretable profile of an individual’s moral concerns which, in recent work, has been linked to behaviour in a range of domains including society, politics, health, and the environment. In this paper, we investigate how moral foundation dimensions can contribute to detecting an individual’s stance on a given target. Specifically, we incorporate moral foundation features extracted from text, along with semantic features, to classify stances at both message-and user-levels using traditional machine learning and Large Language Models …

Neuron Sensitivity Guided Test Case Selection, Dong Huang, Qingwen Bu, Yichao Fu, Yuhao Qing, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been widely deployed in software to address various tasks (e.g., autonomous driving, medical diagnosis). However, they can also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal and repair incorrect behaviors in DNNs, developers often collect rich, unlabeled datasets from the natural world and label them to test DNN models. However, properly labeling a large number of datasets is a highly expensive and time-consuming task. To address the above-mentioned problem, we propose NSS, Neuron Sensitivity Guided Test Case Selection, which can reduce the labeling time by selecting valuable test …

Unraveling The Dynamics Of Stable And Curious Audiences In Web Systems, Rodrigo Alves, Antoine Ledent, Renato Assunção, Pedro Vaz-De-Melo, Marius Kloft

Research Collection School Of Computing and Information Systems

We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time series data such as feeds or search queries. BPoP can distinguish between the slowly-varying regular activity of a stable audience and the bursty activity of a curious audience, often seen in viral threads. Our model consists of two hidden, interacting processes: a self-feeding process (SFP) that generates bursty behavior related to viral threads, and a non-homogeneous Poisson process (NHPP) with step function intensity that is influenced by the bursts from the SFP. The NHPP models the normal background behavior, driven solely by the overall popularity of the …

Ai Coders Are Among Us : Rethinking Programming Language Grammar Towards Efficient Code Generation, Sun Zhensu, Du Xiaoning, Yang Zhou, Li Li, David Lo

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) models have emerged as another important audience for programming languages alongside humans and machines, as we enter the era of large language models (LLMs). LLMs can now perform well in coding competitions and even write programs like developers to solve various tasks, including mathematical problems. However, the grammar and layout of current programs are designed to cater the needs of human developers -- with many grammar tokens and formatting tokens being used to make the code easier for humans to read. While this is helpful, such a design adds unnecessary computational work for LLMs, as each token …

Quantum Relaxation For Solving Multiple Knapsack Problems, Monit Sharma, Jin Yan, Hoong Chuin Lau, Rudy Raymond

Research Collection School Of Computing and Information Systems

Combinatorial problems are a common challenge in business, requiring finding optimal solutions under specified constraints. While significant progress has been made with variational approaches such as QAOA, most problems addressed are unconstrained (such as Max-Cut). In this study, we investigate a hybrid quantum-classical method for constrained optimization problems, particularly those with knapsack constraints that occur frequently in financial and supply chain applications. Our proposed method relies firstly on relaxations to local quantum Hamiltonians, defined through commutative maps. Drawing inspiration from quantum random access code (QRAC) concepts, particularly Quantum Random Access Optimizer (QRAO), we explore QRAO's potential in solving large constrained …

Quantum-Enhanced Simulation-Based Optimization For Newsvendor Problems, Monit Sharma, Hoong Chuin Lau, Rudy Raymond

Research Collection School Of Computing and Information Systems

Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence it is estimated through simulation. Exploiting the enhanced efficiency of Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we make use of a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical …

Ft2ra: A Fine-Tuning-Inspired Approach To Retrieval-Augmented Code Completion, Qi Guo, Shangqing Liu, Xiaofei Xie, Ze Tang Tang

Research Collection School Of Computing and Information Systems

The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these models, especially large models, poses a significant challenge when it comes to fine-tuning them for specific downstream tasks. As an alternative approach, retrieval-based methods have emerged as a promising solution, augmenting model predictions without the need for fine-tuning. Despite their potential, a significant challenge is that the designs of these methods often rely on heuristics, leaving critical questions about what information should be stored or retrieved and how to interpolate such information for …

Efficient Cascaded Multiscale Adaptive Network For Image Restoration, Yichen Zhou, Pan Zhou, Teck Khim Ng

Research Collection School Of Computing and Information Systems

Image restoration, encompassing tasks such as deblurring, denoising, and super-resolution, remains a pivotal area in computer vision. However, efficiently addressing the spatially varying artifacts of various low-quality images with local adaptiveness and handling their degradations at different scales poses significant challenges. To efficiently tackle these issues, we propose the novel Efficient Cascaded Multiscale Adaptive (ECMA) Network. ECMA employs Local Adaptive Module, LAM, which dynamically adjusts convolution kernels across local image regions to efficiently handle varying artifacts. Thus, LAM addresses the local adaptiveness challenge more efficiently than costlier mechanisms like self-attention, due to its less computationally intensive convolutions. To construct a …

Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction, Kaixiang Huang, Jingru Yang, Jin Wang, Shengfeng He, Zhan Wang, Haiyan He, Qifeng Zhang, Guodong Lu

Research Collection School Of Computing and Information Systems

This paper addresses the significant challenges in 3D Semantic Scene Graph (3DSSG) prediction, essential for understanding complex 3D environments. Traditional approaches, primarily using PointNet and Graph Convolutional Networks, struggle with effectively extracting multi-grained features from intricate 3D scenes, largely due to a focus on global scene processing and single-scale feature extraction. To overcome these limitations, we introduce Granular3D, a novel approach that shifts the focus towards multi-granularity analysis by predicting relation triplets from specific sub-scenes. One key is the Adaptive Instance Enveloping Method (AIEM), which establishes an approximate envelope structure around irregular instances, providing shape-adaptive local point cloud sampling, thereby …

Enabling Emg-Based Silent Speech Transcription Through Speech-To-Text Transfer Learning, Alexander T. Garcia

Master's Theses

In recent years, advances in deep learning have allowed various forms of electrographic signals, such as electroencephalography (EEG) and electromyography (EMG), to be used as a viable form of input in artificial intelligence applications, particularly for applications in the medical field. One such topic that EMG inputs have been used is in silent speech interfaces, or devices capable of processing speech without an audio-based input. The goal of this thesis is to explore a novel method of training a machine learning model to be used for silent speech interface development: using transfer learning to leverage a pre-trained speech recognition model …

Two-Dimensional Quantum Material Identification Via Self-Attention And Soft-Labeling In Deep Learning, Xuan Bac Nguyen, Apoorva Bisht, Benjamin Thompson, Hugh O.H. Churchill, Khoa Luu, Samee U. Khan

Electrical Engineering and Computer Science Faculty Publications and Presentations

Detecting two-dimensional (2D) materials in silicon chips presents a significant challenge in the field of quantum machines due to the difficulty of data collection. Specifically, among thousands of flakes, not all flakes are useful or well-annotated, resulting in noisy and hard samples within the dataset, which challenges the deep neural network (DNN) to learn. To address this problem, we propose a novel method for identifying quantum 2D flakes even when there is a high rate of missing annotations in the input images. In particular, we first propose a new mechanism for automatically detecting false negative flakes that are missing annotations. …

Getting To The Point: Contrasting Directness And Warmth In Motivational Embodied Conversational Agents, Michael O'Mahony, Cathy Ennis, Robert Ross

Conference papers

Enhancing long-term engagement with conversational agents remains a significant challenge. Controlling the perceived warmth or directness of an agent’s personality through the style of its generated text could be used to increase user likeability. This paper reports an investigation of a Wizard-of-Oz (WoZ) mediated study of two variants of a motivational embodied conversational agent to measure user perception of and attitudes towards warmth in interaction style. Results show a significant effect of users preferring an agent with a "more direct" personality for this scenario, though this effect is in many ways nuanced.

Multi-Label Classification Using Conformal Prediction, Chhavi Tyagi

Dissertations

In many machine learning applications, such as image tagging, document classi-fication, and medical diagnosis, a data instance can be associated with multiple classes in parallel so that each instance is associated with multiple response variables simultaneously defining multi-label classification. Standard multi-label classification methods that provide point predictions have been developed. They lack in quantifying the uncertainty of predictions. These methods also lack in accounting for label dependencies and are very computationally expensive. This dissertation develops two methods of multi-label classification using conformal prediction that quantify the uncertainty of predictions. Chapter 1 introduces notations and tools that have been used in …

Certifying Stability In Runge-Kutta Schemes: Algebraic Conditions And Semidefinite Programming, Austin Juhl

Dissertations

Numerical stability is a critical property for a time-integration scheme. In the context of Runge-Kutta methods applied to stiff differential equations, A-stability is one of the most basic and practically important notions of stability. Dating back to the work of Dahlquist, it has been known that A-stability is equivalent to the Runge-Kutta stability function satisfying a particular convex feasibility problem. Specifically, up to a transformation, the stability function lies in the convex cone of positive functions. In recent years, sum-of-squares optimization and semidefinite programming have become valuable tools in developing rigorous certificates of stability in dynamical systems. Therefore, it is …

Federated Learning Systems For Mobile Sensing Data, Xiaopeng Jiang

Dissertations

Federated Learning (FL) has emerged as a new distributed Deep Learning (DL) paradigm that enables privacy-aware training and inference on mobile devices with help from the cloud. This dissertation presents a comprehensive exploration of FL with mobile sensing data, covering systems, applications, and optimizations.

First, a mobile-cloud FL system, FLSys, is designed to balance model performance with resource consumption, tolerate communication failures, and achieve scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. In addition, FLSys provides advanced privacy-preserving mechanisms and a common API for third-party app developers to …

A Methodological Framework For Ontology Development, Enrichment, And Application In Natural Language Processing Tasks, Navya Martin Kollapally

Dissertations

Electronic Health Records (EHRs) have been widely used in healthcare to record demographics, vital signs, test results, immunizations, medical imaging reports, differential diagnoses, etc. It is now accepted that non-clinical (e.g., social) factors have a substantial influence on health outcomes. Hence, it is desirable to record these Social and Commercial Determinants of Health (SDoH & CDoH) in an EHR. The "non-text parts" of EHR notes (e.g., data tables) rely on coded terms from underlying ontologies or terminologies to facilitate semantic interoperability. Ontologies help define concepts, the relationships between them, and instances that can be utilized in research.

The first accomplishment …

Numerical Techniques For Improving Simulations Of Tropical Cyclones, Yassine Tissaoui

Dissertations

The increasing frequency and intensity of tropical cyclones (TCs) due to climate change pose significant challenges for forecasting and mitigating their impacts. Despite advancements, accurately predicting TC rapid intensification (RI) remains a challenge. Large eddy simulation (LES) allows for explicitly resolving the large eddies involved in TC turbulence, thus providing an avenue for studying the mechanisms behind their intensification and RI. LES of a full tropical cyclone is very computationally expensive and its accuracy will depend on both explicit and implicit dissipation within an atmospheric model. This dissertation presents two novel numerical methodologies with the potential to improve the efficiency …

Antioxidant, Photoprotective, And Cytotoxic Activities Of Tristaniopsis Merguensis Leaf Fractions With Molecular Docking Study Of Potential Fraction, Boima Situmeang, Respati Tri Swasono, Tri Joko Raharjo

Karbala International Journal of Modern Science

Through experimental along with molecular docking techniques of potential fraction, this study intended to evaluate the essential phytochemical elements of Tristaniopsis merguensis leaflets as well as potential antioxidant, photoprotective, along with cytotoxic activities. The DPPH and ABTS tests were used to evaluate the antioxidant efficacy of the n-hexane (HPF), ethyl acetate (EPF), and methanol (MPF) fractions. Using an in-vitro solar protection factor (SPF) assessment, the photoprotective ability of T. merguensis components against UV damage was examined. Subsequently, invitro cytotoxic research were carried out against MCF-7 cell line (breast cancer line) assay. This was followed by molecular docking stimulation testing against …

Health Benefits And Adverse Effects Of Kratom: A Social Media Text-Mining Approach, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Tareq Nasralah, Ahmed El Noshokaty

All Faculty Scholarship (Archived)

Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about kratom’s benefits and adverse effects. Also, we aim to demonstrate how algorithmic machine learning approaches, qualitative methods, and data visualization techniques can complement each other to discern diverse reactions to kratom’s effects, thereby complementing traditional quantitative and qualitative methods. Methods: Social media data were analyzed using the latent Dirichlet allocation (LDA) algorithm, PyLDAVis, and t-distributed stochastic neighbor …

Adversarial Variational Autoencoders To Extend And Improve Generative Model, Loc Nguyen, Hassan I. Abdalla, Ali A. Amer

All Works

Generative artificial intelligence (GenAI) has been advancing with many notable achievements like ChatGPT and Bard. The deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to the strong role of deep neural networks (DNNs) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims at synaptic plasticity of the human neuron network, fosters the generation ability of DGM, which produces surprising results with the support of statistical flexibility. Two popular approaches in DGM are the variational autoencoder (VAE) and generative adversarial network (GAN). …

Advancing Endovascular Neurosurgery Training With Extended Reality: Opportunities And Obstacles For The Next Decade, Shray Patel, Michael Covell, Saarang Patel, Sandeep Kandregula, Sai Krishna Palepu, Avi Gajjar, Oleg Shekhtman, Georgios Sioutas, Ali Dhanaliwala, Terence Gade, Jan-Karl Burkhardt, Visish Srinivasan

SKMC Student Presentations and Publications

Background: Extended reality (XR) includes augmented reality (AR), virtual reality (VR), and mixed reality (MR). Endovascular neurosurgery is uniquely positioned to benefit from XR due to the complexity of cerebrovascular imaging. Given the different XR modalities available, as well as unclear clinical utility and technical capabilities, we clarify opportunities and obstacles for XR in training vascular neurosurgeons. Methods: A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted. Studies were critically appraised using ROBINS-I. Results: 19 studies were identified. 13 studies used VR, while 3 studies used MR, and 3 studies used AR. …

Hristudio: A Framework For Wizard-Of-Oz Experiments In Human-Robot Interaction Studies, Luiz Felipe Perrone, Sean O'Connor

Faculty Conference Papers and Presentations

Human-robot interaction (HRI) research plays a pivotal role in shaping how robots communicate and collaborate with humans. However, conducting HRI studies, particularly those employing the Wizard-of-Oz (WoZ) technique, can be challenging. WoZ user studies can have complexities in technical and methodological level that may render the results irreproducible. We propose to address these challenges with HRIStudio, a novel web-based platform designed to streamline the design, execution, and analysis of WoZ experiments. HRIStudio offers an intuitive interface for experiment creation, real-time control and monitoring during experimental runs, and comprehensive data logging and playback tools for analysis and reproducibility. By lowering technical …

Ai And Academic Integrity, Max Sparkman Research Instruction Librarian, Milne Library, Brandon West Head Of Research & Instruction, Milne Library

Artificial Intelligence, 2024-25

This short module introduces students to important concepts regarding the use of AI and academic integrity. Concepts covered include a brief overview of generative AI, whether or not their use is considered plagiarism, how to use generative AI tools responsibly, and potential use cases. The module ends with a quiz where students can apply concepts from the module to three scenarios.

Module: Ai And Value-Neutrality, Jonathan Auyer Ph.D., Department Of Philosophy

Artificial Intelligence, 2024-25

Artificial Intelligence is on the tips of everyone’s tongues these days – What exactly is it? What will can it be used for? What will it be used for in the future? What problems will it create or solve or exacerbate? This learning module aims to look at a specific facet of AI — the issue of value-neutrality — by having students look inward at capabilities necessary for human flourishing and then ask whether AI can cultivate (or inhibit) those capabilities. This will lead to a discussion of what values underlie AI and what this says about whether or not …