Physical Sciences and Mathematics Commons^™

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 …

Bugs In Pods: Understanding Bugs In Container Runtime Systems, Jiongchi Yu, Xiaofei Xie, Ceng Zhang, Sen Chen

Research Collection School Of Computing and Information Systems

Container Runtime Systems (CRSs), which form the foundational infrastructure of container clouds, are critically important due to their impact on the quality of container cloud implementations. However, a comprehensive understanding of the quality issues present in CRS implementations remains lacking. To bridge this gap, we conduct the first comprehensive empirical study of CRS bugs. Specifically, we gather 429 bugs from 8,271 commits across dominant CRS projects, including runc, gvisor, containerd, and cri-o. Through manual analysis, we develop taxonomies of CRS bug symptoms and root causes, comprising 16 and 13 categories, respectively. Furthermore, we evaluate the capability of popular testing approaches, …

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 …

How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation, Cen Zhang, Yaowen Zheng, Mingqiang Bai, Yeting Li, Wei Ma, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Fuzz drivers are essential for library API fuzzing. However, automatically generating fuzz drivers is a complex task, as it demands the creation of high-quality, correct, and robust API usage code. An LLM-based (Large Language Model) approach for generating fuzz drivers is a promising area of research. Unlike traditional program analysis-based generators, this text-based approach is more generalized and capable of harnessing a variety of API usage information, resulting in code that is friendly for human readers. However, there is still a lack of understanding regarding the fundamental issues on this direction, such as its e ectiveness and potential challenges. To …

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. …

Cluster-Wide Task Slowdown Detection In Cloud System, Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, Shuiguang Deng

Research Collection School Of Computing and Information Systems

Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, considering millions of concurrent tasks in large-scale cloud computing clusters, it becomes impractical and inefficient. Moreover, single-task slowdowns are very common and do not necessarily indicate a malfunction of a cluster due to its violent fluctuation nature in a virtual environment. Thus, we shift our attention to cluster-wide task slowdowns by utilizing the duration time distribution of tasks across a cluster, so that …

Imbalanced Graph Classification With Multi-Scale Oversampling Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and imbalanced learning loss functions, can be adopted for enabling graph representation learning models to cope with this challenge. However, these methods often directly operate on the graph representations, ignoring rich discriminative information within the graphs and their interactions. To tackle this issue, we introduce a novel multi-scale oversampling graph neural network (MOSGNN) that learns expressive minority graph representations based on intra- and inter-graph semantics resulting from oversampled graphs at multiple scales - …

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 …

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 …

Editorial: Dsaa 2023 Journal Track On Theoretical And Practical Data Science And Analytics., Bin Yang, Feida Zhu, Wei Wei

Research Collection School Of Computing and Information Systems

This special issue of the International Journal of Data Science and Analytics includes the DSAA 2023 Journal Track papers, which cover advances in both theoretical and practical aspects of data science and analytics, with a particular focus on trustworthy data science and analytics. The track contains nine papers, all of which underwent rigorous review by the guest editors and invited reviewers.

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 …

Developer Reactions To Protestware In Open Source Software: The Cases Of Color.Js And Es5.Ext, Youmei Fan, Dong Wang, Supatsara Wattanakriengkrai, Hathaichanok Damrongsiri, Christoph Treude, Hideaki Hata, Raula Gaikovina Kula

Research Collection School Of Computing and Information Systems

There is growing concern about maintainers self-sabotaging their work in order to take political or economic stances, a practice referred to as “protestware”. Our objective is to understand the discourse around discussions on such an attack, how it is received by the community, and whether developers respond to the attack in a timely manner. We study two notable protestware cases i.e., colors.js and es5-ext. Results indicate that protestware discussions are spread more quickly on the GitHub platform, while security vulnerabilities are faster on social media. By establishing a taxonomy of protestware discussions, we identify posts that express stances and provide …

Face It Yourselves: An Llm-Based Two-Stage Strategy To Localize Configuration Errors Via Logs, Shiwen Shi, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Configurable software systems are prone to configuration errors, resulting in significant losses to companies. However, diagnosing these errors is challenging due to the vast and complex configuration space. These errors pose significant challenges for both experienced maintainers and new end-users, particularly those without access to the source code of the software systems. Given that logs are easily accessible to most end-users, we conduct a preliminary study to outline the challenges and opportunities of utilizing logs in localizing configuration errors. Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause …

An Empirical Study Of Static Analysis Tools For Secure Code Review, Wachiraphan Charoenwet, Patanamon Thongtanunam, Van-Thuan Pham, Christoph Treude

Research Collection School Of Computing and Information Systems

Early identification of security issues in software development is vital to minimize their unanticipated impacts. Code review is a widely used manual analysis method that aims to uncover security issues along with other coding issues in software projects. While some studies suggest that automated static application security testing tools (SASTs) could enhance security issue identification, there is limited understanding of SAST’s practical effectiveness in supporting secure code review. Moreover, most SAST studies rely on synthetic or fully vulnerable versions of the subject program, which may not accurately represent real-world code changes in the code review process. To address this gap, …

Fintech Digital Transformation: Generative Ai, Humanoid Robots, Metaverse, Human-Ai Collaboration, And Industry 5.0, Yuxin Liu, Runyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

This paper discusses the transformative impact of emerging digital technologies on the digital transformation of the financial industry, focusing on integrating Generative AI (GenAI), humanoid robots, and the Metaverse within the framework of Industry 5.0. Industry 5.0 emphasizes a human-centric approach to technology, where human-AI collaboration plays a central role in reshaping financial services. By reviewing both academic research and practical applications, the paper highlights the current advancements in FinTech, particularly in AI technologies and the Metaverse, and their future potential, demonstrating how these innovations are driving growth, efficiency, and resilience in the financial sector. Further, the paper proposes a …

A Staged Approach Using Machine Learning And Uncertainty Quantification To Predict The Risk Of Hip Fracture, Anjum Shaik, Kristoffer A. Larsen, Nancy E. Lane, Chen Zhao, Kuan Jui Su, Joyce H. Keyak, Qing Tian, Qiuying Sha, Hui Shen, Hong Wen Deng, Weihua Zhou

Michigan Tech Publications

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. The study cohort included 547 patients, with 94 experiencing hip fracture. To assess the risk of hip fracture, clinical variables and clinical variables combined with hip DXA …

Reclaiming Healing Spaces: A Phenomenological Study On The Transformative Power Of Outdoor Therapy From The Lived Experiences Of Black Clinicians Working With Black Clients, Lynn Murphy

Dissertations

This phenomenological study involved assessing the experiences of Black therapists who engaged Black clients in outdoor therapeutic contexts. The study was founded on the existing literature that shows the quality of the therapeutic relationship is pivotal for client retention and the Western standards that have historically favored treatment within indoor environments. To contextualize this research, a comprehensive literature review was commenced, covering topics such as the decolonization of therapy, the historical and present-day relationship between Blacks and the outdoors in the United States, sedentary lifestyles, the psychological benefits of time spent in nature, various types of outdoor therapy, and the …

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 …

A Micromagnetic Study Of Skyrmions In Thin-Film Multilayered Ferromagnetic Materials, Nicholas J. Dubicki

Dissertations

Magnetic skyrmions are topologically protected, localized, nanoscale spin textures in non-centrosymmetric thin ferromagnetic materials and heterostructures. At present they are of great interest to physicists for potential applications in information technology due to their particle-like properties and stability. In a system of multiple thin ferromagnetic layers, the stray field interaction was typically treated with various simplifications and approximations. It is shown that extensive analysis of the micromagnetic equations leads to an exact representation of the stray field interaction energy in the form of layer interaction kernels, a so-called 'finite thickness' representation. This formulation reveals the competition between perpendicular magnetic anisotropy …

Enhancing Community Flood Resilience By Incorporating Landscape Hydrological Sensitivity And Connectivity, Wenlong Feng

Dissertations

Rapid urban expansion and dramatic climate change have significantly increased the intensity and frequency of floods worldwide. With rising flood risks, conventional flood defense strategies that rely on structural measures become ineffective. The present designations for flood-prone areas, such as FEMA's flood maps, are becoming unreliable. Flood risk management is shifting toward enhancing community flood resilience, highlighting the importance of non-structural approaches. Landscape resilience has become a foundation of community flood resilience. However, past urban development typically undermined natural hydro-ecological functions and landscape resilience because of poor recognition of landscapes' ecological role, hydrological sensitivity, and hydrological connections. This study aims …

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 …

Data Driven Decision Making For Sustainable Planning And Operations Of Large Scale Networks, Bahareh Kargar

Dissertations

This dissertation explores data-driven decision-making networks, focusing on sustainable planning and operations for large-scale systems such as healthcare supply chains and power systems. One significant application in healthcare is the optimization of vaccine supply chains. An agent-based simulation-optimization modeling framework is developed to enhance the efficiency and sustainability of vaccine distribution. First, an agent-based epidemiological model of COVID-19 is extended to capture disease transmission dynamics and forecast the number of susceptible individuals and infections. Then, a sustainable vaccine supply chain considering the impacts of greenhouse gases is developed and integrated with the simulation model to minimize total costs and environmental …

Electrified Membrane For Water Treatment And Resource Recovery: Advancing Multiscale Strategies In Gas-Involving Reactions, Jianan Gao

Dissertations

Multifunctional, modular, and scalable water treatment technologies that operate without chemical additives are crucial for addressing global water pollution challenges. While traditional membrane-based separation processes are crucial for ensuring clean water supplies, they primarily focus on pollutant removal rather than on the upcycling of pollutants into valuable resources. Electrified membrane filtration presents a robust alternative, utilizing minimal or no chemical additives and harnessing electrical power to directly reduce contaminants or produce reactive oxidants or reductants on-site.

This dissertation is dedicated to the development of innovative electrochemically reactive membranes designed to address three major challenges: (1) the engineering of electrocatalysts with …

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 …

Microwave Catalysis-Enabled Membrane Processes For Microbial Inactivation, Fangzhou Liu

Dissertations

The COVID-19 pandemic has highlighted the urgent need for advanced technologies to combat virus transmission. Traditional membrane filtration technologies primarily serve as physical barriers, capturing pollutants but failing to inactivate microbial agents such as bacteria and viruses. These membranes typically target larger particles but are ineffective against sub-micrometer viral particles. Thus, there's a critical demand for membrane filtration processes that combine robust filtration with antifouling and reaction -enabled functions for efficient pathogen disinfection. This research explores a novel microwave-assisted membrane filtration process incorporating catalytic reactions to enhance microbial disinfection and prevent membrane fouling, offering a groundbreaking solution to global challenges …

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 …