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Opportunities And Challenges In Code Search Tools, Chao LIU, Xin XIA, David LO, Cuiying GAO, Xiaohu YANG, John GRUNDY 2022 Zhejiang University

Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy

Research Collection School Of Computing and Information Systems

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique ...


An Empirical Study Of Artifacts And Security Risks In The Pre-Trained Model Supply Chain, Wenxin Jiang, Nicholas Synovic, Rohan Sethi, Aryan Indarapu, Matt Hyattt, Taylor R. Schorlemmer, George K. Thiruvathukal, James C. Davis 2022 Purdue University

An Empirical Study Of Artifacts And Security Risks In The Pre-Trained Model Supply Chain, Wenxin Jiang, Nicholas Synovic, Rohan Sethi, Aryan Indarapu, Matt Hyattt, Taylor R. Schorlemmer, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Deep neural networks achieve state-of-the-art performance on many tasks, but require increasingly complex architectures and costly training procedures. Engineers can reduce costs by reusing a pre-trained model (PTM) and fine-tuning it for their own tasks. To facilitate software reuse, engineers collaborate around model hubs, collections of PTMs and datasets organized by problem domain. Although model hubs are now comparable in popularity and size to other software ecosystems, the associated PTM supply chain has not yet been examined from a software engineering perspective.

We present an empirical study of artifacts and security features in 8 model hubs. We indicate the potential ...


Predictive Self-Organizing Neural Networks For In-Home Detection Of Mild Cognitive Impairment, Seng Khoon TEH, Iris RAWTAER, Ah-hwee TAN 2022 Singapore Management University

Predictive Self-Organizing Neural Networks For In-Home Detection Of Mild Cognitive Impairment, Seng Khoon Teh, Iris Rawtaer, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

In-home sensing of daily living patterns from older adults coupled with machine learning is a promisingapproach to detect Mild Cognitive Impairment (MCI), a potentially reversible condition with early detectionand appropriate intervention. However, the number of subjects involved in such real-world studies istypically limited, posing the so-called small data problem to most predictive models which rely on a sizablenumber of labeled data. In this work, a predictive self-organizing neural network known as fuzzy AdaptiveResonance Associate Map (fuzzy ARAM) is proposed to detect MCI using in-home sensor data collected from aunique Singapore cross-sectional study. Specifically, mean and standard deviation of nine in-home ...


Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi ZHANG, Jun SUN, Jingyi WANG 2022 Singapore Management University

Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi Zhang, Jun Sun, Jingyi Wang

Research Collection School Of Computing and Information Systems

Neural networks are getting increasingly popular thanks to their exceptional performance in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug and subject to fairness issues. To improve people’s trust in the technology, it is often necessary to provide some human-understandable explanation of neural networks’ decisions, e.g., why is that my loan application is rejected whereas hers is approved? That is, the stakeholder would be interested to minimize the chances of not being able to explain the decision consistently and would like to know how often and how ...


Adaptive Fairness Improvement Based Causality Analysis, Mengdi ZHANG, Jun SUN 2022 Singapore Management University

Adaptive Fairness Improvement Based Causality Analysis, Mengdi Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i.e., accuracy). Multiple categories of fairness improving methods have been proposed for neural networks, including pre-processing, in-processing and postprocessing. Our empirical study however shows that these methods are not always effective (e.g., they may improve fairness by paying the price of huge accuracy drop) or even not helpful (e.g., they may even worsen both fairness and accuracy). In this work, we propose an approach which adaptively chooses the fairness improving method based on causality analysis. That is ...


Guaranteeing Timed Opacity Using Parametric Timed Model Checking, Étienne ANDRÉ, Didier LIME, Dylan MARINHO, Jun SUN 2022 Singapore Management University

Guaranteeing Timed Opacity Using Parametric Timed Model Checking, Étienne André, Didier Lime, Dylan Marinho, Jun Sun

Research Collection School Of Computing and Information Systems

Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage occurs whenever an attacker successfully deduces confidential internal information. In this work, we consider that the attacker has access (only) to the system execution time. We address the following timed opacity problem: given a timed system, a private location and a final location, synthesize the execution times from the initial location to the final location for which one cannot deduce whether the system went through the private location. We also consider the full timed opacity problem, asking whether the system is opaque for ...


Qvip: An Ilp-Based Formal Verification Approach For Quantized Neural Networks, Yedi ZHANG, Zhe ZHAO, Guangke CHEN, Fu SONG, Min ZHANG, Taolue CHEN, Jun SUN 2022 Singapore Management University

Qvip: An Ilp-Based Formal Verification Approach For Quantized Neural Networks, Yedi Zhang, Zhe Zhao, Guangke Chen, Fu Song, Min Zhang, Taolue Chen, Jun Sun

Research Collection School Of Computing and Information Systems

Deep learning has become a promising programming paradigm in software development, owing to its surprising performance in solving many challenging tasks. Deep neural networks (DNNs) are increasingly being deployed in practice, but are limited on resource-constrained devices owing to their demand for computational power. Quantization has emerged as a promising technique to reduce the size of DNNs with comparable accuracy as their floating-point numbered counterparts. The resulting quantized neural networks (QNNs) can be implemented energy-efficiently. Similar to their floating-point numbered counterparts, quality assurance techniques for QNNs, such as testing and formal verification, are essential but are currently less explored. In ...


Physical Adversarial Attack On A Robotic Arm, Yifan JIA, Christopher M. POSKITT, Jun SUN, Sudipta CHATTOPADHYAY 2022 Singapore University of Technology and Design

Physical Adversarial Attack On A Robotic Arm, Yifan Jia, Christopher M. Poskitt, Jun Sun, Sudipta Chattopadhyay

Research Collection School Of Computing and Information Systems

Collaborative Robots (cobots) are regarded as highly safety-critical cyber-physical systems (CPSs) owing to their close physical interactions with humans. In settings such as smart factories, they are frequently augmented with AI. For example, in order to move materials, cobots utilize object detectors based on deep learning models. Deep learning, however, has been demonstrated as vulnerable to adversarial attacks: a minor change (noise) to benign input can fool the underlying neural networks and lead to a different result. While existing works have explored such attacks in the context of picture/object classification, less attention has been given to attacking neural networks ...


Snapshot Metrics Are Not Enough: Analyzing Software Repositories With Longitudinal Metrics, Nicholas Synovic, Matt Hyattt, Rohan Sethi, Sohini Thota, Shilpika, Allan J. Miller, Wenxin Jiang, Emmanuel S. Amobi, Austin Pinderski, Konstantin Läufer, Nicholas J. Hayward, Neil Klingensmith, James C. Davis, George K. Thiruvathukal 2022 Loyola University Chicago

Snapshot Metrics Are Not Enough: Analyzing Software Repositories With Longitudinal Metrics, Nicholas Synovic, Matt Hyattt, Rohan Sethi, Sohini Thota, Shilpika, Allan J. Miller, Wenxin Jiang, Emmanuel S. Amobi, Austin Pinderski, Konstantin Läufer, Nicholas J. Hayward, Neil Klingensmith, James C. Davis, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Software metrics capture information about software development processes and products. These metrics support decision-making, e.g., in team management or dependency selection. However, existing metrics tools measure only a snapshot of a software project. Little attention has been given to enabling engineers to reason about metric trends over time -- longitudinal metrics that give insight about process, not just product. In this work, we present PRiME (PRocess MEtrics), a tool for computing and visualizing process metrics. The currently-supported metrics include productivity, issue density, issue spoilage, and bus factor. We illustrate the value of longitudinal data and conclude with a research agenda ...


Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback, Chris Roche, Nathan Deinlein, Darryl Dawkins, Faizan Javed 2022 Southern Methodist University

Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback, Chris Roche, Nathan Deinlein, Darryl Dawkins, Faizan Javed

SMU Data Science Review

For English teachers and students who are dissatisfied with the one-size-fits-all approach of current Automated Essay Scoring (AES) systems, this research uses Natural Language Processing (NLP) techniques that provide a focus on configurability and interpretability. Unlike traditional AES models which are designed to provide an overall score based on pre-trained criteria, this tool allows teachers to tailor feedback based upon specific focus areas. The tool implements a user-interface that serves as a customizable rubric. Students’ essays are inputted into the tool either by the student or by the teacher via the application’s user-interface. Based on the rubric settings, the ...


Model Transformations Between Sequence Diagram And Activity Diagram With Qvto, Yutong Xia 2022 Western University

Model Transformations Between Sequence Diagram And Activity Diagram With Qvto, Yutong Xia

Undergraduate Student Research Internships Conference

Complex software systems are specified by various models denoting the behavior of the system components, the exchanges of messages and data among components, the intents of the system stakeholders, the flow of system processes, and the structure of the system as a collection of modules.

When such systems are maintained and evolved (e.g. by adding new functionality, fixing bugs, or porting to a new operating environment), one or more of these models are altered. This brings the system specification to an inconsistent state since some models reflect the new behavior while other models were not appropriately evolved.

This research ...


Damage Assessment In Aging Structures Using Augmented Reality, Omar Zuhair Awadallah, Ayan Sadhu 2022 Western University

Damage Assessment In Aging Structures Using Augmented Reality, Omar Zuhair Awadallah, Ayan Sadhu

Undergraduate Student Research Internships Conference

Structural Health Monitoring (SHM) is the assessment of bridges and observation of data regarding these bridges over time to monitor their evolution and detect the presence of any possible damages. However, existing methods to perform structural inspections in bridges are high in cost, time-consuming and risky. Inspectors use expensive equipment to reach a certain area of the bridge to inspect it, and at different heights, this can pose a risk to the inspector’s safety. This study aims to find cheaper, faster, and safer ways to perform structural inspections using augmented reality and artificial intelligence. The developed system uses a ...


Data Preprocessing For Machine Learning Modules, Rawan El Moghrabi 2022 Western University

Data Preprocessing For Machine Learning Modules, Rawan El Moghrabi

Undergraduate Student Research Internships Conference

Data preprocessing is an essential step when building machine learning solutions. It significantly impacts the success of machine learning modules and the output of these algorithms. Typically, data preprocessing is made-up of data sanitization, feature engineering, normalization, and transformation. This paper outlines the data preprocessing methodology implemented for a data-driven predictive maintenance solution. The above-mentioned project entails acquiring historical electrical data from industrial assets and creating a health index indicating each asset's remaining useful life. This solution is built using machine learning algorithms and requires several data processing steps to increase the solution's accuracy and efficiency. In this ...


Gpgpu Microbenchmarking For Irregular Application Optimization, Dalton R. Winans-Pruitt 2022 Mississippi State University

Gpgpu Microbenchmarking For Irregular Application Optimization, Dalton R. Winans-Pruitt

Theses and Dissertations

Irregular applications, such as unstructured mesh operations, do not easily map onto the typical GPU programming paradigms endorsed by GPU manufacturers, which mostly focus on maximizing concurrency for latency hiding. In this work, we show how alternative techniques focused on latency amortization can be used to control overall latency while requiring less concurrency. We used a custom-built microbenchmarking framework to test several GPU kernels and show how the GPU behaves under relevant workloads. We demonstrate that coalescing is not required for efficacious performance; an uncoalesced access pattern can achieve high bandwidth - even over 80% of the theoretical global memory bandwidth ...


The Design And Implementation Of A High-Performance Polynomial System Solver, Alexander Brandt 2022 The University of Western Ontario

The Design And Implementation Of A High-Performance Polynomial System Solver, Alexander Brandt

Electronic Thesis and Dissertation Repository

This thesis examines the algorithmic and practical challenges of solving systems of polynomial equations. We discuss the design and implementation of triangular decomposition to solve polynomials systems exactly by means of symbolic computation.

Incremental triangular decomposition solves one equation from the input list of polynomials at a time. Each step may produce several different components (points, curves, surfaces, etc.) of the solution set. Independent components imply that the solving process may proceed on each component concurrently. This so-called component-level parallelism is a theoretical and practical challenge characterized by irregular parallelism. Parallelism is not an algorithmic property but rather a geometrical ...


Task-Based Runtime Optimizations Towards High Performance Computing Applications, Qinglei Cao 2022 University of Tennessee, Knoxville

Task-Based Runtime Optimizations Towards High Performance Computing Applications, Qinglei Cao

Doctoral Dissertations

The last decades have witnessed a rapid improvement of computational capabilities in high-performance computing (HPC) platforms thanks to hardware technology scaling. HPC architectures benefit from mainstream advances on the hardware with many-core systems, deep hierarchical memory subsystem, non-uniform memory access, and an ever-increasing gap between computational power and memory bandwidth. This has necessitated continuous adaptations across the software stack to maintain high hardware utilization. In this HPC landscape of potentially million-way parallelism, task-based programming models associated with dynamic runtime systems are becoming more popular, which fosters developers’ productivity at extreme scale by abstracting the underlying hardware complexity.

In this context ...


Deep Learning Edge Detection In Image Inpainting, Zheng Zheng 2022 California State University, San Bernardino

Deep Learning Edge Detection In Image Inpainting, Zheng Zheng

Electronic Theses, Projects, and Dissertations

In recent years, deep learning has grown rapidly, and it has been creatively implemented for various applications. In 2019, deep learning based EdgeConnect image inpainting algorithm came out and occupied a place in the image inpainting field. Unlike traditional image inpainting methods which mainly read and use the color information of the remaining part of the image to fill the missing regions of the image, EdgeConnect uses the innovative edge-first and color-next approach. It uses an edge detector to generate an edge map of an image with missing regions, then the missing edges are completed by an edge model, finally ...


Finding Meta Winning Ticket To Train Your Maml, Dawei GAO, Yuexiang XIE, Zimu ZHOU, Zhen WANG, Yaliang LI, Bolin. DING 2022 Singapore Management University

Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding

Research Collection School Of Computing and Information Systems

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning ...


Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng WANG, Yongxin TONG, Zimu ZHOU, Ziyao REN, Yi XU, Guobin WU, Weifeng LV 2022 Singapore Management University

Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv

Research Collection School Of Computing and Information Systems

Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. Such data isolation not only impairs user experience but also decreases the potential revenues of the platforms. In this paper, we advocate federated order dispatching for cross-platform ride hailing, where multiple platforms collaboratively make dispatching decisions without sharing their local data. Realizing this concept calls for new federated learning strategies that tackle the unique challenges on effectiveness, privacy and efficiency in the context of order ...


Finding Meta Winning Ticket To Train Your Maml, Dawei GAO, Yuexiang XIE, Zimu ZHOU, Zhen WANG, Yaliang LI, Bolin. DING 2022 Singapore Management University

Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding

Research Collection School Of Computing and Information Systems

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning ...


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