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Full-Text Articles in Physical Sciences and Mathematics

Dialogconv: A Lightweight Fully Convolutional Network For Multi-View Response Selection, Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang Dec 2022

Dialogconv: A Lightweight Fully Convolutional Network For Multi-View Response Selection, Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Research Collection School Of Computing and Information Systems

Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms. Although promising results have been achieved, these models often suffer from slow inference or huge number of parameters. In this paper, we propose a novel lightweight fully convolutional architecture, called DialogConv, for response selection. DialogConv is exclusively built on top of convolution to extract matching features of context and response. Dialogues are modeled in 3D views, where DialogConv performs convolution operations on embedding view, word view and utterance view to capture richer semantic information from multiple contextual views. On the four benchmark datasets, …

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Bank Error In Whose Favor? A Case Study Of Decentralized Finance Misgovernance, Ping Fan Ke, Ka Chung Boris Ng Dec 2022

Bank Error In Whose Favor? A Case Study Of Decentralized Finance Misgovernance, Ping Fan Ke, Ka Chung Boris Ng

Research Collection School Of Computing and Information Systems

Decentralized Finance (DeFi) emerged rapidly in recent years and provided open and transparent financial services to the public. Due to its popularity, it is not uncommon to see cybersecurity incidents in the DeFi landscape, yet the impact of such incidents is under-studied. In this paper, we examine two incidents in DeFi protocol that are mainly caused by misgovernance and mistake in the smart contract. By using the synthetic control method, we found that the incident in Alchemix did not have a significant effect on the total value locked (TVL) in the protocol, whereas the incident in Compound caused a 6.13% …

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Mining Competitively-Priced Bundle Configurations, Ezekiel Ong Young, Hady W. Lauw Dec 2022

Mining Competitively-Priced Bundle Configurations, Ezekiel Ong Young, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We examine the bundle configuration problem in the presence of competition. Given a competitor's bundle configuration and pricing, we determine what to bundle together, and at what prices, to maximize the target firm's revenue. We highlight the difficulty in pricing bundles and propose a scalable alternative and an efficient search heuristic to refine the approximate prices. Furthermore, we extend the heuristics proposed by previous work to accommodate the presence of a competitor. We analyze the effectiveness of our proposed models through experimentation on real-life ratings-based preference data.

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Quote: Quality-Oriented Testing For Deep Learning Systems, Jialuo Chen, Jingyi Wang, Xingjun Ma, Youcheng Sun, Jun Sun, Peixin Zhang, Peng Cheng Dec 2022

Quote: Quality-Oriented Testing For Deep Learning Systems, Jialuo Chen, Jingyi Wang, Xingjun Ma, Youcheng Sun, Jun Sun, Peixin Zhang, Peng Cheng

Research Collection School Of Computing and Information Systems

Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, i.e., given a property of test, defects of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the neuron coverage metrics, commonly used by most existing DL testing approaches, are not necessarily correlated with model quality (e.g., robustness, the most studied model property), and are also not an effective measurement on the confidence of the model …

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Appearance-Preserved Portrait-To-Anime Translation Via Proxy-Guided Domain Adaptation, Wenpeng Xiao, Cheng Xu, Jiajie Mai, Xuemiao Xu, Yue Li, Chengze Li, Xueting Liu, Shengfeng He Dec 2022

Appearance-Preserved Portrait-To-Anime Translation Via Proxy-Guided Domain Adaptation, Wenpeng Xiao, Cheng Xu, Jiajie Mai, Xuemiao Xu, Yue Li, Chengze Li, Xueting Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

Converting a human portrait to anime style is a desirable but challenging problem. Existing methods fail to resolve this problem due to the large inherent gap between two domains that cannot be overcome by a simple direct mapping. For this reason, these methods struggle to preserve the appearance features in the original photo. In this paper, we discover an intermediate domain, the coser portrait (portraits of humans costuming as anime characters), that helps bridge this gap. It alleviates the learning ambiguity and loosens the mapping difficulty in a progressive manner. Specifically, we start from learning the mapping between coser and …

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Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai Dec 2022

Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai

Research Collection School Of Computing and Information Systems

Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough …

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Coresets For Vertical Federated Learning: Regularized Linear Regression And K-Means Clustering, Lingxiao Huang, Zhize Li, Jialin Sun, Haoyu Zhao Dec 2022

Coresets For Vertical Federated Learning: Regularized Linear Regression And K-Means Clustering, Lingxiao Huang, Zhize Li, Jialin Sun, Haoyu Zhao

Research Collection School Of Computing and Information Systems

Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexity for VFL is typically very high. In this paper, we propose a unified framework by constructing coresets in a distributed fashion for communication-efficient VFL. We study two important learning tasks in the VFL setting: regularized linear regression and $k$-means clustering, and apply our coreset framework to both problems. We theoretically show that using coresets can drastically alleviate the communication complexity, while nearly maintain the solution quality. Numerical experiments are conducted to corroborate our theoretical findings.

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Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi Zhang, Jun Sun, Jingyi Wang Nov 2022

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 easy it …

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Real World Projects, Real Faults: Evaluating Spectrum Based Fault Localization Techniques On Python Projects, Ratnadira Widyasari, Gede Artha Azriadi Prana, Stefanus Agus Haryono, Shaowei Wang, David Lo Nov 2022

Real World Projects, Real Faults: Evaluating Spectrum Based Fault Localization Techniques On Python Projects, Ratnadira Widyasari, Gede Artha Azriadi Prana, Stefanus Agus Haryono, Shaowei Wang, David Lo

Research Collection School Of Computing and Information Systems

Spectrum Based Fault Localization (SBFL) is a statistical approach to identify faulty code within a program given a program spectra (i.e., records of program elements executed by passing and failing test cases). Several SBFL techniques have been proposed over the years, but most evaluations of those techniques were done only on Java and C programs, and frequently involve artificial faults. Considering the current popularity of Python, indicated by the results of the Stack Overflow survey among developers in 2020, it becomes increasingly important to understand how SBFL techniques perform on Python projects. However, this remains an understudied topic. In this …

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Vulcurator: A Vulnerability-Fixing Commit Detector, Truong Giang Nguyen, Cong Thanh Le, Hong Jin Kang, Xuan-Bach D. Le, David Lo Nov 2022

Vulcurator: A Vulnerability-Fixing Commit Detector, Truong Giang Nguyen, Cong Thanh Le, Hong Jin Kang, Xuan-Bach D. Le, David Lo

Research Collection School Of Computing and Information Systems

Open-source software (OSS) vulnerability management process is important nowadays, as the number of discovered OSS vulnerabilities is increasing over time. Monitoring vulnerability-fixing commits is a part of the standard process to prevent vulnerability exploitation. Manually detecting vulnerability-fixing commits is, however, time-consuming due to the possibly large number of commits to review. Recently, many techniques have been proposed to automatically detect vulnerability-fixing commits using machine learning. These solutions either: (1) did not use deep learning, or (2) use deep learning on only limited sources of information. This paper proposes VulCurator, a tool that leverages deep learning on richer sources of information, …

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Meta-Complementing The Semantics Of Short Texts In Neural Topic Models, Ce Zhang, Hady Wirawan Lauw Nov 2022

Meta-Complementing The Semantics Of Short Texts In Neural Topic Models, Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Topic models infer latent topic distributions based on observed word co-occurrences in a text corpus. While typically a corpus contains documents of variable lengths, most previous topic models treat documents of different lengths uniformly, assuming that each document is sufficiently informative. However, shorter documents may have only a few word co-occurrences, resulting in inferior topic quality. Some other previous works assume that all documents are short, and leverage external auxiliary data, e.g., pretrained word embeddings and document connectivity. Orthogonal to existing works, we remedy this problem within the corpus itself by proposing a Meta-Complement Topic Model, which improves topic quality …

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Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang Nov 2022

Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper we study how to measure stereotypical bias in pre-trained vision-language models. We leverage a recently released text-only dataset, StereoSet, which covers a wide range of stereotypical bias, and extend it into a vision-language probing dataset called VLStereoSet to measure stereotypical bias in vision-language models. We analyze the differences between text and image and propose a probing task that detects bias by evaluating a model’s tendency to pick stereotypical statements as captions for anti-stereotypical images. We further define several metrics to measure both a vision-language model’s overall stereotypical bias and its intra-modal and inter-modal bias. Experiments on six …

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An Empirical Study Of Blockchain System Vulnerabilities: Modules, Types, And Patterns, Xiao Yi, Daoyuan Wu, Lingxiao Jiang, Yuzhou Fang, Kehuan Zhang, Wei Zhang Nov 2022

An Empirical Study Of Blockchain System Vulnerabilities: Modules, Types, And Patterns, Xiao Yi, Daoyuan Wu, Lingxiao Jiang, Yuzhou Fang, Kehuan Zhang, Wei Zhang

Research Collection School Of Computing and Information Systems

Blockchain, as a distributed ledger technology, becomes increasingly popular, especially for enabling valuable cryptocurrencies and smart contracts. However, the blockchain software systems inevitably have many bugs. Although bugs in smart contracts have been extensively investigated, security bugs of the underlying blockchain systems are much less explored. In this paper, we conduct an empirical study on blockchain’s system vulnerabilities from four representative blockchains, Bitcoin, Ethereum, Monero, and Stellar. Specifically, we first design a systematic filtering process to effectively identify 1,037 vulnerabilities and their 2,317 patches from 34,245 issues/PRs (pull requests) and 85,164 commits on GitHub. We thus build the first blockchain …

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What Motivates Software Practitioners To Contribute To Inner Source?, Zhiyuan Wan, Xin Xia, Yun Zhang, David Lo, Daibing Zhou, Qiuyuan Chen, Ahmed E. Hassan Nov 2022

What Motivates Software Practitioners To Contribute To Inner Source?, Zhiyuan Wan, Xin Xia, Yun Zhang, David Lo, Daibing Zhou, Qiuyuan Chen, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Software development organizations have adopted open source development practices to support or augment their software development processes, a phenomenon referred to as inner source. Given the rapid adoption of inner source, we wonder what motivates software practitioners to contribute to inner source projects. We followed a mixed-methods approach--a qualitative phase of interviews with 20 interviewees, followed by a quantitative phase of an exploratory survey with 124 respondents from 13 countries across four continents. Our study uncovers practitioners' motivation to contribute to inner source projects, as well as how the motivation differs from what motivates practitioners to participate in open source …

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Codematcher: A Tool For Large-Scale Code Search Based On Query Semantics Matching, Chao Liu, Xuanlin Bao, Xin Xia, Meng Yan, David Lo, Ting Zhang Nov 2022

Codematcher: A Tool For Large-Scale Code Search Based On Query Semantics Matching, Chao Liu, Xuanlin Bao, Xin Xia, Meng Yan, David Lo, Ting Zhang

Research Collection School Of Computing and Information Systems

Due to the emergence of large-scale codebases, such as GitHub and Gitee, searching and reusing existing code can help developers substantially improve software development productivity. Over the years, many code search tools have been developed. Early tools leveraged the information retrieval (IR) technique to perform an efficient code search for a frequently changed large-scale codebase. However, the search accuracy was low due to the semantic mismatch between query and code. In the recent years, many tools leveraged Deep Learning (DL) technique to address this issue. But the DL-based tools are slow and the search accuracy is unstable.In this paper, we …

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Artificial Intelligence For Natural Disaster Management, Guansong Pang Nov 2022

Artificial Intelligence For Natural Disaster Management, Guansong Pang

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) can leverage massive amount of diverse types of data, such as geospatial data, social media data, and wireless network sensor data, to enhance our understanding of natural disasters, their forecasting and detection, and humanitarian assistance in natural disaster management (NDM). Due to this potential, different communities have been dedicating enormous efforts to the development and/or adoption of AI technologies for NDM. This article provides an overview of these efforts and discusses major challenges and opportunities in this topic.

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Hapticpuppet: A Kinesthetic Mid-Air Multidirectional Force-Feedback Drone-Based Interface, Martin Feick, Anthony Tang, Antonio Kruger Nov 2022

Hapticpuppet: A Kinesthetic Mid-Air Multidirectional Force-Feedback Drone-Based Interface, Martin Feick, Anthony Tang, Antonio Kruger

Research Collection School Of Computing and Information Systems

Providing kinesthetic force-feedback for human-scale interactions is challenging due to the relatively large forces needed. Therefore, robotic actuators are predominantly used to deliver this kind of haptic feedback; however, they offer limited flexibility and spatial resolution. In this work, we introduce HapticPuppet, a drone-based force-feedback interface which can exert multidirectional forces onto the human body. This can be achieved by attaching strings to different parts of the human body such as fingers, hands or ankles, which can then be affixed to multiple coordinated drones - puppeteering the user. HapticPuppet opens up a wide range of potential applications in virtual, augmented …

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Towards Automated Safety Vetting Of Smart Contracts In Decentralized Applications, Yue Duan, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, Mu Zhang Nov 2022

Towards Automated Safety Vetting Of Smart Contracts In Decentralized Applications, Yue Duan, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, Mu Zhang

Research Collection School Of Computing and Information Systems

We propose VetSC, a novel UI-driven, program analysis guided model checking technique that can automatically extract contract semantics in DApps so as to enable targeted safety vetting. To facilitate model checking, we extract business model graphs from contract code that capture its intrinsic business and safety logic. To automatically determine what safety specifications to check, we retrieve textual semantics from DApp user interfaces. To exclude untrusted UI text, we also validate the UI-logic consistency and detect any discrepancies. We have implemented VetSC and applied it to 34 real-world DApps. Experiments have demonstrated that VetSC can accurately interpret smart contract code, …

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Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li Nov 2022

Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li

Research Collection School Of Computing and Information Systems

Termination is a crucial program property. Non-termination bugs can be subtle to detect and may remain hidden for long before they take effect. Many real-world programs still suffer from vast consequences (e.g., no response) caused by non-termination bugs. As a classic problem, termination proving has been studied for many years. Many termination checking tools and techniques have been developed and demonstrated effectiveness on existing wellestablished benchmarks. However, the capability of these tools in finding practical non-termination bugs has yet to be tested on real-world projects. To fill in this gap, in this paper, we conducted the first large-scale empirical study …

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Delving Deep Into Pixelized Face Recovery And Defense, Zhixuan Zhong, Yong Du, Yang Zhou, Jiangzhong Cao, Shengfeng He Nov 2022

Delving Deep Into Pixelized Face Recovery And Defense, Zhixuan Zhong, Yong Du, Yang Zhou, Jiangzhong Cao, Shengfeng He

Research Collection School Of Computing and Information Systems

Pixelization is arguably one of the most well-adopted deterministic obfuscation techniques for privacy preservation purposes. Although the recovery of pixelized faces is underexplored, the powerful deep neural networks might combat this problem in a data-driven manner. As a consequence, an unbreakable pixelization approach is desired. To achieve this goal, in this paper, we delve into two contradictory problems of unrecoverable pixelization and its counterpart, depixelization, by leveraging the best recovery to strengthen the robustness of the unrecoverable pixelized patterns. In particular, on the offensive end of recovery, we combat the large and continuous nature of pixelized regions by proposing two …

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Api-Related Developer Information Needs In Stack Overflow, Mingwei Liu, Xin Peng, Andrian Marcus, Shuangshuang Xing, Christoph Treude, Chengyuan Zhao Nov 2022

Api-Related Developer Information Needs In Stack Overflow, Mingwei Liu, Xin Peng, Andrian Marcus, Shuangshuang Xing, Christoph Treude, Chengyuan Zhao

Research Collection School Of Computing and Information Systems

Stack Overflow (SO) provides informal documentation for APIs in response to questions that express API related developer needs. Navigating the information available on SO and getting information related to a particular API and need is challenging due to the vast amount of questions and answers and the tag-driven structure of SO. In this paper we focus on identifying and classifying fine-grained developer needs expressed in sentences of API-related SO questions, as well as the specific information types used to express such needs, and the different roles APIs play in these questions and their answers. We derive a taxonomy, complementing existing …

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Inception Transformer, Chenyang Si, Weihao Yu, Pan Zhou, Yichen Zhou, Xinchao Wang, Shuicheng Yan Nov 2022

Inception Transformer, Chenyang Si, Weihao Yu, Pan Zhou, Yichen Zhou, Xinchao Wang, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Recent studies show that Transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose Inception Transformer, or iFormer for short, that effectively learns comprehensive features with both high- and low-frequency information in visual data. Specifically, we design an Inception mixer to explicitly graft the advantages of convolution and max-pooling for capturing the high-frequency information to Transformers. Different from recent hybrid frameworks, the Inception mixer brings greater efficiency through a channel splitting mechanism to adopt parallel convolution/max-pooling path and self-attention path …

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Adaptive Fairness Improvement Based Causality Analysis, Mengdi Zhang, Jun Sun Nov 2022

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, we choose the …

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Tourgether360: Collaborative Exploration Of 360° Videos Using Pseudo-Spatial Navigation, Kartikaeya Kumar, Lev Poretski, Jianan Li, Anthony Tang Nov 2022

Tourgether360: Collaborative Exploration Of 360° Videos Using Pseudo-Spatial Navigation, Kartikaeya Kumar, Lev Poretski, Jianan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Collaborative exploration of 360 videos with contemporary interfaces is challenging because collaborators do not have awareness of one another's viewing activities. Tourgether360 enhances social exploration of 360° tour videos using a pseudo-spatial navigation technique that provides both an overhead "context" view of the environment as a minimap, as well as a shared pseudo-3D environment for exploring the video. Collaborators are embodied as avatars along a track depending on their position in the video timeline and can point and synchronize their playback. We evaluated the Tourgether360 concept through two studies: first, a comparative study with a simplified version of Tourgether360 with …

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A Fine-Grained Data Set And Analysis Of Tangling In Bug Fixing Commits, Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher Ahmed Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier, Bhaveet Nagaria, Philip Makedonski, Matin Nili Ahmadabadi, Kristóf Szabados, Helge Spieker, Matej Madeja, Nathaniel G. Hoy, Christoph Treude, Shangwen Wang, Gema Rodríguez-Pérez, Ricardo Colomo-Palacios, Roberto Verdecchia, Paramvir Singh Nov 2022

A Fine-Grained Data Set And Analysis Of Tangling In Bug Fixing Commits, Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher Ahmed Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier, Bhaveet Nagaria, Philip Makedonski, Matin Nili Ahmadabadi, Kristóf Szabados, Helge Spieker, Matej Madeja, Nathaniel G. Hoy, Christoph Treude, Shangwen Wang, Gema Rodríguez-Pérez, Ricardo Colomo-Palacios, Roberto Verdecchia, Paramvir Singh

Research Collection School Of Computing and Information Systems

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs.Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits.Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the …

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Investigating Bloom's Cognitive Skills In Foundation And Advanced Programming Courses From Students' Discussions, Joel Jer Wei Lim, Gottipati Swapna, Kyong Jin Shim Nov 2022

Investigating Bloom's Cognitive Skills In Foundation And Advanced Programming Courses From Students' Discussions, Joel Jer Wei Lim, Gottipati Swapna, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Programming courses provide students with the skills to develop complex business applications. Teaching and learning programming is challenging, and collaborative learning is proposed to help with this challenge. Online discussion forums promote networking with other learners such that they can build knowledge collaboratively. It aids students open their horizons of thought processes to acquire cognitive skills. Cognitive analysis of discussion is critical to understand students' learning process. In this paper, we propose Bloom's taxonomy based cognitive model for programming discussion forums. We present machine learning (ML) based solution to extract students' cognitive skills. Our evaluations on compupting courses show that …

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Efficient Navigation For Constrained Shortest Path With Adaptive Expansion Control, Wenwen Xia, Yuchen Li, Wentian Guo, Shenghong Li Nov 2022

Efficient Navigation For Constrained Shortest Path With Adaptive Expansion Control, Wenwen Xia, Yuchen Li, Wentian Guo, Shenghong Li

Research Collection School Of Computing and Information Systems

In many route planning applications, finding constrained shortest paths (CSP) is an important and fundamental problem. CSP aims to find the shortest path between two nodes on a graph while satisfying a path constraint. Solving CSPs requires a large search space and is prohibitively slow on large graphs, even with the state-of-the-art parallel solution on GPUs. The reason lies in the lack of effective navigational information and pruning strategies in the search procedure. In this paper, we propose SPEC, a Shortest Path Enhanced approach for solving the exact CSP problem. Our design rationales of SPEC rely on the observation that …

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Mando-Guru: Vulnerability Detection For Smart Contract Source Code By Heterogeneous Graph Embeddings, Huu Hoang Nguyen, Nhat Minh Nguyen, Hong-Phuc Doan, Zahrai Ahmadi, Thanh Nam Doan, Lingxiao Jiang Nov 2022

Mando-Guru: Vulnerability Detection For Smart Contract Source Code By Heterogeneous Graph Embeddings, Huu Hoang Nguyen, Nhat Minh Nguyen, Hong-Phuc Doan, Zahrai Ahmadi, Thanh Nam Doan, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Smart contracts are increasingly used with blockchain systems for high-value applications. It is highly desired to ensure the quality of smart contract source code before they are deployed. This paper proposes a new deep learning-based tool, MANDO-GURU, that aims to accurately detect vulnerabilities in smart contracts at both coarse-grained contract-level and fine-grained line-level. Using a combination of control-flow graphs and call graphs of Solidity code, we design new heterogeneous graph attention neural networks to encode more structural and potentially semantic relations among different types of nodes and edges of such graphs and use the encoded embeddings of the graphs and …

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Towards Automated Safety Vetting Of Smart Contracts In Decentralized Applications, Yue Duan, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, Mu Zhang Nov 2022

Towards Automated Safety Vetting Of Smart Contracts In Decentralized Applications, Yue Duan, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, Mu Zhang

Research Collection School Of Computing and Information Systems

We propose VetSC, a novel UI-driven, program analysis guided model checking technique that can automatically extract contract semantics in DApps so as to enable targeted safety vetting. To facilitate model checking, we extract business model graphs from contract code that capture its intrinsic business and safety logic. To automatically determine what safety specifications to check, we retrieve textual semantics from DApp user interfaces. To exclude untrusted UI text, we also validate the UI-logic consistency and detect any discrepancies. We have implemented VetSC and applied it to 34 real-world DApps. Experiments have demonstrated that VetSC can accurately interpret smart contract code, …

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Itiger: An Automatic Issue Title Generation Tool, Ting Zhang, Ivana Clairine Irsan, Thung Ferdian, Donggyun Han, David Lo, Lingxiao Jiang Nov 2022

Itiger: An Automatic Issue Title Generation Tool, Ting Zhang, Ivana Clairine Irsan, Thung Ferdian, Donggyun Han, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In both commercial and open-source software, bug reports or issues are used to track bugs or feature requests. However, the quality of issues can differ a lot. Prior research has found that bug reports with good quality tend to gain more attention than the ones with poor quality. As an essential component of an issue, title quality is an important aspect of issue quality. Moreover, issues are usually presented in a list view, where only the issue title and some metadata are present. In this case, a concise and accurate title is crucial for readers to grasp the general concept …

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