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SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing

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arXiv 2024年

作者： Guo, An Zhou, Yuan Tian, Haoxiang Fang, Chunrong Sun, Yunjian Sun, Weisong Gao, Xinyu Luu, Anh Tuan Liu, Yang Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China College of Computing and Data Science Nanyang Technological University Singapore

Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal accidents involving ADSs suggests that the desired level of safety has not yet been fully achieved. Consequently, there is a growing need for more comprehensive and targeted testing approaches to ensure safe driving. Scenarios from real-world accident reports provide valuable resources for ADS testing, including critical scenarios and high-quality seeds. However, existing scenario reconstruction methods from accident reports often exhibit limited accuracy in information extraction. Moreover, due to the diversity and complexity of road environments, matching current accident information with the simulation map data for reconstruction poses significant challenges. In this paper, we design and implement SoVAR, a tool for automatically generating road-generalizable scenarios from accident reports. SoVAR utilizes well-designed prompts with linguistic patterns to guide the large language model (LLM) in extracting accident information from textual data. Subsequently, it formulates and solves accident-related constraints in conjunction with the extracted accident information to generate accident trajectories. Finally, SoVAR reconstructs accident scenarios on various map structures and converts them into test scenarios to evaluate its capability to detect defects in industrial ADSs. We experiment with SoVAR, using the accident reports from the National Highway Traffic Safety Administration's (NHTSA) database to generate test scenarios for the industrial-grade ADS Apollo. The experimental findings demonstrate that SoVAR can effectively generate generalized accident scenarios across different road structures. Furthermore, the results confirm that SoVAR identified 5 distinct safety violation types that contributed to the crash of Baidu Apollo. Copyright © 2024, The Authors. All rights reserved.

关键词： Automatic test pattern generation

Lcvd: Loop-Oriented Code Vulnerability Detection Via Graph Neural Network

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SSRN

SSRN 2023年

作者： Wang, Mingke Tao, Chuanqi Guo, Hongjing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Due to the unique mechanism and complex structure, loops in programs can easily lead to various vulnerabilities such as dead loops, memory leaks, resource depletion, etc. Traditional approaches to loop-oriented program analysis (e.g. loop summarization) are costly with a high rate of false positives in complex software systems. To address the issues above, recent works have applied deep learning (DL) techniques to vulnerability detection. However, existing DL-based approaches mainly focused on the general characteristics of most vulnerabilities without considering the semantic information of specific vulnerabilities. As a typical structure in programs, loops are highly iterative with multi-paths. Currently, there is a lack of available approaches to represent loops, as well as useful methods to extract the implicit vulnerability patterns. Therefore, this paper introduces LCVD, an automated loop-oriented code vulnerability detection approach. LCVD represents the source code as the Loop-flow Abstract Syntax Tree (LFAST), which focuses on interleaving multi-paths around loop structures. Then a novel Loop-flow Graph Neural Network (LFGNN) is proposed to learn both the local and overall structure of loop-oriented vulnerabilities. The experimental results demonstrate that LCVD outperforms the three static analysis-based and four state-of-the-art DL-based vulnerability detection approaches across evaluation settings. © 2023, The Authors. All rights reserved.

关键词： Flow graphs

Measurement-based Verification of Quantum Markov Chains

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arXiv 2024年

作者： Guan, Ji Feng, Yuan Turrini, Andrea Ying, Mingsheng Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Beijing100190 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Institute of Intelligent Software Guangzhou511458 China The Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia

Model-checking techniques have been extended to analyze quantum programs and communication protocols represented as quantum Markov chains, an extension of classical Markov chains. To specify qualitative temporal properties, a subspace-based quantum temporal logic is used, which is built on Birkhoff–von Neumann atomic propositions. These propositions determine whether a quantum state is within a subspace of the entire state space. In this paper, we propose the measurement-based linear-time temporal logic MLTL to check quantitative properties. MLTL builds upon classical linear-time temporal logic (LTL) but introduces quantum atomic propositions that reason about the probability distribution after measuring a quantum state. To facilitate verification, we extend the symbolic dynamics-based techniques for stochastic matrices described by Agrawal et al. (JACM 2015) to handle more general quantum linear operators (super-operators) through eigenvalue analysis. This extension enables the development of an efficient algorithm for approximately model checking a quantum Markov chain against an MLTL formula. To demonstrate the utility of our model-checking algorithm, we use it to simultaneously verify linear-time properties of both quantum and classical random walks. Through this verification, we confirm the previously established advantages discovered by Ambainis et al. (STOC 2001) of quantum walks over classical random walks and discover new phenomena unique to quantum walks. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

Hybrid Variational Autoencoder for Collaborative Filtering 25

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Hybrid Variational Autoencoder for Collaborative Filtering

25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Jinxin Xiao, Yingyuan Zhu, Ke Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance. © 2022 IEEE.

关键词： Collaborative filtering

Experience report:investigating bug fixes in machine learning frameworks/libraries

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Frontiers of computer science 2021年第6期15卷 43-58页

作者： Xiaobing SUN Tianchi ZHOU Rongcun WANG Yucong DUAN Lili BO Jianming CHANG School of Information Engineering Yangzhou UniversityYangzhou 225100China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Jiangsu Engineering Research Center of Knowledge Management and Intelligent Service Yangzhou University Yangzhou 225127China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Computer Science and Cyberspace Security Hainan UniversityHaikou 570228China

Machine learning(ML)techniques and algorithms have been successfully and widely used in various areas including software engineering *** other software projects,bugs are also common in ML projects and *** order to more deeply understand the features related to bug fixing in ML projects,we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories,fixing patterns,fixing scale,fixing duration,and types of *** results show that(1)there are commonly seven types of bugs in ML programs;(2)twelve fixing patterns are typically used to fix the bugs in ML programs;(3)68.80%of the patches belong to micro-scale-fix and small-scale-fix;(4)66.77%of the bugs in ML programs can be fixed within one month;(5)45.90%of the bug fixes belong to corrective activity from the perspective of software ***,we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical *** results of our empirical study are basically consistent with the feedback from *** findings from the empirical study provide useful guidance and insights for developers and users to effectively detect and fix bugs in MLprojects.

关键词： bug fixing machine learning project empirical study questionnaire survey

Explaining Model Overfitting in CNNs via GMM Clustering

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arXiv 2024年

关键词： Convolutional neural networks

Optimizing Class Distribution in Memory for Multi-Label Online Continual Learning

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arXiv 2022年

作者： Liang, Yan-Shuo Li, Wu-Jun The National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University Nanjing210023 China

Online continual learning, especially when task identities and task boundaries are unavailable, is a challenging continual learning setting. One representative kind of methods for online continual learning is replay-based methods, in which a replay buffer called memory is maintained to keep a small part of past samples for overcoming catastrophic forgetting. When tackling with online continual learning, most existing replay-based methods focus on single-label problems in which each sample in the data stream has only one label. But multi-label problems may also happen in the online continual learning setting in which each sample may have more than one label. In the online setting with multi-label samples, the class distribution in data stream is typically highly imbalanced, and it is challenging to control class distribution in memory since changing the number of samples belonging to one class may affect the number of samples belonging to other classes. But class distribution in memory is critical for replay-based memory to get good performance, especially when the class distribution in data stream is highly imbalanced. In this paper, we propose a simple but effective method, called optimizing class distribution in memory (OCDM), for multi-label online continual learning. OCDM formulates the memory update mechanism as an optimization problem and updates the memory by solving this problem. Experiments on two widely used multi-label datasets show that OCDM can control the class distribution in memory well and can outperform other state-of-the-art methods. © 2022, CC0.

关键词： Learning systems

LEARNING WITH LOGICAL CONSTRAINTS BUT WITHOUT SHORTCUT SATISFACTION

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arXiv 2024年

作者： Li, Zenan Liu, Zehua Yao, Yuan Xu, Jingwei Chen, Taolue Ma, Xiaoxing Lü, Jian State Key Lab of Novel Software Technology Nanjing University China Department of Mathematics The University of Hong Kong Hong Kong Department of Computer Science Birkbeck University of London United Kingdom

Recent studies have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the knowledge. In this paper, we present a new framework for learning with logical constraints. Specifically, we address the shortcut satisfaction issue by introducing dual variables for logical connectives, encoding how the constraint is satisfied. We further propose a variational framework where the encoded logical constraint is expressed as a distributional loss that is compatible with the model’s original training loss. The theoretical analysis shows that the proposed approach bears salient properties, and the experimental evaluations demonstrate its superior performance in both model generalizability and constraint satisfaction. © 2024, CC BY.

关键词： Signal encoding

Coordination of networking and computing: toward new information infrastructure and new services mode

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Frontiers of Information technology & Electronic Engineering 2024年第5期25卷 629-632页

作者： Xiaoyun WANG Tao SUN Yong CUI Rajkumar BUYYA Deke GUO Qun HUANG Hassnaa MOUSTAFA Chen TIAN Shangguang WANG China Mobile Communications Corporation Beijing 100032China China Mobile Research Institute Beijing 100053China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China CLOUDS Lab School of Computing and Information SystemsUniversity of MelbourneMelbourneVIC 3010Australia National Key Laboratory of Information Systems Engineering National University of Defense TechnologyChangsha 410073China School of Computer Science Peking UniversityBeijing 100871China Intel Corporation Santa ClaraCA 95054USA State Key Laboratory for Novel Sofiware Technology Nanjing UniversityNanjing 210023China State Key Laboratory of Networking and Switching Technology Bejing University of Posts and TelecommunicationsBeijing 100876China

Computing,serving as the cornerstone of information processing,plays a pivotal role in the digital service ***"network"and"computing,"responsible for information transmission and processing respectively,traditionally belong to different stakeholders and have evolved ***,the recent trend toward the coordination and integration of computing and networks has garnered significant attention from both industry and *** such as"computility network"and"computing force network"have emerged,and the International Telecommunication Union-Telecommunication Standardization(ITU-T)has initiated efforts to develop standards for the coordination of networking and computing(CNC),focusing on the architecture and framework.

关键词： computing services network