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Chinese Automation Congress (CAC)

作者： Mingzhi Mao Ji Lu Yang Shi Yunong Zhang School of Software Engineering Sun Yat-sen University Zhuhai P. R. China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P. R. China

We design and present an integral-type Zhang neurodynamics (ITZN) model for handling temporally-varying nonlinear system of equations (TVNSE). To investigate the disturbances inhibition of the model, the general form of the disturbed ITZN model is presented, and it is instantiated by introducing the constant disturbance, bounded random disturbance, and linear-form temporally-varying disturbance, resulting in three corresponding disturbed ITZN variants. Finally, by means of Lyapunov theory and Laplace transformation, we theoretically prove the globally exponential convergence of the ITZN model and the effective disturbances inhibition of the three disturbed ITZN variants.

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ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts

arXiv

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arXiv 2024年

作者： Wang, Che Zhang, Jiashuo Gao, Jianbo Xia, Libin Guan, Zhi Chen, Zhong School of Computer Science Peking University Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

Smart contracts are susceptible to being exploited by attackers, especially when facing real-world vulnerabilities. To mitigate this risk, developers often rely on third-party audit services to identify potential vulnerabilities before project deployment. Nevertheless, repairing the identified vulnerabilities is still complex and labor-intensive, particularly for developers lacking security expertise. Moreover, existing pattern-based repair tools mostly fail to address real-world vulnerabilities due to their lack of high-level semantic understanding. To fill this gap, we propose ContractTinker, a Large Language Models (LLMs)-empowered tool for real-world vulnerability repair. The key insight is our adoption of the Chain-of-Thought approach to break down the entire generation task into sub-tasks. Additionally, to reduce hallucination, we integrate program static analysis to guide the LLM. We evaluate ContractTinker on 48 high-risk vulnerabilities. The experimental results show that among the patches generated by ContractTinker, 23 (48%) are valid patches that fix the vulnerabilities, while 10 (21%) require only minor modifications. A video of ContractTinker is available at https://***/HWFVi-YHcPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Financial Loan Overdue Risk Detection via Meta-path-based Graph Neural Network

Financial Loan Overdue Risk Detection via Meta-path-based Gr...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Jinze Chen Jieli Liu Zhiying Wu Shanhe Zhao Quanzhong Li Jiajing Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China Merchants Union Consumer Finance Company Limited Shenzhen China

Overdue risk detection of consumer loans is a critical issue faced by consumer finance companies. Unlike other types of loans, such as mortgage loans and guaranteed loans, consumer loans only regard personal credit as collateral. As a high overdue rate will result in economic losses to financial companies, it is of great significance for lenders to accurately detect risky customers. However, the large volume of credit data and the variety of customer characteristics make the risk detection via manual expert analysis rather challenging. Additionally, previous loan risk detection approaches based on machine learning classification neglect the relations between different customers, and traditional graph neural networks lack the exploration of loan overdue patterns. In this paper, we construct a heterogeneous graph based on real credit data from a consumer finance company. We analyze the distribution of meta-paths and propose a meta-path-based graph neural network that combines both lower-order and higher-order features. Experimental results show that our model is able to detect more risky customers by exploring overdue patterns and can achieve the best effect in the loan overdue detection task.

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One-to-Multiple Clean-Label Image Camouflage (Omclic) Based Backdoor Attack on Deep Learning

SSRN

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SSRN 2023年

作者： Wang, Guohong Ma, Hua Gao, Yansong Abuadbba, Alsharif Zhang, Zhi Wei, Kang Al-Sarawi, Said F. Zhang, Gongxuan Abbott, Derek School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China School of Electrical and Electronic Engineering The University of Adelaide Australia Department of Computer Science and Software Engineering The University of Western Australia Australia Data61 Commonwealth Scientific and Industrial Research Organisation CSIRO Australia

Image camouflage has been utilized to create clean-label poisoned images for implanting backdoor into a DL model. But there exists a crucial limitation that one attack/poisoned image can only fit a single input size of the DL model, which greatly increases its attack budget when attacking multiple commonly adopted input sizes of DL models. This work proposes to constructively craft an attack image through camouflaging but can fit multiple DL models’ input sizes simultaneously, namely OmClic. Thus, through OmClic, we are able to always implant a backdoor regardless of which common input size is chosen by the user to train the DL model given the same attack budget (i.e., a fraction of the poisoning rate). With our camouflaging algorithm formulated as a multi-objective optimization, M = 5 input sizes can be concurrently targeted with one attack image, which artifact is retained to be almost visually imperceptible at the same time. Extensive evaluations validate the proposed OmClic can reliably succeed in various settings using diverse types of images. Further experiments on OmClic based backdoor insertion to DL models show that high backdoor performances (i.e., attack success rate and clean data accuracy) are achievable no matter which common input size is randomly chosen by the user to train the model. So that the OmClic based backdoor attack budget is reduced by M× compared to the state-of-the-art camouflage based backdoor attack as a baseline. Significantly, the same set of OmClic based poisonous attack images is transferable to different model architectures for backdoor implant. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Explicit Analysis of Age of Synchronization in IoT Networks

Explicit Analysis of Age of Synchronization in IoT Networks

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Zhiwei Jin Siyuan Zhou Yunquan Dong Bin Tang Guoping Tan College of Information Science and Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

The Internet of Things (IoT) has the capability to support the synchronous transmission of dynamically sensed environmental status information to base stations. To improve transmission timeliness, previous studies have commonly utilized Age of Information (AoI) as a metric for network performance. However, AoI falls short in capturing status synchronicity, as synchronization requires knowledge of updates from both the transmitter and the receiver, which AoI cannot reflect due to its inability to track changes at the transmitter. In this work, we adopt the Age of Synchronization (AoS) as a metric to quantify the time elapsed since the freshest information at the receiver becomes desynchronized. Specifically, we develop a model for an IoT communication network to derive closed-form expressions for the average peak AoS (PAoS) and average AoS, considering discrete geometric and uniformly distributed packet arrivals. Ad-ditionally, the analysis has been carried out for both first-come-first-served (FCFS) and last-come-first-served with preemptive resume (LCFS-PR) service disciplines. Within these results, we prove that network's synchronization performance worsens with longer packet arrival intervals but improves with higher arrival rates. LCFS-PR strategy makes synchronization independent of packet arrival, solely influenced by service rate. Additionally, AoS consistently remains lower than AoI in the same system, with both tending to converge as packet arrival rate rises.

关键词： Measurement Closed-form solutions Transmitters Processor scheduling Wireless networks Receivers Signal processing Synchronization Internet of Things Queueing analysis

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Privacy-preserving cloud-fog–based traceable road condition monitoring in VANET

Privacy-preserving cloud-fog–based traceable road condition...

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作者： Wang, Wei Wu, Lei Qu, Wenlei Liu, Zhaoman Wang, Hao School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China Shandong Provincial Key Laboratory for Software Engineering Jinan China

With the development of intelligent vehicles, the research on road condition monitoring has attracted much attention in the vehicular ad hoc network (VANET). The combination of VANET, cloud computing, and fog computing provides on-demand computational resources while creating a lot of new challenges. In this paper, we propose an efficient privacy-preserving cloud-fog–based traceable road surface condition monitoring scheme. We use certificateless aggregate signcryption technology to implement multiple messages aggregation verification, which greatly saves computing resources and bandwidth. Moreover, we also use a traceable vehicle pseudo identity generated by a trace authority (TRA) to achieve identity privacy protection. To ensure the privacy of the fog and cloud server, the road condition information is reported in ciphertext format. In addition, the cloud server can perform ciphertext equivalence test operation to distinguish different road condition information of the same area without compromising the confidentiality. The ciphertext, which exceeds the set threshold, is uploaded to the blockchain. It can be stored permanently and never be tampered with or deleted. Finally, we demonstrate the correctness of the proposed scheme and show that the proposed scheme has higher efficiency. © 2020 John Wiley & Sons, Ltd.

关键词： Fog

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A Crossover Algebra for Solving Minimum Spanning Tree in Network Design with Diameter Constraints

A Crossover Algebra for Solving Minimum Spanning Tree in Net...

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2021 International Conference on Cloud computer, IoT and Intelligence System, CCIIS 2021

作者： Wang, Ziyi Shi, Kai Qiao, Fuqiang Li, Huanran School of Computer Science and Engineering Tianjin University of Technology China College of Software and Communications Tianjin Sino-German University of Applied Sciences Tianjin China

The minimum spanning tree problem with diameter constraint is an important problem for many applications such as network design and reliability. The diameter constraint makes it be different from the common minimum spanning tree problem, it is difficult to get the optimal solution, even the feasible solution can not be obtained by the traditional method. To address this problem, in this paper, a new crossover algebra is designed to ensure that the crossover tree still satisfies the diameter constraint. During the crossover process, structure of the subtree will be retained, thus avoiding the problem that the new generated tree is not a feasible solution. Experimental results show the effectiveness of the improved algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Algebra

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A Video Surveillance System Based on Single-Mode Fiber for Energy and Signal Co-Transmission

A Video Surveillance System Based on Single-Mode Fiber for E...

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Chinese Automation Congress (CAC)

作者： Yuanle Su Xueliang Gu Zhiguo Zhang Xu Wang Haoyu Wu Jialin Wei Xiangning Song State Key Lab of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper introduces an innovative video surveillance system that utilizes single-mode fiber for the simultaneous transmission of energy and information. Traditional surveillance systems relying on electrical cables pose safety risks and operational challenges, particularly in hazardous and remote environments. The proposed system leverages power-over-fiber (PoF) technology, using a single-mode fiber to co-transmit high-power laser energy and video signals. This approach overcomes the limitations of multimode fibers, such as signal loss and modal dispersion, and enables long-distance transmission up to 10 kilometers. At the remote end, the energy is converted into electrical power by a photovoltaic converter, ensuring the operation of surveillance equipment. The system also incorporates low-power micro-controller (MCU) and a lossless compression algorithm to enhance video transmission efficiency, achieving a frame rate of 16–17 frames per second at a resolution of 320×240. Experimental results demonstrate the system's effectiveness in delivering stable energy supply and high-quality video transmission, making it suitable for use in special environments with stringent safety and operational requirements.

关键词： Protocols Energy resolution Optical fiber cables Video surveillance Propagation losses Optical fiber communication Optical fiber devices Safety Signal resolution Optical fiber dispersion

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

引用

Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Tailin Liang Zhaolin Yuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Department of Industrial and Systems Engineering Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications.

关键词： Meters Deep learning Accuracy Ultrasonic variables measurement Transportation Manuals Feature extraction Surface texture Convolutional neural networks Surface treatment

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State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Zhang, Zongyuan Duan, Tianyang Lin, Zheng Huang, Dong Fang, Zihan Sun, Zekai Xiong, Ling Liang, Hongbin Cui, Heming Cui, Yong Department of Computer Science The University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science City University of Hong Kong Hong Kong School of Computer and Software Engineering Xihua University Chengdu610039 China National United Engineering Laboratory of Integrated and Intelligent Transportation The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Department of Computer Science and Technology Ts-inghua University Beijing100084 China

Recently, deep reinforcement learning (DRL) has emerged as a promising approach for robotic control. However, the deployment of DRL in real-world robots is hindered by its sensitivity to environmental perturbations. While existing white-box adversarial attacks rely on local gradient information and apply uniform perturbations across all states to evaluate DRL robustness, they fail to account for temporal dynamics and state-specific vulnerabilities. To combat the above challenge, we first conduct a theoretical analysis of white-box attacks in DRL by establishing the adversarial victim-dynamics Markov decision process (AVD-MDP), to derive the necessary and sufficient conditions for a successful attack. Based on this, we propose a selective state-aware reinforcement adversarial attack method, named STAR, to optimize perturbation stealthiness and state visitation dispersion. STAR first employs a soft mask-based state-targeting mechanism to minimize redundant perturbations, enhancing stealthiness and attack effectiveness. Then, it incorporates an information-theoretic optimization objective to maximize mutual information between perturbations, environmental states, and victim actions, ensuring a dispersed state-visitation distribution that steers the victim agent into vulnerable states for maximum return reduction. Extensive experiments demonstrate that STAR outperforms state-of-the-art benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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