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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jiang, Xinke Qiu, Rihong Xu, Yongxin Zhang, Wentao Zhu, Yichen Zhang, Ruizhe Fang, Yuchen Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science Peking University China University of Electronic Science and Technology of China China Center on Frontiers of Computing Studies Peking University Beijing China Big Data Technology Research Center Nanhu Laboratory Jiaxing China Peking University Information Technology Institute Binhai Tianjin China

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGRAPH), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGRAPH adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGRAPH significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGRAPH consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability. © 2024 Neural information processing systems foundation. All rights reserved.

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Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation

arXiv

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arXiv 2024年

作者： Mou, Xinyi Wei, Zhongyu Huang, Xuanjing School of Data Science Fudan University China Research Institute of Intelligent and Complex Systems Fudan University China School of Computer Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

Social media has emerged as a cornerstone of social movements, wielding significant influence in driving societal change. Simulating the response of the public and forecasting the potential impact has become increasingly important. However, existing methods for simulating such phenomena encounter challenges concerning their efficacy and efficiency in capturing the behaviors of social movement participants. In this paper, we introduce a hybrid framework HiSim for social media user simulation, wherein users are categorized into two types. Core users are driven by Large Language Models, while numerous ordinary users are modeled by deductive agent-based models. We further construct a Twitter-like environment to replicate their response dynamics following trigger events. Subsequently, we develop a multi-faceted benchmark SoMoSiMu-Bench for evaluation and conduct comprehensive experiments across real-world datasets. Experimental results demonstrate the effectiveness and flexibility of our method 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Computational methods

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Visual Servoing under Limited Field of View (FOV) for Lumbar Vertebrae Positioning 8

Visual Servoing under Limited Field of View (FOV) for Lumbar...

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8th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2025

作者： Xiao, Peng Xiong, Chi Wang, Yu Kong, Lingming Cai, Bin Sun, Zhiyong Zou, Le Wang, Xiaofeng Song, Bo Hefei University School of Artificial Intelligence and Big Data Hefei China Institute of Intelligent Machines HFIPS Chinese Academy of Sciences Hefei China University of Science and Technology of China Hefei China Chimed Intelligent Machinery Co. Ltd Hefei China Intelligent Manufacturing Multimodal Data Fusion Engineering Research Center China

ISBN: (纸本)9798331517915

X-ray machines are vital in medical imaging for viewing internal body structures. However, in lumbar vertebrae surgery, the X-ray machine has to move and position frequently and manually, which risks infection, misalignment, and overexposure to radiation. There's a need for mobile X-ray machines with autonomous recognition and positioning. Although visual servoing suitable for X-ray positioning naturally, it still experiencing challenges like limited Field-of-View (FOV) and the structural similarity among vertebrae. In this paper, a Bayesian Estimation based approach is developed to improve the detection accuracy in case of vertebrae positions outside the FOV. Furthermore, a visual servoing method is proposed for X-ray positioning, considering their mechanical structure and imaging characteristics. The experiment results indicates that the proposed approach improves lumbar vertebrae detection accuracy significantly. This approach facilitates precise positioning in X-ray imaging and enhances the safety and effectiveness of lumbar vertebrae surgery. © 2025 IEEE.

关键词： lumbar vertebrae detection visual servoing X-ray machine

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A Cooperative Flow Simulator for Distributed computing Based on Full-Dimensional Definable Network 4

A Cooperative Flow Simulator for Distributed Computing Based...

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4th International Conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Liang, Yuan Xiao, Geyang Yao, Shaofeng Wang, Hongsheng Yi, Xiaoyu Tu, Yonggang Intelligent Network Research Institute Zhejiang Lab Hangzhou China Intelligent Computing Institute Zhejiang Lab Hangzhou China Jiaxing Big Data Center Jiaxing Municipal Peoples Government Jiaxing China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

To improve the efficiency of cluster computing, the research of cooperative flow scheduling algorithms provides another idea for further optimization. Cluster computing frameworks such as Spark and Map-Reduce carry running cluster computing tasks and have distinct stage division and dependency characteristics. With limited network resources, the overall completion time of the job can be reduced by minimizing the wait time of the computing phase due to the bottleneck of transmission capacity. The introduction of an artificial intelligence algorithm can provide a solution to this NP-hard problem. The simulator designed in this paper can realize the overall process of cooperative traffic generation, scheduling policy configuration, and scheduling effect feedback closed-loop through packet sender, packet receiver, scheduler, and programmable switch components according to the dependency between cluster computing stages. © 2022 IEEE.

关键词： Cluster computing

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Plotinus: A Satellite Internet Digital Twin System

arXiv

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arXiv 2024年

作者： Gao, Yue Qiu, Kun Chen, Zhe Zhu, Wenjun Zhang, Qi Luo, Handong Lin, Quanwei Yang, Ziheng Liu, Wenhao The Intelligent Networking and Computing Research Center School of Computer Science Fudan University Shanghai200433 China

The development of integrated space-airground network (SAGIN) requires sophisticated satellite Internet emulation tools that can handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms struggle with challenges like the need for detailed implementation across all network layers, real-time response times, and the ability to scale. Plotinus, a new digital twin system based on microservices for satellite Internet emulation, aims to solve these problems. It features a modular design, allowing for easy replacement of the physical layer to emulate different aerial vehicles and analyze channel interference. It also enables the replacement of path computation methods to simplify testing and deploying algorithms. In particular, Plotinus allows for real-time emulation with live network traffic, enhancing the realism of network models. Evaluation result shows that Plotinus's effective emulation of dynamic satellite networks with real-world devices. Its adaptability for various communication models and algorithm testing highlights Plotinus's role as a vital tool for developing and analyzing SAGIN systems, offering a scalable, real-time response, and flexible digital twin system. Copyright © 2024, The Authors. All rights reserved.

关键词： Network layers

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection 31

Towards Effective and Efficient Error Handling Code Fuzzing ...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin Jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China

ISBN: (纸本)9798350330663

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL-F I are effective that contribute significantly to its overall performance. © 2024 IEEE.

关键词： Errors

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Meta-Path Based Social Relation Reasoning in a Deep and Robust Way 28th

Meta-Path Based Social Relation Reasoning in a Deep and R...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Jiang, Xuhui Shen, Yinghan Wang, Yuanzhuo Shen, Huawei Xu, Chengjin Ma, Shengjie Data Intelligent System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology UCAS Gloucester United Kingdom Big Data Academy Zhongke Zhengzhou China International Digital Economy Academy Shenzhen China Gaoling School of Artificial Intelligence Renmin University of China Beijing China

ISBN: (纸本)9783031306747

Social relation reasoning in heterogeneous social networks (HSNs) should not only infer whether people are connected, but also why they know each other and what their relationship is. This task is challenging because the heterogeneous social network is non-trivial to leverage and the collected data is always noisy and incomplete, which leads that the existing rule-based and representation methods cannot efficiently and accurately reason social relations. To reason social relations in noisy and incomplete HSNs, we shift from simply inferring meta-path based social relations by rule-based methods to reasoning by social relation learning. Then, we present an inductive model named Heterogeneous Graph Variational AutoEncoders (HGVAE) towards robust social relation learning on noisy and incomplete HSN data. In HGVAE, the well-designed heterogeneous graph encoder, multi-signal decoder, and variational inference mechanism bring prominent robustness and significant performance improvement. Extensive experiments are conducted to compare our methods against a set of the most representative baseline methods. The efficiency analysis, robust analysis and ablation studies confirm the value of our proposed approach for modelling meta-path based social relations buried in various HSNs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Tensor and Minimum Connected Dominating Set based Confident Information Coverage Reliability Evaluation for IoT

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2024年第3期10卷 547-561页

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： data reliability

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

引用

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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Computer Modeling in Engineering & sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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