检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Liu, Yanhua Liu, Zhihuang Zhang, Qiu Su, Jinshu Li, Xiaoyan College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer National University of Defense Technology Changsha410073 China

Blockchain-based healthcare IoT technology research promotes increased security for smart healthcare services such as real-time monitoring and remote disease diagnosis. In a healthcare IoT service system, it remains a challenging problem to explore effective incentive mechanisms throughout the healthcare service process and feedback to facilitate the efficiency of the blockchain consensus process. In this paper, we propose a blockchain and trusted reputation assessment-based incentive mechanism for healthcare services (BtRaI). BtRaI provides a realistic and comprehensive reputation assessment with feedback to motivate blockchain consensus node participation, effectively defending against malicious behavior in the healthcare service system. Specifically, BtRaI first introduces multiple moderation factors for comprehensive multidimensional reputation assessment and stores the assessment results credibly on the blockchain. Then, we propose an improved PBFT algorithm based on the reputation assessment to enhance blockchain consensus efficiency. Finally, BtRaI designs a token reward and punishment mechanism to motivate all parties to participate in the blockchain honestly, inhibit potential misbehavior, and encourage the healthcare system to provide better service quality. Theoretical analysis and experimental evaluation demonstrate that BtRaI effectively suppresses malicious attacks in healthcare services, improves blockchain node failure tolerance rates, and achieves blockchain transaction processing efficiency within 0.5 seconds in a 100-node consortium chain. The reputation assessment and token incentive mechanism of BtRaI have a realistic differentiation granularity and change curve, making it suitable for dynamic and complex healthcare service scenarios. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Multi-View Subgraph Neural networks: Self-Supervised Learning with Scarce Labeled Data

arXiv

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arXiv 2024年

作者： Wang, Zhenzhong Zeng, Qingyuan Lin, Wanyu Jiang, Min Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian Xiamen361005 China

While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples. This assumption restricts the classification performance of prevailing GNNs on many real-world applications suffering from low-data regimes. Specifically, features extracted from scarce labeled nodes could not provide sufficient supervision for the unlabeled samples, leading to severe over-fitting. In this work, we point out that leveraging subgraphs to capture long-range dependencies can augment the representation of a node with homophily properties, thus alleviating the low-data regime. However, prior works leveraging subgraphs fail to capture the long-range dependencies among nodes. To this end, we present a novel self-supervised learning framework, called Multi-view subgraph neural networks (Muse), for handling the long-range dependencies. In particular, we propose an information theory-based identification mechanism to identify two types of subgraphs from the views of input space and latent space, respectively. The former is to capture the local structure of the graph, while the latter captures the long-range dependencies among nodes. By fusing these two views of subgraphs, the learned representations can preserve the topological properties of the graph at large, including the local structure and long-range dependencies, thus maximizing their expressiveness for downstream node classification tasks. Theoretically, we provide the generalization error bound based on Rademacher complexity to show the effectiveness of capturing complementary information from subgraphs of multiple views. Empirically, we show a proof-of-concept of Muse on canonical node classification problems on graph data. Experimental results show that Muse outperforms the alternative methods on node classification tasks with limited labeled data. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural networks

arXiv

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arXiv 2024年

作者： Chen, Zhaoliang Wu, Zhihao Sadikaj, Ylli Plant, Claudia Dai, Hong-Ning Wang, Shiping Cheung, Yiu-Ming Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science and with the research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Although Graph Neural networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edgedropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Ask, attend, attack: an effective decision-based black-box targeted attack for image-to-text models 24

Ask, attend, attack: an effective decision-based black-box t...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Qingyuan Zeng Zhenzhong Wang Yiu-ming Cheung Min Jiang Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen Fujian P.R. China Department of Computing The Hong Kong Polytechnic University Department of Computer Science Hong Kong Baptist University School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Xiamen Fujian P.R. China

ISBN: (纸本)9798331314385

While image-to-text models have demonstrated significant advancements in various vision-language tasks, they remain susceptible to adversarial attacks. Existing white-box attacks on image-to-text models require access to the architecture, gradients, and parameters of the target model, resulting in low practicality. Although the recently proposed gray-box attacks have improved practicality, they suffer from semantic loss during the training process, which limits their targeted attack performance. To advance adversarial attacks of image-to-text models, this paper focuses on a challenging scenario: decision-based black-box targeted attacks where the attackers only have access to the final output text and aim to perform targeted attacks. Specifically, we formulate the decision-based black-box targeted attack as a large-scale optimization problem. To efficiently solve the optimization problem, a three-stage process Ask, Attend, Attack, called AAA, is proposed to coordinate with the solver. Ask guides attackers to create target texts that satisfy the specific semantics. Attend identifies the crucial regions of the image for attacking, thus reducing the search space for the subsequent Attack. Attack uses an evolutionary algorithm to attack the crucial regions, where the attacks are semantically related to the target texts of Ask, thus achieving targeted attacks without semantic loss. Experimental results on transformer-based and CNN+RNN-based image-to-text models confirmed the effectiveness of our proposed AAA.

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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Traffic Engineering in Dynamic Hybrid Segment Routing networks

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Computers, Materials & Continua 2021年第7期68卷 655-670页

作者： Yingya Guo Kai Huang Cheng Hu Jiangyuan Yao Siyu Zhou College of Mathematics and Computer Science Fuzhou UniversityFuzhou350000China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350000China Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationFuzhou350003China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyberspace Security Hainan UniversityHaiKou570228China Tandon School of Engineering New York UniversityNew York10012USA Department of Computing Hong Kong Polytechnic UniversityHong Hom999077Hong Kong

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet *** SR architecture,the paths that the packets choose to route on are indicated at the ingress *** with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress *** the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical *** partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable *** the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR *** algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering ***,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)***,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network *** the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in *** extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

关键词： Traffic engineering routing optimization segment routing deep reinforcement learning ultra-scalable spectral clustering

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Application of Fuzzy Neural network in Chromatographic Separation 6th

Application of Fuzzy Neural Network in Chromatographic Separ...

引用

6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Xie, Chao-Fan Xu, Lu-Xiong Wang, Ruobin Zhang, Fuquan Department of Electrical Engineering I-Shou University Kaohsiung84001 Taiwan Electronic Information and Engineering Institute Fuqing Branch of Fujian Normal University FuJian FuQing FuZhou350300 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University FuJian FuQing FuZhou350300 China School of Information Science and Technology North China University of Technology Beijing100144 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811650352

Chromatographic separation process is a continuous and discrete mixed system. It is a fixed bed consisting of chromatographic columns or similar chromatographic columns. The components are separated by valve switching. Therefore, many small and medium-sized enterprises need to control system parameters, so they are vulnerable to external interference. In the actual production process, it is often in the safety aspect, the system operation will deviate from the best operating conditions, so it is difficult to give full play to the separation ability of SMB chromatographic separation process. In order to make the separation system of small and medium-sized enterprises play its potential, the results based on simulation model are particularly important. This paper firstly digitalization the simulation process, then uses FNN to training the simulation data to the influence of observation parameters on the results, searching for the trend of complex parameter change from neural network learning, foundation for verifying the controllability of the system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy neural networks

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A Two-Stage Graph Computation Model with Communication Equilibrium 15th

A Two-Stage Graph Computation Model with Communication Equil...

引用

15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Dong, Yanmei Chen, Rongwang Guo, Kun College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China College of Mathematics and Computer Science Wuyi University Wuyishan Fujian354300 China Digital Fujian Tourism Big Data Institute Wuyishan Fujian354300 China

ISBN: (纸本)9789811625398

Distributed graph computing aims at performing in-depth analysis on large networks in a parallel manner. Iterative communication computation is an important model to perform graph analysis. Moreover, high-efficiency iterative communication computation is necessary for assuring the quality of graph partitioning. Many strategies to improve graph computing are usually based on the hypothesis of single communication iteration which focuses on the optimization of load balance in a single graph partitioning and the improvement of parallel granularity or communication manners. However, a graph in the real-world usually requires complex communication iterations to achieve good analysis results, which often have the problems of data and communication tilting and low analysis efficiency. In this paper, we propose a two-stage graph computing model with communication equilibrium (TSMCE). The model employs a communication-equilibrated graph partitioning strategy (CEGP) for load balancing and a two-stage graph computing mode (TS) to reduce the crossing-partition communication. With the change of graph density, various factors such as load balancing, communication balancing, cross-partition transmission traffic, communication delay, and convergence speed can always maintain an efficient balance. The experiments conducted on the real-world datasets show that the communication-equilibrated graph partitioning strategy can divide a graph with high quality compared with Hash and Metis. Besides, the overall performance of our two-stage graph computing model with communication equilibrium is higher than that of the BSP model. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Multiview Subgraph Neural networks: Self-Supervised Learning With Scarce Labeled Data

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IEEE Transactions on Neural networks and Learning Systems 2024年第6期36卷 11548-11561页

作者： Zhenzhong Wang Qingyuan Zeng Wanyu Lin Min Jiang Kay Chen Tan Department of Computing The Hong Kong Polytechnic University Hong Kong SAR China Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Xiamen Fujian China Department of Computing Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong SAR China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong SAR China

While graph neural networks (GNNs) have become the de facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples. This assumption restricts the classification performance of prevailing GNNs on many real-world applications suffering from low-data regimes. Specifically, features extracted from scarce labeled nodes could not provide sufficient supervision for the unlabeled samples, leading to severe overfitting. We point out that leveraging subgraphs to capture long-range dependencies can augment the node representation, thus alleviating the low-data regime. To this end, we present a novel self-supervised learning (SSL) framework, called multiview subgraph neural networks (Muse), for handling the long-range dependencies. In particular, we propose an information theory-based identification mechanism to identify two types of subgraphs from the views of input space and latent space, respectively. The former is to capture the local structure of the graph, while the latter captures the long-range dependencies among nodes. By fusing these two views of subgraphs, the learned representations can preserve the topological properties of the graph at large, including the local structure and long-range dependencies, thus maximizing their expressiveness. Theoretically, we provide the generalization error bound to show the effectiveness of capturing complementary information from multiview subgraphs. Empirically, we show a proof-of-concept of Muse on canonical node classification problems on graph data.

关键词： Manifolds Semantics Feature extraction Laplace equations Data mining Sugar Social networking (online) Skeleton Manifold learning Graph neural networks

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Interference Coordination Technology of Ultra Dense Deployment Based on Clustering Algorithm

Interference Coordination Technology of Ultra Dense Deployme...

引用

IEEE International Conference on Power, intelligent computing and Systems (ICPICS)

作者： Haoyuan Wang Yue Tian Wenda Li Fujian Key Laboratory of Communication Network and Information Processing Xiamen University of Technology Xiamen China Department of Security and Crime Science University College London London United Kingdom

A clustering algorithm based Ultra Dense Deployment is proposed to address the problem of establishing a reasonable interference model and allocating ABS reasonably to minimize interference and maximize throughput in Ultra Dense Deployment. By analyzing the distribution pattern of low-power nodes in Ultra Dense Deployment, the interference generated by the cluster center is used to replace the interference of other nodes in the class. According to the principle of interference minimization, a distributed allocation scheme for ABS time slots based on K-means clustering is proposed, and a polling algorithm is used for user scheduling. Simulation results show that compared with the generalized interference model and traditional binary models, the interference calculation is more accurate, The throughput of the system is also higher.

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