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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Ye, Enjie Guo, Kun Guo, Wenzhong Chen, Dangrun Zhang, Zihan Li, Fuan Zheng, JiaChen Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923847

Federated graph learning has been widely used in distributed graph machine learning tasks. The data distribution of existing graph-based federated Spatio-temporal prediction methods is mainly segmented by graph topology. However, in the real-world Spatio-temporal traffic speed prediction task, a location will have data from different devices belonging to different companies. A node may have multi-party information in the real-world distributed traffic speed prediction scenario. The difference in multi-party information leads to the information not being fully utilized. Moreover, the direct transmission of node embedding in the federated learning process may also risk privacy leaks. Using homomorphic encryption and other encryption methods will bring a high computational overhead. Therefore we propose a new distributed privacy-preserving traffic speed prediction algorithm, which uses secure node attribute aggregation strategy(SNAAS) to apply to the multi-party collaborative traffic speed prediction scenario when the graph topology structure is public. At the same time, secret sharing technology is used in SNAAS to protect the attribute matrix and reduce the overhead of secret computing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Globally Consistent Vertical Federated Graph Autoencoder for Privacy-Preserving Community Detection 17th

Globally Consistent Vertical Federated Graph Autoencoder for...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Fang, Yutong Huang, Qingqing Ye, Enjie Guo, Wenzhong Guo, Kun Chen, Xiaoqi Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Community detection is a trendy area in research on complex network analysis and has a wide range of real-world applications, like advertising. As people become increasingly concerned about privacy, protecting participants’ privacy in distributed community detection has become a new challenge. When applied to not identically and independently distributed (Non-IID) data, most federated graph algorithms suffer from the weight divergence problem caused by diversified training of local and global models, resulting in accuracy degradation. Furthermore, the privacy protection approaches based on anonymization, such as differential privacy (DP), and cryptography, such as homomorphic encryption (HE), incur accuracy loss and high time consumption, respectively. In this paper, we propose a globally consistent vertical federated graph autoencoder (GCVFGAE) algorithm, which builds a globally consistent model among the coordinator and all participants to solve the Non-IID graph data problem. As well, an attribute blinding strategy based on security aggregation is developed to protect the network privacy of each participant without losing accuracy. Both real-world and artificial networks’ experiments show that our algorithm reaches higher accuracy than the existing vertical federated graph neural networks (GNNs) and the simple distributed graph autoencoder without federated learning and detects communities identical to those found by the standard graph autoencoder (GAE). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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network Traffic Prediction with Attention-based Spatial-Temporal Graph network

Network Traffic Prediction with Attention-based Spatial-Temp...

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IEEE Workshop on High Performance Switching and Routing

作者： Yufei Peng Yingya Guo Run Hao Junda Lin Department of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University

network traffic prediction plays a significant role in network management. Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. Therefore, the prediction accuracy is limited, especially in long-term prediction. To improve the prediction accuracy of the dynamic network traffic in the long term, we propose an Attention-based Spatial-Temporal Graph network (ASTGN) model for network traffic prediction to better capture both the temporal and spatial relations between the network traffic. Specifically, in ASTGN, we exploit an encoder-decoder architecture, where the encoder encodes the input network traffic and the decoder outputs the predicted network traffic sequences, integrating the temporal and spatial information of the network traffic data through the Spatio-Temporal Embedding module. The experimental results demonstrate the superiority of our proposed method ASTGN in long-term prediction.

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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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Research on the Security Defense Strategy of Smart City's Substitution Computer network in Big Data 5

Research on the Security Defense Strategy of Smart City's Su...

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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Ye, Yuguang Quanzhou Normal University fujian Quanzhou362000 China Fujian Provincial Key Laboratory of Data Intensive Computing fujian Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University fujian Quanzhou362000 China

ISBN: (纸本)9781665435246

With the rapid development of the information technology era, the era of big data has also arrived. While computer networks are promoting the prosperity and development of society, their applications have become more extensive and in-depth. Smart city video surveillance systems have entered an era of networked surveillance and business integration. The problems are also endless. This article discusses computer network security in the era of big data, hoping to help strengthen the security of computer networks in our country. This paper studies the computer network security prevention strategies of smart cities in the era of big data. © 2021 IEEE.

关键词： Big data

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Adaptive-Propagating Heterophilous Graph Convolutional network

SSRN

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SSRN 2024年

作者： Huang, Yang Pi, Yueyang Shi, Yiqing Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China College of Photonic and Electronic Engineering Fujian Normal University Fuzhou350007 China

Graph convolutional network have significant advantages in tasks dealing with graph-structured data, but most existing approaches based on graph convolutional network usually potentially assume that nodes belonging to the same class in a graph tend to form edges, yet inter-class edges exist in many real graph-structured data. Due to the propagation mechanism of graph convolutional network, it is usually difficult to avoid the aggregation of interference information of nodes from different classes, leading to the inclusion of noise and irrelevant information in the results, thus affecting the performance of the model. We propose a new framework to address this issue, which can extend graph convolutional network to heterophilous graph-structured data. The proposed method has two main parts: Firstly, from the perspective of feature space, the heterophily of the graph-structured data is modeled so that the model can adaptively change the information propagation process according to the homophily of the edges, and mitigate the influence of inter-class information on the model. Secondly, the implicit node interaction information is captured through the feature space, which is then fused with the original interaction information to aggregate sufficient intra-class information. Extensive experiments on real-world datasets demonstrate the competitiveness of our approach among current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Graphic methods

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OpenViewer: Openness-Aware Multi-View Learning

arXiv

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arXiv 2024年

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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Computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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Computation Offloading Algorithm Based on Deep Reinforcement Learning and Multi-Task Dependency for Edge computing 25th

Computation Offloading Algorithm Based on Deep Reinforcemen...

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25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022

作者： Lin, Tengxiang Lin, Cheng-Kuan Chen, Zhen Cheng, Hongju College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

ISBN: (纸本)9789811995811

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, significantly reducing service latency. In this paper, we aim to divide the task into several sub-tasks through its inherent interrelation, guided by the idea of high concurrency for synchronization, and then offload sub-tasks to other edge servers so that they can be processed to minimize the cost. Furthermore, we propose a DRL-based Multi-Task Dependency Offloading Algorithm (MTDOA) to solve challenges caused by dependencies between sub-tasks and dynamic working scenes. Firstly, we model the Markov decision process as the task offloading decision. Then, we use the graph attention network to extract the dependency information of different tasks and combine Long Short-term Memory (LSTM) with Deep Q network (DQN) to deal with time-dependent problems. Finally, simulation experiments demonstrate that the proposed algorithm boasts good convergence ability and is superior to several other baseline algorithms, proving this algorithm’s effectiveness and reliability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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