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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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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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Gaussian Mixture Model Based Interest Prediction In Social networks

Gaussian Mixture Model Based Interest Prediction In Social N...

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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Dongyun An Xianghan Zheng Chunming Rong Tahar Kechadi ChongCheng Chen College of Mathematics and Computer Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Department of Electrical Engineering and Computer Science Stavanger Norway University College Dublin School of Computer Science and Informatics Ireland

In this paper, we investigate a typical clustering technology, namely, Gaussian mixture model (GMM)-based approach, for user interest prediction in social networks. The establishment of the model follows the following process: collect dataset from 4613 users and more than 16 million messages from Sina Weibo, obtain each user's interest eigenvalue sequence and establish GMM model to clustering users. In theory and experiment, this approach is feasible. The GMM-based approach considers the prediction accuracy and consuming time. A series of experiments are conducted to validate the feasibility and efficiency of the proposed solution and whether it can achieve a higher accuracy of prediction compared with other approaches, such as SVM and K-means. Further experiments show that GMM-based approach could produce higher prediction accuracy of 93.9%, thus leveraging computation complexity.

关键词： Predictive models Feature extraction Social network services Gaussian mixture model Eigenvalues and eigenfunctions Clustering algorithms

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Distributed Traffic Engineering in Hybrid Software Defined networks: A Multi-agent Reinforcement Learning Framework

arXiv

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arXiv 2023年

作者： Guo, Yingya Tang, Qi Ma, Yulong Tian, Han Chen, Kai The College of Computer and Data Science Fuzhou University China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China The Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fujian 350003 China The Department of Computer Science & Engineering Hong Kong University of Science & Technology Hong Kong

Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal, we construct an interactive environment for training the routing agents that own partial link utilization observations. To optimize credit assignment of multi-agent, we introduce the difference reward assignment mechanism for encouraging agents to take better action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen. Copyright © 2023, The Authors. All rights reserved.

关键词： Global optimization

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SPESC: A Specification Language for Smart Contracts

SPESC: A Specification Language for Smart Contracts

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Xiao He Bohan Qin Yan Zhu Xing Chen Yi Liu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China National Computer Network Emergency Response Technical Team Coordination Center of China

ISBN: (纸本)9781538626672

The smart contract is an interdisciplinary concept that concerns business, finance, contract law and information technology. Designing and developing a smart contract may require the close cooperation of many experts coming from different fields. How to support such collaborative development is a challenging problem in blockchain-oriented software engineering. This paper proposes SPESC, a specification language for smart contracts, which can define the specification of a smart contract for the purpose of collaborative design. SPESC can specify a smart contract in a similar form to real-world contracts using a natural-language-like grammar, in which the obligations and rights of parties and the transaction rules of cryptocurrencies are clearly defined. The preliminary study results demonstrated that SPESC can be easily learned and understood by both IT and non-IT users and thus has greater potential to facilitate collaborative smart contract development.

关键词： Contracts Cryptography Contract law Syntactics Collaboration Software

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Model-Driven Approach to Hadoop Deployment in Cloud

Model-Driven Approach to Hadoop Deployment in Cloud

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IEEE International Conference on Mobile Cloud computing, Services, and Engineering (MobileCloud)

作者： Zheyi Chen Tao Xiang Xing Chen Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and Technology (TNLIST) Beijing China College of Mathematics and Computer Science Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou China

Due to the diversification of software and hardware resources in cloud and ever-changing demand for deployment, Hadoop deployment is faced with great challenges in difficulty and complexity. Most of present researches lie in environment configuration and parameter setting of deployment, they do not take into account diversification of infrastructure and scalability issues in cloud. In order to quickly customize, deploy and expand Hadoop services according to the demand, a model-driven approach to Hadoop deployment is proposed in the paper. Firstly, Hadoop demand and deployment models are presented. Secondly, the transformation method from the demand model to the deployment model is proposed. Thirdly, the bidirectional synchronization between Hadoop deployment model and running system is realized based on runtime model.

关键词： Computational modeling Runtime Cloud computing Synchronization Virtual machining

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Hypergraph optimization for multi-structural geometric model fitting

arXiv

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arXiv 2020年

作者： Lin, Shuyuan Xiao, Guobao Yan, Yan Suter, David Wang, Hanzi Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University China School of Science Edith Cowan University Australia

Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points. However, a hypergraph becomes extremely complicated when the input data include a large number of data points (usually contaminated with noises and outliers), which will significantly increase the computational burden. In order to overcome the above problem, we propose a novel hypergraph optimization based model fitting (HOMF) method to construct a simple but effective hypergraph. Specifically, HOMF includes two main parts: an adaptive inlier estimation algorithm for vertex optimization and an iterative hyperedge optimization algorithm for hyperedge optimization. The proposed method is highly efficient, and it can obtain accurate model fitting results within a few iterations. Moreover, HOMF can then directly apply spectral clustering, to achieve good fitting performance. Extensive experimental results show that HOMF outperforms several state-of-the-art model fitting methods on both synthetic data and real images, especially in sampling efficiency and in handling data with severe outliers. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Learning Common and Label-Specific Features for Label Distribution Learning with Correlation information

SSRN

引用

SSRN 2024年

作者： Wu, Yilin Lin, Yaojin 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 Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou363000 China

Label distribution learning quantifies the label space for each instance and has wide applicability in different fields. However, most existing work primarily focuses on common features, ignores the significance of label-specific features. Meanwhile, label correlation is well used in these work, but they still have limitations in capturing instance correlation. Therefore, in this paper, we propose a novel approach for label distribution learning to learn common and label-specific features with correlation information i.e. instance correlation and label correlation. First, we conduct $\ell_{2,1}-$norm regularization to select common and label-specific features, simultaneously. Then, we implement an optimization function to identify instance correlation in feature space. In addition, label correlation is collected with Pearson correlation coefficient. Finally, we design an objective function that enables the joint learning of common and label-specific features while leveraging correlation information. Comprehensive experiments manifest the superiority of our proposed approach against other well-established label distribution learning algorithms. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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DDoS Detection and Defense Based on FLAD and SDN

DDoS Detection and Defense Based on FLAD and SDN

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jie Dong Wenyu Fang Wanling Zheng Jinkun Liu Yanhua Liu College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm.

关键词： Training Data privacy network topology Denial-of-service attack Feature extraction Real-time systems Topology Labeling Computer crime Long short term memory

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Adaptive Multi-Channel Contrastive Graph Convolutional network with Graph and Feature Fusion

SSRN

引用

SSRN 2023年

作者： Zhong, Luying Lu, Jielong Chen, Zhaoliang Song, Na Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Mechanical Electrical and Information Engineering Putian University Putian351100 China

Multi-view semi-supervised classification is an attractive topic in real-world applications. Due to the powerful capability of gathering information from neighbors, Graph Convolutional network (GCN) has become a hotspot in the classification task. However, most multi-view classification works based on GCN only assign weights for feature fusion, and directly consider the weighted sum of the adjacency matrices, ignoring the interaction and correlation among features. These may be problematic because aggregating the matrices from less relevant views may destroy the original topology space, leading to undesired performance. To address these issues, this paper proposes an Adaptive Multi-Channel Graph Convolutional network (AMC-GCN). To extract the interactive information, AMC-GCN designs a deep interactive feature integration network to incorporate consensus and complementary information. To fuse the graph structures, AMC-GCN exploits the relevance between views and imposes an adjacency matrix fusion network to construct multiple GCN channels, thereby delivering valuable information from relevant graphs. To enhance the homogeneity of the framework, AMC-GCN applies a contrastive loss to joint learning during the optimization for classification. With these techniques, AMC-GCN exploits relevant and interactive information between views to promote graph and feature fusion. Extensive experimental results on real-world datasets demonstrate the superiority of the proposed algorithm. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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