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International Conference on Networking and Network Applications (NaNA)

作者： Changbing Bi Youliang Tian Ta Li Shihong Wu Hua Zhou State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang China Guizhou Province Key Laboratory of Cryptography and Block Chain Technology Guizhou University Guiyang China Institute of Cryptography & Data Security Guizhou University Guiyang China College of Big Data and Information Engineering Guizhou University Guiyang China

Vehicle ad-hoc networks (VANETs) play a critical role in providing security and privacy protection for intelligent transportation systems. However, the limited network bandwidth and computing capacity within VANETs have resulted in existing authentication schemes having excessive computational overhead, which cannot meet the low-latency communication requirements of VANETs. To tackle these problems, we propose an efficient blockchain-based certificateless anonymous authentication scheme for VANETs. Firstly, we propose a novel signature scheme without bilinear pairing to reduce the computational overhead in VANETs. Secondly, we utilize blockchain to facilitate trusted information sharing. Thirdly, we provide privacy-preserving through the use of anonymous keys, and enable tracing of vehicle identities in the event of malicious behavior. Finally, security analysis and simulation experiments demonstrate that our proposed scheme has lower communication and computational overhead, while satisfying various security requirements for VANETs, such as anonymity, unlinkability, and traceability.

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Helmet wearing detection method based on lightweight YOLOv5 2

Helmet wearing detection method based on lightweight YOLOv5

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2nd International Conference on Electronic Information Technology, EIT 2023

作者： Wu, Chao Wang, De-Yong Shi, Wen-Xi Fang, Jian Zhao, Xueyi Fu, Yan-Yun School of Information Science and Engineering Xinjiang University Urumqi830000 China XinJiang Lianhai INA-INT Information Technology Co. Ltd Urumqi830000 China Key Laboratory of Big Data of Xinjiang Social Security Risk Prevention and Control Urumqi830000 China Beijing Academy of Science and Technology Beijing100035 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

A lightweight detection algorithm based on YOLOv5 is proposed to solve the problems that the target detection algorithm has many network parameters and the large size of the model is not conducive to the actual deployment. Firstly, the depth separable convolution was introduced to reconstruct the backbone network to reduce the network parameters. Then, the residual element was redesigned by ghost convolution and integrated into the Neck end to further reduce the model volume. Finally, the detection accuracy and recognition effect of the algorithm were improved by modifying the detection layer. When the recognition accuracy of the improved lightweight algorithm is reduced by 3.4%, the model volume is reduced by 75%, and the number of network parameters is reduced by 81%. The results show that the algorithm is able to maintain a high recognition accuracy, and the volume and parameters are greatly reduced. It is suitable for embedded and other system resource limited scenarios, and has a practical application prospect. © 2023 SPIE.

关键词： Convolution

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Zero-Order Optimization in Security Constrained Power System Scheduling 4

Zero-Order Optimization in Security Constrained Power System...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： An, Qi Wang, Jianxiao Wu, Shengyu Ren, Weikai Huang, Ruanming Li, Gengyin North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing China Peking University National Engineering Laboratory for Big Data Analysis and Applications Beijing China State Grid Energy Research Institute CO. LTD. Beijing China Institute of Energy Peking University Beijing China Planning Center State Grid Shanghai Electric Power Company Shanghai China

ISBN: (纸本)9798350395914

The security and resilience of power systems are gradually challenged. As a typical index measuring the security and resilience of power systems, the frequency stability issue is becoming prominent. Due to the increase in the complexity of power systems, the frequency stability boundary is gradually becoming fuzzy, leading to traditional analytical-modeling algorithms hardly applicable for frequency stability constrained dispatch. Existing data-driven methods for characterizing frequency stability boundary typically requires massive simulation calculations, leading to unnecessary computing power consumption. To this end, zeroth-order (ZO) optimization, which aims to solve optimization problems embedding non-analytic functions, is adopted for solving the frequency stability constrained dispatch in this paper. In contrast to traditional data-driven methods, we directly embed frequency stability simulation check into power system dispatch. The gradient of frequency stability simulation check is estimated through ZO optimization, thereby realizing the interaction optimization between power system dispatch and real-time simulation. In this way, the solution for the frequency stability constrained dispatch is modified towards the optimal point along the gradient descent direction. Cases studies based on IEEE-9 bus system verifies that, compared with traditional data-driven methods, the solution approach we propose could reduce the system cost while significantly improving the calculation efficiency. © 2024 IEEE.

关键词： Frequency stability

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Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models 30

Step-by-Step: Controlling Arbitrary Style in Text with Large...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Pusheng Wu, Lianwei Wang, Linyong Guo, Sensen Liu, Yang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China Research & Development Institute of Northwestern Polytechnical University Shenzhen China Chongqing Science & Technology Innovation Center of Northwestern Polytechnical University China State Key Laboratory of Integrated Service Networks Xidian University China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China

ISBN: (纸本)9782493814104

Recently, the autoregressive framework based on large language models (LLMs) has achieved excellent performance in controlling the generated text to adhere to the required style. These methods guide LLMs through prompt learning to generate target text in an autoregressive manner. However, this manner possesses lower controllability and suffers from the challenge of accumulating errors, where early prediction inaccuracies might influence subsequent word generation. Furthermore, existing prompt-based methods overlook specific region editing, resulting in a deficiency of localized control over input text. To overcome these challenges, we propose a novel three-stage prompt-based approach for specific region editing. To alleviate the issue of accumulating errors, we transform the text style transfer task into a text infilling task, guiding the LLMs to modify only a small portion of text within the editing region to achieve style transfer, thus reducing the number of autoregressive iterations. To achieve an effective specific editing region, we adopt both prompt-based and word frequency-based strategies for region selection, subsequently employing a discriminator to validate the efficacy of the selected region. Experiments conducted on several publicly competitive datasets for text style transfer task confirm that our proposed approach achieves state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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MULTIPLE HEADS ARE BETTER THAN ONE: MIXTURE OF MODALITY knowledge EXPERTS FOR ENTITY REPRESENTATION LEARNING

arXiv

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arXiv 2024年

作者： Zhang, Yichi Chen, Zhuo Guo, Lingbing Xu, Yajing Hu, Binbin Liu, Ziqi Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC). The main challenge is to collaboratively model the structural information concealed in massive triples and the multi-modal features of the entities. Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies, yet they overlook the utilization of multi-perspective features concealed within the modalities under diverse relational contexts. To address this issue, we introduce a novel framework with Mixture of Modality knowledge experts (MOMOK for short) to learn adaptive multi-modal entity representations for better MMKGC. We design relation-guided modality knowledge experts to acquire relation-aware modality embeddings and integrate the predictions from multi-modalities to achieve joint decisions. Additionally, we disentangle the experts by minimizing their mutual information. Experiments on four public MMKG benchmarks demonstrate the outstanding performance of MOMOK under complex scenarios. Our code and data are available at https://***/zjukg/MoMoK. Copyright © 2024, The Authors. All rights reserved.

关键词： Modal analysis

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A Novel DDoS Detection Model for SDN Using Single-Class Cluster Oversampling and Weighted Ensemble Method

A Novel DDoS Detection Model for SDN Using Single-Class Clus...

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International Conference on Network Protocols

作者： Hao Zhang Shuqi Wu Jie Pan Zhi Wang Xiaolong Sun 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 Chinese Ministry of Education Fuzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The centralized control plane characteristic of Software Defined Networking (SDN) makes it a prime target for Distributed Denial of Service (DDoS) attacks. A significant issue in network traffic data is the severe imbalance between background traffic and various types of DDoS attack traffic. To address this challenge, we propose a novel DDoS detection model based on single-class cluster oversampling and weighted ensemble method. Initially, we design a multi-strategy feature selection using variance inflation factor and recursive feature elimination. Next, we construct a single-class cluster model based on the spatial distribution of samples, and create minority class samples independently within each cluster. Finally, detection results are determined using the weight vector of each base classifier in ensemble learning. The proposed DDoS detection model's performance is validated through simulations of various DDoS attack scenarios in an SDN environment. Experimental results indicate that the proposed strategy exhibits superior performance, showing improvements in precision, recall, and F1 score compared to state-of-the-art techniques.

关键词： Interpolation Protocols Telecommunication traffic Denial-of-service attack Feature extraction Vectors data models Ensemble learning Computer crime Software defined networking

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Model Predictive Control for Air Source Heat Pump Systems

SSRN

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SSRN 2024年

作者： Zhao, Ruochen Wei, Dong Jia, Yuchen Xu, Rongji Gao, Qiang Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing China Beijing University of Civil Engineering and Architecture School of Environment and Energy Engineering Beijing China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China

Air source heat pump (ASHP) systems exhibit non-linearity with significant inertia, posing challenges to ensure its operational robustness and enhance energy efficiency. This study proposed a non-linear model predictive control (MPC) strategy for an ASHP water heating system. The non-linear modeling methodology, based on thermodynamics and fluid dynamics, was developed. The sequential quadratic programming was employed to handle the non-convex optimization problem for receding horizon optimization. Furthermore, a sensor-less speed control approach was designed to address the issue of the absence of Hall sensors within the DC compressor. Experimental results demonstrated that the proposed strategy reduced heating time by 14.9% and 15.7% compared to PID and fuzzy PID control approaches, diminishing energy consumption by 14.6% and 9.1%, respectively. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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UGCM-LU: A Unified Stream and Batch Graph Computing Model with Local Update for Community Detection 19th

UGCM-LU: A Unified Stream and Batch Graph Computing Model w...

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19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Li, Hong Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Unified stream and batch computing (USBC) aims to incorporate stream and batch computation into a unified framework, thereby enabling the development of a one-stop solution for stream and batch data processing and enhancing the generalization of the framework. However, research on unified graph computing models (UGCMs) faces several challenges. First, existing UGCMs need to consider all graph information in the cache during the incremental update phase, thus leading to decreased execution efficiency. Second, existing UGCMs use fixed bytes to store nodes without considering the actual space occupied by nodes resulting in wasted memory when dealing with large graphs. This paper proposes a UGCM with Local Updates for community detection (UGCM-LU). We first implement a local update strategy to consider partial information of the graph to achieve incremental updates. Secondly, we also designed a byte-compression-based module to store graph data according to the space occupied by nodes. The experimental results show the effectiveness and efficiency of the model in real-world and artificial networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lutetium alloys

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Network Security Situation Prediction Implemented by Attention and BiLSTM

Network Security Situation Prediction Implemented by Attenti...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Zhao, Dongmei Wu, Yaxing Li, Qingru Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Shijiazhuang China Hebei Normal University College of Computer and Cyberspce Security Shijiazhuang China Hebei Normal University Hebei Supply Chain Big Data Analysis Data Security Engineering Research Center Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

With the increasing diversification and complexity of network security attacks, it is becoming more and more difficult to predict the network situation. In order to improve the effect of situation prediction, this paper constructs a network security situation prediction model for a Improved Particle Swarm Optimization and Attention fusion Bidirectional Long Short-Term Memory (IPSO-ABiLSTM). First, there is no real situation value for the UNSW-NB15 data set, and a situation value is generated based on the impact of the attack. Secondly, the particle swarm algorithm is improved. The IPSO algorithm makes the algorithm's global and local search capabilities more balanced and faster to converge. Finally, optimizing the hyperparameters of the BiLSTM network fused with the attention mechanism to obtain the final model, and combined with PSO-BiLSTM network, PSO-LSTM network, BiLSTM model for performance comparison. The experimental results show that the IPSO-ABiLSTM in this paper has a fitting degree of up to 0.9922, and the error value is relatively smaller, which verifies the effectiveness of the model proposed in this paper in the network security situation prediction problem. © 2022 IEEE.

关键词： Long short-term memory

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D-FGNAE: Decentralized Federated Graph Normalized AutoEncoder 19th

D-FGNAE: Decentralized Federated Graph Normalized AutoEncode...

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19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Liang, Yuting Cai, Weixin Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Graphs widely exist in real-world, and Graph Neural Networks (GNNs) have exhibited exceptional efficacy in graph learning in diverse fields. With the strengthening of data privacy protection worldwide in recent years, Federated graph neural networks (FedGNNs) have gained increasing attention in academia and industry owing to their ability to train the model in a collaborative manner while complying with the privacy protection regulations. However, in federated learning, the non-independent and identically distributed (non-IID) problem of local data possessed by multiple participants can significantly undermine model accuracy. We propose a new Decentralized Federated Graph Normalized AutoEncoder (D-FGNAE). First, the model is designed as a decentralized federated learning framework with dynamically assigned tripartite roles. This design eliminates the fixed server role found in traditional federated learning, enhances system fault tolerance, avoids single points of failure, and protects model privacy. Second, the splitting and correcting of calculation by layer in the model, along with the special design of the normalization layer, effectively tackle the non-IID problem in both the structural and attribute aspects. Experimental results on real-world networks demonstrate the effectiveness of D-FGNAE, which can achieve nearly the same accuracy as the centralized model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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