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检索条件"任意字段=2023 International Conference on Computer Network Security and Software Engineering, CNSSE 2023"

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A Hybrid WOA-MTCNN Algorithm for Accurate Face Detection Based on IoT Architecture 9th

A Hybrid WOA-MTCNN Algorithm for Accurate Face Detection Bas...

引用

9th international conference on Intelligent IoT as a Service

作者： Hu, Xi Ruan, Hang Xiong, Xin Zhang, Siqi Huang, Ning Wang, Jun Jianghan Univ State Key Lab Precis Blasting Wuhan 430056 Peoples R China Jianghan Univ Sch Artificial Intelligence Wuhan 430056 Peoples R China Jianghan Univ Artificial Intelligence Inst Wuhan 430056 Peoples R China Dongfeng Motor Corp Tech Ctr Wuhan 430058 Peoples R China

ISBN: (纸本)9783031705069;9783031705076

To further improve the detection accuracy in face detection based on IoT architecture, this paper designs and implements a hybrid face detection system based on the MTCNN model, and optimize the MTCNN model by combining Whale Optimization Algorithm (WOA). Firstly, we select and train the loss function weights and batch_size of MTCNN to estimate the model. Secondly, we utilize the WOA algorithm to further optimize the weights and batch_size parameters. Thirdly, we train and simulate the prediction by applying the obtained optimal parameters. Finally, we design and implement a face detection software based on the MTCNN model to test the practical application capabilities of the model.

关键词： Convolutional Neural network (CNN) Deep learning Face detection Image pyramid Whale Optimization Algorithm (WOA)

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Demystifying Blockchain Scalability: Sibling Chains with Minimal Interleaving 19th

Demystifying Blockchain Scalability: Sibling Chains with Min...

引用

19th international conference on security and Privacy in Communication networks

作者： Ma, Jiangfeng Zhang, Xuetao Li, Xiangxue East China Normal Univ Shanghai Key Lab Trustworthy Comp Sch Software Engn Shanghai 200062 Peoples R China Shanghai Key Lab Trusted Data Circulat & Governan Shanghai 200240 Peoples R China

ISBN: (纸本)9783031649479;9783031649486

Blockchain provides alluring infrastructure for distributed ledgers supporting anonymous online payments. However, existing solutions for blockchain scalability have limitations of either being increasingly cumbersome in security analysis or inherent deficiencies (e.g., surviving on duplicate transactions). Moreover, current state-of-the-art scalable blockchains suffer from low throughput when used for larger transaction blockchains. To improve scalability, we propose sibcha, a novel protocol that equipped with k (power of 2) parallel sibling chains that correspond to k transaction pools (indexed by the rightmost log(2) k bits of transaction payers' addresses). In the protocol, i-th transaction (along with a Merkle tree path) would be announced to the i-th chain based on the rightmost log(2) k bits of the hashing determined in solving proof-ofwork (PoW) puzzle (i is the exact value in decimal format represented by the log2 k bits). To achieve parallel transactions, we design a interchain mechanism without other correlations (such as block ordering, inter-chain transactions, block updates, eventual atomicity decoupling, two-phase PoW puzzle solving, etc.), which makes sibcha considerably simpler than current state-of-the-art solutions (e.g., OHIE at IEEE S&P 2020 and Monoxide at USENIX security 2019). SibCha has much less (e.g., 1.86x similar to 3.16x) confirmation latency than OHIE. Prototype implementations also demonstrate that its throughput scales linearly with available bandwidth (1.5x that of Conflux).

关键词： Scalability

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Unsupervised and Adaptive Tor Website Fingerprinting 19th

Unsupervised and Adaptive Tor Website Fingerprinting

引用

19th international conference on security and Privacy in Communication networks

作者： Zhang, Guoqiang Cao, Jiahao Xu, Mingwei Deng, Xinhao Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace Beijing Peoples R China

ISBN: (纸本)9783031649530;9783031649547

Over the past few years, deep-learning based approaches for Tor website fingerprinting have experienced a significant breakthrough in prediction accuracy. However, many of these approaches suppose that their training and testing datasets share similar distributions, i.e. they belong to the same domain. Unfortunately, this assumption is unrealistic since Tor users' distinctive environmental settings have exerted diverse influence on website trace generation. Although several recent methods attempt to address this problem by utilizing transfer learning techniques, they assume that the adversary has some of the trace labels for each website class in the testing dataset, which is typically irrational in real-world scenarios. In this paper, we propose a novel Tor website fingerprinting framework called Unsupervised and Adaptive Tor Website Fingerprinting (UAF), which minimizes the distribution discrepancies between the training (denoted as the source domain) and testing (denoted as the target domain) datasets by training a "domain-invariant" feature extractor in an unsupervised manner. UAF employs three trace representations on raw Tor traffic to retain discriminative information for classification and combines multiple source-specific classifiers based on their trace length distributions. The experimental results show that UAF outperforms multiple state-of-the-art Tor website fingerprinting approaches in identifying shifted and unlabeled target domains.

关键词： Tor Multi-source domain adaptation Website fingerprinting Unsupervised Transfer learning

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Uncertainty-aware Correspondence Distillation for Deep Image Clustering

Uncertainty-aware Correspondence Distillation for Deep Image...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chang, Luyao Chen, Leiting Zhou, Chuan School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Image clustering is a challenging task in computer vision, with performance heavily dependent on the quality of feature representations due to the inherent complexity of images. However, current image clustering methods overlook the underlying semantic information during representation learning, leading to low-quality feature representations. Moreover, the absence of ground-truth labels amplifies the detrimental effects of unreliable data on semantic guidance, steering the model towards incorrect learning directions. In this work, we propose a novel deep image clustering method named Uncertainty-aware Correspondence Distillation (UCD) to address these issues. Specifically, we introduce the concept of representation correspondence to establish cross-level connections between instances and semantics, which is further employed as a distillation target to improve the network's feature learning by complementing semantic information. To mitigate unnecessary similarity penalties arising from unreliable data, we develop robust dynamic weights for semantic guidance by modeling the uncertainty of image semantics. Extensive experiments on five benchmark datasets demonstrate the superiority of the proposed method. The code is available at https://***/YL616/UCD. © 2025 IEEE.

关键词： Image clustering Knowledge distillation Representation learning Semantic guidance

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Privacy-Preserving k-core Decomposition for Graphs 25th

Privacy-Preserving k-core Decomposition for Graphs

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Liu, Xuyang Zhao, Rong Feng, Bingwen Zhang, Jilian College of Cyber Security Jinan University Guangzhou China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University Guangzhou China

ISBN: (纸本)9789819605750

k-core decomposition is an important task in graph data processing in various fields, such as social network analysis, computational biology, medicine research, etc. In some applications involving massive graph processing, users usually outsource the graph data and k-core decomposition task on the graph, to cloud service providers, so as to alleviate the problem of limited computational resources. This solution, however, may pose threats to the privacy of the data and of the users, for example, leakage of node and edge information, and even the k value. In this paper, we propose a homomorphic encryption-based scheme called HEkc for k-core decomposition in the scenario of graph data outsourcing. We prove that the proposed scheme is secure under the semi-honest model, i.e., it can perform k-core decomposition correctly while preserving the privacy of the graph, the k value, and the computation result. In addition, we design two variants named HEkc-fast and HEkc-safe, to improve the efficiency of HEkc and to prevent it from the known-plaintext attack, respectively. Finally, we empirically evaluate the effectiveness of our proposed schemes through extensive experiments conducted on eight real datasets and the results show that our schemes for privacy-preserving k-core decomposition are promising. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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System Integration and software Platform engineering Implementation of Digital Twin Model for Ultra-high Voltage Converter Valve Side Bushing

System Integration and Software Platform Engineering Impleme...

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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhang, Shiling He, Yongsheng Dai, Liangjun Wang, Pengbo Deng, Baojia State Grid Chongqing Electric Power Company Electric Power Research Institute Yubei District Chongqing401123 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Compared with simulation technology, the prominent feature of digital twin technology is its complex interaction ability with the external information, which enables the digital twin function of the physical objects through complex interaction. Therefore, a complete digital twin system is formed, and the digital twin model can truly and effectively play its role. To achieve this, it is necessary to establish data interfaces that effectively connect with external information, physical field calculations, the visual displays, etc., to realize the system integration of digital twin models and verify their engineering feasibility. This article analyzes the functional requirements of the system integration, designs its functional framework, constructs functional modules of physical hardware layer, twin model layer, and human-computer interaction layer, and realizes the integration of valve side bushing digital twin model system with data exchange capability, data transmission capability, and coordination between various functional modules. Among generated parameters, E-field strength inside 220kV reduced ratio bushing is controlled within 3kV/mm, and the composite insulator umbrella skirt is installed outside the core. Total length of the bushing is 2465mm, the length of the bushing tail is 810mm, and the length of the hollow composite insulator is 1250mm. © 2025 SPIE.

关键词： Bushings

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Practical Privacy-Preserving Community Detection in Decentralized Weighted networks 19th

Practical Privacy-Preserving Community Detection in Decentra...

引用

19th international conference on security and Privacy in Communication networks

作者： Han, Tingxuan Tong, Wei Niu, Jiacheng Zhong, Sheng Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9783031649530;9783031649547

As one of the essential graph analysis tasks, community detection plays a crucial role in various applications. Since a graph can be viewed as a collection of users' personal information and the relationships among them, it is essential to protect individuals' privacy during the process of community detection. Weighted networks are networks with real-valued weights on edges, and the edge weights should be protected when performing community detection in weighted networks. In this paper, we study the privacy-preserving community detection problem for weighted networks where each node is a distributed user and there is no trusted third party. Directly applying homomorphic encryption to implement a secure version of community detection for the untrusted analyst will incur high communication and computational cost, while local perturbation methods, such as local differential privacy, may significantly degrade the accuracy. Therefore, we combine these two types of techniques and propose a practical privacy-preserving algorithm for community detection in weighted networks, by blending a tailored locally differentially private mechanism with a cryptographic component. The proposed method can provide provable privacy guarantees and satisfactory performance. Extensive experiments we conducted have demonstrated the accuracy and efficiency of the proposed algorithm in various cases.

关键词： Community detection Decentralized weighted networks Privacy-preserving Local differential privacy

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7th international conference on Wireless Communications, networking and Applications, WCNA 2023

7th International Conference on Wireless Communications, Net...

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7th international conference on Wireless Communications, networking and Applications, WCNA 2023

ISBN: (纸本)9789819624089

The proceedings contain 38 papers. The special focus in this conference is on Wireless Communications, networking and Applications. The topics include: EffiNet: An Efficient, Low-Complexity Neural network for Monocular Depth Estimation on Embedded Devices;adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection;a Cache Scheduling Method Based on Adaptive Expiration for Data Process System;Research on Airport Communication and Navigation Equipment Inspection Assistance System Based on AR Technology;application of Quality Function Deployment in Assessing Unmanned Assault Capabilities for Mountain Counter-Terrorism Operations;research on the Application of Internet of Things Technology in Power Grid Limited Space security Monitoring;research on the Application of computer Vision Based on Deep Learning in Autonomous Driving Technology;research on Cybersecurity System of Scientific Research Institutions Based on National Cybersecurity Classified Protection 2.0: Take the Chinese Academy of Agricultural Sciences as an Example;Research on DOA Estimation Method of Underwater Acoustic Signal Based on Machine Learning;Design and Implementation of Smart Home Control System Based on STM32;communication Anti-jamming System Based on Deep Reinforcement Learning;fault Diagnosis of Infrared Sensor Based on Convolutional Neural network;graph Neural network Knowledge Graph Recommendation Model Integrating Deep Domain Information and Important Domain Information;an Approach to Microenvironment-Based Particle Swarm Optimization Algorithm;a Verifiable Ciphertext Retrieval Scheme for Smart Grids;panoptic Semantic Mapping Method for Tomato Growing Environment Based on K-Net and OctoMap;An Adversarial Attack Method for Multivariate Time Series Classification Based on AdvGAN;recognition and Calculation of Fish Rafts in Mariculture on the Basis of Artificial Intelligence;digital Twin for Power Load Forecasting.

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15th international conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2023

15th International Conference on e-Infrastructure and e-Serv...

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15th international conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2023

ISBN: (纸本)9783031815720

The proceedings contain 44 papers. The special focus in this conference is on e-Infrastructure and e-Services for Developing Countries. The topics include: Digital Forensics Investigations: Major Challenges in Mobile and Cloud Forensics;a Cloud-Based Drones’ Model for Detection and Tracking of Stationary and Motion-Based Snakes in Farms in Marginalized Rural Areas: A Preliminary Study;A Domain Specific Language (DSL) for Agroecosystems Modelling and Simulation;Leveraging Conversational AI for Accelerating User-Driven software Testing;integration of Artificial Intelligence with Diabetic Data for Increasingly Personalized Medicine;Question Design Using NLP;Edge- AI and Internet of Things for Intelligent Systems: Architectures, Applications and Future Perspectives;comparative Study of Name Entity Recognition Models in Burkina Faso Context;PLAVIDA, an Annotation Tool for Audio and Video in African Languages;towards a Framework for the Preparation of High Quality Data for Use by Machine Learning Algorithms;CAOGen: An Automatic Ontology Constructor Based on Data Mining Techniques;artificial Intelligence for the Analysis of the security Situation in Burkina Faso;analysis, Design and Implementation of a Ripe Mango Detection Program in Burkina Faso;financial Fraud Detection Using Rich Mobile Money Transaction Datasets;culture Ontology to Enhance Social Cohesion;new Zero Watermarking Scheme Based on Hyper-catadioptric System Model and Hyperbolic Geometry;Cotton Disease Detection on UAV Images: A Deep Learning-Based Approach with YOLOv7.

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15th international conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2023

15th International Conference on e-Infrastructure and e-Serv...

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15th international conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2023

ISBN: (纸本)9783031815690

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