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检索条件"任意字段=International Symposium on Computer Networks and Distributed Systems"

共 63334 条记录，以下是71-80 订阅

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CLUES: Collusive Theft of Conditional Generative Adversarial networks 43

CLUES: Collusive Theft of Conditional Generative Adversarial...

引用

43rd international symposium on Reliable distributed systems

作者： Queyrut, Simon Schiavoni, Valerio Chen, Lydia Y. Felber, Pascal Birke, Robert Univ Neuchatel Inst Comp Sci IIUN Neuchatel Switzerland Univ Turin Turin Italy

ISBN: (纸本)9798331530044;9798331530037

Conditional Generative Adversarial networks (cGANs) are increasingly popular web-based synthesis services accessed through a query API, e.g., cGANs generate a cat image based on a "cat" query. However, cGAN-based synthesizers can be stolen via adversaries' queries, i.e., model thieves. The prevailing adversarial assumption is that thieves act independently: they query the deployed cGAN (i.e., the victim), and train a stolen cGAN using the images obtained from the victim. A popular anti-theft defense consists in throttling down the number of queries from any given user. We consider a more realistic adversarial scenario: model thieves collude to query the victim, and then train the stolen cGAN. CLUES is a new collusive model stealing framework, enabling thieves to bypass throttle-based defenses and steal cGANs more efficiently than through individual efforts. Thieves collect queried images and train a stolen cGAN in a federated manner. We evaluate CLUES on three image datasets, e.g., MNIST, FashionMNIST and CelebA. We experimentally show the scalability of the proposed attack strategies against the number of thieves and the queried images, the impact of a classical noise-based defense, a passive watermarking defense and a JPEG-based countermeasure. Our evaluation shows that such a collusive stealing strategy gets close to 4 units of Frechet Inception Distance from a victim model. Our code is readily available to the research community: https://***/records/10224340.

关键词： cGAN distributed stealing model stealing stealing

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Block Channel Analysis and Adaption Approach for distributed Blind Equalization over Sensor networks

Block Channel Analysis and Adaption Approach for Distributed...

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2024 international symposium on Intelligent Signal Processing and Communication systems, ISPACS 2024

作者： Chi, Sulin Shimamura, Tetsuya Otemon Gakuin University 2-1-15 Nishiai Osaka Ibaraki567-8502 Japan Graduate School of Science and Engineering Saitama University 255 Shimo-Okubo Saitama Sakura-ku338-8570 Japan

ISBN: (纸本)9798350389210

In the study of signal estimation, the performance of distributed blind equalization over wireless sensor network (WSN) is significantly affected by channel conditions. However, there has been few studies investigating the effect of channel conditions on distributed blind equalization over WSN. To address this problem, this paper considers that different channel and noise variance conditions for distributed blind equalization. Initially, a smaller step-size is used to mitigate the noise enhancement effect that is caused by a large step-size under ill-channel conditions. Then, a block channel analysis and adaption approach is proposed to analyze the performance of distributed blind equalization under current condition of channel. This approach determines whether to adjust a smaller step-size to a large value and re-designs the weight for each sensor by analyzing the corresponding channel conditions repeated over Q input data samples of equalizer. In the computer simulations, the average mean square error (MSE) is used to assess the performance of distributed blind equalization. Compared to the conventional method, the proposed method demonstrates improved performance for the distributed blind equalization. © 2024 IEEE.

关键词： Mean square error

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Real-Time Jamming Detection, Classification and Logging Using computer Vision in 5G Private networks 19

Real-Time Jamming Detection, Classification and Logging Usin...

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19th international symposium on Wireless Communication systems (ISWCS)

作者： Chitauro, Ronald Brzozowski, Marcin Yener, Onur Langendoerfer, Peter IHP Leibniz Inst Innovat Mikroelekt Technol Pk 25 D-15236 Frankfurt Germany

ISBN: (纸本)9798350362527;9798350362510

Cellular networks, renowned for their robustness and high availability, must consistently meet very stringent standards to ensure service provision and attract deployment in manufacturing industries that cannot tolerate any downtime. Despite the progressive introduction of numerous algorithms over the years across different layers of the 5G communication model to ensure packet delivery and decoding, jamming attacks have consistently disrupted connectivity. Detection and analysis of jamming attack characteristics, including their type and duration, provide comprehensive insights for security analysts to develop educated countermeasures. This paper demonstrates how computer vision can be employed on spectrograms to detect, classify, and log information on three types of jamming attacks in real-time within a 5G campus network. We evaluate the efficiency of our real-time jamming detector by analyzing detection latency, classification accuracy, and attack duration capture accuracy. Our results indicate that our Deep Learning classification model detects all tested jammers with remarkably high accuracy.

关键词： 5G Campus networks Jamming Detection Jamming Classification Deep Learning SDR computer Vision

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High-Performance Simulations for Urban Planning: Implementing Parallel distributed Multi-Agent systems in MATSim 23

High-Performance Simulations for Urban Planning: Implementin...

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23rd international symposium on Parallel and distributed Computing (ISPDC)

作者： Laudan, Janek Heinrich, Paul Nagel, Kai Tech Univ Berlin Transport Syst Planning Berlin Germany

ISBN: (纸本)9798350369205;9798350369199

As semiconductor design approaches physical limits, computer processing speeds are stagnating. This poses significant challenges for traffic simulations, which are becoming more and more computationally demanding. To maintain fast execution times while accommodating more complex simulations, it is essential to utilize the parallel computing capabilities of modern hardware. This paper discusses the need for an updated architectural design in the MATSim traffic simulation framework to take advantage of parallel computing infrastructures. We introduce a prototype that adapts the existing traffic simulation logic to a distributed parallel algorithm. Extensive benchmarks have been conducted to evaluate the prototype's performance and identify its limitations. The results demonstrate that the prototype performs up to 100 times faster than the current implementation. Based on these findings, we advocate for the integration of a distributed traffic simulation within the MATSim framework and outline necessary steps to enhance the prototype.

关键词： Mulit-Agent Transport Simulation distributed Computing MPI Parallel Computing

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Load Balancing in Large WiFi networks Using DQL-MultiMDP with Constrained Clustering 32

Load Balancing in Large WiFi Networks Using DQL-MultiMDP wit...

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32nd international Conference on Modeling, Analysis and Simulation of computer and Telecommunication systems

作者： Bellouch, Mohamed Zitoune, Lynda Lahsen-Cherif, Iyad Veque, Veronique Univ Paris Saclay Lab Signaux & Syst Cent Supelec CNRS F-91190 Gif Sur Yvette France INPT CS Dept Rabat Morocco

ISBN: (纸本)9798331531317;9798331531300

Developing efficient load-balancing techniques remains a persistent research challenge as modern WiFi networks evolve into increasingly complex environments, incorporating new enhancements in their standards. For instance, DQL-MultiMDP is a load-balancing algorithm that learns an optimal STA-to-AP association policy to ensure user fairness and optimize network performance in dense and dynamic WiFi networks. The algorithm leverages a Multi-Markov Decision Process (Multi-MDP) strategy to accommodate the fluctuating number of devices caused by their switching on/off. However, scalability challenges arise due to the exponential expansion of the action space. In this paper, we propose a divide-and-conquer approach that extends the algorithm to operate in extremely large deployments: a dynamic partitioning mechanism divides the network into clusters and assigns a sub-controller to manage the STA-to-AP association in each cluster independently, and a coordination mechanism enables them to exchange their training updates. Experimental investigations validate the effectiveness of the approach and motivate future work.

关键词： WiFi Load balancing Deep Q-Learning Network partitioning

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Federated Learning for V2X Communications: Opportunities and Challenges 19

Federated Learning for V2X Communications: Opportunities and...

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19th IEEE international symposium on Broadband Multimedia systems and Broadcasting (BMSB)

作者： Wang, Heng Qi, Wen Jia, Jing Xing, Yanxia China Telecommun Res Inst 6G Res Ctr Beijing Peoples R China

ISBN: (纸本)9798350364279;9798350364262

This paper delves into the potential of leveraging federated learning (FL) techniques to revolutionize vehicle-to-everything (V2X) wireless communications. We examine the significant opportunities that FL offers in the V2X domain, including distributed model training, privacy preservation and enhanced communication efficiency. Concurrently, we spotlight the formidable challenges associated with adapting FL to the unique characteristics of highly dynamic and heterogeneous vehicular networks. These challenges encompass Non Independent Identically Distribution (Non-IID) data distribution, ensuring model reliability and addressing scoring disparities among participating vehicles. To navigate this intricate landscape, we propose novel research directions, including hierarchical FL, incentive mechanisms and the development of robust training algorithms, aimed at surmounting these obstacles. This paper draws upon valuable insights gleaned from an initial analysis, along with simulation results that underscore the efficacy of tailored FL designs in unlocking the transformative potential of collective learning within vehicular networks, while simultaneously mitigating systemic limitations.

关键词： V2X Federated Learning Heterogeneous Vehicular networks Non-IID data distribution

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Active and reactive power collaborative loss reduction optimization strategy for active distribution networks 9

Active and reactive power collaborative loss reduction optim...

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9th international symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Liu, Wenan Kong, Xiangqing Lv, Dongfei Ma, Tao Song, Yanshan Zhang, Xingyong Zhang, Xinsheng Liu, Hongchen State Grid Shandong Electric Power Company Zibo Power Supply Company Shandong Zibo255000 China Department of Electrical Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China

ISBN: (纸本)9781510683303

The integration of distributed generators (DGs) poses great challenges to the topology, operation planning, control methods, and protection configuration of traditional distribution networks (DN). To solve the optimization control problem of large-scale multi-type DGs, energy storage systems (ESSs), and multi-type loads in DNs, this paper proposes a distributed active and reactive power optimization (ARPO) control method for active distribution networks (ADN). Firstly, the ADN is modeled, taking into account the reactive power (RP) support effects of DGs and ESSs inverters and the suppression of active power (AP) fluctuations in the ESSs, in addition to various grid operation constraints. Afterward, based on the established model, the improved Harris Eagle optimization algorithm (IHHOA) is used to optimize the AP and RP of the ADN to minimize economic efficiency (including operating costs and network loss costs) and voltage fluctuations. The proposed model and method not only ensure the economic operation of the ADN but also improve the voltage quality of the ADN and the self-consumption level of DGs. Finally, simulation verifies the effectiveness and feasibility of the proposed control method. © 2024 SPIE.

关键词： Power distribution networks

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Enhancing Socially Shared Regulation and Equalized Discussions in computer-Supported Collaborative Learning: A Role Scripts Approach 10

Enhancing Socially Shared Regulation and Equalized Discussio...

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10th international symposium on Educational Technology (ISET)

作者： Lian, Shiqiang Yang, Mengxi Fang, Qin Ding, Yuxin Dai, Zhicheng Peng, Xian Cent China Normal Univ Natl Engn Res Ctr Educ Big Data Wuhan Peoples R China

ISBN: (纸本)9798350361414

This study investigates the impact of role fixation (RF) and role rotation (RR) scripts on learners' socially shared regulation (SSR) in computer-supported collaborative learning (CSCL) environments. The research involved 25 undergraduate students from a university in central China, divided into RF and RR groups for an eight-week collaborative learning activity focused on the computer networks course. Data were collected through self-report questionnaires and online discussions platform. Findings indicate that RF groups exhibited significantly higher levels of SSR and more balanced discussions compared to RR groups. RF groups fostered a more evenly distributed interaction network, enhancing cooperation and sharing. These results underscore the effectiveness of role fixation scripts in promoting SSR and improving collaborative learning outcomes.

关键词： role scripts socially shared regulation (SSR) computer-supported collaborative learning (CSCL)

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RobustETH: Ensuring Economic Fairness in ETH2.0's distributed Validator Technology 33

RobustETH: Ensuring Economic Fairness in ETH2.0's Distribute...

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33rd IEEE international Conference on computer Communications and networks (ICCCN)

作者： Chen, Shenghang Xu, Zhicheng Zhou, Kun Zhang, Xiaoli Cheng, Hongbing Zhejiang Univ Technol Dept Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9798350348439;9798350384611

distributed Validator Technology (DVT) mitigates single points of failure in Ethereum validators by distributing validator duties across a node cluster and making decisions within the cluster through consensus algorithms. However, these systems often fail to ensure economic fairness for participants, where misconduct by a few nodes can lead to financial losses for honest nodes within the cluster. To address this issue, we introduce RobustETH, an innovative DVT implementation that prioritizes operational efficiency and economic fairness for participants. Specifically, RobustETH incorporates an efficient consensus protocol, X-IBFT, to enhance DVT's operational efficiency. Additionally, we propose a BFT forensic protocol and a reputation-based recluster strategy to accurately penalize malicious nodes and mitigate their impact, thereby safeguarding the financial interests of honest participants. Our theoretical analysis proves RobustETH's safety, liveness, and accountability. Moreover, through comprehensive evaluations, we demonstrate that RobustETH achieves a 58% reduction in end-to-end latency compared to current state-of-the-art DVT implementations while ensuring economic fairness.

关键词： Ethereum distributed Validator Technology Byzantine faults tolerant

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computer Vision-based Approach for Automated Recognition of Interference Patterns in Digital Inline Holography Microscopy 37

Computer Vision-based Approach for Automated Recognition of ...

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37th international symposium on computer-Based Medical systems (CBMS)

作者： Rockembach, Manoella Souza, Rodrigo Ferraz von Wangenheim, Aldo Sobieranski, Antonio Carlos Univ Fed Santa Catarina Dept Comp DEC Ararangua Brazil Univ Fed Santa Catarina Brazilian Natl Inst Digital Convergence INCoD Florianopolis SC Brazil Univ Fed Santa Catarina Dept Comp DEC Grad Program Informat & Commun Techol PPGTIC Ararangua Brazil

ISBN: (纸本)9798350384734;9798350384727

Digital Inline Holographic Microscopy (DIHM) is a lensless shadow imaging technique where a coherent light-source is utilized to illuminate samples and record interference patterns in a digitalizing device. Those patterns, called holograms, carry volumetric information regarding the inspected sample, requiring complex numerical methods to make them distinguishable as occurs in a conventional bright-field microscopic image. However, for many applications, the real-time analysis of holograms is computationally expensive due to the nature of numerical diffraction methods to reconstruct the signals into visual information. To mitigate this problem, in this paper we investigate the use of deep learning approaches to classify those interference patterns, directly from the raw holograms, without the requirement of phase-recovering methods for diffraction. In our approach, we investigated the use of distinct Convolutional Neural networks (CNNs) architectures and its adaptability to correctly classify holograms in an experimental environment with a dataset generated from synthetic interference patterns produced by the Fresnel Diffraction Method. The computer-generated dataset was produced from 26 classes, resulting in a total of 520 samples after the data augmentation procedure. The obtained results demonstrated the feasibility of the proposed approach to properly classify samples with 96.8% of precision, directly from the holographic interference patterns, avoiding the need for computationally expensive diffraction methods.

关键词： Digital In-line Holography Hologram Classification Interference Patterns Classification Convolutional Neural networks

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