There are several issues with traditional schooling, including poor student participation, rigid scheduling, and restricted accessibility. We require a cutting-edge virtual classroom management system that uses techno...
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Recent studies have revealed the vulnerability of graph neural networks (GNNs) to adversarial poisoning attacks on node classification tasks. Current defensive methods require substituting the original GNNs with defen...
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Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the centra...
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Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central ***,the frequently transmitted local gradients could also leak the participants’private *** protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been ***,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification *** tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission ***,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex ***,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge *** security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and *** last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.
Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management pl...
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Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.
The emergence of coordinated and consistent macro behavior among self-interested individuals competing for limited resources represents a central inquiry in comprehending market mechanisms and collective *** economics...
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The emergence of coordinated and consistent macro behavior among self-interested individuals competing for limited resources represents a central inquiry in comprehending market mechanisms and collective *** economics tackles this challenge through a mathematical and theoretical lens,assuming individuals are entirely rational and markets tend to stabilize through the price *** paper addresses this issue from an econophysics standpoint,employing reinforcement learning to construct a multi-agent system modeled on minority *** study has undertaken a comparative analysis from both collective and individual perspectives,affirming the pivotal roles of reward feedback and individual memory in addressing the aforementioned *** feedback serves as the guiding force for the evolution of collective behavior,propelling it towards an overall increase in ***,drawing insights from their own rewards through accumulated learning,gain information about the collective state and adjust their behavior ***,we apply information theory to present a formalized equation for the evolution of collective *** research supplements existing conclusions regarding the mechanisms of a free market and,at a micro level,unveils the dynamic evolution of individual behavior in synchronization with the collective.
The development of wireless communication technology is reshaping the landscape of intelligent transportation systems, particularly in the realm of internet of vehicles (IoV). Among these, integrated sensing and commu...
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Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possib...
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This article studies composite neural learning-based adaptive failure compensation control issues for the autonomous surface vehicle with full-state constraints. Initially, the control strategy solve the problems of c...
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This paper investigates the design of joint transmit and reflective beamforming for dual intelligent reflecting surfaces (IRSs) aided integrated sensing and communication (ISAC) system. We aim to minimize the Cram...
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The energy crisis has been a hot topic of global *** devices, a typical new energy source, have progressed rapidly and become among the main sources of power generation in the world [1]. Most photovoltaic fields are o...
The energy crisis has been a hot topic of global *** devices, a typical new energy source, have progressed rapidly and become among the main sources of power generation in the world [1]. Most photovoltaic fields are often constructed in large areas, making it difficult to monitor photovoltaic panel situations. Combining remote sensing(RS) and deep learning [2],
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