High reliability applications in dense access scenarios have become one of the main goals of 6G *** solve the access collision of dense Machine Type Communication(MTC)devices in cell-free communication systems,an inte...
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High reliability applications in dense access scenarios have become one of the main goals of 6G *** solve the access collision of dense Machine Type Communication(MTC)devices in cell-free communication systems,an intelligent cooperative secure access scheme based on multi-agent reinforcement learning and federated learning is proposed,that is,the Preamble Slice Orderly Queue Access(PSOQA)*** this scheme,the preamble arrangement is combined with the access *** preamble arrangement is realized by preamble slices which is from the virtual preamble *** access devices learn to queue orderly by deep reinforcement *** orderly queue weakens the random and avoids collision.A preamble slice is assigned to an orderly access queue at each access *** orderly queue is determined by interaction information among multiple *** the federated reinforcement learning framework,the PSOQA scheme is implemented to guarantee the privacy and security of ***,the access performance of PSOQA is compared with other random contention schemes in different load *** results show that PSOQA can not only improve the access success rate but also guarantee low-latency tolerant performances.
The current urban intelligent transportation is in a rapid development stage, and coherence control of vehicle formations has important implications in urban intelligent transportation research. This article focuses o...
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Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least t...
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Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.
The factory has adopted an extensive ecosystem of connected devices and IoT sensors, utilizing cloud computing for real-time decision-making. Secure cloud storage serves as the backbone, managing vast datasets and ena...
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The factory has adopted an extensive ecosystem of connected devices and IoT sensors, utilizing cloud computing for real-time decision-making. Secure cloud storage serves as the backbone, managing vast datasets and enabling centralized control. By leveraging advanced analytics and machine learning on the cloud, the factory has implemented predictive maintenance, minimizing downtime and optimizing production. The integration of Hybrid PSO-GA for machines and supply chain processes streamlines operations, allowing for remote monitoring and control to enhance operational agility. Cutting-edge advancements in New Generation information Technologies (New IT) are crucial in driving the evolution of smart manufacturing. The proliferation of Internet-connected devices in these environments generates substantial data throughout the product lifecycle. Adopting a cloud-based smart manufacturing strategy provides numerous services and applications for analysing massive datasets and fostering significant cooperation in manufacturing operations. However, challenges such as latency, bandwidth congestion, and network unavailability hinder its effectiveness for real-time applications requiring fast, low-latency performance. These issues are efficiently addressed by integrating cloud computing with edge computing, extending the cloud’s capabilities to the edge. This paper presents a hierarchical reference architecture for smart manufacturing, leveraging cloud computing. The proposed approach employs a hybrid PSO-GA scheduling function that combines Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to optimize task start times and reduce latency. The optimal solution from this hybrid approach updates task start times, with subsequent scheduling performed using a selected algorithm. The proposed novel hybrid PSO-GA model integrates AI-driven optimization, IoT, and digital twins to enhance real-time decision-making and adapt to dynamic data streams in smart manufacturing. Its
Finding an appropriate subset of agents (a team) from a larger pool of agents (the source set) so that the team exhibits a desired quality is the essence of the team formation problem. This problem is recognized to ha...
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The life expectancy of a population is a vital measure of its overall health and healthcare quality. This study use machine learning methods, notably XGBoost, to predict life expectancy in industrialized and emerging ...
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In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these chal...
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In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant ...
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In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical *** issues can lead to potential failures in peak-searching-based identification *** address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification *** experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma *** only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain *** proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.
Accurate segmentation of brain tumor regions in MRI images is essential for monitoring tumor growth. In view of this, several automated brain tumor segmentation models are proposed. U-Net is one of the most popular mo...
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There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient su...
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