Quantum digital signature(QDS)can guarantee the information-theoretical security of a signature with the fundamental laws of quantum ***,most current QDS protocols do not take source security into account,leading to a...
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Quantum digital signature(QDS)can guarantee the information-theoretical security of a signature with the fundamental laws of quantum ***,most current QDS protocols do not take source security into account,leading to an overestimation of the signature *** this paper,we propose to utilize Hong–Ou–Mandel interference to characterize the upper bound of the source imperfections,and further to quantify information leakage from potential ***,we combine decoy-state methods and finite-size analysis in analyzing the signature *** results demonstrate the performance and feasibility of our *** current work can improve the practical security of QDS systems,thereby promoting their further networked applications.
Reference-frame-independent quantum key distribution(RFI-QKD) can avoid real-time calibration operation of reference frames and improve the efficiency of the communication process. However, due to imperfections of opt...
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Reference-frame-independent quantum key distribution(RFI-QKD) can avoid real-time calibration operation of reference frames and improve the efficiency of the communication process. However, due to imperfections of optical devices,there will inevitably exist intensity fluctuations in the source side of the QKD system, which will affect the final secure key rate. To reduce the influence of intensity fluctuations, an improved 3-intensity RFI-QKD scheme is proposed in this *** considering statistical fluctuations and implementing global parameter optimization, we conduct corresponding simulation analysis. The results show that our present work can present both higher key rate and a farther transmission distance than the standard method.
Maximize the resource utilization efficiency and guarantee the quality of service(QoS)of users by selecting the network are the key issues for heterogeneous network operators,but the resources occupied by users in dif...
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Maximize the resource utilization efficiency and guarantee the quality of service(QoS)of users by selecting the network are the key issues for heterogeneous network operators,but the resources occupied by users in different networks cannot be compared *** paper proposes a network selection algorithm for heterogeneous ***,the concept of equivalent bandwidth is proposed,through which the actual resources occupied by users with certain QoS requirements in different networks can be compared *** the concept of network applicability is defined to express the abilities of networks to support different *** proposed network selection algorithm first evaluates whether the network has enough equivalent bandwidth required by the user and then prioritizes network with poor applicability to avoid the situation that there are still residual resources in entire network,but advanced services can not be *** simulation results show that the proposed algorithm obtained better performance than the baselines in terms of reducing call blocking probability and improving network resource utilization efficiency.
Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car *** order to highlight visual differences,existing FGI...
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Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car *** order to highlight visual differences,existing FGIR works often follow two steps:discriminative sub-region localization and local feature ***,these works pay less attention on global context *** neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different subregions from a global view ***,in this paper,we consider both global and local information for FGIR,and propose a collaborative teacher-student strategy to reinforce and unity the two types of *** framework is implemented mainly by convolutional neural network,referred to Teacher-Student Based Attention Convolutional Neural network(T-S-ACNN).For fine-grained local information,we choose the classic Multi-Attention network(MA-Net)as our baseline,and propose a type of boundary constraint to further reduce background noises in the local attention *** this way,the discriminative sub-regions tend to appear in the area occupied by fine-grained objects,leading to more accurate sub-region *** fine-grained global information,we design a graph convolution based Global Attention network(GA-Net),which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among *** last,we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes,so as to enhance the cooperative reinforcement of MA-Net and *** experiments on CUB-200-2011,Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.
This article investigates the physical-layer security (PLS) of a satellite-terrestrial integrated network (STIN) consisting of a satellite (S), a destination (D), and several friendly jammers in the presence of an eav...
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Quantum key distribution (QKD) allows the transmission of keys with unconditional security. Nevertheless, a discrepancy remains between the theoretical framework and the practical implementation, as device imperfectio...
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Reconfigurable Intelligent Surfaces (RIS) have recently emerged as a promising and cost-effective technology for enabling stable and reliable communications in high-speed train (HST) environments. However, the coexist...
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Various distributed learning approaches emerge for enabling ubiquitous intelligence in Internet of Things (IoT) without sacrificing data privacy. To improve communication efficiency in frequent knowledge exchange over...
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ISBN:
(数字)9798350368369
ISBN:
(纸本)9798350368376
Various distributed learning approaches emerge for enabling ubiquitous intelligence in Internet of Things (IoT) without sacrificing data privacy. To improve communication efficiency in frequent knowledge exchange over resource-constrained IoT, different techniques for client selection have been proposed. However, the intractable scalability issues remain to be addressed in massive IoT, since highly-coupled co-channel interference adds exponential complexity to combinatorial client selection. In this work, we develop a client selection framework highly-scalable to large-scale networks with thousands of devices, which exploits the inherent graph structure derived from knowledge exchange and co-channel interference. Specifically, we first model a client selection problem for jointly optimizing learning performance and system cost under volatile network conditions. The formulated problem is encoded into a node classification problem by a directed graph. Subsequently, a general yet simple solver is designed based on graph neural networks, which selects clients by classifying node status with recursive neighborhood aggregation of node representations. Finally, extensive experimental results demonstrate that the proposed approach can perform on par with state-of-the-art methods, while scaling to networks whose size is orders of magnitude larger than they can handle.
In this paper, a novel simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhanced uplink non-orthogonal multiple access (NOMA) communicationnetwork is investigated. Specifically, ...
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The application of power over Ethernet (PoE)-enabled lighting has emerged as a groundbreaking technology with the potential to achieve cooperative control and create an energy-saving lighting network. However, fine-gr...
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