At present,most quantum secret sharing(QSS)protocols are more or less designed with the incorporation of classical secret sharing *** the increasing maturity of quantum technology,QSS protocols based on pure quantum m...
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At present,most quantum secret sharing(QSS)protocols are more or less designed with the incorporation of classical secret sharing *** the increasing maturity of quantum technology,QSS protocols based on pure quantum mechanics are becoming more *** secret sharing schemes cannot achieve absolute security,and their involvement can compromise the security of QSS *** paper proposes a QSS scheme based on Greenberger-Horn-Zeilinger(GHZ)basis measurement and quantum entanglement *** this protocol,the secret sender stores the secret information using Pauli *** obtain their shares by measuring the product state ***,participants complete the secret reconstruction through quantum entanglement exchange and other related quantum *** addition,the particles held by participants in the protocol do not contain any secret *** participant's particles are in a state of maximum entanglement,and no participant can deduce the particle information of other participants through their own *** the same time,the protocol is based on pure quantum mechanics and does not involve classical schemes,which avoids the problem of reduced security of the *** analysis indicates that the protocol is not vulnerable to retransmission interception and collusion ***,it is capable of detecting and terminating the protocol promptly when facing with attacks from dishonest participants.
Spatial relations in text refer to how a geographical entity is located in space in relation to a reference *** spatial relations from text is a fundamental task in natural language *** studies have only focused on ge...
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Spatial relations in text refer to how a geographical entity is located in space in relation to a reference *** spatial relations from text is a fundamental task in natural language *** studies have only focused on generic fine-tuning methods with additional classifiers,ignoring the importance of the semantic correlation between different spatial elements and the large offset between the relation extraction task and the pre-trained *** address the above two issues,we propose a spatial relation extraction model based on Dual-view Prompt and Element Correlation(DPEC).Specifically,we first reformulate spatial relation extraction as a mask language model with a Dual-view Prompt(i.e.,Link Prompt and Confidence Prompt).Link Prompt can not only guide the model to incorporate more contextual information related to the spatial relation extraction task,but also better adapt to the original pre-training task of the language ***,Confidence Prompt can measure the confidence of candidate triplets in Link Prompt and work as a supplement to identify those easily confused examples in Link ***,we incorporate the element correlation to measure the consistency between different spatial elements,which is an effective cue for identifying the rationality of spatial *** results on the popular SpaceEval show that our DPEC significantly outperforms the SOTA baselines.
In this paper,we investigate a Reconfigurable Intelligent Surface(RIS)-assisted secure Symbiosis Radio(SR)network to address the information leakage of the primary transmitter(PTx)to potential ***,the RIS serves as a ...
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In this paper,we investigate a Reconfigurable Intelligent Surface(RIS)-assisted secure Symbiosis Radio(SR)network to address the information leakage of the primary transmitter(PTx)to potential ***,the RIS serves as a secondary transmitter in the SR network to ensure the security of the communication between the PTx and the Primary Receiver(PRx),and simultaneously transmits its information to the PTx concurrently by configuring the phase *** the presence of multiple eavesdroppers and uncertain channels in practical scenarios,we jointly optimize the active beamforming of PTx and the phase shifts of RIS to maximize the secrecy energy efficiency of RIS-supported SR networks while satisfying the quality of service requirement and the secure communication *** solve this complicated non-convex stochastic optimization problem,we propose a secure beamforming method based on Proximal Policy Optimization(PPO),which is an efficient deep reinforcement learning algorithm,to find the optimal beamforming strategy against *** results show that the proposed PPO-based method is able to achieve fast convergence and realize the secrecy energy efficiency gain by up to 22%when compared to the considered benchmarks.
In recent years,live streaming has become a popular application,which uses TCP as its primary transport *** UDP Internet Connections(QUIC)protocol opens up new opportunities for live ***,how to leverage QUIC to transm...
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In recent years,live streaming has become a popular application,which uses TCP as its primary transport *** UDP Internet Connections(QUIC)protocol opens up new opportunities for live ***,how to leverage QUIC to transmit live videos has not been studied *** paper first investigates the achievable quality of experience(QoE)of streaming live videos over TCP,QUIC,and their multipath extensions Multipath TCP(MPTCP)and Multipath QUIC(MPQUIC).We observe that MPQUIC achieves the best performance with bandwidth aggregation and transmission ***,network fluctuations may cause heterogeneous paths,high path loss,and band-width degradation,resulting in significant QoE *** by the above observations,we investigate the multipath packet scheduling problem in live streaming and design 4D-MAP,a multipath adaptive packet scheduling scheme over ***,a linear upper confidence bound(LinUCB)-based online learning algorithm,along with four novel scheduling mechanisms,i.e.,Dispatch,Duplicate,Discard,and Decompensate,is proposed to conquer the above problems.4D-MAP has been evaluated in both controlled emulation and real-world networks to make comparison with the state-of-the-art multipath transmission *** results reveal that 4D-MAP outperforms others in terms of improving the QoE of live streaming.
The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band *** such systems,all participants r...
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The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band *** such systems,all participants related to commercial and industrial systems must communicate and generate ***,due to the small storage capacities of IoT devices,they are required to store and transfer the generated data to third-party entity called“cloud”,which creates one single point to store their ***,as the number of participants increases,the size of generated data also ***,such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security,privacy,and *** address these challenges,Federated Learning(FL)has been proposed as a reasonable decentralizing approach,in which clients no longer need to transfer and store real data in the central ***,they only share updated training models that are trained over their private *** the same time,FL enables clients in distributed systems to share their machine learning models collaboratively without their training data,thus reducing data privacy and security ***,slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed ***,these unnecessary communication rounds make the system vulnerable to security and privacy issues,because irrelevant model updates are sent between clients and ***,in this work,we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song(CKKS)to encrypt model parameters for their local information privacy-preserving *** proposed solution uses the impetus term to speed up model convergence during the model training ***,it establishes a secure communication channel between IoT devices and the *** a
This paper considers the asynchronous control for discrete-time Markov jump systems (MJSs) using a multi-node round-robin protocol (MNRRP). Compared to the traditional round-robin protocol, MNRRP increases the number ...
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Currently,the main idea of iterative rendering methods is to allocate a fixed number of samples to pixels that have not been fully rendered by calculating the completion *** is obvious that this strategy ignores the c...
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Currently,the main idea of iterative rendering methods is to allocate a fixed number of samples to pixels that have not been fully rendered by calculating the completion *** is obvious that this strategy ignores the changes in pixel values during the previous rendering process,which may result in additional iterative operations.
Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of ***,existing studies neglect positional information when learni...
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Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of ***,existing studies neglect positional information when learning discrete snapshots,resulting in insufficient network topology *** the same time,due to the lack of appropriate data augmentation methods,it is difficult to capture the evolving patterns of the network *** address the above problems,a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic ***,the global snapshot is built based on the historical snapshots to express the stable pattern of the dynamic graph,and the random walk is used to obtain the position representation by learning the positional information of the ***,a new data augmentation method is carried out from the perspectives of short-term changes and long-term stable structures of dynamic ***,subgraph sampling based on snapshots and global snapshots is used to obtain two structural augmentation views,and node structures and evolving patterns are learned by combining graph neural network,gated recurrent unit,and attention ***,the quality of node representation is improved by combining the contrastive learning between different structural augmentation views and between the two representations of structure and *** results on four real datasets show that the performance of the proposed method is better than the existing unsupervised methods,and it is more competitive than the supervised learning method under a semi-supervised setting.
Recently,many researches have created adversarial samples to enrich the diversity of training data for improving the text classification performance via reducing the loss incurred in the neural network ***,existing st...
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Recently,many researches have created adversarial samples to enrich the diversity of training data for improving the text classification performance via reducing the loss incurred in the neural network ***,existing studies have focused solely on adding perturbations to the input,such as text sentences and embedded representations,resulting in adversarial samples that are very similar to the original *** adversarial samples can not significantly improve the diversity of training data,which restricts the potential for improved classification *** alleviate the problem,in this paper,we extend the diversity of generated adversarial samples based on the fact that adding different disturbances between different layers of neural network has different *** propose a novel neural network with perturbation strategy(PTNet),which generates adversarial samples by adding perturbation to the intrinsic representation of each hidden layer of the neural ***,we design two different perturbation ways to perturb each hidden layer:1)directly adding a certain threshold perturbation;2)adding the perturbation in the way of adversarial *** above settings,we can get more perturbed intrinsic representations of hidden layers and use them as new adversarial samples,thus improving the diversity of the augmented training *** validate the effectiveness of our approach on six text classification datasets and demonstrate that it improves the classification ability of the *** particular,the classification accuracy on the sentiment analysis task improved by an average of 1.79%and on question classification task improved by 3.2%compared to the BERT baseline,respectively.
Image bitmaps,i.e.,data containing pixels and visual perception,have been widely used in emerging applica-tions for pixel operations while consuming lots of memory space and *** with legacy DRAM(dynamic ran-dom access...
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Image bitmaps,i.e.,data containing pixels and visual perception,have been widely used in emerging applica-tions for pixel operations while consuming lots of memory space and *** with legacy DRAM(dynamic ran-dom access memory),non-volatile memories(NVMs)are suitable for bitmap storage due to the salient features of high density and intrinsic ***,writing NVMs suffers from higher energy consumption and latency compared with read *** precise or approximate compression schemes in NVM controllers show limited performance for bitmaps due to the irregular data patterns and variance in *** observe the pixel-level similarity when writing bitmaps due to the analogous contents in adjacent *** exploiting the pixel-level similarity,we propose SimCom,an approximate similarity-aware compression scheme in the NVM module controller,to efficiently compress data for each write access *** idea behind SimCom is to compress continuous similar words into the pairs of base words with *** storage costs for small runs are further mitigated by reusing the least significant bits of base *** adaptively selects an appropriate compression mode for various bitmap formats,thus achieving an efficient trade-off be-tween quality and memory *** implement SimCom on GEM5/zsim with NVMain and evaluate the perfor-mance with real-world image/video *** results demonstrate the efficacy and efficiency of our SimCom with an efficient quality-performance trade-off.
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