Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general ***,most e-auction schemes involve a trusted auctioneer,which is not always credible in **...
详细信息
Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general ***,most e-auction schemes involve a trusted auctioneer,which is not always credible in *** studies have applied cryptography tools to solve this problem by distributing trust,but they ignore the existence of *** this paper,a blockchain-based Privacy-Preserving and Collusion-Resistant scheme(PPCR)for double auctions is proposed by employing both cryptography and blockchain technology,which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy.A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance.A Dispute Resolution agreement(DR)is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and *** addition,a Concise Dispute Resolution protocol(CDR)is designed to handle situations where the number of accused winners is small,significantly reducing the computation cost of dispute *** experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.
Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1...
详细信息
Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1], [2].
Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distri...
详细信息
Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.
This paper highlights the inefficiency of most dis-tributed controls in dealing with dynamic enhancement while coordinating distributed generators(DGs),leading to poor fre-quency *** address this concern,a two-level c...
详细信息
This paper highlights the inefficiency of most dis-tributed controls in dealing with dynamic enhancement while coordinating distributed generators(DGs),leading to poor fre-quency *** address this concern,a two-level coupling-based frequency control strategy for microgrids is proposed in this *** the lower level,an adaptive dynamic compensa-tion algorithm is designed to tackle short-term and long-term frequency fluctuations caused by the uncertainties of renewable energy resources(RESs).At the upper level,an adaptive distrib-uted frequency consensus algorithm is developed to address fre-quency restoration and active power ***,to account for the potential control interaction of the two designed levels,a nonlinear extended state observer(NESO)is intro-duced to couple their control *** tests and hardware-in-the-loop(HIL)experiments confirm the improved frequencydynamics.
This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted ***,the dynamic model of projectile motion is established consider...
详细信息
This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted ***,the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the ***,the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test ***,the probability density evolution method(PDEM)is developed to high-dimensional uncertainty quantification of projectile *** engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation(MCS).Finally,the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is *** results show that the disturbance of the pitch angular,pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle,and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle.
Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1...
Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.
As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention...
详细信息
As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.
Background: Collaborative Representation (CR) has been widely used in Single Image Super Resolution (SISR) with the assumption that Low-resolution (LR) and high-resolution (HR) features can be linearly represented by ...
详细信息
Person Image Synthesis has been widely used in fashion with extensive application *** point of this task is how to synthesise person image from a single source image under arbitrary *** methods generate the person ima...
详细信息
Person Image Synthesis has been widely used in fashion with extensive application *** point of this task is how to synthesise person image from a single source image under arbitrary *** methods generate the person image with target pose well;however,they fail to preserve the fine style details of the source *** address this problem,a robust style injection(RSI)model is proposed,which is a coarse-to-fine framework to synthesise target the person *** develops a simple and efficient cross-attention based module to fuse the features of both source semantic styles and target pose for achieving the coarse aligned *** adaptive instance normalisation is employed to enhance the aligned features in conjunction with source semantic ***,source semantic styles are further injected into the positional normalisation scheme to avoid the fine style details erosion caused by massive *** training losses,optimal transport theory in the form of energy distance is introduced to constrain data distribution to refine the texture style ***,the authors’model is capable of editing the shape and texture of garments to the target style *** experiments demonstrate that the authors’RSI achieves better performance over the state-of-art methods.
Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate ...
详细信息
Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is *** recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean *** it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean *** this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center *** demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learning-based eddy detection method *** results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.
暂无评论