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.
Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is ...
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Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.
The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been *** previous studies,it is common to set ...
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The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been *** previous studies,it is common to set up a one-to-one correspondence between the nodes of a multi-layer network,ignoring the more complex situations in *** the present work,we explore this situation by setting up a partially coupled model of a two-layer network and investigating the impact of asymptomatic infected individuals on *** propose a self-discovery mechanism for asymptomatic infected individuals,taking into account situations such as nucleic acid testing in the community and individuals performing self-antigen testing during the *** these factors together,through the microscopic Markov chain approach(MMCA)and extensive Monte Carlo(MC)numerical simulations,we find that the greater the coupling between the networks,the more information dissemination is *** order to control the epidemics,more asymptomatic infected individuals should be made aware of their *** adoption of nucleic acid testing and individual adoption of antigenic self-testing can help to contain epidemic ***,the epidemic threshold of the proposed model is derived,and then miscellaneous factors affecting the epidemic threshold are also *** results are conducive to devising the prevention and control policies of pandemics.
In the development of static luminescent materials with remarkable optical-thermal performance and low cost, next-generation high-brightness laser lighting faces a key challenge. Herein, a unique composite architectur...
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In the development of static luminescent materials with remarkable optical-thermal performance and low cost, next-generation high-brightness laser lighting faces a key challenge. Herein, a unique composite architecture of Y3Al5O_(12):Ce^(3+) (YAG) phosphor-in-glass film coated on different heat-conducting substrates (PiGF@HCSs), i.e., PiGF@sapphire, PiGF@Al_(2)O_(3), PiGF@AlN, and PiGF@BN–AlN composites, was designed and prepared by a simple film printing and low-temperature sintering technology. The heat-conducting substrates significantly affect the luminescence saturation and phosphor conversion of PiGF@HCSs, allowing substrates with higher thermal conductivity (TC) to have a higher laser power density (LPD) and higher reflectivity to enable higher luminous efficacy (LE). As a consequence, PiGF@sapphire realizes a luminous flux (LF) of 2076 lm@12 W/mm^(2), which is higher than those of PiGF@Al_(2)O_(3) (1890 lm@15 W/mm^(2)) and PiGF@AlN (1915 lm@24 W/mm^(2)), whilePiGF@BN–AlN enables a maximum LF of 3058 lm@21 W/mm^(2). Furthermore, the LE of PiGF@BN–AlN reaches 194 lm/W, which is 1.6 times that of PiGF@AlN, while those of PiGF@sapphire and PiGF@Al_(2)O_(3) are 192 and 150 lm/W, respectively. The working temperature of PiGF@AlN is only 93.3℃ under LPD of 9 W/mm^(2), while those of PiGF@sapphire, PiGF@Al_(2)O_(3), and PiGF@BN–AlN increase to 193.8, 133.6, and 117℃, respectively. These findings provide guidance for commercial applications of PiGF@HCS converters in high-brightness laser lighting and displays.
The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for ...
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Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern fo...
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Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network(LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%,which can greatly enhances the tram driving safety.
Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-...
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Aqueous zinc-ion batteries(AZIBs)are gaining attention owing to their affordability,high safety,and high energy density,making them a promising solution for large-scale energy ***,their performance is hampered by the ...
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Aqueous zinc-ion batteries(AZIBs)are gaining attention owing to their affordability,high safety,and high energy density,making them a promising solution for large-scale energy ***,their performance is hampered by the instability of both the anode-electrolyte interface and the cathode-electrolyte *** use of sodium gluconate(SG),an organic sodium salt with multiple hydroxyl groups,as an electrolyte additive is *** and theoretical analyses demonstrate that Na^(+)from SG can intercalate and deintercalate within the associated V_(2)O_(5) cathode during in situ electrochemical *** action supports the layered structure of V_(2)O_(5),prevents structural collapse and phase transitions,and enhances Zn^(2+)diffusion ***,the gluconate anion disrupts the original Zn^(2+)solvation structure,mitigates water-induced side reactions,and suppresses Zn dendrite *** synchronous regulation of both the V_(2)O_(5) cathode and Zn anode by the SG additive leads to considerable performance ***‖Zn symmetric batteries demonstrate a cycle life exceeding 2800 h at 0.5 mA cm^(-2)and 1 mAh cm^(-2).In Zn‖V_(2)O_(5) full batteries,a high specific capacity of 288.92 mAh g^(-1)and capacity retention of 82.29%are maintained over 1000 cycles at a current density of 2 A g^(-1).This multifunctional additive strategy offers a new pathway for the practical application of AZIBs.
Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadv...
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Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics ***,the efficiency of resource scheduling significantly influences the operation performance o...
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Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics ***,the efficiency of resource scheduling significantly influences the operation performance of *** solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm ***,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated *** new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the ***,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection *** the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible *** the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality *** evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is *** results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.
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