The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avo...
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The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.
Multivariate Time Series(MTS)forecasting is an essential problem in many *** forecasting results can effectively help in making *** date,many MTS forecasting methods have been proposed and widely ***,these methods ass...
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Multivariate Time Series(MTS)forecasting is an essential problem in many *** forecasting results can effectively help in making *** date,many MTS forecasting methods have been proposed and widely ***,these methods assume that the predicted value of a single variable is affected by all other variables,ignoring the causal relationship among *** address the above issue,we propose a novel end-to-end deep learning model,termed graph neural network with neural Granger causality,namely CauGNN,in this *** characterize the causal information among variables,we introduce the neural Granger causality graph in our *** variable is regarded as a graph node,and each edge represents the casual relationship between *** addition,convolutional neural network filters with different perception scales are used for time series feature extraction,to generate the feature of each ***,the graph neural network is adopted to tackle the forecasting problem of the graph structure generated by the *** benchmark datasets from the real world are used to evaluate the proposed CauGNN,and comprehensive experiments show that the proposed method achieves state-of-the-art results in the MTS forecasting task.
The permissioned blockchain is one of the core technologies for Web3.0. However, the transactional relationship leakage on blockchain has become a critical threat to the benefits of users. To prevent the malicious ana...
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The permissioned blockchain is one of the core technologies for Web3.0. However, the transactional relationship leakage on blockchain has become a critical threat to the benefits of users. To prevent the malicious analysis of the sending and receiving addresses of series of transactions, much effort has recently been put into transactional relationship protection (TRP) in blockchain by academia and industry. However, most of the current TRP methods are designed for the particular fungible cryptocurrencies, which have limitations in terms of asset types and scenarios. This paper proposes a TRP-enabled permissioned blockchain framework. First, the framework introduces a ledger structure comprising two distinct types of blocks. The basic block publicly contains the verifiable structure of the transactions, while the transaction block privately contains their content in selected committee. Second, to prevent the committee from analysing the relationships in transaction blocks, the framework includes a confidential transaction replication mechanism that splits the related transactions and replicates them to different committees. Furthermore, we optimize the framework via quantitative analysis to minimize the required replicating size of per transaction, thus enabling the framework to achieve enhanced privacy and scalability. Theoretical analysis and experimental results on datasets demonstrate that the framework achieves more than 95% probabilities of hiding the relationships, and maintains 10 times the throughput compared to the blockchain without our method.
Depthwise convolutions are widely used in lightweight convolutional neural networks (CNNs). The performance of depthwise convolutions is mainly bounded by the memory access rather than the arithmetic operations for cl...
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In recent years, depression, as a serious mental illness, has received widespread attention from various sectors of society. How to identify depressive emotions in a timely manner and detect depression has become an u...
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Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the advances in dynamic graph neural networks...
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Text-to-Image Diffusion Models (T2I DMs) have garnered significant attention for their ability to generate high-quality images from textual descriptions. However, these models often produce images that do not fully al...
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In the present study,the over-constrained hybrid manipulator R(2RPR)R/SP+RR is considered as the research *** this paper,kinematics of the hybrid manipulator,including the forward and inverse position,are ***,the work...
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In the present study,the over-constrained hybrid manipulator R(2RPR)R/SP+RR is considered as the research *** this paper,kinematics of the hybrid manipulator,including the forward and inverse position,are ***,the workspace is checked based on the inverse position solution to evaluate whether the workspace of the hybrid manipulator meets the requirements,and the actual workspace of the hybrid robot is *** that,the force analysis of the over-constrained parallel mechanism is carried out,and an ADAMS-ANSYS rigid-flexible hybrid body model is established to verify the *** on the obtained results from the force analysis,the manipulator structure is ***,the structure optimization is carried out to improve the robot ***,calibration and workspace verification experiments are performed on the prototype,cutting experiment of an S-shaped aluminum alloy workpiece is completed,and the experiment verifies the machining ability of the *** work conducts kinematics,workspace,force analyses,structural optimization design and experiments on the over-constrained hybrid manipulator R(2RPR)R/SP+RR,providing design basis and technical support for the development of the novel hybrid manipulator in practical engineering.
To minimize the propagation of redundant data in wireless sensor networks, conserve energy, and extend network lifespan, we propose an algorithm (R-IEHOBP) that combines radial clustering and an elephant swarm neural ...
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The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simpl...
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The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.
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