Recently, self-supervised learning has garnered significant attention for its ability to extract high-quality features from unlabeled data. However, existing research indicates that backdoor attacks can pose significa...
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In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of positive samples. An effective way to all...
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With the introduction of data protection regulation in various countries, traditional centralized learning for the exploitation of sensitive biological information will gradually become unsustainable. We take face and...
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With the increase of car ownership in China, the problem of difficult parking in cities has become more and more serious. In large parking garage, finding an ideal parking space has become a daily problem for people; ...
With the increase of car ownership in China, the problem of difficult parking in cities has become more and more serious. In large parking garage, finding an ideal parking space has become a daily problem for people; especially during the peak usage period of parking garage, a large number of vehicles will be driven into the parking garage, which will bring congestion, and at the same time, the demand of a large number of users in the area will lead to network congestion under 4G network, and users cannot access the network to check and find parking spaces. Therefore, this paper proposes a global optimal navigation mechanism with 5G network based on the above mentioned problems by analyzing the congestion situation during the peak usage of the parking garage. This mechanism uses an optimal parking space selection model and a global optimal scheduling model that can avoid congestion. Based on the model proposed in this paper, we construct a navigation planning system based on mobile, front-end and back-end. With the ultra-low latency, ultrahigh transmission efficiency and reliability of 5G network, the mobile port can select the optimal parking space according to the user's preference, and the back-end can show the parking space and congestion in the parking garage in real time according to the actual situation of the parking garage. Finally, the performance of the model and system proposed in this paper is verified through experiments.
Disk failure is one of the most important reliability problems in large-scale network storage systems. Disk failure may lead to serious data loss and even disastrous consequences if the missing data cannot be recovere...
Disk failure is one of the most important reliability problems in large-scale network storage systems. Disk failure may lead to serious data loss and even disastrous consequences if the missing data cannot be recovered. Therefore, predicting disk failures is an important means of ensuring storage security in network storage systems. However, since the fault data in the fast degradation stage is smaller than the healthy data in the normal state, the mixture of healthy data and faulty data leads to extremely unbalanced data, which brings great challenges to finding hidden fault information, thus making fault prediction more accurate. Aiming at the above problems, a convolutional transformer model ConvTrans-TPS model for disk failure prediction in large-scale network storage systems is proposed. The ConvTrans-TPS model acquires dependencies between long-term sequence data through transformers and uses convolutional projections for attention computation to enhance attention to local contextual information. data augmentation to predict failures in the next 7 days. Validated by the analysis on the Backblaze dataset, the F1 is 0.96 and the Matthews correlation coefficient (MCC) is 0.92. Compared with the popular CNN-LSTM model in recent years, our proposed method improves F1 and MCC by 4% and 5%, respectively, improving the prediction accuracy.
Because of the complex topology of urban road and the dynamic changes of traffic information over time, how to accurately and efficiently predict traffic flow has become a difficult problem. In order to capture spatio...
Because of the complex topology of urban road and the dynamic changes of traffic information over time, how to accurately and efficiently predict traffic flow has become a difficult problem. In order to capture spatiotemporal dependence at the same time, in this paper we propose BLRGCN, a dynamic traffic flow prediction model based on spatiotemporal graph convolutional network. This model improves the performance of bidirectional long short-term memory network (Bi-LSTM) by using residual network (ResNet), which can directly connect the input short to the output of nonlinear layer, and then combines the improved Bi-LSTM with graph convolutional neural network (GCN). BLRGCN model uses graphs to construct the network information of urban roads. In the graph, nodes express roads and edges express relationship between roads. The attributes of nodes describe traffic information on roads. We use GCN to extract the topological features of road network to capture spatial dependence. Besides, the BLRGCN model uses Bi-LSTM to obtain the information of the whole input sequence and extract the dynamic change characteristics of traffic data on the road to capture the temporal dependence. Compared with other methods on two traffic datasets, BLRGCN model has improved RMSE index by 0.53%-37.03% and 23.69%-43.83% respectively. The MAE index was improved by 34.89%-37.78% and 7.80%-51.88%, the accuracy index was improved by 0.19%-82.53% and 4.55%- 10.45%, the R2 index was improved by 0.24%-25.65% and 16.67%-29.94%, and the var index is improved by 0.25%-24.24% and 16.99%-30.39% respectively, which indicates that BLRGCN model has good prediction performance.
Current adaptive traffic signal control methods based on centralized deep reinforcement learning are not applicable in large-scale adaptive traffic control environment. The scalability problem is overcome by assigning...
Current adaptive traffic signal control methods based on centralized deep reinforcement learning are not applicable in large-scale adaptive traffic control environment. The scalability problem is overcome by assigning global control to each local RL agent through multi-intelligence reinforcement learning, but the environment now becomes partially visible ami non-stationarity from the perspective of each local agent due to limited communication between agents. In this paper, we propose a multi-agent framework called Forgetful Priority Weighed Double Deep Q-learning (FP-WDDQN). We firstly extend Weighted Double Deep Q-Learning(WDDQN) to the multi agent domain, so as to reduce the error caused by the algorithm's underestimation of the target network and get more accurate Q-value. Then we propose a new algorithm based on Forgetful Experience Mechanism(FEM) and Priority Experience Replay Mechanism(PERM). This mechanism makes WDDQN choose experience based on Temporal Difference (TD) error and Identify the importance of experience with FEM. It makes WDDQN preferentially select experience with high TD-error in the process of sampling and training its network based on FEM and TD-error, which improved the model training efficiency and stabilized the learning process. In this paper, we construct an urban traffic network with seven intersections in the simulation platform called Simulation of Urban MObility(SUMO), and the proposed FP-WDDQN is compared against Independent Double Deep Q-learning(IDDQN), WDDQN and Multi-agent Advantage Actor Critic (MA2C) under the condition of simulating the traffic dynamics of peak hours. Experiment shows FP-WDDQN has better performance than other algorithms in vehicle speed, intersection delay and intersection waiting queue length.
Few-shot text classification aims to learn transferable knowledge from a limited dataset to perform classification tasks in unseen domains. Recently, metric-based meta-learning methods have demonstrated their advantag...
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With the development of semantic social networks, social networks become more complex and their size expands rapidly, which brings significant challenges to social network analysis. Network Embedding can transform the...
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ISBN:
(数字)9798350362930
ISBN:
(纸本)9798350362947
With the development of semantic social networks, social networks become more complex and their size expands rapidly, which brings significant challenges to social network analysis. Network Embedding can transform the network from a high-dimensional adjacency matrix to a low-dimensional one, which has excellent foreground in link prediction, vertex classification, and graph visualization. Nowadays, most algorithms are proposed for static networks with little consideration of the dynamic changes in the network. However, most real-world networks change over time. Directly applying static network embedding algorithms to dynamic networks will result in poor stability, flexibility, and efficiency. Because of these shortcomings, we propose a graph convolution-based dynamic network representation learning algorithm (GNEA), which combines Graph Convolutional Network (GCN) with Long Short-term Memory (LSTM) to train the time series networks. GCN is applied to extract features in the network, and LSTM is utilized to capture temporal information. GNEA can not only perform characterization learning on the network but also obtain more abundant information during the dynamic evolution of the network. GNEA is compared with several typical algorithms on the datasets Stochastic Block Model (SBM), Autonomous Systems (AS), and Digital Bibliography and Library Project (DBLP). The results prove that GNEA has good performance in MAP and precision@k.
In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TS...
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