In existing relation extraction methods, there are often issues such as Error propagation or insufficient attention, which limits the improvement of extraction performance. To this end, this paper proposes a relation ...
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The KPN algorithm refines the importance of peer nodes in K-shell to identify important nodes in complex networks, but it does not distinguish the removal order of peer nodes from the proportion weight of K-shell valu...
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To solve the problems of low hidden capacity, weak concealment and low transmission efficiency in the existing blockchain covert communication, a covert communication model of Ethereum based on smart contracts is prop...
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Recently, telemedicine diagnosis via the Internet has been widely used. The transmission of patients' private data over the network is subject to threats such as tampering, forgery and theft, and unauthorized thef...
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Generative adversarial networks (GANs) are widely used for image super-resolution (SR) and have recently attracted increasing attention due to their potential to generate rich details. However, generators are usually ...
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A reversible data hiding method based on double embedding in chunking is proposed for existing data hiding algorithms to improve the embedding capacity and generate a secret-laden image with a high visual effect. Firs...
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para>In order to solve the problem of low classification accuracy of Tibetan medicine-related journals using pre-training model under the condition of unbalanced sample size. This paper proposes an unbalanced data ...
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In order to avoid the disadvantages of complex operation and expensive equipment for the extraction method of motion capture systems and other equipment, a method which can extract key points of characters movements w...
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Ancient books of Tibetan medicine cover the rich history and unique theoretical system of Tibetan medicine, but it is difficult to systematize and digitize Tibetan medicine. In this paper, the BERT-BiGRU-CRF model is ...
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In order to solve the problem of low classification accuracy of Tibetan medicine-related journals using pre-training model under the condition of unbalanced sample size. This paper proposes an unbalanced data augmenta...
In order to solve the problem of low classification accuracy of Tibetan medicine-related journals using pre-training model under the condition of unbalanced sample size. This paper proposes an unbalanced data augmentation for prompt learning based on bert (Unbalanced Data Augmentation for Prompt Learning based on Bert) Based on the BERT model, the unbalanced Tibetan medicine journal data is sent to EDA and SimBERT to generate the balanced data, and then the prompt template is constructed to help understand the downstream tasks, so as to improve the model's extraction of journal subject features. Experiments show that the average subject classification accuracy of the model UDA-PL-BERT is 75%, the accuracy is 97%, the f1 value is 71%, and the recall rate is 60%, which is improved compared with the BiLSTM model, TextCNN model, FastText model and Transformer model. The data modes processed by this method are limited to text form, and the target task is mainly unbalanced multi-classification task of Tibetan medicine-related journal topics, and does not do other dataset classification problems. The UDA-PL-BERT proposed in this paper can effectively improve the extraction and classification performance of unbalanced subject features of Tibetan medicine-related journals.
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