The practical Byzantine fault-tolerant consensus algorithm reduces the operational complexity of Byzantine protocols from an exponential level to a polynomial level, which makes it possible to apply Byzantine protocols in distributed systems. However, it still has some problems, such as high communication overhead, low security, poor scalability, and difficulty in tracking. In this article, we propose a Byzantine fault-tolerant consensus algorithm based on dual administrator short group signatures (GPBFT). Firstly, the certification authority chooses the master node and group administrators based on the credit value. The group administrators organize the nodes into a group, and the members generate the signatures by applying the short group signatures scheme, in which any group member can represent the group during the GroupSign phase. Additionally, the GPBFT algorithm adds the Trace phase. According to member and client authentication information, the group administrator can track the true identity of the malicious node, identify the malicious node, and revoke it. The experimental results show that compared with the PBFT algorithm, the GPBFT algorithm can reduce the network communication overhead, reduce the consensus delay, and greatly improve the security and stability of the system. The algorithm can effectively manage member nodes and enable the tracking of identified malicious nodes while maintaining anonymity in terms of node tracking.

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Background Named entity recognition (NER) of electronic medical records is an important task in clinical medical research. Although deep learning combined with pretraining models performs well in recognizing entities in clinical texts, because Chinese electronic medical records have a special text structure and vocabulary distribution, general pretraining models cannot effectively incorporate entities and medical domain knowledge into representation learning; separate deep network models lack the ability to fully extract rich features in complex texts, which negatively affects the named entity recognition of electronic medical records. Methods To better represent electronic medical record text, we extract the text’s local features and multilevel sequence interaction information to improve the effectiveness of electronic medical record named entity recognition. This paper proposes a hybrid neural network model based on medical MC-BERT, namely, the MC-BERT + BiLSTM + CNN + MHA + CRF model. First, MC-BERT is used as the word embedding model of the text to obtain the word vector, and then BiLSTM and CNN obtain the feature information of the forward and backward directions of the word vector and the local context to obtain the corresponding feature vector. After merging the two feature vectors, they are sent to multihead self-attention (MHA) to obtain multilevel semantic features, and finally, CRF is used to decode the features and predict the label sequence. Results The experiments show that the F1 values of our proposed hybrid neural network model based on MC-BERT reach 94.22%, 86.47%, and 92.28% on the CCKS-2017, CCKS-2019 and cEHRNER datasets, respectively. Compared with the general-domain BERT-based BiLSTM + CRF, our F1 values increased by 0.89%, 1.65% and 2.63%. Finally, we analyzed the effect of an unbalanced number of entities in the electronic medical records on the results of the NER experiment.

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