Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the early days of the COVID-19 outbreak, most studies applied pretrained convolutional neural network (CNN) models, and the features produced by the last convolutional layer were directly passed into the classification head. In this study, the proposed ensemble model consists of three lightweight networks, Xception, MobileNetV2 and NasNetMobile as three original feature extractors, and then three base classifiers are obtained by adding the coordinated attention module, LSTM and a new classification head to the original feature extractors. The classification results from the three base classifiers are then fused by a confidence fusion method. Three publicly available chest X-ray datasets for COVID-19 testing were considered, with ternary (COVID-19, normal and other pneumonia) and quaternary (COVID-19, normal) analyses performed on the first two datasets, bacterial pneumonia and viral pneumonia classification, and achieved high accuracy rates of 95.56% and 91.20%, respectively. The third dataset was used to compare the performance of the model compared to other models and the generalization ability on different datasets. We performed a thorough ablation study on the first dataset to understand the impact of each proposed component. Finally, we also performed visualizations. These saliency maps not only explain key prediction decisions of the model, but also help radiologists locate areas of infection. Through extensive experiments, it was finally found that the results obtained by the proposed method are comparable to the state-of-the-art methods.

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that mainly judges the polarity of a given aspect word in a review. Current methods mainly use graph networks to do aspect-level sentiment classification tasks, most of which use syntactic or semantic graphs, and utilize attention mechanisms to interact and correlate aspect terms and contexts to obtain more useful feature representations. However, these methods may ignore some insignificant syntactic structures and some implicit information in some sentences. The attention mechanism then easily loses the original information, which eventually leads to inaccurate sentiment analysis. In order to solve this problem, this paper proposes a model based on residual attention gating and three-channel graph convolutional network (RAG-TCGCN). Firstly, the model uses a three-channel network composed of syntactic information, semantic information, and public information to simultaneously optimize and fuse through the multi-head attention mechanism to solve the problem of sentences without significant syntactic structure and with implicit information. Through the residual attention gating mechanism the problem of loss of original information is solved. Experimental verification shows that the accuracy and F1 value of the model are improved on the three public datasets.

The log data generated during operation of a software system contain information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most existing log anomaly detection methods are specific to a particular system, have cold-start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pretrained models and domain adaptation, which uses the pretraining model BERT to learn the semantic information of logs. This method can solve problems caused by the multiple meaning of words and log statement updates. The distance to determine anomalies in LogBD is constructed on the basis of domain adaptation, using TCNs to extract common features of different system logs and mapping them to the same hypersphere space. Lastly, experiments were conducted on two publicly available datasets to evaluate the method. The experimental results showed that the method can better solve the log instability problem and exhibits some improvement in the cross-system log anomaly detection effect.

The log data generated during the operation of a software system contains information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most of the existing log anomaly detection methods are specific to a particular system, have cold start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pre-training model and domain adaptation, which uses the pre-training model BERT to learn the semantic information of logs, and can solve the problems caused by multiple meaning words and log statement updates. An anomaly detection method LogBD based on distance to determine anomalies is constructed based on domain adaptation, using TCN networks to extract common features of different system logs and map them to the same hypersphere space. Finally, experiments are conducted on two publicly available datasets to evaluate the method, and the experimental results show that the method can better solve the log instability problem and has some improvement in the cross-system log anomaly detection effect.