Aspect-level sentiment analysis can be more delicate to grasp the user’s emotional tendency, has a wide range of applications in various fields. It has been proved that the method of combining dependency relationship with graph convolutional neural network can be used to improve the effect of aspect-level sentiment analysis. However, user comments are generally conducted from multiple perspectives, resulting in multiple aspects in sentences, and longer comments usually contain more effective information. This will make parsing sentence dependencies more complicated, and generate a large number of parameters in the convolution process, resulting in noise and other problems. Therefore, it is necessary to choose and choose the information and leave the effective information for sentiment analysis. This paper proposes a graph convolution network joint model to improve the dependency relationship(AIDE-GCN). By improving the dependency tree, this model builds multiple matrices and vectors for effectively screening sentences, so that the weight matrix is constructed with the aspect word as the center. When realizing the sentiment analysis of multi-aspect words, only the target dependence is kept, so that the model magnitude is smaller and the accuracy is guaranteed. Through experiments on public datasets SemEval series, Twitter series and more challenging MAMS series, it is proved that the proposed model has obvious advantages.

With the rapid development of economy, more and more free trade areas appear in the market, but in the process of commodity trading, there is a situation that the problem goods flow back to the market, and consequently, the public is more concerned about the subsequent processing flow of the problem goods. In response to the public demand for problematic goods traceability, a problematic goods traceability system based on Fabric embedded with national cryptographic algorithm is designed. We use blockchain technology to provide evidence for the processing flow of goods and embeds the national cryptographic algorithm into the fabric platform, uses Kafka-Zookeeper algorithm to reduce the coalition consensus time, and guarantees the generation and validity of transactions based on smart contracts and endorsements, and uses blockchain and Interplanetary File System as the storage engine for business data requirements. The registration time for the processing flow of the core functions of the system is 0.61 seconds, which meets the expected requirements of the system.

Timely and accurate temperature prediction is crucial to human production and life. However, due to the highly nonlinear nature of temperature prediction, traditional methods cannot meet the medium- and long-term temperature prediction tasks. To address the problems of large prediction errors and inadequate extraction of spatio-temporal features in existing temperature prediction algorithms, an improved deep learning framework: graph convolutional recurrent neural network (GCRNN) is proposed to solve the time series prediction problem in the field of temperature prediction. Specifically, GCRNN uses graph convolution to capture spatial correlation, and uses Encoder-Decoder architecture to capture temporal correlation. In the specific implementation process, the matrix multiplication in recurrent neural network is replaced by graph convolution operator, so as to realize the fusion and extraction of spatio- temporal features. The model is evaluated on three real temperature datasets, and the results show that GCRNN is able to effectively capture the spatio-temporal correlations of real-time temperature datasets, Compared with the existing baseline model, the prediction effect of GCRNN has achieved better results.

Abstract In order to make protocol composition logic (PCL) model satisfy the special needs of anonymous analysis, based on observational equivalence theory, this paper extended PCL to be anonymity PCL (APCL). In anonymity PCL, equivalent messages and equivalent traces were proposed. On the basis of equivalent traces, three kinds of anonymity were defined: sender anonymity, recipient anonymity, and relation anonymity. Finally, taking direct anonymous attestation (DAA) as an example, we formalized the anonymity of DAA by the new framework, the result of which demonstrates that DAA satisfies anonymity and verifies the correctness and feasibility of the new framework. Copyright © 2014 John Wiley & Sons, Ltd.