Aspect-based sentiment analysis tries to estimate different emotional tendencies expressed in different aspects of a sentence. Aiming at the problem that the existing network model based on Recurrent Neural Network (RNN) combined with attention mechanism has too many training parameters and lacks explanation of related syntax constraints and long distance word dependence mechanism, a self-attention gated graph convolutional network was proposed, namely MSAGCN. First, the multi-headed self-attention mechanism was used to encode context words and targets, thus capturing semantic associations within the sentence. Then, a graph convolutional network was established on the sentence's dependency tree to obtain syntactic information and word dependencies. Finally, the sentiment of the specific target was obtained through the GTRU (Gated Tanh-ReLU Unit). Compared with the baseline model, the proposed model has the accuracy and F1 improved by 1%-3.3% and 1.4%-6.3% respectively. At the same time, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model was also applied to the current task to further improve the model effect. Experimental results verify that the proposed model can better grasp the emotional tendencies of user reviews.

To solve the problem of location verification caused by collusion attack in Vehicular Ad Hoc NETworks (VANET), a multi-round vote location verification based on weight and difference was proposed. In the mechanism, a static frame was introduced and the Beacon messages format was redesigned to alleviate the time delay of location verification. By setting malicious vehicles filtering process, the position of the specific region was voted by the neighbors with different degrees of trust, which could obtain credible position verification. The experimental results illustrate that in the case of collusion attack, the scheme achieves a higher accuracy of 93.4% compared to Minimum Mean Square Estimation (MMSE) based location verification mechanism.