Social media makes it easy for individuals to publish and consume news, but it also facilitates the spread of rumors. This paper proposes a novel deep recurrent neural model with a symmetrical network architecture for automatic rumor detection in social media such as Sina Weibo, which shows better performance than the existing methods. In the data preparing phase, we filter the posts according to the followers of the user. We then use sequential encoding for the posts and multiple embedding layers to get better feature representation, and multiple recurrent neural network layers to capture the dynamic temporal signals characteristic. The experimental results on the Sina Weibo dataset show that: 1. the sequential encoding performs better than the term frequency-inverse document frequency (TF-IDF) or the doc2vec encoding scheme; 2. the model is more accurate when trained on the posts from the users with more followers; and 3. the model achieves superior improvements over the existing works on the accuracy of detection, including the early detection.

Many rumors spread quickly and widely on social media, affecting social stability. The rumors of most current detection methods only use textual information or introduce external auxiliary information (such as user information and propagation information) to enhance the detection effect, and the inherent statistical features of the corpus have not been fully used and compared with the external auxiliary features; in addition, statistical features are more certain and can only be obtained from textual information. Therefore, we adopted a method based on the adaptive fusion of word frequency distribution features and textual features to detect rumors. Statistical features were extracted by encoding statistical information through a variational autoencoder. We extracted semantic features and sequence features as textual features through a parallel network comprising a convolutional neural network and a bidirectional long-term memory network. In addition, we also designed an adaptive valve to only fuse useful statistical features with textual features according to the credibility of textual features, which can solve the over-fitting problem caused by the excessive use of statistical features. The accuracy of the model in three public datasets (Twitter15, Twitter16, and Weibo) reached 87.5%, 88.6%, and 95.8%, respectively, and the F1 value reached 87.4%, 88.5%, and 95.8%, respectively, showing that the model can effectively improve the performance of rumor detection.

Power line segmentation is one of the fundamental and important research topic in unmanned aerial vehicles (UAV)-based smart grid inspection. However, due to the dilemma that the power line feature is vulnerable in complex backgrounds but its extension is huge in spatial, even when using CNN-based models, the accuracy of its segmentation remains unsatisfactory. To this end, in this paper, we propose a novel power line segmentation network PLNet. In this network, after the back-bone feature is reinforced by the Multi-Channel Feature Fusion (MCFF) module, the Line Feature Extracting (LFE) module is proposed to robustly encode the global straight-line features by leveraging the Hough domain. Moreover, the output of LEF guides the encoding of lower-level features through the attention mechanism in the proposed Line Feature Attention(LFA) module. Finally, through the multi-level top-down manner structure, the segmentation detail is progressively refined. Experimental results demonstrate that our proposed method achieves state-of-the-art performance in power line segmentation on the public TTPLA dataset. The testing code is available online at https://github.com/JYYMALL/PLNet.