Most current network devices have multiple network interfaces, and multipath transport protocols can utilize multiple network paths (e.g., WiFi and cellular) to improve the performance and reliability of network transmission. The scheduler of the multipath transmission protocol determines the path to which each data packet should be transmitted, and is a key module that affects multipath transmission. However, current multipath schedulers cannot adapt well to various user usage scenarios. In this paper, we propose DRLMS, a deep reinforcement learning based multipath scheduler. DRLMS uses deep reinforcement learning to train neural networks to generate packet scheduling policies. It optimizes the scheduling strategy through feedback to the neural network through the reward function based on the current user usage scenario and QoS. We implement DRLMS in the MPQUIC protocol and compared it with current multipath schedulers. The results show that DRLMS's adaptability to user usage scenarios is significantly outperforms other schedulers.
Traditional rumor detection methods that only focus on text content have achieved certain results. However, with the rapid development of social platforms, graphic information has occupied a large proportion. In this scenario, traditional detection methods cannot make full use of picture information for rumor detection. Aiming at the above scenarios, a rumor detection model integrating multi-modal features is proposed. Firstly, text features and visual features as well as their hidden states are extracted by using the pre-trained deep learning model, and then the preliminary fusion features are obtained by integrating the hidden states of text and image through the attention mechanism. Then, the text features, preliminary fusion features and social features are spliced, and the image features, preliminary fusion features and social features are spliced to obtain two final fusion features. Then the two features are input into different full connection layers to get their respective prediction results. Finally, the two prediction results are integrated to obtain the final detection results. Experimental results show that the proposed model is effective in detecting multimodal rumor data.
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