Fake news on social media is a widespread and serious problem in today's society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, Multi-View Attention Networks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5% in accuracy on average, and produce a reasonable explanation.

Rumors in social media represent a severe problem prevailing in today’s society. Previous studies on automated rumor detection have shown that the topological information specific to social media is a vital clue for debunking rumors. However, existing automatic rumor detection approaches either oversimplify the graph structure or ignore this crucial clue. To address this issue, we propose a model that explores homogeneity and conversation structure to identify rumors. Our model learns more comprehensive and precise representations by modeling follower-following relationships of users, simulating the propagation layout of tweets, and connecting responders’ behavior. The experimental results on two public Twitter datasets show that our model’s performance outperforms other state-of-the-art baseline models. Furthermore, the experimental results prove our hypothesis that birds of a feather rumor together. The results demonstrate that both the conversation structure and the friend network’s homogeneity are significant for checking the veracity of a suspicious tweet.

The early warning monitoring capability of a ship detection algorithm is significant for jurisdictional territorial waters and plays a key role in safeguarding the national maritime strategic rights and interests. In this paper, a Fast Maritime Target Identification algorithm, FMTI, is proposed to identify maritime targets rapidly. The FMTI adopts a Single Feature Map Fusion architecture as its encoder, thereby improving its detection performance for varying scales of ship targets, from tiny-scale targets to large-scale targets. The FMTI algorithm has a decent detection accuracy and computing power, according to the mean average precision (mAP) and floating-point operations (FLOPs). The FMTI algorithm is 7% more accurate than YOLOF for the mAP measure, and FMTI’s FLOPs is equal to 98.016 G. The FMTI can serve the demands of marine vessel identification while also guiding the creation of supplemental judgments for maritime surveillance, offshore military defense, and active warning.

Vessel monitoring technology involves the application of remote sensing technologies to detect and identify vessels in various environments, which is critical for monitoring vessel traffic, identifying potential threats, and facilitating maritime safety and security to achieve real-time maritime awareness in military and civilian domains. However, most existing vessel monitoring models tend to focus on a single remote sensing information source, leading to limited detection functionality and underutilization of available information. In light of these limitations, this paper proposes a comprehensive ship monitoring system that integrates remote satellite devices and nearshore detection equipment. The system employs ResNet, a deep learning model, along with data augmentation and transfer learning techniques to enable bidirectional detection of satellite cloud images and nearshore outboard profile images, thereby alleviating prevailing issues such as low detection accuracy, homogeneous functionality, and poor image recognition applicability. Empirical findings based on two real-world vessel monitoring datasets demonstrate that the proposed system consistently performs best in both nearshore identification and remote detection. Additionally, extensive supplementary experiments were conducted to evaluate the effectiveness of different modules and discuss the constraints of current deep learning-based vessel monitoring models.