Assessing the impact of isolation policies on epidemic dynamics through swarm entropy.

Isolation policies are an effective measure in epidemiological models for the prediction and prevention of infectious diseases. In this paper, we use a multi-agent modeling approach to construct an infectious disease model that considers the influence of isolation policies. The model analyzes the impact of isolation policies on various stages of epidemic from two perspectives: the external environment and agents behavior. It utilizes multiple variables to simulate the extent to which isolation policies influence the spread of the pandemic. Empirical evidence indicates that the progression of the epidemic is primarily driven by factors such as public willingness and regulatory intensity. The improved model, in comparison to traditional infectious disease models, offers greater flexibility and accuracy, addressing the need for frequent modifications in fundamental models within complex environments. Meanwhile, we introduce "swarm entropy" to evaluate infection intensity under various policies. By linking isolation policies with swarm entropy, considering population structure, we quantify the effectiveness of these isolation measures. It provides a novel approach for complex population simulations. These findings have facilitated the enhancement of control strategies and provided decision-makers with guidance in combating the transmission of infectious diseases.

Detecting fake news by exploring the consistency of multimodal data.

During the outbreak of the new Coronavirus (2019-nCoV) in 2020, the spread of fake news has caused serious social panic. Fake news often uses multimedia information such as text and image to mislead readers, spreading and expanding its influence. One of the most important problems in fake news detection based on multimodal data is to extract the general features as well as to fuse the intrinsic characteristics of the fake news, such as mismatch of image and text and image tampering. This paper proposes a Multimodal Consistency Neural Network (MCNN) that considers the consistency of multimodal data and captures the overall characteristics of social media information. Our method consists of five subnetworks: the text feature extraction module, the visual semantic feature extraction module, the visual tampering feature extraction module, the similarity measurement module, and the multimodal fusion module. The text feature extraction module and the visual semantic feature extraction module are responsible for extracting the semantic features of text and vision and mapping them to the same space for a common representation of cross-modal features. The visual tampering feature extraction module is responsible for extracting visual physical and tamper features. The similarity measurement module can directly measure the similarity of multimodal data for the problem of mismatching of image and text. We assess the constructed method in terms of four datasets commonly used for fake news detection. The accuracy of the detection is improved clearly compared to the best available methods.