This study employs an unsupervised learning technique to identify features of misinformation on Weibo, a popular social media platform in China. By utilizing BERT (Bidirectional Encoder Representations from Transformers) for sequence classification, we were able to detect a dataset with a mix of true and false information, achieving a perfect recall rate for false information detection. However, the precision for true information was low, with a 3% precision, 6% recall, and an F1 score of 6%, indicating a high rate of misclassification. To address this, we conducted a Latent Dirichlet Allocation (LDA) analysis on the misclassified true information, identifying specific features that led to incorrect classification. Additionally, social network analysis revealed the presence of structural holes within the information network. This study contributes to the understanding of misinformation detection mechanisms and provides insights into the social dynamics of information spread.

Few-shot anomaly detection (FSAD) aims to identify anomalies using limited samples. While this task has been widely studied in the image field, existing image-based models cannot be directly applied to tabular data. The reason is that the low-level detailed information in tabular data is crucial for few-shot anomaly detection and cannot be overlooked, as it often is when dealing with images. Existing tabular-based few-shot anomaly detection methods rely on generative models to increase the sample size for training. However, these generative models struggle to extract the correct distribution from small amounts of data and suffer from severe overfitting. To address these issues, this paper proposes a few-shot anomaly detection framework for tabular data, called Maxout Low-dimentional Tangent Data Description (MLTDD). Specifically, we design a Maxout Two-Stage Low-Dimensional Representation Extractor (MaxLR) for low-dimensional representation, which trains a random model in the first stage and embeds the data into a low-dimensional space using a non-probabilistic model. Moreover, we propose a few-shot one-class model called Tangent Support Vector Data Description (TSVDD) for anomaly detection. We conduct experiments on ten datasets and show that MLTDD achieves an overall improvement of more than 10% compared to advanced baseline models and generalizes well in the face of unknown anomalies. The ablation experiment also proved the correctness of our constructed module.

Accurate time series modeling plays a critical role in health, financial, and industrial applications. However, real-world deployment faces two key issues: (1) scarcity of clean training data, and (2) valuable information is locked in structured tabular formats, and cannot be imported into time series models. These limitations significantly constrain prediction performance in time series tasks. Cross-modal transfer learning (CMTL) provides a possible solution. Existing CMTL approaches succeed with image, text, and audio data by leveraging their inherent local correlations and hierarchical structures. However, tabular data presents unique difficulties. Specifically, the discrete, heterogeneous nature of tabular features, and lack of explicit temporal relationships in columns, prevent direct geometric alignment with time series waveforms. In fact, although it has no temporal nature, tabular data still contains rich domain knowledge that can be used by time series tasks. In this paper, we propose a cross-modal transfer learning (CMTL) model from tabular to time series data, called TSgraph. TSgraph first extracts the domain topology and feature interaction paradigm from tabular data, and constructs it as a graph structure. Then, the time series graph of each time step is dynamically updated in combination with these graphs, and finally get the classification results. We validate the performance of TSgraph on four real-world datasets from different domains and compare the results with state-of-the-art baseline models. Experiments show that our model achieves the best performance on multiple evaluation metrics.

This study investigated the impact of psychological resilience on job burnout among healthcare workers, with occupational stress as a mediator and organizational trust as a moderator. A total of 303 healthcare workers completed questionnaires measuring psychological resilience, job burnout, occupational stress, and organizational trust. Structural equation modeling was used to analyze the relationships among variables. Results indicated that psychological resilience significantly reduced job burnout both directly and indirectly through decreased occupational stress. Organizational trust moderated the relationship between psychological resilience and occupational stress, strengthening the negative effect of resilience on stress when trust was high. The findings highlight the importance of enhancing healthcare workers’ psychological resilience and fostering organizational trust to mitigate occupational stress and job burnout in healthcare settings.