The rapid spread of misinformation threatens public safety and social stability. Although deep learning-based detection methods have achieved promising results, their effectiveness heavily relies on large amounts of labeled data, limiting their applicability in low-resource scenarios. Existing approaches, such as domain adaptation and metalearning, attempt to transfer knowledge from related source domains but often fail to fully address the challenges of data scarcity and annotation costs. Moreover, traditional active learning strategies typically focus solely on textual uncertainty, overlooking domain-specific discrepancies and the critical role of affective information in misinformation content. To address these challenges, this paper proposes a dual-aspect active learning framework with domain-adversarial training (DDT), tailored for low-resource misinformation detection. The framework integrates a dual-aspect sampling strategy that jointly considers textual and affective features to select samples that are both informative (diverse from labeled data) and uncertain (near decision boundaries). Additionally, a domain-adversarial training module is employed to extract domain-invariant representations, mitigating distribution shifts between source and target domains. Experimental results on multiple benchmark datasets demonstrate that DDT consistently outperforms baseline methods in low-resource settings, enhancing the robustness and generalizability of misinformation detection models.

LiDAR is widely used in autonomous driving. Although LiDAR point cloud data can provide stable and reliable information about the environment, it also faces the problem of a huge amount of data. One of the reasons is that point cloud data contains a large amount of noise and outliers. Outlier removal of point clouds can reduce the impact of these disturbances and improve the quality of the point cloud, but it will inevitably eliminate some valid points, which affects subsequent perception tasks. To overcome this limitation, this paper proposes a fuzzy outlier removal (FOR) method based on fuzzy theory and informativeness. It uses fuzzy theory to model the uncertainty of the membership degree of each point in each dimension, calculates the informativeness sum of each point based on membership degree, and filters points according to the informativeness. FOR is characterized by filtering the point cloud in the edge region on the premise of retaining the point cloud in the center region, so as to preserve the environmental information in the center region and reduce the impact of outlier removal on subsequent perception tasks. The experiments focus on the contradictory relationship between outlier removal and perception accuracy, and verify the effectiveness of FOR with multiple object detection models on the autonomous driving datasets KITTI and nuScenes. The experimental results indicate that, compared with other point cloud outlier removal methods, FOR has the advantage of reducing inference time while retaining detection accuracy, demonstrating balanced high performance across different datasets and detection models.