Software defect prediction (SDP) is an effective approach to ensure software reliability. Machine learning models have been widely employed in SDP, but they ignore the impact of class imbalance, noise and outliers on the prediction performance. This study proposes a fuzzy cost broad learning system (FC‐BLS). FC‐BLS not only handles class imbalance problems but also considers the specific sample distribution to address noise and outliers in software defect datasets. Our approach draws fully on the idea of the cost matrix and fuzzy membership functions. It introduces them to BLS, where the cost matrix prioritises the training errors on the minority samples. Hence, the classification hyperplane position is more reasonable, and fuzzy membership functions calculate the membership degree of the sample in a feature mapping space to remove the prediction error caused by noise and outlier samples. Then, the optimisation problem is constructed based on the idea that the minority class and normal instances have relatively high costs. By contrast, the majority class and noise and outlier instances have relatively small costs. This study conducted experiments on nine NASA SDP datasets, and the experimental findings demonstrated the effectiveness of the proposed methodology on most datasets.

Human pose and shape (HPS) estimation with lensless imaging is not only beneficial to privacy protection but also can be used in covert surveillance scenarios due to the small size and simple structure of this device. However, this task presents significant challenges due to the inherent ambiguity of the captured measurements and lacks effective methods for directly estimating human pose and shape from lensless data. In this paper, we propose the first end-to-end framework to recover 3D human poses and shapes from lensless measurements to our knowledge. We specifically design a multi-scale lensless feature decoder to decode the lensless measurements through the optically encoded mask for efficient feature extraction. We also propose a double-head auxiliary supervision mechanism to improve the estimation accuracy of human limb ends. Besides, we establish a lensless imaging system and verify the effectiveness of our method on various datasets acquired by our lensless imaging system. The code and dataset are available at https://cic.tju.edu.cn/faculty/likun/projects/LPSNet.