Fund loss is an increasingly critical problem caused by the misbehavior of software, especially in fintech and e-commerce platforms. Data reconciliation is one of the most commonly used approaches in detecting and preventing fund loss by executing reconciliation scripts on data storage systems (e.g., database and cache systems). The core of reconciliation scripts is the data constraints, which can be expressed as implications with two parts: preconditions and assertions. However, due to the complexity and diversity of business, the construction of data constraints and reconciliation scripts usually heavily relies on business experts. To this end, we propose AutoReconciler to mine data constraints from business data and generate reconciliation scripts automatically. It can mine assertions via enhanced symbolic regression, discover preconditions via association rule mining, and generate reconciliation scripts in SQL form. We have performed extensive experiments on the synthesized data. The result shows that our approach outperforms the baseline by a large margin (an average improvement in precision and recall of 22.1% and 51.6%, respectively), especially for complex data constraints. Our solution has been implemented, deployed, and adopted in production, and we conducted several case studies further to confirm the benefits of our solution in industrial scenarios.

The computer-aided diagnosis for histopathological images has attracted considerable attention. Principal component analysis network (PCANet) is a novel deep learning algorithm for feature learning with the simple network architecture and parameters. In this study, a color pattern random binary hashing-based PCANet (C-RBH-PCANet) algorithm is proposed to learn an effective feature representation from color histopathological images. The color norm pattern and angular pattern are extracted from the principal component images of R, G, and B color channels after cascaded PCA networks. The random binary encoding is then performed on both color norm pattern images and angular pattern images to generate multiple binary images. Moreover, we rearrange the pooled local histogram features by spatial pyramid pooling to a matrix-form for reducing the dimension of feature and preserving spatial information. Therefore, a C-RBH-PCANet and matrix-form classifier-based feature learning and classification framework is proposed for diagnosis of color histopathological images. The experimental results on three color histopathological image datasets show that the proposed C-RBH-PCANet algorithm is superior to the original PCANet and other conventional unsupervised deep learning algorithms, while the best performance is achieved by the proposed feature learning and classification framework that combines C-RBH-PCANet and matrix-form classifier.