Recently, transaction fraud costs billions of dollars to card issuers. With the increase in fraud rates, it is important to establish a comprehensive monitoring mechanism for detecting abnormal accounts that conform to the characteristics of online fraudulent activities. This paper primarily utilizes statistical methods and data mining techniques to analyze new signals that can represent the latest fraudulent transaction methods based on account static information, account transaction data and fraud blacklists collected by the financial big data system. To safeguard the financial security of customers, interpretable machine learning algorithms are also proposed to construct fraud detection model. Eventually, extensive experiments demonstrate that the proposed framework achieves state-of-the-art results with the highest F1 score, Recall and Accuracy.

Automatic segmentation of retinal vessels is a critical task in fundoscopic image analysis. The emergence of deep learning has shown promising abilities of feature representation, particularly with Convolutional Neural Networks (CNNs). However, the fixed receptive field in CNNs limits their ability to adapt to the scale variation of natural vascular networks and capture nonlocal context dependencies across feature maps. To address these limitations, we propose a novel model called ARA-GAN that can adaptively extract nonlocal feature contexts and aggregate multi-scale information for retinal vessel segmentation. The proposed model comprises a novel Generative Adversarial Network (GAN) as the overall framework to obtain global information and strong robustness. Additionally, we integrate Residual Nonlocal Attention (RNA) Module into the framework to adaptively capture nonlocal context dependencies across the input features. Finally, we add a Pyramid Pooling Module (PPM) to extract the morphological characteristics of natural retinal vessels at multiple scales. Our experimental results demonstrate that our method outperforms state-of-the-art approaches on both the DRIVE and STARE datasets.

In this paper, we investigate a heterogeneous set of listed companies and extract significant features that can accurately evaluate the investment value of them. Specifically, we analyze both financial data and non-financial data, including: corporate annual report, commercial information, industrial information, land acquisition information, financial information, tax report, intellectual property report, etc. In order to effectively handle a large number of categorical features and mitigate over-fitting problem, CatBoost, LightGBM and ensemble learning framework are adopted to output precise value for each enterprise. Furthermore, extensive experiments demonstrate that the proposed framework achieves state-of-the-art result with RMSE as low as 2.97. Finally, we find that in addition to financial features, many non-financial factors such as Number of Patents (NOP) and Number of Qualification Certifications (NOQC) also play important roles in company investment value evaluation.

Automated retinal vessel segmentation plays an important role in the diagnosis of some diseases such as diabetes, arteriosclerosis and hypertension. Recent works attempt to improve segmentation performance by exploring either global or local contexts. However, the context demands are varying from regions in each image and different levels of network. To address these problems, we propose Pyramid Context-aware Network (PCANet), which can adaptively capture multi-scale context representations. Specifically, PCANet is composed of multiple Adaptive Context-aware (ACA) blocks arranged in parallel, each of which can adaptively obtain the context-aware features by estimating affinity coefficients at a specific scale under the guidance of global contextual dependencies. Meanwhile, we import ACA blocks with specific scales in different levels of the network to obtain a coarse-to-fine result. Furthermore, an integrated test-time augmentation method is developed to further boost the performance of PCANet. Finally, extensive experiments demonstrate the effectiveness of the proposed PCANet, and state-of-the-art performances are achieved with AUCs of 0.9866, 0.9886 and F1 Scores of 0.8274, 0.8371 on two public datasets, DRIVE and STARE, respectively.

Automatic segmentation of retinal vessels is an important step in fundoscopic image analysis. Recently, convolutional-neural-network-based methods have been widely explored in this vision task. However, the local fixed receptive field makes network unable to collect global information and adapt to scale variation of retinal vessels. In this paper, we propose a novel RNA-Net which can capture nonlocal context dependencies across the inputs and extract multi-scale features for segmentation task. Firstly, we build a Residual Nonlocal Attention (RNA) Module, which can guide the network to pay more attention to task-related regions of the whole feature map. Secondly, to better capture the morphological characteristics of natural blood vessels, Pyramid Pooling Module (PPM) is added to capture features at multiple scales. Experimental results on two public datasets DRIVE and STARE clearly demonstrate that our method outperforms the current state-of-the-art approaches.