Refined analysis and a hierarchical multi-task learning approach for loan fraud detection

Fraud problems in loan application assessment cause significant losses for finance companies worldwide, and much research has focused on machine learning methods to improve the efficacy of fraud detection in some financial domains. However, diverse information falsification in individual fraud remains one of the most challenging problems in loan applications. To this end, we conducted an empirical study to explore the relationships between various fraud types and analyzed the factors influencing information fabrication. Weak relationships exist among different falsification types, and some essential factors play the same roles in different fraud types. In contrast, others have various or opposing effects on these types of frauds. Based on this finding, we propose a novel hierarchical multi-task learning approach to refine fraud-detection systems. Specifically, we first developed a hierarchical fraud category method to break down this problem into several subtasks according to the information types falsified by customers, reducing fraud identification's difficulty. Second, a heterogeneous network with a meta-path-based random walk and heterogeneous skip-gram model can solve the representation learning problem owing to the sophisticated relationships among the applicants' information. Furthermore, the final subtasks can be predicted using a multi-task learning approach with two prediction layers. The first layer provides the probabilities of general fraud categories as auxiliary information for the second layer, which is for specific subtask prediction. Finally, we conducted extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed approach.     

False dichotomy alert: Improving subjective-probability estimates vs. raising awareness of systemic risk

Taleb et al. (2022) portray the superforecasting research program as a masquerade that purports to build “survival functions for tail assessments via sports-like tournaments.” But that never was the goal. The program was designed to help intelligence analysts make better probability judgments, which required posing rapidly resolvable questions. From a signal detection theory perspective, the superforecasting and Taleb et al. programs are complementary, not contradictory (a point Taleb and Tetlock (2013) recognized). The superforecasting program aims at achieving high hit rates at low cost in false-positives, whereas Taleb et al. prioritize alerting us to systemic risk, even if that entails a high false-positive rate. Proponents of each program should, however, acknowledge weaknesses in their cases. It is unclear: (a) how Taleb et al. (2022) can justify extreme error-avoidance trade-offs, without tacit probability judgments of rare, high-impact events; (b) how much superforecasting interventions can improve probability judgments of such events.     

应用锁相环技术判别混沌相变的方法

针对经典的李氏指数法（Lyapunov Exponential Method）等混沌相变判别方法复杂度高的问题，提出了一种应用锁相环技术判别混沌相变的新方法。首先，理论推导了混沌系统的解析特性，分析了系统在不同相态下含有的频率成分；〖JP2〗然后，构建了针对混沌系统的数字锁相环模型，研究锁相环下混沌态和大周期态呈现的频率特性；最后，提出了一种基于锁相环技术的混沌相变判别新方法。仿真实验显示，相比于李氏指数法，所提方法判别速度快一个数量级，检测差错率为0时，性能提高近2 dB。新方法应用锁相环技术，简便易行，判别速度快，为混沌相变判别的工程应用提供了新的手段。     

基于轻量级卷积神经网络的苹果表面缺陷检测方法

为了解决目前农业信息领域对苹果表面缺陷检测准确率低的问题,提出一种基于轻量级卷积神经网络的苹果表面缺陷检测方法.首先采集苹果缺陷样本图片制作实验数据集用于模型训练和测试;其次在AlexNet网络结构的基础上,引入深度可分离卷积代替原有网络中的标准卷积运算来进行图像特征的提取;最后利用全局平均池化方法代替原有网络中的全连...