Credit Risk Measurement with Wrong Way Risk

I will start with introducing the corporate bond and several important components of it. The existing credit risk model can be categorized into two groups — Structural (Firm Value) Model and Reduced-form (Intensity-based) Models, followed by the risk measure and the risk measure—Value at Risk and its computation. Then I applied the previously introduced material to the given portfolio to calculate its credit VaR using two methods, S-critical and the Monte Carlo simulation. Finally, I present some advanced credit risk models with stochastic interest rate

Stealing Links from Graph Neural Networks

Graph data, such as chemical networks and social networks, may be deemed confidential/private because the data owner often spends lots of resources collecting the data or the data contains sensitive information, e.g., social relationships. Recently, neural networks were extended to graph data, which are known as graph neural networks (GNNs). Due to their superior performance, GNNs have many applications, such as healthcare analytics, recommender systems, and fraud detection. In this work, we propose the first attacks to steal a graph from the outputs of a GNN model that is trained on the graph. Specifically, given a black-box access to a GNN model, our attacks can infer whether there exists a link between any pair of nodes in the graph used to train the model. We call our attacks link stealing attacks. We propose a threat model to systematically characterize an adversary's background knowledge along three dimensions which in total leads to a comprehensive taxonomy of 8 different link stealing attacks. We propose multiple novel methods to realize these 8 attacks. Extensive experiments on 8 real-world datasets show that our attacks are effective at stealing links, e.g., AUC (area under the ROC curve) is above 0.95 in multiple cases. Our results indicate that the outputs of a GNN model reveal rich information about the structure of the graph used to train the model.Comment: To appear in the 30th Usenix Security Symposium, August 2021, Vancouver, B.C., Canad

A Stronger Stitching Algorithm for Fisheye Images based on Deblurring and Registration

Fisheye lens, which is suitable for panoramic imaging, has the prominent advantage of a large field of view and low cost. However, the fisheye image has a severe geometric distortion which may interfere with the stage of image registration and stitching. Aiming to resolve this drawback, we devise a stronger stitching algorithm for fisheye images by combining the traditional image processing method with deep learning. In the stage of fisheye image correction, we propose the Attention-based Nonlinear Activation Free Network (ANAFNet) to deblur fisheye images corrected by Zhang calibration method. Specifically, ANAFNet adopts the classical single-stage U-shaped architecture based on convolutional neural networks with soft-attention technique and it can restore a sharp image from a blurred image effectively. In the part of image registration, we propose the ORB-FREAK-GMS (OFG), a comprehensive image matching algorithm, to improve the accuracy of image registration. Experimental results demonstrate that panoramic images of superior quality stitching by fisheye images can be obtained through our method.Comment: 6 pages, 5 figure

Architecture design of Jing Gangshan virtual tourism system based on WebVR

In view of the current mainstream VR virtual tourism system, because of the heavy virtual scenes and limited network bandwidth, tourists can't browse the WEB pages directly, and they need to download plugins or client systems to browse. This paper studies the lightweight architecture of virtual tourism roaming system. Research technologies such as lightweight modeling of 3D scenes, 3D engine call and lightweight script design, build a cloud storage transmission platform, integrate key technologies such as tour guides, and construct the online Jinggangshan WebVR system. The system based on this architecture will enable visitors to browse Web pages directly, online and quickly in real time, and improve the sense of interaction and immersion of the system

Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration

Since the differences in viewing range, resolution and relative position, the multi-modality sensing module composed of infrared and visible cameras needs to be registered so as to have more accurate scene perception. In practice, manual calibration-based registration is the most widely used process, and it is regularly calibrated to maintain accuracy, which is time-consuming and labor-intensive. To cope with these problems, we propose a scene-adaptive infrared and visible image registration. Specifically, in regard of the discrepancy between multi-modality images, an invertible translation process is developed to establish a modality-invariant domain, which comprehensively embraces the feature intensity and distribution of both infrared and visible modalities. We employ homography to simulate the deformation between different planes and develop a hierarchical framework to rectify the deformation inferred from the proposed latent representation in a coarse-to-fine manner. For that, the advanced perception ability coupled with the residual estimation conducive to the regression of sparse offsets, and the alternate correlation search facilitates a more accurate correspondence matching. Moreover, we propose the first ground truth available misaligned infrared and visible image dataset, involving three synthetic sets and one real-world set. Extensive experiments validate the effectiveness of the proposed method against the state-of-the-arts, advancing the subsequent applications.Comment: 10 pages, 11 figure

Rebounding through the pandemic:towards the digitized and digitalized small hospitality business in China

Purpose Grappling with the sweeping pandemic, the small hospitality business (SHB), smaller in scale and weaker in risk mitigation, has been seriously affected. The purpose of this study aims to supplement the unrepresented area of SHB in China from the digital perspective by drawing on instrumentalization theory (IT). Design/methodology/approach Based on two appropriate and detailed SHB cases, this paper adopted a qualitative approach to understand and conceptualize the focal issue. Findings This study identified the factors affecting SHB at operational, managerial and transformational levels amidst the crisis. It further developed a theoretical framework of the SHB rebound matrix, highlighting the importance of digitization and digitalization. Research limitations/implications The research theoretically confirmed that SHB is internally, externally and essentially restricted and developed a corresponding rebound matrix. It practically supports SHB’s transformation by making recommendations to unleash the potential of digital business. Originality/value This study complements extant descriptive and atheoretical research by focusing on SHB’s underlying digital nature through the lens of IT, providing an evidenced theoretical understanding of SHB’s development amidst and after the pandemic