Comparison of the Methods for Converting Traditional Credit Rating into the Initial Credit Score in Electronic Commerce

When a company undertakes e-commerce transactions for the first time, most major web sites set the initial credit score of the company as zero, which making buyers and sellers can’t judge the partners’ credibility. In recent years, although commercial banks and some specialized credit rating agencies have established more comprehensive and scientific indicators for evaluating the credit of an enterprise, few scholars apply such credit evaluation indicators to the credit management of e-commerce business. Yu Yang and Guangxing Song (2009) put forward a method to convert the traditional credit rating from the credit rating agency—Standard & Poor’s into the initial credit score. Based on this work, the authors convert the traditional credit rating from the credit rating agency—Moody’s into the initial credit score, and compare these two methods, hoping to encourage companies with rating score to participate in e-commerce transactions with true identity. On the background of current internet real-name system implementation, our research is very important for enterprise credit management

Fooling Vision and Language Models Despite Localization and Attention Mechanism

Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial examples for vision and language models, which incorporate natural language understanding and complex structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense captioning model and on two visual question answering (VQA) models. Our evaluation shows that we can generate adversarial examples with a high success rate (i.e., > 90%) for these models. Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks. These observations will inform future work towards building effective defenses.Comment: CVPR 201

Fooling Vision and Language Models Despite Localization and Attention Mechanism

Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial examples for vision and language models, which incorporate natural language understanding and complex structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense captioning model and on two visual question answering (VQA) models. Our evaluation shows that we can generate adversarial examples with a high success rate (i.e., > 90%) for these models. Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks. These observations will inform future work towards building effective defenses.Comment: CVPR 201