Inferring Mobile Payment Passcodes Leveraging Wearable Devices

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs) are the first choice of most consumers to authorize the payment. This work demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, which examines to what extent the user's PIN during mobile payment could be revealed from a single wrist-worn wearable device under different input scenarios involving either two hands or a single hand. Extensive experiments with 15 volunteers demonstrate that an adversary is able to recover a user's PIN with high success rate within 5 tries under various input scenarios

DISC: Deep Image Saliency Computing via Progressive Representation Learning

Salient object detection increasingly receives attention as an important component or step in several pattern recognition and image processing tasks. Although a variety of powerful saliency models have been intensively proposed, they usually involve heavy feature (or model) engineering based on priors (or assumptions) about the properties of objects and backgrounds. Inspired by the effectiveness of recently developed feature learning, we provide a novel Deep Image Saliency Computing (DISC) framework for fine-grained image saliency computing. In particular, we model the image saliency from both the coarse- and fine-level observations, and utilize the deep convolutional neural network (CNN) to learn the saliency representation in a progressive manner. Specifically, our saliency model is built upon two stacked CNNs. The first CNN generates a coarse-level saliency map by taking the overall image as the input, roughly identifying saliency regions in the global context. Furthermore, we integrate superpixel-based local context information in the first CNN to refine the coarse-level saliency map. Guided by the coarse saliency map, the second CNN focuses on the local context to produce fine-grained and accurate saliency map while preserving object details. For a testing image, the two CNNs collaboratively conduct the saliency computing in one shot. Our DISC framework is capable of uniformly highlighting the objects-of-interest from complex background while preserving well object details. Extensive experiments on several standard benchmarks suggest that DISC outperforms other state-of-the-art methods and it also generalizes well across datasets without additional training. The executable version of DISC is available online: http://vision.sysu.edu.cn/projects/DISC.Comment: This manuscript is the accepted version for IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), 201

Exchange Rate Risk and Trade Mode Choice in the Processing Trade: Evidence from Chinese Data

This study investigates the impact of exchange rate fluctuations on trade mode choices among assembly firms. Using the Chinese Customs data from 2000 to 2006, we show that exchange rate pass-through (ERPT) depends on which entity is responsible for importing inputs. Relative to passively receiving inputs under pure assembly (PA) mode, foreign invested assembly firms mainly source inputs by themselves through import and assembly (IA) mode and enjoy lower ERPT by doing so. We then relate exchange rate fluctuations to processing mode choices and find that the share of import through PA increases with exchange rate volatilities. This effect is more pronounced for firms in liquidity constrained industries and is mitigated by better local financial development

Evaluating Chinese policy on post-resettlement support for dam-induced displacement and resettlement

Being resettled is a complex and traumatic process. International experience reveals that people are made worse off by project induced displacement and resettlement. In China, a country with much dam induced resettlement, since 2006 there has been considerable government attention to ensure that post-resettlement outcomes are improved and that people are made better off as a result of being resettled. We describe the context of dam-induced resettlement in China, and analyse the post-resettlement support mechanisms used. We identify the key success factors that have led to effective outcomes. They included: a trigger that prompted the government to take action; a change in development philosophy to a more people-oriented approach and acceptance that resettled people and host communities had to be made better off; a market-oriented approach in the way post-resettlement support was delivered and in terms of cross-subsidizing resettlement from hydroelectricity production; long term support to resettled people and host communities; and considerable public participation so that the post-resettlement support schemes were of value to the resettled people and host communities