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

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.Comment: Submitted to IEEE TM

WristSpy: Snooping Passcodes in Mobile Payment Using Wrist-worn Wearables

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 or patterns) are the first choice of most consumers to authorize the payment. This paper 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, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios

Differences in the impact of land transfer on poverty vulnerability among households with different livelihood structures

IntroductionEradicating poverty is the primary objective of the United Nations 2030 Agenda for Sustainable Development. While China has achieved great success in achieving poverty reduction targets, reducing the poverty vulnerability of rural households is crucial for ensuring the sustainability of poverty reduction gains. The purpose of land transfer is to ensure the continuous increase of farmers’ income through efficient land use; it has become an important initiative for poverty alleviation in rural areas. Existing studies have confirmed the positive effect of land transfer on poverty alleviation, but few have explored the difference in the impact of land transfer on poverty vulnerability of households with different income structures.MethodsUsing data from the China Family Panel Survey (CFPS) from 2010 to 2020, this paper empirically examines the impact of land transfer on poverty vulnerability.Results and discussionThe results show that land transfer has a significant positive impact on poverty vulnerability alleviation among rural households. Further comparing households with different livelihood structures, we find that land transfer is more effective in reducing poverty for non-farm employment-oriented household. Therefore, we suggest that the government should improve the land transfer system, increase agricultural subsidies, and consider the occupational differentiation among farmers to improve the poverty reduction effect of land transfer. These suggestions also provide a reference for promoting sustainable agricultural development and consolidating the achievements of poverty alleviation

Type H vessels: functions in bone development and diseases

Type H vessels are specialized blood vessels found in the bone marrow that are closely associated with osteogenic activity. They are characterized by high expression of endomucin and CD31. Type H vessels form in the cancellous bone area during long bone development to provide adequate nutritional support for cells near the growth plate. They also influence the proliferation and differentiation of osteoprogenitors and osteoclasts in a paracrine manner, thereby creating a suitable microenvironment to facilitate new bone formation. Because of the close relationship between type H vessels and osteogenic activity, it has been found that type H vessels play a role in the physiological and pathological processes of bone diseases such as fracture healing, osteoporosis, osteoarthritis, osteonecrosis, and tumor bone metastasis. Moreover, experimental treatments targeting type H vessels can improve the outcomes of these diseases. Here, we reviewed the molecular mechanisms related to type H vessels and their associated osteogenic activities, which are helpful in further understanding the role of type H vessels in bone metabolism and will provide a theoretical basis and ideas for comprehending bone diseases from the vascular perspective

When Your Wearables Become Your Fitness Mate

Acknowledging the powerful sensors on wearables and smartphones enabling various applications to improve users' life styles and qualities (e.g., sleep monitoring and running rhythm tracking), this paper takes one step forward developing FitCoach, a virtual fitness coach leveraging users' wearable mobile devices (including wrist-worn wearables and arm-mounted smartphones) to assess dynamic postures (movement patterns & positions) in workouts. FitCoach aims to help the user to achieve effective workout and prevent injury by dynamically depicting the short-term and long-term picture of a user's workout based on various sensors in wearable mobile devices. In particular, FitCoach recognizes different types of exercises and interprets fine-grained fitness data (i.e., motion strength and speed) to an easy-to-understand exercise review score, which provides a comprehensive workout performance evaluation and recommendation. Our system further enables contactless device control during workouts (e.g., gesture to pick up an incoming call) through distinguishing customized gestures from regular exercise movement. In addition, FitCoach has the ability to align the sensor readings from wearable devices to the human coordinate system, ensuring the accuracy and robustness of the system. Extensive experiments with over 5000 repetitions of 12 types of exercises involve 12 participants doing both anaerobic and aerobic exercises in indoors as well as outdoors. Our results demonstrate that FitCoach can provide meaningful review and recommendations to users by accurately measure their workout performance and achieve and accuracy for workout analysis and customized control gesture recognition, respectively

Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders

Semantic segmentation of point clouds generates comprehensive understanding of scenes through densely predicting the category for each point. Due to the unicity of receptive field, semantic segmentation of point clouds remains challenging for the expression of multi-receptive field features, which brings about the misclassification of instances with similar spatial structures. In this paper, we propose a graph convolutional network DGFA-Net rooted in dilated graph feature aggregation (DGFA), guided by multi-basis aggregation loss (MALoss) calculated through Pyramid Decoders. To configure multi-receptive field features, DGFA which takes the proposed dilated graph convolution (DGConv) as its basic building block, is designed to aggregate multi-scale feature representation by capturing dilated graphs with various receptive regions. By simultaneously considering penalizing the receptive field information with point sets of different resolutions as calculation bases, we introduce Pyramid Decoders driven by MALoss for the diversity of receptive field bases. Combining these two aspects, DGFA-Net significantly improves the segmentation performance of instances with similar spatial structures. Experiments on S3DIS, ShapeNetPart and Toronto-3D show that DGFA-Net outperforms the baseline approach, achieving a new state-of-the-art segmentation performance.Comment: accepted to AAAI Workshop 202