Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems

Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their self-interest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive realworld trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher

Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.Comment: Submitted to ROBIO 201

The Utility of a Digital Virtual Template for Junior Surgeons in Pedicle Screw Placement in the Lumbar Spine

This study assessed the utility of three-dimensional preoperative image reconstruction as digital virtual templating for junior surgeons in placing a pedicle screw (PS) in the lumbar spine. Twenty-three patients of lumbar disease were operated on with bilateral PS fixation in our hospital. The two sides of lumbar pedicles were randomly divided into "hand-free group" (HFG) and "digital virtual template group" (DVTG) in each patient. Two junior surgeons preoperatively randomly divided into these two groups finished the placement of PSs. The accuracy of PS and the procedure time of PS insertion were recorded. The accuracy of PS in DVTG was 91.8% and that in HFG was 87.7%. The PS insertion procedure time of DVTG was 74.5 ± 8.1 s and that of HFG was 90.9 ± 9.9 s. Although no significant difference was reported in the accurate rate of PS between the two groups, the PS insertion procedure time was significantly shorter in DVTG than in HFG ( < 0.05). Digital virtual template is simple and can reduce the procedure time of PS placement