There have been many proposals and developments to improve smartphone users' location privacy with respect to mobile applications. These include user-centric application permission models and disclosures. However, little attention has been paid to how application developers could build privacypreserving apps. In this paper, we present a laboratory study (N=25) to understand developers' behavior to enhanced APIs, which are a privacy-preserving modification of the existing Android Location API. In contrast to the existing methods, the studied API facilitates acquiring coarse location information without accessing the geocoordinates. Our results indicate that by offering a redesigned API, programmers can be nudged into making choices with their programming that help to preserve privacy of their users. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.
Abstract-Cameras are ubiquitous and increasingly being used not just for capturing images but also for communicating information. For example, the pervasive QR codes can be viewed as communicating a short code to camera-equipped sensors and recent research has explored using screen-to-camera communications for larger data transfers. Such communications could be particularly attractive in pervasive camera based applications, where such camera communications can reuse the existing camera hardware and also leverage from the large pixel array structure for high data-rate communication. While several prototypes have been constructed, the fundamental capacity limits of this novel communication channel in all but the simplest scenarios remains unknown. The visual medium differs from RF in that the information capacity of this channel largely depends on the perspective distortions while multipath becomes negligible. In this paper, we create a model of this communication system to allow predicting the capacity based on receiver perspective (distance and angle to the transmitter). We calibrate and validate this model through lab experiments wherein information is transmitted from a screen and received with a tablet camera. Our capacity estimates indicate that tens of Mbps is possible using a smartphone camera even when the short code on the screen images onto only 15% of the camera frame. Our estimates also indicate that there is room for at least 2.5x improvement in throughput of existing screen -camera communication prototypes.
Smartphone wound image analysis has recently emerged as a viable way to assess healing progress and provide actionable feedback to patients and caregivers between hospital appointments. Segmentation is a key image analysis step, after which attributes of the wound segment (e.g. wound area and tissue composition) can be analyzed. The Associated Hierarchical Random Field (AHRF) formulates the image segmentation problem as a graph optimization problem. Handcrafted features are extracted, which are then classified using machine learning classifiers. More recently deep learning approaches have emerged and demonstrated superior performance for a wide range of image analysis tasks. FCN, U-Net and DeepLabV3 are Convolutional Neural Networks used for semantic segmentation. While in separate experiments each of these methods have shown promising results, no prior work has comprehensively and systematically compared the approaches on the same large wound image dataset, or more generally compared deep learning vs non-deep learning wound image segmentation approaches. In this paper, we compare the segmentation performance of AHRF and CNN approaches (FCN, U-Net, DeepLabV3) using various metrics including segmentation accuracy (dice score), inference time, amount of training data required and performance on diverse wound sizes and tissue types. Improvements possible using various image pre-and post-processing techniques are also explored. As access to adequate medical images/data is a common constraint, we explore the sensitivity of the approaches to the size of the wound dataset. We found that for small datasets (<300 images), AHRF is more accurate than U-Net but not as accurate as FCN and DeepLabV3. AHRF is also over 1000x slower. For larger datasets (>300 images), AHRF saturates quickly, and all CNN approaches (FCN, U-Net and DeepLabV3) are significantly more accurate than AHRF.
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