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In this paper, we propose a browser fingerprinting technique that can track users not only within a single browser but also across different browsers on the same machine. Specifically, our approach utilizes many novel OS and hardware level features, such as those from graphics cards, CPU, and installed writing scripts. We extract these features by asking browsers to perform tasks that rely on corresponding OS and hardware functionalities.Our evaluation shows that our approach can successfully identify 99.24% of users as opposed to 90.84% for state of the art on single-browser fingerprinting against the same dataset. Further, our approach can achieve higher uniqueness rate than the only cross-browser approach in the literature with similar stability.In the paper, we propose a (cross-)browser fingerprinting based on many novel OS and hardware level features, e.g., these from graphics card, CPU, audio stack, and installed writing scripts. Specifically, because many of such OS and hardware level functions are exposed to JavaScript via browser APIs, we can extract features when asking the browser to perform certain tasks through these APIs. The extracted features can be used for both single-and cross-browser fingerprinting.

The lack of reliable techniques to follow up scoliotic deformity from the external asymmetry of the trunk leads to a general use of X-rays and indices of spinal deformity. Young adolescents with idiopathic scoliosis need intensive follow-ups for many years and, consequently, they are repeatedly exposed to ionising radiation, which is hazardous to their long-term health. Furthermore, treatments attempt to improve both spinal and surface deformities, but internal indices do not describe the external asymmetry. The purpose of this study was to assess a commercial, optical 3D digitising system for the 3D reconstruction of the entire trunk for clinical assessment of external asymmetry. The resulting surface is a textured, high-density polygonal mesh. The accuracy assessment was based on repeated reconstructions of a manikin with markers fixed on it. The average normal distance between the reconstructed surfaces and the reference data (markers measured with CMM) was 1.1 +/- 0.9 mm.

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Style transfer describes the rendering of an image's semantic content as different artistic styles. Recently, generative adversarial networks (GANs) have emerged as an effective approach in style transfer by adversarially training the generator to synthesize convincing counterfeits. However, traditional GAN suffers from the mode collapse issue, resulting in unstable training and making style transfer quality difficult to guarantee. In addition, the GAN generator is only compatible with one style, so a series of GANs must be trained to provide users with choices to transfer more than one kind of style. In this paper, we focus on tackling these challenges and limitations to improve style transfer. We propose adversarial gated networks (Gated-GAN) to transfer multiple styles in a single model. The generative networks have three modules: an encoder, a gated transformer, and a decoder. Different styles can be achieved by passing input images through different branches of the gated transformer. To stabilize training, the encoder and decoder are combined as an auto-encoder to reconstruct the input images. The discriminative networks are used to distinguish whether the input image is a stylized or genuine image. An auxiliary classifier is used to recognize the style categories of transferred images, thereby helping the generative networks generate images in multiple styles. In addition, Gated-GAN makes it possible to explore a new style by investigating styles learned from artists or genres. Our extensive experiments demonstrate the stability and effectiveness of the proposed model for multi-style transfer.

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