Machine-to-machine (M2M) techniques have significant application potential in the emerging internet of things, which may cover many fields from intelligence to ubiquitous environment. However, because of the data exposure when transmitted via cable, wireless mobile devices, and other technologies, its security vulnerability has become a great concern during its further extending development. This problem may even get worse if the user privacy and property are considered. Therefore, the authentication process of communicating entities has attracted wide investigation. Meanwhile, the data confidentiality also becomes an important issue in M2M, especially when the data are transmitted in a public and thereby insecure channel. In this paper, we propose a promising M2M application model that connects a mobile user with the home network using the existing popular Time Division-Synchronous Code Division Multiple Access (TD-SCDMA) network. Subsequently, a password-based authentication and key establishment protocol is designed to identify the communicating parties and hence establish a secure channel for data transmissions. The final analysis shows the reliability of our proposed protocol.A security authentication scheme in M2M home network service X. Sun et al.

RGB images differentiate from depth as they carry more details about the color and texture information, which can be utilized as a vital complement to depth for boosting the performance of 3D semantic scene completion (SSC). SSC is composed of 3D shape completion (SC) and semantic scene labeling while most of the existing approaches use depth as the sole input which causes the performance bottleneck. Moreover, the state-of-the-art methods employ 3D CNNs which have cumbersome networks and tremendous parameters. We introduce a light-weight Dimensional Decomposition Residual network (DDR) for 3D dense prediction tasks. The novel factorized convolution layer is effective for reducing the network parameters, and the proposed multi-scale fusion mechanism for depth and color image can improve the completion and segmentation accuracy simultaneously. Our method demonstrates excellent performance on two public datasets. Compared with the latest method SSCNet, we achieve 5.9% gains in SC-IoU and 5.7% gains in SSC-IOU, albeit with only 21% network parameters and 16.6% FLOPs employed compared with that of SSCNet.

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Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, welldocumented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

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