Many deep learning (DL) models have shown exceptional promise in radar-based human activity recognition (HAR) area. For radar-based HAR, the raw data is generally converted into a 2-D spectrogram by using short-time Fourier transform (STFT). All the existing DL methods treat the spectrogram as an optical image, and thus the corresponding architectures such as 2-D convolutional neural networks (2D-CNNs) are adopted in those methods. These 2-D methods that ignore temporal characteristics ordinarily lead to a complex network with a huge amount of parameters but limited recognition accuracy. In this paper, for the first time, the radar spectrogram is treated as a time sequence with multiple channels. Hence, we propose a DL model composed of 1-D convolutional neural networks (1D-CNNs) and long shortterm memory (LSTM). The experiments results show that the proposed model can extract spatio-temporal characteristics of the radar data and thus achieves the best recognition accuracy and relatively low complexity compared to the existing 2D-CNN methods. INDEX TERMS Radar signal processing, human activity recognition, convolutional neural network, recurrent neural network, deep learning.

An efficient higher-order finite difference algorithm is presented in this article for solving systems of twodimensional reaction-diffusion equations with nonlinear reaction terms. The method is fourth-order accurate in both the temporal and spatial dimensions. It requires only a regular five-point difference stencil similar to that used in the standard second-order algorithm, such as the Crank-Nicolson algorithm. The Padé approximation and Richardson extrapolation are used to achieve high-order accuracy in the spatial and temporal dimensions, respectively. Numerical examples are presented to demonstrate the efficiency and accuracy of the new algorithm.

In this article, we discuss a scheme for dealing with Neumann and mixed boundary conditions using a compact stencil. The resulting compact algorithm for solving systems of nonlinear reaction-diffusion equations is fourth-order accurate in both the temporal and spatial dimensions. We also prove that the standard second-order approximation to zero Neumann boundary conditions provides fourth-order accuracy when the nonlinear reaction term is independent of the spatial variables. Numerical examples, including an application of this algorithm to a mathematical model describing frontal polymerization process, are presented in the article to demonstrate the accuracy and efficiency of the scheme.

With its huge real-world demands, large-scale confidential computing still cannot be supported by today's Trusted Execution Environment (TEE), due to the lack of scalable and effective protection of high-throughput accelerators like GPUs, FPGAs, and TPUs etc. Although attempts have been made recently to extend the CPU-like enclave to GPUs, these solutions require change to the CPU or GPU chips, may introduce new security risks due to the side-channel leaks in CPU-GPU communication and are still under the resource constraint of today's CPU TEE.To address these problems, we present the first Heterogeneous TEE design that can truly support large-scale compute or data intensive (CDI) computing, without any chip-level change. Our approach, called HETEE, is a device for centralized management of all computing units (e.g., GPUs and other accelerators) of a server rack. It is uniquely designed to work with today's data centres and clouds, leveraging modern resource pooling technologies to dynamically compartmentalize computing tasks, and enforce strong isolation and reduce TCB through hardware support. More specifically, HETEE utilizes the PCIe ExpressFabric to allocate its accelerators to the server node on the same rack for a non-sensitive CDI task, and move them back into a secure enclave in response to the demand for confidential computing. Our design runs a thin TCB stack for security management on a security controller (SC), while leaving a large set of software (e.g., AI runtime, GPU driver, etc.) to the integrated microservers that operate enclaves. An enclaves is physically isolated from others through hardware and verified by the SC at its inception. Its microserver and computing units are restored to a secure state upon termination.We implemented HETEE on a real hardware system, and evaluated it with popular neural network inference and training tasks. Our evaluations show that HETEE can easily support the CDI tasks on the real-world scale and incurred a maximal throughput overhead of 2.17% for inference and 0.95% for training on ResNet152.Recent years have seen attempts to support the heterogeneous TEE. Examples include Graviton [15] and HIX [16]. However, all these approaches require changes to CPU and (or) GPU chips, which prevents the use of existing hardware, and also incurs a long and expensive development cycle to chip manufacturers and therefore may not happen in the near 1450 2020 IEEE Symposium on Security and Privacy

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