Intel's SGX is a confidential computing technique. It allows key functionalities of C/C++/native applications to be confidentially executed in hardware enclaves. However, numerous cloud applications are written in Java. For supporting their confidential computing, state-of-the-art approaches deploy Java Virtual Machines (JVMs) in enclaves and perform confidential computing on JVMs. Meanwhile, these JVM-in-enclave solutions still suffer from serious limitations, such as heavy overheads of running JVMs in enclaves, large attack surfaces, and deep computation stacks. To mitigate the above limitations, we for-malize a Secure Closed-World (SCW) principle and then propose Lejacon, a lightweight and efficient approach to Java confidential computing. The key idea is, given a Java application, to (1) separately compile its confidential computing tasks into a bundle of Native Confidential Computing (NCC) services; (2) run the NCC services in enclaves on the Trusted Execution Environment (TEE) side, and meanwhile run the non-confidential code on a JVM on the Rich Execution Environment (REE) side. The two sides interact with each other, protecting confidential computing tasks and as well keeping the Trusted Computing Base (TCB) size small. We implement Lejacon and evaluate it against OcclumJ (a state-of-the-art JVM-in-enclave solution) on a set of benchmarks using the BouncyCastle cryptography library. The evaluation results clearly show the strengths of Lejacon: it achieves compet-itive performance in running Java confidential code in enclaves; compared with OcclumJ, Lejacon achieves speedups by up to 16.2x in running confidential code and also reduces the TCB sizes by 90+% on average.

TVM is a popular deep learning (DL) compiler. It is designed for compiling DL models, which are naturally computation graphs, and as well promoting the efficiency of DL computation. State-of-the-art methods, such as Muffin and NNSmith, allow developers to generate computation graphs for testing DL compilers. However, these techniques are inefficient — their generated computation graphs are either type-invalid or inexpressive, and hence not able to test the core functionalities of a DL compiler.

Compared with visible and near-infrared images, the long-wave infrared region hyperspectral image (LWIR HSI) is more vulnerable to noise pollution in the acquisition process due to its specific imaging mode. In this letter, a new restoration method is proposed using total variation based on bidirectional estimation and brightness temperature smoothing (BBSTV), which can remove dead lines and restore junk bands effectively. The proposed method introduces the linear relation between brightness temperature and emissivity derived from radiative transfer model (RTM) to restoration processing. Besides, bilateral estimation is used to complete the loss information of noise-polluted bands to achieve a faster convergence speed of total variation (TV) method. Both simulated and real LWIR HSI experiments were conducted to verify the improvements of the BBSTV method in quantitative and qualitative ways.

Hyperspectral image (HSI) classification methods based on deep learning (DL) algorithms have achieved significant improvements on abundant samples. However, due to the limitation of practically available samples, the difficulty of representative feature extraction from small-sized samples and the loss of subtle diagnostic features in DL iteration results in the accuracy reduction of interclass and intraclass in refined classification, respectively. To address these issues, a water retrieval embedded deep network is proposed in this paper. The relative water content retrieval (RWCR) of the proposed network is embedded as a subnet, which is responsible for extracting subtle diagnostic features of relative water content (RWC) to enhance the representation of features in classification. The experimental results verify the effectiveness of RWCR for improving the interclass and intraclass accuracy in refined classification. Moreover, the superiority of the proposed network is also demonstrated in comparison with state-of-the-art methods.

In hyperspectral image (HSI) classification, the distinction of similar classes has always been a focus of research. In this paper, a new classification module named class confusion merging (CCM) is proposed to improve the classification accuracy, especially for classes with the similar spectral feature. In CCM processing, the merging matrix is firstly constructed based on the confusion matrix to measure the similarity between different classes. Then similar classes are merged as big categories. Finally, for each big category, soft band selection is implemented based on the spectral difference of contained classes for reclassification. To evaluate the performance of CCM, real image experiments are conducted in comparison with no CCM module hyperspectral classification methods. The experiment results demonstrate that the CCM module can improve the classifier performance by providing higher classification accuracy.