Medical images play a crucial role in doctors' clinical diagnosis and treatment. However, the transmission and sharing of such private information raises security concerns. To address this issue, image hiding is used as an effective technique to protect images. To achieve large hiding capacity, lossless recovery and anti-steganalysis, we propose a novel two-stage medical image-hiding method in this paper. In the first stage, a QR code for the patient diagnosis information (PDI) is generated and embedded into a secret medical image using reversible data hiding. In the second stage, the secret medical image containing PDI is hidden in a natural target image. A kind of lossless compression technique named soft compression is innovatively introduced in two hiding stages, to ensure that the reconstructed secret medical image and PDI are exactly identical to the original ones. Moreover, an adaptiven-LSB model is proposed to improve the stego image quality. Extensive experimental results show that our method achieves a PSNR of over 40dB for the stego image at 2 BPP while recovering the PDI and secret medical image with 100% accuracy on the DIV2K, COCO and ImageNet datasets. It outperforms other state-of-the-art methods in terms of hiding invisibility, recovery accuracy and security.

We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system. Though we train using 70% spoken-form data, our teacher models perform comparably to XLM-R and mT5 when evaluated on the written-form Cross-lingual Natural Language Inference (XNLI) corpus. We perform a second stage of pretraining on our teacher models using in-domain data from our system, improving error rates by 3.86% relative for intent classification and 7.01% relative for slot filling. We find that even a 170M-parameter model distilled from our Stage 2 teacher model has 2.88% better intent classification and 7.69% better slot filling error rates when compared to the 2.3B-parameter teacher trained only on public data (Stage 1), emphasizing the importance of in-domain data for pretraining. When evaluated offline using labeled NLU data, our 17M-parameter Stage 2 distilled model outperforms both XLM-R Base (85M params) and DistillBERT (42M params) by 4.23% to 6.14%, respectively. Finally, we present results from a full virtual assistant experimentation platform, where we find that models trained using our pretraining and distillation pipeline outperform models distilled from 85M-parameter teachers by 3.74%-4.91% on an automatic measurement of full-system user dissatisfaction.

Single image dehazing is challenging without scene airlight and transmission map. Most of existing dehazing algorithms tend to estimate key parameters based on manual designed priors or statistics, which may be invalid in some scenarios. Although deep learning-based dehazing methods provide an effective solution, most of them rely on paired training datasets, which are prohibitively difficult to be collected in real world. In this paper, we propose an effective end-to-end generative adversarial network for single image dehazing, named SIDGAN. The proposed SIDGAN adopts a U-net architecture with a novel color-consistency loss derived from dark channel prior and perceptual loss, which can be trained in an unsupervised fashion without paired synthetic datasets. We create a RealHaze dataset for network training, including 4,000 outdoor hazy images and 4,000 haze-free images. Extensive experiments demonstrate that our proposed SIDGAN achieves better performance than existing state-of-the-art methods on both synthetic datasets and real-world datasets in terms of PSNR, SSIM, and subjective visual experience.

Deep learning usually requires big data, with respect to both volume and variety. However, most remote sensing applications only have limited training data, of which a small subset is labeled. Herein, we review three state-of-the-art approaches in deep learning to combat this challenge. The first topic is transfer learning, in which some aspects of one domain, e.g., features, are transferred to another domain. The next is unsupervised learning, e.g., autoencoders, which operate on unlabeled data. The last is generative adversarial networks, which can generate realistic looking data that can fool the likes of both a deep learning network and human. The aim of this article is to raise awareness of this dilemma, to direct the reader to existing work and to highlight current gaps that need solving.

Herein, we present a system for hyperspectral image segmentation that utilizes multiple class-based denoising autoen-coders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system with edge pixels which are almost always mixed pixels. Finally, we utilize a deep neural network and morphological hole-filling to provide robust image classification. Results run on the Salinas dataset verify the high performance of the proposed algorithm.

In this paper, the performance of HTTP protocol and FTP Protocol is analyzed by using OPNET-based simulation. Firstly, related scenarios are modeled with OPNET Simulator. Furthermore, two case studies are conducted by choosing average queuing delay and TCP delay as performance indices respectively. In the case of HTTP protocol's performance, the changing trend with the increase of the number of clients is reported. Additionally, in the case of comparison analysis, TCP delays of HTTP and FTP protocols are studied with the connection of 5 clients. On the basis of this work, performances of related network system with multiple traffics can be conducted.