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The automatic detection of mesoscale ocean eddies is extremely essential to monitor their dynamic changes. Therefore, effective detection of ocean eddies is vital to improve understanding of ocean dynamics. The traditional methods of detecting eddies using remote sensing data are usually based on physical parameters, geometric features, manual features or expert knowledge. In recent years, the deep learning technology has been improved by many experts. In this paper, the deep learning method is used to detect the ocean eddies from the sea surface height (SSH) data. Firstly, a multi-eddy detection model based on deep convolution neural network is proposed aiming to resolve eddy detection challenge on SSH data photoed by satellite. The model can accurately extract the feature information of eddies and fit the relationship between semantic information and sea level anomaly. Secondly, a new dataset, i.e., SCSE-Eddy, is used to train the proposed model and evaluate the performance of eddy detection method based on artificial intelligence (AI). This dataset is composed of the daily satellite remote sensing SSH data covering the South China Sea and its eastern sea area over the past 15 years. Experimental results show that, compared with the existing methods, the model proposed in this paper achieves the best performance and distinguishes close eddies well. [ABSTRACT FROM AUTHOR]

In the scene of document verification，the standard deep learning face recognition method has low accuracy and poor real-time performance on embedded devices. To solve these problems，this paper proposes a modified efficient convolutional neural network（CNN）called Lightnet and adopts the transfer learning method. Lightnet is an efficient CNN module composed of depthwise separable convolution，linear bottleneck structure and attention module. After introducing the loss function AM-Softmax with additive angle margin， the network model can effectively solve the problems of redundancy parameter and vast calculation for standard CNN in the foundation of ensuring the high accuracy of face recognition. The transfer learning method can enhance the scene-identity face matching performance by freezing all the convolution layer weights of the pre-trained model and fine-tuning training in the self-made scene-identity face matching dataset. The experimental results show that the designed efficient scene-identity face matching algorithm has achieved good results in terms of verification accuracy，parameters and verification speed，and has good robustness in life scenarios. [ABSTRACT FROM AUTHOR]

Recommender systems can effectively reduce the information overload by recommending items that users may be interested in. Euclidean-embedding-based collaborative filtering methods map users and items to a unified latent space, which is one of the most important methods to build a recommender system. However, traditional Euclidean-embedding-based collaborative filtering methods only consider the low-order interaction between user latent feature vectors and item latent feature vectors, and cannot efficiently model the complex interaction behavior between users and items in the real world. In this paper, we propose a deep-Euclidean-embedding-based collaborative filtering algorithm, which utilizes deep learning technology to learn the high-order and nonlinear interaction function between user latent feature vectors and item latent feature vectors. This can model the complex interaction behavior between users and items. Experimental results on real-world datasets show that our proposed algorithm outperforms traditional collaborative filtering algorithms. [ABSTRACT FROM AUTHOR]