Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.

Joint iris-periocular recognition based on feature fusion can overcome some inherent drawbacks of unimodal biometrics, but most of the prior works are limited by conventional feature extraction approaches and fixed fusion schemes. To achieve more accurate and adaptive recognition, an end-to-end deep feature fusion network for joint iris-periocular recognition is proposed in this paper. Multiple attention mechanisms including self-attention and co-attention mechanisms are integrated into the network. Specifically, two forms of self-attention mechanisms, spatial attention and channel attention, are inserted into the feature extraction module, aiming to effectively learn the most important features and suppress unnecessary ones. Also, co-attention mechanism is introduced in the feature fusion module, which can adaptively fuse features to obtain more representative iris-periocular features. Additionally, in order to further enhance the discriminative power of the learned features, the proposed network is trained with a joint supervision of softmax loss and center loss. On two publicly available datasets, the proposed network with a small number of parameters outperforms unimodal biometrics and several iris-periocular recognition approaches.

This paper proposes a disocclusion inpainting framework for depth-based view synthesis. It consists of four modules: foreground extraction, motion compensation, improved background reconstruction, and inpainting. The foreground extraction module detects the foreground objects and removes them from both depth map and rendered video; the motion compensation module guarantees the background reconstruction model to suit for moving camera scenarios; the improved background reconstruction module constructs a stable background video by exploiting the temporal correlation information in both 2D video and its corresponding depth map; and the constructed background video and inpainting module is used to eliminate the holes in the synthesized view. The analysis and experiment indicate that the proposed framework has good generality, scalability and effectiveness, which means most of the existing background reconstruction methods and image inpainting methods can be employed or extended as the modules in our framework. Our comparison results have demonstrated that the proposed framework achieves better synthesized quality, temporal consistency, and has lower running time compared to the other methods.

Pseudo-label-based methods of unsupervised domain adaption (UDA) can transfer the knowledge learned from a labeled source domain to an unlabeled target domain and have recently achieved significant progress in the application of person reidentification (re-ID). However, these methods suffer from serious label noise problems that downgrade the retrieval performance in UDA person re-ID. The mutual teaching framework (MTF) with dual networks attempts to tackle this problem by generating reliable soft pseudo labels but results in a mutual convergence problem. In this paper, a novel DiveRsity EnlArged Mutual Teaching framework (DREAMT) is proposed to solve the problem mentioned above. Based on the primary mutual-mean-teaching mechanism two strategies are developed in DREAMT, that is, GAN-based source domain augmentation (GSDA) and cross-branch mutual supervision (CBMS) for dual networks. Specifically, GSDA exploits two GANs to augment source domain datasets in different ways for pre-training to improve the pre-trained models' performance and enlarge the diversity at the beginning of target domain adaption. During target adaption, each network in MTF adopts two branches to extract different features. CBMS based on hard and soft pseudo labels is across branches and networks and can help to maintain the diversity between training peers in the whole training process. Extensive experiments have demonstrated that our proposed DREAMT framework achieves better mAP and CMC performance than the existing mutual teaching methods and outperforms various state-of-the-art methods in UDA person re-ID tasks.

Semantic searching over encrypted data is a crucial task for secure information retrieval in public cloud. It aims to provide retrieval service to arbitrary words so that queries and search results are flexible. In existing semantic searching schemes, the verifiable searching does not be supported since it is dependent on the forecasted results from predefined keywords to verify the search results from cloud, and the queries are expanded on plaintext and the exact matching is performed by the extended semantically words with predefined keywords, which limits their accuracy. In this paper, we propose a secure verifiable semantic searching scheme. For semantic optimal matching on ciphertext, we formulate word transportation (WT) problem to calculate the minimum word transportation cost (MWTC) as the similarity between queries and documents, and propose a secure transformation to transform WT problems into random linear programming (LP) problems to obtain the encrypted MWTC. For verifiability, we explore the duality theorem of LP to design a verification mechanism using the intermediate data produced in matching process to verify the correctness of search results. Security analysis demonstrates that our scheme can guarantee verifiability and confidentiality. Experimental results on two datasets show our scheme has higher accuracy than other schemes.