GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network by Mohammad Farukh Hashmi, B. Kiran Kumar Ashish, Prabhu Chaitanya, Avinash Keskar, Sinan Q. Salih, Neeraj Dhanraj Bokde

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Crowd counting in domain generalization based on multi-scale attention and hierarchy level enhancement by Jiarui Zhou, Jianming Zhang, Yan Gui

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Sliding-Window Dissimilarity Cross-Attention for Near-Real-Time Building Change Detection by Wen Lu, Minh Nguyen

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Attention-enhanced multimodal feature fusion network for clothes-changing person re-identification by Yongkang Ding, Jiechen Li, Hao Wang, Ziang Liu, Anqi Wang

“…To address this issue, we propose a novel network architecture called the Attention-Enhanced Multimodal Feature Fusion Network (AE-Net). …”
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Unleashing the power of pinyin: promoting Chinese named entity recognition with multiple embedding and attention by Jigui Zhao, Yurong Qian, Shuxiang Hou, Jiayin Chen, Kui Wang, Min Liu, Aizimaiti Xiaokaiti

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Construction Factors to which Attention Should Be Paid in Foundation Treatment by Drainage and Consolidation Method by XU Jiang-nan, CHEN Xiao-ling

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Associations of Alpha and Beta Interhemispheric EEG Coherences with Indices of Attentional Control and Academic Performance by Vasavi R. Gorantla, Sarah Tedesco, Merin Chandanathil, Sabyasachi Maity, Vernon Bond, Courtney Lewis, Richard M. Millis

“…Lower TBR, an indicator of attentional control, was associated with higher alpha and beta interhemispheric coherences measured with eyes open at sites overlying the frontal, temporal, and occipital cortices. …”
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An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security by J. Maruthupandi, S. Sivakumar, B. Lakshmi Dhevi, S. Prasanna, R. Karpaga Priya, Shitharth Selvarajan

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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

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Behavioral and Neural Changes Induced by a Blended Essential Oil on Human Selective Attention by Jieqiong Liu, Shi Cai, Danni Chen, Ke Wu, Yang Liu, Ruqian Zhang, Mei Chen, Xianchun Li

“…However, the effects of essential oils on human selective attention and the underlying neural mechanisms remain unclear. …”
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Individual Contribution-Based Spatial-Temporal Attention on Skeleton Sequences for Human Interaction Recognition by Xing Liu, Bo Gao

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NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification by Shuaiqi Liu, Beibei Liang, Siqi Wang, Bing Li, Lidong Pan, Shui-Hua Wang

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The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning by Chuan Mou

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Large Convolution Kernel Network With Edge Self-Attention for Oriented SAR Ship Detection by Yan Feng, Yupeng Zhang, Xiangqing Zhang, Yuning Wang, Shaohui Mei

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Cross-stream attention enhanced central difference convolutional network for CG image detection by HUANG Jinkun, HUANG Yuanhang, HUANG Wenmin, LUO Weiqi

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Encrypted traffic identification method based on deep residual capsule network with attention mechanism by Guozhen SHI, Kunyang LI, Yao LIU, Yongjian YANG

“…With the improvement of users’ security awareness and the development of encryption technology, encrypted traffic has become an important part of network traffic, and identifying encrypted traffic has become an important part of network traffic supervision.The encrypted traffic identification method based on the traditional deep learning model has problems such as poor effect and long model training time.To address these problems, the encrypted traffic identification method based on a deep residual capsule network (DRCN) was proposed.However, the original capsule network was stacked in the form of full connection, which lead to a small model coupling coefficient and it was impossible to build a deep network model.The DRCN model adopted the dynamic routing algorithm based on the three-dimensional convolutional algorithm (3DCNN) instead of the fully-connected dynamic routing algorithm, to reduce the parameters passed between each capsule layer, decrease the complexity of operations, and then build the deep capsule network to improve the accuracy and efficiency of recognition.The channel attention mechanism was introduced to assign different weights to different features, and then the influence of useless features on the recognition results was reduced.The introduction of the residual network into the capsule network layer and the construction of the residual capsule network module alleviated the gradient disappearance problem of the deep capsule network.In terms of data pre-processing, the first 784byte of the intercepted packets was converted into images as input of the DRCN model, to avoid manual feature extraction and reduce the labor cost of encrypted traffic recognition.The experimental results on the ISCXVPN2016 dataset show that the accuracy of the DRCN model is improved by 5.54% and the training time of the model is reduced by 232s compared with the BLSTM model with the best performance.In addition, the accuracy of the DRCN model reaches 94.3% on the small dataset.The above experimental results prove that the proposed recognition scheme has high recognition rate, good performance and applicability.…”
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Negative and Positive Bias for Emotional Faces: Evidence from the Attention and Working Memory Paradigms by Qianru Xu, Chaoxiong Ye, Simeng Gu, Zhonghua Hu, Yi Lei, Xueyan Li, Lihui Huang, Qiang Liu

“…Visual attention and visual working memory (VWM) are two major cognitive functions in humans, and they have much in common. …”
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Characterization of the attention to the Occupational Health in the Policlínico Juan José Apolinaire Pennini. Cienfuegos 2018. by Yoleysi Arteaga Cuéllar, Pedro Luis Veliz Martínez, Sarah Hernánez Malpica, Noelia Castro Ladrón de Guevara, Leticia Castro Morejón, Pedro Ricardo Borges Cabrera

“…<br /><strong>Conclusions:</strong> the research results suggest that there is a relationship between the lack of knowledge about occupational health in polyclinic workers and the insufficient attention perceived by the surveyed patients.</p>…”
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TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection by Xiaoyang Zhang, Genji Yuan, Zhen Hua, Jinjiang Li

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Fall detection method based on spatio-temporal coordinate attention for high-resolution networks by Xiaorui Zhang, Qijian Xie, Wei Sun, Ting Wang

Subjects: “…Spatio-temporal coordinate attention…”
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