Статті в журналах: "Micro-Attention"

1

Wang, Chongyang, Min Peng, Tao Bi, and Tong Chen. "Micro-attention for micro-expression recognition." Neurocomputing 410 (October 2020): 354–62. http://dx.doi.org/10.1016/j.neucom.2020.06.005.

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2

Taylor, Neill R., Matthew Hartley, and John G. Taylor. "The micro-structure of attention." Neural Networks 19, no. 9 (November 2006): 1347–70. http://dx.doi.org/10.1016/j.neunet.2006.08.002.

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3

C, Gnanaprakasam. "Attention Residual Network for Micro-expression Recognition Using Image Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1261–72. http://dx.doi.org/10.5373/jardcs/v12sp7/20202226.

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4

Nenn, Kerry. "Micro-Campaigns Require Attention to Detail, Creativity." Successful Fundraising 23, no. 6 (May 19, 2015): 8. http://dx.doi.org/10.1002/sfr.30131.

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5

Wang, Yan, Yikun Huang, Can Liu, Xiaoying Gu, Dandan Yang, Shuopeng Wang, and Bo Zhang. "Micro Expression Recognition via Dual-Stream Spatiotemporal Attention Network." Journal of Healthcare Engineering 2021 (August 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/7799100.

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Microexpression can manifest the real mood of humans, which has been widely concerned in clinical diagnosis and depression analysis. To solve the problem of missing discriminative spatiotemporal features in a small data set caused by the short duration and subtle movement changes of microexpression, we present a dual-stream spatiotemporal attention network (DSTAN) that integrates dual-stream spatiotemporal network and attention mechanism to capture the deformation features and spatiotemporal features of microexpression in the case of small samples. The Spatiotemporal networks in DSTAN are based on two lightweight networks, namely, the spatiotemporal appearance network (STAN) learning the appearance features from the microexpression sequences and the spatiotemporal motion network (STMN) learning the motion features from optical flow sequences. To focus on the discriminative motion areas of microexpression, we construct a novel attention mechanism for the spatial model of STAN and STMN, including a multiscale kernel spatial attention mechanism and global dual-pool channel attention mechanism. To obtain the importance of each frame in the microexpression sequence, we design a temporal attention mechanism for the temporal model of STAN and STMN to form spatiotemporal appearance network-attention (STAN-A) and spatiotemporal motion network-attention (STMN-A), which can adaptively perform dynamic feature refinement. Finally, the feature concatenate-SVM method is used to integrate STAN-A and STMN-A to a novel network, DSTAN. The extensive experiments on three small spontaneous microexpression data sets of SMIC, CASME, and CASME II demonstrate the proposed DSTAN can effectively cope with the recognition of microexpressions.

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6

LI, Jingting, Zizhao DONG, Ye LIU, Su-Jing WANG, and Dongzhe ZHUANG. "Micro-expression spotting method based on human attention mechanism." Advances in Psychological Science 30, no. 10 (2022): 2143. http://dx.doi.org/10.3724/sp.j.1042.2022.02143.

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7

Bennett, Roger. "Terminology and the computer — attention shifts to the micro." Aslib Proceedings 46, no. 7/8 (July 1994): 188–200. http://dx.doi.org/10.1108/eb051365.

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8

Chen, Boyu, Zhihao Zhang, Nian Liu, Yang Tan, Xinyu Liu, and Tong Chen. "Spatiotemporal Convolutional Neural Network with Convolutional Block Attention Module for Micro-Expression Recognition." Information 11, no. 8 (July 29, 2020): 380. http://dx.doi.org/10.3390/info11080380.

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Анотація:

A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized convolutional neural network (CNN) to extract visual features. Afterwards, it learned to allocate the feature weights in an adaptive manner with the help of a convolutional block attention module. The method was testified in spontaneous micro-expression databases (Chinese Academy of Sciences Micro-expression II (CASME II), Spontaneous Micro-expression Database (SMIC)). The experimental results show that the 3D CNN with convolutional block attention module outperformed other algorithms in micro-expression recognition.

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9

Shuaichao, Li, Li Mingze, Sun Jiaao, and Lu Shuhua. "Micro-expression recognition method integrating LBP and parallel attention mechanism." Journal of Applied Artificial Intelligence 1, no. 3 (October 18, 2024): 310–26. http://dx.doi.org/10.59782/aai.v1i3.333.

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Aiming at the problems of weak intensity of facial micro-expression changes, background noise interference and low feature differentiation, a micro-expression recognition network integrating LBP and parallel attention mechanism is proposed. The network inputs RGB images into the densely connected improved Shuffle Stage branch to extract global facial features and enhance the association of contextual semantic information; the LBP image is input into the local texture feature branch composed of multi-scale hierarchical convolutional neural network to extract detail information; after the dual-branch feature extraction, the parallel attention mechanism is introduced at the back end of the network to improve the feature fusion ability, suppress background interference, and focus on the micro-expression feature interest area; the proposed method is tested on three public datasets including CASME, CASME II and SMIC, and the recognition accuracy rates are , and respectively ; the experimental results show that the proposed method effectively improves the accuracy of micro-expression recognition, which is better than many current advanced methods.

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10

Zhang, Simin, Qin Jie, and Jerito Pereira. "Optimization design and thinking of new knowledge of primary school mathematics under the perspective of high-order thinking cultivation: Take the surface area of the cylinder as an example." Journal Of Teaching And Learning In Elementary Education 6, no. 2 (August 27, 2023): 111. http://dx.doi.org/10.33578/jtlee.v6i2.8032.

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The 20th report clearly emphasizes the importance of training innovative talents, and the cultivation of higher-order thinking has been paid attention to. Micro-lesson is a new teaching method to cultivate and develop students' high-level thinking. Taking cylindrical surface micro-lesson design as an example, this paper, starting from practice, compares the micro-lesson design and fragment record before and after optimization, and puts forward suggestions for optimization design: Pay attention to the construction of knowledge system and develop general thinking; Pay attention to summary reflection, form critical thinking; Pay attention to situation creation and develop innovative thinking; Focus on problem chain design and develop logical thinking. In order to provide reference for primary school mathematics classroom micro-lesson design. In order to provide reference for primary school mathematics classroom micro-lesson design.

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11

Lee, Kate E., Kathryn J. H. Williams, Leisa D. Sargent, Nicholas S. G. Williams, and Katherine A. Johnson. "40-second green roof views sustain attention: The role of micro-breaks in attention restoration." Journal of Environmental Psychology 42 (June 2015): 182–89. http://dx.doi.org/10.1016/j.jenvp.2015.04.003.

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12

Yang, Hongling, Lun Xie, Hang Pan, Chiqin Li, Zhiliang Wang, and Jialiang Zhong. "Multimodal Attention Dynamic Fusion Network for Facial Micro-Expression Recognition." Entropy 25, no. 9 (August 22, 2023): 1246. http://dx.doi.org/10.3390/e25091246.

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Анотація:

The emotional changes in facial micro-expressions are combinations of action units. The researchers have revealed that action units can be used as additional auxiliary data to improve facial micro-expression recognition. Most of the researchers attempt to fuse image features and action unit information. However, these works ignore the impact of action units on the facial image feature extraction process. Therefore, this paper proposes a local detail feature enhancement model based on a multimodal dynamic attention fusion network (MADFN) method for micro-expression recognition. This method uses a masked autoencoder based on learnable class tokens to remove local areas with low emotional expression ability in micro-expression images. Then, we utilize the action unit dynamic fusion module to fuse action unit representation to improve the potential representation ability of image features. The state-of-the-art performance of our proposed model is evaluated and verified on SMIC, CASME II, SAMM, and their combined 3DB-Combined datasets. The experimental results demonstrated that the proposed model achieved competitive performance with accuracy rates of 81.71%, 82.11%, and 77.21% on SMIC, CASME II, and SAMM datasets, respectively, that show the MADFN model can help to improve the discrimination of facial image emotional features.

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13

Qi, Fengyu, Chuanying Yang, Bao Shi, and Shaoying Ma. "Micro-expression Recognition Based on DCBAM-EfficientNet Model." Journal of Physics: Conference Series 2504, no. 1 (May 1, 2023): 012062. http://dx.doi.org/10.1088/1742-6596/2504/1/012062.

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Abstract To address the problems of low accuracy of existing deep learning-based micro-expression recognition models, numerous network parameters, and the difficulty of mobile deployment of micro-expression recognition models, this paper proposes DCBAM-EfficientNet, a micro-expression recognition model that uses the lightweight network EfficientNet as the backbone network and incorporates the attention module. The network can guarantee the accuracy of micro-expression recognition with relatively few network parameters. The attention mechanism allows the more expressive micro-expression features to be highlighted, and the CBAM attention is improved into a DCBAM model, where the large convolution kernel in the spatial attention module of CBAM is replaced by a dilated convolution with the same receptive field, reducing the network parameters while better preserving the spatial features of the image. The integration of the DCBAM model into the main structure of EfficientNet enables better integration of contextual information. Data enhancement is used to process the micro-expression dataset to decrease the occurrence of overfitting and improve the generalization ability of the model. The results demonstrate that the optimized model DCBAM-EfficientNet can effectively promote the recognition accuracy of micro-expressions, significantly reduce the quantity and volume of model parameters, and provide a reference for the deployment of mobile micro-expression recognition models.

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14

Zhou, Haoliang, Shucheng Huang, Jingting Li, and Su-Jing Wang. "Dual-ATME: Dual-Branch Attention Network for Micro-Expression Recognition." Entropy 25, no. 3 (March 6, 2023): 460. http://dx.doi.org/10.3390/e25030460.

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Micro-expression recognition (MER) is challenging due to the difficulty of capturing the instantaneous and subtle motion changes of micro-expressions (MEs). Early works based on hand-crafted features extracted from prior knowledge showed some promising results, but have recently been replaced by deep learning methods based on the attention mechanism. However, with limited ME sample sizes, features extracted by these methods lack discriminative ME representations, in yet-to-be improved MER performance. This paper proposes the Dual-branch Attention Network (Dual-ATME) for MER to address the problem of ineffective single-scale features representing MEs. Specifically, Dual-ATME consists of two components: Hand-crafted Attention Region Selection (HARS) and Automated Attention Region Selection (AARS). HARS uses prior knowledge to manually extract features from regions of interest (ROIs). Meanwhile, AARS is based on attention mechanisms and extracts hidden information from data automatically. Finally, through similarity comparison and feature fusion, the dual-scale features could be used to learn ME representations effectively. Experiments on spontaneous ME datasets (including CASME II, SAMM, SMIC) and their composite dataset, MEGC2019-CD, showed that Dual-ATME achieves better, or more competitive, performance than the state-of-the-art MER methods.

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15

Li, Yante, Xiaohua Huang, and Guoying Zhao. "Micro-expression action unit detection with spatial and channel attention." Neurocomputing 436 (May 2021): 221–31. http://dx.doi.org/10.1016/j.neucom.2021.01.032.

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16

Guo, Jie, Xiushan Nie, Yuling Ma, Kashif Shaheed, Inam Ullah, and Yilong Yin. "Attention based consistent semantic learning for micro-video scene recognition." Information Sciences 543 (January 2021): 504–16. http://dx.doi.org/10.1016/j.ins.2020.05.064.

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17

帕孜来提•, 努尔买提. "HSANet: Hybrid Self-Attention Network Recognition Facial Micro Plastic Method." Computer Science and Application 13, no. 03 (2023): 301–10. http://dx.doi.org/10.12677/csa.2023.133029.

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18

Zhong, Shunshun, Haibo Zhou, YiXiong Yan, Fan Zhang, and Ji'an Duan. "Asymmetric convolutional multi-level attention network for micro-lens segmentation." Engineering Applications of Artificial Intelligence 133 (July 2024): 108355. http://dx.doi.org/10.1016/j.engappai.2024.108355.

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19

Ren, Yili, Ruidong Lu, Guan Yuan, Dashuai Hao, and Hongjue Li. "Attention-Based Spatiotemporal-Aware Network for Fine-Grained Visual Recognition." Applied Sciences 14, no. 17 (September 2, 2024): 7755. http://dx.doi.org/10.3390/app14177755.

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On public benchmarks, current macro facial expression recognition technologies have achieved significant success. However, in real-life scenarios, individuals may attempt to conceal their true emotions. Conventional expression recognition often overlooks subtle facial changes, necessitating more fine-grained micro-expression recognition techniques. Different with prevalent facial expressions, weak intensity and short duration are the two main obstacles for perceiving and interpreting a micro-expression correctly. Meanwhile, correlations between pixels of visual data in spatial and channel dimensions are ignored in most existing methods. In this paper, we propose a novel network structure, the Attention-based Spatiotemporal-aware network (ASTNet), for micro-expression recognition. In ASTNet, we combine ResNet and ConvLSTM as a holistic framework (ResNet-ConvLSTM) to extract the spatial and temporal features simultaneously. Moreover, we innovatively integrate two level attention mechanisms, channel-level attention and spatial-level attention, into the ResNet-ConvLSTM. Channel-level attention is used to discriminate the importance of different channels because the contributions for the overall presentation of micro-expression vary between channels. Spatial-level attention is leveraged to dynamically estimate weights for different regions due to the diversity of regions’ reflections to micro-expression. Extensive experiments conducted on two benchmark datasets demonstrate that ASTNet achieves performance improvements of 4.25–16.02% and 0.79–12.93% over several state-of-the-art methods.

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20

Liu, Peng, Xiaolong Yuan, Qiang Han, Baowen Xing, Xiaolian Hu, and Jianhai Zhang. "Micro-defect Varifocal Network: Channel attention and spatial feature fusion for turbine blade surface micro-defect detection." Engineering Applications of Artificial Intelligence 133 (July 2024): 108075. http://dx.doi.org/10.1016/j.engappai.2024.108075.

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21

Wang, Xinwei. "Perspectives on Energy Transport at the Micro/Nanoscale." Nanomaterials 13, no. 11 (May 26, 2023): 1746. http://dx.doi.org/10.3390/nano13111746.

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Over the last two decades, with the fast development of micro/nanomaterials, including micro/nanoscale and micro/nanostructured materials, significant attention has been attracted to study the energy transport in them [...]

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22

Lai Yee, Ng, Mohd Asyraf Zulkifley, Adhi Harmoko Saputro, and Siti Raihanah Abdani. "Apex Frame Spotting Using Attention Networks for Micro-Expression Recognition System." Computers, Materials & Continua 73, no. 3 (2022): 5331–48. http://dx.doi.org/10.32604/cmc.2022.028801.

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23

Yan, Fangxu, and Jianhui Wang. "Research on Sentiment Analysis of Micro-blog based on Attention-BiLSTM." Frontiers in Computing and Intelligent Systems 7, no. 3 (April 10, 2024): 49–51. http://dx.doi.org/10.54097/dzdmrr39.

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The rapid development of the Internet gives birth to massive data. These data have the characteristics of multiple types, low density, and fast transmission speed. As one of the products of the era of big data, micro-blog provides users with a highly interactive platform. In order to explore the value and sentiment contained in micro-blog comment texts, and solve the problem of semantic loss and over-reliance on manual work in traditional text sentiment analysis, this paper proposes a deep learning model based on Attention-BiLSTM. In the embedding layer, the CBOW method of Word2vec is used to transform the text data into word vectors, and the attention mechanism is integrated to dynamically weight the input word vectors. Secondly, BiLSTM is used to extract text features, then fuse them in concat layer. After that the classification is realized by Softmax whose results of the output text are positive or negative. Finally, by comparing with TextCNN, LSTM and BiLSTM, the result shows that the Attention-BiLSTM model does have better classification effect, strong generalization and practicability.

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24

FU, Yangwei, Jin ZHANG, Zhenxi SUN, Rui ZHANG, Weishi LI, and Haojie XIA. "Design of channel attention network and system for micro target measurement." Optics and Precision Engineering 31, no. 6 (2023): 962–73. http://dx.doi.org/10.37188/ope.20233106.0962.

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25

Ran, Yuhan, Wenming ZHENG, Yuan ZONG, and Jiateng LIU. "Adaptive spatio-temporal attention neural network for crossdatabase micro-expression recognition." Virtual Reality & Intelligent Hardware 5, no. 2 (April 2023): 142–56. http://dx.doi.org/10.1016/j.vrih.2022.03.006.

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26

Li, Hejie, Ying Tan, Jiaqing Miao, Ping Liang, Jinnan Gong, Hui He, Yuhong Jiao, Fan Zhang, Yaolin Xing, and Donghan Wu. "Attention-based and micro designed EfficientNetB2 for diagnosis of Alzheimer’s disease." Biomedical Signal Processing and Control 82 (April 2023): 104571. http://dx.doi.org/10.1016/j.bspc.2023.104571.

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27

Li, Yuanyuan, Zhengguo Zhou, Guanqiu Qi, Gang Hu, Zhiqin Zhu, and Xin Huang. "Remote Sensing Micro-Object Detection under Global and Local Attention Mechanism." Remote Sensing 16, no. 4 (February 9, 2024): 644. http://dx.doi.org/10.3390/rs16040644.

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With the rapid advancement of technology, satellite and drone technologies have had significant impacts on various fields, creating both opportunities and challenges. In areas like the military, urban planning, and environmental monitoring, the application of remote sensing technology is paramount. However, due to the unique characteristics of remote sensing images, such as high resolution, large-scale scenes, and small, densely packed targets, remote sensing object detection faces numerous technical challenges. Traditional detection methods are inadequate for effectively detecting small targets, rendering the accurate and efficient detection of objects in complex remote sensing images a pressing issue. Current detection techniques fall short in accurately detecting small targets compared to medium and large ones, primarily due to limited feature information, insufficient contextual data, and poor localization capabilities for small targets. In response, we propose an innovative detection method. Unlike previous approaches that often focused solely on either local or contextual information, we introduce a novel Global and Local Attention Mechanism (GAL), providing an in-depth modeling method for input images. Our method integrates fine-grained local feature analysis with global contextual information processing. The local attention concentrates on details and spatial relationships within local windows, enabling the model to recognize intricate details in complex images. Meanwhile, the global attention addresses the entire image’s global information, capturing overarching patterns and structures, thus enhancing the model’s high-level semantic understanding. Ultimately, a specific mechanism fuses local details with global context, allowing the model to consider both aspects for a more precise and comprehensive interpretation of images. Furthermore, we have developed a multi-head prediction module that leverages semantic information at various scales to capture the multi-scale characteristics of remote sensing targets. Adding decoupled prediction heads aims to improve the accuracy and robustness of target detection. Additionally, we have innovatively designed the Ziou loss function, an advanced loss calculation, to enhance the model’s precision in small target localization, thereby boosting its overall performance in small target detection. Experimental results on the Visdrone2019 and DOTA datasets demonstrate that our method significantly surpasses traditional methods in detecting small targets in remote sensing imagery.

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28

HUANG, Kai, Feng WANG, Ye WANG, and Yiting CHANG. "Multi-attention micro-expression recognition based on color and optical flow." Chinese Journal of Liquid Crystals and Displays 39, no. 7 (2024): 939–49. http://dx.doi.org/10.37188/cjlcd.2023-0225.

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29

Hashmi, Mohammad Farukh, B. Kiran Kumar Ashish, Vivek Sharma, Avinash G. Keskar, Neeraj Dhanraj Bokde, Jin Hee Yoon, and Zong Woo Geem. "LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network." Sensors 21, no. 4 (February 5, 2021): 1098. http://dx.doi.org/10.3390/s21041098.

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Анотація:

Facial micro expressions are brief, spontaneous, and crucial emotions deep inside the mind, reflecting the actual thoughts for that moment. Humans can cover their emotions on a large scale, but their actual intentions and emotions can be extracted at a micro-level. Micro expressions are organic when compared with macro expressions, posing a challenge to both humans, as well as machines, to identify. In recent years, detection of facial expressions are widely used in commercial complexes, hotels, restaurants, psychology, security, offices, and education institutes. The aim and motivation of this paper are to provide an end-to-end architecture that accurately detects the actual expressions at the micro-scale features. However, the main research is to provide an analysis of the specific parts that are crucial for detecting the micro expressions from a face. Many states of the art approaches have been trained on the micro facial expressions and compared with our proposed Lossless Attention Residual Network (LARNet) approach. However, the main research on this is to provide analysis on the specific parts that are crucial for detecting the micro expressions from a face. Many CNN-based approaches extracts the features at local level which digs much deeper into the face pixels. However, the spatial and temporal information extracted from the face is encoded in LARNet for a feature fusion extraction on specific crucial locations, such as nose, cheeks, mouth, and eyes regions. LARNet outperforms the state-of-the-art methods with a slight margin by accurately detecting facial micro expressions in real-time. Lastly, the proposed LARNet becomes accurate and better by training with more annotated data.

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30

Ruimi, Liad, Yuval Hadash, Ariel Zvielli, Iftach Amir, Pavel Goldstein, and Amit Bernstein. "Meta-Awareness of Dysregulated Emotional Attention." Clinical Psychological Science 6, no. 5 (June 15, 2018): 658–70. http://dx.doi.org/10.1177/2167702618776948.

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We explore the human capacity for and the function(s) of meta-awareness for biased attentional processing of emotional information (MAB) subserving mental (ill) health. We do so by integrating probe-caught sampling methods, signal detection theory, and multilevel modeling of cognitive-experimental laboratory data among daily smokers ( N = 75) known to exhibit biased attentional processing of reward-related (drug) cues in addiction. We found (a) evidence of the capacity for and individual differences in MAB; (b) that momentary MAB was most likely observed in the event of the most extreme micro-expressions of biased attentional processing; and (c) that momentary micro-expressions of biased attention without MAB were more likely followed by attentional dysregulation, whereas momentary micro-expressions of biased attention with MAB were more likely followed by more balanced attentional expression or greater attentional control. We discuss the implications for basic and clinical science of meta-awareness.

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31

Fu, Chenghao, Wenzhong Yang, Danny Chen, and Fuyuan Wei. "AM3F-FlowNet: Attention-Based Multi-Scale Multi-Branch Flow Network." Entropy 25, no. 7 (July 14, 2023): 1064. http://dx.doi.org/10.3390/e25071064.

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Анотація:

Micro-expressions are the small, brief facial expression changes that humans momentarily show during emotional experiences, and their data annotation is complicated, which leads to the scarcity of micro-expression data. To extract salient and distinguishing features from a limited dataset, we propose an attention-based multi-scale, multi-modal, multi-branch flow network to thoroughly learn the motion information of micro-expressions by exploiting the attention mechanism and the complementary properties between different optical flow information. First, we extract optical flow information (horizontal optical flow, vertical optical flow, and optical strain) based on the onset and apex frames of micro-expression videos, and each branch learns one kind of optical flow information separately. Second, we propose a multi-scale fusion module to extract more prosperous and more stable feature expressions using spatial attention to focus on locally important information at each scale. Then, we design a multi-optical flow feature reweighting module to adaptively select features for each optical flow separately by channel attention. Finally, to better integrate the information of the three branches and to alleviate the problem of uneven distribution of micro-expression samples, we introduce a logarithmically adjusted prior knowledge weighting loss. This loss function weights the prediction scores of samples from different categories to mitigate the negative impact of category imbalance during the classification process. The effectiveness of the proposed model is demonstrated through extensive experiments and feature visualization on three benchmark datasets (CASMEII, SAMM, and SMIC), and its performance is comparable to that of state-of-the-art methods.

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32

Zhao, Zejia, Guoqing Zhang, and Wai Sze Yip. "Special Issue: “Micro/Nano Manufacturing Processes: Theories and Optimization Techniques”." Processes 12, no. 8 (August 20, 2024): 1746. http://dx.doi.org/10.3390/pr12081746.

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Анотація:

Manufacturing at the micro/nano scale creates many opportunities to fabricate micro- and nanostructures or to manufacture high-precision components, which has attracted considerable attention in fields such as optics [...]

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33

Benson, T. M., and J. Gregor. "Three-dimensional focus of attention for iterative cone-beam micro-CT reconstruction." Physics in Medicine and Biology 51, no. 18 (August 30, 2006): 4533–46. http://dx.doi.org/10.1088/0031-9155/51/18/006.

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34

Xie, Zhihua, and Chuwei Zhao. "Dual-Branch Cross-Attention Network for Micro-Expression Recognition with Transformer Variants." Electronics 13, no. 2 (January 22, 2024): 461. http://dx.doi.org/10.3390/electronics13020461.

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Анотація:

A micro-expression (ME), as a spontaneous facial expression, usually occurs instantaneously and is difficult to disguise after an emotion-evoking event. Numerous convolutional neural network (CNN)-based models have been widely explored to recognize MEs for their strong local feature representation ability on images. However, the main drawback of the current methods is their inability to fully extracting holistic contextual information from ME images. To achieve efficient ME learning representation from diverse perspectives, this paper uses Transformer variants as the main backbone and the dual-branch architecture as the main framework to extract meaningful multi-modal contextual features for ME recognition (MER). The first branch leverages an optical flow operator to facilitate the motion information extraction between ME sequences, and the corresponding optical flow maps are fed into the Swin Transformer to acquire motion–spatial representation. The second branch directly sends the apex frame in one ME clip to Mobile ViT (Vision Transformer), which can capture the local–global features of MEs. More importantly, to achieve the optimal feature stream fusion, a CAB (cross attention block) is designed to interact the feature extracted by each branch for adaptive learning fusion. The extensive experimental comparisons on three publicly available ME benchmarks show that the proposed method outperforms the existing MER methods and achieves an accuracy of 81.6% on the combined database.

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35

Yamamura, Shohei. "Editorial for the Special Issue on Micro and Nano Devices for Cell Analysis." Micromachines 12, no. 7 (July 19, 2021): 840. http://dx.doi.org/10.3390/mi12070840.

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Анотація:

In recent years, miniaturized systems (micro- and nano-devices) called a lab-on-a-chip or micro-total analysis system (µ-TAS) have received attention as new systems for chemical and biochemical analyses [...]

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Chen, Wen, Chuansheng Chen, Baoping Li, and Jiacai Zhang. "Applying Interleaving Strategy of Learning Materials and Perceptual Modality to Address Secondary Students’ Need to Restore Cognitive Capacity." International Journal of Environmental Research and Public Health 19, no. 12 (June 19, 2022): 7505. http://dx.doi.org/10.3390/ijerph19127505.

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Online courses are prevalent around the world, especially during the COVID-19 pandemic. Long hours of highly demanding online learning can lead to mental fatigue and cognitive depletion. According to Attention Restoration Theory, ‘being away’ or a mental shift could be an important strategy to allow a person to recover from the cognitive overload. The present study aimed to test the interleaving strategy as a mental shift method to help sustain students’ online learning attention and to improve learning outcomes. A total of 81 seventh-grade Chinese students were randomly assigned to four learning conditions: blocked (by subject matter) micro-lectures with auditory textual information (B-A condition), blocked (by subject matter) micro-lectures with visual textual information (B-V condition), interleaved (by subject matter) micro-lectures with auditory textual information (I-A condition), and interleaved micro-lectures by both perceptual modality and subject matter (I-all condition). We collected self-reported data on subjective cognitive load (SCL) and attention level, EEG data during the 40 min of online learning, and test results to assess learning outcomes. The results showed that the I-all condition showed the best overall outcomes (best performance, low SCL, and high attention). This study suggests that interleaving by both subject matter and perceptual modality should be preferred in scheduling and planning online classes.

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Luo, Tao, Xudong Cao, Jin Li, Kun Dong, Rui Zhang, and Xueliang Wei. "Multi-task prediction model based on ConvLSTM and encoder-decoder." Intelligent Data Analysis 25, no. 2 (March 4, 2021): 359–82. http://dx.doi.org/10.3233/ida-194969.

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The energy load data in the micro-energy network are a time series with sequential and nonlinear characteristics. This paper proposes a model based on the encode-decode architecture and ConvLSTM for multi-scale prediction of multi-energy loads in the micro-energy network. We apply ConvLSTM, LSTM, attention mechanism and multi-task learning concepts to construct a model specifically for processing the energy load forecasting of the micro-energy network. In this paper, ConvLSTM is used to encode the input time series. The attention mechanism is used to assign different weights to the features, which are subsequently decoded by the decoder LSTM layer. Finally, the fully connected layer interprets the output. This model is applied to forecast the multi-energy load data of the micro-energy network in a certain area of Northwest China. The test results prove that our model is convergent, and the evaluation index value of the model is better than that of the multi-task FC-LSTM and the single-task FC-LSTM. In particular, the application of the attention mechanism makes the model converge faster and with higher precision.

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Yang, Huanjing, Shibao Sun, and Jingyu Chen. "Deep Learning-Based Micro-Expression Recognition Algorithm Research." International Journal of Computer Science and Information Technology 2, no. 1 (March 21, 2024): 59–70. http://dx.doi.org/10.62051/ijcsit.v2n1.08.

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In order to improve the accuracy and speed of micro-expressions, a modified model based on densenet and eca is proposed. Microfacial expression is a brief, weak facial change, its characteristics are similar, dense, difficult to extract and identify, and the improved model can be adapted to the characteristics and location of the interest. In particular, the eca attention module was added after the densenet model, using the densenet network to extract the rich characteristics of micro-expressions, and the eca attention module to recalibrate the feature channel and focus on the more subtle expression changes. In order to verify the validity of this method, the experiment was conducted in the micro-emotive data set, and compared with the resnet network and the densenet network, the experimental results showed that the improved model significantly improved the performance of micro-expression recognition, and had strong generalized ability and robustness.

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Chen, Junlin, and Weihua Zhang. "Exploration of Ecological Function and Restoration of Small and Micro Wetlands." Journal of Physics: Conference Series 2706, no. 1 (February 1, 2024): 012075. http://dx.doi.org/10.1088/1742-6596/2706/1/012075.

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Abstract Due to their unique ecological functions, small and micro wetlands have received more extensive attention and recognition from the international community, and have become an important part of China’s ecological civilization construction. In this paper, the concept, characteristics and classification of small and micro wetlands are described, and the ecological functions and services of small and micro wetlands are discussed. On this basis, the principles of ecological restoration of small and micro wetlands were analyzed, and suggestions for the protection and management of small and micro wetlands were put forward, in order to provide reference for the protection and restoration of small and micro wetlands in China.

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Boud, David, and Trina Jorre de St Jorre. "The move to micro-credentials exposes the deficiencies of existing credentials." Journal of Teaching and Learning for Graduate Employability 12, no. 1 (February 15, 2021): 18–20. http://dx.doi.org/10.21153/jtlge2021vol12no1art1023.

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The rush to short courses and use of micro-credentials prompted by responses to the pandemic has greatly accelerated a trend already underway. However, few studies have examined the impact of short courses or micro-credentials on skills or employment outcomes, and this hasty move draws attention to major problems in the ways in which higher education credentials - macro and micro -are designed and assessed.

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Fitriano, Yun, Fachruzzaman Fachruzzaman, and Baihaqi Baihaqi. "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBERIAN KREDIT MIKRO DAN KETERKAITANNYA DENGAN KINERJA PT BANK MANDIRI (PERSERO) TBK UNIT KERJA CLUSTER PALEMBANG ARIEF-2 PROVINSI BENGKULU." JURNAL FAIRNESS 3, no. 1 (April 1, 2021): 69–66. http://dx.doi.org/10.33369/fairness.v3i1.15277.

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This research aim to examine if there is or not the influence of the length of business, business capacity, the characteristic of debtor, economy sector which is paid, and the target of performance that is specified by management to the micro credit decision which taken by the Analyst of Micro Credit (MKA) and Manager of Micro Mandiri (MMM) of Mandiri Bank. This research take place in Mandiri Bank work unit Palembang Cluster Arief-2 Bengkulu Province which consist of 17 units of the business partner mandiri/micro branch office. The sample in this research is all of Analyst of Micro Credit (MKA) and the Manager of Micro Credit Palembang (MMM) Cluster Arief-2 Bengkulu Province. The collected data is primer data from the spread of questionnaire then processed by multiple regression examine analyst. The result of this research show that simultaneously the variable of length of business, business capacity, characteristic of debtor, the economic sector which is paid and the target of performance that is specified by management having an effect on micro credit decision. Partially, the length of business variable influence the micro credit decision while other variables do not influence. The result of this research also give an empirical evidence that the main factor which most given attention by the analyst of Micro Credit (MKA) and the Manager of Micro Credit (MMM) of the Mandiri Bank in deciding micro credit is the length of business factor and empirically is proven in this research, by paying attention on the length of business the collectible performance is fluent Palembang Cluster Arief-2 Bengkulu Province can be high with the low number of non performing loan.

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Xu, Xing, Hui Ting Chen, and Lei Feng. "Research on AC-DC Power Flow to Solve the Instability of Micro-Grid." Applied Mechanics and Materials 672-674 (October 2014): 1314–17. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1314.

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Nowadays the disadvantages of large power grids are becoming more and more conspicous. Scholars have come to realize the importance of micro-grids. As the supplement of large power grids, the instability of micro-grids has gathered a huge amount of attention. This paper proposes a solution of the instability problem of micro-grids based on the research on AC-DC power flow. The main idea is to add AC power flow and storage power, which features stable voltage, stable current and stable output, in the micro-grids, thus solving voltage and current fluctuation and other instability problems in micro-grids.

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Du, Li Qun, Qi Jia Wang, and Xiao Lei Zhang. "Application of Ultrasonic Stress Relief in the Fabrication of SU-8 Micro Structure." Key Engineering Materials 483 (June 2011): 3–8. http://dx.doi.org/10.4028/www.scientific.net/kem.483.3.

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SU-8 photoresist has received a lot of attention in the MEMS field because of its excellent lithography properties. However, its high internal stress affects the overall pattern quality of the micro structures. The purpose of this work is to reduce the internal stress in SU-8 micro structure by ultrasonic stress relief technology. The stress relief mechanism of SU-8 micro structure was presented. The effect of ultrasonic stress relief on SU-8 micro structure was studied by experiments. The experimental results show that the internal stress in SU-8 micro structures can be reduced by ultrasonic stress relief technology.

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Glenn, Floyd. "The Case for Micro-models." Proceedings of the Human Factors Society Annual Meeting 33, no. 18 (October 1989): 1228–32. http://dx.doi.org/10.1177/154193128903301813.

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This paper examines the appropriate role of human performance micro-models in simulations of human-machine system operations. Requirements for general human micro-models are considered relative to the objectives of simulation studies, the conditions under which simulations are constructed and used, the status of human performance data bases and models, and the features provided with general-purpose simulation software. This investigation focuses particularly on a new simulation tool for simulating human-machine systems; it is known as the Human Operator Simulator – Version V (HOS-V). A general design principle of HOS-V has been to provide embedded human performance micro-models for the basic performance processes that seem most pervasive and interactive with other processes. These include representations for processes of body movement, cognition, and attention. Key to these representations are the substructures in each area. Body movement models describe locations of body parts and constraints on their movement. Cognition models describe how the human processes information through perception, memory, decision-making, and action initiation. The attention model describes how a limited attentional resource is allocated to the various body movement and cognition processes, each of which has a defined attentional requirement. Plans for implementation of micro-model components of HOS-V are discussed.

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Wang, Peng, Qingshun Bai, Kai Cheng, Liang Zhao, and Yabo Zhang. "Multi-Objective Optimization of Micro-Milling Parameters—The Trade-Offs between Machining Quality, Efficiency, and Sustainability in the Fabrication of Thin-Walled Microstructures." Applied Sciences 13, no. 16 (August 18, 2023): 9392. http://dx.doi.org/10.3390/app13169392.

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Micro-milling has found extensive applications in machining components with thin-walled microstructures, such as terahertz slow-wave structures, microfluidic chips, and micro-molds. Due to the influence of size effects, micro-milling exhibits higher specific energy consumption compared with traditional milling, implying that more energy is consumed to remove a unit volume of material, particularly in challenging-to-machine materials like Ti-6Al-4V. Historically, research on parameter optimization for micro-milling has predominantly focused on enhancing machining quality and efficiency, with limited attention given to energy efficiency. However, in the context of the “double carbon” strategy, energy conservation and emissions reduction have garnered significant attention in the manufacturing industry. Therefore, this paper proposes a micro-milling parameter-based power consumption model. Based on this, a specific energy consumption model can be obtained. Moreover, evolutionary algorithms are utilized for the optimization of micro-milling parameters, which aims to achieve comprehensive enhancements in both machinability and sustainability. The optimization objectives encompass improving surface quality, dimensional accuracy, material removal rate, and specific energy consumption during the micro-milling process for thin-walled micro-structures. Among them, NSGA-III achieves the best optimization results. Under conditions in which cutting energy consumption and processing efficiency are very close, the optimization outcomes based on NSGA-III lead to the best machining quality, including the minimum surface roughness and dimensional errors, and the largest surface fractal dimension. The optimal combination of micro-milling parameters is n = 28,800 rpm, fz = 2.6 μm/t, and ap = 62 μm.

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Huang, Jinghua, Jing Zhang, Yangfan Li, and Zhepeng Lv. "Business Value of Enterprise Micro-blogging." Journal of Global Information Management 22, no. 3 (July 2014): 32–56. http://dx.doi.org/10.4018/jgim.2014070102.

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The increasing use of micro-blogging as a marketing tool has increased research attention on usage and business value of enterprise micro-blogging. Based on research on information system (IS) usage and resource-based view (RBV) theory, this study develops a model to reveal the mechanism of business value creation of enterprise micro-blogging. The model consists of metrics on micro-blogging usage, micro-blogging operational performance, marketing capability, and firm performance. Questionnaires were distributed to firms that use micro-blogging. This study collects 241 valid responses for empirical analysis. The results suggest that the use of enterprise micro-blogging improves operational performance of enterprise micro-blogging directly and indirectly by increasing marketing capability, while operational performance of enterprise micro-blogging significantly affects firm performance. This study extends the stream of research that combines IS usage and RBV theory.

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Xie, Tingxuan, Guoquan Sun, Hao Sun, Qiang Lin, and Xianye Ben. "Decoupling facial motion features and identity features for micro-expression recognition." PeerJ Computer Science 8 (November 14, 2022): e1140. http://dx.doi.org/10.7717/peerj-cs.1140.

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Background Micro-expression is a kind of expression produced by people spontaneously and unconsciously when receiving stimulus. It has the characteristics of low intensity and short duration. Moreover, it cannot be controlled and disguised. Thus, micro-expression can objectively reflect people’s real emotional states. Therefore, automatic recognition of micro-expressions can help machines better understand the users’ emotion, which can promote human-computer interaction. What’s more, micro-expression recognition has a wide range of applications in fields like security systems and psychological treatment. Nowadays, thanks to the development of artificial intelligence, most micro-expression recognition algorithms are based on deep learning. The features extracted by deep learning model from the micro-expression video sequences mainly contain facial motion feature information and identity feature information. However, in micro-expression recognition tasks, the motions of facial muscles are subtle. As a result, the recognition can be easily interfered by identity feature information. Methods To solve the above problem, a micro-expression recognition algorithm which decouples facial motion features and identity features is proposed in this paper. A Micro-Expression Motion Information Features Extraction Network (MENet) and an Identity Information Features Extraction Network (IDNet) are designed. By adding a Diverse Attention Operation (DAO) module and constructing divergence loss function in MENet, facial motion features can be effectively extracted. Global attention operations are used in IDNet to extract identity features. A Mutual Information Neural Estimator (MINE) is utilized to decouple facial motion features and identity features, which can help the model obtain more discriminative micro-expression features. Results Experiments on the SDU, MMEW, SAMM and CASME II datasets were conducted, which achieved competitive results and proved the superiority of the proposed algorithm.

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Liu, Dezhi, Zhengyou Liang, and Yu Sun. "Micro-Expression Recognition Method Based on Spatial Attention Mechanism and Optical Flow Features." Journal of Computer-Aided Design & Computer Graphics 33, no. 10 (October 1, 2021): 1541–52. http://dx.doi.org/10.3724/sp.j.1089.2021.18569.

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Boylan, Ross. "A queuing model of the macro-micro connection, with special attention to stratification." Journal of Mathematical Sociology 16, no. 4 (February 1992): 267–84. http://dx.doi.org/10.1080/0022250x.1992.9990091.

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Wu, Falin, Yu Xia, Tiangyang Hu, Boyi Ma, Jingyao Yang, and Haoxin Li. "Facial micro-expression recognition based on motion magnification network and graph attention mechanism." Heliyon 10, no. 16 (August 2024): e35964. http://dx.doi.org/10.1016/j.heliyon.2024.e35964.

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