Speed-Accuracy Tradeoff Operator Characteristics of Endogenous and Exogenous Covert Orienting of Attention by Peter A. McCormick, Lori Francis

“…There is debate over the mechanisms that govern the orienting of attention. Some argue that the enhanced performance observed at a cued location is the result of increased perceptual sensitivity or preferential access to decision-making processes. …”
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Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition by Fei Yan, Zekai Guo, Abdullah M. Iliyasu, Kaoru Hirota

“…This study proposes a multi-branch convolutional neural network model based on cross-attention mechanism (MCNN-CA) for accurate recognition of different emotions. …”
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Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism by Jun Tang, Dengpan Yang, Mingfei Ding

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Advancing semantic segmentation: Enhanced UNet algorithm with attention mechanism and deformable convolution by Effat Sahragard, Hassan Farsi, Sajad Mohamadzadeh

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Topic-aware neural attention network for malicious social media spam detection by Maged Nasser, Faisal Saeed, Aminu Da’u, Abdulaziz Alblwi, Mohammed Al-Sarem

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Attention-based interactive multi-level feature fusion for named entity recognition by Yiwu Xu, Yun Chen

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STA-HAR: A Spatiotemporal Attention-Based Framework for Human Activity Recognition by Md. Khaliluzzaman, Md. Furquan, Mohammod Sazid Zaman Khan, Md. Jiabul Hoque

“…Human activity recognition (HAR) has gained significant attention in computer vision and human-computer interaction. …”
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Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data by Feng Zhihao

“…This review highlights the significance of cross-attention mechanisms by examining the developments in integrating multi-source heterogeneous event data for traffic prediction. …”
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Mask-wearing affects infants' selective attention to familiar and unfamiliar audiovisual speech by Lauren N. Slivka, Kenna R. H. Clayton, Greg D. Reynolds

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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification by Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed

“…Experimental validation using fivefold cross-validation demonstrates the method’s precision, reliability, and capacity to provide comprehensive insights into cancer molecular mechanisms. The computation of attention coefficients for the edges in the graph, facilitated by the proposed graph attention architecture based on PPIs, proved beneficial for identifying synergies in multi-omics data for cancer classification.…”
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Altered interactions between attentional networks in older adults with mild cognitive impairment by Ling Ma, Hao He, Yuehong Qiu, Yiqi Chen, Qing Guan

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Triple-attentions based salient object detector for strip steel surface defects by Li Zhang, Xirui Li, Yange Sun, Huaping Guo

Subjects: “…Triple attention…”
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Abnormal traffic detection method based on multi-scale attention feature enhancement by YANG Hongyu, ZHANG Haohao, CHENG Xiang

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Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM by Yaping Wang, Chaonan Yang, Di Xu, Jianghua Ge, Wei Cui

“…However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of rolling bearings. …”
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Multi-feature fusion malware detection method based on attention and gating mechanisms by Zhongyuan CHEN, Jianbiao ZHANG

“…With the rapid development of network technology, the number and variety of malware have been increasing, posing a significant challenge in the field of network security.However, existing single-feature malware detection methods have proven inadequate in representing sample information effectively.Moreover, multi-feature detection approaches also face limitations in feature fusion, resulting in an inability to learn and comprehend the complex relationships within and between features.These limitations ultimately lead to subpar detection results.To address these issues, a malware detection method called MFAGM was proposed, which focused on multimodal feature fusion.By processing the .asm and .bytes files of the dataset, three key features belonging to two types (opcode statistics sequences, API sequences, and grey-scale image features) were successfully extracted.This comprehensive characterization of sample information from multiple perspectives aimed to improve detection accuracy.In order to enhance the fusion of these multimodal features, a feature fusion module called SA-JGmu was designed.This module utilized the self-attention mechanism to capture internal dependencies between features.It also leveraged the gating mechanism to enhance interactivity among different features.Additionally, weight-jumping links were introduced to further optimize the representational capabilities of the model.Experimental results on the Microsoft malware classification challenge dataset demonstrate that MFAGM achieves higher accuracy and F1 scores compared to other methods in the task of malware detection.…”
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A generative adversarial network with multiscale and attention mechanisms for underwater image enhancement by Liquan Zhao, Yuda Li, Tie Zhong

Subjects: “…Feature fusion adaptive attention module…”
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‘An Important Exercise of Care and Attention’: How the International Geological Expedition Was Established in Mongolia by Narma A. Kamandzhaev

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IDTransformer: Infrared image denoising method based on convolutional transposed self-attention by Zhengwei Shen, Feiwei Qin, Ruiquan Ge, Changmiao Wang, Kai Zhang, Jie Huang

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Mapping Internet Celebrity on TikTok: Exploring Attention Economies and Visibility Labours by Abidin Crystal

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A simplified model of the process of medical attention. Assistance, educational and investigative implications by Luis Alberto Corona Martínez, Mercedes Fonseca Hernández

“…The process of medical attention constitutes a learning object for the student of Medicine. …”
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