Different Levels Of Attention Research Articles

A Bi-GRU-attention neural network to identify motor units from high-density surface electromyographic signals in real time.

To utilize surface electromyographics (sEMG) for control purposes, it is necessary to perform real-time estimation of the neural drive to the muscles, which involves real-time decomposition of the EMG signals. In this paper, we propose a Bidirectional Gate Recurrent Unit (Bi-GRU) network with attention to perform online decomposition of high-density sEMG signals. The model can give different levels of attention to different parts of the sEMG signal according to their importance using the attention mechanism. The output of gradient convolutional kernel compensation (gCKC) algorithm was used as the training label, and simulated and experimental sEMG data were divided into windows with 120 sample points for model training, the sampling rate of sEMG signal is 2048 Hz. We test different attention mechanisms and find out the ones that could bring the highest F1-score of the model. The simulated sEMG signal is synthesized from Fuglevand method (J. Neurophysiol., 1993). For the decomposition of 10 Motor Units (MUs), the network trained on simulated data achieved an average F1-score of 0.974 (range from 0.96 to 0.98), and the network trained on experimental data achieved an average F1-score of 0.876 (range from 0.82 to 0.97). The average decomposition time for each window was 28 ms (range from 25.6 ms to 30.5 ms), which falls within the lower bound of the human electromechanical delay. The experimental results show the feasibility of using Bi-GRU-Attention network for the real-time decomposition of Motor Units. Compared to the gCKC algorithm, which is considered the gold standard in the medical field, this model sacrifices a small amount of accuracy but significantly improves computational speed by eliminating the need for calculating the cross-correlation matrix and performing iterative computations.

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

The free-flowing traffic environment of the freeway is an important application scenario for automatic driving. In this scenario, the freeway’s geometric design is an important factor because no other vehicle affects the driving process of the target vehicle. The freeway’s combined curves have more safety problems, but there are no quantitative guidelines for their geometric design. They present more challenges for automatic driving or driver assistance functions. If the relationship between human-drivers’ micro-behavior and the geometric design of combined curves is examined, it could provide theoretical support for the enhancement of automated driving and driver assistance functions as well as the quantitative design of combined curves. The paper analyzed the speed change and lane departure behaviors of combined curves, considering downslope curves, upslope curves, sag curves, and crest curves. The relationship between micro-driving behaviors and combined curves’ geometric design were determined using random forest models. The SHAP values of each variable were calculated. The results showed that (1) on a downslope curve and sag curve the speed change behavior should be paid more attention; on an upslope curve and crest curve, the lane departure behavior should be paid more attention; (2) the priority of geometric design parameters for four types of combined curves were different. Based on the results, drivers and autonomous vehicles can pay different levels of attention to their speed change and departure behavior on different combination curves, and take targeted improvement measures in time according to the driving status of the vehicles. Road designers can also prioritize more important road design parameters in the design process to avoid serious accidents caused by excessive speed changes and departures.

Spatio-Temporal Two-stage Fusion for video question answering

Video question answering (VideoQA) has attracted much interest from of scholars as one of the most representative multimodal tasks in recent years. The task requires the model to interact and reason between the video and the question. Most known approaches use pre-trained networks to extract complex embeddings of videos and questions independently before performing multimodal fusion. However, they overlook two factors: (1) These feature extractors are pre-trained for the image or video classification task without taking the question into consideration, therefore may not be suitable for VideoQA task. (2) Using multiple feature extractors to extract features at different levels introduce more irrelevant information to some extent, thus making the task more difficult. For the above reasons, we propose a new model named Spatio-Temporal Two-Stage Fusion, which ties together multiple levels of feature extraction processes and divides them into two distinct stages: spatial fusion and temporal fusion. Specifically, in the spatial fusion stage, we use Vision Transformer to integrate the intra-frame information to generate frame-level features. At the same time, we design a multimodal temporal fusion module that enables the video to fuse textual information and assign different levels of attention to each frame. Then the obtained frame-level features are used to generate global video features by another Vision Transformer. In order to efficiently generate modal interaction information, we design a video–text symmetric fusion module to retain the most relevant information by mutual guidance between the two modalities. Our method is evaluated on three benchmark datasets: MSVD-QA, MSRVTT-QA and TGIF-QA, and achieves competitive results.

Voter attention and electoral accountability

What sorts of policy decisions do voters pay attention to, and why? And how does rational voter attention affect the behavior of politicians in office? We extend the Canes-Wrone et al. (2001) model of electoral accountability to allow the voter to choose when to pay costly attention to learn the consequences of the incumbent’s policy. We find that the voter will pay the most attention in close races, when the instrumental value of information about incumbent performance is greatest. When the voter’s cost of attention is ‘intermediate,” she will pay different levels of attention to different policies, and generally pay more attention to an unpopular policy than a popular one. This asymmetric attention can improve accountability by giving a moderately strong incumbent an opportunity to be “caught in the act of being good” after choosing an unpopular policy. However, it can also harm accountability by leading a moderately strong incumbent to categorically avoid the unpopular policy to evade voter scrutiny. This negative effect can be sufficiently strong to harm the voter’s overall welfare, providing a novel rationale for why subsidizing information about government performance may unintentionally harm voters. Finally, we find that rational voter attention can never induce “fake leadership,” i.e., a moderately weak incumbent choosing an unpopular policy precisely because it draws more scrutiny, hoping that the voter discovers an accidental policy success.

Het Baiae-motief in Cicero’s Pro Caelio

This article focuses on the question how Cicero is able to legitimize the use of the seaside resort Baiae and its negative associations in order to create and widen the moral contradiction between Caelius and Clodia in his Pro Caelio. It claims that Cicero treats the accusations about Baiae in different ways in relation to Clodia and Caelius to create a double standard for two people who are being accused of the same thing. By elaborating different levels of attention, a twofold use of the word Baiae and an emphasis on the frequency and publicity of visits to the place, Cicero manages to legitimize this double standard and thereby broadens the gap between Caelius and Clodia.

Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China

Landslide susceptibility assessment is an important means of helping to reduce and manage landslide risk. The existing studies, however, fail to examine the spatially varying relationships between landslide susceptibility and its explanatory factors. This paper investigates the spatial variation in such relationships in Liangshan, China, leveraging a spatially explicit model, namely, geographical random forest (GRF). By comparing with random forest (RF), we found that GRF achieves a higher performance with an AUC of 0.86 due to its consideration of the spatial heterogeneity among variables. GRF also provides a higher-quality landslide susceptibility map than RF by correctly placing 92.35% of the landslide points in high-susceptibility areas. The local feature importance derived from GRF allows us to understand that the impact of conditioning factors varies across space, which can provide implications for policy development by local governments to place different levels of attention on different conditioning factors in specific counties to prevent and mitigate landslides. To account for the spatial dependence among the data in the model performance assessment, we use spatial cross-validation (CV) to split the data into subsets spatially rather than randomly for model training and testing. The results show that spatial CV can effectively address the over-optimistic bias in model error evaluation.

Observing customer stress and engagement: An intercultural perspective

AbstractSince observing customers outnumber focal customers in most service interactions, service managers aim to engage them despite triggers, such as service incivility. This research contributes to the understanding of the role of stress in observing customers' engagement (CE). It answers two RQs: (1) What is the relationship between their stress and engagement?; (2) What are the triggers of stress? Since ethnically different people pay different levels of attention to contextual and social factors, two sequential scenario‐based experiments are adopted to study two triggers of stress (i.e., availability of information about an incivility incident, and ethnic similarity between the observing customer and the mistreated employee), which impacts CE in an intercultural service encounter. Study 1 compares being exposed to full versus partial information and demonstrates that full information about the incivility incident increases observers' psychological stress, which reduces their behavioral and emotional engagement. Study 2 compares how white and black observers react to ethnic similarity between the observing customer and the mistreated employee. Results show that incivility triggers outward psychological stress in white and black observers. In turn, black observers' outward stress reduces their behavioral engagement, while white observers' behavioral engagement is reduced by both their inward and outward stress.

Library Corporate Social Responsibility: A Systematic Literature Review

A review of library social responsibility is presented in this study to offer an understanding of the future direction of library social responsibility, and to bridge the knowledge gap regarding research patterns and trends in social responsibility of libraries. The purpose of this review was to provide a systematic review of social responsibility in libraries adopted between 2010 and 2021. Searches were conducted on databases in English and Arabic. An analysis of the papers revealed four themes of library social responsibility: workplace and employees, stakeholders, environment, and community. Themes had different levels of attention. The results of this study may contribute to future research. Further highlighting the gaps in the literature could be valuable for future research. In-depth qualitative empirical studies of library social responsibility are recommended.

A Novel End-to-end Network Based on a bidirectional GRU and a Self-Attention Mechanism for Denoising of Electroencephalography Signals

Electroencephalography (EEG) signals are nonlinear and non-stationary sequences that carry much information. However, physiological signals from other body regions may readily interfere with EEG signal capture, having a significant unfavorable influence on subsequent analysis. Therefore, signal denoising is a crucial step in EEG signal processing. This paper proposes a bidirectional gated recurrent unit (GRU) network based on a self-attention mechanism (BG-Attention) for extracting pure EEG signals from noise-contaminated EEG signals. The bidirectional GRU network can simultaneously capture past and future information while processing continuous time sequence. And by paying different levels of attention to the content of varying importance, the model can learn more significant feature of EEG signal sequences, highlighting the contribution of essential samples to denoising. The proposed model is evaluated on the EEGdenoiseNet data set. We compared the proposed model with a fully connected network (FCNN), the one-dimensional residual convolutional neural network (1D-ResCNN), and a recurrent neural network (RNN). The experimental results show that the proposed model can reconstruct a clear EEG waveform with a decent signal-to-noise ratio (SNR) and the relative root mean squared error (RRMSE) value. This study demonstrates the potential of BG-Attention in the pre-processing phase of EEG experiments, which has significant implications for medical technology and brain-computer interface (BCI) applications.

Sparse 3D Point Cloud Parallel Multi-Scale Feature Extraction and Dense Reconstruction with Multi-Headed Attentional Upsampling

Three-dimensional (3D) point clouds have a wide range of applications in the field of 3D vision. The quality of the acquired point cloud data considerably impacts the subsequent work of point cloud processing. Due to the sparsity and irregularity of point cloud data, processing point cloud data has always been challenging. However, existing deep learning-based point cloud dense reconstruction methods suffer from excessive smoothing of reconstruction results and too many outliers. The reason for this is that it is not possible to extract features for local and global features at different scales and provide different levels of attention to different regions in order to obtain long-distance dependence for dense reconstruction. In this paper, we use a parallel multi-scale feature extraction module based on graph convolution and an upsampling method with an added multi-head attention mechanism to process sparse and irregular point cloud data to obtain extended point clouds. Specifically, a point cloud training patch with 256 points is inputted. The PMS module uses three residual connections in the multi-scale feature extraction stage. Each PMS module consists of three parallel DenseGCN modules with different size convolution kernels and different averaging pooling sizes. The local and global feature information of the augmented receptive field is extracted efficiently. The scale information is obtained by averaging the different pooled augmented receptive fields. The scale information was obtained using the different average pooled augmented receptive fields. The upsampling stage uses an upsampling rate of r=4, The self-attentive features with a different focus on different point cloud data regions obtained by fusing different weights make the feature representation more diverse. This operation avoids the bias of one attention, and each focuses on extracting valuable fine-grained feature information. Finally, the coordinate reconstruction module obtains 1024 dense point cloud data. Experiments show that the proposed method demonstrates good evaluation metrics and performance and is able to obtain better visual quality. The problems of over-smoothing and excessive outliers are effectively mitigated, and the obtained sparse point cloud is more dense.

The Role of Space and Place in Organizational and Institutional Change: A Systematic Review of the Literature

AbstractWe present a systematic review of empirical articles investigating the role of place and space within the organizational and institutional change literature. In taking stock of the change literature, our aim is to better understand the nature and degree of scholarly engagement with concepts associated with place and space to inform a future research agenda. Our systematic review identified 290 empirical articles published between 1979 and 2020 that attended to organizational or institutional change and also engaged with space or place. Our analysis generated four archetypal perspectives that represent qualitatively different ways of viewing the role of place and space in how organizations and institutions change: functional perspective, situated perspective, experiential perspective, and mutually constituted perspective. We synthesize the four perspectives into a typology that reveals different levels of attention to change as process and to place and space as lived or physical phenomena, and cast light on different assumptions about the relationships between change and place or space that can guide future research.

A Transformer-Based Method of Multienergy Load Forecasting in Integrated Energy System

Multienergy load forecasting technique is the basis for the operation and scheduling of integrated energy system. Different types of loads in an integrated energy system, i.e., electricity load, heat load, cold load, might have complex and strong coupling relationships among them. If the internal relationship of multienergy load can be considered to realize joint prediction, the accuracy of multienergy load forecasting could be improved. This paper proposes a multi-task model, MultiDeT (Multiple-Decoder Transformer), which firstly adopts the one-encoder multi-decoder structure to realize the multi-task architecture and perform joint prediction of multienergy load. Based on the encoder-decoder architecture, the proposed model encodes all the input data by a uniform encoder and performs each forecasting task by multiple decoders. All tasks share the same encoder parameters, but have dedicated decoders for subtask learning. Therefore, the proposed multi-decoder structure can achieve different levels of attention to the output representation of the encoder by multi-head attention. The entire model is jointly trained end-to-end with losses from each task. Finally, the proposed model is tested on the publicly available datasets. Compared with other forecasting models, the results show that the proposed model has more accurate load forecasting results and has higher generalization capability.

The effect of recycled water information providing on public acceptance of recycled water:An Eye-Tracking Experiment

Public acceptance plays a key role in promoting recycled water. This study attempts to explore how information providing affects public acceptance of recycled water and whether people pay different levels of attention to different types of information for various uses of recycled water. Two experimental groups were provided with information of recycled water for toilet flushing and drinking, respectively. We surveyed the acceptance of recycled water by questionnaires, and compared the acceptance of participants in two experimental groups with those without information. We found that information providing increased the acceptance of recycled water for drinking but decreased that for flushing. An eye-tracking experiment was conducted to explore whether there were differences in the levels of attention to the various types of information for different uses. Results showed that for drinking used recycled water, people spent more time on processing new information, and resolving the incongruity between information and existing schema.

Active Learning in Flipped Classroom and Tutorials: Complementary or Redundant?

Cambridge undergraduates have regular active-learning opportunities in small-group tutorials, in which they solve problems and discuss ideas based on course material. Would they see any value in performing similar tasks in flipped-classroom settings, or would they regard the introduction of a second active-learning modality as redundant? Following the replacement of traditional lectures with flipped teaching within three physiology courses, with tutorials ongoing, questionnaire responses showed that students felt that they learned and understood more, and felt better-prepared for exams. Although similarities were recognised, the context of the active learning evidently made flipped classroom and tutorial teaching feel very different, probably because of the different levels of attention from the instructors. Questionnaire and interview comments suggested a complementarity between the two approaches, in that engaging with problems within a flipped classroom could give students more confidence in tutorials and in essay-writing, while tutorials offered more opportunities for individually-tailored feedback.

SimCLRT: A Simple Framework for Contrastive Learning of Rumor Tracking

As the second stage of the rumor defeat task pipeline, rumor tracking aims to filter tweets related to a specific event. However, due to the different levels of attention of different events, events related to fewer tweets may be masked by related to more tweets. In some research, the researchers give up detecting the events containing a small number of tweets for better results. To this end, we propose a Simple Framework for Contrastive Learning of Rumor Tracking (SimCLRT)-a novel rumor tracking framework that uses contrastive learning to alleviate the cover between tweets. SimCLRT contains three variants SimCLRT-CNN, SimCLRT-Linear, and SimCLRT-RNN. We conduct experiments on the two commonly used rumor tracking datasets PHEME and RumorEval. The results show that SimCLRT completely defeated baselines. Like the detection performance on the events that contain many tweets, SimCLRT can also effectively detect events containing a small number of tweets. Furthermore, we compare and analyze the performance of SimCLRT variants. SimCLRT-CNN is the model that performs best in our experiments. Although SimCLRT-Linear has a slight advantage on the RumorEval dataset, its robustness is weaker than SimCLRT-RNN and SimCLRT-CNN. If in a long text environment, we consider SimCLRT-RNN will perform more competitively.

The New Paradigm Is Already Here

This paper explores how abstract concepts of alignment, such as moving between different levels of attention, were made visible and concrete in a case study carried out in the context of nature tourism. The alignment practices were mapped from a design research project, where the aim was to construct and explore a prototype of future culture based on care and awareness-based co-creation. The paper combines literature form care ethics and ontology of becoming to support the alignment concepts form awareness-based systems change approach, Theory U. In addition, the collaborative and dialogical tradition of constructive design research is being introduced as a potential sense-making approach to explore the deeper meanings of experiences and events in the development process together with local stakeholders. Later, the research findings are discussed in the light of the individual and collective capacity building needs, which aim to change our inner mindset and posture of being and becoming with the world. Finally, the paper argues that the potential for change and the choice to bring about that change can be found within us as a willingness and readiness to re-invent ourselves in every moment, every encounter, and interaction.

A Study of Visual Search based Calibration Protocol for EEG Attention Detection.

Attention, a multi-faceted cognitive process, is essential in our daily lives. We can measure visual attention using an EEG Brain-Computer Interface for detecting different levels of attention in gaming, performance training, and clinical applications. In attention calibration, we use Flanker task to capture EEG data for attentive class. For EEG data belonging to inattentive class calibration, we instruct subject not focusing on a specific position on screen. We then classify attention levels using binary classifier trained with these surrogate ground-truth classes. However, subjects may not be in desirable attention conditions when performing repetitive boring activities over a long experiment duration. We propose attention calibration protocols in this paper that use simultaneous visual search with an audio directional change paradigm and static white noise as 'attentive' and 'inattentive' conditions, respectively. To compare the performance of proposed calibrations against baselines, we collected data from sixteen healthy subjects. For a fair comparison of classification performance; we used six basic EEG band-power features with a standard binary classifier. With the new calibration protocol, we achieved 74.37 ± 6.56% mean subject accuracy, which is about 3.73 ± 2.49% higher than the baseline, but there were no statistically significant differences. According to post-experiment survey results, new calibrations are more effective in inducing desired perceived attention levels. We will improve calibration protocols with reliable attention classifier modeling to enable better attention recognition based on these promising results.

Own-race faces promote integrated audiovisual speech information

The other-race effect indicates a perceptual advantage when processing own-race faces. This effect has been demonstrated in individuals' recognition of facial identity and emotional expressions. However, it remains unclear whether the other-race effect also exists in multisensory domains. We conducted two experiments to provide evidence for the other-race effect in facial speech recognition, using the McGurk effect. Experiment 1 tested this issue among East Asian adults, examining the magnitude of the McGurk effect during stimuli using speakers from two different races (own-race vs. other-race). We found that own-race faces induced a stronger McGurk effect than other-race faces. Experiment 2 indicated that the other-race effect was not simply due to different levels of attention being paid to the mouths of own- and other-race speakers. Our findings demonstrated that own-race faces enhance the weight of visual input during audiovisual speech perception, and they provide evidence of the own-race effect in the audiovisual interaction for speech perception in adults.

Attention to Social Media Ads: The Role of Consumer Recommendations and Smartphones

Despite the increasing presence of advertising on social media, research looking into the relative effects of social media ads is limited and highly dependent on either self-report or basic engagement metrics (e.g., likes). Our understanding of social media advertising is further complicated by (1) constantly changing advertising strategies, such as inclusion of consumer recommendations, and (2) the role of the device used to access social media. In an eye-tracking study (n = 121), we investigate whether consumers dedicate different levels of attention to an ad in a social media newsfeed depending on consumer recommendation (with versus without) and type of device (smartphone versus desktop personal computer [PC]). To increase ecological validity, this study uses a scrollable social media feed in combination with a mobile eye tracker. We find that consumers pay similar attention to the social media ad across recommendation conditions and devices. The results of this study suggest that the effects of advertisements may be smaller than previously assumed and call for more research using more realistic and dynamic exposure situations.

A study of customer perception of visual information in food stands through eye-tracking

PurposeThe study selected five small-scale food operations as visual stimuli and eye-tracking experiment was conducted with 36 female participants in a laboratory setting. Heat maps were used to visualize viewers' visual attention on the storefronts. The eye-movement data were analyzed using one-way repeated ANOVA to identify a significant difference between stimuli in terms of average fixation duration, fixation counts and revisits. An independent t-test was also used to examine statistical difference among text and image in menu board. The significance cut-off of p-value was set to <0.05.Design/methodology/approachThe exteriors of food-service establishments are major business representation. However, few studies have been conducted to examine customers' visual processing toward small-scale restaurants. The present study accordingly aims to discover customers' different levels of attention to the frontage in food stands through eye tracking, which would be practical for future owners to plan their exterior shop design.FindingsThe findings can be summarized as follows: First, upper board shows the highest level of attention, suggesting an optimal location of menu board for grasping customers' attention. Second, customers also gaze the inside of a store along with the food on display, which are related with food hygiene and the perception. Third, textual information on menu boards tends to attract more visual attention than those of images. Overall, the current study indicates various customers' attention toward the location of menu boards as well as the type of visual information on menu board.Originality/valueThe results of this study make a new insight into customers' viewing behavior toward exteriors of food-service establishments. This study is one of the first attempts to explore how customers distribute visual attention to the exterior images of food stand by using eye-tracking technology. The findings of this research thus enrich the food-service literature and offer meaningful discoveries on customers' visual behaviors. For example, this study suggests that customers tend to be attracted to textual information on menu boards rather than graphical ones.