In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi...In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.展开更多
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac...In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.展开更多
With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a...With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing emotions.Textual Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language processing.It is different from the previous tasks of emotion recognition and emotion classification.In addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion source.In this paper,we provide a survey for TECE.First,we introduce the development process and classification of TECE.Then,we discuss the existing methods and key factors for TECE.Finally,we enumerate the challenges and developing trend for TECE.展开更多
Teacher emotion recognition(TER)has a significant impact on student engagement,classroom atmosphere,and teaching quality,which is a research hotspot in the smart education area.However,existing studies lack high-quali...Teacher emotion recognition(TER)has a significant impact on student engagement,classroom atmosphere,and teaching quality,which is a research hotspot in the smart education area.However,existing studies lack high-quality multimodal datasets and neglect common and discriminative features of multimodal data in emotion expression.To address these challenges,this research constructs a multimodal TER dataset suitable for real classroom teaching scenarios.TER dataset contains a total of 102 lessons and 2,170 video segments from multiple educational stages and subjects,innovatively labelled with emotional tags that characterize teacher‒student interactions,such as satisfaction and questions.To explore the characteristics of multimodal data in emotion expression,this research proposes an emotion dual-space network(EDSN)that establishes an emotion commonality space construction(ECSC)module and an emotion discrimination space construction(EDSC)module.Specifically,the EDSN utilizes central moment differences to measure the similarity to assess the correlation between multiple modalities within the emotion commonality space.On this basis,the gradient reversal layer and orthogonal projection are further utilized to construct the EDSC to extract unique emotional information and remove redundant information from each modality.Experimental results demonstrate that the EDSN achieves an accuracy of 0.770 and a weighted F1 score of 0.769 on the TER dataset,outperforming other comparative models.展开更多
This study investigated the impact of problematic mobile phone use(PMPU)on emotion recognition.The PMPU levels of 150 participants were measured using the standardized SAS-SV scale.Based on the SAS-SV cutoff scores,pa...This study investigated the impact of problematic mobile phone use(PMPU)on emotion recognition.The PMPU levels of 150 participants were measured using the standardized SAS-SV scale.Based on the SAS-SV cutoff scores,participants were divided into PMPU and Control groups.These participants completed two emotion recognition experiments involving facial emotion stimuli that had been manipulated to varying emotional intensities using Morph software.Experiment 1(n=75)assessed differences in facial emotion detection accuracy.Experiment 2(n=75),based on signal detection theory,examined differences in hit and false alarm rates across emotional expressions.The results showed that PMPU users demonstrated higher recognition accuracy rates for disgust faces but lower accuracy for happy faces.This indicates a tendency among PMPU users to prioritize specific negative emotions and may have impaired perception of positive emotions.Practically,incorporating diverse emotional stimuli into PMPU intervention may help alleviate the negative emotional focus bias associated with excessive mobile devices use.展开更多
“The blood-washed film will never lie.”When that line was spoken in Dead to Rights,the cinema seemed frozen in a deeper silence than I had ever experienced.It pierced the air with a truth that could not be softened,...“The blood-washed film will never lie.”When that line was spoken in Dead to Rights,the cinema seemed frozen in a deeper silence than I had ever experienced.It pierced the air with a truth that could not be softened,and I felt my chest tighten as if the words had been spoken directly to me.At that moment,I was not merely a spectator of a film,but a witness called to remember.The trembling voice of the actor,the stark finality of the line-it overwhelmed me with an emotion too heavy for words,a blend of grief,anger,and responsibility.展开更多
The Guide to Learning and Development for Children Aged 3–6 states that“a pleasant emotion is one of the important indicators of children’s physical and mental health,and it is also the foundation for learning and ...The Guide to Learning and Development for Children Aged 3–6 states that“a pleasant emotion is one of the important indicators of children’s physical and mental health,and it is also the foundation for learning and development in other areas.”Emotional health in early childhood plays a significant role in their future development.This article focuses on understanding the characteristics of young children’s emotions and the factors that affect their emotional management.Simultaneously,it proposes effective methods for families and kindergartens to jointly protect children’s emotional and mental health,providing a healthy and positive growth environment for preschool children,and jointly supporting their growth.展开更多
In the successive rise of the metaverse and artificial intelligence(AI),rationalism and emotionalism have consistently manifested as symptoms of modernity throughout their developmental trajectories.This paper argues ...In the successive rise of the metaverse and artificial intelligence(AI),rationalism and emotionalism have consistently manifested as symptoms of modernity throughout their developmental trajectories.This paper argues that the achievements and crises of both fields signify the triumph of rationalism in the context of deepening modernity,while simultaneously harboring an intrinsic impetus and possibility for transitioning from rationalism to emotionalism.This dynamic serves as a quintessential representation of modernity’s crises and their transcendence.Centered on embodied cognitive science,AI and the metaverse—rooted in rationalism—now face challenges in shifting toward emotionalism,revealing their inadequacy in addressing this shift.Humanity must explore new modernity-deepening paradigms grounded in emotionalism to confront and transcend the potential crises posed by the metaverse and AI.展开更多
Objectives:Adults with cataracts are often reported with mental health issues,which has driven researchers to identify modifiable factors so that effective intervention programs can be timely implemented.Thus,we inves...Objectives:Adults with cataracts are often reported with mental health issues,which has driven researchers to identify modifiable factors so that effective intervention programs can be timely implemented.Thus,we investigated associations of physical activity(PA)and sedentary behavior(SB)with stress,anxiety,and sleep problems among adultswith cataracts.Methods:In this cross-sectional study,a total of 2219 participantswith cataracts completed self-reported measures on demographic characteristics(e.g.,age and sex),PA,SB,anxiety,stress and sleep problems.Multiple linear regression and logistic analyses adjusted for covariates were employed to examine the associations of PA and SB with outcomes of interest.Results:Meeting PA recommendation was significantly associated with lower stress score(β=−2.920,95%CI:−3.880 to−1.959;p<0.001),a 51.2%reduction in the odds of sleep problems(OR=0.488,95%CI:0.389 to 0.612;p<0.001).Limiting SB to≤8 h/day was significantly associated with reduced stress score(−5.191,95%CI:−6.378 to−4.004;p<0.001),lower odds of anxiety symptoms(OR=0.481,95%CI:0.354 to 0.655;p<0.001),and sleep problems(OR=0.540,95%CI:0.420 to 0.693;p<0.001).The greatest benefit appeared when both PA and SB recommendations were achieved simultaneously.Compared with individuals who met neither recommendation,those who were sufficiently active and sat less than 8 h/day showed a 9.307-point lower stress score(95%CI:−11.12 to−7.49;p<0.001),a 54.9%lower odds of anxiety symptoms(OR=0.451,95%CI:0.262 to 0.776;p=0.004),and a 66.4%lower odds of sleep problems(OR=0.336,95%CI:0.206 to 0.550;p<0.001).Conclusions:Meeting PA and SB recommendations could provide substantial psychosocial benefits for adults with cataracts.展开更多
The present study explores the importance of developing metaphorical thinking skills in students within the framework of English as a Foreign Language(EFL)reading courses at the tertiary educational level.Metaphorical...The present study explores the importance of developing metaphorical thinking skills in students within the framework of English as a Foreign Language(EFL)reading courses at the tertiary educational level.Metaphorical thinking is viewed as the ability to envisage the world figuratively,perceive associatively,and express oneself creatively.It is crucial to recognize metaphors in texts,interpret the complex images they evoke,and generate new metaphors.It is especially needful in the current era of clip thinking and fragmented information processing when students often approach content superficially rather than comprehensively,leading to decreased cognitive activity and a diminished capacity to understand literature.To foster metaphorical thinking,the paper suggests building a text associative-semantic field focusing on metaphors.Due to its hierarchical structure,which can be envisioned as a dense nucleus surrounded by a central region of synonyms and further enveloped by a periphery of more loosely associated linguistic units,the text associative-semantic field is seen as a potent solution for facilitating improved visualization and more holistic comprehension of information,allowing students for expanding their vocabulary and strengthening associative connections.Notably,the study highlights analyzing the metaphors of emotional states as they contribute significantly to a more profound interpretation of the text,understanding the writer’s unique style,deepening the students’engagement with the book,and expanding their emotional experiences.展开更多
The adoption of content and language integrated learning(CLIL)practices has expanded in recent years as higher education institutions adopt a top-down English medium of instruction(EMI)language policy in the hope of e...The adoption of content and language integrated learning(CLIL)practices has expanded in recent years as higher education institutions adopt a top-down English medium of instruction(EMI)language policy in the hope of entering the international knowledge market(De Costa et al.,2022;Isidro&Lasagabaster,2019).However,research focusing on the effects of EMI policy on content teachers needing to implement CLIL,especially within trilingual contexts such as Kazakhstan,has been marginal despite drastic alterations to teachers’professional context and expectations(Karabassova,2022b).Such changes may result in teachers’feelings of professional vulnerability-an emotion that often arises when changes in professional expectations and professional context disrupt one’s professional identity and pedagogical practices(Kelchtermans,2009).Our case study focuses on the professional vulnerability experienced by a Kazakhstani in-service teacher as she negotiated a CLIL pedagogy for the first time.Relying on semi-structured interviews,recorded classroom observation,field notes,and developed material,our findings highlight how macro(e.g.,societal)and meso(e.g.,institutional)language policies can affect teachers’lived experiences and pedagogical practices within their classrooms.Lastly,we provide ways in which administrators can assist teachers in overcoming professional vulnerability as institutions adopt language policies such as CLIL.展开更多
Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion...Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.展开更多
Background:The Canadian 24-h movement guidelines(24-HMG)emphasize the holistic consideration of physical activity(PA),sedentary behavior,and sleep in shaping health outcomes.This study aimed to examine the association...Background:The Canadian 24-h movement guidelines(24-HMG)emphasize the holistic consideration of physical activity(PA),sedentary behavior,and sleep in shaping health outcomes.This study aimed to examine the associations between meeting 24-HMG and emotion regulation-related indicators among children and adolescents.Methods:A total of 534 Chinese children and adolescents aged 12.94±1.10 years(49.81%males)participated in this study and completed self-report measures assessing 24-h movement behaviors,emotion regulation strategies,emotion regulation flexibility,and regulatory emotional self-efficacy.Results:Only 7.12% of theparticipants adhered to two or all three guidelines.The number of guidelines met was positively associated with the use of emotion regulation strategies,emotion regulation flexibility,and regulatory emotional self-efficacy.Compared with meeting none of the guidelines,participants whomet one ormore guidelines reported significantly better performance in these outcomes.Conclusion:Meeting 24-HMG was associated with superior emotion regulation in children and adolescents.The importance of engaging in regular PA,limiting recreational screen time,and getting enough sleep should be highlighted for fostering emotion regulation in this demographic.展开更多
The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classificati...The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.展开更多
In recent years,Speech Emotion Recognition(SER)has developed into an essential instrument for interpreting human emotions from auditory data.The proposed research focuses on the development of a SER system employing d...In recent years,Speech Emotion Recognition(SER)has developed into an essential instrument for interpreting human emotions from auditory data.The proposed research focuses on the development of a SER system employing deep learning and multiple datasets containing samples of emotive speech.The primary objective of this research endeavor is to investigate the utilization of Convolutional Neural Networks(CNNs)in the process of sound feature extraction.Stretching,pitch manipulation,and noise injection are a few of the techniques utilized in this study to improve the data quality.Feature extraction methods including Zero Crossing Rate,Chroma_stft,Mel⁃scale Frequency Cepstral Coefficients(MFCC),Root Mean Square(RMS),and Mel⁃Spectogram are used to train a model.By using these techniques,audio signals can be transformed into recognized features that can be utilized to train the model.Ultimately,the study produces a thorough evaluation of the models performance.When this method was applied,the model achieved an impressive accuracy of 94.57%on the test dataset.The proposed work was also validated on the EMO⁃BD and IEMOCAP datasets.These consist of further data augmentation,feature engineering,and hyperparameter optimization.By following these development paths,SER systems will be able to be implemented in real⁃world scenarios with greater accuracy and resilience.展开更多
This study aimed to determine the reliability,validity and measurement invariance of scores from the Difficulties in Emotion Regulation Scale-8 in Chinese context.A total of 1114 Chinese adolescents were participants ...This study aimed to determine the reliability,validity and measurement invariance of scores from the Difficulties in Emotion Regulation Scale-8 in Chinese context.A total of 1114 Chinese adolescents were participants in three phases:N=424 for the initial DERS-8 measure completion;N=586 the DERS-8,General Anxiety Disorder Scale,Depression Scale and Emotion Regulation Scale completion,with an interval of one month.Then an additional 104 adolescents also completed DERS-8,General Anxiety Disorder Scale,Depression Scale and Emotion Regulation Scale.Both exploratory and confirmatory factor analyses confirmed the one-factor model of the scale,and the fitness indicators wereχ^(2)/df=4.05,RMSEA=0.07,CFI=0.98,and TLI=0.97.Each item of the DERS-8 had good discrimination.The internal consistency reliability coefficient,split-half reliability coefficient and test-retest reliability coefficient of the scale scores were 0.90,0.87 and 0.66,respectively.The findings suggest the Chinese version of the DERS-8 is a reliable measure of difficulty of emotion regulation in Chinese adolescents.展开更多
Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite ...Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite this understanding,the neural circuit mechanisms underlying this phenomenon remain elusive.In this study,we present a biophysical computational model encompassing three crucial regions,including the dorsolateral prefrontal cortex,subgenual anterior cingulate cortex,and ventromedial prefrontal cortex.The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes.The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks.Furthermore,our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex,and network functionality was restored through intervention in the dorsolateral prefrontal cortex.This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.展开更多
Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the dev...Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the development of SER system.To address these issues,this paper proposes a framework that incorporates the Attentive Mask Residual Network(AM-ResNet)and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively,together with a cross-attention module to interact and fuse these two features.The AM-ResNet branch mainly consists of maximum amplitude difference detection,mask residual block,and an attention mechanism.Among them,the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network,respectively,to reduce the impact of silent frames,and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech.In the Wav2vec 2.0 branch,this model is introduced as a feature extractor to obtain general speech features(Wav2vec 2.0 features)through pre-training with a large amount of unlabeled speech data,which can assist the SER task and cope with data sparsity problems.In the cross-attention module,AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features,which are used to predict the final emotion.Furthermore,multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations.Finally,experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches.展开更多
Social cognition constitutes a fundamental component in establishing and maintaining healthy interpersonal relationships,achieving social goals,and effectively regulating emotions within social contexts.Inspired by a ...Social cognition constitutes a fundamental component in establishing and maintaining healthy interpersonal relationships,achieving social goals,and effectively regulating emotions within social contexts.Inspired by a recent study examining the chain mediating roles of perceived social adversity and security in the relationship between impulsive personality and suicidal behaviors among depressed adolescents,this editorial synthesizes advances in social cognition research specific to adolescent depression.Research methodologies,theoretical frameworks,and neuroscientific insights in this domain have evolved substantially over the past fifteen years.Whereas earlier investigations primarily emphasized broad behavioral observations,contemporary research increasingly integrates neuroimaging techniques,computational modeling,and refined experimental paradigms.Current understanding of specific cognitive biases such as distinctions between interpretive and attentional biases has also grown more nuanced.This editorial reflects the evolving nature of the field by presenting shifts in research focus and demonstrating how these changes have deepened our understanding of social-cognitive functioning in adolescent depression.Building on this synthesis,we outline limitations of extant research and suggest promising directions for future inquiry.展开更多
文摘In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.
基金supported by the National Natural Science Foundation of China(62272049,62236006,62172045)the Key Projects of Beijing Union University(ZKZD202301).
文摘In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.
基金partially supported by the National Natural Science Foundation of China under Grant No.62372121the Ministry of education of Humanities and Social Science project under Grant No.20YJAZH118+1 种基金the National Key Research and Development Program of China under Grant No.2020YFB1005804the MOE Project at Center for Linguistics and Applied Linguistics,Guangdong University of Foreign Studies。
文摘With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing emotions.Textual Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language processing.It is different from the previous tasks of emotion recognition and emotion classification.In addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion source.In this paper,we provide a survey for TECE.First,we introduce the development process and classification of TECE.Then,we discuss the existing methods and key factors for TECE.Finally,we enumerate the challenges and developing trend for TECE.
基金supported by the National Natural Science Foundation of China(Grant Nos.62377007 and 62407009)the Chongqing University Graduate Education Teaching Reform Research Key Project,China(Grant No.232073)+1 种基金the Scientific and Technological Research Program of Chongqing Municipal Education Commission,China(Grant Nos.KJZD-M202400606 and KJZD-M202300603)the Chongqing Natural Science Foundation Joint Key Project for Innovation and Development,China(Grant No.2024NSCQ-LZX0057).
文摘Teacher emotion recognition(TER)has a significant impact on student engagement,classroom atmosphere,and teaching quality,which is a research hotspot in the smart education area.However,existing studies lack high-quality multimodal datasets and neglect common and discriminative features of multimodal data in emotion expression.To address these challenges,this research constructs a multimodal TER dataset suitable for real classroom teaching scenarios.TER dataset contains a total of 102 lessons and 2,170 video segments from multiple educational stages and subjects,innovatively labelled with emotional tags that characterize teacher‒student interactions,such as satisfaction and questions.To explore the characteristics of multimodal data in emotion expression,this research proposes an emotion dual-space network(EDSN)that establishes an emotion commonality space construction(ECSC)module and an emotion discrimination space construction(EDSC)module.Specifically,the EDSN utilizes central moment differences to measure the similarity to assess the correlation between multiple modalities within the emotion commonality space.On this basis,the gradient reversal layer and orthogonal projection are further utilized to construct the EDSC to extract unique emotional information and remove redundant information from each modality.Experimental results demonstrate that the EDSN achieves an accuracy of 0.770 and a weighted F1 score of 0.769 on the TER dataset,outperforming other comparative models.
基金supported by the National Social Science Fund of China(Grant Number:20BSH134).
文摘This study investigated the impact of problematic mobile phone use(PMPU)on emotion recognition.The PMPU levels of 150 participants were measured using the standardized SAS-SV scale.Based on the SAS-SV cutoff scores,participants were divided into PMPU and Control groups.These participants completed two emotion recognition experiments involving facial emotion stimuli that had been manipulated to varying emotional intensities using Morph software.Experiment 1(n=75)assessed differences in facial emotion detection accuracy.Experiment 2(n=75),based on signal detection theory,examined differences in hit and false alarm rates across emotional expressions.The results showed that PMPU users demonstrated higher recognition accuracy rates for disgust faces but lower accuracy for happy faces.This indicates a tendency among PMPU users to prioritize specific negative emotions and may have impaired perception of positive emotions.Practically,incorporating diverse emotional stimuli into PMPU intervention may help alleviate the negative emotional focus bias associated with excessive mobile devices use.
文摘“The blood-washed film will never lie.”When that line was spoken in Dead to Rights,the cinema seemed frozen in a deeper silence than I had ever experienced.It pierced the air with a truth that could not be softened,and I felt my chest tighten as if the words had been spoken directly to me.At that moment,I was not merely a spectator of a film,but a witness called to remember.The trembling voice of the actor,the stark finality of the line-it overwhelmed me with an emotion too heavy for words,a blend of grief,anger,and responsibility.
文摘The Guide to Learning and Development for Children Aged 3–6 states that“a pleasant emotion is one of the important indicators of children’s physical and mental health,and it is also the foundation for learning and development in other areas.”Emotional health in early childhood plays a significant role in their future development.This article focuses on understanding the characteristics of young children’s emotions and the factors that affect their emotional management.Simultaneously,it proposes effective methods for families and kindergartens to jointly protect children’s emotional and mental health,providing a healthy and positive growth environment for preschool children,and jointly supporting their growth.
基金supported by the China Metaverse and Digital Talent Development Initiative and the general project of the National Social Science Foundation of China titled“Theoretical Interpretation of Advertising Messaging and the Development of a Typical Case Database”(19BXW085).
文摘In the successive rise of the metaverse and artificial intelligence(AI),rationalism and emotionalism have consistently manifested as symptoms of modernity throughout their developmental trajectories.This paper argues that the achievements and crises of both fields signify the triumph of rationalism in the context of deepening modernity,while simultaneously harboring an intrinsic impetus and possibility for transitioning from rationalism to emotionalism.This dynamic serves as a quintessential representation of modernity’s crises and their transcendence.Centered on embodied cognitive science,AI and the metaverse—rooted in rationalism—now face challenges in shifting toward emotionalism,revealing their inadequacy in addressing this shift.Humanity must explore new modernity-deepening paradigms grounded in emotionalism to confront and transcend the potential crises posed by the metaverse and AI.
文摘Objectives:Adults with cataracts are often reported with mental health issues,which has driven researchers to identify modifiable factors so that effective intervention programs can be timely implemented.Thus,we investigated associations of physical activity(PA)and sedentary behavior(SB)with stress,anxiety,and sleep problems among adultswith cataracts.Methods:In this cross-sectional study,a total of 2219 participantswith cataracts completed self-reported measures on demographic characteristics(e.g.,age and sex),PA,SB,anxiety,stress and sleep problems.Multiple linear regression and logistic analyses adjusted for covariates were employed to examine the associations of PA and SB with outcomes of interest.Results:Meeting PA recommendation was significantly associated with lower stress score(β=−2.920,95%CI:−3.880 to−1.959;p<0.001),a 51.2%reduction in the odds of sleep problems(OR=0.488,95%CI:0.389 to 0.612;p<0.001).Limiting SB to≤8 h/day was significantly associated with reduced stress score(−5.191,95%CI:−6.378 to−4.004;p<0.001),lower odds of anxiety symptoms(OR=0.481,95%CI:0.354 to 0.655;p<0.001),and sleep problems(OR=0.540,95%CI:0.420 to 0.693;p<0.001).The greatest benefit appeared when both PA and SB recommendations were achieved simultaneously.Compared with individuals who met neither recommendation,those who were sufficiently active and sat less than 8 h/day showed a 9.307-point lower stress score(95%CI:−11.12 to−7.49;p<0.001),a 54.9%lower odds of anxiety symptoms(OR=0.451,95%CI:0.262 to 0.776;p=0.004),and a 66.4%lower odds of sleep problems(OR=0.336,95%CI:0.206 to 0.550;p<0.001).Conclusions:Meeting PA and SB recommendations could provide substantial psychosocial benefits for adults with cataracts.
文摘The present study explores the importance of developing metaphorical thinking skills in students within the framework of English as a Foreign Language(EFL)reading courses at the tertiary educational level.Metaphorical thinking is viewed as the ability to envisage the world figuratively,perceive associatively,and express oneself creatively.It is crucial to recognize metaphors in texts,interpret the complex images they evoke,and generate new metaphors.It is especially needful in the current era of clip thinking and fragmented information processing when students often approach content superficially rather than comprehensively,leading to decreased cognitive activity and a diminished capacity to understand literature.To foster metaphorical thinking,the paper suggests building a text associative-semantic field focusing on metaphors.Due to its hierarchical structure,which can be envisioned as a dense nucleus surrounded by a central region of synonyms and further enveloped by a periphery of more loosely associated linguistic units,the text associative-semantic field is seen as a potent solution for facilitating improved visualization and more holistic comprehension of information,allowing students for expanding their vocabulary and strengthening associative connections.Notably,the study highlights analyzing the metaphors of emotional states as they contribute significantly to a more profound interpretation of the text,understanding the writer’s unique style,deepening the students’engagement with the book,and expanding their emotional experiences.
基金funding from the U.S.-Kazakhstan University Partnerships program funded by the U.S.Mission to Kazakhstan and administered by American Councils[Award number SKZ100-19-CA-0149].
文摘The adoption of content and language integrated learning(CLIL)practices has expanded in recent years as higher education institutions adopt a top-down English medium of instruction(EMI)language policy in the hope of entering the international knowledge market(De Costa et al.,2022;Isidro&Lasagabaster,2019).However,research focusing on the effects of EMI policy on content teachers needing to implement CLIL,especially within trilingual contexts such as Kazakhstan,has been marginal despite drastic alterations to teachers’professional context and expectations(Karabassova,2022b).Such changes may result in teachers’feelings of professional vulnerability-an emotion that often arises when changes in professional expectations and professional context disrupt one’s professional identity and pedagogical practices(Kelchtermans,2009).Our case study focuses on the professional vulnerability experienced by a Kazakhstani in-service teacher as she negotiated a CLIL pedagogy for the first time.Relying on semi-structured interviews,recorded classroom observation,field notes,and developed material,our findings highlight how macro(e.g.,societal)and meso(e.g.,institutional)language policies can affect teachers’lived experiences and pedagogical practices within their classrooms.Lastly,we provide ways in which administrators can assist teachers in overcoming professional vulnerability as institutions adopt language policies such as CLIL.
基金supported by the ScientificResearch and Innovation Team Program of Sichuan University of Science and Technology(No.SUSE652A006)Sichuan Key Provincial Research Base of Intelligent Tourism(ZHYJ22-03)In addition,it is also listed as a project of Sichuan Provincial Science and Technology Programme(2022YFG0028).
文摘Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.
基金supported by Zhejiang Provincial Social Science Funding(22NDJC050YB).
文摘Background:The Canadian 24-h movement guidelines(24-HMG)emphasize the holistic consideration of physical activity(PA),sedentary behavior,and sleep in shaping health outcomes.This study aimed to examine the associations between meeting 24-HMG and emotion regulation-related indicators among children and adolescents.Methods:A total of 534 Chinese children and adolescents aged 12.94±1.10 years(49.81%males)participated in this study and completed self-report measures assessing 24-h movement behaviors,emotion regulation strategies,emotion regulation flexibility,and regulatory emotional self-efficacy.Results:Only 7.12% of theparticipants adhered to two or all three guidelines.The number of guidelines met was positively associated with the use of emotion regulation strategies,emotion regulation flexibility,and regulatory emotional self-efficacy.Compared with meeting none of the guidelines,participants whomet one ormore guidelines reported significantly better performance in these outcomes.Conclusion:Meeting 24-HMG was associated with superior emotion regulation in children and adolescents.The importance of engaging in regular PA,limiting recreational screen time,and getting enough sleep should be highlighted for fostering emotion regulation in this demographic.
文摘The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.
文摘In recent years,Speech Emotion Recognition(SER)has developed into an essential instrument for interpreting human emotions from auditory data.The proposed research focuses on the development of a SER system employing deep learning and multiple datasets containing samples of emotive speech.The primary objective of this research endeavor is to investigate the utilization of Convolutional Neural Networks(CNNs)in the process of sound feature extraction.Stretching,pitch manipulation,and noise injection are a few of the techniques utilized in this study to improve the data quality.Feature extraction methods including Zero Crossing Rate,Chroma_stft,Mel⁃scale Frequency Cepstral Coefficients(MFCC),Root Mean Square(RMS),and Mel⁃Spectogram are used to train a model.By using these techniques,audio signals can be transformed into recognized features that can be utilized to train the model.Ultimately,the study produces a thorough evaluation of the models performance.When this method was applied,the model achieved an impressive accuracy of 94.57%on the test dataset.The proposed work was also validated on the EMO⁃BD and IEMOCAP datasets.These consist of further data augmentation,feature engineering,and hyperparameter optimization.By following these development paths,SER systems will be able to be implemented in real⁃world scenarios with greater accuracy and resilience.
基金funded by Science Research Project of Hebei Education Department(BJ2025238)Humanities and Social Science Research Project of Hebei Normal University(S24YX002)Humanities and Social Science Research Foundation of Hebei Normal University(S22B019).
文摘This study aimed to determine the reliability,validity and measurement invariance of scores from the Difficulties in Emotion Regulation Scale-8 in Chinese context.A total of 1114 Chinese adolescents were participants in three phases:N=424 for the initial DERS-8 measure completion;N=586 the DERS-8,General Anxiety Disorder Scale,Depression Scale and Emotion Regulation Scale completion,with an interval of one month.Then an additional 104 adolescents also completed DERS-8,General Anxiety Disorder Scale,Depression Scale and Emotion Regulation Scale.Both exploratory and confirmatory factor analyses confirmed the one-factor model of the scale,and the fitness indicators wereχ^(2)/df=4.05,RMSEA=0.07,CFI=0.98,and TLI=0.97.Each item of the DERS-8 had good discrimination.The internal consistency reliability coefficient,split-half reliability coefficient and test-retest reliability coefficient of the scale scores were 0.90,0.87 and 0.66,respectively.The findings suggest the Chinese version of the DERS-8 is a reliable measure of difficulty of emotion regulation in Chinese adolescents.
基金supported by the Major Research Instrument Development Project of the National Natural Science Foundation of China(82327810)the Foundation of the President of Hebei University(XZJJ202202)the Hebei Province“333 talent project”(A202101058).
文摘Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite this understanding,the neural circuit mechanisms underlying this phenomenon remain elusive.In this study,we present a biophysical computational model encompassing three crucial regions,including the dorsolateral prefrontal cortex,subgenual anterior cingulate cortex,and ventromedial prefrontal cortex.The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes.The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks.Furthermore,our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex,and network functionality was restored through intervention in the dorsolateral prefrontal cortex.This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.
基金supported by Chongqing University of Posts and Telecommunications Ph.D.Innovative Talents Project(Grant No.BYJS202106)Chongqing Postgraduate Research Innovation Project(Grant No.CYB21203).
文摘Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the development of SER system.To address these issues,this paper proposes a framework that incorporates the Attentive Mask Residual Network(AM-ResNet)and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively,together with a cross-attention module to interact and fuse these two features.The AM-ResNet branch mainly consists of maximum amplitude difference detection,mask residual block,and an attention mechanism.Among them,the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network,respectively,to reduce the impact of silent frames,and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech.In the Wav2vec 2.0 branch,this model is introduced as a feature extractor to obtain general speech features(Wav2vec 2.0 features)through pre-training with a large amount of unlabeled speech data,which can assist the SER task and cope with data sparsity problems.In the cross-attention module,AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features,which are used to predict the final emotion.Furthermore,multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations.Finally,experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches.
基金Supported by the National Natural Science Foundation of China,No.82101595.
文摘Social cognition constitutes a fundamental component in establishing and maintaining healthy interpersonal relationships,achieving social goals,and effectively regulating emotions within social contexts.Inspired by a recent study examining the chain mediating roles of perceived social adversity and security in the relationship between impulsive personality and suicidal behaviors among depressed adolescents,this editorial synthesizes advances in social cognition research specific to adolescent depression.Research methodologies,theoretical frameworks,and neuroscientific insights in this domain have evolved substantially over the past fifteen years.Whereas earlier investigations primarily emphasized broad behavioral observations,contemporary research increasingly integrates neuroimaging techniques,computational modeling,and refined experimental paradigms.Current understanding of specific cognitive biases such as distinctions between interpretive and attentional biases has also grown more nuanced.This editorial reflects the evolving nature of the field by presenting shifts in research focus and demonstrating how these changes have deepened our understanding of social-cognitive functioning in adolescent depression.Building on this synthesis,we outline limitations of extant research and suggest promising directions for future inquiry.
