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
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
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
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
To the editor:Non-suicidal self-injury(NSSI)is an array of directly prepense or repetitive self-harm behaviours without suicidal intent.Individuals engage in self-injurious behaviours to reduce negative mental and cog...To the editor:Non-suicidal self-injury(NSSI)is an array of directly prepense or repetitive self-harm behaviours without suicidal intent.Individuals engage in self-injurious behaviours to reduce negative mental and cognitive states or evoke positive emotions.展开更多
Although numerousfindings show that people experience both positive and negative experiences with regards to solitude,the relationship between solitude capacity and emotional experience remains unclear.The current stud...Although numerousfindings show that people experience both positive and negative experiences with regards to solitude,the relationship between solitude capacity and emotional experience remains unclear.The current study investigated the extent to which emotion regulation may play a suppressive role in the relationship between solitude capacity and emotional experience.Questionnaires on solitude capacity,emotion regulation,and emotional experience were completed by a sample of Chinese college students(n=844;432 females;Meanage=19.79 years,SD=1.43 years).The results of the indirect effect test showed that cognitive reappraisal suppresses the prediction of solitude capacity on positive emotions,while the solitude capacity prediction of negative emotions was suppressed by both cognitive reappraisal and expressive suppression.This suggests that solitude capacity does not predict emotional experience directly,but instead is realized through an antagonistic system consisting of adaptive and nonadaptive emotion regulation strategies.Thesefindings provide cross-sectional empirical support for the ecological niche hypothesis of solitude,and are of theoretical significance in clarifying the role of internal mechanisms of solitude capacity on the human emotional experience.展开更多
Previous studies on classroom concentration were conducted from the perspective of teachers,without considering students’real feelings.This study,starting from students’feelings of classroom concentration,sent out q...Previous studies on classroom concentration were conducted from the perspective of teachers,without considering students’real feelings.This study,starting from students’feelings of classroom concentration,sent out questionnaires to undergraduates of different levels and natures of schools.Through analysis and research,it was found that teachers’emotions indirectly affect students’concentration through students’individual emotions and classroom atmosphere.On this basis,it is suggested that teachers can improve classroom concentration through the correct emotional expression.展开更多
In robotics and human-robot interaction,a robot’s capacity to express and react correctly to human emotions is essential.A significant aspect of the capability involves controlling the robotic facial skin actuators i...In robotics and human-robot interaction,a robot’s capacity to express and react correctly to human emotions is essential.A significant aspect of the capability involves controlling the robotic facial skin actuators in a way that resonates with human emotions.This research focuses on human anthropometric theories to design and control robotic facial actuators,addressing the limitations of existing approaches in expressing emotions naturally and accurately.The facial landmarks are extracted to determine the anthropometric indicators for designing the robot head and is employed to the displacement of these points to calculate emotional values using Fuzzy C-Mean(FCM).The rotating angles of skin actuators are required to account for the smaller emotions,which enhance the robot’s ability to perform emotions in reality.In addition,this study contributes a novel approach based on facial anthropometric indicators to tailor emotional expressions to diverse human characteristics,ensuring more personalized and intuitive interactions.The results demonstrated howfuzzy logic can be employed to improve a robot’s ability to express emotions,which are digitized into fuzzy values.This is also the contribution of the research,which laid the groundwork for robots that can interact with humans more intuitively and empathetically.The performed experiments demonstrated that the suitability of proposed models to conduct tasks related to human emotions with the accuracy of emotional value determination and motor angles is 0.96 and 0.97,respectively.展开更多
BACKGROUND Empathetic psychological care improves mood and enhances the quality of life in critically ill patients.AIM To study the impact of combining 222-nm ultraviolet(UV)disinfection with empathetic psychological ...BACKGROUND Empathetic psychological care improves mood and enhances the quality of life in critically ill patients.AIM To study the impact of combining 222-nm ultraviolet(UV)disinfection with empathetic psychological care on emotional states,nosocomial infection rates,and quality of life in critically ill patients.METHODS A total of 202 critically ill patients admitted to Beijing Ditan Hospital(December 2023 to May 2024)were randomly assigned to control(Ctrl,n=101)or observation groups(Obs,n=101).The Ctrl group received 222-nm UV disinfection and routine care,while the Obs group received 222-nm UV disinfection with empathetic psychological care.Emotional states[Self-Rating Anxiety Scale(SAS),Self-Rating Depression Scale(SDS)],hospital infection rates,quality of life(36-Item Short Form Health Survey),and patient satisfaction were evaluated.RESULTS At baseline,there were no significant differences in SAS and SDS scores between the groups(P>0.05).Following care,both groups demonstrated reductions in SAS and SDS scores,with the Obs group exhibiting a significantly greater reduction(P<0.05).The Obs group also experienced a significantly lower overall hospital infection rate(P<0.05).Similarly,while baseline 36-Item Short Form Health Survey scores did not differ significantly between the groups(P>0.05),post-care scores improved in both groups,with a greater improvement observed in the Obs group(P<0.05).Additionally,the Obs group reported higher patient satisfaction ratings(P<0.05).CONCLUSION The combination of 222-nm UV disinfection and empathetic psychological care improves emotional states,reduces hospital infection rates,enhances the quality of life,and increases patient satisfaction among critically ill patients.展开更多
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
BACKGROUND Emotional reactions,such as anxiety,irritability,and aggressive behavior,have attracted clinical attention as behavioral and emotional problems in preschool-age children.AIM To investigate the current statu...BACKGROUND Emotional reactions,such as anxiety,irritability,and aggressive behavior,have attracted clinical attention as behavioral and emotional problems in preschool-age children.AIM To investigate the current status of family rearing,parental stress,and behavioral and emotional problems of preschool children and to analyze the mediating effect of the current status of family rearing on parental stress and behavioral/emo-tional problems.METHODS We use convenience sampling to select 258 preschool children in the physical examination center of our hospital from October 2021 to September 2023.The children and their parents were evaluated using a questionnaire survey.Pearson's correlation was used to analyze the correlation between child behavioral and emotional problems and parental stress and family rearing,and the structural equation model was constructed to test the mediating effect.RESULTS The score for behavioral/emotional problems of 258 preschool children was(27.54±3.63),the score for parental stress was(87.64±11.34),and the score for parental family rearing was(31.54±5.24).There was a positive correlation between the behavioral and emotional problems of the children and the“hostile/mandatory”parenting style;meanwhile,showed a negative correlation with the“support/participation”parenting style(all P<0.05).The intermediary effect value between the family upbringing of parents in parental stress and children's behavior problems was 29.89%.CONCLUSION Parental family upbringing has a mediating effect between parental stress and behavioral and emotional problems of children.Despite paying attention to the behavioral and emotional problems of preschool-age children,clinical medical staff should provide correct and reasonable parenting advice to their parents to promote the mental health of preschool-age children.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
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
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
EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses.However,the high dimensionality of EEG signals and their continuous variability in the time-freque...EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses.However,the high dimensionality of EEG signals and their continuous variability in the time-frequency plane make their analysis challenging.Therefore,advanced deep learning methods are needed to extract meaningful features and improve classification performance.This study proposes a hybrid model that integrates the Swin Transformer and Temporal Convolutional Network(TCN)mechanisms for EEG-based emotion recognition.EEG signals are first converted into scalogram images using Continuous Wavelet Transform(CWT),and classification is performed on these images.Swin Transformer is used to extract spatial features in scalogram images,and the TCN method is used to learn long-term dependencies.In addition,attention mechanisms are integrated to highlight the essential features extracted from both models.The effectiveness of the proposed model has been tested on the SEED dataset,widely used in the field of emotion recognition,and it has consistently achieved high performance across all emotional classes,with accuracy,precision,recall,and F1-score values of 97.53%,97.54%,97.53%,and 97.54%,respectively.Compared to traditional transfer learning models,the proposed approach achieved an accuracy increase of 1.43%over ResNet-101,1.81%over DenseNet-201,and 2.44%over VGG-19.In addition,the proposed model outperformed many recent CNN,RNN,and Transformer-based methods reported in the literature.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
In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challen...In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets containing both emotion and intent labels, we designed a multi-dataset learning approach enabling the model to generate dialogue summaries encompassing both user intent and emotion. Experiments conducted on the MultiWoZ and MELD datasets demonstrate that our model effectively captures user intent and emotion, achieving extremely competitive results in dialogue state tracking tasks.展开更多
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.
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.
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.
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the Innovation 2030-Major Project of Brain Science and Brain-lnspired Intelligence Technology(2021ZD0200600)Shanghai Science and Technology Committee(22YF1439100,YDZX20213100001003)National Natural Science Foundation of China(82201678).
文摘To the editor:Non-suicidal self-injury(NSSI)is an array of directly prepense or repetitive self-harm behaviours without suicidal intent.Individuals engage in self-injurious behaviours to reduce negative mental and cognitive states or evoke positive emotions.
基金supported by grants from the Doctoral Research Project of Yan’an University(2003-205040349)the 2022 General Special Scientific Research Plan Project of the Shaanxi Provincial Department of Education(YDZZYB23-40)the Social Science Foundation of Shaanxi Province(2023P013 and 2024P028).
文摘Although numerousfindings show that people experience both positive and negative experiences with regards to solitude,the relationship between solitude capacity and emotional experience remains unclear.The current study investigated the extent to which emotion regulation may play a suppressive role in the relationship between solitude capacity and emotional experience.Questionnaires on solitude capacity,emotion regulation,and emotional experience were completed by a sample of Chinese college students(n=844;432 females;Meanage=19.79 years,SD=1.43 years).The results of the indirect effect test showed that cognitive reappraisal suppresses the prediction of solitude capacity on positive emotions,while the solitude capacity prediction of negative emotions was suppressed by both cognitive reappraisal and expressive suppression.This suggests that solitude capacity does not predict emotional experience directly,but instead is realized through an antagonistic system consisting of adaptive and nonadaptive emotion regulation strategies.Thesefindings provide cross-sectional empirical support for the ecological niche hypothesis of solitude,and are of theoretical significance in clarifying the role of internal mechanisms of solitude capacity on the human emotional experience.
基金2024 Guangxi Higher Education Undergraduate Teaching Reform Project“OBE-Guided,Digitally Empowered‘Hadoop Big Data Development Technology’Course Ideological and Political Construction Innovation Exploration and Practice”(2024JGA396)。
文摘Previous studies on classroom concentration were conducted from the perspective of teachers,without considering students’real feelings.This study,starting from students’feelings of classroom concentration,sent out questionnaires to undergraduates of different levels and natures of schools.Through analysis and research,it was found that teachers’emotions indirectly affect students’concentration through students’individual emotions and classroom atmosphere.On this basis,it is suggested that teachers can improve classroom concentration through the correct emotional expression.
基金funded by the University of Economics Ho Chi Minh City-UEH,Vietnam.
文摘In robotics and human-robot interaction,a robot’s capacity to express and react correctly to human emotions is essential.A significant aspect of the capability involves controlling the robotic facial skin actuators in a way that resonates with human emotions.This research focuses on human anthropometric theories to design and control robotic facial actuators,addressing the limitations of existing approaches in expressing emotions naturally and accurately.The facial landmarks are extracted to determine the anthropometric indicators for designing the robot head and is employed to the displacement of these points to calculate emotional values using Fuzzy C-Mean(FCM).The rotating angles of skin actuators are required to account for the smaller emotions,which enhance the robot’s ability to perform emotions in reality.In addition,this study contributes a novel approach based on facial anthropometric indicators to tailor emotional expressions to diverse human characteristics,ensuring more personalized and intuitive interactions.The results demonstrated howfuzzy logic can be employed to improve a robot’s ability to express emotions,which are digitized into fuzzy values.This is also the contribution of the research,which laid the groundwork for robots that can interact with humans more intuitively and empathetically.The performed experiments demonstrated that the suitability of proposed models to conduct tasks related to human emotions with the accuracy of emotional value determination and motor angles is 0.96 and 0.97,respectively.
基金Supported by Beijing Ditan Hospital Affiliated to Capital Medical University“Sailing Plan”,No.DTQH-202405.
文摘BACKGROUND Empathetic psychological care improves mood and enhances the quality of life in critically ill patients.AIM To study the impact of combining 222-nm ultraviolet(UV)disinfection with empathetic psychological care on emotional states,nosocomial infection rates,and quality of life in critically ill patients.METHODS A total of 202 critically ill patients admitted to Beijing Ditan Hospital(December 2023 to May 2024)were randomly assigned to control(Ctrl,n=101)or observation groups(Obs,n=101).The Ctrl group received 222-nm UV disinfection and routine care,while the Obs group received 222-nm UV disinfection with empathetic psychological care.Emotional states[Self-Rating Anxiety Scale(SAS),Self-Rating Depression Scale(SDS)],hospital infection rates,quality of life(36-Item Short Form Health Survey),and patient satisfaction were evaluated.RESULTS At baseline,there were no significant differences in SAS and SDS scores between the groups(P>0.05).Following care,both groups demonstrated reductions in SAS and SDS scores,with the Obs group exhibiting a significantly greater reduction(P<0.05).The Obs group also experienced a significantly lower overall hospital infection rate(P<0.05).Similarly,while baseline 36-Item Short Form Health Survey scores did not differ significantly between the groups(P>0.05),post-care scores improved in both groups,with a greater improvement observed in the Obs group(P<0.05).Additionally,the Obs group reported higher patient satisfaction ratings(P<0.05).CONCLUSION The combination of 222-nm UV disinfection and empathetic psychological care improves emotional states,reduces hospital infection rates,enhances the quality of life,and increases patient satisfaction among critically ill patients.
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Supported by the Shijiazhuang Science and Technology Research and Development Program,No.221460383.
文摘BACKGROUND Emotional reactions,such as anxiety,irritability,and aggressive behavior,have attracted clinical attention as behavioral and emotional problems in preschool-age children.AIM To investigate the current status of family rearing,parental stress,and behavioral and emotional problems of preschool children and to analyze the mediating effect of the current status of family rearing on parental stress and behavioral/emo-tional problems.METHODS We use convenience sampling to select 258 preschool children in the physical examination center of our hospital from October 2021 to September 2023.The children and their parents were evaluated using a questionnaire survey.Pearson's correlation was used to analyze the correlation between child behavioral and emotional problems and parental stress and family rearing,and the structural equation model was constructed to test the mediating effect.RESULTS The score for behavioral/emotional problems of 258 preschool children was(27.54±3.63),the score for parental stress was(87.64±11.34),and the score for parental family rearing was(31.54±5.24).There was a positive correlation between the behavioral and emotional problems of the children and the“hostile/mandatory”parenting style;meanwhile,showed a negative correlation with the“support/participation”parenting style(all P<0.05).The intermediary effect value between the family upbringing of parents in parental stress and children's behavior problems was 29.89%.CONCLUSION Parental family upbringing has a mediating effect between parental stress and behavioral and emotional problems of children.Despite paying attention to the behavioral and emotional problems of preschool-age children,clinical medical staff should provide correct and reasonable parenting advice to their parents to promote the mental health of preschool-age children.
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.
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 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.
文摘EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses.However,the high dimensionality of EEG signals and their continuous variability in the time-frequency plane make their analysis challenging.Therefore,advanced deep learning methods are needed to extract meaningful features and improve classification performance.This study proposes a hybrid model that integrates the Swin Transformer and Temporal Convolutional Network(TCN)mechanisms for EEG-based emotion recognition.EEG signals are first converted into scalogram images using Continuous Wavelet Transform(CWT),and classification is performed on these images.Swin Transformer is used to extract spatial features in scalogram images,and the TCN method is used to learn long-term dependencies.In addition,attention mechanisms are integrated to highlight the essential features extracted from both models.The effectiveness of the proposed model has been tested on the SEED dataset,widely used in the field of emotion recognition,and it has consistently achieved high performance across all emotional classes,with accuracy,precision,recall,and F1-score values of 97.53%,97.54%,97.53%,and 97.54%,respectively.Compared to traditional transfer learning models,the proposed approach achieved an accuracy increase of 1.43%over ResNet-101,1.81%over DenseNet-201,and 2.44%over VGG-19.In addition,the proposed model outperformed many recent CNN,RNN,and Transformer-based methods reported in the literature.
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funded by the Science and Technology Foundation of Chongqing EducationCommission(GrantNo.KJQN202301153)the ScientificResearch Foundation of Chongqing University of Technology(Grant No.2021ZDZ025)the Postgraduate Innovation Foundation of Chongqing University of Technology(Grant No.gzlcx20243524).
文摘In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets containing both emotion and intent labels, we designed a multi-dataset learning approach enabling the model to generate dialogue summaries encompassing both user intent and emotion. Experiments conducted on the MultiWoZ and MELD datasets demonstrate that our model effectively captures user intent and emotion, achieving extremely competitive results in dialogue state tracking tasks.
