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Reinforcement learning (RL) is an effective method in training dialogue policies to steer the conversation towards successful task completion. However, most RL-based methods only rely on semantic inputs that lack empathy as they ignore the user emotional information. Moreover, these methods suffer from delayed rewards caused by the user simulator returning valuable results only at dialogue end. Recently, some methods have been proposed to learn the reward function together with user emotions, but they omit considering user emotion in each dialogue turn. In this paper, we proposed an emotion-sensitive dialogue policy model (ESDP), it incorporates user emotions information into dialogue policy and selects the optimal action by the combination of top-k actions with the user emotions. The user emotion information in each turn is used as an immediate reward for the current dialogue state to solve sparse rewards and the dependency on termination. Extensive experiments validate that our method outperforms the baseline approaches when combined with different Q-Learning algorithms, and also surpasses other popular existing dialog policies' performance., (© 2024. The Author(s).)

The proliferation of electric and hybrid vehicles has made it possible for people to read and work in a stationary vehicle for extended periods. However, the current commonly used in–vehicle lighting design is still centered around driving and driving safety. Following recommendations from the literature, a neutral white color band (4000 K–5000 K) with 50–100 lx at the vehicle table area is favored. Whether this lighting environment can meet the needs to enhance the reading performance in a modern vehicle was investigated in this presented study. Therefore, in total, 12 lighting settings were designed based on combinations of four illuminance levels (50 lx, 100 lx, 150 lx and 200 lx) and three correlated color temperatures (3000 K, 4000 K and 5000 K); we recruited 19 subjects (12 females, 7 males) and let study participants evaluate each condition based on electronic and paper reading. Next, subjective preferences, positive and negative emotions, feeling of fatigue and sustained attention were tested. We found that higher illuminance and higher CCT (Correlated Color Temperature) can significantly improve the performance of in–vehicle readers in most aspects following Kruithof's law (p < 0.05). Among them, we recommend the combination of 150 lx and 4000 K as the light parameters for in–vehicle reading as a new development guideline. In addition, we also discovered the inconsistency of people's lighting preferences between in–vehicle spaces and conventional spaces. For indoor lighting, illuminance values up to 1000 lx are still favored. For an in–vehicle function, starting with 200 lx, the preference level and reading performance already declined. In comparison between electronic and paper reading, both were similarly evaluated. These results show that a neutral white light color should be chosen with a horizontal illuminance of maximal 150 lx for a reading light function independent of the reading device. Interdisciplinarily speaking, our findings can be applied in similar small spaces or transportation modes with gentle acceleration and deceleration such as small space hotel rooms, trains, airplanes or ships. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to identify research trends related to emotional leadership among nurse managers by conducting a systematic literature review and meta-analysis. This study sought to derive insights that could contribute to improving emotional leadership in nursing practice., Methods: A systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. Databases including PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, Web of Science, Research Information Sharing Service, Koreanstudies Information Service System, Korean Medical Database, KoreaMed, ScienceON, and DBpia were searched to obtain papers published in English and Korean. Literature searches and screenings were conducted for the period December 1, 2023 to December 17, 2023. The effect size correlation (ESr) was calculated for each variable and the meta-analysis was performed using the statistical software SPSS 29.0, R 4.3.1., Results: Twenty-five (four personal, six job, and fifteen organizational) relevant variables were identified through the systematic review. The results of the meta-analysis showed that the total overall effect size was ESr = .33. Job satisfaction (ESr = .40) and leader-member exchange (ESr = .75) had the largest effect size among the job and organizational-related factors., Conclusion: Emotional leadership helps promote positive changes within organizations, improves organizational effectiveness, and increases member engagement and satisfaction. Therefore, it is considered an important strategic factor in improving organizational performance., Competing Interests: The authors declared no conflict of interest., (© 2024 Korean Society of Nursing Science.)

Emotion is closely associated with language, but we know very little about how children express emotion in their own writing. We used a large-scale, cross-sectional, and data-driven approach to investigate emotional expression via writing in children of different ages, and whether it varies for boys and girls. We first used a lexicon-based bag-of-words approach to identify emotional content in a large corpus of stories (N>100,000) written by 7- to 13-year-old children. Generalized Additive Models were then used to model changes in sentiment across age and gender. Two other machine learning approaches (BERT and TextBlob) validated and extended these analyses, converging on the finding that positive sentiments in children's writing decrease with age. These findings echo reports from previous studies showing a decrease in mood and an increased use of negative emotion words with age. We also found that stories by girls contained more positive sentiments than stories by boys. Our study shows the utility of large-scale data-driven approaches to reveal the content and nature of children's writing. Future experimental work should build on these observations to understand the likely complex relationships between written language and emotion, and how these change over development., (© 2024 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).)

At the present stage, the identification of dangerous behaviors in public places mostly relies on manual work, which is subjective and has low identification efficiency. This paper proposes an automatic identification method for dangerous behaviors in public places, which analyzes group behavior and speech emotion through deep learning network and then performs multimodal information fusion. Based on the fusion results, people can judge the emotional atmosphere of the crowd, make early warning, and alarm for possible dangerous behaviors. Experiments show that the algorithm adopted in this paper can accurately identify dangerous behaviors and has great application value. [ABSTRACT FROM AUTHOR]

This systematic review assessed the requirements and importance of intercultural communication competence (ICC) in human life in the 21st Century, focusing on its practical aspects and elements. The results reported in the current study are from a review of 45 papers published from 2000 to 2023 in 16 different Web of Science and SCOPUS-indexed journals. To record and select the related and required papers, an online search was done using two key terms, "intercultural competence" and "intercultural communication competence," and through the use of WoS and SCOPUS databases. A total of 87 items were recorded from the online search, and after a quick review of all records, 45 papers were selected and reviewed thoroughly. Based on the results from the reviewed papers, ICC is one of the main requirements of human life in today's globalized world, especially in modern education and international business. Findings indicate that the absence of ICC is a crucial reason for failure in many modern professionals. Based on the results, cultural awareness and intercultural sensitivity, language proficiency, empathy, flexibility, and some other traits and skills are among the main components of ICC. The results indicate that staying and studying in multicultural environments, participating in cultural programs, observing felling and emotions, flexibility, and other factors, discussed in the current work, help individuals improve their ICC. [ABSTRACT FROM AUTHOR]

Kazuo Ishiguro's novel Klara and the Sun (2021) philosophizes on how in the current technologically saturated culture, the gradual evolution of the empathetic humanoids has, on one hand, problematized our normative notions of cognitive and affective categories, and on the other, has triggered an order of emotional uncanniness due to our reliance on hyperreal real objects for receiving solace and companionship. The novel may be conceived to be a commentary on the emerging discourse in the domain of cognitive and emotional computing that aspires to transform the inner life and social relationships of the human community. The novelty of the paper lies in its ability to showcase how Kazuo Ishiguro's Klara and the Sun (2021) creates a rupture in the existing research and literary narrative by critiquing the theoretical underpinnings of emotional computing that optimistically foresees a future where simulated empathetic minds will be able to decode the complexities of the human emotions. It discusses how literature turns into an apt tool to reflect on the limitations of the programmed machines to decode the elusiveness of the human mind that defies the one-to-one correlation between words, multiple connotations, and their underlying emotions. Through the lenses of the fictional narrative, the paper foregrounds how the concept of the social robot designed to offer empathy, care, and companionship turns into a failed project. The paper draws on critical perspectives from disposability theory, posthuman affect, and immaterial bodies to foreground the noncodified feature of affective experientialities that emerge as a result of the interface between humans and nonanimate beings. [ABSTRACT FROM AUTHOR]

Introduction: To contribute to addressing diagnostic health inequalities in the United Kingdom, this review aimed to investigate determinants of diagnostic service use amongst people experiencing high deprivation in the United Kingdom. Methods: A systematic review was conducted using three databases (EBSCO, Web of Science and SCOPUS) to search studies pertaining to diagnostic service use amongst people experiencing high deprivation. Search terms related to diagnostics, barriers and facilitators to access and deprivation. Articles were included if they discussed facilitators and/or barriers to diagnostic service access, contained participants' direct perspectives and focussed on individuals experiencing high deprivation in the United Kingdom. Articles were excluded if the full text was unretrievable, only abstracts were available, the research did not focus on adults experiencing high deprivation in the United Kingdom, those not including participants' direct perspectives (e.g., quantitative studies) and papers unavailable in English. Results: Of 14,717 initial papers, 18 were included in the final review. Determinants were grouped into three themes (Beliefs and Behaviours, Emotional and Psychological Factors and Practical Factors), made up of 15 sub‐themes. These were mapped to a conceptual model, which illustrates that Beliefs and Behaviours interact with Emotional and Psychological Factors to influence Motivation to access diagnostic services. Motivation then influences and is influenced by Practical Factors, resulting in a Decision to Access or Not. This decision influences Beliefs and Behaviours and/or Emotional and Psychological Factors such that the cycle begins again. Conclusion: Decision‐making regarding diagnostic service use for people experiencing high deprivation in the United Kingdom is complex. The conceptual model illustrates this complexity, as well as the mediative, interactive and iterative nature of the process. The model should be applied in policy and practice to enable understanding of the factors influencing access to diagnostic services and to design interventions that address identified determinants. Patient or Public Contribution: Consulting lived experience experts was imperative in understanding whether and how the existing literature captures the lived experience of those experiencing high deprivation in South England. The model was presented to lived experience experts, who corroborated findings, highlighted significant factors for them and introduced issues that were not identified in the review. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is becoming part of the everyday. During this transition, people's intention to use AI technologies is still unclear and emotions such as fear are influencing it. In this paper, we focus on autonomous cars to first verify empirically the extent to which people fear AI and then examine the impact that fear has on their intention to use AI-driven vehicles. Our research is based on a systematic survey and it reveals that while individuals are largely afraid of cars that are driven by AI, they are nonetheless willing to adopt this technology as soon as possible. To explain this tension, we extend our analysis beyond just fear and show that people also believe that AI-driven cars will generate many individual, urban and global benefits. Subsequently, we employ our empirical findings as the foundations of a theoretical framework meant to illustrate the main factors that people ponder when they consider the use of AI tech. In addition to offering a comprehensive theoretical framework for the study of AI technology acceptance, this paper provides a nuanced understanding of the tension that exists between the fear and adoption of AI, capturing what exactly people fear and intend to do. [ABSTRACT FROM AUTHOR]

Relieving the emotions of the public through government response is an important part of government emergency management. How governments respond in different situations can avoid stimulating negative emotions during emergencies? This paper analyzes the problem from the perspective of configuration; that is, this paper explores the combined effects of multiple factors on emotions. We construct the theoretical framework "Situation-Responder-Content" from situation, responder and response content, and use the government microblogs (n= 1517) from 23 major production accidents in China for the discussion with the use of fuzzy set qualitative comparison analysis (fsQCA). According to the results, the effective response types of different agencies in emergencies are summarized. Local authorities can respond in ways that include "Measures type" and "Measures-Emotion type". Functional agencies can respond through "Measures type", "Measures-Emotion type" and "Government feature-Driven" type. This study emphasizes that government response in emergencies is a systematic process. Responsive agencies need to release effective information on the basis of fully considering the situation and other factors. Configuration analysis should also be an important direction in government response research, which is helpful to the practice of government response in emergencies. [ABSTRACT FROM AUTHOR]

Mauli ola, optimal health and wellbeing from a Hawaiian perspective, is achieved by being pono, or morally just and upright, and maintaining an intricate balance physically, mentally, spiritually, and emotionally through one's relations. Cultural practices, including practices that foster a connection to the water, land, ocean, and natural environment, may serve as protective and resilience factors, thereby promoting health and wellbeing. This paper starts by sharing the genealogical foundations of cultural practices in Hawai'i as the foundations of Native Hawaiian lifestyles and ways of knowing. The paper proceeds with data analyses that aim to better understand the role of cultural practices in relation to connectedness to 'Āina (the land, nature, and the environment, which nourish our bodies) and Native Hawaiian health based on cross-sectional correlations and qualitative data. The findings demonstrate the importance of cultural practices, specifically 'Āina practices, and identify 'Āina protection, restoration, and conservation as major health priorities. The correlations demonstrate statistically significant relationships between cultural practices; a physical, mental, spiritual, and emotional connection to 'Āina; and health outcomes. These findings continue to support literature and other declarations that support healthcare and medicine that are culturally grounded in Indigenous values and traditional systems of medicine. [ABSTRACT FROM AUTHOR]

Countless articles and books have been written about transformational leadership theory since the late 1970s when it first appeared in the literature. The purpose of this conceptual paper is to illustrate that transformational leadership contains both logical and psychological problems when explaining the nature of leadership and as an empirically supportable and verifiable construct. It aims to show that its failure to garner evidence from a scientific methodological analysis may not invalidate its efficacy if it is viewed from an alternative lens, such as aesthetics. An aesthetic frame is one that recognizes sensuous ways of knowing since feelings and emotions are just as important as reason and logic. An aesthetic approach to leadership would see leadership as more of an art than a science. This paper is not arguing for the abandonment of transformational leadership theory; rather it is saying that an aesthetic lens is likely to yield a richer, more artistic, and more nuanced account of what is understood and enacted as transformational leadership. This way, the manifestations of transformational leadership may live on in the arts and continue to inspire and motivate us. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study is to conduct a systematic review regarding the relationship between positive psychological factors, such as psychological well-being and pleasant emotions, and sports performance. Method: This study, carried out through a systematic review using PRISMA guidelines considering the Web of Science, PsycINFO, PubMed and SPORT Discus databases, seeks to highlight the relationship between other more 'positive' factors, such as well-being, positive emotions and sports performance. Settings: The keywords will be decided by a Delphi Method in two rounds with sport psychology experts. Participants: There are no participants in the present research. Assessment: The main exclusion criteria were: Non-sport thema, sample younger or older than 20–65 years old, qualitative or other methodology studies, COVID-related, journals not exclusively about Psychology. Main outcomes measures: We obtained a first sample of 238 papers, and finally, this sample was reduced to the final sample of 11 papers. Results: The results obtained are intended to be a representation of the 'bright side' of sports practice, and as a complement or mediator of the negative variables that have an impact on athletes' and coaches' performance. Conclusions: Clear recognition that acting on intrinsic motivation continues to be the best and most effective way to motivate oneself to obtain the highest levels of performance, a good perception of competence and a source of personal satisfaction. [ABSTRACT FROM AUTHOR]

This document discusses the emerging trends in biomedical signal processing for intelligent emotion recognition. It highlights the potential impact of these advancements on various fields, including mental health, neuroscience, robotics, advertising, and marketing. The document also presents six research papers that delve into the world of emotion recognition using EEG signals and advanced artificial intelligence techniques. These papers explore novel approaches to subject-independent emotion recognition, spatial-temporal feature learning, electrode combination, ensemble learning techniques, and the application of neuroscience in the tourism industry. The document emphasizes the importance of interdisciplinary approaches and advanced technologies in enhancing our understanding of emotions and their neural substrates. [Extracted from the article]

Undercover videos have become a popular tool among NGOs to influence public opinion and generate engagement for the NGO's cause. These videos are seen as a powerful and cost-effective way of bringing about social change, as they provide first-hand evidence and generate a strong emotional response among those who see them. In this paper, we empirically assess the impact of undercover videos on support for the cause. We in addition analyze whether the increased engagement among viewers is driven by the negative emotional reactions produced by the video. To do so, we design an online experiment that enables us to estimate both the total and emotion-mediated treatment effects on engagement by randomly exposing participants to an undercover video (of animal abuse) and randomly introducing a cooling-off period. Using a representative sample of the French population (N=3,310), we find that the video successfully increases actions in favor of animals (i.e., donations to NGOs and petitions), but we fail to prove that this effect is due to the presence of primary emotions induced by the video. Last, we investigate whether activists correctly anticipate their undercover videos' (emotional) impact via a prediction study involving activists (exploratory analysis). Protocol registration: This manuscript is a Stage-2 working paper of a Registered Report that received In-Principle-Acceptance from Scientific Reports on November 20th, 2023 [Link to Stage-1]. The Stage-1 that received In-Principal-Acceptance can be found here: https://osf.io/8cg2d. [ABSTRACT FROM AUTHOR]

Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency–space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency–space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and arousal binary classification experiments, respectively; the accuracy in the valence–arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and arousal were 97.96% and 98.11%, respectively. The valence–arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments. [ABSTRACT FROM AUTHOR]

Background: The United States is increasingly recognizing period product insecurity, insufficient access to menstrual products and limited private spaces for managing menstruation due to financial constraints, as an issue impacting the well-being and dignity of Americans. One strategy to address period product insecurity has been distributing free period products via period supply banks. The outcomes of period product distribution outside the school setting are absent from the literature. Objectives: This study, a formative evaluation of the free period product distribution efforts of the Alliance for Period Supplies, aims to identify (1) characteristics of individuals receiving products from period supply banks and their experiences of period product insecurity and (2) health and social outcomes experienced by recipients of free period products. Design: Survey data collection occurred at two points: baseline and one-year follow-up. All study participants provided verbal consent. Methods: Between Fall 2018 and Spring 2020, 1863 baseline and 80 follow-up surveys were administered. Participants received free period products for themselves and/or a household member from one of 20 participating Alliance for Period Supplies period supply banks directly or from one of their 64 partner agencies. Results: At baseline, 72.4% of participants had to choose between buying period products and another basic need. One year after accessing a period supply bank, 36.3% of participants reported this experience (p = 0.018). Participants reported at baseline, on average, 7.8 days in the past year of avoiding seeing others, canceling appointments, or skipping work or school because they did not have access to period products. At follow-up, this was reduced to 1.2 days, on average, t (68) = 2.214, p < 0.05. Conclusion: Period supply banks play an essential role in facilitating access to period products and the resulting benefits. Our study highlights the need for sustainable, well-funded policies and interventions to address period product insecurity effectively in society. Plain Language Summary: How people in the United States benefit when they get free period products from a period supply bank Why we did this study: In the United States, many individuals cannot afford to buy period supplies and the other stuff they need to live. Period supply banks want to help by giving them free period products. Researchers do not know if getting free period products is helpful for individuals. What we wanted to learn: The Alliance for Period Supplies is a membership program for period supply banks. We wanted to learn about the individuals who get free period products and whether getting them was helpful. What we did: For a year, starting in Fall 2018, we asked 1863 individuals to fill out paper surveys. A year later, we asked 80 of those same individuals to fill out a second survey. We asked participants to take the survey if they got free period products from a period supply bank for themselves or someone they live with. Individuals only participated in the study if they told us they wanted to. What we learned: Individuals who cannot afford period products must navigate difficult decisions between purchasing products or choosing other basic needs. Providing free products through a period supply bank has lessened the burden on these individuals, reducing the number who had to choose. Individuals unable to afford period products may sometimes opt out of going places they want to go for pleasure or miss important events, like work or school, because they do not have period products. Getting free period products has eased these challenges, letting individuals participate more fully in activities and engagements without worry about period products. Why is this important: Period supply banks are essential places where individuals receive free period products. The period supply banks need more individuals, including our government, to donate period supplies or money to buy them so they can help more individuals. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Facial expression recognition (FER) plays a crucial role in understanding human emotions and is becoming increasingly relevant in educational contexts, where personalized and empathetic interactions are essential. The problems with existing approaches are typically solved using a single deep learning method, which is not robust with complex datasets, such as FER data, which have a characteristic imbalance and multi-class labels. In this research paper, an innovative approach to FER using a homogeneous ensemble convolutional neural network, called HoE-CNN, is presented for future online learning education. This paper aims to transfer the knowledge of models and FER classification using ensembled homogeneous conventional neural network architectures. FER is challenging to research because there are many real-world applications to consider, such as adaptive user interfaces, games, education, and robot integration. HoE-CNN is used to improve the classification performance on an FER dataset, encompassing seven main multi-classes (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral). The experiment shows that the proposed framework, which uses an ensemble of deep learning models, performs better than a single deep learning model. In summary, the proposed model will increase the efficiency of FER classification results and solve FER2013 at a accuracy of 75.51%, addressing both imbalanced datasets and multi-class classification to transfer the application of the model to online learning applications. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment analysis (ABSA) is a pivotal task within the domain of neurorobotics, contributing to the comprehension of fine-grained textual emotions. Despite the extensive research undertaken on ABSA, the limited availability of training data remains a significant obstacle that hinders the performance of previous studies. Moreover, previous works have predominantly focused on concatenating semantic and syntactic features to predict sentiment polarity, which inadvertently severed the intrinsic connection. Several studies have attempted to utilize multi-layer graph convolution for the purpose of extracting syntactic characteristics. However, this approach has encountered the issue of gradient explosion. This paper investigates the possibilities of leveraging ChatGPT for aspect-level text augmentation. Furthermore, we introduce an improved gated attention mechanism specifically designed for graph convolutional networks to mitigates the problem of gradient explosion. By enriching the features of the dependency graph with a sentiment knowledge base, we strengthen the relationship between aspect words and the polarity of the contextual sentiment. It is worth mentioning that we employ cross-fusion to effectively integrate textual semantic and syntactic features. The experimental results substantiate the superiority of our model over the baseline models in terms of performance. [ABSTRACT FROM AUTHOR]

Objective: The concept of caring stress and its specific management has received less attention than other dimensions of stress in nurses. Therefore, to clarify the concept of caring stress, a scoping review study was designed. This scoping review aimed to investigate the concept of caring stress among clinical nurses and examine the strategies used for its management. Methods: This review employed a scoping review methodology to comprehensively map the essential concepts and attributes of the phenomenon by drawing on a wide range of sources. International databases including PubMed, Scopus, Web of Science, Google Scholar, and Scientific Information Database (SID) were searched to gather relevant studies published until October 1, 2023. MESH terms included "caring stress", "care", "stress", "nurse", and "stress management" were used. Two reviewers independently collected data from full-text papers, ensuring that each paper underwent assessment by at least two reviewers. Results: Out of 104,094 articles initially searched, 22 articles were included in this study. High workloads, transmitting the infection, stressful thoughts, stressful emotions, and stressful communications were the significant concepts and factors of caring stress among nurses. Also, rest breaks during patient care shifts, playing music in the ward, and denial of critical situations were examples of positive and negative coping and management ways to reduce caring stress. Conclusion: Effective stress management strategies can lead to better patient care and safety. Stressed nurses are more likely to make errors or become less vigilant in their duties, impacting patient outcomes. By addressing caring stress, clinical practice can prioritize patient well-being. Further research is required to delve deeper into this critical issue concerning nurses in the future. [ABSTRACT FROM AUTHOR]

This research paper investigates the nuanced concepts of bhavna (feeling/emotion) and vichar (thought) in Indian and Western literature and poetics. Drawing upon classical Indian writings of Abhinavgupta, Bhartruhari, Mamata, Bharat Muni and canonical poems by William Wordsworth, Coleridge, John Keats, Matthew Arnold, and Robert Browning, the paper analyzes how these two cultural traditions approach the portrayal and integration of emotion and thought in artistic expression. By examining representative excerpts from these Western poets alongside examples from classical Indian texts, this study aims to elucidate the similarities, differences, and unique characteristics of bhavnaand vichar in shaping the aesthetic experience across cultural traditions. [ABSTRACT FROM AUTHOR]

Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and "Other NNs", which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends. [ABSTRACT FROM AUTHOR]

Background: Emotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal's changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers' performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions. Results: The RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases' results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm's capabilities in precisely determining the emotion label for each participant's EEG features' instance. Conclusion: The findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm's performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result. [ABSTRACT FROM AUTHOR]

Unstable emotions, particularly anger, have been identified as significant contributors to traffic accidents. To address this issue, driver emotion recognition emerges as a promising solution within the realm of cyber-physical-social systems (CPSS). In this paper, we introduce SVGG, an emotion recognition model that leverages the attention mechanism. We validate our approach through comprehensive experiments on two distinct datasets, assessing the model's performance using a range of evaluation metrics. The results suggest that the proposed model exhibits improved performance across both datasets. [ABSTRACT FROM AUTHOR]

Emotionally intelligent people are thought to be more skilled in recognizing, thinking about, using, and regulating emotions. This construct has garnered considerable interest, but initial enthusiasm has faded and it is time to take stock. There is consensus that ability-related measures of emotional intelligence (EI) can be favored to self-report tests, in part because the resulting scores cannot be equated with personality traits. However, there are questions surrounding measurement as well as predictive value. Experts in the field were encouraged to chart new directions, with the idea that these new directions could reinvigorate EI scholarship. Special Issue papers speak to theory, mechanism, measurement, and training. In addition, these papers seek to forge links with research traditions focused on interpersonal perception, emotional awareness, and emotion regulation. As a result of these efforts, new insights into what EI is and how it works can be anticipated in upcoming years. [ABSTRACT FROM AUTHOR]

Copyright of Ius et Praxis (07172877) is the property of Universidad de Talca and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, self-report measures are the primary assessment tool for anxiety disorders. Since they have some limitations, alternative measurements, such as language-based measures, are worth investigating. This paper explores which language markers signal anxiety in fictitious stories written in response to four Thematic Apperception Test (TAT) cards. Participants (n = 492) from a non-probabilistic convenience sample were asked to write a short story next to each TAT card after completing the Generalized Anxiety Disorder-7. We used RoLIWC2015 to conduct the text analysis and applied the LASSO method to identify which language markers predict anxiety. The results showed that the respondents scoring high on anxiety also tend to use more words expressing negative emotions, and fewer words expressing positive emotions. Moreover, their language contained a higher frequency of words that implied semantic differentiation (i.e., but, else) and a lower frequency of words indicating leisure. In conclusion, this paper aims to shed new light on the multimethod assessment of anxiety, mainly focused on specific language signatures as reliable predictors of anxiety symptoms. Further research using more extensive text data is recommended to discover more linguistic markers and improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Text mining has led to growth in sentiment analysis (SA) across various research disciplines. The pandemic has provided a unique and special context for analysing students’ written expression. We utilised comments from a survey conducted during the pandemic to create a corpus for SA. The corpus comprises 25,197 words extracted from over 600 comments in Spanish, collected during a survey that lasted around 20 days. We aim to detect sentiments and emotions from this corpus using SA. However, some essential and little-discussed issues in literature should be addressed, such as its relationship with post-cognitivist theory. This paper uses the post-cognitivist approach to analyse emotions and sentiments through SA with the Spanish lexicon in the economics of education. Literature in this area needs further development, especially in Spanish. The article shows that the emotions and sentiments of students in challenging situations can be identified through a corpus of student comments. However, specific elements should be considered while interpreting emotions and sentiments within the framework of post-cognitivism methodologies. Recognising that the human experience is a complex interaction, it is essential to consider the emotional nuances within the context in which they develop. Addressing this issue from the post-cognitivist approach is one of several ways to carry out this task. Using SA and emotions to analyse a text corpus is still helpful for researchers who follow the post-cognitivist approach. However, combining this technique with other qualitative and in-depth computational methods is essential to fully understanding the emotional experiences within their respective contexts. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study was to gain consensus among young people with a stoma due to inflammatory bowel disease (IBD) on the priorities for the content of an intervention for the self‐management of stoma‐related distress. The current identification and management of distress in young people with a stoma is often suboptimal in clinical settings and there is a need for improved support resources. Methods: Two consensus group meetings were carried out via online video conferencing, using nominal group technique. Participants generated, rated on a Likert scale and discussed, topics for inclusion in a future self‐management intervention. Results: Nineteen young people, aged 19–33, with a stoma due to IBD took part in one of two group meetings. Participants were located across England, Scotland, and Northern Ireland. Twenty‐nine topics were generated by participants, seven of which reached consensus of ≥80%, that is, a mean of ≥5.6 on a 7‐point Likert scale. These were: receiving advice from young people with lived experience of stoma surgery; advice on/addressing concerns about romantic relationships, sex and intimacy; information about fertility and pregnancy related to stoma surgery; stoma 'hacks', for example, useful everyday tips regarding clothing, making bag changes easier and so forth; reflecting on and recognising own emotional response to surgery; tips on managing the stoma during the night; and processing trauma related to the illness and surgery journey. Conclusions: Findings extend previous research on young people's experiences of stoma surgery, by generating consensus on young people's priorities for managing distress related to surgery and living with a stoma. These priorities include topics not previously reported in the literature, including the need for information about fertility and pregnancy. Findings will inform the development of a self‐management resource for young people with an IBD stoma and have relevance for the clinical management of stoma‐related distress in this population. Patient or Public Contribution: Three patient contributors are co‐authors on this paper, having contributed to the study design, interpretation of results and writing of the manuscript. The study's Patient and Public Involvement and Engagement advisory group also had an integral role in the study. They met with the research team for four 2‐h virtual meetings, giving input on the aims and purpose of the study, recruitment methods, and interpretation of findings. The group also advised on the age range for participants. The views of young people with a stoma are the central component of the study reported in this paper, which aims to gain consensus among young people with an IBD stoma on their priorities for the content of a resource to self‐manage distress related to stoma surgery. [ABSTRACT FROM AUTHOR]

Background: This paper is devoted to a narrative review of the literature on emotions and academic performance in medicine. The review aims to examine the role emotions play in the academic performance of undergraduate medical students., Methods: Eight electronic databases were used to search the literature from 2013 to 2023, including Academic Search Ultimate, British Education Index, CINAHL, Education Abstract, ERIC, Medline, APA Psych Articles and APA Psych Info. Using specific keywords and terms in the databases, 3,285,208 articles were found. After applying the predefined exclusion and inclusion criteria to include only medical students and academic performance as an outcome, 45 articles remained, and two reviewers assessed the quality of the retrieved literature; 17 articles were selected for the narrative synthesis., Result: The findings indicate that depression and anxiety are the most frequently reported variables in the reviewed literature, and they have negative and positive impacts on the academic performance of medical students. The included literature also reported that a high number of medical students experienced test anxiety during their study, which affected their academic performance. Positive emotions lead to positive academic outcomes and vice versa. However, Feelings of shame did not have any effect on the academic performance of medical students., Discussion: The review suggests a significant relationship between emotions and academic performance among undergraduate medical students. While the evidence may not establish causation, it underscores the importance of considering emotional factors in understanding student performance. However, reliance on cross-sectional studies and self-reported data may introduce recall bias. Future research should concentrate on developing anxiety reduction strategies and enhancing mental well-being to improve academic performance., (© 2024. The Author(s).)

Better communication with older people requires not only improving speech intelligibility but also understanding how well emotions can be conveyed and the effect of age and hearing loss (HL) on emotion perception. In this paper, emotion discrimination experiments were conducted using a vocal morphing method and an HL simulator in young normal hearing (YNH) and older participants. Speech sounds were morphed to represent intermediate emotions between all combinations of happiness, sadness, and anger. Discrimination performance was compared when the YNH listened to normal sounds, when the same YNH listened to HL simulated sounds, and when older people listened to the same normal sounds. The results showed that there was no significant difference between discrimination with and without HL simulation, suggesting that peripheral HL may not affect emotion perception. The discrimination performance of the older participants was significantly worse only for the anger-happiness pair than for the other emotion pairs and for the YNH. It was also found that the difficulty increases with age, not just with hearing level., (© 2024. The Author(s).)

Speech emotion recognition (SER) technology involves feature extraction and prediction models. However, recognition efficiency tends to decrease because of gender differences and the large number of extracted features. Consequently, this paper introduces a SER system based on gender. First, gender and emotion features are extracted from speech signals to develop gender recognition and emotion classification models. Second, according to gender differences, distinct emotion recognition models are established for male and female speakers. The gender of speakers is determined before executing the corresponding emotion model. Third, the accuracy of these emotion models is enhanced by utilizing an advanced differential evolution algorithm (ADE) to select optimal features. ADE incorporates new difference vectors, mutation operators, and position learning, which effectively balance global and local searches. A new position repairing method is proposed to address gender differences. Finally, experiments on four English datasets demonstrate that ADE is superior to comparison algorithms in recognition accuracy, recall, precision, F1-score, the number of used features and execution time. The findings highlight the significance of gender in refining emotion models, while mel-frequency cepstral coefficients are important factors in gender differences., (© 2024. The Author(s).)

Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition., (© 2024. The Author(s).)

Background: Emotional clarity has often been assessed with self-report measures, but efforts have also been made to measure it passively, which has advantages such as avoiding potential inaccuracy in responses stemming from social desirability bias or poor insight into emotional clarity. Response times (RTs) to emotion items administered in ecological momentary assessments (EMAs) may be an indirect indicator of emotional clarity. Another proposed indicator is the drift rate parameter, which assumes that, aside from how fast a person responds to emotion items, the measurement of emotional clarity also requires the consideration of how careful participants were in providing responses., Objective: This paper aims to examine the reliability and validity of RTs and drift rate parameters from EMA emotion items as indicators of individual differences in emotional clarity., Methods: Secondary data analysis was conducted on data from 196 adults with type 1 diabetes who completed a 2-week EMA study involving the completion of 5 to 6 surveys daily. If lower RTs and higher drift rates (from EMA emotion items) were indicators of emotional clarity, we hypothesized that greater levels (ie, higher clarity) should be associated with greater life satisfaction; lower levels of neuroticism, depression, anxiety, and diabetes distress; and fewer difficulties with emotion regulation. Because prior literature suggested emotional clarity could be valence specific, EMA items for negative affect (NA) and positive affect were examined separately., Results: Reliability of the proposed indicators of emotional clarity was acceptable with a small number of EMA prompts (ie, 4 to 7 prompts in total or 1 to 2 days of EMA surveys). Consistent with expectations, the average drift rate of NA items across multiple EMAs had expected associations with other measures, such as correlations of r=-0.27 (P<.001) with depression symptoms, r=-0.27 (P=.001) with anxiety symptoms, r=-0.15 (P=.03) with emotion regulation difficulties, and r=0.63 (P<.001) with RTs to NA items. People with a higher NA drift rate responded faster to NA emotion items, had greater subjective well-being (eg, fewer depression symptoms), and had fewer difficulties with overall emotion regulation, which are all aligned with the expectation for an emotional clarity measure. Contrary to expectations, the validities of average RTs to NA items, the drift rate of positive affect items, and RTs to positive affect items were not strongly supported by our results., Conclusions: Study findings provided initial support for the validity of NA drift rate as an indicator of emotional clarity but not for that of other RT-based clarity measures. Evidence was preliminary because the sample size was not sufficient to detect small but potentially meaningful correlations, as the sample size of the diabetes EMA study was chosen for other more primary research questions. Further research on passive emotional clarity measures is needed., (©Raymond Hernandez, Claire Hoogendoorn, Jeffrey S Gonzalez, Elizabeth A Pyatak, Gladys Crespo-Ramos, Stefan Schneider. Originally published in JMIR Mental Health (https://mental.jmir.org), 18.07.2024.)

Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Nonetheless, existing methods are mostly unable to offer correct predictions with a minimum error rate. In this paper, an innovative facial emotion recognition framework, termed extended walrus-based deep learning with Botox feature selection network (EWDL-BFSN), was designed to accurately detect facial emotions. The main goals of the EWDL-BFSN are to identify facial emotions automatically and effectively by choosing the optimal features and adjusting the hyperparameters of the classifier. The gradient wavelet anisotropic filter (GWAF) can be used for image pre-processing in the EWDL-BFSN model. Additionally, SqueezeNet is used to extract significant features. The improved Botox optimization algorithm (IBoA) is then used to choose the best features. Lastly, FER and classification are accomplished through the use of an enhanced optimization-based kernel residual 50 (EK-ResNet50) network. Meanwhile, a nature-inspired metaheuristic, walrus optimization algorithm (WOA) is utilized to pick the hyperparameters of EK-ResNet50 network model. The EWDL-BFSN model was trained and tested with publicly available CK+ and FER-2013 datasets. The Python platform was applied for implementation, and various performance metrics such as accuracy, sensitivity, specificity, and F1-score were analyzed with state-of-the-art methods. The proposed EWDL-BFSN model acquired an overall accuracy of 99.37 and 99.25% for both CK+ and FER-2013 datasets and proved its superiority in predicting facial emotions over state-of-the-art methods.

The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA's video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson's correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions.

Mental health chatbots (MHCBs) designed to support individuals in coping with mental health issues are rapidly advancing. Currently, these MHCBs are predominantly used in commercial rather than clinical contexts, but this might change soon. The question is whether this use is ethically desirable. This paper addresses a critical yet understudied concern: assuming that MHCBs cannot have genuine emotions, how this assumption may affect psychotherapy, and consequently the quality of treatment outcomes. We argue that if MHCBs lack emotions, they cannot have genuine (affective) empathy or utilise countertransference. Consequently, this gives reason to worry that MHCBs are (a) more liable to harm and (b) less likely to benefit patients than human therapists. We discuss some responses to this worry and conclude that further empirical research is necessary to determine whether these worries are valid. We conclude that, even if these worries are valid, it does not mean that we should never use MHCBs. By discussing the broader ethical debate on the clinical use of chatbots, we point towards how further research can help us establish ethical boundaries for how we should use mental health chatbots., (© 2024 The Authors. Bioethics published by John Wiley & Sons Ltd.)

Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people., Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions., Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults., Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review., Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather insufficient level of detail., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Qualitative research has traditionally relied on specific methodologies for data collection, with interviews being the predominant and preferred approach. Vast quantities of information have been published regarding traditional mechanisms for collecting qualitative data, but few studies have addressed more unconventional mechanisms such as body mapping. This is a distinctive technique commonly used in health sciences. It offers a versatile alternative that encourages participant creativity, reflexivity, and protagonism. It was adapted for use in an educational study with the aim of eliciting participants' constructivist experiences and emotions related to a particular social phenomenon. It was used as part of a descriptive case study which employed two other triangulation techniques, namely semi-structured virtual interviews, and document collection. After the researchers' reflective processes, it was concluded that body mapping represented an ideal means to facilitate the data collection process. The primary objective of the study, grounded in qualitative research, was to explore the perceptions of five primary English teachers in the Sarapiquí Regional Directorate of Education in Costa Rica regarding the return to in-person education amid the COVID-19 public health crisis. For the body mapping technique, three participants engaged in a face-to-face session, structured into seven phases: map outlining, reflections on distance education, reflections on combined education, reflections on in-person education, finalization of body maps, testimony giving, and concluding reflections on the technique. Although the focus was on in-person education (2022), the distance and combined modalities (2020-2021) were used to activate participants' prior knowledge and to provide context for the study. This paper demonstrates the procedure followed in applying the body mapping technique for qualitative data collection in a study on the perspective and experiences of teachers, and highlights the valuable insights gained through its use. [ABSTRACT FROM AUTHOR]

The paper aims to develop an information system for human emotion recognition in streaming data obtained from a PC or smartphone camera, using different methods of modality merging (image, sound and text). The objects of research are the facial expressions, the emotional color of the tone of a conversation and the text transmitted by a person. The paper proposes different neural network structures for emotion recognition based on unimodal flows and models for the margin of the multimodal data. The analysis determined that the best classification accuracy is obtained for systems with data fusion after processing each channel separately and obtaining individual characteristics. The final analysis of the model based on data from a camera and microphone or recording or broadcast of the screen, which were received in the "live" mode, gave a clear understanding that the quality of the obtained results is highly dependent on the quality of the data preparation and labeling. This is directly related to the fact that the data on which the neural network is trained is highly qualified. The neural network with combined data on the penultimate layer allows a psycho-emotional state recognition accuracy of 0.90 to be obtained. The spatial distribution of emotion analysis was also analyzed for each data modality. The model with late fusion of multimodal data demonstrated the best recognition accuracy. [ABSTRACT FROM AUTHOR]

Emotion detection from text plays a very critical role in different domains, including customer service, social media analysis, healthcare, financial services, education, human-to-computer interaction, psychology, and many more. Nowadays, deep learning techniques become popular due to their capabilities to capture inherent complex insights and patterns from raw data. In this paper, we have used the Word2Vec embedding approach that takes care of the semantic and contextual understanding of text making it more realistic while detecting emotions. These embeddings act as input to the convolution neural network (CNN) to capture insights using feature maps. The Word2Vec and CNN models applied to the international survey on emotion antecedents and reactions (ISEAR) dataset outperform the models in the literature in terms of accuracy and F1-score as model evaluation metrics. The proposed approach not only obtains high accuracy in emotion detection tasks but also generates interpretable representations that contribute to the understanding of emotions in textual data. These findings carry significant implications for applications in diverse domains, such as social media analysis, market research, clinical assessment and counseling, and tailored recommendation systems. [ABSTRACT FROM AUTHOR]

Research focused on the human emotional sphere is primarily carried out by psychologists. However, evaluating emotion naming competence is an interdisciplinary issue, and it is vital also for speech therapy. Researchers have observed that many children with neurodevelopmental disorders, e.g., autism spectrum disorder (ASD), experience difficulties in recognising and understanding emotions, as well as naming them. The lateer is also challenging for many neurotypical children. Nonetheless, linguistic studies into emotion naming competence are still insufficiently undertaken. The objective of this paper is to present problems encountered while constructing tasks that assess the level of proficiency in emotion naming under test conditions. It also offers preliminary results of a pilot study involving neurotypical kindergarten children. The paper constitutes part of a more extensive research project on the naming and expression of emotions in children with ASD. [ABSTRACT FROM AUTHOR]

Introduction: Multimodal emotion recognition has become a hot topic in human-computer interaction and intelligent healthcare fields. However, combining information from different human different modalities for emotion computation is still challenging. Methods: In this paper, we propose a three-dimensional convolutional recurrent neural network model (referred to as 3FACRNN network) based on multimodal fusion and attention mechanism. The 3FACRNN network model consists of a visual network and an EEG network. The visual network is composed of a cascaded convolutional neural network--time convolutional network (CNNTCN). In the EEG network, the 3D feature building module was added to integrate band information, spatial information and temporal information of the EEG signal, and the band attention and self-attention modules were added to the convolutional recurrent neural network (CRNN). The former explores the effect of different frequency bands on network recognition performance, while the latter is to obtain the intrinsic similarity of different EEG samples. Results: To investigate the effect of different frequency bands on the experiment, we obtained the average attention mask for all subjects in different frequency bands. The distribution of the attention masks across the different frequency bands suggests that signals more relevant to human emotions may be active in the high frequency bands γ (31--50 Hz). Finally, we try to use the multi-task loss function Lc to force the approximation of the intermediate feature vectors of the visual and EEG modalities, with the aim of using the knowledge of the visual modalities to improve the performance of the EEG network model. The mean recognition accuracy and standard deviation of the proposed method on the two multimodal sentiment datasets DEAP and MAHNOB-HCI (arousal, valence) were 96.75 ± 1.75, 96.86 ± 1.33; 97.55 ± 1.51, 98.37 ± 1.07, better than those of the state-of-the-art multimodal recognition approaches. Discussion: The experimental results show that starting from the multimodal information, the facial video frames and electroencephalogram (EEG) signals of the subjects are used as inputs to the emotion recognition network, which can enhance the stability of the emotion network and improve the recognition accuracy of the emotion network. In addition, in future work, we will try to utilize sparse matrix methods and deep convolutional networks to improve the performance of multimodal emotion networks. [ABSTRACT FROM AUTHOR]

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