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Purpose: This study aims to consider the role of emotions, especially those related to empathy, in promoting a more humane education that enables students to reach out across kinship chasms to promote the development of communities predicated on a shared value on mutual respect. This attention to empathy includes a review of the rational basis for much schooling, introduces skepticism about the façade of rational thinking, reviews the emotionally flat character of classrooms, attends to the emotional dimensions of literacy education, argues on behalf of taking emotions into account in developmental theories and links empathic connections with social justice efforts. The study's main thrust is that empathy is a key emotional quality that does not come naturally or easily to many, yet is important to cultivate if social justice is a goal of education. Design/methodology/approach: The author clicked Essay and Conceptual Paper. Yet the author required to write the research design. Findings: The author clicked Essay and Conceptual Paper. Yet the author required to write the research design. Research limitations/implications: The author clicked Essay and Conceptual Paper. Yet the author required to write the research design. Originality/value: The paper challenges the rational emphasis of schooling and argues for more attention to the ways in which emotions shape thinking. [ABSTRACT FROM AUTHOR]

The man-machine interface encompasses a crucial area-emotion recognition through facial expressions. Despite its significance, emotion recognition faces challenges such as facial accessories, nonuniform illuminations, pose variations, audio speeches, text conversations, and hand and facial gestures. Understanding emotions like happiness, anger, anxiety, joy, and shock, along with their varying degrees and overlaps, is essential for accurate recognition. These nuances, inherent to humans, pose difficulties and costs in achieving standard results through facial recognition. Recognizing someone's mood through facial expression, conversation, voice modulation, and gestures is a skill humans excel at. However, replicating this ability through facial recognition has proven challenging and costly. This paper addresses these challenges by proposing diverse approaches to emotion detection. By exploring various modes, including facial expressions, conversation analysis, voice modulation, and gestures, the paper tackles current research problems and holds practical applications in public experiments and exhaustive sentiment analysis. The paper presents a good combo of various modes of emotion recognition on multiple datasets (tried and tested widely before amalgamating all to produce an excellent optimal result as an output of the model). [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie 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.)

Consumer feedback is often used for various purposes in many fields. However, traditional paper questionnaires or surveys cannot fully meet the demands for accurately understanding consumers' feelings. Consumers often convey their feelings through their facial expressions, whether consciously or unconsciously. Understanding these feelings can provide very direct and useful feedbacks. Yet, humans may miss those subtle changes because the micro expression is too brief to be captured. Therefore, in this paper. we proposed a deep learning based recognition approach of fucial micro expressions, in which more realistic emotional feedback of users can be extracted. To achieve this goal, we integrated several approaches including: ( 1) using trained face detection model to capture face image from input; (2) training a high accurate 468- point landmark detection model with multiple face dataset. Based on the FACS (Facial Action Coding System) table, we categorized these landmarks into 13 groups of facial regions. These regions with specific emotion labels are used as our target units of AU (Action Unit) detection; (3) training CNN model to detect and analyze AUs from facial landmark data: (4) implying FACS to evaluate the facial expressions and emotions: and (5) using a straightforward GUI plotter to show the digitized emotions. The experiment results show that not only the primaty emotion but also the secondary emotion of users in movies can be detected and evaluated successfully. Therefore, our system has a great potential for obtaining users' feedbacks iii a more accurate and comprehensive manner. [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.)

Electroencephalogram (EEG)-based emotion recognition plays an important role in brain-computer interface and mental health monitoring. The large amount of EEG data but the lacks of labeling, multi-feature attribute, and data uncertainty are the difficulties in its recognition problem. A multi-view semi-supervised Takagi–Sugeno–Kang (MV-SS-TSK) fuzzy system is developed for EEG emotion classification in this paper. In the learning of fuzzy system consequent, firstly, a novel joint learning of semi-supervised learning, sparse representation, and low-rank coding is developed for semi-supervised sparse consequent factor learning, which makes the consequent parameter learning as a pseudo-label-only optimization problem. In particular, to simplify fuzzy rules, the sparse constraint term ensures the consequent parameters to be sparse in rows. Secondly, the consequent factor learning in a single feature view is extended into the multi-view learning model. In particular, low-rank coding is considered in multi-view semi-supervised consequent parameter learning. The low-rank constraint on view-shared component of consequent factor is implemented to exploit global data structure. The sparse constraint on view-dependent component of consequent factor is implemented to retain the feature diversity representation. By minimizing the intersection between view-shared component and view-specific components for different views, MV-SS-TSK can take advantage of the intrinsic relationship between various features and capture the consistency from multi-view features. Experiments on the SEED dataset show the superior performance of the proposed fuzzy system. [ABSTRACT FROM AUTHOR]

While research is emerging on how the emotional state of voice users can impact voice quality and production, the emotion of shame has been underrepresented in that research. This paper examines what research currently exists regarding shame and offers future directions for future research in this area. [ABSTRACT FROM AUTHOR]

As the landscape of social networks evolves, the dissemination of public opinion through online channels has become increasingly dominant. Consequently, researchers have turned their attention to the impact of emotional factors introduced by communicators when conveying public opinion. This paper proposes a novel SEInIeR public opinion communication prediction model, incorporating an extreme emotion mechanism, which acknowledges the inuential nature of speech under heightened emotional states. By integrating the traditional infectious disease SEIR model with fractional order differentiation, our model not only ensures the existence and stability of the disease-free equilibrium point E0 but also allows for more accurate prediction. Finally, the model has been validated and analyzed through simulation experiments and numerical simulations. Furthermore, example data were collected for analysis and tting. The experimental results provide substantial evidence for the accuracy of our theoretical analyses and the plausibility of the proposed model. [ABSTRACT FROM AUTHOR]

This paper explores one way in which the view that emotions can be epistemically justified stands in tension with two common views in epistemology; namely, that doxastic justification entails propositional justification, and that propositional justification is entirely determined by the (inferential) support relations between one's evidence and a given proposition. A tentative solution to the tension is provided. [ABSTRACT FROM AUTHOR]

A key development towards enhancing computer-human interaction is emotion recognition. This publication describes a technique called EmoCNN, which uses deep learning techniques to precisely identify and classify human emotions, emphasizing improving model performance using different optimizers. Our research intends to contribute to the creation of more effective systems that improve computer-human interaction by solving the problems associated with emotion recognition. By bridging the gap between humans and robots, accurate emotion detection enables systems to perceive emotions for customized and responsive interactions. AI-powered assistants, chatbots, and social robots all benefit from emotion recognition by providing more responsive, empathic and interesting user experiences. Emotion-aware technologies can also enhance user feedback analysis, human-centered design, and monitoring of mental health. Using a human emotion detection dataset, we carried out comprehensive experiments focusing on the happy, sad, and neutral emotion classes. Constructing a customized EmoCNN model with convolutional layers, a hidden layer, ReLU activation, and max-pooling was the focus of our computational work. We investigated various optimizers and evaluated how they affected accuracy, convergence speed and loss minimization. The results demonstrated that the EmoCNN model, which had been trained using the Adam optimizer, gave the best accuracy in distinguishing between emotions. Our paper provides a comparative analysis, highlighting the superiority of EmoCNN over existing models, showcasing its ability to achieve higher validation accuracy (89%) and more efficient emotion recognition when compared to previous approaches with minimal loss. Our research advances the field of emotional computing by demonstrating how well EmoCNN can identify and categorizes various human emotions. This discovery has significant ramifications for the creation of emotion-aware computers, which can better understand and react to human emotions, enhancing computer-human interaction. [ABSTRACT FROM AUTHOR]

Copyright of Studii de Ştiintă şi Cultură is the property of Studii de Stiinta si Cultura 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.)

Coronavirus (COVID-19) is the infectious agent responsible for the transmission of SARS (severe acute respiratory syndrome). When an infected person coughs, talks, or breathes, it spreads as small droplets of fluid from the mouth and nose of the infected person. Sanitary masks help prevent the spread of the virus from the person wearing the mask to others. This new behavior may cause a number of problems in interpersonal interactions. The goal of this paper is the emotion estimation of masked faces. It presents two major parts. At the first level, we created a system for identifying sanitary masks, using CNN. At the second one, we developed an emotion estimation system in order to estimate the classification rates of a masked face. It consists of three steps: face element detection, feature point localization, and classification. We used the well-known Viola and Jones algorithm in order to achieve the first step. We used several techniques to estimate emotions (SVM, KNN and deep learning). We made comparisons of the obtained results. Particular attention is also given to the effect of face masks on the performance of various methods. [ABSTRACT FROM AUTHOR]

With the metaverse here to stay, we are seeing ever more advances in capability. This includes the ability to incorporate real-time body and facial animation, so that personalised avatars can display our changing expressions, allowing those we meet to gauge our mood. Given this advance, the opportunity now exists to utilise emotion recognition from avatars. This positional paper explores the benefits of utilising emotion prediction within the metaverse. [ABSTRACT FROM AUTHOR]

Down syndrome is the most common form of intellectual disability. However, there is a paucity of educational research focused on this vulnerable segment of learners especially in the present novel situation. This paper aimed to explore how teachers tailor remote special education for children with down syndrome amid the COVID-19 pandemic in the Philippines. It draws from a phenomenological qualitative study that collected information from online interviews with nine special education teachers handling children with down syndrome. The results revealed five themes: (1) contextualize individual educational plans; (2) meet sensory and movement needs; (3) emotionally connect with children; (4) communicate often with families; and (5) collaborate with community. This study provides practical insight into the enabling practices that teachers use to design the remote special education for children with down syndrome amid the current crisis. [ABSTRACT FROM AUTHOR]

Embodied cognition claims that how we move our body is central for experience. Exploring dimensions of bodily engagement should, therefore, also be central for engaging art. However, little attention has been paid to the actual ways viewers move in front of art and how this impacts experiences. We aim to close this gap, using a new paradigm in a gallery-like setting in which we tracked movements of participants that engaged an abstract artwork. Guided by a literature review, we relate objective movement factors and subjective body awareness to mobile viewing behavior, art experience, and expertise. We also—for the first time—define shared movement patterns employing principal component/cluster analysis and relate these to experience outcomes, noting, for example, that moving more/more dynamically related to more reported insight. As a proof-of-concept paper, we hope to support a more embodied, enactive understanding of art engagements, and provide practical guidelines for future research. [ABSTRACT FROM AUTHOR]

This paper examines a segment of Tucker Carlson Tonight from 2021 in which Carlson contextualizes the start of the trial against Derek Chauvin, whose killing of George Floyd in 2020 sparked nationwide protests. By employing an analytical framework that includes 'feeling rules' and 'framing rules,' this study argues that Carlson frames Chauvin's trial as an existential threat to the United States' national security, locating notions of (in)security within affective structures of fear. Notions of 'appropriate feelings' are promoted through emotive framing and the visual comparison of protest and terrorism. The study also highlights how mass media shape and negotiate the opposing ideas of 'feeling American' and analyzes how Carlson employs discourses of (in)security to enable the polarization of two distinct 'emotional communities' in the US. This analysis shows that political and cultural divisions in the US might be more deeply entrenched than previously acknowledged, as they constitute fundamentally different experiences of 'feeling American.' [ABSTRACT FROM AUTHOR]

This paper reveals how a popular UK reality TV programme Your Home Made Perfect mobilises emotions to challenge conceptualisations of architecture while simultaneously reinforcing regressive ideas of race, class and sexuality. Drawing on our thematic analysis of fourteen episodes of Your Home , the paper shows how architectural entertainment is uniquely positioned through the use of VR technology, to generate and mobilise client emotions, towards critique. This includes the critique of architectural drawings and the power imbalances of architect-client relationships. We trace how 'happy' emotions are tied to being able to read architectural visualisations through virtual reality (VR) rather than the 'sad' emotion enforced by traditional architectural forms of communication; how positive emotions fostered through care-in-action attach to the architecture and home. By foregrounding clients' emotional responses to the redesign of their homes by architects, the paper reveals architectural entertainment programmes as popular and powerful forms of architectural critique that nonetheless simultaneously reinforce exclusionary social logics that limits owner-occupation for the white middle class. In so doing, this paper contributes to unpacking both the emotional value of architecture and how this complex form of taste-making occurs within popular culture, that is, conveying to people the place of architecture within society. • Analysis of an award-winning Reality Television show, Your Home Made Perfect. • Tracing emotional intersections between people, homes and society – embedded in professional practices and processes. • How positive emotions attach to the profession of architecture through the medium of Reality Television and Virtual Reality. • Persuasive circulation of social and spatial emotions and norms at the conjunction of VR and Reality Television. [ABSTRACT FROM AUTHOR]

In this article, a concept of Reversible Neural Network, unexplored as yet, is introduced. When identifying facial expressions using a set of salient features such Reversible Neural Network is found to play a crucial role in recognizing the facial expressions into six basic categories. These salient characteristics are extracted by utilizing distance and shape signatures from three benchmark datasets to produce a noteworthy feature set. Such distance and shape signatures are utilized for finding stability indices capable of identifying a particular expression quite effectively. Statistical metrics like skewness, entropy, and moment are computed using distance and shape signatures in order to ensure the discriminative behavior in the selected feature set. The proposed Reversible Neural Network typically receives such discriminative feature set and transforms it into six basic facial expressions. The proposed network is tested and validated on the MMI, CK+, and JAFFE data sets in order to conduct our experiment and confirm the efficacy of facial expression identification. Additionally, its superiority in the context of facial expression recognition is justified by comparison with the state-of-the-art approaches. • This paper introduces a Reversible Neural Network (RNN) for identifying human emotions that is a new contribution. • According to Universal Approximation Theorem, it proves that a feedforward network with a single layer is sufficient to approximate any continuous function or precisely can predict any kind of classification. Here we propose a single hidden layer of Reversible Neural Network (RNN) for identifying the emotions. • Low dimensional feature set is employed to proposed RNN for identifying the human emotions. As we are considering the low dimensional feature set for recognizing the emotions, we have not required to employ deep neural network for identifying emotions. For this reason, we simply proposed a new RNN for identifying the emotions. • On benchmark data sets, such as CK+, MMI, and JAFFE, the suggested RNN is used to ascertain its performance and is validated using cutting-edge techniques. [ABSTRACT FROM AUTHOR]

Emotion recognition in conversations (ERC) has been challenging due to the dynamics and complexity of emotions in conversations. Most current emotion recognition studies have focused on modeling temporal dimensions, such as context-sensitive dependencies while ignoring the spatial dimensional relationships between discourses. In this paper, we propose a dual-channel information fusion network based on a voting mechanism, in which we model modal information in two dimensions: distance dependence is constructed in the spatial dimension, and context dependence is constructed in the temporal dimension. We aim to extract more comprehensive and accurate sentiment features to recognize verbal emotions from limited data. In addition, we discard the traditional modal fusion method and propose a fusion strategy based on the voting mechanism, which significantly accelerates the model convergence speed and recognition performance. We conducted experiments on two benchmark datasets, IEMOCAP and MELD, respectively, and the experimental results demonstrate the superiority of the designed network in emotion recognition. • Modeling node characteristics in both temporal and spatial dimensions. • Modal fusion using voting mechanisms inspired by integrated learning. • Achieved higher accuracy compared to other modal fusion methods. [ABSTRACT FROM AUTHOR]

This paper investigates the structural connections between hotel customers' general perceptions of technology, their perceptions toward service robots, emotions expectations, brand-related perceptions, and their behavioural intentions toward hotel brands that use service robots. It employs a quantitative approach by analysing data gathered through an online questionnaire from residents in Oman with PLS-SEM. The results indicate that attitudes toward service robots are significantly affected by optimism and insecurity. Customers' discomfort and insecurity significantly influence robot-related anxiety, which in turn impacts their emotional expectations. Attitudes toward service robots significantly affect positive and negative emotional expectations while positive emotions impact brand perceptions that in turn impact service robots' use intentions. Willingness-to-pay for robotic service is not affected by brand perceptions or use intentions. Theoretical and managerial contributions, limitations, and future research directions are outlined as well. • Examines the links among customers' perceptions, emotions expectations, perceptions, and behaviour toward service robots. • Adopts Technology Readiness Index, Cognitive Appraisal Theory, and Attachment Theory. • Customers' discomfort and insecurity influence their anxiety, which in turn affects emotions expectations. • Positive emotions drive brand perceptions, which positively influence consumer intentions to use service robots. • Brand perceptions and use intentions have no effect on willingness to pay for robotic services. [ABSTRACT FROM AUTHOR]

In this paper, we assess the role of emotions in choices. We elicited emotions using an innovative facial expression analysis approach, comparing the results to those from a traditional hedonic liking scale. To this end, we conduct an experiment combing surveys and actual wine tasting. The results show a positive and significant relationship between positive emotions (joy), positive experiences (valence), and wine choices, especially in the case of wines with credence attributes (organic and selected vintage organic wines). Moreover, we conclude that facial recognition mechanisms can be useful to elicit evoked emotions and provide complementary information to the traditional liking scales to better understand decision making. • Abundant literature shows that emotions affect choices. • Previous literature has measured emotions by mostly applying surveys and liking scales. • We employ an innovative method based on facial expressions and emotions detection. • These emotion variables are included into a discrete choice experiment framework. • We find that new facial expression recognition methods are quite useful for research. [ABSTRACT FROM AUTHOR]

Introduction. The paper explores high school students' experiences of information overload in the context of their studies, investigating its causes and how they manage it. Method. Data was collected through qualitative interviews with six students aged 13-16. Analysis. A thematic content analysis was applied to discern patterns and themes in the students' experiences. Results. All informants had experienced information overload, but how and to which degree it manifested differed between them. The most common trigger was encountering complex information, followed by passive exposure to large amounts of information over time. Students primarily responded to information overload by losing attention, while some also reported negative emotions such as irritation and sadness. Conclusion. The findings reveal that high school students frequently experience information overload in the context of their schoolwork. Personal vulnerabilities seemed to influence the extent in which they experienced information overload. [ABSTRACT FROM AUTHOR]

Recently, electroencephalogram (EEG) emotion analysis has attracted increasing attention in many fields, such as neuroscience and brain-computer fusion. Due to the spatial channel difference and time continuity of emotional responses, it is necessary to capture relevant properties from EEG signals. In this paper, spectrum-based spatial channel attention is developed to reveal the emotional responses of different electrode channels located in different cerebral cortex regions. In addition, a time continuity encoding mechanism is developed to encode the temporal relations of time series signals in the transformer. In our model, the spectrum-based spatial channel attention cooperates with the time continuity encoding mechanism to fully utilize the spectral, spatial and temporal features of EEG signals (SST-Emo). Extensive experiments with different window sizes (1 s and 3 s) are carried out on DEAP for both binary and multiclass classification tasks. The accuracies are 96.28 %, 95.25 %, 95.52 %, 95.83 % and 93.88 % for 1 s window-size on arousal, valence, dominance, liking and 5-class, respectively. For 3 s window-size, the accuracies are 96.13 %, 95.74 %, 95.23 %, 95.7 % and 95.83 %, respectively. The results show that SST-Emo is superior to the state-of-the-art methods, which indicates that SST-Emo is able to extract more discriminative emotional representations. [ABSTRACT FROM AUTHOR]

• We introduce the notion of 'transition pain' in transitions-in-the-making. • We explain how emotions in transitions are process-dependent, culturally and socially embedded and political. • We suggest a 'coping with transition pain' perspective for more integrated engagements with phase-outs. With this perspective paper, we aim to raise awareness of and offer starting points for studying the role of emotions and associated behavioural responses to losses in relation to phase-outs. We start from a psychological perspective and explain how losses due to phasing out dominant practices, structures, and cultures may threaten core psychological needs and lead to - what we introduce as - 'transition pain'. We borrow insights from the psychological coping literature to explain that different forms of transition pain may elicit characteristic coping responses (e.g. opposition, escape, negotiation), shaping individual meaning-making and behaviour in ongoing sustainability transitions. We then expand this psychological lens and present three additional perspectives, namely, that transition pain is (1) dynamic and process-dependent, (2) collectively shared and socially conditioned, and (3) political. We discuss how a 'coping with transition pain' lens can contribute to a better understanding of individual and collective meaning-making, behaviour and agency in transitions as well as a more emotion-sensitive governance of phase-outs. [ABSTRACT FROM AUTHOR]

Copyright of Papers: Revista de Sociologia is the property of Universitat Autonoma de Barcelona 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.)