True Emotions Research Articles

Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition

Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them. Due to their short duration and low intensity, the recognition of micro-expressions is a difficult task in affective computing. The early work based on handcrafted spatio-temporal features which showed some promise, has recently been superseded by different deep learning approaches which now compete for the state of the art performance. Nevertheless, the problem of capturing both local and global spatio-temporal patterns remains challenging. To this end, herein we propose a novel spatio-temporal transformer architecture -- to the best of our knowledge, the first purely transformer based approach (i.e. void of any convolutional network use) for micro-expression recognition. The architecture comprises a spatial encoder which learns spatial patterns, a temporal aggregator for temporal dimension analysis, and a classification head. A comprehensive evaluation on three widely used spontaneous micro-expression data sets, namely SMIC-HS, CASME II and SAMM, shows that the proposed approach consistently outperforms the state of the art, and is the first framework in the published literature on micro-expression recognition to achieve the unweighted F1-score greater than 0.9 on any of the aforementioned data sets.

The Paralysis of Perceived Powerlessness – How Emotional Tensions Influence Corporate Climate Action

In responding to the climate crisis, organizations navigate in an increasingly volatile emotional milieu in which feelings of fear, guilt and anger may influence corporate sustainability transformations and climate action. We conduct an eight-month ethnographic case study of an Energy Company (EnCo) in Germany to understand how intra-individual emotional tensions influence the externally visible corporate sustainability transformation process over time. Through analysis of observational, interview, and documentary data we explore emotional tensions that emerge from paradox corporate role expectations and emotional display rules which compete against corporate actors’ true emotions and beliefs about climate change. We develop a dynamic leader-follower-interaction model that shows how the leaders’ emotional display affects the perceived sustainability commitment and sustainability action of followers over time. We argue that emotional dissonance fosters the delegation of responsibility to act against climate change to authorities with more perceived power. The paper contributes to the literature of paradox theory and organizational responses to climate change by drawing attention to the relevance of emotions in navigating the corporate sustainability paradox over time.

Fallen Castles in the Air: Marriage, Labor, and Suppression in Louisa May Alcott’s 'Little Women'

Suppression of one’s emotions is defined as a shared experience among the March sisters in Louisa May Alcott’s Little Women, and a large portion of women after the Civil War. Drawing on the failed careers of Jo and Amy and the struggling marriage of Meg, this essay argues that in learning how to suppress oneself and be seen as socially acceptable, the sisters lose their prospects for success and happiness. When the sisters stray from home, their flaws are exposed: Meg’s jealousy, Jo’s anger, and Amy’s greed. A return trip home and time completing labor provide them training for wifehood. Within this labor, each sister confronts their greatest desire, forcing them to contain their desires toward what they want in order to prevent another outburst. The domestic sphere becomes a place for them to come face-to-face with their largest flaw and use the home as a testing ground for how to suppress their true emotions and survive within the expectations of being a wife. In stifling their ambitions and desires outside the domestic sphere through suppression training, the March sisters lose the prospects for unhappy marriages with men that continuously inflame their flaws through belittling behavior and condemnation.

A Data-Driven Adaptive Emotion Recognition Model for College Students Using an Improved Multifeature Deep Neural Network Technology.

With the increasing pressure on college students in terms of study, work, emotion, and life, the emotional changes of college students are becoming more and more obvious. For college student management workers, if they can accurately grasp the emotional state of each college student in all aspects of the whole process, it will be of great help to student management work. The traditional way to understand students' emotions at a certain stage is mostly through chats, questionnaires, and other methods. However, data collection in this way is time-consuming and labor-intensive, and the authenticity of the collected data cannot be guaranteed because students will lie out of impatience or unwillingness to reveal their true emotions. In order to explore an accurate and efficient emotion recognition method for college students, more objective physiological data are used for emotion recognition research. Since emotion is generated by the central nervous system of the human brain, EEG signals directly reflect the electrophysiological activity of the brain. Therefore, in the field of emotion recognition based on physiological signals, EEG signals are favored due to their ability to intuitively respond to emotions. Therefore, a deep neural network (DNN) is used to classify the collected emotional EEG data and obtain the emotional state of college students according to the classification results. Considering that different features can represent different information of the original data, in order to express the original EEG data information as comprehensively as possible, various features of the EEG are first extracted. Second, feature fusion is performed on multiple features using the autosklearn model integration technique. Third, the fused features are input to the DNN, resulting in the final classification result. The experimental results show that the method has certain advantages in public datasets, and the accuracy of emotion recognition exceeds 88%. This proves the used emotion recognition is feasible to be applied in real life.

Deep learning for facial emotion recognition using custom CNN architecture

Human facial expressions are an indication of true emotions. To recognize facial expressions accurately is useful in the field of Artificial Intelligence, Computing, Medical, e-Education, and many more. The facial expression recognition (FER) system detects emotion through facial expression. But, it is challenging to detect facial emotions accurately. However, recent advancements in technology, research, and availability of facial expression datasets have led to the development of many FER systems which can accurately detect facial emotions. Past research in the field of FER indicates With Convolutional Neural Networks (CNNs), deep learning techniques are the most advanced presently. Custom CNN Architecture is used to implement basic facial emotion recognition in static images in this paper. A K-fold cross-validation method was used to train them using FER13, CK+, and the JAFFE data set. On the seven classes of fundamental emotions, including anger, disgust, fear, happiness, neutrality, sorrow, and surprise, the FER13, CK+, and JAFFE datasets had an accuracy rate of 91.58 percent. Given the difficulty of developing unique CNN architecture, this study’s accurate findings contrast well with those of previous studies.

The Beginning of Memory

The Beginning of Memory

Wavelet ELM-AE Based Data Augmentation and Deep Learning for Efficient Emotion Recognition Using EEG Recordings

Emotion perception is critical for behavior prediction. There are many ways to capture emotional states by observing the body and copying actions. Physiological markers such as electroencephalography (EEG) have gained popularity, as facial emotions may not always adequately convey true emotion. This study has two main aims. The first is to measure four emotion categories using deep learning architectures and EEG data. The second purpose is to increase the number of samples in the dataset. To this end, a novel data augmentation approach namely the Extreme Learning Machine Wavelet Auto Encoder (ELM-W-AE) is proposed for data augmentation. The proposed data augmentation approach is both simple and faster than the other synthetic data augmentation approaches. For deep architectures, large datasets are important for performance. For this reason, data multiplexing approaches with classical and synthetic methods have become popular recently. The proposed synthetic data augmentation is the ELM-W-AE because of its efficiency and detail reproduction. The ELM-AE structure uses wavelet activation functions such as Gaussian, GgW, Mexican, Meyer, Morlet, and Shannon. Deep convolutional architectures classify EEG signals as images. EEG waves are scalogram using Continuous Wavelet Transform (CWT). The ResNet18 architecture recognizes emotions. The proposed technique uses GAMEMO data collected during gameplay. Each of these states is represented in the GAMEMO data collection. The visual data set created from the signal was divided into two groups as 70% training and 30% testing. ResNet18 has been fine-tuned with augmented photos, training images only. It achieved 99.6% classification accuracy in tests. The proposed method is compared with the other approaches on the same dataset, and an approximately 22% performance improvement is achieved.

К вопросу о дискурсивных практиках в конструировании нового человека средствами советской фотографии (на материале публикаций в газете «Хальмг үнн» («Калмыцкая правда») в 1957–1964 гг.)

Introduction. The struggle for historical truth systematically conducted by the Government makes it essential enough to address the topic of the Great Patriotic War for further insights into actual discursive practices across Soviet regions. Goals. The work aims at identifying certain patterns of discursive practices once instrumental in the shaping of new man through means of Soviet photography and, specifically, the images published by the Khalmg Ünn (Kalm. ‘Kalmyk truth’) newspaper, a key periodical of the Kalmyk ASSR, between 1957 and 1964. Materials and methods. The continuous sampling method has been instrumental in examining over 10.000 photographs published in 1957 to 1964, the analysis having included both local images and ones delivered by the ‘Center’ (TASS) for further publication in the republican media. The research methods employed are the photograph analysis method (at various levels), quantitative methods, and that of extrapolation. Conclusions. The analysis of 1957–1964 Khalmg Ünn photographs as visual evidence of the past reveals that the then ideological techniques did not involve the theme of the Great Patriotic War to the full: despite the latter served a key element in the shaping of common Soviet identity, it was thenceforth closely associated with the drama of Kalmyk Deportation to Siberia. The newspaper’s visual imagery depicts a republic under reconstruction which was allegedly determined by the specific situation to have resulted from that the Deportation had been undertaken and explained by false accusations of treason and collaborationism — during war years. So, the Center was articulating the idea of restoration and revival of the Republic to be perceived by the Kalmyks as a revival of the ethnos, withdrawal of any charges, complete rehabilitation — and this is manifested in all photographs (construction sites, new buildings and facilities), this key discursive practice employed was to meet the sentiments of Kalmyks to have just relieved and arrived in ancestral lands. The combination of true emotions and ideological patterns created a platform for the formation of new Soviet man in the Kalmyk ASSR.

The importance of understanding and making sense of Zulu traditional hymns as a symbol of expressing worship: Cognitive hermeneutics and hermeneutics of hymnody theories

Singing and understanding Zulu traditional hymns among charismatic churches and gospel groups have become a fundamental worship tool. Zulu traditional hymns are at the centre of Christian lives in South Africa. Singing Zulu traditional hymns (iCilongo Levangeli) is predominant for many South African musicians and gospel groups using modern musical styles. However, contemporary churches, musicians and gospel groups tend not to understand the authenticity of these hymns. The issue of Zulu traditional hymns in the modern gospel industry is a matter of great concern. Therefore, this article addresses and discusses the importance of understanding and making sense of Zulu traditional hymns as a symbol of expressing worship. Data were collected for a research practice using a hermeneutic phenomenology paradigm to obtain a precise understanding and the original meaning of the prominent Zulu traditional hymns. The study reveals that there are Zulu traditional hymns that were misinterpreted and misunderstood. The misinterpretation of Zulu traditional hymns is partly attributable to the ignorance of the underlying true meaning, emotions, state and purpose of the original composer.Contribution: This study recommends that contemporary gospel musicians sing Zulu traditional hymns in the original text to ensure that they do not misinterpret the hymns.

Behind male Saudi nursing students\u2019 mental health facade: a husserlian phenomenological approach

BackgroundEvery person has a persona (or mask) which is the façade that every person shows to the world. Thus, males use façade to reveal or conceal their true feelings and emotions. Also, the male uses mental health façade to protect themselves from prejudice and judgment. Thus, the study aimed to explore the experiences of male Saudi nursing students of mental health.MethodHusserl’s descriptive phenomenology was used as a guiding lens to explore. Eleven participants were involved in the study by using the referral sampling technique. An unstructured interview was performed to gather information from the participants. The seven steps of the descriptive Colaizzi process were followed to investigate and examine the obtained data. The credibility, dependability, confirmability, transferability, and reflexivity criteria were observed to ensure the rigor of the study.ResultsThe findings have two major themes. The first theme is the unadulterated smile that describes optimism in the family and mutual guarantee. The second theme is the orchestrated smile, which describes avoiding diverting burdens, social responsibility, protection of self, and reputation.ConclusionsThe findings document that the mental health façade of male Saudi nursing students is associated with the expectation of family optimism, mutual guarantee, the expectation of society, and self-protection.

A Study on Social Anxiety and Aggression among Young Adults

Social anxiety is an excessive fear of potentially embarrassing or humiliating situations and it is a common mental health problem. Aggression is overt or covert, frequently harmful, social interplay with the aim of causing harm or different damage upon every other individual. The relationship between social anxiety and aggression is being studied but in a scattered manner. The combination of social anxiety and aggression cause many problems to the individual as well as to the society. It is important to know how social anxiety and aggression affects the individuals in a detailed way. The goal of the study was to assess the impact of social anxiety among young adults and the impact of aggression due to social anxiety in young adults. In the study it was evident that social anxiety is correlated with aggression. People with social anxiety are often perplexed about expressing their emotions and thoughts with someone. This causes social anxiety. As a result of their inability to express their true emotions, their anger builds up. They engage in some socially unacceptable activities to get rid of the unease.

A Short Story as a Special Genre of Vietnamese National Literature

There is still a gap between prosperous foundation of national literary heritage, its genre diversity and real practical understanding of its value for society development and national identification. The article is deals with to the study of the short Vietnamese story as a genre of Vietnamese literature, its history, specifics, and the current stage of development. The purpose of the research is to study the short Vietnamese story as a national genre of Vietnamese literature, periodization, identification of its features. In this study, several methods of scientific research were used. Historical and comparative analysis, with the help of which it is possible to identify the features of the genre under study by establishing similarities and differences with other genres, to identify the stages of its development in the historical context; and empirical analysis to summarize, classify and describe the results obtained. The contribution of the study is to present the history of the Vietnamese short story as a genre of Vietnamese literature, reveal its specifics, features due to the cultural and religious basis, the historical heritage of the country and socio-economic development. The periodization of the genre evolution is carried out. Special attention is paid to the analysis of the current stage short story development. The author shows the transformation of the genre, reveals new features inherent in the genre of the modern short Vietnamese story, related to the socio-economic development of Vietnam and the echoes of the military events of the mid-XX century. Namely, the approach to realism and showing the true emotions of the hero. We show that it is the analysis of literary works of this genre that can become an important element, which can help to preserve the depth and beauty of literature, to maintain the continuity of generations. This is especially important in the context of globalization and the erosion of traditional cultural values in Vietnamese society.

Delivering Bad News: Should Physicians Express their Emotions?

The traditionally assumed medical dictum is that a physician who expresses his or her emotions in font of patients or their families is almost deemed unprofessional. This feeling may be common place, particularly among traditional physicians who still hold the belief that professionalism is endangered if physicians deliver bad news laced with their true emotions. Discussion of this important topic surfaces now and then and are then hidden away without definite answers possibly due to lack of attention by physician's fraternity to dispense with this out-of-date dictum. Though we understand the protection of professionalism is the basis for this practice, the topic is rarely revisited because the community of physicians remains distant from this sensitive issue, in spite of its importance in achieving high care satisfaction from patients and their families. In view of the increased emphasis on enhancing patients’ hospital experiences and satisfaction, the demonstration of sensitivity on the part of physicians in some form as a component of their compassionate care may require renewed attention.

Exploring customer satisfaction in cold chain logistics using a text mining approach

PurposeWith the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.Design/methodology/approachThis research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.FindingsThe results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.Research limitations/implicationsThe data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.Originality/valuePrior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.

Perceptions and experiences of nurses transitioning to primary care: A qualitative study.

Transitioning to primary care is a highly stressful process for nurses due to the diverse roles and responsibilities required. Despite the global shift of health services requiring more nurses in primary care, there is a dearth of literature regarding their transition experiences. To explore the perceptions and experiences of nurses transitioning to primary care. A qualitative descriptive design was used. Nineteen nurses from five primary care clinics in Singapore participated in the semi-structured interviews. These interviews gathered the experiences of nurses transitioning to primary care. Interviews were analysed using thematic analysis through an inductive and theoretical approach. Rigour was established through an audit trail, peer examination and member checking. Three themes of Idealistic assumptions, Role realignment and Seeking enlightenment emerged. These themes described the nurses' initial disorientation to the unfamiliar primary care setting; their attempts to fit in by embracing the new environment and suppressing their true emotions; and the support received coupled with their ownership to learn. The findings highlight the importance for nurse managers to promote positive transition experiences through the modification of existing support programmes and organizational cultures. To further enhance the nurses' retention within primary care, it is imperative for nurse educators and policy makers to revise the nursing curricula, primary care nursing career framework and recruitment campaigns at the national level. This brings clarity to the misconceptions in the roles and expectations required of nurses in primary care.

Perceived organizational support and emotional labour among police communicators: what can organizational context tell us about posttraumatic stress?

As central members of the emergency response system, communicators are regularly exposed to potentially traumatic events and experience some of the highest rates of posttraumatic stress. Given elevated rates of distress, they are regularly called upon to manage emotions-their own and others'-during high-risk and high-stress situations, within a highly controlled organizational context. Emotional labour (EL) theory suggests that many individuals faced with this challenge utilize a strategy in which emotions are suppressed or faked (surface acting-SA) in keeping with organizational expectations. This study was designed to examine the relationships among reported EL, perceived organizational support, job stress, and severity of posttraumatic stress among a population of communicators. Job pressure and perceived lack of organizational support were positively associated with posttraumatic stress. Although the highest reported levels of SA occurred when interacting with members of the public, this SA was not associated with posttraumatic stress, unlike SA with co-workers and supervisors. SA with co-workers and supervisors was further related to perceptions of lack of organizational support. Thus, an organization perceived as unsupportive may create a culture in which individuals are dissuaded from expressing true emotions with colleagues and supervisors, potentially magnifying the traumatic effects of exposure to critical incidents.

A Magnitude and Angle Combined Optical Flow Feature for Microexpression Spotting

Microexpression (ME) spotting is a crucial step for emotion analysis to detect people’s true emotions. However, the short duration, small motion amplitude, and limited sample number make accurate spotting and ME-locating challenging. To address these problems, we make two contributions in this article, including an ME dataset SDU2 and a spotting method. The dataset SDU2 contains hybrid expressions of 1602 video clips labeled by professional psychologists, covering six main categories of emotions with balanced distribution. Our ME spotting method is based on a magnitude- and angle-combined optical flow feature, exploiting the angle information, which has been overlooked by other spotting methods. In this method, we select four most discriminative regions of interest (ROIs) and determine the appearance of MEs using the proposed feature and a novel decision criterion based on the characteristic of the optical flow angle and magnitude. We have conducted experiments on SDU2 and CASME II datasets. The results demonstrate that our method achieves much better performance compared to other state-of-the-art methods in terms of spotting accuracy.

Spontaneous emotional speech in European Portuguese using feeltrace system – first approaches

The present work studies spontaneous emotional speech in European Portuguese, comparing the values obtained in measuring acoustic parameters in this study with the ones known for other languages and for simulated. The spontaneous expression of emotions recorded in the street or from television programs may bring very relevant results, but even in those situations it is difficult to determine the individual’s true emotion (it is often only possible to be identify through the context) (Patrick, 2008). The main focus of our study is on voice related parameters such as F0, jitter, shimmer and HNR that will be liable to compare with previous studies of emotions produced by an actor. Using the Feeltrace system it is possible to better describe the sensation caused by the stimuli (in any of the emotions in question) and describe in more dynamic manner the emotion. We also realize that, although the emotions produced by actors show some differences, because they are necessarily exaggerated, the parameter values are all very similar, valuing the studies that are usually obtained based on utterances produced by performers. Given the scarcity of studies on voice quality in European Portuguese, it is important to highlight that this work presents an original corpus specifically created for this study. We recognize the limitations of the presented research seeing the results as first approaches that must be deeply studied being important to areas such as robotics and health technology.

Research on the current situation of Chinese national vocal music education from the perspective of educational psychology

National vocal music is unique in the field of music in our country. Through its beautiful melody, distinctive vocal, clear language and true emotions, it has a beneficial influence on people's hearts and provides listeners with a good hearing experience. Therefore, national vocal music can stimulate listeners' resonance with national music culture. At this stage, educational psychology has been integrated with the teaching of various courses in colleges and universities. Integrating the focus of educational psychology with Chinese national vocal music education can help teachers change their educational concepts from the perspective of psychology. By observing the physical and psychological changes of students, we can give targeted guidance to students, so as to ensure the orderly progress of education, so as to improve students' values and artistic accomplishments. With the continuous reform and advancement of education, in order to bring students a different learning experience, colleges and universities gradually pay attention to the impact of the concepts and theories of educational psychology on students, and actively encourage college educators to use educational psychology knowledge to adjust and optimize teaching design. Based on this, this article will start from the concepts and theoretical characteristics of "educational psychology" and "Chinese national vocal music education", analyze the internal connection between the two, analyze the needs of educational psychology and Chinese national vocal music education, analyzes the current problems of Chinese national vocal music education, and proposes corresponding strategies.

Cross-Domain Few-Shot Micro-Expression Recognition Incorporating Action Units

Micro-expression, different from ordinary facial expressions, is an involuntary, spontaneous, and subtle facial movement that reveals true emotions which people intend to conceal. As it usually occurs within a fraction of a second (less than 1/2 second) with a low action intensity, capturing micro-expressions among facial movements in a video is difficult. Moreover, when a micro-expression recognition system works in cold-start conditions, it has to recognize novel classes of micro-expressions in a new scenario, suffering from the lack of sufficient labeled samples. Inconsistency in micro-expression labeling criteria makes it difficult to use existing labeled datasets in other scenarios. To tackle the challenges, we present a micro-expression recognizer, which on one hand leverages the knowledge of facial action units (AU) to enhance facial representations, and on the other hand performs cross-domain few-shot learning to transfer knowledge acquired from other domains with different data labeling protocols and feature distribution to overcome the scarcity of labeled samples in the cold-starting scenario. In particular, we draw inspirations from the correlation between micro-expression and facial action units (AUs), and design an action unit module, aiming to extract subtle AU-related features from videos. We then fuse AU-related features and general features extracted by optical-flow facial images. Through fine-tuning, we transfer knowledge from datasets in different domains to the target domain. The experimental results on two datasets show that: (1) the proposed recognizer can effectively learn to recognize new categories of micro-expressions in different domains with a very few labeled samples with the UF1 score of 0.544 on CASME dataset, outperforming the state-of-the-art methods by 0.089; (2) the performance of the recognizer is more competitive when it distinguishes micro-expression videos of more categories; and (3) the action unit module enables to improve the recognition performance by 0.072 and 0.047 on CASME and SMIC, respectively.