Search results for: negative emotion

Authors: Bubai Maji, Monorama Swain

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Speech is considered an essential and most natural medium for the interaction between machines and humans. However, extracting effective features for speech emotion recognition (SER) is remains challenging. The present studies show that the temporal information captured but high-level temporal-feature learning is yet to be investigated. In this paper, we present an efficient novel method using the Self-attention (SA) mechanism in a combination of Convolutional Neural Network (CNN) and Bi-directional Gated Recurrent Unit (Bi-GRU) network to learn high-level temporal-feature. In order to further enhance the representation of the high-level temporal-feature, we integrate a Bi-GRU output with learnable weights features by SA, and improve the performance. We evaluate our proposed method on our created SITB-OSED and IEMOCAP databases. We report that the experimental results of our proposed method achieve state-of-the-art performance on both databases.

Keywords: Bi-GRU, 1D-CNNs, self-attention, speech emotion recognition

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Authors: García López

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The Government Departments of Health have the task of informing and educating citizens about public health issues. For this, they use channels like Twitter, key in the search for health information and the propagation of content. The tweets, important in the virality of the content, may contain emotions that influence the contagion and exchange of knowledge. The goal of this study is to perform an analysis of the emotional projection of health information shared on Twitter by official American accounts: the disease control account CDCgov, National Institutes of Health, NIH, the government agency HHSGov, and the professional organization PublicHealth. For this, we used Tone Analyzer, an International Business Machines Corporation (IBM) tool specialized in emotion detection in text, corresponding to the categorical model of emotion representation. For 15 days, all tweets from these accounts were analyzed with the emotional analysis tool in text. The results showed that their tweets contain an important emotional load, a determining factor in the success of their communications. This exposes that official accounts also use subjective language and contain emotions. The predominance of emotion joy over sadness and the strong presence of emotions in their tweets stimulate the virality of content, a key in the work of informing that government health departments have.

Keywords: emotions in tweets, emotion detection in the text, health information on Twitter, American health official accounts, emotions on Twitter, emotions and content

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Authors: Marlene Weber, Joseph Giacomin, Alessio Malizia, Lee Skrypchuk, Voula Gkatzidou

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Technological and sociological developments in the automotive sector are shifting the focus of design towards developing a better understanding of driver needs, desires and emotions. Human centred design methods are being more frequently applied to automotive research, including the use of systems to detect human emotions in real-time. One method for a non-contact measurement of emotion with low intrusiveness is Facial-Expression Analysis (FEA). This paper describes a research study investigating emotional responses of 22 participants in a naturalistic driving environment by applying a multi-method approach. The research explored the possibility to investigate emotional responses and their frequencies during naturalistic driving through real-time FEA. Observational analysis was conducted to assign causes to the collected emotional responses. In total, 730 emotional responses were measured in the collective study time of 440 minutes. Causes were assigned to 92% of the measured emotional responses. This research establishes and validates a methodology for the study of emotions and their causes in the driving environment through which systems and factors causing positive and negative emotional effects can be identified.

Keywords: affective computing, case study, emotion recognition, human computer interaction

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Tracie Catlett

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Students enrolled in high-achieving schools are under tremendous pressure to perform at high levels inside and outside the classroom. Achievement pressure is a prevalent source of stress for students enrolled in high-achieving schools, and female students, in particular, experience a higher frequency and higher levels of stress compared to their male peers. The practice of mindfulness in a school setting is one tool that has been linked to improved self-regulation of emotions, increased positive emotions, and stress reduction. A mixed methods randomized pretest-posttest no-treatment control trial evaluated the effects of a six-session mindfulness intervention taught during a regularly scheduled life skills period in an independent day school, one type of high-achieving school. Twenty-nine students in Grades 10 and 11 were randomized by class, where Grade 11 students were in the intervention group (n = 14) and Grade 10 students were in the control group (n = 15). Findings from the study produced mixed results. There was no evidence that the mindfulness program reduced participants’ stress levels and negative emotions. In fact, contrary to what was expected, students enrolled in the intervention group experienced higher levels of stress and increased negative emotions at posttreatment when compared to pretreatment. Neither the within-group nor the between-groups changes in stress level were statistically significant, p > .05, and the between-groups effect size was small, d = .2. The study found evidence that the mindfulness program may have had a positive impact on students’ ability to regulate their emotions. The within-group comparison and the between-groups comparison at posttreatment found that students in the mindfulness course experienced statistically significant improvement in the in their ability to regulate their emotions at posttreatment, p = .009 < .05 and p =. 034 < .05, respectively. The between-groups effect size was medium, d =.7, suggesting that the positive differences in emotion regulation difficulties were substantial and have practical implications. The analysis of gender differences, as they relate to stress and emotions, revealed that female students perceive higher levels of stress and report experiencing stress more often than males. There were no gender differences when analyzing sources of stress experienced by the student participants. Both females and males experience regular achievement pressures related to their school performance and worry about their future, college acceptance, grades, and parental expectations. Females reported an increased awareness of their stress and actively engaged in practicing mindfulness to manage their stress. Students in the treatment group expressed that the practice of mindfulness resulted in feelings of relaxation and calmness.

Keywords: achievement pressure, adolescents, emotion regulation, emotions, high-achieving schools, independent schools, mindfulness, negative affect, positive affect, stress

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Authors: Saba Harati, Nasrin Arian Parsa

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The current research was an attempt to investigate the effect of resilience on mental health and life satisfaction. In one Cross Sectional research, 287 (173 females and 114 males) students of Tehran University were participated their average age was 23.17 years old (SD=4.9). The instruments used for assessing the research variables included: Cutter and Davidson resilience scale (CD-RISC), the short form of the depression-anxiety-stress scale, and life satisfaction scale. The data analysis was done in the form of structural equation model. The results of Simultaneous Hierarchical Multiple Regression Analysis indicated that there was a significant mediating role of the negative emotions (depression, anxiety, and stress), in the relationship between the family resilience (p < 0.001) and satisfaction with life (p < 0.001). Resilience results in life satisfaction by reducing the emotional problems (or increasing the mental health level). The effect of the resilience variable on life satisfaction was indirect.

Keywords: resilience, negative emotion, mental health, life satisfaction

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Authors: Begum Serim-Yildiz

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The aim of this study is to examine the role of parents' emotional and behavioral reactions on fears of children in the COVID-19 pandemic considering Rachman’s Three Pathways Theory. For this purpose, a phenomenological qualitative study was conducted. Thirteen participants living with their children were utilized through criterion and snowball sampling. In semi-structured interviews parents were asked about their own and their children’s beahavioral and emotional reactions in the COVID-19 pandemic, and they were expected to give detailed information about fears of their children before and in pandemic. Firstly, parents were asked about their behavioral and emotional reactions in the COVID-19 pandemic. As behavioral reactions, precautions taken by parents to protect the rest of the family from negative physical and emotional impact of the pandemic were mentioned, while emotional reactions were defined as acquisition of negative emotions like fear, anxiety, and worry. Secondly, parents were asked about their children’s behavioral and emotional reactions. Some of the parents talked about positive behavioral changes such as gaining self-control, while some others explained negative behavioral changes like increased time spent with technological tools. In the emotional changes section, all of the parents explained at least one negative emotion. All of the parents stated that their children had COVID-19 related fears. According to parents’ expressions, fears of children in pandemic were examined in two dimensions. Fears directly related to COVID-19 were fear of virus/microbes, illness or death of someone in family and death and fears. Fears indirectly related to COVID-19 were fear of going out, sleep alone at night, separation, touching stuff outside the home, and cold. Considering existing literature and based on the findings of this study, it can be concluded that children’s modelling experiences have impact on acquisition of negative emotions, especially fear, therefore, preventive interventions involving caregivers should be provided by mental health professionals working with children.

Keywords: children’s fears, COVID-19 pandemic, modelling experiences, parents’ reactions

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Authors: Hyojung Shin, Munhee Kweon

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Non-suicide Self Injuri (NSSI) can be defined as the act of an individual who does not intend to die directly and intentionally damaging his or her body tissues. According to a study conducted by the Korean Ministry of Education in 2018, the NSSI is widely spreading among teenagers, with 7.9 percent of all middle school students and 6.4 percent of high school students reporting experience in NSSI. As such, it is understood that the first time of the NSSI is in adolescence. However, the NSSI may not start and stop at a certain time, but may last longer. However, despite the widespread prevalence of NSSI among teenagers, little is known about the process and maintenance of NSSI college students on a continuous development basis. Korea's NSSI research trends are mainly focused on individual internal vulnerabilities (high levels of painful emotions/awareness, lack of pain tolerance) and interpersonal vulnerabilities (poor communication skills and social problem solving), and little studies have been done on individuals' unique characteristics and environmental factors such as substrate or environmental vulnerability factors. In particular, environmental factors are associated with the occurrence of NSSI by acting as a vulnerability factor that can interfere with the emotional control of individuals, whereas individual factors play a more direct role by contributing to the maintenance of NSSI, so it is more important to consider this for personal environmental involvement in NSSI. This study focused on the Fight-Flight-Freeze System as a factor in the defensive avoidance system of Reward Sensitivity in individual factors. Also, Environmental factors include the level of expressed emotion in family. Wedig and Nock (2007) said that if parents with a self-critical cognitive style take the form of criticizing their children, the experience of NSSI increases. The high level of parental criticism is related to the increasing frequency of NSSI acts as well as to serious levels of NSSI. If the normal coping mechanism fails to control emotions, people want to overcome emotional difficulties even through NSSI, and emotional disturbances experienced by individuals within an unsupported social relationship increase vulnerability to NSSI. Based on these theories, this study is to find ways to prevent NSSI and intervene in counseling effectively by verifying the differences between the characteristics experienced NSSI persons and non-experienced NSSI persons. Therefore, the purpose of this research was to examine the relationship of Fight-Flight-Freeze System (FFFS), level of expressed emotion in family and emotion regulation difficulties, comparing those who experienced Non-Suicidal Self-Injury (NSSI) with those who did not experienced Non-Suicidal Self-Injury (NSSI). The data were collected from university students in Seoul Korea and Gyeonggi-do province. 99 subjects were experienced student of NSSI, while 375 were non- experienced student of NSSI. The results of this study are as follows. First, the result of t-test indicated that NSSI attempters showed a significant difference in fight-flight-freeze system, level of expressed emotion and emotion regulation difficulties, compared with non-attempters. Second, fight-flight-freeze system, level of expressed emotion in family and emotion regulation difficulties of NSSI attempters showed a significant difference in correlation. The correlation was significant only freeze system of fight-flight-freeze system, Level of expressed emotion in family and emotion regulation difficulties. Third, freeze system and level of expressed emotion in family predicted emotion regulation difficulties of NSSI attempters. Fight-freeze system and level of expressed emotion in family predicted emotion regulation difficulties of non-NSSI attempters. Lastly, Practical implications for counselors and limitations of this study are discussed.

Keywords: fight-flight-freeze system, level of expressed emotion in family, emotion regulation difficulty, non-suicidal self injury

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Authors: Amber Hatch, Eric Wright, Feihong Wang

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Research suggests that attachment in close relationships affects one’s emotional processes, mindfulness, conflict-management behaviors, and interpersonal interactions. Attachment insecurity is often associated with maladaptive social interactions and suboptimal relationship qualities. Past studies have considered how the nature of emotion regulation and mindfulness in teachers may be related to student or classroom outcomes. Still, no research has examined how the relationship between such internal experiences and classroom management outcomes may also be related to teachers’ attachment insecurity. This study examined the interrelationships between teachers’ attachment insecurity, mindfulness tendencies, emotion regulation abilities, and classroom management efficacy as indexed by students’ classroom behavior and teachers’ response effectiveness. Teachers’ attachment insecurity was evaluated using the global ECRS-SF, which measures both attachment anxiety and avoidance. The present study includes a convenient sample of 357 American elementary school teachers who responded to a survey regarding their classroom management efficacy, attachment in/security, dispositional mindfulness, emotion regulation strategies, and difficulties in emotion regulation, primarily assessed via pre-existing instruments. Good construct validity was demonstrated for all scales used in the survey. Sample demographics, including gender (94% female), race (92% White), age (M = 41.9 yrs.), years of teaching experience (M = 15.2 yrs.), and education level were similar to the population from which it was drawn, (i.e., American elementary school teachers). However, white women were slightly overrepresented in our sample. Correlational results suggest that teacher attachment insecurity is associated with poorer classroom management efficacy as indexed by students’ disruptive behavior and teachers’ response effectiveness. Attachment anxiety was a much stronger predictor of adverse student behaviors and ineffective teacher responses to adverse behaviors than attachment avoidance. Mindfulness, emotion regulation abilities, and years of teaching experience predicted positive classroom management outcomes. Attachment insecurity and mindfulness were more strongly related to frequent adverse student behaviors, while emotion regulation abilities were more strongly related to teachers’ response effectiveness. The teaching experience was negatively related to attachment insecurity and positively related to mindfulness and emotion regulation abilities. Although the data were cross-sectional, path analyses revealed that attachment insecurity is directly related to classroom management efficacy. Through two routes, this relationship is further mediated by emotion regulation and mindfulness in teachers. The first route of indirect effect suggests double mediation by teacher’s emotion regulation and then teacher mindfulness in the relationship between teacher attachment insecurity and classroom management efficacy. The second indirect effect suggests mindfulness directly mediated the relationship between attachment insecurity and classroom management efficacy, resulting in improved model fit statistics. However, this indirect effect is much smaller than the double mediation route through emotion regulation and mindfulness in teachers. Given the significant predication of teacher attachment insecurity, mindfulness, and emotion regulation on teachers’ classroom management efficacy both directly and indirectly, the authors recommend improving teachers’ classroom management efficacy via a three-pronged approach aiming at enhancing teachers’ secure attachment and supporting their learning adaptive emotion regulation strategies and mindfulness techniques.

Keywords: Classroom management efficacy, student behavior, teacher attachment, teacher emotion regulation, teacher mindfulness

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Authors: Trisha Malhotra

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Sentiment analysis is a recent field of study that computationally assesses the emotional nature of a body of text. To assess its test-validity, sentiment analysis was carried out on the emotional corpus of text from a personal 15-day mood diary. Self-reported mood scores varied more or less accurately with daily mood evaluation score given by the software. On further assessment, it was found that while sentiment analysis was good at assessing ‘global’ mood, it was not able to ‘locally’ identify and differentially score synonyms of various emotional words. It is further critiqued for treating the intensity of an emotion as universal across cultures. Finally, the software is shown not to account for emotional complexity in sentences by treating emotions as strictly positive or negative. Hence, it is posited that a better output could be two (positive and negative) affect scores for the same body of text.

Keywords: analysis, data, diary, emotions, mood, sentiment

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Authors: Leah Lee

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This paper examines the correlation between deep learning models and emotions with noises to see whether or not noises mask emotions. The deep learning models used are plain convolutional neural networks (CNN), auto-encoder, long short-term memory (LSTM), and Visual Geometry Group-16 (VGG-16). Emotion datasets used are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Toronto Emotional Speech Set (TESS), and Surrey Audio-Visual Expressed Emotion (SAVEE). To make it four times bigger, audio set files, stretch, and pitch augmentations are utilized. From the augmented datasets, five different features are extracted for inputs of the models. There are eight different emotions to be classified. Noise variations are white noise, dog barking, and cough sounds. The variation in the signal-to-noise ratio (SNR) is 0, 20, and 40. In summation, per a deep learning model, nine different sets with noise and SNR variations and just augmented audio files without any noises will be used in the experiment. To compare the results of the deep learning models, the accuracy and receiver operating characteristic (ROC) are checked.

Keywords: auto-encoder, convolutional neural networks, long short-term memory, speech emotion recognition, visual geometry group-16

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Authors: Merve Denizci Nazligul

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Dancers seem to have much higher risk levels for the development of eating disorders, compared to non-dancing counterparts. In a remarkably competitive nature of dance environment, perfectionism and emotion regulation difficulties become inevitable risk factors. Moreover, early maladaptive schemas are associated with various eating disorders. In the current study, it was aimed to investigate the mediating role of difficulties with emotion regulation on the relationship between perfectionism and disordered eating behaviors, as well as on the relationship between early maladaptive schemas and disordered eating behaviors. A total of 70 volunteer dancers (n = 47 women, n = 23 men) were recruited in the study (M age = 25.91, SD = 8.9, range 19–63) from the university teams or private clubs in Turkey. The sample included various types of dancers (n = 26 ballets or ballerinas, n =32 Latin, n = 10 tango, n = 2 hiphop). The mean dancing hour per week was 11.09 (SD = 7.09) within a range of 1-30 hours. The participants filled a questionnaire set including demographic information form, Dutch Eating Behavior Questionnaire, Multidimensional Perfectionism Scale, three subscales (Emotional Inhibition, Unrelenting Standards-Hypercriticalness, Approval Seeking-Recognition Seeking) from Young Schema Questionnaire-Short Form-3 and Difficulties in Emotion Regulation Scale. The mediation hypotheses were tested using the PROCESS macro in SPSS. The findings revealed that emotion regulation difficulties significantly mediated the relationship between three distinct subtypes of perfectionism and emotional eating. The results of the Sobel test suggested that there were significant indirect effects of self-oriented perfectionism (b = .06, 95% CI = .0084, .1739), other-oriented perfectionism (b = .15, 95% CI = .0136, .4185), and socially prescribed perfectionism (b = .09, 95% CI = .0104, .2344) on emotional eating through difficulties with emotion regulation. Moreover, emotion regulation difficulties significantly mediated the relationship between emotional inhibition and emotional eating (F(1,68) = 4.67, R2 = .06, p < .05). These results seem to provide some evidence that perfectionism might become a risk factor for disordered eating behaviors when dancers are not able to regulate their emotions. Further, gaining an understanding of how inhibition of emotions leads to inverse effects on eating behavior may be important to develop intervention strategies to manage their disordered eating patterns in risk groups. The present study may also support the importance of using unified protocols for transdiagnostic approaches which focus on identifying, accepting, prompting to express maladaptive emotions and appraisals.

Keywords: dancers, disordered eating, emotion regulation difficulties, perfectionism

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Authors: Mikel Alonso López

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The early eighties saw the rise of a new research trend in several prestigious journals, mainly articles that related emotions with the decision-making processes of the consumer, and stopped treating them as external elements. That is why we ask questions such as: what are emotions? Are there different types of emotions? What components do they have? Which theories exist about them? In this study, we will review the main theories and components of emotion analysing the cognitive factor and the different emotional states that are generally recognizable with a focus in the classic debate as to whether they occur before the cognitive process or the affective process.

Keywords: emotion, consumer behaviour, feelings, decision making

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Authors: Avyarthana Dey, Naren P. Rao, Arpitha Jacob, Chaitra V. Hiremath, Shivarama Varambally, Ganesan Venkatasubramanian, Rose Dawn Bharath, Bangalore N. Gangadhar

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Objective: Impaired facial emotion recognition is widely reported in schizophrenia. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. However, its effect on facial emotion recognition deficits seen in schizophrenia is not well explored. In this study, we examined the effect of intranasal OXT on processing facial emotions and its neural correlates in patients with schizophrenia. Method: 12 male patients (age= 31.08±7.61 years, education= 14.50±2.20 years) participated in this single-blind, counterbalanced functional magnetic resonance imaging (fMRI) study. All participants underwent three fMRI scans; one at baseline, one each after single dose 24IU intranasal OXT and intranasal placebo. The order of administration of OXT and placebo were counterbalanced and subject was blind to the drug administered. Participants performed a facial emotion recognition task presented in a block design with six alternating blocks of faces and shapes. The faces depicted happy, angry or fearful emotions. The images were preprocessed and analyzed using SPM 12. First level contrasts comparing recognition of emotions and shapes were modelled at individual subject level. A group level analysis was performed using the contrasts generated at the first level to compare the effects of intranasal OXT and placebo. The results were thresholded at uncorrected p < 0.001 with a cluster size of 6 voxels. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. Results: Compared to placebo, intranasal OXT attenuated activity in inferior temporal, fusiform and parahippocampal gyri (BA 20), premotor cortex (BA 6), middle frontal gyrus (BA 10) and anterior cingulate gyrus (BA 24) and enhanced activity in the middle occipital gyrus (BA 18), inferior occipital gyrus (BA 19), and superior temporal gyrus (BA 22). There were no significant differences between the conditions on the accuracy scores of emotion recognition between baseline (77.3±18.38), oxytocin (82.63 ± 10.92) or Placebo (76.62 ± 22.67). Conclusion: Our results provide further evidence to the modulatory effect of oxytocin in patients with schizophrenia. Single dose oxytocin resulted in significant changes in activity of brain regions involved in emotion processing. Future studies need to examine the effectiveness of long-term treatment with OXT for emotion recognition deficits in patients with schizophrenia.

Keywords: recognition, functional connectivity, oxytocin, schizophrenia, social cognition

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Authors: K. Hiro, Y. Imai, T. Sasayama

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Cavitation under static negative pressure is not revealed well. The Berthelot method to generate such negative pressure can be a means to study cavitation inception. In this study, metal Berthelot tubes built in observation windows are newly developed and are checked whether high static negative pressure is generated or not. Negative pressure in the tube with a pair of a corundum plate and an aluminum gasket increased with temperature cycles. The trend was similar to that as reported before.

Keywords: Berthelot method, cavitation, negative pressure, observation

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Authors: Methawee Yuttapongtada

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Intensifier is the linguistic term and device that is generally found in different languages in order to enhance and give additional quantity, quality or emotion to the words of each language. In fact, each language in the world has both of the similar and dissimilar intensifying device. More specially, the wide variety of intensifying device is used for Thai language and one of those is usage of the impolite word or the word that used to mean something negative as intensifier. The data collection in this study was done throughout the spoken language style by collecting from intensifiers regarded as impolite words because these words as employed in the other contexts will be held as the rude, swear words or the words with negative meaning. Then, backward study to the past was done in order to consider the historical change. Explanation of the original meaning and the contexts of words use from the past till the present time were done by use of both textual documents and dictionaries available in different periods. It was found that regarding the semantics and pragmatic aspects, subjectification also is the significant motivation that changed the impolite words to intensifiers. At last, it can explain pathway of the semantic change of these very words undoubtedly. Moreover, it is found that use tendency in the impolite word or the word that used to mean something negative will more be increased and this phenomenon is commonly found in many languages in the world and results of this research may support to the belief that human language in the world is universal and the same still reflected that human has the fundamental thought as the same to each other basically.

Keywords: impolite word, intensifier, Thai, semantic change

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Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

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Authors: Gabrielle Veloso, Welison Ty

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Trait anxiety is a pervasive tendency to attend to and experience fears and worries to a disproportionate degree, across various situations. This study sought to determine if participants who undergo emotional working memory training will have significantly lower scores on the trait anxiety scales post-intervention. The study also sought to determine if emotional regulation mediated the relationship between working memory training and trait anxiety. Forty-nine participants underwent 20 days of computerized emotional working memory training called Emotional Dual n-back, which involves viewing a continuous stream of emotional content on a grid, and then remembering the location and color of items presented on the grid. Participants of the treatment group had significantly lower trait anxiety compared to controls post-intervention. Mediation analysis determined that working memory training had no significant relationship to anxiety as measured by the Beck’s Anxiety Inventory-Trait (BAIT), but was significantly related to anxiety as measured by form Y2 of the Spielberger State-Trait Anxiety Inventory (STAI-Y2). Emotion regulation, as measured by the Emotional Regulation Questionnaire (ERQ), was found not to mediate between working memory training and trait anxiety reduction. Results suggest that working memory training may be useful in reducing psychoemotional symptoms rather than somatic symptoms of trait anxiety. Moreover, it proposes for future research to further look into the mediating role of emotion regulation via neuroimaging and the development of more comprehensive measures of emotion regulation.

Keywords: anxiety, emotion regulation, working-memory, working-memory training

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Authors: H. Abas Nejad, A. R. Teymoori

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Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: Rafid Saeed Abdulrazak Alshkaki

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In this paper, zero-one inflated negative binomial distribution is considered, along with some of its structural properties, then its parameters were estimated using the method of moments. It is found that the method of moments to estimate the parameters of the zero-one inflated negative binomial models is not a proper method and may give incorrect conclusions.

Keywords: zero one inflated models, negative binomial distribution, moments estimator, non negative integer sampling

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Authors: Martina Katalin Szabó, Bernadett Lipóczi, Csenge Guba, István Uveges

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In this study, an exhaustive analysis was carried out about the historical development of negative emotive intensifiers in the Hungarian language via NLP methods. Intensifiers are linguistic elements which modify or reinforce a variable character in the lexical unit they apply to. Therefore, intensifiers appear with other lexical items, such as adverbs, adjectives, verbs, infrequently with nouns. Due to the complexity of this phenomenon (set of sociolinguistic, semantic, and historical aspects), there are many lexical items which can operate as intensifiers. The group of intensifiers are admittedly one of the most rapidly changing elements in the language. From a linguistic point of view, particularly interesting are a special group of intensifiers, the so-called negative emotive intensifiers, that, on their own, without context, have semantic content that can be associated with negative emotion, but in particular cases, they may function as intensifiers (e.g.borzasztóanjó ’awfully good’, which means ’excellent’). Despite their special semantic features, negative emotive intensifiers are scarcely examined in literature based on large Historical corpora via NLP methods. In order to become better acquainted with trends over time concerning the intensifiers, The exhaustively analysed a specific historical corpus, namely the Magyar TörténetiSzövegtár (Hungarian Historical Corpus). This corpus (containing 3 millions text words) is a collection of texts of various genres and styles, produced between 1772 and 2010. Since the corpus consists of raw texts and does not contain any additional information about the language features of the data (such as stemming or morphological analysis), a large amount of manual work was required to process the data. Thus, based on a lexicon of negative emotive intensifiers compiled in a previous phase of the research, every occurrence of each intensifier was queried, and the results were stored in a separate data frame. Then, basic linguistic processing (POS-tagging, lemmatization etc.) was carried out automatically with the ‘magyarlanc’ NLP-toolkit. Finally, the frequency and collocation features of all the negative emotive words were automatically analyzed in the corpus. Outcomes of the research revealed in detail how these words have proceeded through grammaticalization over time, i.e., they change from lexical elements to grammatical ones, and they slowly go through a delexicalization process (their negative content diminishes over time). What is more, it was also pointed out which negative emotive intensifiers are at the same stage in this process in the same time period. Giving a closer look to the different domains of the analysed corpus, it also became certain that during this process, the pragmatic role’s importance increases: the newer use expresses the speaker's subjective, evaluative opinion at a certain level.

Keywords: historical corpus analysis, historical linguistics, negative emotive intensifiers, semantic changes over time

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Authors: Sabahat Nadeem, Farman Ali Khan

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Emotions are considered as a vital factor affecting the process of information handling, level of attention, memory capacity and decision making. Latest e-Learning systems are therefore taking into consideration the effective state of learners to make the learning process more effective and enjoyable. One such use of user’s affective information is in the systems that tend to regulate users’ emotions to a state optimally desirable for learning. So for, this objective has been tried to be achieved with the help of teaching strategies, background music, guided imagery, video clips and odors. Nevertheless, we know that colors can affect human emotions. Relationship between color and emotions has a strong influence on how we perceive our environment. Similarly, the colors of the interface can also affect the user positively as well as negatively. This affective behavior of color and its use as emotion regulation agent is not yet exploited. Therefore, this research proposes a Color-based Emotion Regulation Model (CERM), a new framework that can automatically adapt its colors according to user’s emotional state and her personality type and can help in producing a desirable emotional effect, aiming at providing an unobtrusive emotional support to the users of e-learning environment. The evaluation of CERM is carried out by comparing it with classical non-adaptive, static colored learning management system. Results indicate that colors of the interface, when carefully selected has significant positive impact on learner’s emotions.

Keywords: effective learning, e-learning, emotion regulation, emotional design

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Authors: Caihong Sun, Xizi Zhang

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Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.

Keywords: factorization machines, feature engineering, negative ratings, recommendation systems

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Authors: R. Borghol, T. Aguili

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In this paper, we investigate theoretically the waves propagation in a lossless double-negative grounded slab (DNG). This study is performed by the Transverse Resonance Method (TRM). The proper or improper nature of real and complex modes is observed. They are highly dependent on metamaterial parameters, i.e. ɛ_r-negative, µ_r-negative, or both. Numerical results provided that only the proper complex modes (i.e., leaky modes) exist in DNG slab, and only the improper complex modes exist in single-negative grounded slab.

Keywords: double negative grounded slab, real and complex modes, single negative grounded slab, transverse resonance method

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Authors: Brianne Nichols, John A. Parkinson

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Positive Psychology has provided a rich understanding of the beneficial effects of positive emotions in relation to optimal functioning, and research has been devoted to promote states of positive feeling and thinking. While this is a worthwhile pursuit, positive emotions are not useful in all contexts - some situations may require the individual to make use of their negative emotions to reach a desired end state. To account for the potential value of a wider range of emotional experiences that are common to the human condition, Positive Psychology needs to expand its horizons and investigate how individuals achieve positive outcomes using varied means. The current research seeks to understand the positive psychology of fear of failure (FF), which is a commonly experienced negative emotion relevant to most life domains. On the one hand, this emotion has been linked with avoidance motivation and self-handicap behaviours, on the other; FF has been shown to act as a drive to move the individual forward. To fully capture the depth of this highly subjective emotional experience and understand the circumstances under which FF may be adaptive, this study adopted a mixed methods design using SenseMaker; a web-based tool that combines the richness of narratives with the objectivity of numerical data. Two hundred participants consisting mostly of undergraduate university students shared a story of a time in the recent past when they feared failure of achieving a valued goal. To avoid researcher bias in the interpretation of narratives, participants self-signified their stories in a tagging system that was based on researchers’ aim to explore the role of past failures, the cognitive, emotional and behavioural profile of individuals high and low in FF, and the relationship between these factors. In addition, the role of perceived personal control and self-esteem were investigated in relation to FF using self-report questionnaires. Results from quantitative analyses indicated that individuals with high levels of FF, compared to low, were strongly influenced by past failures and preoccupied with their thoughts and emotions relating to the fear. This group also reported an unwillingness to accept their internal experiences, which in turn was associated with withdrawal from goal pursuit. Furthermore, self-esteem was found to mediate the relationship between perceived control and FF, suggesting that self-esteem, with or without control beliefs, may have the potential to buffer against high FF. It is hoped that the insights provided by the current study will inspire future research to explore the ways in which ‘acceptance’ may help individuals keep moving towards a goal despite the presence of FF, and whether cultivating a non-contingent self-esteem is the key to resilience in the face of failures.

Keywords: fear of failure, goal-pursuit, negative emotions, optimal functioning, resilience

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Authors: Sakshi Ghai

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The word ‘emotion’ has been microscopically studied through psychological, anthropological and biological lenses and have indubitably been one of the most researched concepts as, in all situations and reactions that constitute human life, emotions form the very niche of our mutual existence. While understanding the social aspects of cognition, one can realize that emotions are deeply interwoven with language and thereby are pivotal in inducing human actions and behavior. The society or the outward social structure is the result of the inward psychological structure of our human relationships, for the individual is the result of the total experience, knowledge and conduct of man. The aim of this paper is threefold: first, to establish the relation between mental representations of emotions and its neuropsychological connection with language on a conscious and sub-conscious level; secondly, to describe how innate, basic and higher cognitive emotions affect the constantly changing state of an agent and peruse its assistance in determining the moral compass within all beings. Lastly, in the course of this paper, the concept of the architecture of mind is explored considering how it has developed an ability to display adaptive emotional states and responses, which are in sync with the language of thought. For every response to the social environment is so deeply determined by the very social milieu in which one is situated, language has a fundamental role in constructing emotions and articulating behavior. Being linguistic beings, we tend to associate emotion, feelings and other aspects of inwards mental states intrinsically with the language we use. This paper aims to devise a discursive approach to understand how emotions are fabricated, intertwined with the mental constructs further expressed and communicated through the various units of language.

Keywords: mental representation, emotion, language, psychology

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Authors: Yishu Gong, Liangliang Yang, Jianyu Zhang, Zhengyu Chen, Sihong He, Xusheng Zhang, Wei Zhang

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and is characterized by cognitive decline and behavioral changes. People living with Alzheimer’s disease often find it hard to complete routine tasks. However, there are limited objective assessments that aim to quantify the difficulty of certain tasks for AD patients compared to non-AD people. In this study, we propose to use speech emotion recognition (SER), especially the frustration level, as a potential biomarker for quantifying the difficulty patients experience when describing a picture. We build an SER model using data from the IEMOCAP dataset and apply the model to the DementiaBank data to detect the AD/non-AD group difference and perform longitudinal analysis to track the AD disease progression. Our results show that the frustration level detected from the SER model can possibly be used as a cost-effective tool for objective tracking of AD progression in addition to the Mini-Mental State Examination (MMSE) score.

Keywords: Alzheimer’s disease, speech emotion recognition, longitudinal biomarker, machine learning

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Authors: Richard Abel

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In the United States, increased incarceration rates have caused many adolescents to feel the strain of parental absence. This absence is then manifest through adolescent feelings of parental rejection. Additionally, upon reentry maternal incarceration may be related to adolescents experienced perceived excessive disciple. It is possible parents engage in this manner of discipline attempting to prevent the child from taking the same path to incarceration as the parent. According to General Strain Theory, adolescents encountering strain are likely to experience negative emotions. The emotion that is most likely to lead to delinquency is anger through reduced inhibitions and motivation to act. Additionally, males are more likely to engage in delinquent behavior, regardless of experiencing strain. This is not the case for every male who experiences maternal incarceration, parental rejection, excessive discipline, or anger. There are protective factors that enable agency within individuals. One such protective factor is mastery, or the perception that one is in control of his or her own future. The model proposed in this research suggests maternal incarceration is associated with increased parental rejection and excessive discipline in males. Males experiencing parental rejection and excessive discipline are likely to experience increased anger, which is then associated with increases in delinquent behavior. This model explores whether agency, in the form of mastery, mediates the relationship between strains and negative emotions, or between negative emotions and delinquent behavior. The Kaplan Longitudinal and Multigenerational Study (KLAMS) dataset is uniquely situated to analyze this model providing longitudinal data collected from both parents and their offspring. Maternal incarceration is constructed using parental responses such that the mother was incarcerated after the child’s birth, and any incarceration that happened prior to birth is excluded. The remaining variables of the study are all constructed from varying waves of the adolescent survey. Parental rejection, along with control variables for age, race, parental socioeconomic status, neighborhood effects, delinquent peers, and prior delinquent behavior are all constructed using Wave I data. To increase causal inference, the negative emotion of anger and the mediating variable of mastery are measured during Wave II. Lastly, delinquent behavior is measured at Wave III. Results of the analysis show expected relationships such that adolescent males encountering maternal incarceration show increased perception of parental rejection and excessive discipline. Additionally, there is a positive relationship between parental rejection and excessive discipline at Wave I and feelings of anger at Wave II for males. For males experiencing either of these strains in Wave I, feelings of anger in Wave II are found to be associated with increased delinquent behavior in Wave III. Mastery was found to mediate the relationship between both parental rejection and excessive discipline and anger, but no such mediation occurs in the relationship between anger and delinquency, regardless of the strain being experienced. These findings suggest adolescent males who feel they are in control of their own lives are less likely to experience negative emotions produced by the occurrence of strain, thereby decreasing male engagement in delinquent behavior later in life.

Keywords: delinquency, mastery, maternal incarceration, strain

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Authors: Joanna Maj

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Awe inspiring is the idea that emotions could be present in human-machine interactions, both on the human side as well as the machine side. Human factors present intriguing components and are examined in detail while discussing this controversial topic. Mood, attention, memory, performance, assessment, causes of emotion, and neurological responses are analyzed as components of the interaction. Problems in computer-based technology, revenge of the system on its users and design, and applications comprise a major part of all descriptions and examples throughout this paper. It also allows for critical thinking while challenging intriguing questions regarding future directions in research, dealing with emotion in human-machine interactions.

Keywords: biocomputing, biomedical engineering, emotions, human-machine interaction, interfaces

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Authors: Cristina Costescu, Carmen David, Adrian Roșan

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Executive functions (EF) and emotion regulation strategies are processes that allow individuals to function in an adaptative way and to be goal-oriented, which is essential for success in daily living activities, at school, or in social contexts. The Emotion Regulation and Executive Functioning Scale for Children and Adolescents (REMEX) represents an empirically based tool (based on the model of EF developed by Diamond) for evaluating significant dimensions of child and adolescent EFs and emotion regulation strategies, mainly in school contexts. The instrument measures the following dimensions: working memory, inhibition, cognitive flexibility, executive attention, planning, emotional control, and emotion regulation strategies. Building the instrument involved not only a top-down process, as we selected the content in accordance with prominent models of FE, but also a bottom-up one, as we were able to identify valid contexts in which FE and ER are put to use. For the construction of the instrument, we implemented three focus groups with teachers and other professionals since the aim was to develop an accurate, objective, and ecological instrument. We used the focus group method in order to address each dimension and to yield a bank of items to be further tested. Each dimension is addressed through a task that the examiner will apply and through several items derived from the main task. For the validation of the instrument, we plan to use item response theory (IRT), also known as the latent response theory, that attempts to explain the relationship between latent traits (unobservable cognitive processes) and their manifestations (i.e., observed outcomes, responses, or performance). REMEX represents an ecological scale that integrates a current scientific understanding of emotion regulation and EF and is directly applicable to school contexts, and it can be very useful for developing intervention protocols. We plan to test his convergent validity with the Childhood Executive Functioning Inventory (CHEXI) and Emotion Dysregulation Inventory (EDI) and divergent validity between a group of typically developing children and children with neurodevelopmental disorders, aged between 6 and 9 years old. In a previous pilot study, we enrolled a sample of 40 children with autism spectrum disorders and attention-deficit/hyperactivity disorder aged 6 to 12 years old, and we applied the above-mentioned scales (CHEXI and EDI). Our results showed that deficits in planning, bebavior regulation, inhibition, and working memory predict high levels of emotional reactivity, leading to emotional and behavioural problems. Considering previous results, we expect our findings to provide support for the validity and reliability of the REMEX version as an ecological instrument for assessing emotion regulation and EF in children and for key features of its uses in intervention protocols.

Keywords: executive functions, emotion regulation, children, item response theory, focus group

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