Form Of Facial Expressions Research Articles

Emotion Mining User Review of the BRImo Mobile Banking Application Using the Decision Tree Algorithm

As consumer transaction preferences shifted from analog to digital, banks were compelled to develop digital transactions in the form of mobile banking. Users of mobile banking provide feedback regarding the application's usability. The opinions of users can be emotive. Emotions influence what a person emits or applies. Emotions are the behavioral response of a person when he is happy or unhappy. Thus, the manifestation of a person's emotions, whether in the form of facial expressions, verbal communication, written text, or judgment, can be used as a source of information to aid in decision making. The objective of this study is to apply emotion mining to the analysis of user evaluations of the BRImo application, one of the three most popular platforms in Indonesia as of August 2022, with a total of 800,000 reviews on the Play Store. Emotion Mining can be used to analyze the four categories of emotions expressed by users in the comments section: happy, angry, sad, and afraid. According to BRImo user evaluations, the decision tree algorithm is used to categorize happy, sad, afraid, and angry feelings. Using a decision tree to manage large data category sets is effective. The obtained dataset included 2959 happy classes, 2196 sad classes, 387 angry classes, and 81 scared classes. According to the findings of the analysis, a significant number of users of the BRImo application express positive sentiments in their evaluations, which are indicative of happy emotions. The Decision Tree algorithm yields results with a performance specification of 84.5%, sensitivity of 85.5%, and precision of 84.4%.

Differential processing ofrisk andreward indelinquent andnon-delinquent youth.

The present study examined the behavioral and neural differences in risky decision-making between delinquent (n = 23) and non-delinquent (n = 27) youth ages 13-17 years (M = 16, SD = 0.97) in relation to reward processing. While undergoing functional neuroimaging, participants completed an experimental risk task wherein they received feedback about the riskiness of their behavior in the form of facial expressions that morphed from happy to angry. Behavioral results indicated that delinquent youth took fewer risks and earned fewer rewards on the task than non-delinquent youth. Results from whole-brain analyses indicated no group differences in sensitivity to punishments (i.e.angry faces), but instead showed that delinquent youth evinced greater neural tracking of reward outcomes (i.e. cash-ins) in regions including the ventral striatum and inferior frontal gyrus. While behavioral results show that delinquent youth were more risk-averse, the neural results indicated that delinquent youth were also more reward-driven, potentially suggesting a preference for immediate rewards. Results offer important insights into differential decision-making processes between delinquent and non-delinquent youth.

Budaya Komunikasi Keluarga di Perdesaan: Konteks Komunikasi Verbal dan Nonverbal

Communication determines a person's quality of life and success including in the family. Communication in the family can increase harmony in the family and quality of human resources. This study aims to analyze the communication culture of parents and children in rural areas. This research uses qualitative methods. The research was conducted in Agam Regency in June-September 2021, research informants were selected purposively or intentionally. Data were collected through in-depth interviews and observations. Data analysis was carried out using the Miles and Huberman technique. The results showed that the form of verbal communication that occurs in the family is in the form of using local language when calling children, telling children, advising children, telling stories with children, or chatting with children. Parents communicate with children according to the situation and conditions of the child. Parents still use harsh words to children. The use of non-verbal communication in the family in the form of facial expressions, body movements, voice intonation used differently by parents when calling children, telling children, advising children, telling stories with children and scolding children. Parents do not maximize non-verbal communication in terms of providing warmth to children such as hugging children, holding shoulders and giving appreciation or praise

Implementation of Reinforcement Skill in Teaching English in The Pandemic Situation

This study aimed to identify the types of reinforcement skills and investigate the implementation of reinforcement skills used during the online learning process at SMP Laboratorium Undiksha Singaraja. This study used a qualitative descriptive method as the research design. This study also used an English teacher as the research subject. In collecting data, this study used three methods, namely; observation, interview, and questionnaires. This research was conducted online due to the COVID-19 pandemic. Through the data obtained, this study found the results of the type of reinforcement and the implementation used by teachers during online learning. There are four types of reinforcement used by the teachers, such as; (1) positive verbal reinforcement in the form of praise and partial reinforcement, (2) positive non-verbal reinforcement in the form of gestures, (3) negative verbal reinforcement in the form of enforceable statements, questions, calling out the students' names, and (4) negative non-verbal reinforcement in the form of facial expressions and record the students' misbehavior. In addition, the reinforcement implementation was relevant to the theories suggested by experts by following the five principles in giving reinforcement. Further study is needed to get a deeper understanding toward the implementation of reinforcement in EFL classroom.

Implementation of Reinforcement Skill in Teaching English in The Pandemic Situation

This study aimed to identify the types of reinforcement skills and investigate the implementation of reinforcement skills used during the online learning process at SMP Laboratorium Undiksha Singaraja. This study used a qualitative descriptive method as the research design. This study also used an English teacher as the research subject. In collecting data, this study used three methods, namely; observation, interviews, and questionnaires. This research was conducted online due to the COVID-19 pandemic. Through the data obtained, this study found the results of the type of reinforcement and the implementation used by teachers during online learning. There are four types of reinforcement used by the teachers, such as; (1) positive verbal reinforcement in the form of praise and partial reinforcement, (2) positive non-verbal reinforcement in the form of gestures, (3) negative verbal reinforcement in the form of enforceable statements, questions, calling out the students' names, and (4) negative non-verbal reinforcement in the form of facial expressions and record the students' misbehavior. In addition, the reinforcement implementation were in line with theories suggested by experts by following the five principles in giving reinforcement.

Implementasi Convolutional Neural Network Untuk Deteksi Nyeri Bayi Melalui Citra Wajah Dengan YOLO

Pain in a baby is difficult to detect is because the method for detecting pain is self-reporting even though babies themselves still cannot describe the pain verbally, then by observing changes in behavior in the form of facial expressions. Statistically, it is also recorded that about 80% of the world's population pays less attention to pain assessment, especially for children, even though this pain gives children a bad experience so that it can interfere with pain responses in the future or psychological trauma. Based on these problems, a prototype system was made using the NVIDIA Jetson Nano Developer kit to help detect pain, especially in infants 0-12 months by using the Convolutional Neural Network (CNN) model with the PyTorch framework and the You Only Look Once (YOLO) algorithm with three detection classification is sad, neutral and sick. From the results of the study, it was found that the YOLO algorithm was able to detect the three classifications with a sad mAP value of 77.8%, neutral 76.7%, in pain 68.9%. With a precision value of 71.4%, recall 62.5% and f1-score 66.6%. The average value of Confidence is 53.57%.

Study of Mental Disorder Prediction Model approaches its issues and Challenges

Mental disorder is becoming one of the major health issues in society today. (WHO) depression will be the leading mental disorder all over the world by 2030. The timely prediction of such disorders is very essential for maintaining the health of human beings. These mental disorders are associated with different symptoms, some of these symptoms are visible in the form of facial expressions, gestures, change in voice modulation, etc. and some of them even cannot be noticed by naked eyes. Therefore, it is highly important to collect appropriate verbal and non-verbal symptom details about any individual along with using best-suited algorithms for accurate prediction using information technology. This paper explores different types of such symptoms associated with different types of mental disorders, their causes, and existing prediction-based solutions. The paper further presents the critical analysis of these aspects and proposes usages of biometrics-based traits for building better prediction systems for mental disorders.

EMOTION MINING IN ENGLISH TEXT USING BAYES & SVM APPROACH

Emotions have captivated researchers for years, as is obvious in the huge body of research work related to emotion in area of mental characteristics, language, socialism, and interaction. Human emotion manifests itself in the form of facial expressions, speech utterances, writings, and in gestures and actions. As a result, technical research in emotion has been pursued along several proportions and has drawn upon research from various areas. This paper results in the chore of emotion gratitude by attempting to robotically learn emotions from text. In this paper, a new technique to mine the emotions from an English text has been introduced. We have used 10 categories from which we can extract the motions. Proposed system use bays and SVM approach to perform the given task. The accuracy of the proposed system is better as compared to the existing system.

Affective interaction: Using emotions as a user interface in games

Affective computing, as a novel paradigm, adds a new feature into the human-computer interactions era that brings emotions into the process of design and evaluation. Such condition influences digital games’ not only structural dimension but also content and evaluation areas. This study aims to evaluate the effect of using emotions in the form of facial expressions as a User Interface (UI) and the input of a system in virtual scenarios. After studying the findings and methods of previous researches, a conceptual framework for Affective User Interface Design and Evaluation (AFFUIDE) has been proposed. Then, a game in the genre of shoot ‘em up (called Facial Battle) in four interaction modes has been developed. The evaluation of this study has been performed in terms of the players’ performance and experience. For this purpose, 18 male students of the Multimedia Faculty of Tabriz Islamic Art University took part in the experiments, who were asked to play all four modes of the game. Then, after analyzing the normalization of collected data, one-way ANOVA and U Mann Whitney tests were used for the statistical analysis. The findings indicate that the Traditional UI is the most usable and the Full Affective UI has the best sense of fun and user experience among other methods. However, the hybrid mode (affective shooting), is not only usable as traditional mode, but it also works better in terms of user experience compared to other modes.

Appearance Based Feature Extraction and Selection Methods for Facial Expression Recognition

Faces contain a plethora of information such as identity of a person, facial expressions and other properties like race etc. Human face conveys the wealth of their opinions, emotions in the form of facial expressions. The problem of automatic facial expression is both interesting and quite challenging with a strong impact on many application areas such as animation and human-computer interaction (HCI). The field has shown a tremendous growth over past years with its benchmarking efforts and progress. In this paper, the study of facial expression recognition based feature extraction and selection techniques is presented. The paper offers an insight into the work of researchers who have considered appearance based feature extraction techniques for facial expression recognition. In addition, a discussion is made by specifying some of the open question related to facial expression recognition.

Elderly People Benefit More from Positive Feedback Based on Their Reactions in the Form of Facial Expressions during Human-Computer Interaction

In this brief report, we present the results of our investigation into the impact of age on reactions in the form of facial expressions to positive and negative feedback during human-computer interaction. In total, 30 subjects were analyzed after a video-recorded mental task in the style of a Wizard of Oz scenario. All subjects and their facial reactions were coded using the Facial Expression Coding System (FACES). To summarize briefly, we can conclude from our facial expression analysis that compared with their younger counterparts, elderly people show significantly lower levels of negative expression in response to positive feedback from the technical system (“Your performance is improving!”). This result indicates that elderly people seem to benefit more from praise during interaction than younger people, which is significant for the design of future companion technologies.

Benefits of social vs. non-social feedback on learning and generosity. Results from the Tipping Game.

Although much work has recently been directed at understanding social decision-making, relatively little is known about how different types of feedback impact adaptive changes in social behavior. To address this issue quantitatively, we designed a novel associative learning task called the “Tipping Game,” in which participants had to learn a social norm of tipping in restaurants. Participants were found to make more generous decisions from feedback in the form of facial expressions, in comparison to feedback in the form of symbols such as ticks and crosses. Furthermore, more participants displayed learning in the condition where they received social feedback than participants in the non-social condition. Modeling results showed that the pattern of performance displayed by participants receiving social feedback could be explained by a lower sensitivity to economic costs.

Involvement of Right STS in Audio-Visual Integration for Affective Speech Demonstrated Using MEG

Speech and emotion perception are dynamic processes in which it may be optimal to integrate synchronous signals emitted from different sources. Studies of audio-visual (AV) perception of neutrally expressed speech demonstrate supra-additive (i.e., where AV>[unimodal auditory+unimodal visual]) responses in left STS to crossmodal speech stimuli. However, emotions are often conveyed simultaneously with speech; through the voice in the form of speech prosody and through the face in the form of facial expression. Previous studies of AV nonverbal emotion integration showed a role for right (rather than left) STS. The current study therefore examined whether the integration of facial and prosodic signals of emotional speech is associated with supra-additive responses in left (cf. results for speech integration) or right (due to emotional content) STS. As emotional displays are sometimes difficult to interpret, we also examined whether supra-additive responses were affected by emotional incongruence (i.e., ambiguity). Using magnetoencephalography, we continuously recorded eighteen participants as they viewed and heard AV congruent emotional and AV incongruent emotional speech stimuli. Significant supra-additive responses were observed in right STS within the first 250 ms for emotionally incongruent and emotionally congruent AV speech stimuli, which further underscores the role of right STS in processing crossmodal emotive signals.

Facial Expression Recognition Using Eigenspaces

A lot of information is conveyed by human beings in the form of facial expression apart from just what is spoken. Proper recognition of such expression has thus become important for any modern human computer interface. We present here a method of facial expression recognition based on Eigenfaces. It is a modified method from the original Eigenfaces approach [1] and starts out with the human vision as a standard reference point – by making use of the standard JAFFE database and computes the expression contained by the image of a test face. It is a unique approach which directly classifies a test image as belonging to one of the six standard expressions - anger, disgust, fear, happy, sad or surprise with great accuracy. In this paper, we present with experimental proof the accuracy of such a strategy with analysis and discussion on further ways to improve upon it.

Facilitation of learning by social-emotional feedback in humans is beta-noradrenergic-dependent

Adaptive behavior in dynamic environments critically depends on the ability to learn rapidly and flexibly from the outcomes of prior choices. In social environments, facial expressions of emotion often serve as performance feedback and thereby guide declarative learning. Abundant evidence implicates beta-noradrenergic signaling in the modulatory influence of emotion on declarative learning. It is currently unclear whether a similar mechanism also mediates a guidance of declarative learning by social-emotional feedback administered in the form of facial expressions. We therefore conducted a double-blind randomized placebo-controlled trial to test the effects of a 40-mg single oral dose of the nonspecific beta-noradrenergic antagonist propranolol in a behavioral task that required gradual declarative learning of item-category associations from either social-emotional (happy vs. angry faces) or nonsocial (green vs. red color signals) trial-by-trial feedback. As predicted on the basis of our previous experiments, learning from social-emotional feedback was more effective than learning from nonsocial feedback in placebo-treated subjects. This advantage of social-emotional over nonsocial feedback was abolished by propranolol treatment. Propranolol had no effect on learning during the nonsocial feedback condition. Our findings suggest that a facilitation of declarative learning by social-emotional feedback critically involves signaling via beta-noradrenergic receptors.

Text Mining for Fuzzy-based Emotion Expressions

A model of emotion representation in the form of facial expressions using text mining technique, followed by fuzzy-based mechanisms using Fuzzy Inference System (FIS) is proposed. The model classifies the emotional content of sentences from text input and expresses corresponding emotions by a facial expression. Text input is classified using Na¨ıve Bayes text classifier, while facial expression of a virtual character are controlled by Mamdani fuzzy inference system utilizing results from text classifier. This model is able to show the facial expression with admixture blending emotions. As a demonstration, examples of facial expressions with corresponding text inputs as results from the implementation of our model are shown.

Processing Affective Stimuli in Children with Attention-Deficit Hyperactivity Disorder

Neuropsychological investigations have suggested a contribution of right hemisphere dysfunction in attention-deficit hyperactivity disorder (ADHD). Right hemisphere dysfunction has been implicated in deficits of attention, motor impersistence, and processing emotion-laden stimuli. The current study investigated the ability of ADHD children to perceive emotional stimuli in the form of facial expressions and speech intonation. The subjects consisted of 37 ADHD and 37 control children aged 7 to 12 years. ANCOVA analysis indicated that ADHD children demonstrate mild-to-moderate deficits in the perception of affect. Furthermore, deficits in attention may contribute to inaccurate or incomplete encoding of stimulus properties. The results lend tentative support for the notion that the right cerebral hemisphere may play a critical role in ADHD.

The role of locus of control orientation in speed of discriminating facial affect

Individuals with an internal locus of control have been found to be more socially successful than those with an external orientation. In the present study, the possibility was tested that a significant contributor to the internals' social success was their ability to discriminate nonverbal information in the form of facial expressions more quickly than externals. Forty-two females were asked to discriminate which of a series of two facial expressions and which of a series of two words were similar, and which were different. Word discrimination performance was used to evaluate whether speed of discrimination differences between internals and externals was specific to facial expressions or to a more generalized ability to discriminate all information more quickly. As predicted, internals, compared to externals, were significantly faster discriminators of facial expressions but not of words. The results were discussed within an interpersonal communication perspective.