Model Construction and Empirical Study on Unsustainable Usage Behavior of Paid Knowledge Users

Knowledge payment is a new method of electronic learning that has developed in the era of social media. With the impact of the COVID-19 pandemic, the market for knowledge payment is rapidly expanding. Exploring the factors that influence users' sustained willingness is beneficial for better communication between knowledge payment platforms and users, and for achieving a healthier and more sustainable development of the knowledge payment industry. The model of unsustainable usage behavior of knowledge payment users was constructed on the basis of expectation inconsistency theory, price equilibrium theory, and perceived value theory, using the 'cognitive-emotional-behavioral' model framework of cognitive emotion theory. The data were collected from 348 users through a web-based questionnaire and analyzed using structural equation modeling. Findings show that expectation inconsistency, price equilibrium, and quality value, emotional value, and social value have significant effects on discontinuous use intentions. Discontinuous use intentions also significantly affect discontinuous use behavior. © 2013 IEEE.

A Survey of Human-Computer Interaction Technology in Intelligent Home for Children's Learning

The development of the COVID-19 epidemic has increased the home learning time of children. More researchers began to pay attention to children's learning in home. This survey reviewed the frontier and classic cases in the field of interactive design of children's home learning in the past five years, analyzed tangible user interface, augmented reality, and multimodal interaction in human-computer interaction of children's home learning. This paper reviewed the application of interactive system in children's learning and points out its positive side in development of ability, process of learning, habits of learning, and environment of learning of children. Through analysis, we advise that it is necessary to create home learning applications, link smart home systems, and build an interactive learning environment for smart home learning environment design. Finally, we point out the technical and ethical problems existing in the current research, proposes that intelligent perception, emotion recognition, and expression technologies should be introduced in the future, and looks forward to the development of this field. © 2023 Institute of Computing Technology. All rights reserved.

What is behind the mask? Social representation of mask wearing

Background: During the COVID-19 pandemic, a preventive and widely mandatory use of face masks was a dominant segment of the infection prevention and control of the epidemic. Covering about 60-70% of the facial surface, face masks dramatically affect social interactions-especially emotion recognition, expression and mentalization. Difficulties in communication in the doctor-patient relationship become of paramount importance to the effectiveness of the healing work. This becomes even more critical when the patient suffers from a disorder characterized by a mentalization deficit. In our study, we use the theory of social representations to examine the contents with which mask wearing has become part of our everyday knowledge. Objectives: We aimed to explore the social representations of mask wearing considering its impact on interpersonal communication, in groups where the effectiveness of mutual understanding is critical. Methods: In our study, carried out during the second and third waves of the coronavirus epidemic in Hungary, we gave a free association task to the target word mask-wearing" in a group of medical doctors, and hospitalized somatic and psychiatric patients and healthy controls (total of 81 subjects, mean age 43.1 [13.83] years), then used the obtained associations to form semantic categories and to map the structure of social representations within the groups using a rank-frequency method. Results: The positive experience of safety and the negative experience of physiological discomfort caused by the facemasks were consistently central to the social representations of mask-wearing in all study groups. Differences were found between groups in terms of more mature elaborative categories, as well as anxiety, aggression, helplessness, damaged dependency needs, and forced conformity. Conclusions: The analysis of the social representations revealed ambivalent meanings of the mask wearing. Although there were significant differences in the structure of mask-related social representations, the mask was recognized as an "inconvenient but necessary" health protection measure in most of the groups studied. Based on the results, each group may be at risk in a different way or deal differently with the pandemic based on their specific representations. (PsycInfo Database Record (c) 2023 APA, all rights reserved) (Hungarian) Elmeleti hatter: A COVID-19-pandemia idejen a jarvanyugyi intezkedesek meghatarozo reszeve valt az arcmaszkok viselesenek preventiv es szeles koru alkalmazasa. Az arcmaszkok az arcfelulet mintegy 60-70%-at lefedve jelentosen befolyasoljak a szocialis interakciokat - kulonosen az erzelemfelismerest, erzelemkifejezest es mentalizalast. A kommunikacioban fellepo nehezsegek a gyogyito munka hatekonysaga szempontjabol kiemelt jelentoseguve valnak az orvos-beteg kapcsolatban. Ennek meg kritikusabb esetei azok a helyzetek, amikor a paciens mentalizacios deficittel jellemezheto zavarban szenved. Tanulmanyunkban a szocialis reprezentaciok elmeletet hasznaljuk annak vizsgalatara, hogy a maszkviseles milyen tartalmakkal valt a kozos tudas reszeve. Celkituzes: Vizsgalatunkban a maszkviseles szocialis reprezentaciojanak felterkepezeset tuztuk ki celul, figyelembe veve annak interperszonalis kommunikaciora gyakorolt hatasat, olyan csoportokban, ahol a kolcsonos megertes hatekonysaga kiemelt jelentoseggel bir. Modszerek: Kutatasunkban a koronavirus-jarvany masodik es harmadik magyarorszagi hullama idejen, orvos, szomatikus es pszichiatriai beteg csoportban, valamint kontrollcsoportban (osszesen 81 fo, atlageletkor 43,1 [SD = 13,83] ev) szabad asszociacios feladatot adtunk a maszkviseles" hivoszora. A nyert adatokbol szemantikus kategoriakat kepeztunk, majd ranggyakorisag-eljarassal felterkepeztuk a szocialis reprezentaciok szerkezetet az egyes csoportokon belul. Eredmenyek: A vizsgalati csoportok maszkhasznalathoz kapcsolodo szocialis reprezentaciojaban egysegesen kozponti elemkent jelent meg a maszkviseles altal nyujtott biztonsagelmeny, valamint a maszk zavaro testerzetet kelto hatasa. Kulonbseget talaltunk az egyes csoportok kozott elaborativ kategoriak megjelenese, illetve szorongas, agresszio, tehetetlenseg, serult dependenciaszukseglet, valamint a kenyszeru alkalmazkodas tekinteteben. Kovetkeztetesek: A maszkviseles szocialis reprezentaciojanak elemzese alapjan a maszkviseles ambivalens jelentestartalmakat hordoz. Bar a maszkviseleshez kapcsolodo szocialis reprezentaciok strukturajaban szamottevo kulonbsegek is mutatkoztak, ugyanakkor a legtobb vizsgalt csoportban a maszk a virusvedelem szempontjabol kenyelmetlen, de szukseges" eszkozkent kerult felismeresre. Az eredmenyek alapjan az egyes csoportok sajatos reprezentacioik alapjan eltero modokon lehetnek veszelyeztetettek, illetve kuzdhetnek meg a pandemia idejen kialakult helyzettel. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Study on the Online Learning Evaluation Based on 2D and 3D Image Processing Technology

In recent years, the outbreak of the COVID-19 epidemic has posed a serious threat to the life safety of people around the world, which has also led to the development of a series of online learning assessment technologies. Through the research and development of a variety of online learning platforms such as WeChat, Tencent Classroom and Netease Cloud Classroom, schools can carry out online learning assessment, which also promotes the rapid development of online learning technology. Through 2D and 3D recognition technology, the online learning platform can recognize face and pose changes. Based on 2D and 3D image processing technology, we can evaluate students' online learning, which will identify students' learning state and emotion. Through the granulation of teaching evaluation, online learning platform can accurately evaluate and analyze the teaching process, which can realize real-time teaching evaluation of students' learning status, including no one, many people, distraction and fatigue. Through relevant algorithms, the online learning platform can realize the assessment of students' head posture, which will give real-time warning of learning fatigue. Firstly, this paper analyzes the framework of online learning quality assessment. Then, this paper analyzes the face recognition and head pose recognition technology. Finally, some suggestions are put forward. © 2023 SPIE.

Emotion Detection of People Wearing Face Mask

Though many facial emotion recognition models exist, after the Covid-19 pandemic, majority of such algorithms are rendered obsolete as everybody is compelled to wear a facemask to protect themselves against the deadly virus. Face masks can hinder emotion recognition systems, as crucial facial features are not visible in the image. This is because facemasks cover essential parts of the face such as the mouth, nose, and cheeks which play an important role in differentiating between various emotions. This study intends to recognize the emotional states of anger-disgust, neutral, surprise-fear, joy, sadness, of the person in the image with a face mask. In the proposed method, a CNN model is trained using images of people wearing masks. To achieve higher accuracy, the classes in the dataset are combined. Different combinations of clubbing are performed, and results are recorded. Images are taken from FER2013 dataset which consists of a huge number of manually annotated facial images of people. © 2023 IEEE.

Social emotional processes during the third wave of COVID-19: Results from a close replication study in a Turkish sample.

Until recently, for almost 3 years, we used face masks to protect against COVID-19. Face masks disrupted our perception of socially relevant information, and impacted our social judgements as a result of the new social norms around wearing masks imposed by the pandemic. To shed light on such pandemic-induced changes in social emotional processes, Calbi et al. analysed data from an Italian sample collected in Spring 2020. They assessed valence, social distance and physical distance ratings for neutral, happy and angry male and female faces covered with a scarf or a mask. A year later, we used the same stimuli to investigate the same measures in a Turkish sample. We found that females attributed more negative valence ratings than males to angry faces, and that angry and neutral faces of females were rated more negatively than those of males. Scarf stimuli were evaluated more negatively in terms of valence. Participants attributed greater distance to more negative faces (angry > neutral > happy) and to scarf than the mask stimuli. Also, females attributed greater social and physical distance than males. These results may be explained by gender-stereotypic socialisation processes, and changes in people's perception of health behaviours during the pandemic.

Fully robotic social environment for teaching and practicing affective interaction: Case of teaching emotion recognition skills to children with autism spectrum disorder, a pilot study.

21st century brought along a considerable decrease in social interactions, due to the newly emerged lifestyle around the world, which became more noticeable recently of the COVID-19 pandemic. On the other hand, children with autism spectrum disorder have further complications regarding their social interactions with other humans. In this paper, a fully Robotic Social Environment (RSE), designed to simulate the needed social environment for children, especially those with autism is described. An RSE can be used to simulate many social situations, such as affective interpersonal interactions, in which observational learning can take place. In order to investigate the effectiveness of the proposed RSE, it has been tested on a group of children with autism, who had difficulties in emotion recognition, which in turn, can influence social interaction. An A-B-A single case study was designed to show how RSE can help children with autism recognize four basic facial expressions, i.e., happiness, sadness, anger, and fear, through observing the social interactions of two robots speaking about these facial expressions. The results showed that the emotion recognition skills of the participating children were improved. Furthermore, the results showed that the children could maintain and generalize their emotion recognition skills after the intervention period. In conclusion, the study shows that the proposed RSE, along with other rehabilitation methods, can be effective in improving the emotion recognition skills of children with autism and preparing them to enter human social environments.

Emotion Recognition and Analysis Of Netizens Based On Micro-Blog During Covid-19 Epidemic

The research is about emotion recognition and analysis based on Micro-blog short text. Emotion recognition is an important field of text classification in Natural Language Processing. The data of this research comes from Micro-blog 100K record related to COVID-19 theme collected by Data fountain platform, the data are manually labeled, and the emotional tendencies of the text are negative, positive and neutral. The empirical part adopts dictionary emotion recognition method and machine learning emotion recognition respectively. The algorithms used include support vector machine and naive Bayes based on TFIDF, support vector machine and LSTM based on wod2vec. The five results are compared. Combined with statistical analysis methods, the emotions of netizens in the early stage of the epidemic are analyzed for public opinion. This research uses machine learning algorithm combined with statistical analysis to analyze current events in real time. It will be of great significance for the introduction and implementation of national policies.

Multiroom Speech Emotion Recognition

Automated audio systems, such as speech emotion recognition, can benefit from the ability to work from another room. No research has yet been conducted on the effectiveness of such systems when the sound source originates in a different room than the target system, and the sound has to travel between the rooms through the wall. New advancements in room-impulse-response generators enable a large-scale simulation of audio sources from adjacent rooms and integration into a training dataset. Such a capability improves the performance of data-driven methods such as deep learning. This paper presents the first evaluation of multiroom speech emotion recognition systems. The isolating policies due to COVID-19 presented many cases of isolated individuals suffering emotional difficulties, where such capabilities would be very beneficial. We perform training, with and without an audio simulation generator, and compare the results of three different models on real data recorded in a real multiroom audio scene. We show that models trained without the new generator achieve poor results when presented with multiroom data. We proceed to show that augmentation using the new generator improves the performances for all three models. Our results demonstrate the advantage of using such a generator. Furthermore, testing with two different deep learning architectures shows that the generator improves the results independently of the given architecture.

Empathising with masked targets: limited side effects of face masks on empathy for dynamic, context-rich stimuli.

Multiple studies revealed detrimental effects of face masks on communication, including reduced empathic accuracy and enhanced listening effort. Yet, extant research relied on artificial, decontextualised stimuli, which prevented assessing empathy under more ecologically valid conditions. In this preregistered online experiment (N = 272), we used film clips featuring targets reporting autobiographical events to address motivational mechanisms underlying face mask effects on cognitive (empathic accuracy) and emotional facets (emotional congruence, sympathy) of empathy. Surprisingly, targets whose faces were covered by a mask (or a black bar) elicited the same level of empathy motives (affiliation, cognitive effort), and accordingly, the same level of cognitive and emotional empathy compared to targets with uncovered faces. We only found a negative direct effect of face coverings on sympathy. Additional analyses revealed that older (compared to young) adults showed higher empathy, but age did not moderate face mask effects. Our findings speak against strong negative face mask effects on empathy when using dynamic, context-rich stimuli, yet support motivational mechanisms of empathy.

Engagement in Video Graphic Online Learning Using the Emotional Dimensions in the Learning Context

The COVID-19 pandemic has become the prime reason for organizations across the world to shift their entire workforce onto virtual platforms. One of the major drawbacks of these virtual platforms is that it lacks a real-time metric which could be used to detect whether a person is attentive during the lectures and meetings or not. This was most evident in the case of educational institutions, where students would often fail to pay attention to the content that was being taught by teachers and professors at home. With this research work, our aim is to create a solution for this problem with the help of AI-FER (Artificial Intelligence Facial Emotion Recognition). For this, we have proposed our own Convolutional Neural Network model achieving an overall accuracy of 59.03%. We have also used several pre-trained models available in Google's Tensorflow library like DenseNET and VGG. © 2023 IEEE.

BERIS: An mBERT-based Emotion Recognition Algorithm from Indian Speech

Emotions, the building blocks of the human intellect, play a vital role in Artificial Intelligence (AI). For a robust AI-based machine, it is important that the machine understands human emotions. COVID-19 has introduced the world to no-touch intelligent systems. With an influx of users, it is critical to create devices that can communicate in a local dialect. A multilingual system is required in countries like India, which has a large population and a diverse range of languages. Given the importance of multilingual emotion recognition, this research introduces BERIS, an Indian language emotion detection system. From the Indian sound recording, BERIS estimates both acoustic and textual characteristics. To extract the textual features, we used Multilingual Bidirectional Encoder Representations from Transformers. For acoustics, BERIS computes the Mel Frequency Cepstral Coefficients and Linear Prediction coefficients, and Pitch. The features extracted are merged in a linear array. Since the dialogues are of varied lengths, the data are normalized to have arrays of equal length. Finally, we split the data into training and validated set to construct a predictive model. The model can predict emotions from the new input. On all the datasets presented, quantitative and qualitative evaluations show that the proposed algorithm outperforms state-of-the-art approaches. © 2022 Association for Computing Machinery.

DevEmo—Software Developers' Facial Expression Dataset

The COVID-19 pandemic has increased the relevance of remote activities and digital tools for education, work, and other aspects of daily life. This reality has highlighted the need for emotion recognition technology to better understand the emotions of computer users and provide support in remote environments. Emotion recognition can play a critical role in improving the remote experience and ensuring that individuals are able to effectively engage in computer-based tasks remotely. This paper presents a new dataset, DevEmo, that can be used to train deep learning models for the purpose of emotion recognition of computer users. The dataset consists of 217 video clips of 33 students solving programming tasks. The recordings were collected in the participants' actual work environment, capturing the students' facial expressions as they engaged in programming tasks. The DevEmo dataset is labeled to indicate the presence of the four emotions (anger, confusion, happiness, and surprise) and a neutral state. The dataset provides a unique opportunity to explore the relationship between emotions and computer-related activities, and has the potential to support the development of more personalized and effective tools for computer-based learning environments. © 2023 by the authors.

Concentration Monitoring in Online Classes for Smart Education Applications Based on Neural Network and C-PAD Emotion Model

The arrival of COVID-19 has changed the way traditional classes are conducted, and online teaching has never been more popular. While there are many advantages to online teaching, there are also extremely obvious disadvantages, one of which is the tendency to lack concentration. For this reason, this study uses video images from the DAiSee dataset, a new sampling script, deep learning neural networks, and a new PAD emotion model to systematically assess student concentration. Our test set uses 21 short videos from the DAISee dataset, sampling a total of 1,866 frames. The final results showed that the accuracy of the neural network was approximately 80%. The results of the test set on the PAD model showed that the percentage of attentive listeners was 65.9%, while the percentage of highly inattentive listeners was 6.2%. This study constructed a complete concentration monitoring system for online classrooms centred on smart education which can provide the information of students' concentration in real time. © 2022 ACM.

Student Attention Base Facial Emotion State Recognition Using Convolutional Neural Network

E-learning system advancements give students new opportunities to better their academic performance and access e-learning education. Because it provides benefits over traditional learning, e-learning is becoming more popular. The coronavirus disease pandemic situation has caused educational institution cancelations all across the world. Around all over the world, more than a billion students are not attending educational institutions. As a result, learning criteria have taken on significant growth in e-learning, such as online and digital platform-based instruction. This study focuses on this issue and provides learners with a facial emotion recognition model. The CNN model is trained to assess images and detect facial expressions. This research is working on an approach that can see real-time facial emotions by demonstrating students' expressions. The phases of our technique are face detection using Haar cascades and emotion identification using CNN with classification on the FER 2013 datasets with seven different emotions. This research is showing real-time facial expression recognition and help teachers adapt their presentations to their student's emotional state. As a result, this research detects that emotions' mood achieves 62% accuracy, higher than the state-of-the-art accuracy while requiring less processing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Text Emotion Analysis Using Deep Learning For COVID- 19 Dataset

Managing mental health and psychological well-being is just as critical as managing physical health throughout COVID-19. The difficulty of detecting, classifying, and quantifying emotions in text in any form are addressed in this study. We consider English text collected from social media sites such as Twitter and various Kaggle datasets that can provide information useful in a variety of ways, particularly opinion mining. However, analysing and categorising text based on emotions is a difficult task and might be thought of as a more advanced kind of Sentiment Analysis. This work provides a system for categorising text into three types of emotions: positive, negative, and neutral. This analysis can be utilized by authorities to better understand people's mental health and to make appropriate policy decisions to combat the coronavirus, which is hurting the world's social well-being and economy. © 2022 IEEE.

Automated speech emotion polarization for a distance education system based on orbital local binary pattern and an appropriate sub-band selection technique.

The distance education system was widely adopted during the Covid-19 pandemic by many institutions of learning. To measure the effectiveness of this system, it is essential to evaluate the performance of the lecturers. To this end, an automated speech emotion recognition model is a solution. This research aims to develop an accurate speech emotion recognition model that will check the lecturers/instructors' emotional state during lecture presentations. A new speech emotion dataset is collected, and an automated speech emotion recognition (SER) model is proposed to achieve this aim. The presented SER model contains three main phases, which are (i) feature extraction using multi-level discrete wavelet transform (DWT) and one-dimensional orbital local binary pattern (1D-OLBP), (ii) feature selection using neighborhood component analysis (NCA), (iii) classification using support vector machine (SVM) with ten-fold cross-validation. The proposed 1D-OLBP and NCA-based model is tested on the collected dataset, containing three emotional states with 7101 sound segments. The presented 1D-OLBP and NCA-based technique achieved a 93.40% classification accuracy using the proposed model on the new dataset. Moreover, the proposed architecture has been tested on the three publicly available speech emotion recognition datasets to highlight the general classification ability of this self-organized model. We reached over 70% classification accuracies for all three public datasets, and these results demonstrated the success of this model.

The Eyes Have It: Psychotherapy in the Era of Masks.

Nonverbal communication is integral to the success of psychotherapy and facial expression is an important component of nonverbal communication. The SARS CoV-2 pandemic has caused alterations in how psychotherapy services are provided. In this paper, potential issues that may arise from conducting psychotherapy when both the patient and therapist are wearing masks are explored. These include higher likelihood of misidentifying facial expression, especially when expression is incongruent with body language, and when the lower face is more important for correct identification of emotion. These issues may be particularly problematic for patient populations for whom emotion recognition may be a problem at baseline, or for those more prone to biases in emotional recognition. Suggestions are made for therapists to consider when seeing patients in-person when masks are necessary.

The effect of face masks on the stereotype effect in emotion perception

The accurate and swift decoding of emotional expressions from faces is fundamental for social communication. Yet, emotion perception is prone to error. For example, the ease with which emotions are perceived is affected by stereotypes (Bijlstra, Holland, & Wigboldus, 2010). Moreover, the introduction of face masks mandates in response to the Covid-19 pandemic additionally impedes accurate emotion perception by introducing ambiguity to the emotion perception process. Predictive coding frameworks of visual perception predict that in such situations of increased ambiguity of the sensory input (i.e., faces with masks), people increasingly rely on their prior beliefs (i.e., their stereotypes). Using specification curve analysis, we tested this prediction across two experiments, featuring different social categories (Study 1: Gender;Study 2: Ethnicity) and corresponding emotion stereotypes. We found no evidence that face masks increase reliance on prior stereotypes. In contrast, in Study 1 (but not in Study 2), we found preliminary evidence that face masks decrease reliance on prior stereotypes. We discuss these findings in relation to predictive coding frameworks and dual process models and emphasize the need for up-to-date analytic methods in social cognition research. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Investigation on MLP, CNNs and Vision Transformer models performance for Extracting a Human Emotions via Facial Expressions

One of the difficult challenges in AI development is to make machine understand the human feeling through expression because human can express feeling in various ways, for example, through voices, facial actions or behaviors. Facial Emotion Recognition (FER) has been used in interrogating suspects and being a tool to help detect emotions in people with nerve damage or even in the COVID-19 pandemic when patients hide their timelines. It can be applied to detect lies through micro expression. In this work will mainly focus on FER. The results of Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Vision Transformer were compared. Human emotion expressions were classified by using facial expression datasets from AffectNet, Tsinghua, Extended Cohn Kanade (CK+), Karolinska Directed Emotional Faces (KDEF) and Real-world Affective Faces (RAF). Finally, all models were evaluated on the testing dataset to confirm their performance. The result shows that Vision Transformer model outperforms other models. © 2023 IEEE.