Collection and Analysis of Narratives for a Values Charter of the Italian Society for Hospital Pharmacy

In recent years, the field of Narrative Pharmacy was introduced, which particularly addresses the pharmacist not only to guide a relationship of listening to and caring for the patient but also to strengthen and motivate toward the profession, improve relationships with colleagues, enhance the ability to teamwork, and understand emotions. In this paper, we report the analysis behind the construction of the Value Chart from the personal narratives of members of the Italian Society of Hospital Pharmacy. Each member's subjective professional experiences and their own view of themselves within society were collected through a semi-structured interview. Personal thinking, including experiences, feelings, opinions, desires, and regrets was classified by objective methods, from which main concepts were extracted for the Value Chart. The feedback to the survey, including activities during the Covid-19 pandemic management, is classified according to the analytical methods of Kleinman, Frank, Bury and Launer-Robinson. Regarding sentiment analysis, the emotional and subjective context of the text provides an ideal baseline to validate the result. The analysis was implemented using neural networks trained on dictionaries and natural language (i.e., Tweets). The originality of the work lies in the fact that generally value charters are built on a Society's values. In contrast, in this case, individual contributions were gathered to complement the ethical values on which the society is founded. © 2022 IEEE.

Automatic emotion regulation of psychological overwhelm for software developers

Due to the steadily increasing digitalization and the lack of social contact during the Covid-19 pandemic, the workload, and stress of software developers increases. This may lead to psychological overwhelm, when negative emotions caused by heavy stress are not detected early enough to be treated effectively. Machine Learning made it possible to recognize emotions in human beings using physiological features automatically. Nonetheless, current research lacks of methods to detect psychological overwhelm in software developers during work early. Furthermore, means are necessary to react to such detection properly. In this research, we investigate the methods for enabling an automatic emotion regulation for psychological overwhelm of software developers using multimodal physiological sensors, Machine Learning and the qualitative inquiry method of Interpretative Phenomenological Analysis. The goal is to find solutions to improve the psychological well-being of software developers and the associated quality of software development through the use of emotion regulation techniques. Raising awareness of the problem of psychological overwhelm among software developers will lead to a more profound understanding of its impact on the overall quality of software development and the mental health of software developers. © 2022 IEEE.

A Systematic Literature Survey of Emotion Detection of Students in E-learning

Affective computing is a part of artificial intelligence, which is becoming more important and widely used in education to process and analyze large amounts of data. Consequently, the education system has shifted to an E-learning format because of the COVID-19 epidemic. Then, e-learning is becoming more common in higher education, primarily through Massive Open Online Courses (MOOCs). This study reviewed many prior studies on bolstering educational institutions using AI methods, including deep learning, machine learning, and affective computing. According to the findings, these methods had a very high percentage of success. These studies also helped academic institutions, as well as teachers, understand the emotional state of students in an e-learning environment. © 2022 IEEE.

Speech emotion recognition with artificial intelligence for contact tracing in the COVID-19 pandemic

If understanding sentiments is already a difficult task in human-human communication, this becomes extremely challenging when a human-computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network-based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot virtual assistant whose task was to perform contact tracing during the COVID-19 pandemic. The system was tested on a novel dataset of audio samples, provided by the company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID-19. The dataset provided was unlabelled for the emotions associated to the conversations. Therefore, the work was structured using a sort of transfer learning strategy. First, the model is trained using the labelled and publicly available Italian-language dataset EMOVO Corpus. The accuracy achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact tracing, shedding lights towards the importance of the use of such techniques in virtual assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SER. © 2023 The Authors. Cognitive Computation and Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Shenzhen University.

Design and Evaluation of Proactive Behavior in Conversational Assistants: Approach with the Eva Companion Robot

The importance of providing emotional support and assistance to older adults has been highlighted by the COVID-19 pandemic. An increasing number of older adults live alone, which promotes loneliness and depression risks. Also, the digital divide exacerbates these issues and other social difficulties, since older adults are not able to use technology to communicate. A socially assistive robot could help to address these loneliness and digital divide problems. However, it is critical to incorporate affectiveness and naturalness to promote the user acceptance of the robot. This project makes use of the existing EVA open-source robotics platform. The aim is to improve the quality of life of older adults by boosting their independence and alleviating loneliness or other emotional issues that can arise. To improve the user acceptance and to get a more natural, affective, non-passive behavior, this paper contributes to integrate several aspects to the EVA robot: a) assistiveness through conversations and a social messaging end-user skill to reduce the digital divide;b) proactivity by means of proactive interventions so EVA is able to start conversations;c) affectivity by means of showing emotions with eyes expressions, user recognition and emotion analysis in user input;and d) naturalness by blending all these characteristics with a low response time in the interaction and the novel wakeface activation method. Finally, a technical evaluation of the proposed solution provides evidence of its appropriate performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Towards Stress Detection During Gameplay: A Survey

In day-to-day life stress can arise due to various factors including work life demands, external situations and health issues. Stress becomes a concern when it affects a person's mental health, and sometimes it can even result in other chronic illnesses. Currently, stress detection studies in the literature are often limited to laboratory studies or to specific situations. However, daily stressors are on-going and therefore detection of stress in everyday life (outside the laboratory environments) is important to improve the wellbeing of individuals. Computer games is an entertainment media that can be found in almost every household in the modern society. Statistics show that people spend hours playing computer games daily. The amount of data that gameplay generates and interactivity they provide via various human computer interfaces have a lot of potential in identifying behaviour patterns of the players that could assist in the process of stress detection. As such, this survey attempts to identify the extent to which computer games can be used as a medium for stress detection. Towards this end, this survey reviews the existing stress detection studies, both laboratory techniques, as well as the techniques that can be used in a home-based environment. Finally, it summarises the stress detection techniques that can be used within games in order to make it an everyday technology that can be used to detect and monitor stress. In addition, it is expected that development of such a technology will be useful in providing objective data to the health care professionals for intervention and management. Such a technology is even more required in the current unprecedented situation the world has faced due to the COVID-19 pandemic as it can be developed as a technology to manage mental health issues people are facing due to home isolation. IEEE

Emotion Recognition of Students' Bilingual Tweets during COVID-19 Pandemic using Attention-based Bi-GRU

This paper studied the emotions manifested by students from March 2020 to April 2021, a year of the Coronavirus Disease-2019 (COVID-19) pandemic. Our tweet compromises Taglish (Tagalog - English) texts, a low-resource code-switching language. The texts were cleaned and translated from Taglish to English. WordNet Affect was used to annotate the text with Happy, Angry, Sad, Surprise, and Fear as the output. A neural network, Bidirectional Gated Recurrent unit (Bi-GRU) with Attention layer, was used, and it was compared to Bernoulli Naïve Bayes (BNB) and Support Vector Machine (SVM), which are commonly used algorithms for Taglish emotion recognition. A 100-dimensional GloVe word embedding was applied to the data before training. The augmentation method does not affect the model's performance negatively;instead has helped the Bi-GRU with Attention boost its performance. Bi-GRU with attention has a higher F1-score on all emotions compared to the other three algorithms but, as expected, requires a large amount of data. The results show that the most dominant emotion manifested by students throughout the year is surprise immediately followed by Sad and Fear. The three are close in values. © 2022 IEEE.

Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse, when patient emotion is fear, cry or sad. The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm. The proposed Convolution Neural Networks (CNN) architecture with DnCNN preprocessing enhances the performance of recognition. The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras. The proposed method accuracy is more when compared to other methods. It detects the chances of suicide attempt based on stress level and emotional recognition. © 2023 CRL Publishing. All rights reserved.

Real-time Facial Expression Recognition via Dense & Squeeze-and-Excitation Blocks

Due to the coronavirus disease 2019 (COVID-19) pandemic, traditional face-to-face courses have been transformed into online and e-learning courses. Although online courses provide flexible teaching and learning in terms of time and place, teachers cannot be fully aware of their students’ individual learning situation and emotional state. The cognition of learning emotion with facial expression recognition has been a vital issue in recent years. To achieve affective computing, the paper presented a fast recognition model for learning emotions through Dense Squeeze-and-Excitation Networks (DSENet), which rapidly recognizes students’ learning emotions, while the proposed real-time online feedback system notifies teacher instantaneously. Firstly, DSENet is trained and validated by an open dataset called Facial Expression Recognition 2013. Then, we collect students’ learning emotions from e-learning classes and apply transfer learning and data augmentation techniques to improve the testing accuracy. The proposed DSENet model and real-time online feedback system aim to realize effective e-learning for any teaching and learning environments, especially in the COVID-19 environment of late © This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Critique of Data Visualisation, Graphic Medicine and the COVID-19 Pandemic

Data sets were plentifully used in the wake of the COVID-19 pandemic. Although they were utilized for documentation, policy formulation, course correction, and research among others, data sets relentlessly reduced human beings to mere numbers and glossed over the affective and emotional experiences which characterize our lived experience of the COVID-19 pandemic. Quarrelling with such decontextualized, depersonalized, and hegemonic impacts of data, graphic medicine while not entirely dismissive of the performative authority of data, criticizes and supplements data only to arrive at a complex model of data. Using close reading of comic panels created by Andy Warner, Sarah Firth, and Randall Munroe, the present article demonstrates how graphic medicine imagines different ways of engaging data through enfolding the social/individual and structures of feeling to convey the embodied nature of our existence. Put differently, graphic medicine rematerializes and reclaims the individuals from datasets through a process which we call "redrawing." Redrawing is a textual practice and strategic engagement with the authority of visual/verbal discourses and its attendant technologies through rhetorical operations of irony, satire and genre blending among others. The article concludes by emphasizing the need to humanize, contextualize, and sensitively present data so as to convey the collective, entangled and affective nature of our existence. [ FROM AUTHOR] Copyright of QScience Connect is the property of Hamad bin Khalifa University Press (HBKU Press) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

EMO-Learning: Towards an intelligent tutoring system to assess online students' emotions

Due to the COVID-19 pandemic, most universities have adapted their learning infrastructure to an increasing demand for online training modalities. However, this type of learning, usually through Learning Management Systems (LMSs), suffer from a lack of direct feedback between students and the educational staff. For that reason, the present work introduces the EMO-learning project, whose key goal is to capture the emotions of students. This is done by means of a deep learning approach, able to timely analyse the face expressions of the students during online lectures. The module has been tested with different students during the academic year 2020-21, showing quite promising results. © 2022 IEEE.

Application of Affective Computing in the Analysis of Emotions of Educational Content for the Prevention of COVID-19

Affective computing is an emerging research area focused on the development of devices and systems that have the ability to recognize, interpret, process and simulate human emotions in order to improve a user's experience when interacting with a software system. One of the possible fields of application of the techniques provided by affective computing is in the design and generation of multimedia content in the context of formal and non-formal education, which can generate greater interest in students through the transmission of different emotions throughout this content. Based on the above, in this article, an analysis of emotions is carried out on a set of content provided by the Ministry of Health of Colombia as a measure for the prevention and mitigation of contagion by COVID-19. For the development of the study, a tool has been built in the Java language, which allows the segmentation of the audio fragments of the multimedia content, as well as the extraction of the acoustic parameters of arousal and valence, and the application of clustering models on the set of properties extracted from the segments. Copyright © 2022 Praise Worthy Prize S.r.l.-All rights reserved. © 2022 Praise Worthy Prize S.r.l.-All rights reserved.

Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks.

The automatic generation of features without human intervention is the most critical task for biomedical sentiment analysis. Regarding the high dynamicity of shared patient narrative data, the lack of formal medical language sentiment dictionaries prevents retrieval of the appropriate sentiment, which is unapproachable and can be prone to annotator bias. We propose a novel affective biomedical concept-based encoding via sentic computing and neural networks. The main contributions include four aspects. First, a biomedical embedding, in which a medical entity is defined, normalized, and synthesized from a text, is built using online patient narratives after being combined with label propagation from a widely used comprehensive biomedical vocabulary. Second, considering the dependence on biomedical definitions, drug reaction sample selection based on general matching is suggested. These feature settings are then used to build and recognize affective semantics and sentics based on an extreme learning machine. Finally, a semisupervised LSTM-BiLSTM model for biomedical sentiment analysis is constructed. There was a massive influx of patient self-reports related to the COVID-19 pandemic. A study was conducted in this direction, and we tested the validity, medical language familiarity, and transferability of our approach by analyzing millions of COVID-19 tweets. Comparisons to affective lexicons also indicate that integrating extreme learning machine cognitive capabilities has advantages over biomedical sentiment analysis. By considering sentics vectors on top of the formed embeddings, our semisupervised LSTM-BiLSTM achieved an accuracy of 87.5%. The evaluations of unsupervised learning approximated the results of the previous model when dealing with a serious loss of biomedical data. In this paper, we demonstrate the effectiveness of integrating deep-learning-based cognitive capabilities for both enhancing distributed biomedical definitions and inferring sentiment compositions from many patient self-reports on social networks. The relevant encoding of affective information conveyed regarding medication subjects clearly reveals defined roles and expectations that can have a positive impact on public health.

Investigating the Relationship between Facial Mimicry and Empathy.

Facial expressions play a key role in interpersonal communication when it comes to negotiating our emotions and intentions, as well as interpreting those of others. Research has shown that we can connect to other people better when we exhibit signs of empathy and facial mimicry. However, the relationship between empathy and facial mimicry is still debated. Among the factors contributing to the difference in results across existing studies is the use of different instruments for measuring both empathy and facial mimicry, as well as often ignoring the differences across various demographic groups. This study first looks at the differences in the empathetic abilities of people across different demographic groups based on gender, ethnicity and age. The empathetic ability is measured based on the Empathy Quotient, capturing a balanced representation of both emotional and cognitive empathy. Using statistical and machine learning methods, this study then investigates the correlation between the empathetic ability and facial mimicry of subjects in response to images portraying different emotions displayed on a computer screen. Unlike the existing studies measuring facial mimicry using electromyography, this study employs a technology detecting facial expressions based on video capture and deep learning. This choice was made in the context of increased online communication during and after the COVID-19 pandemic. The results of this study confirm the previously reported difference in the empathetic ability between females and males. However, no significant difference in empathetic ability was found across different age and ethnic groups. Furthermore, no strong correlation was found between empathy and facial reactions to faces portraying different emotions shown on a computer screen. Overall, the results of this study can be used to inform the design of online communication technologies and tools for training empathy team leaders, educators, social and healthcare providers.

The Influence of Affective Feedback Adaptive Learning System on Learning Engagement and Self-Directed Learning.

The outbreak of the two-year corona virus has made a great difference on existing methods of learning and instruction. Online education has become a crucial role to maintain non-stop learning after the post-epidemic period. The advanced technologies and growing popularity of network equipment have made it easy to deploy remote connections. However, teachers still face challenges when they actually implement distance courses. During the learning process, the quality of learning can be improved if the researchers consider multiple factors, including emotions, attitudes, engagement, cognition, neuroscientific and cultural psychology. After analyzing these factors, instructors can have better understanding of students' mental building and cognitive understanding in their process of learning, and be familiar with the way of interaction with students and appropriately adjust their teaching. Therefore, the current study established a learning system that aimed to understand learners' emotional signals during learning by applying the adaptive-feedback emotional computing technology. The purpose of the system was to allow learners to (1) self-examine their learning condition, (2) enhance their self-directed learning, (3) help learners who are in negative learning emotions or settings to lower anxieties, and (4) promote their learning attitudes and engagement. Result showed that the system with the adaptive-feedback emotional computing technology has significantly improved the learning effectiveness, lowered learning anxieties and increased students' self-directed learning.

Human-annotated dataset for social media sentiment analysis for Albanian language.

Social media was a heavily used platform by people in different countries to express their opinions about different crises, especially during the Covid-19 pandemics. This dataset is created through collecting people's comments in the news items on the official Facebook site of the National Institute of Public Health of Kosovo. The dataset contains a total of 10,132 comments that are human-annotated in the Albanian language as a low-resource language. The dataset was collected from March 12, 2020, and this coincides with the emergence of the first confirmed Covid-19 case in Kosovo until August 31, 2020, when the second wave started. Due to the scarcity of labeled data for low-resource languages, the dataset can be used by the research community in the field of machine learning, information retrieval, affective computing, as well as by the public agencies and decision makers.

Exercise becomes Music: Creating communication through new music experiences using exercise data

This is a new communication proposal using data acquired by Physiological signal measurement. Since the COVID-19 pandemic, opportunities for exercise have been decreasing as people have fewer opportunities to go outside. For this reason, we have created several exercise guidance contents that can be used on a daily basis. The data obtained from the exercise guidance contents can be used in various ways. For example, in a physical education class, the children were motivated to improve their exercise by seeing the actual data of their exercise. There are three exercise guidance contents, each of which has its own feedback loop of acquiring data and utilizing it. "Exercise becomes Music"is an attempt to use them in a more meta way to motivate people to exercise, and to create a larger feedback loop throughout. The data acquired from the exercise guidance content is converted into music that makes the most of the characteristics of each content, and the content is designed to be played by the experiencer while combining the generated music. The surprise of having one's own data converted into music, and the experience of playing with the combination of data, will create feedback for each exercise, and we also hope that sharing the combined music online will create new communication. © 2022 IEEE.

How Virtual Reality Influenced EmotionalWell-being Worldwide During the Covid-19 Pandemics

Use in the Virtual Reality (VR) world has spiked with the outbreak of domestic lockdowns and social distancing measures for the covid-19 pandemics and the simultaneous market launch of the Oculus Quest 2 standalone VR device. TRIPP is a VR app in the VR world as a wellness platform based on scientific research, e.g., Artificial Intelligence (AI), binaural beats, differential color tuning, VR-based relaxation, and mindfulness. This paper analyses the dynamic of emotional and mood transitions of a large set of TRIPP users exposed to VR devices' emotional experiences from the end of 2019 (i.e., when the pandemic started) to April 2021, when our study has begun. A clear and well-documented data analysis is presented, focusing on emotional well-being. In particular, this study presents a data breakdown of users' accesses and analyzed the impact on emotional well-being based on the feedback given within the app, access time, and users' anagraphical distributions. The systematic experimental analysis of anonymized data found the studied AI and VR wellness techniques to be indicators and positive modifiers of users' mental and emotional states during the most impactful global crisis of the millennium. © 2021 ACM.

Emotional Speech Recognition Method Based on Word Transcription.

The emotional speech recognition method presented in this article was applied to recognize the emotions of students during online exams in distance learning due to COVID-19. The purpose of this method is to recognize emotions in spoken speech through the knowledge base of emotionally charged words, which are stored as a code book. The method analyzes human speech for the presence of emotions. To assess the quality of the method, an experiment was conducted for 420 audio recordings. The accuracy of the proposed method is 79.7% for the Kazakh language. The method can be used for different languages and consists of the following tasks: capturing a signal, detecting speech in it, recognizing speech words in a simplified transcription, determining word boundaries, comparing a simplified transcription with a code book, and constructing a hypothesis about the degree of speech emotionality. In case of the presence of emotions, there occurs complete recognition of words and definitions of emotions in speech. The advantage of this method is the possibility of its widespread use since it is not demanding on computational resources. The described method can be applied when there is a need to recognize positive and negative emotions in a crowd, in public transport, schools, universities, etc. The experiment carried out has shown the effectiveness of this method. The results obtained will make it possible in the future to develop devices that begin to record and recognize a speech signal, for example, in the case of detecting negative emotions in sounding speech and, if necessary, transmitting a message about potential threats or riots.

Sense the classroom: AI-supported synchronous online education for a resilient new normal

Following the COVID-19 pandemic, as the user-base of online synchronous communication systems skyrocketed, the shortcomings of synchronous online learning systems became more visible. Any attempt to overcome these shortcomings should be considered worthwhile due to the magnitude of potential impact. Improving the quality and addressing the shortcomings of online education is more important than ever. The goal of this multidisciplinary study that lies in the intersection of the fields of Education Science and Computer Science is to address a number of challenges of online education by incorporating AI. This study focuses on developing methods and means to ethically collect and use non-verbal cues of participants of online classrooms to assist teachers, students, and course coordinators by providing real-time and after-the-fact feedback of the students’ learning-centered affective states. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).