Benchmarking Micro-Action Recognition: Dataset, Methods, and Applications

The currently growing awareness about the climate change is in conflict with one’s own behavior resulting from global trends such as the globalization and new lifestyles, that involve an increase of long-distance travel. Whereas people living in urban areas tend to travel short distances with environmentally friendly means of transport in everyday life, studies indicate a higher amount of long-distance travel of urbanites. The question arises whether behavior and norms in everyday life affects long-distance travel behavior. By using data from surveys in Hamburg and Berlin (Germany), we analyze the long-distance travel behavior of urban people in a comprehensive context including everyday travel, attitudes and norms, sociodemographic as well as spatial characteristics as a proxy for urbanity. Of particular relevance is, whether there are discrepancies or similarities between the behavior in everyday life and the long-distance travel behavior. The results indicate, that people who live in highly urban areas are most likely to travel more than 3,000 km, which involves primarily air travel. This effect is reduced only by the ecological norm of the individuals. People who pay attention to sustainable means of transport in their daily lives are less likely to be long-haul travelers. We see, however, that the effect of living in dense urban areas overcompensates the effect of the ecological norm. Shifting leisure activities to everyday life also reduces the probability of traveling to distant places. This indicates an offset between the everyday and the long-distance travel behavior of individuals.

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human–Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam.

Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human-computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education, psychology, and military domains. Electroencephalographic (EEG) signals have gained widespread application in emotion recognition due to their inherent characteristics of being non-concealable and directly reflecting brain activity. In recent years, with the establishment of open datasets and advancements in deep learning, an increasing number of researchers have integrated EEG with deep learning methods for emotion recognition studies. This review summarizes commonly used deep learning models in EEG-based emotion recognition along with their applications in this field, including the design of different network architectures, optimization strategies, and model designs based on EEG signal features. We also discuss limitations from the perspectives of commonality-individuality (C-I) and suggest improvements. The review outlines future research directions and provided a minimal C-I framework to assess models. Through this review, we aim to provide researchers in this field with a comprehensive reference and approach to balance universality and personalization to promote the development of deep learning-based EEG emotion recognition methods.

Our aim is to determine the factors influencing a visitor’s decision to stay in a green accommodation facility. Visitors from six tourist sites throughout the Czech Republic were surveyed. Out of 944 respondents, 506 have never visited a “green” accommodation facility and only 93 respondents stayed mainly in a “green” accommodation facility. Several independent variables from four factors were studied—the socio-demographic status of respondents, pro-environmental behaviour in tourism, pro-environmental behaviour in everyday life and the pro-environmental attributes of accommodation facilities. Logistic regression and decision tree model were used to identify the most influential variables that are responsible for the choosing of “green” accommodation. Expenses during holiday, attitudes, personal norms, pro-environmental behaviour in everyday life and the attributes of a hotel reception were found as important by logistic regression, and out of them, pro-environmental behaviour in everyday life and personal norms are the most important. It is important that variables from all four factors were found as being statistically important for the choosing of a “green” accommodation facility. Inquiry at the tourist attractions resulted in diversified information from the respondents that has a predicative ability to link “green” accommodation to the visit of mass tourist destinations. An extremely low proportion of those who spend their holidays only in “green” accommodation facilities was found.

Emotions are a crucial aspect of human life and the researchers have tried to build an automatic emotion recognition system that helps to provide important real-world applications. The psychologists have shown that emotions differ across culture, considering this fact, we provide and describe the first audio-visual Arabic emotional dataset which called (AVANEmo). In this work we aim to fill the gap between studies of emotion recognition for Arabic content and other languages by provided an Arabic dataset which is a major and fundamental part of build emotion recognition application. Our dataset contains 3000 clips for video and audio data, and it covers six basic emotional labels (Happy, Sad, Angry, Surprise, Disgust, Neutral). Also, we provide some baseline experiments to measure the primitive performance for automated audio and visual emotion recognition application using the AVANEmo dataset. The best accuracy that we achieved was 54.5% and 57.9% using the audio and visual data respectively. The data will be available for distribution to researchers.

The implementation of the litter behavior initiative aims to mitigate the generation of litter by hikers within the hiking trails of China’s national park. The present study employed the extended norm activation model (NAM) to examine the determinants of hikers’ intention to engage in litter behavior within the context of Wuyishan national park. This analysis incorporated three supplementary explanatory variables, namely environmental knowledge, connectedness to nature, and pro-environmental behavior in everyday life. Additionally, three control variables were included, namely gender, age, and education. A survey was conducted by researchers at Wuyishan national park, with a sample size of 466 hikers. The data obtained from the survey was analyzed using the statistical technique known as structural equation modeling. The findings indicate that the NAM effectively captured hikers’ litter behavior in national parks. Factors such as hikers’ awareness of consequences, ascription of responsibility, personal norm, environmental knowledge, connectedness to nature, and pro-environmental behavior in everyday life were found to significantly influence hikers’ intention to engage in litter behavior. The influence of age on hikers’ intention was shown to be significant and positive, suggesting that older hikers exhibited a greater willingness to engage in litter behavior compared to younger hikers. In conclusion, the study provided practical recommendations for improving the management of hiker trash and promoting sustainable development inside national parks.

The aim of this study was to describe the results of the Auditory Behavior in Everyday Life (ABEL) questionnaire adapted to Hebrew and to Arabic and its association to clinical test results in children with cochlear implants. As assessment of hearing by audiometry does not always adequately reflect performance in daily life, questionnaires have been developed to assess functioning in natural surroundings and to track progress. In order to evaluate cochlear-implanted children's verbal and communicative abilities, the parental ABEL questionnaire was developed in 2002. The advantages of the ABEL questionnaire are that it is intended for a wide age range, is quick to administer, and is filled out by parents themselves. The ABEL questionnaire was translated into Hebrew and into Arabic and routinely used in the clinic. A total of 61 questionnaires were thus filled out by parents of children with cochlear implants (ages 3.9-14.3 years) when they came for routine mapping. Retrospectively, data were analyzed and questionnaire results were compared with performance with the implant on several clinical tests: audiometric thresholds, discrimination (percentage) of vowel-consonant-vowel nonsense syllables, and results of speech perception tests with monosyllabic and bisyllabic words and with sentences in quiet and in noise. A correlation was found between the different sections of the questionnaire, and age at implantation had a significant effect on questionnaire scores. However, correlations between questionnaire score and clinical tests were found only for speech perception tests in noise and not in quiet or to audiogram and speech reception threshold. As has been reported previously, self-evaluation or parental evaluation does not always correlate with all measured results of hearing performance. However, the subjective information collected through questionnaires can be valuable for evaluation of progress, for counseling and rehabilitation training, as well as for mapping.

Introduction This article is about relationships between religious experiences and everyday life. It is an attempt to answer the question of how religious experiences influence an individual's psychology and Investigators' opinions differ in their understanding of relationships between religious experiences and the rest of the life experience. Some authors emphasize the extraordinary, short-time, episodic nature of these experiences. They claim that religious experience is something special and unexpected, occurring rarely over the span of one's life. An example here is Hardy (1979:29) who described these experiences as follows: At certain times in their lives many people have had specific, deeply felt, transcendental experiences which have made them all aware of the presence of this power ... Although Hardy concentrated on an analysis of particular experiences shared by the respondents, his study provides evidence that there is a close relationship between life before and after the experience. Other authors who have analyzed specific aspects of particular religious experiences also report evidence of the impact of religious experience on the rest of one's life. Their analysis, however, has been confined to a particular issue, such as the structure of religious experience (Girgensohn 1921, Gruehn 1926), the essence of the experience (Otto, 1950), similarities between and generalizations about different experiences (Hay & Morisy, 1978), or a chemical ecstasy (Clark 1969, Huxley 1954, Smith 2000). Karl Girgensohn (1921) might be mentioned as one example of a researcher studying episodic religious experiences. Girgensohn and his co-workers analyzed reports of religious experiences the subjects had had during and after reading different religious texts. His goal was to find out and explain the functions of the emotional, rational and sensational components of a religious experience. Although Girgensohn acknowledged the impact of religious experiences on human psychology and behavior in everyday life, he primarily studied particular religious experiences manifested in an experimental situation. Other authors relate religious experiences to the larger psychological context. This means that religious experience is not studied as a separate phenomenon but as part of one's lifelong experience. One good example of this is the study of Spilka, Brown and Cassidy (1992) on the structure of religious experience in relation to one's lifestyle before and after the experience. Their general conclusion was that it is evident that reported mystical experiences, as theorized, relate to remembered pre-experience life circumstances and present perceptions of one's thinking and behavior. Hjalmar Sunden (1966, 1969) understood religious experience to be a process of learning in which there is a need for preparation by getting to know the roles of man and God described in a religious narrative. Sunden relates religious experiences directly to the rest of the experiences during one's life. First, learning the description of the roles through religious narratives is a long process lasting from childhood to death. Second, religious experiences may appear and will appear in ordinary life situations. Third, personal motivation is needed to experience the role of God in certain situations. Therefore, according to Sunden, religious experience is very much a part of one's total life experience. Paul Pruyser's (1976) criteria of pastoral diagnostics have affected later investigations of religious experiences from a lifelong perspective. He described seven dimensions of Christian faith that are most closely related to everyday life: awareness of the holy, acceptance of God's grace, being repentant and responsible, knowing God's leadership and guidance, involvement in organized religion, experiencing fellowship, and a sense of vocation. These dimensions, which have personal meaning, will determine the portrait of one's religious life. …

Smartphone sensing methods for studying behavior in everyday life

이미지를 감성으로 쾌 또는 불쾌, 긴장 또는 평온의 구분 감성을 트리 형식으로 가중치를 부여하여 평가한다. 이미지 대표평가 감성인 명도대비를 평가 기준으로 1차는 쾌, 불쾌 또는 긴장 또는 평온이고 2차는 세분화 이미지 감성으로 구분한다. 4개의 감성인식을 수치화 된 명도대비 데이터로 측정한다. 평가 구현은 OpenCV를 통해 명도대비를 그래프화하여 긴장, 평온, 쾌, 불쾌 값 변화에 따라 4개 감성으로 구분하여 컴퓨팅한다. 감성 컴퓨팅으로 명도대비의 입력된 값에 따라 '불쾌'를 '쾌' 또는 '긴장'을 '평온'으로 감성적 변화를 줄 수 있다. 이미지 감성의 규칙성을 계산화 된 컴퓨팅 시스템으로 제어할 수 있고 향후 산업방향에 감성 인식의 적용에 대한 긍정적인 역할을 할 것이다. Image of emotional pleasure or displeasure, tension or emotional division of tranquility in the form of a tree is evaluated by weighting. Image representative evaluation of the sensitivity of the brightness contrast ratings 1 car pleasure, displeasure or stress or emotional tranquility and two cars are separated by image segmentation. Emotion Recognition of four compared to the numerical data is measured by brightness. OpenCV implementation through evaluation graph the stress intensity contrast, tranquility, pleasure, displeasure, depending on changes in the value of the computing is divided into four emotional. Contrast sensitivity of computing the brightness depending on the value entered 'nuisance' to 'excellent' or 'stress' to 'calm' the emotional changes can give. Calculate the sensitivity of the image regularity of localized computing system can control the future direction of industry on the application of emotion recognition will play a positive role.

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.

Social Internet of Things (SIoT) is an expansion of the Internet of Things (IoT) that meets with person-to-person communication ideas to make informal organizations of interconnected smart objects. This integration enables the two paradigms to nourish each other, resulting in new ecosystems. SIoT adds human-to-thing (H2T) interactions to the IoT's two interaction paradigms: human-to-human (H2H) and thing-to-thing (T2T). Smart “social objects” that automatically emulate human social behaviour in everyday life are enabled by SIoT. SIoT device security is also a big concern. It aids in the protection of devices or nodes used for communication. In this work, we address numerous SIoT security concerns at various layers, as well as a complete literature study and various risks and security features on each tier of the SIoT architecture.

Distant emotion recognition (DER) extends the application of speech emotion recognition to the very challenging situation that is determined by variable speaker to microphone distances. The performance of conventional emotion recognition systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker. This is due to a broad variety of effects such as background noise, feature distortion with distance, overlapping speech from other speakers, and reverberation. This paper presents a novel solution for DER, addressing the key challenges by identification and deletion of features from consideration which are significantly distorted by distance, creating a novel, called Emo2vec, feature modeling and overlapping speech filtering technique, and the use of an LSTM classifier to capture the temporal dynamics of speech states found in emotions. A comprehensive evaluation is conducted on two acted datasets (with artificially generated distance effect) as well as on a new emotional dataset of spontaneous family discussions with audio recorded from multiple microphones placed in different distances. Our solution achieves an average 91.6%, 90.1% and 89.5% accuracy for emotion happy, angry and sad, respectively, across various distances which is more than a 16% increase on average in accuracy compared to the best baseline method.

This paper presents the various emotion classification and recognition systems which implement methods aiming at improving Human Machine Interaction. The modalities and approaches used for affect detection vary and contribute to accuracy and efficacy in detecting emotions of human beings. This paper discovers them in a comparison and descriptive manner. Various applications that use the methodologies in different contexts to address the challenges in real time are discussed. This survey also describes the databases that can be used as standard data sets in the process of emotion identification. Thus an integrated discussion of methods, databases used and applications pertaining to the emerging field of Affective Computing (AC) is done and surveyed.This paper presents the various emotion classification and recognition systems which implement methods aiming at improving Human Machine Interaction. The modalities and approaches used for affect detection vary and contribute to accuracy and efficacy in detecting emotions of human beings. This paper discovers them in a comparison and descriptive manner. Various applications that use the methodologies in different contexts to address the challenges in real time are discussed. This survey also describes the databases that can be used as standard data sets in the process of emotion identification. Thus an integrated discussion of methods, databases used and applications pertaining to the emerging field of Affective Computing (AC) is done and surveyed.

Existing research highlights the significance of prosocial behavior (voluntary, intentional behavior that results in benefits for another) to people's well-being. Yet, the extent to which this expected positive relation operates at the within-person level (e.g., is more prosocial behavior than usual related to a higher than usual level of well-being?) while taking into account stable interindividual differences, remains a research question that deserves further investigation. In this study, we aimed to explore the relations between prosocial behavior and hedonic (HWB; subjective assessment of life satisfaction and happiness) and eudaimonic (EWB; actualization of human potential in alignment with personal goals, including concepts like meaning in life and closeness to others) well-being in daily life. Using ecological momentary assessment for 4 weeks, data were collected from two British samples, comprising 82 adolescents and 166 adults. Dynamic Structural Equation Modeling revealed a positive relations between prosocial behavior and HWB/EWB at both between and within-person levels across the samples. In summary, these findings further support the positive link between prosocial behavior and well-being in everyday life. Notably, this association was consistent across different age groups (adolescent and adults) at both between and within-person levels.