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With the advancement of artificial intelligence, conversational agents are now capable of displaying intelligent and emotionally empathetic responses, which are essential for the continued use of AI-based agents. However, there is a scarcity of formal measures that can comprehensively evaluate how well they react to the user's emotional needs. The objective of this research is to develop and validate a set of new measures, collectively called the agent empathic reactivity index (AERI), an adaptation of the interpersonal reactivity index (IRI) developed for the human-human relationship evaluation to the human-agent interaction context. By rigorously following the measure development procedures suggested by prior research, four dimensions of AERI measures of empathic concern, perspective-taking, fantasy, and personal distress were developed. Multiple pilot tests and surveys involving various conversational agents were conducted to validate the four AERI measures. The study results show that the new measures have strong psychometric properties and nomological validity. [ABSTRACT FROM AUTHOR]

As interest in online learning has increased, studies utilizing a social system for the innovation of lecture/learning environments have attracted attention recently. To establish a sustainable social environment in the online learning system, prior research investigated strategies to improve and manage the social presence of collaborators (e.g., students, AI facilitators, etc.) in an online lecture. Nevertheless, the negative effect of social presence was often neglected, which leads to a lack of comprehensiveness in managing social presence in an online lecturing environment. In the study, we intend to investigate the influence of social presence with both positive (student engagement) and negative (information overload) aspects on the learning experience by formulating a structural equation model. To test the model, we implemented an experimental online lecture system for the introductory session of human–computer interaction, and data from 83 participants were collected. The model was analyzed with Partial Least Square Structural Equation Modeling (PLS-SEM). The result shows the social presence of the collaborators influences both student engagement (other learners: β = 0.239, t = 2.187) and information overload (agent facilitator: β = 0.492, t = 6.163; other learners: β = 0.168, t = 1.672). The result also supports that student engagement is influenced by information overload as well (β = −0.490, t = 3.712). These positive and negative factors of social presence influence learning attainment (student engagement: β = 0.183, t = 1.680), satisfaction (student engagement: β = 0.385, t = 3.649; information overload: β = −0.292, t = 2.343), and learning efficacy (student engagement: β = 0.424, t = 2.543). Thus, it corroborates that a change in the level of social presence influences student engagement and information overload; furthermore, it confirms that the effect of changes in social presence is reflected differently depending on learning attainment and experience. [ABSTRACT FROM AUTHOR]

The swine industry is one of the industries that progressively incorporates smart livestock farming (SLF) to monitor the grouped-housed pigs' welfare. In recent years, pigs' positive welfare has gained much attention. One of the evident behavioral indicators of positive welfare is playing behaviors. However, playing behavior is spontaneous and temporary, which makes the detection of playing behaviors difficult. The most direct method to monitor the pigs' behaviors is a video surveillance system, for which no comprehensive classification framework exists. In this work, we develop a comprehensive pig playing behavior classification framework and build a new video-based classification model of pig playing behaviors using deep learning. We base our deep learning framework on an end-to-end trainable CNN-LSTM network, with ResNet34 as the CNN backbone model. With its high classification accuracy of over 92% and superior performances over the existing models, our proposed model highlights the importance of applying the global maximum pooling method on the CNN final layer's feature map and leveraging a temporal attention layer as an input to the fully connected layer for final prediction. Our work has direct implications on advancing the welfare assessment of group-housed pigs and the current practice of SLF. [ABSTRACT FROM AUTHOR]

Fueled by the power of AI, chatbots are becoming more personal. Prior research showed that a chatbot has great potential to elicit its user's self-disclosure because it does not judge the user. However, the chatbot's features beyond the conversational characteristics in eliciting a user's self-disclosure are not as well researched. In this study, we have developed a chatbot and implemented two non-conversation features: (1) co-activity (COA), conducting an activity together, and (2) conversation atmosphere visualization (CAV), visually displaying the emotional feelings conveyed in the conversation, to examine their effects on self-disclosure and user experience. We conducted a field study involving 87 participants who were randomly assigned to one of the four experimental conditions (control, COA only, CAV only, CAV + COA) and asked to use the assigned chatbot for 10 days in their natural life setting. Our results from this field study show that both the COA and CAV features have significant effects on a user's self-disclosure. In addition, interaction effects between COA and CAV have been found to affect a user's intention to use. Based on the findings, we provide design implications for a user's self-disclosure and trusting relationship development with a chatbot. [ABSTRACT FROM AUTHOR]

The advent of information and communication technology has made people practice prosocial behavior in social networking services (SNSs) more easily. For this reason, the aim of the study was to identify the social and individual factors that induce prosociality in SNS. The concept of isomorphism for categorizing the characteristics of each social networks was adopted. The study also considered the concept of social presence for representing each individual. The experiment manipulated types of isomorphism (Mimetic, Normative, and Coercive) and degrees of social presence in an experimental SNS context. The study also measured individuals’ intention and activity of prosocial behavior. The experiment results indicate that mimetic and normative isomorphic conditions induce higher levels of prosocial intention and activity than coercive isomorphic condition. Also, a higher degree of social presence induces a higher level of prosocial intention. More interesting, the impact of mimetic condition is stronger when the social presence is higher. [ABSTRACT FROM PUBLISHER]

Life cycle assessment (LCA) and life cycle cost (LCC) are two primary methods used to assess the environmental and economic feasibility of building construction. An estimation of the building's life span is essential to carrying out these methods. However, given the diverse factors that affect the building's life span, it was estimated typically based on its main structural type. However, different buildings have different life spans. Simply assuming that all buildings with the same structural type follow an identical life span can cause serious estimation errors. In this study, we collected 1,812,700 records describing buildings built and demolished in South Korea, analysed the actual life span of each building, and developed a building life-span prediction model using deep-learning and traditional machine learning. The prediction models examined in this study produced root mean square errors of 3.72–4.6 and the coefficients of determination of 0.932–0.955. Among those models, a deep-learning based prediction model was found the most powerful. As anticipated, the conventional method of determining a building's life expectancy using a discrete set of specific factors and associated assumptions of life span did not yield realistic results. This study demonstrates that an application of deep learning to the LCA and LCC of a building is a promising direction, effectively guiding business planning and critical decision making throughout the construction process. • Actual life span of building is vastly different from mainframe-based life span. • The computational models were trained to predict building life span using big data. • The proposed computational approach is superior over the mainframe-based approach. [ABSTRACT FROM AUTHOR]