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It is widely recognised that paper remains a pervasive resource for collaboration and yet there has been uncertain progress in developing technologies that aim to enhance paper documents with computational capabilities. In this article, we discuss the design of a technology that interweaves developments in hardware and materials, electronics and software, and seeks to create new affinities between digital content and paper. The design of the technology drew from findings from naturalistic studies of the uses of paper, particularly when considering how ‘users’ might ‘interact’ with the augmented technology. We briefly review these studies and discuss the results of an evaluation of the emerging technology. Analysis of the fine details of the conduct of participants in these assessments suggest how, even when we design simple forms of interaction with a device, these can be shaped and transformed by the participation and collaboration of others. [ABSTRACT FROM AUTHOR]

This paper describes the work done by sixth grade students to achieve and sustain productive and personally meaningful lines of inquiry with computational models. The capacity to frame interactions with tools as dialogic exchanges with co-participants is a productive practice for disciplinary engagement in science and for computational thinking (Chandrasekharan and Nersessian 2015; Dennet 1989; Latour 1993; Pickering 1995). We propose that computational models have unique affordances for dialogic interaction because they are probabilistic and iteratively executable, features that provide an entry point for students to adopt stances that treat computational models as participants. Our analysis reveals that existing patterns in students' social interactions are resources for interacting flexibly with computational tools as participants. In particular, we found that students treated computational models as participants in three ways: (1) as conversational peers, (2) as co-constructors of lines of inquiry, and (3) as projections of students' agency and identity. Our data also demonstrate that students take on flexible, rather than fixed, stances toward computational participants. These stances parallel scientists' interactions with non-human entities, which often involve treating tools as agentive participants in inquiry (Latour 1999; Pickering 1995), affording students a pathway to practices at the intersection of disciplinary engagement and computational thinking. [ABSTRACT FROM AUTHOR]

This paper reports ongoing investigations into the use and role of paper-mail communications in domestic environments. It utilises ethnographically informed data to analyse how paper-mail supports various social roles within the home, particularly a division of labour whereby women tend to be largely responsible for what may be called "managing the home". Implications for the future of paper-mail are considered, as well as suggestions made about how email tools may be designed to reflect the patterns of social organisation within the home. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between paper and pencil measures and molecular behavioral indicators of social skills in three widely used assessment options. A total of 221 students engaged in either a “get acquainted” or “role play” interaction, in which they were given instructions and knew they were being observed. A third group engaged in a “waiting period” interaction, in which they were given no instructions to interact and had no knowledge of being observed. Results showed that subjects in the “get acquainted” and “role play” situations scored higher on several measures of social skills than those in the “waiting period” situation. In addition, there was most variability in the measures of social skills in the waiting period situation. In general, trait measures exhibited poor correspondence with behavioral measures of social skills in all situations. However, state paper and pencil measures of social skills exhibited strong correspondence with behaviors in the waiting period, but not in the get acquainted or role play situations. Implications for the assessment of social communication skills through commonly used assessment procedures are discussed. [ABSTRACT FROM AUTHOR]

Why have Singapore's unique developmental state arrangements persisted in a region which has experienced democratic change? This paper argues that this is due to the PAP state's successful legitimation of its unique brand of meritocracy, one which has both competitive and interventionist elements. During the colonial era, a culture of economic meritocracy evolved in a bottom-up process through social and commercial interactions between the British class and Chinese community. This was then transmuted by the PAP's top-down imposition of the institutions and discourses of political meritocracy. This cultural hybrid allows the state to sustain its hegemony in the face of progressive social change. Accordingly, our emphasis on the wider institutional environment within which merit is conceived helps to better illuminate Singapore's challenges of encouraging organic innovation, alleviating social stratification, and opening up its political arena. This paper suggests that the problems in these areas stem not from meritocracy per se, but from the PAP's "monocentric meritocracy" where merit is narrowly defined and singularly imposed in the post-colonial era. [ABSTRACT FROM AUTHOR]

The paradox of predictability refers to situations in which, even in a deterministic universe, a correct prediction of a future action is seemingly impossible because the agent whose action is predicted is determined to act counterpredictively. In a recent contribution to this journal, Victor Gijsbers provides an example of the paradox in which the undecidability of the situation plays an essential role and claims, additionally, that this undecidability is at the root of all examples of the paradox. This paper argues, first, that the latter claim is not correct because there are clear examples of the paradox in which the situation remains fully decidable. The paper argues, secondly, that, because of its reliance on rather artificial conditions and in contrast with examples referring to the physical nature of the predictor, the example presented by Gijsbers, though technically correct, has little relevance for our understanding of the role of (counter-)predictability in the context of human interactions. [ABSTRACT FROM AUTHOR]

Undergraduate student engagement in research publication facilitates their intellectual development and professional social interaction and leads to increased sense of self-efficacy and trust in science and recognition as a researcher. Scientific publication is a pillar which gauges intellectual human capital of countries in the global innovation index. This paper presents the effectiveness of using technology-based tools in ensuring the quality of articles for journal publication. It fundamentally employed pre- and post-test research design to determine the effectiveness of online technology-based tools before and after the intervention. It employed descriptive presentation of the different on-line technology tools used in the 21 specimens of student researches written in publishable article format. It examined the quality of references, level of readability, writing quality, originality, and grammar of the papers before and after the review process and interventions. The study highlighted that using online tools improved the quality of the papers with respect to grammar and lexical quality, similarity index, readability index, number of references, number of correct bibliographic entries for submission in high impact journals. A higher level of a cleansing process using the online technology tools ensures the quality of publishable articles. Implications of this study will facilitate journal article writing skill of academic community to effectively disseminate results of research studies with higher chances of being accepted in respected global databases as contribution to knowledge generation and development of the country. [ABSTRACT FROM AUTHOR]

The article presents abstracts on psychological topics which include biofeedback, social interaction and electroencephalography.

A central issue in postphenomenology is how to explain the multistability of technologies: how can it be that specific technologies can be used for a wide variety of purposes (the "multi"), while not for all purposes (the "stability")? For example, a table can be used for the purpose of sleeping, having dinner at, or even for staging a fencing match, but not for baking a cake. One explanation offered in the literature is that the (material) design of a technology puts constraints on the purposes for which technologies can be used. In this paper, I argue that such an explanation—while partly correct—fails to address the role of the environment in which human beings operate in putting constraints on technology use. I suggest that James Gibson's affordance theory helps highlighting how stabilities in technology use arise in the interaction between human being and environment. Building on more recent approaches in affordance theory, I suggest that the environment can be conceptualized as a "rich landscape of affordances" that solicits certain actions, which are not just cued by the environment's material structure, but also by the normativity present in the form of life in which a human being participates. I briefly contrast the approach to affordances developed in this paper with how Klenk (2020) and Tollon (2021) have conceptualized the "affordance character" of technological artifacts, and highlight how a focus on the situated nature of affordances augments these earlier conceptualizations. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including data mining, subgroup discovery and social relationship in online social network.

Mental rotation is the ability to rotate objects in one's mind. Large age-related decreases in accuracy and processing time are often found in studies using paper-and-pencil or computerized mental rotation tests. For older participants, these tests are often too difficult. In the present study, real models consisting of cube figures were used to assess the mental rotation performance of middle and older aged adults. It should be investigated whether these tests were comparable to paper-and-pencil or chronometric tests and if very old participants were able to solve them. Eighty-four participants (49 females) between 40 and 90 years took part and were divided into middle (40–68 years) and older aged (69–90 years) and groups with higher (with college degree) and lower education (without college degree). For accuracy, main effects of gender and age group as well as interactions of age group and education were found. Younger participants outperformed older ones only in the group with lower education. For processing time, a main effect of age group as well as an interaction of age group and education was found. The age-related cognitive decline in the higher educated group was moderate, while a large effect appeared for the group without college degree. Age and gender effects of our new test with real objects were comparable to paper–pencil and computerized tests. Furthermore, a protective effect of education on the cognitive decline in mental rotation performance is discussed. [ABSTRACT FROM AUTHOR]

Navigating mobile robots in crowded environments poses a significant challenge and is essential for the coexistence of robots and humans in future intelligent societies. As a pragmatic data-driven approach, deep reinforcement learning (DRL) holds promise for addressing this challenge. However, current DRL-based navigation methods have possible improvements in understanding agent interactions, feedback mechanism design, and decision foresight in dynamic environments. This paper introduces the model inductive bias enhanced deep reinforcement learning (MIBE-DRL) method, drawing inspiration from a fusion of data-driven and model-driven techniques. MIBE-DRL extensively incorporates model inductive bias into the deep reinforcement learning framework, enhancing the efficiency and safety of robot navigation. The proposed approach entails a multi-interaction network featuring three modules designed to comprehensively understand potential agent interactions in dynamic environments. The pedestrian interaction module can model interactions among humans, while the temporal and spatial interaction modules consider agent interactions in both temporal and spatial dimensions. Additionally, the paper constructs a reward system that fully accounts for the robot's direction and position factors. This system's directional and positional reward functions are built based on artificial potential fields (APF) and navigation rules, respectively, which can provide reasoned evaluations for the robot's motion direction and position during training, enabling it to receive comprehensive feedback. Furthermore, the incorporation of Monte-Carlo tree search (MCTS) facilitates the development of a foresighted action strategy, enabling robots to execute actions with long-term planning considerations. Experimental results demonstrate that integrating model inductive bias significantly enhances the navigation performance of MIBE-DRL. Compared to state-of-the-art methods, MIBE-DRL achieves the highest success rate in crowded environments and demonstrates advantages in navigation time and maintaining a safe social distance from humans. [ABSTRACT FROM AUTHOR]

Both participation and argumentation (OECD, 2022) are important keywords in educational contexts. While participation is seen as a crucial prerequisite for education and collaborative learning in general, argumentation as a discursive practice serves to convey and negotiate—also school-specific—knowledge. This paper explores repetition in argumentative events as a technique of establishing—or even hindering—participation in terms of alignment and affiliation. It can serve as a strategy for participation by signalling responsiveness and thematic coherence—and thus inclusion. At the same time, however, studies show that repetition can also signal contradiction and rejection—and thus exclusion. So far, we know little about how exactly these functional differences are produced—especially in younger children. Therefore, the paper explores how children use repetition as a resource for negotiating participation in argumentative events. Using authentic data in the form of observations and transcriptions of audio and video recordings from child-child-interactions of 15 Hebrew- and 31 German-speaking children aged 3–6 years, we identify oral argumentative events and investigate different forms of repetitions and their respective relevance for enabling participation. Our results show that, on the one hand, minimal and partial repetitions are used by the children in an inclusive way, creating closeness between the participants. On the other hand, children use complete repetitions more as an excluding technique, displaying misalignment and disaffiliation, in order to challenge and mock each other. The findings suggest that this line of research has significant potential to provide new insights into the formation of social relationships between peers, into the prevention or establishment of participation, which itself is a prerequisite for joint learning, as well as insights into the acquisition of argumentative competence. [ABSTRACT FROM AUTHOR]

Air pollution poses a great danger to human health and economic development, and understanding people's willingness to pay for improved air quality (WTPA) impacts environmental protection. This paper investigates WTPA based on the perspective of subjective well-being (SWB) and analyzes the mediating role of social interaction on the relationship between the two. This paper distinguishes social interactions into online and offline interactions and analyzes whether the mediating effect of the two different interactions on SWB and WTPA exists separately. Using data from the 2018 China General Social Survey (CGSS), we find that SWB has a significant positive effect on WTPA, individuals with higher well-being have higher pro-environmental willingness; there is no age, education level, sex of the person, or regional heterogeneity in the effect of SWB on WTPA; offline social interactions play a partially mediating role between SWB and WTPA, while online social interactions failed to mediate between the two. This paper's policy implication is that improving residents' subjective well-being is both an important development goal and an essential way to resolve the conflict between economic development and environmental protection. [ABSTRACT FROM AUTHOR]

Scholars have demonstrated that common ways of performing charitable food aid in high-income countries maintain a powerless and alienated status of recipients. Aiming to protect the dignity of recipients, alternative forms of food aid have taken shape. However, an in-depth understanding of dignity in the context of food aid is missing. We undertook a scoping review to outline ways in which the dignity of recipients is violated or protected across various forms of food aid in high-income countries. By bringing scientific results together through a social dignity lens, this paper offers a complex understanding of dignity in the context of food aid. The online database Scopus was used to identify scientific literature addressing food aid in relation to the dignity of recipients in high-income countries. The final selection included 37 articles representing eight forms of food aid in twelve countries. Across diverse forms of food aid, the selected studies report signs of (in)dignity concerning five dimensions: access to food aid, social interactions, the food, the physical space, and needs beyond food. Research gaps are found in the diversity of forms of food aid studied, and the identification of social standards important for recipients. Bringing the results of 37 articles together through a social dignity lens articulates the complex and plural ways in which the dignity of recipients is violated or protected. In addition, this review has demonstrated the usefulness of a social dignity lens to understand dignity across and in particular food aid contexts. [ABSTRACT FROM AUTHOR]

Political goals are often sought by groups, whether they be political parties, trade associations, interest groups, or of any other sort. The formalities of modern political systems rely on individual voting, but group identities shape both individual behavior and the workings of government. To build a metaphor from the natural sciences, fluids (groups) are composed of molecules (individuals), but the fluid has properties of its own that affect its motion (actions) and interactions with other fluids (groups). This paper develops a model based on fluid dynamics relationships to explain group interactions based on observable group characteristics. It complements many strands of literature by explaining the circumstances in which existing models are valid, and in which they are incomplete. In so doing, it does not refute any existing models, but links them together and addresses their shortcomings. [ABSTRACT FROM AUTHOR]

Introduction: Conflicts of interest are a normal part of human social intercourse. They become problematic when there is a power differential between participants in the setting of relationships requiring a high degree of trust, as in healthcare. In this white paper we consider how these conflicts may be detected and mitigated.Methods: Following Medline search and reference chaining, we undertook a narrative review of the literature with iterative discussion.Results: Conflicts of interest may be financial, professional or personal, and may operate at the level of the individual or the organisation. Unmanaged, they become a source of bias which places the interests of the professional or the organisation before those of the patient. Reported with increasing frequency, conflicts damage trust, harm patients, and defraud the health system. We make 15 recommendations for minimising conflicts of interest.Conclusions: Nationally funded open-access registries should be established to permit complete disclosure of financial, professional, and personal relationships with the potential for driving bias in research, clinical practice, or health management. Governance of disclosure should be the responsibility of employing organisations through annual staff appraisals, audited by national research integrity committees. Research fraud should incur suspension of the license to practice. Organisations should monitor staff perceptions of ethical climate to enhance awareness of staff behaviours and the potential for misconduct driven by academic pressures. Clear separation of advisory and voting roles is needed in best practice guideline panels. Professional societies and scientific journals should display conflict of interest policies for their own staff and officers as well as for speakers and authors. Undergraduates should not be exposed to pharmaceutical promotions masquerading as education. Undergraduate and postgraduate training programmes should include teaching about managing conflicts of interest and identifying research misconduct. [ABSTRACT FROM AUTHOR]

The following study investigates the measurement equivalence of an online and paper-and-pencil (PAP) survey of human values. For this purpose, a total of 250 respondents completed the 21-item version of the Portrait Value Questionnaire either online ( n = 125) or by PAP ( n = 125). This questionnaire was developed by Schwartz (Advances in experimental social psychology, Academic Press, New York, 1992) and has been included in the European Social Survey since 2002 to test his theory of basic human values. Measurement invariance was tested via a multiple group confirmatory factor analysis. The assessment of invariance included the three levels of configural, metric, and scalar invariance, and the latent means of the values between both samples were compared. Results of this study show that the measurements are invariant at the three levels (configural, metric, and scalar), but there are latent mean differences between the values across the surveys. These differences may be partly explained by age and level of education differences between the two samples. Based on these findings we conclude that the methods of measurement are essentially invariant. [ABSTRACT FROM AUTHOR]

An ensemble of classifiers combines several single classifiers to deliver a final prediction or classification decision. An increasingly provoking question is whether such an ensemble can outperform the single best classifier. If so, what form of ensemble learning system (also known as multiple classifier learning systems) yields the most significant benefits in the size or diversity of the ensemble? In this paper, the ability of ensemble learning to predict and identify factors that influence or contribute to autism spectrum disorder therapy (ASDT) for intervention purposes is investigated. Given that most interventions are typically short-term in nature, henceforth, developing a robotic system that will provide the best outcome and measurement of ASDT therapy has never been so critical. In this paper, the performance of five single classifiers against several multiple classifier learning systems in exploring and predicting ASDT is investigated using a dataset of behavioural data and robot-enhanced therapy against standard human treatment based on 3000 sessions and 300 h, recorded from 61 autistic children. Experimental results show statistically significant differences in performance among the single classifiers for ASDT prediction with decision trees as the more accurate classifier. The results further show multiple classifier learning systems (MCLS) achieving better performance for ASDT prediction (especially those ensembles with three core classifiers). Additionally, the results show bagging and boosting ensemble learning as robust when predicting ASDT with multi-stage design as the most dominant architecture. It also appears that eye contact and social interaction are the most critical contributing factors to the ASDT problem among children. [ABSTRACT FROM AUTHOR]

This paper investigates the intersection of cognitive sciences and social network theory and its counterpart, the complexity sciences, aiming to shed light on the compatibility and potential integration of these frameworks into institutional theory. Institutional scholars have for long selectively adopted notions linked with the cognitive sciences and complexity sciences, such as the notion of path dependence, without exploring the broader implications of systematically integrating such perspectives into institutionalism. This paper aims to advance such a comprehensive theoretical integration, by investigating the effective combination of these approaches and their significant implications. It shows how the complexity sciences contribute to dissolving the barriers between the cognitive and social realms and illustrates how this impacts notions of human agency and reflexivity. Theoretical integration also involves acknowledging considerable diversity in individual human agency, which in turn prompts a reconsideration of how notions of institutional stability, change, diffusion and adaptation are understood. Furthermore, the paper addresses the epistemological challenge presented by the complexity sciences, before it highlights the general relevance of institutional theory in analyzing complex social phenomena. Finally, the paper explores implications for research methodology, proposing that a fusion of institutional theory and the complexity sciences provides a metatheoretical framework for assessing the contextual suitability of different theoretical and methodological approaches. [ABSTRACT FROM AUTHOR]

Bodily engagement with the material and sociocultural world is ubiquitous in doing and learning science. However, science education researchers have often tended to emphasize the disembodied and nonmaterial aspects of science learning, thereby overlooking the crucial role of the body in meaning-making processes. While in recent years we have seen a turn towards embracing embodied perspectives, there persist considerable theoretical and methodological differences within research on embodiment in science education that hamper productive discourse. What is needed is a careful examination of how different traditions and disciplines, among them philosophy, social semiotics, and cognitive science, bear on embodiment in science education research. This paper aims to explore and articulate the differences and convergences of embodied perspectives in science education research in the form of a dialogue between three fictitious personas that stand for the cognitive, social-interactionist, and phenomenological research traditions. By bringing these traditions into dialogue, we aim to better position the role of the body in the science education research landscape. In doing so, we take essential steps towards unifying terminology across different research traditions and further exploring the implications of embodiment for science education research. [ABSTRACT FROM AUTHOR]

In this paper, we study the interactional organization of an instructed object exploration among sighted and visually impaired people (VIPs) in order to contribute to studies of instructional activities and the observable accomplishment of haptic perception. We do this by showing the situated, interactional, and co-operative organization of achieving object understanding. We focus on the dynamics of haptic perception as being reliant on instructions, while at the same time being an observable production that furnishes further instructions. We show the organization of visual and verbal instructions versus the touching of objects for haptic perception. Based on ethnomethodological conversation analysis of video data, we study a VIP's haptic actions in interaction with a professional, sighted ICT consultant who provides instructions on what an object is and what it can do. We show how the instructions are sequentially adjusted to make them relevant for a simultaneous, emerging exploration in which the VIP uses their hands and fingers to perceive very specific details of the object. We argue that achieving object understanding is accomplished in and through the fine-tuned coordination of haptic exploration, both as a response to verbal instructions and also as a means of conveying perception-related actions, which the ICT uses to build new actions. The paper thus makes a case for instructed and distributed haptic perception as observable in social interaction and as a resource for building object understanding within phenomenal fields. [ABSTRACT FROM AUTHOR]

With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability. [ABSTRACT FROM AUTHOR]

Personality and emotion as intrinsic factors often have great influences on the cognition of people's behavior. In computer vision, there is a lot of work done on the recognition of emotions, such as classification of a person's emotions via analyzing facial expressions. Relatively there is less work done on personality estimation. Personality, as a long-term characteristic pattern of behavior, influences the emotion generation of a person. In this paper, we present a new method to analyze and estimate personality and emotions in dyadic and multiparty social interactions. We first propose a context-aware deep learning framework that automatically estimates the personality of a target person based on his/her own and the interlocutor's body behavioral and facial information recorded in the interaction process. Then, we expand this architecture to form a method for jointly estimating personality and recognizing emotions. We conduct a series of experiments on two datasets and the experimental results show that the proposed method has good performance in both personality estimation and emotion recognition. [ABSTRACT FROM AUTHOR]

This study examines a child's personal experiences with peer relationship building in the classroom and is guided by Vygotsky's cultural historical concepts of the social situation of development and cultural tools and Hedegaard's (2012) model for learning and development. Hedegaard's (2012) dialectical—interactive approach was adopted to analyse the data for this study which was gathered using digital video observations of a grade one classroom within the western suburbs of Melbourne, Victoria, Australia over a five week period. This paper explores the strategies used by children that enables them to balance their desire for peer relationships while remaining within what is considered acceptable behaviours by their teachers within the classroom. It is argued that it is important for schools to continue to strive to find the balance between institutional demands while creating social situations that foster peer relationships. [ABSTRACT FROM AUTHOR]

Many online services allow users to participate in various group activities such as online meeting or group buying and thus need to provide user groups with services that they are interested. The group recommender systems emerge as required and provide personalized services for various online user groups. Data sparsity is an important issue in group recommender systems, since even fewer group-item interactions are observed. Transfer learning has been one efficient tool to alleviate the data sparsity issue in recommender systems for individual users, but have not been utilized for group recommendation. Moreover, the group and the group members have complex and mutual relationships with each other, which exacerbates the difficulty in modelling the preferences of both a group and its members for recommendation. Therefore, group recommender systems face three main challenges that may significantly impact its quality and accuracy: (1) taking consideration of group member relationship and their interactions in modelling user and group preferences; (2) ensuring latent feature spaces between the users and groups are maximally matched; and (3) constructing a deep group recommendation method that both the individual user and group domains can benefit from a knowledge exchange. Hence, in this paper, we propose a deep adversarial group recommendation method, called DA-GR. User feature are separated into two subspaces to ensure only consistent group members' feature knowledge can be extracted and shared with group preference modelling. Adversarial learning is used to effectively transfer consistent knowledge from individual user interactions to the group interaction domain through the bridge of group-user relationships. Extensive experiments, which demonstrate the effectiveness and superiority of our proposal, providing accurate recommendation for both individual users and groups, are conducted on public datasets. The source code of DA-GR is in https://github.com/ccnu-mathits/DA-GR. [ABSTRACT FROM AUTHOR]

The concept of engagement is widely adopted in the human–robot interaction (HRI) field, as a core social phenomenon in the interaction. Despite the wide usage of the term, the meaning of this concept is still characterized by great vagueness. A common approach is to evaluate it through self-reports and observational grids. While the former solution suffers from a time-discrepancy problem, since the perceived engagement is evaluated at the end of the interaction, the latter solution may be affected by the subjectivity of the observers. From the perspective of developing socially intelligent robots that autonomously adapt their behaviors during the interaction, replicating the ability to properly detect engagement represents a challenge in the social robotics community. This systematic review investigates the conceptualization of engagement, starting with the works that attempted to automatically detect it in interactions involving robots and real users (i.e., online surveys are excluded). The goal is to describe the most worthwhile research efforts and to outline the commonly adopted definitions (which define the authors' perspective on the topic) and their connection with the methodology used for the assessment (if any). The research was conducted within two databases (Web of Science and Scopus) between November 2009 and January 2023. A total of 590 articles were found in the initial search. Thanks to an accurate definition of the exclusion criteria, the most relevant papers on automatic engagement detection and assessment in HRI were identified. Finally, 28 papers were fully evaluated and included in this review. The analysis illustrates that the engagement detection task is mostly addressed as a binary or multi-class classification problem, considering user behavioral cues and context-based features extracted from recorded data. One outcome of this review is the identification of current research barriers and future challenges on the topic, which could be clustered in the following fields: engagement components, annotation procedures, engagement features, prediction techniques, and experimental sessions. [ABSTRACT FROM AUTHOR]

The intelligent service of the digital twin machine tool provides convenience for the human operation interaction in the machine tool, and the real-time operation interaction between the human and the machine tool has high requirements for the real-time machining model simulation algorithm. Firstly, this paper proposes a simulation method based on the combination of augmented reality (AR) and digital twin of machine tool machining virtual-real fusion. Secondly, to improve the real-time interoperability between human and machine tools in the AR virtual-real fusion machining process, this paper proposes a fast Dexel model generation method based on binary tree space segmentation. The method is based on the 3D model of the workpiece preprocessing to generate a one-way Dexel model of the binary tree storage structure, using the tool and the workpiece overlap envelope in the binary tree structure to determine the interference region, and ultimately calculating the intersection line between the one-way generation line and the tool geometry to get the model of the workpiece after cutting. By analyzing the real-time performance of the algorithm, the algorithm satisfies the simulation calculation of Dexel models of different scales. Finally, through the example of real-time interactive operation between human and machine tool AR, the results show that the average display frame rate of this algorithm in the machining process reaches 55–60 frames, and the parameter error between the model after virtual machining and the actual machining model is within 1.3%. At the same time, 100 people were randomly selected to carry out AR interaction training, and the real-time performance experience of AR virtual-real fusion machining was comprehensively evaluated, and the results showed that the system can meet the real-time demand of interaction operation of most participants. [ABSTRACT FROM AUTHOR]

Higher-order structures such as simplicial complexes are ubiquitous in numerous real-world networks. Empirical evidence reveals that interactions among nodes occur not only through edges but also through higher-dimensional simplicial structures such as triangles. Nevertheless, classic models such as the threshold model fail to capture group interactions within these higher-order structures. In this paper, we propose a higher-order non-Markovian social contagion model, considering both higher-order interactions and the non-Markovian characteristics of real-world spreading processes. We develop a mean-field theory to describe its evolutionary dynamics. Simulation results reveal that the theory is capable of predicting the steady state of the model. Our theoretical analyses indicate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions. Besides, we find that non-Markovian recovery can boost the system resilience to withstand a large-scale infection or a small-scale infection under different conditions. This work deepens our understanding of the behaviors of higher-order non-Markovian social contagions in the real world. High-order structures are ubiquitous in numerous real-world networks and play a significant role in social contagion phenomena, the authors introduce a novel higher-order non-Markovian social contagion model, addressing limitations of traditional models. Through mean-field theory and simulations, the authors demonstrate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions and reveal the resilience enhancement conferred by non-Markovian recovery, shedding light on real-world contagion dynamics. [ABSTRACT FROM AUTHOR]

Robots must develop the ability to socially navigate in uncontrolled urban environments to be able to be included in our daily lives. This paper presents a new robot navigation framework called the adaptive social planner (ASP) and a robotic system, which includes the ASP. Our results and previous work show that the ASP can adapt to different collaborative tasks involving humans and robots, such as independent robot navigation, human-robot accompaniment, a robot approaching people, robot navigation tasks that combine learning techniques, and human-drone interactions. Our approach in this paper focuses on demonstrating how the ASP can be customized to implement two new methods for group accompaniment: the adaptive social planner using a V-formation model to accompany groups of people (ASP-VG) and the adaptive social planner using a side-by-side model to accompany groups of people (ASP-SG). These two methods result in a robot accompanying groups of people by anticipating human and uncontrolled urban environment behaviors. Also, we develop four new robot skills to deal with unexpected human behaviors, such as rearrangement of the position of the companions inside the group, unforeseen changes in the velocity of the robot companions, occlusions among group members, and changes in the direction toward destinations in the environment. Moreover, we develop different performance metrics, based on social distances, to evaluate the tasks of the robot. In addition, we present the guidelines followed in performing the real-life experiments with volunteers, including a human-robot speech interaction to help humans create a relationship with the robot to be genuinely involved in the mutual accompaniment. Finally, we include an exhaustive validation of the methods by evaluating the behavior of the robot through synthetic and real-life experiments. We incorporate five user studies to evaluate aspects related to social acceptability and preferences of people regarding both types of robot group accompaniment. [ABSTRACT FROM AUTHOR]

Three papers propose machine learning-based models for the realization of socially competent human-robot interactions whether it be cognitive or physical. Searching for proper strategies to evaluate the quality of social interactions is essential for machine learning technologies in social robotics. [Extracted from the article]

While loneliness has been linked to various mental and physical health problems, the sense in which loneliness is a cause of these conditions has so far attracted little philosophical attention. This paper aims to fill this gap by analyzing research on health effects of loneliness and therapeutic interventions through current approaches to causality. To deal with the problem of causality between psychological, social, and biological variables, the paper endorses a biopsychosocial model of health and disease. I will investigate how three main approaches to causality used in psychiatry and public health apply to loneliness: interventionism, mechanisms, and dispositional theories. Interventionism can specify whether loneliness causes specific effects, or whether a treatment works, incorporating results from randomized controlled trials. Mechanisms help explain how loneliness brings about negative health effects, spelling out psychological processes involved in lonely social cognition. Dispositional approaches help stress particular features of loneliness connected to negative social interactions, such as defensiveness. I will conclude by showing that previous research alongside emerging approaches to health effects of loneliness lend themselves to analysis in terms of the causal models under discussion. [ABSTRACT FROM AUTHOR]

Collaborative robots, also known as cobots, are designed to work alongside humans in a shared workspace and provide assistance to them. With the rapid development of robotics and artificial intelligence in recent years, cobots have become faster, smarter, more accurate, and more dependable. They have found applications in a broad range of scenarios where humans require assistance, such as in the home, healthcare, and manufacturing. In manufacturing, in particular, collaborative robots combine the precision and strength of robots with the flexibility of human dexterity to replace or aid humans in highly repetitive or hazardous manufacturing tasks. However, human–robot interaction still needs improvement in terms of adaptability, decision making, and robustness to changing scenarios and uncertainty, especially in the context of continuous interaction with human operators. Collaborative robots and humans must establish an intuitive and understanding rapport to build a cooperative working relationship. Therefore, human–robot interaction is a crucial research problem in robotics. This paper provides a summary of the research on human–robot interaction over the past decade, with a focus on interaction methods in human–robot collaboration, environment perception, task allocation strategies, and scenarios for human–robot collaboration in manufacturing. Finally, the paper presents the primary research directions and challenges for the future development of collaborative robots. [ABSTRACT FROM AUTHOR]

Ranking schemes drive many real-world decisions, like, where to study, whom to hire, what to buy, etc. Many of these decisions often come with high consequences. For example, a university can be deemed less prestigious if not featured in a top-k list, and consumers might not even explore products that do not get recommended to buyers. At the heart of most of these decisions are opaque ranking schemes, which dictate the ordering of data entities, but their internal logic is inaccessible or proprietary. Drawing inferences about the ranking differences is like a guessing game to the stakeholders, like, the rankees (i.e., the entities who are ranked, like product companies) and the decision-makers (i.e., who use the rankings, like buyers). In this paper, we aim to enable transparency in ranking interpretation by using algorithmic rankers that learn from available data and by enabling human reasoning about the learned ranking differences using explainable AI (XAI) methods. To realize this aim, we leverage the exploration–explanation paradigm of human–data interaction to let human stakeholders explore subsets and groupings of complex multi-attribute ranking data using visual explanations of model fit and attribute influence on rankings. We realize this explanation paradigm for transparent ranking interpretation in TRIVEA, a visual analytic system that is fueled by: (i) visualizations of model fit derived from algorithmic rankers that learn the associations between attributes and rankings from available data and (ii) visual explanations derived from XAI methods that help abstract important patterns, like, the relative influence of attributes in different ranking ranges. Using TRIVEA, end users not trained in data science have the agency to transparently reason about the global and local behavior of the rankings without the need to open black-box ranking models and develop confidence in the resulting attribute-based inferences. We demonstrate the efficacy of TRIVEA using multiple usage scenarios and subjective feedback from researchers with diverse domain expertise. [ABSTRACT FROM AUTHOR]

Autistic Children often struggle with social interaction and communication, studies have found that many of them prefer to interact with objects than people. However, there is a lack of research exploring the specific characteristics and factors involved in interactions within families with autistic children where objects are the center of the interaction. This paper describes the process and findings of a diary study exploring how young autistic children interact with their families through objects in natural scenarios. A one-week diary study was conducted with six families with young autistic children. Diary videos were recorded onsite and coded later according to a social interaction behavior scheme with corresponding diary entries. Qualitative data analysis was conducted to reveal possible patterns. Results revealed ongoing difficulties in establishing and maintaining family interaction and identified influential factors of object-centered family interaction. The most prevalent pattern observed was parents taking the lead in interactions, followed by the child's confirmation response. Remarkably, daily necessities emerged as potential physical mediums for enhancing family interactions, opening avenues for exploring tangible designs in human–computer interaction. These findings offer valuable implications for future research and the development of innovative designs that promote enriching interactions for autistic children and their families. [ABSTRACT FROM AUTHOR]

One of the greatest challenges of computational argumentation research consists of creating persuasive strategies that can effectively influence the behaviour of a human user. From the human perspective, argumentation represents one of the most effective ways to reason and to persuade other parties. Furthermore, it is very common that humans adapt their discourse depending on the audience in order to be more persuasive. Thus, it is of utmost importance to take into account user modelling features for personalising the interactions with human users. Through computational argumentation, we can not only devise the optimal solution, but also provide the rationale for it. However, synergies between computational argumentative reasoning and computational persuasion have not been researched in depth. In this paper, we propose a new formal framework aimed at improving the persuasiveness of arguments resulting from the computational argumentative reasoning process. For that purpose, our approach relies on an underlying abstract argumentation framework to implement this reasoning and extends it with persuasive features. Thus, we combine a set of user modelling and linguistic features through the use of a persuasive function in order to instantiate abstract arguments following a user-specific persuasive policy. From the results observed in our experiments, we can conclude that the framework proposed in this work improves the persuasiveness of argument-based computational systems. Furthermore, we have also been able to determine that human users place a high level of trust in decision support systems when they are persuaded using arguments and when the reasons behind the suggestion to modify their behaviour are provided. [ABSTRACT FROM AUTHOR]

In this paper, I outline a grammar of lifting (i.e., resistance training) and compare it to that of language. I approach lifting as a system of generating complex meaning–form correspondences from regularized elements and describe the levels of mental representations and relationships between them that are involved in full command of this system. To be able to do so, I adopt a goal-based conception of meaning, which allows us to talk about mappings from complex goals to complex surface outputs in systems of intentional action, signaling and non-signaling, interactive and non-interactive, in a unified way, and show how it applies in lifting. I then proceed to argue that the grammar of lifting is architecturally very similar to that of language. First, I show that both involve stable (idiomatized/lexicalized) pairings of regularized forms with regularized meanings. Second, I argue that in both lifting and language, meaning–form mapping is mediated by syntax, which, crucially, operates on non-linearized hierarchical structures of abstract objects that include both content morphemes and functional morphemes. I conclude, following and expanding on some insights from prior literature and offering further evidence for them, that neither of these architectural phenomena (idiomatized meaning–form pairings and abstract syntax) is specific to language, with both of them likely emerging in skilled action that does not necessarily involve social interaction, due to considerations of repeatability and reusability of elements in new contexts. [ABSTRACT FROM AUTHOR]

Social media has expanded the possibilities for citizens around the world to share knowledge and interact about scientific advancements, facilitating to raise public awareness of and interest in science. Amidst this context, scientists in all disciplines are intensifying the use of social media as a data source to capture what citizens express about their achievements, beyond dissemination purposes. Content analysis is the generalised method used by researchers to explore the interactions of citizens in social media about science. In this commentary paper, we explore the social media communicative observations as an emerging technique in the social media analytics to include the communicative dimension of science in the analysis of interactions between scientists and citizens. The implications to empower dialogically the social media communities interested in science are shared. [ABSTRACT FROM AUTHOR]

With the rapid expansion of online socializing, social media fatigue has become increasingly common among users. Envy and admiration are very common emotions in online social interactions. We usually consider envy a negative emotion and admiration a positive emotion. Nevertheless, does envy inevitably increase social media fatigue? Does admiration always alleviate social media fatigue? To answer these questions, this paper explores the impacts of two forms of envy (i.e., benign envy and malicious envy) and two forms of admiration (i.e., elevation and skill admiration) on social media fatigue through the mediators of social media loneliness and social media anxiety. The data were collected from 581 WeChat users, and the results showed that benign envy aggravated social media fatigue through the mediator of social media anxiety, while malicious envy aggravated social media fatigue through the mediators of both social media loneliness and anxiety. However, although admiration is often considered a positive emotion, it does not always have a positive effect. Elevation relieved social media fatigue through the mediator of social media loneliness, while skill admiration aggravated social media fatigue through the mediator of social media anxiety. Overall, the findings offer useful implications for alleviating social media fatigue. [ABSTRACT FROM AUTHOR]

Time geography was conceptualized in the 1960s when the technology was very different from what we have today. Conventional time-geographic concepts therefore were developed with a focus on human activities and interactions in physical space. We now live in a smart, connected, and dynamic world with human activities and interactions increasingly taking place in virtual space enabled by modern information and communications technology. Coupled with recent advances in sensing and mobile technologies, it is now feasible to collect human dynamics data in both physical and virtual spaces with unprecedented spatial and temporal details in the so-called Big Data era. The Big Data era brings both opportunities and challenges to time geography. While the unprecedented data collected in the Big Data era can serve as useful data sources to time-geographic research, we also notice that some classical concepts in time geography are insufficient to properly handle human dynamics in today's hybrid physical–virtual world in many cases. This paper first discusses the evolving human dynamics enabled by technological advances to illustrate different types of hybrid physical–virtual space performed through internet applications, digital twins, and augmented reality/virtual reality/metaverse. We then review the classical time-geographic concepts of constraints, space–time path, space–time prism, bundle, project/situation, and diorama in a hybrid physical–virtual world to discuss potential extensions of some classical time-geographic concepts to bolster human dynamics research in today's hybrid physical–virtual world. [ABSTRACT FROM AUTHOR]

Nowadays, various interfaces are used to control smart home appliances. The human and smart home appliances interaction may be based on input devices such as a mouse, keyboard, microphone, or webcam. The interaction between humans and machines can be established via speech using a microphone as one of the input modes. The Speech-based human and machine interaction is a more natural way of communication in comparison to other types of interfaces. Existing speech-based interfaces in the smart home domain suffer from some problems such as limiting the users to use a fixed set of pre-defined commands, not supporting indirect commands, requiring a large training set, or depending on some specific speakers. To solve these challenges, we proposed several approaches in this paper. We exploited ontology as a knowledge base to support indirect commands and remove user restrictions on expressing a specific set of commands. Moreover, Long Short-Term Memory (LSTM) has been exploited for detecting spoken commands more accurately. Additionally, due to the lack of Persian voice commands for interacting with smart home appliances, a dataset of speaker-independent Persian voice commands for communicating with TV, media player, and lighting system has been designed, recorded, and evaluated in this research. The experimental results show that the LSTM-based voice command detection system performed almost 1.5% and 13% more accurately than the Hidden Markov Model-based one, in scenarios 'with' and 'without ontology', respectively. Furthermore, using ontology in the LSTM-based method has improved the system performance by about 40%. [ABSTRACT FROM AUTHOR]

Purpose The development of assistive technologies that support people in social interactions has attracted increased attention in HCI. This paper presents a systematic review of studies of Socially Assistive Systems targeted at older adults and people with disabilities. The purpose is threefold: (1) Characterizing related assistive systems with a special focus on the system design, primarily including HCI technologies used and user-involvement approach taken; (2) Examining their ways of system evaluation; (3) Reflecting on insights for future design research. Methods A systematic literature search was conducted using the keywords "social interactions" and "assistive technologies" within the following databases: Scopus, Web of Science, ACM, Science Direct, PubMed, and IEEE Xplore. Results Sixty-five papers met the inclusion criteria and were further analyzed. Our results showed that there were 11 types of HCI technologies that supported social interactions for target users. The most common was cognitive and meaning understanding technologies, often applied with wearable devices for compensating users' sensory loss; 33.85% of studies involved end-users and stakeholders in the design phase; Four types of evaluation methods were identified. The majority of studies adopted laboratory experiments to measure user-system interaction and system validation. Proxy users were used in system evaluation, especially in initial experiments; 42.46% of evaluations were conducted in field settings, primarily including the participants' own homes and institutions. Conclusion We contribute an overview of Socially Assistive Systems that support social interactions for older adults and people with disabilities, as well as illustrate emerging technologies and research opportunities for future work. [ABSTRACT FROM AUTHOR]

A large and fast-growing number of studies across the social sciences use experiments to better understand the role of race in human interactions, particularly in the American context. Researchers often use names to signal the race of individuals portrayed in these experiments. However, those names might also signal other attributes, such as socioeconomic status (e.g., education and income) and citizenship. If they do, researchers would benefit greatly from pre-tested names with data on perceptions of these attributes; such data would permit researchers to draw correct inferences about the causal effect of race in their experiments. In this paper, we provide the largest dataset of validated name perceptions to date based on three different surveys conducted in the United States. In total, our data include over 44,170 name evaluations from 4,026 respondents for 600 names. In addition to respondent perceptions of race, income, education, and citizenship from names, our data also include respondent characteristics. Our data will be broadly helpful for researchers conducting experiments on the manifold ways in which race shapes American life. [ABSTRACT FROM AUTHOR]

This paper aims at contributing to the debate in Mathematics Education about the understanding of the dynamics of students' group interactions by proposing an interpretative lens, which defines four modes of participating in a group on the basis of different kinds of utterances, gestures, postures, and glances that each student makes. We apply this lens to two selected cases of students working in a small group, and, by comparing and contrasting similarities and differences observed through our interpretative lens, we attempt to understand how, and under which circumstances, the students reach mathematical understanding as a group, or not. [ABSTRACT FROM AUTHOR]

A decision delay can translate into significant financial and business losses. One way to accelerate the decision process is through improved communication among the stakeholders engaged in the project. Capturing, transferring, managing, and reusing data, information, and knowledge in the context it is generated can lead to higher productivity, effective communication, reduced number of requests for clarification, and a shorter time-to-market cycle. We formalized the concept of reflection in interaction during communicative events among multiple project stakeholders. This concept extends Donald Schon’s theory of reflection in action of a single practitioner. We model the observed reflection in interaction with a prototype system called TalkingPaperTM. It is a ubiquitous client-server collaborative environment that facilitates knowledge capture, sharing, and reuse during synchronous and asynchronous communicative events. TalkingPaperTM bridges the paper and digital worlds. It transforms the analog verbal discours, annotated paper corporate documents, and the paper and pencil sketches into indexed and synchronized digital content that is published on and streamed on-demand from a TalkingPaperTM web server. The TalkingPaperTM sessions can be accessed by all stakeholders for rapid knowledge sharing and decision-making. [ABSTRACT FROM AUTHOR]

The prevalence of teamwork in contemporary science has raised new questions about collaboration networks and the potential impact on research outcomes. Previous studies primarily focused on pairwise interactions between scientists when constructing collaboration networks, potentially overlooking group interactions among scientists. In this study, we introduce a higher-order network representation using algebraic topology to capture multi-agent interactions, i.e., simplicial complexes. Our main objective is to investigate the influence of higher-order structures in local collaboration networks on the productivity of the focal scientist. Leveraging a dataset comprising more than 3.7 million scientists from the Microsoft Academic Graph, we uncover several intriguing findings. Firstly, we observe an inverted U-shaped relationship between the number of disconnected components in the local collaboration network and scientific productivity. Secondly, there is a positive association between the presence of higher-order loops and individual scientific productivity, indicating the intriguing role of higher-order structures in advancing science. Thirdly, these effects hold across various scientific domains and scientists with different impacts, suggesting strong generalizability of our findings. The findings highlight the role of higher-order loops in shaping the development of individual scientists, thus may have implications for nurturing scientific talent and promoting innovative breakthroughs. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on the development of qualitative social network analysis (QSNA) and its evolution over time, using the analysis of bibliographic networks. The dataset consists of articles from the Web of Science Clarivate Analytics database obtained by searching for the keyword"Social network* + (Qualitative OR Mixed method*)"(in total 21,823 publications). From the data, we constructed a citation network, which was followed by an evaluation of QSNA field's growth and the identification of the most cited works. Using the normalized Search path count weights, we extracted the Main path and Key-routes in the citation network. Starting from 1988, when the first works appeared, the field has grown significantly. The analysis of the citation network shows that there are two main scientific spheres where QSNA was developed—in the social science and the medical science. In the social science the main empirical research subjects are migrant networks and teacher's networks. In the medical science the research object moved from sexual contacts to social contacts in infection studies. [ABSTRACT FROM AUTHOR]

Several externalities arise when agents shield optimally to avoid infection during an epidemic. We classify externalities into static and dynamic and compare the decentralized and optimal solutions when agents derive utility from social interaction. For low infection costs agents shield too little; for high costs they shield too much because of a "rat race to shield": they delay social action until other agents contract the disease and society reaches herd immunity. Other externalities drive more wedges between the private and social outcomes. The expectation of a fully effective vaccine that ends the disease faster changes results, reversing excessive shielding. [ABSTRACT FROM AUTHOR]

Social network group decision-making (SNGDM) is a new type of group decision-making (GDM) paradigm that has emerged in recent years. The traditional consensus feedback adjustment model for GDM is difficult to adapt to the characteristics of SNGDM, with a large number of participants, unrestricted by time and space, and unfixed decision-making rules. Opinion dynamics is an important tool for predicting the evolution of group opinions based on established opinion evolution rules, relying solely on the initial opinions of participants. The combination of opinion dynamics and SNGDM is natural. However, this combination still faces many problems, such as the current opinion dynamics models having difficulty handling the common heterogeneous preferences in GDM, and little consideration being given to the interaction between social relationships and opinion evolution. This paper studies heterogeneous opinion dynamics phenomena considering consensus evolution in SNGDM. We process heterogeneous preferences based on the measurement of distance and similarity, improve Friedkin and Johnsen model considering the stubbornness of the decision-makers with respect to their own latest opinions dynamically, and mainly focus on the interaction between opinion evolution and social relationships. A case study on enterprise team risk management is given to illustrate the effectiveness of the proposed method. Through comparative analysis, we find that when the group is in a connected network with consistent goals, the interaction between opinion evolution and social relationships can achieve consensus faster than in other situations. [ABSTRACT FROM AUTHOR]