Pervasive Social Computing: augmenting five facets of human intelligence

Zhou, Jiehan; Sun, Jun-Zhao; Athukorala, Kumaripaba; Wijekoon, Dinesh; Ylianttila, Mika

doi:10.1007/s12652-011-0081-z

Cited by 20 publications

(12 citation statements)

References 60 publications

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“…These systems can extract various kinds of behavioral cues and social signals, such as physical appearance, gesture and posture, gaze and face, vocal behavior, and use of space and environment (Zhou et al 2012). Analyzing this information can enable the visually representation of social features, such as identity, reputation, trust, accountability, presence, social role, expertise, knowledge, and ownership (Zhou et al 2012).…”

Section: Understanding Of the Datamentioning

confidence: 99%

The use of data science for education: The case of social-emotional learning

Liu

Huang

2017

Smart Learn. Environ.

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The broad availability of educational data has led to an interest in analyzing useful knowledge to inform policy and practice with regard to education. A data science research methodology is becoming even more important in an educational context. More specifically, this field urgently requires more studies, especially related to outcome measurement and prediction and linking these to specific interventions. Consequently, the purpose of this paper is first to incorporate an appropriate data-analytic thinking framework for pursuing such goals. The well-defined model presented in this work can help ensure the quality of results, contribute to a better understanding of the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Second, a case study of social-emotional learning is presented. We hope the issues we have highlighted in this paper help stimulate further research and practice in the use of data science for education.

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Section: Understanding Of the Datamentioning

confidence: 99%

The use of data science for education: The case of social-emotional learning

Liu

Huang

2017

Smart Learn. Environ.

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“…Pervasive technologies are motivating communities in voluntary groupware sensing endeavors. People in a social context use pervasive technologies to augment their collective knowledge, experience and efforts in reaching collective goals [14]. Deciphering human intention, gleaning of meaning from social media data, etc., are the ultimate challenges for Pervasive Computing if they are to evolve to Pervasive Social computing as proposed by Mokhtar and Capra 2009 [15].…”

Section: Socio-economicmentioning

confidence: 99%

Ubiquitous Computing in the context of developing countries

Eblewongel¹,

Amanuel²,

Mekuria³

et al. 2016

Proccedings of the ICAIIT2016

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Abstract.Ubiquitous computing is an ultra-interdisciplinary field. Because of its capacity to affect every sphere of human life, it is endowed with a wide range of characteristics and features. This paper attempts to describe the more salient and diverse features of Pervasive Computing in an organized manner that appreciates the multi-disciplinary nature of the field. Five categories: Setup, design, architecture, deployment and operations; interface; security; engineering and socio-economic; are used to systematically outline the current characteristics of Ubiquitous computing. Sample applications from the field of Ubicomp are also discussed to illustrate some of the characteristics of pervasive computing. Moreover, we have also tried to highlight the challenges and opportunities for implementing Pervasive Computing in the context of developing countries. .

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“…The output of online capture of socio-environmental conditions would be expressed in terms of interpersonal distances, age and sex of individuals (inferred from vocal parameters) and estimates of emotional arousal. Tools for the extraction of contextual information from the physical environment are being developed (Beadle et al 1997), and ideas for their integration into a more complex computing environment have emerged (Ben-Mokhtar and Capra 2009; Henricksen and Indulska 2006;Zhou et al 2010). Automatic analysis of social context has, however, not yet been developed, possibly due to a lack of consensus on how to annotate social situations.…”

Section: Multimodal Signalsmentioning

confidence: 99%

A psycho-ethological approach to social signal processing

Méhu

Scherer

2012

Cogn Process

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The emerging field of social signal processing can benefit from a theoretical framework to guide future research activities. The present article aims at drawing attention to two areas of research that devoted considerable efforts to the understanding of social behaviour: ethology and social psychology. With a long tradition in the study of animal signals, ethology and evolutionary biology have developed theoretical concepts to account for the functional significance of signalling. For example, the consideration of divergent selective pressures responsible for the evolution of signalling and social cognition emphasized the importance of two classes of indicators: informative cues and communicative signals. Social psychology, on the other hand, investigates emotional expression and interpersonal relationships, with a focus on the mechanisms underlying the production and interpretation of social signals and cues. Based on the theoretical considerations developed in these two fields, we propose a model that integrates the processing of perceivable individual features (social signals and cues) with contextual information, and we suggest that output of computer-based processing systems should be derived in terms of functional significance rather than in terms of absolute conceptual meaning.

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Pervasive Social Computing: augmenting five facets of human intelligence

Cited by 20 publications

References 60 publications

The use of data science for education: The case of social-emotional learning

The use of data science for education: The case of social-emotional learning

Ubiquitous Computing in the context of developing countries

A psycho-ethological approach to social signal processing

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