51,215 research outputs found
Virtual EQ – the talent differentiator in 2020?
In an increasingly competitive, globalised world, knowledge-intensive industries/ services are seen as engines for success. Key to this marketplace is a growing army of ‘talent’ i.e. skilled and dedicated knowledge workers. These knowledge workers engage in non-routine problem solving through combining convergent, divergent and creative thinking across organizational and company boundaries - a process often facilitated though the internet and social media, consequently forming networks of expertise. For knowledge workers, sharing their learning with others through communities of practice embedded in new information media becomes an important element of their personal identity and the creation of their individual brand or e-social reputation. Part of the new knowledge/skills needed for this process becomes not only emotional intelligence (being attuned to the emotional needs of others) but being able to do this within and through new media, thus the emergence of virtual emotional intelligence (EQ). Our views of current research found that HRD practitioners in 2020 might need to consider Virtual EQ as part of their talent portfolio. However it seems that new technology has created strategies for capturing and managing knowledge that are readily duplicated and that a talent differentiator in 2020 might simply be the ability and willingness to learn
First impressions: A survey on vision-based apparent personality trait analysis
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft
Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequities
and unfairness is receiving increasing popular and academic attention. A surge
of recent work has focused on the development of algorithmic tools to assess
and mitigate such unfairness. If these tools are to have a positive impact on
industry practice, however, it is crucial that their design be informed by an
understanding of real-world needs. Through 35 semi-structured interviews and an
anonymous survey of 267 ML practitioners, we conduct the first systematic
investigation of commercial product teams' challenges and needs for support in
developing fairer ML systems. We identify areas of alignment and disconnect
between the challenges faced by industry practitioners and solutions proposed
in the fair ML research literature. Based on these findings, we highlight
directions for future ML and HCI research that will better address industry
practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in
Computing Systems (CHI 2019
Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools
Many people struggle to control their use of digital devices. However, our
understanding of the design mechanisms that support user self-control remains
limited. In this paper, we make two contributions to HCI research in this
space: first, we analyse 367 apps and browser extensions from the Google Play,
Chrome Web, and Apple App stores to identify common core design features and
intervention strategies afforded by current tools for digital self-control.
Second, we adapt and apply an integrative dual systems model of self-regulation
as a framework for organising and evaluating the design features found. Our
analysis aims to help the design of better tools in two ways: (i) by
identifying how, through a well-established model of self-regulation, current
tools overlap and differ in how they support self-control; and (ii) by using
the model to reveal underexplored cognitive mechanisms that could aid the
design of new tools.Comment: 11.5 pages (excl. references), 6 figures, 1 tabl
Teaching psychology to computing students
The aim of this paper is twofold. The first aim is to discuss some observations gained from teaching Psychology to Computing students, highlighting both the wide range of areas where Psychology is relevant to Computing education and the topics that are relevant at different stages of students’ education. The second aim is to consider findings from research investigating the characteristics of Computing and Psychology students. It is proposed that this information could be considered in the design and use of Psychology materials for Computing students.
The format for the paper is as follows. Section one will illustrate the many links between the disciplines of Psychology & Computing; highlighting these links helps to answer the question that many Computing students ask, what can Psychology offer to Computing? Section two will then review some of the ways that I have been involved in teaching Psychology to Computing students, from A/AS level to undergraduate and postgraduate level. Section three will compare the profiles of Computing and Psychology students (e.g. on age, gender and motivation to study), to highlight how an understanding of these factors can be used to adapt Psychology teaching materials for Computing students. The conclusions which cover some practical suggestions are presented in section four
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