6,434 research outputs found

    First impressions: A survey on vision-based apparent personality trait analysis

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    © 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

    Toward future 'mixed reality' learning spaces for STEAM education

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    Digital technology is becoming more integrated and part of modern society. As this begins to happen, technologies including augmented reality, virtual reality, 3d printing and user supplied mobile devices (collectively referred to as mixed reality) are often being touted as likely to become more a part of the classroom and learning environment. In the discipline areas of STEAM education, experts are expected to be at the forefront of technology and how it might fit into their classroom. This is especially important because increasingly, educators are finding themselves surrounded by new learners that expect to be engaged with participatory, interactive, sensory-rich, experimental activities with greater opportunities for student input and creativity. This paper will explore learner and academic perspectives on mixed reality case studies in 3d spatial design (multimedia and architecture), paramedic science and information technology, through the use of existing data as well as additional one-on-one interviews around the use of mixed reality in the classroom. Results show that mixed reality can provide engagement, critical thinking and problem solving benefits for students in line with this new generation of learners, but also demonstrates that more work needs to be done to refine mixed reality solutions for the classroom

    Discourse Analysis

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    This chapter (a) presents discourse analysis as both epistemology and methodology; (b) suggests a sociolinguistic toolkit that could be used as one type of approach to conducting discourse analysis; (c) reviews and points to literature in music education and music therapy that have used such epistemological and methodological tools; and (d) suggests that, by engaging with discourse analysis, we can begin to ask questions about participants and their interactions within environments where music therapists operate and analyze prevailing discourses within structures and systems of music therapy. [excerpt

    Stressful first impressions in job interviews

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    Stress can impact many aspects of our lives, such as the way we interact and work with others, or the first impressions that we make. In the past, stress has been most commonly assessed through self-reported questionnaires; however, advancements in wearable technology have enabled the measurement of physiological symptoms of stress in an unobtrusive manner. Using a dataset of job interviews, we investigate whether first impressions of stress (from annotations) are equivalent to physiological measurements of the electrodermal activity (EDA). We examine the use of automatically extracted nonverbal cues stemming from both the visual and audio modalities, as well EDA stress measurements for the inference of stress impressions obtained from manual annotations. Stress impressions were found to be significantly negatively correlated with hireability ratings i.e individuals who were perceived to be more stressed were more likely to obtained lower hireability scores. The analysis revealed a significant relationship between audio and visual features but low predictability and no significant effects were found for the EDA features. While some nonverbal cues were more clearly related to stress, the physiological cues were less reliable and warrant further investigation into the use of wearable sensors for stress detection

    Negotiation of meaning via virtual exchange in immersive virtual reality environments

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    This study examines how English-as-lingua-franca (ELF) learners employ semiotic resources, including head movements, gestures, facial expression, body posture, and spatial juxtaposition, to negotiate for meaning in an immersive virtual reality (VR) environment. Ten ELF learners participated in a Taiwan-Spain VR virtual exchange project and completed two VR tasks on an immersive VR platform. Multiple datasets, including the recordings of VR sessions, pre- and post-task questionnaires, observation notes, and stimulated recall interviews, were analyzed quantitatively and qualitatively with triangulation. Built upon multimodal interaction analysis (Norris, 2004) and Varonis and Gass’ (1985a) negotiation of meaning model, the findings indicate that ELF learners utilized different embodied semiotic resources in constructing and negotiating meaning at all primes to achieve effective communication in an immersive VR space. The avatar-mediated representations and semiotic modalities were shown to facilitate indication, comprehension, and explanation to signal and resolve non-understanding instances. The findings show that with space proxemics and object handling as the two distinct features of VR-supported environments, VR platforms transform learners’ social interaction from plane to three-dimensional communication, and from verbal to embodied, which promotes embodied learning. VR thus serves as a powerful immersive interactive environment for ELF learners from distant locations to be engaged in situated languacultural practices that goes beyond physical space. Pedagogical implications are discussed

    Computational Analysis Of Behavior In Employment Interviews And Video Resumes

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    Used in nearly every organization, employment interviews are a ubiquitous process where job applicants are evaluated by an employer for an open position. Consisting of an interpersonal interaction between at least one interviewer and a job applicant, they are used to assess interviewee knowledge, skills, abilities, and behavior in order to select the most suitable person for the job at hand. Because they require face-to-face interaction between at least two protagonists, they are inherently social, and all that recruiters have as a basis to forge their opinion is the applicant's behavior during the interview (in addition to his resume); in such settings, first impressions are known to play an important role. First impressions can be defined as snap judgments of others made based on a low amount of information. Interestingly, social psychology research has shown that humans are quite accurate at making inferences about others, even if the information is minimal. Social psychologists long studied job interviews, with the aim of understanding the relationships between behavior, interview outcomes, and job performance. Until recently, psychology studies relied on the use of time-intensive manual annotations by human observers. However, the advent of inexpensive audio and video sensors in the last decade, in conjunction with improved perceptual processing methods, has enabled the automatic and accurate extraction of behavioral cues, facilitating the conduct of social psychology studies. The use of automatically extracted nonverbal cues in combination with machine learning inference techniques has led to promising computational methods for the automatic prediction of individual and group social variables such as personality, emergent leadership, or dominance. In this thesis, we addressed the problem of automatically predicting hirability impressions from interview recordings by investigating three main aspects. First, we explored the use of state-of-the-art computational methods for the automatic extraction of nonverbal cues. As a rationale for selecting the behavioral features to be extracted, we reviewed the psychology literature for nonverbal cues which were shown to play a role in job interviews. While the main focus of this thesis is nonverbal behavior, we also investigated the use of verbal content and standard questionnaire outputs. Also, we did not limit ourselves to the use of existing techniques: we developed a multimodal nodding detection method based on previous findings in psychology stating that head gestures are conditioned on the speaking status of the person under analysis, and results showed that considering the speaking status improved the accuracy. Second, we investigated the use of supervised machine learning techniques for the prediction of hirability impressions in a regression task, and up to 36% of the variance could be explained, demonstrating that the automatic inference of hirability is a promising task. Finally, we analyzed the predictive validity of thin slices, short segments of interaction, and showed that short excerpts of job interviews could be predictive of the outcome, with up to 34% of the variance explained by nonverbal behavior extracted from thin slices. As another trend, online social media is changing the landscape of personnel recruitment. Until now, resumes were among the most widely used tools for the screening of job applicants. [...
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