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The article explores whether the traits representing the dimensions underlying the structure of facial and non‐facial impressions are similarly mapped in the face space. Two studies examine whether the trustworthiness‐by‐dominance and the warmth‐by‐competence two‐dimensional models overlap in face perception. In Study 1 (N = 200), we used a reverse‐correlation task to obtain classification images (CIs) reflecting how each dimension is mapped onto a face. Results show that the similarity between CIs was higher between warmth and trustworthiness than between competence and dominance. In Study 2 (N = 31) the evaluations of each CI on each social dimension show a higher dissociation between dominance and competence than between trustworthiness and warmth. These results, obtained at both perceptual and judgment levels, suggest that there is only a partial correspondence between the two models that seems to be driven by the relationship that the competence and dominance dimensions establish with valence.

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Automatically monitoring and quantifying stressinduced thermal dynamic information in real-world settings is an extremely important but challenging problem. In this paper, we explore whether we can use mobile thermal imaging to measure the rich physiological cues of mental stress that can be deduced from a person's nose temperature. To answer this question we build i) a framework for monitoring nasal thermal variable patterns continuously and ii) a novel set of thermal variability metrics to capture a richness of the dynamic information. We evaluated our approach in a series of studies including laboratory-based psychosocial stress-induction tasks and real-world factory settings. We demonstrate our approach has the potential for assessing stress responses beyond controlled laboratory settings.

Operators in industrial manufacturing environments are under pressure to cope with increasing flexibility and complexity of work. The automation of manufacturing requires operators to adopt new techniques and shifts the focus from lowcomplexity repetitive tasks to dealing with the execution of highcomplexity tasks in cooperation with machines. The emergence of wearable technologies makes it possible to equip operators with miniaturized sensors that may be used to determine the physical and mental stress experienced by operators. Process mining technologies are suited to analyze such sensor data in the context of the manufacturing process with the ultimate goal of improving the operator's well-being through reorganization of work and the work place. However, the storage and processing of such highly personalized data comes with many privacy challenges. Whereas there are many potential benefits, such as improve the work environment, there are also many justified reasons for operators to oppose the processing of their data. Apart from employee concerns, data protection regulations, such as EU GDPR (Europe's General Data Protection Regulation), imposes many compliance challenges for the design of a process mining systems dealing with personal data. We contribute an analysis of the privacy challenges of using process mining on data recorded from sensorized operators in human-centered industrial environments. Guided by privacy research and the regulation imposed by the GDPR, we describe guidelines for privacy in process mining systems.