What does your profile picture say about you? The accuracy of thin-slice personality judgments from social networking sites made at zero-acquaintance

The myocardium exhibits heterogeneous nature due to scarring after Myocardial Infarction (MI). In Cardiac Magnetic Resonance (CMR) imaging, Late Gadolinium (LG) contrast agent enhances the intensity of scarred area in the myocardium. In this paper, we propose a probability mapping technique using Texture and Intensity features to describe heterogeneous nature of the scarred myocardium in Cardiac Magnetic Resonance (CMR) images after Myocardial Infarction (MI). Scarred tissue and non-scarred tissue are represented with high and low probabilities, respectively. Intermediate values possibly indicate areas where the scarred and healthy tissues are interwoven. The probability map of scarred myocardium is calculated by using a probability function based on Bayes rule. Any set of features can be used in the probability function. In the present study, we demonstrate the use of two different types of features. One is based on the mean intensity of pixel and the other on underlying texture information of the scarred and non-scarred myocardium. Examples of probability maps computed using the mean intensity of pixel and the underlying texture information are presented. We hypothesize that the probability mapping of myocardium offers alternate visualization, possibly showing the details with physiological significance difficult to detect visually in the original CMR image. The probability mapping obtained from the two features provides a way to define different cardiac segments which offer a way to identify areas in the myocardium of diagnostic importance (like core and border areas in scarred myocardiu

Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis

One of the main findings of cognitive sciences is that automatic processes of which we are unaware shape, to a significant extent, our perception of the environment. The phenomenon applies not only to the real world, but also to multimedia data we consume every day. Whenever we look at pictures, watch a video or listen to audio recordings, our conscious attention efforts focus on the observable content, but our cognition spontaneously perceives intentions, beliefs, values, attitudes and other constructs that, while being outside of our conscious awareness, still shape our reactions and behavior. So far, multimedia technologies have neglected such a phenomenon to a large extent. This paper argues that taking into account cognitive effects is possible and it can also improve multimedia approaches. As a supporting proof-of-concept, the paper shows not only that there are visual patterns correlated with the personality traits of 300 Flickr users to a statistically significant extent, but also that the personality traits (both self-assessed and attributed by others) of those users can be inferred from the images these latter post as "favourite"

The Role of Person-Organization Fit in Organizational Selection Decisions

This paper presents and tests a theoretical model of person-organization fit and organizational selection decisions using data from 35 organizations making hiring decisions. Results suggested that (a) interviewers were able to assess applicants\u27 values with above-chance levels of accuracy, (b) interviewers compare their perceptions of applicants\u27 values with their organizations\u27 values to assess person-organization fit, and (c) it is perceived values congruence and not actual values congruence between applicants and organizations that predicted interviewers\u27 person-organization fit perceptions. Results also suggested that interviewers\u27 person-organization fit assessments had the largest effect on their hiring recommendations even after controlling for competing applicant characteristics (e.g., demographics, human capital), and that interviewers\u27 hiring recommendations had large and significant effects on organizations\u27 hiring decisions (e.g., job offers)

Belbin Revisited: The Construct Validity of the Interplace II Team Role Instrument

In the present study the construct validity of the revised edition of the Belbin Team Roles measure, the so-called Interplace II program, is tested. Three parallel parts were used to determine someone’s team roles. The sample included 1434 persons who were asked to fill out the self-perception inventory and the self-perception assessment, whereas the observer assessment sheet was filled out by at least four observers. The inter-rater reliability appeared to be satisfactory across all team roles. As for the construct validity, which was studied in a multitrait-multimethod design using structural equation modeling, the results revealed that the discriminant and convergent validity for the instrument as a whole is good; only a small effect could be contributed to method variance.Belbin;multitrait-multimethod methodology;team roles;teams

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Agreement on the perception of moral character

This study tested for inter-judge agreement on moral character. A sample of students and community members rated their own moral character using a measure that tapped six moral character traits. Friends, family members, and/or acquaintances rated these targets on the same traits. Self/other and inter-informant agreement was found at the trait level for both a general character factor and for residual variance explained by individual moral character traits, as well as at the individual level (judges agreed on targets’ “moral character profiles”). Observed inter-judge agreement constitutes evidence for the existence of moral character, and raises questions about the nature of moral character traits

Investigating the veracity of self-reported post-traumatic growth: a profile analysis approach

Research into posttraumatic growth—positive psychological change that people report in their relationships, priorities in life, and self-perception after experiences of adversity—has been severely critiqued. We investigated the degree to which community members’ friends and relatives corroborated targets’ self-perceived positive and negative changes as measured by the Posttraumatic Growth Inventory-42. We found corroboration only for negative changes when we examined overall (averaged) scores. However, using a profile analysis procedure, we found significant participant–informant agreement on the domains of change that had relatively higher scores in the target’s profile and those that had relatively lower scores. Our results demonstrate that informants were able to observe that targets had changed and were sensitive to the idiosyncratic ways in which these changes had manifested in targets’ behavior

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