89 resultados para Affective intelligence

em Queensland University of Technology - ePrints Archive


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For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

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The proliferation of news reports published in online websites and news information sharing among social media users necessitates effective techniques for analysing the image, text and video data related to news topics. This paper presents the first study to classify affective facial images on emerging news topics. The proposed system dynamically monitors and selects the current hot (of great interest) news topics with strong affective interestingness using textual keywords in news articles and social media discussions. Images from the selected hot topics are extracted and classified into three categorized emotions, positive, neutral and negative, based on facial expressions of subjects in the images. Performance evaluations on two facial image datasets collected from real-world resources demonstrate the applicability and effectiveness of the proposed system in affective classification of facial images in news reports. Facial expression shows high consistency with the affective textual content in news reports for positive emotion, while only low correlation has been observed for neutral and negative. The system can be directly used for applications, such as assisting editors in choosing photos with a proper affective semantic for a certain topic during news report preparation.

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Background and Objectives: Although depression is a commonly occurring mental illness, research concerning strategies for early detection and prophylaxis has not until now focused on the possible utility of measures of Emotional Intelligence (EI) as a potential predictive factor. The current study aimed to investigate the relationship between EI and a clinical diagnosis of depression in a cohort of adults. Methods: Sixty-two patients (59.70% female) with a DSM-IV-TR diagnosis of a major affective disorder and 39 aged matched controls (56.40% female) completed self-report instruments assessing EI and depression in a cross-sectional study. Results: Significant associations were observed between severity of depression and the EI dimensions of Emotional Management (r = -0.56) and Emotional Control (r = -0.62). The results show a reduced social involvement, an increased prior institutionalization and an increased incidence of "Schizophrenic Psychosis" and "Abnormal Personalities" in the sub-group of repeated admissions. Conclusions: Measures of EI may have predictive value in terms of early identification of those at risk for developing depression. The current study points to the potential value of conducting further studies of a prospective nature.

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Wisdom and emotional intelligence are increasingly popular topics among happiness scholars. Despite their conceptual overlap, no empirical research has examined their interrelations and incremental predictive validities. The aims of this study were (a) to investigate associations between multidimensional conceptualizations of self-reported wisdom (Ardelt in Res Aging 25(3):275-324, 2003, 2004) and emotional intelligence (Davies et al. in J Pers Soc Psychol 75:989-1015, 1998) and (b) to examine the joint effects of self-reported wisdom and emotional intelligence on dimensions of happiness (life satisfaction as well as positive and negative affect). Data were provided by two samples: 175 university students and 400 online workers. Correlations between a composite wisdom score, a composite emotional intelligence score, and happiness facets were positive and moderate in size. Regression analyses showed that the effects of composite wisdom on life satisfaction and positive affect (but not negative affect) became weaker and non-significant when composite emotional intelligence was controlled. Additional analyses including three dimensions of the self-reported wisdom (cognitive, reflective, and affective wisdom) and four dimensions of emotional intelligence (self- and others-emotions appraisal, use and regulation of emotion) revealed a more differentiated pattern of results. Implications for future research on wisdom and happiness are discussed.

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The lack of satisfactory consensus for characterizing the system intelligence and structured analytical decision models has inhibited the developers and practitioners to understand and configure optimum intelligent building systems in a fully informed manner. So far, little research has been conducted in this aspect. This research is designed to identify the key intelligent indicators, and develop analytical models for computing the system intelligence score of smart building system in the intelligent building. The integrated building management system (IBMS) was used as an illustrative example to present a framework. The models presented in this study applied the system intelligence theory, and the conceptual analytical framework. A total of 16 key intelligent indicators were first identified from a general survey. Then, two multi-criteria decision making (MCDM) approaches, the analytic hierarchy process (AHP) and analytic network process (ANP), were employed to develop the system intelligence analytical models. Top intelligence indicators of IBMS include: self-diagnostic of operation deviations; adaptive limiting control algorithm; and, year-round time schedule performance. The developed conceptual framework was then transformed to the practical model. The effectiveness of the practical model was evaluated by means of expert validation. The main contribution of this research is to promote understanding of the intelligent indicators, and to set the foundation for a systemic framework that provide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed components design configurations.

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Sponsorship is increasingly important in a firm’s communication mix. Research to date has focused on the impact of sponsorship on brand awareness and its subsequent consequences for image congruency and consumer attitudes towards sponsors’ brands. A lesser studied area is the effect of sponsorship on consumers’ purchase intentions and behaviours. We argue that existing models of sponsorship driven purchase behaviour fail to account for affective commitment, which mediates relationship between affiliation with the team and social identification with the team. We propose a modified framework describing the effect of sponsorship on purchase intentions in the context of low and high performing sports teams. The framework is tested using structural equations modelling; employing PLS estimation and data collected via online survey of AFL chat room participants. Results confirm the role of affective commitment in sport sponsorship purchase intentions and indicate that team success has a significant influence on fans’ purchase behaviours.

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The assessment of intellectual ability is a core competency in psychology. The results of intelligence tests have many potential implications and are used frequently as the basis for decisions about educational placements, eligibility for various services, and admission to specific groups. Given the importance of intelligence test scores, accurate test administration and scoring are essential; yet there is evidence of unacceptably high rates of examiner error. This paper discusses competency and postgraduate training in intelligence testing and presents a training model for postgraduate psychology students. The model aims to achieve high levels of competency in intelligence testing through a structured method of training, practice and feedback that incorporates peer support, self-reflection and multiple methods for evaluating competency.

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This study was designed to examine affective leader behaviours, and their impact on cognitive, affective and behavioural engagement. Researchers (e.g., Cropanzano & Mitchell, 2005; Moorman et al., 1998) have called for more research to be directed toward modelling and testing sets of relationships which better approximate the complexity associated with contemporary organisational experience. This research has attempted to do this by clarifying and defining the construct of engagement, and then by examining how each of the engagement dimensions are impacted by affective leader behaviours. Specifically, a model was tested that identifies leader behaviour antecedents of cognitive, affective and behavioural engagement. Data was collected from five public-sector organisations. Structural equation modelling was used to identify the relationships between the engagement dimensions and leader behaviours. The results suggested that affective leader behaviours had a substantial direct impact on cognitive engagement, which in turn influenced affective engagement, which then influenced intent to stay and extra-role performance. The results indicated a directional process for engagement, but particularly highlighted the significant impact of affective leader behaviours as an antecedent to engagement. In general terms, the findings will provide a platform from which to develop a robust measure of engagement, and will be helpful to human resource practitioners interested in understanding the directional process of engagement and the importance of affective leadership as an antecedent to engagement.

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The purpose of the present study was to examine the role of fluid (gf), social (SI) and emotional intelligence (EI) in faking the Beck Depression Inventory (2nd ed., BDI-II). Twenty-two students and 26 non-students completed Raven’s Advanced Progressive Matrices (APM), a social insight test, the Schutte et al. self-report EI scale, and the BDI-II under honest and faking instructions. Results were consistent with a new model of successful faking, in which a participant’s original response must be manipulated into a strategic response, which must match diagnostic criteria. As hypothesised, the BDI-II could be faked, and gf was not related to faking ability. Counter to expectations, however, SI and EI were not related to faking ability. A second study explored why EI failed to facilitate faking. Forty-nine students and 50 non-students completed the EI measure, the Marlowe-Crown Scale and the Levenson et al. Psychopathy Scale. As hypothesised, EI was negatively correlated with psychopathy, but EI showed no relationship with socially desirable responding. It was concluded that in the first experiment, high-EI people did fake effectively, but high-psychopathy people (who had low EI) were also faking effectively, resulting in a distribution that showed no advantage to high EI individuals.

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This research examined for the first time the relationship between emotional manipulation, emotional intelligence, and primary and secondary psychopathy. As predicted, in Study 1 (N = 73), emotional manipulation was related to both primary and secondary psychopathy. Only secondary psychopathy was related to perceived poor emotional skills. Secondary psychopathy was also related to emotional concealment. Emotional intelligence was negatively related to perceived poor emotional skills, emotional concealment, and primary and secondary psychopathy. In Study 2 (N = 275), two additional variables were included: alexithymia and ethical position. It was found that for males, primary psychopathy and emotional intelligence predicted emotional manipulation, while for females emotional intelligence acted as a suppressor, and ethical idealism and secondary psychopathy were additional predictors. For males, emotional intelligence and alexithymia were related to perceived poor emotional skills, while for females emotional intelligence, but not alexithymia, predicted perceived poor emotional skills, with ethical idealism acting as a suppressor. For both males and females, alexithymia predicted emotional concealment. These findings suggest that the mechanisms behind the emotional manipulation–psychopathy relationship differ as a function of gender. Examining the different aspects of emotional manipulation as separate but related constructs may enhance understanding of the construct of emotional manipulation.