Intergroup Variability in Personality Recognition

Automatic Identification of personality in conversational speech has many applications in natural language processing such as leader identification in a meeting, adaptive dialogue systems, and dating websites. However, the widespread acceptance of automatic personality recognition through lexical and vocal characteristics is limited by the variability of error rate in a general purpose model among speakers from different demographic groups. While other work reports accuracy, we explored error rates of automatic personality recognition task using classification models for different genders and native language groups (L1). We also present a statistical experiment showing the influence of gender and L1 on the relation between acoustic-prosodic features and NEO- FFI self-reported personality traits. Our results show the impact of demographic differences on error rate varies considerably while predicting “Big Five” personality traits from speaker’s utterances. This impact can also be observed through differences in the statistical relationship of voice characteristics with each personality inventory. These findings can be used to calibrate existing personality recognition models or to develop new models that are robust to intergroup variability

Personality Recognition For Deception Detection

Personality aims at capturing stable individual characteristics, typically measurable in quantitative terms, that explain and predict observable behavioral differences. Personality has been proved to be very useful in many life outcomes, and there has been huge interests on predicting personality automatically. Previously, there are tremendous amount of approaches successfully predicting personality. However, most previous research on personality detection has used personality scores assigned by annotators based solely on the text or audio clip, and found that predicting self-reported personality is a much more difficult task than predicting observer-report personality. In our study, we will demonstrate how to accurately detect self-reported personality from speech using various technique include feature engineering and machine learning algorithms. Individual speaker differences such as personality play an important role in deception detection, adding considerably to its difficulty. We therefore hypothesize that personality scores may provide useful information to a deception classifier, helping to account for interpersonal differences in verbal and deceptive behavior. In final step of this study, we focus upon the personality differences between deceivers as well as their common characteristics. We helped collect within- and cross-cultural data to train new automatic procedures to identify deceptive behavior in American and Mandarin speakers. We examined whether personality recognition can help to predict individual differences in deceivers’ behavior. Therefore, we embedded personality recognition classifier into the deception classifier using deep neural network to improve the performance of deception detection

INTERACTIVE EMPATHY AND LEADER EFFECTIVENESS: AN EVALUATION OF HOW SENSING EMOTION AND RESPONDING WITH EMPATHY INFLUENCE CORPORATE LEADER EFFECTIVENESS

Empathy has been shown to be a very powerful social and work ability. This study surveyed 754 employees of a privately held eastern United States company, and incorporated annual performance evaluations to empirically link interactive empathy to leader performance of 102 leaders. Data was collected from the leader’s followers, peers, and supervisors and from self-report personality evaluations. The results of this study show that leaders that are willing to engage their followers with empathic displays are seen as better leaders from their supervisors and have more engaged employees. Other contributions of this study include validation of the interactive empathy scale in a corporate environment and empirical support to show how interactive empathy adds incremental explanatory power of leader’s performance above and beyond that explained by personality. Directions for future research and practical implications of these results are also offered

Factors controlling virus-vector-host plant interactions : the model system Frankliniella occidentalis and Tomato spotted wilt virus

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Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition

Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate

Listener Background in L2 Speech Evaluation

Listeners are integral parts of second language (L2) oral performance assessment. However, evaluation of listeners is susceptible to listener background variables and biases. These variables and preexisting biases distort native speaker (NS) listeners’ perceptions of non-native speakers’ (NNSs) speech performance and contribute errors into their oral performance assessment. Among listener background variables, listeners’ first language status, the amount of exposure to different English varieties, listeners’ educational background, prior language teaching experience, NNSs’ linguistic stereotyping, and listener attitude have been investigated in the literature and assumed to exert sizable amount of variation in speakers’ oral proficiency true scores. To minimize listeners’ bias in the assessment context, listeners are provided with intensive training programs in which they are trained how to rate NNSs’ speech more objectively utilizing scoring rubrics. To mediate listeners’ bias in social contexts, the literature has provided strands of evidence in favor of structured intergroup contact programs, which are inoculations particularly devised to improve NSs’ attitude, thereby making them more receptive to NNSs’ English varieties. To enhance L2 listeners’ self-efficacy and foster their autonomy, L2 instructors are encouraged to emphasize explicit instruction of listening strategies

Magistrates' decision-making: personality, process and outcome

The thesis examined personality and socio-demographic characteristics of individuals and their relationship to the way in which magistrates approach the sentencing of offenders and the choices they make. It was based on a review of the theoretical approaches to models of decision-making and the concept of individual differences. A pluralistic methodology was adopted combining a quasi-experimental approach in the first study, with two further qualitative studies. Study 1 reported the profile data for the participants, all practising magistrates, and their responses to case study vignettes. Study 2 considered participants' perception of the sentencing process and the factors that influenced their decisions using an interpretative phenomenological approach, while Study 3 applied content and discourse analysis to transcripts of a sentencing training exercise in which magistrates had participated. Analyses of the first study were mostly correlational. Modest associations between Locus of Control and Legal Authoriarianism with severity of sentence were demonstrated and also small gender differences in sentencing choice. The study concluded that there was no support for hypotheses linking other personality trait measurements with the severity of sentence or the approach adopted, using an algebraic model to represent the process. In the subsequent studies, evidence emerged to suggest a more holistic approach to sentencing, guided by advice on structured decision-making, while accommodating the influences of probation service reports, diverse sentencing aims and the advice of the legal professionals. The impact of group interactions was also apparent. This varied with individual characteristics and acquired competences necessary for satisfactory appraisal. The interpretation of 'roles' on a sentencing Bench and their potential effects on the process and outcome of sentencing was observed

Attention Restraint, Working Memory Capacity, and Mind Wandering: Do Emotional Valence or Intentionality Matter?

Attention restraint appears to mediate the relationship between working memory capacity (WMC) and mind wandering (Kane et al., 2016). Prior work has identifed two dimensions of mind wandering—emotional valence and intentionality. However, less is known about how WMC and attention restraint correlate with these dimensions. Te current study examined the relationship between WMC, attention restraint, and mind wandering by emotional valence and intentionality. A confrmatory factor analysis demonstrated that WMC and attention restraint were strongly correlated, but only attention restraint was related to overall mind wandering, consistent with prior fndings. However, when examining the emotional valence of mind wandering, attention restraint and WMC were related to negatively and positively valenced, but not neutral, mind wandering. Attention restraint was also related to intentional but not unintentional mind wandering. Tese results suggest that WMC and attention restraint predict some, but not all, types of mind wandering

Book of abstracts, 4th World Congress on Agroforestry

International audienc

Biomarkers, Concussions, and the Duty of Care

Article published in the Michigan State Law Review