Bell lysaker emotion recognition test: a contribution for the italian validation

INTRODUCTION: Emotion recognition deficits in psychopathology have been extensively studied with a variety of measures. The Bell Lysaker Emotion Recognition Test (BLERT; Bell et al., 1997) is an effective method to assess emotion recognition by presenting affect stimuli which may have greater verisimilitude with real life events. Indeed, BLERT combines facial expressions with affective information transmitted in prosody or body posture. This method has allowed the study of emotion recognition deficit in psychotic patients, as well as its relationships with other aspects of psychopathology (Vohs et al., 2014). OBJECTIVES: We aimed at testing the validity and reliability of an Italian version of the BLERT. AIMS: First, a group-comparison was carried out between clinical and nonclinical participants. Then, correlations among BLERT scores and other indices of psychological functioning were explored. METHODS: We recruited 12 inpatients with psychotic disorders (mean age= 54.75; 58.3% female) and 45 nonclinical participants (mean age= 24.04; 75.6% female). We administered the BLERT (Bell et al., 1997), along with the following measures: Empathy Quotient (Lawrence et al., 2004), Interpersonal Reactivity Index (Davis, 1980), Difficulties in Emotion Regulation Scale (Gratz & Roemer, 2004), and the Inventory of Interpersonal Problems-47 (Pilkonis et al., 1996). RESULTS: Clinical participants resulted impaired in all indices of the BLERT. Further, the construct validity of the BLERT was confirmed by associations with measures of empathy, emotion dysregulation, and interpersonal problems. CONCLUSIONS: The use of the Italian version of the BLERT seemed promising for the study of emotion recognition in both clinical and nonclinical samples

Online user behavioural modeling with applications to price steering

Price steering is the practice of “changing the order of search results to highlight specific products” and products prices. In this paper, we show an initial investigation to quantify the price steering level in search results shown to different kind of users on Google Shopping. We mimic the category of affluent users. Affluent users visit websites offering expensive services, search for luxury goods and always click on the most costly items results at Google Shopping. The goal is checking if users trained in specific ways get different search results, based on the price of the products in the results. Evaluation is based on well known metrics to measure page results differences and similarities. Experiments are automised, rendering large-scale investigations feasible. Results of our experiments, based on a preliminary experimental setting, show that users trained on some particular topics are not always influenced by previous search and click activities. However, different trained users actually achieve different search results, thus paving the way for further investigation

The role of bot squads in the political propaganda on Twitter

Nowadays, Social Media are a privileged channel for news spreading, information exchange, and fact checking. Unexpectedly for many users, automated accounts, known as social bots, contribute more and more to this process of information diffusion. Using Twitter as a benchmark, we consider the traffic exchanged, over one month of observation, on the migration flux from Northern Africa to Italy. We measure the significant traffic of tweets only, by implementing an entropy-based null model that discounts the activity of users and the virality of tweets. Results show that social bots play a central role in the exchange of significant content. Indeed, not only the strongest hubs have a number of bots among their followers higher than expected, but furthermore a group of them, that can be assigned to the same political tendency, share a common set of bots as followers. The retweeting activity of such automated accounts amplifies the hubs’ messages

“I Do Not Like Being Me”: the Impact of Self-hate on Increased Risky Sexual Behavior in Sexual Minority People

Background: Increased risky sexual behaviors (RSB) in sexual minority people relative to heterosexual individuals are well documented. However, the role of trans-diagnostic factors that are not sexual orientation-specific, such as self-criticism, in predicting RSB was understudied. The present study aimed to test participants’ gender and sexual orientation as moderators between self-criticism and RSB. Methods: Data were collected during 2019. The total sample included 986 sexual minority people (Nwomen = 51%) and 853 heterosexual people (Nwomen = 46%), ranging from 18 to 35 years of age. Self-criticism dimensions (self-hate, self-inadequacy, self-reassurance), types of positive affect (relaxed, safe/content, and activated affect), and RSB were assessed. Bivariate, multivariate analyses, and moderated regression analyses were conducted. Results: Sexual minority participants showed higher levels of RSB, self-hate, and self-inadequacy than heterosexual people. Only in sexual minority men, RSB correlated positively with self-hate and negatively with safe/content positive affect. Moderated regressions showed that only for sexual minority participants, higher RSB were predicted by higher levels of self-hate. At the same time, this association was not significant for heterosexual people controlling the effects of age, presence of a stable relationship, other self-criticism dimensions, and activation safe/content affect scale. The two-way interaction between sexual orientation and gender was significant, showing that regardless of self-hate, the strength of the association between sexual orientation and RSB is stronger for sexual minority men than sexual minority women and heterosexual participants. Conclusions: Findings highlight the distinctive role of self-hate in the occurrence of RSB in sexual minority people and support the usefulness of developing a compassion-focused intervention to target self-hate in sexual minority people

Automated synthesis and ranking of secure BPMN orchestrators

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A Multilevel Multidimensional Finite Mixture Item Response Model to Cluster Respondents and Countries: The Forms of Self-Criticising/Attacking and Self-Reassuring Scale

The aim of this study was to test the multilevel multidimensional finite mixture item response model of the Forms of Self-Criticising/Attacking and Self-Reassuring Scale (FSCRS) to cluster respondents and countries from 13 samples (N = 7,714) and from 12 countries. The practical goal was to learn how many discrete classes there are on the level of individuals (i.e., how many cut-offs are to be used) and countries (i.e., the magnitude of similarities and dissimilarities among them). We employed the multilevel multidimensional finite mixture approach which is based on an extended class of multidimensional latent class Item Response Theory (IRT) models. Individuals and countries are partitioned into discrete latent classes with different levels of self-criticism and self-reassurance, taking into account at the same time the multidimensional structure of the construct. This approach was applied to the analysis of the relationships between observed characteristics and latent trait at different levels (individuals and countries), and across different dimensions using the three-dimensional measure of the FSCRS. Results showed that respondents' scores were dependent on unobserved (latent class) individual and country membership, the multidimensional structure of the instrument, and justified the use of a multilevel multidimensional finite mixture item response model in the comparative psychological assessment of individuals and countries. Latent class analysis of the FSCRS showed that individual participants and countries could be divided into discrete classes. Along with the previous findings that the FSCRS is psychometrically robust we can recommend using the FSCRS for measuring self-criticism

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Analysis of offsite Emergency Planning Zones (EPZ) for the Rocky Flats Plant

This project plan for Phase II summarizes the design of a project to complete analysis of offsite Emergency Planning Zones (EPZ) for the Rocky Flats Plant. Federal, state, and local governments develop emergency plans for facilities that may affect the public in the event of an accidental release of nuclear or hazardous materials. One of the purposes of these plans is to identify EPZs where actions might be necessary to protect public health. Public protective actions include sheltering, evacuation, and relocation. Agencies use EPZs to develop response plans and to determine needed resources. The State of Colorado, with support from the US Department of Energy (DOE) and Rocky Flats contractors, has developed emergency plans and EPZs for the Rocky Flats Plant periodically beginning in 1980. In Phase II, Interim Emergency Planning Zones Analysis, Maximum Credible Accident'' we will utilize the current Rocky Flats maximum credible accident (MCA), existing dispersion methodologies, and upgraded dosimetry methodologies to update the radiological EPZs. Additionally, we will develop recommendations for EPZs for nonradiological hazardous materials releases and evaluate potential surface water releases from the facility. This project will allow EG G Rocky Flats to meet current commitments to the state of Colorado and make steady, tangible improvements in our understanding of risk to offsite populations during potential emergencies at the Rocky Flats Plant. 8 refs., 5 figs., 4 tabs

The factor structure of the Forms of Self-Criticising/Attacking & Self-Reassuring Scale in thirteen distinct populations

There is considerable evidence that self-criticism plays a major role in the vulnerability to and recovery from psychopathology. Methods to measure this process, and its change over time, are therefore important for research in psychopathology and well-being. This study examined the factor structure of a widely used measure, the Forms of Self-Criticising/Attacking & Self-Reassuring Scale in thirteen nonclinical samples (N = 7510) from twelve different countries: Australia (N = 319), Canada (N = 383), Switzerland (N = 230), Israel (N = 476), Italy (N = 389), Japan (N = 264), the Netherlands (N = 360), Portugal (N = 764), Slovakia (N = 1326), Taiwan (N = 417), the United Kingdom 1 (N = 1570), the United Kingdom 2 (N = 883), and USA (N = 331). This study used more advanced analyses than prior reports: a bifactor item-response theory model, a two-tier item-response theory model, and a non-parametric item-response theory (Mokken) scale analysis. Although the original three-factor solution for the FSCRS (distinguishing between Inadequate-Self, Hated-Self, and Reassured-Self) had an acceptable fit, two-tier models, with two general factors (Self-criticism and Self-reassurance) demonstrated the best fit across all samples. This study provides preliminary evidence suggesting that this two-factor structure can be used in a range of nonclinical contexts across countries and cultures. Inadequate-Self and Hated-Self might not by distinct factors in nonclinical samples. Future work may benefit from distinguishing between self-correction versus shame-based self-criticism.Peer reviewe