Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.Comment: To be presented at EMNLP 2018. 15 page

Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following

How robust is the evidence of an emerging or increasing female excess in physical morbidity between childhood and adolescence? Results of a systematic literature review and meta-analyses

For asthma and psychological morbidity, it is well established that higher prevalence among males in childhood is replaced by higher prevalence among females by adolescence. This review investigates whether there is evidence for a similar emerging female ‘excess’ in relation to a broad range of physical morbidity measures. Establishing whether this pattern is generalised or health outcome-specific will further understandings of the aetiology of gender differences in health. Databases (Medline; Embase; CINAHL; PsycINFO; ERIC) were searched for English language studies (published 1992–2010) presenting physical morbidity prevalence data for males and females, for at least two age-bands within the age-range 4–17 years. A three-stage screening process (initial sifting; detailed inspection; extraction of full papers), was followed by study quality appraisals. Of 11 245 identified studies, 41 met the inclusion criteria. Most (n = 31) presented self-report survey data (five longitudinal, 26 cross-sectional); 10 presented routinely collected data (GP/hospital statistics). Extracted data, supplemented by additional data obtained from authors of the included studies, were used to calculate odds ratios of a female excess, or female:male incident rate ratios as appropriate. To test whether these changed with age, the values were logged and regressed on age in random effects meta-regressions. These showed strongest evidence of an emerging/increasing female excess for self-reported measures of headache, abdominal pain, tiredness, migraine and self-assessed health. Type 1 diabetes and epilepsy, based on routinely collected data, did not show a significant emerging/increasing female excess. For most physical morbidity measures reviewed, the evidence broadly points towards an emerging/increasing female excess during the transition to adolescence, although results varied by morbidity measure and study design, and suggest that this may occur at a younger age than previously thought

Associations between body dissatisfaction and self-reported anxiety and depression in otherwise healthy men: a systematic review and meta-analysis

Introduction It is unknown whether male body dissatisfaction is related to anxiety and depression. This study investigates whether there is an association between body dissatisfaction and self-reported anxiety and/or depression in otherwise healthy adult males. Method A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta Analyses as the reporting guideline. Four databases including CINAHL complete, Health Source: Nursing/Academic Edition, MEDLINE and PsycINFO were searched for observational studies with a correlational design. Studies were appraised using the Appraisal tool for Cross-Sectional Studies to measure quality and risk of bias. Data were extracted from studies to analyse and synthesise findings using content analysis and random effects meta-analyses in male body dissatisfaction and anxiety, depression, and both anxiety and depression. Results Twenty-three cross-sectional studies were included in the review. Nineteen studies found positive correlations between male body dissatisfaction and anxiety and/or depression. Meta-analyses of Pearson’s correlation coefficients found statistically significant associations with body satisfaction for anxiety 0.40 (95% CI 0.28 to 0.51) depression 0.34 (95% CI 0.22 to 0.45) and both anxiety and depression outcomes 0.47 (95% CI 0.33 to 0.59). The quality appraisal found study samples were homogeneous being mostly ascertained through academic institutions where participants were predominantly young, Caucasian and with relatively high educational attainment. Measures of body satisfaction focused predominantly on muscularity and thinness. Discussion This study provides the first pooled estimates of the correlation between body dissatisfaction and anxiety and depression in men. Findings need to be interpreted with respect to the samples and outcomes of the included studies. It is recommended that future research should increase the diversity of men in studies. Studies should measure a wider range of body dissatisfaction types found in men. Conclusion The findings demonstrate that an association between male body dissatisfaction and anxiety and depression is likely to exist. Future research should address the temporal relationship between body dissatisfaction and anxiety and depression

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