Vine copula modeling dependence among cyber risks: A dangerous regulatory paradox

Dependence among different cyber risk classes is a fundamentally underexplored topic in the literature. However, disregarding the dependence structure in cyber risk management leads to inconsistent estimates of potential unintended losses. To bridge this gap, this article adopts a regulatory perspective to develop vine copulas to capture dependence. In quantifying the solvency capital requirement gradient for cyber risk measurement according to Solvency II, a dangerous paradox emerges: an insurance company does not tend to provide cyber risk hedging products as they are excessively expensive and would require huge premiums that it would not be possible to find policyholders

Complete Genome Sequences of Mycobacterium smegmatis Phages Chewbacca, Reptar3000, and Riparian, Isolated in Las Vegas, Nevada

Here, we present the complete genome sequences of Mycobacterium smegmatis phages Chewbacca, Reptar3000, and Riparian, isolated from soil in Las Vegas, NV. The phages were isolated and annotated by undergraduate students enrolled in the Phage Discovery course offered by the School of Life Sciences at the University of Nevada, Las Vega

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Ensemble learning on visual and textual data for social image emotion classification

Texts, images and other information are posted everyday on the social network and provides a large amount of multimodal data. The aim of this work is to investigate if combining and integrating both visual and textual data permits to identify emotions elicited by an image. We focus on image emotion classification within eight emotion categories: amusement, awe, contentment, excitement, anger, disgust, fear and sadness. Within this classification task we here propose to adopt ensemble learning approaches based on the Bayesian model averaging method, that combine five state-of-the-art classifiers. The proposed ensemble approaches consider predictions given by several classification models, based on visual and textual data, through respectively a late and an early fusion schemes. Our investigations show that an ensemble method based on a late fusion of unimodal classifiers permits to achieve high classification performance within all of the eight emotion classes. The improvement is higher when deep image representations are adopted as visual features, compared with hand-crafted ones

Detecting Sexist MEME On The Web: A Study on Textual and Visual Cues

In recent years, it is evident the interest in the role of women within society and, in particular, the way we approach and refer to them. However, sexism as a form of discrimination towards women spread exponentially through the web and at a very high frequency, especially in the form of memes. Memes, which are typically composed of pictorial and textual components, can convey messages ranging from women stereotype, shaming, objectification to violence. In order to counterattack this phenomenon, in this paper we give a first insight in the field of automatic detection of sexist memes, by investigating both unimodal and multimodal approaches to understand the contribution of textual and visual cues

Dall’High-Tech alla Ceramica Tradizionale – Un esempio tutto Salentino di ricostruzione di lacune ceramiche con l’ausilio della tecnica stereolitografica laser

L’esperienza ventennale del gruppo di Scienza e Tecnologia dei Materiali dell’Università del Salento (http://mstg.unile.it/) nel settore dei nuovi materiali e processi combinata con la professionalità e la passione del Museo delle Ceramiche di Cutrofiano per il recupero e la conservazione dei Beni Culturali ha dato luogo ad un’interessante proposta per la ricostruzione dei reperti ceramici. Viene qui descritta in modo articolato l’applicazione pratica della tecnica per la ricostruzione della parte superiore di un’anfora ottocentesca, messa a disposizione dal Museo