Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology, and Framework

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers

Multi-hazard resilience assessment of prestressed highway bridge

The exposure of structures to natural hazard has significant consequences on national economies and societies. A significant example of the importance of multiple hazard effects is the 2011 Tohoku (Japan) earthquake and the resulting tsunami. During this catastrophic event, the country's rail network was strongly affected: 23 stations were washed away, tracks and bridge piers were either eroded or buried, passenger and freight trains derailed. Therefore, resilience assessment of an infrastructure asset to extreme events and sequences of different hazards plays a key role on the development of suitable risk and resilience-based managing strategies. Hazard interactions and cascading effects can be classified differently, while modelling of multiple hazards is a relatively new research area [5], [6]. Scientific literature provides some examples of multi-hazard fragility curves, but very few integrated frameworks for assessing the resilience of a structure struck by multiple hazard have been treated so far. Despite many applications of resilience analysis for residential buildings, industrial facilities and bridges subject to a single catastrophic event (mainly earthquakes) are present in scientific literature, very little theory has been developed for multiple extreme events. Therefore, this paper aims to help filling this gap by showing the impact on resilience of a flood-scoured bridge struct by a seismic event

A Novel Approach for Supporting Italian Satire Detection Through Deep Learning

Satire is a way of criticizing people (or ideas) by ridiculing them on political, social, and morals topics often used to denounce political and societal problems, leveraging comedic devices such as parody exaggeration, incongruity, etc.etera. Detecting satire is one of the most challenging computational linguistics tasks, natural language processing, and social multimedia sentiment analysis. In particular, as satirical texts include figurative communication for expressing ideas/opinions concerning people, sentiment analysis systems may be negatively affected; therefore, satire should be adequately addressed to avoid such systems’ performance degradation. This paper tackles automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidaino, and significant Italian newspapers. Experiments show an optimal performance achieved by the network capable of detecting satire in a context where it is not marked

Comparison of variable-thread tapered implant designs to a standard tapered implant design after immediate loading. A 3-year multicentre randomised controlled trial

This randomised, controlled multicentre trial aimed at comparing two versions of a variable-thread dental implant design to a standard tapered dental implant design in cases of immediate functional loading for 36 months after loading