This work proposes a new approach to deception detection, based on finding significant differences between liars and truth tellers through the analysis of their behavior, verbal and non-verbal. This is based on the combination of two factors: multimodal data collection, and t-pattern analysis. Multimodal approach has been acknowledged in literature about deception detection and on several studies concerning the understanding of any communicative phenomenon. We believe a methodology such as T-pattern analysis could be able to get the best advantages from an approach that combines data coming from multiple signaling systems. In fact, T-pattern analysis is a recent methodology for the analysis of behavior that unveil the complex structure at the basis of the organization of human behavior. For this work, we conducted an experimental study and analyzed data related to a single subject. Results showed how T-pattern analysis allowed to find differences between truth telling and lying. This work aims at making progress in the state of knowledge about deception detection, with the final goal to propose a useful tool for the improvement of public security and well-being

In the age of Big Data, which is clearly affecting also the Healthcare sector, one of the most valuable challenge is the one connected with the information extraction from raw data that implies the automatic detection of significant facts in unstructured texts and their transformation into structured documents, which are indexable and queryable exactly like databases. The volume, variety, velocity, verification and value of data raise the necessity of managing information with the most sophisticated linguistic and computational architectures, which are able to approach the semantic dimension of words and sentences. The present paper introduces ABC, A knowledge Based Collaborative framework that consists in a double-faced system aiming to support clinical processes in a more effective way (diagnostics and therapy). All the ABC's area of intervention (improving security from clinical risk, enhancing services' quality/results and perfecting the effects of the spending management) help to make hospitals more compliant with national and international laws and observant of many standards. ABC is intended as way to perform auto-diagnoses of the safety and the quality in hospitals, so the medical staff is always prepared to successfully overcome authorities' inspections; it must not be viewed as a supervisory control instrument. Moreover, ABC guarantees protection from every illegal use of sensitive data regarding patients and health users

This paper presents a Lexicon-Grammar based method for automatic extraction of spatial relations from Italian non-structured data. We used the software Nooj to build sophisticated local grammars and electronic dictionaries associated with the lexicon-grammar classes of the Italian intransitive spatial verbs (i.e. 234 verbal entries) and we applied them to the Italian text Il Codice da Vinci ('The Da Vinci Code', by Dan Brown) in order to parse the spatial predicate-arguments structures. In addition, Nooj allowed us to automatically annotate (in XML format) the words (or the sequence of words) that in each sentence (S) of the text play the 'spatial roles' of Figure (F), Motion (M) and Ground (G). Finally the results of the experiment and the evaluation of this method will be discussed

The present research deals with the automatic annotation and classification of vulgar ad offensive speech on social media. In this paper we will test the effectiveness of the computational treatment of the taboo contents shared on the web, the output is a corpus of 31,749 Facebook comments which has been automatically annotated through a lexicon-based method for the automatic identification and classification of taboo expressions.

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