Arricchimento semantico nel contesto dell'Existing Building Information Modelling: aspetti metodologici e applicazione al caso della Rocca Estense di San Felice sul Panaro.

(ITA) L’industria mondiale odierna nel campo dell’architettura e dell’ingegneria si esprime quasi esclusivamente mediante l’approccio BIM, Building Information Modeling. Anche se sviluppato pensando alle nuove costruzioni ed ancora in via di perfezionamento, è entrato prepotentemente nei capitoli normativi di molti stati all’urlo dell“interoperability”. Su questo tema è recente l’interesse e la possibilità di adozione per l’intervento sul costruito, ovvero di Existing Building Information Modelling, eBIM. Gli studi applicativi-sperimentali in questo ambito sono sempre più numerosi e convergono, purtroppo, sulla delicata correlazione tra la gestione del contenuto semantico e la perdita di interoperabilità. Questa tesi si incentra sull’analisi di tale correlazione valutando in particolare l’aspetto metodologico-applicativo dell’arricchimento semantico adottando come caso studio la Torre Nord della Rocca Estense di San Felice sul Panaro. (ENG)Today's global industry in architecture and engineering fields, expresses itself almost entirely focusing on BIM, Building Information Modeling. Even though it was developed taking in consideration new buildings and the ones that are in the process of improvement, it has entered the regulatory chapters of many states in the hymn of "interoperability". Concerning this topic is recent the interest and possibility of adopting a process to intervene on the already built constructions, Existing Building Information Modeling, eBIM. Application-experimental studies in this area are increasingly numerous and unfortunately converge, on the delicate correlation between the management of the semantic content and the loss of interoperability. This thesis focuses on the analysis of this correlation by evaluating in particular the methodological-applicative aspect of semantic enrichment by adopting the North Tower of the Rocca Estense in San Felice sul Panaro as a case study.

SynBA: A contextualized Synonim-Based adversarial Attack for text classification

With the advent of high-performance computing devices, deep neural networks have gained a lot of popularity in solving many Natural Language Processing tasks. However, they are also vulnerable to adversarial attacks, which are able to modify the input text in order to mislead the target model. Adversarial attacks are a serious threat to the security of deep neural networks, and they can be used to craft adversarial examples that steer the model towards a wrong decision. In this dissertation, we propose SynBA, a novel contextualized synonym-based adversarial attack for text classification. SynBA is based on the idea of replacing words in the input text with their synonyms, which are selected according to the context of the sentence. We show that SynBA successfully generates adversarial examples that are able to fool the target model with a high success rate. We demonstrate three advantages of this proposed approach: (1) effective - it outperforms state-of-the-art attacks by semantic similarity and perturbation rate, (2) utility-preserving - it preserves semantic content, grammaticality, and correct types classified by humans, and (3) efficient - it performs attacks faster than other methods.