La strategia BIM a supporto del progetto della sicurezza nel cantiere edile: il caso dell'ospedale di primo soccorso a Lampedusa.

Throughout this research, the whole life cycle of a building will be analyzed, with a special focus on the most common issues that affect the construction sector nowadays, such as safety. In fact, the goal is to enhance the management of the entire construction process in order to reduce the risk of accidents. The contemporary trend is that of researching new tools capable of reducing, or even eliminating, the most common mistakes that usually lead to safety risks. That is one of the main reasons why new technologies and tools have been introduced in the field. The one we will focus on is the so-called BIM: Building Information Modeling. With the term BIM we refer to wider and more complex analysis tool than a simple 3D modeling software. Through BIM technologies we are able to generate a multi-dimension 3D model which contains all the information about the project. This innovative approach aims at a better understanding and control of the project by taking into consideration the entire life cycle and resulting in a faster and more sustainable way of management. Furthermore, BIM software allows for the sharing of all the information among the different aspects of the project and among the different participants involved thus improving the cooperation and communication. In addition, BIM software utilizes smart tools that simulate and visualize the process in advance, thus preventing issues that might not have been taking into consideration during the design process. This leads to higher chances of avoiding risks, delays and cost increases. Using a hospital case study, we will apply this approach for the completion of a safety plan, with a special focus onto the construction phase.

Smart Data Analysis on Lamborghini Connected Vehicles

The thesis is the result of work conducted during a period of six months at the Strategy department of Automobili Lamborghini S.p.A. in Sant'Agata Bolognese (BO) and concerns the study and analysis of Big Data relating to Lamborghini's connected cars. The Big Data is a project of Connected Car Project House, that is an inter-departmental team which works toward the definition of the Lamborghini corporate connectivity strategy and its implementation in the product portfolio. The Data of the connected cars is one of the hottest topics right now in the automotive industry; in fact, all the largest automotive companies are investi,ng a lot in this direction, in order to derive the greatest advantages both from a purely economic point of view, because from these data you can understand a lot the behaviors and habits of each driver, and from a technological point of view because it will increasingly promote the development of 5G that will be an important enabler for the future of connectivity. The main purpose of the work by Lamborghini prospective is to analyze the data of the connected cars, in particular a data-set referred to connected Huracans that had been already placed on the market, and, starting from that point, derive valuable Key Performance Indicators (KPIs) on which the company could partly base the decisions to be made in the near future. The key result that we have obtained at the end of this period was the creation of a Dashboard, in which is possible to visualize many parameters and indicators both related to driving habits and the use of the vehicle itself, which has brought great insights on the huge potential and value that is present behind the study of these data. The final Demo of the project has received great interest, not only from the whole strategy department but also from all the other business areas of Lamborghini, making mostly a great awareness that this will be the road to follow in the coming years.

NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation

Driven by recent deep learning breakthroughs, natural language generation (NLG) models have been at the center of steady progress in the last few years. However, since our ability to generate human-indistinguishable artificial text lags behind our capacity to assess it, it is paramount to develop and apply even better automatic evaluation metrics. To facilitate researchers to judge the effectiveness of their models broadly, we suggest NLG-Metricverse—an end-to-end open-source library for NLG evaluation based on Python. This framework provides a living collection of NLG metrics in a unified and easy- to-use environment, supplying tools to efficiently apply, analyze, compare, and visualize them. This includes (i) the extensive support of heterogeneous automatic metrics with n-arity management, (ii) the meta-evaluation upon individual performance, metric-metric and metric-human correlations, (iii) graphical interpretations for helping humans better gain score intuitions, (iv) formal categorization and convenient documentation to accelerate metrics understanding. NLG-Metricverse aims to increase the comparability and replicability of NLG research, hopefully stimulating new contributions in the area.