Requirements definition and testing of the new version of multi-mission generic monitoring system (GEMS) v 4.4 for operations at Eumetsat

EUMETSAT (www.eumetsat.int) e’ l’agenzia europea per operazioni su satelliti per monitorare clima, meteo e ambiente terrestre. Dal centro operativo situato a Darmstadt (Germania), si controllano satelliti meteorologici su orbite geostazionarie e polari che raccolgono dati per l’osservazione dell’atmosfera, degli oceani e della superficie terrestre per un servizio continuo di 24/7. Un sistema di monitoraggio centralizzato per programmi diversi all’interno dell’ambiente operazionale di EUMETSAT, e’ dato da GEMS (Generic Event Monitoring System). Il software garantisce il controllo di diverse piattaforme, cross-monitoring di diverse sezioni operative, ed ha le caratteristiche per potere essere esteso a future missioni. L’attuale versione della GEMS MMI (Multi Media Interface), v. 3.6, utilizza standard Java Server Pages (JSP) e fa uso pesante di codici Java; utilizza inoltre files ASCII per filtri e display dei dati. Conseguenza diretta e’ ad esempio, il fatto che le informazioni non sono automaticamente aggiornate, ma hanno bisogno di ricaricare la pagina. Ulteriori inputs per una nuova versione della GEMS MMI vengono da diversi comportamenti anomali riportati durante l’uso quotidiano del software. La tesi si concentra sulla definizione di nuovi requisiti per una nuova versione della GEMS MMI (v. 4.4) da parte della divisione ingegneristica e di manutenzione di operazioni di EUMETSAT. Per le attivita’ di supporto, i test sono stati condotti presso Solenix. Il nuovo software permettera’ una migliore applicazione web, con tempi di risposta piu’ rapidi, aggiornamento delle informazioni automatico, utilizzo totale del database di GEMS e le capacita’ di filtri, insieme ad applicazioni per telefoni cellulari per il supporto delle attivita’ di reperibilita’. La nuova versione di GEMS avra’ una nuova Graphical User Interface (GUI) che utilizza tecnologie moderne. Per un ambiente di operazioni come e’ quello di EUMETSAT, dove l’affidabilita’ delle tecnologie e la longevita’ dell’approccio scelto sono di vitale importanza, non tutti gli attuali strumenti a disposizione sono adatti e hanno bisogno di essere migliorati. Allo stesso tempo, un’ interfaccia moderna, in termini di visual design, interattivita’ e funzionalita’, e’ importante per la nuova GEMS MMI.

Traduttori specializzati e business center nel processo di internazionalizzazione d'impresa: Studio esplorativo e prospettive future

This exploratory study aims at investigating the professional opportunities available to a specialised translator within the process of international business development. Firstly, the analysis focuses on a review of the theoretical principles affirming the need of a fine-tuned language strategy, especially in the process of internationalization, managed by professionals with translation, language and cultural skills. Against this background, the focus is on the role played by a specialised translator within this process. With the aim of exploring the translator’s role within this process, the analysis focuses on business centers in Italy, which bring together demand and supply of language services and services for company internationalisation. According to the results that shows the opportunities available to a specialised translator with extra skills, the analysis focuses on the placement of this professional within the process. A specialised translator can be a language and cultural consultant for the internationalised company, as well as a Project Manager working for its international development. The conclusions describe the role which a specialised translator with economic or international marketing skills might play within this framework, and pave the way for future developments of this analysis.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.