Qualita' del processo di sviluppo e manutenzione del software di configurazione e controllo di Reti Fotoniche: il caso di studio del Local Craft Terminal.

La presente tesi ha l’obiettivo di ottimizzare l’efficacia e l’efficienza di un processo di Manutenzione attraverso una Business Process Analysis. Nello specifico si sono applicate metodologie e tecniche tese alla diagnosi, alla verifica, alla misurazione delle perfomance al fine di verificare, in termini quantitativi, l’efficienza e l’efficacia dei processi. Il presente lavoro evidenzia e approfondisce concetti come il “modeling”, utilizzando l’estensione Eriksson Penker per UML, e “l’analisi dimensionale”, utilizzando l’approccio dei Key Performance Indicator (KPI). Nella tesi vengono affrontate le suddette tematiche, attraverso il caso di studio del processo di sviluppo e manutenzione di un prodotto destinato al controllo e alla configurazione di Apparati Fotonici. Il lavoro si articola in quattro fasi principali : 1)Modeling: sviluppo del modello di business attraverso le quattro Business View dell’estensione Eriksson-Penker per UML: - Business Vision View : definisce gli obiettivi del business; - Business Process View: definisce il flusso operativo e le interazioni tra processi e risorse; - Business Structure View: definisce l’organizzazione delle risorse; - Business Behavior View: definisce come evolvono le risorse; 2) Analisi Dimensionale: profilatura degli indicatori KPI e rilevamento dei risultati; 3) Diagnosi: Analisi dei risultati e individuazione di punti di debolezza e opportunità; 4) Re-Modeling: sviluppo del nuovo modello di business al fine di eliminare le cause dei problemi e cogliere le opportunità individuati nella fase precedente

Generative Adversarial Network to evaluate quantity of information in financial markets

Nowadays, the information obtainable from the markets are potentially limitless. Economic theory has always supported the possible advantage obtainable from having more information than competitors, however quantifying the advantage that these can give has always been a problem. In particular, in this paper we study the amount of information obtainable from the markets taking into account only the time series of the prices, through the use of a specific Generative Adversarial Network. We consider two types of financial instruments traded on the market, stocks and cryptocurrencies: the first are traded in a market subject to opening and closing hours, whereas cryptocurrencies are traded in a 24/7 market. Our goal is to use this GAN to be able to “convert” the amount of information that the different instruments can have in discriminative and predictive power, useful to improve forecast. Finally, we demonstrate that by using the initial dataset with the 5 most important feature useds by traders, the prices of cryptocurrencies present higher discriminatory and predictive power than stocks, while by adding a feature the situation can be completely reversed

How Boltzmann Entropy Improves Prediction with LSTM

In this paper we want to demonstrate how it is possible to improve the forecast by using Boltzmann entropy like the classic financial indicators, throught neural networks. In particular, we show how it is possible to increase the scope of entropy by moving from cryptocurrencies to equities and how this type of architectures highlight the link between the indicators and the information that they are able to contain

Boltzmann Entropy in Cryptocurrencies: A Statistical Ensemble Based Approach

In this paper we try to build a statistical ensemble to describe a cryptocurrency-based system, emphasizing an "affinity" between the system of agents trading in these currencies and statistical mechanics. We focus our study on the concept of entropy in the sense of Boltzmann and we try to extend such a definition to a model in which the particles are replaced by N agents completely described by their ability to buy and to sell a certain quantity of cryptocurrencies. After providing some numerical examples, we show that entropy can be used as an indicator to forecast the price trend of cryptocurrencies

Legal Management in der sich digital transformierenden Versicherungswirtschaft

Die Digitalisierungswelle breitet sich weiterhin über die davon besonders betroffene Versicherungswirtschaft aus. Seit einiger Zeit wird – im Zusammenhang mit diesbezüglichen Veränderungsprozessen – von der digitalen Transformation gesprochen. Bisherige Transformationsbestrebungen von grossen Versicherungsunternehmen sind als Vorbereitung auf eine noch tiefergreifende Veränderungsphase zu betrachten. Produkt-, Prozess- und gar Geschäftsmodellinnovationen werden die Entwicklungsabteilungen von Versicherungsunternehmen in den kommenden Jahren zunehmend beschäftigen. Derartige Change-Vorhaben können sich ebenfalls auf die Rechtsabteilung und damit auf das Legal Management auswirken. Darüber hinaus beschäftigen sich Rechtsabteilungen vermehrt damit, die Rechtsfunktion selbst den digitalen Gegebenheiten entsprechend zu optimieren. Die vorliegende Masterarbeit untersucht, welche Auswirkungen die gegenwärtig und künftig weiterhin stattfindende digitale Transformation der Versicherungsbranche auf das Legal Management von Versicherungsunternehmen in der Schweiz hat. Zur Erreichung der besagten Zielsetzung wurde eine Methodenkombination eingesetzt, die auf juristischen und betriebswirtschaftlichen Ansätzen basiert. Im Rahmen einer interdisziplinären Literaturanalyse wurden künftige Markttrends der Versicherungswirtschaft aufgezeigt, hernach zu erwartende rechtliche Herausforderungen herausgearbeitet und schliesslich davon abgeleitete Auswirkungen auf das Legal Management von Versicherungsunternehmen ergründet und interpretiert. Um der betriebsökonomischen Verankerung des Themas entsprechend Rechnung zu tragen und einen zusätzlichen Praxisbezug herzustellen, wurden – im Sinne einer empirischen Datenerhebung – Experteninterviews durchgeführt. Die Ergebnisse wurden in einem letzten Schritt interpretiert sowie mit den Ergebnissen der Literaturanalyse verglichen, kombiniert und diskutiert. Teil der finalen Erkenntnis ist die Feststellung, dass sich die digitale Transformation in vielerlei Hinsicht auf die Rechtsabteilung von Versicherungsunternehmen auswirkt. Das Aufgabenspektrum sämtlicher Teilaspekte des Legal Managements, wie insbesondere Legal Controlling, Legal Engineering und Legal Operations Management, wird sich voraussichtlich zunehmend ausweiten. Dadurch ergibt sich einerseits Wandlungsbedarf der Rechtsabteilung als Einheit und andererseits wandeln sich die Anforderungen an Mitarbeitende der Rechtsabteilung selbst.The digitization wave continues to spread across the insurance industry, which is particularly affected by it. For some time now, people have been talking about digital transformation in connection with change processes in this area. Previous transformation efforts by large insurance companies should be seen as preparation for an even more far-reaching phase of change. Product, process and even business model innovations will increasingly occupy the development departments of insurance companies in the coming years. Such change projects can also have an impact on the legal department and thus on legal management. In addition, legal departments are increasingly concerned with optimizing the legal function itself in line with the digital realities. This master's thesis examines the impact of the current and future digital transformation of the insurance industry on the legal management of insurance companies in Switzerland. To achieve the said objective, a combination of methods based on legal and business management approaches was used. Within the framework of an interdisciplinary literature analysis, future digital trends in the insurance industry were identified, expected legal challenges were then worked out, and finally the resulting effects on the legal management of insurance companies were explored and interpreted. In order to take account of the fact that the topic is anchored in business administration and to establish an additional practical reference, expert interviews were conducted in the sense of an empirical data collection. In a final step, the results were interpreted and compared, combined and discussed with the results of the literature analysis. Part of the final insight is that digital transformation is impacting the legal department of insurance companies in many ways. The range of tasks of all sub-aspects of legal management, such as legal controlling, legal engineering and legal operations management in particular, is expected to expand increasingly. On the one hand, this results in a need for change in the legal department as a unit, and on the other hand, the requirements for employees in the legal department itself are changing

Neural Network Contribute to Reverse Cryptographic Processes in Bitcoin Systems: attention on SHA256

Bitcoin is a digital currency created in January 2009 following the housing market crash that promises lower transaction fees than traditional online payment mechanisms. Though each bitcoin transaction is recorded in a public log, the names of buyers and sellers are never revealed. While that keeps bitcoin users' transactions private, it also lets them buy or sell anything without easily tracing it back to them. Bitcoin is based on cryptographic evidence, which therefore does not suffer from the weakness present in a model based on trust in guarantee authorities. The use of cryptography is of crucial importance in the Bitcoin system. In addition to maintaining data secrecy, in the case of Bitcoin, cryptography is used to make it impossible for anyone to spend money from another user's wallet. In our paper, we develop the idea that it is possible to reverse the cryptography process based on hash functions (one-way) through Machine Translation with neural networks. Assuming this hypothesis is true and considering some quantistic algorithms to decrypt certain types of hash functions, we will highlight their effects on the Bitcoin system

BERT's sentiment score for portfolio optimization: a fine-tuned views in Black and Litterman model

In financial markets, sentiment analysis on natural language sentences can improve forecasting. Many investors rely on information extracted from newspapers or their feelings. Therefore, this information is expressed in their language. Sentiment analysis models classify sentences (or entire texts) with their polarity (positive, negative, or neutral) and derive a sentiment score. In this paper, we use this sentiment (polarity) score to improve the forecasting of stocks and use it as a new ‘‘view’’ in the Black and Litterman model. This score is related to various events (both positive and negative) that have affected some stocks. The sentences used to determine the scores are taken from articles published in Financial Times (an international financial newspaper). To improve the forecast using this average sentiment score, we use a Monte Carlo method to generate a series of possible paths for several trading hours after the article was published to discretize (or approximate) the Wiener measure, which is applied to the paths and returning an exact price as results. Finally, we use the price determined in this way to calculate a yield to be used as views in a new type of ‘‘dynamic’’ portfolio optimization, based on hourly prices. We compare the results by applying the views obtained, disregarding the sentiment and leaving the initial portfolio unchanged