Analyse quantitative de la carrière criminelle des internautes effectuant du leurre d’enfants

Objectifs: L’objectif principal de ce mémoire consiste à comprendre les caractéristiques des carrières criminelles d’individus connus de la police pour avoir perpétré une infraction de leurre d’enfants sur Internet. Aussi, par une analyse typologique à l’aide des antécédents criminels, il sera possible d’établir une typologie d’individus ayant leurré des enfants sur Internet. Également, il sera question de vérifier s’il y a un lien entre les caractéristiques des antécédents criminels de ces individus sur la perpétration de l’agression sexuelle hors ligne. Méthodologie: Provenant de données officielles de la communauté policière du Québec, l’échantillon comprend les parcours de criminels ayant perpétré une infraction de leurre d’enfants sur Internet. Des analyses descriptives en lien avec les différents paramètres de la carrière criminelle seront effectuées. Ensuite, des tests de moyenne et une analyse de régression Cox permettront de vérifier la présence ou non d’un lien statistique entre les caractéristiques des antécédents criminels des individus connus de la police pour leurre d’enfants sur Internet et le passage à l’acte physique. Résultats: Les analyses ont montré que la majorité des sujets n’avaient aucun antécédent judiciaire. Pour la plupart, le leurre d’enfants est le crime le plus grave perpétré au cours de leur carrière criminelle. Trois catégories d’individus ont été décelées : les amateurs, les spécialistes et les généralistes. Ce sont les individus polymorphes ayant une carrière criminelle plus grave et plus longue qui sont portés à agresser sexuellement avant le leurre. Cependant, ce sont des individus spécialisés ayant une importante proportion de délits sexuels dans leurs antécédents criminels qui ont plus de chance d’agresser sexuellement suite à l’exploitation sexuelle sur Internet.

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.

Analyse quantitative de la carrière criminelle des internautes effectuant du leurre d’enfants

Objectifs: L’objectif principal de ce mémoire consiste à comprendre les caractéristiques des carrières criminelles d’individus connus de la police pour avoir perpétré une infraction de leurre d’enfants sur Internet. Aussi, par une analyse typologique à l’aide des antécédents criminels, il sera possible d’établir une typologie d’individus ayant leurré des enfants sur Internet. Également, il sera question de vérifier s’il y a un lien entre les caractéristiques des antécédents criminels de ces individus sur la perpétration de l’agression sexuelle hors ligne. Méthodologie: Provenant de données officielles de la communauté policière du Québec, l’échantillon comprend les parcours de criminels ayant perpétré une infraction de leurre d’enfants sur Internet. Des analyses descriptives en lien avec les différents paramètres de la carrière criminelle seront effectuées. Ensuite, des tests de moyenne et une analyse de régression Cox permettront de vérifier la présence ou non d’un lien statistique entre les caractéristiques des antécédents criminels des individus connus de la police pour leurre d’enfants sur Internet et le passage à l’acte physique. Résultats: Les analyses ont montré que la majorité des sujets n’avaient aucun antécédent judiciaire. Pour la plupart, le leurre d’enfants est le crime le plus grave perpétré au cours de leur carrière criminelle. Trois catégories d’individus ont été décelées : les amateurs, les spécialistes et les généralistes. Ce sont les individus polymorphes ayant une carrière criminelle plus grave et plus longue qui sont portés à agresser sexuellement avant le leurre. Cependant, ce sont des individus spécialisés ayant une importante proportion de délits sexuels dans leurs antécédents criminels qui ont plus de chance d’agresser sexuellement suite à l’exploitation sexuelle sur Internet.

La convivialità urbana nei quartieri di Milano, Bologna e Roma. Un'analisi mixed-methods sulle Social Streets

Le Social Street sono gruppi di vicini di casa che vogliono ricreare legami di convivialità avendo notato un indebolimento delle relazioni sociali nei loro quartieri. Nascono come gruppi online, tramite la piattaforma Facebook, per materializzarsi in incontri offline andando a costruire legami conviviali grazie pratiche di socialità, inclusività e gratuità. Questa Tesi ha come obiettivo l’analisi dei profili socio-demografici degli Streeter e dei quartieri coinvolti per comprendere come sia possibile creare convivialità e come la variabile urbana intervenga in questi processi. Inoltre, si vuole comprendere le dinamiche di attaccamento al quartiere, gli interessi portati avanti dagli Streeter, il loro profilo civico e il posizionamento di quest’esperienza rispetto all’associazionismo tradizionale. Per perseguire l’obiettivo della ricerca, sono state studiate le tre città che vedono la maggiore presenza di Social Street: Milano, Bologna, Roma. La ricerca ha previsto sia un’analisi degli Streeter grazie a un questionario online replicato in tutti i contesti. Inoltre, sono state realizzate 131 interviste ad amministratori e fondatori di Social Street e condotte osservazioni etnografiche e netnografiche. I risultati mostrano come gli Streeter siano appartenenti alle classi medio-alte, tra trenta e cinquanta anni, che hanno sperimentato la mobilità tra un quartiere e l’altro o tra diversi contesi nazionali ed internazionali e trovano nelle Social Street un modo per creare legami di vicinato che hanno perso nei loro trasferimenti. Gli stessi quartieri dove si diffondono le Social Street sono agiati e vi è una buona corrispondenza tra Streeter e modello della centralità sociale elaborato da Milbrath (1965) per cui anche la partecipazione civica è molto sentita tra gli aderenti alle Social Street. Il contributo di questa Tesi al dibattito sociologico risiede nell’aver offerto un’analisi empirica di un’azione collettiva a livello urbano, quella delle Social Street, mostrando come vi sia circolarità tra azione e contesto grazie all’azione mutualistica conviviale.

Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.

Pattern peristaltico uterino nell'adenomiosi isolata: studio prospettico

Obiettivi dello studio: valutare con l’ecografia transvaginale la peristalsi uterina in fase periovulatoria in donne con adenomiosi isolata, confrontandola con un gruppo di controllo e, secondariamente, valutare il grado di accordo tra gli sperimentatori nella descrizione dei pattern di contrattilità. Disegno dello studio: studio osservazione prospettico condotto presso il Policlinico S. Orsola- Malpighi di Bologna, Italia. Materiali e Metodi: sono state reclutate pazienti afferenti al Centro per valutazione ambulatoriale, suddivise sulla base dei criteri di inclusione ed esclusione nei gruppi A (adenomiosi) e B (controlli) e sono state sottoposte da un unico ecografista esperto a ecografia transvaginale con registrazione di un video della durata di 180 secondi della scansione sagittale dell’utero. La registrazione è stata rivalutata off line da due sperimentatori esperti ecografisti, non a conoscenza della storia clinica delle pazienti e in cieco l’uno rispetto all’altro, che hanno descritto il pattern contrattile. È stata stimata una numerosità campionaria di 18 pazienti per gruppo per ottenere una differenza del 20% nell’obiettivo primario con una significatività del 5% (power 80%). Risultati: di 51 pazienti reclutate nello studio, a seguito di drop out 36 sono state sottoposte alla videoregistrazione ecografica (18 per gruppo). Il pattern peristaltico nel gruppo A è risultato alterato in maniera statisticamente significativa rispetto al gruppo B con un p value= 0,02. Sono stati osservati un pattern retrogrado nel 27,8% vs 72,2%, anterogrado del 11,1% vs 16,7%, opposto 38,9% vs 5,6% e random nel 22,2% vs 5,6%, rispettivamente nel gruppo A e B. Il calcolo dell’accordo interosservatore ha portato a un κ value di 0,92. Conclusioni: l’adenomiosi isolata è associata a disperistalsi uterina, che concorrerebbe nello sviluppo dei sintomi tipici dell’adenomiosi. L’ecografia transvaginale rappresenta uno strumento accessibile e utile nella valutazione della contrattilità uterina in quanto il grado di accordo tra gli sperimentatori è ottimo.

Essays in Empirical Political Economics

This thesis takes two perspectives on political institutions. From the one side, it examines the long-run effects of institutions on cultural values. From the other side, I study strategic communication, and its determinants, of politicians, a pivotal actor inside those institutions. The first chapter provides evidence for the legacy of feudalism - a set of labor coercion and migration restrictions -, on interpersonal distrust. I combining administrative data on the feudal system in the Prussian Empire (1816 – 1849) with the geo-localized survey data from the German Socio-Economic Panel (1980 – 2020). I show that areas with strong historical exposure to feudalism have lower levels of inter-personal trust today, by means of OLS- and mover specifications. The second chapter builds a novel dataset that includes the Twitter handles of 18,000+ politicians and 61+ million tweets from 2008 – 2021 from all levels of government. I find substantial partisan differences in Twitter adoption, Twitter activity and audience engagement. I use established tools to measure ideological polarization to provide evidence that online-polarization follows similar trends to offline-polarization, at comparable magnitude and reaches unprecedented heights in 2018 and 2021. I develop a new tool to demonstrate a marked increase in affective polarization. The third chapter tests whether politicians disseminate distortive messages when exposed to bad news. Specifically, I study the diffusion of misleading communication from pro-gun politicians in the aftermath of mass shootings. I exploit the random timing of mass shootings and analyze half a million tweets between 2010 – 2020 in an event-study design. I develop and apply state-of-the-art text analysis tools to show that pro- gun politicians seek to decrease the salience of the mass shooting through distraction and try to alter voters’ belief formation through misrepresenting the causes of the mass shootings.

Big Data approaches as a support for precision livestock farming techniques

With the advent of new technologies it is increasingly easier to find data of different nature from even more accurate sensors that measure the most disparate physical quantities and with different methodologies. The collection of data thus becomes progressively important and takes the form of archiving, cataloging and online and offline consultation of information. Over time, the amount of data collected can become so relevant that it contains information that cannot be easily explored manually or with basic statistical techniques. The use of Big Data therefore becomes the object of more advanced investigation techniques, such as Machine Learning and Deep Learning. In this work some applications in the world of precision zootechnics and heat stress accused by dairy cows are described. Experimental Italian and German stables were involved for the training and testing of the Random Forest algorithm, obtaining a prediction of milk production depending on the microclimatic conditions of the previous days with satisfactory accuracy. Furthermore, in order to identify an objective method for identifying production drops, compared to the Wood model, typically used as an analytical model of the lactation curve, a Robust Statistics technique was used. Its application on some sample lactations and the results obtained allow us to be confident about the use of this method in the future.

Design and testing of innovative, artificial intelligence-based techniques to model and control the combustion process and to reduce the Co2 emissions in modern, high-performance, gasoline direct injection engines

This work deals with the development of calibration procedures and control systems to improve the performance and efficiency of modern spark ignition turbocharged engines. The algorithms developed are used to optimize and manage the spark advance and the air-to-fuel ratio to control the knock and the exhaust gas temperature at the turbine inlet. The described work falls within the activity that the research group started in the previous years with the industrial partner Ferrari S.p.a. . The first chapter deals with the development of a control-oriented engine simulator based on a neural network approach, with which the main combustion indexes can be simulated. The second chapter deals with the development of a procedure to calibrate offline the spark advance and the air-to-fuel ratio to run the engine under knock-limited conditions and with the maximum admissible exhaust gas temperature at the turbine inlet. This procedure is then converted into a model-based control system and validated with a Software in the Loop approach using the engine simulator developed in the first chapter. Finally, it is implemented in a rapid control prototyping hardware to manage the combustion in steady-state and transient operating conditions at the test bench. The third chapter deals with the study of an innovative and cheap sensor for the in-cylinder pressure measurement, which is a piezoelectric washer that can be installed between the spark plug and the engine head. The signal generated by this kind of sensor is studied, developing a specific algorithm to adjust the value of the knock index in real-time. Finally, with the engine simulator developed in the first chapter, it is demonstrated that the innovative sensor can be coupled with the control system described in the second chapter and that the performance obtained could be the same reachable with the standard in-cylinder pressure sensors.

Exact and heuristic algorithms for the integrated planning of multi-energy systems

The aim of this thesis is to present exact and heuristic algorithms for the integrated planning of multi-energy systems. The idea is to disaggregate the energy system, starting first with its core the Central Energy System, and then to proceed towards the Decentral part. Therefore, a mathematical model for the generation expansion operations to optimize the performance of a Central Energy System system is first proposed. To ensure that the proposed generation operations are compatible with the network, some extensions of the existing network are considered as well. All these decisions are evaluated both from an economic viewpoint and from an environmental perspective, as specific constraints related to greenhouse gases emissions are imposed in the formulation. Then, the thesis presents an optimization model for solar organic Rankine cycle in the context of transactive energy trading. In this study, the impact that this technology can have on the peer-to-peer trading application in renewable based community microgrids is inspected. Here the consumer becomes a prosumer and engages actively in virtual trading with other prosumers at the distribution system level. Moreover, there is an investigation of how different technological parameters of the solar Organic Rankine Cycle may affect the final solution. Finally, the thesis introduces a tactical optimization model for the maintenance operations’ scheduling phase of a Combined Heat and Power plant. Specifically, two types of cleaning operations are considered, i.e., online cleaning and offline cleaning. Furthermore, a piecewise linear representation of the electric efficiency variation curve is included. Given the challenge of solving the tactical management model, a heuristic algorithm is proposed. The heuristic works by solving the daily operational production scheduling problem, based on the final consumer’s demand and on the electricity prices. The aggregate information from the operational problem is used to derive maintenance decisions at a tactical level.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.

Trasferimento di Token Inter-Blockchain: attacchi e soluzioni in uno scenario di Blockchain locali.

La disponibilità di connessioni a internet poco costose ed affidabili rappresenta un lusso in molti paesi in via di sviluppo e in zone rurali di nazioni sviluppate. L’utilizzo di nuove tecnologie come le blockchain risulta quindi difficile in queste aree, per quanto esse trarrebbero certamente beneficio dalla disponibilità di sistemi di pagamento digitali, decentralizzati e tolleranti ai guasti; inoltre l’uso delle blockchain in zone rurali potrebbe rendere l’abitabilità di tali aree maggiormente appetibile. Una possibile soluzione è costituita dalle blockchain locali, ossia catene a servizio di una ristretta area geografica in cui è disponibile solamente una rete locale, da cui potrebbero ricevere vantaggio sia paesi in via di sviluppo, sia scenari industriali dove si necessiti di blockchain il cui accesso debba avvenire solamente dall’interno dell’intranet aziendale. L’utilità delle blockchain locali risulterebbe tuttavia limitata qualora questi sistemi rimanessero totalmente isolati dal mondo esterno. Utilizzando tecnologie che permettono l’interoperabilità tra blockchain è però possibile interconnettere questi sistemi, rendendo possibile il trasferimento di asset tra diverse chain. Le particolarità degli scenari ipotizzati lasciano però spazio ad alcune vulnerabilità, che, se sfruttate, possono condurre ad attacchi ai danni degli utenti e dell’economia delle blockchain locali. Per risolvere le problematiche individuate, sono stati quindi discussi ed implementati una serie di requisiti per la messa in sicurezza del sistema.

Panson: An interactive sonification framework for real-time applications

Many sonification systems face a number of common design challenges. These are addressed in every project with different, specific-purpose solutions. We present Panson – an interactive sonification framework implemented in Python that can ease the development of sonification systems. Panson allows the user to implement sonifications using the sc3nb library as interface to the SuperCollider sound synthesis engine. The framework provides support for both offline and online (real-time) sonification through a set of composable classes; these classes are designed to natively support interaction in Jupyter Notebooks. Using Panson, we will show an example of its application by implementing a facial expression sonification Jupyter Notebook based on OpenFace 2.0.

Construction and test of a cosmic ray telescope based on CMS Drift Tube chambers and data acquisition prototypes of the Phase-2 upgrade

In this thesis work, a cosmic-ray telescope was set up in the INFN laboratories in Bologna using smaller size replicas of CMS Drift Tubes chambers, called MiniDTs, to test and develop new electronics for the CMS Phase-2 upgrade. The MiniDTs were assembled in INFN National Laboratory in Legnaro, Italy. Scintillator tiles complete the telescope, providing a signal independent of the MiniDTs for offline analysis. The telescope readout is a test system for the CMS Phase-2 upgrade data acquisition design. The readout is based on the early prototype of a radiation-hard FPGA-based board developed for the High Luminosity LHC CMS upgrade, called On Board electronics for Drift Tubes. Once the set-up was operational, we developed an online monitor to display in real-time the most important observables to check the quality of the data acquisition. We performed an offline analysis of the collected data using a custom version of CMS software tools, which allowed us to estimate the time pedestal and drift velocity in each chamber, evaluate the efficiency of the different DT cells, and measure the space and time resolution of the telescope system.

Integration of ROS for the control of industrial robotic systems in a proprietary environment

The paper deals with the integration of ROS, in the proprietary environment of the Marchesini Group company, for the control of industrial robotic systems. The basic tools of this open-source software are deeply studied to model a full proprietary Pick and Place manipulator inside it, and to develop custom ROS nodes to calculate trajectories; speaking of which, the URDF format is the standard to represent robots in ROS and the motion planning framework MoveIt offers user-friendly high-level methods. The communication between ROS and the Marchesini control architecture is established using the OPC UA standard; the tasks computed are transmitted offline to the PLC, supervisor controller of the physical robot, because the performances of the protocol don’t allow any kind of active control by ROS. Once the data are completely stored at the Marchesini side, the industrial PC makes the real robot execute a trajectory computed by MoveIt, so that it replicates the behaviour of the simulated manipulator in Rviz. Multiple experiments are performed to evaluate in detail the potential of ROS in the planning of movements for the company proprietary robots. The project ends with a small study regarding the use of ROS as a simulation platform. First, it is necessary to understand how a robotic application of the company can be reproduced in the Gazebo real world simulator. Then, a ROS node extracts information and examines the simulated robot behaviour, through the subscription to specific topics.