Content And Multimedia Database Management Systems

A database management system is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications. The main characteristic of the ‘database approach’ is that it increases the value of data by its emphasis on data independence. DBMSs, and in particular those based on the relational data model, have been very successful at the management of administrative data in the business domain. This thesis has investigated data management in multimedia digital libraries, and its implications on the design of database management systems. The main problem of multimedia data management is providing access to the stored objects. The content structure of administrative data is easily represented in alphanumeric values. Thus, database technology has primarily focused on handling the objects’ logical structure. In the case of multimedia data, representation of content is far from trivial though, and not supported by current database management systems

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Bayesian Neural Networks as a pricing model to reduce information costs in peer-to-peer online marketplaces

Treballs Finals del Grau d'Economia i Estadística. Doble titulació interuniversitària, Universitat de Barcelona i Universitat Politècnica de Catalunya. Curs: 2017-2018. Tutors: Xavier Puig Oriol; Salvador Torra Porras(eng) The main purpose of this thesis is to disclose the potential of exploiting the synergies between Statistical Science and Machine Learning. In particular, we propose a specific feed-forward Bayesian Neural Network (BNN) as a parametric statistical model able to both yield better punctual predictions than linear models and handle uncertainty through more grounded intervals than the ones offered by bootstrapping conventional Neural Networks. On top of proposing a complete methodology (based on DoE for architecture selection and MCMC to conduct inference) to apply BNNs in real cases, we analyze, using theoretical arguments from Microeconomics, the positive effect on society that it would have to use BNNs as pricing model for peer-to-peer online marketplaces and, moreover, we implement them for the case of Airbnb in Barcelona.(cat) El principal objectiu d’aquest treball consisteix en demostrar el potencial de combinar el coneixement de l’estadística i de l’aprenentatge automàtic (Machine Learning) per tal de proporcionar noves eines que permetin aprofitar les oportunitats que les Tecnologies de la Informació i Comunicació han generat en els darrers anys. Aquestes oportunitats es basen en la creació de tot un univers de dades que està esperant a ser analitzat i convertit en informació útil. En particular, aquest projecte es centra en les plataformes online de transaccions entre iguals (P2P OM) com ara Airbnb, Uber o Blablacar, ja que estant tenint un gran impacte en la nostra societat al modificar el procés mitjançant el qual adquirim béns i serveis. En aquest projecte, s’argumenta que, construint un model de predicció de preus mitjançant totes les transaccions realitzades en la plataforma, es podria proporcionar als usuaris oferents una eina de recomanació de preus per tal de determinar el preu de mercat del bé o servei que ofereixen, de forma ràpida i objectiva. L’objectiu és aconseguir que els oferents prenguin decisions més acurades sobre els preus, apropant-los, així, a les preferències de la demanda i incrementant el nombre de transaccions realitzades. Per tal de construir el model de predicció de preus es proposa treballar amb Xarxes Neuronals Bayesianes (BNN), amb l’objectiu d’oferir millors prediccions puntuals que el model lineal i, sobretot, intervals pel preu de mercat que realment capturin el comportament del mercat, cosa que les Xarxes Neuronals Artificials convencionals (ANN) tenen serioses dificultats per aconseguir-ho. Ara bé, i aquí és on es fan paleses les sinergies entre l’estadística i l’aprenentatge automàtic, en aquest projecte, a diferència del treball d’autors previs, es proposa les BNN com un model estadístic paramètric i, com a conseqüència, es desenvolupa tota una metodologia per tal de poder implementar-les en problemes aplicats, com ara el cas de les P2P OM. Aquesta metodologia es fonamenta en tres pilars principals que són: La manera d’implementar els mètodes MCMC per tal de capturar la multimodalitat inherent en BNN i com determinar que s’estan obtenint mostres de la a posteriori, l’ús de Disseny d’experiments en comptes de validació creuada per tal de determinar una arquitectura adient per la BNN i, finalment, el desenvolupament de tècniques pròpies i adaptació de aportacions d’altres autors per tal d’entendre com està funcionant la BNN. Finalment, s’implementa la BNN proposada d’acord amb la metodologia dissenyada pel cas de Airbnb a Barcelona, amb l’objectiu de demostrar tant el major rendiment i capacitats de la BNN respecte al model lineal i les ANN, com la utilitat que tindria un model de predicció de preus a l’hora d’ajudar els usuaris de les P2P OM a decidir un preu. A més a més, i fent ús de les tècniques d’interpretació de la BNN també s’observa com es poden extreure conclusions sobre el nivell de competència en cadascun dels barris de Barcelona i, a més a més, es pot explorar els efectes de canviar algunes característiques de l’apartament sobre el preu de mercat, cosa que pot ajudar als usuaris a decidir canvis i inversions

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Bayesian probability encoding in medical decision analysis

Ph.DDOCTOR OF PHILOSOPH

Bayesian Neural Networks as a pricing model to reduce information costs in peer-to-peer online marketplaces

The main purpose of this thesis is to disclose the potential of exploiting the synergies between Statistical Science and Machine Learning. In particular, we propose a specific feed-forward Bayesian Neural Network (BNN) as a parametric statistical model able to both yield better punctual predictions than linear models and handle uncertainty through more grounded intervals than the ones offered by bootstrapping conventional Neural Networks. On top of proposing a complete methodology (based on DoE for architecture selection and MCMC to conduct inference) to apply BNNs in real cases, we analyze, using theoretical arguments from Microeconomics, the positive effect on society that it would have to use BNNs as pricing model for peer-to-peer online marketplaces and, moreover, we implement them for the case of Airbnb in Barcelon

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically