Anàlisi i comparativa de frameworks dins de les 3 capes (Vista-Model-Dades) d'una arquitecturatípica JEE

Analitzarem, mitjançant proves de codi que realitzen la mateixa tasca, dels diferentsFramework escollits, la seva eficiència (velocitat i memòria entre d'altres).

Disseny i implementació d'un marc de treball (Framework) de presentació per aplicacions J2EE

El projecte consisteix en l'estudi i avaluació de diferents alternatives existents al mercat per a realitzar l'anàlisi i desenvolupament d'un conjunt de components que constitueixin un marc de treball per a simplificar i agilitzar el desenvolupament de la capa de presentació per a les aplicacions de client prim d'un determinat Framework desenvolupades amb la plataforma J2EE i basats en el patró de disseny Model-Vista-Controlador.

Integration in strategic alliances : a conceptual framework of IT use in marketing as NPD key factor

En una economia basada en el coneixement, la innovació del producte es considera un factor clau a l'hora de determinar la competitivitat, la productivitat i el creixement d'una companyia. No obstant això, l'experiència de les companyies demostra la necessitat d'un nou model de gestió de la innovació del producte: una gestió basada en el màrqueting, en què la cooperació i l'ús intensiu de les tecnologies de la informació i de la comunicació (TIC) són especialment importants. En els darrers anys, la bibliografia sobre màrqueting ha analitzat el paper de la cooperació en l'èxit del procés d'innovació. No obstant això, fins ara pocs treballs han estudiat el paper que té l'ús de les TIC en el màrqueting en l'èxit del desenvolupament de nous productes (NPD, New Product Development en anglès). És una omissió curiosa, tenint en compte que el nou entorn competitiu és definit per una economia i una societat basades principalment en l'ús intensiu de les TIC i del coneixement. L'objectiu d'aquest treball és investigar el paper que l'ús de les TIC en el màrqueting té en el procés de desenvolupament de nous productes, com a element que reforça la integració d'agents al projecte, afavorint l'establiment de relacions dirigides a la cooperació i l'adquisició d'intel·ligència de mercat útil en el procés de desenvolupament de nous productes. L'estudi d'una mostra de 2.038 companyies de tots els sectors de l'activitat econòmica a Catalunya ens permet contrastar hipòtesis inicials i establir un perfil de companyia innovadora basat en les importants relacions que hi ha entre la innovació, l'ús de TIC en el màrqueting i la integració. Sobresurten dues idees en la nostra anàlisi. En primer lloc, l'ús intensiu de les TIC en el màrqueting fa que la companyia sigui més innovadora, ja que percep que el seu ús ajuda a superar barreres a la innovació i accelera els processos, que es tornen més eficients. En segon lloc, incrementant l'ús de les TIC en el màrqueting es fa augmentar la predisposició de la companyia a integrar agents particulars en l'entorn de negoci en el desenvolupament del procés d'innovació i a col·laborar-hi, de manera que es millora el grau d'adaptació del nou producte a les demandes del mercat.

Explaining Fiscal Balances with a Simultaneous Equation Model of Revenue and Expenditure : A Case Study of Swiss Cantons Using Panel Data

Empirical literature on the analysis of the efficiency of measures for reducing persistent government deficits has mainly focused on the direct explanation of deficit. By contrast, this paper aims at modeling government revenue and expenditure within a simultaneous framework and deriving the fiscal balance (surplus or deficit) equation as the difference between the two variables. This setting enables one to not only judge how relevant the explanatory variables are in explaining the fiscal balance but also understand their impact on revenue and/or expenditure. Our empirical results, obtained by using a panel data set on Swiss Cantons for the period 1980-2002, confirm the relevance of the approach followed here, by providing unambiguous evidence of a simultaneous relationship between revenue and expenditure. They also reveal strong dynamic components in revenue, expenditure, and fiscal balance. Among the significant determinants of public fiscal balance we not only find the usual business cycle elements, but also and more importantly institutional factors such as the number of administrative units, and the ease with which people can resort to political (direct democracy) instruments, such as public initiatives and referendum.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

The Relevance of the Principal-Agent Model for the Analysis of Public Policies : Do the Objectives Conflict ?

This paper aims to provide empirical support for the use of the principal-agent framework in the analysis of public sector and public policies. After reviewing the different conditions to be met for a relevant analysis of the relationship between population and government using the principal-agent theory, our paper focuses on the assumption of conflicting goals between the principal and the agent. A principal-agent analysis assumes in effect that inefficiencies may arise because principal and agent pursue different goals. Using data collected during an amalgamation project of two Swiss municipalities, we show the existence of a gap between the goals of the population and those of the government. Consequently, inefficiencies as predicted by the principal-agent model may arise during the implementation of a public policy, i.e. an amalgamation project. In a context of direct democracy where policies are regularly subjected to referendum, the conflict of objectives may even lead to a total failure of the policy at the polls.

Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation

Overcoming the rare species modelling paradox: test of a novel hierarchical framework with an Iberian endemic plant

Rare species have restricted geographic ranges, habitat specialization, and/or small population sizes. Datasets on rare species distribution usually have few observations, limited spatial accuracy and lack of valid absences; conversely they provide comprehensive views of species distributions allowing to realistically capture most of their realized environmental niche. Rare species are the most in need of predictive distribution modelling but also the most difficult to model. We refer to this contrast as the "rare species modelling paradox" and propose as a solution developing modelling approaches that deal with a sufficiently large set of predictors, ensuring that statistical models aren't overfitted. Our novel approach fulfils this condition by fitting a large number of bivariate models and averaging them with a weighted ensemble approach. We further propose that this ensemble forecasting is conducted within a hierarchic multi-scale framework. We present two ensemble models for a test species, one at regional and one at local scale, each based on the combination of 630 models. In both cases, we obtained excellent spatial projections, unusual when modelling rare species. Model results highlight, from a statistically sound approach, the effects of multiple drivers in a same modelling framework and at two distinct scales. From this added information, regional models can support accurate forecasts of range dynamics under climate change scenarios, whereas local models allow the assessment of isolated or synergistic impacts of changes in multiple predictors. This novel framework provides a baseline for adaptive conservation, management and monitoring of rare species at distinct spatial and temporal scales.

Dense Deformation Field Estimation for Atlas Registration using the Active Contour Framework

In this paper, we propose a new paradigm to carry outthe registration task with a dense deformation fieldderived from the optical flow model and the activecontour method. The proposed framework merges differenttasks such as segmentation, regularization, incorporationof prior knowledge and registration into a singleframework. The active contour model is at the core of ourframework even if it is used in a different way than thestandard approaches. Indeed, active contours are awell-known technique for image segmentation. Thistechnique consists in finding the curve which minimizesan energy functional designed to be minimal when thecurve has reached the object contours. That way, we getaccurate and smooth segmentation results. So far, theactive contour model has been used to segment objectslying in images from boundary-based, region-based orshape-based information. Our registration technique willprofit of all these families of active contours todetermine a dense deformation field defined on the wholeimage. A well-suited application of our model is theatlas registration in medical imaging which consists inautomatically delineating anatomical structures. Wepresent results on 2D synthetic images to show theperformances of our non rigid deformation field based ona natural registration term. We also present registrationresults on real 3D medical data with a large spaceoccupying tumor substantially deforming surroundingstructures, which constitutes a high challenging problem.

Analysis and Monte Carlo simulations of a model for the spread of infectious diseases in heterogeneous metapopulations

We present a study of the continuous-time equations governing the dynamics of a susceptible infected-susceptible model on heterogeneous metapopulations. These equations have been recently proposed as an alternative formulation for the spread of infectious diseases in metapopulations in a continuous-time framework. Individual-based Monte Carlo simulations of epidemic spread in uncorrelated networks are also performed revealing a good agreement with analytical predictions under the assumption of simultaneous transmission or recovery and migration processes

Weighted Shape-based Averaging with Neighborhood Prior Model for Multiple Atlas Fusion-based Medical Image Segmentation

In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: "Global Weighted Shape-Based Averaging" (GWSBA) and "Local Weighted Shape-Based Averaging" (LWSBA). These methods extend the well known Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.

Forensic intelligence framework. Part II: study of the main generic building blocks and challenges through the examples of illicit drugs and false identity documents monitoring

The development of forensic intelligence relies on the expression of suitable models that better represent the contribution of forensic intelligence in relation to the criminal justice system, policing and security. Such models assist in comparing and evaluating methods and new technologies, provide transparency and foster the development of new applications. Interestingly, strong similarities between two separate projects focusing on specific forensic science areas were recently observed. These observations have led to the induction of a general model (Part I) that could guide the use of any forensic science case data in an intelligence perspective. The present article builds upon this general approach by focusing on decisional and organisational issues. The article investigates the comparison process and evaluation system that lay at the heart of the forensic intelligence framework, advocating scientific decision criteria and a structured but flexible and dynamic architecture. These building blocks are crucial and clearly lay within the expertise of forensic scientists. However, it is only part of the problem. Forensic intelligence includes other blocks with their respective interactions, decision points and tensions (e.g. regarding how to guide detection and how to integrate forensic information with other information). Formalising these blocks identifies many questions and potential answers. Addressing these questions is essential for the progress of the discipline. Such a process requires clarifying the role and place of the forensic scientist within the whole process and their relationship to other stakeholders.

Gender inequality in health among elderly people in a combined framework of socioeconomic position, family characteristics and social support

This study analyses gender inequalities in health among elderly people in Catalonia (Spain) by adopting a conceptual framework that globally considers three dimensions of health determinants : socio-economic position, family characteristics and social support. Data came from the 2006 Catalonian Health Survey. For the purposes of this study a sub-sample of people aged 65–85 years with no paid job was selected (1,113 men and 1,484 women). The health outcomes analysed were self-perceived health status, poor mental health status and long-standing limiting illness. Multiple logistic regression models separated by sex were fitted and a hierarchical model was fitted in three steps. Health status among elderly women was poorer than among the men for the three outcomes analysed. Whereas living with disabled people was positively related to the three health outcomes and confidant social support was negatively associated with all of them in both sexes, there were gender differences in other social determinants of health. Our results emphasise the importance of using an integrated approach for the analysis of health inequalities among elderly people, simultaneously considering socio-economic position, family characteristics and social support, as well as different health indicators, in order fully to understand the social determinants of the health status of older men and women.

Monitoring of illicit pill distribution networks using an image collection exploration framework

This paper proposes a novel approach for the analysis of illicit tablets based on their visual characteristics. In particular, the paper concentrates on the problem of ecstasy pill seizure profiling and monitoring. The presented method extracts the visual information from pill images and builds a representation of it, i.e. it builds a pill profile based on the pill visual appearance. Different visual features are used to build different image similarity measures, which are the basis for a pill monitoring strategy based on both discriminative and clustering models. The discriminative model permits to infer whether two pills come from the same seizure, while the clustering models groups of pills that share similar visual characteristics. The resulting clustering structure allows to perform a visual identification of the relationships between different seizures. The proposed approach was evaluated using a data set of 621 Ecstasy pill pictures. The results demonstrate that this is a feasible and cost effective method for performing pill profiling and monitoring.

Randomized observation periods for the compound Poisson risk model: Dividends

In the framework of the classical compound Poisson process in collective risk theory, we study a modification of the horizontal dividend barrier strategy by introducing random observation times at which dividends can be paid and ruin can be observed. This model contains both the continuous-time and the discrete-time risk model as a limit and represents a certain type of bridge between them which still enables the explicit calculation of moments of total discounted dividend payments until ruin. Numerical illustrations for several sets of parameters are given and the effect of random observation times on the performance of the dividend strategy is studied.