EmotiBlog: a model to learn subjective information detection in the new textual genres of the Web 2.0 -a multilingual and multi-genre approach-

Tesis doctoral con mención europea en procesamiento del lenguaje natural realizada en la Universidad de Alicante por Ester Boldrini bajo la dirección del Dr. Patricio Martínez-Barco. El acto de defensa de la tesis tuvo lugar en la Universidad de Alicante el 23 de enero de 2012 ante el tribunal formado por los doctores Manuel Palomar (Universidad de Alicante), Dr. Paloma Moreda (UA), Dr. Mariona Taulé (Universidad de Barcelona), Dr. Horacio Saggion (Universitat Pompeu Fabra) y Dr. Mike Thelwall (University of Wolverhampton). Calificación: Sobresaliente Cum Laude por unanimidad.

MDD vs. traditional software development: a practitioner’s subjective perspective

Context: Today’s project managers have a myriad of methods to choose from for the development of software applications. However, they lack empirical data about the character of these methods in terms of usefulness, ease of use or compatibility, all of these being relevant variables to assess the developer’s intention to use them. Objective: To compare three methods, each following a different paradigm (Model-Driven, Model-Based and Code-Centric) with respect to their adoption potential by junior software developers engaged in the development of the business layer of a Web 2.0 application. Method: We have conducted a quasi-experiment with 26 graduate students of the University of Alicante. The application developed was a Social Network, which was organized around a fixed set of modules. Three of them, similar in complexity, were used for the experiment. Subjects were asked to use a different method for each module, and then to answer a questionnaire that gathered their perceptions during such use. Results: The results show that the Model-Driven method is regarded as the most useful, although it is also considered the least compatible with previous developers’ experiences. They also show that junior software developers feel comfortable with the use of models, and that they are likely to use them if the models are accompanied by a Model-Driven development environment. Conclusions: Despite their relatively low level of compatibility, Model-Driven development methods seem to show a great potential for adoption. That said, however, further experimentation is needed to make it possible to generalize the results to a different population, different methods, other languages and tools, different domains or different application sizes.

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.

Model Probability in Self-organising Maps

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.