A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps

The Self-OrganizingMap (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topologypreservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topologypreservation, particularly using Kohonen's model. In this work, two methods for measuring the topologypreservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving map

Desarrollos para la explotación de datos integrados mediante fotogrametría y láser escáner en proyectos de patrimonio arquitectónico

La presente tesis doctoral describe los desarrollos realizados, y finalmente materializados en patentes con registro de la propiedad intelectual, para la integración de las nuevas tecnologías de documentación fotogramétrica y las bases de datos de los barredores láser terrestres, en los procesos de elaboración, redacción y ejecución de proyectos de restauración y rehabilitación arquitectónicos. Los avances tecnológicos aparecidos en control métrico, junto con las técnicas de imagen digital y los desarrollos fotogramétricos, pueden aportar mejoras significativas en el proceso proyectual y permiten aplicar nuevos procedimientos de extracción de datos para generar de forma sencilla, bajo el control directo y supervisión de los responsables del proyecto, la información métrica y documental más adecuada. Se establecen como principios, y por tanto como base para el diseño de dicha herramienta, que los desarrollos aparecidos sí han producido el uso extendido del sistema CAD (como instrumento de dibujo) así como el uso de la imagen digital como herramienta de documentación. La herramienta a diseñar se fundamenta por tanto en la imagen digital (imágenes digitales, imágenes rectificadas, ortofotografías, estéreo- modelos, estereo- ortofotografías) así como su integración en autocad para un tratamiento interactivo. En la aplicación de la fotogrametría a la disciplina arquitectónica, se considera de interés estructurar aplicaciones con carácter integrador que, con mayores capacidades de interactuación y a partir de información veraz y rigurosa, permitan completar o elaborar documentos de interés proyectual, ABSTRACT This doctoral thesis explains the developments carried out, and finally patented with intellectual property rights, for the integration of the new photogrammetric technology documentation and terrestrial scanner databases in the preparation, documentation and implementation processes of restoration projects and architectural renovation. The technological advances in metric control, as well as the digital image techniques and photogrammetric developments, can together bring a significant improvement to the projecting process, and, under the direct control and supervision of those in charge of the project, can allow new data extraction processes to be applied in order to easily generate the most appropriate metric information and documentation. The principles and, therefore, the basis for the design of this tool are that the developments have indeed produced the widespread use of the CAD system (as a drawing instrument) and the use of digital images as a documentation tool. The tool to be designed is therefore based on digital images (rectify images, orthophotos, stereomodels, stereo-orthophotos) as well as its integration in Autocad for interactive processing. In the application of photogrammetry to the architectural discipline, what interests us is to structure applications of an integrative nature which, with a greater capacity for interaction and from accurate and thorough information, enable the completion or elaboration of documents that are of interest to the project.

Autonomous classification models in ubiquitous environments

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behaviour to the volatile underlying data distributions, what has been called concept drift. Moreover, it is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. This could be extremely useful in ubiquitous environments that are characterized by the existence of resource constrained devices. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. By means of using meta-models as a recurrent drift detection mechanism, the ability to share concepts representations among different data mining processes is open. That kind of exchanges could improve the accuracy of the resultant local model as such model may benefit from patterns similar to the local concept that were observed in other scenarios, but not yet locally. This would also improve the efficiency of training instances used during the classification process, as long as the exchange of models would aid in the application of already trained recurrent models, that have been previously seen by any of the collaborative devices. Which it is to say that the scope of recurrence detection and representation is broaden. In fact the detection, representation and exchange of concept drift patterns would be extremely useful for the law enforcement activities fighting against cyber crime. Being the information exchange one of the main pillars of cooperation, national units would benefit from the experience and knowledge gained by third parties. Moreover, in the specific scope of critical infrastructures protection it is crucial to count with information exchange mechanisms, both from a strategical and technical scope. The exchange of concept drift detection schemes in cyber security environments would aid in the process of preventing, detecting and effectively responding to threads in cyber space. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classification models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of: the MMPRec system, that integrates a meta-model mechanism and a fuzzy similarity function; a collaborative environment to share meta-models between different devices; a recurrent drift generator that allows to test the usefulness of recurrent drift systems, as it is the case of MMPRec. Moreover, this thesis presents an experimental validation of the proposed contributions using synthetic and real datasets.

Augmenting PROV with Plans in P-PLAN: Scientific Processes as Linked Data

Provenance models are crucial for describing experimental results in science. The W3C Provenance Working Group has recently released the PROV family of specifications for provenance on the Web. While provenance focuses on what is executed, it is important in science to publish the general methods that describe scientific processes at a more abstract and general level. In this paper, we propose P-PLAN, an extension of PROV to represent plans that guid-ed the execution and their correspondence to provenance records that describe the execution itself. We motivate and discuss the use of P-PLAN and PROV to publish scientific workflows as Linked Data.

Replication Data Management,needs and solutions: an initial evaluation of conceptual approaches for integrating heterogeneous replication study data

Replication Data Management (RDM) aims at enabling the use of data collections from several iterations of an experiment. However, there are several major challenges to RDM from integrating data models and data from empirical study infrastructures that were not designed to cooperate, e.g., data model variation of local data sources. [Objective] In this paper we analyze RDM needs and evaluate conceptual RDM approaches to support replication researchers. [Method] We adapted the ATAM evaluation process to (a) analyze RDM use cases and needs of empirical replication study research groups and (b) compare three conceptual approaches to address these RDM needs: central data repositories with a fixed data model, heterogeneous local repositories, and an empirical ecosystem. [Results] While the central and local approaches have major issues that are hard to resolve in practice, the empirical ecosystem allows bridging current gaps in RDM from heterogeneous data sources. [Conclusions] The empirical ecosystem approach should be explored in diverse empirical environments.