959 resultados para Pedro Rebelo de Sousa


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In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets.

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Se analizan las posibilidades que tiene el cultivo de mariposas y la utilización de las plantas en la alimentación, sanidad y otros usos en el medio rural de Honduras, como complemento para su desarrollo.

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Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

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Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today’s smart phones. The state of the art in mobile activity recognition uses traditional classification learning techniques. Thus, the learning process typically involves: i) collection of labelled sensory data that is transferred and collated in a centralised repository; ii) model building where the classification model is trained and tested using the collected data; iii) a model deployment stage where the learnt model is deployed on-board a mobile device for identifying activities based on new sensory data. In this paper, we demonstrate the Mobile Activity Recognition System (MARS) where for the first time the model is built and continuously updated on-board the mobile device itself using data stream mining. The advantages of the on-board approach are that it allows model personalisation and increased privacy as the data is not sent to any external site. Furthermore, when the user or its activity profile changes MARS enables promptly adaptation. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practise that MARS quickly adapts to user profile changes while at the same time being scalable and efficient in terms of consumption of the device resources.

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El proyecto de la pasarela sobre el cauce, hoy Parque del Turia, entre la Calle Pedro Aleixandre y Avda. de Francia nº1, queda enmarcado dentro de un proyecto mucho más ambicioso, que pretende revolucionar la ciudad de Valencia. La integración total del parque dentro de la ciudad sin que éste suponga una barrera. Por dicha razón su diseño y concepción debe englobarse dentro de un contexto determinado que sea coherente con todo lo proyectado y diseñado hasta el momento. En la actualidad el parque es un lugar de encuentro referente en Valencia con más de tres millones de visitantes anuales, es el más visitado y grande de España. El parque posee integrado en su interior la Ciudad de las Artes y las Ciencias, el Parque Gulliver, el Palau de la Música, el Parque de Cabecera, el Bioparc y el Zoo de Valencia. La pasarela será un paso de referencia para la gran cantidad de usuarios que cruzan a diario y especialmente los fines de semana, por esta zona de gran importancia por su valor turístico y comercial. Por tanto, será una infraestructura discreta pero estética, útil y confortable, que tiende a estar integrada con el entorno, manteniendo una inversión y mantenimiento adecuados.

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Bibliografía: p. 325

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We propose a novel measure to assess the presence of meso-scale structures in complex networks. This measure is based on the identi?cation of regular patterns in the adjacency matrix of the network, and on the calculation of the quantity of information lost when pairs of nodes are iteratively merged. We show how this measure is able to quantify several meso-scale structures, like the presence of modularity, bipartite and core-periphery con?gurations, or motifs. Results corresponding to a large set of real networks are used to validate its ability to detect non-trivial topological patterns.

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Hay un ejemplar encuadernado con: Manifestacion iuridica sobre el derecho de inmunidad y sagrado de las iglesias y monasterios, paraque [sic] no se hagá extracciones viole>tas de los refugiados à ellas por la Iusticia secular.. (XVII/42).