Recent studies concerning Spiking Neural Networks show that they are a powerful tool for multiple applications as pattern recognition, image tracking, and detection tasks. The basic functional properties of SNN reside...
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Recent studies concerning Spiking Neural Networks show that they are a powerful tool for multiple applications as pattern recognition, image tracking, and detection tasks. The basic functional properties of SNN reside in the use of spike information encoding as the neurons are specifically designed and trained using spike trains. We present a novel and efficient frequency encoding algorithm with Gabor-like receptive fields using probabilistic methods and targeted to FPGA for online pro-cessing. The proposed encoding is versatile, modular and, when applied to images, it is able to perform simple image transforms as edge detection, spot detection or removal, and Gabor-like filtering without any further computation requirements. The algorithm is implemented in FPGA and ready to be used in embedded systems, being capable of processing images or video stream up to 40 megapixel per second per single core. Results show an improvement in hardware occupation and encoding speed up to 2.5x over existing state of the art implementations.
General Concept Inclusion (GCIs) absorption algorithms have shown to play an important role in classical Description Logics (DLs) reasoners, as they allow to transform GCIs into simpler forms to which apply specialise...
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General Concept Inclusion (GCIs) absorption algorithms have shown to play an important role in classical Description Logics (DLs) reasoners, as they allow to transform GCIs into simpler forms to which apply specialised inference rules, resulting in an important performance gain. In this work, we develop a first absorption algorithm for fuzzy DLs, and evaluate it over some ontologies.
Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge that is imprecise or vague by nature. In fuzzy DLs, restricting to a finite set of degrees of truth has proved to be useful...
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Classical ontologies are not suitable to represent imprecise or vague information, which has led to several extensions using non-classical logics. In particular, several fuzzy extensions have been proposed in the lite...
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The ubiquitous learning is used as a means of supporting teaching processes by means of the use of mobile and wireless communication technologies, sensors and tracking mechanisms / tracking, working together to integr...
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The ubiquitous learning is used as a means of supporting teaching processes by means of the use of mobile and wireless communication technologies, sensors and tracking mechanisms / tracking, working together to integrate students with their environment. The process of clinical learning for nursing students may be the main cause of increased stress on teachers and students, given the problematic nature of the patients. This paper presents an ubiquitous learning system based on active learning methodology that provides context awareness support for nursing courses. An experiment with control and experimental groups with nursing students demonstrates that for theoretical concepts to be successfully transferred into practice, the context of practice needs to be considered.
The aim of this work is to present an experiment of liquid level process controlled by a reset PI+CI compensator. The results are compared with a well tuned linear PI compensator, showing that the reset compensator im...
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This paper explores the applicability of the software prototype developed for personalized access to semantically enriched art collection of the Rijksmuseum in Amsterdam in a different environment-city rather than mus...
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This paper explores the applicability of the software prototype developed for personalized access to semantically enriched art collection of the Rijksmuseum in Amsterdam in a different environment-city rather than museum. As a case study we take Amsterdam, a World Heritage City, i.e. a city that includes urban areas designated as World Heritage (WH). This is the first step towards turning our prototype into a generic tool applicable for generating recommendations/personalized routes in indoor and outdoor environments based on semantically described data of the museum collection or points of interest in the city. Moreover we allow for user model information reuse between various domains/scenarios served by our Web-based application therefore addressing the cold start problem while starting to use a new application.
The issue of the automatic classification of research articles into one or more fields of science is of primary importance for scientific databases and digital libraries. A sophisticated classification strategy render...
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ISBN:
(纸本)9781450316569
The issue of the automatic classification of research articles into one or more fields of science is of primary importance for scientific databases and digital libraries. A sophisticated classification strategy renders searching more effective and assists the users in locating similar relevant items. Although the most publishing services require from the authors to categorize their articles themselves, there are still cases where older documents remain unclassified, or the taxonomy changes over time. In this work we attempt to address this interesting problem by introducing a machine learning algorithm which combines several parameters and meta-data of a research article. In particular, our model exploits the training set to correlate keywords, authors, co-authorship, and publishing journals to a number of labels of the taxonomy. In the sequel, it applies this information to classify the rest of the documents. The experiments we have conducted with a large dataset comprised of about 1,5 million articles, demonstrate that in this specific application, our model outperforms the *** and SVM methods. Copyright 2013 ACM.
Classification problems in which the number of attributes is larger than the number of examples are increasingly common with rapid technological advances in data collection. Also numerical data are predominant in real...
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ISBN:
(纸本)9789898565778
Classification problems in which the number of attributes is larger than the number of examples are increasingly common with rapid technological advances in data collection. Also numerical data are predominant in real world applications and many algorithms in supervised learning are restricted to discrete attributes. Focusing on these issues, we proposed an improvement in a fuzzy discretization method by means of the introduction of a bagging process in the different phases of the method. The bagging process tries to solve problems which can appear with small size datasets. Also we show the benefits that bagging introduces in the method by means of several experiments. The experiments have been validated by means of statistical tests.
Mechanised labour and games with a purpose are the two most popular human computation genres, frequently employed to support research activities in fields as diverse as natural language processing, semantic web or dat...
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