Digital library of historical cartography: open access to cultural heritage and public good

This paper describes the integration of information between Digital Library of Historical Cartography and Bibliographical Database (DEDALUS), both of the University of São Paulo (USP), to guarantee open, public access by Internet to the maps in the collection and make them available to users everywhere. This digital library was designed by Historical Cartography Studies Laboratory team (LECH/USP), and provides maps images on the Web, of high resolution, as well as such information on these maps as technical-scientific data (projection, scale, coordinates), printing techniques and material support that have made their circulation and cultural consumption possible. The Digital Library of Historical Cartography is accessible not only to the historical cartography researchers, but also to students and the general public. Beyond being a source of information about maps, the Digital Library of Historical Cartography seeks to be interactive, exchanging information and seeking dialogue with different branches of knowledge

USO DO DUBLIN CORE NA DESCRIÇÃO DE OBRAS RARAS NA WEB: A COLEÇÃO DA BIBLIOTECA BRASILIANA DIGITAL

Acessível ao público desde junho de 2009, a Biblioteca Brasiliana Digital, da Universidade de São Paulo tem por objetivo facultar para a pesquisa, a maior Brasiliana custodiada por uma universidade. Sua intenção é disponibilizar virtualmente parte do acervo da Universidade oferecendo-se como um instrumento útil e funcional para a pesquisa e o estudo dos temas e cultura brasileiros, além de oferecer um modelo tecnológico de gestão que possa ser difundido a outras coleções, acervos e instituições. Este trabalho apresenta os resultado da implantação de um esquema de metadados baseado no formato Dublin Core, para a descrição de obras raras e especiais na web. Especificamente, apresenta os procedimentos e processos de descrição de conteúdos das diversas tipologias documentais (livros, periódicos, gravuras etc.) e formatos digitais (pdf, jpeg entre outros). Palavras-Chave: Bibliotecas digitais; Metadados; Dublin Core.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Unveiling the relationship between complex networks metrics and word senses

The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012

PLANEJAMENTO, OBJETIVOS E DEFINIÇÃO DE PADRÕES NA DESCRIÇÃO DOS METADADOS DA COLEÇÃO DA BIBLIOTECA BRASILIANA DIGITAL

O projeto „Por uma Biblioteca Brasiliana Digital‟ é parte integrante do projeto BRASILIANA USP, uma iniciativa da Reitoria da Universidade de São Paulo (USP), que tem por objetivo facultar para a pesquisa a maior Brasiliana custodiada por uma universidade, tornando-a disponível na internet. O trabalho que ora apresentamos é resultado da implantação de um modelo de biblioteca digital que atende aos padrões de interoperabilidade e compartilhamento de informações. Especificamente, apresentaremos os procedimentos e processos de descrição de conteúdos das diversas tipologias documentais (livros, periódicos, mapas, gravuras etc.) e formatos digitais (pdf, mp3, jpeg entre outros), bem como a consolidação de um esquema de metadados gerenciais e administrativos que contemplam as informações e dados produzidos pelo Projeto da Brasiliana Digital.

Techniques for measuring aerosol attenuation using the Central Laser Facility at the Pierre Auger Observatory

The Pierre Auger Observatory in Malargüe, Argentina, is designed to study the properties of ultra-high energy cosmic rays with energies above 1018 eV. It is a hybrid facility that employs a Fluorescence Detector to perform nearly calorimetric measurements of Extensive Air Shower energies. To obtain reliable calorimetric information from the FD, the atmospheric conditions at the observatory need to be continuously monitored during data acquisition. In particular, light attenuation due to aerosols is an important atmospheric correction. The aerosol concentration is highly variable, so that the aerosol attenuation needs to be evaluated hourly. We use light from the Central Laser Facility, located near the center of the observatory site, having an optical signature comparable to that of the highest energy showers detected by the FD. This paper presents two procedures developed to retrieve the aerosol attenuation of fluorescence light from CLF laser shots. Cross checks between the two methods demonstrate that results from both analyses are compatible, and that the uncertainties are well understood. The measurements of the aerosol attenuation provided by the two procedures are currently used at the Pierre Auger Observatory to reconstruct air shower data.