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Register a new userThe Benefits of Model-Driven Development in Institutional Repositories - Los Beneficios del Desarrollo Dirigido por Modelos en los Repositorios Institucionales
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Added by
Jose Texier
Publication date
2012
fields
Informatics Engineering
and research's language is
English
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The Institutional Repositories (IR) have been consolidated into the institutions in scientific and academic areas, as shown by the directories existing open access repositories and the deposits daily of articles made by different ways, such as by self-archiving of registered users and the cataloging by librarians. IR systems are based on various conceptual models, so in this paper a bibliographic survey Model-Driven Development (MDD) in systems and applications for RI in order to expose the benefits of applying MDD in IR. The MDD is a paradigm for building software that assigns a central role models and active under which derive models ranging from the most abstract to the concrete, this is done through successive transformations. This paradigm provides a framework that allows interested parties to share their views and directly manipulate representations of the entities of this domain. Therefore, the benefits are grouped by actors that are present, namely, developers, business owners and domain experts. In conclusion, these benefits help make more formal software implementations, resulting in a consolidation of such systems, where the main beneficiaries are the end users through the services are offered
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Institutional repositories are deposits of different types of digital files for access, disseminate and preserve them. This paper aims to explain the importance of repositories in the academic field of engineering as a way to democratize knowledge by teachers, researchers and students to contribute to social and human development. These repositories, usually framed in the Open Access Initiative, allow to ensure access free and open (unrestricted legal and economic) to different sectors of society and, thus, can make use of the services they offer. Finally, that repositories are evolving in the academic and scientific, and different disciplines of engineering should be prepared to provide a range of services through these systems to society of today and tomorrow.
The attitudes and beliefs of teachers and future physics teachers about epistemological aspects of Physics play a very important role in teaching the discipline. In this paper, after describing some of the most important results reported in the literature, we present the main results of the implementation of the CLASS test on attitudes and beliefs to hundreds of student and physics teacher in Uruguay. The results corresponding to the teaching students are similar to those who choose careers with an emphasis on Physics and Mathematics and, in turn, are clearly below those presented by professors and researchers. We highlight then the need to make explicit the issues related to epistemological attitudes and beliefs in our classes.
GitHub has become an important platform for code sharing and scientific exchange. With the massive number of repositories available, there is a pressing need for topic-based search. Even though the topic label functionality has been introduced, the majority of GitHub repositories do not have any labels, impeding the utility of search and topic-based analysis. This work targets the automatic repository classification problem as textit{keyword-driven hierarchical classification}. Specifically, users only need to provide a label hierarchy with keywords to supply as supervision. This setting is flexible, adaptive to the users needs, accounts for the different granularity of topic labels and requires minimal human effort. We identify three key challenges of this problem, namely (1) the presence of multi-modal signals; (2) supervision scarcity and bias; (3) supervision format mismatch. In recognition of these challenges, we propose the textsc{HiGitClass} framework, comprising of three modules: heterogeneous information network embedding; keyword enrichment; topic modeling and pseudo document generation. Experimental results on two GitHub repository collections confirm that textsc{HiGitClass} is superior to existing weakly-supervised and dataless hierarchical classification methods, especially in its ability to integrate both structured and unstructured data for repository classification.
Here we show the preliminary results of a study where there seems to be a bias effect in the size distributions of the detected low-surface brightness (LSB) galaxies in different environments. In this sense, more distant groups/clusters would lack small effective radius objects, while large systems would not found in the Local Group and nearby environments. While there may be an actual shortage of large LSB galaxies in low-density environments like the Local Group, the non-detection of small (and faint) systems at large distances is clearly a selection effect. As an example, LSB galaxies with similar sizes to those of the satellites of Andromeda in the Local Group, will be certainly missed in a visual identification at the distance of Pegasus I.
An alternative definition of the concept is given of functional dependence among the attributes of the relational schema in the Relational Model, this definition is obtained in terms of the set theory. For that which a theorem is demonstrated that establishes equivalence and on the basis theorem an algorithm is built for the search of the functional dependences among the attributes. The algorithm is illustrated by a concrete example
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