9,595 research outputs found

    A link-bridged topic model for cross-domain document classification

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    Proceedings of the 2nd Computer Science Student Workshop: Microsoft Istanbul, Turkey, April 9, 2011

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    Online visibility of software-related web sites: The case of biomedical text mining tools

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    Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.ipm.2018.11.011.Internet, in general, and the WWW, in particular, have become an immediate, practical means of introducing software tools and resources, and most importantly, a key vehicle to attract the attention of the potential users. In this scenario, content organization as well as different development practices may affect the online visibility of the target resource. Therefore, the careful selection, organization and presentation of contents are critical to guarantee that the main features of the target tool can be easily discovered by potential visitors, while ensuring a proper indexation by automatic online systems and resource recognizers. Understanding how software is depicted in scientific manuscripts and comparing these texts with the corresponding online descriptions can help to improve the visibility of the target website. It is particularly relevant to be able to align online descriptions and those found in literature, and use the resulting knowledge to improve software indexing and grouping. Therefore, this paper presents a novel method for formally defining and mining software-related websites and related literature with the ultimate aim of improving the global online visibility of the software. As a proof of concept, the method was used to evaluate the online visibility of biomedical text mining tools. These tools have evolved considerably in the last decades, and are gathering together a heterogeneous development community as well as various user groups. For the most part, these tools are not easily discovered via general search engines. Hence, the proposed method enabled the identification of specific issues regarding the visibility of these online contents and the discussion of some possible improvements.SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure. This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE2020(POCI-01-0145-FEDER-006684).The authors also acknowledge the Ph.D.grants of MartínPérez-Pérez and Gael Pérez - Rodríguez, funded by the Xunta de Galicia.info:eu-repo/semantics/publishedVersio

    Conceptual Representations for Computational Concept Creation

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    Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.Peer reviewe

    Multistep Fuzzy Bridged Refinement Domain Adaptation Algorithm and Its Application to Bank Failure Prediction

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    © 2015 IEEE. Machine learning plays an important role in data classification and data-based prediction. In some real-world applications, however, the training data (coming from the source domain) and test data (from the target domain) come from different domains or time periods, and this may result in the different distributions of some features. Moreover, the values of the features and/or labels of the datasets might be nonnumeric and involve vague values. Traditional learning-based prediction and classification methods cannot handle these two issues. In this study, we propose a multistep fuzzy bridged refinement domain adaptation algorithm, which offers an effective way to deal with both issues. It utilizes a concept of similarity to modify the labels of the target instances that were initially predicted by a shift-unaware model. It then refines the labels using instances that are most similar to a given target instance. These instances are extracted from mixture domains composed of source and target domains. The proposed algorithm is built on a basis of some data and refines the labels, thus performing completely independently of the shift-unaware prediction model. The algorithm uses a fuzzy set-based approach to deal with the vague values of the features and labels. Four different datasets are used in the experiments to validate the proposed algorithm. The results, which are compared with those generated by the existing domain adaptation methods, demonstrate a significant improvement in prediction accuracy in both the above-mentioned datasets

    Patterns and Interactions in Network Security

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    Networks play a central role in cyber-security: networks deliver security attacks, suffer from them, defend against them, and sometimes even cause them. This article is a concise tutorial on the large subject of networks and security, written for all those interested in networking, whether their specialty is security or not. To achieve this goal, we derive our focus and organization from two perspectives. The first perspective is that, although mechanisms for network security are extremely diverse, they are all instances of a few patterns. Consequently, after a pragmatic classification of security attacks, the main sections of the tutorial cover the four patterns for providing network security, of which the familiar three are cryptographic protocols, packet filtering, and dynamic resource allocation. Although cryptographic protocols hide the data contents of packets, they cannot hide packet headers. When users need to hide packet headers from adversaries, which may include the network from which they are receiving service, they must resort to the pattern of compound sessions and overlays. The second perspective comes from the observation that security mechanisms interact in important ways, with each other and with other aspects of networking, so each pattern includes a discussion of its interactions.Comment: 63 pages, 28 figures, 56 reference
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