80,813 research outputs found

    Notice of Duplicate Publication

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    Editorial stance on duplicate and salami publication

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    In this edition of the British Orthoptic Journal the notice to contributors has been amended. The sentence ‘Papers are considered for publication on the understanding that they are not being submitted elsewhere at the same time’ has been extended to address the problem of duplicate publication and now appears under ‘Terms of submission’

    Notice of Inadvertent Duplicate Publication

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    ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

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    Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called "matching dependencies" (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language "LogiQL" -an extended form of Datalog supported by the "LogicBlox" platform- for all activities related to data processing, and the specification and enforcement of MDs.Comment: Final journal version, with some minor technical corrections. Extended version of arXiv:1508.0601

    ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

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    Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating three components of ER: (a) Classifiers for duplicate/non-duplicate record pairs built using machine learning (ML) techniques, (b) MDs for supporting both the blocking phase of ML and the merge itself; and (c) The use of the declarative language LogiQL -an extended form of Datalog supported by the LogicBlox platform- for data processing, and the specification and enforcement of MDs.Comment: To appear in Proc. SUM, 201

    Publications ethics

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    The editor of any medical journal has to be aware of the ethical and legal framework within which medical research is conducted. When research and publications relate to children, then particularly high standards are required in the design, conduct, and reporting of research in order to protect the rights of children and their families. Authors have a number of duties and responsibilities that are mirrored by those of editors and publishers. Of particular importance are the principles of transparency and integrity. Authors should be explicit about who carried out the work and who funded the study. They should declare whether the work has been published before and is not being considered for publication elsewhere. The authors must protect the rights of research participants including their anonymity. Editors and publishers have a duty to ensure high editorial standards and efficient and effective peer review systems. They should follow ethical and responsible publication practices and should safeguard the intellectual property of the authors. This review discusses in detail the duties and responsibilities of authors, editors, and publishers in modern medical publishing
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