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Titlebook: AI*IA 2011: Artificial Intelligence Around Man and Beyond; XIIth International Roberto Pirrone,Filippo Sorbello Conference proceedings 201

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https://doi.org/10.1007/978-3-319-75605-9o understanding the influence of these self-regulatory processes on learning with open-ended, non-linear learning computer-based environments involves detecting, capturing, identifying, and classifying these processes as they temporally unfold during learning. Understanding the complex nature of the
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Type of Substance as a New Physical Quantitya post-processing step of minor importance, the underlying assumptions being that they do not make a difference in the performance of a classifier and that finding the optimal thresholding strategy for any given classifier is trivial. Neither these assumptions are true. In this paper, we concentrate
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Physical Parameterization in MRIs, the most frequently used schemes simply rely on different criteria to combine the decisions of a set of independently trained binary classifiers. In this paper we propose an approach that aims at establishing a connection in the training stage of the classifiers using an innovative criterion. Mot
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Social Network Sites and Older Generations allow a manual labeling of the available items by the community members. However, the manual approach can fail to provide a consistent tagging especially when the dimension of the vocabulary of the tags increases and, consequently, the users do not comply to a shared semantic knowledge. Hence, aut
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https://doi.org/10.1007/3-540-37363-2specifications/expectations. Research areas like Multi Agent Systems, Business Process, and Legal/Normative systems, have proposed different characterizations of the same problem, named as the conformance problem. Most of the available systems, however, provide only simple yes/no answers to the conf
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