1,163 research outputs found

    Ontologies, Mental Disorders and Prototypes

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    As it emerged from philosophical analyses and cognitive research, most concepts exhibit typicality effects, and resist to the efforts of defining them in terms of necessary and sufficient conditions. This holds also in the case of many medical concepts. This is a problem for the design of computer science ontologies, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. However, the need of representing concepts in terms of typical traits concerns almost every domain of real world knowledge, including medical domains. In particular, in this article we take into account the domain of mental disorders, starting from the DSM-5 descriptions of some specific mental disorders. On this respect, we favor a hybrid approach to the representation of psychiatric concepts, in which ontology oriented formalisms are combined to a geometric representation of knowledge based on conceptual spaces

    Star Architecture as Socio-Material Assemblage

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    Taking inspiration from new materialism and assemblage, the chapter deals with star architects and iconic buildings as socio-material network effects that do not pre-exist action, but are enacted in practice, in the materiality of design crafting and city building. Star architects are here conceptualized as part of broader assemblages of actors and practices ‘making star architecture’ a reality, and the buildings they design are considered not just as unique and iconic objects, but dis-articulated as complex crafts mobilizing skills, technologies, materials, and forms of knowledge not necessarily ascribable to architecture. Overcoming narrow criticism focusing on the symbolic order of icons as unique creations and alienated repetitions of capitalist development, the chapter’s main aim is to widen the scope of critique by bridging culture and economy, symbolism and practicality, making star architecture available to a broad, fragmented arena of (potential) critics, unevenly equipped with critical tools and differentiated experiences

    Empowering Executive Functions in 5- and 6-Year-Old Typically Developing Children Through Educational Robotics: An RCT Study

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    Educational Robotics (ER) is a new learning approach that is known mainly for its effects on scientific academic subjects such as science, technology, engineering, and mathematics. Recent studies indicate that ER can also affect cognitive development by improving critical reasoning and planning skills. This study aimed to quantify the ability of ER to empower Executive Functions (EF), including the ability to control, update, and program information, in 5- and 6-year-old children attending first grade, a crucial evolutionary window for the development of such abilities. A total of 187 typically developing children were enrolled and randomly allocated into two experimental conditions: A, for immediate ER training, and B, for waitlist. ER-Laboratories (ER-Lab) for small groups were organized at schools, using a child-friendly, bee-shaped robot called Bee-BotÂź (Campus Store). Activities were intensive, enjoyable, and progressively more challenging over the 20 twice-weekly sessions. Outcome measures, based on standardized tests, were used to quantify the effects of ER on EF. Compared to the control group, the ER-Lab group showed significantly better ability to actively manipulate information in short-term memory and suppress automatic responses in favor of goal-appropriate actions. This RCT study provides the first quantitative evidence of the positive effects of ER activities for improving working memory and inhibition in the early school years

    Semantically Aware Text Categorisation for Metadata Annotation

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    In this paper we illustrate a system aimed at solving a longstanding and challenging problem: acquiring a classifier to automatically annotate bibliographic records by starting from a huge set of unbalanced and unlabelled data. We illustrate the main features of the dataset, the learning algorithm adopted, and how it was used to discriminate philosophical documents from documents of other disciplines. One strength of our approach lies in the novel combination of a standard learning approach with a semantic one: the results of the acquired classifier are improved by accessing a semantic network containing conceptual information. We illustrate the experimentation by describing the construction rationale of training and test set, we report and discuss the obtained results and conclude by drawing future work.</p

    Extending ontological categorization through a dual process conceptual architecture

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    In this work we present a hybrid knowledge representation system aiming at extending the representational and reasoning capabilities of classical ontologies by taking into account the theories of typicality in conceptual processing. The system adopts a categorization process inspired to the dual process theories and, from a representational perspective, is equipped with a heterogeneous knowledge base that couples conceptual spaces and ontological formalisms. The system has been experimentally assessed in a conceptual categorization task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially improves the representational and reasoning \ue2\u80\u9cconceptual\ue2\u80\u9d capabilities of standard ontology-based systems

    Characterization of Strombolian events by using independent component analysis

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    We apply Independent Component Analysis (ICA) to seismic signals recorded at Stromboli volcano. Firstly, we show how ICA works considering synthetic signals, which are generated by dynamical systems. We prove that Strombolian signals, both tremor and explosions, in the high frequency band (>0.5 Hz), are similar in time domain. This seems to give some insights to the organ pipe model generation for the source of these events. Moreover, we are able to recognize in the tremor signals a low frequency component (<0.5 Hz), with a well defined peak corresponding to 30s
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