This study provides empirical evidence that an animated avatar offers the opportunity to transmit vital sign information significantly more quickly than conventional monitoring and with improved confidence and reduced cognitive effort. This could help care providers gain situation awareness more efficiently.
Model-Driven Engineering (MDE) uses models as its main assets in the software development process. The structure of a model is described through a metamodel. Even though modelling and meta-modelling are recurrent activities in MDE and a vast amount of MDE tools exist nowadays, they are tasks typically performed in an unassisted way. Usually, these tools cannot extract useful knowledge available in heterogeneous information sources like XML, RDF, CSV or other models and meta-models. We propose an approach to provide modelling and meta-modelling assistance. The approach gathers heterogeneous information sources in various technological spaces, and represents them uniformly in a common data model. This enables their uniform querying, by means of an extensible mechanism, which can make use of services, e.g., for synonym search and word sense analysis. The query results can then be easily incorporated into the (meta-)model being built. The approach has been realized in the Extremo tool, developed as an Eclipse plugin. Extremo has been validated in the context of two domains-production systems and process modelling-taking into account a large and complex industrial standard for classification and product description. Further validation results indicate that the integration of Extremo in various modelling environments can be achieved with low effort, and that the tool is able to handle information from most existing technological spaces.
Large-scale software repository mining typically requires substantial storage and computational resources, and often involves a large number of calls to (rate-limited) APIs such as those of GitHub and StackOverflow. This creates a growing need for distributed execution of repository mining programs to which remote collaborators can contribute computational and storage resources, as well as API quotas (ideally without sharing API access tokens or credentials). In this paper we introduce CROSSFLOW, a novel framework for building distributed repository mining programs. We demonstrate how CROSSFLOW can delegate mining jobs to remote workers and cache their results, and how workers can implement advanced behaviour such as load balancing and rejecting jobs they cannot perform (e.g. due to lack of space, credentials for a specific API).
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