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In the context of medical team leaders training, we present a multiagent communication model that can introduce errors in a team of agents. This model is built from existing work from the literature in multiagents systems and information science, but also from a corpus of dialogues collected during actual field training for medical teams. Our model supports four types of communication errors (misunderstanding,...
In this paper we present the computational study of one class of discrete models of collective behavior. In the context of these models a set of agents, that form a collective, is represented by a network. Each agent is assigned a special weight function. The behavior of a collective in discrete time moments is specified with a vector function, the coordinates of which are defined by values of agents...
In Human Activity Recognition (HAR) supervised and semi-supervised training are important tools for devising parametric activity models. For the best modelling performance, typically large amounts of annotated sample data are required. Annotating often represents the bottleneck in the overall modelling process as it usually involves retrospective analysis of experimental ground truth, like video footage...
In this article we illustrate and discuss a techno-centric aspect of re-engineering realized on an existent TEL system: the Apprenticeship Electronic Booklet. Although this system has been designed with end-users following a participatory process, the first version had also been found too rigid in regard to the roles management and to the underlying academic structures. In order to improve this TEL...
The highly cross-disciplinary emerging field of neuromorphic computing architectures for cognitive information processing applications requires knowledge within many research fields: computer architecture, neuroscience, cognitive psychology, cognitive modeling, dynamical systems, belief systems, software, computer engineering, etc. In our effort to develop cognitive systems atop a neuromorphic computing...
Major recommendation systems don't consider the user's current composite state in time. And also they do not consider the affect on the recommendation results when the user updates the context. In the process of analyzing the user's interests and preferences, it is not enough to determine the user state only based on the browsing records about the user, so more context information from the user's...
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