Download Discovery Science: 18th International Conference, DS 2015, by Nathalie Japkowicz, Stan Matwin PDF

By Nathalie Japkowicz, Stan Matwin

This ebook constitutes the complaints of the seventeenth overseas convention on Discovery technology, DS 2015, held in banff, AB, Canada in October 2015. The sixteen lengthy and 12 brief papers presendted including four invited talks during this quantity have been rigorously reviewed and chosen from forty four submissions.

The blend of modern advances within the improvement and research of equipment for locating scienti c wisdom, coming from laptop studying, information mining, and clever facts research, in addition to their software in a variety of scienti c domain names, at the one hand, with the algorithmic advances in computer studying concept, however, makes each example of this joint occasion specific and tasty.

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Additional resources for Discovery Science: 18th International Conference, DS 2015, Banff, AB, Canada, October 4–6, 2015, Proceedings

Sample text

As well, swarming midges do not interact with each other via long-distance cohesive influences [9]. 2 cm of each other [9]. Therefore, different tendencies should be used to simulate swarming from those typically used for schooling and flocking simulations. Couzin et al. proposed a collective behaviour model with separation, cohesion, and parallel alignment, which is able to simulate four collective behaviours: swarm, torus, dynamic parallel group, and highly parallel group [2]. The swarming behaviour model, implemented with long-range cohesion, was determined not to be biologically realistic by Puckett, Kelley, and Ouellette, who compared the acceleration statistics of the model with those of a real swarm [9].

Previous researchers who proposed such models validated their results by qual‐ itative assessments of simulations and animations. Our basic model can be made more complex by considering different circumstances. For example, our current model assumes that the air is still, whereas, in nature, there is almost always at least a light breeze, which is known to affect how the swarm behaves [4]. Additionally, one could adjust the decision-making of the simulated midges to allow them able to judge swarm size and density, and then join or leave swarms based on their size.

The resulting animations will still need to be evaluated by a person, in a further step we do not attempt in this paper. The remainder of the paper is organized as follows. The second section describes back‐ ground information on midges and swarms of midges, with emphasis on previous models of their swarming behaviour. The third section describes our approach, including mention of the relevant tendencies, the range of possible values for parameters, and a scoring func‐ tion for evaluating behaviours in simulation runs.

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