By Ignacio Rojas, Gonzalo Joya, Joan Cabestany
This two-volume set LNCS 7902 and 7903 constitutes the refereed complaints of the twelfth overseas Work-Conference on man made Neural Networks, IWANN 2013, held in Puerto de los angeles Cruz, Tenerife, Spain, in June 2013. The 116 revised papers have been rigorously reviewed and chosen from quite a few submissions for presentation in volumes. The papers discover sections on mathematical and theoretical tools in computational intelligence, neurocomputational formulations, studying and version emulation of cognitive features, bio-inspired structures and neuro-engineering, complicated themes in computational intelligence and functions
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Additional info for Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Proceedings, Part 1
His description may well be the reason that the developers of the first commercial GUIs used the analogy of the desktop. That analogy is central to our most-used computerized devices – the laptop and the smartphone. The thing is, when Bush proposed this possible future technology he wasn’t doing so just for the sake of talking about technology. Bush was saying that this access to huge amounts of data would affect our cognitive processing, changing us “as we may think”. We have not followed his example, but have slipped into the pattern of developing new technology without worrying at all about how the use of it might affect human thought.
Of course, it would be against aviation regulations to actually turn off the channel, but lowering the volume situationally is allowed. Most pilots would tell you that if they reduce the volume during chatter, they will certainly increase it again as soon as they notice a need for it. This, of course, assumes that they will notice the need. No one has said that these two pilots had the emergency contact channel turned too low, but it would explain why they didn’t hear any of the attempts to contact them.
On the leftmost part of the figure, the fit by the ELM is good, but when a pure random noise variable is added, on the rightmost figure (the added noise variable is not pictured on the figure), the fit becomes loose and spread. 8 Fig. 1. Illustration of the ELM model fit (light blue dots) on a toy example (sum of sines, black crosses), for the normal data (leftmost part) and for the same data augmented with a random noise variable (not displayed), on the rightmost part. Due to the irrelevant additional variable, the fit of the ELM model is less accurate.