By Jörg Müller, Dennis Wilmsmann, Juliane Exeler, Markus Buzeck, Albrecht Schmidt, Tim Jay (auth.), Hideyuki Tokuda, Michael Beigl, Adrian Friday, A. J. Bernheim Brush, Yoshito Tobe (eds.)
This ebook constitutes the refereed court cases of the seventh foreign convention on Pervasive Computing, Pervasive 2009, held in Nara, Japan, in may perhaps 2009.
The 20 revised complete papers and seven revised brief papers offered have been conscientiously reviewed and chosen from 147 preliminary submissions. The papers are geared up in topical sections on electronic screens, navigation, at domestic with pervasive purposes, sensors, sensors, all over the place, operating jointly, tagging and monitoring, equipment and instruments, and the significance of context.
Read Online or Download Pervasive Computing: 7th International Conference, Pervasive 2009, Nara, Japan, May 11-14, 2009. Proceedings PDF
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Extra info for Pervasive Computing: 7th International Conference, Pervasive 2009, Nara, Japan, May 11-14, 2009. Proceedings
UbiComp 2001. LNCS, vol. 2201, pp. 332–345. Springer, Heidelberg (2001) 6. : Machine Learning. McGraw-Hill Science/Engineering/Math (1997) 7. : Placing Words. Symbols, Space, and the City. MIT Press, Cambridge (2005) 8. : How much to bid in digital signage advertising auctions? In: Adjunct proceedings of Pervasive 2007 (2007) 9. : Situated public news and reminder displays. In: Proc. European Conference on Ambient Intelligence, pp. 248–265 (2007) 10. : Auction mechanisms for eﬃcient advertisement selection on public displays.
As the system starts with no data, there is a problem of many probabilities being zero at the beginning. This problem is circumvented by applying the m-estimate to individual probabilities. The eﬀect of this is to give the system a set of values for a hot-start. 4 Implementation The ReﬂectiveSigns prototype consists of four digital signs installed at a university department with approximately 60 employees. One sign is located at an entrance (see Figure 2), one in a sofa corner, one in a hallway and one in a coﬀee kitchen.
In the learning loop, currently the only sensor is the face detection that measures the audience’s view time and then calculates the expected view time. Whenever some content is shown, the number of faces that are oriented towards the sign as well as the duration of time that these faces look at the sign are determined. The sum of these view times is then stored to a database. In order to be able to estimate the view time even with only few data, we used the 20 J. M¨ uller et al. Naive Bayes approach to calculate the expected view time.