By Sumi Helal (auth.), Zhiwen Yu, Ramiro Liscano, Guanling Chen, Daqing Zhang, Xingshe Zhou (eds.)
Ubiquitous sensors, units, networks and data are paving the best way towards a sensible global during which computational intelligence is shipped in the course of the actual setting to supply trustworthy and suitable prone to humans. This ubiquitous intelligence will swap the computing panorama since it will permit new breeds of purposes and platforms to be built, and the world of computing chances may be considerably prolonged. through improving daily gadgets with intelligence, many projects and approaches will be simplified, the actual areas the place humans have interaction, like places of work and houses, may turn into extra effective, more secure and extra relaxing. Ubiquitous computing, or pervasive computing, makes use of those many “smart issues” or “u-things” to create clever environments, providers and purposes. a wise factor could be endowed with assorted degrees of intelligence, and will be c- text-aware, lively, interactive, reactive, proactive, assistive, adaptive, automatic, sentient, perceptual, cognitive, autonomic and/or considering. examine on ubiquitous intelligence is an rising learn box overlaying many disciplines. a sequence of grand demanding situations exists to maneuver from the present point of computing companies to the clever international of adaptive and clever prone. began in 2005, the sequence of UIC meetings has been held in Taipei, Nagasaki, 3 Gorges (China), Hong Kong, Oslo and Brisbane. The complaints comprise the papers awarded on the seventh overseas convention on Ubiquitous Intelligence and Computing (UIC 2010), held in Xi’an, China, October 26–29, 2010. The convention was once observed via six brilliant workshops on numerous examine demanding situations in the zone of ubiquitous intelligence and computing.
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Additional resources for Ubiquitous Intelligence and Computing: 7th International Conference, UIC 2010, Xi’an, China, October 26-29, 2010. Proceedings
5. Then we ﬁnd the Euclidean distance of each trip using these 3 dimensions as in Equation 14. M axI refers to a maximum interest density value of all candidate trips which we use for normalization. We can assign diﬀerent weight values for the factors by setting α1 , α2 , and α3 . For our system we treat three factors equally important by setting α1 = α2 = α3 = 1. 6 (14) Re-ranking by Travel Sequence We have cut down the number of candidate trips from n to k. These k trips will likely have similar Euclidean distance values.
The classical travel sequence integrates three aspects, the authority score of going in and out and the hub scores, to score travel sequences . Figure 5 demonstrates the calculation of the classical score for a 2-length sequence l1 ⇒ l3 . The connected edges represent people’s transition sequence and the values on the edges show the times users have taken the sequence. Equation 15 shows the calculation based on the following parts. 1) The authority score of location l1 (al1 ) weighted by the probability of people moving out from this sequence (Outl1 ,l3 ).
We evaluated our method in algorithmic level by using 4,000 simulated user queries in four diﬀerent time levels to conﬁrm the performance gain in the overall quality over baseline algorithms. We achieved the best performance when it was used for the longer duration. Also active residents used our system to generate real queries and evaluated the resulting itinerary. As a result, we found that our algorithm recommends itineraries that are nearly as good for a single attribute focused baseline algorithms such as Rank-by-Time and Rank-by-Interest and better in overall quality.
Ubiquitous Intelligence and Computing: 7th International Conference, UIC 2010, Xi’an, China, October 26-29, 2010. Proceedings by Sumi Helal (auth.), Zhiwen Yu, Ramiro Liscano, Guanling Chen, Daqing Zhang, Xingshe Zhou (eds.)