Advances in Swarm Intelligence: Third International by Seyed Naser Razavi, Nicolas Gaud, Abderrafiâa Koukam, Naser

By Seyed Naser Razavi, Nicolas Gaud, Abderrafiâa Koukam, Naser Mozayani (auth.), Ying Tan, Yuhui Shi, Zhen Ji (eds.)

This e-book and its significant other quantity, LNCS vols. 7331 and 7332, represent the lawsuits of the 3rd foreign convention on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The one hundred forty five complete papers provided have been conscientiously reviewed and chosen from 247 submissions. The papers are geared up in 27 cohesive sections overlaying all significant subject matters of swarm intelligence examine and developments.

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Extra info for Advances in Swarm Intelligence: Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part II

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Yan Sample 1 hh Sample 2 hh ow ow m m Fig. 3. Phone Duration of the Word ”home” where N is the number of the phones, p(ndi )|pi ) is the probability of the i-th phone whose normalized duration was ndi . The normalized duration ndi was calculated as: di ndi = (4) sumN j dj where di is the phone duration to be normalized, N is the phone number of the sentence. ROS Score. We use the Rate of Speech (ROS) [2] as another feature to describe the fluency. ROS evaluate the reading speech, which reflects a side of the fluency of the reading.

The GMM-based duration cumulative probability features contributes 24 J. Zhang, F. Pan, and Y. 80 when even being used alone, that is because the duration cumulative probability reflects whether the student can master the phone duration in reading. Using the combination of the pronunciation and fluency features performanced better than using each of them alone. Table 1. 2 below. In the indicators ASAD and RR, the machine scoring is better than human scoring. But the CC of machine scoring is less than human scoring.

It assumes if user’s rating is similar in lots of items, their rating on other items will be similar, too. So the user’s rating for the items is predicted by neighbors’ weighted average values [4]. Content-based Recommendation [5] assumes if user is interest in an item before, user will interest in the similar items, too. It usually using vector space model [6] to describe user’s interest. The aim of Association Rule-based Recommendation [7] is to find the relations between items, such as the famous story about beer and diaper.

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