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

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

This publication and its spouse quantity, LNCS vols. 7331 and 7332, represent the complaints of the 3rd foreign convention on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The one hundred forty five complete papers offered have been rigorously reviewed and chosen from 247 submissions. The papers are prepared in 27 cohesive sections masking all significant issues of swarm intelligence learn 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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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. This result suggested the machine scoring is less accuracy than human in small score range but it is more hardly to make extraordinary failure than human scoring.

Variable selection for Cox’s proportional hazards model and frailty model. Ann. Statist. 30, 74–99 (2002) 5. : Adaptive Lasso for Cox’s proportional hazards model. Biometrika 94(3), 691–703 (2007) 6. : Penalized Cox regression analysis in the high- dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 21, 3001–3008 (2005) 7. : L1/2 regularization. Science in China, Series F 40(3), 1–11 (2010) 8. : Variable selection and sparse reconstruction via L1/2 Regularization, Sparsity in Machine Learning and Statistic, London (2009) 9.

In order to embody the technical features of rich client-side, the functions of data cache and logic calculation should be provided in the client logic layer of client-side. Making full use of the computing capability of client-side can improve the response time of e-Learning system and reduce the load of server-side. The reusability and maintainability of client-side application can be improved significantly by designing the tool class of e-Learning system to implement the above functions. Service calling layer is responsible for calling service offered by the service layer of server-side and returning the result to client logic layer and client presentation layer.

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