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By Lei Chen, Chengfei Liu, Xiao Zhang, Shan Wang, Darijus Strasunskas, Stein L. Tomassen, Jinghai Rao, Wen-Syan Li, K. Selcuk Candan, Dickson K. W. Chiu, Yi Zhuang

This quantity contains papers from 4 APWeb/WAIM 2009 workshops, that are 1. foreign Workshop on Web-based Contents administration applied sciences (WCMT 2009), 2. foreign Workshop on Real-Time enterprise Intelligence (RTBI 2009), three. foreign Workshop on DataBase and data Retrieval and points in comparing Holistic caliber of Ontology-based details Retrieval (DBIR-ENQOIR 2009), in addition to four. overseas Workshop on procedure conscious details structures (PAIS 2009. those 4 workshops have been chosen from a public call-for-proposals technique. The workshop organizers have placed an enormous volume of attempt into soliciting and choosing examine papers with a stability of top quality and new principles and new purposes.

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Extra resources for Advances in Web and Network Technologies and Information Management: AP Web WAIM 2009 International Workshops: WCMT 2009, RTBI 2009, DBIR-ENQOIR 2009, ... Applications, incl. Internet Web, and HCI)

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Chrominance Conspicuity Map (CCM) P1: It is known that face region generally exhibits the skin-color feature. Therefore, using the skin-color information, the facial saliency map can be easily constructed to locate the potential face areas. The P1 is defined as P1 ( x , y ) = exp{−(ωCr ( x , y ) Cr '( x , y ) 2 Cb '( x , y ) 2 + ω )} Cb 2 2 2Δ Cr 2 Δ Cb Cr '( x , y ) = (Cr ( x , y ) − μCr ) cos(θ ) + (Cb( x , y ) − μCb )sin(θ ) Cb '( x, y ) = (Cb( x, y ) − μCb )cos(θ ) + (Cr ( x, y ) − μCr )sin(θ ) (5) (6) (7) π θ= ω ( x, y ) ( v = Cr or Cb ) is a where μCr = 153 , μCb = 102 , ΔCr = 20 , ΔCb = 25 , 4 and v weight coefficient.

E P]: 3. E’=E;T’=0 4. [A|B]: 5. =TSConvert(A,T) 6. =TSConvert(B,T) 7. E’=A’|B’;T’=max(Ta,Tb) 8. [A+B]: 9. =TSConvert(A,T) 10. =TSConvert(B,T) 11. E’=A’+B’;T’=∞ 12. [A-B]: 13. =TSConvert(A,T) 14. =TSConvert(B,min(Ta ,T)) 15. E’=A’-B’;T’= Ta 16. [A;B] 17. =TSConvert(A,T) 18. =TSConvert(B,T) 19. IF Tb≤T 20. E’=A’; Tb B’;T’=∞ 21. ELSE 22. E’=A’; T B’;T’=∞ 23. [AT’’]: 24. =TSConvert(A,min(T,T’’)) 25. IF Ta≤min(T,T’’) 26. E’=A’;T’= Ta 27.

Suppose we label the new cluster formed by merging clusters r and s as t. } (6) Ward's method. Ward proposed a clustering procedure seeking to form the partitions Pk, Pk-1, …, P1 in a manner that minimizes the loss associated with each grouping and to quantifies that loss in readily interpretable form. Information loss is defined by Ward in terms of an error sum-of-squares (ESS) criterion. ESS is defined as the following ESS K p ¦ ¦ ¦ ( xij  xkj ) K 1 xiCk j 1 2 (7) 1 ¦ xiCk xij , where xij denotes the value for the i-th nk individual in the j-cluster, k is the total number of clusters at each stage, and n j is the number of individuals in the j-th cluster.

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