Download Advances in Computer Science and Engineering: 13th by Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi, PDF

By Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi, Shaahin Hessabi

This publication constitutes the revised chosen papers of the thirteenth foreign CSI laptop convention, CSICC 2008 hung on Kish Island, Iran, in March 2008. The eighty four general papers awarded including sixty eight poster displays have been rigorously reviewed and chosen from a complete of 426 submissions.

The papers are equipped in topical sections on learning/soft computing, set of rules concept, SoC and NoC, wireless/sensor networks, video processing and comparable subject matters, processor structure, AI/robotics/control, clinical photograph processing, p2p/cluster/grid platforms, cellular advert hoc networks, net, sign processing/speech processing, misc, protection, picture processing functions in addition to VLSI.

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Additional resources for Advances in Computer Science and Engineering: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, March 9-11, 2008 Revised Selected Papers

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The most well-known type of MS is Kurchan square posed by Rodolfo Kurchan (1989), which is originated from magic square. The maximum product minus the minimum one is as small as possible in a Kurchan Square. However, in the MAX version of MS, sum of the following products is maximum. The score function of a MS of dimension 3 is illustrated in Fig. 1. Rows: 5*1*8 = 40, 3*9*4 = 108, 7*2*6 = 84 Columns: 5*3*7 = 105, 1*9*2 = 18, 8*4*6 = 192 Diagonals: 5*9*6 = 270, 1*4*7 = 28, 8*3*2 = 48 Anti-diagonals: 8*9*7 = 504, 1*3*6 = 18, 5*4*2 = 40 MAXMS: SF= 40+108+84+105+18+192+270+28+48+504+18+40= 1455 Kurchan MS: SF= 504-18 = 486 5 3 7 1 9 2 8 4 6 Fig.

In this paper we use the web, the most comprehensive text corpus, in order to improve the low values of recall. Therefore, we use the search engines for searching web pages. Two information elements provided by search engines are the number of pages retrieved for the query Q and an abstraction of the document that contains the concept. In this paper we use these two elements for extracting ‘is-a’ relations. Another group of methods for extracting ‘is-a’ relations is based on distribution hypothesis [2].

Bayesian network provides a compact representation or factorization of the joint probability distribution for a group of variables [4]. In this way, the joint probability distribution for the entire network can be specified. This relationship can be captured mathematically using the chain rule in Equation 1 [6]. n p( x) = ∏ p( xi | parents( xi )) (1) i =1 We are interested in learning BN from training data D consisting of examples x. The two major tasks in learning a BN are: learning the graphical structure, and then learning the parameters (CP table entries) for that structure.

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