Computer and Information Science (Studies in Computational - download pdf or read online

ISBN-10: 3319105094

ISBN-13: 9783319105093

This edited e-book provides medical result of the thirteenth IEEE/ACIS overseas convention on desktop and data technological know-how (ICIS 2014) which used to be hung on June 4-6, 2014 in Taiyuan, China. the purpose of this convention was once to compile researchers and scientists, businessmen and marketers, academics, engineers, machine clients, and scholars to debate the various fields of desktop technology and to percentage their reviews and trade new principles and data in a significant means. examine effects approximately all elements (theory, functions and instruments) of laptop and data technology, and to debate the sensible demanding situations encountered alongside the best way and the ideas followed to resolve them.

The convention organizers chosen the simplest papers from these papers approved for presentation on the convention. The papers have been selected in keeping with evaluate ratings submitted through individuals of this system committee, and underwent additional rigorous rounds of overview. This book captures 14 of the conference’s such a lot promising papers, and we impatiently look forward to the real contributions that we all know those authors will convey to the sphere of machine and knowledge technology.

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In order to overcome the need to specify the number of clusters apriori, a nice property of the possibilistic approach to generate duplicated clusters can be investigated in order to find the optimal number of clusters in an unsupervised manner. Acknowledgments This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University (Project RC131013). The authors are grateful for this support. References 1. , Smokey: Automatic recognition of hostile messages.

Test cases are developed in the process of the leaning scenario design and used to test the target scenario. ReStart-Me checks formally inclusion relation between dependability requirements and test cases. If the formal checking succeeds, then we can assure that the dependability requirements are well implemented in the ubiquitous learning environment. This paper is organised as follows. The following section will be devoted to define challenges of ubiquitous learning scenario. Section 3 presents different dimensions that should be evaluated in order to satisfy dependability’s requirement.

2 Fusion Based on Possibilistic Context Extraction Let N training observations with desired output T = {t j | j = 1, . . N }. These outputs were obtained using K classifiers. Each classifier k extracts its own feature set X k = {x kj | j = 1, . . N } and generates confidence values, Y k = {yk j | j = 1, . . N }. The K feature sets are then concatenated to generate one global descriptor, χ = K k χ = {x j = [x 1j , . . , x Kj | j = 1, . . , N ]}. The original Context Extraction for k=1 Local Fusion algorithm [9] minimizes N J= C K u mji j=1 i=1 s=1 q vik di2jk N + βi u mji j=1 i=1 2 K C k=1 ωik yk j − t j , (1) C K subject to i=1 u ji = 1 ∀ j, u ji ∈ [0, 1] ∀i, j, k=1 vik = 1 ∀i, vik ∈ [0, 1] ∀i, k, K and k=1 ωik = 1 ∀i.

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Computer and Information Science (Studies in Computational Intelligence, Volume 566)


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