Computer and Information Science (Studies in Computational

This edited ebook offers clinical result of the thirteenth IEEE/ACIS foreign convention on desktop and data technology (ICIS 2014) which was once hung on June 4-6, 2014 in Taiyuan, China. the purpose of this convention was once to compile researchers and scientists, businessmen and marketers, lecturers, engineers, desktop clients, and scholars to debate the various fields of desktop technology and to percentage their stories and trade new principles and data in a significant method. learn effects approximately all features (theory, functions and instruments) of computing device and knowledge technology, and to debate the sensible demanding situations encountered alongside the way in which and the recommendations followed to unravel 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 evaluation rankings submitted through contributors of this system committee, and underwent additional rigorous rounds of overview. This booklet captures 14 of the conference’s such a lot promising papers, and we impatiently wait for the real contributions that we all know those authors will deliver to the sector of desktop and knowledge technology.

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Table 1 A comparative study of learning paradigm Learning paradigm Level of Level of embeddedness mobility E-learning Mobile learning Pervasive learning Ubiquitous learning + + +++ +++ legend: +: Low ++: Medium +++: High + +++ ++ +++ Level of interactivity Level of contextawareness + ++ +++ +++ + ++ +++ +++ 42 M. BenSassi et al. 3 What Can We Evaluate in Ubiquitous Learning Scenario? As computers have become more rapid and powerful, educational software has flourished and there are numerous claims conceived by designers.

In other words, we do not consider misclassifying an insult comment as serious as misclassifying a non-insult one. Moreover, since the testing data contains 720 insulting comments only out of 2674 comments, the accuracy cannot be an appropriate performance measure for this application. In Table 1, we show the aggregation weights learned by the proposed algorithms. As it can be seen, the relative performances of the individual classifiers varies significantly from one cluster to another. For instance for cluster 1, classifier 3 outperforms the other classifiers.

The second term in (1) attempts to learn cluster-dependent aggregation weights of the K algorithm outputs. ωik is the aggregation weight assigned to classifier k within cluster i. This term is minimized when the aggregated partial output values match the desired output. When both terms are combined and β is chosen properly, the algorithm seeks to partition the data into compact and homogeneous clusters while learning optimal aggregation weights for each algorithm within each cluster. For real world classification problems, multiple sources of information and multiple classifiers for each source may be needed to obtain satisfactory results.

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