Intelligent Data Engineering and Automated Learning – IDEAL by Li Ying, Li Fan-jun (auth.), Hujun Yin, Ke Tang, Yang Gao,

By Li Ying, Li Fan-jun (auth.), Hujun Yin, Ke Tang, Yang Gao, Frank Klawonn, Minho Lee, Thomas Weise, Bin Li, Xin Yao (eds.)

This publication constitutes the refereed lawsuits of the 14th overseas convention on clever facts Engineering and automatic studying, excellent 2013, held in Hefei, China, in October 2013. The seventy six revised complete papers provided have been conscientiously reviewed and chosen from greater than a hundred thirty submissions. those papers supplied a beneficial choice of most modern learn results in facts engineering and automatic studying, from methodologies, frameworks and strategies to purposes. as well as a number of subject matters akin to evolutionary algorithms, neural networks, probabilistic modelling, swarm clever, multi-objective optimisation, and sensible purposes in regression, type, clustering, organic info processing, textual content processing, video research, together with a few distinctive periods on rising subject matters corresponding to model and studying multi-agent structures, vast information, swarm intelligence and information mining, and mixing studying and optimisation in clever facts engineering.

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Extra info for Intelligent Data Engineering and Automated Learning – IDEAL 2013: 14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013. Proceedings

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In: Proceedings of ACM SIGIR Conference, Melbourne, Australia, pp. pl Abstract. The article describes a new concept of voice and visual communication between the human operator and a system for monitoring and optimization of processes of micro- and nano-machining. The remote system for monitoring and optimization of process quality, which is equipped with a speech interface and artificial intelligence, is presented in exemplary application in the precision grinding process. The developed concept proposes an architecture of the system equipped with a data analysis layer, process supervision layer, decision layer, communication subsystem by speech and natural language, and visual communication subsystem using voice descriptions.

In TREC 2000, the curves of RP and P@10 are very close to each other. In general, such results are as expected. It confirms that NDCG is the best metric, which is followed by AP, RP, P@10, and RR, in that order, if we consider a metric’s stability and discrimination power together. Further, comparison of any two metrics can be quantitative. For example, consider NDCG and RP in TREC 2004. 05. 8% in such a setting. 1 Level of significance Fig. 2. The situation of metric NDCG in four different year groups H.

Shi et al. 1 Fig. 3. Effect of query numbers on metric (AP) with the TREC 2008 data set For any of the metrics involved, it performs very differently from one year group to another. TREC 2004 is the most favourable, TREC 2008 is in second place, while TREC 2000 and TREC 2012 are the worst. For clarity, Figure 2 shows the curves of NDCG. The situation is the same for the other 4 metrics. We hypothesize that the major impact factor is the number of queries in each year group, because TREC 2004 has the largest number (249), which is followed by TREC 2008 (150), while TREC 2000 and TREC 2012 only have 50 and 47 queries, respectively.

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