By Tran Khanh Dang, Roland Wagner, Erich J. Neuhold, Makoto Takizawa, Josef Küng, Nam Thoai
This publication constitutes the refereed lawsuits of the 1st foreign convention on destiny information and defense Engineering, FDSE 2014, held in Ho Chi Minh urban, Vietnam, in November 2014. The 23 complete papers awarded have been conscientiously reviewed and chosen from sixty six submissions. they've been geared up within the following topical sections: gigantic info analytics and functions; safeguard and privateness engineering; crowdsourcing and social community information analytics; biometrics and information safety in clever units; cloud info administration and purposes; and advances in question processing and optimization.
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Additional resources for Future Data and Security Engineering: First International Conference, FDSE 2014, Ho Chi Minh City, Vietnam, November 19-21, 2014, Proceedings
A. Le-Khac, and M-T. Kechadi Fig. 2. FTK case configuration According to the FTK processing log, the FTK processing time of the testing dataset, with the criteria shown above is 10 hours and 54 minutes. For LES tool, the total processing time is 1 hour and 24 minutes including 34 minutes of text extraction, 38 minutes of generating NLP NER databases and 12 minutes of generating the searchable keyword index based on the testing datasets. In fact, the LES tool was evaluated by running various experiments on the testing datasets.
It is essential to choose keyword combinations well. Next, we use the five known evidence items to locate and use an AND combination of keywords to evaluate response time and retrieval time of evidence items. Table 2 shows the results of this experiment. Table 1. 020 Table 2. 5 40 M. -A. Le-Khac, and M-T. Kechadi When a combination of keywords is used we see that the response times for FTK are worser than single keyword search. On the other hand, using multiple keywords in LES the response time is also milliseconds for the performed evaluations.
Thus, the strength of decision stumps is reduced, particularly when dealing with datasets having dependencies among dimensions (see figure 1). Due to this situation, one can thus build oblique decision trees using the multivariate splitting criteria . Recently ensemble of oblique decision trees  using SVM , has attracted much research interests. Our forest algorithms constructs a collection of oblique decision stumps in the same framework of classical Bagging and Arcx4. The main difference is that each oblique decision stump in the forest uses the linear SVM for performing multivariate non-terminal node splitting (using the combination between attributes, instead of choosing a best one 20 Van T.