• Korea Information Processing Society Review
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• v.29 no.1
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• pp.61-73
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• 2022

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.276-282
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• 2011

CoreNet is a valuable resource to use in the domain of natural language processing including Korean-Chinese-Japanese multilingual text analysis, and translation among natural languages. CoreNet is mapped to SUMO in order to encourage its application in broader fields and enhance its international status as a multilingual lexical semantic network. To do this, indirect and direct mapping methodologies are used. Through the indirect mapping among CoreNet-KorLex-PWN-SUMO, we alleviate the difficulty of translating CoreNet concept terms in Korean into SUMO concepts in English, and maximize recall of SUMO concepts corresponding to the concept of CoreNet.

• Journal of Bio-Environment Control
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• v.20 no.4
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• pp.341-345
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• 2011

This study was aimed to select the optimal mat condition using existing plant-mats for the efficient planting of bedding Chrysanthemum. At fifty days after cutting with Dendranthema grandiflorum 'Ford', root formation among the treatments using eight different mats outstood with the treatment using 10 mm thick coir net, which has medium inserted between mat layers - called C treatment, compared to other treatments; this treatment had the highest values in the plant height and shoot fresh weight, which were 29 cm and 5.6 g, respectively. On the contrary, in 40 days after transplanting root-formed mats to field, 12 mm thick jute net, which has medium inserted between mat layers, had the highest plants compared to other treatments. However, there was no significant difference in shoot weight compared to C treatment. In experiment of different lengths of cut, the results of growth after transplanting showed that 5 cm long cut performed best compared to 3 and 8 cm long cuts.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.427-432
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• 2019

In this paper, we propose three approaches to modeling Android malware. The first method involves human security experts for meticulously selecting feature sets. With the second approach, we choose 300 features with the highest importance among the top 99% features in terms of occurrence rate. The third approach is to combine multiple models and identify malware through weighted voting. In addition, we applied a novel method of eliminating permission information which used to be regarded as a critical factor for distinguishing malware. With our carefully generated feature sets and the weighted voting by the ensemble algorithm, we were able to reach the highest malware detection accuracy of 97.8%. We also verified that discarding the permission information lead to the improvement in terms of false positive and false negative rates.