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Abdusalam Dawut,Hiroshi Nakayama,Rintarou Iwasaki,Toshiki Matsuda 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.7
As a method of instruction of information morals education, Tamada and others proposes the method of instruction by three sorts of knowledge, and also supposes that the method called “feedback method” of making one's act examine from a victim's position is effective to the student of low information morals (Tamada et al.1987). In this research, the new teaching materials which combined a feedback method and virtual reality (VR) technology were developed, and the effect was verified. In order for the student of low information morals to make it specifically look back upon the act performed within VR from a victim's position, within VR, the subject's head shot was attached to the face of the avatar which serves as an assailant, and victim experience was carried out. As a result, before and after carrying out victim experience, improvement in the consciousness to information morals and reduction of the act contrary to information morals were seen.
Uyghur Stemming and Lemmatization Approach based on Multi-Morphological Features
Abdurahim Mahmoud,Sediyegvl Enwer,Abdusalam Dawut,Palidan Tuerxun,Askar Hamdulla 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.11
This paper describes a stemming and lemmatization approach for Uyghur using Conditional Random Fields (CRFs). In the proposed approach, we used syllable-level training and test corpus with the combination of some automatically tagged positional and morphological feature tags. The training and test corpus has been manually tagged with a stemming tag set which includes eight kinds of tags which fully reflect the morphological feature of Uyghur word. It has been observed that some morphological features are very helpful for improving the evaluating results. The syllable-level Precision, Recall and F-score of the best evaluation result respectively are 98.79%,98.71% and 98.75% respectively, and the word-level accuracy we achieved is 95.9%.The experimental results show that the efficiency of this approach is very ideal.