• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.455-466
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• 2013

The purpose of this study was to develop a school violence prevention program "Modujikimi" for young children to be embedded in IrobiQ, the teacher assistive robot. The themes of this program consisted of basic character education, bullying prevention education and sexual violence prevention education. The activity types included large group, individual and small group activities, free choice activities, and finally parents' education, which included poems, fairy tales, music, art, sharing stories. Finally, the multi modal functions of the robot were employed: image on the screen, TTS (Text To Speech), touch function, recognition of sound and recording system. The robot content was demonstrated to thirty early childhood educators whose acceptability of the content was measured using questionnaires. And also the content was applied to children in daycare center. As a result, majority of them responded positively in acceptability. The results of this study suggest that the further research is needed to improve two-way interactivity of teacher assistive robot.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.347-363
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• 2019

Malware has its own unique behavior characteristics, like DNA for living things. To respond APT (Advanced Persistent Threat) attacks in advance, it needs to extract behavioral characteristics from malware. To this end, it needs to do classification for each malware based on its behavioral similarity. In this paper, various similarity of Windows malware is estimated; and based on these similarity values, malware's family is predicted. The similarity measures used in this paper are as follows: 'TF-IDF cosine similarity', 'Nilsimsa similarity', 'malware function cosine similarity' and 'Jaccard similarity'. As a result, we find the prediction rate for each similarity measure is widely different. Although, there is no similarity measure which can be applied to malware classification with high accuracy, this result can be helpful to select a similarity measure to classify specific malware family.