Signature Verification for Multiuser Online Kanji Learning System

Multiuser online system is useful, but the administrator must be nervous at security problem. To solve this problem, the authors propose applying signature verification to multiuser online system. At the authors＇ research, they attempt adding signature verification function based on DP （Dynamic Programming） matching to existing multiuser online kanji learning system. In this paper, the authors propose the construction of the advance system and methods of signature verification, and evaluate performance of those signature verification methods that difference is combination of using features. From signature verification＇s experimental results, the authors adopted to use writing velocity and writing speed differential as using feature to verify the writer for the system. By using signature database which is construct with 20 genuine signatures and 20 forged signatures with 40 writers and written mostly by English or Chinese literal, experimental results of signature verification records 12.71% as maximum EER （Equal Error Rate）, 6.00% as minimum EER, and 8.22% as average EER. From mentioned above, the authors realized to advance the reliability and usefulness of the multiuser online kanji learning system.

The comparison between traditional Chinese literacy strategies and English vocabulary strategies

There is a positive transfer from native language vocabulary learning strategy to that of the second language. The comparison between them shows that the traditional Chinese character learning strategies have profound effect on English vocabulary learning on the basis of morphology, lexicon as well as discourse categories. If the mutual effect can be applied in English vocabulary learning effectively, positive transfer emerges.

Droid Detector:Android Malware Characterization and Detection Using Deep Learning

Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.