A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.
Based on the improved algorithm for analyzing log and the detection and defense system of SSH Dictionary-Attack for Multi-Platform Environment (Su, Chen, Chung & Wu), we developed the upgrade detection and defense system of SSH Dictionary-Attack for Multi-Platform Environment. In this study, we introduced the current threats and the types of SSH Dictionary-Attack. Then, we explained the functions and differences between the current defense software and defense types of SSH Dictionary-Attack; and described the current system of SSH Dictionary-Attack for Multi-Platform Environment. Moreover, based on the study of Su, Chen, Chung and Wu, we improved the algorithm of analyzing log in order to increase the defense capability of SSH Dictionary-Attack. After that, we designed the upgrade detection and defense system of SSH Dictionary-Attack for Multi-Platform Environment. The contribution of this study is to provide the upgrade detection and defense system of SSH Dictionary-Attack which was to keep the functions of original system of SSH Dictionary-Attack, and to improve the effectiveness of the algorithm of analyzing log
The researcher discussed the SSH dictionary attack defense system in the multi platform environments through the analyzing log. The study introduced the current formats and threats of the SSH dictionary attack. Then, the research explained the types of the SSH dictionary attack defense system, and compared the functions and differences between the traditional and current detection systems. Moreover, the study based on the research of Y.N. Su and Y. H. Chen (2010), the SSH dictionary attack defense system, and advanced the detection functions in the multi platforms. According to those literatures, the study developed the SSH dictionary attack and detection system in the multi platforms environments using analyzing log . The contributions of the study were to provide an easy way for detection, and to get the list of the SSH dictionary attack sources by typing into the defection system in order to achieve the purpose of the SSH dictionary attack system in the multi platform defection environments.
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