Hi, I am a Ph.D. candidate in Software Engineer at The University of Texas at Dallas (UTD) under supervisor Dr. I-Ling Yen, and had cooperated with Dr. Tien Nguyen in some projects. I am original from Vietnam. I spent 5 years for doing Bachelor in Software Enginner in Russia at Volgograd State Technical University (VSTU).
My research interests: Software Engineer, Data Mining, Program Analysis, IoT Data Discovery, Machine Learning, Big Data;
Designing technique to store data efficiently in IoT Network based on summarization for purpose of data discovery.
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The most of the people have their account on social networks (e.g. Facebook, Vkontakte) where they express their attitude to different situations and events. Facebook provides only the positive mark as a like button and share. However, it is important to know the position of a certain user on posts even though the opinion is negative. Positive, negative and neutral attitude can be extracted from the comments of users. Overall information about positive, negative and neutral opinion can bring understanding how people react in a position. Moreover, it is important to know how attitude is changing during the time period. The contribution of the paper is a new method based on sentiment text analysis for detection and prediction negative and positive patterns for Facebook comments which combines (i) real-time sentiment text analysis for pattern discovery and (ii) batch data processing for creating opinion forecasting algorithm. To perform forecast we propose two-steps algorithm where: (i) patterns are clustered using unsupervised clustering techniques and (ii) trend prediction is performed based on finding the nearest pattern from the certain cluster. Case studies show the efficiency and accuracy (Avg. MAE = 0.008) of the proposed method and its practical applicability. Also, we discovered three types of users attitude patterns and described them.
This block allows teachers to plan and start supervised sessions in particular classroom, with particular group and of particular lesson type. This block is devoted to track supervised sessions, where students work on the site in classroom under teacher (or staff) supervision. The session can have it's type (lesson type), classroom and group of students. The classroom is defined via IP subnet, so students off class won't be recorded as participating in sessions. The block allows you to see logs on what is going in each particular session. More importantly, you can control students permissions, given them special abilities (like attempting exam quiz) only during supervised sessions. For now there is quiz access control rule, allowing you to restrict quiz access to supervised sessions. There will be more options later.