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In this paper, an automatic lesson generation system is presented which is suitable in a learning-by-mimicking scenario where the learning objects can be represented as multiattribute time series data. The dance is used as an example in this paper to illustrate the idea. Given a dance motion sequence as the input, the proposed lesson generation system automatically generates the lesson plan for students. It first extracts patterns from the input dance sequence to form the learning objects. The prerequisite structure is then built by considering the relations between the learning objects. Afterward the knowledge structure is constructed from the prerequisite structure based on the knowledge space theory. Finally, the learning path is derived according to an easy-to-complex manner while respecting the prerequisite relations. A user study that involved 40 students was conducted to evaluate the proposed work. The average learning time required for the treatment group (learning with the proposed system) was found to be lower than that of the control group (learning by free browsing) thus demonstrating the learning efficiency of the proposed system. The feedback from the questionnaires indicated that a majority of the subjects showed positive response toward the usefulness and rationality of our proposed system. (Contains 12 figures and 1 table.)

Background: The Surveillance, Epidemiology, and End Results (SEER) database and National Cancer Database (NCDB) show improved overall survival (OS) in patients with multiple myeloma (MM) over the last 15 years. This analysis evaluated the validity of the largely community‐based Connect MM Registry as a national reference for MM. Methods: Baseline disease characteristics and survival in US newly diagnosed MM patients were examined using the Connect MM Registry as well as SEER and NCDB databases. Baseline characteristics predictive of longer survival in Connect MM were also identified. Results: As of February 2017, 3011 patients were enrolled in the Connect MM Registry; 2912 were treated. Median age at time of MM diagnosis and age range were numerically similar from 2010 to 2015 across all 3 registries; SEER had a higher representation of nonwhite racial groups than that in the other 2 registries. OS rates suggest proportionate improvement with year of diagnosis among the 3 registries. A Cox proportional hazards model suggests that younger age (<65 years) is associated with longer survival (vs ≥75; HR, 0.39; 95% confidence interval, 0.34‐0.46) in the Connect MM Registry. However, sex (HR, 0.91; P =.15) and race (black vs white; HR, 0.88; P =.21) were not associated with longer OS. Conclusions: Data from the Connect MM Registry appear to be largely representative of national trends, comprehensive, and reliable representations of the national MM population. Baseline characteristics were comparable, and survival similarly improved over time among the 3 registries. ClinicalTrials.gov, identifier: NCT01081028. [ABSTRACT FROM AUTHOR]

In this paper, an automatic lesson generation system is presented which is suitable in a learning-by-mimicking scenario where the learning objects can be represented as multiattribute time series data. The dance is used as an example in this paper to illustrate the idea. Given a dance motion sequence as the input, the proposed lesson generation system automatically generates the lesson plan for students. It first extracts patterns from the input dance sequence to form the learning objects. The prerequisite structure is then built by considering the relations between the learning objects. Afterward the knowledge structure is constructed from the prerequisite structure based on the knowledge space theory. Finally, the learning path is derived according to an easy-to-complex manner while respecting the prerequisite relations. A user study that involved 40 students was conducted to evaluate the proposed work. The average learning time required for the treatment group (learning with the proposed system) was found to be lower than that of the control group (learning by free browsing) thus demonstrating the learning efficiency of the proposed system. The feedback from the questionnaires indicated that a majority of the subjects showed positive response toward the usefulness and rationality of our proposed system. [ABSTRACT FROM PUBLISHER]

Abstract: Aspect-Oriented programming (AOP) is a newly developed technology which provides a graceful solution to the code crosscutting problem in the OO (object-oriented) system. In an OO system, the codes belonging to one module always scatter to other modules, which makes the system customization difficult to carry out. That''s an instinct of OOP due to its incomplete encapsulation. AOP uses a linguistic unit called “Aspect” to make the crosscutting codes localized in one single place to eliminate the ripple effect of changing these codes. Database management system (DBMS) is a complicated software system with many interactive modules in its architecture. It''s widely used in many different areas which demands we can easily make a DBMS customizable to meet the needs of different application backgrounds. In this paper, we propose a technical implementation on how to convert an OOP system to AOP system. We first introduce 5 kinds of extracting rules and bring out an extracting methodology based on the location of the crosscutting code. Next we use Berkeley DB (BDB) as a case study to demonstrate how the rules can help us to make a DBMS tailorable so as easier to customize. Through our experiment we determine that AOP is a very efficacy way for separating crosscutting concerns. It can improve the modularity and maintainability of the original system. But there still existing some disadvantages and drawbacks about this technology. We also evaluate these challenges in this paper. [Copyright &y& Elsevier]