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For a number of services with similar functionality reputation has been regarded as one of the most important methods to identify good ones from bad ones. However, a composite service, which is composed of multiple component services, obtains only one score (or feedback) after every invocation. In order to compute the reputation of each component service, it is necessary for the composite service to distribute this score to its component services. How to achieve a fair distribution is a challenging issue, as each component service may perform differently in contributing to the success or failure of the composite service. Although several efforts have been made for this problem, they do not consider the context of composition, which makes the distribution unfair. Therefore, in this paper, we propose a fair score distribution framework which combines the context of component services and their runtime performance. We distinguish two aspects contexts of a component service: structure-related importance and community-related replaceability, and adopt graph theory and dominating relationship technique to compute them, respectively. Experimental results show that our approach can achieve a more reasonable and fair score distribution than other existing methods. [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]

We evaluated the prognostic significance of p34cdc2 expression in 50 tongue squamous cell carcinomas (SCC) using immunohistochemical methods. The p34cdc2 protein was expressed in 33 cases of 50 tumor tissues (66.0%), compared with 15 cases of 42 controlled epithelia (35.7%). The expression of p34cdc2 was significantly correlated with the histological grade of tongue carcinoma (P<0.01). In addition, on evaluation of prognosis of tumor, the p34cdc2 protein was overexpressed in recurrent tumors or in those with lymph node metastasis. Statistics showed a significant reduction in the 5-year accumulative survival rate of p34cdc2 positive cases compared with p34cdc2 negative cases (P<0.01). Namely, the p34cdc2 positive patients had worse prognosis. The results suggested that the expression of p34cdc2 suited to the histological grade might reflect the malignant degree of tongue carcinoma biologically. Therefore, the evaluation of p34cdc2 expression was of benefit in elucidating the nature of tumor malignancy and the prognostic prediction of tongue SCC. [Copyright &y& Elsevier]