Link to the original article: http://iospress.metapress.com/content/c6226856gj862408/ In the Abstract, the sentence "Three groups of subjects: BPH (57 patients), prostate cancer (53 patients) and controls (46 subjects) were recruited" should read:"Three groups of subjects: BPH (53 patients), prostate cancer (57 patients) and controls (46 subjects) were recruited."We are thankful to Professor Peichao Tian for pointing out this correction. Prof. Tian can be reached at the Department of Pediatrics, the first

This study provides an analytical model to predict the fixing pattern of issues in the open-source software (OSS) packages to assist developers in software development and maintenance. Moreover, the continuous evolution of software due to bugs removal, new features addition or existing features modification results in the source code complexity. The proposed model quantifies the complexity in the source code using the Shannon entropy measure. In addition, the issues fixing growth behavior is viewed as a function of continuation time of the software in the field environment and amount of uncertainty or complexity present in the source code. Therefore, a two-dimensional function called Cobb–Douglas production function is applied to model the intensity function of the issues fixing rate. Furthermore, the rate of fixing the different issue types is considered variable that may alter after certain time points. Thus, this study incorporates the concept of multiple change-points to predict and assess the fixing behavior of issues in the software system. The performance of the proposed model is validated by fitting the proposed model to the actual issues data of three open-source projects. Findings of the data analysis exhibit excellent prediction and estimation capability of the model.

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Online shopping has become the buzzword in this information age. Users want to purchase the best possible item and services at the shortest span of time. In this information age Recommender system is a very useful tool, because it has the capability of filtering the information according to user interest and provide personalized suggestion. One of the major drawbacks of the classical recommender system is that, they deal with the only single domain.In real world scenario domains could be related to each other by some common information. There are many approaches available for cross domain recommendation, but they are not able to provide better accuracy of high dimensional data and these approaches are suffering from data sparsity problem. In this paper, we deal with cross domain recommendation where we exploit knowledge from auxiliary domains (e.g., movies) which contains additional user preference data to improve recommendation on the target domain (e.g., books).. In order to achieve a high level of accuracy, we make use of semantic similarity measure of common information by which domains are related and Tensor decomposition to exploiting the latent factor for high dimensional data. Tensor decomposition with semantic similarity is used for making cross domain recommendation where in the data sparsity problem is avoided by normalizing and clustering the data in auxiliary domain. We provide experimental results on real world data sets and compared our proposed method with other similar approaches based on hit ratio and the results show that we achieve a better hit ratio.

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