With the arrival of the era of 3G mobile communications, people need not only to meet the call, SMS service, and begin to pursue more realistic visual and interactive experience. The paper developed the virtual campus roaming system of the Wuhan University of Technology for the Android platform based on the Baidu map of the mobile version of the API (Android) and OpenGL ES development tools. Users may select the Campus location of Wuhan University of Technology in the Android mobile phone and can query the campus road, the corresponding function architecture position and 3D virtual scene graph; at the same time they can accept the new information campus. The system of design cannot only provide users with a conventional mobile map services, but also show their location of the 3D scene graph. It is more intuitive to display location information, enhance the campus scenery and culture readability.

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Machine learning-based code smell detection has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for code smell detection, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques on machine learning-based code smell detection, we examine 31 imbalanced learning techniques with seven classifiers to build code smell detection models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed-rank test and Cliff’s δ. The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over-sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best-performing imbalanced learning techniques and the top-3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.

For the first time, we demonstrate a Ti-MOF (Ti-metal organic framework) single-crystal featuring intracrystal macro-microporous hierarchy (Hier-NTU-9) by a vapor-assisted polymer-templated method. Such Hier-NTU-9 possesses macropores (100-1000 nm) derived from...

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