Tax consulting service is one of various professional consulting services and is interested to study by many researchers. Nevertheless, this issue has not been interested to research in Vietnam. This paper performs confirmatory factors analysis (CFA) and structural equation modeling (SEM) to identify the factors influencing the intentions of using tax consulting services of firms in Ho Chi Minh city, Vietnam. Specifically, this paper finds that the intentions depend on the “attitude toward the behavior” and “replacement”. In addition, through Chi-square test, it can be proven that the intentions also depend on type of firms and whether they have ever used tax consulting service or not. Based on the obtained results, the discussion and recommendation are also proposed. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The indoor positioning system (IPS) is a significant factor to develop many applications such as direction and navigation in large commercial centers, tracking valuable goods, or determining position of children or old people. With the rapid growth of Internet, Wi-Fi access points (APs) can be found almost everywhere. This widespread infrastructure offers the possibility to locate mobile devices in an economical way. Therefore, Wi-Fi based positioning systems have become an attractive positioning technology in urban areas where GPS signals can be weaker, or where the use of GPS puts too much strain on the device's battery [1]. In order to enhance accuracy and stability of the Wi-Fi based positioning system, we proposed a robust, ubiquitous, and precise indoor positioning solution for smart mobile devices based-on the combination of Wi-Fi positioning system and embedded sensors on the smart devices.The new method will be analyzed and tested in real experiments.Experiment results have shown that our method can provide better accuracy to compare with traditional methods.

With the explosion of computer science in the last decade, data banks and networksmanagement present a huge part of tomorrows problems. One of them is the development of the best classication method possible in order to exploit the data bases. In classication problems, a representative successful method of the probabilistic model is a Naïve Bayes classier. However, the Naïve Bayes effectiveness still needs to be upgraded. Indeed, Naïve Bayes ignores misclassied instances instead of using it to become an adaptive algorithm. Different works have presented solutions on using Boosting to improve the Gaussian Naïve Bayes algorithm by combining Naïve Bayes classier and Adaboost methods. But despite these works, the Boosted Gaussian Naïve Bayes algorithm is still neglected in the resolution of classication problems. One of the reasons could be the complexity of the implementation of the algorithm compared to a standard Gaussian Naïve Bayes. We present in this paper, one approach of a suitable solution with a pseudo-algorithm that uses Boosting and Gaussian Naïve Bayes principles having the lowest possible complexity. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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