Fueled by the booming online games,there is an increasing demand for monitoring online games in various settings.One of the application scenarios is the monitor of computer games in school computer labs,for which an i...Fueled by the booming online games,there is an increasing demand for monitoring online games in various settings.One of the application scenarios is the monitor of computer games in school computer labs,for which an intelligent game recognition method is required.In this paper,a method to identify game processes in accordance with private working sets(i.e.,the amount of memory occupied by a process but cannot be shared among other processes)is introduced.Results of the W test showed that the memory sizes occupied by the legitimate processes(e.g.,the processes of common native windows applications)and game processes followed normal distribution.Using the T-test,a significant difference was identified between the legitimate processes and C/S-based computer games,in terms of the means and variances of their private working sets.Subsequently,we derived the density functions of the private working sets of the considered game processes and those of the legitimate processes.Given the private working set of a process and the derived probability density functions,the probability that the process is a legitimate process and the probability that the process is a game process can be determined.After comparing the two probabilities,we can easily determine whether the process is a game process or not.As revealed from the test results,the recognition accuracy of this method for C/S-based computer games was approximately 90%.展开更多
The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classificati...The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classification machines (SVM). The manuscript describes the ALFPGM algorithm, provides numerical results for training SVM on large data sets, and compares the training times of ALFPGM and Sequential Minimal Minimization algorithms (SMO) from Scikit-learn library. The numerical results demonstrate that ALFPGM with the improved working selection scheme is capable of training SVM with tens of thousands of training examples in a fraction of the training time of some widely adopted SVM tools.展开更多
基金This work is funded in part by the National Nature Science Foundation of China(File Nos.61872451 and 61872452)in part by the Science and Technology Development Fund,Macao SAR(File Nos.0098/2018/A3 and 0076/2019/A2).Li Feng is the corresponding author.
文摘Fueled by the booming online games,there is an increasing demand for monitoring online games in various settings.One of the application scenarios is the monitor of computer games in school computer labs,for which an intelligent game recognition method is required.In this paper,a method to identify game processes in accordance with private working sets(i.e.,the amount of memory occupied by a process but cannot be shared among other processes)is introduced.Results of the W test showed that the memory sizes occupied by the legitimate processes(e.g.,the processes of common native windows applications)and game processes followed normal distribution.Using the T-test,a significant difference was identified between the legitimate processes and C/S-based computer games,in terms of the means and variances of their private working sets.Subsequently,we derived the density functions of the private working sets of the considered game processes and those of the legitimate processes.Given the private working set of a process and the derived probability density functions,the probability that the process is a legitimate process and the probability that the process is a game process can be determined.After comparing the two probabilities,we can easily determine whether the process is a game process or not.As revealed from the test results,the recognition accuracy of this method for C/S-based computer games was approximately 90%.
文摘The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classification machines (SVM). The manuscript describes the ALFPGM algorithm, provides numerical results for training SVM on large data sets, and compares the training times of ALFPGM and Sequential Minimal Minimization algorithms (SMO) from Scikit-learn library. The numerical results demonstrate that ALFPGM with the improved working selection scheme is capable of training SVM with tens of thousands of training examples in a fraction of the training time of some widely adopted SVM tools.
