Identifying Game Processes Based on Private Working Sets

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%.

Difference of Visual Information Metric Based on Entropy of Primitive

Image sparse representation is a method of efficient compression and coding of image signal in the process of digital image processing.Image after sparse representation,to enhance the transmission efficiency of the image signal.Entropy of Primitive(EoP)is a statistical representation of the sparse representation of the image,which indicates the probability of each base element.Based on the EoP,this paper presents an image quality evaluation method-Difference of Visual Information Metric(DVIM).The principle of this method is to evaluate the image quality with the difference between the original image and the distorted image.The comparative experiments between DVIM&PSNR&SSIM are carried out.It was found that there was a great improvement in the image quality evaluation of geometric changes.This method is an effective image quality evaluation method,which overcomes the weakness of other quality evaluation methods for geometrically changing images to a certain extent,and is more consistent with the subjective observation of the human eye.

Who Will Come: Predicting Freshman Registration Based on Decision Tree

The registration rate of freshmen has been a great concern at many colleges and universities,particularly private institutions.Traditionally,there are two inquiry methods:telephone and tuition-payment-status.Unfortunately,the former is not only time-consuming but also suffers from the fact that many students tend to keep their choices secret.On the other hand,the latter is not always feasible because only few students are willing to pay their university tuition fees in advance.It is often believed that it is impossible to predict incoming freshmen’s choice of university due to the large amount of subjectivity.However,if we look at the two major considerations a potential freshman contemplates in making a choice,such as the geographical location of the university in relation to his/her home town,and testimonies about of that college life experience by previous graduates,we believe it is possible to predict future enrollment decisions.This paper is the first to find a way to solve the problem of predicting the choice of university a freshman will attend.Our contributions include the following:1.we present a dataset on freshman registration;2.we propose a decision-tree-based approach for freshman registration prediction.Study results show that freshman registration is predictable.

TCPW BR: A Wireless Congestion Control Scheme Base on RTT

The wireless network is limited by the transmission medium,and the transmission process is subject to large interference and jitter.This jitter can cause sporadic loss and is mistaken for congestion by the congestion control mechanism.The TCP Westwood protocol(referred to as TCPW)is such that it cannot distinguish between congestion loss and wireless jitter loss,which makes the congestion mechanism too sensitive and reduces bandwidth utilization.Based on this,the TCPW protocol is modified based on the estimate of the Round-Trip Time(referred to as RTT)value-called TCPW BR.The algorithm uses the measured smooth RTT value and divides the congestion level according to the weighted average idea to determine congestion loss and wireless jitter loss.The simulation results show that the TCPW BR algorithm enhances the wireless network’s ability to judge congestion and random errors.