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Research on dangerous driving behavior recognition is beneficial to regulate the driving behavior of drivers. As the existing algorithms are sensitive to noise, and abnormal data often affects the process of identifying dangerous driving behaviors. This paper proposes a novel driving behavior research method. Such method establishes a driving behavior recognition model based on Support Vector Machine (SVM) and oversampling. The experimental results show that the proposed model demonstrates a higher recognition rate.

The continuous integration of computer technology and art has promoted the development of digital music. In recent years, the massive music data has promoted the problems of music inquiry, music classification, music content understanding and so on, making a new subject develop continuously, that is music information retrieval. The current mainstream feature extraction of music is based on acoustics, such as pitch, timbre, loudness, zero-crossing rate, etc. Direct symbolic music feature extraction and music analysis are relatively rare. This paper aims to present an empirical study on text clustering ideas in natural language processing into the field of symbolic music style analysis. Firstly, Symbolic music is textualized and proposed by us as an inspiration. To be precise, textualization of symbolic music is converted into weighted structured data through tf-idf algorithm. In the following step, three different types of mainstream clustering algorithms, K-Means, OPTICS, and Birch are used to perform cluster analysis and comparison on the traditional Chinese folk music dataset we crawled, T-SNE algorithm is used to visualize the dimensionality reduction of high-dimensional data. Finally, a series of objective evaluation indicators of clustering are used to evaluate the three clustering algorithms. Through comprehensive evaluation of indicators, it is proved that the clustering algorithm has achieved an excellent clustering effect on the midi note dataset we extracted. As a result of the clustering, the professional music theory knowledge and the historical development characteristics of traditional Chinese folk music are comprehensively integrated, which reversely verifies that the 1300 midi music data sets have distinct modal characteristics of traditional Chinese folk music.

In this study, an analytical hierarchy process based model is proposed to perform the decision-making for virtual machine migration towards green cloud computing. The virtual machine migration evaluation index system is established based on the process of constructing hierarchies for evaluation of virtual machine migration, and selection of task usage. A comparative judgment of two hierarchies has been conducted. In the experimental study, five-point rating scale has been adopted to map the raw data to the scaled rating score; this rating method is used to analyze the performance of each virtual machine and its task usage data. The results show a significant improvement in the decision-making process for the virtual machine migration. The deduced results are useful for the system administrators to migrate the exact virtual machine, and then switch on the power of physical machine that the migrated virtual machine resides on. Thus the proposed method contributes to the green cloud computing environment.

This paper uses the ID3 algorithm to analyze and extract the classification rules hidden in the original data of the physical fitness test of junior college students in mobile app platform database of “Running Shida”. These classification rules are highly consistent with the actual data in the database and are highly consistent with the results of individual survey of students. The forecasting conclusion of these classification rules is of great significance for quickly and scientifically determining students’ physique, putting forward reasonable suggestions for sports training and promoting the reform of “integration in-and-outside class” teaching mode of physical education in colleges and universities.

A virtual machine placement optimization model based on optimized ant colony algorithm is proposed. The model is able to determine the physical machines suitable for hosting migrated virtual machines. Thus, it solves the problem of redundant power consumption resulting from idle resource waste of physical machines. First, based on the utilization parameters of the virtual machine, idle resources and energy consumption models are proposed. The models are dedicated to quantifying the features of virtual resource utilization and energy consumption of physical machines. Next, a multi-objective optimization strategy is derived for virtual machine placement in cloud environments. Finally, an optimal virtual machines placement scheme is determined based on feature metrics, multi-objective optimization, and the ant colony algorithm. Experimental results indicate that compared with the traditional genetic algorithms-based MGGA model, the convergence rate is increased by 16%, and the optimized highest average energy consumption is reduced by 18%. The model exhibits advantages in terms of algorithm efficiency and efficacy.

Botnet has become a popular technique for deploying Internet crimes. The command of botnet has evolved into a major way for attackers to launch Distributed Denial of Service attacks on network servers. Modelized analysis methods need to be studied for botnet attacks implements, defense, and prediction. In this paper, we propose a novel game theory-based model to describe the scenario, in which the botmaster launching Distributed Denial of Service attacks using a botnet while the defender equipped a firewall defending. In our model, we consider the following: firstly, the botmaster and the defender can be rational or irrational; secondly, the interaction between the botmaster and the defender is modeled as a dynamic game; thirdly, their supporting or not self-learning databases. We detail the analysis of eight sub-scenarios for the assumptions and give an easy-to-use algorithm for adjustment of offensive and defensive strategy. We use the OPNET to validate our model and its effectiveness. The experiment result shows that our strategy can improve the firewall abilities to lower false alarm rate FR and improve the botmaster lower exposure rate of botnet to avoid detection. Furthermore, the model is helpful to evaluate defense ability of the defender towards current botmaster attacks by analyzing attack log in sandbox. Copyright © 2016 John Wiley & Sons, Ltd.

The security improvement of service provided by peer-to-peer (P2P) network has been widely studied. Se- curity issue of P2P is mainly related to the users of P2P services and threat of malicious software. Especially, some malicious software could threaten other P2P users and even threaten the security of the entire P2P network. In this paper, based on the analytical hierarchy process, a degenerated node evaluation model has been proposed to evaluate the node resource usage of the P2P. Moreover, an attribute clustering based collaborative filtering algorithm is de- picted for calculating the similarities between the identified degenerated node and all other nodes within a hybrid autonomous cluster. The algorithm utilizes similarity characteristics of node task resource attributes, especially CPU related attributes, to filter redundant data by feature selection. Experiments show that, the proposed model and method is feasible and has the vital significance for providing decision support and recommendation to the search node. It is practical for secure computing of P2P network.

Virtual machines (VM) migration can improve availability, manageability, performance and fault tolerance of systems. Current migration researches mainly focus on the promotion of the efficiency by using shared storage, priority-based policy etc.. But the effect of migration is not well concerned. In fact, once physical servers are overloaded from denial-of-service attack (DDoS) attack, a hasty migration operation not only unable to alleviate the harm of the attack, but also increases the harmfulness. In this paper, a novel DDoS attack, Cloud-Droplet-Freezing (CDF) attack, is described according to the characteristics of cloud computing cluster. Our experiments show that such attack is able to congest internal network communication of cloud server cluster, whilst consume resources of physical server. Base on the analysis of CDF attack, we highlight the method of evaluating potential threats hidden behind the normal VM migration and analyze the flaws of existing intrusion detection systems/prevention system for defensing the CDF attack.

This paper analyzes the system of OLAP physical examination data mining system, the concept of classification, and the nature of decision tree to define the implementation process of ID3 algorithm. Transaction data of the physical examination information management system of Xi’an Shiyou University Hospital from 2015 to 2018 are selected and prepared for cleaning and preprocessing, then the data is dimensionally reduced and transformed, to design a direct data model suitable for classification analysis. The sampling data set is classified and analyzed by ID3 algorithm, and the classification rules are extracted. Using the prediction conclusions of these classification rules, the medical examiner can quickly and scientifically predict the individual physique of each college teacher, so as to classify college teachers of different physiques, and specifically guide the teacher’s further treatment plan of exercise, diet and work. It has high reference value and promotion towards teachers’ health.

In this paper, we review the classical secure communication issues, which is always described as a set of interactive rules following a specified sequence, in the perspective of game theory. By introducing rational communication participants, we model the secure communication process in the manner of game theory to capture the interactions of distrusted communication parties. More specifically, we propose a formal framework to provide a precise description of the computation and communication rules in a secure communication game. Each player tends to behave in a way that maximizes their profits in this framework. Following the framework, the fairness of a protocol is presented according to Nash equilibrium of the communication game, as well as the equivalence condition towards a fair protocol. To verify the effectiveness of this framework, we design and implement a series of experiments. The experimental results show that our rational framework is more secure and closer to the practice compared with traditional cryptographic models, which can be a promising analysis solution for future cryptographic protocols.

In this paper, we proposed a testing method to evaluate the system which is developed by IPv6 multicasting for conform the international and industry standards, implementing the functionality as well as meeting business requirements during practical use. The paper also presents a design and implements method for hybrid multiplex integrated network access equipment System by IPv6 multicasting under AN6016-01 system research project context. Moreover, it conducts an IPv6 multicast simulation test on system basic factors through Spirent Testcenter. Testing result shows, it is difficult for instrument ports of Testcenter to meet testing requirements when handle massive users in an application senario. But long time performance testing among massive users may be conducted via aggregation switch. IPv6 multicast simulation testing may effectively discover problems in system development and application. But this testing method does not suitable for the actual application scenarios (ONU+STB+TV), it is only suitable in laboratory. Finally, the paper proposes a common solution about laboratory testing.

In this paper, an attribute clustering based collaborative filtering algorithm is applied for development well recommendation towards exploratory wells argumentation. The algorithm utilizes similarity characteristics of exploratory and development well related attributes, especially porosity, permeability and oil saturation, to filter redundant data by feature selection. Experiment use practical well data of the oil company for clustering. By integration of a scaled rating scheme on properties and the collaborative filtering philosophy to provide the recommend cluster. Such cluster contains the candidate development wells for recommendation. Finally, by calculating the similarity between exploratory and development wells, the scheme is able to provide the geologist the selected development wells for the decided exploratory in well argumentation.

The chest bitmap is the most widely used in live-firing. The target surface information extraction is the chief question need to be solved for automatic target-scoring system. Therefore, this paper proposes a new low-complexity algorithm for target surface feature information extraction according to the characteristics of the chest ring image. Based on the pre-processing for image, background interference of the image is eliminated by using regional feature elimination algorithm. Besides, target’s eye position is determined by employing gray two-way clipping projection, and all of the effective feature information of the target surface is extracted. The result of simulation shows that the target surface information extraction algorithm for chest bitmap is characterized by low-complexity, short time-consuming and high efficiency, and meets the real-time requirement.

Security and efficiency of messages transmission are crucial requirements for multicast data origin authentication, especially in time-critical scenarios, such as pay-TV and the smart grid. However, how to balance such equirements is still a difficult issue. Through analysis, we find that TV-HORS is more suitable for the time-critical scenarios than RSA-based and MAC-based scheme. However, the reuse times of TV-HORS scheme is still too small to suit some scenarios. In this paper, we present a Game Model of Multicast Data Origin Authentication (GMMDOA), to describe the relationship between security and efficiency for the time-critical multicast communication, contra posing the TV-HORS scheme. By analyzing our model with Shannons information and game theory, we propose an optimization scheme for update the private key. Through the security analysis of our model, the signature valid time is negligible compared with the adversary attacking time. Thus, the security of our scheme is still secure even in the lowest risk-level parameter. We present measurement results from experiments and discuss performance comparisons. It shows that, our scheme does significantly to promote the efficiency of multicast data origin authentication.

In compare with the traditional Artificial Neural Network, PSO-BP neutral network has fast convergence and is immune to local minimum. This paper presents an application of PSO-BP neural network for dynamic predicting small layer reservoir parameters of fault block E1f11-1 in well ZHuang 2. By defining input and output neuron number, our method firstly realizes quantization of input neuron. Then we choose proper samples for training neural network in order to build a dynamic prediction model of reservoir parameters. Such model has been successfully tested and the model itself is appropriate for predicting unknown reservoir parameters. Testing result indicates that PSO-BP neural network is superior to the genetic algorithm optimized BP neural network and the pure neural network. Finally, PSO-BP neural network gained certain achievements for dynamically predicting reservoir parameters according as dynamic production information.

A great challenge of providing authentication to time-critical multicast data is the existence of low end-to-end delay in many applications such as pay-TV and the power grid scenarios. Consequently, to balance security and efficiency is still a difficult issue. The group of secret keys reused scheme of TV-HORS still cannot meet the requirements of low time delay of computation. To describe the relationship between security and efficiency for the time-critical multicast communication scenarios, we present a Game Model of Multicast Data Origin Authentication, in this paper. By analysing our model with Shannon's information and game theory, we propose an optimised re-keying scheme, contraposing the TV-HORS scheme. The advantage of breaking the security goal is negligible, even in the lowest risk-level parameter. The experiment results show that our scheme does significantly promote the efficiency of TV-HORS scheme and is the most efficient multicast data origin authentication signature scheme.