Trust evolvement method of Web service combination based on network behavior

Based on the problem that the service entity only has the partial field of vision in the network environment,a trust evolvement method of the macro self-organization for Web service combination was proposed.In the method,the control rule of the trust degree in the Dempster-Shafer(D-S)rule was utilized based on the entity network interactive behavior,and a proportion trust control rule was put up.The control rule could make the Web service self-adaptively study so as to gradually form a proper trust connection with its cooperative entities and to improve the security performance of the whole system.The experimental results show that the historical successful experience is saved during the service combination alliance,and the method can greatly improve the reliability and success rate of Web service combination.

Research on College Frustrated Students＇ Network Mentality and Behaviors

Network has not only become a habit and lifestyle for university student, but also brought all sorts of ethical misconducts and ethical issues in society. Based on the analysis of college students＇ frustrations, this paper explores the causes of network behavior anomie for college students, which mainly include： dissatisfaction in real communication, game addiction to the network, craving online pornography, and hooking on online shopping. In addition, it also investigates the ways to wipe out mental frustration in such a cyber era. These ways mainly are to strenzthen online education and management, to make psychological counseling, and to carry on frustration education.

Efficient Feature Extraction Using Apache Spark for Network Behavior Anomaly Detection

Extracting and analyzing network traffic feature is fundamental in the design and implementation of network behavior anomaly detection methods. The traditional network traffic feature method focuses on the statistical features of traffic volume. However, this approach is not sufficient to reflect the communication pattern features. A different approach is required to detect anomalous behaviors that do not exhibit traffic volume changes, such as low-intensity anomalous behaviors caused by Denial of Service/Distributed Denial of Service （DoS/DDoS） attacks, Internet worms and scanning, and BotNets. We propose an efficient traffic feature extraction architecture based on our proposed approach, which combines the benefit of traffic volume features and network communication pattern features. This method can detect low-intensity anomalous network behaviors and conventional traffic volume anomalies. We implemented our approach on Spark Streaming and validated our feature set using labelled real-world dataset collected from the Sichuan University campus network. Our results demonstrate that the traffic feature extraction approach is efficient in detecting both traffic variations and communication structure changes. Based on our evaluation of the MIT-DRAPA dataset, the same detection approach utilizes traffic volume features with detection precision of 82.3% and communication pattern features with detection precision of 89.9%. Our proposed feature set improves precision by 94%.

An Unknown Trojan Detection Method Based on Software Network Behavior

Aiming at the difficulty of unknown Trojan detection in the APT flooding situation, an improved detecting method has been proposed. The basic idea of this method originates from advanced persistent threat （APT） attack intents： besides dealing with damaging or destroying facilities, the more essential purpose of APT attacks is to gather confidential data from target hosts by planting Trojans. Inspired by this idea and some in-depth analyses on recently happened APT attacks, five typical communication characteristics are adopted to describe application’s network behavior, with which a fine-grained classifier based on Decision Tree and Na ve Bayes is modeled. Finally, with the training of supervised machine learning approaches, the classification detection method is implemented. Compared with general methods, this method is capable of enhancing the detection and awareness capability of unknown Trojans with less resource consumption.

Research on behavior recognition algorithm based on SE-I3D-GRU network

In order to effectively solve the problems of low accuracy and large amount of calculation of current human behavior recognition,a behavior recognition algorithm based on squeeze-and-excitation network(SENet) combined with 3 D Inception network(I3 D) and gated recurrent unit(GRU) network is proposed.The algorithm first expands the Inception module to three-dimensional,and builds a network based on the three-dimensional module,and expands SENet to three-dimensional,making it an attention mechanism that can pay attention to the three-dimensional channel.Then SENet is introduced into the 13 D network,named SE-I3 D,and SENet is introduced into the CRU network,named SE-GRU.And,SE-13 D and SE-GRU are merged,named SE-13 D-GRU.Finally,the network uses Softmax to classify the results in the UCF-101 dataset.The experimental results show that the SE-I3 D-GRU network achieves a recognition rate of 93.2% on the UCF-101 dataset.

Calculation of the Behavior Utility of a Network System： Conception and Principle

The service and application of a network is a behavioral process that is oriented toward its operations and tasks, whose metrics and evaluation are still somewhat of a rough comparison, This paper describes sce- nes of network behavior as differential manifolds, Using the homeomorphic transformation of smooth differential manifolds, we provide a mathematical definition of network behavior and propose a mathe- matical description of the network behavior path and behavior utility, Based on the principle of differen- tial geometry, this paper puts forward the function of network behavior and a calculation method to determine behavior utility, and establishes the calculation principle of network behavior utility, We also provide a calculation framework for assessment of the network＇s attack-defense confrontation on the strength of behavior utility, Therefore, this paper establishes a mathematical foundation for the objective measurement and precise evaluation of network behavior,

The Research on E-mail Users' Behavior of Participating in Subjects Based on Social Network Analysis

The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.

Container Networking Performance Analysis for Large-Scale User Behavior Simulation

Accurately simulating large-scale user behavior is important to improve the similarity between the cyber range and the real network environment. The Linux Container provides a method to simulate the behavior of large-scale users under the constraints of limited physical resources. In a container-based virtualization environment, container networking is an important component. To evaluate the impact of different networking methods between the containers on the simulation performance, the typical container networking methods such as none, bridge, macvlan were analyzed, and the performance of different networking methods was evaluated according to the throughput and latency metrics. The experiments show that under the same physical resource constraints, the macvlan networking method has the best network performance, while the bridge method has the worst performance. This result provides a reference for selecting the appropriate networking method in the user behavior simulation process.

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.

Empirical Study on Influence of Brand Crisis of Agricultural Products on Network Cluster Behavior of Consumers

From the perspective of psychological contract,this paper discusses mechanism of consumers' network cluster behavior in the context of brand crisis. On the basis of Simmel's conflict theory,it presented new findings of network cluster behavior. It is concluded that brand crisis exerts significant influence on breach of psychological contract. Particularly,functional brand crisis more easily leads to breach of transactional psychological contract,while value brand crisis more easily leads to breach of relational psychological contract. Breach of transactional psychological contract more easily leads to realistic network cluster behavior,while breach of relational psychological contract does not necessarily lead to non-realistic network cluster behavior.

A Co-Evolution Model for Dynamic Social Network and Behavior

Individual behaviors, such as drinking, smoking, screen time, and physical activity, can be strongly influenced by the behavior of friends. At the same time, the choice of friends can be influenced by shared behavioral preferences. The actor-based stochastic models (ABSM) are developed to study the interdependence of social networks and behavior. These methods are efficient and useful for analysis of discrete behaviors, such as drinking and smoking;however, since the behavior evolution function is in an exponential format, the ABSM can generate inconsistent and unrealistic results when the behavior variable is continuous or has a large range, such as hours of television watched or body mass index. To more realistically model continuous behavior variables, we propose a co-evolution process based on a linear model which is consistent over time and has an intuitive interpretation. In the simulation study, we applied the expectation maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to find the maximum likelihood estimate (MLE) of parameter values. Additionally, we show that our assumptions are reasonable using data from the National Longitudinal Study of Adolescent Health (Add Health).

LONG-TIME BEHAVIOR OF TRANSIENT SOLUTIONS FOR CELLULAR NEURAL NETWORK SYSTEMS

By establishing concept an transient solutions of general nonlinear systems converging to its equilibrium set, long-time behavior of solutions for cellular neural network systems is studied. A stability condition in generalized sense is obtained. This result reported has an important guide to concrete neural network designs.

Analog-Circuit Model of FGH96 Superalloy Hot Deformation Behaviors Based on Artificial Neural Network

At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network （ANN） model of FGH96 superalloy during hot deformation is established by using back propagation （BP） network. Then according to electrical analogy theory, its analog-circuit （AC） model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy＇s dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.

Nonlinear Dynamical Behavior in BS Evolution Model Based on Small-World Network Added with Nonlinear Preference

We introduce a modified small-world network adding new links with nonlinearly preferential connectioninstead of adding randomly,then we apply Bak-Sneppen(BS)evolution model on this network.We study severalimportant structural properties of our network such as the distribution of link-degree,the maximum link-degree,and thegth of the shortest path.We further argue several dynamical characteristics of the model such as the important criticalvalue f_c,the f_0 avalanche,and the mutating condition,and find that those characteristics show panticular behaviors.

网络舆情用户传播行为的心理距离感知强度测度及实证研究

[目的/意义]网络舆情用户传播行为的形成与网民的心理活动密切相关。从心理学视角出发,对网民传播群体的心理距离进行解析量化,有助于从心理根源上厘清网民传播行为的形成机理,为政府各级网信办提供理论和实践方法参考。[方法/过程]基于心理学理论视角,对心理距离进行归纳演绎,析出时空涨落度、语义关联度和情感认同度三个子参数,用以测度心理距离感知强度,并对感知强度进行分型。[结果/结论]为网络舆情研究提供了新的理论视角,心理距离感知强度能够很好地展现出舆情发展的时空热度、语义主题演化和舆情情感反转态势,能为舆情管控工作提供科学的理论支撑和量化依据。

基于双流自适应图卷积网络的管制员睡岗行为识别

为识别空中交通管制员的睡岗行为,减少管制差错,保障航空器飞行安全,提出了一种基于双流自适应图卷积网络的管制员睡岗行为识别方法。该方法设计双流网络分别处理管制员骨架的一阶信息和二阶信息,实现对骨架数据的充分提取;通过自适应学习的骨骼拓扑连接矩阵,挖掘管制员不同关节之间的功能连接关系;同时在卷积层引入时空通道注意力机制,增强管制员睡岗行为识别模型在时间、空间、通道3个方向提取重要信息的能力。仿真结果表明,该方法能有效识别管制员3种睡岗行为,相较于传统的时空图卷积网络,识别准确率提高了3.08百分点,达到95.03%,可以提高民航运行安全管理水平。

高阶结构对无标度网络上合作行为演化的影响

为研究高阶结构对无标度网络上合作行为演化的影响,构建基于囚徒困境博弈的网络博弈模型。在无标度网络上引入二阶高阶结构,定义含成对博弈的三角形面博弈,用高阶结构参数联系成对博弈收益与面博弈收益,并通过仿真实验分析高阶结构对合作行为演化的影响。结果表明,当高连接度个体优先合作并获得高收益时,会促使其他连接度个体也选择合作,博弈个体间一旦形成稳定的“全合作”三角形策略结构,就能显著提高每个合作者收益,进而促进合作行为的产生。

Travel Patterns and Street Networks: A Novel Visualization Methodology of City-Level Traffic Flows and Network Usability

Street Networks, knitted in the urban fabric, facilitate spatial movement and control the flow of urbanization. The interrelation between a city’s spatial network and how the residents travel over it has always been of high interest to scholars. Over the years, multifaceted visualization methods have emerged to better express this travel trend from small to large scale. This study proposes a novel approach to 1) visualize city-wide travel patterns with respect to the street network orientation and 2) analyze the discrepancies between travel patterns and streets to evaluate network usability. The visualizations adopt histograms and rose diagrams to provide several insights into network-wide traffic flows. The visualization of four New York City (NYC) boroughs including Queens, Brooklyn, Bronx, and Staten Island was generated for the daily traffic and the average hourly flows in the morning and evening rush hours. Then the contrasts between built-in street network topology and travel orientation were drawn to show where people travel over the network, travel demand, and finally which segments experience high or light traffic, revealing the true picture of network usability. The findings of the study provide an insight into the novel and innovative approach that can help better understand the travel behavior lucidly and assist policymakers in decision making to maintain a balance between urban topology and travel demands. In addition, the study demonstrates how to further investigate city street networks and urbanization from different diverse dimensions.

不确定环境下收费与信息对路径选择行为影响的实验研究

在不确定环境下考虑信息发布与拥挤收费两种措施,基于交通网络均衡,推导了两路径简单路网的最优收费模型,并推广至一般路网。基于两路径路网设计逐日路径选择行为实验,分析了两种措施对路径选择行为的影响。结果表明:完美信息与收费的实施均能减少流量波动,单独实施收费的效果最佳,但只有两种措施相结合时,路网流量趋于用户均衡的趋势最稳定;完美信息会增加路径切换行为,而收费可有效抑制大幅的路径切换;无收费时路径切换往往会造成出行成本增加,而收费时则相反;收费与信息结合时的出行时间比两种措施单独实施时小。