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Social media marketing is a new mode of marketing industry. KOL (Key Opinion Leader) marketing is a popular way of social media marketing, which is profit-oriented. During the brand building in the early stage of marketing, the product side generally carries out corresponding advertising promotion, so as to achieve the purpose of promoting marketing. As decision-makers, different KOLs selection affects the final promotion effect. Therefore, to understand the advertising promotion effect of social media, this paper considers the instability of the network environment and the uncertainty of a KOL’s promotion ability, solves the advertising promotion problem in the absence of historical data, and provides meaningful insights for decision-makers. First, this paper takes the advertising promotion effect of the KOL belonging to different levels and the cost of advertisers as uncertain variables and constructs an uncertain KOL selection model considering the constraints of income (promotion effect), cost and risk. Second, based on the relevant algorithm of uncertainty theory, the uncertainty of the model is eliminated, the uncertainty model is transformed into a corresponding clear model, and the KOL’s optimal choice at each level is calculated. Finally, the effectiveness and practicability of the model and the algorithm are verified.

The research of text similarity, especially for rumor texts, which constructed the calculation model by known rumors and calculated its similarity. From which, people can recognize the rumor in advance, and improve their vigilance to effectively block and control rumors dissemination. Based on the Bayesian network, the similarity calculation model of microblog rumor texts was built. At the same time, taking into account not only the rumor texts have similar characters, but also the rumor producers have similar characters, and therefore the similarity calculation model of rumor texts makers was constructed. Then, the similarity between the text and the user was integrated, and the microblog similarity calculation model was established. Finally, also experimentally studied the performance of the proposed model on the microblog rumor text and the user data set. The experimental results indicated that the similarity algorithm proposed in this paper could be used to identify the rumors of texts and predict the characters of users more accurately and effectively

Targeted cell ablation is a powerful approach for studying the role of specific cell populations in a variety of organotypic functions, including cell differentiation, and organ generation and regeneration. Emerging tools for permanently or conditionally ablating targeted cell populations and transiently inhibiting neuronal activities exhibit a diversity of application and utility. Each tool has distinct features, and none can be universally applied to study different cell types in various tissue compartments. Although these tools have been developed for over 30 years, they require additional improvement. Currently, there is no consensus on how to select the tools to answer the specific scientific questions of interest. Selecting the appropriate cell ablation technique to study the function of a targeted cell population is less straightforward than selecting the method to study a gene’s functions. In this review, we discuss the features of the various tools for targeted cell ablation and provide recommendations for optimal application of specific approaches.

The doubly fed induction generator (DFIG) has insufficient capacity to respond to the frequency inertia of the power grid due to the isolation between the rotor kinetic energy and the power grid by the generator-side converter. At the same time, the ‘source’ of the DFIG cannot be as stable as the thermal generator, so sudden changes in the wind speed on the ultra-short time scale can affect the system inertia. In this study, the inertia of a doubly fed wind generator with a super capacitor energy storage system (SCESS-DFIG) is defined from the standpoint of a single machine. Based on fuzzy logic controller, an inertia response control strategy of multi energy coordinated control is proposed. Then the SCESS-DFIG inertia response control strategy under variable wind speed is proposed at the wind field level. Based on the establishment of DFIG, DC bidirectional buck–boost converter, and supercapacitor energy storage unit model, a wind farm with three SCESS-DFIGs is simulated. When the grid frequency is dropped, the effectiveness of the proposed control strategy is verified by simulating their operation under the same rated parameters, different wind speed curves, and different initial voltages of the supercapacitors.

Internet rumors are confusing and destructive, and it is difficult to identify them from texts, this is why rumors are flying all over the place. The rapid identification of Internet rumors can effectively control their spread and achieve good public governance. Considering the similarity between multiple rumor texts, based on the fractal theory, a fractal dimension calculation method for Internet rumors covered by pixel points is constructed. The Internet rumor text is divided into keywords and non-keywords to form a binarization matrix, so as to build a two-dimensional image with the same resolution, in order to realize the rapid recognition of rumors. When using the Internet rumors title text as a sample for model verification, the box-counting dimension solved by different matrices constructed by a rumor is quite different because of the effect of the increased pixel points when the equal-length heading text generates different matrices, that is, the shape of the two-dimensional image has a great influence on the fractal dimension. Therefore, it is difficult to realize the recognition of Internet rumors using the box-counting dimension algorithm based on the pixel coverage method.

In the face of increasingly complex social public opinion situations, the traditional network public opinion governance models have increasingly prominent issues such as information transmission efficiency and information security. In order to establish a new model of fair, credible, and community co-management of network public opinion governance, this article uses blockchain as a key technology for collaborative governance of network public opinion. In order to meet the requirements of network governance, the Delegated Proof-of-Stake(DPoS) algorithm is improved to solve problems. After proposing a reputation model that considers time dynamic factors, this paper constructs a reputation-based voting mechanism and rewards and punishments incentive mechanism, and also designs a new method of counting votes. From the experiment results, it was found that the node participation has increased significantly, the proportion of error nodes was obviously reduced, and the operating efficiency was improved. It shows that the improved consensus algorithm can not only improve the security of the system, reduce the possibility of false public opinion spreading, but also improve the efficiency of information processing, so it can be well applied to information sharing and public opinion governance scenarios.

In this paper,the fractal characteristics of the rumor spreading are studied by the perspective of time series constructed with the influence of rumor spreading.The values of the Hurst exponent calculated through rescaled range analysis indicate that the time series of rumor spreading are fractal with long-range dependence and self-similarity.The visual method is used to convert the time series into a complex network. The scale-free and small-world nature of the network further verify that the time series of rumor spread has fractal characteristics. These conclusions will help scholars to study the spread of rumor from a new perspective and provide ideas for the governance of rumor.

To effectively identify network rumors and block their spread, this paper uses fractal theory to analyze a network rumor spreading situation time series, reveal its inner regularity, extract features, and establish a network rumor recognition model. The model is based on an empirical mode decomposition (EMD) correlation dimension and K-nearest neighbor (KNN) approach. Firstly, a partition function is used to determine if the time series of the rumor spreading situation is a uniform fractal process. Secondly, the rumor spreading situation is subjected to EMD to obtain a series of intrinsic mode functions (IMFs), construct the IMF1–IMF6 components containing effective feature information as the principal components, and reconstruct the phase space of the principal components, respectively. Finally, the correlation dimensions of the principal components IMF1–IMF6 as obtained by the Grassberger-Procaccia algorithm are used as feature parameters and are imported into the KNN model for rumor recognition. The experimental results show that the correlation dimension of a spreading situation can better reflect the characteristic information; as combined with the KNN model for identifying rumors, the recognition rate reaches 87.5%. This result verifies the effectiveness of fractal theory in network rumors recognition, expands the thinking for the research of rumors recognition, and provides theoretical support for rumor governance.

Exploring the law of the spread of online rumors is an important way to rumor governance. In the analogy of thermal radiation process, this paper constructs a thermal radiation model of network rumor propagation. First, an analogy analysis of the thermal radiation process and the rumor propagation process, and the parameters involved in the process of thermal radiation are analyzed and corresponding to various influencing factors in the process of Network rumor propagation. Then the UTS rumor propagation model is constructed. Finally, the experimental simulation results verify the rationality of the UTS model, which provides an important basis for network rumor governance.

Based on the fractal theory, the fractal dimension calculation model of Internet rumor is built to explore the characteristics and rules of Internet rumor propagation. This paper takes health rumors as an example, selects typical Internet rumors in recent three years, classifies rumors according to Internet influence, and explores the relationship between fractal dimension and influence of Internet rumors. The results show that there is self-similarity between a class of rumors and has fractal features. The more widespread and influential a rumor is, the greater its fractal dimension will be, which provides a new research idea for the control and governance of Internet rumor propagation.

Because rumors are hard to distinguish, social groups will inevitably fall into it, be bewildered by them, and spread them, thus bringing great impact on national security, social development and personal progress. In view of the high similarity between rumor spread and seismic activity, based on the BK seismic model, this paper analyzes the process of rumor seismic from the perspective of user state transformation, establishes the rumor spread seismic model, discusses the law and evolution mechanism of rumor spread. Finally, the simulation results show that the user status update has a great influence on the rumor dissemination process. The update of the dynamic state can slow down the spread of rumors and reduce the scope of rumors. The change of the user's own seismic parameters has an effect on the evolution of rumor dissemination, and an effective rumor control strategy is proposed.

Laws of rumor makers’ behaviors are the root of curbing rumor and effective way to block rumor occurrence. Therefore, based on system dynamics model, this paper proposed the rumors behavior evolution model of rumor makers, aimed at discovering the laws of rumor makers’ behaviors to achieve rumors blocking. First, by refining the driving factors in disinformation behavior, we constructed causal diagram of disinformation behavior evolution; secondly, by means of causal diagram, we constructed stock-flow diagram for quantitative analysis; finally, simulation experiment was carried out by using Vensim Personal Learning Edition software (Vensim PLE). The results showed that negative attitude is a major factor in the occurrence of disinformation behavior; personal factors are more pronounced than the factors of social and government on the impact of disinformation propensity score.

Recent advances in deep neural networks (DNNs) have substantially improved the accuracy and speed of a variety of intelligent applications. Nevertheless, one obstacle is that DNN inference imposes heavy computation burden to end devices, but offloading inference tasks to the cloud causes transmission of a large volume of data. Motivated by the fact that the data size of some intermediate DNN layers is significantly smaller than that of raw input data, we design the DNN surgery, which allows partitioned DNN processed at both the edge and cloud while limiting the data transmission. The challenge is twofold: (1) Network dynamics substantially influence the performance of DNN partition, and (2) State-of-the-art DNNs are characterized by a directed acyclic graph (DAG) rather than a chain so that partition is greatly complicated. In order to solve the issues, we design a Dynamic Adaptive DNN Surgery (DADS) scheme, which optimally partitions the DNN under different network condition. Under the lightly loaded condition, DNN Surgery Light (DSL) is developed, which minimizes the overall delay to process one frame. The minimization problem is equivalent to a min-cut problem so that a globally optimal solution is derived. In the heavily loaded condition, DNN Surgery Heavy (DSH) is developed, with the objective to maximize throughput. However, the problem is NP-hard so that DSH resorts an approximation method to achieve an approximation ratio of 3. Real-world prototype based on self-driving car video dataset is implemented, showing that compared with executing entire the DNN on the edge and cloud, DADS can improve latency up to 6.45 and 8.08 times respectively, and improve throughput up to 8.31 and 14.01 times respectively.

With the development of network technology, the rumors of network rumors are getting more and more serious, and the spread of network rumors has become a research hotspot. Firstly, based on the content characteristics of the information, this paper combines the three characteristics of rumors: subject universality, impact severity and event ambiguity, and establishes a content trust function. Secondly, the micro-Markov chain approximation method is used to establish the discreteness of rumor communication. The model, continuity model and continuous model based on trust function, and then calculate the corresponding rumor propagation threshold. Finally, through numerical simulation, the validity of the three properties of rumors is verified, and the validity of the model is proved. Good suppression of rumor spread.

With the wide application of mobile social networks, rumor spreading is more explosive, more quickly, and has a bad impact. Therefore, exploring the law of rumor spreading has always been a hot topic of current research. Inspired by the explosion process of explosives, this paper constructs an explosion model of network rumor spreading. Firstly, analogy the explosion process of explosives and the process of rumor spreading and establish the theoretical basis of the model. Secondly, the UBEN network rumor propagation model based on the explosive explosion principle is constructed. By establishing the average field equation, the rumor impact threshold is studied, and the influence of the factors on the rumor spreading process is simulated. The experimental results verify the rationality of the model and provide a theoretical basis for the formulation of relevant policies. The research results of this paper not only provide new ideas for the study of rumor spreading, but also help to further understand the law of rumor spreading, which has certain theoretical and practical value.

As an indispensable means of transportation in modern buildings, elevators have become increasingly demanding in terms of safety, reliability, comfort, efficiency and energy saving. How to use the control method and the composition of the structure to be able to lift the elevator to provide better service and to better meet people's needs has always been the core of the field of elevator control research. This paper introduces an elevator control system based on Siemens S7-1200PLC and G120 inverter. The system takes PLC and inverter as the core control devices to realize the speed control of the tractor and the optimization control of elevator group control, and finally realizes the smooth running control function of two six-storey elevators. The simulation results show that the system can make the elevator run smoothly, safely and efficiently, and meet the design requirements.

Based on the Dissipation Structure Theory, this paper constructs the rumor spreading behavior evolution model. First, we analyze the Dissipative structural characteristics of network rumor spreading behavior. Then, we compare the evolution of rumor spreading behavior with the evolution of dynamic self-organizing behavior of system dissipative structure, and further construct the nonlinear differential equations of rumor spreading behavior, analyzing the dissipation threshold and stability of system spreading behavior. Finally, simulation experiments are conducted. The experimental results show that behavior preference effect, behavior dissipation effect and external environment effect all play an important role in the evolution of individual rumor spreading behavior. The interactions among them make the system reach a stable state, providing reference for the blocking and control of rumor spreading.

The continuous breeding and rapid spread of rumors in social networks poses a severe challenge to the effective utilization and scientific management of social media. Therefore, it is of great theoretical significance and application value to study the decision-making behavior of users in rumors spread in social networks and to reveal the rumor transmission rules. Based on the Ising model, this paper constructs a social network rumor propagation dynamics model and then reveals the rumor transmission rules. In the model, the Monte Carlo method is used to simulate the interaction between the user’s self-identity attribute (micropart), the user-user interaction (the middle part), and the social environment’s influence (the macroscopic part) to study user decision behavior of rumor spread in a social network system. The results show that, in the rumor propagation system, von Neumann entropy can quantify well the phase transition of the system and is consistent with the phase transition information obtained by measuring the spontaneous magnetization and magnetic susceptibility of the system. In addition, the introduction of the self-identity characteristics of individual users into the Ising model has greatly changed the users’ decision-making behavior of rumor spreading and changed the internal structure of the system, thus changing the type of phase transition. As the degree of self-identification increases, the need for lower temperatures can change the orderly state of the system, and user behavior changes more rapidly. For the rumor transmission system, the state of close order is conducive to the blocking of rumors, thus maintaining social stability.

The rumor propagation in social networks has great harmfulness. Classic researches are mostly based on infectious disease model, without considering the characteristics of the users in the network itself. Based on the theory of combustion, this paper proposes a new network rumor propagation model CRP Model (Combustion-inspired Rumor Propagation Model) to explore the propagation rule of rumor among users. The model is based on three combustion elements to simulate the process of network rumor propagation including combustible material, combustion improver and ignition temperature. It can be seen from the experiment that the fire users' influence and the firefighters in the network play a very important role in the spreading of rumors.

The study of the spread of rumors is helpful to understand the rumors spread in time so as to predict and block rumors spreading. This paper selects the propagation situation of similar network rumors, uses R/S analysis method to calculate Hurst index, further analyzes the structural characteristics of rumors communication situation, and finds that rumors spreading has the characteristics of state continuity and fractal distribution. Then we set up a fractal interpolation model to depict changes over time and predict the trend in the short term, and found that it is basically consistent with the original data trend. The results show that using the Hurst index to estimate the vertical scale factor and the fractal interpolation model to predict the rumors are feasible and effective, and this paper provides theoretical basis for correctly predicting the development of rumors and further implementing governance.

The widely used applications of Microblog, WeChat, and other social networking platforms (that we call MicroNet) shorten the period of information dissemination and expand the range of information dissemination, which allows rumors to cause greater harm and have more influence. A hot topic in the information dissemination field is how to identify and block rumors. Based on the maximum entropy model, this paper constructs the recognition mechanism of rumor information in the micronetwork environment. First, based on the information entropy theory, we obtained the characteristics of rumor information using the maximum entropy model. Next, we optimized the original classifier training set and the feature function to divide the information into rumors and nonrumors. Finally, the experimental simulation results show that the rumor identification results using this method are better than the original classifier and other related classification methods.

As a popular innovation, Web Service provides a flexible solution to integrate diverse online applications with existing Internet protocols and open standards. The availability and flexibility of Web Service enable its potential to handle dynamic requests in distributed online collaboration. However, this potential is limited by Web Service's security concerns due to its uncertainty, openness and fraudulence. A solution to this problem, Trust, an important social concept in all human interactions, has been proven to be a promising way to resolve the security issues raised by these distributed collaborations. This paper introduces a novel evaluation model of Web Service by leveraging trust as an approach. We first incorporate a trust management module into the standard Service Oriented Architecture (SOA). Then, after transforming a Web Service network to a small-world network based on the trust relationships of service entities, we propose a trust evaluation model with an amendatory subjective logic. The simulation experiments we ran compared our trust evaluation model with two other popular models. The result shows our proposed model outperforms in terms of both detection capability and stability.

The spread of network rumors has always been a research hotspot. How information flows in social networks is a question of interest to us. Based on the theory of fluid mechanics, the dissemination model is constructed, and the dissemination mechanism of network rumor is explored. Through numerical simulation, it is found that the flow of rumor information has a strong correlation with the subject of rumor dissemination and their relationship. The dissemination law of rumor information is revealed, which provides theoretical support for rumor control and governance.

In order to effectively identify rumors and further block their propagation, this present study established a rumor situation identification model based on multifractals and support vector machine (SVM), which considered the fractal theory and the situation of rumor propagation. First, the representative rumors and nonrumors about food safety were selected. The multifractal analysis was used to calculate the generalized fractal dimension spectrum of the two target states. After this, the generalized fractal dimension values were used as the input target eigenvector for the SVM, which was subsequently trained to obtain the best classification results. The experimental results show that the model can quickly and effectively identify rumors from the communication situation, thus providing further help for governance.

Trust is an important mechanism to promote cooperation among persons in social network. As a security mechanism, it is widely implemented computer networks. In P2P applications, trust among cooperating peers is an essential precondition. It is becoming increasing vital in open, distributed, dynamic and anonymous networks to protect against malicious peers. This paper proposed a trust-aware propagation model based on the social network and with the basis of analyzing aware-computing to attempt to discover the propagation characteristics and regularity of trust being as network stability factor, and to reveal the functional mechanism of trust in the network security, which can improve the stability and robustness of the network. Firstly, we proposed a trust-aware propagation framework that contains three layers: propagation layer, aware layer, and computation layer. And then, the trust-aware propagation algorithm is presented to implement the formation of trust networks as fast as in P2P. A fitness function for peers computes their fitness degree based on contextual information to provide an initial entity identification mechanism, and the process of trust-aware propagation based on Markov process is defined to apperceive of trust among peers to ulteriorly form trust alliance. Thirdly, we built the dynamics model of trust propagation in order to deeply analyze the stability of trust networks. Next, three propagation strategies is proposed for implementing trust propagation. Finally, simulation experiments are carried in the bio-network platform. The results show that the trust-aware propagation model can effectively enhance the security and stability of P2P network, and improve the availability of the peer's resources.

With deep and broad applications in distributed computing, how to promote cooperation between entities becomes more and more important. Trust has been proven to be essential to enforce cooperative behaviors in distributed environments. To build trust relationship depends on some factors, such as context, behaviors, and experiences, and it is more challenging to accurately measure them. In this paper, we present a context-sensitive trust computing model to address this problem. Firstly, a trust space with fit-degree is defined based on the contextual information and a context-sensitive fit-law is proposed to judge the abilities of entities. Then, a trust computing model is proposed and followed by an expatiated dynamics trust analysis. Next, considering a new entity without trustworthiness can almost not do anything, we further present an algorithm of initial trustworthiness based on the new entity's abilities. The entity's trustworthiness is divided into the initial trustworthiness, the direct trustworthiness, and the recommended trustworthiness. Based on the trustworthiness obtained by the trust fusion algorithm, a mechanism of making trust decision is presented to promote cooperation. The simulation results show that our model can enhance the cooperation among entities. The malicious behaviors can be controlled because of the trustworthiness threshold of services. As such, the honest peers can be incentive, the network stability can be promoted, and the extent of network security can be improved. The proposed dynamic trust computing model is proven to be reasonable, practical, comparable, and workable in distributed services environments.

With the rapid development, Microblog as an important interactive media, has become a kind of transmission carrier of the false information. Therefore, the research significance of Micro-blog information credibility becomes more and more important today. In this paper, different representative factors are selected from three facets--text contents, information dissemination and information source--which influence the information credibility of Micro-blog. We choose Netica software to build Bayesian network model and use the rumors grabbed from Sina Weibo as experimental data in order to get the relationship between conditions and phenomena from the changes of probability distribution in Bayesian network. On the basis of this, we find the influences of the representative factors on the subjective credibility of objective unreliable information.

There are a number of dirty data in observation data set derived from ocean observing network. These data should be carefully and reasonably processed before they are used for forecasting or analysis in oceanic warning system (OWS). Due to high-dimensional and dynamic oceanic data, we propose an intelligent integrated data processing model for the OWS. Firstly, we design an integrated framework of the oceanic data processing and present its processing model. The function of each module of this model is analyzed in details. Then, we propose several intelligent data processing methods, such as an intelligent data cleaning method based on the fuzzy c-means algorithm, a data filtering and clustering method based on the greedy clustering algorithm, and a data processing method based on the maximum entropy for the OWS. The efficiency and accuracy of the proposed model is proved by experimental results of observation data of the Red Tide. The proposed model can automatically find the new clustering center with the updated sample data, and outperforms several algorithms in data processing for the OWS.

How to develop a trust management model and then to efficiently control and manage nodes is an important issue in the scope of social network security. In this paper, a trust management model based on a cloud model is proposed. The cloud model uses a specific computation operator to achieve the transformation from qualitative concepts to quantitative computation. Additionally, this can also be used to effectively express the fuzziness, randomness and the relationship between them of the subjective trust. The node trust is divided into reputation trust and transaction trust. In addition, evaluation methods are designed, respectively. Firstly, the two-dimension trust cloud evaluation model is designed based on node’s comprehensive and trading experience to determine the reputation trust. The expected value reflects the average trust status of nodes. Then, entropy and hyper-entropy are used to describe the uncertainty of trust. Secondly, the calculation methods of the proposed direct transaction trust and the recommendation transaction trust involve comprehensively computation of the transaction trust of each node. Then, the choosing strategies were designed for node to trade based on trust cloud. Finally, the results of a simulation experiment in P2P network file sharing on an experimental platform directly reflect the objectivity, accuracy and robustness of the proposed model, and could also effectively identify the malicious or unreliable service nodes in the system. In addition, this can be used to promote the service reliability of the nodes with high credibility, by which the stability of the whole network is improved.

The recommender systems encounter a series of challenges as E-commerce widens its scale and scope. This paper explores the current E-commerce recommender algorithms and proposes a personalized recommender approach based on immune learning, clonal selection and self-adaption of natural immune system. Our approach first clusters initialized antibody of immune network. Then it applies self-adaptive aiNet algorithm on cluster centers for clonal variation. Compared to collaborative filtering, our approach provides more accuracy prediction on users' interest and improves the quality of recommender systems. Our experiment verifies its effectiveness and feasibility in real recommender systems.

Trust is a vital control mechanism in computer network and social network, but there is a big gap between web trust and social trust. To eliminate this difference to make web trust more similar to social trust, we proposed a theory of quantum trust based on quantum mechanics. And then, the framework of quantum trust computation is presented for resolving network security problems. Finally, we use a scenario to illustrate the need for a quantum trust management in a distributed environment.

As popular web applications, web services can provide very flexible application integration taking advantage of existing Internet protocols and open standards. However, there are a few stubborn problems which exist in its architectures, for example, security. Especially in distributed services environment, trust which is a proved way to resolve security problem of web service is crucial to service providers and service customers for protecting service transactions and message delivering. In this paper, a web services trust evaluation model based on small world model is proposed. Firstly, an expanded subjective logic model is proposed. Secondly, a framework of SOA is built upon trust management module. Thirdly, the computing of subjective logic is improved based on small world, and the components in SOA are organized in small world. And then, the trust architecture of web service is presented. Finally, a simulation platform is used to study the evolution process of trust value in the model. The experiments and analysis show that the model can support evolving the web services trust.

The spin current, orbit angular momentum current and total angular momentum current in a tensor form have been universally defined according to the quantum electrodynamics. Their conservation quantities and the continuity equations have been discussed in different cases. Non-relativistic approximation forms are deduced in order to explain their physical meanings, and to analyze some experimental results. The spin current of helical edge states in HgTe/CdTe quantum wells is calculated to demonstrate the properties of the spin current of the two dimensional quantum spin-Hall system. A generalized spin-orbit coupling term in the semiconducting media is deduced based on the theory of the electrodynamics in the moving media. It is recommended to use the effective total angular momentum current instead of the pure spin current to describe the distribution of polarization and the transport properties in spintronics.

Distributed resource sharing in P2P networks has received a great deal of attention. Resources overload make users increasingly difficult to locate the right resource. Recommender systems have been proven to resolve this problem. But Recommender systems have natural weaknesses such as their vulnerability to various attacks. So, in this paper, we present a model of recommender system based on trust to figure this problem out. Firstly, we propose a computation model to compute trust value for each peer owning resources shared based on interactive histories or recommendations from other peers interacted. Then, right resources in right peers would be found for users based on trust value. So it can prevent some attacks from shilling of malicious peers. The experiment result has shown the reliability and robustness.

Community related applications such as instant messengers or file-sharing tools enjoy a great popularity in the Internet. But, the forming of communities is unprompted and the joining of users is based on their interesting. The opportunity to spread malicious-programs or purloin user's information is provided to malicious users. So, a clustering algorithm based on trust is presented to form a safe and reliable community. First, the node trust value is computed by their past behaviors. And then, based the trust value computed, the clustering algorithm is promoted to form community. Finally, the simulation result has shown the credibility and safety of the communities in network applications.

With the application of peer-to-peer network, how to promote cooperation between peers has gotten more and more important. Most of traditional security technologies can not be applied in P2P network very well to promote the cooperation because of the special characteristics of P2P network such as openness and anonymity, etc. Trust has been proven to be essential to enforcing cooperative behavior in peer-to-peer networks. Trust relationship depends on trustee's trustworthiness. So, in this paper, we present a three-dimensional computing model of dynamic trust to try to find a way to address the problem. Firstly, we give a three-dimensional computing model of trust and make a dynamics analysis to trust of peer. Next, considered the new peer without trustworthiness can not do anything almost, we propose an algorithm of initial trustworthiness based on the new peer's abilities. To compute the direct trustworthiness and the recommended trustworthiness, we colligate the time as a dynamic factor. Finally, based on the trustworthiness computed by trust fusion algorithm, we present a mechanism of making trust decision to promote cooperation. The simulation results have showed that our model can enhance the cooperation between peers and avoid the malicious peers from destroying behaviors.

Successful electronic commerce allows businesses to create more efficient channels for product sales or to create new business opportunities. Trust plays an important role in successful online businesses. In this paper, we consider the important case of interactions in peer communities, and then propose a trust establish model based on social computing which aims at avoiding interaction with undesirable participants. Our specific approach based on game to trust computing leads to a decentralized society in which agents help each other weed out undesirable players.

Trust plays an important role in successful online businesses. In this paper, we consider the important case of interactions in peer communities, and then propose a trust established model based on social computing which aims at avoiding interaction with undesirable participants. Our specific approach based on game to trust computing leads to a decentralized society in which agents help each other weed out undesirable players.

Trust has been shown to have fundamental effects on the security induced by interaction of more general networks, such as social networks. These effects have, however, proved to be sensitive to the details of the dynamics management of relationship network like the small world network in human society. Inspired from this, we derive a dynamics security mechanism of trust computing in P2P networks, starting from fundamental individual level such as autonomic and anonymous peers. Then we describe components of trust computing framework to demonstrate how one can derive the trust enhanced security solutions for peerspsila protection with the proposed trust computing architecture. Applying the computing model, we investigate how a trust mechanism to promote network security and performance.

Induced by computation of more general networks, such as social networks, trust has been shown to have fundamental effects on the dynamics of evolutionary games. These effects have, however, proved to be sensitive to the details of the dynamics. Inspired this, we derive the dynamics governing the evolution of such a system, ad hoc network, starting from fundamental individual level. We show that how computation is modeled in game theory and how the ideas of evolutionary game theory apply to a specific trust game. Applying the evolutionary framework, we investigate how a reputation mechanism to promote trustworthiness. Simulation result concerning the dynamics of trust evolutionary computation in networking lead to the emergence of a trust scenario.

The anonymous, open nature of P2P networks offers an almost ideal environment for the free-riders and the malicious peers spreading the inauthentic files and viruses. Evaluation of trustworthiness of participating peers is an effective method to simulate to offer content and services and mitigate the selfish behavior of unreliable or malicious peers. Similar to other security related protocols, trust management is an attractive target for adversaries. In this paper, we present a trust management model inspired from energy mechanism of ecological network, and then implement a trust evaluation that peer's trust value is transformed into own energy for controlling peers' behaviors in networks. Through P2P network simulation experiments, we find significant performance gains in using this system. The trust management model can significantly improve the network resources sharing as well as effectively help minimize selfish and malicious peers in the network.

Induced by computing of more general networks, such as social networks, trust has been shown to have fundamental effects on the dynamics of evolution. Inspired this, we derive the dynamics governing the evolution of such a system, P2P network, starting from fundamental individual level of trust. We show that how computing is modeled based on game theory and how the ideas of evolutionary theory apply to a specific trust game to analysis the evolutionary dynamics of trust computing. Simulation result concerning the dynamics of evolution in networking lead to the emergence of a trust scenario.

Comprehensive Ocean Observation Information Systems (COOIS) comprises a massive and complex resource data, which is vital to an increasingly important application of Integrated Intelligent Ocean Information Management System (IIOIMS). IIOIMS is just complex, but uses a holistic approach to deal with the sophistication. The application domain and its resource require a tool of matching characteristics, which is facilitated by the current wide availability of high performance computing. In this paper, we first build the COOIS network and experimental platform to aggregate the comprehensive ocean observation information. Then, we present a fuzzy-based data assimilation framework and its algorithm for doing with the sophistication of COOIS. Data assimilation is widely applied for those different type data, but we firstly propose to solve this problem with fuzzy set. Theoretically, this will efficiently reduce the complexity and enhance the accuracy and utility of COOIS. Finally, we provide a demonstrated example to demonstrate the efficiency of the fuzzy-based data assimilation algorithm.

Inspired social network, we derive the trust management of such a system, Ad Hoc, starting from fundamental individual level. Applying the management model, we investigate how a trust mechanism to promote network performance.