Multi-level access control model for tree-like hierarchical organizations

An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning security tags to subjects and objects is greatly simplified.The interoperation among different departments is implemented through assigning multiple security tags to one post, and the more departments are closed on the organization tree,the more secret objects can be exchanged by the staff of the departments.The access control matrices of the department,post and staff are defined.By using the three access control matrices,a multi granularity and flexible discretionary access control policy is implemented.The outstanding merit of the BLP model is inherited,and the new model can guarantee that all the information flow is under control.Finally,our study shows that compared to the BLP model,the proposed model is more flexible.

DECISION-MAKING OF SLICING SCHEME IN FUSED DEPOSITION MODELING PROCESS BASED ON ANALYTICAL HIERARCHICAL PROCESS

Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluation is introduced to make decision in slicing schemes for a processing part. The application in determining the slicing scheme for a computer mouse during prototyping shows that the method increases the rationality during decision- making and improves quality and efficiency for the prototyping part.

Hierarchical interacting multiple model algorithm based on improved current model

Interacting multiple models is the hotspot in the research of maneuvering target models at present. A hierarchical idea is introduced into IMM algorithm. The method is that the whole models are organized as two levels to co-work, and each cell model is an improved ＂current＂ statistical model. In the improved model, a kind of nonlinear fuzzy membership function is presented to get over the limitation of original model, which can not track weak maneuvering target precisely. At last, simulation experiments prove the efficient of the novel algorithm compared to interacting multiple model and hierarchical interacting multiple model based original ＂current＂ statistical model in tracking precision.

Hierarchical hybrid testability modeling and evaluation method based on information fusion

In order to meet the demand of testability analysis and evaluation for complex equipment under a small sample test in the equipment life cycle, the hierarchical hybrid testability model- ing and evaluation method （HHTME）, which combines the testabi- lity structure model （TSM） with the testability Bayesian networks model （TBNM）, is presented. Firstly, the testability network topo- logy of complex equipment is built by using the hierarchical hybrid testability modeling method. Secondly, the prior conditional prob- ability distribution between network nodes is determined through expert experience. Then the Bayesian method is used to update the conditional probability distribution, according to history test information, virtual simulation information and similar product in- formation. Finally, the learned hierarchical hybrid testability model （HHTM） is used to estimate the testability of equipment. Compared with the results of other modeling methods, the relative deviation of the HHTM is only 0.52%, and the evaluation result is the most accu rate.

Optimal Credibility Estimation of Random Parameters in Hierarchical Random Effect Linear Model

In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.

An Intrusion Detection Method Based on Hierarchical Hidden Markov Models

This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model （HHMM） is used to represent a temporal profile of normal behavior in a computer system. The HHMM of the norm profile is learned from historic data of the system＇s normal behavior. The observed behavior of the system is analyzed to infer the probability that the HHMM of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The model was implemented and tested on the UNIX system call sequences collected by the University of New Mexico group. The testing results showed that the model can clearly identify the anomaly activities and has a better performance than hidden Markov model.

Multiscale Hierarchical Digital Rock Mass Models and Numerical Testing on Mechanical and Hydaulic Properties of Rock Mass

Underground space utilization and exploration is an irreversible trend for promoting sustainable development especially in megacities.Geotechnical engineering safety is always one of the most important issues in all phases,including planning,design, construction and operation,of the underground project.Engineering geological and hydro-geological characteristics of the foundation rock mass

A Hierarchical Model Architecture for Enterprise Integration in Chemical Industries

Towards integration of supply chain, manufacturing/production and investment decision making, this paper presents a hierarchical model architecture which contains six sub-models covering the areas of manufacturing control, production operation, design and revamp, production management, supply chain and investment decision making. Six types of flow, material, energy, information, humanware, partsware and capital are clasified. These flows connect enterprise components/subsystems to formulate system topology and logical structure. Enterprise components/subsystems are abstracted to generic elementary and composite classes. Finally, the model architecture is applied to a management system of an integrated suply chain, and suggestion are made on the usage of the model architecture and further development of the model as well as implementation issues.

Cluster based hierarchical resource searching model in P2P network

For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

Community effects on public health in India: A hierarchical model

The data on any aspect of public health, including that on infant mortality, has inbuilt hierarchical structure. Using traditional regression approach in data analysis, i.e., ignoring hierarchical structure, either at micro (individual) or at macro (community) level will be avoiding desired assumption related to independence of records. Accordingly, this may result into distortion in the results due to probable underestimation of standard error of the regression coefficients. To be more specific, an irrelevant co-variate may emerge as an important covariate leading to inappropriate public health implications. To overcome this problem, the objective of the present work was to deal with multilevel analysis of the data on infant mortality available under second round of National family Health Survey and notify changes in results under traditional regression analysis that ignores hierarchical structure of data. This method provides more accurate results leading to meaningful public health implications. In addition, estimation of variability at different levels and their covariance are also obtained. The results indicate that the community (e.g., state) level characteristics still have major role regarding infant mortality in India. Further, if computational facilities are available, multilevel analysis may be preferred in dealing with data involving hierarchical structure leading to accurate results having meaningful public health implications.

Local and regional flood frequency analysis based on hierarchical Bayesian model in Dongting Lake Basin,China

This study developed a hierarchical Bayesian(HB)model for local and regional flood frequency analysis in the Dongting Lake Basin,in China.The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model.The generalized extreme value(GEV)distribution was selected as the extreme flood distribution,and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate.The Markov chain Monte Carlo(MCMC)method with Gibbs sampling was employed to calculate the posterior distribution in the HB model.The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles,while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles,due to the fact that the lower confidence bounds tended to decrease as the return periods increased.Furthermore,the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method,such as the homogeneity region assumption and the scale invariance assumption.The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site,but the index flood method with L-moments does not demonstrate this uncertainty directly.Therefore,the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme,and of quantifying the associated predictive uncertainty.

Strategic games on a hierarchical network model

Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.

Hierarchical Modeling by Recursive Unsupervised Spectral Clustering and Network Extended Importance Measures to Analyze the Reliability Characteristics of Complex Network Systems

The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We exemplify this by referring to the reliability analysis of the 380 kV Italian Power Transmission Network (IPTN). In this work of analysis, the classical component Importance Measures (IMs) of reliability theory have been extended to render them compatible and applicable to a complex distributed network system. By utilizing these extended IMs, the reliability properties of the IPTN system can be evaluated in the framework of the hierarchical system model, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.

A multidimensional and hierarchical model of library mobile service quality

Purpose:Based on the understanding of the characteristics of mobile services(m-services)provided by libraries,we attempt to develop a multidimensional and hierarchical model and a specifically designed measurement scale for the assessment of libraries' m-service quality.Design/methodology/approach:The research has been conducted sequentially in two stages.At the first stage,a multidimensional and hierarchical model of library m-service quality and a measurement scale are developed through literature review and focus group interviews.At the second stage the model is tested using partial disaggregation technique for an analysis of the data collected from over 400 library users in 3 Chinese universities.Findings:The proposed model is shown to fit the data well.The empirical analysis provides strong support for the model,which includes 4 dimensions(service environment,service interaction,information control and service effect) and 12 corresponding sub-dimensions.Research limitations:The sample size needs to be enlarged and the user types need to be expanded to make the sample more representative.Practical implications:The study provides a conceptual framework and measurement tool that can help library managers understand the users' needs in the context of mobile network environment,and boost their libraries' competitiveness by carrying out better services to meet the user demands.Originality/value:The proposed model and measurement scale specifically designed for the assessment of libraries' m-service quality fully reflect the hierarchical structure of libraries' m-service quality.In addition,by using partial disaggregation technique for model testing,we believe our research can serve as a practical reference to those engaged in the development and examination of a multidimensional and hierarchical model.

Modeling and Analyzing Web Application with Aspect-Oriented Hierarchical Coloured Petri Nets

Aspect-oriented modeling can uncover potential design faults, yet most existing work fails to achieve both separation and composition in a natural and succinct way. This study presents an aspect-oriented modeling and analysis approach with hierarchical Coloured Petri Nets(HCPN). HCPN has sub-models and well-defined semantics combining a set of submodels. These two characteristics of HCPN are nicely integrated into aspect oriented modeling. Submodels are used to model aspects while the combination mechanism contributes to aspects weaving. Furthermore, the woven aspect oriented HCPN model can be simulated and analyzed by the CPN Tools. A systematic web application case study is conducted. The results show the system original properties are satisfied after weaving aspects and all design flaws are revealed. As such, the approach can support web application design and analysis in an aspect-oriented fashion concisely and effectively.

Differentiated digital library evaluation in a hierarchical model stemming from its operational scope and complexity

Digital libraries are complex systems and this brings difficulties for their evaluation. This paper proposes a hierarchical model to solve this problem, and puts the entangled matters into a clear-layered structure. Firstly, digital libraries(DLs thereafter)are classified into 5 groups in ascending gradations, i.e. mini DLs, small DLs, medium DLs,large DLs, and huge DLs by their scope of operation. Then, according to the characteristics of DLs at different operational scope and level of sophistication, they are further grouped into unitary DLs, union DLs and hybrid DLs accordingly. Based on this simulated structure,a hierarchical model for digital library evaluation is introduced, which evaluates DLs differentiatingly within a hierarchical scheme by using varying criteria based on their specific level of operational complexity such as at the micro-level, medium-level, and/or at the macro-level. Based on our careful examination and analysis of the current literature about DL evaluation system, an experiment is conducted by using the DL evaluation model along with its criteria for unitary DLs at micro-level. The main contents resulting from this evaluation experimentation and also those evaluation indicators and relevant issues of major concerns for DLs at medium-level and macro-level are also to be presented at some length.

A Comprehensive Survey of Recent Transformers in Image,Video and Diffusion Models

Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism.This study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion models.We begin by discussing existing surveys of vision transformers and comparing them to this work.Then,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,etc.In the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for Segmentation.We also provide a comprehensive overview of recent transformer models for video tasks and diffusion models.We compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark datasets.Finally,we explore some future research directions to further improve the field.

Comparing of Aviation Network Structure of Mid-south,Northwest and Southwest of China Based on Hierarchical Index Model

In order to compare the aviation network of mid-south,northwest and southwest of China to reveal the structure similarity and difference for providing quantitative evidence to construct regional aviation network and improve its structure,hierarchical index model of regional aviation network was established through dividing the aviation network into layers to research its structure characters.Data matrixes were defined to record the basic state of regional aviation network.Index matrixes were constructed to describe the quantitative features of regional aviation network.On the basis of these indexes,several structure indexes of all layers of aviation network were calculated to show the structure features of aviation network,such as ratio of passenger volume within the region with across the region,share rate of passenger volume among layers,ratio of average number of airline for each airport,ratio of average passenger volume for each airline and ratio of airline rate.According to the statistical data,similar structure of share rate of passenger volume among layers and average passenger volume for each airline in their regional aviation network was found after calculating.But on the side of ratio of passenger volume within the region with across the region,ratio of average number of airlines for each airport and ratio of airline rate were different.

Parameter-driven Level of Detail Derivation Method for Semantic Building Facade Model

The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.