一种基于属性的用户角色自动指派研究

基于角色的访问控制模型没有给出用户角色指派的实现方式,一种基于属性的用户角色自动指派机制,既可以实现细粒度的用户角色自动指派,又可以有效地减少为用户分配角色过程中的代价。本文详细介绍了用户角色自动指派的模型、基于静态属性的指派规则、基于动态属性约束的指派规则以及在某开放系统中的角色自动指派实现实例。     

基于属性与安全上下文约束的AURA模型

在基于角色的访问控制(RBAC)系统中,用户承担的角色由管理员指定,对一个大型系统,这样的用户角色指派方式既繁琐且易出错。该文建立基于属性与安全上下文约束的用户角色自动指派模型,给出用户角色自动指派算法,实现用户角色指派的自动化,保证指派后系统的灵活性与安全性,为RBAC模型在大型系统中的应用创造条件。     

支持动态角色切换的RBAC模型

动态角色切换是信息系统依据用户属性改变而部分或整体改变用户-角色指派的一种自动授权机制。本文将动态角色切换引入到RBAC96模型,论述了动态角色切换的各种形态、不同切换间的相互关系及模型实现。基于动态角色切换,系统可以自动处理触发角色切换条件而引起的用户-角色指派变更问题,整个过程无须人工参与,减轻了系统管理员的工作负担,提高了授权管理的效率与安全性。     

基于角色限制条件的用户-角色指派研究

URA97和URA02作为两个主要的用户-角色指派模型,得到广泛的应用。但这两个模型的指派先决条件较弱,不能满足一些对用户限制较强情况下的用户角色指派。本文认为同一角色下的不同用户之间存在差异,用户在指派某角色时除了常见的约束外,还要受到其他限制。提出了角色和用户属性概念,定义了角色限制条件角色资格条件,改进了指派先决条件,实现了指派先决条件的统一和更严格的用户角色指派限制。对RBAC96模型做了相应的修改。     

普适环境下的动态模糊访问控制模型研究

普适计算环境下用于授权决策的上下文条件满足程度、用户的信任程度以及授予用户权限后产生的安全风险程度都具有模糊性,现有的访问控制模型大都不支持对模糊信息的授权推理.提出了一个基于角色的模糊访问控制模型(FRBAC模型),它把对用户到角色的指派(UA)和角色到权限的指派(PA)分为独立的两部分.在UA指派中,用户可以激活的角色是通过对上下文条件的满足程度、用户的信任程度以及激活角色可能产生的安全风险进行模糊推理自动生成的.FRBAC模型实现了普适环境下的动态模糊授权和用户角色的自动分配,简化了模型的安全管理工作.最后给出了FRBAC模型实现的体系结构,还给出了模糊授权推理器的设计以及模糊授权规则库.模糊授权推理算法的实现.FRBAC模型实现了普适计算环境下的动态模糊授权,为智能访问控制授权系统的研究提供了新思路.     

一种基于可信度和属性的RBAC授权模型*

针对传统RBAC模型中存在用户角色指派的模糊性、用户授权认证决策的单一性及角色数量与管理的冲突等问题,提出一种结合属性与可信度的改进型RBAC授权模型——TA-RBAC模型。该模型通过增加对用户及所在平台的可信性认证,使得传统模型的认证方式得到了完善,保证了系统授权过程更为安全可靠;同时利用可信度和属性概念对传统模型的授权机制进行了扩展,通过用户认证可信度指派相应的系统角色,实现了动态的用户角色指派;在权限指派过程中引入属性实现对象激活操作,有效地减少了角色的设置数量并实现了更细粒度的授权。最后给出模型授     

引入属性和分组概念简便实用的ARBAC扩展模型

通过考察基于角色的访问控制RBAC模型,提出了一个实用的扩展模型。扩展模型主要引入属性和分组的概念,将具有相同角色的用户定义为一个组,按用户组指派相应的角色。并对权限和属性分组,按组为角色指派相应的属性和权限,解决了原模型在用户指派时不易表达对用户特征的限制。实体分为用户组、权限组、属性组等,简化了对RBAC系统中大量实体的管理,减轻了安全管理员进行用户指派、权限指派和属性指派时的工作量,增强了实用性。扩展模型中的实体与面向对象的编程方法OOP中的概念存在对应关系,软件开发人员很容易理解和实现。     

引入属性和分组概念的ARBAC扩展模型

通过考察基于角色的访问控制RBAC模型,提出了一个实用的扩展模型.扩展模型主要引入属性和分组的概念,将具有相同角色的用户定义为一个组,按用户组指派相应的角色.并对权限和属性分组,按组为角色指派相应的属性和权限,解决了原模型在用户指派时不易表达对用户特征的限制.实体分为用户组、权限组、属性组等,简化了对RBAC系统中大量实体的管理,减轻了安全管理员进行用户指派、权限指派和属性指派时的工作量,增强了实用性.扩展模型中的实体与面向对象的编程方法OOP中的概念存在对应关系,软件开发人员很容易理解和实现.     

基于E—cargo模型的RBAC冲突约束研究

RBAC是一种基于角色访问控制模型,该模型通过角色为核心,并加以各种约束,完成对用户权限的管理指派,一奎妻通荽引用E-CARG．O模型对RBA．C进彳亍形式化建模,并用该模型解决RBAC中的角色指派和用户指派冲突。     

角色指派规则生成研究

RBAC中,用户-角色指派常常由系统管理员完成,适用于用户数量不大,指派关系简单的环境。在分布环境中,用户数量巨大,指派关系复杂且易变,传统的用户-角色指派方法效率较低。角色指派规则的出现为实现URA过程的自动化提供了可能,也是角色对用户的一种限制条件。用户只有满足指派规则后才能获得角色。已有研究工作未说明如何生成指派规则,本文提出了角色指派规则的生成方法。根据角色获得权限的方式,研究了PRA过程中角色指派规则的生成,以及角色继承关系中子角色的角色指派规则生成。同时,本文也对上面两种情况所引起的角色权限变化而带来的角色指派规则的变化进行了研究。     

基于XACML的用户角色自动指派

AURA(Automatic User Role Assignment)能够大幅降低RBAC的管理开销。基于属性规则的访问控制机制能提供细粒度的访问控制。本文详细介绍基于XACML的AURA扩展、AURA中的XACML的策略语言模型、基于XACML的AURA的应用实例、基于XACML的AURA中存在的问题以及基于XACML的参考实现。     

Short-term prediction of traffic flow using a binary neural network

This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine whether the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be set-up for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems.     

Development of a model for the estimation of photosynthetically active radiation from geostationary satellite data in a tropical environment

This paper presents a model for the estimation of photosynthetically active radiation (PAR) from geostationary satellite data. The model is aimed to estimate the monthly average hourly PAR in a tropical environment. This model represents a physical relation of PAR incident on the earth's surface and satellite-derived earth-atmospheric albedo together with the absorption and scattering coefficients of various atmospheric constituents. The earth-atmospheric albedo was obtained from the Multifunctional Transport Satellite-1R (MTSAT-1R). The absorption of PAR by water vapor, an important process for the tropics, was computed from the ambient temperature and relative humidity. The absorption of PAR by aerosols was estimated by using the visibility data and aerosol optical properties obtained from the Aerosol Robotic Network (AERONET) of NASA in this region. The total column ozone from the Ozone Monitoring Instrument onboard of AURA satellite (OMI/AURA) was used for the estimation of the absorption of PAR by ozone. The model was validated against the monthly average hourly PAR from measurements at four solar radiation measuring stations situated in the tropical environment of Thailand. The values of the monthly average hourly PAR estimated from the model and those obtained from the measurement were in good agreement, with the root mean square error (RMSE) and mean bias error (MBE) of 9.8% and 0.6%, respectively. After the validation, the model was employed to estimate the monthly average hourly PAR over Thailand using a 4-year period of data from MTSAT-1R and other ancillary surface data. Values of the monthly average hourly PAR were presented as maps showing the geographical distribution of PAR. These maps reveal the diurnal and seasonal variation of PAR over the country.     

A comparison of a novel neural spell checker and standard spell checking algorithms

In this paper, we propose a simple and flexible spell checker using efficient associative matching in the AURA modular neural system. Our approach aims to provide a pre-processor for an information retrieval (IR) system allowing the user's query to be checked against a lexicon and any spelling errors corrected, to prevent wasted searching. IR searching is computationally intensive so much so that if we can prevent futile searches we can minimise computational cost. We evaluate our approach against several commonly used spell checking techniques for memory-use, retrieval speed and recall accuracy. The proposed methodology has low memory use, high speed for word presence checking, reasonable speed for spell checking and a high recall rate.     

Distribution forecasting of high frequency time series

The availability of high frequency data sets in finance has allowed the use of very data intensive techniques using large data sets in forecasting. An algorithm requiring fast k-NN type search has been implemented using AURA, a binary neural network based upon Correlation Matrix Memories. This work has also constructed probability distribution forecasts, the volume of data allowing this to be done in a nonparametric manner. In assistance to standard statistical error measures the implementation of simulations has allowed actual measures of profit to be calculated.     

A comparison of standard spell checking algorithms and a novel binary neural approach

In this paper, we propose a simple, flexible, and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning, and associative matching in the AURA neural system. We integrate Hamming Distance and n-gram algorithms that have high recall for typing errors and a phonetic spell-checking algorithm in a single novel architecture. Our approach is suitable for any spell checking application though aimed toward isolated word error correction, particularly spell checking user queries in a search engine. We use a novel scoring scheme to integrate the retrieved words from each spelling approach and calculate an overall score for each matched word. From the overall scores, we can rank the possible matches. We evaluate our approach against several benchmark spellchecking algorithms for recall accuracy. Our proposed hybrid methodology has the highest recall rate of the techniques evaluated. The method has a high recall rate and low-computational cost.     

Introduction to the FY-3A Total Ozone Unit: instrument,performance and results

The first 18 months of data from the FY-3A Total Ozone Unit (FY-3A/TOU) was collected to analyse instrument performance. Retrieval tests were carried out using post-launch calibration coefficients. Validation was analysed at 74 ground-based stations of total ozone network by inter-comparison with AURA/Ozone Monitoring Instrument (OMI) Total Ozone Mapping Spectrometer (TOMS) V8 data and comparison with ground-based measurements. The validation results show that the TOU total column ozone has an average root mean square (rms) error of less than 3.1% compared with OMI products, and less than 4.4% compared with ground-based measurements. The solar irradiance is measured continuously, and the post-launch calibration coefficients will be updated only if the change of instrument sensitivity has noticeable influence on the retrieval. The instrument is currently stable.     

An investigation of temporal variability of the total column ozone over tropical urban,high altitude,and coastal stations in Western Maharashtra,India

Distribution and variability of ozone are vital to the atmospheric thermal structure as it can exert great influence on climate. In this study, the Microtops II Ozonometer (Microtops)-measured total column ozone (TCO) data archived at the tropical urban, high altitude, and coastal observing sites during 2012–2015 are analysed to investigate the temporal structure of ozone. Results reveal that the TCO exhibits a non-negligible diurnal variability depicting distinct seasonal behaviour, which corroborates well with the Indian as well as the worldwide measurements of TCO. The mean rate of ozone diurnal change (V_s) in winter is found to be maximum (approximately 2.1 DU h^–1) while it is minimum (about 0.53 DU h^–1) in pre-monsoon. In spite of the prevalent variability of the order of about 2–9 DU amongst Microtops channels and Ozone Monitoring Instrument on board the NASA EOS/AURA spacecraft (OMI-AURA) measurements, there exists a strong monthly/seasonal variation in both the ground- and satellite-based TCO measurements. Monthly mean OMI-AURA TCO variation presents a nearly perfect sinusoidal wave with a coefficient of determination (R²) equal to 0.76. Monthly TCO is maximum in May/June and minimum in December/January. The noticeable diurnal and monthly TCO variability could be due to a complex combination of photochemical processes in the lower troposphere and the transport in the middle and upper troposphere. Linear regression technique applied to the Microtops and OMI-AURA data sets show that the two data sets are better correlated with a correlation coefficient (r) taking values 0.71, 0.77, and 0.61 for channels I, II, and III, respectively. The three Microtops channels show the dispersion of about 8–11 DU around 1:1 regression line which is of the order of one standard deviation of the daily mean data set. The TCO data at all Microtops channels either underestimate or overestimate with respect to the OMI-AURA measurements since the values for slopes of the linear regression line for all the three channels are ≤1. Pearson’s product moment correlation analysis indicates that the TCO anti-correlates with ultraviolet-B (UV-B) irradiance (vis-à-vis through UV index) as the Pearson’s product moment correlation coefficients are found to be in the range –0.52 to –0.97.     

基于Visual C ++的自动组卷系统的实现

自动组卷系统是在已有的题库基础上,按照考核目的随机生成一份满足一定要求的试卷管理系统.在该系统中,计算机如何自动选题形成一套科学合理的试卷是最关键的问题,本文以《C语言程序设计》试题库为例,详细阐述在试题库中自动组卷的过程.