一种基于不确定方法的虚拟人眨眼控制模型*

针对采用确定性方法合成的虚拟人眨眼效果单一、重复、不能刻画眨眼表达深层信息的不确定性,提出一种基于正态云模型的虚拟人眨眼非确定性控制方法.设计了眨眼间隔云、眼皮旋转速度云、睁眼最大角度云和闭眼保持时间云.分别运用正向云生成算法产生眨眼间隔、眼皮旋转速度、睁眼最大角度和闭眼保持时间,且四个参数符合各自云模型的模糊性和随机...     

基于云模型的服务信任模型

针对Web服务中信任的主观性和不确定性等特点,提出一种基于云模型的信任建模方法,并给出了直接信任云、推荐信任云、综合信任云的计算方法及基于时间权重的信任更新算法．同时,该模型运用具体的服务能力评价信息来量化服务的信任度,增强了模型的客观性,使其能较好地量化实体的真实信任水平,提高网络中实体的交互成功率．实验与分析结果表明,该模型在网络环境中具有较好的效果．     

CSCW系统中基于角色的感知模型

感知是CSCW系统中需要解决的关键技术问题之一,因此,本文提出一种基于层次图的角色感知模型:模型通过角色与原子任务之间的归属关系和原子任务的交互关系建立层次图,同时对非原子任务进行分解;通过起始原子任务发起简化算法,并在算法中引入角色合并规则;以简化后的角色为基础在P2P网络下建立虚拟工作组,给出了虚拟工作组间的消息分发机制.实验表明,该方案在协作节点大量增加的情况下降低了数据反馈延时,更易于满足CSCW中感知的高实时性的要求.     

基于多维信任云的信任模型研究

针对主观信任的模糊性和不确定性以及现有的基于云模型的信任模型中粒度粗糙的问题,提出了一种基于多维信任云的信任模型。首先,依据实体间的直接交互经验和交互时间,利用加权逆向云生成算法计算被评估实体的直接信任云;然后,通过评估推荐实体的推荐可信度,计算被评估实体的推荐信任云;最后,综合直接信任云和推荐信任云产生综合信任云,并依此进行可信实体的选择。仿真结果表明,基于多维信任云的信任模型能够有效地识别系统中的各类服务实体,可提高实体间的交易成功率。     

有关基于云模型的聚类特性的应用研究

随着科学技术的发展,云模型逐渐开始得到更进一步的研究与发展,并开始应用于实际的实践当中。云模型刻画聚类的随机与模糊特性存在着一定的关联性,分析聚类过程中存在着不确定性。本文首先对云模型的相关算法进行理论分析,对它的聚类特性通过试验验证的方式进行分析。     

云模型研究的回顾与展望*

云模型融合了概率论和模糊集合论的观点,更好地刻画了自然语言中概念的不确定性,反映了随机性与模糊性之间的内在关联。综述了云模型的产生背景、理论基础及发展历程,着重梳理了云模型的主要理论、技术和方法及其在数据挖掘、算法改进、网络安全等众多领域的应用成果,并针对云模型研究存在的一些问题提出了云模型进一步研究的思路。     

面向普适计算应用的模型分析与设计语言研究

传统的建模方法主要面向具体的计算环境和设备,其接口、执行上下文和实施行为的方式都是可以预测的,难以准确刻画普适计算应用的不确定性自适应行为。针对此种情况,提出一种普适计算应用模型及设计语言。它将计算应用解构为相互联系的四元素,并通过定义元素内部执行和外部交互语义,支持搭建具备分布、上下文感知与自适应执行特性的普适计算应用。以智能导航应用为例给出基于四元素的应用描述。案列结果说明该应用模型能够帮助设计人员快速刻画操作对象和交互实体的物理属性和行为语义,支持对应用逻辑的高效设计。     

云决策分析方法

提出一种基于不确定性人工智能的决策分析方法,该方法利用云理论中的云变换和概念提升方法来刻画属性因素的概念云模型,并对每个因素都分配相应的云权重;使用X-条件云和极大判定法,找出每个因素的最佳评判语言云模型;让云权重参与综合评判,最终通过云计算得到基于云滴分布的综合评价结果,实验结果表明,与传统的模糊决策分析相比,它更具有易理解性、广普适性和高准确性等特点.     

一种交互式的云模型图像分割方法

论文针对图像分割中存在的不确定性问题,通过研究不确定性人工智能中定性和定量的转换模型—云模型,提出一种新的基于云模型的图像分割方法。该方法采取交互式的方式选择训练样区,利用训练样区中的像素生成云模型,并通过泛概念树生成算法生成泛概念树,最后通过极大判定法判定像素所属类别,实现图像分割。这种方法能较好地描述图像目标的不确定性。通过几组实验,证明该方法可以准确地分割出目标,并优于传统的图像分割算法。     

基于云模型的多粒度彩色图像分割

图像信息中存在的不确定性问题会影响图像的分割效果.为此,提出一种基于粒计算和云模型的彩色图像分割算法.研究多粒度认知模型,在HSV颜色空间中利用云模型构建彩色图像的信息粒,进行多粒度、多层次的云粒合成,实现彩色图像分割.实验结果表明,与PCNN算法和K均值算法相比,该算法的分割效果较好.     

基于云模型的虚拟人摇头动作不确定性控制*

以虚拟人摇头表达否定性信息为例,设计了最大摇头角否定云、摇头次数否定云和摇头速度否定云。分别运用正向云生成算法产生云滴,获得摇头动作序列的最大摇头角度、摇头次数和摇头速度。三个控制参数均以各自的期望值为中心正态分布。仿真实验表明,所提出的基于云模型的虚拟人摇头控制算法能够产生呈规律性差异的摇头动作序列控制曲线。给出了三类云模型数字特征的建议值。     

基于马尔可夫模型的云系统安全性与性能建模

针对云环境缺乏安全性评估的问题,提出一种评估系统安全性的建模方法,并建立了云环境下的安全性-性能（S-P）关联模型。首先,针对云系统中最重要的组成部分,即虚拟机,建立了评估其安全性的模型,该模型充分反映了安全机制和恶意攻击两个安全因素对虚拟机的影响;随后基于虚拟机与云系统之间的关系,提出评估云系统安全性的指标;其次,提出一种分层建模方法来建立S-P关联模型。利用队列理论对云计算系统的性能进行建模,然后基于贝叶斯理论和相关分析建立了安全性和性能之间的关联关系,并提出评估复杂S-P相关性的新指标。实验结果验证了理论模型的正确性,并揭示了安全因素引起的性能动态变化规律。     

A framework for semi-automatically identifying fully occluded objects in 3D models: Towards comprehensive construction design review in virtual reality

Virtual Reality (VR)-based construction design review applications have shown potential to enhance user performance in many research projects and experiments. Currently, visualizing occluded objects in VR is a challenge, and this function is indispensable for construction design review and coordination. This paper proposes an occlusion detection framework that semi-automatically identifies occluded objects in 3D construction models. The framework determines the visibility status of an object by converting the object to a point cloud and comparing the point cloud to the virtual laser scanning result of the original model. It exports models that are interoperable with VR development software so that visualization effects can be easily employed to occluded objects. The authors validated the framework using two building information models. The algorithm achieved a recall rate of 90.30% and a precision rate of 75.05% in a gasoline refinery facility model. It reached a higher 98.06% recall rate and a 97.53% precision rate in an academic building model. This paper contributes to the body of knowledge by proposing a semi-automatic occlusion detection framework and validating that point cloud-based algorithms are appropriate for this classification task.     

Linking building data in the cloud: Integrating cross-domain building data using linked data

Within the operational phase buildings are now producing more data than ever before, from energy usage, utility information, occupancy patterns, weather data, etc. In order to manage a building holistically it is important to use knowledge from across these information sources. However, many barriers exist to their interoperability and there is little interaction between these islands of information.As part of moving building data to the cloud there is a critical need to reflect on the design of cloud-based data services and how they are designed from an interoperability perspective. If new cloud data services are designed in the same manner as traditional building management systems they will suffer from the data interoperability problems.Linked data technology leverages the existing open protocols and W3C standards of the Web architecture for sharing structured data on the web. In this paper we propose the use of linked data as an enabling technology for cloud-based building data services. The objective of linking building data in the cloud is to create an integrated well-connected graph of relevant information for managing a building. This paper describes the fundamentals of the approach and demonstrates the concept within a Small Medium sized Enterprise (SME) with an owner-occupied office building.     

A Proposed Biometric Authentication Model to Improve Cloud Systems Security

Most user authentication mechanisms of cloud systems depend on the credentials approach in which a user submits his/her identity through a username and password. Unfortunately, this approach has many security problems because personal data can be stolen or recognized by hackers. This paper aims to present a cloud-based biometric authentication model (CBioAM) for improving and securing cloud services. The research study presents the verification and identification processes of the proposed cloud-based biometric authentication system (CBioAS), where the biometric samples of users are saved in database servers and the authentication process is implemented without loss of the users’ information. The paper presents the performance evaluation of the proposed model in terms of three main characteristics including accuracy, sensitivity, and specificity. The research study introduces a novel algorithm called “Bio_Authen_as_a_Service” for implementing and evaluating the proposed model. The proposed system performs the biometric authentication process securely and preserves the privacy of user information. The experimental result was highly promising for securing cloud services using the proposed model. The experiments showed encouraging results with a performance average of 93.94%, an accuracy average of 96.15%, a sensitivity average of 87.69%, and a specificity average of 97.99%.     

基于云的Web应用性能测试服务平台

云计算作为一种新型的计算资源交付模型,为软件性能测试带来了新的机遇．设计了一种四层架构的性能测试服务平台,实现传统性能测试软件向云计算环境的迁移,同时设计一种准入控制和任务调度算法,实现负载发生资源的共享管理和动态分配,满足多租户性能测试需求．基于上述工作,完成已有性能测试工具Bench4Q的迁移．     

Framework for cloud-based software test data generation service

This paper presents the framework of cloud-based software test data generation service (CSTS) that caters to cost-effective test data generation service in a cloud environment. In contrast to existing conventional or cloud-based testing frameworks, CSTS has a number of unique benefits. First, CSTS is designed to facilitate test data generation in minimum time and cost. Second, unlike existing frameworks which mandates clients to opt for resources to test their jobs, CSTS guides customer for selecting best cluster configuration in order to minimize the cost. While the existing models do not provide any solution for trust establishment in cloud computing services, CSTS delivers it by implementing security mechanism with the provision of role based access control. The security mechanism proposed in this paper ensures the protection of data and code of different users. Third, CSTS provides a mathematical pricing model to fulfill the expectations of customers and also to maximize the net profit of service providers. Cloud service request model has also been designed that postulates service level agreements between customers and service providers. We have evaluated, compared, and analyzed our framework and have found that it outperforms other existing cloud-based frameworks.     

Latency based group discovery algorithm for network aware cloud scheduling

Cloud computing is a big paradigm shift of computing mechanism. It provides high scalability and elasticity with a range of on-demand services. We can execute a variety of distributed applications on cloud’s virtual machines (computing nodes). In a distributed application, virtual machine nodes need to communicate and coordinate with each other. This type of coordination requires that the inter-node latency should be minimal to improve the performance. But in the case of nodes belonging to different clusters of the same cloud or in a multi-cloud environment, there can be a problem of higher network latency. So it becomes more difficult to decide, which node(s) to choose for the distributed application execution, to keep inter-node latency at minimum. In this paper, we propose a solution for this problem. We propose a model for the grouping of nodes with respect to network latency. The application scheduling is done on the basis of network latency. This model is a part of our proposed Cloud Scheduler module, which helps the scheduler in scheduling decisions on the basis of different criteria. Network latency and resultant node grouping on the basis of this latency is one of those criteria. The main essence of the paper is that our proposed latency grouping algorithm not only has no additional network traffic overheads for algorithm computation but also works well with incomplete latency information and performs intelligent grouping on the basis of latency. This paper addresses an important problem in cloud computing, which is locating communicating virtual machines for minimum latency between them and group them with respect to inter-node latency.