Incorporating linguistic structure into maximum entropy language models

In statistical language models,how to integrate diverse linguistic knowledge in a general framework for long-distance dependencies is a challenging issue,In this paper,an improved language model incorporating linguistic structure into maximum entropy framework is presented.The poposed model combines trigram with the structure knowledge of base phrase in which trigram is used to capture the local relation between words.while the structure knowledge of base phrase is considered to represent the long-distance relations between syntactical structures.The knowledge of syntax,semantics and vocabulary is is integrated into the maximum entropy framework,Experimental results show that the proposed model improves by 24% for language model perplexity and increases about3% for sign language recognition rate compared with the trigram model.     

Fuzzy Logic Based Group Maturity Rating for Software Performance Prediction

Driven by market requirements,software services organizations have adopted various software engineering process models (such as capability maturity model (CMM),capability maturity model integration (CMMI),ISO 9001:2000,etc.) and practice of the project management concepts defined in the project management body of knowledge.While this has definitely helped organizations to bring some methods into the software development madness,there always exists a demand for comparing various groups within the organization in terms of the practice of these defined process models.Even though there exist many metrics for comparison,considering the variety of projects in terms of technology,life cycle,etc.,finding a single metric that caters to this is a difficult task.This paper proposes a model for arriving at a rating on group maturity within the organization.Considering the linguistic or imprecise and uncertain nature of software measurements,fuzzy logic approach is used for the proposed model.Without the barriers like technology or life cycle difference,the proposed model helps the organization to compare different groups within it with reasonable precision.     

缺值背景中的概念分析与知识获取

In formal concept analysis ,concept lattice as the fundamental data structure can be construct-ed front a formal context. Howevt, r,it is required that the relation between object and feature in the for-real context should be certain, For uncertain relation,this paper uses the thoughts of upper and lowerapproximation in rough set theory to deal with it ,and gives out the corresponding definitions of missing-value context and rough formal concept, Based on them, this paper employs rough concept lattice,formed by rough formal concepts and partial order relation on them,as the basic data structure for con-cept analysis and knowledge acquisition. Then a theroem is presented to describe the method of extract-ing rules from constructed rough formal concept lattice,and the semantic interpretation of discoveredrules is explained.     

Modeling and analyzing of the interaction between worms and antiworms during network worm propagation

Interaction of antiworms with a worm population of e.g. hosts of worm infected and hosts of antiworm infected must be considered as a dynamic process. This study is an attempt for the first time to understand how introduction of antiworm affects the dynamic of network worm propagation. In this paper, we create a mathematical model (SIAR model) using ordinary differential equations to describe the interaction of worms and antiworms. Although idealized, the model demonstrates how the combination of a few proposed nonlinear interaction rules between antiworms and worms is able to generate a considerable variety of different kinds of responses. Taking the Blaster and Nachi worms as an example, we give a brief analysis for designing a practical antiworm system. To the best of our knowledge, there is no model for the spread of an antiworm that employs the passive scan and the finite lifetime and we believe that this is the first attempt on understanding the interaction between worms and antiworms.     

A measurement approach of trust relation in web service

With the advent and proliferation of web services, quality of service serves as a benchmark to differentiate and select the services. Because trust has been regarded as an essential security relationship within a distributed network environment, quality evaluation of services from the viewpoint of trust presents a significant trend in decision support for selecting service over Internet. Unfortunately, the current research in the context of web services does not accurately reflect the randomness and fuzziness of trust, the basic issue in related research about trust. In this paper, we designed two kinds of trust cloud based on cloud model theory to describe the randomness and fuzziness of trust factors. Furthermore, a formalized calculation was provided to obtain the trust degree. To our knowledge, this is the first investigation into trust of web Service considering the integration of randomness and fuzziness, and applying the cloud model to quantitative evaluation of trust in the field of web services.     

An Attack-Finding Algorithm for Security Protocols

This paper proposes an automatic attack construction algorithm in order to find potential attacks on ecurity protocols.It is based on a dynamic strand space model,which enhances the original strand space model by introducing active nodes on strands so as to characterize the dynamic procedure of protocol execution.With exact causal dependency relations between messages considered in the model,this algorithm can avoid state space explo-sion caused by asynchronous composition.In order to get a finite state space,a new method called strand-added on demand is exploited,which extends a bundle in an incremental manner without requiring explicit configuration of protocol execution parameters.A finer granularity model of term structure is also introduced, in which subterms are divided into check subterms and data subterms .Moreover,data subterms can be further classified based on the compatible data subterm relation to obtain automatically the finite set of valid acceptable terms for an honest principal.In this algorithm,terms core is designed to represent the intruder‘s knowledge compactly,and forward search technology is used to simulate attack patterns easily.Using this algorithm,a new attack on the Dolve-Yao protocol can be found,which is even more harmful beeause the secret is revealed before the session terminates.     

KDD中知识的自动评价系统

How to automatically evaluate the discovered knowledge is very important in KDD,but the research on this problem is very little. The paper gives one automatic system for evaluating the knowledge, and provides many solutions. First some relative concepts are described,and the construction of this system is given,and uses the case to prove it.     

Hybrid Bayesian estimation tree learning with discrete and fuzzy labels

Classical decision tree model is one of the classical machine learning models for its simplicity and effectiveness in applications. However, compared to the DT model, probability estimation trees （PETs） give a better estimation on class probability. In order to get a good probability estimation, we usually need large trees which are not desirable with respect to model transparency. Linguistic decision tree （LDT） is a PET model based on label semantics. Fuzzy labels are used for building the tree and each branch is associated with a probability distribution over classes. If there is no overlap between neighboring fuzzy labels, these fuzzy labels then become discrete labels and a LDT with discrete labels becomes a special case of the PET model. In this paper, two hybrid models by combining the naive Bayes classifier and PETs are proposed in order to build a model with good performance without losing too much transparency. The first model uses naive Bayes estimation given a PET, and the second model uses a set of small-sized PETs as estimators by assuming the independence between these trees. Empirical studies on discrete and fuzzy labels show that the first model outperforms the PET model at shallow depth, and the second model is equivalent to the naive Bayes and PET.     

An OSGi-based flexible and adaptive pervasive cloud infrastructure

There is a trend that different computing paradigms such as cloud computing, pervasive, and mobile computing are converging with each other. Due to this convergence, there arise unprecedented complexities, such as huge number of computing devices, flexibilities and adaptation of service infrastructure （infrastructure elasticity） for fitting dynamics of large smart city applications, expectations of powerful computing and storage capabilities on handhold devices, and so on. Therefore, a supporting infrastructure is needed, which can flexibly switch services at run time and can be used to enhance capabilities for small devices through component/service migrations. In this paper, we propose an elastic open service gateway initiative （OSGi）-based pervasive cloud （OSGi-PC） infrastructure which can make use of both the cloud computing capabilities and the component flexibilities from OSCi. OSGi-PC provides flexible management of component migrations between small devices themselves and powerful nodes in between, which is remaining a critical challenge for enabling mobile clouds. We have evaluated the OSGi-PC in terms of performance for adaptive service provision, and power consumption during service adaptation, performance and power consumption for component migrations in different scenarios, which show the usability of OSGi-PC.     

State-Based Regression with Sensing and Knowledge

This paper develops a state-based regression method for planning domains with sensing operators and a representation of the knowledge of the planning agent. The language includes primitive actions, sensing actions, and conditional plans. The regression operator is direct in that it does not depend on a progression operator for its formulation. We prove the soundness and completeness of the regression formulation with respect to the definition of progression and the semantics of a propositional modal logic of knowledge. The approach is illustrated with a running example that can not be handled by related methods that utilize an approximation of knowledge instead of the full semantics of knowledge as is used here. It is our expectation that this work will serve as the foundation for the extension of work on state-based regression planning to include sensing and knowledge as well.     

用语言云模型发掘关联规则

该文提出用语言云模型用于KDD中知识表达和不确定性处理,引入了多维云模型作为一维模型的扩展.语言云的数字特征量将语言值的模糊性和随机性用统一的方式巧妙地综合到一起,基于云模型的概念层次结构可以跨越定量和定性知识之间的鸿沟.为了发现强关联规则,属性值要在较高的概念层上泛化,同时允许相邻属性值或语言项间有重叠.这种软划分可以模仿人类的思想,使发现的知识具有稳健性.将基于云模型的泛化方法与Apriori算法结合起来,从空间数据库中发掘关联规则.试验显示了其有效性、高效性和灵活性.     

云理论及其在空间数据发掘和知识发现中的应用

云理论是以研究定性定量间的不生转换为基础的系统处理不确定性问题的一新理论,包括云模型,虚云,云运算,云变换,不确定性推理等内容,云理论为数据发掘和知识发现中的许多基础性关键问题提供了新的解决方法,如概念和知识表达,定性定量转换,概念的综合与分解,从数据中生成概念和概念层次结构等。     

基于多维云模型的入侵检测

通过研究多维云模型机理，给出了定性与定量之间转换的云模型的形式化表示，用来反映语言值中蕴涵的模糊性和随机性，解释了多条定性规则同时被激活的不确定推理过程。实验采用了KDD99的测试数据，结果表明，该方法能够比较有效地检测真实网络数据中的未知入侵行为。     

A novel hesitant fuzzy linguistic hybrid cloud model and extended best-worst method for multicriteria decision making

Developing effective and accurate model to handle complex uncertainties of linguistic assessments in multicriteria decision making (MCDM) has important theoretical significance and practical value of engineering. This paper proposes a novel hesitant fuzzy linguistic hybrid cloud (HFLHC) model that integrates hesitant fuzzy linguistic term set and cloud model to handle the hesitancy, fuzziness, and randomness of linguistic expression. The normal cloud and trapezium cloud are integrated to represent hybrid-length linguistic variables of HFLHC model, which can effectively avoid evaluation information loss and distortion. Aiming at applying HFLHC model to MCDM, some hybrid operations for normal cloud and trapezium cloud are developed. Moreover, an improved method for aggregating multiple linguistic concepts into an integrated trapezium cloud in HFLHC model is proposed, with consideration of the different representation region of each linguistic concept. Furthermore, a novel HFLHC-based best-worst method is proposed to obtain optimal criteria weights with developing a HFLHC optimization programming model and a modified consistency ratio. Finally, an illustrative example of sustainable supplier selection is presented. Several comparative analyses demonstrate that our method can provide more consistency and greater reliability.     

定性规则的云表示

云模型是定性定量间的不确定转换模型，它将概念的模糊性和随机性集成在一起，利用云模型可以有效地表示基于语言值的定性规则。但已有的文献中，定性规则的云表示多种多样，文章根据各种云的内在联系提出了一维正态云的定性规则的统一表示。     

基于云综合方法的三支群决策模型

在三支决策问题中，领域专家群决策是一种确定损失函数的最直接方法。相较于体现单一不确定性的语言变量模型和模糊集模型，云模型描述的专家评价更能够反映认知过程中复杂的不确定性形式，并能通过云综合的方法获得综合评价函数。但当前的云综合方法仅对数字特征进行简单的线性组合，缺乏对概念语义差异上的描述，难以获得令人信服的结果。因此首先证明了在云模型的距离空间中赋权距离和是一个凸函数，并将综合云模型定义为此函数的最小值点。然后，将该定义推广到多个云模型的场景下，提出了一种新的云综合方法——基于密度中心的云综合方法。群决策过程中，该方法在保证综合评价与基础评价之间的相似度最高的同时获得最精确的综合评价，为损失函数的确定提供了一种新的语义解释。实验结果表明，在与简单线性组合和合理粒度方法对比中，该方法所确定的损失函数使得三支决策中的误分类率最低。     

基于云计算的网站群架构及安全性设计与实践

该文以政府机构网站群建设为对象,搭建了网站群云计算构建的实验与应用平台,阐述了应用云计算支撑政府机构网站群架构及其安全性设计和应用方法i,该文主要从两方面满足网站群应用需求,一方面,以IaaS（Infrastructure as a Service）的模式搭建支撑网站群应用的云计算基础平台,以SaaS（Software as a Service）的模式搭建网站群内容管理云计算平台,另一方面,通过构建公共云和私有云环境,保障网站群系统安全和信息安全。以云计算为平台,向用户提供一站式的网站群建设、运行、监控服务,将成为政府网站群建设的新的方向．     

基于云模型和BP神经网络的入侵检测

云模型是进行定性定量转换的有效工具,正态云模型通过期望、熵和超熵构成特定结构发生器,这种特定结构使得正态云模型更具有普遍适用性,更简单、直接地完成了定性与定量之间的相互转换过程.结合 BP 网络局部精确搜索的特性,提出一种基于云模型与 BP 网络的入侵检测方法,将云模型和 BP算法有机结合.通过 KDD99 CUP 数...     

基于云模型具有语言评价信息的多属性群决策研究

针对多属性群决策中具有语言评价信息偏好的表示与集结的关键问题．研究了基于云模型的决策专家个体偏好表示、偏好集结和方案优选方法．首先采用云模型表示决策者给出的自然语言评价信息，而属性和决策者权重大小则用云的语气运算表示；然后用浮动云进行偏好集结，根据云模型的相对距离进行方案的排序和优选．此方法可充分表达评价语言的模糊性和随机性，具有较大的客观性．