Problem representation for refinement

In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements which are binary predicates on state variables. Then, the statement representing the set of goal states can be partitioned into its subsets each of which becomes a subgoal of the resulting strategy. The techniques involved in partitioning a goal into its subgoals are presented with examples.     

基于新型语言评估标度的二元语义改进模型

为了有效地求解基于语言评价信息的多属性决策问题,针对目前常用的二元语义分析方法中存在的标度转换问题,提出一种处理语言评价信息的新型复合标度(该标度综合了指数标度和-n～n标度的优点),建立了基于新型复合标度的二元语义改进模型.算例验证结果表明,新型复合标度为处理定性的语言评价信息提供了科学依据,所提出的改进模型可以有效提高决策结果的精度和可信度.     

A 2-tuple fuzzy linguistic representation model for computing withwords

The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation of this approach imposed by its information representation model and the computation methods used when fusion processes are performed on linguistic values. This limitation is the loss of information; this loss of information implies a lack of precision in the final results from the fusion of linguistic information. In this paper, we present tools for overcoming this limitation. The linguistic information is expressed by means of 2-tuples, which are composed of a linguistic term and a numeric value assessed in (-0.5, 0.5). This model allows a continuous representation of the linguistic information on its domain, therefore, it can represent any counting of information obtained in a aggregation process. We then develop a computational technique for computing with words without any loss of information. Finally, different classical aggregation operators are extended to deal with the 2-tuple linguistic model     

A new version of 2-tuple fuzzy linguistic representation model for computing with words

In this paper, we provide a new (proportional) 2-tuple fuzzy linguistic representation model for computing with words (CW), which is based on the concept of "symbolic proportion." This concept motivates us to represent the linguistic information by means of 2-tuples, which are composed by two proportional linguistic terms. For clarity and generality, we first study proportional 2-tuples under ordinal contexts. Then, under linguistic contexts and based on canonical characteristic values (CCVs) of linguistic labels, we define many aggregation operators to handle proportional 2-tuple linguistic information in a computational stage for CW without any loss of information. Our approach for this proportional 2-tuple fuzzy linguistic representation model deals with linguistic labels, which do not have to be symmetrically distributed around a medium label and without the traditional requirement of having "equal distance" between them. Moreover, this new model not only provides a space to allow a "continuous" interpolation of a sequence of ordered linguistic labels, but also provides an opportunity to describe the initial linguistic information by members of a "continuous" linguistic scale domain which does not necessarily require the ordered linguistic terms of a linguistic variable being equidistant. Meanwhile, under the assumption of equally informative (which is defined by a condition based on the concept of CCV), we show that our model reduces to Herrera and Mart/spl inodot//spl acute/nez's (translational) 2-tuple fuzzy linguistic representation model.     

二元语义信息集结算子的性质分析

研究语言评价信患处理中有关二元语义集结算子的性质。首先描述了二元语义信患集结的有序加权平均(T-OWA)算子，并提出一种有序加权几何(T-OWG)算子；然后分析了T-OWA算子和T-OWG算子所具有的性质。该研究结果丰富了二元语义集结算子的分析方法。     

Applications of linguistic techniques for use case analysis

Use cases are effective techniques to express the functional requirements of a system in a very simple and easy-to-learn way. Use cases are mainly composed of natural language (NL) sentences, and the use of NL to describe the behaviour of a system is always a critical point, due to the inherent ambiguities originating from the different possible interpretations of NL sentences. We discuss in this paper the application of analysis techniques based on a linguistic approach to detect, within requirements documents, defects related to such an inherent ambiguity. Starting from the proposed analysis techniques, we will define some metrics that will be used to perform a quality evaluation of requirements documents. Some available automatic tools supporting the linguistic analysis of NL requirements have been used to evaluate an industrial use cases document according to the defined metrics. A discussion on the application of linguistic analysis techniques to support the semantic analysis of use cases is also reported.     

Dimensionality reduction via kernel sparse representation

Dimensionality reduction (DR) methods based on sparse representation as one of the hottest research topics have achieved remarkable performance in many applications in recent years. However, it’s a challenge for existing sparse representation based methods to solve nonlinear problem due to the limitations of seeking sparse representation of data in the original space. Motivated by kernel tricks, we proposed a new framework called empirical kernel sparse representation (EKSR) to solve nonlinear problem. In this framework, nonlinear separable data are mapped into kernel space in which the nonlinear similarity can be captured, and then the data in kernel space is reconstructed by sparse representation to preserve the sparse structure, which is obtained by minimizing a ?1 regularization-related objective function. EKSR provides new insights into dimensionality reduction and extends two models: 1) empirical kernel sparsity preserving projection (EKSPP), which is a feature extraction method based on sparsity preserving projection (SPP); 2) empirical kernel sparsity score (EKSS), which is a feature selection method based on sparsity score (SS). Both of the two methods can choose neighborhood automatically as the natural discriminative power of sparse representation. Compared with several existing approaches, the proposed framework can reduce computational complexity and be more convenient in practice.     

Market research for requirements analysis using linguistic tools

Numerous studies in recent months have proposed the use of linguistic instruments to support requirements analysis. There are two main reasons for this: (i) the progress made in natural language processing and (ii) the need to provide the developers of software systems with support in the early phases of requirements definition and conceptual modelling. This paper presents the results of an online market research intended (a) to assess the economic advantages of developing a CASE (computer-aided software engineering) tool that integrates linguistic analysis techniques for documents written in natural language, and (b) to verify the existence of the potential demand for such a tool. The research included a study of the language – ranging from completely natural to highly restricted – used in documents available for requirements analysis, an important factor given that on a technological level there is a trade-off between the language used and the performance of the linguistic instruments. To determine the potential demand for such tool, some of the survey questions dealt with the adoption of development methodologies and consequently with models and support tools; other questions referred to activities deemed critical by the companies involved. Through statistical correspondence analysis of the responses, we were able to outline two profiles of companies that correspond to two potential market niches, which are characterised by their very different approach to software development.

How to use linguistic instruments for object-oriented analysis

One of object orientation's main limitations is the object-oriented analysis process's immaturity. This article proposes an approach that is based on using linguistic information from information specifications to apply during this process. Our method helps to analyze this information semantically and syntactically and employs a semi-formal procedure to extract an object-oriented system's components     

面向问题域的概念模型描述方法

现有概念模型开发普遍存在着应用领域知识获取难、不能满足多方面用户需求、可重用性低的问题.在分析上述问题的基础上,提出了分层次概念模型开发方法,即面向领域用户的非形式化概念模型开发和面向技术人员的形式化概念模型开发.重点研究了基于本体的概念模型描述方法,并采用UML-OCL方法对概念模型进行了形式化描述.     

Selected issues of frame of knowledge representation realized by means of linguistic labels

The article concentrates on selected aspects of knowledge representation. It is considered how fuzzy sets viewed as linguistic labels (information granules) determine a representation frame. the representation scheme is discussed with respect to its perception level and a scope of individual labels. Both the features can be handled by fuzzy sets and easily modeled by changing associated parameters of corresponding membership functions. Problems of prototype determination in the original space (in which the objects are characterized) as well as in the space of linguistic labels are studied. It is argued that the space of grades of membership functions is more suitable to capture a notion of the prototype. Different aspects of matching are also brought into account.     

时间序列数据降维及特征表示新方法

数据降维和特征表示是解决时间序列维灾问题的关键技术和重要方法, 它们在时间序列数据挖掘中起基础性作用. 鉴于此, 提出一种新的时间序列数据降维和特征表示方法, 利用正交多项式回归模型对时间序列实现特征提取, 结合特征序列长度对时间序列的拟合分析结果, 运用奇异值分解方法对特征序列进一步降维处理, 进而得到保存大部分信息且维数更低的特征序列. 数值实验结果表明, 新方法可以在维度较低的特征空间下取得较好的数据挖掘聚类和分类效果.

     

Combined numerical and linguistic knowledge representation and its application to medical diagnosis

In this paper, we propose a novel hybrid intelligent system (HIS) which provides a unified integration of numerical and linguistic knowledge representations. The proposed HIS is a hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a linguistic model, i.e., fuzzy expert system (FES), optimized via the genetic algorithm (GA). The ILFN is a self-organizing network. The linguistic model is constructed based on knowledge embedded in the trained ILFN or provided by the domain expert. The knowledge captured from the low-level ILFN can be mapped to the higher level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy, comprehensibility, completeness, compactness, and consistency. The resulted HIS is capable of dealing with low-level numerical computation and higher level linguistic computation. After the system is completely constructed, it can incrementally learn new information in both numerical and linguistic forms. To evaluate the system's performance, the well-known benchmark Wisconsin breast cancer data set was studied for an application to medical diagnosis. The simulation results have shown that the proposed HIS performs better than the individual standalone systems. The comparison results show that the linguistic rules extracted are competitive with or even superior to some well-known methods. Our interest is not only on improving the accuracy of the system, but also enhancing the comprehensibility of the resulted knowledge representation.     

基于稀疏表示的半监督降维方法

提出一种基于稀疏表示的半监督降维方法（SpSSDR）。不同于其他基于图的半监督降维方法分步构图,SpSSDR通过稀疏重构系数来同时定义图上边连接性及边权重,再结合边约束信息进行降维。在高维人脸数据上的实验表明,SpSSDR不仅对噪声鲁棒,对边信息的利用也更有效。     

Functional Knowledge Representation Based on Problem Reduction

This paper proposes an approach for functional knowledge representation based on problem reuction,which represents the organization of problem-solving activities in two levels:reduction and reasoning.The former makes the functional plans for problem-solving while the latter constructs functional units, called handlers,for executing subproblems designated by these plans.This approach emphasizes that the representation of domain knowledge should be closely combined with(rather than separated from)its use,therefore provides a set of reasoning-level primitives to construct handlers and formulate the control strategies for executing them,As reduction-level primitives,handlers are used to construct handler-associative networks,which become the executable representation of problem-reduction graphs,in order to realize the problem-solving methods suited to domain features.Besides,handlers and their control slots can be used to focus the attention of knowledge acquisition and reasoning control.