Intelligent control of the hierarchical agglomerative clustering process

The basic process of Hierarchical Agglomerative (HAG) clustering is described as a merging of clusters based on their proximity. The importance of the selected cluster distance measure in the determination of resulting clusters is pointed out. We note a fundamental distinction between the nearest neighbor cluster distance measure, Min, and the furthest neighbor measure, Max. The first favors the merging of large clusters while the later favors the merging of smaller clusters. We introduce a number of families of intercluster distance measures each of which can be parameterized along a scale characterizing their preference for merging larger or smaller clusters. We then consider the use of this distinction between distance measures as a way of controlling the hierarchical clustering process. Combining this with the ability of fuzzy systems modeling to formalize linguistic specifications, we see the emergence of a tool to add human like intelligence to the clustering process.     

Some accuracy and resolution aspects of computer vision distance measurements

The distance between an object and stereo vision sensors can be measured using image processing and known system parameters. A detailed distance measurement synthesis procedure to meet system specifications is presented and illustrated with an example. An error analysis shows that error is proportional to distance. System parameters such as separation between sensor elements, sensor focal length, and sensor array dimensions are related in the design and error equations presented. The main desired design goal is to establish the smallest image sensor array size which will meet system operating specifications. Minimum and maximum distance, object height, optic parameters, scene shift, and sensor array parameters are related.     

Specifications: Formal and informal—a case study

Formal specifications (algebraic) are given for an informally specified small subsystem of the Change Management Automatic Build System. A comparison of the two specifications shows that although informal specifications are easier to read, the formal specifications are clearer, specify operation domains precisely, define the interaction between the operations, show the incompleteness of the informal specifications and are devoid of implementation details. The formal specifications pointed to the need of a function not in the subsystem whose inclusion would improve the system design. This inclusion is now being considered. However, the use of algebraic specifications requires practice and experience. Although the formal specification of large systems is somewhat impractical at the moment, experience in using formal specifications can lead to better informal specifications.     

Comparing top-k XML lists

Systems that produce ranked lists of results are abundant. For instance, Web search engines return ranked lists of Web pages. There has been work on distance measure for list permutations, like Kendall tau and Spearman's footrule, as well as extensions to handle top-k lists, which are more common in practice. In addition to ranking whole objects (e.g., Web pages), there is an increasing number of systems that provide keyword search on XML or other semistructured data, and produce ranked lists of XML sub-trees. Unfortunately, previous distance measures are not suitable for ranked lists of sub-trees since they do not account for the possible overlap between the returned sub-trees. That is, two sub-trees differing by a single node would be considered separate objects. In this paper, we present the first distance measures for ranked lists of sub-trees, and show under what conditions these measures are metrics. Furthermore, we present algorithms to efficiently compute these distance measures. Finally, we evaluate and compare the proposed measures on real data using three popular XML keyword proximity search systems.     

地方高校软件设计系列课程群实践体系的建立

软件设计类课程在计算机科学与技术专业的教学体系中占有重要的地位。针对目前地方高校计算机软件设计课程设置和教学现状,在分析工作岗位对人才培养规格要求的基础上,本文介绍我院软件设计课程群及实践体系教学改革的思路、举措以及所取得的成果。     

Determining semantic similarity among entity classes from different ontologies

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.     

Model checking probabilistic systems against pushdown specifications

Model checking is a fully automatic verification technique traditionally used to verify finite-state systems against regular specifications. Although regular specifications have been proven to be feasible in practice, many desirable specifications are non-regular. For instance, requirements which involve counting cannot be formalized by regular specifications but using pushdown specifications, i.e., context-free properties represented by pushdown automata. Research on model-checking techniques for pushdown specifications is, however, rare and limited to the verification of non-probabilistic systems.In this paper, we address the probabilistic model-checking problem for systems modeled by discrete-time Markov chains and specifications that are provided by deterministic pushdown automata over infinite words. We first consider finite-state Markov chains and show that the quantitative and qualitative model-checking problem is solvable via a product construction and techniques that are known for the verification of probabilistic pushdown automata. Then, we consider recursive systems modeled by probabilistic pushdown automata with an infinite-state Markov chain semantics. We first show that imposing appropriate compatibility (visibility) restrictions on the synchronizations between the pushdown automaton for the system and the specification, decidability of the probabilistic model-checking problem can be established. Finally we prove that slightly departing from this compatibility assumption leads to the undecidability of the probabilistic model-checking problem, even for qualitative properties specified by deterministic context-free specifications.     

SPARE: a development environment for program analysis algorithms

A tool that bridges the gap between the theory and practice of program analysis specifications is described. The tool supports a high-level specification language that enables clear and concise expression of analysis algorithms. The denotational nature of the specifications eases the derivation of formal proofs of correctness for the analysis algorithm. SPARE (structured program analysis refinement environment) is based on a hybrid approach that combines the positive aspects of both the operational and the semantics-driven approach. An extended denotational framework is used to provide specifications in a modular fashion. Several extensions to the traditional denotational specification language have been designed to allow analysis algorithms to be expressed in a clear and concise fashion. This extended framework eases the design of analysis algorithms as well as the derivation of correctness proofs. The tool provides automatic implementation for testing purposes     

On the J-divergence of intuitionistic fuzzy sets with its application to pattern recognition

The importance of suitable distance measures between intuitionistic fuzzy sets (IFSs) arises because of the role they play in the inference problem. A concept closely related to one of distance measures is a divergence measure based on the idea of information-theoretic entropy that was first introduced in communication theory by Shannon (1949). It is known that J-divergence is an important family of divergences. In this paper, we construct J-divergence between IFSs. The proposed J-divergence can induce some useful distance and similarity measures between IFSs. Numerical examples demonstrate that the proposed measures perform well in clustering and pattern recognition.     

Efficient color histogram indexing for quadratic form distancefunctions

In image retrieval based on color, the weighted distance between color histograms of two images, represented as a quadratic form, may be defined as a match measure. However, this distance measure is computationally expensive and it operates on high dimensional features (O(N)). We propose the use of low-dimensional, simple to compute distance measures between the color distributions, and show that these are lower bounds on the histogram distance measure. Results on color histogram matching in large image databases show that prefiltering with the simpler distance measures leads to significantly less time complexity because the quadratic histogram distance is now computed on a smaller set of images. The low-dimensional distance measure can also be used for indexing into the database     

Viewpoint: effectiveness of focused mentoring to improve requirements engineering industrial practice

Requirements engineering practice in industry is often encumbered by practical limits of time, resource, and attention. In our environment, most requirements authors have had no formal training in the practice, yet are expected to produce high-quality specifications that drive subsequent work. Authors are often unaware of different techniques for requirements specification and are rarely equipped to navigate all of the techniques and tools that are potentially available to them, to the extent that they are even aware of the various options. A small cadre of coaches, providing focused, just-in-time mentoring of authors in a handful of basic techniques can increase the proficiency of these authors, who in turn produce higher-quality requirements specifications. From this foundation, authors may be able to later adopt additional techniques to further improve the quality and maturity of their work, thus resulting in improved work products built on the basis of those specifications.     

Some cosine similarity measures and distance measures between q-rung orthopair fuzzy sets

In this paper, we consider some cosine similarity measures and distance measures between q-rung orthopair fuzzy sets (q-ROFSs). First, we define a cosine similarity measure and a Euclidean distance measure of q-ROFSs, their properties are also studied. Considering that the cosine measure does not satisfy the axiom of similarity measure, then we propose a method to construct other similarity measures between q-ROFSs based on the proposed cosine similarity and Euclidean distance measures, and it satisfies with the axiom of the similarity measure. Furthermore, we obtain a cosine distance measure between q-ROFSs by using the relationship between the similarity and distance measures, then we extend technique for order of preference by similarity to the ideal solution method to the proposed cosine distance measure, which can deal with the related decision-making problems not only from the point of view of geometry but also from the point of view of algebra. Finally, we give a practical example to illustrate the reasonableness and effectiveness of the proposed method, which is also compared with other existing methods.     

Intuitive distance for intuitionistic fuzzy sets with applications in pattern recognition

Distance is an important fundamental concept of the set theory. Since the intuitionistic fuzzy sets (IFSs) was put forward, distance between IFSs has been widely concerned by some researchers and many types of measures have been proposed. However, although intuitionistic fuzzy sets have the advantage of being able to consider waver (lack of knowledge), existing distance measures have not yet considered waver and most of them have counter-intuitive cases. To deal with this problem, this paper introduces the concept of intuitive distance for IFSs, which embodies the property requirements of classical distance measure and highlights the characteristics of intuitionistic fuzzy information. Then, a new intuitive distance measure for IFSs is proposed along with its proofs. After that, a comparative analysis between the intuitive distance measure and the existing distance measures is conducted based on an extended artificial benchmark test set, in which eleven pairs of single-element IFSs are used as an illustration of six typical counter-intuitive cases. Finally, the proposed distance measure is applied to deal with pattern recognition. Results show that the proposed distance does not provide any counter-intuitive cases and the waver that brought from hesitance index can be well reflected.     

Vague值与Vague集的多测度相似度量

针对Vague值(集)相似度量问题,指出了采用单一测度构造Vague值(集)相似度量的不足,根据不同测度表现的不同相似性度量特点,提出了两种由距离测度、未知度测度和熵测度三种测度结合的Vague值(集)多测度相似度量,并给出了相应的定义和性质.多测度相似度量体现出了多特征度量相似性的特点,若距离与熵测度失效,未知度测度仍能发挥作用,而距离与熵测度度量结果的综合,则进一步提高了分辨力.实例验证了该多测度相似度量的有效性和优越性.     

A visualization model based on adjacency data

In this paper, we describe a model whose focus is on data visualization. We assume the data are provided in adjacency format, as is frequently the case in practice. As an example, individuals who buy item a are likely to buy or consider buying items b, c, and d, also. We present a simple technique for obtaining distance measures between data points. Armed with the resulting distance matrix, we show how Sammon maps can be used to visualize the data points. An application to the college selection process is discussed in detail.     

Egocentric distance perception in large screen immersive displays

Many scientists have demonstrated that compared to the real world egocentric distances in head-mounted display virtual environments are underestimated. However, distance perception in large screen immersive displays has received less attention. We investigate egocentric distance perception in a virtual office room projected using a semi-spherical, a Max Planck Institute CyberMotion Simulator cabin and a flat large screen immersive display. The goal of our research is to systematically investigate distance perception in large screen immersive displays with commonly used technical specifications. We specifically investigate the role of distance to the target, stereoscopic projection and motion parallax on distance perception. We use verbal reports and blind walking as response measures for the real world experiment. Due to the limited space in the three large screen immersive displays we use only verbal reports as the response measure for the experiments in the virtual environment. Our results show an overall underestimation of distance perception in the large screen immersive displays, while verbal estimates of distances are nearly veridical in the real world. We find that even when providing motion parallax and stereoscopic depth cues to the observer in the flat large screen immersive display, participants estimate the distances to be smaller than intended. Although stereo cues in the flat large screen immersive display do increase distance estimates for the nearest distance, the impact of the stereoscopic depth cues is not enough to result in veridical distance perception. Further, we demonstrate that the distance to the target significantly influences the percent error of verbal estimates in both the real and virtual world. The impact of the distance to the target on the distance judgments is the same in the real world and in two of the used large screen displays, namely, the MPI CyberMotion Simulator cabin and the flat displays. However, in the semi-spherical display we observe a significantly different influence of distance to the target on verbal estimates of egocentric distances. Finally, we discuss potential reasons for our results. Based on the findings from our research we give general suggestions that could serve as methods for improving the LSIDs in terms of the accuracy of depth perception and suggest methods to compensate for the underestimation of verbal distance estimates in large screen immersive displays.     

Experimentally feasible measures of distance between quantum operations

We present two measures of distance between quantum processes which can be measured directly in laboratory without resorting to process tomography. The measures are based on the superfidelity, introduced recently to provide an upper bound for quantum fidelity. We show that the introduced measures partially fulfill the requirements for distance measure between quantum processes. We also argue that they can be especially useful as diagnostic measures to get preliminary knowledge about imperfections in an experimental setup. In particular we provide quantum circuit which can be used to measure the superfidelity between quantum processes. We also provide a physical interpretation of the introduced metrics based on the continuity of channel capacity.     

Distance-based intuitionistic multiplicative multiple criteria decision-making methods for healthcare management in West China Hospital

Intuitionistic multiplicative sets use an asymmetric, unbalanced scale to express information from positive, negative, and indeterminate information. They have been found capable of comprehensively and objectively representing a person's intuitive understanding and hence have attracted much attention. Distance techniques are widely used to measure the degree to which arguments deviate from one another. Several fuzzy set extensions have been developed, but little research has been conducted on measures of distance between intuitionistic multiplicative sets. In this paper, we start by presenting a variety of measures of the distance between intuitionistic multiplicative sets, including Hausdorff distance measures, weighted distance measures, ordered weighted distance measures, and continuous weighted distance measures. We then develop a distance-based intuitionistic multiplicative-technique for order preference by similarity to ideal solution method and a distance-based intuitionistic multiplicative-Vlsekriterijumska Optimizacija I Kompromisno Resenje method for handling multiple criteria decision-making problems with intuitionistic multiplicative evaluation information. To demonstrate the practical application of these distance measures and the proposed methods, we provide a case study of hospital management of inpatient admission. The paper ends with comparative analyses of the two methods and some concluding remarks.     

一种新的直觉模糊集距离及其在决策中的应用

首先针对直觉模糊集距离中是否包含直觉模糊集通过隶属度、非隶属度以及犹豫度这三种信息,以及直觉模糊集距离是否满足相应距离度量的条件对其进行了详细分析,发现现有方法都是直接将犹豫度直接引入到直觉模糊集距离中,从而会产生不一致性。鉴于此,定义了一种新的直觉模糊集距离度量方法,其不仅考虑隶属度和非隶属度信息,同时还考虑犹豫度对隶属度和非隶属度的分配,从而间接地将犹豫度也引入到直觉模糊集距离中。其次,证明了所提距离度量满足距离度量条件,并结合实例将其与现有距离度量方法进行比较分析,说明了新方法的合理性。最后,将所提出方法应用于多准则模糊决策中,进一步说明了新方法的有效性和可行性。