Generating approximate region boundaries from heterogeneous spatial information: An evolutionary approach

Spatial information takes different forms in different applications, ranging from accurate coordinates in geographic information systems to the qualitative abstractions that are used in artificial intelligence and spatial cognition. As a result, existing spatial information processing techniques tend to be tailored towards one type of spatial information, and cannot readily be extended to cope with the heterogeneity of spatial information that often arises in practice. In applications such as geographic information retrieval, on the other hand, approximate boundaries of spatial regions need to be constructed, using whatever spatial information that can be obtained. Motivated by this observation, we propose a novel methodology for generating spatial scenarios that are compatible with available knowledge. By suitably discretizing space, this task is translated to a combinatorial optimization problem, which is solved using a hybridization of two well-known meta-heuristics: genetic algorithms and ant colony optimization. What results is a flexible method that can cope with both quantitative and qualitative information, and can easily be adapted to the specific needs of specific applications. Experiments with geographic data demonstrate the potential of the approach.     

RFuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over Prolog

We present the RFuzzy framework, a Prolog-based tool for representing and reasoning with fuzzy information. The advantages of our framework in comparison to previous tools along this line of research are its easy, user-friendly syntax, and its expressivity through the availability of default values and types.In this approach we describe the formal syntax, the operational semantics and the declarative semantics of RFuzzy (based on a lattice). A least model semantics, a least fixpoint semantics and an operational semantics are introduced and their equivalence is proven. We provide a real implementation that is free and available. (It can be downloaded from http://babel.ls.fi.upm.es/software/rfuzzy/.) Besides implementation details, we also discuss some actual applications using RFuzzy.     

A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: A railway risk management system

Risk management is becoming increasingly important for railway companies in order to safeguard their passengers and employees while improving safety and reducing maintenance costs. However, in many circumstances, the application of probabilistic risk analysis tools may not give satisfactory results because the risk data are incomplete or there is a high level of uncertainty involved in the risk data. This article presents the development of a risk management system for railway risk analysis using fuzzy reasoning approach and fuzzy analytical hierarchy decision making process. In the system, fuzzy reasoning approach (FRA) is employed to estimate the risk level of each hazardous event in terms of failure frequency, consequence severity and consequence probability. This allows imprecision or approximate information in the risk analysis process. Fuzzy analytical hierarchy process (fuzzy-AHP) technique is then incorporated into the risk model to use its advantage in determining the relative importance of the risk contributions so that the risk assessment can be progressed from hazardous event level to hazard group level and finally to railway system level. This risk assessment system can evaluate both qualitative and quantitative risk data and information associated with a railway system effectively and efficiently, which will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management of railway systems and set safety standards. A case study on risk assessment of shunting at Hammersmith depot is used to illustrate the application of the proposed risk assessment system.     

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, using syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommending only items that bear strong resemblance to those the user already knows. Traditional collaborative approaches face up to overspecialization by considering the preferences of other users, which causes other severe limitations. In this paper, we avoid the intrinsic pitfalls of collaborative solutions and diversify the recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations. We have adopted these mechanisms to fulfill the personalization requirements of recommender systems, enabling to discover extra knowledge about the user’s preferences and leading to more accurate and diverse suggestions. Our approach is generic enough to be used in a wide variety of domains and recommender systems. The proposal has been preliminary evaluated by statistics-driven tests involving real users in the recommendation of Digital TV contents. The results reveal the users’ satisfaction regarding the accuracy and diversity of the reasoning-driven content-based recommendations.     

On the estimation of adequate test set size using fault failure rates

Test set size in terms of the number of test cases is an important consideration when testing software systems. Using too few test cases might result in poor fault detection and using too many might be very expensive and suffer from redundancy. We define the failure rate of a program as the fraction of test cases in an available test pool that result in execution failure on that program. This paper investigates the relationship between failure rates and the number of test cases required to detect the faults. Our experiments based on 11 sets of C programs suggest that an accurate estimation of failure rates of potential fault(s) in a program can provide a reliable estimate of adequate test set size with respect to fault detection and should therefore be one of the factors kept in mind during test set construction. Furthermore, the model proposed herein is fairly robust to incorrect estimations in failure rates and can still provide good predictive quality. Experiments are also performed to observe the relationship between multiple faults present in the same program using the concept of a failure rate. When predicting the effectiveness against a program with multiple faults, results indicate that not knowing the number of faults in the program is not a significant concern, as the predictive quality is typically not affected adversely.     

Diagnosing and correcting design inconsistencies in source code with logical abduction

Correcting design decay in source code is not a trivial task. Diagnosing and subsequently correcting inconsistencies between a software system’s code and its design rules (e.g., database queries are only allowed in the persistence layer) and coding conventions can be complex, time-consuming and error-prone. Providing support for this process is therefore highly desirable, but of a far greater complexity than suggesting basic corrective actions for simplistic implementation problems (like the “declare a local variable for non-declared variable” suggested by Eclipse).We present an abductive reasoning approach to inconsistency correction that consists of (1) a means for developers to document and verify a system’s design and coding rules, (2) an abductive logic reasoner that hypothesizes possible causes of inconsistencies between the system’s code and the documented rules and (3) a library of corrective actions for each hypothesized cause. This work builds on our previous work, where we expressed design rules as equality relationships between sets of source code artifacts (e.g., the set of methods in the persistence layer is the same as the set of methods that query the database). In this paper, we generalize our approach to design rules expressed as user-defined binary relationships between two sets of source code artifacts (e.g., every state changing method should invoke a persistence method).We illustrate our approach on the design of IntensiVE, a tool suite that enables defining sets of source code artifacts intensionally (by means of logic queries) and verifying relationships between such sets.     

一种基于证据推理的混合型灰色多属性群决策方法

针对方案的属性值为区间灰数与确定语言等级,或在两个连续的语言等级之间权重完全已知的混合型灰色多属性群决策问题,提出一种新的决策方法．该方法可根据决策者的偏好给出定量属性的白化值和定性属性的信用结构,确定了每个决策者和群体的等级信用结构矩阵;提出了求解群体集成权重的新方法,并利用证据推理算法求出各方案在各等级的信任度;最后利用期望效用和区间数排序法对方案进行了排序．实例分析表明了该方法的合理性和有效性．     

程度推理与程度控制

文章把语言值规则（模糊规则）视为语言值及其程度（隶属度）之间的一种对应关系,提出了程度函数和程度规则的概念,并由此建立了一种称为程度推理和程度控制的推理与控制方法。采用程度推理和程度控制,传统的模糊推理就变为简单的符号推演和函数计算,传统的模糊控制由数值／（语,度）转换、（语,度）变换和（语,度）／数值转换等三步来实现。     

基于软件需求分析方法的机械产品需求建模研究

通过对软件与机械产品的需求进行比较,说明了将软件需求分析方法应用于机械产品的需求分析建模的可行性,同时通过对OOSE的用例图方法进行详细分析与扩展以实现这一目标。这种机械产品需求建模方法具有系统化、表达方式直观简洁、支持全生命周期后续各阶段等特点,并为全生命周期的产品模型集成打下良好的基础。