Supporting Roadmapping of Quality Requirements

Would slightly better performance be significantly more valuable from a market perspective? Would significantly better performance be just slightly more expensive to implement? When dealing with performance, usability, reliability, and so on, you often end up in difficult trade-off analysis. You must take into account aspects such as release targets, end-user experience, and business opportunities. At the same time, you must consider what is feasible with the evolving system architecture and the available development resources.Quality requirements are of major importance in the development of systems for software-intensive products. To be successful, a company must find the right balance among competing quality attributes. How should you balance, for example, investments for improved usability of a mobile phone's phone book and better mobile positioning? In the context of quality requirements, decision making typically combines market considerations and design issues in activities such as roadmapping, release planning, and platform scoping. Models that address requirements prioritization in a market-driven context often emphasize functional aspects. (For a comparison of other relevant techniques with Quper, see the sidebar.) Quper provides concepts for reasoning about quality in relation to cost and value and can be used in combination with existing prioritization approaches.     

Evaluating the Generation of Domain Ontologies in the Knowledge Puzzle Project

One of the goals of the knowledge puzzle project is to automatically generate a domain ontology from plain text documents and use this ontology as the domain model in computer-based education. This paper describes the generation procedure followed by TEXCOMON, the knowledge puzzle ontology learning tool, to extract concept maps from texts. It also explains how these concept maps are exported into a domain ontology. Data sources and techniques deployed by TEXCOMON for ontology learning from texts are briefly described herein. Then, the paper focuses on evaluating the generated domain ontology and advocates the use of a three-dimensional evaluation: structural, semantic, and comparative. Based on a set of metrics, structural evaluations consider ontologies as graphs. Semantic evaluations rely on human expert judgment, and finally, comparative evaluations are based on comparisons between the outputs of state-of-the-art tools and those of new tools such as TEXCOMON, using the very same set of documents in order to highlight the improvements of new techniques. Comparative evaluations performed in this study use the same corpus to contrast results from TEXCOMON with those of one of the most advanced tools for ontology generation from text. Results generated by such experiments show that TEXCOMON yields superior performance, especially regarding conceptual relation learning.     

Enhancing Learning Objects with an Ontology-Based Memory

The reusability in learning objects has always been a hot issue. However, we believe that current approaches to e-Learning failed to find a satisfying answer to this concern. This paper presents an approach that enables capitalization of existing learning resources by first creating "content metadatardquo through text mining and natural language processing and second by creating dynamically learning knowledge objects, i.e., active, adaptable, reusable, and independent learning objects. The proposed model also suggests integrating explicitly instructional theories in an on-the-fly composition process of learning objects. Semantic Web technologies are used to satisfy such an objective by creating an ontology-based organizational memory able to act as a knowledge base for multiple training environments.     

A Multiobjective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems

In this paper, we propose the use of a multiobjective evolutionary approach to generate a set of linguistic fuzzy-rule-based systems with different tradeoffs between accuracy and interpretability in regression problems. Accuracy and interpretability are measured in terms of approximation error and rule base (RB) complexity, respectively. The proposed approach is based on concurrently learning RBs and parameters of the membership functions of the associated linguistic labels. To manage the size of the search space, we have integrated the linguistic two-tuple representation model, which allows the symbolic translation of a label by only considering one parameter, with an efficient modification of the well known (2 + 2) Pareto archived evolution strategy (PAES). We tested our approach on nine real world datasets of different sizes and with different numbers of variables. Besides the (2 + 2)PAES, we have also used the well known nondominated sorting genetic algorithm (NSGA-II) and an accuracy-driven single-objective evolutionary algorithm (EA). We employed these optimization techniques both to concurrently learn rules and parameters and to learn only rules. We compared the different approaches by applying a nonparametric statistical test for pairwise comparisons, thus taking into consideration three representative points from the obtained Pareto fronts in the case of the multiobjective EAs. Finally, a data complexity measure, which is typically used in pattern recognition to evaluate the data density in terms of average number of patterns per variable, has been introduced to characterize regression problems. Results confirm the effectiveness of our approach, particularly for (possibly high dimensional) datasets with high values of the complexity metric.     

Matching-Pursuits Dissimilarity Measure for Shape-Based Comparison and Classification of High-Dimensional Data

In this paper, a new matching pursuits dissimilarity measure (MPDM) is presented that compares two signals using the information provided by their matching pursuits (MP) approximations, without requiring any prior domain knowledge. MPDM is a flexible and differentiable measure that can be used to perform shape-based comparisons and fuzzy clustering of very high-dimensional, possibly compressed, data. A novel prototype based classification algorithm, which is termed the computer aided minimization procedure (CAMP), is also proposed. The CAMP algorithm uses the MPDM with the competitive agglomeration (CA) fuzzy clustering algorithm to build reliable shape based prototypes for classification. MP is a well known sparse signal approximation technique, which is commonly used for video and image coding. The dictionary and coefficient information produced by MP has previously been used to define features to build discrimination and prototype based classifiers. However, existing MP based classification applications are quite problem domain specific, thus making their generalization to other problems quite difficult. The proposed CAMP algorithm is the first MP based classification system that requires no assumptions about the problem domain and builds a bridge between the MP and fuzzy clustering algorithms. Experimental results also show that the CAMP algorithm is more resilient to outliers in test data than the multilayer perceptron (MLP) and support-vector-machine (SVM) classifiers, as well as prototype-based classifiers using the Euclidean distance as their dissimilarity measure.     

Guaranteed search of a target object by an electromechanical system with minimal light power inputs

An optimal guaranteed control problem for electromechanical manipulator with the goal of finding an immovable or mobile target objects is considered. Unlike [1–6], as the optimality criterion, a functional taking into account power inputs of the light source on the gripper of the manipulator is taken. The sought object is assumed to be detected if it falls into the light circle with given illumination. The control algorithms of the gripper motion and corresponding laws of electric current variation in the light source circuit providing the detection of both immovable and mobile sought object in a guaranteed time for minimal light power inputs during the search are developed. The gain in light power inputs achieved for the proposed search algorithms is estimated.     

Investigation of stability of nonlinear continuous-discrete models of economic dynamics using vector Lyapunov function. I

Modification and extension of procedures of strict analysis of stability and estimates of domains of attraction based on the reduction method with sublinear vector Lyapunov function is given for sufficiently wide class of nonlinear stabilization systems of continuous objects using piecewise constant control formed using discrete state measurements. The new type of realization of heterogeneous comparison systems in the form of interconnected differential and discrete impulse subsystems with varying right-hand side allowing to avoid preliminary discretization of the original system and thus increasing accuracy of investigations is proposed. In the case of nonlinearities (including those with respect to control and measurements), limited by semi-homogeneous functions. Constructive conditions of exponential stability with necessary quantitative estimates are formulated. Application to investigation of stability of economic growth in the Phillips-Bergstrom model with discrete monetary regulation is presented. The paper consists of two parts. In this part, the studied models are described, the studied stability property is defined, procedures for construction of vector Lyapunov function and comparison system are given, and some of their specific features are established.     

TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data

Statistical analysis of air mass back trajectories combined with long-term ambient air pollution measurements are useful tools for source identification. Using these methods, the geographic information system (GIS) based software, TrajStat, was developed to view, query, and cluster the trajectories and compute the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses when measurement data are included.