Models for the Reverse Engineering of Java/Swing Applications

Abstract. Interest in design and development of graphical user interface (GUIs) is growing in the last few years. However, correctness of GUI's code is essential to the correct execution of the overall software. Models can help in the evaluation of interactive applications by allowing designers to concentrate on its more important aspects. This paper describes our approach to reverse engineering abstract GUI models directly from the Java/Swing code.

Color Models and Illumination Changes

Color model representation allows characterizing in a quantitative manner, any defined color spectrum of visible light, i.e. with a wavelength between 400nm and 700nm. To accomplish that, each model, or color space, is associated with a function that allows mapping the spectral power distribution of the visible electromagnetic radiation, in a space defined by a set of discrete values that quantify the color components composing the model. Some color spaces are sensitive to changes in lighting conditions. Others assure the preservation of certain chromatic features, remaining immune to these changes. Therefore, it becomes necessary to identify the strengths and weaknesses of each model in order to justify the adoption of color spaces in image processing and analysis techniques. This chapter will address the topic of digital imaging, main standards and formats. Next we will set the mathematical model of the image acquisition sensor response, which enables assessment of the various color spaces, with the aim of determining their invariance to illumination changes.

Extracting and verifying coordination models from source code

Current software development relies increasingly on non-trivial coordination logic for com- bining autonomous services often running on di erent platforms. As a rule, however, in typical non-trivial software systems, such a coordination layer is strongly weaved within the application at source code level. Therefore, its precise identi cation becomes a major methodological (and technical) problem which cannot be overestimated along any program understanding or refactoring process. Open access to source code, as granted in OSS certi cation, provides an opportunity for the devel- opment of methods and technologies to extract, from source code, the relevant coordination information. This paper is a step in this direction, combining a number of program analysis techniques to automatically recover coordination information from legacy code. Such information is then expressed as a model in Orc, a general purpose orchestration language

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Multilevel and multidimensional scale for online trust

This paper examines the nature of the construct of consumers' trust toward the electronic channel of their financial institution. Through a study of a total of 372 individual users of Internet banking in Spain, we have managed to develop a third-order measuring instrument that integrates a total of seven dimensions. The exploratory and confirmatory factor analyses were used to test the validation and reliability of the proposed scale. Findings provide useful information to professionals who seek to identify how customer's trust is formed in the online channel and in the financial sector.

ABOUT PSYCHOLOGICAL VARIABLES IN APPLICATION SCORING MODELS

The purpose of this study is to investigate the contribution of psychological variables and scales suggested by Economic Psychology in predicting individuals’ default. Therefore, a sample of 555 individuals completed a self-completion questionnaire, which was composed of psychological variables and scales. By adopting the methodology of the logistic regression, the following psychological and behavioral characteristics were found associated with the group of individuals in default: a) negative dimensions related to money (suffering, inequality and conflict); b) high scores on the self-efficacy scale, probably indicating a greater degree of optimism and over-confidence; c) buyers classified as compulsive; d) individuals who consider it necessary to give gifts to children and friends on special dates, even though many people consider this a luxury; e) problems of self-control identified by individuals who drink an average of more than four glasses of alcoholic beverage a day.

USE AND CONSEQUENCES OF PARTICIPATORY GIS IN A MEXICAN MUNICIPALITY: APPLYING A MULTILEVEL FRAMEWORK

This paper seeks to understand the use and the consequences of Participatory Geographic Information System (PGIS) in a Mexican local community. A multilevel framework was applied, mainly influenced by two theoretical lenses – structurationist view and social shaping of technology – structured in three dimensions – context, process and content – according to contextualist logic. The results of our study have brought two main contributions. The first is the refinement of the theoretical framework in order to better investigate the implementation and use of Information and Communication Technology (ICT) artifacts by local communities for social and environmental purposes. The second contribution is the extension of existing IS (Information Systems) literature on participatory practices through identification of important conditions for helping the mobilization of ICT as a tool for empowering local communities.

A Feedback Learning and Mental Models Perspective on Strategic Decision Making

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term success. We present a set of hypotheses about the influence of feedback information and systems thinking facilitation on mental models and management performance. We explore, under controlled conditions, the role of mental models in terms of structure and behaviour. A test based on a simulation experiment with a system dynamics model was performed. Three out of the four hypotheses were confirmed. Causal diagramming positively influences mental model structure similarity, mental model structure similarity positively influences mental model behaviour similarity, and mental model behaviour similarity positively influences the quality of the decision.

A Feedback Learning and Mental Models Perspective on Strategic Decision Making

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term success. We present a set of hypotheses about the influence of feedback information and systems thinking facilitation on mental models and management performance. We explore, under controlled conditions, the role of mental models in terms of structure and behaviour. A test based on a simulation experiment with a system dynamics model was performed. Three out of the four hypotheses were confirmed. Causal diagramming positively influences mental model structure similarity, mental model structure similarity positively influences mental model behaviour similarity, and mental model behaviour similarity positively influences the quality of the decision

On the Problem of Balancing the DC Capacitor Voltage Divider in Back-to-Back Multilevel Converters

This paper presents a new generalized solution for DC bus capacitors voltage balancing in back-to-back m level diode-clamped multilevel converters connecting AC networks. The solution is based on the DC bus average power flow and exploits the switching configuration redundancies. The proposed balancing solution is particularized for the back-to-back multilevel structure with m=5 levels. This back-to-back converter is studied working with bidirectional power flow, connecting an induction machine to the power grid.

Stability Condition Based Sliding Mode Modulators for Multilevel Power Converters

Sliding mode controllers for power converters usually employ hysteresis comparators to directly generate the power semiconductors switching states. This paper presents a new sliding mode modulator based on the direct implementation of the sliding mode stability condition, which for multilevel power converters shows advantages, as branch equalized switching frequencies and less distortion on the ac currents when operating near the rated converter power. The new sliding mode multilevel modulator is used to control a three-phase multilevel converter, operated as a reactive power compensator (STATCOM), implementing the stability condition in a digital signal processing system. The performance of this new sliding mode modulator is compared with a multilevel modulator based on hysteresis comparators. Simulation and experimental results are presented in order to highlight the system operation and control robustness.

CP properties of symmetry-constrained two-Higgs-doublet models

The two-Higgs-doublet model can be constrained by imposing Higgs-family symmetries and/or generalized CP symmetries. It is known that there are only six independent classes of such symmetry-constrained models. We study the CP properties of all cases in the bilinear formalism. An exact symmetry implies CP conservation. We show that soft breaking of the symmetry can lead to spontaneous CP violation (CPV) in three of the classes.

Renormalization-group constraints on Yukawa alignment in multi-Higgs-doublet models

We write down the renormalization-group equations for the Yukawa-coupling matrices in a general multi-Higgs-doublet model. We then assume that the matrices of the Yukawa couplings of the various Higgs doublets to right-handed fermions of fixed quantum numbers are all proportional to each other. We demonstrate that, in the case of the two-Higgs-doublet model, this proportionality is preserved by the renormalization-group running only in the cases of the standard type-I, II, X, and Y models. We furthermore show that a similar result holds even when there are more than two Higgs doublets: the Yukawa-coupling matrices to fermions of a given electric charge remain proportional under the renormalization-group running if and only if there is a basis for the Higgs doublets in which all the fermions of a given electric charge couple to only one Higgs doublet.