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

Style and performance of international socially responsible funds in Europe

This paper analyses the performance and investment styles of internationally oriented Socially Responsible Investment (SRI)funds, domiciled in eight European markets, in comparison with characteristics-matched conventional funds. To the best of our knowledge, this is the first multi-country study, focused on international SRI funds (investing in Global and in European equities), to combine the matched-pairs approach with the use of robust conditional multi-factor performance evaluation models, which allow for both time-varying alphas and betas and also control for home biases and spurious regression biases.In general, the results show that differences in the performance of international SRI funds and their conventional peers are not statistically significant. Regarding investment styles, SRI and conventional funds exhibit similar factor exposures in most cases. In addition,conventional benchmarks present a higher explaining power of SRI fund returns than SRI benchmarks. Our results also show significant differences in the investment styles of SRI funds according to whether they use “best-in-class” screening strategies or not. When compared to SRI funds that employ simple negative and/or positive screens, SRI “best-in-class” funds present significantly lower exposures to small caps and momentum strategies and significantly higher exposures to local stocks.

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.

The determinants of international equity investment: Do they differ between institutional and noninstitutional investors?

In this paper we investigate whether the determinants of international equity investment differ between investors with different degrees of sophistication. For this purpose, we analyse and compare the determinants of international equity investment of institutional and noninstitutional investors from 20 OECD countries (US not included) in the period 2001-2009. The results show that there are significant differences in the determinants of international equity investment between institutional and noninstitutional investors. In particular, noninstitutional investors tend to exhibit a more pronounced preference for equities of geographical nearby, contiguous and more transparent countries than institutional investors. The preference for more developed equity markets and the contrarian behaviour are also significantly more pronounced for noninstitutional than for institutional investors. These results support the argument that international equity investment of less sophisticated investors is more affected by information costs and familiarity than that of more sophisticated investors. Moreover, business cycles exert an influence on international equity investment decisions of both institutional and noninstitutional investors.

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.

Transaction costs: an empirical analysis of their relationship with investment and foreign direct investment

As stated by the New Institutional Economics theory, transaction costs play a relevant role in economics and, according to the extent of such costs, agents make investment decisions. Actually, transaction costs may represent a disincentive to entrepreneurship. This work aims to verify whether transaction costs are related to investment rate and foreign direct investment rate (FDI) in different business environments. The results suggest that foreign investors do not have precise information about other countries as domestic investors do; as it is observed, only the relation between transaction costs and investment rate is significant. Furthermore, there is evidence that the business environments of BRIC countries are less developed when compared to business environments of other countries in the study

Earnings management and economic crises in the brazilian capital market

The 2008 economic crisis challenged accounting, either demanding recognition and measurement criteria well adjusted to this scenario or even questioning its ability to inform appropriately entities' financial situation before the crisis occurred. So, our purpose was to verify if during economic crises listed companies in the Brazilian capital market tended to adopt earnings management (EM) practices. Our sample consisted in 3,772 firm-years observations, in 13 years - 1997 to 2009. We developed regression models considering discretionary accruals as EM proxy (dependent variable), crisis as a macroeconomic factor (dummy variable of interest), ROA, market-to-book, size, leverage, foreign direct investment (FDI) and sector as control variables. Different for previous EM studies two approaches were used in data panel regression models and multiple crises were observed simultaneously. Statistics tests revealed a significant relation between economic crisis and EM practices concerning listed companies in Brazil in both approaches used.

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.

INTERNATIONALIZATION OF STATE-OWNED ENTERPRISES THROUGH FOREIGN DIRECT INVESTMENT

ABSTRACT State-owned enterprises (SOEs) are created to focus on domestic needs, and yet recent evidence points to increasing outward foreign direct investment by SOEs. Existing International Business (IB) theories focus on efficiency-based motives for internationalization; therefore, they do not fully capture SOEs' internalization dynamics, which are driven largely by political factors and social welfare considerations. We integrate public management and IB theories to develop propositions that combine these questions: why SOEs internationalize; what are their motivations; and what are the main managerial outcomes of SOEs' internationalization. Our findings suggest that SOEs display little hesitancy in entering international markets, and that SOE international expansion is not contradictory with the goals of state-ownership if the purpose is to adjust the company to changing institutional environments both in the domestic and international markets. Our propositions about SOE internationalization are based on an in-depth case study of the outward foreign direct investment conducted by Brazil's Petrobras over the past three decades.

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.