Contributions in Merlin

This paper examines the impact of microfinance institutions on development in an empirical setting, and therewith aims at filling a gap in econometric assessments of microfinance institutions. Using data of MFIs operating in selected African and Asian countries and choosing average savings and loan balances per client as proxies for development, there is empirical evidence for significant positive impact of microfinance institutions on development. Microcredit is the most robust mechanism to enhance development in recent years. While an MFI's size is mostly irrelevant, its experience was found to be especially enhancing for the amount of credit granted to the poor. Savings is found to be the best estimator for development in recent years, yet a structural break between 2003 and 2006 is possible. While African development is generally in arrears compared to Asia, there is no statistical evidence for differences in the marginal impact of microfinance institutions subject to geographical positions, which allows for the conclusion of environment independent positive impact of microfinance institutions on development in low-income countries.

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Software engineering researchers have long been interested in where and why bugs occur in code, and in predicting where they might turn up next. Historical bug-occurence data has been key to this research. Bug tracking systems, and code version histories, record when, how and by whom bugs were fixed; from these sources, datasets that relate file changes to bug fixes can be extracted. These historical datasets can be used to test hypotheses concerning processes of bug introduction, and also to build statistical bug prediction models. Unfortunately, processes and humans are imperfect, and only a fraction of bug fixes are actually labelled in source code version histories, and thus become available for study in the extracted datasets. The question naturally arises, are the bug fixes recorded in these historical datasets a fair representation of the full population of bug fixes? In this paper, we investigate historical data from several software projects, and find strong evidence of systematic bias. We then investigate the potential effects of "unfair, imbalanced" datasets on the performance of prediction techniques. We draw the lesson that bias is a critical problem that threatens both the effectiveness of processes that rely on biased datasets to build prediction models and the generalizability of hypotheses tested on biased data.

A brief narrative description of the journal article, document, or resource. This paper provides a critical analysis of Mobile Learning projects published before the end of 2007. The review uses a Mobile Learning framework to evaluate and categorize 102 Mobile Learning projects, and to briefly introduce exemplary projects for each category. All projects were analysed with the criteria: context, tools, control, communication, subject and objective. Although a significant number of projects have ventured to incorporate the physical context into the learning experience, few projects include a socializing context. Tool support ranges from pure content delivery to content construction by the learners. Although few projects explicitly discuss the Mobile Learning control issues, one can find all approaches from pure teacher control to learner control. Despite the fact that mobile phones initially started as a communication device, communication and collaboration play a surprisingly small role in Mobile Learning projects. Most Mobile Learning projects support novices, although one might argue that the largest potential is supporting advanced learners. All results show the design space and reveal gaps in Mobile Learning research.

Biophysical chemistry of mesoscale systems and quantitative modeling in systems biology now require a simulation methodology unifying chemical reaction kinetics with essential collective physics. This will enable the study of the collective dynamics of complex chemical and structural systems in a spatially resolved manner with a combinatorially complex variety of different system constituents. In order to allow a direct link-up with experimental data (e.g. high-throughput fluorescence images) the simulations must be constructed locally, i.e. mesoscale phenomena have to emerge from local composition and interactions that can be extracted from experimental data. Under suitable conditions, the simulation of such local interactions must lead to processes such as vesicle budding, transport of membrane-bounded compartments and protein sorting, all of which result from a sophisticated interplay between chemical and mechanical processes and require the link-up of different length scales. In this work, we show that introducing multipolar interactions between particles in dissipative particle dynamics (DPD) leads to extended membrane structures emerging in a self-organized manner and exhibiting the necessary mechanical stability for transport, correct scaling behavior, and membrane fluidity so as to provide a two-dimensional self-organizing dynamic reaction environment for kinetic studies in the context of cell biology.

Although accumulating evidence highlights a crucial role of the insular cortex in feelings, empathy and processing uncertainty in the context of decision making, neuroscientific models of affective learning and decision making have mostly focused on structures such as the amygdala and the striatum. Here, we propose a unifying model in which insula cortex supports different levels of representation of current and predictive states allowing for error-based learning of both feeling states and uncertainty. This information is then integrated in a general subjective feeling state which is modulated by individual preferences such as risk aversion and contextual appraisal. Such mechanisms could facilitate affective learning and regulation of body homeostasis, and could also guide decision making in complex and uncertain environments.

It has been shown in past research that unemployment has a large negative impact on subjective well-being of individuals. In this paper, I explore whether and to what extent people with more social capital are sheltered from the harmful effects of unemployment. Using data from the German Socio-Economic Panel 1984–2004, I find that social capital is an important predictor of well-being levels, but there is no evidence that it moderates the effect of unemployment on well-being. Possible reasons for these findings are discussed, and suggestions for future research given.

Software systems have to evolve continually to meet their changing requirements. Refactorings are systematic, behavior-preserving changes to software systems intended to improve their inter- nal structure. Today, refactoring is considered a state of the art technique in software develop- ment. Refactorings have a positive impact on the understandability and maintainability of a software system. Another important property is that their catalogued names capture the intent of the underlying changes and provide an accurate vocabulary for communicating changes at a much higher level of abstraction than the insertion or removal of individual lines of code. A similar property has been attributed to the design patterns of Gamma et al.; giving mean- ingful names not only reveals the intent and structure of a pattern but also allows for efficient communication of design concepts. It is not only crucial to understand what changes have hap- pened but also what changes need to be made in order to maintain the understandability and extensibility of a software system. Therefore, another goal of refactoring research is to develop approaches for anticipating and suggesting possible refactorings to developers—which requires understanding past changes and refactorings. Based on the EVOLIZER framework, which offers advanced modeling and data extraction fa- cilities, and FAMIX, a meta model for modeling the structure of object-oriented software systems, this thesis presents the approach, implementation and evaluation of REFACTORIZER, a prototype tool for detecting refactorings in the development history of a software system. The main goals of the approach are to be intuitive and light-weight. This means that the ap- proach builds on the vocabulary and understanding of a software developer about how programs are composed and uses a simple representation of the structure of a software system to formulate intuitive FAMIX-based heuristics.

This Paper examines the correlation between age and flow experience on learning activities at computers. The study includes the definition of the age groups, the analysis of their flow experience and the finding of group specific behavior patterns. In the experiment the test persons had to play a flash game. Based on this activity the flow and pulse values were recorded. The result was that all age groups achieved similar flow values. Nevertheless there were found differences between the age groups concerning the behavior patterns and the pulse values. Additionally a second Experiment with a smaller sample was performed. Therefore the brain waves and the flow values were recorded and compared.