Abstract
Research data management is acknowledged as an important concern for institutions and several platforms to support data deposits have emerged. In this paper we start by overviewing the current practices in the data management workflow and identifying the stakeholders in this process. We then compare four recently proposed data repository platforms-DSpace, CKAN, Zenodo and Figshare-considering their architecture, support for metadata, API completeness, as well as their search mechanisms and community acceptance. To evaluate these features, we take into consideration the identified stakeholders' requirements. In the end, we argue that, depending on local requirements, different data repositories can meet some of the stakeholders requirements. Nevertheless, there is still room for improvements, mainly regarding the compatibility with the description of data from different research domains, to further improve data reuse.

Abstract
Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations. Copyright

Abstract
Bidirectional transformations, in particular lenses, are programs with a forward get transformation and a backward putback transformation that keep source and view data types synchronized. Several bidirectional programming languages exist to aid programmers in writing a (sort of) forward transformation, and deriving a backward transformation for free. However, the maintainability offered by such languages comes at the cost of expressiveness and (more importantly) predictability because the ambiguity of synchronization -handled by the putback transformation- is solved by default strategies over which programmers have little control. In this paper, we argue that controlling such ambiguity is essential for bidirectional transformations and propose a novel language in which programmers write a (sort of) putback transformation, and get the unique get transformation for free. Like traditional bidirectional languages, our put-oriented language allows reasoning about the correctness of defined transformations from the properties of their building blocks. But it allows programmers to describe the behavior of a bidirectional transformation much more precisely, while retaining the maintainability of writing a single program. We demonstrate the practical power of the new approach through a series of examples, ranging from simple ones that illustrate traditional lenses to complex ones for which our putback-based approach is central to specifying nontrivial update strategies. Categories and Subject Descriptors D.1.1 [Programming Techniques]: Applicative (Functional) Programming; D.3.1 [Programming Languages]: Formal Definitions and Theory; F.3.2 [Logics and Meanings of Programs]: Semantics of Programming Languages-Algebraic approaches to semantics.

Abstract
The Query/View/Transformation Relations (QVT-R) standard for bidirectional model transformation is notorious for its underspecified semantics. When restricted to transformations between pairs of models, most of the ambiguities and omissions have been addressed in recent work. Nevertheless, the application of the QVT-R language is not restricted to that scenario, and similar issues remain unexplored for the multidirectional case (maintaining consistency between more than two models), that has been overlooked so far. In this paper we first discuss ambiguities and omissions in the QVT-R standard concerning the mutidirectional transformation scenario, and then propose a simple extension and formalization of the checking and enforcement semantics that clarifies some of them. We also discuss how such proposal could be implemented in our Echo bidirectional model transformation tool. Ours is just a small step towards making QVT-R a viable language for bidirectional transformation in realistic applications, and a considerable amount of basic research is still needed to fully accomplish that goal.

Abstract
Currently, the teaching-learning process in domains, such as computer programming, is characterized by an extensive curricula and a high enrolment of students. This poses a great workload for faculty and teaching assistants responsible for the creation, delivery, and assessment of student exercises. The main goal of this chapter is to foster practice-based learning in complex domains. This objective is attained with an e-learning framework-called Ensemble-as a conceptual tool to organize and facilitate technical interoperability among services. The Ensemble framework is used on a specific domain: computer programming. Content issues are tacked with a standard format to describe programming exercises as learning objects. Communication is achieved with the extension of existing specifications for the interoperation with several systems typically found in an e-learning environment. In order to evaluate the acceptability of the proposed solution, an Ensemble instance was validated on a classroom experiment with encouraging results. © 2015, IGI Global.

Abstract
Courses in computer programming combine a number of different concepts, from general problem-solving to mathematical precepts such as algorithms and computational intelligence. Due to the complex nature of computer science education, teaching the novice programmer can be a challenge. Innovative Teaching Strategies and New Learning Paradigms in Computer Programming brings together pedagogical and technological methods to address the recent challenges that have developed in computer programming courses. Focusing on educational tools, computer science concepts, and educational design, this book is an essential reference source for teachers, practitioners, and scholars interested in improving the success rate of students.