E/Valuating new media in language development

This paper addresses the need for a new approach to the educational evaluation of software that falls under the rubric "new media" or "multimedia" as distinct from previous generations of Computer-Assisted Language Learning (CALL) software. The authors argue that present approaches to CALL software evaluation are not appropriate for a new genre of CALL software distinguished by its shared assumptions about language learning and teaching as well as by its technical design. The paper sketches a research-based program called "E/Valuation" that aims to assist language educators to answer questions about the educational effectiveness of recent multimedia language learning software. The authors suggest that such program needs to take into account not only the nature of the new media and its potential to promote language learning in novel ways, but also current professional knowledge about language learning and teaching

Using grounded theory to understand software process improvement: A study of Irish software product companies

Software Process Improvement (SPI) aims to understand the software process as it is used within an organisation and thus drive the implementation of changes to that process to achieve specific goals such as increasing development speed, achieving higher product quality or reducing costs. Accordingly, SPI researchers must be equipped with the methodologies and tools to enable them to look within organisations and understand the state of practice with respect to software process and process improvement initiatives, in addition to investigating the relevant literature. Having examined a number of potentially suitable research methodologies, we have chosen Grounded Theory as a suitable approach to determine what was happening in actual practice in relation to software process and SPI, using the indigenous Irish software product industry as a test-bed. The outcome of this study is a theory, grounded in the field data, that explains when and why SPI is undertaken by the software industry. The objective of this paper is to describe both the selection and usage of grounded theory in this study and evaluate its effectiveness as a research methodology for software process researchers. Accordingly, this paper will focus on the selection and usage of grounded theory, rather than results of the SPI study itself

Bioconductor: open software development for computational biology and bioinformatics.

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples

SANTO: Social Aerial NavigaTion in Outdoors

In recent years, the advances in remote connectivity, miniaturization of electronic components and computing power has led to the integration of these technologies in daily devices like cars or aerial vehicles. From these, a consumer-grade option that has gained popularity are the drones or unmanned aerial vehicles, namely quadrotors. Although until recently they have not been used for commercial applications, their inherent potential for a number of tasks where small and intelligent devices are needed is huge. However, although the integrated hardware has advanced exponentially, the refinement of software used for these applications has not beet yet exploited enough. Recently, this shift is visible in the improvement of common tasks in the field of robotics, such as object tracking or autonomous navigation. Moreover, these challenges can become bigger when taking into account the dynamic nature of the real world, where the insight about the current environment is constantly changing. These settings are considered in the improvement of robot-human interaction, where the potential use of these devices is clear, and algorithms are being developed to improve this situation. By the use of the latest advances in artificial intelligence, the human brain behavior is simulated by the so-called neural networks, in such a way that computing system performs as similar as possible as the human behavior. To this end, the system does learn by error which, in an akin way to the human learning, requires a set of previous experiences quite considerable, in order for the algorithm to retain the manners. Applying these technologies to robot-human interaction do narrow the gap. Even so, from a bird's eye, a noticeable time slot used for the application of these technologies is required for the curation of a high-quality dataset, in order to ensure that the learning process is optimal and no wrong actions are retained. Therefore, it is essential to have a development platform in place to ensure these principles are enforced throughout the whole process of creation and optimization of the algorithm. In this work, multiple already-existing handicaps found in pipelines of this computational gauge are exposed, approaching each of them in a independent and simple manner, in such a way that the solutions proposed can be leveraged by the maximum number of workflows. On one side, this project concentrates on reducing the number of bugs introduced by flawed data, as to help the researchers to focus on developing more sophisticated models. On the other side, the shortage of integrated development systems for this kind of pipelines is envisaged, and with special care those using simulated or controlled environments, with the goal of easing the continuous iteration of these pipelines.Thanks to the increasing popularity of drones, the research and development of autonomous capibilities has become easier. However, due to the challenge of integrating multiple technologies, the available software stack to engage this task is restricted. In this thesis, we accent the divergencies among unmanned-aerial-vehicle simulators and propose a platform to allow faster and in-depth prototyping of machine learning algorithms for this drones