Enabling the classroom and the curriculum: higher education, literary studies and disability

In this article the tripartite model of disability is applied to the lived experience of twenty-first-century higher education. The tripartite model facilitates a complex understanding of disability that recognises assumptions and discrimination but not at the cost of valued identity. This being so, not only the normative positivisms and non-normative negativisms but also the non-normative positivisms of the classroom and the curriculum are explored. Inclusion is taken as the starting point and the argument progresses to a profound and innovational appreciation of disability. The problem addressed is that inclusion, as shown in The Biopolitics of Disability, constitutes little more than inclusion-ism until disability is recognised in the context of alternative lives and values that neither enforce nor reify normalcy. Informed by this understanding, the article adopts the disciplinary example of literary studies and refers to Brian Friel’s Molly Sweeney as a primary text. The conclusion is that, despite passive and active resistance, disability enters higher education in many ways, most of which are beneficial to students and educators alike

Automated Bug Assignment : Ensemble-based Machine Learning in Large Scale Industrial Contexts

Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classification. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classifiers. We collect more than 50,000 bug reports from five development projects from two companies in different domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. We show that SG scales to large scale industrial application and that it outperforms the use of individual classifiers for bug assignment, reaching prediction accuracies from 50 % to 89 % when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment

Alfvén: magnetosphere--ionosphere connection explorers

International audienceThe Alfvén concept is designed specifically to take the next step in studying the aurora, by making the crucial high-time resolution, multi-scale measurements in the AAR, needed to address the key science questions of auroral plasma physics. The new knowledge that the mission will produce will find application in studies of the Sun, the processes that accelerate the solar wind and that produce aurora on other planets