Save up to 99% of your time in mapping validation

Identifying semantic correspondences between different vocabularies has been recognized as a fundamental step towards achieving interoperability. Several manual and automatic techniques have been recently proposed. Fully manual approaches are very precise, but extremely costly. Conversely, automatic approaches tend to fail when domain specific background knowledge is needed. Consequently, they typically require a manual validation step. Yet, when the number of computed correspondences is very large, the validation phase can be very expensive. In order to reduce the problems above, we propose to compute the minimal set of correspondences, that we call the minimal mapping, which are sufficient to compute all the other ones. We show that by concentrating on such correspondences we can save up to 99% of the manual checks required for validation

ILS and RTP: Support to Researchers Provided by Information and Learning Services as Part of the Research Training Programme at the University of Worcester, Past, Present and Future.

The purpose of this article is to investigate the involvement of Information and Learning Services staff in the delivery of the Research Training Programme at the University of Worcester, UK with a focus on researcher receptivity. I believe that by constantly reflecting on the development of that part of the programme delivered by ILS and by examining feedback from the sessions, it is possible to improve and increase the level of researcher receptivity. It is hoped that such examination and reflection will be of value and relevance to the IL community since by reflecting on success and failure in a local context and by mapping this reflection to existing research enables librarians to improve the support provided to researchers within their institutions. This article outlines the support given to research students at the University of Worcester in the past, examines the changes leading to present programme delivery and reflects on considerations for future support. The article is underpinned by reference to current research undertaken in international (albeit Western-centric) contexts. I note that the rationale behind changes is embedded in current adult learning and teaching theory. In an increasingly competitive research environment where funding is dependent on a statistically monitored research output, the aim of such support is to integrate any IL contribution into the wider research training programme. Thus resource discovery becomes part of the reflexive research cycle. Implicit in this investigative reflection is the desire of the IL community to constantly strive towards the positive reception of IL into research support programmes which are perceived by researchers as highly valuable to the process and progress of their work

Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use

The creation of Domain Specific Languages(DSL) counts as one of the main goals in the field of Model-Driven Software Engineering (MDSE). The main purpose of these DSLs is to facilitate the manipulation of domain specific concepts, by providing developers with specific tools for their domain of expertise. A natural approach to create DSLs is to reuse existing modeling standards and tools. In this area, the Eclipse Modeling Framework (EMF) has rapidly become the defacto standard in the MDSE for building Domain Specific Languages (DSL) and tools based on generative techniques. However, the use of EMF generated tools in domains like Internet of Things (IoT), Cloud Computing or Models@Runtime reaches several limitations. In this paper, we identify several properties the generated tools must comply with to be usable in other domains than desktop-based software systems. We then challenge EMF on these properties and describe our approach to overcome the limitations. Our approach, implemented in the Kevoree Modeling Framework (KMF), is finally evaluated according to the identified properties and compared to EMF.Comment: ISBN 978-2-87971-131-7; N° TR-SnT-2014-11 (2014

Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.Comment: 19 pages, 1 table, 3 figure

Children's Well-being in Contexts of Poverty: Approaches to Research, Monitoring and Participation

Monitoring, protecting and promoting 'well-being' are central to realisation of children's rights. Yet definitions of the concept are both variable and can appear conceptually confused. Competing research paradigms engage with the concept and its measurement, while applications of well-being in policy are equally contested. This paper outlines some of the major debates, as a starting point for reviewing three contrasting approaches to well-being: indicator-based, participatory and longitudinal research. In particular, it focuses on applications of the concept in contexts of child poverty worldwide. We suggest there are some promising signs of integration amongst these approaches, and argue that well-being does have potential as a bridging concept, at the same time highlighting inequalities, acknowledging diversities, and respecting children's agency

Reuse It Or Lose It: More Efficient Secure Computation Through Reuse of Encrypted Values

Two-party secure function evaluation (SFE) has become significantly more feasible, even on resource-constrained devices, because of advances in server-aided computation systems. However, there are still bottlenecks, particularly in the input validation stage of a computation. Moreover, SFE research has not yet devoted sufficient attention to the important problem of retaining state after a computation has been performed so that expensive processing does not have to be repeated if a similar computation is done again. This paper presents PartialGC, an SFE system that allows the reuse of encrypted values generated during a garbled-circuit computation. We show that using PartialGC can reduce computation time by as much as 96% and bandwidth by as much as 98% in comparison with previous outsourcing schemes for secure computation. We demonstrate the feasibility of our approach with two sets of experiments, one in which the garbled circuit is evaluated on a mobile device and one in which it is evaluated on a server. We also use PartialGC to build a privacy-preserving "friend finder" application for Android. The reuse of previous inputs to allow stateful evaluation represents a new way of looking at SFE and further reduces computational barriers.Comment: 20 pages, shorter conference version published in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Pages 582-596, ACM New York, NY, US