A Coq-based synthesis of Scala programs which are correct-by-construction

The present paper introduces Scala-of-Coq, a new compiler that allows a Coq-based synthesis of Scala programs which are "correct-by-construction". A typical workflow features a user implementing a Coq functional program, proving this program's correctness with regards to its specification and making use of Scala-of-Coq to synthesize a Scala program that can seamlessly be integrated into an existing industrial Scala or Java application.Comment: 2 pages, accepted version of the paper as submitted to FTfJP 2017 (Formal Techniques for Java-like Programs), June 18-23, 2017, Barcelona , Spai

Semantic Web meets Web 2.0 (and vice versa): The Value of the Mundane for the Semantic Web

Web 2.0, not the Semantic Web, has become the face of “the next generation Web” among the tech-literate set, and even among many in the various research communities involved in the Web. Perceptions in these communities of what the Semantic Web is (and who is involved in it) are often misinformed if not misguided. In this paper we identify opportunities for Semantic Web activities to connect with the Web 2.0 community; we explore why this connection is of significant benefit to both groups, and identify how these connections open valuable research opportunities “in the real” for the Semantic Web effort

Informatics Research Institute (IRIS) July 2004 newsletter

This summer period has been rich in presence and dissemination related activities. Several important conferences, which have enjoyed a great international participation and success, have been organized by IRIS academics in Salford. These include NLDB04, CRIS 2004 and the LTSN workshop. Also, a substantial number of research projects have been secured from national as well as European funding sources. All these activities are contributing to reinforcing the leading position that IRIS is currently enjoying in the field of Informatics. This newsletter gives an overview of all research activities that took place during this reporting period. It is hoped that this will help trigger further collaboration with existing and future colleagues from academia, research and industry to work together towards addressing the many societal and technological challenges engendered by the information age

Privacy, security, and trust issues in smart environments

Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning

RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

Extreme learning machine (ELM) as an emerging branch of shallow networks has shown its excellent generalization and fast learning speed. However, for blended data, the robustness of ELM is weak because its weights and biases of hidden nodes are set randomly. Moreover, the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different groups concurrently, then employs selective ensemble to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the results have shown that RMSE-ELM significantly improves robustness with slightly computational time compared with representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.Comment: Accepted for publication in Mathematical Problems in Engineering, 09/22/201