The CHAIN-REDS Semantic Search Engine

e-Infrastructures, and in particular Data Repositories and Open Access Data Infrastructures, are essential platforms for e-Science and e-Research and are being built since several years both in Europe and the rest of the world to support diverse multi/inter-disciplinary Virtual Research Communities. So far, however, it is difficult for scientists to correlate papers to datasets used to produce them and to discover data and documents in an easy way. In this paper, the CHAINREDS project’s Knowledge Base and its Semantic Search Engine are presented, which attempt to address those drawbacks and contribute to the reproducibility of science

New science on the Open Science Grid

The Open Science Grid (OSG) includes work to enable new science, new scientists, and new modalities in support of computationally based research. There are frequently significant sociological and organizational changes required in transformation from the existing to the new. OSG leverages its deliverables to the large-scale physics experiment member communities to benefit new communities at all scales through activities in education, engagement, and the distributed facility. This paper gives both a brief general description and specific examples of new science enabled on the OSG. More information is available at the OSG web site: www.opensciencegrid.org

EGI: anOpen e-Infrastructure Ecosystem for the Digital European Research Area

Bringing the digital European Research Area (ERA) online means modernising Europe’s research infrastructure by promoting open science through the availability, accessibility and reuse of scientific data and results, the use of web- based tools that facilitate scientific collaboration and ensuring public access to research. As the European Grid Infrastructure (EGI) is the largest European distributed computing infrastructure providing 24/7 access to large scale computing, storage and data resources through a federation of national resource providers, it allows scientists from all disciplines to make the most out of the latest computing technologies for the benefit of their research. This paper describes the methodology and approach for defining EGI’s role in bringing this digital ERA online. The work presented defines the roles and functions of EGI as an open ICT ecosystem, required service redesign, the added value of EGI for the European research communities and demonstrates the role that EGI plays in contributing to the Europe 2020 strategy for social-economic impact

System Requirements Analysis for e-learning systems using grid

Until recent years network-based education and grid technologies were two distinct areas. But e-learning systems have been increasingly addressing learning resources sharing (text, images, video, on-line data, etc.) and reuse, interoperability and other more different modes of interactions. E-learning systems consist of complex activities and most of them have been designed based on client-server or peer to peer, and recently web services architecture. These systems have major drawback because of their limitations in scalability, availability, distribution of computing power and storage systems, as well as sharing information between users that contribute to these systems. In this context the use of grid technology reveals its utility and availability, as scalable, flexible coordinated and secure resource sharing among geographically distributed individuals or institutions, in the perspective of e-learning.networked-based, education, grid technologies, e-learning systems,resouce sharing, interoperability, standardisation.

Utilizing Deep Neural Networks for Brain–Computer Interface-Based Prosthesis Control

Limb amputations affect a significant portion of the world’s population every year. The necessity for these operations can be associated with related health conditions or a traumatic event. Currently, prosthetic devices intended to alleviate the burden of amputation lack many of the premier features possessed by their biological counterparts. The foremost of these features are agility and tactile function. In an effort to address the former, researchers here investigate the fundamental connection between agile finger movement and brain signaling. In this study each subject was asked to move his or her right index finger in sync with a time-aligned finger movement demonstration while each movement was labeled and the subject’s brain waves were recorded via a single-channel electroencephalograph. This data was subsequently used to train and test a deep neural network in an effort to classify each subject’s intention to rest and intention to extend his or her right index finger. On average, the employed model yielded an accuracy of 63.3%, where the most predictable subject’s movements were classified with an accuracy of 70.5%