From e-learning to integrated learning architectures. A novel approach to learning management in corporate and higher education contexts

E-learning has taken an important role in the work of human resources departments, thus increasing both the importance of, and the demands placed upon, learning management processes and technologies. High-performance learning platforms enable a wide range of functionalities and process choice. In addition, they need interfaces for integration into other corporate IT systems. And finally, corporate learning solutions also need to reach all relevant partners who might participate in learning and information exchange processes. Providing examples from the architecture of the leading European learning management system CLIX�, the article outlines the conceptual, technical, process-related and organizational framework for a successful implementation of viable and sustainable learning solutions in companies, higher education and public organizations.corporate learning, E-learning, learning management, management education, performance management.

Multimedia and e-Learning integration for supporting training programs in agriculture by MOODLE

The NODES project aims at facilitating, for adult training / lifelong training, the use of multimedia knowledge to improve competitiveness employability and mobility of handicapped adults (physical and sensorial) and of adults victims of the digital divide or of some of its components such as distance, initial level of knowledge, language, use of complex technologies. The NODES project is focused, on the wide sense, on the production and diffusion of knowledge created within public and private organizations dedicated to adult training or by individuals, through Europe. Within the project the MOODLE e-Learning system was selected and more multimedia content will be integrated into the knowledge base. The EU-Index metadatabase collects content sources for the project partners. Another target is to integrate video files into the systems. This parts are integrated by the logical and physical architectures of the NODES

Enhanced electronic whiteboard

This disclosure describes an enhanced electronic whiteboard for visualization of cloud-based computing solutions. The electronic whiteboard includes technical icons that represent cloud computing solution elements and enables users to depict and manipulate various computer architectures. The electronic whiteboard is integrated with cloud migration solution software for collaborative design, sales presentations, and simulation. Depicting technical architectures is made possible by providing icons for servers, storage, networking appliances, etc. that can be dragged and dropped onto the whiteboard. With user permission, machine learning techniques are utilized to auto-populate operating data relevant to different architectures

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Towards better understanding of gradient-based attribution methods for Deep Neural Networks

Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, there have been only a few attempts to compare them from a theoretical perspective. What is more, no exhaustive empirical comparison has been performed in the past. In this work, we analyze four gradient-based attribution methods and formally prove conditions of equivalence and approximation between them. By reformulating two of these methods, we construct a unified framework which enables a direct comparison, as well as an easier implementation. Finally, we propose a novel evaluation metric, called Sensitivity-n and test the gradient-based attribution methods alongside with a simple perturbation-based attribution method on several datasets in the domains of image and text classification, using various network architectures.Comment: ICLR 201