A workflow for learning objects lifecycle and reuse: Towards evaluating cost effective reuse

Over the last decade Learning Objects (LOs) have gained a lot of attention as a common format for developing and sharing digital educational content in the field of technology-enhanced learning. The main advantage of LOs is considered to be their potential for component-based reuse in different learning settings supporting different learning activities. However, despite the importance of the concept of reuse and its potential benefits in digital educational content production and deployment, there are only sporadic efforts to study issues related to LOs reuse that would allow interested parties (such as people, organizations and initiatives) to assess the conditions for and eventually implement systematic LOs reuse within the context of learning activities design and development. This is a drawback in adopting the LOs paradigm towards reducing costs and effort. In this paper, we study existing efforts for the definition of the different steps involved during the LOs lifecycle, we identify the aspects of LOs reuse within the context of learning activities design and development, we propose a thorough workflow for LOs lifecycle that can support LOs reuse and enable us to define a set of metrics for cost effective LOs reuse, and discuss the cost effectiveness conditions in various use cases

Software development: A paradigm for the future

A new paradigm for software development that treats software development as an experimental activity is presented. It provides built-in mechanisms for learning how to develop software better and reusing previous experience in the forms of knowledge, processes, and products. It uses models and measures to aid in the tasks of characterization, evaluation and motivation. An organization scheme is proposed for separating the project-specific focus from the organization's learning and reuse focuses of software development. The implications of this approach for corporations, research and education are discussed and some research activities currently underway at the University of Maryland that support this approach are presented

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

Using Neural Networks In Software Metrics

Software metrics provide effective methods for characterizing software. Metrics have traditionally been composed through the definition of an equation, but this approach is limited by the fact that all the interrelationships among all the parameters be fully understood. Derivation of a polynomial providing the desired characteristics is a substantial challenge. In this paper instead of using conventional methods for obtaining software metrics, we will try to use a neural network for that purpose. Experiments performed in the past on two widely known metrics, McCabe and Halstead, indicate that this approach is feasible.neural networks, software metrics, halstead, mccabe

Next generation software environments : principles, problems, and research directions

The past decade has seen a burgeoning of research and development in software environments. Conferences have been devoted to the topic of practical environments, journal papers produced, and commercial systems sold. Given all the activity, one might expect a great deal of consensus on issues, approaches, and techniques. This is not the case, however. Indeed, the term "environment" is still used in a variety of conflicting ways. Nevertheless substantial progress has been made and we are at least nearing consensus on many critical issues.The purpose of this paper is to characterize environments, describe several important principles that have emerged in the last decade or so, note current open problems, and describe some approaches to these problems, with particular emphasis on the activities of one large-scale research program, the Arcadia project. Consideration is also given to two related topics: empirical evaluation and technology transition. That is, how can environments and their constituents be evaluated, and how can new developments be moved effectively into the production sector