Real‐time interactive social environments: A review of BT's generic learning platform

Online learning in particular and lifelong learning in general require a learning platform that makes sense both pedagogically and commercially. This paper sets out to describe what we mean by generic, learning and platform. The technical requirements are described, and various trials that test the technical, educational and commercial nature of the platform are described Finally, the future developments planned for the Real‐time Interactive Social Environments (RISE) are discusse

Building capacity in climate change policy analysis and negotiation: methods and technologies

Capacity building is often cited as the reason “we cannot just pour money into developing countries” and why so many development projects fail because their design does not address local conditions. It is therefore a key technical and political concept in international development. Some of the poorest countries in the world are also some of the most vulnerable to the impacts of climate change. Their vulnerability is in part due to a lack of capacity to plan and anticipate the effects of climate change on crops, water resources, urban electricity demand etc. What capacities do these countries lack to deal with climate change? How will they cope? What steps can they take to reduce their vulnerability? This innovative and high-profile research project was part of a larger project (called C3D) and conducted with non-governmental organisations in Senegal, South Africa and Sri Lanka. The research involved several participatory workshops and a questionnaire to all three research centres

The Virtual University and Avatar Technology: E-learning Through Future Technology

E-learning gains increasingly importance in academic education. Beyond present distance learning technologies a new opportunity emerges by the use of advanced avatar technology. Virtual robots acting in an environment of a virtual campus offer opportunities of advanced learning experiences. Human Machine Interaction (HMI) and Artificial Intelligence (AI) can bridge time zones and ease professional constraints of mature students. Undergraduate students may use such technology to build up topics of their studies beyond taught lectures. Objectives of the paper are to research the options, extent and limitations of avatar technology for academic studies in under- and postgraduate courses and to discuss students' potential acceptance or rejection of interaction with AI. The research method is a case study based on Sir Tony Dyson's avatar technology iBot2000. Sir Tony is a worldwide acknowledged robot specialist, creator of Star Wars' R2D2, who developed in recent years the iBot2000 technology, intelligent avatars adaptable to different environments with the availability to speak up to eight different languages and capable to provide logic answers to questions asked. This technology underwent many prototypes with the latest specific goal to offer blended E-learning entering the field of the virtual 3-D university extending Web2.0 to Web3.0 (Dyson. 2009). Sir Tony included his vast experiences gained in his personal (teaching) work with children for which he received his knighthood. The data was mainly collected through interviews with Sir Tony Dyson, which helps discover the inventor’s view on why such technology is of advantage for academic studies. Based on interviews with Sir Tony, this research critically analyses the options, richness and restrictions, which avatar (iBot2000) technology may add to academic studies. The conclusion will discuss the opportunities, which avatar technology may be able to bring to learning and teaching activities, and the foreseeable limitations – the amount of resources required and the complexity to build a fully integrated virtual 3-D campus. Key Words: virtual learning, avatar technology, iBot2000, virtual universit

The practice of web conferencing: where are we now?

The use of web conferencing tools to support learning and teaching and facilitate interaction and collaboration is common in many higher education environments as is the replacement of face-to-face meetings with virtual collaboration tools. This paper reports on Phase 3 of a trial of web conferencing conducted at a regional Australian university and further explores the use of web conferencing to support and enhance learning and teaching. Preliminary findings from Phases 1 and 2 of the trial have been reported on in a previous paper. The action research method which framed the initial trial has again been applied to Phase 3. In Phase 1, collaborative mathematical problem-solving in an undergraduate course was carried out using web conferencing and tablet PCs. In addition, students in postgraduate Education courses were linked across the globe to participate in interactive and collaborative web conference activity. In Phase 2, a university-wide trial across disciplines was conducted. Phase 3 represents an analysis of feedback received from teachers who were involved in Phases 1 and 2 and who have had some time to reflect on the impact web conferencing has had on their teaching practice. From this, and their own experience, the authors make further observations regarding the use of web conferencing to support learning and teaching and raise a number of questions and issues to guide future research

Online learning and detection of faces with low human supervision

The final publication is available at link.springer.comWe present an efficient,online,and interactive approach for computing a classifier, called Wild Lady Ferns (WiLFs), for face learning and detection using small human supervision. More precisely, on the one hand, WiLFs combine online boosting and extremely randomized trees (Random Ferns) to compute progressively an efficient and discriminative classifier. On the other hand, WiLFs use an interactive human-machine approach that combines two complementary learning strategies to reduce considerably the degree of human supervision during learning. While the first strategy corresponds to query-by-boosting active learning, that requests human assistance over difficult samples in function of the classifier confidence, the second strategy refers to a memory-based learning which uses ¿ Exemplar-based Nearest Neighbors (¿ENN) to assist automatically the classifier. A pre-trained Convolutional Neural Network (CNN) is used to perform ¿ENN with high-level feature descriptors. The proposed approach is therefore fast (WilFs run in 1 FPS using a code not fully optimized), accurate (we obtain detection rates over 82% in complex datasets), and labor-saving (human assistance percentages of less than 20%). As a byproduct, we demonstrate that WiLFs also perform semi-automatic annotation during learning, as while the classifier is being computed, WiLFs are discovering faces instances in input images which are used subsequently for training online the classifier. The advantages of our approach are demonstrated in synthetic and publicly available databases, showing comparable detection rates as offline approaches that require larger amounts of handmade training data.Peer ReviewedPostprint (author's final draft

Interactive multiple object learning with scanty human supervision

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/We present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human robot interaction, the user just needs to annotate a small fraction of frames to compute object specific classifiers based on random ferns which share the same features. The resulting methodology is fast (in a few seconds, complex object appearances can be learned), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier.; We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds. (C) 2016 Elsevier Inc. All rights reserved.Peer ReviewedPostprint (author's final draft

A framework for interactive learning in emerging technologies

Innovation is an interactive learning process which is of special interest for emerging technologies in which complex complementary knowledge from heterogeneous stakeholders is combined. In the emerging phase of technology development a lot of knowledge is tacit and can only be transferred face-to-face. At the same time a shared vision between stakeholders is being formed that acts as a driver for innovation. Although the importance of interactive learning is widely acknowledged, an adequate framework for studying interactive learning processes in emerging technologies is still missing. Therefore we formulated the leading research question: How to understand and conceptualize interactive learning in the context of emerging technologies? We did not only take the outcome of interactive learning into account, but also focused on opening the black box of the interactive learning process. We developed a framework based on characteristic elements of the interactive learning process in emerging technologies (i.e. prime mover, intermediaries, network formation and knowledge flows), influencing conditions (geographical, cognitive, regulatory, cultural and organisational proximity), and the outcome of the interactive learning process (single-loop and double-loop, tacit and codified knowledge). Clarifying examples are taken from the empirical field of the development of novel food products (functional foods).