937,853 research outputs found

    Collaborative trails in e-learning environments

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    This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

    iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking

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    Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present {\em iTelescope}, the first intelligent, inexpensive, and scalable SDN-based solution for identifying and classifying video flows in real-time. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95\% accuracy in identifying and classifying video streams from many providers including Youtube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.Comment: 12 pages, 16 figure

    Assessing the effects of power quality on partial discharge behaviour through machine learning

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    Partial discharge (PD) is commonly used as an indicator of insulation health in high voltage equipment, but research has indicated that power quality, particularly harmonics, can strongly influence the discharge behaviour and the corresponding pattern observed. Unacknowledged variation in harmonics of the excitation voltage waveform can influence the insulation's degradation, leading to possible misinterpretation of diagnostic data and erroneous estimates of the insulation's ageing state, thus resulting in inappropriate asset management decisions. This paper reports on a suite of classifiers for identifying pertinent harmonic attributes from PD data, and presents results of techniques for improving their accuracy. Aspects of PD field monitoring are used to design a practical system for on-line monitoring of voltage harmonics. This system yields a report on the harmonics experienced during the monitoring period

    Between the Lines: documenting the multiple dimensions of computer supported collaborations

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    When we consider the possibilities for the design and evaluation of Computer Supported Collaborative Learning (CSCL) we probably constrain the CS in CSCL to situations in which learners, or groups of learners collaborate with each other around a single computer, across a local intranet or via the global internet. We probably also consider situations in which the computer itself acts as a collaborative partner giving hints and tips either with or without the addition of an animated pedagogical agent. However, there are now many possibilities for CSCL applications to be offered to learners through computing technology that is something other than a desktop computer, such as the TV or a digital toy. In order to understand how such complex and novel interactions work, we need tools to map out the multiple dimensions of collaboration using a whole variety of technologies. This paper discusses the evolution of a documentation technique for collaborative interactions from its roots in a situation where a single learner is collaborating with a software learning partner, through its second generation: group use of multimedia, to its current test-bed: young children using digital toys and associated software. We will explore some of the challenges these different learning situations pose for those involved in the evaluation of collaborative learning

    Transcribing Content from Structural Images with Spotlight Mechanism

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    Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured symbols), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what" solution. Specifically, we first decide "where-to-look" through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine the framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18

    Revisiting On-Line Discussion as Practice for Reflective Thinking in Three Sequential Classes

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    In a previous study, the authors questioned the potential of an on-line environment for increasing productive reflection in three sequential education classes. Of their findings, the issue of consistency stood out as particularly perplexing, namely, why did students exhibit high level reflections sometimes, but not all the time, in an on-line environment? In this follow-up study, the authors question whether in-class reflections coupled with on-line prompts could yield consistently high level pre-service teacher reflections, as measured by individual and class progress over time. This study also examines perceived relationships between the length of a student\u27s reflection and its productivity, as well as a student\u27s depth of focus and productivity. Using the same scoring approach as our previous study, our discussion of the results examines the usefulness of on-line environments for promoting consistently high level pre-service teacher reflection

    Requirements for an Adaptive Multimedia Presentation System with Contextual Supplemental Support Media

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    Investigations into the requirements for a practical adaptive multimedia presentation system have led the writers to propose the use of a video segmentation process that provides contextual supplementary updates produced by users. Supplements consisting of tailored segments are dynamically inserted into previously stored material in response to questions from users. A proposal for the use of this technique is presented in the context of personalisation within a Virtual Learning Environment. During the investigation, a brief survey of advanced adaptive approaches revealed that adaptation may be enhanced by use of manually generated metadata, automated or semi-automated use of metadata by stored context dependent ontology hierarchies that describe the semantics of the learning domain. The use of neural networks or fuzzy logic filtering is a technique for future investigation. A prototype demonstrator is under construction
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