A Framework for Sharing Handwritten Notes

NotePals is an ink-based, collaborative note taking application that runs on personal digital assistants (PDAs). Meeting participants write notes in their own handwriting on a PDA. These notes are shared with other participants by synchronizing later with a shared note repository that can be viewed using a desktop-based web browser. NotePals is distinguished by its lightweight process, interface, and hardware. This demonstration illustrates the design of two different NotePals clients and our web-based note browser. Keywords PDA, pen-based user interface, CSCW, informal user interfaces, gestures, digital ink, mobile computin

Informal learning evidence in online communities of mobile device enthusiasts

This chapter describes a study that investigated the informal learning practices of enthusiastic mobile device owners. Informal learning is far more widespread than is often realized. Livingston (2000) pointed out that Canadian adults spend an average of fifteen hours per week on informal learning activities, more than they spend on formal learning activities. The motivation for these learning efforts generally comes from the individual, not from some outside force such as a school, university, or workplace. Therefore, in the absence of an externally imposed learning framework, informal learners will use whatever techniques,resources, and tools best suit their learning needs and personal preferences. As ownership of mobile technologies becomes increasingly widespread in the western world, it is likely that learners who have access to this technology will use it to support their informal learning efforts. This chapter presents the findings of a study into the various and innovative ways in which PDA and Smartphone users exploit mobile device functionality in their informal learning activities. The findings suggested that mobile device users deploy the mobile, connective, and collaborative capabilities of their devices in a variety of informal learning contexts, and in quite innovative ways. Trends emerged, such as the increasing importance of podcasting and audio and the use of built-in GPS, which may have implications for future studies. Informal learners identified learning activities that could be enhanced by the involvement of mobile technology, and developed methods and techniques that helped them achieve their learning goals

Public yet private: the status, durability and visibility of handover sheets

Drawing on data from a multi-site case study of a range of clinical settings, this paper explores the form of nursing handover sheets and the processes through which they are created and updated. We argue that these documents function as both public and private documents, having relevance for the whole ward while also acting as a personal workspace. Such dual functionality needs to be supported by any technology that seeks to provide for the work of handover, if the handover sheet is to continue to act as a space for work, rather than just a repository of information

Using non-participant observation to uncover mechanisms: insights from a realist evaluation

This article outlines how a realist evaluation of dementia care in hospitals used non-participant observation to support the refinement and testing of mechanisms likely to lead to the use of person-centred care. We found that comments and explanations of their actions from hospital staff during observation periods provided insights into the reasoning that generated their actions for care in real time. This informed subsequent data collection and analysis. Two worked examples of mechanisms first identified during non-participant observation demonstrate (1) how they were uncovered, and (2) how this informed research activities for theory refinement. Early, iterative engagement with the analytic process, primarily involving reflection and debate with the research team, maximised the potential of observation data to support surfacing underlying mechanisms, linking them to specific contexts and outcomes.Peer reviewedFinal Accepted Versio

The Potential of the Intel Xeon Phi for Supervised Deep Learning

Supervised learning of Convolutional Neural Networks (CNNs), also known as supervised Deep Learning, is a computationally demanding process. To find the most suitable parameters of a network for a given application, numerous training sessions are required. Therefore, reducing the training time per session is essential to fully utilize CNNs in practice. While numerous research groups have addressed the training of CNNs using GPUs, so far not much attention has been paid to the Intel Xeon Phi coprocessor. In this paper we investigate empirically and theoretically the potential of the Intel Xeon Phi for supervised learning of CNNs. We design and implement a parallelization scheme named CHAOS that exploits both the thread- and SIMD-parallelism of the coprocessor. Our approach is evaluated on the Intel Xeon Phi 7120P using the MNIST dataset of handwritten digits for various thread counts and CNN architectures. Results show a 103.5x speed up when training our large network for 15 epochs using 244 threads, compared to one thread on the coprocessor. Moreover, we develop a performance model and use it to assess our implementation and answer what-if questions.Comment: The 17th IEEE International Conference on High Performance Computing and Communications (HPCC 2015), Aug. 24 - 26, 2015, New York, US