914 research outputs found

    HSLIC Annual Report FY2006-07

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    https://digitalrepository.unm.edu/hslic-annual-reports/1001/thumbnail.jp

    The MARVEL EU project: A social constructivist approach to remote experimentation

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    The work presented in this paper proposes a conceptual model where remote experimentation is envisaged as a tool to achieve instructional objectives via social constructivist learning methods. The conceptual model described considers remote experiments as embedded activities in an e-learning framework that integrates the necessary tools to support the acquisition of theoretical concepts, synchronous communication via video-conferencing, interface panels to the equipments available in the remote workbench, and the necessary management tools to support this architecture. Each remote experiment is perceived by the students as a broader activity (called workshop activity) that enables them to achieve pre-defined learning goals, where collaborative actions and peer-review activities are at the basis of the underlying social constructivist learning model. The outcome of these activities is itself a learning object that provides evidence that the learning goals were achieved

    Health consensus: a digital adapted Delphi for healthcare

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    New tools are needed to facilitate the involvement of health professionals in healthcare participative processes, partially because a relevant segment of healthcare knowledge and decision-making is capillary distributed among them. A collaborative design strategy has been applied to the creation of an Internet tool to produce digitally adapted Delphi for healthcare purposes. During the period 2012-16 the prototype of the tool has been gradually improved through its application to 18 real cases. It is proposed the model Health Consensus as a digitally adapted Delphi supported by the various capabilities of Internet. The authors agree that Health Consensus is a useful and expandable tool for participative processes. The Internet provides several opportunities to overcome many of the limitations of conventional Delphi, as well as improving the final studies with new functionalities.Peer ReviewedPostprint (author's final draft

    Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging

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    Deep learning at scale is dominated by communication time. Distributing samples across nodes usually yields the best performance, but poses scaling challenges due to global information dissemination and load imbalance across uneven sample lengths. State-of-the-art decentralized optimizers mitigate the problem, but require more iterations to achieve the same accuracy as their globally-communicating counterparts. We present Wait-Avoiding Group Model Averaging (WAGMA) SGD, a wait-avoiding stochastic optimizer that reduces global communication via subgroup weight exchange. The key insight is a combination of algorithmic changes to the averaging scheme and the use of a group allreduce operation. We prove the convergence of WAGMA-SGD, and empirically show that it retains convergence rates similar to Allreduce-SGD. For evaluation, we train ResNet-50 on ImageNet; Transformer for machine translation; and deep reinforcement learning for navigation at scale. Compared with state-of-the-art decentralized SGD variants, WAGMA-SGD significantly improves training throughput (e.g., 2.1x on 1,024 GPUs for reinforcement learning), and achieves the fastest time-to-solution (e.g., the highest score using the shortest training time for Transformer).Comment: Published in IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS), vol. 32, no. 7, pp. 1725-1739, 1 July 202

    Computer-Mediated Deception: Collective Language-action Cues as Stigmergic Signals for Computational Intelligence

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    Collective intelligence is easily observable in group-based or interpersonal pairwise interaction, and is enabled by environment-mediated stigmertic signals. Based on innate ability, human sensors not only sense and coordinate, but also tend to solve problems through these signals. This paper argues the efficacy of computational intelligence for adopting the collective language-action cues of human intelligence as stigmertic signals to differentiate deception. A study was conducted in synchronous computer-mediated communication environment with a dataset collected from 2014 to 2015. An online game was developed to examine the accuracy of certain language-action cues (signs), deceptive actors (agents) during pairwise interaction (environment). The result of a logistic regression analysis demonstrates the computational efficacy of collective language-action cues in differentiating and sensing deception in spontaneous communication. This study contributes to the computational modeling in adapting human intelligence as a base to attribute computer-mediated deception

    Saudi College Students’ Attitudes towards Online Collaborative Learning

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    Online learning has the potential to expand collaborative learning and teaching. It has tremendous potential in the educational field, as it allows people to access computing services to share and edit data over the Internet. Yet few studies investigate the growing impacts of online learning on students’ learning skills, such as collaborative learning. This study investigates attitudes, factors, and challenges to adopt online applications by Saudi students at King Abdul-Aziz University to support collaborative learning. The hypothesized model was developed through the Technology Acceptance Model of Davis, and the Diffusion of Innovation model of Rogers. Three hundred and six students participated in an electronic survey (138 female and168 male). The findings reveal the students have positive attitudes toward collaborative learning with their classmates (M = 4.07, SD = .78), and have positive attitudes toward adopting online collaborative learning, (M = 3.96, SD = .77). Of the participants, 60.1% use online applications for their learning, and 69.9% preferred the learning style that mix between collaborative and individual learning style. There was a significant relationship between the overall attitudes of the students (M = 3.96, SD = .77), and perceived usefulness of online applications in collaborative learning (M = 4.09, SD = .68), with r (306) = .774, p = .00. Students reported facing three major barriers to adopt online collaborative learning, which are data concerns (M = 3.86, SD = 1.01), privacy issues (M = 3.64 and SD = 1.22), and security issues (M = 3.47 and SD = 1.19). Of three predictors: age, gender, and education major, none were significant predictors of student attitudes towards adopting online collaborative learning (F (3,302) = 1.32, p .05). Given that the online applications can be a very useful solution for education, as it may reduce the costs incurred for the purchase of computers, other equipment, and for employing IT people, it is urgent that universities and administrators start implementing this solution

    ALT-C 2010 - Conference Introduction and Abstracts

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