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This article aims to rediscover the tactile aspects of radio listening, which until now have been underestimated by radio scholars, and to describe how haptic radio listening has evolved, from the beginning of broadcasting to the arrival of digital media. This article will first perform an overview of the studies that have dealt with the haptic dimension of media, then focus on what we call “haptically mediated” radio listening, a specific form of listening made possible by the interaction with radio content through mobile digital devices, and conclude with a critical depiction of the implications of haptically mediated listening for audience commodification. 相似文献
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Hierarchical Text Categorization (HTC) is the task of generating (usually by means of supervised learning algorithms) text classifiers that operate on hierarchically
structured classification schemes. Notwithstanding the fact that most large-sized classification schemes for text have a hierarchical
structure, so far the attention of text classification researchers has mostly focused on algorithms for “flat” classification,
i.e. algorithms that operate on non-hierarchical classification schemes. These algorithms, once applied to a hierarchical
classification problem, are not capable of taking advantage of the information inherent in the class hierarchy, and may thus
be suboptimal, in terms of efficiency and/or effectiveness. In this paper we propose TreeBoost.MH, a multi-label HTC algorithm consisting of a hierarchical variant of AdaBoost.MH, a very well-known member of the family of “boosting” learning algorithms. TreeBoost.MH embodies several intuitions that had arisen before within HTC: e.g. the intuitions that both feature selection and the selection
of negative training examples should be performed “locally”, i.e. by paying attention to the topology of the classification
scheme. It also embodies the novel intuition that the weight distribution that boosting algorithms update at every boosting
round should likewise be updated “locally”. All these intuitions are embodied within TreeBoost.MH in an elegant and simple way, i.e. by defining TreeBoost.MH as a recursive algorithm that uses AdaBoost.MH as its base step, and that recurs over the tree structure. We present the results of experimenting TreeBoost.MH on three HTC benchmarks, and discuss analytically its computational cost.
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| Fabrizio SebastianiEmail: |
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Chiara Milanese Valentina Cavedon Stefano Corte Tiziano Agostini 《Journal of sports sciences》2017,35(5):476-483
Improving motor skills represents one of the major issues in motor control and motor learning literature. The aim of this study was to investigate which of two strategies, method of amplification of error (MAE) or direct instruction (DI), would be more beneficial for error correction of the snatch technique. Thirty well-trained male weightlifters were randomly assigned to one of three training conditions (MAE, DI and Control). The experiment took place in only one practice session in which each lifter performed 3 pretraining trials, 8 training intervention trials, and 3 post-training trials, and a retention test session after 1 week. An optoelectronic motion capture system was used to measure the kinematic parameters of the weightlifting performance. After the training intervention, data showed that the MAE group revealed a greater improvement in several kinematic parameters when compared to the DI and Control groups, and the benefits derived from its application were still present 1 week later in the retention test. Nevertheless, the findings of the present study should be interpreted with caution due to the relatively small sample size; further research will also be necessary to evaluate the effects of MAE with different ability levels and other sport skills. The present findings could have practical implications for sport psychology and physical education because while practice is obviously necessary for improving learning, the efficacy of the learning process is essential in enhancing learners’ motivation and sport enjoyment. 相似文献
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