Ubiquitous Learning (U-Learning), as an emerging learning paradigm, makes it possible for learners to carry out the learning activities at any places and at anytime. With the advantages of the devices, learners can ob...
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ISBN:
(纸本)9781424467082
Ubiquitous Learning (U-Learning), as an emerging learning paradigm, makes it possible for learners to carry out the learning activities at any places and at anytime. With the advantages of the devices, learners can obtain a variety of supplementary materials from the internet. In the scope of distance learning, LOR (Learning Object Repository) stands for managing and sharing of these kinds of materials which also be named as learning objects. However, some challenges raise while performing these activities. For instance, a huge amount of learning objects may appear while learners utilize the search service provided by the system. Learners have to spend time on collecting relevant resources for specific purposes. This situation may discourage the reusability of learning objects especially in a ubiquitous environment. In this paper, based on systematic re-examination of reuse scenarios, a resource retrieval mechanism, as a search middleware, was proposed to assist learners in obtaining relevant objects. The achievement of proposed mechanism can re-rank the search results in order of relevant degree based on the combination of learners' geographical information and input query. It may make resource retrieval more efficient in u-learning environment.
Traditionally, the modeling of sensory neurons has focused on the characterization and/or the learning of input-output relations. Motivated by the view that different neurons impose different partitions on the stimulu...
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Wafer bonding technology is applied to the GaN/quantum dots/GaN system, where CdSe/ZnS Quantum Dots serve as both the active layer and the binding layer. Photoluminescence is observed from quantum dots following wafer...
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In this research will show a method for sound-recognition with artificial neural network backpropagation concept. The artificial neural network use sigmoid activation function to all layer. Steps to the extraction, fi...
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The worldwide Digital Signage market has been getting increasingly popular in recent years. Nevertheless, for service providers, the Digital Signage business is still not easy to manage and time-consuming to operate. ...
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Recently, some variants of the l_1 norm, particularly matrix norms such as the l_(1,2) and l_(1,∞) norms, have been widely used in multi-task learning, compressed sensing and other related areas to enforce sparsity v...
ISBN:
(纸本)9781617823800
Recently, some variants of the l_1 norm, particularly matrix norms such as the l_(1,2) and l_(1,∞) norms, have been widely used in multi-task learning, compressed sensing and other related areas to enforce sparsity via joint regularized on. In this paper, we unify the l_(1,2) and l_(1,∞) norms by considering a family of l_(1,q) norms for 1 < q < ∞ and study the problem of determining the most appropriate sparsity enforcing norm to use in the context of multi-task feature selection. Using the generalized normal distribution, we provide a probabilistic interpretation of the general multi-task feature selection problem using the l_(1,q) norm. Based on this probabilistic interpretation, we develop a probabilistic model using the noninfor-mative Jeffreys prior. We also extend the model to learn and exploit more general types of pairwise relationships between tasks. For both versions of the model, we devise expectation-maximization (EM) algorithms to learn all model parameters, including q, automatically. Experiments have been conducted on two cancer classification applications using microarray gene expression data.
Crystal growth of Rb2CdI4 was performed by Czochralski method. Transparent colourless crystals with monoclinic structure were obtained. Temperature dependence of the dielectric constant along the b-axis was measured w...
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Neuron models are frequently used to represent the dynamics of real neurons but hardly ever to evaluate the electrochemical energy required to maintain that dynamics. This paper discusses the interpretation of a Hodgk...
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The paper developed a block-wise approach for ICA algorithms which can improve the computational efficiency of ICA without the degradation of performance for the separation of biomedical signals. Source signals includ...
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