Through a detailed analysis of the theoretical basis for MOOCs in terms of educational learning theories, this paper attempts at finding the corresponding relationship between the theories and MOOCs, which not only th...
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ISBN:
(纸本)9789881925367
Through a detailed analysis of the theoretical basis for MOOCs in terms of educational learning theories, this paper attempts at finding the corresponding relationship between the theories and MOOCs, which not only theoretically explains the reasons why MOOCs exceed all the previous education models, but also empirically proves it with voluminous facts and data. The author conducts this research in the hope of enlightening the online education platform of other types. Besides, this paper provides the model framework and strategy to multidimensional educational review based on MOOCs. The model and strategy are applicable not only to MOOCs, the large-scale online education, but also to various online education platforms.
Recent research has shown that the improvement of mean retrieval effectiveness (e.g., MAP) may sacrifice the retrieval stability across queries, implying a tradeoff between effectiveness and stability. The evaluation ...
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ISBN:
(纸本)9781450325981
Recent research has shown that the improvement of mean retrieval effectiveness (e.g., MAP) may sacrifice the retrieval stability across queries, implying a tradeoff between effectiveness and stability. The evaluation of both effectiveness and stability are often based on a baseline model, which could be weak or biased. In addition, the effectiveness-stability tradeoff has not been systematically or quantitatively evaluated over TREC participated systems. The above two problems, to some extent, limit our awareness of such tradeoff and its impact on developing future IR models. In this paper, motivated by a recently proposed bias-variance based evaluation, we adopt a strong and unbiased "baseline", which is a virtual target model constructed by the best performance (for each query) among all the participated systems in a retrieval task. We also propose generalized bias-variance metrics, based on which a systematic and quantitative evaluation of the effectiveness-stability tradeoff is carried out over the participated systems in the TREC Ad-hoc Track (1993-1999) and Web Track (2010-2012). We observe a clear effectiveness-stability tradeoff, with a trend of becoming more obvious in more recent years. This implies that when we pursue more effective IR systems over years, the stability has become problematic and could have been largely overlooked. Copyright 2014 ACM.
Network coding is able to address output conflicts when fanout splitting is allowed for multicast ***,it successfully achieves a larger rate region than non-coding approaches in crossbar ***,network coding requires la...
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Network coding is able to address output conflicts when fanout splitting is allowed for multicast ***,it successfully achieves a larger rate region than non-coding approaches in crossbar ***,network coding requires large coding buffers and a high computational cost on encoding and *** this paper,we propose a novel Online Network Coding framework called Online NC for multicast switches,which is adaptive to constrained ***,it enjoys a much lower decoding complexity by a Vandermonde matrix based approach,as compared to conven-tional randomized network coding Our approach realizes online coding with one coding algo-rithm that synchronizes buffering and ***,we significantly reduce requirements on buffer space,while also sustaining high *** confirm the superior advantages of our contributions using empirical studies.
MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelle...
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MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context).
Multimodal sentiment analysis leverages information from multiple sensors to achieve a comprehensive interpretation of emotions. However, different modalities do not always boost each other as expected. They compete w...
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Multimodal sentiment analysis leverages information from multiple sensors to achieve a comprehensive interpretation of emotions. However, different modalities do not always boost each other as expected. They compete with each other, leading to some modalities being under-optimized during the training process. To address this issue, we propose Adaptive Gradient Scaling with Sparse Mixture-of-Experts (AGS-SMoE). We first discuss the issue of modal preemption in unified multimodal learning from the perspective of causal preemption. Driven by actual cause, we use the gradient norms from different encoders at two fusion stages as evidence, estimating the current modal preemption state using a parameter-free method. Then, based on the dynamic preemption factor, we design a gradient scaling method to balance optimization for different encoders. Furthermore, we use Mixture-of-Experts to sparsify and perceive multimodal tokens in different preemption states. As a result, our experiments on four multimodal sentiment analysis datasets have achieved state-of-the-art results. Moreover, our method improves modal representation learning at different stages. Extensive experiments confirm that our method can alleviate the modal preemption problem in a plug-and-play manner. Our code is available at https://***/TheShy-Dream/AGS-SMoE.
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