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Titlebook: Advances in Brain Inspired Cognitive Systems; 10th International C Jinchang Ren,Amir Hussain,Yinyin Xiao Conference proceedings 2020 Spring

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Building Global Security Through Cooperation to authentication. An eye-tracking machine was used to record cognitive measurements that were used for decision analysis. Multi Criteria Decision Analysis (MCDA) approach was then used to evaluate the participants’ decisions. The result showed that participants security decisions are varied depend
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Semi-supervised Batch Mode Active Learning for Multi-class Classification978-3-8349-8639-9
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Dimensionality Reduction with Extreme Learning Machine Based on Manifold Preserving978-1-4419-8813-3
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Conference proceedings 2020in Guangzhou, China, in July 2019...The 57 papers presented in this volume were carefully reviewed and selected from 129 submissions. The papers are organized in topical sections named: neural computation; biologically inspired systems; image recognition: detection, tracking and classification; and
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https://doi.org/10.1007/978-1-4842-3492-1linguistic context of translation results. We evaluated the performance of BLEU on the benchmark dataset of MSCOCO 2014. Experimental results show the effectiveness of the proposed method. Specifically, the performance of BLEU-1, BLEU-2, BLEU-3 BLEU-4, and METEOR is improved by 0.2%, 0.7%, 0.6%, 0.5%, and 0.1, respectively.
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Does Ocean Acidification Even Matter?ency map through further learning. The proposed model can efficiently and flexibly aggregate multi-layer convolution features and provides accurate saliency maps. The extensive experiments show that our method achieves satisfactory results compared with some current representative methods.
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Isn’t the Ozone Hole Sorted Now?se that non-overlapping bounding boxes in training phase. Experimental validations have shown that our tracker performs substantially improvement on the tracking benchmarks OTB100 and is effective to deformation, occlusion and other challenges in object tracking.
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Non-Military Aspects of European Securitytch mode sample selection strategy for multi-class active learning. By minimizing the expected error with respect to the optimal classifier, we can select the most valuable unlabeled samples for labeling. Experimental results on three public image datasets have demonstrated the effectiveness of our proposed method.
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