Titlebook: IT Convergence and Security; Proceedings of ICITC Hyuncheol Kim,Kuinam J. Kim Conference proceedings 2021 The Editor(s) (if applicable) and

Bravura

aterials.Includes supplementary material: .This volume covers all aspects of carbon and oxide based nanostructured materials. The topics include synthesis, characterization and application of carbon-based namely carbon nanotubes, carbon nanofibres, fullerenes, carbon filled composites etc. In additi

Canary

Abdulsalam K. Alhazmi,Fatima Al-Hammadi,Ezzadeen Kaed,Athar Imtiazaterials.Includes supplementary material: .This volume covers all aspects of carbon and oxide based nanostructured materials. The topics include synthesis, characterization and application of carbon-based namely carbon nanotubes, carbon nanofibres, fullerenes, carbon filled composites etc. In additi

Exposition

aterials.Includes supplementary material: .This volume covers all aspects of carbon and oxide based nanostructured materials. The topics include synthesis, characterization and application of carbon-based namely carbon nanotubes, carbon nanofibres, fullerenes, carbon filled composites etc. In additi

abolish

Justin Bryce Torres,Josiah Eleazar Regencia,William Emmanuel S. Yuaterials.Includes supplementary material: .This volume covers all aspects of carbon and oxide based nanostructured materials. The topics include synthesis, characterization and application of carbon-based namely carbon nanotubes, carbon nanofibres, fullerenes, carbon filled composites etc. In additi

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Minutes

An NMT-Based Approach to Translate Natural Language Questions to SPARQL Queries deep neural networks, Neural Machine Translation (NMT) models are employed to directly translate natural language questions into SPARQL queries in recent years. In this paper, we propose an NMT-based approach with Transformer model to generate SPARQL queries. Transformer model is chosen due to its

飞镖

Efficient Training Convolutional Neural Networks on Edge Devices with Gradient-Pruned Sign-SymmetricHowever, the lack of training capability for edge devices significantly limits the energy efficiency of distributed learning in real life. This paper describes a novel approach of training DNNs exploiting the redundancy and the weight asymmetry potential of conventional back propagation. We demonstr

Entropion

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