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Advances in brain inspired cognitive systems : 10th International Conference, BICS 2019, Guangzhou, China, July 13-14, 2019, proceedings / Jinchang Ren [and 7 more] (eds.)

This book constitutes the refereed proceedings of the 10th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2019, held in Guangzhou, China, in July 2019. The 57 papers presented in this volume were carefully reviewed and selected from 129 submissions. The papers are org...

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Bibliographic Details
Online Access: Full Text (via Springer)
Corporate Author: Brain-inspired Cognitive Systems (Conference) Guangzhou, China)
Other Authors: Ren, Jinchang (Editor)
Other title:BICS 2019.
Format: Conference Proceeding eBook
Language:English
Published: Cham, Switzerland : Springer, [2020]
Series:Lecture notes in computer science ; 11691.
LNCS sublibrary. Artificial intelligence.
Lecture notes in computer science. Lecture notes in artificial intelligence.
Subjects:
Artificial intelligence > Congresses.
Cognitive science > Data processing > Congresses.
User-centered system design > Congresses.
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Neural Computation
  • Improving Image Caption Performance with Linguistic Context
  • 1 Introduction
  • 2 Related Work
  • 3 Attention-Based Framework
  • 4 Attention-Based Network with Linguistic Context
  • 5 Experiment
  • 6 Conclusion
  • References
  • Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification
  • 1 Introduction
  • 2 Methodology
  • 2.1 Problem Definition
  • 2.2 Model Architecture
  • 3 Experiments
  • 3.1 Datasets
  • 3.2 Embedding Results
  • 3.3 Zero-Shot Recognition
  • 4 Conclusion
  • References.
  • A Multi-view Images Classification Based on Shallow Convolutional Neural Network
  • 1 Introduction
  • 2 Related Works
  • 3 Method
  • 3.1 Dropout to Improve Accuracy
  • 3.2 SCNN-a and SCNN Model
  • 3.3 Training and Testing Process with SCNN-a and SCNN
  • 4 Experiments
  • 4.1 Dataset
  • 4.2 Experimental Parameter
  • 4.3 Results
  • 5 Conclusions
  • References
  • Low-Rank Laplacian Similarity Learning
  • 1 Introduction
  • 2 Methodology
  • 2.1 LE
  • 2.2 Low-Rank Laplacian Similarity Learning
  • 3 Optimization Algorithm
  • 4 Experiments
  • 4.1 Datasets
  • 4.2 Clustering.
  • 4.3 Semi-supervised Classification
  • 4.4 Classification
  • 5 Conclusions
  • References
  • Long Short-Term Attention
  • 1 Introduction
  • 2 Related Work
  • 2.1 LSTM
  • 2.2 Models Using the Attention Mechanism
  • 3 Long Short-Term Attention
  • 3.1 The Attention Gate
  • 3.2 LSTA
  • 4 Experiments
  • 4.1 Experiments on the Image Classification Task
  • 4.2 Experiments on the Sentiment Analysis Task
  • 5 Conclusion
  • References
  • EEG-Based Emotion Estimate Using Shallow Fully Convolutional Neural Network with Boost Training Strategy
  • Abstract
  • 1 Introduction
  • 2 Methodology.
  • 2.1 Shallow Fully Convolutional Network (SFCN)
  • 2.2 Boost Training Strategy
  • 3 Experiments and Results
  • 3.1 Database and Preprocessing
  • 3.2 Training Details
  • 3.3 Performance of Shallow Fully Convolutional Network
  • 3.4 Performance of Boost Training Strategy
  • 4 Conclusions
  • Acknowledgments
  • References
  • Emotion Recognition Using Eye Gaze Based on Shallow CNN with Identity Mapping
  • Abstract
  • 1 Introduction
  • 2 Eye Gaze Feature Set
  • 3 Methodology
  • 3.1 Sample Construction
  • 3.2 Shallow CNN with Identity Mapping
  • 3.3 Validation and Classification Strategy.
  • 4 Experimental Results
  • 4.1 Comparison with Baseline
  • 4.2 Comparison of Using and Not Using Classification Strategy
  • 4.3 Comparison of Features
  • 4.4 Comparison of Networks
  • 5 Conclusion
  • Acknowledgments
  • References
  • Height Prediction for Growth Hormone Deficiency Treatment Planning Using Deep Learning
  • 1 Introduction
  • 2 Related Knowledge
  • 2.1 Deep Feed-Forward Neural Network
  • 2.2 Data Acquisition
  • 2.3 Data Preprocessing
  • 2.4 Data Normalisation
  • 2.5 Collinearity
  • 2.6 Missing Values
  • 2.7 Feature Selection
  • 3 The Proposed Deep Learning Prediction Model.

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