scholarly journals Deep Coupling Recurrent Auto-Encoder with Multi-Modal EEG and EOG for Vigilance Estimation

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1316
Author(s):  
Kuiyong Song ◽  
Lianke Zhou ◽  
Hongbin Wang
Keyword(s):  
Data Analysis ◽  
Traffic Safety ◽  
Feature Fusion ◽  
Pearson Correlation ◽  
Metric Learning ◽  
Feature Space ◽  
Research Field ◽  
Function Optimization ◽  
Analysis Model ◽  
New Feature

Vigilance estimation of drivers is a hot research field of current traffic safety. Wearable devices can monitor information regarding the driver’s state in real time, which is then analyzed by a data analysis model to provide an estimation of vigilance. The accuracy of the data analysis model directly affects the effect of vigilance estimation. In this paper, we propose a deep coupling recurrent auto-encoder (DCRA) that combines electroencephalography (EEG) and electrooculography (EOG). This model uses a coupling layer to connect two single-modal auto-encoders to construct a joint objective loss function optimization model, which consists of single-modal loss and multi-modal loss. The single-modal loss is measured by Euclidean distance, and the multi-modal loss is measured by a Mahalanobis distance of metric learning, which can effectively reflect the distance between different modal data so that the distance between different modes can be described more accurately in the new feature space based on the metric matrix. In order to ensure gradient stability in the long sequence learning process, a multi-layer gated recurrent unit (GRU) auto-encoder model was adopted. The DCRA integrates data feature extraction and feature fusion. Relevant comparative experiments show that the DCRA is better than the single-modal method and the latest multi-modal fusion. The DCRA has a lower root mean square error (RMSE) and a higher Pearson correlation coefficient (PCC).

scholarly journals Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm

2019 ◽  
Author(s):  
Mengdi Huai ◽  
Hongfei Xue ◽  
Chenglin Miao ◽  
Liuyi Yao ◽  
Lu Su ◽  
...  
Keyword(s):  
Error Bound ◽  
Metric Learning ◽  
Feature Space ◽  
Retention Rates ◽  
Generalization Error ◽  
Unseen Data ◽  
Deep Metric Learning ◽  
Generalization Error Bound ◽  
Real World Datasets ◽  
New Feature

As an effective way to learn a distance metric between pairs of samples, deep metric learning (DML) has drawn significant attention in recent years. The key idea of DML is to learn a set of hierarchical nonlinear mappings using deep neural networks, and then project the data samples into a new feature space for comparing or matching. Although DML has achieved practical success in many applications, there is no existing work that theoretically analyzes the generalization error bound for DML, which can measure how good a learned DML model is able to perform on unseen data. In this paper, we try to fill up this research gap and derive the generalization error bound for DML. Additionally, based on the derived generalization bound, we propose a novel DML method (called ADroDML), which can adaptively learn the retention rates for the DML models with dropout in a theoretically justified way. Compared with existing DML works that require predefined retention rates, ADroDML can learn the retention rates in an optimal way and achieve better performance. We also conduct experiments on real-world datasets to verify the findings derived from the generalization error bound and demonstrate the effectiveness of the proposed adaptive DML method.

scholarly journals Sentiment Analysis of Social Media via Multimodal Feature Fusion

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2010
Author(s):  
Kang Zhang ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Jianxin Liu ◽  
Wenxiao Li
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Feature Fusion ◽  
Image Features ◽  
Research Field ◽  
Sentiment Classification ◽  
Analysis Model ◽  
Noise Interference ◽  
Proposed Model ◽  
Multimodal Sentiment Analysis

In recent years, with the popularity of social media, users are increasingly keen to express their feelings and opinions in the form of pictures and text, which makes multimodal data with text and pictures the con tent type with the most growth. Most of the information posted by users on social media has obvious sentimental aspects, and multimodal sentiment analysis has become an important research field. Previous studies on multimodal sentiment analysis have primarily focused on extracting text and image features separately and then combining them for sentiment classification. These studies often ignore the interaction between text and images. Therefore, this paper proposes a new multimodal sentiment analysis model. The model first eliminates noise interference in textual data and extracts more important image features. Then, in the feature-fusion part based on the attention mechanism, the text and images learn the internal features from each other through symmetry. Then the fusion features are applied to sentiment classification tasks. The experimental results on two common multimodal sentiment datasets demonstrate the effectiveness of the proposed model.

The Influence of Leadership on the Transfer of Knowledge and Work Effectivity at Secretariat Office in Boven Digoel Regency

2020 ◽  
Vol 2 (4) ◽  
pp. 44-52
Author(s):  
Yoseph Awunim ◽  
Abdul Rahman Kadir ◽  
Mahlia Muis
Keyword(s):  
Data Analysis ◽  
Knowledge Transfer ◽  
Path Analysis ◽  
Positive Impact ◽  
Direct Impact ◽  
Transfer Of Knowledge ◽  
Analysis Model ◽  
Work Effectiveness ◽  
Statistical Results ◽  
Path Analysis Model

The research objective is to analyze the direct impact of leadership toward transfer knowledge and work effectiveness in Boven Digoel. Data analysis in this research is quantitative using a path analysis model (path analysis) with the help of Smart PLS Software version 3.2 .8. The research distributed questionnaires to 89 respondents of officers assigned at the secretariat office in Boven Digoel Regency. On the basis of statistical results, it was found that leadership can be said to have impacted positively and significant knowledge transfer and work effectiveness. The knowledge transfer also has a positive impact on work effectiveness.

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

Genetic variant effect prediction by supervised nonnegative matrix tri-factorization

2021 ◽  
Author(s):  
Asieh Amousoltani Arani ◽  
Mohammadreza Sehhati ◽  
Mohammad Amin Tabatabaiefar
Keyword(s):  
Input Data ◽  
Genetic Variant ◽  
Nonnegative Matrix ◽  
Feature Space ◽  
Variant Effect ◽  
New Feature ◽  
Variant Effect Prediction

A new feature space, which can discriminate deleterious variants, was constructed by the integration of various input data using the proposed supervised nonnegative matrix tri-factorization (sNMTF) algorithm.

scholarly journals Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

2021 ◽  
Vol 13 (4) ◽  
pp. 812
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Feature Space ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Forest Model ◽  
High Profile ◽  
Deep Forest ◽  
Positive Effect ◽  
Weak Target

Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

A new Feature-Fusion method based on training dataset prototype for surface defect recognition

2021 ◽  
Vol 50 ◽  
pp. 101392
Author(s):  
Yucheng Wang ◽  
Xinyu Li ◽  
Yiping Gao ◽  
Lijian Wang ◽  
Liang Gao
Keyword(s):  
Surface Defect ◽  
Feature Fusion ◽  
Training Dataset ◽  
Fusion Method ◽  
New Feature ◽  
Defect Recognition
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