scholarly journals Affective EEG-Based Person Identification Using Channel Attention Convolutional Neural Dense Connection Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yunxia Zhang ◽  
Xin Li ◽  
Changming Zhao ◽  
Wenyin Zheng ◽  
Manqing Wang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Recognition Rate ◽  
The State ◽  
Attention Mechanism ◽  
Affective States ◽  
Biometric Recognition ◽  
Person Identification ◽  
Data Set ◽  
Approach Channel ◽  
Channel Information

In the biometric recognition mode, the use of electroencephalogram (EEG) for biometric recognition has many advantages such as anticounterfeiting and nonsteal ability. Compared with traditional biometrics, EEG biometric recognition is safer and more concealed. Generally, EEG-based biometric recognition is to perform person identification (PI) through EEG signals collected by performing motor imagination and visual evoked tasks. The aim of this paper is to improve the performance of different affective EEG-based PI using a channel attention mechanism of convolutional neural dense connection network (CADCNN net) approach. Channel attention mechanism (CA) is used to handle the channel information from the EEG, while convolutional neural dense connection network (DCNN net) extracts the unique biological characteristics information for PI. The proposed method is evaluated on the state-of-the-art affective data set HEADIT. The results indicate that CADCNN net can perform PI from different affective states and reach up to 95%-96% mean correct recognition rate. This significantly outperformed a random forest (RF) and multilayer perceptron (MLP). We compared our method with the state-of-the-art EEG classifiers and models of EEG biometrics. The results show that the further extraction of the feature matrix is more robust than the direct use of the feature matrix. Moreover, the CADCNN net can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.

scholarly journals A Benchmark and Evaluation of Non-Rigid Structure from Motion

2020 ◽  
Author(s):  
Sebastian Hoppe Nesgaard Jensen ◽  
Mads Emil Brix Doest ◽  
Henrik Aanæs ◽  
Alessio Del Bue
Keyword(s):  
Computer Vision ◽  
Structure From Motion ◽  
State Of The Art ◽  
The State ◽  
Quality Data ◽  
Data Set ◽  
Rigid Structure ◽  
Public Data ◽  
3D Information ◽  
Further Development

AbstractNon-rigid structure from motion (nrsfm), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrsfm, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrsfm. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.

Ensemble-based deep meta learning for medical image segmentation

2021 ◽  
pp. 1-7
Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
The State ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Meta Learning ◽  
Small Set

Deep learning methods have led to a state of the art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders to collaborate. However, limited labelled data set limits the deep learning algorithm to generalize for one domain into another. To handle the problem, meta-learning helps to learn from a small set of data. We proposed a meta learning-based image segmentation model that combines the learning of the state-of-the-art model and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segments part and remove noise from the new test image. The proposed model can achieve 0.94 precision and 0.92 recall. The ability to increase 3.3% among the state-of-the-art algorithms.

SANTM: Efficient Self-attention-driven Network for Text Matching

2022 ◽  
Vol 22 (3) ◽  
pp. 1-21
Author(s):  
Prayag Tiwari ◽  
Amit Kumar Jaiswal ◽  
Sahil Garg ◽  
Ilsun You
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
The State ◽  
Attention Mechanism ◽  
Matching Problems ◽  
Attention Model ◽  
Extra Information ◽  
Textual Entailment ◽  
Benchmark Datasets ◽  
Text Matching

Self-attention mechanisms have recently been embraced for a broad range of text-matching applications. Self-attention model takes only one sentence as an input with no extra information, i.e., one can utilize the final hidden state or pooling. However, text-matching problems can be interpreted either in symmetrical or asymmetrical scopes. For instance, paraphrase detection is an asymmetrical task, while textual entailment classification and question-answer matching are considered asymmetrical tasks. In this article, we leverage attractive properties of self-attention mechanism and proposes an attention-based network that incorporates three key components for inter-sequence attention: global pointwise features, preceding attentive features, and contextual features while updating the rest of the components. Our model follows evaluation on two benchmark datasets cover tasks of textual entailment and question-answer matching. The proposed efficient Self-attention-driven Network for Text Matching outperforms the state of the art on the Stanford Natural Language Inference and WikiQA datasets with much fewer parameters.

scholarly journals A Span-based Joint Model for Opinion Target Extraction and Target Sentiment Classification

2019 ◽  
Author(s):  
Yan Zhou ◽  
Longtao Huang ◽  
Tao Guo ◽  
Jizhong Han ◽  
Songlin Hu
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Joint Model ◽  
The State ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Global Information ◽  
Target Extraction ◽  
Benchmark Datasets ◽  
Tagging Methods

Target-Based Sentiment Analysis aims at extracting opinion targets and classifying the sentiment polarities expressed on each target. Recently, token based sequence tagging methods have been successfully applied to jointly solve the two tasks, which aims to predict a tag for each token. Since they do not treat a target containing several words as a whole, it might be difficult to make use of the global information to identify that opinion target, leading to incorrect extraction. Independently predicting the sentiment for each token may also lead to sentiment inconsistency for different words in an opinion target. In this paper, inspired by span-based methods in NLP, we propose a simple and effective joint model to conduct extraction and classification at span level rather than token level. Our model first emulates spans with one or more tokens and learns their representation based on the tokens inside. And then, a span-aware attention mechanism is designed to compute the sentiment information towards each span. Extensive experiments on three benchmark datasets show that our model consistently outperforms the state-of-the-art methods.

scholarly journals Modified Deep Neural Networks for Dog Breeds Identification

2018 ◽  
Author(s):  
Aydin Ayanzadeh ◽  
Sahand Vahidnia
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Data Sets ◽  
Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

scholarly journals Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Wenjun Du ◽  
Bo Sun ◽  
Jiating Kuai ◽  
Jiemin Xie ◽  
Jie Yu ◽  
...  
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
State Of The Art ◽  
The State ◽  
Attention Mechanism ◽  
Critical Parameters ◽  
Depth Information ◽  
Highway Traffic ◽  
Residual Network ◽  
Traffic Diversion

Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).

scholarly journals Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction

2020 ◽  
Vol 34 (05) ◽  
pp. 7594-7601
Author(s):  
Pierre Colombo ◽  
Emile Chapuis ◽  
Matteo Manica ◽  
Emmanuel Vignon ◽  
Giovanna Varni ◽  
...  
Keyword(s):  
Machine Translation ◽  
Random Fields ◽  
Conditional Random Fields ◽  
State Of The Art ◽  
The State ◽  
Attention Mechanism ◽  
Accuracy Score ◽  
Beam Search ◽  
Conversational Agents ◽  
Neural Machine Translation

The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.

scholarly journals Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Junfeng Yang ◽  
Yuwen Huang ◽  
Fuxian Huang ◽  
Gongping Yang
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Rate ◽  
K Nearest Neighbor ◽  
Biometric Recognition ◽  
Recognition Model ◽  
The Third ◽  
Model Based ◽  
Current State ◽  
Biometric Trait

Photoplethysmography (PPG) biometric recognition has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on PPG biometric recognition have been reported, challenges in noise sensitivity and poor robustness remain. To address these issues, a PPG biometric recognition framework is presented in this article, that is, a PPG biometric recognition model based on a sparse softmax vector and k-nearest neighbor. First, raw PPG data are rerepresented by sliding window scanning. Second, three-layer features are extracted, and the features of each layer are represented by a sparse softmax vector. In the first layer, the features are extracted by PPG data as a whole. In the second layer, all the PPG data are divided into four subregions, then four subfeatures are generated by extracting features from the four subregions, and finally, the four subfeatures are averaged as the second layer features. In the third layer, all the PPG data are divided into 16 subregions, then 16 subfeatures are generated by extracting features from the 16 subregions, and finally, the 16 subfeatures are averaged as the third layer features. Finally, the features with first, second, and third layers are combined into three-layer features. Extensive experiments were conducted on three PPG datasets, and it was found that the proposed method can achieve a recognition rate of 99.95%, 97.21%, and 99.92% on the respective sets. The results demonstrate that the proposed method can outperform current state-of-the-art methods in terms of accuracy.

scholarly journals Interpretable Rumor Detection in Microblogs by Attending to User Interactions

2020 ◽  
Vol 34 (05) ◽  
pp. 8783-8790 ◽  
Author(s):  
Ling Min Serena Khoo ◽  
Hai Leong Chieu ◽  
Zhong Qian ◽  
Jing Jiang
Keyword(s):  
State Of The Art ◽  
Attention Mechanism ◽  
Data Sets ◽  
Long Distance ◽  
User Interactions ◽  
Data Set ◽  
Structure Information ◽  
Attention Model ◽  
Tree Models ◽  
Rumor Detection

We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.

scholarly journals Asteroid Family Identification: History and State of the Art

2015 ◽  
Vol 10 (S318) ◽  
pp. 16-27 ◽  
Author(s):  
Zoran Knežević
Keyword(s):  
State Of The Art ◽  
The State ◽  
Classification Methods ◽  
Data Set ◽  
The Third ◽  
Proper Elements ◽  
The Future ◽  
History Of ◽  
Asteroid Families ◽  
Collisional Evolution

AbstractThe history of asteroid families, from their discovery back in 1918, until the present time, is briefly reviewed. Two threads have been followed: on the development of the theories of asteroid motion and the computation of proper elements, and on the methods of classification themselves. Three distinct periods can be distinguished: the first one until mid-1930s, devoted to discovery and first attempts towards understanding of the properties of families; the second one, until early 1980s, characterized by a growing understanding of their importance as key evidence of the collisional evolution; the third one, characterized by an explosion of work and results, comprises the contemporary era. An assessment is given of the state-of-the-art and possible directions for the future effort, focusing on the dynamical studies, and on improvements of classification methods to cope with ever increasing data set.

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