2021 International Joint Conference on Neural Networks (IJCNN)最新文献_第8页

Neural inverse optimal control applied to design therapeutic options for patients with COVID-19 应用神经逆最优控制设计COVID-19患者治疗方案

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9534240

V. Chan, E. Hernández-Vargas, E. Sánchez

引用次数: 0

Large-scale singer recognition using deep metric learning: an experimental study 基于深度度量学习的大规模歌手识别实验研究

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533911

Shichao Hu, B. Liang, Zhouxuan Chen, Xiao Lu, Ethan Zhao, Simon Lui

{"title":"Large-scale singer recognition using deep metric learning: an experimental study","authors":"Shichao Hu, B. Liang, Zhouxuan Chen, Xiao Lu, Ethan Zhao, Simon Lui","doi":"10.1109/IJCNN52387.2021.9533911","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533911","url":null,"abstract":"Singer recognition aims to automatically recognize the singer of a given recording. Compared to spoken voices, singing voice is characterized by a much higher degree of vocal style. The task becomes more challenging when it operates on numerous singers. This paper explores different strategies in a deep metric learning framework, with special focus on their performance in a large-scale dataset consisting of audio samples from 5057 singers. We conduct thorough experiments to compare loss functions, including triplet loss, generalized end-to-end (GE2E) loss, and prototypical network (PN) loss. Effects of vocal source separation is also investigated. Using audio inputs with separated vocals, our model trained with PN loss outperforms other evaluated methods in the identification task. While in the verification task with one-on-one comparison of two single embeddings, triplet loss achieves the best results. However, verification using PN loss shows superior performance to methods with triplet loss when using the centroid of 5 embed dings to represent the singer embedding. Using longer segments for a singer representation consistently improves the performance for all evaluated tasks.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124128735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

GKT-CD: Make Cognitive Diagnosis Model Enhanced by Graph-based Knowledge Tracing GKT-CD:基于图的知识追踪增强认知诊断模型

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533298

Junrui Zhang, Yun Mo, Changzhi Chen, Xiaofeng He

{"title":"GKT-CD: Make Cognitive Diagnosis Model Enhanced by Graph-based Knowledge Tracing","authors":"Junrui Zhang, Yun Mo, Changzhi Chen, Xiaofeng He","doi":"10.1109/IJCNN52387.2021.9533298","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533298","url":null,"abstract":"Recent advancements in online education platforms have caused an increase in research on adaptive learning system, wherein student performance on coursework exercises is predicted over time and directed exercises are recommended. In adaptive learning systems, knowledge tracing and cognitive diagnosis are critical techniques for predicting student performance. The traditional cognitive diagnosis model's terms are suitable for the student abilities analysis, but they rely on handcrafted interaction functions and only use student's response records so that it is difficult to capture the dynamic knowledge mastery ability of students. Although using the knowledge tracing to enhance cognitive diagnosis is a meaningful attempt towards towards capturing student performance, the RNN-based know-eledge tracing model have limited effect. This paper proposes a new model, named GKT-CD, which fuses knowledge tracing and cognitive diagnosis in a synergistic framework. In GKT-CD, we develop Gated-GNN to trace the student-knowledge response records and extract students' latent trait. And then, we use hierarchical structure in knowledge to construct exercise latent vector. At last, we use two-dimensional item response theory (IRT) to predict the probability of students answering exercises correctly. Extensive experiments conducted on realworld datasets show that the GKT-CD model is feasible and obtain excellent performance.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127759548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Nacre: Proactive Recurrent Concept Drift Detection in Data Streams 珍珠:数据流中的主动循环概念漂移检测

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533926

Ocean Wu, Yun Sing Koh, G. Dobbie, Thomas Lacombe

{"title":"Nacre: Proactive Recurrent Concept Drift Detection in Data Streams","authors":"Ocean Wu, Yun Sing Koh, G. Dobbie, Thomas Lacombe","doi":"10.1109/IJCNN52387.2021.9533926","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533926","url":null,"abstract":"Concept drift detection is used to signal to a learning algorithm that there has been a change in the underlying distribution of the data stream. However, there is a delay in detecting the actual drifts, leading to performance loss between the start of the drift and the detection point. There are two major challenges in reducing such performance loss, specifically the difficulty in anticipating the location of the next drift point and determining the exact concept that will appear for timely concept adaptation. In this research, we leverage concept recurrences in data streams. We proposed a framework called Nacre, which can perform proactive drift detection and online updates to allow for smooth adaptation of concept drifts. We present a novel technique, called drift coordinator, that anticipates the next drift point and assesses the incoming concept. This will ultimately increase accuracy in the classification performance. We demonstrate that our method is able to learn and predict drift trends in streams with recurring drifts. This allows the anticipation of future changes which enables users and detection methods to be more proactive. We empirically show that our technique outperforms baselines in terms of accuracy, kappa, accuracy gain per drift and cumulative accuracy gain on both synthetic and real-world datasets.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126546501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Cascaded Hierarchical Context-Aware Vehicle Re-Identification 级联分层上下文感知车辆再识别

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533385

Wancheng Mo, Jianming Lv

{"title":"Cascaded Hierarchical Context-Aware Vehicle Re-Identification","authors":"Wancheng Mo, Jianming Lv","doi":"10.1109/IJCNN52387.2021.9533385","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533385","url":null,"abstract":"Vehicle Re-Identification (Re-ID) is a challenging task, which aims to match the surveillance images containing the same vehicle. Since vehicles of the same type tend to share very similar appearance, slight difference in local areas are usually critical in the vehicle Re-ID task. Recently, some fine-grained Re-ID algorithms have achieved superior performance by modeling the key areas with specific semantics such as windows, lights, car orientation, etc. However, such methods are labor-intensive to label the key areas for object detection. This work proposes a Cascaded Hierarchical Context-Aware scheme namely CHCA, which is free of fine-grained labeling, to adaptively extract the visual features of discriminative local areas based on surrounding hierarchical context information with a specially designed recursive cross-level attention mechanism. It does not require any additional supervision and is easy to be embedded in existing networks. Extensive experiments on three popular vehicle Re-ID benchmarks demonstrate the effectiveness of CHCA, which has competitive results with existing state-of-the-art methods applying fine-grained labels.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125970168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Towards the Overcome of Performance Pitfalls in Data Stream Mining Tools 数据流挖掘工具中性能缺陷的克服

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533375

Lucca Portes Cavalheiro, M. Alves, J. P. Barddal

引用次数: 0

Temporal Dependent Local Learning for Deep Spiking Neural Networks 深度尖峰神经网络的时间依赖局部学习

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9534390

Chenxiang Ma, Junhai Xu, Qiang Yu

{"title":"Temporal Dependent Local Learning for Deep Spiking Neural Networks","authors":"Chenxiang Ma, Junhai Xu, Qiang Yu","doi":"10.1109/IJCNN52387.2021.9534390","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9534390","url":null,"abstract":"Spiking neural networks (SNNs) are promising to replicate the efficiency of the brain by utilizing a paradigm of spike-based computation. Training a deep SNN is of great importance for solving practical tasks as well as discovering the fascinating capability of spike-based computation. The biologically plausible scheme of local learning motivates many approaches that enable training deep networks in an efficient parallel way. However, most of the existing spike-based local learning approaches show relatively low performances on challenging tasks. In this paper, we propose a new spike-based temporal dependent local learning (TDLL) algorithm, where each hidden layer of a deep SNN is independently trained with an auxiliary trainable spiking projection layer, and temporal dependency is fully employed to construct local errors for adjusting parameters. We examine the performance of the proposed TDLL with various networks on the MNIST, Fashion-MNIST, SVHN and CIFAR-10 datasets. Experimental results highlight that our method can scale up to larger networks, and more importantly, achieves relatively high accuracies on all benchmarks, which are even competitive with the ones obtained by global backpropagation-based methods. This work therefore contributes to providing an effective and efficient local learning method for deep SNNs, which could greatly benefit the developments of distributed neuromorphic computing.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"305 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115934968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Noisy Mammogram Classification Method Based on New Weighted Fusion Framework 基于新加权融合框架的乳腺x线图像噪声分类方法

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533752

Jianhui Zhao, Saifeng Feng, Jing Yang, Zhiyong Yuan, Wenyuan Zhao, Tingbao Zhang

{"title":"Noisy Mammogram Classification Method Based on New Weighted Fusion Framework","authors":"Jianhui Zhao, Saifeng Feng, Jing Yang, Zhiyong Yuan, Wenyuan Zhao, Tingbao Zhang","doi":"10.1109/IJCNN52387.2021.9533752","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533752","url":null,"abstract":"Convolutional neural network (CNN) has made outstanding performance in the classification of natural light images. However, images in many fields have the characteristics of high noise, low resolution, no color information and small data set, such as mammogram, which will affect the accuracy and robustness of the model. In order to improve the classification accuracy and the noise robustness of convolution network for mammogram images, we design a novel classification model based on the new weighted fusion convolution framework. This method has been improved from the following aspects: firstly, we take the place of traditional max-pooling layer with convolution layer with increased step, which achieves the purpose of down-sampling and extracts features more rationally through back-propagation. Secondly, we fuse multi-level feature maps to make full use of the information contained in the shallow levels and deep levels. At the same time, we design a new fusion method to effectively fuse the feature maps from different layers with different sizes. Finally, our model is tested on the mammographic image analysis society (MIAS), which is a mammographic medical image dataset. The experimental results show that the average accuracy of the model is as high as 97.6%, and the convolution layer with increased step has better robustness than the traditional max-pooling layer.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131351730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seasonal-Trend decomposition based on Loess + Machine Learning: Hybrid Forecasting for Monthly Univariate Time Series 基于黄土+机器学习的季节趋势分解:月度单变量时间序列的混合预测

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533644

Gabriel Dalforno Silvestre, M. Santos, A. Carvalho

引用次数: 0

Multi-Modal Emotion Recognition Based On deep Learning Of EEG And Audio Signals 基于脑电和音频信号深度学习的多模态情绪识别

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533663

Zhongjie Li, Gaoyan Zhang, J. Dang, Longbiao Wang, Jianguo Wei

{"title":"Multi-Modal Emotion Recognition Based On deep Learning Of EEG And Audio Signals","authors":"Zhongjie Li, Gaoyan Zhang, J. Dang, Longbiao Wang, Jianguo Wei","doi":"10.1109/IJCNN52387.2021.9533663","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533663","url":null,"abstract":"Automatic recognition of human emotional states has attracted many researchers' attention in Human-Computer Interactions and emotional brain-computer interface recently. However, the accuracy of emotion recognition is not satisfying. Considering the advantage of information supplement based on deep learning of multi-modal signals related to emotion, this study proposed a novel emotion recognition architecture to fuse emotional features from brain electroencephalography (EEG) signal and the corresponding audio signal in emotion recognition on DEAP dataset. We used convolutional neural network (CNN) to extract EEG features and bidirectional long short term memory (BiLSTM) neural networks to extract audio features. After that, we combine the multi-modal features into a deep learning architecture to recognize arousal and valence levels. Results showed an improved accuracy compared with previous studies that merely used the EEG signals in both arousal level and valence level, which suggests the effectiveness of our proposed multi-modal fused emotion recognition model. In future work, multi-modal data from nature interaction scenes will be collected and inputted into this architecture to further validate the effectiveness of the method.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132528461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5