2020 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献

Imbalanced Toxicity Prediction Using Multi-Task Learning and Over-Sampling 使用多任务学习和过度抽样的不平衡毒性预测

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469546

Jincheng Li

{"title":"Imbalanced Toxicity Prediction Using Multi-Task Learning and Over-Sampling","authors":"Jincheng Li","doi":"10.1109/ICMLC51923.2020.9469546","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469546","url":null,"abstract":"Chemical compound toxicity prediction is a challenge learning problem that the number of active chemicals obtained for toxicity assays are far smaller than the inactive chemicals, i.e. imbalanced data. Neural Networks learned from these tasks with imbalanced data tend to misclassify the minority samples into majority samples. In this paper, we propose a novel learning method that combine multi-task deep neural networks learning with over-sampling method to handle imbalanced data and lack of training data problems of toxicity prediction. Over-sampling is a kind of re-sampling method that tackle the class imbalance problem by replicating the minority class samples. For each toxicity prediction task, we apply over-sampling method on training set to generate synthetic samples of the minority class to balance the training data. Then, we train the multi-task deep neural network on the tasks with balanced training set. Multi-task learning can share common information among tasks and the balanced data set have larger number of training data that benefit the multi-task deep neural networks learning.Experiment results on tox21 toxicity prediction data set shows that our method significantly relieve imbalanced data problem of multi-task deep neural networks learning and outperforms multi-task deep neural network method that without over-sampling and many other computational approaches like support vector machine and random forests.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116388453","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

Research on Hotspot Mining Method of Twitter News Report Based on LDA and Sentiment Analysis 基于LDA和情感分析的Twitter新闻报道热点挖掘方法研究

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469557

Lingfei Zhang, Chunfang Li

引用次数: 1

Hybrid Separable Convolutional Inception Residual Network for Human Facial Expression Recognition 混合可分离卷积初始残差网络用于人脸表情识别

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469558

Xinqi Fan, Rizwan Qureshi, A. Shahid, Jianfeng Cao, Luoxiao Yang, H. Yan

{"title":"Hybrid Separable Convolutional Inception Residual Network for Human Facial Expression Recognition","authors":"Xinqi Fan, Rizwan Qureshi, A. Shahid, Jianfeng Cao, Luoxiao Yang, H. Yan","doi":"10.1109/ICMLC51923.2020.9469558","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469558","url":null,"abstract":"Facial expression recognition has been applied widely in human-machine interactions, security and business applications. The aim of facial expression recognition is to classify human expressions from their face images. In this work, we propose a novel neural network-based pipeline for facial expression recognition, Hybrid Separable Convolutional Inception Residual Network, using transfer learning with Inception residual network and depth-wise separable convolution. Specifically, our method uses multi-task convolutional neural network for face detection, then modifies the last two blocks of the original Inception residual network using depthwise separable convolution to reduce the computation cost, and finally utilizes transfer learning to take advantages of the transferable weights from a large face recognition dataset. Experimental result on three different databases - the Radboud Faces Database, Compounded Facial Expression of Emotions Database, and Real-word Affective Face Database, shows superior performance compared with the existing studies. Moreover, the proposed method is computationally efficient and reduces the trainable parameters by approximately 25% than the original Inception residual network.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123889317","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: 6

Identification of Inverted Pendulum System Using Frequency Domain Maximum Likelihood Estimation 基于频域极大似然估计的倒立摆系统辨识

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469529

Si-Ting Zou, Qing Sun, Dangdang Du

引用次数: 1

Sentiment Analysis of Online Product Reviews Based On SenBERT-CNN 基于SenBERT-CNN的在线产品评论情感分析

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469551

F. Wu, Zhenjie Shi, Zhaowei Dong, C. Pang, Bailing Zhang

引用次数: 4

Comparative Study of Speech Emotion Recognition Based On CNN and CRNN 基于CNN和CRNN的语音情感识别比较研究

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469540

Nan Jiang, Junwei Jia, Dongmei Shao

引用次数: 1

A Modified Q-Learning Algorithm for Control of Two-Qubit Systems 一种用于双量子比特系统控制的改进q -学习算法

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469044

Omar Shindi, Qi Yu, D. Dong, Jiangjun Tang

引用次数: 0

Conservative Generalisation for Small Data Analytics –An Extended Lattice Machine Approach 小数据分析的保守推广——扩展格机方法

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469579

Shuangshuang Kong, Hui Wang, Kaijun Wang

{"title":"Conservative Generalisation for Small Data Analytics –An Extended Lattice Machine Approach","authors":"Shuangshuang Kong, Hui Wang, Kaijun Wang","doi":"10.1109/ICMLC51923.2020.9469579","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469579","url":null,"abstract":"Small data analytics is to tackle the data analysis challenges such as overfitting when the data set is small. There are different approaches to small data analytics, including knowledge-based learning, but most of these approaches need experience to use. In this paper we consider another approach, lattice machine. Lattice machine is a conservative generalisation based learning algorithm. It is a learning paradigm that \"learns\" by generalising data in a consistent, conservative and parsimonious way. A lattice machine model built from a dataset is a set of hyper tuples that tightly \"wraps around\" clusters of data, each of which is a conservative generalisation of the underlying cluster. A key feature of lattice machine, indeed any conservative generalisation based learning algorithm, is that it has high precision and low recall, limiting its applications as high recall is needed in some applications such as disease (e.g. covid-19) screening. It is thus necessary to improve lattice machine’s recall whilst retaining his high precision. In this paper, we present a study on how to achieve this for lattice machine.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116212585","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

The Realization of Cross-Media Knowledge Graph of Tang and Song Poetry 唐宋诗歌跨媒介知识图谱的实现

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469590

Chan-liang Wu, Chunfang Li, Wenjuan Jiang

{"title":"The Realization of Cross-Media Knowledge Graph of Tang and Song Poetry","authors":"Chan-liang Wu, Chunfang Li, Wenjuan Jiang","doi":"10.1109/ICMLC51923.2020.9469590","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469590","url":null,"abstract":"As the two peaks of Chinese culture, poems of the Tang and Song Dynasties attracts numerous scholars to devote themselves to research. \"China Biographical Database Project\" (CBDB) is a relational database of Chinese historical figures established by Harvard University, and \"Chinese poetry: the most complete database of ancient Chinese poetry\" is an open source project of GitHub. This paper uses the above databases, takes poetry in Tang and Song Dynasties as the main body, combines the related video of the cultural variety show \"Chinese Poetry Conference\" and the Chinese textbooks for primary school to establish the system of \"Knowledge Graph of Tang and Song Dynasties\", and achieves the integration of video and teaching, characters and works across the media. It provides the comprehensive search function of characters, poems and related videos, the online generation function of knowledge graph, and the display function of the chronology of the top 100 figures divided by the emperors of past dynasties. Users can interact with the knowledge graph by clicking, typing, dragging and so on to complete exploratory visual analysis.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127877048","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

Digitalization of Electronic Textbook Based on OPENCV 基于OPENCV的电子教科书数字化

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469536

Zhi-Ming Deng, Minyong Shi, Chunfang Li

引用次数: 2