Reconstruction of phylogenetic tree from biological sequences is a fundamental step in molecular biology, but it is computationally exhausting. Our goal is to use neural network to learn the heuristic strategy of phylogenetic tree reconstruction algorithm. We propose an attention model to learn heuristic strategies for constructing circular ordering related to phylogenetic trees. We use alignment-free K-mer frequency vector representation to represent biological sequences and use unlabeled sequence data sets to train attention model through reinforcement learning. Comparing with traditional methods, our approach is alignment-free and can be easily extended to large-scale data with computational efficiency. With the rapid growth of public biological sequence data, our method provides a potential way to reconstruct phylogenetic tree.
{"title":"Applying Neural Network to Reconstruction of Phylogenetic Tree","authors":"T. Zhu, Yunpeng Cai","doi":"10.1145/3457682.3457704","DOIUrl":"https://doi.org/10.1145/3457682.3457704","url":null,"abstract":"Reconstruction of phylogenetic tree from biological sequences is a fundamental step in molecular biology, but it is computationally exhausting. Our goal is to use neural network to learn the heuristic strategy of phylogenetic tree reconstruction algorithm. We propose an attention model to learn heuristic strategies for constructing circular ordering related to phylogenetic trees. We use alignment-free K-mer frequency vector representation to represent biological sequences and use unlabeled sequence data sets to train attention model through reinforcement learning. Comparing with traditional methods, our approach is alignment-free and can be easily extended to large-scale data with computational efficiency. With the rapid growth of public biological sequence data, our method provides a potential way to reconstruct phylogenetic tree.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124942004","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}
Low-light image is easily degraded by real noise, which brings great challenges for image enhancement task because the enhancement process will amplify the noise. To address this problem, we propose an amplified noise map guided network (AMG-Net), which simultaneously achieves the low-light enhancement and noise removal by extracting amplified noise map to guide the network training. Specifically, we build an encoder-decoder network as the basic enhancement model to get a preliminary enhanced image that usually includes amplified noise. Subsequently, we fed the preliminary enhanced image into a noise map estimator to continuously estimating the amplified noise map during the enhancement process by adopting residual connection. Finally, a residual block with adaptive instance normalization (AIN) is used to build a denoising model, which is guided by the noise map estimator to remove the amplified noise. Extensive experimental results demonstrate that the proposed AMG-Net can achieve competitive results compared with the existing state-of-the-art methods.
{"title":"Amplified Noise Map Guided Network for Low-Light Image Enhancement","authors":"Kai Xu, Huaian Chen, Yi Jin, Chang'an Zhu","doi":"10.1145/3457682.3457731","DOIUrl":"https://doi.org/10.1145/3457682.3457731","url":null,"abstract":"Low-light image is easily degraded by real noise, which brings great challenges for image enhancement task because the enhancement process will amplify the noise. To address this problem, we propose an amplified noise map guided network (AMG-Net), which simultaneously achieves the low-light enhancement and noise removal by extracting amplified noise map to guide the network training. Specifically, we build an encoder-decoder network as the basic enhancement model to get a preliminary enhanced image that usually includes amplified noise. Subsequently, we fed the preliminary enhanced image into a noise map estimator to continuously estimating the amplified noise map during the enhancement process by adopting residual connection. Finally, a residual block with adaptive instance normalization (AIN) is used to build a denoising model, which is guided by the noise map estimator to remove the amplified noise. Extensive experimental results demonstrate that the proposed AMG-Net can achieve competitive results compared with the existing state-of-the-art methods.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124732413","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}
Feature selection is a process to select key features from the initial feature set. It is commonly used as a preprocessing step to improve the efficiency and accuracy of a classification model in artificial intelligence and machine learning domains. This paper proposes a redundancy based unsupervised feature selection method for high-dimensional data called as RUFS. Firstly, RUFS roughly descending order the features by the average SU with the other features. Secondly, RUFS orderly check each feature to decide whether it is redundant or not. Finally, it selects the proper feature subset by repeating the second step until all the features are checked. After key features are selected, the research implements classifiers to check the quality of the selected feature subset. Compared with the other existing methods, the proposed RUFS method improves the mean classification of 11 real datasets by 8.1 percent at least.
{"title":"A Redundancy Based Unsupervised Feature Selection Method for High-Dimensional Data","authors":"Jian Zhou, Ding Liu","doi":"10.1145/3457682.3457725","DOIUrl":"https://doi.org/10.1145/3457682.3457725","url":null,"abstract":"Feature selection is a process to select key features from the initial feature set. It is commonly used as a preprocessing step to improve the efficiency and accuracy of a classification model in artificial intelligence and machine learning domains. This paper proposes a redundancy based unsupervised feature selection method for high-dimensional data called as RUFS. Firstly, RUFS roughly descending order the features by the average SU with the other features. Secondly, RUFS orderly check each feature to decide whether it is redundant or not. Finally, it selects the proper feature subset by repeating the second step until all the features are checked. After key features are selected, the research implements classifiers to check the quality of the selected feature subset. Compared with the other existing methods, the proposed RUFS method improves the mean classification of 11 real datasets by 8.1 percent at least.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122759945","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}
As a widely used method in relation extraction at the present stage suggests, distant supervision is affected by label noise. The data noise is introduced artificially due to the theory and the performance of distant supervision will be restricted during the modeling process. To solve this problem on the sentence level, the task of relation extraction in our project is modeled with two parts: sentence selector and relation extractor. Sentence selector, based on the theory of reinforcement learning, processes the corpus in units of entity pairs. The training corpus is divided into three parts including selected sentences, discarded sentences, and unlabeled sentences. We try to obtain more semantic information of the training corpus by introducing the intra-class attention and inter-class similarity. To make the operation of filtering noise data more accurate, this model evaluates the predicted value produced by the relation extractor between the selected and discarded sentences in the sentence package. The result shows that the redesigned reinforcement learning algorithm WPR-RL in this study can significantly improve the deficiencies of the existing approach. At the same time, we also carry out a number of composite tests to discuss the impact of each improvement on the performance of the model.
{"title":"Distant Supervision for Relation Extraction via Noise Filtering","authors":"Jing Chen, Zhiqiang Guo, Jie Yang","doi":"10.1145/3457682.3457743","DOIUrl":"https://doi.org/10.1145/3457682.3457743","url":null,"abstract":"As a widely used method in relation extraction at the present stage suggests, distant supervision is affected by label noise. The data noise is introduced artificially due to the theory and the performance of distant supervision will be restricted during the modeling process. To solve this problem on the sentence level, the task of relation extraction in our project is modeled with two parts: sentence selector and relation extractor. Sentence selector, based on the theory of reinforcement learning, processes the corpus in units of entity pairs. The training corpus is divided into three parts including selected sentences, discarded sentences, and unlabeled sentences. We try to obtain more semantic information of the training corpus by introducing the intra-class attention and inter-class similarity. To make the operation of filtering noise data more accurate, this model evaluates the predicted value produced by the relation extractor between the selected and discarded sentences in the sentence package. The result shows that the redesigned reinforcement learning algorithm WPR-RL in this study can significantly improve the deficiencies of the existing approach. At the same time, we also carry out a number of composite tests to discuss the impact of each improvement on the performance of the model.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133378654","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}
Construction of mathematical models to investigate genetic circuit design is a powerful technique in synthetic biology with real-world applications in biomanufacturing and biosensing. The challenge of building such models is to accurately discover flow of information in simple as well as complex biological systems. However, building synthetic biological models is often a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of various machine learning (ML) techniques to accurately construct mathematical models for predicting gene expressions in genetic circuit designs. Specifically, classification and regressions models were built using Random Forrest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). The obtained accuracy of the regression model using RF and ANN yielded R2 scores of 0.97 and 0.95, respectively, compared to the best score of 0.63 obtained in an earlier study. Furthermore, a classifier model was built using the green fluorescent protein (GFP) measurements obtained from the experiments conducted in this work. Biologists use GFP as an indicator of gene expression, enabling easy measurement of its protein level in the living cells. The measured GFP values were predicted with 100% accuracy by both RF and ANN classifier models while identifying various synthetic gene circuit patterns. The paper also highlights importance of the relevant data preparation techniques to ensure high accuracy is obtained by the utilized ML models.
{"title":"Applications of Machine Learning Techniques in Genetic Circuit Design","authors":"Jiajie Zhu, Qi Zhang, B. Forouraghi, Xiao Wang","doi":"10.1145/3457682.3457683","DOIUrl":"https://doi.org/10.1145/3457682.3457683","url":null,"abstract":"Construction of mathematical models to investigate genetic circuit design is a powerful technique in synthetic biology with real-world applications in biomanufacturing and biosensing. The challenge of building such models is to accurately discover flow of information in simple as well as complex biological systems. However, building synthetic biological models is often a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of various machine learning (ML) techniques to accurately construct mathematical models for predicting gene expressions in genetic circuit designs. Specifically, classification and regressions models were built using Random Forrest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). The obtained accuracy of the regression model using RF and ANN yielded R2 scores of 0.97 and 0.95, respectively, compared to the best score of 0.63 obtained in an earlier study. Furthermore, a classifier model was built using the green fluorescent protein (GFP) measurements obtained from the experiments conducted in this work. Biologists use GFP as an indicator of gene expression, enabling easy measurement of its protein level in the living cells. The measured GFP values were predicted with 100% accuracy by both RF and ANN classifier models while identifying various synthetic gene circuit patterns. The paper also highlights importance of the relevant data preparation techniques to ensure high accuracy is obtained by the utilized ML models.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133638887","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}
Recently, various neural network frameworks have achieved good results in sentiment classification task, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). However, these methods only take into account semantic information in local contexts and ignore the global syntactic structure information due to the network structure. To solve this problem, we propose a novel neural architecture called SC-DGCN that combines Graph Convolutional Network (GCN) and Bi-LSTM. In SC-DGCN model, we utilize a GCN over the dependency tree of a sentence to exploit syntactical information and words dependencies. In addition, we further introduce dense connection strategy into GCN blocks to aggregate more syntactic information from neighbors and multi-hops in the dependency tree, and employ attention mechanism to generate the final representation of text. Our proposed SC-DGCN model can automatically extract semantic feature in local contexts and the global syntactic structure feature. A series of experiments on MR and SST datasets also indicate that our model is effective for sentiment classification task.
{"title":"SC-DGCN: Sentiment Classification Based on Densely Connected Graph Convolutional Network","authors":"Renhao Zhao, Menghan Wang, Qiong Yin, Chao Chen","doi":"10.1145/3457682.3457724","DOIUrl":"https://doi.org/10.1145/3457682.3457724","url":null,"abstract":"Recently, various neural network frameworks have achieved good results in sentiment classification task, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). However, these methods only take into account semantic information in local contexts and ignore the global syntactic structure information due to the network structure. To solve this problem, we propose a novel neural architecture called SC-DGCN that combines Graph Convolutional Network (GCN) and Bi-LSTM. In SC-DGCN model, we utilize a GCN over the dependency tree of a sentence to exploit syntactical information and words dependencies. In addition, we further introduce dense connection strategy into GCN blocks to aggregate more syntactic information from neighbors and multi-hops in the dependency tree, and employ attention mechanism to generate the final representation of text. Our proposed SC-DGCN model can automatically extract semantic feature in local contexts and the global syntactic structure feature. A series of experiments on MR and SST datasets also indicate that our model is effective for sentiment classification task.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115028424","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}
The surveillance of public transportation is of great significance to improve public safety. However, the low resolution of vehicle images becomes a bottleneck in real scenarios. Since high-low resolution vehicle images pairs are not available in traffic surveillance scenarios, this paper aims to study the problem of unsupervised super-resolution to reconstruct the high quality vehicle image. Most of the existing super-resolution algorithms adopt pre-defined down-sampling methods for paired training, however, the models trained in this pattern cannot achieve the expected results in traffic surveillance scenarios. Therefore, we propose a super-resolution method that does not require paired data, and raise a novel down-sampling network to generate low-resolution images of vehicles close to the real-world data, and then utilize the synthesized pairs for pair-wise training. Our extensive experiments on private real-world dataset Vehicle5k demonstrate the advantages of the proposed approach over baseline approaches.
{"title":"Unsupervised Super Resolution Reconstruction of Traffic Surveillance Vehicle Images","authors":"Yaoyuan Liang","doi":"10.1145/3457682.3457734","DOIUrl":"https://doi.org/10.1145/3457682.3457734","url":null,"abstract":"The surveillance of public transportation is of great significance to improve public safety. However, the low resolution of vehicle images becomes a bottleneck in real scenarios. Since high-low resolution vehicle images pairs are not available in traffic surveillance scenarios, this paper aims to study the problem of unsupervised super-resolution to reconstruct the high quality vehicle image. Most of the existing super-resolution algorithms adopt pre-defined down-sampling methods for paired training, however, the models trained in this pattern cannot achieve the expected results in traffic surveillance scenarios. Therefore, we propose a super-resolution method that does not require paired data, and raise a novel down-sampling network to generate low-resolution images of vehicles close to the real-world data, and then utilize the synthesized pairs for pair-wise training. Our extensive experiments on private real-world dataset Vehicle5k demonstrate the advantages of the proposed approach over baseline approaches.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115928031","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}
Although the heterogeneous of financial markets is attracting interest both among scholars and practitioners, however, attention was almost exclusively given to networks in which all individuals were treated indifference, while neglecting all the extra information about the context-related or temporal-spatial properties of the interactions under study. Here introduces a new learnable relation inference model—based on graph networks—which implements an inference for entity- and relation-centric representations of multilayer, dynamical systems. The results show that as a learnable model, the approach supports accurate predictions from real and simulated data.
{"title":"Graph Networks as Learnable Engines for Relations Inference of Interacting Financial Systems","authors":"Jiayu Pi, Yuan Deng","doi":"10.1145/3457682.3457713","DOIUrl":"https://doi.org/10.1145/3457682.3457713","url":null,"abstract":"Although the heterogeneous of financial markets is attracting interest both among scholars and practitioners, however, attention was almost exclusively given to networks in which all individuals were treated indifference, while neglecting all the extra information about the context-related or temporal-spatial properties of the interactions under study. Here introduces a new learnable relation inference model—based on graph networks—which implements an inference for entity- and relation-centric representations of multilayer, dynamical systems. The results show that as a learnable model, the approach supports accurate predictions from real and simulated data.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123794388","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}
In this paper, we propose a new model for text line segmentation and classification, which consists of convolutional and two-layer bi-directional long short-term memory (BiLSTM) networks. Trained on the synthetic text dataset, it performs excellently when predicting the real data. Without labelling every line on the real data, a generalized standard for evaluating the accuracy is proposed. We also propose a simplified IoU loss to improve the execution speed greatly. In the experiments, it achieves 98.1% line segmentation accuracy and 99.5% classification accuracy on the English fiction Pride and Prejudice by Jane Austen, and achieves 98.5% line segmentation accuracy and 99.7% classification accuracy on the The Secret Of Plato's Atlantis by John Arundell, outperforming the traditional methods. Furthermore, for 1024 × 724 input samples, it gets 2.95 FPS speed when using a Tesla K80 GPU. Index Terms—Text line segmentation, Text classification, Synthetic text, BiLSTM, Convolutional network.
{"title":"A Model for Text Line Segmentation and Classification in Printed Documents","authors":"Xin Wang, Jun Guo","doi":"10.1145/3457682.3457760","DOIUrl":"https://doi.org/10.1145/3457682.3457760","url":null,"abstract":"In this paper, we propose a new model for text line segmentation and classification, which consists of convolutional and two-layer bi-directional long short-term memory (BiLSTM) networks. Trained on the synthetic text dataset, it performs excellently when predicting the real data. Without labelling every line on the real data, a generalized standard for evaluating the accuracy is proposed. We also propose a simplified IoU loss to improve the execution speed greatly. In the experiments, it achieves 98.1% line segmentation accuracy and 99.5% classification accuracy on the English fiction Pride and Prejudice by Jane Austen, and achieves 98.5% line segmentation accuracy and 99.7% classification accuracy on the The Secret Of Plato's Atlantis by John Arundell, outperforming the traditional methods. Furthermore, for 1024 × 724 input samples, it gets 2.95 FPS speed when using a Tesla K80 GPU. Index Terms—Text line segmentation, Text classification, Synthetic text, BiLSTM, Convolutional network.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122384584","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}
Jingjing Lu, Shuangyan Yi, Jiaoyan Zhao, Yongsheng Liang, Wei Liu
Dimension reduction is a hot topic in data processing field. The challenge lies in how to find a suitable feature subset in low-dimensional space to accurately summarize the important information in high-dimensional space, rather than redundant information or noise. This requires the proposed model to reasonably explain the importance of features and be robust to noise. In order to solve this problem, this paper proposes an interpretable robust feature selection method, in which both the reconstruction error term and the regularization term are constrained by -norm. The reconstruction error term can capture samples corroded by noise, while the regular term automatically finds a group of discriminative features on relatively clean samples. Experimental results show the effectiveness of the proposed method, especially on noise data sets.
{"title":"Interpretable Robust Feature Selection via Joint -Norms Minimization","authors":"Jingjing Lu, Shuangyan Yi, Jiaoyan Zhao, Yongsheng Liang, Wei Liu","doi":"10.1145/3457682.3457693","DOIUrl":"https://doi.org/10.1145/3457682.3457693","url":null,"abstract":"Dimension reduction is a hot topic in data processing field. The challenge lies in how to find a suitable feature subset in low-dimensional space to accurately summarize the important information in high-dimensional space, rather than redundant information or noise. This requires the proposed model to reasonably explain the importance of features and be robust to noise. In order to solve this problem, this paper proposes an interpretable robust feature selection method, in which both the reconstruction error term and the regularization term are constrained by -norm. The reconstruction error term can capture samples corroded by noise, while the regular term automatically finds a group of discriminative features on relatively clean samples. Experimental results show the effectiveness of the proposed method, especially on noise data sets.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129573468","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}
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