2021 13th International Conference on Machine Learning and Computing最新文献

An Improved K-means Algorithm Based on Multiple Clustering and Density 基于多聚类和密度的改进K-means算法

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457695

Yulong Ling, Xiao Zhang

引用次数: 2

Active Learning for Concept Prerequisite Learning in Wikipedia 维基百科中概念前提学习的主动学习

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457771

Xinying Hu, Yu He, Guangzhong Sun

引用次数: 2

Algorithmic Generation of Positive Samples for Compound-Target Interaction Prediction 化合物-靶标相互作用预测阳性样本的生成算法

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457689

Ebenezer Nanor, Wei-Ping Wu, S. Bayitaa, V. K. Agbesi, Brighter Agyemang

{"title":"Algorithmic Generation of Positive Samples for Compound-Target Interaction Prediction","authors":"Ebenezer Nanor, Wei-Ping Wu, S. Bayitaa, V. K. Agbesi, Brighter Agyemang","doi":"10.1145/3457682.3457689","DOIUrl":"https://doi.org/10.1145/3457682.3457689","url":null,"abstract":"Machine Learning (ML) methods have become the preferred computational methods for Compound-Target Interaction (CTI) prediction in small drug development in Bioinformatics, because they have been proven to be very efficient. However, the extremely imbalance nature of CTI datasets presents a major challenge when ML methods are leveraged to predict CTIs. To a large extent, these methods inaccurately predict the class of the minority samples, i.e. positive samples, which are rather of much interest to players in the business of drug development. In this study, we aim to improve the performance of ML-based methods for prediction of CTIs, particularly the positive samples, by addressing the challenge of class imbalance. We applied the technique of deep generative modeling to oversample selected positive samples from the original dataset in order to construct balance datasets. The process of oversampling espoused the General-based approach and a novel Domain Specific-based approach. In the experimental section, 3 Deep Learning (DL) methods and 6 classical ML methods were trained on the original imbalance dataset and two constructed sets of balance data to investigate their performance in the prediction of CTIs. To ensure robustness of the ML-based predictive methods, a Grid Search with 5-fold Cross Validation (CV) was performed to estimate the best hyperparameters for training. Convolutional Neural Network (CNN) produced the most competitive results in predicting positive samples following evaluation carried out with Recall metric.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"2 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":"128799153","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

Multi-Objective Optimal Design of Excitation Systems of Synchronous Condensers for HVDC Systems Based on MOEA/D 基于MOEA/D的高压直流系统同步冷凝器励磁系统多目标优化设计

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457770

Fan Shi, Hong-hua Wang, Tianhang Lu, Chengliang Wang

{"title":"Multi-Objective Optimal Design of Excitation Systems of Synchronous Condensers for HVDC Systems Based on MOEA/D","authors":"Fan Shi, Hong-hua Wang, Tianhang Lu, Chengliang Wang","doi":"10.1145/3457682.3457770","DOIUrl":"https://doi.org/10.1145/3457682.3457770","url":null,"abstract":"In order to optimize the reactive power characteristics of synchronous condensers and improve the capability of condensers to support the voltage of AC systems, in this paper, the outer loop control of the reactive power of condensers and the outer loop control of the voltage of AC systems are introduced into the design of the main excitation systems of condensers in high voltage direct current (HVDC) systems. Meanwhile, taking the integral values, peak values and steady-state values of voltage deviations of AC systems as objective functions, the multi-objective optimization design of the proportional adjustment coefficients in the outer loop control of the reactive power of condensers and the voltage of AC systems is carried out via utilizing a multi-objective evolutionary algorithm based on decomposition (MOEA/D) combining with fuzzy control method. Its purpose is to alleviate the overvoltage problems of power grids caused by the feedback of the reactive power of condensers and the voltage of AC systems. Lastly, the simulation model of ±100 kV HVDC system with a synchronous condenser is established. The simulation results show that the optimal design method of excitation systems of synchronous condensers proposed in this paper can optimize the reactive power characteristics of the condenser, ensure the rapid regulation of the voltage of the AC system by the condenser, and solve the overvoltage problem in the AC system caused by the reactive power regulation of the condenser which can not change suddenly and the feedback links of the reactive power of the condenser and the voltage of the AC system in the excitation system.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"68 6 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":"116282801","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: 1

A Practical Indoor and Outdoor Seamless Navigation System Based on Electronic Map and Geomagnetism 一种实用的基于电子地图和地磁的室内外无缝导航系统

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457772

K. Qiu, Ruizhi Chen, He Huang

引用次数: 3

Biological Named Entity Recognition and Role Labeling via Deep Multi-task Learning 基于深度多任务学习的生物命名实体识别和角色标记

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457751

Fei Deng, Dongdong Zhang, Jing Peng

{"title":"Biological Named Entity Recognition and Role Labeling via Deep Multi-task Learning","authors":"Fei Deng, Dongdong Zhang, Jing Peng","doi":"10.1145/3457682.3457751","DOIUrl":"https://doi.org/10.1145/3457682.3457751","url":null,"abstract":"Bioscience is an experimental science. The qualitative and quantitative findings of the biological experiments are often exclusively available in the form of figures in published papers. In this paper, we introduce the SourceData model, which captures a key aspect of the biological experimental design by categorizing biological entity involved in the experiment into one of the six roles. Our work aims at determining whether a given entity is subjected to a perturbation or is the object of a measurement (entity role labeling) through automatic natural language algorithms. We use state-of-the-art transformer models (e.g., Bert and its variants) as a strong baseline, find that after jointly trained with biological named entity recognition task by deep multi-task learning (MTL), the F1 score gets improved by 2% compared to previous single-task architecture. Also, for named entity recognition task, the MTL method achieves comparable performance in five public datasets. Further analysis reveals the importance of fusing entity information at the input layer of entity role labeling task and incorporating global context.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"8 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":"133927145","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: 1

Leveraging CNN and Bi-LSTM in Indonesian G2P Using Transformer 利用CNN和Bi-LSTM在印尼G2P使用变压器

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457706

A. Rachman, S. Suyanto, Ema Rachmawati

{"title":"Leveraging CNN and Bi-LSTM in Indonesian G2P Using Transformer","authors":"A. Rachman, S. Suyanto, Ema Rachmawati","doi":"10.1145/3457682.3457706","DOIUrl":"https://doi.org/10.1145/3457682.3457706","url":null,"abstract":"We apply a transformer called tensor2tensor toolkit, which is based on Tensorflow, to overcome the Grapheme-to-Phoneme conversion problem. This study performs conversions to produce pronunciation symbols for certain letter sequences in Indonesian particularly. The unavailability of the G2P conversion system in Indonesian is currently being faced, so research is being carried out to create a system that can solve this problem by applying the Transformer. The transformer has a simple network architecture based solely on the attention mechanism, so we took advantage of eliminating convolution and redundancies—complex recurrent and convolution neural networks including encoders and decoders as the basis for the sequence transduction model. The excellent performance of the model is obtained through the attention mechanism by connecting the encoder and decoder. By using this tool, we carry out to compare among KBBI and CMU dictionary datasets. We attained a word error rate (WER) of 6,7% on the KBBI data set after training for three days on two core CPUs, which has an accuracy of 93,3%, improving over the existing best results CMU dictionary dataset for 26% word error rate. In this study, we carried out a detailed experimental evaluation by assessing the processing time and the error rate of words and then compared it with state of the art. By demonstrating this Transformer, this tool successfully generalizes and then applies it to several Indonesian elements with limited training data and large training data. We concluded that the transformer model is suitable for dealing with the G2P problem at hand for this task.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"1 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":"130889070","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

Visualization Analysis of Library Research in the Context of Big Data Based on Knowledge Map 基于知识地图的大数据背景下图书馆研究可视化分析

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457775

Chen Ke

引用次数: 1

InSAR Deformation Time-series Reconstruction for Rainfall-induced Landslides Based on Gaussian Process Regression 基于高斯过程回归的降雨诱发滑坡InSAR变形时间序列重建

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457700

Zhiyong Li, Yunqi Wang, Jinghan Mu, Wei Liao, Kui Zhang

{"title":"InSAR Deformation Time-series Reconstruction for Rainfall-induced Landslides Based on Gaussian Process Regression","authors":"Zhiyong Li, Yunqi Wang, Jinghan Mu, Wei Liao, Kui Zhang","doi":"10.1145/3457682.3457700","DOIUrl":"https://doi.org/10.1145/3457682.3457700","url":null,"abstract":"Multi-baseline interferometric synthetic aperture radar (InSAR) techniques have been accepted as effective remote sensing tools for detecting and monitoring landslide movements. With the use of stacked synthetic aperture radar (SAR) imageries, it is capable of generating precise ground displacement time-series. In order to further suppress noise induced by atmospheric effects, a post-process step, named as temporal filter, is required to be applied to the final displacement time-series in most applications. As displacement signals are strongly correlated in time, the traditional window-based/least squares filter is widely adopted. Since the window-based filter balances a tradeoff between noise smoothing and signal smoothing, the resulting time-series may strongly deviate from the true values when ground displacements appear high nonlinearity. In this paper, a new approach is proposed to reconstruct the InSAR deformation time-series for rainfall-induced landslides. This method establishes a nonparametric model based on the idea of Gaussian process regression (GPR) and introduces precipitation data as a priori knowledge. A strong relationship between rainfall history and ground movements is therefore constructed, which is extremely helpful in preventing the loss of high-frequency displacement signals. The proposed approach was applied to the InSAR landslide displacement time-series obtained from 108 European Space Agency (ESA) Sentinel-1A satellite SAR images. Experimental results demonstrate that it is capable of preserving the details of the temporal evolution of ground displacements effectively compared to the traditional window-based method, in particular on the surface of sliding mass.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"148 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":"124146100","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

Bird Songs Recognition Based on Ensemble Extreme Learning Machine 基于集成极限学习机的鸟鸣识别

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457750

S. Xie, Haifeng Xu, Jiang Liu, Yan Zhang, Danjv Lv

引用次数: 0