2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献

Evaluation of GAN Architectures For Visualisation of HPV Viruses From Microscopic Images 从显微图像中评估用于HPV病毒可视化的GAN结构

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00137

Xiaohong W. Gao, X. Wen, Dong Li, Weiping Liu, Jichun Xiong, Bin Xu, Juan Liu, Heng Zhang, Xuefeng Liu

引用次数: 3

Anomaly Detection of actual IoT traffic flows through Deep Learning 通过深度学习实现物联网实际流量异常检测

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00275

Lerina Aversano, M. Bernardi, Marta Cimitile, R. Pecori

引用次数: 0

MetaPrep: Data preparation pipelines recommendation via meta-learning MetaPrep:通过元学习推荐数据准备管道

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00194

F. Zagatti, L. C. Silva, Lucas Nildaimon Dos Santos Silva, B. S. Sette, Helena de Medeiros Caseli, D. Lucrédio, D. F. Silva

{"title":"MetaPrep: Data preparation pipelines recommendation via meta-learning","authors":"F. Zagatti, L. C. Silva, Lucas Nildaimon Dos Santos Silva, B. S. Sette, Helena de Medeiros Caseli, D. Lucrédio, D. F. Silva","doi":"10.1109/ICMLA52953.2021.00194","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00194","url":null,"abstract":"Data preparation is a mandatory phase in the machine learning pipeline. The goal of data preparation is to convert noisy and disordered data into refined data that can be used by the algorithms. However, data preparation is time-consuming and requires specialized knowledge about the data and algorithms. Therefore, automating data preparation is essential to decrease the effort made by data scientists to develop satisfactory models. Despite its relevance, current AutoML platforms disregard or make simple hardcoded data preparation pipelines. Trying to fill this gap, we present a meta-learning-based recommendation system for data preparation. Our system recommends five pipelines, ranked by their relevance, making it useful for users with varying degrees of experience. Using the top-1 pipeline we demonstrated that our proposal allows a better performance of an AutoML system. Furthermore, the accuracy rates of our method were comparable to those achieved by a reinforcement-learning-based algorithm with the same goal, but it was up to two orders of magnitude faster. Moreover, we tested our method in a real-world application and evaluated its benefits and limitations in this scenario.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"26 1","pages":"1197-1202"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81413978","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

Elastic distributed training with fast convergence and efficient resource utilization 具有快速收敛和高效资源利用的弹性分布式训练

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00160

Guojing Cong

引用次数: 0

Text Mining Approach To Predict Non-Adherence 预测非依从性的文本挖掘方法

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00236

Yufan Wang, Mahsa Mohaghegh

引用次数: 0

A proposal to identify stakeholders from news for the institutional relationship management activities of an institution based on Named Entity Recognition using BERT 基于BERT的命名实体识别，从新闻中识别机构关系管理活动的利益相关者

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00251

Eric Hans Messias Da Silva, J. Laterza, Marcos Paulo Pereira Da Silva, M. Ladeira

{"title":"A proposal to identify stakeholders from news for the institutional relationship management activities of an institution based on Named Entity Recognition using BERT","authors":"Eric Hans Messias Da Silva, J. Laterza, Marcos Paulo Pereira Da Silva, M. Ladeira","doi":"10.1109/ICMLA52953.2021.00251","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00251","url":null,"abstract":"For an organization’s institutional relationship activities, it is strategic that there is an efficient process of identification and characterization of stakeholders based on available information. Given the increasing volume of data currently available, this strategic process has commonly been supported by information technology solutions, with high potential for the use of data mining techniques such as textual analysis and natural language processing (NLP). In this work we analyzed the possibility of using a mechanism of Named Entity Recognition (NER) based on the use of Bidirectional Encoder Representations from Transformers (BERT) with Conditional Random Field (CRF), which in the future can be used as the stakeholder identification solution as a replacement of the rule based identification. We applied the proposed solution in news dataset to evaluate its performance. The experiment results showed us that pre-trained Portuguese models performed better than Multilingual ones by a good margin of at least 3.43 percentage points on Test Dataset. We also added a post processing Prediction Masking to correct invalid tagging scheme transitions to improve Micro F1 Score in both datasets ranging from 0.38 percentage points to 1.29 percentage points of improvement. Thus, we achieved the objective of improving stakeholder detection by proposing a NER model that far surpasses the naive rules-based approach of current application, which consisted of an exact text match of stakeholders based on a dictionary built manually.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"633 1","pages":"1569-1575"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77083273","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

Hashtags: an essential aspect of topic modeling of city events through social media. 话题标签:通过社交媒体对城市事件进行主题建模的一个重要方面。

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00255

Mikhail V. Kovalchuk, D. Nasonov

{"title":"Hashtags: an essential aspect of topic modeling of city events through social media.","authors":"Mikhail V. Kovalchuk, D. Nasonov","doi":"10.1109/ICMLA52953.2021.00255","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00255","url":null,"abstract":"Today, the city is full of digital information, which can be extremely useful in various applications. Instagram, Facebook, VKontakte, and other popular social networks contain a vast amount of valuable data. This information reflects individual stories of people and the background of the city, its events, and current activities in different areas and places of attraction. City events have essential attributes like the time of occurrence, geographical coverage, audience, and often expressed interests or topics. Owning the subject of events, you can solve a whole range of tasks - from individual recommendation systems for leisure activities for citizens and tourists to providing services in the field of food (food trucks) and transport (taxis). To determine the topic (subject) of events, it is necessary to solve two crucial tasks: to identify the events themselves from a variety of city posts and to develop an approach based on modern natural language processing methods for identifying events topics. To determine the events, we suggest an improved algorithm that we had previously developed that integrates time window and area coverage strategy. However, the focus of the work is on the analysis of different approaches to identifying topics, considering the heterogeneity of posts, both in semantic meaning and in size and structure. The focus of this paper is the importance of using post hashtags in various variations to set up more accurate models. In addition, the analysis of features for different language groups was carried out.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"1594-1599"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82039740","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

Influence of Training Data on the Invertability of Neural Networks for Handwritten Digit Recognition 训练数据对手写体数字识别神经网络可逆性的影响

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00122

Antonia Adler, Michaela Geierhos, Eleanor Hobley

{"title":"Influence of Training Data on the Invertability of Neural Networks for Handwritten Digit Recognition","authors":"Antonia Adler, Michaela Geierhos, Eleanor Hobley","doi":"10.1109/ICMLA52953.2021.00122","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00122","url":null,"abstract":"Model inversion attacks aim to extract details of training data from a trained model, potentially revealing sensitive information about a person’s identity. To abide with protection of personal privacy requirements, it is important to understand the mechanisms that increase the privacy of training data. In this work, we systematically investigated the impact of the training data on a model’s susceptibility to model inversion attacks for models trained at the task of hand-written digit recognition with the openly available MNIST dataset. Using an optimization-based inversion approach, we studied the impacts of the quantity and diversity of training data, and the number and selection of classes on the susceptibility of models to inversion. Our model inversion attack strategy was less successful for models with a larger number of training data and greater training data diversity. Moreover, atypical training records provided additional protection against model inversion. We discovered that not every class was equally susceptible to model inversion attacks and that the inversion results of one class were changed when models were trained with a different selection of classes. However, we did not detect a clear relationship between the number of classes and a model’s susceptibility to inversion. Our study shows that the inversion susceptibility of a model depends on the training data-not only the data used to train the class that is inverted, but also the data used to train the other classes.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"61 1","pages":"730-737"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84195234","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

An Augmented Image Captioning Model: Incorporating Hierarchical Image Information 一种融合分层图像信息的增强图像字幕模型

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00257

Nathan Funckes, Erin Carrier, Greg Wolffe

引用次数: 1

Auto-encoder LSTM for Li-ion SOH prediction: a comparative study on various benchmark datasets 用于锂离子SOH预测的自编码器LSTM:不同基准数据集的比较研究

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00246

Paul Audin, I. Jorge, T. Mesbahi, Ahmed Samet, F. D. Beuvron, R. Boné

引用次数: 4