2022 14th International Conference on Machine Learning and Computing (ICMLC)最新文献_第8页

Hard Anchor Attention in Anchor-based Detector 基于锚点的探测器中的硬锚注意

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529940

Shuai Jiang, Di Zhao, Tao Wang, Jing Zhang, Xiao Sun

引用次数: 0

Automated Single-Label Patent Classification using Ensemble Classifiers 使用集成分类器的自动单标签专利分类

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529849

Eleni Kamateri, Vasileios Stamatis, K. Diamantaras, M. Salampasis

{"title":"Automated Single-Label Patent Classification using Ensemble Classifiers","authors":"Eleni Kamateri, Vasileios Stamatis, K. Diamantaras, M. Salampasis","doi":"10.1145/3529836.3529849","DOIUrl":"https://doi.org/10.1145/3529836.3529849","url":null,"abstract":"Many thousands of patent applications arrive at patent offices around the world every day. One important task when a patent application is submitted is to assign one or more classification codes from the complex and hierarchical patent classification schemes that will enable routing of the patent application to a patent examiner who is knowledgeable about the specific technical field. This task is typically undertaken by patent professionals, however due to the large number of applications and the potential complexity of an invention, they are usually overwhelmed. Therefore, there is a need for this code assignment manual task to be supported or even fully automated by classification systems that will classify patent applications, hopefully with an accuracy close to patent professionals. Like in many other text analysis problems, in the last years, this intellectually demanding task has been studied using word embeddings and deep learning techniques. In this paper these research efforts are shortly reviewed and re-produced with similar deep learning techniques using different feature representations on automatic patent classification in the level of sub-classes. On top of that, an innovative method of ensemble classifiers trained with different parts of the patent document is proposed. To the best of our knowledge, this is the first time that an ensemble method was proposed for the patent classification problem. Our first results are quite promising showing that an ensemble architecture of classifiers significantly outperforms current state-of-the-art techniques using the same classifiers as standalone solutions.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"496 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129244735","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

A Segmentation Model of Lung Parenchyma in Chest CT Based on ResUnet 基于ResUnet的胸部CT肺实质分割模型

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529917

Bingdong Liu, Chengxu Ye, Ping Yang, Zhikun Miao, R. Liu, Ying Chen

引用次数: 1

Analysis Method of Ship Detention Judgment based on optimized DBSCAN Cluster Algorithm 基于优化DBSCAN聚类算法的船舶滞留判断分析方法

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529951

Lei Han, Hanting Zhao, Mengao Li

引用次数: 0

Spherical Tree Structured Self-Organizing Map 球形树结构自组织映射

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529900

H. Dozono, Koki Yoshioka, Gen Niina

引用次数: 0

Neighborhood Extended Dynamic Graph Neural Network 邻域扩展动态图神经网络

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529851

Da-ming Yu, Junli Wang, Changjun Jiang

{"title":"Neighborhood Extended Dynamic Graph Neural Network","authors":"Da-ming Yu, Junli Wang, Changjun Jiang","doi":"10.1145/3529836.3529851","DOIUrl":"https://doi.org/10.1145/3529836.3529851","url":null,"abstract":"Representation learning on dynamic graphs has drawn much attention due to its ability to learn hidden relationships as well as capture temporal patterns in graphs. It can be applied to represent a broad spectrum of graph-based data like social networks and further use the learned representations to solve the downstream tasks including link prediction and edge classification. Although some approaches have been proposed for dynamic graphs in recent years, most of them paid little attention to the evolution of the entire graph topology, leading to the lack of global information of what happened in nodes’ neighborhoods during their update. We propose NEDGNN, a novel Neighborhood Extended Dynamic Graph Neural Network on dynamic graphs represented as sequences of time-stamped events. We introduce a temporal attention propagation module to generate messages for n-hop neighbors through a self-attention mechanism, which can help disseminate information among nodes’ n-hop neighbors. Besides, a FIFO message box module is also applied to gain some time efficiency. Due to the introduction of these modules, NEDGNN outperforms many state-of-the-art baselines in several tasks. We also perform a detailed ablation study to test the effectiveness and time cost of each module.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"108 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134476515","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 Deep Neural Networks model for Restaurant Recommendation systems in Thailand 泰国餐厅推荐系统的深度神经网络模型

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529925

Apisara Saelim, B. Kijsirikul

引用次数: 1

Chaos Prediction of Power Systems by Using Deep Learning 基于深度学习的电力系统混沌预测

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529843

Ying-Ling Lu, D. Wei

{"title":"Chaos Prediction of Power Systems by Using Deep Learning","authors":"Ying-Ling Lu, D. Wei","doi":"10.1145/3529836.3529843","DOIUrl":"https://doi.org/10.1145/3529836.3529843","url":null,"abstract":"Ensuring the stability of power systems is an important issue that should be considered in order to ensure the social and economic development of a country. Therefore, predicting the chaotic behavior of power systems in order to develop protection measures and keep power systems stable is vital. In this paper, a deep learning algorithm was proposed to predict the chaotic behavior of power systems by using deep long short-term memory (DLSTM) networks, which have two forms: deep long short-term memory with static scenario (DLSTM-s) and deep long-term memory with dynamic scenario (DLSTM-d). The genetic algorithm was used to optimize the hyperparameters of the networks. Then, taking interconnected power systems as an example, the effectiveness of the proposed DLSTM network was verified via numerical simulation. Finally, the experimental results of the DLSTM network were compared with those of the echo state network, multi-recurrent neural network, deep gated recurrent unit, and long short-term memory. Experimental results illustrated that a trained DLSTM network can predict the chaotic behavior of power systems by using the time series data of a single state variable. Moreover, the DLSTM-s network proposed in this paper can achieve competitive prediction performance compared with other baseline methods.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"1 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114116729","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

Evaluation System of Push-up Action Based on Kinect 基于Kinect的俯卧撑动作评价系统

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529915

Xia Liu, Mingdie Yan, Yazhuo Li, Xiao Li

引用次数: 0

SGCNN for 3D Point Cloud Classification SGCNN用于3D点云分类

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529847

Shiyun Liu, Dongrui Liu, Chuanchuan Chen, Changqing Xu

引用次数: 1