Proceedings of the 2022 6th International Conference on Deep Learning Technologies最新文献

Deep-Learning-Based Automated Scoring for the Severity of Toxic Comments Using Electra 基于深度学习的使用Electra的有毒评论严重性自动评分

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556693

Tiancong Zhang

{"title":"Deep-Learning-Based Automated Scoring for the Severity of Toxic Comments Using Electra","authors":"Tiancong Zhang","doi":"10.1145/3556677.3556693","DOIUrl":"https://doi.org/10.1145/3556677.3556693","url":null,"abstract":"With the increasing popularity of the Internet, social media plays a crucial role in people's daily communication. However, due to the anonymity of Internet, toxic comments emerge in an endless stream on the Internet, which seriously affects the health of online social environment. To effectively reduce the impact of toxic comments, automated scoring methods for the severity of toxic comments are in great demand. For that purpose, a deep-learning-based natural language processing technique is proposed using ELECTRA to automatically score the toxicity of a comment in this work. The backbone of our model is the ELECTRA discriminator, and the downstream regression task is accomplished by the following head layer. Three head layers are implemented separately: multi-layer perceptron, convolutional neural network, and attention. The dataset used for model training is from the Kaggle competition Toxic Comment Classification Challenge, and the model performance is evaluated through another Kaggle competition Jigsaw Rate Severity of Toxic Comments. By a boost from the K-Fold cross validation and an ensemble of three models with different head layers, our method can reach a competition score 0.80343. Such score ranks 71/2301 (top 3.1%) in the leaderboard and can get a silver medal in the competition. The results in this work would help filter the toxic comments and harmful text information automatically and effectively on the Internet, and could greatly reduce the cost of manual review and help build a healthier Internet environment.","PeriodicalId":350340,"journal":{"name":"Proceedings of the 2022 6th International Conference on Deep Learning Technologies","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126173202","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

Prediction of Bitcoin Price Since COVID-19 by Using Neural Network Models 基于神经网络模型的COVID-19以来比特币价格预测

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556679

Zhiheng Jiang

引用次数: 0

Smoke Detection Algorithm Based on Improved EfficientDet 基于改进effentdet的烟雾检测算法

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556678

Zengquan Yang, Han Huang, Fuming Xia, Zhen Shi

引用次数: 0

An Image-based Transfer Learning Framework for Classification of E-Commerce Products 基于图像的电子商务产品分类迁移学习框架

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556689

Vrushali Atul Surve, Pramod Pathak, Mohammed Hasanuzzaman, Rejwanul Haque, Paul Stynes

{"title":"An Image-based Transfer Learning Framework for Classification of E-Commerce Products","authors":"Vrushali Atul Surve, Pramod Pathak, Mohammed Hasanuzzaman, Rejwanul Haque, Paul Stynes","doi":"10.1145/3556677.3556689","DOIUrl":"https://doi.org/10.1145/3556677.3556689","url":null,"abstract":"Classification of e-commerce products involves identifying the products and placing those products into the correct category. For example, men’s Nike Air Max will be in the men’s category shoes on an e-Commerce platform. Identifying the correct classification of a product from hundreds of categories is time-consuming for businesses. This research proposes an Image-based Transfer Learning Framework to classify the images into the correct category in the shortest time. The framework combines Image-based algorithms with Transfer Learning. This research compares the time to predict the category and accuracy of traditional CNN and transfer learning models such as VGG19, InceptionV3, ResNet50, and MobileNet. A visual classifier is trained CNN and transfer learning models such as VGG19, InceptionV3, ResNet50, and MobileNet. The models are trained on an e-commerce product dataset that combines the ImageNet dataset with pre-trained weights. The dataset consists of 15000 images scraped from the web. Results demonstrate that Inception V3 outperforms all other models based on a TIMING of 0.10 seconds and an accuracy of 85%.","PeriodicalId":350340,"journal":{"name":"Proceedings of the 2022 6th International Conference on Deep Learning Technologies","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123170021","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

Household Load Identification Based on Multi-label and Convolutional Neural Networks 基于多标签和卷积神经网络的家庭负荷识别

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556695

Zhengquan Wang, Qi Xie

引用次数: 0

Comparison of cancer classification algorithms based on clustering analysis 基于聚类分析的癌症分类算法比较

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556684

Jiawei Guo, Yu-shan Cai

引用次数: 0

Semi supervised ocean mesoscale vortex detection method based on feature invariance 基于特征不变性的半监督海洋中尺度涡旋检测方法

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556682

Haiyan Liu, Bo Qin, Y. Liu

{"title":"Semi supervised ocean mesoscale vortex detection method based on feature invariance","authors":"Haiyan Liu, Bo Qin, Y. Liu","doi":"10.1145/3556677.3556682","DOIUrl":"https://doi.org/10.1145/3556677.3556682","url":null,"abstract":"Ocean mesoscale eddy detection is an important hotspot of Marine scientific research. Over the last few years, with the development of machine learning research, eddy detection methods based on machine learning have been applied in various fields. However, the traditional scroll detection algorithm has weak generalization ability and low detection accuracy, and the fully supervised scroll detection algorithm needs a large amount of marker data, which is costly and has poor readability. In this paper, a new semi-supervised ocean mesoscale eddy detection method based on feature invariance is proposed. The fully supervised loss calculation model is optimized to solve the problem of serious imbalance of positive and negative samples in loss calculation, so as to achieve the purpose of training the model. In addition, based on the feature invariance, an interpolation consistency calculation method based on flipped image and original image is proposed, which is combined with the consistency method algorithm put forward in CSD networks to increase the precision of detection. Compared with SSD and ISD networks, the proposed meso-scale eddy detection algorithm achieves better performance, with the AP value increasing by 1.7% and 1.1%, respectively.","PeriodicalId":350340,"journal":{"name":"Proceedings of the 2022 6th International Conference on Deep Learning Technologies","volume":"55 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123659024","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 Instance Segmentation Model to Categorize Clothes from Wild Fashion Images 基于实例分割模型的服装分类方法

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556690

Rohan Indrajeet Jadhav, Paul Stynes, Pramod Pathak, Rejwanul Haque, Mohammed Hasanuzzaman

引用次数: 1

Hybrid Feature Selection for Efficient Detection of DDoS Attacks in IoT 物联网中高效检测DDoS攻击的混合特征选择

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556687

Liang Hong, Khadijeh Wehbi, Tulha Hasan Alsalah

{"title":"Hybrid Feature Selection for Efficient Detection of DDoS Attacks in IoT","authors":"Liang Hong, Khadijeh Wehbi, Tulha Hasan Alsalah","doi":"10.1145/3556677.3556687","DOIUrl":"https://doi.org/10.1145/3556677.3556687","url":null,"abstract":"The increasing Distributed Denial of Service (DDoS) attacks on the Internet of Things (IoT) is leading to the need for an efficient detection approach. Although much research has been conducted to detect DDoS attacks on traditional networks, such as machine learning (ML) based approaches that have improved accuracy and confidence, the limited bandwidth and computation resources in IoT networks restrict the application of ML, especially deep learning (DL) based solutions that require extensive input data. In order to appropriately address the security issues in the resources-constrained IoT network, this paper is aimed to reduce the input data dimensions by extracting a subset of the most relevant features from the original features and using this subset to detect DDoS attacks on IoT without degrading the detection performance. A cost-effective model is developed to clean and prepare raw data before dimensionality reduction. A hybrid feature selection that uses Mutual Information (MI), Analysis of Variance (ANOVA), Chi-Squared, L1-based feature selection, and Tree-based feature selection algorithms is designed to identify important data features and reduce the data inputs needed for detection. Simulation results show that detection accuracy is improved with the combination of features chosen by the proposed hybrid feature selection approach. The training time is much less than the combination of each individual feature selection method.","PeriodicalId":350340,"journal":{"name":"Proceedings of the 2022 6th International Conference on Deep Learning Technologies","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121264942","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: 3

Topology-oriented 3D ocean flow field feature classification and tracking algorithm 面向拓扑的三维海洋流场特征分类与跟踪算法

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556683

Y. Liu, Bo Qin, Haiyan Liu

{"title":"Topology-oriented 3D ocean flow field feature classification and tracking algorithm","authors":"Y. Liu, Bo Qin, Haiyan Liu","doi":"10.1145/3556677.3556683","DOIUrl":"https://doi.org/10.1145/3556677.3556683","url":null,"abstract":"The tracking analysis of ocean feature phenomena exists many problems, such as incomplete topological structure information extraction and unclear time-varying law information display, etc. In this paper, a topology-oriented 3D ocean flow field feature classification and tracking algorithm is proposed to solve the problem of flow field feature tracking in different scales. The algorithm consists of three parts: Initially, the adaptive circular sampling space manner is optimized and improved to adapt to the extraction of flow field feature regions at different scales in view of the imprecise definition of traditional feature regions. Secondly, feature seed points were screened by setting information entropy threshold and denoised by template detection method. Eventually, combined with the eigenvalues of Jacobian matrix at critical points, the extracted two-dimensional feature regions are classified, and the continuous three-dimensional flow field features are visually tracked. By analyzing the experimental results of ocean flow field data of different depth and dimension, the validity and feasibility of topological feature structure classification and tracking algorithm are proved.","PeriodicalId":350340,"journal":{"name":"Proceedings of the 2022 6th International Conference on Deep Learning Technologies","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127513010","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