2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献_第2页

Exploring Meta Learning: Parameterizing the Learning-to-learn Process for Image Classification 探索元学习:参数化图像分类的学习到学习过程

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415205

Chaehan So

{"title":"Exploring Meta Learning: Parameterizing the Learning-to-learn Process for Image Classification","authors":"Chaehan So","doi":"10.1109/ICAIIC51459.2021.9415205","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415205","url":null,"abstract":"Meta-learning has emerged as a new paradigm in AI to challenge the limitation of conventional deep learning to acquire only task-specific knowledge. Meta-learning transcends this limitation by extracting the general concepts when learning tasks to apply these concepts later when learning new tasks. One popular meta-learning approach is model-agnostic meta-learning (MAML) which learns tasks by optimizing parameters towards highest generalizability of future tasks. The present paper applied a practical implementation of MAML to conduct an image classification task. Results showed that performance on learning new tasks neared training performance without overfitting. Furthermore, optimal values for inner-loop and outer-loop learning rate were close to default parameter values. Smaller batch sizes with more epochs improved learning in earlier epochs compared to larger batch sizes with fewer epochs. These findings show that MAML is able to transfer the concepts extracted during training effectively on to new tasks which it had not been trained on, similarly to how humans transfer knowledge.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129662407","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

Machine Learning-Based Service Differentiation in the 5G Core Network 5G核心网中基于机器学习的业务差异化

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415263

Mohamad Rimas Mohamad Anfar, Joyce B. Mwangama

引用次数: 1

Verbal Abuse Classification Using Multiple Deep Neural Networks 基于多个深度神经网络的言语虐待分类

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415218

Hyunju Park, Hong Kook Kim

引用次数: 1

Bug Report Summarization using Believability Score and Text Ranking Bug报告总结使用可信度评分和文本排名

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415267

Youngjin Koh, Sungwon Kang, Seonah Lee

引用次数: 3

A Study on Attack Pattern Generation and Hybrid MR-IDS for In-Vehicle Network 车载网络攻击模式生成及混合MR-IDS研究

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415261

D. Kang, Sang-Hun Yoon, D. Shin, Young Yoon, Hyeon Min Kim, Soohyun Jang

{"title":"A Study on Attack Pattern Generation and Hybrid MR-IDS for In-Vehicle Network","authors":"D. Kang, Sang-Hun Yoon, D. Shin, Young Yoon, Hyeon Min Kim, Soohyun Jang","doi":"10.1109/ICAIIC51459.2021.9415261","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415261","url":null,"abstract":"The CAN (Controller Area Network) bus, which transmits and receives ECU control information in vehicle, has a critical risk of external intrusion because there is no standardized security system. Recently, the need for IDS (Intrusion Detection System) to detect external intrusion of CAN bus is increasing, and high accuracy and real-time processing for intrusion detection are required. In this paper, we propose Hybrid MR (Machine learning and Ruleset) -IDS based on machine learning and ruleset to improve IDS performance. For high accuracy and detection rate, feature engineering was conducted based on the characteristics of the CAN bus, and the generated features were used in detection step. The proposed Hybrid MR-IDS can cope to various attack patterns that have not been learned in previous, as well as the learned attack patterns by using both advantages of rule set and machine learning. In addition, by collecting CAN data from an actual vehicle in driving and stop state, five attack scenarios including physical effects during all driving cycle are generated. Finally, the Hybrid MR-IDS proposed in this paper shows an average of 99% performance based on F1-score.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122470208","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

Online Fall Detection Using Attended Memory Reference Network 使用出席记忆参考网络的在线跌倒检测

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415258

Sunah Min, Jinyoung Moon

{"title":"Online Fall Detection Using Attended Memory Reference Network","authors":"Sunah Min, Jinyoung Moon","doi":"10.1109/ICAIIC51459.2021.9415258","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415258","url":null,"abstract":"Falls cause serious injuries that make daily activities difficult; therefore, they are a common target action for intelligent monitoring systems. Existing vision-based methods for fall actions classify well-trimmed short videos as either fall or non-fall actions. However, critical limitations exist when applying these methods to untrimmed videos including fall and non-fall actions as well as background. These methods can determine whether there is a fall or not for an input video with many frames related to either fall or non-fall actions. In addition, these methods require offline processing for a whole video as input, while there is strong demand for quicker responses to fall injuries provided by online fall detection. To this end, we introduce an attended memory reference network that detects a current action online for a given video segment consisting of past and current frames. To integrate contextual information used for detecting a current action, we propose a new recurrent unit, called an attended memory reference unit, which accumulates input information based on visual memory attended by current information. In an experiment using a fall detection dataset obtained from the abnormal event detection dataset for CCTV videos publicized by AI Hub, the proposed method outperforms state-of-the-art online action detection methods. By conducting ablation studies, we also demonstrate the effectiveness of the proposed modules related to the attended visual memory.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122729318","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

Consideration of Convolutional Neural Networks for Image Processing of Capillaries 卷积神经网络在毛细血管图像处理中的应用

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415270

Ha Phuong, Hieyong Jeong, Choonsung Shin

{"title":"Consideration of Convolutional Neural Networks for Image Processing of Capillaries","authors":"Ha Phuong, Hieyong Jeong, Choonsung Shin","doi":"10.1109/ICAIIC51459.2021.9415270","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415270","url":null,"abstract":"The Convolutional Neural Network (CNN) is an effective algorithm in deep learning and the performance which the CNN brings in life problem is recognized worthily. Tobacco is one of the biggest public health threats and results in 8 million deaths every year through cardiovascular diseases, lung disorders, cancers, diabetes, and hypertension. There are several methods used in hospitals for inspecting their own health, however, they are difficult to use in daily life because all inspecting devices are large-scale and complex. Thus, the purpose of this study was to propose a new method to self-check the effect of smoking on capillaries and surface skin in daily life, then evaluate the usefulness of the proposed method. The dataset was collected from the 26 human subjects through the capillaroscopy; 13 subjects were the smoker and the 13 were the non-smoker. Through all of the results for the recognition of the difference between smokers and non-smokers, it was confirmed that conventional methods to extract featured points from the edge or corner points such as ssim (structural similarity) and sift (scale-invariant feature transform) was not so good for the image processing of capillaries. However, it was found that CNN worked well with over 80% accuracy. It was discussed that efficientnet with the compound scaling was so good for the small dataset with the comparison of resnet50, vgg16, densenet121 with one scaling factor, although COVID-19 virus affected the dataset making procedure measured from human subjects directly.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122866209","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

Pedestrian Detection System with Edge Computing Integration on Embedded Vehicle 嵌入式车辆边缘计算集成行人检测系统

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415262

Ching-Lung Su, W. Lai, C. Li

引用次数: 3

Joint Demodulation and Decoding with Multi-Label Classification Using Deep Neural Networks 基于深度神经网络的多标签分类联合解调与解码

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415182

I. Ahmed, Wenjie Xu, R. Annavajjala, Woo-Sung Yoo

引用次数: 1

Recursive Feature Elimination for Machine Learning-based Landslide Prediction Models 基于机器学习的滑坡预测模型递归特征消除

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415232

Kusala Munasinghe, Piyumika Karunanayake

引用次数: 4