2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献_第3页

An Empirical Comparison of BERT, RoBERTa, and Electra for Fact Verification BERT、RoBERTa和Electra在事实验证中的实证比较

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415192

Muchammad Naseer, M. Asvial, R. F. Sari

引用次数: 8

Parallelization of Levelset-based Text Baseline Detection in Document Images 文档图像中基于水平集的文本基线检测的并行化

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415268

Hyeonwoo Jeong, Ye-Chan Choi, Kang-Sun Choi

引用次数: 1

Deep Q-learning for 5G network slicing with diverse resource stipulations and dynamic data traffic 基于深度q学习的5G网络切片，具有多种资源规定和动态数据流量

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415190

Debaditya Shome, Ankit Kudeshia

{"title":"Deep Q-learning for 5G network slicing with diverse resource stipulations and dynamic data traffic","authors":"Debaditya Shome, Ankit Kudeshia","doi":"10.1109/ICAIIC51459.2021.9415190","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415190","url":null,"abstract":"5G wireless networks use the network slicing technique that provides a suitable network to a service requirement raised by a network user. Further, the network performs effective slice management to improve the throughput and massive connectivity along with the required latency towards an appropriate resource allocation to these slices for service requirements. This paper presents an online Deep Q-learning based network slicing technique that considers a sigmoid transformed Quality of Experience, price satisfaction, and spectral efficiency as the reward function for bandwidth allocation and slice selection to serve the network users. The Next Generation Mobile Network (NGMN) vertical use cases have been considered for the simulations which also deals with the problem of international roaming and diverse intra-use case requirement variations by using only three standard network service slices termed as enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC). Our Deep Q-Learning model also converges significantly faster than the conventional Deep Q-Learning based approaches used in this field. The environment has been prepared based on ITU specifications for eMBB, uRLLC, mMTC. Our proposed method demonstrates a superior Quality-of-experience for the different users and the higher network bandwidth efficiency compared to the conventional slicing technique.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"15 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":"131298754","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: 7

IoT-Based Vibration Sensor Data Collection and Emergency Detection Classification using Long Short Term Memory (LSTM) 基于物联网的振动传感器数据采集及基于长短期记忆的应急检测分类

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415228

C. I. Nwakanma, F. Islam, Mareska Pratiwi Maharani, Dong-Seong Kim, Jae-Min Lee

{"title":"IoT-Based Vibration Sensor Data Collection and Emergency Detection Classification using Long Short Term Memory (LSTM)","authors":"C. I. Nwakanma, F. Islam, Mareska Pratiwi Maharani, Dong-Seong Kim, Jae-Min Lee","doi":"10.1109/ICAIIC51459.2021.9415228","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415228","url":null,"abstract":"In this paper, we used a vibration sensor known as G-Link 200 to collect real time vibration data. The sensor is connected through the internet gateway and Long Short Term Memory (LSTM) used for the classification of sensor data. The classification allows for detecting normal and anomaly activity situation which allows for triggering emergency situation. This is implemented in smart homes where privacy is an issue of concern. Example of such places are toilets, bedrooms and dressing rooms. It can also be applied to smart factory where detecting excessive or abnormal vibration is of critical importance to factory operation. The system eliminates the discomfort for video surveillance to the user. The data collected is also useful for the research community in similar research areas of sensor data enhancement. MATLAB R2019b was used to develop the LSTM. The result showed that the accuracy of the LSTM is 97.39% which outperformed other machine learning algorithm and is reliable for emergency classification.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"1 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":"130202951","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: 10

High-speed Optimization of User Pairing in NOMA System Using Laser Chaos Based MAB Algorithm 基于激光混沌MAB算法的NOMA系统用户配对高速优化

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415234

Zengchao Duan, Norihiro Okada, Aohan Li, M. Naruse, N. Chauvet, M. Hasegawa

{"title":"High-speed Optimization of User Pairing in NOMA System Using Laser Chaos Based MAB Algorithm","authors":"Zengchao Duan, Norihiro Okada, Aohan Li, M. Naruse, N. Chauvet, M. Hasegawa","doi":"10.1109/ICAIIC51459.2021.9415234","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415234","url":null,"abstract":"NOMA has become one of the most concerned technologies in 5G technology due to its efficiency in improving spectrum utilization by exploiting power domain technology. Efficient pairing techniques are needed to avoid system performance decrease affected by Successive Interference Cancellation (SIC) execution errors in NOMA. Generally, user pairing in NOMA is based on the distance between the Base Station (BS) and the users, which may lead extra time consumption in measuring distance. To reduce the communication latency caused by user pairing, we propose an ultra-fast user pairing algorithm using laser chaos based Multi-Armed-Bandit (MAB) algorithm. It has been shown that ultra-fast and high-performance decision making on the order of GHz can be achieved by applying laser chaos based MAB algorithm. In our proposed algorithm, user pairing is based on all possible pairing combination instead of distance. Simulation results show that our proposed algorithm can achieve higher throughput than the state-of-the-arts NOMA pairing algorithms, i.e., C-NOMA and UCGD-NOMA, with high speed under different radii of communication area.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"71 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":"114936465","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

Differentially-Private Text Generation via Text Preprocessing to Reduce Utility Loss 基于文本预处理的差分私有文本生成减少效用损失

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415242

Taisho Sasada, Masataka Kawai, Yuzo Taenaka, Doudou Fall, Y. Kadobayashi

{"title":"Differentially-Private Text Generation via Text Preprocessing to Reduce Utility Loss","authors":"Taisho Sasada, Masataka Kawai, Yuzo Taenaka, Doudou Fall, Y. Kadobayashi","doi":"10.1109/ICAIIC51459.2021.9415242","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415242","url":null,"abstract":"To provide user-generated texts to third parties, various anonymization used to process the texts. Since this anonymization assume the knowledge possessed by the adversary, sensitive information may be leaked depending on the adversary’s knowledge even after this anonymization. Moreover, setting the strongest assumptions about the adversary’s knowledge leads to the degradation of the utility as the data by removing any quasi-identifiers. Therefore, instead of providing original data, a method to generate differentially-private synthetic data has been proposed. Differential privacy is more flexible than anonymization technologies because it does not require the assumption of the adversary’s knowledge. However, if a large noise is added to the gradient in text generative model to satisfy differential privacy, the utility of the synthetic text is degraded. Since differential privacy can be satisfied with a small noise in data containing duplicates, it is possible to reduce utility loss as text by creating duplicates before adding noise. In this study, we reduce the amount of noise added by creating duplicates through generalization, thereby minimizing text utility loss. By constructing a differentially-private text generation model, we can provide synthetic text and promote text utilization while protecting privacy information in the text.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"63 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":"128283201","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

Feature-Based Deep LSTM Network for Indoor Localization Using UWB Measurements 基于特征的深度LSTM网络在超宽带室内定位中的应用

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415277

Alwin Poulose, D. Han

{"title":"Feature-Based Deep LSTM Network for Indoor Localization Using UWB Measurements","authors":"Alwin Poulose, D. Han","doi":"10.1109/ICAIIC51459.2021.9415277","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415277","url":null,"abstract":"Indoor localization using ultra-wideband (UWB) measurements is an effective localization approach when the localization system exists in non-line of sight (NLOS) conditions from the indoor experiment area. In UWB-based indoor localization, the system estimates the user’s distance information using anchor-tag communication. The user’s distance information in the UWB system is an influencing factor to determine localization performance. A deep learning-based localization system uses the raw distance information for model training and testing and the model predicts the user’s current positions. Recently developed deep learning-based UWB localization approaches achieve the best localization results when compared to conventional approaches. However, when the deep learning models use raw distance information, the system lacks sufficient features for training and this is reflected in the model’s performance. To solve this problem, we propose a feature-based localization approach for UWB localization. The proposed approach uses deep long short-term memory (DLSTM) network for training and testing. Using extracted features from the user’s distance information gives a better model performance than raw distance data and the DLSTM network is capable of encoding temporal dependencies and learn high-level representation from the extracted feature data. The simulation results show that the proposed feature-based DLSTM localization system achieved a 5cm mean localization error as compared to conventional UWB localization approaches.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"4 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":"123993848","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: 10

Distortion based Watermark Extraction Technique Using 1D CNN 基于失真的一维CNN水印提取技术

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415200

Yuto Matsunaga, N. Aoki, Y. Dobashi, T. Kojima

引用次数: 0

Two-Stage Classification Technique for Malicious DNS Identification 恶意DNS识别的两阶段分类技术

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415225

G. Amaizu, Danielle Jaye S. Agron, Jae-Min Lee, Dong-Seong Kim

引用次数: 0

Multi-View 3D Human Pose Estimation with Self-Supervised Learning 基于自监督学习的多视图三维人体姿态估计

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415244

Inho Chang, Min-Gyu Park, Jaewoo Kim, J. Yoon

引用次数: 4