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2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)最新文献

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Mobility-Aware Proactive Video Segment Caching Based on Deep Reinforcement Learning 基于深度强化学习的机动感知主动视频片段缓存
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660434
Xuefei Li, Jiawei Wang, Zhilong Zhang, Danpu Liu
{"title":"Mobility-Aware Proactive Video Segment Caching Based on Deep Reinforcement Learning","authors":"Xuefei Li, Jiawei Wang, Zhilong Zhang, Danpu Liu","doi":"10.1109/IC-NIDC54101.2021.9660434","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660434","url":null,"abstract":"Maintaining efficient and successive video streaming services in cellular networks is challenging due to user mobility and ever-increasing volume of data traffic. A promising solution is to cache popular contents at the edge of wireless networks. Although caching schemes have been widely discussed, few of them jointly considered the characteristics of streamed video data and user mobility. In this paper, we construct a dynamic caching decision framework based on Long Short-Term Memory (LSTM) and Deep Q-network (DQN). Based on this framework, a mobility-aware segment-level caching strategy is proposed to maximize the cache hit rate. Simulation results show that our proposed method can achieve 20% performance improvement by comparing with baseline caching algorithms.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132549189","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
The Experimental Comparison of Under-Panel-Sensing (UPS) Using FMCW Radar Sensor FMCW雷达传感器面板下传感(UPS)的实验比较
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660454
Dingyang Wang, Junyoung Park, S. Cho
{"title":"The Experimental Comparison of Under-Panel-Sensing (UPS) Using FMCW Radar Sensor","authors":"Dingyang Wang, Junyoung Park, S. Cho","doi":"10.1109/IC-NIDC54101.2021.9660454","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660454","url":null,"abstract":"According to the trend of full displays on phones and TVs. The camera and fingerprints are shifted from the trimmed display panel or wider edges to the underside of the display. However, no attempt has been made to use radar for interaction sensing under the panel. In this paper, we present a comparison of the under-panel-sensing (UPS) and normal sensing (without panel) using frequency modulation continuous wave (FMCW) radar. In our experiments, we compare the range estimation, Doppler, and direction of arrival (DOA) distortion due to refraction and diffraction as the radio frequency (RF) signal penetrates the liquid crystal panel and glass. The signal-to-noise ratio shows no significant difference in the range estimation for the five different colors of liquid crystal display (LCD). However, the amplitude is reduced by about 6 dB compared to the measurement without LCD. The Doppler frequency is almost same even the LCD is installed. The DOA results show that there is a noise component in range-azimuth map at the specific angle along the distance, and by removing the DC component, the DOA information of the moving target can be extracted.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130174635","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
A Safety-Helmet Detection Algorithm Based on Attention Mechanism 基于注意机制的安全帽检测算法
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660439
Haotian Sun, Ping Gong
{"title":"A Safety-Helmet Detection Algorithm Based on Attention Mechanism","authors":"Haotian Sun, Ping Gong","doi":"10.1109/IC-NIDC54101.2021.9660439","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660439","url":null,"abstract":"Wearing safety helmet is one of the most effective methods to prevent the head injury of construction workers. However, the existing safety helmet detection algorithms based on deep learning mostly have the defects of high false detection rate of similar targets. Therefore, we propose an improved object detection algorithm based on YOLOv3 by integrating attention mechanism, to increase the accuracy of helmet detection. Firstly, due to being combined with the attention mechanism, the ability of expression of the feature graph in the neural network is enhanced, this improves the robustness of the object detection model. Considering the imbalance of samples in existing helmet detection datasets, the loss function was redesigned to ameliorate the imbalance of positive and negative samples, and the accuracy of detection is improved when the targets overlap each other. The experimental results show that our new algorithm improves the mean average precision (mAP) of helmet detection by 6.4% compared with the previous algorithm and has applicability for helmets at different scenes and in different scales.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134031920","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 Practical Approach for SNR-Based Subchannel Allocation Considering Inter-Beam Interference in a Satellite Communication System 考虑波束间干扰的卫星通信系统基于信噪比的子信道分配方法
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660446
Masaki Takahashi, S. Suzuki, Y. Kawamoto, N. Kato
{"title":"A Practical Approach for SNR-Based Subchannel Allocation Considering Inter-Beam Interference in a Satellite Communication System","authors":"Masaki Takahashi, S. Suzuki, Y. Kawamoto, N. Kato","doi":"10.1109/IC-NIDC54101.2021.9660446","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660446","url":null,"abstract":"With the recent increase in the demand for satellite communications, the research efforts on resource allocation using a digital channelizer aim to improve the utilization efficiency of the limited frequency resources. However, the conventional resource allocation methods using a digital channelizer focus on almost completely avoiding inter-beam interference between adjacent beams, which results in the problem that the amount of over- or under-allocated resources does not decrease depending on the traffic demand distribution. In addition, in situations where the Global Positioning System (GPS) information of ground terminals is difficult to use from the viewpoint of security and privacy, it is unrealistic to accurately identify ground terminal location. Thus, we propose a practical approach for subchannel allocation based on the expected interference power of ground terminals calculated on the basis of the signal-to-noise ratio (SNR) information as a new alternative to GPS information from ground terminals. The simulation results show that the proposed approach achieves a higher traffic capacity ratio than the conventional resource allocation method for all possible variations in the traffic demand distribution.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122200233","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
Network Flow Generation Based on Reinforcement Learning Powered Generative Adversarial Network 基于强化学习生成对抗网络的网络流生成
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660491
Jianxu Li, Yang Xiao, Jiawei Wu, Jialong Feng, Jun Liu
{"title":"Network Flow Generation Based on Reinforcement Learning Powered Generative Adversarial Network","authors":"Jianxu Li, Yang Xiao, Jiawei Wu, Jialong Feng, Jun Liu","doi":"10.1109/IC-NIDC54101.2021.9660491","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660491","url":null,"abstract":"Mining anomalies and special events from massive network flows based on machine learning and deep learning is a promising approach for network management. However, it is difficult to build labeled network flow data sets for training machine learning and deep learning models. In this paper, we propose a novel reinforcement learning (RL) powered generative adversarial network (GAN) model named NF-GAN for network flow generation. The generator of NF-GAN is designed as a stochastic policy model to generate labeled network flow data. In terms of the discriminator, a check reward is integrated into the network reward to capture the correlations among attributes. Experiment results demonstrate that the majority of the generated flows conform to the strict network protocols of the standard OSI stack, and the success rate of network flows generation achieves 99.96%. To the best of our knowledge, this is the first time of applying RL powered GAN on network flow generation tasks.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124892388","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
A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion 基于注意机制和多任务融合的织物缺陷检测网络
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660399
Z. Peng, Xinyi Gong, Zhenfeng Lu, Xiangyi Xu, Bengang Wei, Mukesh Prasad
{"title":"A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion","authors":"Z. Peng, Xinyi Gong, Zhenfeng Lu, Xiangyi Xu, Bengang Wei, Mukesh Prasad","doi":"10.1109/IC-NIDC54101.2021.9660399","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660399","url":null,"abstract":"Fabric is an important material, which is applied in the entire process of textile manufacturing, such as spinning, weaving, dyeing, printing, and finishing, and garments manufacturing. As defects on the surface of the fabric are inevitable in the process of fabric production, the defect detection of fabric is significant for fabric manufacture. The current CNN-based defect detection methods face several challenges when tackling the fabric defect with a tiny shape, the low grayscale difference with background, and ambiguous defect type. To deal with the problem, we proposed a novel fabric defect detection network – AMTFNet based on the attention mechanism and multi-task fusion module in this paper. On one hand, the attention mechanism module forced networks to pay attention to defects. On the other hand, the multi-task fusion module helps AMTFNet to further improve the classification effect using feature concatenated. The experimental result indicates that the precision-score, recall-score, and F1-score of AMTFNet reach 0.980, 0.994, and 0.987, respectively. The proposed method can be successfully applied in the detection of industrial fabric material.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130081648","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
Data-Driven Receiver for OTFS System with Deep Learning 基于深度学习的OTFS系统数据驱动接收机
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660432
Qingyu Li, Yi Gong, Fanke Meng, Zhan Xu
{"title":"Data-Driven Receiver for OTFS System with Deep Learning","authors":"Qingyu Li, Yi Gong, Fanke Meng, Zhan Xu","doi":"10.1109/IC-NIDC54101.2021.9660432","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660432","url":null,"abstract":"Recently researches about receiver structures for orthogonal time-frequency space (OTFS) have been received widespread attention. Previous OTFS receiver algorithms are based on model-driven, which would lead to complex structures. Motivated by recent advances in data-driven receivers, this paper proposes a data-driven OTFS receiver with a deep neural network (DNN). We demonstrate that the proposed data-driven receiver for OTFS can be generalized to different high mobility scenarios. Specifically, this scheme combines the power of deep learning (DL), which is widely used in various fields. With DL, the proposed algorithm can achieve excellent robustness and strong generalization ability for channel parameters, which are ubiquitous challenges in the design of receiver algorithms. Through a good deal of numerical experiments, simulation results show that the proposed data-driven receiver based on DNN for OTFS can achieve superior performance than comparison methods.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127965524","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: 6
An Explainable Educational Resource Recommendation Model Based on Matrix Factorization 基于矩阵分解的可解释教育资源推荐模型
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660549
Xiaolin Gui, Fuying Wu, Xiaoyan Liu, Yugen Yi, Zhenzhen Luo, Bing Li
{"title":"An Explainable Educational Resource Recommendation Model Based on Matrix Factorization","authors":"Xiaolin Gui, Fuying Wu, Xiaoyan Liu, Yugen Yi, Zhenzhen Luo, Bing Li","doi":"10.1109/IC-NIDC54101.2021.9660549","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660549","url":null,"abstract":"The recommendation algorithm based on hidden variables are widely used in educational resource recommendation systems. However, such algorithms and their recommendation results lack explainability, which affects the application effect of recommendation. Therefore, we propose an explainable educational resource recommendation (EERR) model to solve this problem. The model is constructed by three steps. To begin with, we extract explainable features from educational resource manually. Then, the recessive feature is correlated with explicit feature by using of matrix decomposition. Finally, the alternating least square algorithm is used to obtain the recommended results. Experiment results show that the proposed model has better performance under the RMSE evaluation criteria, and it can improve users' trust in the recommendation system.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130480664","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
Sequence Elimination Algorithm for Multi-Server Multi-User Mobile Edge Computing Offloading 多服务器多用户移动边缘计算卸载的序列消去算法
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660420
Yi Chu, Ruixiang Li, Fang Wang, Junlong Ren, Yuanyuan Qiao, Jie Yang
{"title":"Sequence Elimination Algorithm for Multi-Server Multi-User Mobile Edge Computing Offloading","authors":"Yi Chu, Ruixiang Li, Fang Wang, Junlong Ren, Yuanyuan Qiao, Jie Yang","doi":"10.1109/IC-NIDC54101.2021.9660420","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660420","url":null,"abstract":"Edge computing is a new paradigm of distributed computing, in which computing offloading attracts wide attention from scholars. Existing work mainly focuses on single-server and single-user, and generally only optimizes energy consumption or execution time respectively. However, optimizing one may lead to the decline in another. In order to remedy this defect, we propose sequence elimination algorithm based on genetic algorithm. The algorithm can optimize energy consumption or execution time respectively in the scenario of multi-user and multi-server. The simulation results show that the sequence elimination algorithm can effectively reduce the energy consumption and execution time in the edge computing offloading problem.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"5 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122982750","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
Analysis and Research of Abstractive Automatic Summarization Based on Sequential Facts 基于顺序事实的抽象自动摘要分析与研究
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660463
Yinan Liu, Yiyang Li, Lei Li
{"title":"Analysis and Research of Abstractive Automatic Summarization Based on Sequential Facts","authors":"Yinan Liu, Yiyang Li, Lei Li","doi":"10.1109/IC-NIDC54101.2021.9660463","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660463","url":null,"abstract":"Automatic summarization is a task of converting text, and the summary result obtained should be able to accurately describe the facts that occurred in the original text. But so far, there are a lot of factual errors in the results obtained by generative summary models, resulting in low quality and poor readability. We believe that adding factual information in the encoding stage can effectively improve the readability of the summary and generate more accurate facts. To this end, we propose an abstractive summary model based on sequential facts and conduct experiments on the CNN/Daily Mail dataset. Experiments have proved that the integration of factual information can effectively improve the ROUGE value and factual accuracy of the summary.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128195277","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
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