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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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Location-Aided Window based Beam Alignment for mmWave Communications 毫米波通信中基于定位辅助窗口的波束对准
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660423
I. Orikumhi, Jeongwan Kang, Sunwoo Kim
{"title":"Location-Aided Window based Beam Alignment for mmWave Communications","authors":"I. Orikumhi, Jeongwan Kang, Sunwoo Kim","doi":"10.1109/IC-NIDC54101.2021.9660423","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660423","url":null,"abstract":"Beam alignment is required in millimeter wave communication to ensure high data rate transmission. However, with narrow beamwidth in massive MIMO, beam alignment could be computationally intensive due to the large number of beam pairs to be measured. In this paper, we propose an efficient beam alignment framework by exploiting the location information of the user equipment (UE) and potential reflecting points. The proposed scheme allows the UE and the base station to perform a coordinated beam search from a small set of beams within the error boundary of the location information, the selected beams are then used to guide the search of future beams. To further reduce the number of beams to be searched, we propose an intelligent search scheme within a small window of beams to determine the direction of the actual beam. The proposed beam alignment algorithm is verified on simulation with some location uncertainty.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"97 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":"125089826","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
An Object Detection Framework for Span Extraction in Question Answering 问答中跨度提取的目标检测框架
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660483
Tianyu Zhou, Ping Gong
{"title":"An Object Detection Framework for Span Extraction in Question Answering","authors":"Tianyu Zhou, Ping Gong","doi":"10.1109/IC-NIDC54101.2021.9660483","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660483","url":null,"abstract":"Machine Reading Comprehension(MRC), including a series of tasks that test the ability of models to understand natural language, has received quite a few attention in Natural Language Processing(NLP). Most existing works deal with MRC tasks by exploiting the expression capability of neural networks. Some of them have achieved impressive performance. Despite the rapid iteration of the models used, few work have focused on output layer and prediction method of answer span - also known as span extraction. In this paper, we focus on span extraction in the Question Answering(QA) task. A cross-sectional comparison of widely used span extraction methods is presented, with their strengths and weaknesses noted in detail. Furthermore, inspired by Faster R-CNN, we propose a brand new span extraction method. Experiment results show that our proposed method outperforms existing span extraction methods on both English and Chinese MRC tasks.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"43 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":"122484758","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
Ancient Chinese Recognition Method Based on Attention Mechanism 基于注意机制的古汉语识别方法
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660518
Lingjing Wu, Chuang Zhang, Mengqiu Xu, Ming Wu
{"title":"Ancient Chinese Recognition Method Based on Attention Mechanism","authors":"Lingjing Wu, Chuang Zhang, Mengqiu Xu, Ming Wu","doi":"10.1109/IC-NIDC54101.2021.9660518","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660518","url":null,"abstract":"Characters and symbols play an important role of historical development and cultural transmission. Automatic ancient character recognition has become a meaningful and typical task. However, the existing recognition methods mostly focus on the detection and classification of modern Chinese, there are lack of the research on ancient Chinese, especially pre-Qin characters. And the methods are mainly computer graphics, topology, support vector machines (SVM) and convolutional neural networks (CNN), these methods lack attention to character features. Thus, based on ancient Chinese characters dataset of Tsinghua Bamboo Slips, the method proposed in this paper add attention mechanism to recognition algorithms to replace traditional convolution in order to improve recognition accuracy. Besides, we propose a data augmentation method specifically for character images, as much as possible without changing the writing form of Chinese characters. Experimental results demonstrated that our method has achieved a top5 accuracy of 99.98% which is higher compared with other methods.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"55 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":"122764671","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
Asynchronous Multi-Nets Detailed Routing in VLSI using Multi-Agent Reinforcement Learning 基于多智能体强化学习的VLSI异步多网络详细路由
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660569
Xuhua Ju, Konglin Zhu, Yibo Lin, Lin Zhang
{"title":"Asynchronous Multi-Nets Detailed Routing in VLSI using Multi-Agent Reinforcement Learning","authors":"Xuhua Ju, Konglin Zhu, Yibo Lin, Lin Zhang","doi":"10.1109/IC-NIDC54101.2021.9660569","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660569","url":null,"abstract":"Detailed routing is a crucial challenge in modern integrated circuit (IC) design. Due to the continuous increase in design complexity and complicated design rules, avoiding routing conflicts between nets becomes more and more challenging. Conventional routing strategies like the rip-up and re-route scheme may need to spend huge efforts on avoiding conflicts between nets with overlapping routing areas. To resolve this challenge, in this paper, we propose a detailed router based on multi-agent reinforcement learning for handling conflicting nets. First, we approximate nets of detailed routing as agents and regard the pin-connection task as path planning to achieve the asynchronization of routing. Second, we assign each agent a local field of view to reduce feature size and difficulty in training. Finally, in order to eliminate routing congestion, we set an information storage unit for the information communication of each agent. The evaluation results show that the proposed multi-agent reinforcement learning scheme outperforms the baseline learning methods by 11.6%.","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":"122784515","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
Anomaly Detection in Unstructured Logs Using Attention-based Bi-LSTM Network 基于注意力的Bi-LSTM网络非结构化日志异常检测
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660476
Dongqing Yu, Xia Hou, Ce Li, Qiujian Lv, Yan Wang, Ning Li
{"title":"Anomaly Detection in Unstructured Logs Using Attention-based Bi-LSTM Network","authors":"Dongqing Yu, Xia Hou, Ce Li, Qiujian Lv, Yan Wang, Ning Li","doi":"10.1109/IC-NIDC54101.2021.9660476","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660476","url":null,"abstract":"System logs record valuable information about the runtime status of IT systems. Therefore, system logs are a naturally excellent source of information for anomaly detection. Most of the existing studies on log-based anomaly detection construct a detection model to identify anomalous logs. Generally, the model treats historical logs as natural language sequences and learns the normal patterns from normal log sequences, and detects deviations from normal patterns as anomalies. However, the majority of existing methods focus on sequential and quantitative information and ignore semantic information hidden in log sequence so that they are inefficient in anomaly detection. In this paper, we propose a novel framework for automatically detecting log anomalies by utilizing an attention-based Bi-LSTM model. To demonstrate the effectiveness of our proposed model, we evaluate the performance on a public production log dataset. Extensive experimental results show that the proposed approach outperforms all comparison methods for anomaly detection.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"60 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":"121276597","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: 5
Research on Object Detection Algorithm Based on UVA Aerial Image 基于UVA航拍图像的目标检测算法研究
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660571
Huan Liu, Chengpo Mu, Ruixin Yang, Yang He, Nan Wu
{"title":"Research on Object Detection Algorithm Based on UVA Aerial Image","authors":"Huan Liu, Chengpo Mu, Ruixin Yang, Yang He, Nan Wu","doi":"10.1109/IC-NIDC54101.2021.9660571","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660571","url":null,"abstract":"In this paper a new object detection network is proposed to process UVA aerial images. The detection network based on single-stage object detection algorithm, and reduces the calculation of the network through cross phase partial connection modules. Resource consumption makes the network lighter, through multi-scale feature fusion, the ability to detect small objects of the network we proposed is improved.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"64 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":"133488084","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
Enhancing Class-semantics Features' Locating Performance for Temporal Action Localization 增强类语义特征在时间动作定位中的定位性能
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660459
Jianming Zhang, Jianqin Yin
{"title":"Enhancing Class-semantics Features' Locating Performance for Temporal Action Localization","authors":"Jianming Zhang, Jianqin Yin","doi":"10.1109/IC-NIDC54101.2021.9660459","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660459","url":null,"abstract":"Temporal action localization is a fundamental video understanding task. Meanwhile, due to the complex video background, the varied duration and amplitude of the actions, it is also a considerable challenge. Currently, offline class-semantics representation is the mainstream input of this task since untrimmed videos occupy a large memory, high-quality untrimmed videos and annotations are difficult to access. Because these representations only focus on the class-semantics information, they are sub-optimal for the temporal action localization tasks. At the same time, the exploration of localization-semantics representation is very few due to the high resource consumption. Therefore, it is necessary to improve the detection capability of class-semantics representation directly. As an exploration, we propose the ForeBack module to enhance class-semantics features’ locating performance by augmenting the distinction modeling between foreground and background clips. This module could also eliminate part of the noise of inference probability sequences. Furthermore, we use phased training to learn and use the ForeBack module more effectively. Finally, we reveal the effectiveness of our approach by conduct experiments on THUMOS-14 and the mAP at tIoU@0.5 is improved from 38.8% (BMN action detection baseline) to 47.1%.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"38 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":"133946747","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
Auxiliary Smart Glasses for Visually Impaired People Based on Two-dimensional Code Positioning 基于二维码定位的视障辅助智能眼镜
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660558
Da Liu, Wenjun Zhang, Yonggang Qi, F. Liu, Jun Liu, Junlong Ren, Yu Wang, Zehao Wang
{"title":"Auxiliary Smart Glasses for Visually Impaired People Based on Two-dimensional Code Positioning","authors":"Da Liu, Wenjun Zhang, Yonggang Qi, F. Liu, Jun Liu, Junlong Ren, Yu Wang, Zehao Wang","doi":"10.1109/IC-NIDC54101.2021.9660558","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660558","url":null,"abstract":"In order to solve the travel problem of the large number of visually impaired people, wearable visual auxiliary equipment has been proposed and extensively studied as a key technology. However, the current research work focused on the perception of external objects in the environment, rather than the location of the visually impaired people. In this paper, we proposed a positioning scheme based on two-dimensional (QR) code to obtain the location of the visually impaired. Specifically, we designed the three-point positioning scheme. Then we proposed a two-point plus orientation positioning scheme to compensate for the low accuracy of distance estimation. Especially, we have implemented a smart glass to provide intelligent guidance for the visually impaired. Through the analysis and calculation of the image, smart glasses use voice broadcast to provide obstacle warning, walking navigation and other guidance services for the visually impaired. Experimental results show that compared with the existing Bluetooth, WiFi and other methods, the QR code positioning solution we proposed has better positioning accuracy and lower cost. Moreover, compared with three-point positioning, the two-point plus orientation positioning method greatly improves the success rate of positioning, which proves that the introduction of orientation can solve the problem of insufficient ranging accuracy and meet the engineering requirements of smart glasses.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"12 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":"115543303","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 Temporal Convolutional Network for Weakly Supervised Action Segmentation 弱监督动作分割的时间卷积网络
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660442
Z. Zou, Jiaqi Zou, Junzhe Liu, Songlin Sun
{"title":"A Temporal Convolutional Network for Weakly Supervised Action Segmentation","authors":"Z. Zou, Jiaqi Zou, Junzhe Liu, Songlin Sun","doi":"10.1109/IC-NIDC54101.2021.9660442","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660442","url":null,"abstract":"The task of video action segmentation in weakly supervised learning is one of the key points of video content understanding. The ground truth only provides a set of actions but not frame level features. A popular type uses a neural network framework to train the prediction model. Our key contribution is a new Hidden Markov Model (HMM) grounded on a Temporal Convolutional Network (TCN) to label video frames, and thus generate a pseudo-ground truth for the subsequent pseudo-supervised training. In testing, we use Viterbi algorithm to generate the time action sequence to be selected, and finally get the largest posteriori sequence. We evaluate the performance of action segmentation task on breakfast dataset. The research experiments on this dataset show that our model gets efficient performance.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"8 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":"115021944","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 Federated Learning Based Chinese Text Classification Model with Parameter Factorization Weighting 基于联邦学习的参数分解加权中文文本分类模型
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660471
Huan Wang, Zerong Zeng, Ruifang Liu, Sheng Gao
{"title":"A Federated Learning Based Chinese Text Classification Model with Parameter Factorization Weighting","authors":"Huan Wang, Zerong Zeng, Ruifang Liu, Sheng Gao","doi":"10.1109/IC-NIDC54101.2021.9660471","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660471","url":null,"abstract":"Federated learning (FL), as an emerging field of machine learning, has received wide attention since this concept was proposed. In this, paper, we conduct research on text classification based on Federated Learning, and propose a Federated Learning via Local Batch Normalization and Parameter Factorization Weighting based Chinese Text Classification Model (FedBN-PW-CTC). We evaluate our approach on both homogenous and non-homogenous datasets and confirm its effect of 2.95% improvement of accuracy and 4.7% improvement of F1 score on non-homogeneous dataset.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"20 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":"115130151","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
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