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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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Physio6: A Sensor-Based Monitoring System for 6-Minute Walking Test in the Era of COVID-19 Physio6:基于传感器的新冠肺炎时代6分钟步行测试监测系统
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660605
Haoran Xu, Zhicheng Yang, Yingjia She, Wenya Chu, Yaning Zang, Jianli Pan, Desen Cao, Yuzhu Li, Zhengbo Zhang
{"title":"Physio6: A Sensor-Based Monitoring System for 6-Minute Walking Test in the Era of COVID-19","authors":"Haoran Xu, Zhicheng Yang, Yingjia She, Wenya Chu, Yaning Zang, Jianli Pan, Desen Cao, Yuzhu Li, Zhengbo Zhang","doi":"10.1109/IC-NIDC54101.2021.9660605","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660605","url":null,"abstract":"Coronavirus disease of 2019 (COVID-19) is still severe nowadays, and plentiful COVID-19 patients need careful rehabilitation. The 6-minute walking test (6MWT) is a common clinical trial that requires the patient to walk as far as possible in a corridor for 6 minutes, significantly indicating patients' cardiopulmonary disease conditions and rehabilitation. A traditional 6MWT provides the 6-minute walking distance (6MWD) as the primary result for clinical analysis. In this paper, we propose Physio6, a sensor-based monitoring system for 6MWT, which monitors one patient's various physiological signals and indicates her/his condition during the test. The system also provides the functions of early warning based on physiological signal monitoring and automatically or manually recording the adverse events, such as hypoxia or dyspnea. Moreover, Physio6 is able to communicate with the existing systems in hospitals, and to generate a comprehensive report that summarizes the performance of the patient in the current 6MWT and even in the past ones. Our system has been deployed in four hospitals. Compared with the conventional distance-based measurement, our preliminary validation reveals that the extracted physiological parameters are promisingly valuable for clinical decision-making. System quality and device comfort are also confirmed by questionnaires. The potential of leveraging this system to perform the remote 6MWT at home/in communities as a solution of COVID-19 patient rehabilitation monitoring is also discussed.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"25 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":"114449865","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
Recorrect Net: Visual Guidance for Image Captioning recrectnet:图像标题的视觉指导
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660494
Qilin Guo, Yajing Xu, Sheng Gao
{"title":"Recorrect Net: Visual Guidance for Image Captioning","authors":"Qilin Guo, Yajing Xu, Sheng Gao","doi":"10.1109/IC-NIDC54101.2021.9660494","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660494","url":null,"abstract":"Most image caption methods directly learn the mapping relationship from image to text. In practice, however, paying attention to both sentence structure and visual content at the same time can be difficult. In this paper, we propose a model, called Re-correct Net, which aims to use the existing caption information by other captioners, to guide the visual content in the generation of new caption. In addition, to obtain the more accurate caption, our method uses the existing textured entity as additional prior knowledge. Experiments show that our model can be used as re-correct block after all captioner training, which is beneficial to improve the quality of caption and is also flexible.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"15 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":"123964855","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
DSAMT: Dual-Source Aligned Multimodal Transformers for TextCaps DSAMT:文本大写的双源对齐多模态变压器
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660575
Chenyang Liao, Ruifang Liu, Sheng Gao
{"title":"DSAMT: Dual-Source Aligned Multimodal Transformers for TextCaps","authors":"Chenyang Liao, Ruifang Liu, Sheng Gao","doi":"10.1109/IC-NIDC54101.2021.9660575","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660575","url":null,"abstract":"When generating captions for images, previous caption methods tend to consider the visual features of the image but ignore the Optical Character Recognition (OCR) in it, which makes the generated caption lack text information in the image. By integrating OCR modal as well as visual modal into caption prediction, TextCaps task is aimed at producing concise sentences recapitulating the image and the text information. We propose Dual-Source Aligned Multimodal Transformers (DSAMT), which utilize words from two sources (object tags and OCR tokens) as the supplement to vocabulary. These extra words are applied to align caption embedding and visual embedding through randomly masking some tokens in caption and calculating the masked token loss. A new object detection module is used in DSAMT to extract image visual features and object tags on TextCaps. We additionally use BERTSCORE to evaluate our predictions. We demonstrate our approach achieves superior results compared to state-of-the-art models on TextCaps dataset.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"85 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":"121895159","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
Attention-guided Soft Ranking Loss for Resource-constrained Head Pose Estimation 资源受限头部姿态估计的注意引导软排序损失
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660542
Wenqi Xu, Tangzheng Lian, Wei Liu, Kaili Zhao
{"title":"Attention-guided Soft Ranking Loss for Resource-constrained Head Pose Estimation","authors":"Wenqi Xu, Tangzheng Lian, Wei Liu, Kaili Zhao","doi":"10.1109/IC-NIDC54101.2021.9660542","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660542","url":null,"abstract":"This paper presents a novel model for head-pose estimation from a single image with a compact model size. Previous state-of-the-art methods often rely on large training models and converge slowly on standard GPUs. In this paper, we introduce attention-guided soft ranking loss that reduces the size of the state-of-the-art method while increasing its performance. Specifically, we design an attention module to encourage learning on salient features. In addition, we propose a pair-wise soft ranking loss that supervises the model with paired samples and penalizes incorrect ordering of head-pose prediction. Considering the lack of large-pose data, we also introduce a minority head-pose oversampling algorithm to balance the distribution of yaw, pitch, and roll angles. Experiments on BIWI and AFLW2000 datasets demonstrate that our approach significantly outperforms the state-of-the-art methods. Extensive ablation studies further validate the effectiveness and robustness of the design of our framework. Code will be made availablel.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"16 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":"127745717","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 Method of Establishing a Synthetic Dataset for Stored-Grain Insects 一种建立储粮昆虫综合数据集的方法
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660507
Kuo Cai, Jiangtao Li, Yudong Wang, An-yi Lan, Huiling Zhou
{"title":"A Method of Establishing a Synthetic Dataset for Stored-Grain Insects","authors":"Kuo Cai, Jiangtao Li, Yudong Wang, An-yi Lan, Huiling Zhou","doi":"10.1109/IC-NIDC54101.2021.9660507","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660507","url":null,"abstract":"This paper presents a method to establish the image dataset for stored-grain insects based on image composition, for the development of stored-grain insect recognition. The proposed method comprises 3 parts: 3D modeling of insects, the dynamic generation equation for synthetic image of stored-grain insects, and a material conversion neural network. As a result, a synthetic image dataset, named VirtualInsect, is established for adults of insects from 13 species 11 families in main stored grains (paddy rice, wheat and corn) in China, which is the most diverse in species of insects at present.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"183 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":"124644289","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
Channel Tracking and Detection Based on Long-Short Term Memory in Millimeter Wave System 基于长短期记忆的毫米波系统信道跟踪与检测
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660607
Qingqing Li, Chao Dong, Shiqiang Suo, K. Niu
{"title":"Channel Tracking and Detection Based on Long-Short Term Memory in Millimeter Wave System","authors":"Qingqing Li, Chao Dong, Shiqiang Suo, K. Niu","doi":"10.1109/IC-NIDC54101.2021.9660607","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660607","url":null,"abstract":"Millimeter wave communication is one of the most promising technology for 5G and beyond in the future. Massive MIMO technology and beamforming technology are deployed to compensate for the severe path loss. However, millimeter wave channels still exist some problems such as susceptibility to channel abrupt changes (CAC) due to environmental impacts. Therefore, detecting the CAC of the millimeter wave channel effectively is one of the key issues in keeping high service quality. This paper proposes a Long-Short Term Memory (LSTM) algorithm for the detection of CAC. Specifically, extended kalman filter (EKF) is exploited for channel tracking, and the obtained channel state information (CSI) is collected to train the LSTM network in the offline training phase. Then, the trained LSTM network would detect CAC consecutively in the online learning phase. The key of this algorithm is to make full use of the effective information in different slots to further improve the detection performance. The results prove that, compared with traditional algorithms, the proposed algorithms decrease the false detection rate (FDR) by 47% while the missed detection rate (MDR) can be maintained at a stable level.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"106 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":"122422690","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
I-MMCCN: Improved MMCCN for RGB-T Crowd Counting of Drone Images I-MMCCN:用于无人机图像RGB-T人群计数的改进MMCCN
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660586
Binyu Zhang, Yunhao Du, Yanyun Zhao, Jun-Jun Wan, Zhihang Tong
{"title":"I-MMCCN: Improved MMCCN for RGB-T Crowd Counting of Drone Images","authors":"Binyu Zhang, Yunhao Du, Yanyun Zhao, Jun-Jun Wan, Zhihang Tong","doi":"10.1109/IC-NIDC54101.2021.9660586","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660586","url":null,"abstract":"Crowd counting is a critical technique in many artificial intelligent applications, such as security monitoring and automatic transportation management. However, due to the variations in object scales, illumination and image quality, crowd counting from drone images is full of challenges. To fully delve the information hidden in the multi-modal RGB-T images shot by drones for crowd counting, we proposed a hard examples mining module and a novel Block Mean Absolute Error loss (BMAE) to improve Multi-Modal Crowd Counting Network (MMCCN). With the local structural supervision introduced by BMAE loss, the network can incorporate local spatial correlation within each block and focus on the local pattern of people. Besides, BMAE is more similar to the evaluation metrics. By combining our proposed hard example mining module and BMAE loss with MMCCN, we obtain our Improved MMCCN, named as I-MMCCN. Experiments on the DroneRGBT dataset verify the effectiveness of our I-MMCCN. It achieves 1.01 MAE and 1.48 RMSE lower than MMCCN on DroneRGBT validation set.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"1 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":"125856765","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
Symbol Error Rate of M-ary PSK with I/Q Imbalances Over an Impulsive Noise Channel 脉冲噪声信道中具有I/Q不平衡的m型PSK的符号错误率
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660482
Geunbae Kim, Juntaek Park, D. Yoon
{"title":"Symbol Error Rate of M-ary PSK with I/Q Imbalances Over an Impulsive Noise Channel","authors":"Geunbae Kim, Juntaek Park, D. Yoon","doi":"10.1109/IC-NIDC54101.2021.9660482","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660482","url":null,"abstract":"In this paper, we analyze the effect of the in-phase/quadrature imbalances on the error performance of M-ary phase shift keying (PSK) systems in the presence of impulsive noise. We adopt Middleton class A model as an impulsive noise model and derive symbol error rate (SER) and SER floor expressions of M-ary PSK over an impulsive noise channel. Through computer simulations, we validate the theoretical results.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"82 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":"131392739","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
Enhancing Recommendation Diversity Using Determinantal Point Process Forward Inference and Backward Elimination 利用确定性点过程前向推理和后向消除增强推荐多样性
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660543
Xiaohan Yang, Kun Niu, Xiao Li, Ruijie Yu
{"title":"Enhancing Recommendation Diversity Using Determinantal Point Process Forward Inference and Backward Elimination","authors":"Xiaohan Yang, Kun Niu, Xiao Li, Ruijie Yu","doi":"10.1109/IC-NIDC54101.2021.9660543","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660543","url":null,"abstract":"Top-N recommendation refers to mining a few specific items that are supposed to be most appealing to the user. While relevancy has been the prevailing issue of the recommendation problem for the last decades, diversity, which is associated with increasing user satisfaction with the presented recommendation lists and mitigating the overfitting problem, also plays a central role in the success of predictive models. Existing work applied determinantal point processes (DPP) to provide a favorable trade-off between relevance and diversity. However, the maximum a posteriori (MAP) inference for DPP is generally NP-hard. To attain an approximate solution with sufficient accuracy, popular approximation approaches such as forward and backward greedy algorithms are used. Despite their intuitive manner, they are not adequate and still be too computationally expensive to be used in large-scale domains. Thus, this paper aims to enhance forward greedy algorithms incorporating backward elimination algorithms and accelerate the greedy MAP inference for DPP by introducing the Cholesky decomposition and Givens rotation. Experimental results show that our proposed algorithm is faster than most competitors and ensures a substantial improvement over the accuracy-diversity trade-off on the Netflix Prize dataset.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"24 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":"124368981","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
[Copyright notice] (版权)
2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/ic-nidc54101.2021.9660589
{"title":"[Copyright notice]","authors":"","doi":"10.1109/ic-nidc54101.2021.9660589","DOIUrl":"https://doi.org/10.1109/ic-nidc54101.2021.9660589","url":null,"abstract":"","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":"122880704","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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