Pub Date : 2022-07-18DOI: 10.1109/ICMEW56448.2022.9859304
Yaozong Gan, Ren Togo, Takahiro Ogawa, M. Haseyama
This paper presents a transformer-based multimodal soccer scene recognition method for both visual and audio modalities. Our approach directly uses the original video frames and audio spectrogram from the soccer video as the input of the transformer model, which can capture the spatial information of the action at a moment and the contextual temporal information between different actions in the soccer videos. We fuse both video frames and audio spectrogram information output from the transformer model in order to better identify scenes that occur in real soccer matches. The late fusion performs a weighted average of visual and audio estimation results to obtain complete information of a soccer scene. We evaluate the proposed method on SoccerNet-V2 dataset and confirm that our method achieves the best performance compared with the recent and state-of-the-art methods.
{"title":"Transformer Based Multimodal Scene Recognition in Soccer Videos","authors":"Yaozong Gan, Ren Togo, Takahiro Ogawa, M. Haseyama","doi":"10.1109/ICMEW56448.2022.9859304","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859304","url":null,"abstract":"This paper presents a transformer-based multimodal soccer scene recognition method for both visual and audio modalities. Our approach directly uses the original video frames and audio spectrogram from the soccer video as the input of the transformer model, which can capture the spatial information of the action at a moment and the contextual temporal information between different actions in the soccer videos. We fuse both video frames and audio spectrogram information output from the transformer model in order to better identify scenes that occur in real soccer matches. The late fusion performs a weighted average of visual and audio estimation results to obtain complete information of a soccer scene. We evaluate the proposed method on SoccerNet-V2 dataset and confirm that our method achieves the best performance compared with the recent and state-of-the-art methods.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124999507","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}
Pub Date : 2022-07-18DOI: 10.1109/ICMEW56448.2022.9859468
Abderrezzaq Sendjasni, M. Larabi
Since the introduction of 360-degree images, a significant number of deep learning based image quality assessment (IQA) models have been introduced. Most of them are based on multichannel architectures where several convolutional neural networks (CNNs) are used together. Despite the competitive results, these models come with a higher cost in terms of complexity. To significantly reduce the complexity and ease the training of the CNN model, this paper proposes a patch-based scheme dedicated to 360-degree IQA. Our framework is developed including patches selection and extraction based on latitude to account for the importance of the equatorial region, data normalization, CNN-based architecture and a weighted average pooling of predicted local qualities. We evaluate the proposed model on two widely used databases and show the superiority to state-of-the-art models, even multichannel ones. Furthermore, the cross-database assessment revealed the good generalization ability, demonstrating the robustness of the proposed model.
{"title":"SAL-360IQA: A Saliency Weighted Patch-Based CNN Model for 360-Degree Images Quality Assessment","authors":"Abderrezzaq Sendjasni, M. Larabi","doi":"10.1109/ICMEW56448.2022.9859468","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859468","url":null,"abstract":"Since the introduction of 360-degree images, a significant number of deep learning based image quality assessment (IQA) models have been introduced. Most of them are based on multichannel architectures where several convolutional neural networks (CNNs) are used together. Despite the competitive results, these models come with a higher cost in terms of complexity. To significantly reduce the complexity and ease the training of the CNN model, this paper proposes a patch-based scheme dedicated to 360-degree IQA. Our framework is developed including patches selection and extraction based on latitude to account for the importance of the equatorial region, data normalization, CNN-based architecture and a weighted average pooling of predicted local qualities. We evaluate the proposed model on two widely used databases and show the superiority to state-of-the-art models, even multichannel ones. Furthermore, the cross-database assessment revealed the good generalization ability, demonstrating the robustness of the proposed model.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116028312","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}
In this work, we propose a 3D style transfer framework, 3DSTNet, to transfer shape or geometric properties from style to content 3D objects. We analyze the effects of multiple model hyperparameters on 3D style transfer. To evaluate the proposed 3D style transfer framework, we conduct a user study with 3D designers. Our evaluation results demonstrate that our approach effectively generates new designs and the generated designs aid in designers’ creativity.
{"title":"3DSTNet: Neural 3D Shape Style Transfer","authors":"Abhinav Upadhyay, Alpana Dubey, Suma Mani Kuriakose, Devasish Mahato","doi":"10.1109/ICMEW56448.2022.9859470","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859470","url":null,"abstract":"In this work, we propose a 3D style transfer framework, 3DSTNet, to transfer shape or geometric properties from style to content 3D objects. We analyze the effects of multiple model hyperparameters on 3D style transfer. To evaluate the proposed 3D style transfer framework, we conduct a user study with 3D designers. Our evaluation results demonstrate that our approach effectively generates new designs and the generated designs aid in designers’ creativity.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124549340","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}
Pub Date : 2022-07-18DOI: 10.1109/ICMEW56448.2022.9859417
Yi-Ping Hung, Jerry Chin-Han Goh, Yuan-An Chan, Hsiao-Yuan Chin, You-Shin Tsai, Chien-Hsin Ju
SmileVerse is a continuation of our previous work, Smiling Buddha [1], which aims to achieve emotional contagion. In the interactive installation of Smiling Buddha, we have designed a natural interactive process to let the smile that can be contagious. Based on the innovative VR/AI technologies, we upgraded our previous artwork to build a virtual universe and use facial trackers to detect the user’s expressions and let the virtual characters respond interactively to achieve smiles that can be contagious.
{"title":"Smileverse : A VR Experience","authors":"Yi-Ping Hung, Jerry Chin-Han Goh, Yuan-An Chan, Hsiao-Yuan Chin, You-Shin Tsai, Chien-Hsin Ju","doi":"10.1109/ICMEW56448.2022.9859417","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859417","url":null,"abstract":"SmileVerse is a continuation of our previous work, Smiling Buddha [1], which aims to achieve emotional contagion. In the interactive installation of Smiling Buddha, we have designed a natural interactive process to let the smile that can be contagious. Based on the innovative VR/AI technologies, we upgraded our previous artwork to build a virtual universe and use facial trackers to detect the user’s expressions and let the virtual characters respond interactively to achieve smiles that can be contagious.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126899395","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}
Pub Date : 2022-07-18DOI: 10.1109/ICMEW56448.2022.9859510
Zihan Chen, X. Chen
Tachie painting is an emerging digital portrait art form that shows a character in a standing pose. Automatic generation of a Tachie picture from a real photo would facilitate many creation tasks. However, it is non-trivial to represent Tachie’s artistic styles and establish a delicate mapping from the real-world image domain to the Tachie domain. Existing approaches generally suffer from inaccurate style transformation and severe structure distortion when applied to Tachie style transfer. In this paper, we propose the first approach for Tachie stylization of portrait photographs. Based on the unsupervised CycleGAN framework, we design two novel loss functions to emphasize lines and tones in the Tachie style. Furthermore, we design a character-enhanced stylization framework by introducing an auxiliary body mask to better preserve the global body structure. Experiment results demonstrate the robustness and better generation capability of our method in Tachie stylization from photos in a wide range of poses, even trained on a small dataset.
{"title":"Tachiegan: Generative Adversarial Networks for Tachie Style Transfer","authors":"Zihan Chen, X. Chen","doi":"10.1109/ICMEW56448.2022.9859510","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859510","url":null,"abstract":"Tachie painting is an emerging digital portrait art form that shows a character in a standing pose. Automatic generation of a Tachie picture from a real photo would facilitate many creation tasks. However, it is non-trivial to represent Tachie’s artistic styles and establish a delicate mapping from the real-world image domain to the Tachie domain. Existing approaches generally suffer from inaccurate style transformation and severe structure distortion when applied to Tachie style transfer. In this paper, we propose the first approach for Tachie stylization of portrait photographs. Based on the unsupervised CycleGAN framework, we design two novel loss functions to emphasize lines and tones in the Tachie style. Furthermore, we design a character-enhanced stylization framework by introducing an auxiliary body mask to better preserve the global body structure. Experiment results demonstrate the robustness and better generation capability of our method in Tachie stylization from photos in a wide range of poses, even trained on a small dataset.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"38 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120897453","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}
Pub Date : 2022-07-18DOI: 10.1109/icmew56448.2022.9859515
{"title":"ICMEW 2022 Cover Page","authors":"","doi":"10.1109/icmew56448.2022.9859515","DOIUrl":"https://doi.org/10.1109/icmew56448.2022.9859515","url":null,"abstract":"","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133543674","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}
Pub Date : 2022-07-18DOI: 10.1109/ICMEW56448.2022.9859467
Jaakko Laitinen, Alexandre Mercat, Jarno Vanne, H. R. Tavakoli, Francesco Cricri, Emre B. Aksu, M. Hannuksela
A Neural Network Compression (NNC) standard aims to define a set of coding tools for efficient compression and transmission of neural networks. This paper addresses the high-level syntax (HLS) of NNC and proposes three HLS techniques for network topology coding and payload partitioning. Our first technique provides an efficient way to code prune topology information. It removes redundancy in the bitmask and thereby improves coding efficiency by 4–99% over existing approaches. The second technique processes bitmasks in larger chunks instead of one bit at a time. It is shown to reduce computational complexity of NNC encoding by 63% and NNC decoding by 82%. Our third technique makes use of partial data counters to partition an NNC bitstream into uniformly sized units for more efficient data transmission. Even though the smaller partition sizes introduce some overhead, our network simulations show better throughput due to lower packet retransmission rates. To our knowledge, this the first work to address the practical implementation aspects of HLS. The proposed techniques can be seen as key enabling factors for efficient adaptation and economical deployment of the NNC standard in a plurality of next-generation industrial and academic applications.
{"title":"Efficient Topology Coding and Payload Partitioning Techniques for Neural Network Compression (NNC) Standard","authors":"Jaakko Laitinen, Alexandre Mercat, Jarno Vanne, H. R. Tavakoli, Francesco Cricri, Emre B. Aksu, M. Hannuksela","doi":"10.1109/ICMEW56448.2022.9859467","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859467","url":null,"abstract":"A Neural Network Compression (NNC) standard aims to define a set of coding tools for efficient compression and transmission of neural networks. This paper addresses the high-level syntax (HLS) of NNC and proposes three HLS techniques for network topology coding and payload partitioning. Our first technique provides an efficient way to code prune topology information. It removes redundancy in the bitmask and thereby improves coding efficiency by 4–99% over existing approaches. The second technique processes bitmasks in larger chunks instead of one bit at a time. It is shown to reduce computational complexity of NNC encoding by 63% and NNC decoding by 82%. Our third technique makes use of partial data counters to partition an NNC bitstream into uniformly sized units for more efficient data transmission. Even though the smaller partition sizes introduce some overhead, our network simulations show better throughput due to lower packet retransmission rates. To our knowledge, this the first work to address the practical implementation aspects of HLS. The proposed techniques can be seen as key enabling factors for efficient adaptation and economical deployment of the NNC standard in a plurality of next-generation industrial and academic applications.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115035964","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}
Pub Date : 2022-07-18DOI: 10.1109/ICMEW56448.2022.9859502
V. V. Menon, Hadi Amirpour, Christian Feldmann, M. Ghanbari, C. Timmerer
In live streaming applications, typically a fixed set of bitrateresolution pairs (known as a bitrate ladder) is used during the entire streaming session in order to avoid the additional latency to find scene transitions and optimized bitrateresolution pairs for every video content. However, an optimized bitrate ladder per scene may result in (i) decreased storage or delivery costs or/and (ii) increased Quality of Experience (QoE). This paper introduces an Online Per-Scene Encoding (OPSE) scheme for adaptive HTTP live streaming applications. In this scheme, scene transitions and optimized bitrate-resolution pairs for every scene are predicted using Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features. Experimental results show that, on average, OPSEyields bitrate savings of up to 48.88% in certain scenes to maintain the same VMAF, compared to the reference HTTP Live Streaming (HLS) bitrate ladder without any noticeable additional latency in streaming.
在实时流媒体应用中，通常在整个流媒体会话期间使用一组固定的比特率分辨率对(称为比特率阶梯)，以避免为每个视频内容寻找场景转换和优化比特率分辨率对的额外延迟。然而，优化每个场景的比特率阶梯可能会导致(i)降低存储或传输成本或/和(ii)提高体验质量(QoE)。本文介绍了一种用于自适应HTTP直播应用的在线逐场景编码(OPSE)方案。在该方案中，使用基于能量的低复杂度空间和时间特征的离散余弦变换(DCT)预测场景转换和优化的比特率分辨率对。实验结果表明，与参考HTTP Live Streaming (HLS)比特率阶梯相比，平均而言，opse在某些场景中可以节省高达48.88%的比特率，以保持相同的VMAF，而在流媒体中没有任何明显的额外延迟。
{"title":"OPSE: Online Per-Scene Encoding for Adaptive Http Live Streaming","authors":"V. V. Menon, Hadi Amirpour, Christian Feldmann, M. Ghanbari, C. Timmerer","doi":"10.1109/ICMEW56448.2022.9859502","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859502","url":null,"abstract":"In live streaming applications, typically a fixed set of bitrateresolution pairs (known as a bitrate ladder) is used during the entire streaming session in order to avoid the additional latency to find scene transitions and optimized bitrateresolution pairs for every video content. However, an optimized bitrate ladder per scene may result in (i) decreased storage or delivery costs or/and (ii) increased Quality of Experience (QoE). This paper introduces an Online Per-Scene Encoding (OPSE) scheme for adaptive HTTP live streaming applications. In this scheme, scene transitions and optimized bitrate-resolution pairs for every scene are predicted using Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features. Experimental results show that, on average, OPSEyields bitrate savings of up to 48.88% in certain scenes to maintain the same VMAF, compared to the reference HTTP Live Streaming (HLS) bitrate ladder without any noticeable additional latency in streaming.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"27 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120854096","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}
Pub Date : 2022-07-18DOI: 10.1109/ICMEW56448.2022.9859419
Sana Alamgeer, Mylène C. Q. Farias
Light Field (LF) cameras capture angular and spatial information and, consequently, require a large amount of resources in memory and bandwidth. To reduce these requirements, LF contents generally need to undergo compression and transmission protocols. Since these techniques may introduce distortions, the design of Light-Field Image Quality Assessment (LFI-IQA) methods are important to monitor the quality of the LFI content at the user side. In this work, we present a No-Reference (NR) LFIIQA method that is based on a Long Short-Term Memory based Deep Neural Network (LSTM-DNN). The method is composed of two streams. The first stream extracts long-term dependent distortion related features from horizontal epipolar plane images, while the second stream processes bottleneck features of micro-lens images. The outputs of both streams are fused, and supplied to a regression operation that generates a scalar value as a predicted quality score. Results show that the proposed method is robust and accurate, outperforming several state-of-the-art LF-IQA methods.
{"title":"No-Reference Light Field Image Quality Assessment Method Based on a Long-Short Term Memory Neural Network","authors":"Sana Alamgeer, Mylène C. Q. Farias","doi":"10.1109/ICMEW56448.2022.9859419","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859419","url":null,"abstract":"Light Field (LF) cameras capture angular and spatial information and, consequently, require a large amount of resources in memory and bandwidth. To reduce these requirements, LF contents generally need to undergo compression and transmission protocols. Since these techniques may introduce distortions, the design of Light-Field Image Quality Assessment (LFI-IQA) methods are important to monitor the quality of the LFI content at the user side. In this work, we present a No-Reference (NR) LFIIQA method that is based on a Long Short-Term Memory based Deep Neural Network (LSTM-DNN). The method is composed of two streams. The first stream extracts long-term dependent distortion related features from horizontal epipolar plane images, while the second stream processes bottleneck features of micro-lens images. The outputs of both streams are fused, and supplied to a regression operation that generates a scalar value as a predicted quality score. Results show that the proposed method is robust and accurate, outperforming several state-of-the-art LF-IQA methods.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128494271","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}
Pub Date : 2022-07-18DOI: 10.1109/ICMEW56448.2022.9859488
Haonan Cheng, Xiaoying Huang, Ruyu Zhang, Long Ye
In this work, we develop PerSong, a music recommendation system that can recommend personalised songs based on the user’s current status. First, multi-modal physiological signals, namely visual and heart rate, are collected and combined to construct multi-level temporal sequences. Then, we propose a Global-Local Similarity Function (GLSF) based music recommendation algorithm to establish a mapping between the user’s current state and the music. Our demonstrations have attended a quite number of exhibitions and shown remarkable performance under diverse circumstances. We have made the core of our work publicly available: https://github.com/yrz7991/GLSF/tree/master.
{"title":"Persong: Multi-Modality Driven Music Recommendation System","authors":"Haonan Cheng, Xiaoying Huang, Ruyu Zhang, Long Ye","doi":"10.1109/ICMEW56448.2022.9859488","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859488","url":null,"abstract":"In this work, we develop PerSong, a music recommendation system that can recommend personalised songs based on the user’s current status. First, multi-modal physiological signals, namely visual and heart rate, are collected and combined to construct multi-level temporal sequences. Then, we propose a Global-Local Similarity Function (GLSF) based music recommendation algorithm to establish a mapping between the user’s current state and the music. Our demonstrations have attended a quite number of exhibitions and shown remarkable performance under diverse circumstances. We have made the core of our work publicly available: https://github.com/yrz7991/GLSF/tree/master.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127159979","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}
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