Cíntia Souza, José Ronaldo Júnior, Cássio V. S. Prazeres
The rise of Decentralized Online Social Networks (DOSNs) and the increase in the number of active users on these networks offer an opportunity to develop solutions related to verifying origin, description paths and indicating the trajectory of the data that traffic in these networks. Provenance information is a key aspect of social networks because it is possible to evaluate the authenticity, reliability, and relevance of the information through its results. The speed of information generation and sharing, the decentralized storage strategy associated with the large volume of data represents a challenge for data provenance. Thus, this paper proposes DOSNPROV, a data provenance ontological model based on the W3C PROV-O specification. In addition, this paper proposes services based on DOSN-PROV model to support capture and tracking of provenance information in DOSNs. We evaluated DOSN-PROV model in two stages and demonstrated its quality and compliance with the proposed domain. The services underwent an evaluation of their performance and their results indicated acceptable response times.
{"title":"An Ontological Model and Services for Capturing and Tracking Provenance in Decentralized Social Networks","authors":"Cíntia Souza, José Ronaldo Júnior, Cássio V. S. Prazeres","doi":"10.1145/3470482.3479637","DOIUrl":"https://doi.org/10.1145/3470482.3479637","url":null,"abstract":"The rise of Decentralized Online Social Networks (DOSNs) and the increase in the number of active users on these networks offer an opportunity to develop solutions related to verifying origin, description paths and indicating the trajectory of the data that traffic in these networks. Provenance information is a key aspect of social networks because it is possible to evaluate the authenticity, reliability, and relevance of the information through its results. The speed of information generation and sharing, the decentralized storage strategy associated with the large volume of data represents a challenge for data provenance. Thus, this paper proposes DOSNPROV, a data provenance ontological model based on the W3C PROV-O specification. In addition, this paper proposes services based on DOSN-PROV model to support capture and tracking of provenance information in DOSNs. We evaluated DOSN-PROV model in two stages and demonstrated its quality and compliance with the proposed domain. The services underwent an evaluation of their performance and their results indicated acceptable response times.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128671653","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}
Sidney Loyola de Sá, A. Paes, Antonio A. de A. Rocha
The Internet's popularization has increased the amount of content produced and consumed on the Web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract most users' attention. For example, in the data explored in this paper, only 6% of the most popular videos are responsible for 85% of the total views. Finding out in advance which videos will be popular is not trivial, specially because of the large amount of influencing variables. Nevertheless, a tool with this ability would be of great value to help dimensioning network infrastructure and to properly recommend new content to users. In this work, we propose two approaches to obtaining features to classify the popularity of a video before it is published. The first one builds upon predictive attributes defined by feature engineering. The second leverages word embeddings from the descriptions and titles of the videos. We experiment with the proposed approaches on a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of both engineered features and the embeddings using Random Forest machine learning algorithm reached the best result, with an accuracy of 87%.
{"title":"Extracting Textual Features from Video Streaming Services Publications to Predict their Popularity","authors":"Sidney Loyola de Sá, A. Paes, Antonio A. de A. Rocha","doi":"10.1145/3470482.3479624","DOIUrl":"https://doi.org/10.1145/3470482.3479624","url":null,"abstract":"The Internet's popularization has increased the amount of content produced and consumed on the Web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract most users' attention. For example, in the data explored in this paper, only 6% of the most popular videos are responsible for 85% of the total views. Finding out in advance which videos will be popular is not trivial, specially because of the large amount of influencing variables. Nevertheless, a tool with this ability would be of great value to help dimensioning network infrastructure and to properly recommend new content to users. In this work, we propose two approaches to obtaining features to classify the popularity of a video before it is published. The first one builds upon predictive attributes defined by feature engineering. The second leverages word embeddings from the descriptions and titles of the videos. We experiment with the proposed approaches on a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of both engineered features and the embeddings using Random Forest machine learning algorithm reached the best result, with an accuracy of 87%.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126578529","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}
Eric Ferreira dos Santos, Danilo S. Carvalho, Jonice Oliveira
The massive use of online social media is a reality nowadays. Such an increasing usage also raises growth in malicious activities in social media, one of which is the use of automated users (bots) that disseminate false information and can insert bias in analyses done on gathered social media data. Based on the concept of media literacy, this research presents a method to teach the human user to identify a pattern of a text produced by a bot, providing a tool (guide) to analyze social media text. Users who learned to identify a bot user with the guide had an average of 90% accuracy in the classification of new messages, against 57% of the participants who had no contact with the guide. The produced guide received a usefulness rating between 4 and 5 by the participants (scale from 1 to 5, with 5 being the highest value).
{"title":"Pattern Identification of Bot Messages for Media Literacy","authors":"Eric Ferreira dos Santos, Danilo S. Carvalho, Jonice Oliveira","doi":"10.1145/3470482.3479452","DOIUrl":"https://doi.org/10.1145/3470482.3479452","url":null,"abstract":"The massive use of online social media is a reality nowadays. Such an increasing usage also raises growth in malicious activities in social media, one of which is the use of automated users (bots) that disseminate false information and can insert bias in analyses done on gathered social media data. Based on the concept of media literacy, this research presents a method to teach the human user to identify a pattern of a text produced by a bot, providing a tool (guide) to analyze social media text. Users who learned to identify a bot user with the guide had an average of 90% accuracy in the classification of new messages, against 57% of the participants who had no contact with the guide. The produced guide received a usefulness rating between 4 and 5 by the participants (scale from 1 to 5, with 5 being the highest value).","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131141939","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}
Itamar Rocha Filho, Felipe Honorato, J. W. Lucena, J. P. Teixeira, T. Araújo
Audio Description (AD) or Video Description is a vital accessibility concept in blind and visually impaired people's life. Automating this task is not easy and involves many problems, such as describing the scenario, actions, emotions, and characters. This paper presents an approach to automatically describe characters --- in a video or image --- combining Deep Learning (DL), Face detection, Facial Expression detection techniques, and audio synthesizers. Our proposal uses the detection tools, applies some DL models to the analyzed data, and generates an audio description. To evaluate the feasibility of our proposal, we have developed a proof of concept of the solution and performed some computational experiments to evaluate it.
{"title":"An Approach for Automatic Description of Characters for Blind People","authors":"Itamar Rocha Filho, Felipe Honorato, J. W. Lucena, J. P. Teixeira, T. Araújo","doi":"10.1145/3470482.3479617","DOIUrl":"https://doi.org/10.1145/3470482.3479617","url":null,"abstract":"Audio Description (AD) or Video Description is a vital accessibility concept in blind and visually impaired people's life. Automating this task is not easy and involves many problems, such as describing the scenario, actions, emotions, and characters. This paper presents an approach to automatically describe characters --- in a video or image --- combining Deep Learning (DL), Face detection, Facial Expression detection techniques, and audio synthesizers. Our proposal uses the detection tools, applies some DL models to the analyzed data, and generates an audio description. To evaluate the feasibility of our proposal, we have developed a proof of concept of the solution and performed some computational experiments to evaluate it.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130240013","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}
Thiago Feijó, J. M. David, Regina M. M. Braga, M. Otenio, V. R. Paula, Gabriele Medeiros Santos, Fernanda Campos, Victor Ströele
In agribusiness, the treatment and sustainable management of waste in intensive production systems must consider productivity and economic gains in the short term and the sustainability of agricultural production. The acquisition of technical information, and the knowledge about the characteristics of the rural property, are fundamental for stakeholders, especially considering the adequacy of sustainable environmental practices. It is necessary to provide a web-based system to support stakeholders in discovering and understanding their productive context. This paper proposes a solution to support stakeholders' decision-making to improve sustainable practices in agribusiness. The proposed solution is applied in the agricultural domain, focusing on sustainability, by implementing a web system supported by an ontology to organize and provide strategic information through a mobile app. Technical and regulatory environmental practices documents are recommended for a relevant context, considering a feasibility study with experts in the field. As a result, the approach provided decision-making support offering technical information efficiently and agile for stakeholders.
{"title":"@grogest_Ambiental: A Web-based Decision Support System for agribusiness","authors":"Thiago Feijó, J. M. David, Regina M. M. Braga, M. Otenio, V. R. Paula, Gabriele Medeiros Santos, Fernanda Campos, Victor Ströele","doi":"10.1145/3470482.3479612","DOIUrl":"https://doi.org/10.1145/3470482.3479612","url":null,"abstract":"In agribusiness, the treatment and sustainable management of waste in intensive production systems must consider productivity and economic gains in the short term and the sustainability of agricultural production. The acquisition of technical information, and the knowledge about the characteristics of the rural property, are fundamental for stakeholders, especially considering the adequacy of sustainable environmental practices. It is necessary to provide a web-based system to support stakeholders in discovering and understanding their productive context. This paper proposes a solution to support stakeholders' decision-making to improve sustainable practices in agribusiness. The proposed solution is applied in the agricultural domain, focusing on sustainability, by implementing a web system supported by an ontology to organize and provide strategic information through a mobile app. Technical and regulatory environmental practices documents are recommended for a relevant context, considering a feasibility study with experts in the field. As a result, the approach provided decision-making support offering technical information efficiently and agile for stakeholders.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128375748","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}
Guilherme de A. P. Marques, A. Busson, Alan Livio Vasconcelos Guedes, S. Colcher
A crucial task to overall video understanding is the recognition and localisation in time of different actions or events that are present along the scenes. To address this problem, action segmentation must be achieved. Action segmentation consists of temporally segmenting a video by labeling each frame with a specific action. In this work, we propose a novel action segmentation method that requires no prior video analysis and no annotated data. Our method involves extracting spatio-temporal features from videos in samples of 0.5s using a pre-trained deep network. Data is then transformed using a positional encoder and finally a clustering algorithm is applied with the use of a silhouette score to find the optimal number of clusters where each cluster presumably corresponds to a different single and distinguishable action. In experiments, we show that our method produces competitive results on Breakfast and Inria Instructional Videos dataset benchmarks.
{"title":"A Cluster-Based Method for Action Segmentation Using Spatio-Temporal and Positional Encoded Embeddings","authors":"Guilherme de A. P. Marques, A. Busson, Alan Livio Vasconcelos Guedes, S. Colcher","doi":"10.1145/3470482.3479632","DOIUrl":"https://doi.org/10.1145/3470482.3479632","url":null,"abstract":"A crucial task to overall video understanding is the recognition and localisation in time of different actions or events that are present along the scenes. To address this problem, action segmentation must be achieved. Action segmentation consists of temporally segmenting a video by labeling each frame with a specific action. In this work, we propose a novel action segmentation method that requires no prior video analysis and no annotated data. Our method involves extracting spatio-temporal features from videos in samples of 0.5s using a pre-trained deep network. Data is then transformed using a positional encoder and finally a clustering algorithm is applied with the use of a silhouette score to find the optimal number of clusters where each cluster presumably corresponds to a different single and distinguishable action. In experiments, we show that our method produces competitive results on Breakfast and Inria Instructional Videos dataset benchmarks.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"322 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123270201","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}
A. Serra, A. Busson, Alan Livio Vasconcelos Guedes, S. Colcher
Audio quality degradation can have many causes. For musical applications, this fragmentation may lead to highly unpleasant experiences. Restoration algorithms may be employed to reconstruct missing parts of the audio in a similar way as for image reconstruction --- in an approach called audio inpainting. Current state-of-the art methods for audio inpainting cover limited scenarios, with well-defined gap windows and little variety of musical genres. In this work, we propose a Deep-Learning-based (DL-based) method for audio inpainting accompanied by a dataset with random fragmentation conditions that approximate real impairment situations. The dataset was collected using tracks from different music genres to provide a good signal variability. Our best model improved the quality of all musical genres, obtaining an average of 12.9 dB of PSNR, although it worked better for musical genres in which acoustic instruments are predominant.
{"title":"Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies","authors":"A. Serra, A. Busson, Alan Livio Vasconcelos Guedes, S. Colcher","doi":"10.1145/3470482.3479635","DOIUrl":"https://doi.org/10.1145/3470482.3479635","url":null,"abstract":"Audio quality degradation can have many causes. For musical applications, this fragmentation may lead to highly unpleasant experiences. Restoration algorithms may be employed to reconstruct missing parts of the audio in a similar way as for image reconstruction --- in an approach called audio inpainting. Current state-of-the art methods for audio inpainting cover limited scenarios, with well-defined gap windows and little variety of musical genres. In this work, we propose a Deep-Learning-based (DL-based) method for audio inpainting accompanied by a dataset with random fragmentation conditions that approximate real impairment situations. The dataset was collected using tracks from different music genres to provide a good signal variability. Our best model improved the quality of all musical genres, obtaining an average of 12.9 dB of PSNR, although it worked better for musical genres in which acoustic instruments are predominant.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127690500","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}
The presentation of sensory effects in sync with 360° content has the potential to increase user immersion. However, the authoring process for such effects is laborious and slow. It requires the author to specify in time and space the presentation characteristics of effects. This paper contributes to address this need with a generic architecture for inferring the presentation characteristics of sensory effects in 360° content. The proposed architecture decouples and eases the adaptation of multimedia content analysis techniques for recognizing sensory effects. We highlight the original contribution of this work of presenting the automatic recognition of sensory effects location in 360° content.
{"title":"Sensory Effect Extraction for 360° Media Content","authors":"R. Abreu, J. Santos, D. Muchaluat-Saade","doi":"10.1145/3470482.3479620","DOIUrl":"https://doi.org/10.1145/3470482.3479620","url":null,"abstract":"The presentation of sensory effects in sync with 360° content has the potential to increase user immersion. However, the authoring process for such effects is laborious and slow. It requires the author to specify in time and space the presentation characteristics of effects. This paper contributes to address this need with a generic architecture for inferring the presentation characteristics of sensory effects in 360° content. The proposed architecture decouples and eases the adaptation of multimedia content analysis techniques for recognizing sensory effects. We highlight the original contribution of this work of presenting the automatic recognition of sensory effects location in 360° content.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126073827","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}
L. Pacífico, Larissa F. S. Britto, Teresa B Ludermir
Recipe sharing websites have become even more popular in the past few decades, and such repositories are able to keep hundreds of thousands of cooking recipes at the same time. Many recipe websites are developed with the participation of their community of users, which are allowed to upload new recipes and to provide evaluations and comments on the available recipes. However, in such repositories, the amount of recipes that are safe for users with special needs, such as food restrictions or allergies, is much smaller than ordinary food recipes, what may restrict the access and usability provided by such websites to that public. In this work, we propose a new recipe recommendation and generation system, based on a data-driven approach for single ingredient substitution, in such a way that recipes containing forbidden ingredients, according to a category of user food restrictions, are adapted by replacing such ingredients by safe ingredients. The proposed ingredient substitute recommendation system is based on a filtering process that takes into consideration the original recipe context, the relationship among sets of ingredients and the user preferences, towards the generation of recipes that are safe, and at the same time contemplate both user needs and tastes. The proposed system is evaluated by means of a qualitative analysis, showing promising results.
{"title":"Ingredient Substitute Recommendation Based on Collaborative Filtering and Recipe Context for Automatic Allergy-Safe Recipe Generation","authors":"L. Pacífico, Larissa F. S. Britto, Teresa B Ludermir","doi":"10.1145/3470482.3479622","DOIUrl":"https://doi.org/10.1145/3470482.3479622","url":null,"abstract":"Recipe sharing websites have become even more popular in the past few decades, and such repositories are able to keep hundreds of thousands of cooking recipes at the same time. Many recipe websites are developed with the participation of their community of users, which are allowed to upload new recipes and to provide evaluations and comments on the available recipes. However, in such repositories, the amount of recipes that are safe for users with special needs, such as food restrictions or allergies, is much smaller than ordinary food recipes, what may restrict the access and usability provided by such websites to that public. In this work, we propose a new recipe recommendation and generation system, based on a data-driven approach for single ingredient substitution, in such a way that recipes containing forbidden ingredients, according to a category of user food restrictions, are adapted by replacing such ingredients by safe ingredients. The proposed ingredient substitute recommendation system is based on a filtering process that takes into consideration the original recipe context, the relationship among sets of ingredients and the user preferences, towards the generation of recipes that are safe, and at the same time contemplate both user needs and tastes. The proposed system is evaluated by means of a qualitative analysis, showing promising results.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116207445","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}
Welton Santos, E. Fazzion, D. Dias, M. Guimarães, Elisa Tuler, L. Rocha
Exploring large news collections created by media outlets with traditional search engines is impractical for demanding users. We propose a temporal exploration tool that aims to facilitate the consultation of news collections. We concentrated our efforts on two fronts: (i) allowing users to make queries with the addition of information from documents represented by word embbedings, and; (ii) retrieving temporal information to generate timelines presented by an appropriate interface. We evaluated our solution in a collection of a Brazilian newspaper, demonstrating that it can draw different timelines, covering different subtopics of the same theme.
{"title":"Semantically Time Tracking of Events from Web Documents","authors":"Welton Santos, E. Fazzion, D. Dias, M. Guimarães, Elisa Tuler, L. Rocha","doi":"10.1145/3470482.3479627","DOIUrl":"https://doi.org/10.1145/3470482.3479627","url":null,"abstract":"Exploring large news collections created by media outlets with traditional search engines is impractical for demanding users. We propose a temporal exploration tool that aims to facilitate the consultation of news collections. We concentrated our efforts on two fronts: (i) allowing users to make queries with the addition of information from documents represented by word embbedings, and; (ii) retrieving temporal information to generate timelines presented by an appropriate interface. We evaluated our solution in a collection of a Brazilian newspaper, demonstrating that it can draw different timelines, covering different subtopics of the same theme.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126507051","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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