Pub Date : 2023-09-30DOI: 10.33851/jmis.2023.10.3.237
Yanlong Yang, Mengzhu Lu, Xiaohan Niu
The further development of Internet of things (IoT) makes the number of various terminal devices grow rapidly. At the same time, the amount of data collected and transmitted through terminal devices is also increasing. However, in the communication between devices and servers, most of them lack efficient identity authentication and encrypted communication mechanisms suitable for IoT environment. Therefore, in order to secure the communication between these devices and servers, they need to be protected by password technology. This paper proposes a secure communication protocol based on chaotic mapping algorithm, which is used to ensure the bidirectional identity authentication and data encryption between IoT devices and servers. The protocol is proved by Burrows Abadi Needham(BAN) logic, Scyther and informal security analysis that it satisfies the security and can resist various security attacks, and achieve anonymity and nontraceability. Finally, the performance comparison analysis with similar protocols shows that the proposed protocol significantly improves the security and has high efficiency.
{"title":"Secure and Lightweight Authentication Protocol in Internet of Things","authors":"Yanlong Yang, Mengzhu Lu, Xiaohan Niu","doi":"10.33851/jmis.2023.10.3.237","DOIUrl":"https://doi.org/10.33851/jmis.2023.10.3.237","url":null,"abstract":"The further development of Internet of things (IoT) makes the number of various terminal devices grow rapidly. At the same time, the amount of data collected and transmitted through terminal devices is also increasing. However, in the communication between devices and servers, most of them lack efficient identity authentication and encrypted communication mechanisms suitable for IoT environment. Therefore, in order to secure the communication between these devices and servers, they need to be protected by password technology. This paper proposes a secure communication protocol based on chaotic mapping algorithm, which is used to ensure the bidirectional identity authentication and data encryption between IoT devices and servers. The protocol is proved by Burrows Abadi Needham(BAN) logic, Scyther and informal security analysis that it satisfies the security and can resist various security attacks, and achieve anonymity and nontraceability. Finally, the performance comparison analysis with similar protocols shows that the proposed protocol significantly improves the security and has high efficiency.","PeriodicalId":477174,"journal":{"name":"Journal of multimedia information system","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135083597","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 : 2023-09-30DOI: 10.33851/jmis.2023.10.3.207
Ren Wang, Tae Sung Kim, Tae-Ho Lee, Jin-Sung Kim, Hyuk-Jae Lee
Aggregating predicted outputs from multiple views helps boost multi-view single object detection performance. Decision-making strategies are flexible to perform this result-level aggregation. However, the relationship among multiple views is not exploited in aggregation. This study proposes a novel decision-making model with graph convolutional networks (DM-GCN) to address this issue by establishing a relationship among predicted outputs with graph convolutional networks. Through training, the proposed DM-GCN learns to make a correct decision by enhancing the contributions from informative views. DM-GCN is light, fast, and can be applied to any object detector with a negligible computational cost. Moreover, a real captured dataset named Yogurt10 with a new metric is proposed to investigate the performance of DM-GCN in the multi-view single object detection task. Experimental results show that DM-GCN achieves superior performance compared to classical decision-making strategies. A visual explanation is also provided to interpret how DM-GCN makes a correct decision.
{"title":"Decision-Making for Multi-View Single Object Detection with Graph Convolutional Networks","authors":"Ren Wang, Tae Sung Kim, Tae-Ho Lee, Jin-Sung Kim, Hyuk-Jae Lee","doi":"10.33851/jmis.2023.10.3.207","DOIUrl":"https://doi.org/10.33851/jmis.2023.10.3.207","url":null,"abstract":"Aggregating predicted outputs from multiple views helps boost multi-view single object detection performance. Decision-making strategies are flexible to perform this result-level aggregation. However, the relationship among multiple views is not exploited in aggregation. This study proposes a novel decision-making model with graph convolutional networks (DM-GCN) to address this issue by establishing a relationship among predicted outputs with graph convolutional networks. Through training, the proposed DM-GCN learns to make a correct decision by enhancing the contributions from informative views. DM-GCN is light, fast, and can be applied to any object detector with a negligible computational cost. Moreover, a real captured dataset named Yogurt10 with a new metric is proposed to investigate the performance of DM-GCN in the multi-view single object detection task. Experimental results show that DM-GCN achieves superior performance compared to classical decision-making strategies. A visual explanation is also provided to interpret how DM-GCN makes a correct decision.","PeriodicalId":477174,"journal":{"name":"Journal of multimedia information system","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135083464","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 : 2023-09-30DOI: 10.33851/jmis.2023.10.3.227
Thanh Hien Truong, Tae-Ho Lee, Viduranga Munasinghe, Tae Sung Kim, Jin-Sung Kim, Hyuk-Jae Lee
Image blending is a scheme for image composition to make the composite image looks as natural and realistic as possible. Image blending should ensure that the edges of the object look seamless and do not distort colors. Recently, numerous studies investigated image blending methods adopting deep learning-based image processing algorithms and contributed to generating natural blended images. Although the previous studies show remarkable performance in many cases, they suffer from quality drop when blending incompletely cropped object. This is because partial loss and unnecessary extra information on the cropped object image interferes with image blending. This paper proposes a new scheme that significantly reduce the unnatural edges and the color distortion. First, to detect and handle the incompletely cropped region, an adaptive binary line mask generation utilizing color difference checking algorithm (CDC) is proposed. The generated mask is exploited to improve image blending performance by isolating incompletely cropped image edges from image blending. Second, in order to perform inpainting the missing or masked area of the object image and image blending together, the inpainting generative adversarial model is adopted. Experimental results show that the blended images are not only more natural than those of the previous works but the color information is also well preserved.
{"title":"Inpainting GAN-Based Image Blending with Adaptive Binary Line Mask","authors":"Thanh Hien Truong, Tae-Ho Lee, Viduranga Munasinghe, Tae Sung Kim, Jin-Sung Kim, Hyuk-Jae Lee","doi":"10.33851/jmis.2023.10.3.227","DOIUrl":"https://doi.org/10.33851/jmis.2023.10.3.227","url":null,"abstract":"Image blending is a scheme for image composition to make the composite image looks as natural and realistic as possible. Image blending should ensure that the edges of the object look seamless and do not distort colors. Recently, numerous studies investigated image blending methods adopting deep learning-based image processing algorithms and contributed to generating natural blended images. Although the previous studies show remarkable performance in many cases, they suffer from quality drop when blending incompletely cropped object. This is because partial loss and unnecessary extra information on the cropped object image interferes with image blending. This paper proposes a new scheme that significantly reduce the unnatural edges and the color distortion. First, to detect and handle the incompletely cropped region, an adaptive binary line mask generation utilizing color difference checking algorithm (CDC) is proposed. The generated mask is exploited to improve image blending performance by isolating incompletely cropped image edges from image blending. Second, in order to perform inpainting the missing or masked area of the object image and image blending together, the inpainting generative adversarial model is adopted. Experimental results show that the blended images are not only more natural than those of the previous works but the color information is also well preserved.","PeriodicalId":477174,"journal":{"name":"Journal of multimedia information system","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135083607","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 : 2023-09-30DOI: 10.33851/jmis.2023.10.3.259
Zhi Li, Xiao Chen
Art can purify the soul and cultivate sentiment. Artists promote the dissemination and popularization of ecological awareness through the power of art. Painting has a strong visual aesthetic ideology and closely relates to people, nature, and society. Landscape oil paintings use visual metaphor and symbolic expression techniques to endow paintings with multiple and rich ecological concepts, conveying anxiety about various ecological imbalances in human society. However, the exploration of oil painting teaching has stopped in universities recently. Therefore, it is necessary to study the diversified landscape oil painting teaching mode from the perspective of ecology to promote its sustainable development. To further immerse students in nature and advance the sustainable development of oil painting teaching from an ecological perspective, teachers can utilize VR to create natural scenery by introducing the metaverse into the landscape oil painting course. However, in the 360-degree VR landscape sampling video, if the texture cannot be processed well, the student’s experience will be significantly reduced. To this end, the texture synthesis of VR videos is studied. The simulation results show that the proposed texture synthesis method performs better in time and space, which undoubtedly improves the students’ experience of watching VR landscape videos. Finally, this study uses questionnaires to examine the application effect of metaverse-based landscape oil painting courses. The experimental results demonstrate that metaverse-based landscape oil painting courses can increase students’ sense of immersion, most strikingly, which is of great help to the improvement of grades.
{"title":"Sustainable Development of Diversified Teaching Mode from Ecological Perspective: A Case Study on Metaverse-Based Landscape Oil Painting Course","authors":"Zhi Li, Xiao Chen","doi":"10.33851/jmis.2023.10.3.259","DOIUrl":"https://doi.org/10.33851/jmis.2023.10.3.259","url":null,"abstract":"Art can purify the soul and cultivate sentiment. Artists promote the dissemination and popularization of ecological awareness through the power of art. Painting has a strong visual aesthetic ideology and closely relates to people, nature, and society. Landscape oil paintings use visual metaphor and symbolic expression techniques to endow paintings with multiple and rich ecological concepts, conveying anxiety about various ecological imbalances in human society. However, the exploration of oil painting teaching has stopped in universities recently. Therefore, it is necessary to study the diversified landscape oil painting teaching mode from the perspective of ecology to promote its sustainable development. To further immerse students in nature and advance the sustainable development of oil painting teaching from an ecological perspective, teachers can utilize VR to create natural scenery by introducing the metaverse into the landscape oil painting course. However, in the 360-degree VR landscape sampling video, if the texture cannot be processed well, the student’s experience will be significantly reduced. To this end, the texture synthesis of VR videos is studied. The simulation results show that the proposed texture synthesis method performs better in time and space, which undoubtedly improves the students’ experience of watching VR landscape videos. Finally, this study uses questionnaires to examine the application effect of metaverse-based landscape oil painting courses. The experimental results demonstrate that metaverse-based landscape oil painting courses can increase students’ sense of immersion, most strikingly, which is of great help to the improvement of grades.","PeriodicalId":477174,"journal":{"name":"Journal of multimedia information system","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135082602","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 : 2023-09-30DOI: 10.33851/jmis.2023.10.3.215
ChangMan Zou, Wang-Su Jeon, Sang-Yong Rhee, MingXing Cai
Optical remote sensing image detection has wide-ranging applications in both military and civilian sectors. Addressing the specific challenge of false positives and missed detections in optical remote sensing image analysis due to object size variations, a lightweight remote sensing image detection method based on an improved YOLOv5n has been proposed. This technology allows for rapid and effective analysis of remote sensing images, real-time detection, and target localization, even in scenarios with limited computational resources in current machines/systems. To begin with, the YOLOv5n feature fusion network structure incorporates an adaptive spatial feature fusion mechanism to enhance the algorithm’s ability to fuse features of objects at different scales. Additionally, an SIoU loss function has been developed based on the original YOLOv5n positional loss function, redefining the vector angle between position frame regressions and the penalty index. This adjustment aids in improving the convergence speed of model training and enhancing detection performance. To validate the effectiveness of the proposed method, experimental comparisons were conducted using optical remote sensing image datasets. The experimental results on optical remote sensing images serve to demonstrate the efficiency of this advanced technology. The findings indicate that the average mean accuracy of the improved network model has increased from the original 81.6% to 84.9%. Moreover, the average detection speed and network complexity are significantly superior to those of the other three existing object detection algorithms.
{"title":"A Method for Detecting Lightweight Optical Remote Sensing Images Using Improved Yolov5n","authors":"ChangMan Zou, Wang-Su Jeon, Sang-Yong Rhee, MingXing Cai","doi":"10.33851/jmis.2023.10.3.215","DOIUrl":"https://doi.org/10.33851/jmis.2023.10.3.215","url":null,"abstract":"Optical remote sensing image detection has wide-ranging applications in both military and civilian sectors. Addressing the specific challenge of false positives and missed detections in optical remote sensing image analysis due to object size variations, a lightweight remote sensing image detection method based on an improved YOLOv5n has been proposed. This technology allows for rapid and effective analysis of remote sensing images, real-time detection, and target localization, even in scenarios with limited computational resources in current machines/systems. To begin with, the YOLOv5n feature fusion network structure incorporates an adaptive spatial feature fusion mechanism to enhance the algorithm’s ability to fuse features of objects at different scales. Additionally, an SIoU loss function has been developed based on the original YOLOv5n positional loss function, redefining the vector angle between position frame regressions and the penalty index. This adjustment aids in improving the convergence speed of model training and enhancing detection performance. To validate the effectiveness of the proposed method, experimental comparisons were conducted using optical remote sensing image datasets. The experimental results on optical remote sensing images serve to demonstrate the efficiency of this advanced technology. The findings indicate that the average mean accuracy of the improved network model has increased from the original 81.6% to 84.9%. Moreover, the average detection speed and network complexity are significantly superior to those of the other three existing object detection algorithms.","PeriodicalId":477174,"journal":{"name":"Journal of multimedia information system","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135083452","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 : 2023-09-30DOI: 10.33851/jmis.2023.10.3.271
Faiza Sabir, Sarfaraz Ahmed, Gihwon Kwon
Digital healthcare system requires safety critical software and more attention among human perception and assistance of computer. Apparently, it becomes serious challenge for engineers and doctors in assuring the secure and reliable healthcare system. The main target is patient safety here, thus important factor to consider in healthcare is system protection. In recent research vital signs such as heart rate (HR) and breathing rate (BR) are extracted using a non-invasive IR-UWB radar sensor. Hence, safety of the contactless device is necessary for proper and accurate measurement of HR and BR in digital healthcare system. Therefore, in our research we drawn and performed fault tree analysis (FTA) of such system which analyze potential hazards of measuring subject’s HR and BR in a non-contact fashion. We emphasize on crucial issues and safety factors which are important in patient safety and protection before the proper deployment of such healthcare system.
{"title":"Failure Analysis of Vital Sign Monitoring System in Digital Healthcare with FTA","authors":"Faiza Sabir, Sarfaraz Ahmed, Gihwon Kwon","doi":"10.33851/jmis.2023.10.3.271","DOIUrl":"https://doi.org/10.33851/jmis.2023.10.3.271","url":null,"abstract":"Digital healthcare system requires safety critical software and more attention among human perception and assistance of computer. Apparently, it becomes serious challenge for engineers and doctors in assuring the secure and reliable healthcare system. The main target is patient safety here, thus important factor to consider in healthcare is system protection. In recent research vital signs such as heart rate (HR) and breathing rate (BR) are extracted using a non-invasive IR-UWB radar sensor. Hence, safety of the contactless device is necessary for proper and accurate measurement of HR and BR in digital healthcare system. Therefore, in our research we drawn and performed fault tree analysis (FTA) of such system which analyze potential hazards of measuring subject’s HR and BR in a non-contact fashion. We emphasize on crucial issues and safety factors which are important in patient safety and protection before the proper deployment of such healthcare system.","PeriodicalId":477174,"journal":{"name":"Journal of multimedia information system","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135083471","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 : 2023-09-30DOI: 10.33851/jmis.2023.10.3.279
Mengmeng LIU
Campus heterogeneity has become prominent with the deeper popularization of higher education, necessitating more focused ideological and political instruction. English classes are crucial because they help students develop their humanistic traits and capacity for intercultural dialogue. Although it is a realistic strategy, integrating ideological and political education into English instruction depends on scientific assessment of the educational quality. Existing assessment approaches, however, need to be more particular for English courses and flexible enough to accommodate diverse student populations. Traditional questionnaire surveys are only sometimes accurate, timely, or complete. Therefore, based on the unmanned aerial vehicle (UAV) network, this research suggests a novel ideological and political education quality evaluation approach for English courses at varied campuses. A consistency feature extraction method is used to identify the ideological and political factors in English teaching by analyzing the consistency between English courses and ideological and political courses. The analytic hierarchy process determines the indicator weights. Teachers’ education roles are quantified based on educational psychology theories. A UAV network is leveraged to collect real-time classroom data adaptively across various campus types—fuzzy comprehensive evaluation aggregates multi-source data for objective and pertinent assessment. Experiments on three campus types and 60 teachers validate the effectiveness. The model achieves over 84% accuracy, significantly higher than conventional questionnaire and fixed sensor methods. The results match expert opinions and offer diagnostic suggestions to improve teaching. The model provides a practical data-driven approach to evaluate and enhance the ideological and political education quality through English courses on heterogeneous campuses.
{"title":"Ideological and Political Evaluation of English Courses in Heterogeneous Campuses Based on UAV Network","authors":"Mengmeng LIU","doi":"10.33851/jmis.2023.10.3.279","DOIUrl":"https://doi.org/10.33851/jmis.2023.10.3.279","url":null,"abstract":"Campus heterogeneity has become prominent with the deeper popularization of higher education, necessitating more focused ideological and political instruction. English classes are crucial because they help students develop their humanistic traits and capacity for intercultural dialogue. Although it is a realistic strategy, integrating ideological and political education into English instruction depends on scientific assessment of the educational quality. Existing assessment approaches, however, need to be more particular for English courses and flexible enough to accommodate diverse student populations. Traditional questionnaire surveys are only sometimes accurate, timely, or complete. Therefore, based on the unmanned aerial vehicle (UAV) network, this research suggests a novel ideological and political education quality evaluation approach for English courses at varied campuses. A consistency feature extraction method is used to identify the ideological and political factors in English teaching by analyzing the consistency between English courses and ideological and political courses. The analytic hierarchy process determines the indicator weights. Teachers’ education roles are quantified based on educational psychology theories. A UAV network is leveraged to collect real-time classroom data adaptively across various campus types—fuzzy comprehensive evaluation aggregates multi-source data for objective and pertinent assessment. Experiments on three campus types and 60 teachers validate the effectiveness. The model achieves over 84% accuracy, significantly higher than conventional questionnaire and fixed sensor methods. The results match expert opinions and offer diagnostic suggestions to improve teaching. The model provides a practical data-driven approach to evaluate and enhance the ideological and political education quality through English courses on heterogeneous campuses.","PeriodicalId":477174,"journal":{"name":"Journal of multimedia information system","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135083592","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 : 2023-09-30DOI: 10.33851/jmis.2023.10.3.249
Shunji Cui
The emergence and development of multimedia forms provide technical support for online Korean language teaching. However, in many aspects, there are still many problems in online Korean teaching, such as noise interference, inaccurate translation, and unstable translation models. In this paper, we propose a Korean speech enhancement model based on temporal convolutional neural network and GRU neural network. We explore a Korean speech enhancement technology based on deep neural network, to make Korean speech teaching clearer and smoother, and to provide a robust support technology for online Korean teaching. First, we construct a temporal convolutional neural network to process and extract temporal feature in language data. Second, we introduce the sliding window mechanism and the maximum pooling structure to extract the feature in the speech time series data effectively and reduced the data scale. Third, we employ the Bi-GRU neural network and encoder-decoder for temporal data enhancement, which effectively avoids the problem that the hidden layer information cannot be effectively used in the traditional model, thereby improving the prediction accuracy and speed of speech data. The experimental outcomes demonstrate the effective evaluation performance of the method proposed in this paper.
{"title":"A Robust Online Korean Teaching Support Technology Based on TCNN","authors":"Shunji Cui","doi":"10.33851/jmis.2023.10.3.249","DOIUrl":"https://doi.org/10.33851/jmis.2023.10.3.249","url":null,"abstract":"The emergence and development of multimedia forms provide technical support for online Korean language teaching. However, in many aspects, there are still many problems in online Korean teaching, such as noise interference, inaccurate translation, and unstable translation models. In this paper, we propose a Korean speech enhancement model based on temporal convolutional neural network and GRU neural network. We explore a Korean speech enhancement technology based on deep neural network, to make Korean speech teaching clearer and smoother, and to provide a robust support technology for online Korean teaching. First, we construct a temporal convolutional neural network to process and extract temporal feature in language data. Second, we introduce the sliding window mechanism and the maximum pooling structure to extract the feature in the speech time series data effectively and reduced the data scale. Third, we employ the Bi-GRU neural network and encoder-decoder for temporal data enhancement, which effectively avoids the problem that the hidden layer information cannot be effectively used in the traditional model, thereby improving the prediction accuracy and speed of speech data. The experimental outcomes demonstrate the effective evaluation performance of the method proposed in this paper.","PeriodicalId":477174,"journal":{"name":"Journal of multimedia information system","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135083463","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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