The key challenges that recommendation systems must overcome are data isolation and privacy protection issues. Federated learning can efficiently train global models using decentralized data while preserving privacy. In real-world applications, however, it is difficult to achieve high prediction accuracy due to the heterogeneity of devices, the lack of data, and the limited generalization capacity of models. In this research, we introduce a personalized federated knowledge distillation model for a recommendation system based on a multi-head attention mechanism for recommendation systems. Specifically, we first employ federated distillation to improve the performance of student models and introduce a multi-head attention mechanism to enhance user encoding information. Next, we incorporate Wasserstein distance into the objective function of combined distillation to reduce the distribution gap between teacher and student networks and also use an adaptive learning rate technique to enhance convergence. We show that the proposed approach achieves better effectiveness and robustness through benchmarks.
{"title":"MAFD: A Federated Distillation Approach with Multi-head Attention for Recommendation Tasks","authors":"Aming Wu, Young-Woo Kwon","doi":"10.1145/3555776.3577849","DOIUrl":"https://doi.org/10.1145/3555776.3577849","url":null,"abstract":"The key challenges that recommendation systems must overcome are data isolation and privacy protection issues. Federated learning can efficiently train global models using decentralized data while preserving privacy. In real-world applications, however, it is difficult to achieve high prediction accuracy due to the heterogeneity of devices, the lack of data, and the limited generalization capacity of models. In this research, we introduce a personalized federated knowledge distillation model for a recommendation system based on a multi-head attention mechanism for recommendation systems. Specifically, we first employ federated distillation to improve the performance of student models and introduce a multi-head attention mechanism to enhance user encoding information. Next, we incorporate Wasserstein distance into the objective function of combined distillation to reduce the distribution gap between teacher and student networks and also use an adaptive learning rate technique to enhance convergence. We show that the proposed approach achieves better effectiveness and robustness through benchmarks.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76952591","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}
João Francisco, Miguel E. Coimbra, P. Neto, Felix Freitag, L. Veiga
The model of approximate computing can be used to increase performance or optimize resource usage in stream and graph processing. It can be used to satisfy performance requirements (e.g., throughput, lag) in stream processing by reducing the effort that applications need to process datasets. There are currently multiple stream processing platforms, and most of them do not natively support approximate results. A recent one, Stateful Functions, is an API that uses Flink to enable developers to easily build stream and graph processing applications. It also retains Flink's features like stateful computations, fault-tolerance, scalability, control events and its graph processing library Gelly. Herein we present Approxate, an extension over this platform to support approximate results. It can also support more efficient stream and graph processing by allocating available resources adaptively, driven by user-defined requirements on throughput, lag, and latency. This extension enables flexibility in computational trade-offs such as trading accuracy for performance. The user can choose which metrics should be guaranteed at the cost of others, and/or the accuracy. Approxate incorporates approximate computing (using load shedding) with adaptive accuracy and resource manegement in state-of-the-art stream processing platforms, which are not targeted in other relevant related work. It does not require significant modifications to application code, and minimizes imbalance in data source representation when dropping events.
{"title":"Stateful Adaptive Streams with Approximate Computing and Elastic Scaling","authors":"João Francisco, Miguel E. Coimbra, P. Neto, Felix Freitag, L. Veiga","doi":"10.1145/3555776.3577858","DOIUrl":"https://doi.org/10.1145/3555776.3577858","url":null,"abstract":"The model of approximate computing can be used to increase performance or optimize resource usage in stream and graph processing. It can be used to satisfy performance requirements (e.g., throughput, lag) in stream processing by reducing the effort that applications need to process datasets. There are currently multiple stream processing platforms, and most of them do not natively support approximate results. A recent one, Stateful Functions, is an API that uses Flink to enable developers to easily build stream and graph processing applications. It also retains Flink's features like stateful computations, fault-tolerance, scalability, control events and its graph processing library Gelly. Herein we present Approxate, an extension over this platform to support approximate results. It can also support more efficient stream and graph processing by allocating available resources adaptively, driven by user-defined requirements on throughput, lag, and latency. This extension enables flexibility in computational trade-offs such as trading accuracy for performance. The user can choose which metrics should be guaranteed at the cost of others, and/or the accuracy. Approxate incorporates approximate computing (using load shedding) with adaptive accuracy and resource manegement in state-of-the-art stream processing platforms, which are not targeted in other relevant related work. It does not require significant modifications to application code, and minimizes imbalance in data source representation when dropping events.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78035215","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}
Graph related tasks, such as graph classification and clustering, have been substantially improved with the advent of graph neural networks (GNNs). However, existing graph embedding models focus on homogeneous graphs that ignore the heterogeneity of the graphs. Therefore, using homogeneous graph embedding models on heterogeneous graphs discards the rich semantics of graphs and achieves average performance, especially by utilizing unlabeled information. However, limited work has been done on whole heterogeneous graph embedding as a supervised task. In light of this, we investigate unsupervised distributed representations learning on heterogeneous graphs and propose a novel model named G-HIN2Vec, Graph-Level Heterogeneous Information Network to Vector. Inspired by recent advances of unsupervised learning in natural language processing, G-HIN2Vec utilizes negative sampling technique as an unlabeled approach and learns graph embedding matrix from different pre-defined meta-paths. We conduct a variety of experiments on three main graph downstream applications on different socio-demographic cardholder features, graph regression, graph clustering, and graph classification, such as gender classification, age, and income prediction, which shows superior performance of our proposed GNN model on real-world financial credit card data.
{"title":"G-HIN2Vec: Distributed heterogeneous graph representations for cardholder transactions","authors":"Farouk Damoun, H. Seba, Jean Hilger, R. State","doi":"10.1145/3555776.3577740","DOIUrl":"https://doi.org/10.1145/3555776.3577740","url":null,"abstract":"Graph related tasks, such as graph classification and clustering, have been substantially improved with the advent of graph neural networks (GNNs). However, existing graph embedding models focus on homogeneous graphs that ignore the heterogeneity of the graphs. Therefore, using homogeneous graph embedding models on heterogeneous graphs discards the rich semantics of graphs and achieves average performance, especially by utilizing unlabeled information. However, limited work has been done on whole heterogeneous graph embedding as a supervised task. In light of this, we investigate unsupervised distributed representations learning on heterogeneous graphs and propose a novel model named G-HIN2Vec, Graph-Level Heterogeneous Information Network to Vector. Inspired by recent advances of unsupervised learning in natural language processing, G-HIN2Vec utilizes negative sampling technique as an unlabeled approach and learns graph embedding matrix from different pre-defined meta-paths. We conduct a variety of experiments on three main graph downstream applications on different socio-demographic cardholder features, graph regression, graph clustering, and graph classification, such as gender classification, age, and income prediction, which shows superior performance of our proposed GNN model on real-world financial credit card data.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78581158","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}
R. Hassanpour, Niels Netten, Tony Busker, Mortaza Shoae Bargh, Sunil Choenni
Machine learning models have been an inevitable tool for analyzing medical images by radiologists. These models provide important information about the contents of these images using extracted radiomic features. However, the dimensionality of the feature space can cause reduction in the accuracy of prediction, a phenomenon known as the curse of dimensionality. In this study we propose a feature selection method using an autoencoder, which incorporates the performance of a classifier within the feature selection process. This is achieved by automatically adjusting a threshold value used for selecting the features fed to the classifier. The contribution of this study is twofold. The first contribution is an improvement to group lasso to include the group size as a cost parameter of the autoencoder. The second contribution is to automate the selection of the threshold value used for eliminating redundant input features. The threshold value in our proposed method is learned during training phase of the proposed model. Our experimental results indicates that the proposed model can successfully converge to appropriate feature selection parameters.
{"title":"Adaptive Feature Selection Using an Autoencoder and Classifier: Applied to a Radiomics Case","authors":"R. Hassanpour, Niels Netten, Tony Busker, Mortaza Shoae Bargh, Sunil Choenni","doi":"10.1145/3555776.3577861","DOIUrl":"https://doi.org/10.1145/3555776.3577861","url":null,"abstract":"Machine learning models have been an inevitable tool for analyzing medical images by radiologists. These models provide important information about the contents of these images using extracted radiomic features. However, the dimensionality of the feature space can cause reduction in the accuracy of prediction, a phenomenon known as the curse of dimensionality. In this study we propose a feature selection method using an autoencoder, which incorporates the performance of a classifier within the feature selection process. This is achieved by automatically adjusting a threshold value used for selecting the features fed to the classifier. The contribution of this study is twofold. The first contribution is an improvement to group lasso to include the group size as a cost parameter of the autoencoder. The second contribution is to automate the selection of the threshold value used for eliminating redundant input features. The threshold value in our proposed method is learned during training phase of the proposed model. Our experimental results indicates that the proposed model can successfully converge to appropriate feature selection parameters.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74668413","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}
Recently, there has been a growing interest in improving students' competitiveness in STEM education. Self-reporting and observation are the most used tools for the assessment of STEM education. Despite their effectiveness, such assessment tools face several challenges, such as being labor-intensive and time-consuming, prone to subjective awareness, depending on memory limitations, and being influenced due to social expectations. To address these challenges, in this research, we propose an approach called Image4Assess that---by benefiting from state-of-the-art machine learning like convolutional neural networks and transfer learning---automatically and uninterruptedly assesses students' learning processes during STEM activities using image processing. Our findings reveal that the Image4Assess approach can achieve accuracy, precision, and recall higher than 85% in the learning process recognition of students. This implies that it is feasible to accurately measure the learning process of students in STEM education using their imagery data. We also found that there is a significant correlation between the learning processes automatically identified by our proposed approach and students' post-test, confirming the effectiveness of the proposed approach in real-world classrooms.
{"title":"Image4Assess: Automatic learning processes recognition using image processing","authors":"Hsin-Yu Lee, Maral Hooshyar, Chia-Ju Lin, Wei-Sheng Wang, Yueh-Min Huang","doi":"10.1145/3555776.3577643","DOIUrl":"https://doi.org/10.1145/3555776.3577643","url":null,"abstract":"Recently, there has been a growing interest in improving students' competitiveness in STEM education. Self-reporting and observation are the most used tools for the assessment of STEM education. Despite their effectiveness, such assessment tools face several challenges, such as being labor-intensive and time-consuming, prone to subjective awareness, depending on memory limitations, and being influenced due to social expectations. To address these challenges, in this research, we propose an approach called Image4Assess that---by benefiting from state-of-the-art machine learning like convolutional neural networks and transfer learning---automatically and uninterruptedly assesses students' learning processes during STEM activities using image processing. Our findings reveal that the Image4Assess approach can achieve accuracy, precision, and recall higher than 85% in the learning process recognition of students. This implies that it is feasible to accurately measure the learning process of students in STEM education using their imagery data. We also found that there is a significant correlation between the learning processes automatically identified by our proposed approach and students' post-test, confirming the effectiveness of the proposed approach in real-world classrooms.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74765397","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}
Distributed monitoring is an essential functionality to allow large cluster federations to efficiently schedule applications on a set of available geo-distributed resources. However, periodically reporting the precise status of each available server is both unnecessary to allow accurate scheduling and unscalable when the number of servers grows. This paper proposes Acala, a monitoring framework for geo-distributed cluster federations which aims to provide the management cluster with aggregate information about the entire cluster instead of individual servers. Our evaluations, based on actual deployment under controlled environment in the geo-distributed Grid'5000 testbed, show that Acala reduces the cross-cluster network traffic by up to 99% and the scrape duration by up to 55%.
{"title":"Acala: Aggregate Monitoring for Geo-Distributed Cluster Federations","authors":"Chih-Kai Huang, G. Pierre","doi":"10.1145/3555776.3577716","DOIUrl":"https://doi.org/10.1145/3555776.3577716","url":null,"abstract":"Distributed monitoring is an essential functionality to allow large cluster federations to efficiently schedule applications on a set of available geo-distributed resources. However, periodically reporting the precise status of each available server is both unnecessary to allow accurate scheduling and unscalable when the number of servers grows. This paper proposes Acala, a monitoring framework for geo-distributed cluster federations which aims to provide the management cluster with aggregate information about the entire cluster instead of individual servers. Our evaluations, based on actual deployment under controlled environment in the geo-distributed Grid'5000 testbed, show that Acala reduces the cross-cluster network traffic by up to 99% and the scrape duration by up to 55%.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72444214","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}
Non-Fungible Tokens (NFT) have gained popularity since 2021, reaching a total market valuation of several billion US dollars, especially in art. This paper highlights the findings of our statistically representative survey of more than 1850 Americans, e.g., 5.7% have already bought an NFT. Unfortunately, that trust has been misplaced on many occasions due to technical and legal issues of most created NFTs. We detail those issues and evaluate them in the case of the most well-known NFT marketplace, i.e., OpenSea.
{"title":"NFT Trust Survey","authors":"Jean-Marc Seigneur, Suzana Moreno","doi":"10.1145/3555776.3577824","DOIUrl":"https://doi.org/10.1145/3555776.3577824","url":null,"abstract":"Non-Fungible Tokens (NFT) have gained popularity since 2021, reaching a total market valuation of several billion US dollars, especially in art. This paper highlights the findings of our statistically representative survey of more than 1850 Americans, e.g., 5.7% have already bought an NFT. Unfortunately, that trust has been misplaced on many occasions due to technical and legal issues of most created NFTs. We detail those issues and evaluate them in the case of the most well-known NFT marketplace, i.e., OpenSea.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79323879","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}
HotStuff is a Byzantine fault-tolerant state machine replication protocol that incurs linear communication costs to achieve consensus. This linear scalability promoted the protocol to be adopted as the consensus mechanism in permissioned blockchains. This paper discusses the architecture and evaluation of our extensible framework to implement three HotStuff variants. This reimplementation demonstrates the extensibility of our framework to implement other HotStuff-like protocols. Leveraging our deployment tool, we evaluated our implementation on a wide variety of configurations.
{"title":"An Extensible Framework for Implementing Byzantine Fault-Tolerant Protocols","authors":"Hanish Gogada, J. Olsen, H. Meling, Leander Jehl","doi":"10.1145/3555776.3578614","DOIUrl":"https://doi.org/10.1145/3555776.3578614","url":null,"abstract":"HotStuff is a Byzantine fault-tolerant state machine replication protocol that incurs linear communication costs to achieve consensus. This linear scalability promoted the protocol to be adopted as the consensus mechanism in permissioned blockchains. This paper discusses the architecture and evaluation of our extensible framework to implement three HotStuff variants. This reimplementation demonstrates the extensibility of our framework to implement other HotStuff-like protocols. Leveraging our deployment tool, we evaluated our implementation on a wide variety of configurations.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83409586","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}
Lucas Pereira, B. Guterres, Kauê Sbrissa, Amanda Mendes, Francisca Vermeulen, L. Lain, Marié Smith, Javier Martinez, Paulo L. J. Drews-Jr, Nelson Duarte, Vinicus Oliveira, S. Botelho, M. Pias
Edge computing is a new development paradigm that brings computational power to the network edge through novel intelligent end-user services. It allows latency-sensitive applications to be placed where the data is created, thus reducing communication overhead and improving security, mobility and power consumption. There is a plethora of applications benefiting from this type of processing. Of particular interest is emerging edge-based image classification at the microscopic level. The scale and magnitude of the objects to segment, detect and classify are very challenging, with data collected using order of magnitude in magnification. The required data processing is intense, and the wish list of end-users in this space includes tools and solutions that fit into a desk-based device. Taking heavy-lift classification models initially built in the cloud to desk-based image analysis devices is a hard job for application developers. This work looks at the performance limitations and energy consumption footprint in embedding deep learning classification models in a representative edge computing device. Particularly, the dataset and heavy-lift models explored in the case study are phytoplankton images to detect Harmful Algae Blooms (HAB) in aquaculture at early stages. The work takes a deep learning model trained for phytoplankton classification and deploys it at the edge. The embedded model, deployed in a base form alongside optimised options, is submitted to a series of system stress experiments. The performance and power consumption profiling help understand system limitations and their impact on the microscopic grade image classification task.
{"title":"The not-so-easy task of taking heavy-lift ML models to the edge: a performance-watt perspective","authors":"Lucas Pereira, B. Guterres, Kauê Sbrissa, Amanda Mendes, Francisca Vermeulen, L. Lain, Marié Smith, Javier Martinez, Paulo L. J. Drews-Jr, Nelson Duarte, Vinicus Oliveira, S. Botelho, M. Pias","doi":"10.1145/3555776.3577742","DOIUrl":"https://doi.org/10.1145/3555776.3577742","url":null,"abstract":"Edge computing is a new development paradigm that brings computational power to the network edge through novel intelligent end-user services. It allows latency-sensitive applications to be placed where the data is created, thus reducing communication overhead and improving security, mobility and power consumption. There is a plethora of applications benefiting from this type of processing. Of particular interest is emerging edge-based image classification at the microscopic level. The scale and magnitude of the objects to segment, detect and classify are very challenging, with data collected using order of magnitude in magnification. The required data processing is intense, and the wish list of end-users in this space includes tools and solutions that fit into a desk-based device. Taking heavy-lift classification models initially built in the cloud to desk-based image analysis devices is a hard job for application developers. This work looks at the performance limitations and energy consumption footprint in embedding deep learning classification models in a representative edge computing device. Particularly, the dataset and heavy-lift models explored in the case study are phytoplankton images to detect Harmful Algae Blooms (HAB) in aquaculture at early stages. The work takes a deep learning model trained for phytoplankton classification and deploys it at the edge. The embedded model, deployed in a base form alongside optimised options, is submitted to a series of system stress experiments. The performance and power consumption profiling help understand system limitations and their impact on the microscopic grade image classification task.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75864732","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 paper, we study device driver architectures on two different operating systems, Fuchsia and Linux. Fuchsia is a relatively new operating system developed by Google and it is based on a microkernel named Zircon, while Linux-based operating system is based on a monolithic kernel. This paper examines technical details of device driver on Fuchsia and Linux operating systems with the focus on different kernel designs. We also quantitatively evaluate the performance of device drivers on both operating systems by measuring I/O throughput in a real device.
{"title":"Comparative Study on Fuchsia and Linux Device Driver Architecture","authors":"Taejoon Song, Youngjin Kim","doi":"10.1145/3555776.3577828","DOIUrl":"https://doi.org/10.1145/3555776.3577828","url":null,"abstract":"In this paper, we study device driver architectures on two different operating systems, Fuchsia and Linux. Fuchsia is a relatively new operating system developed by Google and it is based on a microkernel named Zircon, while Linux-based operating system is based on a monolithic kernel. This paper examines technical details of device driver on Fuchsia and Linux operating systems with the focus on different kernel designs. We also quantitatively evaluate the performance of device drivers on both operating systems by measuring I/O throughput in a real device.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78792967","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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