To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.
{"title":"Personalized Web Service Recommendation Method Based on Hybrid Social Network and Multi-Objective Immune Optimization","authors":"Huashan Cao","doi":"10.3745/JIPS.01.0071","DOIUrl":"https://doi.org/10.3745/JIPS.01.0071","url":null,"abstract":"To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114471029","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 unbalance between system ergodic sum rate and high fairness is one of the key issues affecting the performance of non-orthogonal multiple access (NOMA) system. To solve the problem, this paper proposes a power allocation algorithm to realize the ergodic sum rate maximization of NOMA system. The scheme is mainly achieved by the construction algorithm of fair model based on α fair utility function and the optimal solution algorithm based on the interior point method of penalty function. Aiming at the construction of fair model, the fair target is added to the traditional power allocation model to set the reasonable target function. Simultaneously, the problem of ergodic sum rate and fairness in power allocation is weighed by adjusting the value of α. Aiming at the optimal solution algorithm, the interior point method of penalty function is used to transform the fair objective function with unequal constraints into the unconstrained problem in the feasible domain. Then the optimal solution of the original constrained optimization problem is gradually approximated within the feasible domain. The simulation results show that, compared with NOMA and time division multiple address (TDMA) schemes, the proposed method has larger ergodic sum rate and lower Fairness Index (FI) values.
{"title":"Power Allocation Method of Downlink Non-orthogonal Multiple Access System Based on α Fair Utility Function","authors":"Jianpo Li, Qiwei Wang","doi":"10.3745/JIPS.03.0157","DOIUrl":"https://doi.org/10.3745/JIPS.03.0157","url":null,"abstract":"The unbalance between system ergodic sum rate and high fairness is one of the key issues affecting the performance of non-orthogonal multiple access (NOMA) system. To solve the problem, this paper proposes a power allocation algorithm to realize the ergodic sum rate maximization of NOMA system. The scheme is mainly achieved by the construction algorithm of fair model based on α fair utility function and the optimal solution algorithm based on the interior point method of penalty function. Aiming at the construction of fair model, the fair target is added to the traditional power allocation model to set the reasonable target function. Simultaneously, the problem of ergodic sum rate and fairness in power allocation is weighed by adjusting the value of α. Aiming at the optimal solution algorithm, the interior point method of penalty function is used to transform the fair objective function with unequal constraints into the unconstrained problem in the feasible domain. Then the optimal solution of the original constrained optimization problem is gradually approximated within the feasible domain. The simulation results show that, compared with NOMA and time division multiple address (TDMA) schemes, the proposed method has larger ergodic sum rate and lower Fairness Index (FI) values.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130785960","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, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.
{"title":"Vehicle Image Recognition Using Deep Convolution Neural Network and Compressed Dictionary Learning","authors":"Yanyan Zhou","doi":"10.3745/JIPS.01.0073","DOIUrl":"https://doi.org/10.3745/JIPS.01.0073","url":null,"abstract":"In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125235080","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}
Increasingly, there have been attempts to utilize physiological information collected from different non-intrusive devices and sensors (e.g., electroencephalogram, electrocardiograph, electrodermal activity, and skin conductance) for different activities and studies, such as using the data to train machine-/deep-learning models in order to facilitate medical diagnosis and other decision-making. Given the constant advances in machine and deep learning methods, such as deep learning, transfer learning, reinforcement learning, and federated learning, we can also utilize such techniques in cognitive computing to facilitate human behavior analysis. For example, transfer learning uses data or knowledge acquired on solved problems to help solve unsolved but very relevant problems. Transfer learning is often used in cognitive computing to use differences between individuals or tasks to improve learning efficiency and effectiveness. Transfer learning can also be integrated with deep learning to take advantage of the progress of deep learning and transfer learning.
{"title":"A Special Section on Deep & Advanced Machine Learning Approaches for Human Behavior Analysis","authors":"Yizhang Jiang, Kim-Kwang Raymond Choo, Hoon Ko","doi":"10.3745/JIPS.01.0074","DOIUrl":"https://doi.org/10.3745/JIPS.01.0074","url":null,"abstract":"Increasingly, there have been attempts to utilize physiological information collected from different non-intrusive devices and sensors (e.g., electroencephalogram, electrocardiograph, electrodermal activity, and skin conductance) for different activities and studies, such as using the data to train machine-/deep-learning models in order to facilitate medical diagnosis and other decision-making. Given the constant advances in machine and deep learning methods, such as deep learning, transfer learning, reinforcement learning, and federated learning, we can also utilize such techniques in cognitive computing to facilitate human behavior analysis. For example, transfer learning uses data or knowledge acquired on solved problems to help solve unsolved but very relevant problems. Transfer learning is often used in cognitive computing to use differences between individuals or tasks to improve learning efficiency and effectiveness. Transfer learning can also be integrated with deep learning to take advantage of the progress of deep learning and transfer learning.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121742787","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}
Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.
{"title":"Query Optimization on Large Scale Nested Data with Service Tree and Frequent Trajectory","authors":"Li Wang, Guodong Wang","doi":"10.3745/JIPS.04.0205","DOIUrl":"https://doi.org/10.3745/JIPS.04.0205","url":null,"abstract":"Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127353880","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 an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.
{"title":"Effective and Efficient Similarity Measures for Purchase Histories Considering Product Taxonomy","authors":"Yu-Jeong Yang, K. Lee","doi":"10.3745/JIPS.04.0209","DOIUrl":"https://doi.org/10.3745/JIPS.04.0209","url":null,"abstract":"In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125918973","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}
Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.
{"title":"Default Prediction of Automobile Credit Based on Support Vector Machine","authors":"Ying Chen, Ruirui Zhang","doi":"10.3745/JIPS.04.0207","DOIUrl":"https://doi.org/10.3745/JIPS.04.0207","url":null,"abstract":"Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130609916","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}
Object detection is a fundamental yet challenging task in computer vision that plays an important role in object recognition, tracking, scene analysis and understanding. This paper aims to propose a multiproperty fusion algorithm for moving object detection. First, we build a scale-invariant feature transform (SIFT) vector field and analyze vectors in the SIFT vector field to divide vectors in the SIFT vector field into different classes. Second, the distance of each class is calculated by dispersion analysis. Next, the target and contour can be extracted, and then we segment the different images, reversal process and carry on morphological processing, the moving objects can be detected. The experimental results have good stability, accuracy and efficiency.
{"title":"Multiple Properties-Based Moving Object Detection Algorithm","authors":"Changjian Zhou, Jinge Xing, Haibo Liu","doi":"10.3745/JIPS.02.0153","DOIUrl":"https://doi.org/10.3745/JIPS.02.0153","url":null,"abstract":"Object detection is a fundamental yet challenging task in computer vision that plays an important role in object recognition, tracking, scene analysis and understanding. This paper aims to propose a multiproperty fusion algorithm for moving object detection. First, we build a scale-invariant feature transform (SIFT) vector field and analyze vectors in the SIFT vector field to divide vectors in the SIFT vector field into different classes. Second, the distance of each class is calculated by dispersion analysis. Next, the target and contour can be extracted, and then we segment the different images, reversal process and carry on morphological processing, the moving objects can be detected. The experimental results have good stability, accuracy and efficiency.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115909457","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 modern market is highly competitive. It has progressed from traditional competition between enterprises to competition between supply chains. To ensure that enterprise can form the best strategy consistently, it is necessary to evaluate the trust of other enterprises in the supply chain. First, this paper analyzes the background and significance of supply chain trust research, analyzes and expounds on the qualitative and quantitative methods of supply chain trust evaluation, and summarizes the research in this field. Analytic hierarchy process (AHP) is the most frequently used method in the literature to evaluate and rank criteria through data analysis. However, the input data for AHP analysis is based on human judgment, and hence there is every possibility that the data may be vague to some extent. Therefore, in view of the above problems, this study improves the global trust method based on chain iteration. The improved global trust evaluation method based on chain iteration is more flexible and practical, hence, it can more accurately evaluate supply chain trust. Finally, combined with an actual case of Zhaoxian Chengji Food Co. Ltd., the paper qualitatively analyzes the current situation of supply chain trust management and effectively strengthens the supervision of enterprises to cooperative enterprises. Thus, the company can identify problems on time and strategic adjustments can be implemented accordingly. The effectiveness of the evaluation method proposed in this paper is demonstrated through a quantitative evaluation of its trust in downstream enterprise A. Results suggest that the subjective preferences of and historical transactions together affect the final evaluation of trust.
{"title":"Supply Chain Trust Evaluation Model Based on Improved Chain Iteration Method","authors":"Hongqiang Jiao, Wanning Ding, Xinxin Wang","doi":"10.3745/JIPS.04.0203","DOIUrl":"https://doi.org/10.3745/JIPS.04.0203","url":null,"abstract":"The modern market is highly competitive. It has progressed from traditional competition between enterprises to competition between supply chains. To ensure that enterprise can form the best strategy consistently, it is necessary to evaluate the trust of other enterprises in the supply chain. First, this paper analyzes the background and significance of supply chain trust research, analyzes and expounds on the qualitative and quantitative methods of supply chain trust evaluation, and summarizes the research in this field. Analytic hierarchy process (AHP) is the most frequently used method in the literature to evaluate and rank criteria through data analysis. However, the input data for AHP analysis is based on human judgment, and hence there is every possibility that the data may be vague to some extent. Therefore, in view of the above problems, this study improves the global trust method based on chain iteration. The improved global trust evaluation method based on chain iteration is more flexible and practical, hence, it can more accurately evaluate supply chain trust. Finally, combined with an actual case of Zhaoxian Chengji Food Co. Ltd., the paper qualitatively analyzes the current situation of supply chain trust management and effectively strengthens the supervision of enterprises to cooperative enterprises. Thus, the company can identify problems on time and strategic adjustments can be implemented accordingly. The effectiveness of the evaluation method proposed in this paper is demonstrated through a quantitative evaluation of its trust in downstream enterprise A. Results suggest that the subjective preferences of and historical transactions together affect the final evaluation of trust.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122174334","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}
This paper proposes two novel methods to model and simulate a mobile Internet of Things (IoT) system using the discrete event system specification (DEVS) formalism. In traditional simulation methods, it is advantageous to partition the simulation area hierarchically to reduce simulation time; however, in this case, the structure of the model may change as the IoT nodes to be modeled move. The proposed methods reduce the simulation time while maintaining the model structure, even when the IoT nodes move. To evaluate the performance of the proposed methods, a prototype mobile IoT system was modeled and simulated. The simulation results show that the proposed methods achieve good performance, even if the number of IoT nodes or the movement of IoT nodes increases.
{"title":"Simulation of a Mobile IoT System Using the DEVS Formalism","authors":"J. Im, Ha-Ryoung Oh, Y. Seong","doi":"10.3745/JIPS.03.0155","DOIUrl":"https://doi.org/10.3745/JIPS.03.0155","url":null,"abstract":"This paper proposes two novel methods to model and simulate a mobile Internet of Things (IoT) system using the discrete event system specification (DEVS) formalism. In traditional simulation methods, it is advantageous to partition the simulation area hierarchically to reduce simulation time; however, in this case, the structure of the model may change as the IoT nodes to be modeled move. The proposed methods reduce the simulation time while maintaining the model structure, even when the IoT nodes move. To evaluate the performance of the proposed methods, a prototype mobile IoT system was modeled and simulated. The simulation results show that the proposed methods achieve good performance, even if the number of IoT nodes or the movement of IoT nodes increases.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975252","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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