Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494394
K. Jobczyk
In 1969, Per LindstrØm proved his famous theorem and established criteria for the first-order definability of formal theories for discrete structures. The results were extrapolated for systems of modal logic and even for theories for continuous structures. This paper aims to formulate and prove LindstrØm's theorem for analytic structures based on measures. In particular, Hajek's Logic of Integral is redefined as an abstract logic with a new type of Hajek's satisfiability and considered as a minimal logic in the class of analytic structures with Lebesgue integrals.
{"title":"The LindstrØm-Type Characterization of Hajek's Fuzzy Logic of Integrals","authors":"K. Jobczyk","doi":"10.1109/FUZZ45933.2021.9494394","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494394","url":null,"abstract":"In 1969, Per LindstrØm proved his famous theorem and established criteria for the first-order definability of formal theories for discrete structures. The results were extrapolated for systems of modal logic and even for theories for continuous structures. This paper aims to formulate and prove LindstrØm's theorem for analytic structures based on measures. In particular, Hajek's Logic of Integral is redefined as an abstract logic with a new type of Hajek's satisfiability and considered as a minimal logic in the class of analytic structures with Lebesgue integrals.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132375765","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494530
B. Pandya, A. Pourabdollah, Ahmad Lotfi, G. Acampora
Fuzzy logic systems are customarily related to specific hardware or software systems. Nevertheless, it has been observed that distributed and cloud-based architectures of various intelligent systems are pouring intensifying attention. While the distributed architectures can potentially add values in developing fuzzy systems, a lack of standard methods and practices may limit their public use. This study aims to provide a standard solution for developing cloud-based service-oriented architectures for fuzzy logic systems, based on extending IEEE-1855 (2016) in the defining system and exchanging data. Experiments were performed employing simulation concerning collection, processing and monitoring of data in a distributed manner over the web. A real-time human activity recognition simulated scenario is also demonstrated through a cloud-based fuzzy system.
{"title":"Developing a cloud-based service-oriented architecture for fuzzy logic systems","authors":"B. Pandya, A. Pourabdollah, Ahmad Lotfi, G. Acampora","doi":"10.1109/FUZZ45933.2021.9494530","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494530","url":null,"abstract":"Fuzzy logic systems are customarily related to specific hardware or software systems. Nevertheless, it has been observed that distributed and cloud-based architectures of various intelligent systems are pouring intensifying attention. While the distributed architectures can potentially add values in developing fuzzy systems, a lack of standard methods and practices may limit their public use. This study aims to provide a standard solution for developing cloud-based service-oriented architectures for fuzzy logic systems, based on extending IEEE-1855 (2016) in the defining system and exchanging data. Experiments were performed employing simulation concerning collection, processing and monitoring of data in a distributed manner over the web. A real-time human activity recognition simulated scenario is also demonstrated through a cloud-based fuzzy system.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126976463","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494470
Patryk Żywica, J. Siwek, M. Jasiulewicz-Kaczmarek
The article discusses the topic of combining various numerical indicators (indexes, models, scales, etc.) into a new synthetic index, which will allow for effective decision making within the expert system. Mentioned problem already has a well-researched solution based on aggregation operators. However, the aggregation-based approach did not create a new, consistent decision-making index. Moreover, some problems may occur because the interaction between input data is omitted during the aggregation. The methods proposed in this paper will focus on the possibility of maintaining interactions between attributes. The described methods will then be applied to the real-life problem of monitoring and assessment of maintenance in an enterprise.
{"title":"Interaction-driven aggregation of multiple numeric indicators with applications to decision-making support systems","authors":"Patryk Żywica, J. Siwek, M. Jasiulewicz-Kaczmarek","doi":"10.1109/FUZZ45933.2021.9494470","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494470","url":null,"abstract":"The article discusses the topic of combining various numerical indicators (indexes, models, scales, etc.) into a new synthetic index, which will allow for effective decision making within the expert system. Mentioned problem already has a well-researched solution based on aggregation operators. However, the aggregation-based approach did not create a new, consistent decision-making index. Moreover, some problems may occur because the interaction between input data is omitted during the aggregation. The methods proposed in this paper will focus on the possibility of maintaining interactions between attributes. The described methods will then be applied to the real-life problem of monitoring and assessment of maintenance in an enterprise.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125802445","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494437
A. Carniel, Markus Schneider
Spatial data science emerges as an important subclass of data science and focuses on extracting meaningful information and knowledge from spatial data to enable effective communication and interpretation of both spatial data and analytic results. It emphasizes the importance of location and spatial interaction by storing, analyzing, retrieving, and visualizing spatial and geometric information. Frequently, spatial objects are afflicted by spatial fuzziness, characterizing spatial objects with blurred interiors, uncertain boundaries, and imprecise locations. Fuzzy set theory and fuzzy logic have become powerful tools to adequately represent spatial fuzziness. This paper provides a survey and a review of the literature to understand the application of fuzzy approaches to spatial data science (projects) with the objective of proposing, motivating, and envisioning fuzzy spatial data science.
{"title":"A Survey of Fuzzy Approaches in Spatial Data Science","authors":"A. Carniel, Markus Schneider","doi":"10.1109/FUZZ45933.2021.9494437","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494437","url":null,"abstract":"Spatial data science emerges as an important subclass of data science and focuses on extracting meaningful information and knowledge from spatial data to enable effective communication and interpretation of both spatial data and analytic results. It emphasizes the importance of location and spatial interaction by storing, analyzing, retrieving, and visualizing spatial and geometric information. Frequently, spatial objects are afflicted by spatial fuzziness, characterizing spatial objects with blurred interiors, uncertain boundaries, and imprecise locations. Fuzzy set theory and fuzzy logic have become powerful tools to adequately represent spatial fuzziness. This paper provides a survey and a review of the literature to understand the application of fuzzy approaches to spatial data science (projects) with the objective of proposing, motivating, and envisioning fuzzy spatial data science.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124711516","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494562
M. Witczak, Lothar Seybold, G. Bocewicz, M. Mrugalski, A. Gola, Z. Banaszak
A permanent growth of electrical forklifts' applications makes them a dominating indoor and outdoor transportation tool. In spite of an unquestionable appeal of e-mobility, forklifts accumulators undergo a gradual degradation, which has to be suitably maintained. Thus, an appropriate work scheduling and human operator skills are crucial for their remaining useful life control. The paper proposes a comprehensive practical solution, which can be used for settling the above problem. It starts with shaping an appropriate IoT infrastructure located on a designed shopfloor. Using the above infrastructure, a strategy for an identification of Takagi-Sugeno operator model is proposed. Subsequently, a tool for assessing a forklift's accumulator remaining useful life is introduced and integrated with the operator model. These constitute a core component for the final scheduling framework, which can tolerate inevitable delays caused by human operators. All these factor contribute towards the remaining useful life control of the cooperating forklifts accumulators. Finally, the performance of the proposed strategy is verified using selected simulation scenarios involving forklift-based transportation tasks.
{"title":"A fuzzy logic approach to remaining useful life control and scheduling of cooperating forklifts","authors":"M. Witczak, Lothar Seybold, G. Bocewicz, M. Mrugalski, A. Gola, Z. Banaszak","doi":"10.1109/FUZZ45933.2021.9494562","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494562","url":null,"abstract":"A permanent growth of electrical forklifts' applications makes them a dominating indoor and outdoor transportation tool. In spite of an unquestionable appeal of e-mobility, forklifts accumulators undergo a gradual degradation, which has to be suitably maintained. Thus, an appropriate work scheduling and human operator skills are crucial for their remaining useful life control. The paper proposes a comprehensive practical solution, which can be used for settling the above problem. It starts with shaping an appropriate IoT infrastructure located on a designed shopfloor. Using the above infrastructure, a strategy for an identification of Takagi-Sugeno operator model is proposed. Subsequently, a tool for assessing a forklift's accumulator remaining useful life is introduced and integrated with the operator model. These constitute a core component for the final scheduling framework, which can tolerate inevitable delays caused by human operators. All these factor contribute towards the remaining useful life control of the cooperating forklifts accumulators. Finally, the performance of the proposed strategy is verified using selected simulation scenarios involving forklift-based transportation tasks.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130383023","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494589
Jean-Philippe Poli, W. Ouerdane, Régis Pierrard
Semantic image annotation is a field of paramount importance in which deep learning excels. However, some application domains, like security or medicine, may need an explanation of this annotation. Explainable Artificial Intelligence is an answer to this need. In this work, an explanation is a sentence in natural language that is dedicated to human users to provide them clues about the process that leads to the decision: the labels assignment to image parts. We focus on semantic image annotation with fuzzy logic that has proven to be a useful framework that captures both image segmentation imprecision and the vagueness of human spatial knowledge and vocabulary. In this paper, we present an algorithm for textual explanation generation of the semantic annotation of image regions.
{"title":"Generation of Textual Explanations in XAI: the Case of Semantic Annotation","authors":"Jean-Philippe Poli, W. Ouerdane, Régis Pierrard","doi":"10.1109/FUZZ45933.2021.9494589","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494589","url":null,"abstract":"Semantic image annotation is a field of paramount importance in which deep learning excels. However, some application domains, like security or medicine, may need an explanation of this annotation. Explainable Artificial Intelligence is an answer to this need. In this work, an explanation is a sentence in natural language that is dedicated to human users to provide them clues about the process that leads to the decision: the labels assignment to image parts. We focus on semantic image annotation with fuzzy logic that has proven to be a useful framework that captures both image segmentation imprecision and the vagueness of human spatial knowledge and vocabulary. In this paper, we present an algorithm for textual explanation generation of the semantic annotation of image regions.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127261196","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494584
Aykut Beke, T. Kumbasar
In this paper, we propose a learning approach for interval Fuzzy Logic Systems (FLSs) to end up with models that are capable to cover an expected amount of uncertainty with a high accuracy by exploiting a composite learning method with quantile regression. Within this paper, we construct two interval FLSs that have a different representation of uncertainty. One of them models the uncertainty in its consequents while the other one within its antecedents that are defined with interval type-2 Fuzzy Sets (FSs). The learning approach uses a multi-objective composite loss that is formed by the mean square error for accuracy purposes along with tilted loss for enforcing the bounds of the FLSs to capture the expected amount of uncertainty. In that way, it is not only possible to learn the FLSs that represent the uncertainty within their MFs (which can be used as prediction intervals) but also to improve the regression performance since the composite loss provides a more complete representation of the data. We present the proposed learning approach alongside parameterization tricks so that they can be trained within the frameworks of deep learning while not violating the definitions of FSs. We present comparative results on benchmark datasets that have different characteristics.
{"title":"Capturing Uncertainty with Interval Fuzzy Logic Systems through Composite Deep Learning","authors":"Aykut Beke, T. Kumbasar","doi":"10.1109/FUZZ45933.2021.9494584","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494584","url":null,"abstract":"In this paper, we propose a learning approach for interval Fuzzy Logic Systems (FLSs) to end up with models that are capable to cover an expected amount of uncertainty with a high accuracy by exploiting a composite learning method with quantile regression. Within this paper, we construct two interval FLSs that have a different representation of uncertainty. One of them models the uncertainty in its consequents while the other one within its antecedents that are defined with interval type-2 Fuzzy Sets (FSs). The learning approach uses a multi-objective composite loss that is formed by the mean square error for accuracy purposes along with tilted loss for enforcing the bounds of the FLSs to capture the expected amount of uncertainty. In that way, it is not only possible to learn the FLSs that represent the uncertainty within their MFs (which can be used as prediction intervals) but also to improve the regression performance since the composite loss provides a more complete representation of the data. We present the proposed learning approach alongside parameterization tricks so that they can be trained within the frameworks of deep learning while not violating the definitions of FSs. We present comparative results on benchmark datasets that have different characteristics.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"0 550 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130446288","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 : 2021-07-11DOI: 10.1109/fuzz45933.2021.9494532
{"title":"[Copyright notice]","authors":"","doi":"10.1109/fuzz45933.2021.9494532","DOIUrl":"https://doi.org/10.1109/fuzz45933.2021.9494532","url":null,"abstract":"","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128994453","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494455
K. Rudnik, A. Chwastyk, I. Pisz, G. Bocewicz
This paper proposes a novel approach for building transparent knowledge-based systems by generating interpretable fuzzy rules that allow for present dependences between quantitative variables by accounting for uncertainty and the dynamics of their values. In the approach, IF-THEN rules are used to show the conditional relationship between the ordered fuzzy numbers, which contain additional information about the tendencies of variables' value changes. This paper elaborates an approach of mining ordered fuzzy rules from numerical data included in an incremental database. This approach develops the ability to record uncertainty and its change in the context of rapidly changing data. In addition, it is the basis for the development of research on the inference method with ordered fuzzy rules, which may become an indispensable tool for decision-making in an uncertain environment.
{"title":"Ordered fuzzy rules generation based on incremental dataset","authors":"K. Rudnik, A. Chwastyk, I. Pisz, G. Bocewicz","doi":"10.1109/FUZZ45933.2021.9494455","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494455","url":null,"abstract":"This paper proposes a novel approach for building transparent knowledge-based systems by generating interpretable fuzzy rules that allow for present dependences between quantitative variables by accounting for uncertainty and the dynamics of their values. In the approach, IF-THEN rules are used to show the conditional relationship between the ordered fuzzy numbers, which contain additional information about the tendencies of variables' value changes. This paper elaborates an approach of mining ordered fuzzy rules from numerical data included in an incremental database. This approach develops the ability to record uncertainty and its change in the context of rapidly changing data. In addition, it is the basis for the development of research on the inference method with ordered fuzzy rules, which may become an indispensable tool for decision-making in an uncertain environment.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127832066","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494439
Rafael R. C. Silva, W. Caminhas, P. C. de Lima e Silva, F. Guimarães
In the present work we extend the traditional C4.5 decision tree method for regression and forecasting of multivariate time series. In the proposed method, time series data is first fuzzified leading to a fuzzy time series (FTS) representation of the data. A fuzzy decision tree (FDT) based on C4.5 is employed to form the knowledge base of the FTS model. The method can deal with high-order and multivariate fuzzy time series, offering an explainable model. The FDT-FTS method is tested with data from IBOVESPA stock market index, which tracks the performance of around 50 most liquid stocks traded on the Sao Paulo Stock Exchange in Brazil. The method is applied to the IBOVESPA mini future contract time series in order to forecast future values using a mix of historical values and technical analysis indicators. This method is compared with Support Vector Regression (SVR) and Random Forest Regression (RFR), both methods implemented in the Scikit-Learn open-source library. The FDT-FTS model was implemented in Python programming language in the open-source pyFTS library. Although all three methods have similar performance, according to the MAPE, SMAPE, RMSE, NRMSE and MAE metrics, the proposed method is computationally faster and explainable.
{"title":"A C4.5 Fuzzy Decision Tree Method for Multivariate Time Series Forecasting","authors":"Rafael R. C. Silva, W. Caminhas, P. C. de Lima e Silva, F. Guimarães","doi":"10.1109/FUZZ45933.2021.9494439","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494439","url":null,"abstract":"In the present work we extend the traditional C4.5 decision tree method for regression and forecasting of multivariate time series. In the proposed method, time series data is first fuzzified leading to a fuzzy time series (FTS) representation of the data. A fuzzy decision tree (FDT) based on C4.5 is employed to form the knowledge base of the FTS model. The method can deal with high-order and multivariate fuzzy time series, offering an explainable model. The FDT-FTS method is tested with data from IBOVESPA stock market index, which tracks the performance of around 50 most liquid stocks traded on the Sao Paulo Stock Exchange in Brazil. The method is applied to the IBOVESPA mini future contract time series in order to forecast future values using a mix of historical values and technical analysis indicators. This method is compared with Support Vector Regression (SVR) and Random Forest Regression (RFR), both methods implemented in the Scikit-Learn open-source library. The FDT-FTS model was implemented in Python programming language in the open-source pyFTS library. Although all three methods have similar performance, according to the MAPE, SMAPE, RMSE, NRMSE and MAE metrics, the proposed method is computationally faster and explainable.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117008228","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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