People have a tendency to mimic the actions of the larger group. Herd behavior is one of the most common phenomena in our daily life. Previous studies criticized that homogeneity of herd behavior and blind conformity will cause the bubble to burst. Crowd psychology plays an important role in service marketing. However, is it possible for service operation to thrive in service industry by manipulating the herd behavior? In this study, we proposed and validated that the Cascade-based service innovation (CSI) design which utilizes the attraction effect that highlights the service value and meaning interpretation enable service innovation opportunities. The cascade-based innovation design uses service meaning interpretation as the decoy and applies the attraction effect to drive the cascade-based service innovations. The result indicates that the CSI design could facilitate service providers to manipulate and reshape their service design to achieve sustainable development of service marketing. This study applied hybrid research methodologies that combine empirical research to examine the CSI design for fads operation and qualitative case study, and explore the criteria that enables service providers to operate and manipulate fads phenomenon successfully.
{"title":"Fads Phenomenon Operation through Cascade-Based Service Innovation Design","authors":"Pin-Rui Hwang","doi":"10.1109/SCC.2016.114","DOIUrl":"https://doi.org/10.1109/SCC.2016.114","url":null,"abstract":"People have a tendency to mimic the actions of the larger group. Herd behavior is one of the most common phenomena in our daily life. Previous studies criticized that homogeneity of herd behavior and blind conformity will cause the bubble to burst. Crowd psychology plays an important role in service marketing. However, is it possible for service operation to thrive in service industry by manipulating the herd behavior? In this study, we proposed and validated that the Cascade-based service innovation (CSI) design which utilizes the attraction effect that highlights the service value and meaning interpretation enable service innovation opportunities. The cascade-based innovation design uses service meaning interpretation as the decoy and applies the attraction effect to drive the cascade-based service innovations. The result indicates that the CSI design could facilitate service providers to manipulate and reshape their service design to achieve sustainable development of service marketing. This study applied hybrid research methodologies that combine empirical research to examine the CSI design for fads operation and qualitative case study, and explore the criteria that enables service providers to operate and manipulate fads phenomenon successfully.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130706749","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}
Service Assured task execution is an exemplary need for Enterprise Task Crowdsourcing, even when crowdsourcing tasks on open and public crowdosourcing platforms, which are characteristic of non-committed and discretionary task execution by crowd workers. Service Assurance (SA) has broader semantics than SLA fulfillment in the context of enterprise task crowdsourcing, as explained in our previous work on Service Assurance Framework for Enterprise task crowdsourcing. The framework is designed for open crowd platforms and enables to map a given requester SLA to worker SA requirements and select crowd workers who evince high probability of SA requirement fulfillment. Even with these select crowd workers, it might not be possible to implicitly assume the sustenance of workers' expected SA (which is computed based on workers' prior task executions) in the day to day task executions on the Enterprise Requester tasks, owing to the unbinding task execution facilitated by the open crowd platforms. This might result in defaulting in requester SLA fulfillment, rendering the worker selection (by the framework) futile. Hence, in addition to selecting the required SA fulfilling workers, it is essential to employ suitable SA sustaining strategies in place for sustaining the SA expected of workers. The SA Sustaining Strategies and the Service Assurance Framework together constitute for the Service Assurance Sustaining Enterprise Task Crowdsourcing Service (SAS Service)for Enterprise Tasks, which is deliberated in this paper. The SAS Service's performance is validated using appropriate crowd experiments.
{"title":"Service Assurance Sustaining Enterprise Task Crowdsourcing Service","authors":"Chithralekha Balamurugan, K. K. Budhraja","doi":"10.1109/SCC.2016.100","DOIUrl":"https://doi.org/10.1109/SCC.2016.100","url":null,"abstract":"Service Assured task execution is an exemplary need for Enterprise Task Crowdsourcing, even when crowdsourcing tasks on open and public crowdosourcing platforms, which are characteristic of non-committed and discretionary task execution by crowd workers. Service Assurance (SA) has broader semantics than SLA fulfillment in the context of enterprise task crowdsourcing, as explained in our previous work on Service Assurance Framework for Enterprise task crowdsourcing. The framework is designed for open crowd platforms and enables to map a given requester SLA to worker SA requirements and select crowd workers who evince high probability of SA requirement fulfillment. Even with these select crowd workers, it might not be possible to implicitly assume the sustenance of workers' expected SA (which is computed based on workers' prior task executions) in the day to day task executions on the Enterprise Requester tasks, owing to the unbinding task execution facilitated by the open crowd platforms. This might result in defaulting in requester SLA fulfillment, rendering the worker selection (by the framework) futile. Hence, in addition to selecting the required SA fulfilling workers, it is essential to employ suitable SA sustaining strategies in place for sustaining the SA expected of workers. The SA Sustaining Strategies and the Service Assurance Framework together constitute for the Service Assurance Sustaining Enterprise Task Crowdsourcing Service (SAS Service)for Enterprise Tasks, which is deliberated in this paper. The SAS Service's performance is validated using appropriate crowd experiments.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125986534","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}
Simulation is known to be an effective technique to understand and manage traffic in cities of developed countries. However, in developing countries, traffic management is lacking due to a wide diversity of vehicles on the road, their chaotic movement, little instrumentation to sense traffic state and limited funds to create IT and physical infrastructure to ameliorate the situation. Under these conditions, in this paper, we present our approach of using the Megaffic traffic simulator as a service to gain actionable insights for two use-cases and cities in India, a first. Our approach is general to be readily used in other use cases and cities, and our results give new insights: (a) using demographics data, traffic demand can be reduced if timings of government offices are altered in Delhi, (b) using a mobile company's Call Data Record (CDR) data to mine trajectories anonymously, one can take effective traffic actions while organizing events in Mumbai at local scale.
{"title":"Case Studies in Managing Traffic in a Developing Country with Privacy-Preserving Simulation as a Service","authors":"B. Srivastava, M. Pallan, M. Madhavan, Ravi Kokku","doi":"10.1109/SCC.2016.130","DOIUrl":"https://doi.org/10.1109/SCC.2016.130","url":null,"abstract":"Simulation is known to be an effective technique to understand and manage traffic in cities of developed countries. However, in developing countries, traffic management is lacking due to a wide diversity of vehicles on the road, their chaotic movement, little instrumentation to sense traffic state and limited funds to create IT and physical infrastructure to ameliorate the situation. Under these conditions, in this paper, we present our approach of using the Megaffic traffic simulator as a service to gain actionable insights for two use-cases and cities in India, a first. Our approach is general to be readily used in other use cases and cities, and our results give new insights: (a) using demographics data, traffic demand can be reduced if timings of government offices are altered in Delhi, (b) using a mobile company's Call Data Record (CDR) data to mine trajectories anonymously, one can take effective traffic actions while organizing events in Mumbai at local scale.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128819399","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 investigates the multi-agent cooperation problems in Web services domain. For Pareto-optimal Nash equilibrium, reinforcement learning algorithms are used to solve the coordination problem in cooperative environments. Most previous works study the deterministic gain of a state. However, in practical service environments, the gain may be nondeterministic due to unstable Quality of Service (QoS). To avoid local optimal solution, we use an improved update function. The experimental results show that proposed reinforcement learning algorithm outperforms other learning methods.
{"title":"Multi-agent Reinforcement Learning for Service Composition","authors":"Yu Lei, Philip S. Yu","doi":"10.1109/SCC.2016.108","DOIUrl":"https://doi.org/10.1109/SCC.2016.108","url":null,"abstract":"This paper investigates the multi-agent cooperation problems in Web services domain. For Pareto-optimal Nash equilibrium, reinforcement learning algorithms are used to solve the coordination problem in cooperative environments. Most previous works study the deterministic gain of a state. However, in practical service environments, the gain may be nondeterministic due to unstable Quality of Service (QoS). To avoid local optimal solution, we use an improved update function. The experimental results show that proposed reinforcement learning algorithm outperforms other learning methods.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122196420","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 realization of business process with services has gain more importance in the last decade. Several organizations are focusing nowadays in the modeling and execution of their business processes, seizing the advantages provided by both the BPMN 2.0 notation which allows these models to be executed, and the emergence of BPMS platforms which are able to execute business processes invoking internal and external services from partners and/or the cloud when needed. Although many lifecycle proposals exist to guide the definition and management of both business process and services, there is no clear relationship defined between them, i.e. how services should be defined and managed to support business processes. This is a key element that should be taken into account when implementing services for this kind of systems, in order to systematize the work and obtain better results. In this paper we present a service lifecycle to support business processes, which helps developing services for business process systems.
{"title":"A Services Lifecycle to Support the Business Processes Lifecycle: From Modeling to Execution and Beyond","authors":"Andrea Delgado","doi":"10.1109/SCC.2016.117","DOIUrl":"https://doi.org/10.1109/SCC.2016.117","url":null,"abstract":"The realization of business process with services has gain more importance in the last decade. Several organizations are focusing nowadays in the modeling and execution of their business processes, seizing the advantages provided by both the BPMN 2.0 notation which allows these models to be executed, and the emergence of BPMS platforms which are able to execute business processes invoking internal and external services from partners and/or the cloud when needed. Although many lifecycle proposals exist to guide the definition and management of both business process and services, there is no clear relationship defined between them, i.e. how services should be defined and managed to support business processes. This is a key element that should be taken into account when implementing services for this kind of systems, in order to systematize the work and obtain better results. In this paper we present a service lifecycle to support business processes, which helps developing services for business process systems.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115046959","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}
Jinliang Xu, Shangguang Wang, Sen Su, S. Kumar, Chou Wu
Latent Factor Model (LFM) is extensively used in dealing with user-item bipartite networks in service recommendation systems. To alleviate the limitations of LFM, this papers presents a novel unsupervised learning model, Latent Interest and Topic Mining model (LITM), to automatically mine the latent user interests and item topics from user-item bipartite networks. In particular, we introduce the motivation and objectives of this bipartite network based approach, and detail the model development and optimization process of the proposed LITM. This work not only provides an efficient method for latent user interest and item topic mining, but also highlights a new way to improve the accuracy of service recommendation. Experimental studies are performed and the results validate the LITM's efficiency in model training, and its ability to provide better service recommendation performance based on user-item bipartite networks are demonstrated.
{"title":"Latent Interest and Topic Mining on User-Item Bipartite Networks","authors":"Jinliang Xu, Shangguang Wang, Sen Su, S. Kumar, Chou Wu","doi":"10.1109/SCC.2016.105","DOIUrl":"https://doi.org/10.1109/SCC.2016.105","url":null,"abstract":"Latent Factor Model (LFM) is extensively used in dealing with user-item bipartite networks in service recommendation systems. To alleviate the limitations of LFM, this papers presents a novel unsupervised learning model, Latent Interest and Topic Mining model (LITM), to automatically mine the latent user interests and item topics from user-item bipartite networks. In particular, we introduce the motivation and objectives of this bipartite network based approach, and detail the model development and optimization process of the proposed LITM. This work not only provides an efficient method for latent user interest and item topic mining, but also highlights a new way to improve the accuracy of service recommendation. Experimental studies are performed and the results validate the LITM's efficiency in model training, and its ability to provide better service recommendation performance based on user-item bipartite networks are demonstrated.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117005939","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}
Kai Zhang, Zhiyong Feng, Shizhan Chen, Keman Huang, Guiling Wang
With the rapid development of mobile internet and wireless network technologies, more and more people use the mobile app to call a taxicab to pick them up. Therefore, understanding the passengers' travel demand becomes crucial to improve the utilization of the taxicabs and reduce their cost. In this paper, based on spatio-temporal clustering, we propose a demand hotspots prediction framework to generate recommendation for taxi drivers. Specially, an adaptive prediction approach is presented to demand hotspots and their hotness, and then combing the driver's location and the hotness, top candidates are recommended and visually presented to drivers. Based on the dataset provided by CAR INC., the experiment shows that our approach gains a significant improvement in hotspots prediction and recommendation, with 15.21% improvement on average f-measure for prediction and 79.6% hit ratio for recommendation.
{"title":"A Framework for Passengers Demand Prediction and Recommendation","authors":"Kai Zhang, Zhiyong Feng, Shizhan Chen, Keman Huang, Guiling Wang","doi":"10.1109/SCC.2016.51","DOIUrl":"https://doi.org/10.1109/SCC.2016.51","url":null,"abstract":"With the rapid development of mobile internet and wireless network technologies, more and more people use the mobile app to call a taxicab to pick them up. Therefore, understanding the passengers' travel demand becomes crucial to improve the utilization of the taxicabs and reduce their cost. In this paper, based on spatio-temporal clustering, we propose a demand hotspots prediction framework to generate recommendation for taxi drivers. Specially, an adaptive prediction approach is presented to demand hotspots and their hotness, and then combing the driver's location and the hotness, top candidates are recommended and visually presented to drivers. Based on the dataset provided by CAR INC., the experiment shows that our approach gains a significant improvement in hotspots prediction and recommendation, with 15.21% improvement on average f-measure for prediction and 79.6% hit ratio for recommendation.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133058208","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}
Meiling Zhu, Chen Liu, Jianwu Wang, Xiongbin Wang, Yanbo Han
Traveling companions are object groups that move together in a period of time. In this paper, we introduce a special kind of traffic data stream, which is called Automatic Number Plate Recognition (ANPR) data. In order to quickly identify traveling companions on ANPR data stream, this paper proposes an analysis algorithm named COINCIDENT. Owing to the privacy and security of ANPR data stream, the algorithm is encapsulated as a data stream service. The main contributions include: 1) we consummate our previous stream data service model and design a traveling companion discovery service on the model. 2) the proposed COINCIDENT algorithm can instantly and continuously discover traveling companions on the live ANPR data stream. Final experiments show the effectiveness and efficiency of our developed service.
{"title":"A Service-Friendly Approach to Discover Traveling Companions Based on ANPR Data Stream","authors":"Meiling Zhu, Chen Liu, Jianwu Wang, Xiongbin Wang, Yanbo Han","doi":"10.1109/SCC.2016.29","DOIUrl":"https://doi.org/10.1109/SCC.2016.29","url":null,"abstract":"Traveling companions are object groups that move together in a period of time. In this paper, we introduce a special kind of traffic data stream, which is called Automatic Number Plate Recognition (ANPR) data. In order to quickly identify traveling companions on ANPR data stream, this paper proposes an analysis algorithm named COINCIDENT. Owing to the privacy and security of ANPR data stream, the algorithm is encapsulated as a data stream service. The main contributions include: 1) we consummate our previous stream data service model and design a traveling companion discovery service on the model. 2) the proposed COINCIDENT algorithm can instantly and continuously discover traveling companions on the live ANPR data stream. Final experiments show the effectiveness and efficiency of our developed service.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133582315","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}
Yan Wang, Jiantao Zhou, Jing Liu, Tenghe Au, Xiaoyu Song
The quality-of-service (QoS) is a common focus which users and service providers pay close attention to at present. For service providers, it is one of their main targets to find the optimal QoS strategy based on user preferences. However, because of the fuzziness of user preferences and the complexity of service environment, searching an optimal service strategy becomes a difficult problem. In the paper, how the QoS affects a user's satisfaction is analyzed, and then a quantitative relationship between QoS and user satisfaction is built. Based on the relationship, a user utility model of cloud service is established. In order to maximize user utility, a QoS evolutionary algorithm based on user utility model is proposed. In the algorithm, some improvement is designed to balance the contradiction between search scope and search speed in the traditional genetic algorithm. It can be seen through the experiments that the QoS optimization strategy of cloud service output by the QoS evolutionary algorithm is consistent with the target user's preferences, which can effectively enhance the cost effectiveness of service resources.
{"title":"A QoS Evolutionary Method of Cloud Service Based on User Utility Model","authors":"Yan Wang, Jiantao Zhou, Jing Liu, Tenghe Au, Xiaoyu Song","doi":"10.1109/SCC.2016.79","DOIUrl":"https://doi.org/10.1109/SCC.2016.79","url":null,"abstract":"The quality-of-service (QoS) is a common focus which users and service providers pay close attention to at present. For service providers, it is one of their main targets to find the optimal QoS strategy based on user preferences. However, because of the fuzziness of user preferences and the complexity of service environment, searching an optimal service strategy becomes a difficult problem. In the paper, how the QoS affects a user's satisfaction is analyzed, and then a quantitative relationship between QoS and user satisfaction is built. Based on the relationship, a user utility model of cloud service is established. In order to maximize user utility, a QoS evolutionary algorithm based on user utility model is proposed. In the algorithm, some improvement is designed to balance the contradiction between search scope and search speed in the traditional genetic algorithm. It can be seen through the experiments that the QoS optimization strategy of cloud service output by the QoS evolutionary algorithm is consistent with the target user's preferences, which can effectively enhance the cost effectiveness of service resources.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"39 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133137271","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}
Log-based business operation analysis is getting more and more attention from enterprise decision makers. However, at the very first step of the analysis service, two primary obstacles are always encountered: how to process a wide variety of local event log formats and how to handle personally identifiable information weaved in an event log. Due to these obstacles, typical business analysts who do not have programming skills have lost business opportunities at the early stages. We propose a privacy-preserving data curation specification language, BELAS, for such analysts and present experimental results that show how most of a real-life event log could be processed in process analysis services.
{"title":"Adaptable Privacy-Preserving Data Curation for Business Process Analysis Services","authors":"M. Kudo, Kumiko Maeda, Fumiko Satoh","doi":"10.1109/SCC.2016.60","DOIUrl":"https://doi.org/10.1109/SCC.2016.60","url":null,"abstract":"Log-based business operation analysis is getting more and more attention from enterprise decision makers. However, at the very first step of the analysis service, two primary obstacles are always encountered: how to process a wide variety of local event log formats and how to handle personally identifiable information weaved in an event log. Due to these obstacles, typical business analysts who do not have programming skills have lost business opportunities at the early stages. We propose a privacy-preserving data curation specification language, BELAS, for such analysts and present experimental results that show how most of a real-life event log could be processed in process analysis services.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131557796","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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