Complex Adaptive Systems are systems composed of distributed, decentralized and autonomous agents (software components, systems and people) and exhibit non-deterministic interactions between these agents. These interactions can often lead to the appearance of "emergent" behaviour or properties at the system level. These emergents can be harmful to the system or individual constituents, but are by their nature impossible to predict in advance and must therefore be detected at run-time. The characteristics of these systems mean that detecting emergence at run-time presents a significant challenge, one that cannot be met by existing methods that depend on a centralized controller with a global view of the system state. In this paper we present an important step towards decentralised detection of emergence in Complex Adaptive Systems. Our approach is based on observing the consequence of naturally arising feedback that occurs from the system level (macro) to the component level (micro) when emergent behaviour or properties appear in a system. This feedback results in the appearance of correlations, where none existed before, between the internal variables of individual agents and the properties that an agent detects in its local environment. In a case study of five different multi-agent systems we demonstrate that the number of agents that report these correlations increases as emergence occurs in each system. This provides the constituent agents with sufficient information to collaboratively detect when emergence has occurred at a system level without the need for a centralized, global view of the system.
{"title":"Towards Decentralised Detection of Emergence in Complex Adaptive Systems","authors":"E. O'Toole, Vivek Nallur, S. Clarke","doi":"10.1145/3019597","DOIUrl":"https://doi.org/10.1145/3019597","url":null,"abstract":"Complex Adaptive Systems are systems composed of distributed, decentralized and autonomous agents (software components, systems and people) and exhibit non-deterministic interactions between these agents. These interactions can often lead to the appearance of \"emergent\" behaviour or properties at the system level. These emergents can be harmful to the system or individual constituents, but are by their nature impossible to predict in advance and must therefore be detected at run-time. The characteristics of these systems mean that detecting emergence at run-time presents a significant challenge, one that cannot be met by existing methods that depend on a centralized controller with a global view of the system state. In this paper we present an important step towards decentralised detection of emergence in Complex Adaptive Systems. Our approach is based on observing the consequence of naturally arising feedback that occurs from the system level (macro) to the component level (micro) when emergent behaviour or properties appear in a system. This feedback results in the appearance of correlations, where none existed before, between the internal variables of individual agents and the properties that an agent detects in its local environment. In a case study of five different multi-agent systems we demonstrate that the number of agents that report these correlations increases as emergence occurs in each system. This provides the constituent agents with sufficient information to collaboratively detect when emergence has occurred at a system level without the need for a centralized, global view of the system.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"174 1","pages":"60-69"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82957142","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}
Biological organisms show a remarkable flexibility in how they organize their behavior and adapt it to changed circumstances. In this paper, we apply some of the more interesting concepts from biological theory to cyber-physical systems, especially those in such remote or hazardous environments that we cannot expect our control of them to be adequate for success or even survival. We propose a software architecture based on our Wrappings infrastructure, and show how it manages all of the resources necessary for autonomous operation, how it uses interacting planning and decision processes to organize its activity (determining that it cannot do something is one important aspect of the decision and planning processes), and how it uses various analyses of detailed behavioral instrumentation to improve that behavior or determine that improvement is not possible. We describe several difficult questions that arise when implementing our system architecture, and discuss how they might be addressed.
{"title":"Process Planning and Self-Improvement in Cyber-Physical Systems","authors":"C. Landauer, K. Bellman","doi":"10.1109/SASOW.2014.24","DOIUrl":"https://doi.org/10.1109/SASOW.2014.24","url":null,"abstract":"Biological organisms show a remarkable flexibility in how they organize their behavior and adapt it to changed circumstances. In this paper, we apply some of the more interesting concepts from biological theory to cyber-physical systems, especially those in such remote or hazardous environments that we cannot expect our control of them to be adequate for success or even survival. We propose a software architecture based on our Wrappings infrastructure, and show how it manages all of the resources necessary for autonomous operation, how it uses interacting planning and decision processes to organize its activity (determining that it cannot do something is one important aspect of the decision and planning processes), and how it uses various analyses of detailed behavioral instrumentation to improve that behavior or determine that improvement is not possible. We describe several difficult questions that arise when implementing our system architecture, and discuss how they might be addressed.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"71 1","pages":"144-149"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85917669","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 new design of self optimization for organic computing systems is investigated. Its main task, i.e., beside load-balancing, is to assign services with different importance levels to nodes so that the more important services are assigned to more trustworthy nodes. The evaluation results showed that the proposed algorithm is able to balance the workload between nodes nearly optimal. Moreover, it improves significantly the availability of important services.
{"title":"A Trust- and Load-Based Self-Optimization Algorithm for Organic Computing Systems","authors":"Nizar Msadek, Rolf Kiefhaber, T. Ungerer","doi":"10.1109/SASO.2014.32","DOIUrl":"https://doi.org/10.1109/SASO.2014.32","url":null,"abstract":"In this paper a new design of self optimization for organic computing systems is investigated. Its main task, i.e., beside load-balancing, is to assign services with different importance levels to nodes so that the more important services are assigned to more trustworthy nodes. The evaluation results showed that the proposed algorithm is able to balance the workload between nodes nearly optimal. Moreover, it improves significantly the availability of important services.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"32 1","pages":"177-178"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80845460","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 notion of a computational field has been proposed as a unifying abstraction for constructing and reasoning about large and self-organising networks of devices, focusing on the computations and coordination of aggregates of devices instead of individual behaviour. Recently, firm mathematical foundations have been established for this approach, in the form of a minimal universal field calculus and a more restricted syntax that guarantees self-stabilisation. We now aim to raise the abstraction level for system construction by identifying a collection of general and reusable "building block" algorithms. By functional combination of these building blocks, it is possible to construct complex adaptive behaviours. Moreover, the building blocks we present are all self-stabilising, ensuring that any system constructed from them is guaranteed to rapidly converge to a correct behaviour.
{"title":"Building Blocks for Aggregate Programming of Self-Organising Applications","authors":"J. Beal, Mirko Viroli","doi":"10.1109/SASOW.2014.6","DOIUrl":"https://doi.org/10.1109/SASOW.2014.6","url":null,"abstract":"The notion of a computational field has been proposed as a unifying abstraction for constructing and reasoning about large and self-organising networks of devices, focusing on the computations and coordination of aggregates of devices instead of individual behaviour. Recently, firm mathematical foundations have been established for this approach, in the form of a minimal universal field calculus and a more restricted syntax that guarantees self-stabilisation. We now aim to raise the abstraction level for system construction by identifying a collection of general and reusable \"building block\" algorithms. By functional combination of these building blocks, it is possible to construct complex adaptive behaviours. Moreover, the building blocks we present are all self-stabilising, ensuring that any system constructed from them is guaranteed to rapidly converge to a correct behaviour.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"80 1","pages":"8-13"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77797677","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}
Thorsteinn S. Rögnvaldsson, Henrik Norrman, S. Byttner, E. Järpe
Deviation detection is important for self-monitoring systems. To perform deviation detection well requires methods that, given only "normal" data from a distribution of unknown parametric form, can produce a reliable statistic for rejecting the null hypothesis, i.e. evidence for devating data. One measure of the strength of this evidence based on the data is the p-value, but few deviation detection methods utilize p-value estimation. We compare three methods that can be used to produce p-values: one class support vector machine (OCSVM), conformal anomaly detection (CAD), and a simple "most central pattern" (MCP) algorithm. The SVM and the CAD method should be able to handle a distribution of any shape. The methods are evaluated on synthetic data sets to test and illustrate their strengths and weaknesses, and on data from a real life self-monitoring scenario with a city bus fleet in normal traffic. The OCSVM has a Gaussian kernel for the synthetic data and a Hellinger kernel for the empirical data. The MCP method uses the Mahalanobis metric for the synthetic data and the Hellinger metric for the empirical data. The CAD uses the same metrics as the MCP method and has a k-nearest neighbour (kNN) non-conformity measure for both sets. The conclusion is that all three methods give reasonable, and quite similar, results on the real life data set but that they have clear strengths and weaknesses on the synthetic data sets. The MCP algorithm is quick and accurate when the "normal" data distribution is unimodal and symmetric (with the chosen metric) but not otherwise. The OCSVM is a bit cumbersome to use to create (quantized) p-values but is accurate and reliable when the data distribution is multimodal and asymmetric. The CAD is also accurate for multimodal and asymmetric distributions. The experiment on the vehicle data illustrate how algorithms like these can be used in a self-monitoring system that uses a fleet of vehicles to conduct deviation detection without supervision and without prior knowledge about what is being monitored.
{"title":"Estimating p-Values for Deviation Detection","authors":"Thorsteinn S. Rögnvaldsson, Henrik Norrman, S. Byttner, E. Järpe","doi":"10.1109/SASO.2014.22","DOIUrl":"https://doi.org/10.1109/SASO.2014.22","url":null,"abstract":"Deviation detection is important for self-monitoring systems. To perform deviation detection well requires methods that, given only \"normal\" data from a distribution of unknown parametric form, can produce a reliable statistic for rejecting the null hypothesis, i.e. evidence for devating data. One measure of the strength of this evidence based on the data is the p-value, but few deviation detection methods utilize p-value estimation. We compare three methods that can be used to produce p-values: one class support vector machine (OCSVM), conformal anomaly detection (CAD), and a simple \"most central pattern\" (MCP) algorithm. The SVM and the CAD method should be able to handle a distribution of any shape. The methods are evaluated on synthetic data sets to test and illustrate their strengths and weaknesses, and on data from a real life self-monitoring scenario with a city bus fleet in normal traffic. The OCSVM has a Gaussian kernel for the synthetic data and a Hellinger kernel for the empirical data. The MCP method uses the Mahalanobis metric for the synthetic data and the Hellinger metric for the empirical data. The CAD uses the same metrics as the MCP method and has a k-nearest neighbour (kNN) non-conformity measure for both sets. The conclusion is that all three methods give reasonable, and quite similar, results on the real life data set but that they have clear strengths and weaknesses on the synthetic data sets. The MCP algorithm is quick and accurate when the \"normal\" data distribution is unimodal and symmetric (with the chosen metric) but not otherwise. The OCSVM is a bit cumbersome to use to create (quantized) p-values but is accurate and reliable when the data distribution is multimodal and asymmetric. The CAD is also accurate for multimodal and asymmetric distributions. The experiment on the vehicle data illustrate how algorithms like these can be used in a self-monitoring system that uses a fleet of vehicles to conduct deviation detection without supervision and without prior knowledge about what is being monitored.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"94 1","pages":"100-109"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80683082","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}
C. V. Leeuwen, J. Gier, Julio A. de Oliveira Filho, Z. Papp
This short paper introduces a closed-loop design optimization method for self-organizing and self-optimizing networked systems with a focus on signal processing and control. The design process starts with creating graph-based model of the system using a dedicated modelling language. The design is exported and converted to executable code in order to obtain the properties of the runtime behaviour of the system using a simulation environment. The embedding optimization loop iteratively invokes the evaluation and searches for optimal architectures and parameterization in the user defined design space. A distinguishing feature of the tool is that it allows for runtime changes in the models, i.e. it is capable of evaluating runtime reconfigurable architectures. The design space is split into two disjunct sub-spaces: one of them defines the runtime reconfigurability (the self-capabilities), the other defines the region of design time optimization. The tool is demonstrated via a real-time monitoring application.
{"title":"Model-Based Architecture Optimization for Self-Adaptive Networked Signal Processing Systems","authors":"C. V. Leeuwen, J. Gier, Julio A. de Oliveira Filho, Z. Papp","doi":"10.1109/SASO.2014.37","DOIUrl":"https://doi.org/10.1109/SASO.2014.37","url":null,"abstract":"This short paper introduces a closed-loop design optimization method for self-organizing and self-optimizing networked systems with a focus on signal processing and control. The design process starts with creating graph-based model of the system using a dedicated modelling language. The design is exported and converted to executable code in order to obtain the properties of the runtime behaviour of the system using a simulation environment. The embedding optimization loop iteratively invokes the evaluation and searches for optimal architectures and parameterization in the user defined design space. A distinguishing feature of the tool is that it allows for runtime changes in the models, i.e. it is capable of evaluating runtime reconfigurable architectures. The design space is split into two disjunct sub-spaces: one of them defines the runtime reconfigurability (the self-capabilities), the other defines the region of design time optimization. The tool is demonstrated via a real-time monitoring application.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"28 1","pages":"187-188"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90065322","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}
F. Bagnoli, A. Guazzini, Giovanna Pacini, I. Stavrakakis, Evangelia Kokolaki, George Theodorakopoulos
Collective awareness platforms (CAPs) are internet and mobile tools for collaboration, sustainability and social innovation that can allows drastic improvement of our lifestyle, beyond the standard economic model. However, their development is often driven (and motivated) by technology, while their adoption and usage characteristics are determined by the social interactions and can be affected by many items, up to failure. We describe here our approach to CAPs modelling that includes elements from cognitive and evolutionary sciences, in the hope of providing instruments for the improvement and the assessment of CAPs.
{"title":"Cognitive Structure of Collective Awareness Platforms","authors":"F. Bagnoli, A. Guazzini, Giovanna Pacini, I. Stavrakakis, Evangelia Kokolaki, George Theodorakopoulos","doi":"10.1109/SASOW.2014.38","DOIUrl":"https://doi.org/10.1109/SASOW.2014.38","url":null,"abstract":"Collective awareness platforms (CAPs) are internet and mobile tools for collaboration, sustainability and social innovation that can allows drastic improvement of our lifestyle, beyond the standard economic model. However, their development is often driven (and motivated) by technology, while their adoption and usage characteristics are determined by the social interactions and can be affected by many items, up to failure. We describe here our approach to CAPs modelling that includes elements from cognitive and evolutionary sciences, in the hope of providing instruments for the improvement and the assessment of CAPs.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"3 1","pages":"96-101"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86298212","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}
Payam Zahadat, M. Bodi, Ziad Salem, Frank Bonnet, Marcelo Elias de Oliveira, F. Mondada, Karlo Griparic, Tomislav Haus, S. Bogdan, Rob Mills, Pedro Mariano, L. Correia, O. Kernbach, S. Kernbach, T. Schmickl
The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be accepted by the animals aspart of the society and/or as part of the collectively formed environment. For that, we have developed two sets of robotic hardware for integrating into societies of two different animals: zebra fish and young honeybees. We also developed mechanisms to provide feedback from the behaviours of societies for the controllers of the robotic system. Two different computational methods are then used as the controllers of the robots in simulation and successfully adapted by evolutionary algorithms to influence the simulated animals for desired behaviours. Together, these advances in mechatronic hardware, feedback mechanisms, and controller methodology are laying essential foundations to facilitate experiments on modulating self-organised behaviour in mixed animal -- robot societies.
{"title":"Social Adaptation of Robots for Modulating Self-Organization in Animal Societies","authors":"Payam Zahadat, M. Bodi, Ziad Salem, Frank Bonnet, Marcelo Elias de Oliveira, F. Mondada, Karlo Griparic, Tomislav Haus, S. Bogdan, Rob Mills, Pedro Mariano, L. Correia, O. Kernbach, S. Kernbach, T. Schmickl","doi":"10.1109/SASOW.2014.13","DOIUrl":"https://doi.org/10.1109/SASOW.2014.13","url":null,"abstract":"The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be accepted by the animals aspart of the society and/or as part of the collectively formed environment. For that, we have developed two sets of robotic hardware for integrating into societies of two different animals: zebra fish and young honeybees. We also developed mechanisms to provide feedback from the behaviours of societies for the controllers of the robotic system. Two different computational methods are then used as the controllers of the robots in simulation and successfully adapted by evolutionary algorithms to influence the simulated animals for desired behaviours. Together, these advances in mechatronic hardware, feedback mechanisms, and controller methodology are laying essential foundations to facilitate experiments on modulating self-organised behaviour in mixed animal -- robot societies.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"158 1","pages":"55-60"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80031948","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}
Nino Antulov-Fantulin, Alen Lancic, H. Štefančić, M. Šikić, T. Šmuc
We introduce a statistical inference framework for maximum likelihood estimation of the contagion source from a partially observed contagion spreading process on an arbitrary network structure. The framework is based on simulations of a contagion spreading process from a set of potential sources which were infected in the observed realization. We present a number of different likelihood estimators for determining the conditional probabilities of potential initial sources producing the observed epidemic realization, which are computed in scalable and parallel way. This statistical inference framework is applicable to arbitrary networks with different dynamical spreading processes.
{"title":"Statistical Inference Framework for Source Detection of Contagion Processes on Arbitrary Network Structures","authors":"Nino Antulov-Fantulin, Alen Lancic, H. Štefančić, M. Šikić, T. Šmuc","doi":"10.1109/SASOW.2014.35","DOIUrl":"https://doi.org/10.1109/SASOW.2014.35","url":null,"abstract":"We introduce a statistical inference framework for maximum likelihood estimation of the contagion source from a partially observed contagion spreading process on an arbitrary network structure. The framework is based on simulations of a contagion spreading process from a set of potential sources which were infected in the observed realization. We present a number of different likelihood estimators for determining the conditional probabilities of potential initial sources producing the observed epidemic realization, which are computed in scalable and parallel way. This statistical inference framework is applicable to arbitrary networks with different dynamical spreading processes.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"29 1","pages":"78-83"},"PeriodicalIF":0.0,"publicationDate":"2013-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90700647","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}
Saber Mansour, Nicolas Wiest, Olivier Lefevre, Sébastien Mazac
{"title":"Hemis: Hybrid Multi-agent Architecture for Energy Management and Home Automation","authors":"Saber Mansour, Nicolas Wiest, Olivier Lefevre, Sébastien Mazac","doi":"10.1109/SASO.2012.44","DOIUrl":"https://doi.org/10.1109/SASO.2012.44","url":null,"abstract":"","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"85 1","pages":"229-230"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77224497","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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