Pub Date : 2018-11-01DOI: 10.1109/ictai.2018.00001
{"title":"[Title page i]","authors":"","doi":"10.1109/ictai.2018.00001","DOIUrl":"https://doi.org/10.1109/ictai.2018.00001","url":null,"abstract":"","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114107880","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 : 2018-11-01DOI: 10.1109/ICTAI.2018.00118
Iosif Papadakis Ktistakis, N. Bourbakis
This paper presents a new multimodal Human-Machine Interaction (HMI) scheme-model for the co-operation of a robotic-nurse (here a robotic wheelchair) and its human user. The HMI model processes vocal commands through a Personalized Isolated Word Recognition System (PIWRS) along with the recognition of Body Pose Angles (BPA) for decision-making in real time. In particular, the HMI scheme is able to recognize: (i) a set of voice commands, (ii) a set of body postures and poses and (iii) calculate the appropriate body angles associated to skeletal data obtained through a set of cameras. Furthermore, the HMI scheme receives specific values provided by pressure sensors, which are being utilized by the user throughout the duration of the tasks to be executed that compose the Active Participation System (APS). All these variables are appropriately combined for the safe control of an Autonomous Intelligent Robotic Wheelchair (AIRW) used by people in need. More specifically, the stand-up, turn-around and sit-down are the procedural steps under study.
{"title":"A Multimodal Human-Machine Interaction Scheme for an Intelligent Robotic Nurse","authors":"Iosif Papadakis Ktistakis, N. Bourbakis","doi":"10.1109/ICTAI.2018.00118","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00118","url":null,"abstract":"This paper presents a new multimodal Human-Machine Interaction (HMI) scheme-model for the co-operation of a robotic-nurse (here a robotic wheelchair) and its human user. The HMI model processes vocal commands through a Personalized Isolated Word Recognition System (PIWRS) along with the recognition of Body Pose Angles (BPA) for decision-making in real time. In particular, the HMI scheme is able to recognize: (i) a set of voice commands, (ii) a set of body postures and poses and (iii) calculate the appropriate body angles associated to skeletal data obtained through a set of cameras. Furthermore, the HMI scheme receives specific values provided by pressure sensors, which are being utilized by the user throughout the duration of the tasks to be executed that compose the Active Participation System (APS). All these variables are appropriately combined for the safe control of an Autonomous Intelligent Robotic Wheelchair (AIRW) used by people in need. More specifically, the stand-up, turn-around and sit-down are the procedural steps under study.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116831493","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 : 2018-11-01DOI: 10.1109/ICTAI.2018.00117
Guillaume Baud-Berthier, Laurent Simon
Model Checking is at the heart of formal methods for software and hardware verification. In this area of active research, Bounded Model Checking (BMC) and k-induction have reached very impressive results, especially when both methods are working together. They are based on a common approach that unrolls the transition relation, but each method serves a different purpose in practice. BMC is usually used for bugs findings, while k-induction aims at building inductive invariants. The ZigZag approach, proposed 15 years ago, takes benefit from both strategies by successively calling each one of them, while trying to share a lot of information between calls thanks to the mechanism of SAT clauses learning. Despite the practical importance of the ZigZag algorithm, it was mainly used forwardly until last year. The transition relation was unrolled by increasing depths only. However, as stated by the authors of ZigZag themselves, it was possible to consider the ZigZag approach backwardly. The experimental study of backward zigzag performances was only proposed one year ago. In this paper, we propose to extend the idea of the ZigZag algorithm by allowing to unroll the transitions from the middle. This has the nice property of allowing the SAT solver to keep learnt clauses that are both close to the initial state and to the bad state in the search. Our experimental study however shows that the best option for ZigZag is still to perform it backward, as stated in a previous work. However, we also show that our hybrid approach offers the same performances as forward ZigZag, while allowing more flexible strategies to be developed in the future, for example by choosing the right transition to expand.
{"title":"Zigzagging Strategies for Temporal Induction","authors":"Guillaume Baud-Berthier, Laurent Simon","doi":"10.1109/ICTAI.2018.00117","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00117","url":null,"abstract":"Model Checking is at the heart of formal methods for software and hardware verification. In this area of active research, Bounded Model Checking (BMC) and k-induction have reached very impressive results, especially when both methods are working together. They are based on a common approach that unrolls the transition relation, but each method serves a different purpose in practice. BMC is usually used for bugs findings, while k-induction aims at building inductive invariants. The ZigZag approach, proposed 15 years ago, takes benefit from both strategies by successively calling each one of them, while trying to share a lot of information between calls thanks to the mechanism of SAT clauses learning. Despite the practical importance of the ZigZag algorithm, it was mainly used forwardly until last year. The transition relation was unrolled by increasing depths only. However, as stated by the authors of ZigZag themselves, it was possible to consider the ZigZag approach backwardly. The experimental study of backward zigzag performances was only proposed one year ago. In this paper, we propose to extend the idea of the ZigZag algorithm by allowing to unroll the transitions from the middle. This has the nice property of allowing the SAT solver to keep learnt clauses that are both close to the initial state and to the bad state in the search. Our experimental study however shows that the best option for ZigZag is still to perform it backward, as stated in a previous work. However, we also show that our hybrid approach offers the same performances as forward ZigZag, while allowing more flexible strategies to be developed in the future, for example by choosing the right transition to expand.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128644623","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 : 2018-11-01DOI: 10.1109/ICTAI.2018.00140
Hongcui Wang, Erwei Wang, Di Jin, Xiao Wang, Jing Wang, Dongxiao He
Network embedding, aiming at learning the low-dimensional representations of nodes in a network, is a key to many network analysis tasks. All the current network embedding methods primarily explore the network topology or node attributes, while no effort has been made to analyze the edge content for network embedding. The edge content, such as the email content between two users in an email network, is often naturally associated with edges. They carry rich information to describe the interaction between nodes, and provide valuable supervision to learn the representations of nodes. In this paper, we propose a novel edge content enhanced network embedding model, which incorporates the edge content to guide the network representation learning process. We provide the efficient updating rules to infer the parameters in the model, along with theoretical analysis on correctness and convergence guarantees. Extensive experiments, in comparison with the state-of-the-arts, show the superior performance of our proposed new approach on different network analysis tasks.
{"title":"Edge Content Enhanced Network Embedding","authors":"Hongcui Wang, Erwei Wang, Di Jin, Xiao Wang, Jing Wang, Dongxiao He","doi":"10.1109/ICTAI.2018.00140","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00140","url":null,"abstract":"Network embedding, aiming at learning the low-dimensional representations of nodes in a network, is a key to many network analysis tasks. All the current network embedding methods primarily explore the network topology or node attributes, while no effort has been made to analyze the edge content for network embedding. The edge content, such as the email content between two users in an email network, is often naturally associated with edges. They carry rich information to describe the interaction between nodes, and provide valuable supervision to learn the representations of nodes. In this paper, we propose a novel edge content enhanced network embedding model, which incorporates the edge content to guide the network representation learning process. We provide the efficient updating rules to infer the parameters in the model, along with theoretical analysis on correctness and convergence guarantees. Extensive experiments, in comparison with the state-of-the-arts, show the superior performance of our proposed new approach on different network analysis tasks.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133718329","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 : 2018-11-01DOI: 10.1109/ICTAI.2018.00097
Jason Bernard, Ian McQuillan
Stochastic context-free Lindenmayer systems (S0L-systems) are a formal grammar system that produce sequences of strings based on parallel rewriting rules over a probability distribution. The resulting words can be treated as symbolic instructions to create visual models by simulation software. S0L-system have been used to model different natural and engineered processes. One issue with S0L-systems is the difficulty in determining an S0L-systems to model a process. Current approaches either infer S0L-systems based on aesthetics or rely on a priori expert knowledge. This work introduces PMIT-S0L, a tool for inferring S0L-systems from a sequence of strings generated by a (hidden) L-system, using a greedy algorithm hybridized with search algorithms. PMIT-S0L was evaluated using 3600 procedurally generated S0L-systems and is able to infer the test set with 100% success so long as there are 12 or less rewriting rules in total in the L-system. This makes PMIT-S0L applicable for many practical applications.
{"title":"Inferring Stochastic L-Systems Using a Hybrid Greedy Algorithm","authors":"Jason Bernard, Ian McQuillan","doi":"10.1109/ICTAI.2018.00097","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00097","url":null,"abstract":"Stochastic context-free Lindenmayer systems (S0L-systems) are a formal grammar system that produce sequences of strings based on parallel rewriting rules over a probability distribution. The resulting words can be treated as symbolic instructions to create visual models by simulation software. S0L-system have been used to model different natural and engineered processes. One issue with S0L-systems is the difficulty in determining an S0L-systems to model a process. Current approaches either infer S0L-systems based on aesthetics or rely on a priori expert knowledge. This work introduces PMIT-S0L, a tool for inferring S0L-systems from a sequence of strings generated by a (hidden) L-system, using a greedy algorithm hybridized with search algorithms. PMIT-S0L was evaluated using 3600 procedurally generated S0L-systems and is able to infer the test set with 100% success so long as there are 12 or less rewriting rules in total in the L-system. This makes PMIT-S0L applicable for many practical applications.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"13 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133174597","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 : 2018-11-01DOI: 10.1109/ICTAI.2018.00139
Jingjie Mo, Neng Gao, Yujing Zhou, Yang Pei, Jiong Wang
Attributed network embedding, which aims to map the structural and attribute information into a latent vector space jointly, has attracted a surge of research attention in recent years. However, a vast majority of existing work explores the correlation between node structure and attribute values whereas the attribute type information which can be potentially complementary is ignored. How to effectively model the nodes, attribute types and attribute values as well as their relations in a unified framework is an open yet challenging problem. To this end, we propose a translation-based attributed network embedding method named TransANE. In our approach, the whole attributed network is considered as a coupled network which consists of two components, i.e., node relation network and attribute correlation network. We construct attribute correlation network by the co-occurrence of attribute values. Each node-attribute relation is regarded as an attributional triple, e.g., (Tom, Gender, Male). We introduce knowledge representation method to model the mapping between nodes, attribute types and attribute values. Empirically, experiments on two real-world datasets including node multi-class classification and network visualization are conducted to evaluate the effectiveness of our method TransANE in this paper. Our method achieves significant performance compared with state-of-the-art baselines.
{"title":"Translation-Based Attributed Network Embedding","authors":"Jingjie Mo, Neng Gao, Yujing Zhou, Yang Pei, Jiong Wang","doi":"10.1109/ICTAI.2018.00139","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00139","url":null,"abstract":"Attributed network embedding, which aims to map the structural and attribute information into a latent vector space jointly, has attracted a surge of research attention in recent years. However, a vast majority of existing work explores the correlation between node structure and attribute values whereas the attribute type information which can be potentially complementary is ignored. How to effectively model the nodes, attribute types and attribute values as well as their relations in a unified framework is an open yet challenging problem. To this end, we propose a translation-based attributed network embedding method named TransANE. In our approach, the whole attributed network is considered as a coupled network which consists of two components, i.e., node relation network and attribute correlation network. We construct attribute correlation network by the co-occurrence of attribute values. Each node-attribute relation is regarded as an attributional triple, e.g., (Tom, Gender, Male). We introduce knowledge representation method to model the mapping between nodes, attribute types and attribute values. Empirically, experiments on two real-world datasets including node multi-class classification and network visualization are conducted to evaluate the effectiveness of our method TransANE in this paper. Our method achieves significant performance compared with state-of-the-art baselines.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122053780","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 : 2018-11-01DOI: 10.1109/ICTAI.2018.00160
Kostas Kolomvatsos, M. Koziri, Thanasis Loukopoulos
Current networking applications involve the definition and utilization of multiple virtualized resources on top of the available infrastructure. Software defined networks increase the performance compared with legacy systems as any functionality is managed through software. Securing the quality of service in such environments is significant to support novel applications that deliver their results in real time. In this paper, we propose a monitoring mechanism that observes the performance of the virtualized resources and identifies possible quality of service violations. Our model can be applied to any application domain, however, it is adapted to virtualized resources. We rely on a simple model that collects performance data, focuses on multiple parts of a virtualized functions chain and immediately concludes potential violations in real time. The proposed mechanism is incorporated in an SDN controller that is responsible to manage the virtualized resources. We provide an analytical description of the model and through a large set of simulations, we reveal its performance. Our results exhibit the timely identification of quality of service violations even in very dynamic environments where the performance of the network changes continuously.
{"title":"An Intelligent Scheme for the Identification of QoS Violations in Virtualized Environments","authors":"Kostas Kolomvatsos, M. Koziri, Thanasis Loukopoulos","doi":"10.1109/ICTAI.2018.00160","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00160","url":null,"abstract":"Current networking applications involve the definition and utilization of multiple virtualized resources on top of the available infrastructure. Software defined networks increase the performance compared with legacy systems as any functionality is managed through software. Securing the quality of service in such environments is significant to support novel applications that deliver their results in real time. In this paper, we propose a monitoring mechanism that observes the performance of the virtualized resources and identifies possible quality of service violations. Our model can be applied to any application domain, however, it is adapted to virtualized resources. We rely on a simple model that collects performance data, focuses on multiple parts of a virtualized functions chain and immediately concludes potential violations in real time. The proposed mechanism is incorporated in an SDN controller that is responsible to manage the virtualized resources. We provide an analytical description of the model and through a large set of simulations, we reveal its performance. Our results exhibit the timely identification of quality of service violations even in very dynamic environments where the performance of the network changes continuously.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126098241","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 : 2018-11-01DOI: 10.1109/ictai.2018.00008
{"title":"Message from the Applications of AI in Smart Cities Track Chairs","authors":"","doi":"10.1109/ictai.2018.00008","DOIUrl":"https://doi.org/10.1109/ictai.2018.00008","url":null,"abstract":"","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123600844","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 : 2018-11-01DOI: 10.1109/ICTAI.2018.00085
C. Pizzuti, Annalisa Socievole
A method to enhance the robustness of a network, based on Genetic Algorithms, is proposed. The approach optimizes the effective graph resistance of a network, a measure of robustness derived from the field of electric circuit analysis, that can be computed as a cumulative sum of the eigenvalues of the Laplacian matrix associated with the network. Specialized variation operators allow the method to find a solution almost always coinciding with that obtained by the exhaustive search. Experiments on synthetic and real life networks show that the approach outperforms heuristic strategies extensively investigated, by giving the exact solution in a high percentage of the considered networks.
{"title":"A Genetic Algorithm for Improving Robustness of Complex Networks","authors":"C. Pizzuti, Annalisa Socievole","doi":"10.1109/ICTAI.2018.00085","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00085","url":null,"abstract":"A method to enhance the robustness of a network, based on Genetic Algorithms, is proposed. The approach optimizes the effective graph resistance of a network, a measure of robustness derived from the field of electric circuit analysis, that can be computed as a cumulative sum of the eigenvalues of the Laplacian matrix associated with the network. Specialized variation operators allow the method to find a solution almost always coinciding with that obtained by the exhaustive search. Experiments on synthetic and real life networks show that the approach outperforms heuristic strategies extensively investigated, by giving the exact solution in a high percentage of the considered networks.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"128 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123640244","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 : 2018-11-01DOI: 10.1109/ICTAI.2018.00090
Mhd Mousa Hamad, M. Skowron, M. Schedl
Social media represents a valuable data source for researchers to analyze how people feel about a variety of topics, from politics to products to entertainment. This paper addresses the detection of controversies involving music artists, based on microblogs. In particular, we develop a new controversy detection dataset consisting of 53,441 tweets related to 95 music artists, and we devise and evaluate a comprehensive set of user-and content-based feature candidates to regress controversy. The evaluation results show a strong performance of the presented approach in the controversy detection task: F1 score of 0.811 in a classification task and RMSE of 0.688 in a regression task, using controversy scores in the range [1, 4]. In addition, the results obtained in applying the presented approach on a dataset from a different domain (CNN news controversy) demonstrate transferability of the developed feature set, with a significant improvement over prior approaches. A combination of the adopted Gradient Boosting based classifier and the developed feature set results in an F1 score of 0.775, which represents an improvement of 9.8% compared to the best prior result on this dataset.
{"title":"Regressing Controversy of Music Artists from Microblogs","authors":"Mhd Mousa Hamad, M. Skowron, M. Schedl","doi":"10.1109/ICTAI.2018.00090","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00090","url":null,"abstract":"Social media represents a valuable data source for researchers to analyze how people feel about a variety of topics, from politics to products to entertainment. This paper addresses the detection of controversies involving music artists, based on microblogs. In particular, we develop a new controversy detection dataset consisting of 53,441 tweets related to 95 music artists, and we devise and evaluate a comprehensive set of user-and content-based feature candidates to regress controversy. The evaluation results show a strong performance of the presented approach in the controversy detection task: F1 score of 0.811 in a classification task and RMSE of 0.688 in a regression task, using controversy scores in the range [1, 4]. In addition, the results obtained in applying the presented approach on a dataset from a different domain (CNN news controversy) demonstrate transferability of the developed feature set, with a significant improvement over prior approaches. A combination of the adopted Gradient Boosting based classifier and the developed feature set results in an F1 score of 0.775, which represents an improvement of 9.8% compared to the best prior result on this dataset.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126018910","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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