Pub Date : 2002-11-04DOI: 10.1109/TAI.2002.1180792
Saswat Anand, W. Chin, Siau-Cheng Khoo
A divide and conquer strategy enables a problem to be divided into subproblems, which are solved independently and later combined to form solutions of the original problem. For solving constraint satisfaction problems, however, the divide and conquer technique has not been shown to be effective. This is because it is not possible to cleanly divide a problem into independent subproblems in the presence of constraints that involve variables belonging to different subproblems. Consequently, solutions of one subproblem may prune solutions of another subproblem, making those solutions of the latter subproblem redundant. In this paper we propose a divide and conquer approach to constraint solving in a lazy evaluation framework. In this framework, a subproblem is solved on demand, which eliminates redundant consistency checks. Moreover, once solved, the solutions of a subproblem can be reused in the satisfaction of various global constraints connecting this subproblem with others, thus reducing the search space. We also demonstrate the effectiveness of our algorithm in solving a practical problem: finding all instances of a user-defined pattern in stock market price charts.
{"title":"A lazy divide and conquer approach to constraint solving","authors":"Saswat Anand, W. Chin, Siau-Cheng Khoo","doi":"10.1109/TAI.2002.1180792","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180792","url":null,"abstract":"A divide and conquer strategy enables a problem to be divided into subproblems, which are solved independently and later combined to form solutions of the original problem. For solving constraint satisfaction problems, however, the divide and conquer technique has not been shown to be effective. This is because it is not possible to cleanly divide a problem into independent subproblems in the presence of constraints that involve variables belonging to different subproblems. Consequently, solutions of one subproblem may prune solutions of another subproblem, making those solutions of the latter subproblem redundant. In this paper we propose a divide and conquer approach to constraint solving in a lazy evaluation framework. In this framework, a subproblem is solved on demand, which eliminates redundant consistency checks. Moreover, once solved, the solutions of a subproblem can be reused in the satisfaction of various global constraints connecting this subproblem with others, thus reducing the search space. We also demonstrate the effectiveness of our algorithm in solving a practical problem: finding all instances of a user-defined pattern in stock market price charts.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133960200","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 : 2002-11-04DOI: 10.1109/TAI.2002.1180796
L. Khan, Feng Luo
Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while ensuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user specified keywords. But many documents convey desired semantic information without containing these keywords. One can overcome this problem by indexing documents according to meanings rather than words, although this will entail a way of converting words to meanings and the creation of ontology. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontology. Ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. We propose a new mechanism that can generate ontology automatically in order to make our approach scalable. For this we modify the existing self-organizing tree algorithm (SOTA) that constructs a hierarchy from top to bottom. Furthermore, in order to find an appropriate concept for each node in the hierarchy we propose an automatic concept selection algorithm from WordNet called linguistic ontology. To illustrate the effectiveness of our automatic ontology construction method, we have explored our ontology construction in text documents. The Reuters21578 text document corpus has been used. We have observed that our modified SOTA outperforms hierarchical agglomerative clustering (HAC).
{"title":"Ontology construction for information selection","authors":"L. Khan, Feng Luo","doi":"10.1109/TAI.2002.1180796","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180796","url":null,"abstract":"Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while ensuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user specified keywords. But many documents convey desired semantic information without containing these keywords. One can overcome this problem by indexing documents according to meanings rather than words, although this will entail a way of converting words to meanings and the creation of ontology. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontology. Ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. We propose a new mechanism that can generate ontology automatically in order to make our approach scalable. For this we modify the existing self-organizing tree algorithm (SOTA) that constructs a hierarchy from top to bottom. Furthermore, in order to find an appropriate concept for each node in the hierarchy we propose an automatic concept selection algorithm from WordNet called linguistic ontology. To illustrate the effectiveness of our automatic ontology construction method, we have explored our ontology construction in text documents. The Reuters21578 text document corpus has been used. We have observed that our modified SOTA outperforms hierarchical agglomerative clustering (HAC).","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115398340","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 : 2002-11-04DOI: 10.1109/TAI.2002.1180807
P. Lucic, D. Teodorovic
Artificial life (ALife) uses biological knowledge and techniques to help solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. The main goal of this paper is to show how we can use ALife concepts (inspired by some principles of natural swarm intelligence) when solving complex problems in traffic and transportation. The bee system that represents the new approach in the field of swarm intelligence is described. It is also shown in the paper that ALife approach can be successful to "attack" transportation problems characterized by uncertainty. The fuzzy ant system (FAS) described in the paper represents an attempt to handle the uncertainty that sometimes exists in some complex transportation problems. The potential applications of the bee system and the fuzzy ant system in the field of traffic and transportation engineering are discussed.
{"title":"Transportation modeling: an artificial life approach","authors":"P. Lucic, D. Teodorovic","doi":"10.1109/TAI.2002.1180807","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180807","url":null,"abstract":"Artificial life (ALife) uses biological knowledge and techniques to help solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. The main goal of this paper is to show how we can use ALife concepts (inspired by some principles of natural swarm intelligence) when solving complex problems in traffic and transportation. The bee system that represents the new approach in the field of swarm intelligence is described. It is also shown in the paper that ALife approach can be successful to \"attack\" transportation problems characterized by uncertainty. The fuzzy ant system (FAS) described in the paper represents an attempt to handle the uncertainty that sometimes exists in some complex transportation problems. The potential applications of the bee system and the fuzzy ant system in the field of traffic and transportation engineering are discussed.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124727717","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 : 2002-11-04DOI: 10.1109/TAI.2002.1180785
Xidong Jin, R. Reynolds
In the paper we demonstrate how evolutionary search for functional optima can be used as a vehicle for data mining, that is, in the process of searching for optima in a multi-dimensional space we can keep track of the constraints that must be placed on related variables in order to move towards the optima. Thus, a side effect of evolutionary search can be the mining of constraints for related variables. We use a cultural algorithm framework to embed the search and store the results in regional schemata. An application to a large-scale real world archaeological data set is presented.
{"title":"Data mining using cultural algorithms and regional schemata","authors":"Xidong Jin, R. Reynolds","doi":"10.1109/TAI.2002.1180785","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180785","url":null,"abstract":"In the paper we demonstrate how evolutionary search for functional optima can be used as a vehicle for data mining, that is, in the process of searching for optima in a multi-dimensional space we can keep track of the constraints that must be placed on related variables in order to move towards the optima. Thus, a side effect of evolutionary search can be the mining of constraints for related variables. We use a cultural algorithm framework to embed the search and store the results in regional schemata. An application to a large-scale real world archaeological data set is presented.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125411406","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 : 2002-11-04DOI: 10.1109/TAI.2002.1180797
I. Yen, F. Bastani, F. Mohamed, Hui Ma, J. Linn
Rapid growth in the demand for embedded systems and the increased complexity of embedded software pose an urgent need for advanced embedded software development techniques. One attractive approach is to enable semi-automated code generation and integration of systems from components. However, the implementation and validation of these systems requires a steep learning curve due to the large number, variety, and complexity of software components. In this paper, we discuss the potential application of AI planning techniques in assisting with the synthesis of glue code for assembling a system from existing components as well as automated testing of the system. The approach works by transforming component specifications into rules that operate on a domain-specific state space. Each rule captures the semantics of a method in a class. The code assembly and testing requirements are described by identifying conditions (goals) that should be achieved. An automated planning system computes a sequence of rules and their instantiations that will achieve the goal state. This sequence is then used to synthesize the code or to generate test cases. The approach is illustrated using an example.
{"title":"Application of AI planning techniques to automated code synthesis and testing","authors":"I. Yen, F. Bastani, F. Mohamed, Hui Ma, J. Linn","doi":"10.1109/TAI.2002.1180797","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180797","url":null,"abstract":"Rapid growth in the demand for embedded systems and the increased complexity of embedded software pose an urgent need for advanced embedded software development techniques. One attractive approach is to enable semi-automated code generation and integration of systems from components. However, the implementation and validation of these systems requires a steep learning curve due to the large number, variety, and complexity of software components. In this paper, we discuss the potential application of AI planning techniques in assisting with the synthesis of glue code for assembling a system from existing components as well as automated testing of the system. The approach works by transforming component specifications into rules that operate on a domain-specific state space. Each rule captures the semantics of a method in a class. The code assembly and testing requirements are described by identifying conditions (goals) that should be achieved. An automated planning system computes a sequence of rules and their instantiations that will achieve the goal state. This sequence is then used to synthesize the code or to generate test cases. The approach is illustrated using an example.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116185146","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 : 2002-11-04DOI: 10.1109/TAI.2002.1180850
C. Ramamoorthy
Complex systems, services and enterprises are very dependent on computer-communication technology and in particular to their supporting and controlling software systems. This paper describes aspects of software system quality from different points of view. It provides a historical overview of the evolution of software quality and the means and methods used to achieve and enhance it. The traditional software quality models trace their origins to the well-established manufacturing industries. Since quality of a product consists of many attributes, we identify the commonly accepted established entities that conform to our vision of good quality. Notions and meanings of quality vary across disciplines and application domains.
{"title":"Evolution and evaluation of software quality models","authors":"C. Ramamoorthy","doi":"10.1109/TAI.2002.1180850","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180850","url":null,"abstract":"Complex systems, services and enterprises are very dependent on computer-communication technology and in particular to their supporting and controlling software systems. This paper describes aspects of software system quality from different points of view. It provides a historical overview of the evolution of software quality and the means and methods used to achieve and enhance it. The traditional software quality models trace their origins to the well-established manufacturing industries. Since quality of a product consists of many attributes, we identify the commonly accepted established entities that conform to our vision of good quality. Notions and meanings of quality vary across disciplines and application domains.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196777","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 : 2002-11-04DOI: 10.1109/TAI.2002.1180848
Helmut Meisel, E. Compatangelo
We propose a system which supports knowledge sharing through the articulation of the overlapping components in two or more schemas or ontologies. EER-CONCEPTOOL uses description logics (DLs) to formalise and capture some of the relevant features of knowledge described using an enhanced entity-relationship (EER) model. We describe how DL-based reasoning can provide a relevant part of the semi-automated deductive support needed to specify the articulation (i.e. the shared content) of two EER knowledge bases. We also show how a more effective level of support can be provided by the EER-CONCEPTOOL architecture, which combines DL-based deductions with lexical analysis and heuristic inferences. We illustrate the approach to knowledge articulation in our system by way of an example.
{"title":"EER-CONCEPTOOL: a \"reasonable\" environment for schema and ontology sharing","authors":"Helmut Meisel, E. Compatangelo","doi":"10.1109/TAI.2002.1180848","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180848","url":null,"abstract":"We propose a system which supports knowledge sharing through the articulation of the overlapping components in two or more schemas or ontologies. EER-CONCEPTOOL uses description logics (DLs) to formalise and capture some of the relevant features of knowledge described using an enhanced entity-relationship (EER) model. We describe how DL-based reasoning can provide a relevant part of the semi-automated deductive support needed to specify the articulation (i.e. the shared content) of two EER knowledge bases. We also show how a more effective level of support can be provided by the EER-CONCEPTOOL architecture, which combines DL-based deductions with lexical analysis and heuristic inferences. We illustrate the approach to knowledge articulation in our system by way of an example.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127745194","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 : 2002-11-04DOI: 10.1109/TAI.2002.1180810
Panagiotis D. Alevizos, B. Boutsinas, D. Tasoulis, M. Vrahatis
Clustering, that is the partitioning of a set of patterns into disjoint and homogeneous meaningful groups (clusters), is a fundamental process in the practice of science. k-windows is an efficient clustering algorithm that reduces the number of patterns that need to be examined for similarity. using a windowing technique. It exploits well known spatial data structures, namely the range free, that allows fast range searches. From a theoretical standpoint, the k-windows algorithm is characterized by lower time complexity compared to other well-known clustering algorithms. Moreover it achieves high quality clustering results. However, it appears that it cannot be directly applicable in high-dimensional settings due to the superlinear space requirements for the range tree. In this paper an improvement of the k-windows algorithm, aiming at resolving this deficiency, is presented. The improvement is based on an alternative solution to the orthogonal range search problem.
{"title":"Improving the orthogonal range search k-windows algorithm","authors":"Panagiotis D. Alevizos, B. Boutsinas, D. Tasoulis, M. Vrahatis","doi":"10.1109/TAI.2002.1180810","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180810","url":null,"abstract":"Clustering, that is the partitioning of a set of patterns into disjoint and homogeneous meaningful groups (clusters), is a fundamental process in the practice of science. k-windows is an efficient clustering algorithm that reduces the number of patterns that need to be examined for similarity. using a windowing technique. It exploits well known spatial data structures, namely the range free, that allows fast range searches. From a theoretical standpoint, the k-windows algorithm is characterized by lower time complexity compared to other well-known clustering algorithms. Moreover it achieves high quality clustering results. However, it appears that it cannot be directly applicable in high-dimensional settings due to the superlinear space requirements for the range tree. In this paper an improvement of the k-windows algorithm, aiming at resolving this deficiency, is presented. The improvement is based on an alternative solution to the orthogonal range search problem.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130965869","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 : 2002-11-04DOI: 10.1109/TAI.2002.1180817
S. V. Delden, F. Gomez
A method has been developed and implemented that assigns syntactic roles to commas. Text that has been tagged using a part-of-speech tagger serves as the input to the system. A set of Finite State Automata first assigns temporary syntactic roles to each comma in the sentence. A greedy learning algorithm is then used to determine the final syntactic roles of the commas. The system requires no training and is not domain specific. The performance of the system on numerous corpora is given.
{"title":"Combining finite state automata and a greedy learning algorithm to determine the syntactic roles of commas","authors":"S. V. Delden, F. Gomez","doi":"10.1109/TAI.2002.1180817","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180817","url":null,"abstract":"A method has been developed and implemented that assigns syntactic roles to commas. Text that has been tagged using a part-of-speech tagger serves as the input to the system. A set of Finite State Automata first assigns temporary syntactic roles to each comma in the sentence. A greedy learning algorithm is then used to determine the final syntactic roles of the commas. The system requires no training and is not domain specific. The performance of the system on numerous corpora is given.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134225596","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 : 2002-11-04DOI: 10.1109/TAI.2002.1180839
B. Scherrer, F. Charpillet
Solving multiagent reinforcement learning problems is a key issue. Indeed, the complexity of deriving multiagent plans, especially when one uses an explicit model of the problem, is dramatically increasing with the number of agents. This papers introduces a general iterative heuristic: at each step one chooses a sub-group of agents and update their policies to optimize the task given the rest of agents have fixed plans. We analyse this process in a general purpose and show how it can be applied to Markov decision processes, partially observable Markov decision processes and decentralized partially observable Markov decision processes.
{"title":"Cooperative co-learning: a model-based approach for solving multi-agent reinforcement problems","authors":"B. Scherrer, F. Charpillet","doi":"10.1109/TAI.2002.1180839","DOIUrl":"https://doi.org/10.1109/TAI.2002.1180839","url":null,"abstract":"Solving multiagent reinforcement learning problems is a key issue. Indeed, the complexity of deriving multiagent plans, especially when one uses an explicit model of the problem, is dramatically increasing with the number of agents. This papers introduces a general iterative heuristic: at each step one chooses a sub-group of agents and update their policies to optimize the task given the rest of agents have fixed plans. We analyse this process in a general purpose and show how it can be applied to Markov decision processes, partially observable Markov decision processes and decentralized partially observable Markov decision processes.","PeriodicalId":197064,"journal":{"name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133861996","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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