D. Dasgupta, Fernando Niño, D. Garrett, Koyel Chaudhuri, Soujanya Medapati, Aishwarya Kaushal, James Simien
This paper investigates a multiobjective formulation of the United States Navy's Task based Sailor Assignment Problem and examines the performance of a multiobjective evolutionary algorithm (MOEA), called NSGA-II, on large instances of this problem. Our previous work [3, 5, 4], consider the sailor assignment problem (SAP) as a static assignment, while the present work assumes it as a time dependent multitask SAP, making it a more complex problem, in fact, an NP-complete problem. Experimental results show that the presented genetic-based solution is appropriate for this problem.
{"title":"A multiobjective evolutionary algorithm for the task based sailor assignment problem","authors":"D. Dasgupta, Fernando Niño, D. Garrett, Koyel Chaudhuri, Soujanya Medapati, Aishwarya Kaushal, James Simien","doi":"10.1145/1569901.1570099","DOIUrl":"https://doi.org/10.1145/1569901.1570099","url":null,"abstract":"This paper investigates a multiobjective formulation of the United States Navy's Task based Sailor Assignment Problem and examines the performance of a multiobjective evolutionary algorithm (MOEA), called NSGA-II, on large instances of this problem. Our previous work [3, 5, 4], consider the sailor assignment problem (SAP) as a static assignment, while the present work assumes it as a time dependent multitask SAP, making it a more complex problem, in fact, an NP-complete problem. Experimental results show that the presented genetic-based solution is appropriate for this problem.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129012411","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}
Datasets with a large number of attributes are a difficult challenge for evolutionary learning techniques. The recently proposed attribute list rule representation has shown to be able to significantly improve the overall performance (e.g. run-time, accuracy, rule set size) of the BioHEL Iterative Evolutionary Rule Learning system. In this paper we, first, extend the attribute list rule representation so it can handle not only continuous domains, but also datasets with a very large number of mixed discrete-continuous attributes. Secondly, we benchmark the new representation with a diverse set of large-scale datasets and, third, we compare the new algorithms with several well-known machine learning methods. The experimental results we describe in the paper show that the new representation is equal or better than the state of-the-art in evolutionary rule representations both in terms of the accuracy obtained with the benchmark datasets used, as well as in terms of the computational time requirements needed to achieve these improved accuracies. The new attribute list representation puts BioHEL on an equal footing with other well-established machine learning techniques in terms of accuracy. In the paper, we also analyse and discuss the current weaknesses behind the current representation and indicate potential avenues for correcting them.
{"title":"A mixed discrete-continuous attribute list representation for large scale classification domains","authors":"J. Bacardit, N. Krasnogor","doi":"10.1145/1569901.1570057","DOIUrl":"https://doi.org/10.1145/1569901.1570057","url":null,"abstract":"Datasets with a large number of attributes are a difficult challenge for evolutionary learning techniques. The recently proposed attribute list rule representation has shown to be able to significantly improve the overall performance (e.g. run-time, accuracy, rule set size) of the BioHEL Iterative Evolutionary Rule Learning system. In this paper we, first, extend the attribute list rule representation so it can handle not only continuous domains, but also datasets with a very large number of mixed discrete-continuous attributes. Secondly, we benchmark the new representation with a diverse set of large-scale datasets and, third, we compare the new algorithms with several well-known machine learning methods. The experimental results we describe in the paper show that the new representation is equal or better than the state of-the-art in evolutionary rule representations both in terms of the accuracy obtained with the benchmark datasets used, as well as in terms of the computational time requirements needed to achieve these improved accuracies. The new attribute list representation puts BioHEL on an equal footing with other well-established machine learning techniques in terms of accuracy. In the paper, we also analyse and discuss the current weaknesses behind the current representation and indicate potential avenues for correcting them.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128455506","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 the recent past, hybrid metaheuristics became famous as successful optimization methods. The motivation for the hybridization is a notion of combining the best of two worlds: evolutionary black box optimization and local search. Successful hybridizations in large combinatorial solution spaces motivate to transfer the idea of combining the two worlds to continuous domains as well. The question arises: Can local search also improve the convergence to the Pareto front in continuous multiobjective solutions spaces? We introduce a relay and a concurrent hybridization of the successful multiobjective optimizer SMS-EMOA and local optimization methods like Hooke & Jeeves and the Newton method. The concurrent approach is based on a parameterized probability function to control the local search. Experimental analyses on academic test functions show increased convergence speed as well as improved accuracy of the solution set of the new hybridizations.
{"title":"On the hybridization of SMS-EMOA and local search for continuous multiobjective optimization","authors":"P. Koch, Oliver Kramer, G. Rudolph, N. Beume","doi":"10.1145/1569901.1569985","DOIUrl":"https://doi.org/10.1145/1569901.1569985","url":null,"abstract":"In the recent past, hybrid metaheuristics became famous as successful optimization methods. The motivation for the hybridization is a notion of combining the best of two worlds: evolutionary black box optimization and local search. Successful hybridizations in large combinatorial solution spaces motivate to transfer the idea of combining the two worlds to continuous domains as well. The question arises: Can local search also improve the convergence to the Pareto front in continuous multiobjective solutions spaces? We introduce a relay and a concurrent hybridization of the successful multiobjective optimizer SMS-EMOA and local optimization methods like Hooke & Jeeves and the Newton method. The concurrent approach is based on a parameterized probability function to control the local search. Experimental analyses on academic test functions show increased convergence speed as well as improved accuracy of the solution set of the new hybridizations.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128556721","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}
Image segmentation is an essential process in many image analysis applications and is mainly used for automatic object recognition purposes. In this paper, we define a new genetic programming based image segmentation algorithm (GPIS). It uses a primitive image-operator based approach to produce linear sequences of MATLAB® code for image segmentation. We describe the evolutionary architecture of the approach and present results obtained after testing the algorithm on a biomedical image database for cell segmentation. We also compare our results with another EC-based image segmentation tool called GENIE Pro. We found the results obtained using GPIS were more accurate as compared to GENIE Pro. In addition, our approach is simpler to apply and evolved programs are available to anyone with access to MATLAB®.
{"title":"Genetic programming based image segmentation with applications to biomedical object detection","authors":"T. Singh, N. Kharma, M. Daoud, R. Ward","doi":"10.1145/1569901.1570052","DOIUrl":"https://doi.org/10.1145/1569901.1570052","url":null,"abstract":"Image segmentation is an essential process in many image analysis applications and is mainly used for automatic object recognition purposes. In this paper, we define a new genetic programming based image segmentation algorithm (GPIS). It uses a primitive image-operator based approach to produce linear sequences of MATLAB® code for image segmentation. We describe the evolutionary architecture of the approach and present results obtained after testing the algorithm on a biomedical image database for cell segmentation. We also compare our results with another EC-based image segmentation tool called GENIE Pro. We found the results obtained using GPIS were more accurate as compared to GENIE Pro. In addition, our approach is simpler to apply and evolved programs are available to anyone with access to MATLAB®.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115456419","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}
RNAnet [3] http://bioinformatics.essex.ac.uk/users/wlangdon/rnanet/ allows the user to calculate correlations of gene expression, both between genes and between components within genes. We investigate all of Ensembl http://www.ensembl.org and find all the Homo Sapiens exons for which there are sufficient robust Affymetrix HG-U133 Plus 2 GeneChip probes. Calculating correlation between mRNA probe measurements for the same exon shows many exons whose components are consistently up regulated and down regulated. However we identify other Ensembl exons where sub-regions within them are self consistent but these transcript blocks are not well correlated with other blocks in the same exon. We suggest many current Ensembl exon definitions are incomplete. Secondly, having identified exon with substructure we use machine learning to try and identify patterns in the DNA sequence lying between blocks of high correlation which might yield biological or technological explanations. A Backus-Naur form (BNF) context-free grammar constrains strongly typed genetic programming (STGP) to evolve biological motifs in the form of regular expressions (RE) (e.g. TCTTT) which classify gene exons with potential alternative mRNA expression from those without. We show biological patterns can be data mined by a GP written in gawk and using egrep from NCBI's GEO http://www.ncbi.nlm.nih.gov/geo/ database. The automatically produced DNA motifs suggest that alternative polyadenylation is not responsible. (Full version in TR-09-02 [7].) Blocky exons can be found in http://bioinformatics.essex.ac.uk/users/wlangdon/tr-09-02.tar.gz
RNAnet [3] http://bioinformatics.essex.ac.uk/users/wlangdon/rnanet/允许用户计算基因之间以及基因内组件之间的基因表达相关性。我们研究了所有的Ensembl http://www.ensembl.org，并找到了所有具有足够健壮的Affymetrix gg - u133 Plus 2基因芯片探针的智人外显子。计算相同外显子的mRNA探针测量之间的相关性显示，许多外显子的成分一致上调和下调。然而，我们发现了其他的Ensembl外显子，其中的子区域是自一致的，但这些转录片段与同一外显子中的其他片段没有很好的相关性。我们认为许多当前的Ensembl外显子定义是不完整的。其次，在确定了外显子和亚结构之后，我们使用机器学习来尝试识别DNA序列中的模式，这些模式位于可能产生生物学或技术解释的高相关性块之间。Backus-Naur形式(BNF)上下文无关语法限制强类型遗传编程(STGP)以正则表达式(RE)的形式进化生物基序(例如TCTTT)，将具有潜在替代mRNA表达的基因外显子与没有的基因外显子进行分类。我们展示了生物模式可以通过用gawk编写的GP和使用NCBI的GEO http://www.ncbi.nlm.nih.gov/geo/数据库中的egrep来挖掘数据。自动产生的DNA基序表明，选择性聚腺苷酸化不是原因。(完整版本见TR-09-02[7]。)块状外显子可以在http://bioinformatics.essex.ac.uk/users/wlangdon/tr-09-02.tar.gz上找到
{"title":"Creating regular expressions as mRNA motifs with GP to predict human exon splitting","authors":"W. Langdon, Joanna Rowsell, A. Harrison","doi":"10.1145/1569901.1570162","DOIUrl":"https://doi.org/10.1145/1569901.1570162","url":null,"abstract":"RNAnet [3] http://bioinformatics.essex.ac.uk/users/wlangdon/rnanet/ allows the user to calculate correlations of gene expression, both between genes and between components within genes. We investigate all of Ensembl http://www.ensembl.org and find all the Homo Sapiens exons for which there are sufficient robust Affymetrix HG-U133 Plus 2 GeneChip probes. Calculating correlation between mRNA probe measurements for the same exon shows many exons whose components are consistently up regulated and down regulated. However we identify other Ensembl exons where sub-regions within them are self consistent but these transcript blocks are not well correlated with other blocks in the same exon. We suggest many current Ensembl exon definitions are incomplete. Secondly, having identified exon with substructure we use machine learning to try and identify patterns in the DNA sequence lying between blocks of high correlation which might yield biological or technological explanations. A Backus-Naur form (BNF) context-free grammar constrains strongly typed genetic programming (STGP) to evolve biological motifs in the form of regular expressions (RE) (e.g. TCTTT) which classify gene exons with potential alternative mRNA expression from those without. We show biological patterns can be data mined by a GP written in gawk and using egrep from NCBI's GEO http://www.ncbi.nlm.nih.gov/geo/ database. The automatically produced DNA motifs suggest that alternative polyadenylation is not responsible. (Full version in TR-09-02 [7].) Blocky exons can be found in http://bioinformatics.essex.ac.uk/users/wlangdon/tr-09-02.tar.gz","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115672671","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, we investigate the value of a new selection mechanism that is inspired from biochemistry, namely, vesicular selection. We test its effectiveness when used in evolutionary algorithms on a number of benchmark problems in both static and dynamic environments. The results suggest beneficial attributes for the new selection mechanism.
{"title":"The effect of vesicular selection in dynamic environments","authors":"Yun-Geun Lee, R. McKay, N. X. Hoai, Dong-Kyun Kim","doi":"10.1145/1569901.1570207","DOIUrl":"https://doi.org/10.1145/1569901.1570207","url":null,"abstract":"In this paper, we investigate the value of a new selection mechanism that is inspired from biochemistry, namely, vesicular selection. We test its effectiveness when used in evolutionary algorithms on a number of benchmark problems in both static and dynamic environments. The results suggest beneficial attributes for the new selection mechanism.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114335802","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 studies the multi-objectivization of single-objective optimization problems (SOOP) using evolutionary multi-objective algorithms (EMOAs). In contrast to the single-objective case, diversity can be introduced by the multi-objective view of the algorithm and the dynamic use of objectives. Using the travelling salesman problem as an example we illustrate that two basic approaches, a) the addition of new objectives to the existing problem and b) the decomposition of the primary objective into sub-objectives, can improve performance compared to a single-objective genetic algorithm when objectives are used dynamically. Based on decomposition we propose the concept "Multi-Objectivization via Segmentation" (MOS), at which the original problem is reassembled. Experiments reveal that this new strategy clearly outperforms both the traditional genetic algorithm (GA) and the algorithms based on existing multiobjective approaches even without changing objectives.
{"title":"Evolutionary algorithms and multi-objectivization for the travelling salesman problem","authors":"Martin Jähne, Xiaodong Li, J. Branke","doi":"10.1145/1569901.1569984","DOIUrl":"https://doi.org/10.1145/1569901.1569984","url":null,"abstract":"This paper studies the multi-objectivization of single-objective optimization problems (SOOP) using evolutionary multi-objective algorithms (EMOAs). In contrast to the single-objective case, diversity can be introduced by the multi-objective view of the algorithm and the dynamic use of objectives. Using the travelling salesman problem as an example we illustrate that two basic approaches, a) the addition of new objectives to the existing problem and b) the decomposition of the primary objective into sub-objectives, can improve performance compared to a single-objective genetic algorithm when objectives are used dynamically. Based on decomposition we propose the concept \"Multi-Objectivization via Segmentation\" (MOS), at which the original problem is reassembled. Experiments reveal that this new strategy clearly outperforms both the traditional genetic algorithm (GA) and the algorithms based on existing multiobjective approaches even without changing objectives.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114854885","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}
{"title":"Session details: Track 7: evolutionary multiobjective optimization","authors":"D. Corne, Joshua D. Knowles","doi":"10.1145/3257486","DOIUrl":"https://doi.org/10.1145/3257486","url":null,"abstract":"","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127452498","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 proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm's efficiency considerably, while introducing a negligible computational overhead.
{"title":"An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing","authors":"J. Ribeiro, M. Z. Rela, F. F. Vega","doi":"10.1145/1569901.1570253","DOIUrl":"https://doi.org/10.1145/1569901.1570253","url":null,"abstract":"This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm's efficiency considerably, while introducing a negligible computational overhead.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125576384","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 major contribution of this paper is a hybrid GA-PSO fuzzy user identification system, UGuard, for smart phones. Our system gets 3 phone usage features as input to identify a user or an imposter. We show that these phone usage features for different users are diffused; therefore, we justify the need of a front end fuzzy classifier for them. We further show that the fuzzy classifier must be optimized using a back end online dynamic optimizer. The dynamic optimizer is a hybrid of Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA). We have collected phone usage data of 10 real users having Symbian smart phones for 8 days. We evaluate our UGuard system on this dataset. The results of our experiments show that UGuard provides on the average an error rate of 2% or less. We also compared our system with four classical classifiers -- Na¨1ve Bayes, Back Propagation Neural Networks, J48 Decision Tree, and Fuzzy System -- and three evolutionary schemes -- fuzzy system optimized by ACO, PSO, and GA. To the best of our knowledge, the current work is the first system that has achieved such a small error rate. Moreover, the system is simple and efficient; therefore, it can be deployed on real world smart phones.
{"title":"A hybrid GA-PSO fuzzy system for user identification on smart phones","authors":"Muhammad Shahzad, Saira Zahid, M. Farooq","doi":"10.1145/1569901.1570117","DOIUrl":"https://doi.org/10.1145/1569901.1570117","url":null,"abstract":"The major contribution of this paper is a hybrid GA-PSO fuzzy user identification system, UGuard, for smart phones. Our system gets 3 phone usage features as input to identify a user or an imposter. We show that these phone usage features for different users are diffused; therefore, we justify the need of a front end fuzzy classifier for them. We further show that the fuzzy classifier must be optimized using a back end online dynamic optimizer. The dynamic optimizer is a hybrid of Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA). We have collected phone usage data of 10 real users having Symbian smart phones for 8 days. We evaluate our UGuard system on this dataset. The results of our experiments show that UGuard provides on the average an error rate of 2% or less. We also compared our system with four classical classifiers -- Na¨1ve Bayes, Back Propagation Neural Networks, J48 Decision Tree, and Fuzzy System -- and three evolutionary schemes -- fuzzy system optimized by ACO, PSO, and GA. To the best of our knowledge, the current work is the first system that has achieved such a small error rate. Moreover, the system is simple and efficient; therefore, it can be deployed on real world smart phones.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"435 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106015","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：