Z. Pan, B. Cheng, Jianxi Fan, Yan Wang, Xiajing Li
{"title":"A parallel algorithm to construct edge independent spanning trees on the line graphs of conditional bijective connection networks","authors":"Z. Pan, B. Cheng, Jianxi Fan, Yan Wang, Xiajing Li","doi":"10.2139/ssrn.4111842","DOIUrl":"https://doi.org/10.2139/ssrn.4111842","url":null,"abstract":"","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"39 1","pages":"33-46"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84635129","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":"Complexity and approximability of Minimum Path-Collection Exact Covers","authors":"S. V. Ravelo, Cristina G. Fernandes","doi":"10.2139/ssrn.4072697","DOIUrl":"https://doi.org/10.2139/ssrn.4072697","url":null,"abstract":"","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"58 1","pages":"21-32"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72883526","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 : 2022-11-01DOI: 10.4230/LIPIcs.ISAAC.2021.54
E. Allender, John Gouwar, Shuichi Hirahara, Caleb Robelle
A version of time-bounded Kolmogorov complexity, denoted KT , has received attention in the past several years, due to its close connection to circuit complexity and to the Minimum Circuit Size Problem MCSP . Essentially all results about the complexity of MCSP hold also for MKTP (the problem of computing the KT complexity of a string). Both MKTP and MCSP are hard for SZK (Statistical Zero Knowledge) under BPP -Turing reductions; neither is known to be NP -complete. Recently, some hardness results for MKTP were proved that are not (yet) known to hold for MCSP . In particular, MKTP is hard for DET (a subclass of P ) under nonuniform ≤ NC 0 m reductions. In this paper, we improve this, to show that MKTP is hard for the (apparently larger) class NISZK L under not only ≤ NC 0 m reductions but even under projections. Also MKTP is hard for NISZK under ≤ P / poly m reductions. Here, NISZK is the class of problems with non-interactive zero-knowledge proofs, and NISZK L is the non-interactive version of the class SZK L that was studied by Dvir et al. As an application, we provide several improved worst-case to average-case reductions to problems in NP , and we obtain a new lower bound on MKTP (which is currently not known to hold for MCSP ).
{"title":"Cryptographic Hardness under Projections for Time-Bounded Kolmogorov Complexity","authors":"E. Allender, John Gouwar, Shuichi Hirahara, Caleb Robelle","doi":"10.4230/LIPIcs.ISAAC.2021.54","DOIUrl":"https://doi.org/10.4230/LIPIcs.ISAAC.2021.54","url":null,"abstract":"A version of time-bounded Kolmogorov complexity, denoted KT , has received attention in the past several years, due to its close connection to circuit complexity and to the Minimum Circuit Size Problem MCSP . Essentially all results about the complexity of MCSP hold also for MKTP (the problem of computing the KT complexity of a string). Both MKTP and MCSP are hard for SZK (Statistical Zero Knowledge) under BPP -Turing reductions; neither is known to be NP -complete. Recently, some hardness results for MKTP were proved that are not (yet) known to hold for MCSP . In particular, MKTP is hard for DET (a subclass of P ) under nonuniform ≤ NC 0 m reductions. In this paper, we improve this, to show that MKTP is hard for the (apparently larger) class NISZK L under not only ≤ NC 0 m reductions but even under projections. Also MKTP is hard for NISZK under ≤ P / poly m reductions. Here, NISZK is the class of problems with non-interactive zero-knowledge proofs, and NISZK L is the non-interactive version of the class SZK L that was studied by Dvir et al. As an application, we provide several improved worst-case to average-case reductions to problems in NP , and we obtain a new lower bound on MKTP (which is currently not known to hold for MCSP ).","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"23 1","pages":"206-224"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87023935","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}
Agriculture exhibits the prime driving force for growth of agro-based economies globally. In the field of agriculture, detecting and preventing crops from attacks of pests is the major concern in today's world. Early detection of plant disease becomes necessary to prevent the degradation in the yield of crop production. In this paper, we propose an ensemble based Convolutional Neural Network (CNN) architecture that detects plant disease from the images of the leaves of the plant. The proposed architecture takes into account CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). The approach helped us in building a generalized model for disease detection with an accuracy of 97.9 % under test conditions.
{"title":"Plant Disease Detection using Ensembled CNN Framework","authors":"Subhash Mondal, Suharta Banerjee, Subinoy Mukherjee, Diganta Sengupta","doi":"10.7494/csci.2022.23.3.4376","DOIUrl":"https://doi.org/10.7494/csci.2022.23.3.4376","url":null,"abstract":"Agriculture exhibits the prime driving force for growth of agro-based economies globally. In the field of agriculture, detecting and preventing crops from attacks of pests is the major concern in today's world. Early detection of plant disease becomes necessary to prevent the degradation in the yield of crop production. In this paper, we propose an ensemble based Convolutional Neural Network (CNN) architecture that detects plant disease from the images of the leaves of the plant. The proposed architecture takes into account CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). The approach helped us in building a generalized model for disease detection with an accuracy of 97.9 % under test conditions.","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72577816","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 : 2022-10-02DOI: 10.7494/csci.2022.23.3.4356
Yasmina Rahmoune, A. Chaoui
Model Driven Engineering (MDE) provides available tools, concepts and languages to create and transform models. One of the most important successes of MDE is model transformation; it permits transforming models used by one community to equivalent models used by another one. Moreover, each community of developers has its own tools for verification, testing and test case generation. Hence, a developer of one community who moves to work with another community needs a transformation process from the second community to (his/her) own community and vice versa. Therefore, the target community can benefit from the expertise of the source one and the developers do not begin from zero.In this context, we propose in this paper an automatic transformation to create a bridge between the BPMN and UML communities. We propose an approach and a visual tool for the automatic transformation of BPMN models to UML Activity Diagrams (UML-AD). The proposed approach is based on Meta-Modeling and Graph Transformation, and uses the AToM3 tool. Indeed, we were inspired by the OMG meta-models of BPMN and UML-AD and implemented versions of both meta-models using AToM3. This last allows generating automatically a visual modeling tool for each proposed meta-model. Based on these two meta-models, we propose a graph grammar composed of sixty rules that perform the transformation process. The proposed approach is illustrated through three case studies.
{"title":"Automatic Bridge between BPMN Models and UML Activity Diagrams based on Graph Transformation","authors":"Yasmina Rahmoune, A. Chaoui","doi":"10.7494/csci.2022.23.3.4356","DOIUrl":"https://doi.org/10.7494/csci.2022.23.3.4356","url":null,"abstract":"Model Driven Engineering (MDE) provides available tools, concepts and languages to create and transform models. One of the most important successes of MDE is model transformation; it permits transforming models used by one community to equivalent models used by another one. Moreover, each community of developers has its own tools for verification, testing and test case generation. Hence, a developer of one community who moves to work with another community needs a transformation process from the second community to (his/her) own community and vice versa. Therefore, the target community can benefit from the expertise of the source one and the developers do not begin from zero.In this context, we propose in this paper an automatic transformation to create a bridge between the BPMN and UML communities. We propose an approach and a visual tool for the automatic transformation of BPMN models to UML Activity Diagrams (UML-AD). The proposed approach is based on Meta-Modeling and Graph Transformation, and uses the AToM3 tool. Indeed, we were inspired by the OMG meta-models of BPMN and UML-AD and implemented versions of both meta-models using AToM3. This last allows generating automatically a visual modeling tool for each proposed meta-model. Based on these two meta-models, we propose a graph grammar composed of sixty rules that perform the transformation process. The proposed approach is illustrated through three case studies.","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84048463","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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