Pub Date : 2020-06-27DOI: 10.1016/j.jocs.2020.101179
Praveen Kumar Kolluru, M. Atif, S. Ansumali
{"title":"Extended BGK model for diatomic gases","authors":"Praveen Kumar Kolluru, M. Atif, S. Ansumali","doi":"10.1016/j.jocs.2020.101179","DOIUrl":"https://doi.org/10.1016/j.jocs.2020.101179","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"70 5 1","pages":"101179"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87711519","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 : 2020-06-26DOI: 10.1016/j.jocs.2020.101182
J. A. R. Barraza, R. Deiterding
{"title":"Towards a generalised lattice Boltzmann method for aerodynamic simulations","authors":"J. A. R. Barraza, R. Deiterding","doi":"10.1016/j.jocs.2020.101182","DOIUrl":"https://doi.org/10.1016/j.jocs.2020.101182","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"45 1","pages":"101182"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84306701","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 : 2020-06-26DOI: 10.1016/j.jocs.2020.101178
F. Milan, F. Milan, Luca Biferale, M. Sbragaglia, F. Toschi, F. Toschi
{"title":"Sub-Kolmogorov droplet dynamics in isotropic turbulence using a multiscale lattice Boltzmann scheme","authors":"F. Milan, F. Milan, Luca Biferale, M. Sbragaglia, F. Toschi, F. Toschi","doi":"10.1016/j.jocs.2020.101178","DOIUrl":"https://doi.org/10.1016/j.jocs.2020.101178","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"28 1","pages":"101178"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76642958","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 : 2020-06-26DOI: 10.1016/j.jocs.2020.101174
K. Fujita, Masashi Horikoshi, T. Ichimura, L. Meadows, K. Nakajima, M. Hori, Lalith Maddegedara
{"title":"Development of element-by-element kernel algorithms in unstructured finite-element solvers for many-core wide-SIMD CPUs: Application to earthquake simulation","authors":"K. Fujita, Masashi Horikoshi, T. Ichimura, L. Meadows, K. Nakajima, M. Hori, Lalith Maddegedara","doi":"10.1016/j.jocs.2020.101174","DOIUrl":"https://doi.org/10.1016/j.jocs.2020.101174","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"135 1","pages":"101174"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79624237","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 : 2020-04-02DOI: 10.1016/j.jocs.2022.101562
Anastasia A. Funkner, A. Yakovlev, S. Kovalchuk
{"title":"Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: Application to evolutionary modeling of clinical pathways","authors":"Anastasia A. Funkner, A. Yakovlev, S. Kovalchuk","doi":"10.1016/j.jocs.2022.101562","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101562","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"19 1","pages":"101562"},"PeriodicalIF":0.0,"publicationDate":"2020-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78466590","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 : 2020-04-01DOI: 10.1016/j.jocs.2020.101160
A. Montessori, A. Tiribocchi, M. Lauricella, S. Succi
{"title":"A coupled lattice Boltzmann-Multiparticle collision method for multi-resolution hydrodynamics","authors":"A. Montessori, A. Tiribocchi, M. Lauricella, S. Succi","doi":"10.1016/j.jocs.2020.101160","DOIUrl":"https://doi.org/10.1016/j.jocs.2020.101160","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"32 1","pages":"101160"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89887560","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 : 2020-01-01DOI: 10.17706/jcp.15.3.98-105
Laishui Lv, Kun Zhang
Numerous centrality measures have been established to identify the important nodes in static networks, among them, HITS centrality is widely used as a ranking method. In this paper, we extend the classical HITS centrality to rank nodes in multilayer temporal networks with directed edges. First, we use a sixth-order tensor to represent multilayer temporal network and then introduce random walks in the established sixth-order tensor by constructing six transition probability tensors. Second, we establish tensor equations based on these constructed tensors to obtain six centrality vectors: two for the nodes, two for the layers and two for the time stamps. Besides, we prove the existence of the proposed centrality measure under some conditions. Finally, we experimentally show the effectiveness of the proposed centrality on an synthetic network and a real-world network.
{"title":"Multi-Dimensional HITS Based on Random Walks for Multilayer Temporal Networks","authors":"Laishui Lv, Kun Zhang","doi":"10.17706/jcp.15.3.98-105","DOIUrl":"https://doi.org/10.17706/jcp.15.3.98-105","url":null,"abstract":"Numerous centrality measures have been established to identify the important nodes in static networks, among them, HITS centrality is widely used as a ranking method. In this paper, we extend the classical HITS centrality to rank nodes in multilayer temporal networks with directed edges. First, we use a sixth-order tensor to represent multilayer temporal network and then introduce random walks in the established sixth-order tensor by constructing six transition probability tensors. Second, we establish tensor equations based on these constructed tensors to obtain six centrality vectors: two for the nodes, two for the layers and two for the time stamps. Besides, we prove the existence of the proposed centrality measure under some conditions. Finally, we experimentally show the effectiveness of the proposed centrality on an synthetic network and a real-world network.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"94 1","pages":"98-105"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74933359","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}
Intan and Mukaidono discussed that knowledge plays an important role in determining the membership function of a given fuzzy set by introducing a concept, called Knowledge-based Fuzzy Sets (KFS) in 2002. Here, the membership degree of an element given a fuzzy set is subjectively determined by the knowledge. Every knowledge may have each different membership degree of the element given the fuzzy set. In 1988, Wang et al. extended the concept of fuzzy set, called Dynamic Fuzzy Sets (DFS) by considering that the membership degree of an element given a fuzzy set might be dynamically changeable over the time. Both generalized concepts, KFS and DFS, were hybridized by Intan et al. to be a Knowledge-based Dynamic Fuzzy Set (KDFS). As usually happened in the real-world application, the KDFS showed that a membership function of a given fuzzy set subjectively determined by a certain knowledge may be dynamically changeable over time. Moreover, the concept of fuzzy granularity was discussed dealing with the KDFS. Related to the concept of fuzzy granularity in KDFS, this paper discusses the concept of approximate reasoning of KDFS in representing fuzzy production rules as generally applied in the fuzzy expert system.
{"title":"Approximate Reasoning in the Knowledge-Based Dynamic Fuzzy Sets","authors":"R. Intan, S. Halim, L. P. Dewi","doi":"10.17706/jcp.15.2.59-72","DOIUrl":"https://doi.org/10.17706/jcp.15.2.59-72","url":null,"abstract":"Intan and Mukaidono discussed that knowledge plays an important role in determining the membership function of a given fuzzy set by introducing a concept, called Knowledge-based Fuzzy Sets (KFS) in 2002. Here, the membership degree of an element given a fuzzy set is subjectively determined by the knowledge. Every knowledge may have each different membership degree of the element given the fuzzy set. In 1988, Wang et al. extended the concept of fuzzy set, called Dynamic Fuzzy Sets (DFS) by considering that the membership degree of an element given a fuzzy set might be dynamically changeable over the time. Both generalized concepts, KFS and DFS, were hybridized by Intan et al. to be a Knowledge-based Dynamic Fuzzy Set (KDFS). As usually happened in the real-world application, the KDFS showed that a membership function of a given fuzzy set subjectively determined by a certain knowledge may be dynamically changeable over time. Moreover, the concept of fuzzy granularity was discussed dealing with the KDFS. Related to the concept of fuzzy granularity in KDFS, this paper discusses the concept of approximate reasoning of KDFS in representing fuzzy production rules as generally applied in the fuzzy expert system.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"58 1","pages":"59-72"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91347962","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}
Muhammad Ashad Baloch, Sajid Ali, Mubashir H. Malik, Aamir Hussain, Abdul Mustaan Madni
Deep neural networks are providing a powerful solution for remote-sensing scene image classification. However, a limited number of training samples, inter-class similarity among scene categories, and to get the benefits of multi-layer features remains a significant challenge in the remote sensing domain. Many efforts have been proposed to deal the above challenges by adapting knowledge of state-of-the-art networks such as AlexNet, GoogleNet, OverFeat, etc. However, these networks have high number of parameters. This research proposes a five-layer architecture which has fewer parameters compared with above state-of-the-art networks, and can be also complementary to other convolutional neural network features. Extensive experiments on UC Merced and WHU-RS datasets prove that although our network decreases the number of parameters dramatically, it generates more accurate results than AlexNet, OverFeat, and its accuracy is comparable with other state-of-the-art methods.
{"title":"An Efficient Convolutional Neural Network for Remote-Sensing Scene Image Classification","authors":"Muhammad Ashad Baloch, Sajid Ali, Mubashir H. Malik, Aamir Hussain, Abdul Mustaan Madni","doi":"10.17706/jcp.15.2.48-58","DOIUrl":"https://doi.org/10.17706/jcp.15.2.48-58","url":null,"abstract":"Deep neural networks are providing a powerful solution for remote-sensing scene image classification. However, a limited number of training samples, inter-class similarity among scene categories, and to get the benefits of multi-layer features remains a significant challenge in the remote sensing domain. Many efforts have been proposed to deal the above challenges by adapting knowledge of state-of-the-art networks such as AlexNet, GoogleNet, OverFeat, etc. However, these networks have high number of parameters. This research proposes a five-layer architecture which has fewer parameters compared with above state-of-the-art networks, and can be also complementary to other convolutional neural network features. Extensive experiments on UC Merced and WHU-RS datasets prove that although our network decreases the number of parameters dramatically, it generates more accurate results than AlexNet, OverFeat, and its accuracy is comparable with other state-of-the-art methods.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"35 1","pages":"48-58"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77102674","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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