Pub Date : 2023-06-09DOI: 10.1142/s1793351x23620027
A. Kriegler, Csaba Beleznai, M. Gelautz, Markus Murschitz, Kai Gobel
{"title":"PrimitivePose: Generic Model and Representation for 3D Bounding Box Prediction of Unseen Objects","authors":"A. Kriegler, Csaba Beleznai, M. Gelautz, Markus Murschitz, Kai Gobel","doi":"10.1142/s1793351x23620027","DOIUrl":"https://doi.org/10.1142/s1793351x23620027","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89199591","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 : 2023-04-18DOI: 10.1142/s1793351x23600012
Carsten Felix Draschner, Hajira Jabeen, Jens Lehmann
In recent years, exciting sources of data have been modeled as knowledge graphs (KGs). This modeling represents both structural relationships and the entity-specific multi-modal data in KGs. In various data analytics pipelines and machine learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all-pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile contents like categorical relations between classes or their attribute literals like strings, timestamps or numeric data. In this paper, we propose the SimE4KG framework as a resource providing generic open-source modules that perform semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF KG, including the experiment results, hyper-parameter setup and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed Semantic ANalytics StAck (SANSA).
{"title":"SimE4KG: Distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs","authors":"Carsten Felix Draschner, Hajira Jabeen, Jens Lehmann","doi":"10.1142/s1793351x23600012","DOIUrl":"https://doi.org/10.1142/s1793351x23600012","url":null,"abstract":"In recent years, exciting sources of data have been modeled as knowledge graphs (KGs). This modeling represents both structural relationships and the entity-specific multi-modal data in KGs. In various data analytics pipelines and machine learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all-pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile contents like categorical relations between classes or their attribute literals like strings, timestamps or numeric data. In this paper, we propose the SimE4KG framework as a resource providing generic open-source modules that perform semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF KG, including the experiment results, hyper-parameter setup and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed Semantic ANalytics StAck (SANSA).","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135861826","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 : 2023-03-29DOI: 10.1142/s1793351x23610019
Kevin Cedric Guyard, Michel Deriaz
Recommendation systems are becoming more and more present in our daily lives, whether it is for suggesting items to buy, movies to watch or music to listen. They can be used in a large number of contexts. In this paper, we propose the use of a recommendation system in the context of a recruitment platform. The use of the recommendation system allows to obtain precise profile recommendations based on the competences of a candidate to meet the stated requirements and to avoid companies to have to perform a very time-consuming manual sorting of the candidates. Thus, this paper presents the context in which we propose this recommendation system, the data preprocessing, the general approach based on a hybrid content-based filtering (CBF) and similarity index (SI) system, as well as the means implemented to reduce the computational cost of such a system with the increasing evolution of the platform.
{"title":"A Scalable Recommendation System Approach for a Companies — Seniors Matching","authors":"Kevin Cedric Guyard, Michel Deriaz","doi":"10.1142/s1793351x23610019","DOIUrl":"https://doi.org/10.1142/s1793351x23610019","url":null,"abstract":"Recommendation systems are becoming more and more present in our daily lives, whether it is for suggesting items to buy, movies to watch or music to listen. They can be used in a large number of contexts. In this paper, we propose the use of a recommendation system in the context of a recruitment platform. The use of the recommendation system allows to obtain precise profile recommendations based on the competences of a candidate to meet the stated requirements and to avoid companies to have to perform a very time-consuming manual sorting of the candidates. Thus, this paper presents the context in which we propose this recommendation system, the data preprocessing, the general approach based on a hybrid content-based filtering (CBF) and similarity index (SI) system, as well as the means implemented to reduce the computational cost of such a system with the increasing evolution of the platform.","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135468878","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 : 2022-10-03DOI: 10.1142/s1793351x2299001x
{"title":"Author Index Volume 16 (2022)","authors":"","doi":"10.1142/s1793351x2299001x","DOIUrl":"https://doi.org/10.1142/s1793351x2299001x","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78571717","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 : 2022-09-20DOI: 10.1142/s1793351x22400177
Sovon Chakraborty1, F. J. M. Shamrat, Rasel Ahammad, J. Uddin, M. Billah, Jannatun Naeem Muna, Jannatul Ferdaous
{"title":"An iot based bearing fault detection using convolutional neural network","authors":"Sovon Chakraborty1, F. J. M. Shamrat, Rasel Ahammad, J. Uddin, M. Billah, Jannatun Naeem Muna, Jannatul Ferdaous","doi":"10.1142/s1793351x22400177","DOIUrl":"https://doi.org/10.1142/s1793351x22400177","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73354233","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 : 2022-09-20DOI: 10.1142/s1793351x22400165
K. Govinda, Mali Shrikant Deelip
{"title":"Expsfroa-based drn: exponential sunflower rider optimization algorithm-driven deep residual network for the intrusion detection in iot-based plant disease monitoring","authors":"K. Govinda, Mali Shrikant Deelip","doi":"10.1142/s1793351x22400165","DOIUrl":"https://doi.org/10.1142/s1793351x22400165","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91025553","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 : 2020-12-01DOI: 10.1142/s1793351x20990019
{"title":"Author Index Volume 14 (2020)","authors":"","doi":"10.1142/s1793351x20990019","DOIUrl":"https://doi.org/10.1142/s1793351x20990019","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82546231","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 : 2019-12-01DOI: 10.1142/s1793351x19990010
{"title":"Author Index Volume 13 (2019)","authors":"","doi":"10.1142/s1793351x19990010","DOIUrl":"https://doi.org/10.1142/s1793351x19990010","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85888654","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 : 2018-12-01DOI: 10.1142/s1793351x18990015
{"title":"Author Index Volume 12 (2018)","authors":"","doi":"10.1142/s1793351x18990015","DOIUrl":"https://doi.org/10.1142/s1793351x18990015","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75098118","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 : 2015-12-01DOI: 10.1142/S1793351X16400031
J. C. S. J. Júnior, S. Musse
Pedestrian segmentation is a problem of considerable practical interest. In this work we present an extended version of our shape-based model for pedestrian segmentation, which can also be used to give an initial guess of the 2D pedestrians pose/orientation. The proposed model is initialized by a bounding-box of the person under analysis, which can be estimated by a person detector. The basic idea of the proposed model is to create a graph around the detected person, based on a scale invariant shape model and the estimated contour is given by a path in the graph that maximizes certain boundary energy. In practice, such energy should be large in the boundary between the foreground/background. To cope with pose/shape variations, the final estimate is given by a selection scheme, which takes into consideration the individual estimate given by different generated graphs. Experimental results indicated that the proposed technique works well in non trivial images, with comparable accuracy to the state-of-the-art.
{"title":"Shape-Based Pedestrian Segmentation in Still Images","authors":"J. C. S. J. Júnior, S. Musse","doi":"10.1142/S1793351X16400031","DOIUrl":"https://doi.org/10.1142/S1793351X16400031","url":null,"abstract":"Pedestrian segmentation is a problem of considerable practical interest. In this work we present an extended version of our shape-based model for pedestrian segmentation, which can also be used to give an initial guess of the 2D pedestrians pose/orientation. The proposed model is initialized by a bounding-box of the person under analysis, which can be estimated by a person detector. The basic idea of the proposed model is to create a graph around the detected person, based on a scale invariant shape model and the estimated contour is given by a path in the graph that maximizes certain boundary energy. In practice, such energy should be large in the boundary between the foreground/background. To cope with pose/shape variations, the final estimate is given by a selection scheme, which takes into consideration the individual estimate given by different generated graphs. Experimental results indicated that the proposed technique works well in non trivial images, with comparable accuracy to the state-of-the-art.","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72717475","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：