{"title":"A new collective anomaly detection approach using pitch frequency and dissimilarity: Pitchy anomaly detection (PAD)","authors":"E. C. Erkus, Vilda Purutçuoglu Gazi","doi":"10.2139/ssrn.4349068","DOIUrl":"https://doi.org/10.2139/ssrn.4349068","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"4 1","pages":"102084"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77572349","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":"Distributed source scheme for Poisson equation using finite element method","authors":"N. Goona, S. Parne","doi":"10.2139/ssrn.4397801","DOIUrl":"https://doi.org/10.2139/ssrn.4397801","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"13 1","pages":"102103"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86029853","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}
Zahra Ahmadi, Hoang H. Nguyen, Zijian Zhang, Dmytro Bozhkov, D. Kudenko, Maria Jofre, F. Calderoni, Noa Cohen, Yosef Solewicz
{"title":"Inductive and transductive link prediction for criminal network analysis","authors":"Zahra Ahmadi, Hoang H. Nguyen, Zijian Zhang, Dmytro Bozhkov, D. Kudenko, Maria Jofre, F. Calderoni, Noa Cohen, Yosef Solewicz","doi":"10.2139/ssrn.4331130","DOIUrl":"https://doi.org/10.2139/ssrn.4331130","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"21 1","pages":"102063"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91088055","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}
D. Helbing, Sachit Mahajan, Regula Hänggli Fricker, Andrea Musso, C. Hausladen, C. Carissimo, Dino Carpentras, Elisabeth Stockinger, Javier Argota Sánchez-Vaquerizo, Joshua Yang, M. Ballandies, Marcin Korecki, R. Dubey, Evangelos Pournaras
{"title":"Democracy by Design: Perspectives for Digitally Assisted, Participatory Upgrades of Society","authors":"D. Helbing, Sachit Mahajan, Regula Hänggli Fricker, Andrea Musso, C. Hausladen, C. Carissimo, Dino Carpentras, Elisabeth Stockinger, Javier Argota Sánchez-Vaquerizo, Joshua Yang, M. Ballandies, Marcin Korecki, R. Dubey, Evangelos Pournaras","doi":"10.2139/ssrn.4266038","DOIUrl":"https://doi.org/10.2139/ssrn.4266038","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"33 1","pages":"102061"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81333803","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 : 2023-04-30DOI: 10.48550/arXiv.2305.00540
Huai-Shui Tong, Kuanren Qian, Eni Halilaj, Y. Zhang
High-quality mesh generation is the foundation of accurate finite element analysis. Due to the vast interior vertices search space and complex initial boundaries, mesh generation for complicated domains requires substantial manual processing and has long been considered the most challenging and time-consuming bottleneck of the entire modeling and analysis process. In this paper, we present a novel computational framework named ``SRL-assisted AFM"for meshing planar geometries by combining the advancing front method with neural networks that select reference vertices and update the front boundary using ``policy networks."These deep neural networks are trained using a unique pipeline that combines supervised learning with reinforcement learning to iteratively improve mesh quality. First, we generate different initial boundaries by randomly sampling points in a square domain and connecting them sequentially. These boundaries are used for obtaining input meshes and extracting training datasets in the supervised learning module. We then iteratively improve the reinforcement learning model performance with reward functions designed for special requirements, such as improving the mesh quality and controlling the number and distribution of extraordinary points. Our proposed supervised learning neural networks achieve an accuracy higher than 98% on predicting commercial software. The final reinforcement learning neural networks automatically generate high-quality quadrilateral meshes for complex planar domains with sharp features and boundary layers.
{"title":"SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method","authors":"Huai-Shui Tong, Kuanren Qian, Eni Halilaj, Y. Zhang","doi":"10.48550/arXiv.2305.00540","DOIUrl":"https://doi.org/10.48550/arXiv.2305.00540","url":null,"abstract":"High-quality mesh generation is the foundation of accurate finite element analysis. Due to the vast interior vertices search space and complex initial boundaries, mesh generation for complicated domains requires substantial manual processing and has long been considered the most challenging and time-consuming bottleneck of the entire modeling and analysis process. In this paper, we present a novel computational framework named ``SRL-assisted AFM\"for meshing planar geometries by combining the advancing front method with neural networks that select reference vertices and update the front boundary using ``policy networks.\"These deep neural networks are trained using a unique pipeline that combines supervised learning with reinforcement learning to iteratively improve mesh quality. First, we generate different initial boundaries by randomly sampling points in a square domain and connecting them sequentially. These boundaries are used for obtaining input meshes and extracting training datasets in the supervised learning module. We then iteratively improve the reinforcement learning model performance with reward functions designed for special requirements, such as improving the mesh quality and controlling the number and distribution of extraordinary points. Our proposed supervised learning neural networks achieve an accuracy higher than 98% on predicting commercial software. The final reinforcement learning neural networks automatically generate high-quality quadrilateral meshes for complex planar domains with sharp features and boundary layers.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"142 1","pages":"102109"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74327447","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":"A novel global clustering coefficient-dependent degree centrality (GCCDC) metric for large network analysis using real-world datasets","authors":"Ubaida Fatima, Saman Hina, Muhammad Wasif","doi":"10.2139/ssrn.4284451","DOIUrl":"https://doi.org/10.2139/ssrn.4284451","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"36 1","pages":"102008"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88555196","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":"Numerical solution of extended black-oil model incorporating capillary effects based on a high-resolution central scheme","authors":"H. Biglarian, M. Salimi","doi":"10.2139/ssrn.4281400","DOIUrl":"https://doi.org/10.2139/ssrn.4281400","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"75 1","pages":"102003"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86043831","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 : 2023-01-03DOI: 10.48550/arXiv.2301.01209
David Lenz, Raine Yeh, V. Mahadevan, I. Grindeanu, T. Peterka
B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. The proposed method selectively incorporates regularization terms based on first and second derivatives to maintain model accuracy while minimizing numerical artifacts. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations. In addition, a key tuning parameter is highlighted and the effects of this parameter are presented in detail. This paper is an extension of our previous conference paper at the 2022 International Conference on Computational Science (ICCS) [Lenz et al. 2022].
b样条模型是用函数近似表示科学数据集的一种强大方法。然而，当拟合数据不均匀分布时，这些模型可能会出现伪振荡。模型正则化(即平滑)传统上被用来最小化这些振荡;不幸的是，如果不平滑数据集的关键特征，有时不可能充分去除不需要的工件。在本文中，我们提出了一种模型正则化方法，该方法保留了数据集的重要特征，同时最小化了人为振荡。我们的方法在整个域内自动改变平滑参数的强度，去除约束较差区域的伪影，同时保持其他区域不变。提出的方法选择性地结合基于一阶导数和二阶导数的正则化项，在保持模型精度的同时最小化数值伪影。我们的方法的行为在科学模拟产生的二维和三维数据集的集合上得到了验证。此外，重点介绍了一个关键的调优参数，并详细介绍了该参数的效果。本文是我们之前在2022年国际计算科学会议(ICCS)上发表的会议论文的延伸[Lenz et al. 2022]。
{"title":"Customizable Adaptive Regularization Techniques for B-Spline Modeling","authors":"David Lenz, Raine Yeh, V. Mahadevan, I. Grindeanu, T. Peterka","doi":"10.48550/arXiv.2301.01209","DOIUrl":"https://doi.org/10.48550/arXiv.2301.01209","url":null,"abstract":"B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. The proposed method selectively incorporates regularization terms based on first and second derivatives to maintain model accuracy while minimizing numerical artifacts. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations. In addition, a key tuning parameter is highlighted and the effects of this parameter are presented in detail. This paper is an extension of our previous conference paper at the 2022 International Conference on Computational Science (ICCS) [Lenz et al. 2022].","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"25 3 1","pages":"102037"},"PeriodicalIF":0.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79741039","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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