Pub Date : 2024-07-01DOI: 10.1007/978-3-031-63775-9_17
Geethu Joy, Christian Huyck, Xin-She Yang
{"title":"Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods","authors":"Geethu Joy, Christian Huyck, Xin-She Yang","doi":"10.1007/978-3-031-63775-9_17","DOIUrl":"https://doi.org/10.1007/978-3-031-63775-9_17","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"79 1","pages":"242-253"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696021","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 : 2024-04-11DOI: 10.1007/978-3-031-08760-8_41
Przemyslaw Wrona, M. Grzenda, Marcin Luckner
{"title":"Streaming Detection of Significant Delay Changes in Public Transport Systems","authors":"Przemyslaw Wrona, M. Grzenda, Marcin Luckner","doi":"10.1007/978-3-031-08760-8_41","DOIUrl":"https://doi.org/10.1007/978-3-031-08760-8_41","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"14 8","pages":"486-499"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140714520","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 : 2023-06-15DOI: 10.48550/arXiv.2306.09538
Anahita Pakiman, J. Garcke, A. Schumacher
In this work, we establish a method for abstracting information from Computer Aided Engineering (CAE) into graphs. Such graph representations of CAE data can improve design guidelines and support recommendation systems by enabling the comparison of simulations, highlighting unexplored experimental designs, and correlating different designs. We focus on the load-path in crashworthiness analysis, a complex sub-discipline in vehicle design. The load-path is the sequence of parts that absorb most of the energy caused by the impact. To detect the load-path, we generate a directed weighted graph from the CAE data. The vertices represent the vehicle's parts, and the edges are an abstraction of the connectivity of the parts. The edge direction follows the temporal occurrence of the collision, where the edge weights reflect aspects of the energy absorption. We introduce and assess three methods for graph extraction and an additional method for further updating each graph with the sequences of absorption. Based on longest-path calculations, we introduce an automated detection of the load-path, which we analyse for the different graph extraction methods and weights. Finally, we show how our method for the detection of load-paths helps in the classification and labelling of CAE simulations.
{"title":"Graph Extraction for Assisting Crash Simulation Data Analysis","authors":"Anahita Pakiman, J. Garcke, A. Schumacher","doi":"10.48550/arXiv.2306.09538","DOIUrl":"https://doi.org/10.48550/arXiv.2306.09538","url":null,"abstract":"In this work, we establish a method for abstracting information from Computer Aided Engineering (CAE) into graphs. Such graph representations of CAE data can improve design guidelines and support recommendation systems by enabling the comparison of simulations, highlighting unexplored experimental designs, and correlating different designs. We focus on the load-path in crashworthiness analysis, a complex sub-discipline in vehicle design. The load-path is the sequence of parts that absorb most of the energy caused by the impact. To detect the load-path, we generate a directed weighted graph from the CAE data. The vertices represent the vehicle's parts, and the edges are an abstraction of the connectivity of the parts. The edge direction follows the temporal occurrence of the collision, where the edge weights reflect aspects of the energy absorption. We introduce and assess three methods for graph extraction and an additional method for further updating each graph with the sequences of absorption. Based on longest-path calculations, we introduce an automated detection of the load-path, which we analyse for the different graph extraction methods and weights. Finally, we show how our method for the detection of load-paths helps in the classification and labelling of CAE simulations.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124165885","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 : 2023-06-13DOI: 10.1007/978-3-031-36027-5_45
Ye. V. Yudin, David Coster, U. Toussaint, F. Jenko
{"title":"Epistemic and Aleatoric Uncertainty Quantification and Surrogate Modelling in High-Performance Multiscale Plasma Physics Simulations","authors":"Ye. V. Yudin, David Coster, U. Toussaint, F. Jenko","doi":"10.1007/978-3-031-36027-5_45","DOIUrl":"https://doi.org/10.1007/978-3-031-36027-5_45","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124718314","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 : 2023-05-26DOI: 10.48550/arXiv.2305.16750
Anna Wr'oblewska, Bartosz Pieli'nski, Karolina Seweryn, Sylwia Sysko-Roma'nczuk, Karol Saputa, Aleksandra Wichrowska, Hanna Schreiber
This paper explores the automatic knowledge extraction of formal institutional design - norms, rules, and actors - from international agreements. The focus was to analyze the relationship between the visibility and centrality of actors in the formal institutional design in regulating critical aspects of cultural heritage relations. The developed tool utilizes techniques such as collecting legal documents, annotating them with Institutional Grammar, and using graph analysis to explore the formal institutional design. The system was tested against the 2003 UNESCO Convention for the Safeguarding of the Intangible Cultural Heritage.
{"title":"Automating the Analysis of Institutional Design in International Agreements","authors":"Anna Wr'oblewska, Bartosz Pieli'nski, Karolina Seweryn, Sylwia Sysko-Roma'nczuk, Karol Saputa, Aleksandra Wichrowska, Hanna Schreiber","doi":"10.48550/arXiv.2305.16750","DOIUrl":"https://doi.org/10.48550/arXiv.2305.16750","url":null,"abstract":"This paper explores the automatic knowledge extraction of formal institutional design - norms, rules, and actors - from international agreements. The focus was to analyze the relationship between the visibility and centrality of actors in the formal institutional design in regulating critical aspects of cultural heritage relations. The developed tool utilizes techniques such as collecting legal documents, annotating them with Institutional Grammar, and using graph analysis to explore the formal institutional design. The system was tested against the 2003 UNESCO Convention for the Safeguarding of the Intangible Cultural Heritage.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127061741","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 : 2023-05-01DOI: 10.48550/arXiv.2305.00724
J. Adamczyk, W. Czech
We present the analysis of the topological graph descriptor Local Degree Profile (LDP), which forms a widely used structural baseline for graph classification. Our study focuses on model evaluation in the context of the recently developed fair evaluation framework, which defines rigorous routines for model selection and evaluation for graph classification, ensuring reproducibility and comparability of the results. Based on the obtained insights, we propose a new baseline algorithm called Local Topological Profile (LTP), which extends LDP by using additional centrality measures and local vertex descriptors. The new approach provides the results outperforming or very close to the latest GNNs for all datasets used. Specifically, state-of-the-art results were obtained for 4 out of 9 benchmark datasets. We also consider computational aspects of LDP-based feature extraction and model construction to propose practical improvements affecting execution speed and scalability. This allows for handling modern, large datasets and extends the portfolio of benchmarks used in graph representation learning. As the outcome of our work, we obtained LTP as a simple to understand, fast and scalable, still robust baseline, capable of outcompeting modern graph classification models such as Graph Isomorphism Network (GIN). We provide open-source implementation at href{https://github.com/j-adamczyk/LTP}{GitHub}.
{"title":"Strengthening structural baselines for graph classification using Local Topological Profile","authors":"J. Adamczyk, W. Czech","doi":"10.48550/arXiv.2305.00724","DOIUrl":"https://doi.org/10.48550/arXiv.2305.00724","url":null,"abstract":"We present the analysis of the topological graph descriptor Local Degree Profile (LDP), which forms a widely used structural baseline for graph classification. Our study focuses on model evaluation in the context of the recently developed fair evaluation framework, which defines rigorous routines for model selection and evaluation for graph classification, ensuring reproducibility and comparability of the results. Based on the obtained insights, we propose a new baseline algorithm called Local Topological Profile (LTP), which extends LDP by using additional centrality measures and local vertex descriptors. The new approach provides the results outperforming or very close to the latest GNNs for all datasets used. Specifically, state-of-the-art results were obtained for 4 out of 9 benchmark datasets. We also consider computational aspects of LDP-based feature extraction and model construction to propose practical improvements affecting execution speed and scalability. This allows for handling modern, large datasets and extends the portfolio of benchmarks used in graph representation learning. As the outcome of our work, we obtained LTP as a simple to understand, fast and scalable, still robust baseline, capable of outcompeting modern graph classification models such as Graph Isomorphism Network (GIN). We provide open-source implementation at href{https://github.com/j-adamczyk/LTP}{GitHub}.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129393060","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 : 2023-04-27DOI: 10.1007/978-3-031-36024-4_10
M. Dul, Michal K. Grzeszczyk, E. Nojszewska, A. Sitek
{"title":"Estimation of the Impact of COVID-19 Pandemic Lockdowns on Breast Cancer Deaths and Costs in Poland Using Markovian Monte Carlo Simulation","authors":"M. Dul, Michal K. Grzeszczyk, E. Nojszewska, A. Sitek","doi":"10.1007/978-3-031-36024-4_10","DOIUrl":"https://doi.org/10.1007/978-3-031-36024-4_10","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117045661","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: 10.1007/978-3-031-36021-3_32
M. Bolanowski, Alicja Gerka, A. Paszkiewicz, M. Ganzha, M. Paprzycki
{"title":"Application of genetic algorithm to load balancing in networks with a homogeneous traffic flow","authors":"M. Bolanowski, Alicja Gerka, A. Paszkiewicz, M. Ganzha, M. Paprzycki","doi":"10.1007/978-3-031-36021-3_32","DOIUrl":"https://doi.org/10.1007/978-3-031-36021-3_32","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123220671","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 : 2023-04-15DOI: 10.48550/arXiv.2304.07535
Edoardo Di Paolo, M. Petrocchi, A. Spognardi
Online Social Networks have revolutionized how we consume and share information, but they have also led to a proliferation of content not always reliable and accurate. One particular type of social accounts is known to promote unreputable content, hyperpartisan, and propagandistic information. They are automated accounts, commonly called bots. Focusing on Twitter accounts, we propose a novel approach to bot detection: we first propose a new algorithm that transforms the sequence of actions that an account performs into an image; then, we leverage the strength of Convolutional Neural Networks to proceed with image classification. We compare our performances with state-of-the-art results for bot detection on genuine accounts / bot accounts datasets well known in the literature. The results confirm the effectiveness of the proposal, because the detection capability is on par with the state of the art, if not better in some cases.
{"title":"From Online Behaviours to Images: A Novel Approach to Social Bot Detection","authors":"Edoardo Di Paolo, M. Petrocchi, A. Spognardi","doi":"10.48550/arXiv.2304.07535","DOIUrl":"https://doi.org/10.48550/arXiv.2304.07535","url":null,"abstract":"Online Social Networks have revolutionized how we consume and share information, but they have also led to a proliferation of content not always reliable and accurate. One particular type of social accounts is known to promote unreputable content, hyperpartisan, and propagandistic information. They are automated accounts, commonly called bots. Focusing on Twitter accounts, we propose a novel approach to bot detection: we first propose a new algorithm that transforms the sequence of actions that an account performs into an image; then, we leverage the strength of Convolutional Neural Networks to proceed with image classification. We compare our performances with state-of-the-art results for bot detection on genuine accounts / bot accounts datasets well known in the literature. The results confirm the effectiveness of the proposal, because the detection capability is on par with the state of the art, if not better in some cases.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124097817","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 : 2023-04-11DOI: 10.48550/arXiv.2304.05243
Klaudia Bałazy, Lukasz Struski, Marek Śmieja, J. Tabor
Nowadays artificial neural network models achieve remarkable results in many disciplines. Functions mapping the representation provided by the model to the probability distribution are the inseparable aspect of deep learning solutions. Although softmax is a commonly accepted probability mapping function in the machine learning community, it cannot return sparse outputs and always spreads the positive probability to all positions. In this paper, we propose r-softmax, a modification of the softmax, outputting sparse probability distribution with controllable sparsity rate. In contrast to the existing sparse probability mapping functions, we provide an intuitive mechanism for controlling the output sparsity level. We show on several multi-label datasets that r-softmax outperforms other sparse alternatives to softmax and is highly competitive with the original softmax. We also apply r-softmax to the self-attention module of a pre-trained transformer language model and demonstrate that it leads to improved performance when fine-tuning the model on different natural language processing tasks.
{"title":"r-softmax: Generalized Softmax with Controllable Sparsity Rate","authors":"Klaudia Bałazy, Lukasz Struski, Marek Śmieja, J. Tabor","doi":"10.48550/arXiv.2304.05243","DOIUrl":"https://doi.org/10.48550/arXiv.2304.05243","url":null,"abstract":"Nowadays artificial neural network models achieve remarkable results in many disciplines. Functions mapping the representation provided by the model to the probability distribution are the inseparable aspect of deep learning solutions. Although softmax is a commonly accepted probability mapping function in the machine learning community, it cannot return sparse outputs and always spreads the positive probability to all positions. In this paper, we propose r-softmax, a modification of the softmax, outputting sparse probability distribution with controllable sparsity rate. In contrast to the existing sparse probability mapping functions, we provide an intuitive mechanism for controlling the output sparsity level. We show on several multi-label datasets that r-softmax outperforms other sparse alternatives to softmax and is highly competitive with the original softmax. We also apply r-softmax to the self-attention module of a pre-trained transformer language model and demonstrate that it leads to improved performance when fine-tuning the model on different natural language processing tasks.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121521591","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：