Littérature scientifique sur le sujet « Network data representation »
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Articles de revues sur le sujet "Network data representation"
R.Tamilarasu et G. Soundarya Devi. « Improvising Connection In 5g By Means Of Particle Swarm Optimization Techniques ». South Asian Journal of Engineering and Technology 14, no 2 (30 avril 2024) : 1–6. http://dx.doi.org/10.26524/sajet.2023.14.2.
Texte intégralYe, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu et Zhaoyang Wang. « An Optimized Network Representation Learning Algorithm Using Multi-Relational Data ». Mathematics 7, no 5 (21 mai 2019) : 460. http://dx.doi.org/10.3390/math7050460.
Texte intégralArmenta, Marco, et Pierre-Marc Jodoin. « The Representation Theory of Neural Networks ». Mathematics 9, no 24 (13 décembre 2021) : 3216. http://dx.doi.org/10.3390/math9243216.
Texte intégralAristizábal Q, Luz Angela, et Nicolás Toro G. « Multilayer Representation and Multiscale Analysis on Data Networks ». International journal of Computer Networks & ; Communications 13, no 3 (31 mai 2021) : 41–55. http://dx.doi.org/10.5121/ijcnc.2021.13303.
Texte intégralNguyễn, Tuấn, Nguyen Hai Hao, Dang Le Dinh Trang, Nguyen Van Tuan et Cao Van Loi. « Robust anomaly detection methods for contamination network data ». Journal of Military Science and Technology, no 79 (19 mai 2022) : 41–51. http://dx.doi.org/10.54939/1859-1043.j.mst.79.2022.41-51.
Texte intégralDu, Xin, Yulong Pei, Wouter Duivesteijn et Mykola Pechenizkiy. « Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data ». Proceedings of the AAAI Conference on Artificial Intelligence 34, no 04 (3 avril 2020) : 3809–16. http://dx.doi.org/10.1609/aaai.v34i04.5792.
Texte intégralDongming Chen, Dongming Chen, Mingshuo Nie Dongming Chen, Jiarui Yan Mingshuo Nie, Jiangnan Meng Jiarui Yan et Dongqi Wang Jiangnan Meng. « Network Representation Learning Algorithm Based on Community Folding ». 網際網路技術學刊 23, no 2 (mars 2022) : 415–23. http://dx.doi.org/10.53106/160792642022032302020.
Texte intégralZhang, Xiaoxian, Jianpei Zhang et Jing Yang. « Large-scale dynamic social data representation for structure feature learning ». Journal of Intelligent & ; Fuzzy Systems 39, no 4 (21 octobre 2020) : 5253–62. http://dx.doi.org/10.3233/jifs-189010.
Texte intégralKapoor, Maya, Michael Napolitano, Jonathan Quance, Thomas Moyer et Siddharth Krishnan. « Detecting VoIP Data Streams : Approaches Using Hidden Representation Learning ». Proceedings of the AAAI Conference on Artificial Intelligence 37, no 13 (26 juin 2023) : 15519–27. http://dx.doi.org/10.1609/aaai.v37i13.26840.
Texte intégralGiannarakis, Nick, Alexandra Silva et David Walker. « ProbNV : probabilistic verification of network control planes ». Proceedings of the ACM on Programming Languages 5, ICFP (22 août 2021) : 1–30. http://dx.doi.org/10.1145/3473595.
Texte intégralThèses sur le sujet "Network data representation"
Lim, Chong-U. « Modeling player self-representation in multiplayer online games using social network data ». Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82409.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (p. 101-105).
Game players express values related to self-expression through various means such as avatar customization, gameplay style, and interactions with other players. Multiplayer online games are now often integrated with social networks that provide social contexts in which player-to-player interactions take place, such as conversation and trading of virtual items. Building upon a theoretical framework based in machine learning and cognitive science, I present results from a novel approach to modeling and analyzing player values in terms of both preferences in avatar customization and patterns in social network use. To facilitate this work, I developed the Steam-Player- Preference Analyzer (Steam-PPA) system, which performs advanced data collection on publicly available social networking profile information. The primary contribution of this thesis is the AIR Toolkit Status Performance Classifier (AIR-SPC), which uses machine learning techniques including k-means clustering, natural language processing (NLP), and support vector machines (SVM) to perform inference on the data. As an initial case study, I use Steam-PPA to collect gameplay and avatar customization information from players in the popular, and commercially successful, multi-player first-person-shooter game Team Fortress 2 (TF2). Next, I use AIR-SPC to analyze the information from profiles on the social network Steam. The upshot is that I use social networking information to predict the likelihood of players customizing their profile in several ways associated with the monetary values of their avatars. In this manner I have developed a computational model of aspects of players' digital social identity capable of predicting specific values in terms of preferences exhibited within a virtual game-world.
by Chong-U Lim.
S.M.
Lee, John Boaz T. « Deep Learning on Graph-structured Data ». Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.
Texte intégralAzorin, Raphael. « Traffic representations for network measurements ». Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS141.
Texte intégralMeasurements are essential to operate and manage computer networks, as they are critical to analyze performance and establish diagnosis. In particular, per-flow monitoring consists in computing metrics that characterize the individual data streams traversing the network. To develop relevant traffic representations, operators need to select suitable flow characteristics and carefully relate their cost of extraction with their expressiveness for the downstream tasks considered. In this thesis, we propose novel methodologies to extract appropriate traffic representations. In particular, we posit that Machine Learning can enhance measurement systems, thanks to its ability to learn patterns from data, in order to provide predictions of pertinent traffic characteristics.The first contribution of this thesis is a framework for sketch-based measurements systems to exploit the skewed nature of network traffic. Specifically, we propose a novel data structure representation that leverages sketches' under-utilization, reducing per-flow measurements memory footprint by storing only relevant counters. The second contribution is a Machine Learning-assisted monitoring system that integrates a lightweight traffic classifier. In particular, we segregate large and small flows in the data plane, before processing them separately with dedicated data structures for various use cases. The last contributions address the design of a unified Deep Learning measurement pipeline that extracts rich representations from traffic data for network analysis. We first draw from recent advances in sequence modeling to learn representations from both numerical and categorical traffic data. These representations serve as input to solve complex networking tasks such as clickstream identification and mobile terminal movement prediction in WLAN. Finally, we present an empirical study of task affinity to assess when two tasks would benefit from being learned together
SURANO, FRANCESCO VINCENZO. « Unveiling human interactions : approaches and techniques toward the discovery and representation of interactions in networks ». Doctoral thesis, Politecnico di Torino, 2023. https://hdl.handle.net/11583/2975708.
Texte intégralWoodbury, Nathan Scott. « Representation and Reconstruction of Linear, Time-Invariant Networks ». BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.
Texte intégralMartignano, Anna. « Real-time Anomaly Detection on Financial Data ». Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281832.
Texte intégralDetta arbete presenterar en undersökning av tillämpningar av Network Representation Learning (NRL) inom den finansiella industrin. Metoder inom NRL möjliggör datadriven kondensering av grafstrukturer till lågdimensionella och lätthanterliga vektorer.Dessa vektorer kan sedan användas i andra maskininlärningsuppgifter. Närmare bestämt, kan metoder inom NRL underlätta hantering av och informantionsutvinning ur beräkningsintensiva och storskaliga grafer inom den finansiella sektorn, till exempel avvikelsehantering bland finansiella transaktioner. Arbetet med data av denna typ försvåras av det faktum att transaktionsgrafer är dynamiska och i konstant förändring. Utöver detta kan noderna, dvs transaktionspunkterna, vara vitt skilda eller med andra ord härstamma från olika fördelningar.I detta arbete har Graph Convolutional Network (ConvGNN) ansetts till den mest lämpliga lösningen för nämnda tillämpningar riktade mot upptäckt av avvikelser i transaktioner. GraphSAGE har använts som utgångspunkt för experimenten i två olika varianter: en dynamisk version där vikterna uppdateras allteftersom nya transaktionssekvenser matas in, och en variant avsedd särskilt för bipartita (tvådelade) grafer. Dessa varianter har utvärderats genom användning av faktiska datamängder med avvikelsehantering som slutmål.
GARBARINO, DAVIDE. « Acknowledging the structured nature of real-world data with graphs embeddings and probabilistic inference methods ». Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1092453.
Texte intégralRANDAZZO, VINCENZO. « Novel neural approaches to data topology analysis and telemedicine ». Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850610.
Texte intégralLucke, Helmut. « On the representation of temporal data for connectionist word recognition ». Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239520.
Texte intégralCori, Marcel. « Modèles pour la représentation et l'interrogation de données textuelles et de connaissances ». Paris 7, 1987. http://www.theses.fr/1987PA077047.
Texte intégralLivres sur le sujet "Network data representation"
service), SpringerLink (Online, dir. Guide to Computer Network Security. 2e éd. London : Springer London, 2013.
Trouver le texte intégralHill, Richard. Guide to Cloud Computing : Principles and Practice. London : Springer London, 2013.
Trouver le texte intégralVarlamov, Oleg. Mivar databases and rules. ru : INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Texte intégralLaszlo, Berke, Murthy P. L. N et United States. National Aeronautics and Space Administration., dir. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC] : National Aeronautics and Space Administration, 1994.
Trouver le texte intégralLaszlo, Berke, Murthy P. L. N et United States. National Aeronautics and Space Administration., dir. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC] : National Aeronautics and Space Administration, 1994.
Trouver le texte intégralBrath, Richard Karl. Effective information visualization guidelines and metrics for 3D interactive representations of business data. [Toronto] : Brath, 1999.
Trouver le texte intégralS, Drew Mark, dir. Fundamentals of multimedia. Upper Saddle River, NJ : Pearson Prentice Hall, 2004.
Trouver le texte intégralRiaño, David. Knowledge Representation for Health-Care : ECAI 2010 Workshop KR4HC 2010, Lisbon, Portugal, August 17, 2010, Revised Selected Papers. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.
Trouver le texte intégralDiagrams 2010 (2010 Portland, Or.). Diagrammatic representation and inference : 6th international conference, Diagrams 2010, Portland, OR, USA, August 9-11, 2010 : proceedings. Berlin : Springer, 2010.
Trouver le texte intégralGerhard, Friedrich, Gottlob Georg, Katzenbeisser Stefan, Turán György et SpringerLink (Online service), dir. SOFSEM 2012 : Theory and Practice of Computer Science : 38th Conference on Current Trends in Theory and Practice of Computer Science, Špindlerův Mlýn, Czech Republic, January 21-27, 2012. Proceedings. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.
Trouver le texte intégralChapitres de livres sur le sujet "Network data representation"
Gaudel, Bijay, Donghai Guan, Weiwei Yuan, Deepanjal Shrestha, Bing Chen et Yaofeng Tu. « Graph Representation Learning Using Attention Network ». Dans Big Data, 137–47. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0705-9_10.
Texte intégralSchestakov, Stefan, Paul Heinemeyer et Elena Demidova. « Road Network Representation Learning with Vehicle Trajectories ». Dans Advances in Knowledge Discovery and Data Mining, 57–69. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33383-5_5.
Texte intégralWang, Binglei, Tong Xu, Hao Wang, Yanmin Chen, Le Zhang, Lintao Fang, Guiquan Liu et Enhong Chen. « Author Contributed Representation for Scholarly Network ». Dans Web and Big Data, 558–73. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_41.
Texte intégralZhang, Si, Yinglong Xia, Yan Zhu et Hanghang Tong. « Representation Learning on Dynamic Network of Networks ». Dans Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 298–306. Philadelphia, PA : Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch34.
Texte intégralZhang, Yan, Zhao Zhang, Zheng Zhang, Mingbo Zhao, Li Zhang, Zhengjun Zha et Meng Wang. « Deep Self-representative Concept Factorization Network for Representation Learning ». Dans Proceedings of the 2020 SIAM International Conference on Data Mining, 361–69. Philadelphia, PA : Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.41.
Texte intégralScheider, Simon, et Werner Kuhn. « Road Networks and Their Incomplete Representation by Network Data Models ». Dans Geographic Information Science, 290–307. Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87473-7_19.
Texte intégralZhang, Shaowei, Zhao Li, Xin Wang, Zirui Chen et WenBin Guo. « TKGAT : Temporal Knowledge Graph Representation Learning Using Attention Network ». Dans Advanced Data Mining and Applications, 46–61. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_4.
Texte intégralSkabek, Krzysztof, et Łukasz Ząbik. « Network Transmission of 3D Mesh Data Using Progressive Representation ». Dans Computer Networks, 325–33. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02671-3_38.
Texte intégralChen, Weizheng, Jinpeng Wang, Zhuoxuan Jiang, Yan Zhang et Xiaoming Li. « Hierarchical Mixed Neural Network for Joint Representation Learning of Social-Attribute Network ». Dans Advances in Knowledge Discovery and Data Mining, 238–50. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_19.
Texte intégralAnuradha, T., Arun Tigadi, M. Ravikumar, Paparao Nalajala, S. Hemavathi et Manoranjan Dash. « Feature Extraction and Representation Learning via Deep Neural Network ». Dans Computer Networks, Big Data and IoT, 551–64. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0898-9_44.
Texte intégralActes de conférences sur le sujet "Network data representation"
Luo, Xuexiong, Jia Wu, Chuan Zhou, Xiankun Zhang et Yuan Wang. « Deep Semantic Network Representation ». Dans 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00141.
Texte intégralGao, Li, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan et Yue Hu. « Active Discriminative Network Representation Learning ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/296.
Texte intégralHansen, Brian, Leya Breanna Baltaxe-Admony, Sri Kurniawan et Angus G. Forbes. « Exploring Sonic Parameter Mapping for Network Data Structures ». Dans ICAD 2019 : The 25th International Conference on Auditory Display. Newcastle upon Tyne, United Kingdom : Department of Computer and Information Sciences, Northumbria University, 2019. http://dx.doi.org/10.21785/icad2019.055.
Texte intégralZhang, Xiangliang. « Mining Streaming and Temporal Data : from Representation to Knowledge ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/821.
Texte intégralHou, Mingliang, Jing Ren, Falih Febrinanto, Ahsan Shehzad et Feng Xia. « Cross Network Representation Matching with Outliers ». Dans 2021 International Conference on Data Mining Workshops (ICDMW). IEEE, 2021. http://dx.doi.org/10.1109/icdmw53433.2021.00124.
Texte intégralBandyopadhyay, Sambaran, Manasvi Aggarwal et M. Narasimha Murty. « Self-supervised Hierarchical Graph Neural Network for Graph Representation ». Dans 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377860.
Texte intégralYu, Yanlei, Zhiwu Lu, Jiajun Liu, Guoping Zhao et Ji-rong Wen. « RUM : Network Representation Learning Using Motifs ». Dans 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019. http://dx.doi.org/10.1109/icde.2019.00125.
Texte intégralZhang, Chuxu, Meng Jiang, Xiangliang Zhang, Yanfang Ye et Nitesh V. Chawla. « Multi-modal Network Representation Learning ». Dans KDD '20 : The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3394486.3406475.
Texte intégralYang, Hong, Shirui Pan, Ling Chen, Chuan Zhou et Peng Zhang. « Low-Bit Quantization for Attributed Network Representation Learning ». Dans Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California : International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/562.
Texte intégralGuan, Zhanming, Bin Wu, Bai Wang et Hezi Liu. « Personality2vec : Network Representation Learning for Personality ». Dans 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). IEEE, 2020. http://dx.doi.org/10.1109/dsc50466.2020.00013.
Texte intégralRapports d'organisations sur le sujet "Network data representation"
Haynes, T., et D. Noveck, dir. Network File System (NFS) Version 4 External Data Representation Standard (XDR) Description. RFC Editor, mars 2015. http://dx.doi.org/10.17487/rfc7531.
Texte intégralShepler, S., M. Eisler et D. Noveck, dir. Network File System (NFS) Version 4 Minor Version 1 External Data Representation Standard (XDR) Description. RFC Editor, janvier 2010. http://dx.doi.org/10.17487/rfc5662.
Texte intégralHaynes, T. Network File System (NFS) Version 4 Minor Version 2 External Data Representation Standard (XDR) Description. RFC Editor, novembre 2016. http://dx.doi.org/10.17487/rfc7863.
Texte intégralZanoni, Wladimir, Jimena Romero, Nicolás Chuquimarca et Emmanuel Abuelafia. Dealing with Hard-to-Reach Populations in Panel Data : Respondent-Driven Survey (RDS) and Attrition. Inter-American Development Bank, octobre 2023. http://dx.doi.org/10.18235/0005194.
Texte intégralHenderson, Tim, Mincent Santucci, Tim Connors et Justin Tweet. National Park Service geologic type section inventory : Chihuahuan Desert Inventory & ; Monitoring Network. National Park Service, avril 2021. http://dx.doi.org/10.36967/nrr-2285306.
Texte intégralHenderson, Tim, Vincent Santucci, Tim Connors et Justin Tweet. National Park Service geologic type section inventory : Northern Colorado Plateau Inventory & ; Monitoring Network. National Park Service, avril 2021. http://dx.doi.org/10.36967/nrr-2285337.
Texte intégralHenderson, Tim, Vincent Santucci, Tim Connors et Justin Tweet. National Park Service geologic type section inventory : Klamath Inventory & ; Monitoring Network. National Park Service, juillet 2021. http://dx.doi.org/10.36967/nrr-2286915.
Texte intégralHenderson, Tim, Vincent Santucci, Tim Connors et Justin Tweet. National Park Service geologic type section inventory : Mojave Desert Inventory & ; Monitoring Network. National Park Service, décembre 2021. http://dx.doi.org/10.36967/nrr-2289952.
Texte intégralHenderson, Tim, Vincet Santucci, Tim Connors et Justin Tweet. National Park Service geologic type section inventory : North Coast and Cascades Inventory & ; Monitoring Network. National Park Service, mars 2022. http://dx.doi.org/10.36967/nrr-2293013.
Texte intégralHenderson, Tim, Vincent Santucci, Tim Connors et Justin Tweet. National Park Service geologic type section inventory : Central Alaska Inventory & ; Monitoring Network. National Park Service, mai 2022. http://dx.doi.org/10.36967/nrr-2293381.
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