Literatura científica selecionada sobre o tema "Network data representation"
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Artigos de revistas sobre o assunto "Network data representation"
R.Tamilarasu e G. Soundarya Devi. "Improvising Connection In 5g By Means Of Particle Swarm Optimization Techniques". South Asian Journal of Engineering and Technology 14, n.º 2 (30 de abril de 2024): 1–6. http://dx.doi.org/10.26524/sajet.2023.14.2.
Texto completo da fonteYe, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu e Zhaoyang Wang. "An Optimized Network Representation Learning Algorithm Using Multi-Relational Data". Mathematics 7, n.º 5 (21 de maio de 2019): 460. http://dx.doi.org/10.3390/math7050460.
Texto completo da fonteArmenta, Marco, e Pierre-Marc Jodoin. "The Representation Theory of Neural Networks". Mathematics 9, n.º 24 (13 de dezembro de 2021): 3216. http://dx.doi.org/10.3390/math9243216.
Texto completo da fonteAristizábal Q, Luz Angela, e Nicolás Toro G. "Multilayer Representation and Multiscale Analysis on Data Networks". International journal of Computer Networks & Communications 13, n.º 3 (31 de maio de 2021): 41–55. http://dx.doi.org/10.5121/ijcnc.2021.13303.
Texto completo da fonteNguyễn, Tuấn, Nguyen Hai Hao, Dang Le Dinh Trang, Nguyen Van Tuan e Cao Van Loi. "Robust anomaly detection methods for contamination network data". Journal of Military Science and Technology, n.º 79 (19 de maio de 2022): 41–51. http://dx.doi.org/10.54939/1859-1043.j.mst.79.2022.41-51.
Texto completo da fonteDu, Xin, Yulong Pei, Wouter Duivesteijn e Mykola Pechenizkiy. "Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3809–16. http://dx.doi.org/10.1609/aaai.v34i04.5792.
Texto completo da fonteDongming Chen, Dongming Chen, Mingshuo Nie Dongming Chen, Jiarui Yan Mingshuo Nie, Jiangnan Meng Jiarui Yan e Dongqi Wang Jiangnan Meng. "Network Representation Learning Algorithm Based on Community Folding". 網際網路技術學刊 23, n.º 2 (março de 2022): 415–23. http://dx.doi.org/10.53106/160792642022032302020.
Texto completo da fonteZhang, Xiaoxian, Jianpei Zhang e Jing Yang. "Large-scale dynamic social data representation for structure feature learning". Journal of Intelligent & Fuzzy Systems 39, n.º 4 (21 de outubro de 2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.
Texto completo da fonteKapoor, Maya, Michael Napolitano, Jonathan Quance, Thomas Moyer e Siddharth Krishnan. "Detecting VoIP Data Streams: Approaches Using Hidden Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junho de 2023): 15519–27. http://dx.doi.org/10.1609/aaai.v37i13.26840.
Texto completo da fonteGiannarakis, Nick, Alexandra Silva e David Walker. "ProbNV: probabilistic verification of network control planes". Proceedings of the ACM on Programming Languages 5, ICFP (22 de agosto de 2021): 1–30. http://dx.doi.org/10.1145/3473595.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteCataloged 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.
Texto completo da fonteAzorin, Raphael. "Traffic representations for network measurements". Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS141.
Texto completo da fonteMeasurements 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.
Texto completo da fonteWoodbury, Nathan Scott. "Representation and Reconstruction of Linear, Time-Invariant Networks". BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.
Texto completo da fonteMartignano, 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.
Texto completo da fonteDetta 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.
Texto completo da fonteRANDAZZO, VINCENZO. "Novel neural approaches to data topology analysis and telemedicine". Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850610.
Texto completo da fonteLucke, 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.
Texto completo da fonteCori, Marcel. "Modèles pour la représentation et l'interrogation de données textuelles et de connaissances". Paris 7, 1987. http://www.theses.fr/1987PA077047.
Texto completo da fonteLivros sobre o assunto "Network data representation"
service), SpringerLink (Online, ed. Guide to Computer Network Security. 2a ed. London: Springer London, 2013.
Encontre o texto completo da fonteHill, Richard. Guide to Cloud Computing: Principles and Practice. London: Springer London, 2013.
Encontre o texto completo da fonteVarlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Texto completo da fonteLaszlo, Berke, Murthy P. L. N e United States. National Aeronautics and Space Administration., eds. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC]: National Aeronautics and Space Administration, 1994.
Encontre o texto completo da fonteLaszlo, Berke, Murthy P. L. N e United States. National Aeronautics and Space Administration., eds. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC]: National Aeronautics and Space Administration, 1994.
Encontre o texto completo da fonteBrath, Richard Karl. Effective information visualization guidelines and metrics for 3D interactive representations of business data. [Toronto]: Brath, 1999.
Encontre o texto completo da fonteS, Drew Mark, ed. Fundamentals of multimedia. Upper Saddle River, NJ: Pearson Prentice Hall, 2004.
Encontre o texto completo da fonteRiaño, 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.
Encontre o texto completo da fonteDiagrams 2010 (2010 Portland, Or.). Diagrammatic representation and inference: 6th international conference, Diagrams 2010, Portland, OR, USA, August 9-11, 2010 : proceedings. Berlin: Springer, 2010.
Encontre o texto completo da fonteGerhard, Friedrich, Gottlob Georg, Katzenbeisser Stefan, Turán György e SpringerLink (Online service), eds. 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.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Network data representation"
Gaudel, Bijay, Donghai Guan, Weiwei Yuan, Deepanjal Shrestha, Bing Chen e Yaofeng Tu. "Graph Representation Learning Using Attention Network". In Big Data, 137–47. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0705-9_10.
Texto completo da fonteSchestakov, Stefan, Paul Heinemeyer e Elena Demidova. "Road Network Representation Learning with Vehicle Trajectories". In 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.
Texto completo da fonteWang, Binglei, Tong Xu, Hao Wang, Yanmin Chen, Le Zhang, Lintao Fang, Guiquan Liu e Enhong Chen. "Author Contributed Representation for Scholarly Network". In Web and Big Data, 558–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_41.
Texto completo da fonteZhang, Si, Yinglong Xia, Yan Zhu e Hanghang Tong. "Representation Learning on Dynamic Network of Networks". In 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.
Texto completo da fonteZhang, Yan, Zhao Zhang, Zheng Zhang, Mingbo Zhao, Li Zhang, Zhengjun Zha e Meng Wang. "Deep Self-representative Concept Factorization Network for Representation Learning". In 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.
Texto completo da fonteScheider, Simon, e Werner Kuhn. "Road Networks and Their Incomplete Representation by Network Data Models". In Geographic Information Science, 290–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87473-7_19.
Texto completo da fonteZhang, Shaowei, Zhao Li, Xin Wang, Zirui Chen e WenBin Guo. "TKGAT: Temporal Knowledge Graph Representation Learning Using Attention Network". In Advanced Data Mining and Applications, 46–61. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_4.
Texto completo da fonteSkabek, Krzysztof, e Łukasz Ząbik. "Network Transmission of 3D Mesh Data Using Progressive Representation". In Computer Networks, 325–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02671-3_38.
Texto completo da fonteChen, Weizheng, Jinpeng Wang, Zhuoxuan Jiang, Yan Zhang e Xiaoming Li. "Hierarchical Mixed Neural Network for Joint Representation Learning of Social-Attribute Network". In 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.
Texto completo da fonteAnuradha, T., Arun Tigadi, M. Ravikumar, Paparao Nalajala, S. Hemavathi e Manoranjan Dash. "Feature Extraction and Representation Learning via Deep Neural Network". In 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Network data representation"
Luo, Xuexiong, Jia Wu, Chuan Zhou, Xiankun Zhang e Yuan Wang. "Deep Semantic Network Representation". In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00141.
Texto completo da fonteGao, Li, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan e Yue Hu. "Active Discriminative Network Representation Learning". In 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.
Texto completo da fonteHansen, Brian, Leya Breanna Baltaxe-Admony, Sri Kurniawan e Angus G. Forbes. "Exploring Sonic Parameter Mapping for Network Data Structures". In 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.
Texto completo da fonteZhang, Xiangliang. "Mining Streaming and Temporal Data: from Representation to Knowledge". In 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.
Texto completo da fonteHou, Mingliang, Jing Ren, Falih Febrinanto, Ahsan Shehzad e Feng Xia. "Cross Network Representation Matching with Outliers". In 2021 International Conference on Data Mining Workshops (ICDMW). IEEE, 2021. http://dx.doi.org/10.1109/icdmw53433.2021.00124.
Texto completo da fonteBandyopadhyay, Sambaran, Manasvi Aggarwal e M. Narasimha Murty. "Self-supervised Hierarchical Graph Neural Network for Graph Representation". In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377860.
Texto completo da fonteYu, Yanlei, Zhiwu Lu, Jiajun Liu, Guoping Zhao e Ji-rong Wen. "RUM: Network Representation Learning Using Motifs". In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019. http://dx.doi.org/10.1109/icde.2019.00125.
Texto completo da fonteZhang, Chuxu, Meng Jiang, Xiangliang Zhang, Yanfang Ye e Nitesh V. Chawla. "Multi-modal Network Representation Learning". In 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.
Texto completo da fonteYang, Hong, Shirui Pan, Ling Chen, Chuan Zhou e Peng Zhang. "Low-Bit Quantization for Attributed Network Representation Learning". In 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.
Texto completo da fonteGuan, Zhanming, Bin Wu, Bai Wang e Hezi Liu. "Personality2vec: Network Representation Learning for Personality". In 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). IEEE, 2020. http://dx.doi.org/10.1109/dsc50466.2020.00013.
Texto completo da fonteRelatórios de organizações sobre o assunto "Network data representation"
Haynes, T., e D. Noveck, eds. Network File System (NFS) Version 4 External Data Representation Standard (XDR) Description. RFC Editor, março de 2015. http://dx.doi.org/10.17487/rfc7531.
Texto completo da fonteShepler, S., M. Eisler e D. Noveck, eds. Network File System (NFS) Version 4 Minor Version 1 External Data Representation Standard (XDR) Description. RFC Editor, janeiro de 2010. http://dx.doi.org/10.17487/rfc5662.
Texto completo da fonteHaynes, T. Network File System (NFS) Version 4 Minor Version 2 External Data Representation Standard (XDR) Description. RFC Editor, novembro de 2016. http://dx.doi.org/10.17487/rfc7863.
Texto completo da fonteZanoni, Wladimir, Jimena Romero, Nicolás Chuquimarca e Emmanuel Abuelafia. Dealing with Hard-to-Reach Populations in Panel Data: Respondent-Driven Survey (RDS) and Attrition. Inter-American Development Bank, outubro de 2023. http://dx.doi.org/10.18235/0005194.
Texto completo da fonteHenderson, Tim, Mincent Santucci, Tim Connors e Justin Tweet. National Park Service geologic type section inventory: Chihuahuan Desert Inventory & Monitoring Network. National Park Service, abril de 2021. http://dx.doi.org/10.36967/nrr-2285306.
Texto completo da fonteHenderson, Tim, Vincent Santucci, Tim Connors e Justin Tweet. National Park Service geologic type section inventory: Northern Colorado Plateau Inventory & Monitoring Network. National Park Service, abril de 2021. http://dx.doi.org/10.36967/nrr-2285337.
Texto completo da fonteHenderson, Tim, Vincent Santucci, Tim Connors e Justin Tweet. National Park Service geologic type section inventory: Klamath Inventory & Monitoring Network. National Park Service, julho de 2021. http://dx.doi.org/10.36967/nrr-2286915.
Texto completo da fonteHenderson, Tim, Vincent Santucci, Tim Connors e Justin Tweet. National Park Service geologic type section inventory: Mojave Desert Inventory & Monitoring Network. National Park Service, dezembro de 2021. http://dx.doi.org/10.36967/nrr-2289952.
Texto completo da fonteHenderson, Tim, Vincet Santucci, Tim Connors e Justin Tweet. National Park Service geologic type section inventory: North Coast and Cascades Inventory & Monitoring Network. National Park Service, março de 2022. http://dx.doi.org/10.36967/nrr-2293013.
Texto completo da fonteHenderson, Tim, Vincent Santucci, Tim Connors e Justin Tweet. National Park Service geologic type section inventory: Central Alaska Inventory & Monitoring Network. National Park Service, maio de 2022. http://dx.doi.org/10.36967/nrr-2293381.
Texto completo da fonte