Auswahl der wissenschaftlichen Literatur zum Thema „Network data representation“
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Zeitschriftenartikel zum Thema "Network data representation"
R.Tamilarasu und G. Soundarya Devi. „Improvising Connection In 5g By Means Of Particle Swarm Optimization Techniques“. South Asian Journal of Engineering and Technology 14, Nr. 2 (30.04.2024): 1–6. http://dx.doi.org/10.26524/sajet.2023.14.2.
Der volle Inhalt der QuelleYe, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu und Zhaoyang Wang. „An Optimized Network Representation Learning Algorithm Using Multi-Relational Data“. Mathematics 7, Nr. 5 (21.05.2019): 460. http://dx.doi.org/10.3390/math7050460.
Der volle Inhalt der QuelleArmenta, Marco, und Pierre-Marc Jodoin. „The Representation Theory of Neural Networks“. Mathematics 9, Nr. 24 (13.12.2021): 3216. http://dx.doi.org/10.3390/math9243216.
Der volle Inhalt der QuelleAristizábal Q, Luz Angela, und Nicolás Toro G. „Multilayer Representation and Multiscale Analysis on Data Networks“. International journal of Computer Networks & Communications 13, Nr. 3 (31.05.2021): 41–55. http://dx.doi.org/10.5121/ijcnc.2021.13303.
Der volle Inhalt der QuelleNguyễn, Tuấn, Nguyen Hai Hao, Dang Le Dinh Trang, Nguyen Van Tuan und Cao Van Loi. „Robust anomaly detection methods for contamination network data“. Journal of Military Science and Technology, Nr. 79 (19.05.2022): 41–51. http://dx.doi.org/10.54939/1859-1043.j.mst.79.2022.41-51.
Der volle Inhalt der QuelleDu, Xin, Yulong Pei, Wouter Duivesteijn und Mykola Pechenizkiy. „Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 04 (03.04.2020): 3809–16. http://dx.doi.org/10.1609/aaai.v34i04.5792.
Der volle Inhalt der QuelleDongming Chen, Dongming Chen, Mingshuo Nie Dongming Chen, Jiarui Yan Mingshuo Nie, Jiangnan Meng Jiarui Yan und Dongqi Wang Jiangnan Meng. „Network Representation Learning Algorithm Based on Community Folding“. 網際網路技術學刊 23, Nr. 2 (März 2022): 415–23. http://dx.doi.org/10.53106/160792642022032302020.
Der volle Inhalt der QuelleZhang, Xiaoxian, Jianpei Zhang und Jing Yang. „Large-scale dynamic social data representation for structure feature learning“. Journal of Intelligent & Fuzzy Systems 39, Nr. 4 (21.10.2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.
Der volle Inhalt der QuelleKapoor, Maya, Michael Napolitano, Jonathan Quance, Thomas Moyer und Siddharth Krishnan. „Detecting VoIP Data Streams: Approaches Using Hidden Representation Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 13 (26.06.2023): 15519–27. http://dx.doi.org/10.1609/aaai.v37i13.26840.
Der volle Inhalt der QuelleGiannarakis, Nick, Alexandra Silva und David Walker. „ProbNV: probabilistic verification of network control planes“. Proceedings of the ACM on Programming Languages 5, ICFP (22.08.2021): 1–30. http://dx.doi.org/10.1145/3473595.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleCataloged 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.
Der volle Inhalt der QuelleAzorin, Raphael. „Traffic representations for network measurements“. Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS141.
Der volle Inhalt der QuelleMeasurements 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.
Der volle Inhalt der QuelleWoodbury, Nathan Scott. „Representation and Reconstruction of Linear, Time-Invariant Networks“. BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.
Der volle Inhalt der QuelleMartignano, 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.
Der volle Inhalt der QuelleDetta 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.
Der volle Inhalt der QuelleRANDAZZO, VINCENZO. „Novel neural approaches to data topology analysis and telemedicine“. Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850610.
Der volle Inhalt der QuelleLucke, 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.
Der volle Inhalt der QuelleCori, Marcel. „Modèles pour la représentation et l'interrogation de données textuelles et de connaissances“. Paris 7, 1987. http://www.theses.fr/1987PA077047.
Der volle Inhalt der QuelleBücher zum Thema "Network data representation"
service), SpringerLink (Online, Hrsg. Guide to Computer Network Security. 2. Aufl. London: Springer London, 2013.
Den vollen Inhalt der Quelle findenHill, Richard. Guide to Cloud Computing: Principles and Practice. London: Springer London, 2013.
Den vollen Inhalt der Quelle findenVarlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Der volle Inhalt der QuelleLaszlo, Berke, Murthy P. L. N und United States. National Aeronautics and Space Administration., Hrsg. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC]: National Aeronautics and Space Administration, 1994.
Den vollen Inhalt der Quelle findenLaszlo, Berke, Murthy P. L. N und United States. National Aeronautics and Space Administration., Hrsg. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC]: National Aeronautics and Space Administration, 1994.
Den vollen Inhalt der Quelle findenBrath, Richard Karl. Effective information visualization guidelines and metrics for 3D interactive representations of business data. [Toronto]: Brath, 1999.
Den vollen Inhalt der Quelle findenS, Drew Mark, Hrsg. Fundamentals of multimedia. Upper Saddle River, NJ: Pearson Prentice Hall, 2004.
Den vollen Inhalt der Quelle findenRiaño, 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.
Den vollen Inhalt der Quelle findenDiagrams 2010 (2010 Portland, Or.). Diagrammatic representation and inference: 6th international conference, Diagrams 2010, Portland, OR, USA, August 9-11, 2010 : proceedings. Berlin: Springer, 2010.
Den vollen Inhalt der Quelle findenGerhard, Friedrich, Gottlob Georg, Katzenbeisser Stefan, Turán György und SpringerLink (Online service), Hrsg. 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.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Network data representation"
Gaudel, Bijay, Donghai Guan, Weiwei Yuan, Deepanjal Shrestha, Bing Chen und 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.
Der volle Inhalt der QuelleSchestakov, Stefan, Paul Heinemeyer und 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.
Der volle Inhalt der QuelleWang, Binglei, Tong Xu, Hao Wang, Yanmin Chen, Le Zhang, Lintao Fang, Guiquan Liu und 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.
Der volle Inhalt der QuelleZhang, Si, Yinglong Xia, Yan Zhu und 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.
Der volle Inhalt der QuelleZhang, Yan, Zhao Zhang, Zheng Zhang, Mingbo Zhao, Li Zhang, Zhengjun Zha und 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.
Der volle Inhalt der QuelleScheider, Simon, und 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.
Der volle Inhalt der QuelleZhang, Shaowei, Zhao Li, Xin Wang, Zirui Chen und 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.
Der volle Inhalt der QuelleSkabek, Krzysztof, und 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.
Der volle Inhalt der QuelleChen, Weizheng, Jinpeng Wang, Zhuoxuan Jiang, Yan Zhang und 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.
Der volle Inhalt der QuelleAnuradha, T., Arun Tigadi, M. Ravikumar, Paparao Nalajala, S. Hemavathi und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Network data representation"
Luo, Xuexiong, Jia Wu, Chuan Zhou, Xiankun Zhang und 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.
Der volle Inhalt der QuelleGao, Li, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan und 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.
Der volle Inhalt der QuelleHansen, Brian, Leya Breanna Baltaxe-Admony, Sri Kurniawan und 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.
Der volle Inhalt der QuelleZhang, 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.
Der volle Inhalt der QuelleHou, Mingliang, Jing Ren, Falih Febrinanto, Ahsan Shehzad und 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.
Der volle Inhalt der QuelleBandyopadhyay, Sambaran, Manasvi Aggarwal und 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.
Der volle Inhalt der QuelleYu, Yanlei, Zhiwu Lu, Jiajun Liu, Guoping Zhao und 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.
Der volle Inhalt der QuelleZhang, Chuxu, Meng Jiang, Xiangliang Zhang, Yanfang Ye und 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.
Der volle Inhalt der QuelleYang, Hong, Shirui Pan, Ling Chen, Chuan Zhou und 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.
Der volle Inhalt der QuelleGuan, Zhanming, Bin Wu, Bai Wang und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Network data representation"
Haynes, T., und D. Noveck, Hrsg. Network File System (NFS) Version 4 External Data Representation Standard (XDR) Description. RFC Editor, März 2015. http://dx.doi.org/10.17487/rfc7531.
Der volle Inhalt der QuelleShepler, S., M. Eisler und D. Noveck, Hrsg. Network File System (NFS) Version 4 Minor Version 1 External Data Representation Standard (XDR) Description. RFC Editor, Januar 2010. http://dx.doi.org/10.17487/rfc5662.
Der volle Inhalt der QuelleHaynes, T. Network File System (NFS) Version 4 Minor Version 2 External Data Representation Standard (XDR) Description. RFC Editor, November 2016. http://dx.doi.org/10.17487/rfc7863.
Der volle Inhalt der QuelleZanoni, Wladimir, Jimena Romero, Nicolás Chuquimarca und Emmanuel Abuelafia. Dealing with Hard-to-Reach Populations in Panel Data: Respondent-Driven Survey (RDS) and Attrition. Inter-American Development Bank, Oktober 2023. http://dx.doi.org/10.18235/0005194.
Der volle Inhalt der QuelleHenderson, Tim, Mincent Santucci, Tim Connors und Justin Tweet. National Park Service geologic type section inventory: Chihuahuan Desert Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285306.
Der volle Inhalt der QuelleHenderson, Tim, Vincent Santucci, Tim Connors und Justin Tweet. National Park Service geologic type section inventory: Northern Colorado Plateau Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285337.
Der volle Inhalt der QuelleHenderson, Tim, Vincent Santucci, Tim Connors und Justin Tweet. National Park Service geologic type section inventory: Klamath Inventory & Monitoring Network. National Park Service, Juli 2021. http://dx.doi.org/10.36967/nrr-2286915.
Der volle Inhalt der QuelleHenderson, Tim, Vincent Santucci, Tim Connors und Justin Tweet. National Park Service geologic type section inventory: Mojave Desert Inventory & Monitoring Network. National Park Service, Dezember 2021. http://dx.doi.org/10.36967/nrr-2289952.
Der volle Inhalt der QuelleHenderson, Tim, Vincet Santucci, Tim Connors und Justin Tweet. National Park Service geologic type section inventory: North Coast and Cascades Inventory & Monitoring Network. National Park Service, März 2022. http://dx.doi.org/10.36967/nrr-2293013.
Der volle Inhalt der QuelleHenderson, Tim, Vincent Santucci, Tim Connors und 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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