Littérature scientifique sur le sujet « Network measures »
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Articles de revues sur le sujet "Network measures"
Kincaid, Rex K., et David J. Phillips. « Network topology measures ». Wiley Interdisciplinary Reviews : Computational Statistics 3, no 6 (14 juin 2011) : 557–65. http://dx.doi.org/10.1002/wics.180.
Texte intégralZhang, Wen Jie. « Network Security Vulnerabilities and Preventive Measures ». Applied Mechanics and Materials 433-435 (octobre 2013) : 1674–78. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1674.
Texte intégralAytaç, Aysun, et Tufan Turaci. « Vulnerability Measures of Transformation Graph Gxy+ ». International Journal of Foundations of Computer Science 26, no 06 (septembre 2015) : 667–75. http://dx.doi.org/10.1142/s0129054115500379.
Texte intégralBoehmke, Frederick J., Olga Chyzh et Cameron G. Thies. « Addressing Endogeneity in Actor-Specific Network Measures ». Political Science Research and Methods 4, no 1 (24 août 2015) : 123–49. http://dx.doi.org/10.1017/psrm.2015.34.
Texte intégralKansky, Karl, et Pascal Danscoine. « Measures of network structure ». Flux 5, no 1 (1989) : 89–121. http://dx.doi.org/10.3406/flux.1989.913.
Texte intégralBanisch, Ralf, Péter Koltai et Kathrin Padberg-Gehle. « Network measures of mixing ». Chaos : An Interdisciplinary Journal of Nonlinear Science 29, no 6 (juin 2019) : 063125. http://dx.doi.org/10.1063/1.5087632.
Texte intégralLi, Yan. « Network Security Protection Measures ». Advanced Materials Research 971-973 (juin 2014) : 1659–62. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1659.
Texte intégralVardi, Yehuda, et Cun-Hui Zhang. « Measures of Network Vulnerability ». IEEE Signal Processing Letters 14, no 5 (mai 2007) : 313–16. http://dx.doi.org/10.1109/lsp.2006.888290.
Texte intégralBibi, Fizza, Hikmat Khan, Tassawar Iqbal, Muhammad Farooq, Irfan Mehmood et Yunyoung Nam. « Ranking Authors in an Academic Network Using Social Network Measures ». Applied Sciences 8, no 10 (4 octobre 2018) : 1824. http://dx.doi.org/10.3390/app8101824.
Texte intégralLordan, Oriol, et Jose M. Sallan. « Dynamic measures for transportation networks ». PLOS ONE 15, no 12 (3 décembre 2020) : e0242875. http://dx.doi.org/10.1371/journal.pone.0242875.
Texte intégralThèses sur le sujet "Network measures"
Traore, Abdoulaye S. « Mixed Network Interference Management with Multi-Distortion Measures ». International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/604294.
Texte intégralThis paper presents a methodology for the management of interference and spectrum for iNET. It anticipates a need for heavily loaded test environments with Test Articles (TAs) operating over the horizon. In such cases, it is anticipated that fixed and ad hoc networks will be employed, and where spectrum reuse and interference will limit performance. The methodology presented here demonstrates how this can be accomplished in mixed networks.
Grando, Felipe. « Methods for the approximation of network centrality measures ». reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/186166.
Texte intégralCentrality measures are an important analysis mechanism to uncover vital information about complex networks. However, these metrics have high computational costs that hinder their applications in large real-world networks. I propose and explain the use of artificial neural learning algorithms can render the application of such metrics in networks of arbitrary size. Moreover, I identified the best configuration and methodology for neural learning to optimize its accuracy, besides presenting an easy way to acquire and generate plentiful and meaningful training data via the use of a complex networks model that is adaptable for any application. In addition, I compared my prosed technique based on neural learning with different centrality approximation methods proposed in the literature, consisting of sampling and other artificial learning methodologies, and, I also tested the neural learning model in real case scenarios. I show in my results that the regression model generated by the neural network successfully approximates the metric values and is an effective alternative in real-world applications. The methodology and machine learning model that I propose use only a fraction of computing time with respect to other commonly applied approximation algorithms and is more robust than the other tested machine learning techniques.
Pellegrinet, Sarah <1988>. « Systemic Risk Measures and Connectedness : a network approach ». Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/6009.
Texte intégralBenbrook, Jimmie Glen 1943. « A SYSTEM ANALYSIS OF A MULTILEVEL SECURE LOCAL AREA NETWORK (COMPUTER) ». Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/275531.
Texte intégralKim, Hyoungshick. « Complex network analysis for secure and robust communications ». Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610134.
Texte intégralFu, Zehua. « Confidence measures in deep neural network based stereo matching ». Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEC014.
Texte intégralDespite decades of enhancement since the first proposal of Barnard and Fischler’s, stereo matching approaches still suffer from imprecision, especially in the presence of occlusion, extreme lighting conditions and ambiguity. To overcome these inaccuracies, many methods, called confidence measures, have been proposed to assess the accuracy of the matching. In this thesis, we study state-of-the-art confidence measures and propose two measures, based on neural networks and deep learning, to improve the performance of stereo matching. A first proposed approach uses multi-modal data including the initial disparity and reference RGB images. The multi-modal architecture is subsequently improved by enlarging the Effective Receptive Field (ERF) enabling learning with more contextual information and thus leading to better detection of matching errors. Evaluated on KITTI2012 and KITTI2015 datasets, our multi-modal approach had achieved the best performance during the time. As a second approach, a Recurrent Neural Network (RNN) is proposed in order to refine the result of the stereo matching, step by step. The Gated Recurrent Units (GRU), combined with our multi-modal dilated convolutional network, use information from one step to guide refinement in the next. To the best of our knowledge, this is the first attempt to refine stereo matching based on an RNN. The proposed approach is easily applicable to different Convolutional Neural Networks (CNNs) in stereo matching to produce an effective and precise end-to-end solution. The experimental results prove significant improvements both on KITTI2012 and KITTI2015 datasets
Olsson, Eric J. « Literature survey on network concepts and measures to support research in network-centric operations ». Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FOlsson.pdf.
Texte intégralFuloria, Shailendra. « Robust security for the electricity network ». Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610100.
Texte intégralWoldearegay, Yonas, et Oumar Traore. « Optimization of Nodes in Mixed Network Using Three Distance Measures ». International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595764.
Texte intégralThis paper presents a method for the management of mixed networks as envisioned in future iNET applications and develops a scheme for global optimal performance for features that include signal to Noise Ratio (SNR), Quality of service (QoS), and Interference. This scheme demonstrates potential for significant enhancement of performance for dense traffic environments envisioned in future telemetry applications. Previous research conducted at Morgan State University has proposed a cellular and Ad hoc mixed network for optimum capacity and coverage using two distance measures: QoS and SNR. This paper adds another performance improvement technique, interference, as a third distance measure using an analytical approach and using extensive simulation with MATLAB. This paper also addresses solutions where performance parameters are correlated and uncorrelated. The simulations show the optimization of mixed network nodes using distance, traffic and interference measures all at one time. This has great potential in mobile communication and iNET.
Mooi, Roderick David. « A model for security incident response in the South African National Research and Education network ». Thesis, Nelson Mandela Metropolitan University, 2014. http://hdl.handle.net/10948/d1017598.
Texte intégralLivres sur le sujet "Network measures"
Hunt, Craig. Network Security. Sebastopol, CA : O’Reilly Media, 1998.
Trouver le texte intégralR, Simon Alan, dir. Network security. Boston : AP Professional, 1994.
Trouver le texte intégralAlbanese, Massimiliano (Computer scientist), author et Jajodia Sushil author, dir. Network hardening : An automated approach to improving network security. Cham : Springer, 2014.
Trouver le texte intégralJohn, Mallery, dir. Hardening network security. New York : McGraw-Hill/Osborne, 2005.
Trouver le texte intégralEric, Cole, dir. Network security fundamentals. Hoboken, N.J : Wiley, 2008.
Trouver le texte intégralNetwork security foundations. San Francisco, Calif : SYBEX, 2004.
Trouver le texte intégralNetwork defense. Clifton Park, NY : Course Technology, Cengage Learning, 2011.
Trouver le texte intégralRuss, Rogers, Criscuolo Paul et Petruzzi Mike, dir. Nessus network auditing. 2e éd. Burlington, MA : Syngress, 2008.
Trouver le texte intégralNetwork security auditing. Indianapolis, Ind : Cisco Press, 2010.
Trouver le texte intégralNetwork Perimeter Security. London : Taylor and Francis, 2003.
Trouver le texte intégralChapitres de livres sur le sujet "Network measures"
Horvath, Steve. « Association Measures and Statistical Significance Measures ». Dans Weighted Network Analysis, 249–77. New York, NY : Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8819-5_10.
Texte intégralZweig, Katharina A. « Classic Network Analytic Measures ». Dans Lecture Notes in Social Networks, 91–108. Vienna : Springer Vienna, 2016. http://dx.doi.org/10.1007/978-3-7091-0741-6_4.
Texte intégralMiyazaki, Syuji. « Gibbs measures for the network ». Dans Emergent Intelligence of Networked Agents, 129–37. Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71075-2_10.
Texte intégralAl-khateeb, Samer, et Nitin Agarwal. « Social Network Measures and Analysis ». Dans SpringerBriefs in Cybersecurity, 27–44. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13690-1_2.
Texte intégralHe, Baozhu, et Zhen He. « Centrality Measures in Telecommunication Network ». Dans Advanced Research on Electronic Commerce, Web Application, and Communication, 337–43. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20370-1_55.
Texte intégralZweig, Katharina A. « Understanding and Designing Network Measures ». Dans Lecture Notes in Social Networks, 215–42. Vienna : Springer Vienna, 2016. http://dx.doi.org/10.1007/978-3-7091-0741-6_8.
Texte intégralAleskerov, Fuad, Sergey Shvydun et Natalia Meshcheryakova. « Centrality Indices in Network Analysis ». Dans New Centrality Measures in Networks, 1–38. Boca Raton : Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003203421-1.
Texte intégralMulekar, Madhuri S., et C. Scott Brown. « Distance and Similarity Measures ». Dans Encyclopedia of Social Network Analysis and Mining, 1–16. New York, NY : Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_141-1.
Texte intégralMulekar, Madhuri S., et C. Scott Brown. « Distance and Similarity Measures ». Dans Encyclopedia of Social Network Analysis and Mining, 385–400. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_141.
Texte intégralMulekar, Madhuri S., et C. Scott Brown. « Distance and Similarity Measures ». Dans Encyclopedia of Social Network Analysis and Mining, 647–62. New York, NY : Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_141.
Texte intégralActes de conférences sur le sujet "Network measures"
Singh, Gurpreet, Srinath Balaji, Jami J. Shah, David Corman, Ron Howard, Raju Mattikalli et D. Stuart. « Evaluation of Network Measures as Complexity Metrics ». Dans ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70483.
Texte intégralZheng, Xiaoxia. « Computer network security and measures ». Dans Mechanical Engineering and Information Technology (EMEIT). IEEE, 2011. http://dx.doi.org/10.1109/emeit.2011.6023622.
Texte intégralHou, Lvlin, Gang Liu, Songyang Lao et Liang Bai. « Measures of network topology invulnerability ». Dans 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI 2012). IEEE, 2012. http://dx.doi.org/10.1109/carpi.2012.6356504.
Texte intégralRheinwalt, Aljoscha, Norbert Marwan, Jurgen Kurths, Peter Werner et Friedrich-Wilhelm Gerstengarbe. « Boundary Effects in Network Measures of Spatially Embedded Networks ». Dans 2012 SC Companion : High Performance Computing, Networking, Storage and Analysis (SCC). IEEE, 2012. http://dx.doi.org/10.1109/sc.companion.2012.72.
Texte intégralScellato, Salvatore, Ilias Leontiadis, Cecilia Mascolo, Prithwish Basu et Murtaza Zafer. « Understanding robustness of mobile networks through temporal network measures ». Dans IEEE INFOCOM 2011 - IEEE Conference on Computer Communications. IEEE, 2011. http://dx.doi.org/10.1109/infcom.2011.5935006.
Texte intégralCarneiro, Murillo G., Barbara C. Gama et Otavio S. Ribeiro. « Complex Network Measures for Data Classification ». Dans 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533608.
Texte intégralHuang, Q., Y. Yuan, J. Goncalves et M. A. Dahleh. « H2 norm based network volatility measures ». Dans 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859249.
Texte intégralDehmer, Matthias, Stephan Borgert et Frank Emmert-Streib. « Network Classes and Graph Complexity Measures ». Dans 2008 First International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems. Medical Applications of the Complex Systems. Biomedical Computing (CANS). IEEE, 2008. http://dx.doi.org/10.1109/cans.2008.17.
Texte intégralWinch, A. « Network performance improvement initiatives : a regional electricity company perspective ». Dans IEE Colloquium on Measures to Prevent Power Blackouts. IEE, 1998. http://dx.doi.org/10.1049/ic:19980480.
Texte intégralCarter, Martha A., et Mark E. Oxley. « Generalized measures of artificial neural network capabilities ». Dans AeroSense '99, sous la direction de Kevin L. Priddy, Paul E. Keller, David B. Fogel et James C. Bezdek. SPIE, 1999. http://dx.doi.org/10.1117/12.342875.
Texte intégralRapports d'organisations sur le sujet "Network measures"
Young, Stanley, et Dennis So Ting Fong. Arterial Network Performance Measures Software. Purdue University, décembre 2017. http://dx.doi.org/10.5703/1288284316570.
Texte intégralFrantz, Terrill L., et Kathleen M. Carley. Relating Network Topology to the Robustness of Centrality Measures. Fort Belvoir, VA : Defense Technical Information Center, mai 2005. http://dx.doi.org/10.21236/ada456108.
Texte intégralYoung, Stanley, Christopher Day et Dennis So Ting Fong. Network Performance Measures for Arterials—A Systematic Level Perspective. Purdue University, décembre 2017. http://dx.doi.org/10.5703/1288284316561.
Texte intégralDuvvuri, Sarvani, et Srinivas S. Pulugurtha. Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics. Mineta Transportation Institute, juillet 2021. http://dx.doi.org/10.31979/mti.2021.1946.
Texte intégralBalza, Lenin H., Camilo De Los Rios, Alfredo Guerra, Luis Herrera-Prada et Osmel Manzano. Unraveling the Network of the Extractive Industries. Inter-American Development Bank, avril 2021. http://dx.doi.org/10.18235/0003191.
Texte intégralMartínez-Ventura, Constanza, Jorge Ricardo Mariño-Martínez et Javier Iván Miguélez-Márquez. Redundancy of Centrality Measures in Financial Market Infrastructures. Banco de la República de Colombia, août 2022. http://dx.doi.org/10.32468/be.1206.
Texte intégralLausche, Barbara, Aaron Laur et Mary Collins. Marine Connectivity Conservation Rules of Thumb for MPA and MPA Network Design. IUCN WCPA Connectivity Conservation Specialist Group’s Marine Connectivity Working Group, août 2021. http://dx.doi.org/10.53847/jxqa6585.
Texte intégralBielinskyi, Andrii O., et Vladimir N. Soloviev. Complex network precursors of crashes and critical events in the cryptocurrency market. [б. в.], décembre 2018. http://dx.doi.org/10.31812/123456789/2881.
Texte intégralHoaglund, Robert, et Walter Gazda. Assessment of Performance Measures for Security of the Maritime Transportation Network. Port Security Metrics : Proposed Measurement of Deterrence Capability. Fort Belvoir, VA : Defense Technical Information Center, janvier 2007. http://dx.doi.org/10.21236/ada471403.
Texte intégralDuffy-Turner, M., I. M. Nettleton, M. G. Winter et I. Webber. Forensic Examination of Critical Special Geotechnical Measures : Soil Nails Information Note. TRL, juin 2022. http://dx.doi.org/10.58446/eprl1160.
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