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Journal articles on the topic 'Information flow'

To see the other types of publications on this topic, follow the link: Information flow.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 October 2023

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1

Mygal, Valeriy, and Galyna Mygal. "Problems of Digitized Information Flow Analysis: Cognitive Aspects." Information & Security: An International Journal 43, no. 2 (2019): 134–44. http://dx.doi.org/10.11610/isij.4312.

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2

Gruska, Damas P. "Information Flow Testing." Fundamenta Informaticae 128, no. 1-2 (2013): 81–95. http://dx.doi.org/10.3233/fi-2013-934.

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3

Ahlswede, R., Ning Cai, S. Y. R. Li, and R. W. Yeung. "Network information flow." IEEE Transactions on Information Theory 46, no. 4 (July 2000): 1204–16. http://dx.doi.org/10.1109/18.850663.

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4

Carlowicz, Michael. "Flow of information." Eos, Transactions American Geophysical Union 77, no. 27 (July 2, 1996): 254. http://dx.doi.org/10.1029/eo077i027p00254-02.

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5

Godlee, F. "Global information flow." BMJ 321, no. 7264 (September 30, 2000): 776–77. http://dx.doi.org/10.1136/bmj.321.7264.776.

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6

Godlee, Fiona, Richard Horton, and Richard Smith. "Global information flow." Lancet 356, no. 9236 (September 2000): 1129–30. http://dx.doi.org/10.1016/s0140-6736(00)02752-5.

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7

Beraia, M., and G. Beraia. "Energy/information dissipation and blood flow in human body." Cardiology Research and Reports 3, no. 2 (May 10, 2021): 01–08. http://dx.doi.org/10.31579/2692-9759/017.

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The amount of work done to displace blood in systemic arteries and capillaries exceeds the work done by the left ventricle. Besides, at the heartbeat, electromagnetic energy dissipates from the heart to the whole human body. For the problem study, the dielectric spectroscopy method was used. Ringer’s, amino acid solution, and heparinized venous blood were affected by the external electromagnetic oscillations (100-65000Hz, 1-8MHz.) in 17 healthy individuals. Correlations were noted between the initial and induced signal forms/frequencies according to the impedance of the system. The electric impulse from the heart initiates an oscillating electric field around the charged cells/particles and an emerging repulsing electromagnetic force, based on the electroacoustic phenomena, promotes the blood flow, in addition to the pulse pressure from the myocardial contraction. Blood conduces mechanical, electromagnetic waves of different frequencies and transmits energy/information to implement the spontaneous chemical processes in the human body.

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8

McCamant, Stephen, and Michael D. Ernst. "Quantitative information flow as network flow capacity." ACM SIGPLAN Notices 43, no. 6 (May 30, 2008): 193–205. http://dx.doi.org/10.1145/1379022.1375606.

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9

Hongladarom, Soraj. "Information Divide, Information Flow and Global Justice." International Review of Information Ethics 7 (September 1, 2007): 77–81. http://dx.doi.org/10.29173/irie8.

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10

Hu, Wei, Armaiti Ardeshiricham, and Ryan Kastner. "Hardware Information Flow Tracking." ACM Computing Surveys 54, no. 4 (May 2021): 1–39. http://dx.doi.org/10.1145/3447867.

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Information flow tracking (IFT) is a fundamental computer security technique used to understand how information moves through a computing system. Hardware IFT techniques specifically target security vulnerabilities related to the design, verification, testing, manufacturing, and deployment of hardware circuits. Hardware IFT can detect unintentional design flaws, malicious circuit modifications, timing side channels, access control violations, and other insecure hardware behaviors. This article surveys the area of hardware IFT. We start with a discussion on the basics of IFT, whose foundations were introduced by Denning in the 1970s. Building upon this, we develop a taxonomy for hardware IFT. We use this to classify and differentiate hardware IFT tools and techniques. Finally, we discuss the challenges yet to be resolved. The survey shows that hardware IFT provides a powerful technique for identifying hardware security vulnerabilities, as well as verifying and enforcing hardware security properties.

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11

Roberts, Franklin D. "Let the Information Flow." Journal of Intellectual Freedom and Privacy 1, no. 2-3 (December 30, 2016): 4. http://dx.doi.org/10.5860/jifp.v1i2-3.6165.

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About twenty years ago I became the library director at a small state college in a far-northern state, known mostly for liberal social stances even among its conservative electorate. So I was taken aback when the head of computing recommended I install the same filtering software used in the public school system to protect students from getting into “mischief” online. College students are taking their first steps into adulthood, I explained when declining his offer, and they should get into mischief. They should be testing their boundaries, finding out the world is much different than the dinner conversations they grew up with surrounded by people whose main goal was protecting them.

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12

Bracher, Shane, and Padmanabhan Krishnan. "Supporting Secure Information Flow." International Journal of e-Collaboration 8, no. 1 (January 2012): 17–35. http://dx.doi.org/10.4018/jec.2012010102.

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The authors describe a model to provide access control for information flow that crosses organisational boundaries. The model specifies a distributed access control enforcement approach for workflow objects (e.g., a document assigned to a pre-defined workflow) using software agents and data encryption techniques. Access to restricted content within the workflow object is based on the possession of encryption keys and role enactment. The model relies on trusted software agents to verify and ensure the validity of the workflow object. The authors construct a prototype and report on a case study that demonstrates the feasibility of the proposal.

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13

Recanatesi, Stefano. "Untangling network information flow." Nature Computational Science 2, no. 8 (August 18, 2022): 475–76. http://dx.doi.org/10.1038/s43588-022-00284-3.

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14

Camlek, Victor. "Healthcare mobile information flow." Information Services & Use 31, no. 1-2 (September 6, 2011): 23–30. http://dx.doi.org/10.3233/isu-2011-0626.

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15

Kozyri, Elisavet, Stephen Chong, and Andrew C. Myers. "Expressing Information Flow Properties." Foundations and Trends® in Privacy and Security 3, no. 1 (2022): 1–102. http://dx.doi.org/10.1561/3300000008.

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16

Bhardwaj, Chandrika, and Sanjiva Prasad. "Secure information flow connections." Journal of Logical and Algebraic Methods in Programming 127 (June 2022): 100761. http://dx.doi.org/10.1016/j.jlamp.2022.100761.

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17

Lowe, Gavin. "Defining information flow quantity." Journal of Computer Security 12, no. 3-4 (May 1, 2004): 619–53. http://dx.doi.org/10.3233/jcs-2004-123-410.

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18

Guchenko, E., and E. Vasilev. "Flow simulation textual information." Актуальные направления научных исследований XXI века: теория и практика 3, no. 5 (November 6, 2015): 196–99. http://dx.doi.org/10.12737/14487.

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19

Ikuta, Masaki. "Information Flow in Asia." Media Asia 12, no. 3 (January 1985): 147–50. http://dx.doi.org/10.1080/01296612.1985.11726185.

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20

Sinebare, Musawe. "One-Way Information Flow." Media Asia 24, no. 1 (January 1997): 33–39. http://dx.doi.org/10.1080/01296612.1997.11726520.

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21

Lourenço, Luísa, and Luís Caires. "Dependent Information Flow Types." ACM SIGPLAN Notices 50, no. 1 (May 11, 2015): 317–28. http://dx.doi.org/10.1145/2775051.2676994.

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22

Martin, Vivien. "Managing an information flow." Nursing Management 8, no. 5 (September 2001): 34–37. http://dx.doi.org/10.7748/nm2001.09.8.5.34.c2082.

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23

Cotton-Nessler, Natalie C. "Information Flow in Interactions." Academy of Management Proceedings 2014, no. 1 (January 2014): 16253. http://dx.doi.org/10.5465/ambpp.2014.16253abstract.

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24

BITITCI, UMIT S., and ALLAN S. CARRIE. "INFORMATION MATERIAL FLOW MAPPING." Logistics Information Management 3, no. 1 (January 1990): 31–36. http://dx.doi.org/10.1108/eb007494.

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25

Chiu, Kuang-Hui. "Information flow over process." Journal of Information and Optimization Sciences 27, no. 1 (January 2006): 167–92. http://dx.doi.org/10.1080/02522667.2006.10699685.

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26

Phan, Quoc-Sang, Pasquale Malacaria, Oksana Tkachuk, and Corina S. Păsăreanu. "Symbolic quantitative information flow." ACM SIGSOFT Software Engineering Notes 37, no. 6 (November 27, 2012): 1–5. http://dx.doi.org/10.1145/2382756.2382791.

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27

Humphrey, Curtis M., and Julie A. Adams. "Cognitive information flow analysis." Cognition, Technology & Work 15, no. 2 (October 29, 2011): 133–52. http://dx.doi.org/10.1007/s10111-011-0198-z.

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28

Han, Sang Hun, Aziz Nasridinov, and Keun Ho Ryu. "Information Flow Monitoring System." IEEE Access 6 (2018): 23820–27. http://dx.doi.org/10.1109/access.2018.2829495.

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29

Rosinberg, Martin Luc, and Jordan M. Horowitz. "Continuous information flow fluctuations." EPL (Europhysics Letters) 116, no. 1 (October 1, 2016): 10007. http://dx.doi.org/10.1209/0295-5075/116/10007.

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30

Georgatos, K. "Logic and Information Flow." Computer Journal 38, no. 3 (January 1, 1995): 259–60. http://dx.doi.org/10.1093/comjnl/38.3.259.

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31

Polikarpova, Nadia, Deian Stefan, Jean Yang, Shachar Itzhaky, Travis Hance, and Armando Solar-Lezama. "Liquid information flow control." Proceedings of the ACM on Programming Languages 4, ICFP (August 2, 2020): 1–30. http://dx.doi.org/10.1145/3408987.

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32

Peres, Yuval, and Elchanan Mossel. "Information flow on trees." Annals of Applied Probability 13, no. 3 (August 2003): 817–44. http://dx.doi.org/10.1214/aoap/1060202828.

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33

Kane, Jeffrey, and Pavel Naumov. "Symmetry in information flow." Annals of Pure and Applied Logic 165, no. 1 (January 2014): 253–65. http://dx.doi.org/10.1016/j.apal.2013.07.013.

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34

Russell, Lucian, Ouri Wolfson, and Clement Yu. "Information flow in the DAMA project beyond database managers: information flow managers." Distributed Systems Engineering 3, no. 4 (December 1996): 263–75. http://dx.doi.org/10.1088/0967-1846/3/4/006.

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35

Kovtun, Svitlana, Oleksandr Ponomarenko, and Oleg Nazarenko. "Quality of the information flow management at stochastic energy consumption conditions." System Research in Energy 2023, no. 3 (August 25, 2023): 78–84. http://dx.doi.org/10.15407/srenergy2023.03.078.

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Modern energy systems are rapidly changing and becoming increasingly complex. This process is facilitated by the growing demand for distributed energy resources, renewable energy sources, and distributed generation. This makes the energy system highly stochastic. An integral part of the concept of smart grids is the production of energy from distributed energy sources. Modernization of the energy network with the help of intelligent technologies allows us to fully take into account the peculiarities of energy consumption. The smart grid paradigm envisions flexible energy demand and storage to cope with the variability of renewable energy sources. A necessary condition for the implementation of demand response is an improved infrastructure, especially information and communication technologies. The paper presents the results of research on the quality of information flow management under conditions of stochastic energy consumption by maintaining a stationary queue of information transmission through the network and preventing overloading of the controlled network segment. The stages of monitoring and assessment of the network state, as well as the methodology of identification of network parameters are presented. According to the results of the analysis of the system of key network parameters and the specifics of their application for managing the quality of network service, it has been established that using the statistical approach one can distinguish between the key parameters of the network. The wireless network has been researched, which allows us to build a quality of service management system by regulating and shaping traffic. It is shown that when using a polling system with feedback on the speed of buffer filling to organize requests in the network, it is possible to maintain a stationary queue with a non-stationary flow of requests. Keywords: wireless network, information delay, stochastic energy consumption.

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36

Kassir, Abdallah, Robert Fitch, and Salah Sukkarieh. "Communication-aware information gathering with dynamic information flow." International Journal of Robotics Research 34, no. 2 (December 15, 2014): 173–200. http://dx.doi.org/10.1177/0278364914556911.

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37

Ling, Bin, Colin Allison, J. Ross Nicholl, Luke Moodley, and Dave Roberts. "Disabilities Information Flow: a disabilities information management system." British Journal of Educational Technology 37, no. 2 (March 2006): 289–94. http://dx.doi.org/10.1111/j.1467-8535.2006.00535.x.

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38

Matsumoto, K., and I. Tsuda. "Calculation of information flow rate from mutual information." Journal of Physics A: Mathematical and General 21, no. 6 (March 21, 1988): 1405–14. http://dx.doi.org/10.1088/0305-4470/21/6/016.

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39

Phan, Khoa Tran, Mihaela van der Schaar, and William R. Zame. "Congestion, Information, and Secret Information in Flow Networks." IEEE Journal of Selected Topics in Signal Processing 6, no. 2 (April 2012): 117–26. http://dx.doi.org/10.1109/jstsp.2011.2182496.

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40

Jiang, Di. "Watching information flow inside cells." Science 370, no. 6514 (October 15, 2020): 306.2–307. http://dx.doi.org/10.1126/science.370.6514.306-b.

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41

Reinitzer, Sigrid. "Free flow of bibliographic information." Organizacija znanja 16, no. 4 (2011): 156–58. http://dx.doi.org/10.3359/oz1104156.

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42

KAKUDA, Yuzuru. "Information Flow and Design Systems." Annals of the Japan Association for Philosophy of Science 15, no. 1 (2006): 1–24. http://dx.doi.org/10.4288/jafpos1956.15.1.

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43

Chandra, Akhilesh, Balaji Rajagopalan, and Ravindra Krovi. "Flow Turbulence And Information Quality." Review of Business Information Systems (RBIS) 8, no. 1 (January 1, 2004): 83–96. http://dx.doi.org/10.19030/rbis.v8i1.4507.

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44

Azevedo de Amorim, Arthur, Nathan Collins, André DeHon, Delphine Demange, Cătălin Hriţcu, David Pichardie, Benjamin C. Pierce, Randy Pollack, and Andrew Tolmach. "A verified information-flow architecture." Journal of Computer Security 24, no. 6 (December 1, 2016): 689–734. http://dx.doi.org/10.3233/jcs-15784.

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45

Kozyri, Elisavet, and Fred B. Schneider. "RIF: Reactive information flow labels." Journal of Computer Security 28, no. 2 (March 17, 2020): 191–228. http://dx.doi.org/10.3233/jcs-191316.

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46

Bossi, Annalisa, Damiano Macedonio, Carla Piazza, and Sabina Rossi. "Information flow in secure contexts." Journal of Computer Security 13, no. 3 (August 24, 2005): 391–422. http://dx.doi.org/10.3233/jcs-2005-13303.

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47

Clarkson, Michael R., Andrew C. Myers, and Fred B. Schneider. "Quantifying information flow with beliefs." Journal of Computer Security 17, no. 5 (October 12, 2009): 655–701. http://dx.doi.org/10.3233/jcs-2009-0353.

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48

Jantsch, Michael F. "Editing the flow of information." RNA Biology 10, no. 10 (October 2013): 1609–10. http://dx.doi.org/10.4161/rna.26446.

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49

Lanotte, Ruggero, Andrea Maggiolo-Schettini, and Simone Tini. "Information flow in hybrid systems." ACM Transactions on Embedded Computing Systems 3, no. 4 (November 2004): 760–99. http://dx.doi.org/10.1145/1027794.1027799.

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50

Kruscha, K. J. G., and B. Pompe. "Information Flow in 1D Maps." Zeitschrift für Naturforschung A 43, no. 2 (February 1, 1988): 93–104. http://dx.doi.org/10.1515/zna-1988-0201.

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An information theoretical description is given of the action of 1D maps on probability measures (e.g. on ergodic invariant measures of chaotic maps). On the basis of a detailed analysis of the elements of information flow the problem of optimum measuring of initial states for state predictions is discussed. Moreover, we give an information theoretical description of the relaxation, under the action of a map, of an initial probability distribution to any, not necessarily steady, final distribution. In this connection we formulate an H-theorem for 1D maps.

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