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Journal articles on the topic 'Distributed'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Burton, A. Mike. "The many ways to distribute distributed representations." Behavioral and Brain Sciences 23, no. 4 (August 2000): 472–73. http://dx.doi.org/10.1017/s0140525x00273354.

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Distributed representations can be distributed in very many ways. The specific choice of representation for a specific model is based on considerations unique to the area of study. General statements about the effectiveness of distributed models are therefore of little value. The popularity of these models is discussed, particularly with respect to reporting conventions.

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2

Fox, Peter T., and Karl J. Friston. "Distributed processing; distributed functions?" NeuroImage 61, no. 2 (June 2012): 407–26. http://dx.doi.org/10.1016/j.neuroimage.2011.12.051.

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3

Takamura, Seishi. "Distributed Video Coding." Journal of the Institute of Image Information and Television Engineers 61, no. 4 (2007): 443–46. http://dx.doi.org/10.3169/itej.61.443.

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4

Pester, Andreas, and Thomas Klinger. "Distributed Experiments and Distributed Learning." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 06 (May 28, 2020): 19. http://dx.doi.org/10.3991/ijoe.v16i06.13661.

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The paper, an enhanced version of conference key presentation, investigates, how the development of new needs in education and developments in IT triggered technology enhanced learning. Online labs play an important in that area. Federation of labs is the new direction to answer the challenges, which come from the intensive use of online labs in the modern learning environments. VISIR Federation is a concrete attempt to implement such a federation. Distributed labs in a federation need commitments from the participants in organization, sharing lab and learning resources.

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5

Giere, Ronald N. "Distributed Cognition without Distributed Knowing." Social Epistemology 21, no. 3 (July 2007): 313–20. http://dx.doi.org/10.1080/02691720701674197.

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6

Fiore, Stephen M., Haydee M. Cuevas, Eduardo Salas, and Jonathan W. Schooler. "Distributed Teams and Distributed Memory." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 3 (September 2002): 398–402. http://dx.doi.org/10.1177/154193120204600339.

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The nature of teams is changing in that the implementation of distributed teams as a definable organizational unit has substantially increased. In this paper we discuss a portion of the cognitive processes potentially impacting distributed team performance. We elaborate on how team opacity arising from distributed interaction can impact team cognition, with an emphasis on the critical memory components that are foundational to the development and implementation of shared mental models.

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7

Mukhopadhyay, Snehasis. "Distributed control and distributed computing." ACM SIGAPP Applied Computing Review 7, no. 1 (April 1999): 23–24. http://dx.doi.org/10.1145/570150.570157.

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8

McLeod, Poppy Lauretta. "Distributed People and Distributed Information." Small Group Research 44, no. 6 (September 5, 2013): 627–57. http://dx.doi.org/10.1177/1046496413500696.

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9

Harnad, Stevan, and Itiel E. Dror. "Distributed cognition." Distributed Cognition 14, no. 2 (September 21, 2006): 209–13. http://dx.doi.org/10.1075/pc.14.2.03har.

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Some of the papers in this Special Issue distribute cognition between what is going on inside individual cognizers’ heads and their outside worlds; others distribute cognition among different individual cognizers. Turing’s criterion for cognition was for individual, autonomous input/output capacity. It is not clear that distributed cognition could pass the Turing Test.

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10

Kojima, Hiroshi, Tohru Katsuno, Yosuke Nakanishi, Yoshikazu Fukuyama, Hideki Matsuda, and Yasuhisa Kanazawa. "E202 AN INTRODUCTION EFFECT EVALUATION TOOL FOR DISTRIBUTED GENERATORS(Distributed Energy System-3)." Proceedings of the International Conference on Power Engineering (ICOPE) 2009.2 (2009): _2–371_—_2–376_. http://dx.doi.org/10.1299/jsmeicope.2009.2._2-371_.

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11

Bhagwath, S. G., and Dr Mallikarjun Math. "Distributed Systems and Recent Innovations: Challenges Benefits and Security Issues in Distributed Systems." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 37–42. http://dx.doi.org/10.9756/bijsesc.8239.

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12

Alistarh, Dan. "Distributed Computing Column 82 Distributed Computability." ACM SIGACT News 52, no. 2 (June 14, 2021): 91. http://dx.doi.org/10.1145/3471469.3471483.

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Overview. In this edition of the column, we have a very useful and interesting piece from Michel Raynal (IRISA Rennes & Hong Kong Polytechnic), whose goal is to provide a systematic view of the computability results in distributed computing which should be well-understood by any Masters-level student with an interest in our area. Michel's motivation for the column is that, given the intense proliferation of computational models, tasks, and correctness and progress conditions in recent years, for instance, in the context of Blockchain technology, it may be hard for newcomers to the field to understand the boundary between what is essentially possible and what is impossible in a distributed system.

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13

Kindler, Ekkart, and Sibylle Peuker. "Integrating Distributed Algorithms into Distributed Systems." Fundamenta Informaticae 37, no. 3 (1999): 291–309. http://dx.doi.org/10.3233/fi-1999-37306.

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14

Doeppner, Thomas W. "Distributed file systems and distributed memory." ACM Computing Surveys 28, no. 1 (March 1996): 229–31. http://dx.doi.org/10.1145/234313.234409.

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15

Keidar, Idit. "Distributed computing column 36 distributed computing." ACM SIGACT News 40, no. 4 (January 25, 2010): 64–67. http://dx.doi.org/10.1145/1711475.1711489.

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16

Reed, Morton W. "Distributed simulation using distributed control systems." ACM SIGSIM Simulation Digest 20, no. 4 (April 1990): 143–51. http://dx.doi.org/10.1145/99637.99656.

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17

Hazari, Cyrus, and Hussein Zedan. "A distributed algorithm for distributed termination." Information Processing Letters 24, no. 5 (March 1987): 293–97. http://dx.doi.org/10.1016/0020-0190(87)90152-9.

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18

DOI, Yusuke, Shirou WAKAYAMA, and Satoshi OZAKI. "Data Recovery of Distributed Hash Table with Distributed-to-Distributed Data Copy." IEICE Transactions on Information and Systems E92-D, no. 10 (2009): 1999–2006. http://dx.doi.org/10.1587/transinf.e92.d.1999.

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19

Naidu, V. Balakrishnama, Dr P. Surya Kumar Dr. P. Surya Kumar, and Dr M. Sateeshnadha Reddy. "Distributed Lags: A New Estimator." Indian Journal of Applied Research 3, no. 6 (October 1, 2011): 131–33. http://dx.doi.org/10.15373/2249555x/june2013/43.

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20

Khethavath, Praveenkumar, and Doyel Pal. "Privacy Preserving Distributed Cloud Storage." Journal of Computers 10, no. 5 (2015): 329–35. http://dx.doi.org/10.17706/jcp.10.5.329-335.

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21

Gottinger, Hans W. "Internet Economics of Distributed Systems." Archives of Business Research 3, no. 1 (February 25, 2015): 36–52. http://dx.doi.org/10.14738/abr.31.715.

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22

Yang, Qi, Meng Cong Liu, Ying Chen, and Yun Xiao Bai. "Distributed Network Power Flow of Distributed Generation." Advanced Materials Research 614-615 (December 2012): 1693–99. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1693.

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With constrains imposed by natural conditions, the power generated from wind power, solar power and other intermittent energy system has great randomness and volatility. Thus, the impact from the intermittent energy to the system voltage and power flow is uncertainty. In view of this problem, this paper studies the characteristics of these double random variables and correlation. By using the typical probabilistic load flow and Monte Carlo simulation method, this paper poses the probability load flow adapted to the intermittent distributed generation. Finally, taking IEEE33 distribution system for example, this paper obtains the mutual relationship between the power from the intermittent distributed generation and the probability distribution of the node voltage, current distribution of each branch.

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23

Kim, Heung-Mook, Sung-Ik Park, Ho-Min Eum, Jae-Hyun Seo, Yong-Tae Lee, Hyoung-Soo Lim, Soo-In Lee, and Hyuck-Jae Lee. "Distributed Translator Part 1: Distributed Translator Technology." Journal of Broadcast Engineering 15, no. 1 (January 30, 2010): 14–28. http://dx.doi.org/10.5909/jbe.2010.15.1.014.

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24

Demidenko, Nikolai D., and Lyudmila V. Kulagina. "Distributed Control for Systems with Distributed Parametres." Journal of Siberian Federal University. Engineering & Technologies 11, no. 2 (March 2018): 221–28. http://dx.doi.org/10.17516/1999-494x-0025.

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25

Harnad, Stevan. "Distributed processes, distributed cognizers, and collaborative cognition." Cognitive Technologies and the Pragmatics of Cognition 13, no. 3 (December 20, 2005): 501–14. http://dx.doi.org/10.1075/pc.13.3.06har.

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Cognition is thinking; it feels like something to think, and only those who can feel can think. There are also things that thinkers can do. We know neither how thinkers can think nor how they are able to do what they can do. We are waiting for cognitive science to discover how. Cognitive science does this by testing hypotheses about what processes can generate what doing (“know-how”).This is called the Turing Test. It cannot test whether a process can generate feeling, hence thinking — only whether it can generate doing. The processes that generate thinking and know-how are “distributed” within the heads of thinkers, but not across thinkers’ heads. Hence there is no such thing as distributed cognition, only collaborative cognition. Email and the Web have spawned a new form of collaborative cognition that draws upon individual brains’ real-time interactive potential in ways that were not possible in oral, written or print interactions.

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26

Sarma, Atish Das, Stephan Holzer, Liah Kor, Amos Korman, Danupon Nanongkai, Gopal Pandurangan, David Peleg, and Roger Wattenhofer. "Distributed Verification and Hardness of Distributed Approximation." SIAM Journal on Computing 41, no. 5 (January 2012): 1235–65. http://dx.doi.org/10.1137/11085178x.

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27

Girone, Maria. "Distributed Data Management and Distributed File Systems." Journal of Physics: Conference Series 664, no. 4 (December 23, 2015): 042022. http://dx.doi.org/10.1088/1742-6596/664/4/042022.

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28

Bamieh, Bassam, and Petros Voulgaris. "OPTIMAL DISTRIBUTED CONTROL WITH DISTRIBUTED DELAYED MEASUREMENTS." IFAC Proceedings Volumes 35, no. 1 (2002): 95–100. http://dx.doi.org/10.3182/20020721-6-es-1901.00584.

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29

Michaelian, Kourken. "JFGI: FROM DISTRIBUTED COGNITION TO DISTRIBUTED RELIABILISM*." Philosophical Issues 24, no. 1 (September 23, 2014): 314–46. http://dx.doi.org/10.1111/phis.12036.

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30

Mirovitskii, D. I. "Distributed and quasi-distributed optical fiber sensors." Measurement Techniques 34, no. 12 (December 1991): 1269–72. http://dx.doi.org/10.1007/bf00982572.

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31

Arora, R. K., S. P. Rana, and M. N. Gupta. "Distributed termination detection algorithm for distributed computations." Information Processing Letters 22, no. 6 (May 1986): 311–14. http://dx.doi.org/10.1016/0020-0190(86)90072-4.

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32

Thapar, M., and B. Delagi. "Distributed-directory scheme: Stanford distributed-directory protocol." Computer 23, no. 6 (June 1990): 78–80. http://dx.doi.org/10.1109/2.55504.

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33

Burdette, Steven, Tracy Camp, and Bill Bynum. "Distributed BACI: a toolkit for distributed applications." Concurrency: Practice and Experience 12, no. 1 (January 2000): 35–52. http://dx.doi.org/10.1002/(sici)1096-9128(200001)12:1<35::aid-cpe458>3.0.co;2-8.

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34

Li, Yiwen. "Elastic distributed dataset RDD (Resilient Distributed Datasets)." Applied and Computational Engineering 31, no. 1 (January 22, 2024): 184–93. http://dx.doi.org/10.54254/2755-2721/31/20230149.

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In recent years, the amount of data to be processed has become larger and larger, and it is difficult for a single processor to handle data, so we need to work hard to develop the expansion mode of cluster mode. Mapreduce, Storm, etc. are a solution for clusters to process data. However, we still have parallel processing, user resource sharing and other issues to be addressed. RDD data model is the key to solve these problems. In this paper, RDD is applied to Spark for detailed expansion, and project experiments are carried out to study the RDD data model. The programming language used in this article is python. The advantage of studying distributed iterative computing is that during the calculation process, the task calculation fails and can be recovered. The default is to allow four failures recoverable. If the stage fails, one can recover, and if the partition fails, one can also recover. Multiple partitions can improve parallelism and efficiency. After a shuffle, a partition failed because the many-to-one relationship needs to be recalculated.

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35

Bivens, Kristin Marie, and Kelli Cargile Cook. "Coordinating Distributed Memory." Journal of Business and Technical Communication 32, no. 3 (March 4, 2018): 285–307. http://dx.doi.org/10.1177/1050651918762028.

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This case study of an environmental engineer’s proposal-writing process reveals how the engineer (Beatrice) reifies, archives, and accesses her distributed memory across physical and digital sources in order to write proposals. Based on the authors’ observations of Beatrice’s proposal-writing process and their interviews with her, they arrived at three key conclusions: Beatrice distributes her memory across multiple physical and digital sources, the (spreadsheet) product calculator helps Beatrice to manage her cognitive load and relieve her working memory, and the product calculator allows Beatrice to reassemble her distributed memory and coordinate her cognition.

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36

Nikov, Ventzislav, Svetla Nikova, and Bart Preneel. "On Distributed Oblivious Transfer." Serdica Journal of Computing 1, no. 3 (September 26, 2007): 313–36. http://dx.doi.org/10.55630/sjc.2007.1.313-336.

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This paper is about unconditionally secure distributed protocols for oblivious transfer, as proposed by Naor and Pinkas and generalized by Blundo et al. In this setting a Sender has ζ secrets and a Receiver is interested in one of them. The Sender distributes the information about the secrets to n servers, and a Receiver must contact a threshold of the servers in order to compute the secret. We present a non-existence result and a lower bound for the existence of one-round, threshold, distributed oblivious transfer protocols, generalizing the results of Blundo et al. A threshold based construction implementing 1-out-of-ζ distributed oblivious transfer achieving this lower bound is described. A condition for existence of distributed oblivious transfer schemes based on general access structures is proven. We also present a general access structure protocol implementing 1-out-of-ζ distributed oblivious transfer.

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37

Zhang, Zhongwei Jake, and Nirmal-Kumar Nair. "Distributor “time value” pricing framework to schedule distributed energy resources." International Transactions on Electrical Energy Systems 27, no. 9 (May 15, 2017): e2374. http://dx.doi.org/10.1002/etep.2374.

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38

Bartal, Yair, and Adi Rosén. "The Distributedk-Server Problem—A Competitive Distributed Translator fork-Server Algorithms." Journal of Algorithms 23, no. 2 (May 1997): 241–64. http://dx.doi.org/10.1006/jagm.1996.0826.

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39

Mündges, Stephan. "Distributed Content." MedienWirtschaft 13, no. 4 (2016): 26–33. http://dx.doi.org/10.15358/1613-0669-2016-4-26.

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Die mobile Nutzung des Internets nimmt zu und damit auch die Zeit, die Nutzer in Walled Gardens wie Facebook oder Snapchat verbringen. Dieser Aufstieg von mobiler und sozialer Mediennutzung führt zu einer neuen Vertriebsform von Medieninhalten: dem Distributed Content. Der Begriff beschreibt die Veröffentlichung und das Hosting von durch Medienunternehmen produzierten Inhalten auf digitalen Plattformen, die in vereinzelter Form an Nutzer über algorithmisch kuratierte Feeds ausgespielt werden. Es gibt prototypische Beispiele wie „Instant Articles“ auf Facebook, aber auch andere Formen wie Apple News oder Snapchat Discover. Medienunternehmen versprechen sich von dieser neuen Distributionsform höhere Reichweiten und neue Erlösmodelle. Doch gehen Unternehmen, die sich darauf einlassen, auch Risiken ein: Es können Abhängigkeiten entstehen, die den Unternehmen wirtschaftlich schaden können.

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40

Boyle, Randall J., Charles J. Kacmar, and Joey F. George. "Distributed Deception." International Journal of e-Collaboration 4, no. 3 (July 2008): 14–39. http://dx.doi.org/10.4018/jec.2008070102.

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41

De Belder, Marijke, and Jan Don. "Distributed Morphology." Nederlandse Taalkunde 27, no. 1 (July 1, 2022): 75–104. http://dx.doi.org/10.5117/nedtaa2022.1.007.beld.

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42

Frankston, Bob. "Distributed Cooperation." IEEE Consumer Electronics Magazine 11, no. 6 (November 1, 2022): 6–10. http://dx.doi.org/10.1109/mce.2022.3202104.

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43

Raynal, Michel. "Distributed Computability." ACM SIGACT News 52, no. 2 (June 14, 2021): 92–110. http://dx.doi.org/10.1145/3471469.3471484.

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As today Computer Science is more and more (driven) consumed by its applications, it becomes more and more important to know what is and what is not computable. For a long time now, this has been relatively well understood in sequential computing. But today the computing world becomes more and more distributed, and consequently more and more applications are distributed. As a result, it becomes important, or even crucial, to understand what is distributed computing and which are its power and its limits. This article is a step in this direction from an agreement-oriented and fault-tolerance perspective.

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44

Slanec, Konrad. "Distributed Intelligence." ATZautotechnology 4, no. 6 (November 2004): 64–67. http://dx.doi.org/10.1007/bf03246864.

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45

HARADA, RYUJI. "Distributed Reduplication." ENGLISH LINGUISTICS 29, no. 1 (2012): 100–111. http://dx.doi.org/10.9793/elsj.29.1_100.

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46

Suarez, Eugenio Dante, and Manuel Castañón-Puga. "Distributed Agency." International Journal of Agent Technologies and Systems 5, no. 1 (January 2013): 32–52. http://dx.doi.org/10.4018/jats.2013010103.

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Distributed Agency is the name of a conceptual framework for describing complex adaptive systems that this paper develops. To understand the complexity of the world in a holistic fashion, the field of Modeling and Simulation is currently lacking a common terminology in which different bodies of knowledge can communicate with each other in a general language. In this work, agency is proposed as the common link between the different dimensions of reality, expressing the influence of one dimension on another. This conceptualization is based on a process of backwards induction where nested actors such as an evolved organism or a human choice can be represented as the resulting force of intertwined aims and constraints. The theoretical framework can serve as a point of reference for the social and computational researcher by communicating structural and emergent properties that are essential for the understanding of social and evolutionary phenomena such as companies, economies, governments, and ecosystems.

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47

Kamrad, M., and J. Cross. "Distributed communications." ACM SIGAda Ada Letters X, no. 9 (November 1990): 85–93. http://dx.doi.org/10.1145/102456.102477.

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48

Roelofsen, Floris. "Distributed knowledge." Journal of Applied Non-Classical Logics 17, no. 2 (January 2007): 255–73. http://dx.doi.org/10.3166/jancl.17.255-273.

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49

Erbe, Heinz-H. "DISTRIBUTED WORKSPACES." IFAC Proceedings Volumes 39, no. 3 (2006): 255–60. http://dx.doi.org/10.3182/20060517-3-fr-2903.00142.

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50

Denniston, Tyler, Shoaib Kamil, and Saman Amarasinghe. "Distributed Halide." ACM SIGPLAN Notices 51, no. 8 (November 9, 2016): 1–12. http://dx.doi.org/10.1145/3016078.2851157.

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