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Journal articles on the topic 'Best Effort'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Goergen, N., W. S. Lin, K. J. R. Liu, and T. C. Clancy. "Best-Effort Cooperative Relaying." IEEE Transactions on Wireless Communications 10, no. 6 (June 2011): 1833–43. http://dx.doi.org/10.1109/twc.2011.051211.100683.

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2

Carlberg, Ken, Panos Gevros, and Jon Crowcroft. "Lower than best effort." ACM SIGCOMM Computer Communication Review 31, no. 2 supplement (April 2001): 244–65. http://dx.doi.org/10.1145/844193.844208.

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3

Breslau, Lee, and Scott Shenker. "Best-effort versus reservations." ACM SIGCOMM Computer Communication Review 28, no. 4 (October 1998): 3–16. http://dx.doi.org/10.1145/285243.285248.

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4

Bernstein, Sondra. "“HUSHED CRISIS” BEST DOCUMENTARY EFFORT." Juvenile Court Judges Journal 22, no. 1 (March 18, 2009): 21. http://dx.doi.org/10.1111/j.1755-6988.1971.tb00423.x.

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5

Wydrowski, B., and M. Zukerman. "QoS in best-effort networks." IEEE Communications Magazine 40, no. 12 (December 2002): 44–49. http://dx.doi.org/10.1109/mcom.2002.1106158.

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6

Diot, C., and J. Le Boudec. "Control of best effort traffic." IEEE Network 15, no. 3 (May 2001): 14–15. http://dx.doi.org/10.1109/mnet.2001.923936.

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7

Plasser, E., T. Ziegler, P. Reichl, and G. Mittenecker. "Internet services beyond best effort." e & i Elektrotechnik und Informationstechnik 119, no. 2 (February 2002): 30–36. http://dx.doi.org/10.1007/bf03161578.

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8

Dong In Kim. "Selective relative best scheduling for best-effort downlink packet data." IEEE Transactions on Wireless Communications 5, no. 6 (June 2006): 1254–59. http://dx.doi.org/10.1109/twc.2006.1638644.

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9

Menzies, Tim, Zhihao Chen, Jairus Hihn, and Karen Lum. "Selecting Best Practices for Effort Estimation." IEEE Transactions on Software Engineering 32, no. 11 (November 2006): 883–95. http://dx.doi.org/10.1109/tse.2006.114.

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10

Cho, Seong-ho, Ku-bong Min, and Chong-kwon Kim. "Cyclic best-effort multicasting with FEC." Electronics Letters 36, no. 4 (2000): 315. http://dx.doi.org/10.1049/el:20000310.

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11

Wang, Chi, Hongbo Wang, Yu Lin, and Shanzhi Chen. "Evolution of below-best-effort service." Frontiers of Electrical and Electronic Engineering in China 5, no. 1 (November 3, 2009): 15–22. http://dx.doi.org/10.1007/s11460-009-0069-z.

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12

Audsley, N. C., A. Burns, R. I. Davis, and A. J. Wellings. "Integrating Best Effort and Fixed Priority Scheduling." IFAC Proceedings Volumes 27, no. 6 (June 1994): 45–50. http://dx.doi.org/10.1016/s1474-6670(17)45965-9.

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13

Peng Li and B. Ravindran. "Fast, best-effort real-time scheduling algorithms." IEEE Transactions on Computers 53, no. 9 (September 2004): 1159–75. http://dx.doi.org/10.1109/tc.2004.61.

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14

Mohamedin, Mohamed, Roberto Palmieri, Ahmed Hassan, and Binoy Ravindran. "Managing Resource Limitation of Best-Effort HTM." IEEE Transactions on Parallel and Distributed Systems 28, no. 8 (August 1, 2017): 2299–313. http://dx.doi.org/10.1109/tpds.2017.2668415.

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15

Cao, Jin, William S. Cleveland, and Don X. Sun. "Bandwidth Estimation for Best-Effort Internet Traffic." Statistical Science 19, no. 3 (August 2004): 518–43. http://dx.doi.org/10.1214/088342304000000260.

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16

Imhoff, F., O. Spaniol, C. Linnhoff-Popien, and M. Garschhammer. "Aachen-Münchener Teleteaching unter Best-Effort-Bedingungen." PIK - Praxis der Informationsverarbeitung und Kommunikation 23, no. 3 (September 2000): 156–63. http://dx.doi.org/10.1515/piko.2000.156.

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17

Audsley, N. C., A. Burns, R. I. Davis, and A. J. Wellings. "Integrating best effort and fixed priority scheduling." Annual Review in Automatic Programming 18 (January 1994): 45–50. http://dx.doi.org/10.1016/0066-4138(94)90009-4.

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18

Shim, Hyotaek, Jin-soo Kim, and Seungryoul Maeng. "BEST: Best-effort energy saving techniques for NAND flash-based hybrid storage." IEEE Transactions on Consumer Electronics 58, no. 3 (August 2012): 841–48. http://dx.doi.org/10.1109/tce.2012.6311326.

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19

Petrere Jr., M., HC Giacomini, and P. De Marco Jr. "Catch-per-unit-effort: which estimator is best?" Brazilian Journal of Biology 70, no. 3 (March 19, 2010): 483–91. http://dx.doi.org/10.1590/s1519-69842010005000010.

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In this paper we examine the accuracy and precision of three indices of catch-per-unit-effort (CPUE). We carried out simulations, generating catch data according to six probability distributions (normal, Poisson, lognormal, gamma, delta and negative binomial), three variance structures (constant, proportional to effort and proportional to the squared effort) and their magnitudes (tail weight). The Jackknife approach of the index is recommended, whenever catch is proportional to effort or even under small deviations from proportionality assumption, when a ratio estimator is to be applied and little is known about the underlying behaviour of variables, as is the case for most fishery studies.

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20

Quislant, Ricardo, Eladio Gutierrez, Emilio L. Zapata, and Oscar Plata. "Lazy Irrevocability for Best-Effort Transactional Memory Systems." IEEE Transactions on Parallel and Distributed Systems 28, no. 7 (July 1, 2017): 1919–32. http://dx.doi.org/10.1109/tpds.2016.2638815.

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21

Titos-Gil, Ruben, Ricardo Fernandez-Pascual, Alberto Ros, and Manuel E. Acacio. "Concurrent Irrevocability in Best-Effort Hardware Transactional Memory." IEEE Transactions on Parallel and Distributed Systems 31, no. 6 (June 1, 2020): 1301–15. http://dx.doi.org/10.1109/tpds.2019.2963030.

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22

Clark, D. D., and Wenjia Fang. "Explicit allocation of best-effort packet delivery service." IEEE/ACM Transactions on Networking 6, no. 4 (1998): 362–73. http://dx.doi.org/10.1109/90.720870.

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23

Ng, T. S. Eugene, Donpaul C. Stephens, Ion Stoica, and Hui Zhang. "Supporting best-effort traffic with fair service curve." ACM SIGMETRICS Performance Evaluation Review 27, no. 1 (June 1999): 218–19. http://dx.doi.org/10.1145/301464.301580.

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24

Ma, Huadong, G. Kang Shin, and Weibiao Wu. "Best-Effort Patching for Multicast True VoD Service." Multimedia Tools and Applications 26, no. 1 (May 2005): 101–22. http://dx.doi.org/10.1007/s11042-005-6851-x.

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25

Keung, Jacky, Ekrem Kocaguneli, and Tim Menzies. "Finding conclusion stability for selecting the best effort predictor in software effort estimation." Automated Software Engineering 20, no. 4 (May 12, 2012): 543–67. http://dx.doi.org/10.1007/s10515-012-0108-5.

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26

Watson, David M. "Sampling effort determination in bird surveys: do current norms meet best-practice recommendations?" Wildlife Research 44, no. 3 (2017): 183. http://dx.doi.org/10.1071/wr16226.

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A critical design component of studies measuring diversity is sampling effort. Allocation of sampling effort dictates how many sites can be sampled within a particular time-frame or budget, as well as sample duration, frequency and intensity, thereby determining the resolution and reliability of emergent inferences. Conventional survey techniques use fixed-effort methods that assume invariant detectabilities among sites and species. Several approaches have been developed in the past decade that account for variable detectability by using alternative sampling methods or by adjusting standard counts before analysis, but it is unclear how widely adopted these techniques have been or how current bird surveying norms compare with best-practice recommendations. I conducted a systematic search of the primary literature to ascertain how sampling effort is determined, how much effort is devoted to sampling each site and how variation in detectability is dealt with. Of 225 empirical studies of bird diversity published between 2004 and 2016, five used results-based stopping rules (each derived independently), 54 used proportional sampling, and 159 (71%) used implicit effort-based stopping rules (fixed effort). Effort varied widely, but 61% of studies used samples of 10min or less and 62% of studies expended total effort per datum of 2h or less, with 78% providing no justification for sampling efforts used and just 15% explicitly accounting for estimated detectability. Given known variation in detectability, relying on short-duration fixed-effort approaches without validation or post hoc correction means that most bird diversity studies necessarily under-sample some sites and/or species. Having identified current bird surveying norms and highlighted their shortcomings, I provide five practical solutions to improve sampling effort determination, urging contributors and consumers of empirical ecological literature to consider survey data in terms of sample completeness.

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27

KOSHIMIZU, Seiichi. "Our Effort for The Best Quality Whiskies and Brandies." JOURNAL OF THE BREWING SOCIETY OF JAPAN 90, no. 10 (1995): 744–48. http://dx.doi.org/10.6013/jbrewsocjapan1988.90.744.

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28

Park, Jong Seung, and Joo-Ho Lee. "Best Effort Location for a Device in Reconfigurable Environment." Journal of Advances in Information Technology 7, no. 3 (2016): 146–55. http://dx.doi.org/10.12720/jait.7.3.146-155.

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29

Jiang, Z., Y. Ge, and Y. Li. "Max-utility wireless resource management for best-effort traffic." IEEE Transactions on Wireless Communications 4, no. 1 (January 2005): 100–111. http://dx.doi.org/10.1109/twc.2004.840210.

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30

Roy, Senjuti Basu, Tina Eliassi-Rad, and Spiros Papadimitriou. "Fast Best-Effort Search on Graphs with Multiple Attributes." IEEE Transactions on Knowledge and Data Engineering 27, no. 3 (March 1, 2015): 755–68. http://dx.doi.org/10.1109/tkde.2014.2345482.

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31

Shao, Xu, Yuebin Bai, Xiaofei Cheng, Yong-Kee Yeo, Luying Zhou, and Lek Heng Ngoh. "Best Effort SRLG Failure Protection for Optical WDM Networks." Journal of Optical Communications and Networking 3, no. 9 (August 30, 2011): 739. http://dx.doi.org/10.1364/jocn.3.000739.

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32

Jaradat, Ashraf, Ahmed Abu Halimeh, Aziz Deraman, and Fadi Safieddine. "A best-effort integration framework for imperfect information spaces." International Journal of Intelligent Information and Database Systems 11, no. 4 (2018): 296. http://dx.doi.org/10.1504/ijiids.2018.096592.

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33

Safieddine, Fadi, Aziz Deraman, Ahmed Abu Halimeh, and Ashraf Jaradat. "A best-effort integration framework for imperfect information spaces." International Journal of Intelligent Information and Database Systems 11, no. 4 (2018): 296. http://dx.doi.org/10.1504/ijiids.2018.10017942.

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34

Quislant, Ricardo, Eladio Gutierrez, Emilio L. Zapata, and Oscar Plata. "Enhancing scalability in best-effort hardware transactional memory systems." Journal of Parallel and Distributed Computing 104 (June 2017): 73–87. http://dx.doi.org/10.1016/j.jpdc.2017.01.002.

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35

Gevros, P., J. Crowcroft, P. Kirstein, and S. Bhatti. "Congestion control mechanisms and the best effort service model." IEEE Network 15, no. 3 (2001): 16–26. http://dx.doi.org/10.1109/65.923937.

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36

Gibbens, R., and P. Key. "Distributed control and resource marking using best-effort routers." IEEE Network 15, no. 3 (2001): 54–59. http://dx.doi.org/10.1109/65.923941.

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37

Hurley, P., J. Y. Le Boudec, P. Thiran, and M. Kara. "ABE: providing a low-delay service within best effort." IEEE Network 15, no. 3 (2001): 60–69. http://dx.doi.org/10.1109/65.923942.

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38

Macilwain, Colin. "$40m plant genome sequencing effort targets the best science." Nature 390, no. 6660 (December 1997): 539–40. http://dx.doi.org/10.1038/37412.

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39

Turunen, Jari, Pekka Loula, and Tarmo Lipping. "Assessment of objective voice quality over best-effort networks." Computer Communications 28, no. 5 (March 2005): 582–88. http://dx.doi.org/10.1016/j.comcom.2004.02.027.

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40

Li, Xianxu, Qing Chang, and Yong Xu. "Queueing Characteristics of the Best Effort Network Coding Strategy." IEEE Access 4 (2016): 5990–97. http://dx.doi.org/10.1109/access.2016.2611619.

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41

Farber, Alik, Kenny Rosenfield, Jeff Siracuse, Cari Reynolds, and Matthew Menard. "The BEST-CLI Trial: A Multidisciplinary Effort at Work!" Annals of Vascular Surgery 41 (May 2017): 4–5. http://dx.doi.org/10.1016/j.avsg.2017.03.118.

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42

Thompson, R. M., E. D. Edwards, A. R. McIntosh, and C. R. Townsend. "Allocation of effort in stream food-web studies: the best compromise?" Marine and Freshwater Research 52, no. 3 (2001): 339. http://dx.doi.org/10.1071/mf00041.

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The amount and allocation of effort needed to characterize stream food webs was investigated in five replicate streams. Two areas were considered:analysis of community composition (number of individuals sampled)and of diets (number of individuals gutted per animal taxon). Food webs were described by use of consistent methodology, then the effort was retrospectively reduced by considering half of the gut samples (halving dietary analysis effort)and by successively reducing the number of individuals included. Food webs with a reduced number of individuals overestimated connectance and prey:predator ratios, and underestimated species richness, links per species and mean chain lengths. These changes were due to loss of some invertebrate predator species when effort was reduced. In contrast, for dietary analysis the amount of effort expended on non-predatory invertebrates was more influential; halving effort in dietary analysis of non-predatory taxa reduced estimates of connectance and links per species. This study suggests that the effort needed to produce a reasonable estimate is highly dependent on the food-web attribute in question, and that aiming for equity of effort across taxonomic groups is as important as expending greater effort in general.

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43

Russo, Daniel. "Simple Bayesian Algorithms for Best-Arm Identification." Operations Research 68, no. 6 (November 2020): 1625–47. http://dx.doi.org/10.1287/opre.2019.1911.

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This paper considers the optimal adaptive allocation of measurement effort for identifying the best among a finite set of options or designs. An experimenter sequentially chooses designs to measure and observes noisy signals of their quality with the goal of confidently identifying the best design after a small number of measurements. Just as the multiarmed bandit problem crystallizes the tradeoff between exploration and exploitation, this “pure exploration” variant crystallizes the challenge of rapidly gathering information before committing to a final decision. The paper proposes several simple Bayesian algorithms for allocating measurement effort and, by characterizing fundamental asymptotic limits on the performance of any algorithm, formalizes a sense in which these seemingly naive algorithms are the best possible.

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44

Jourdan, Dawn, Eric Strauss, and Madeline Hunter. "Sign Code Development Process: Best Practices." Interdisciplinary Journal of Signage and Wayfinding 2, no. 1 (December 29, 2017): 34–59. http://dx.doi.org/10.15763/issn.2470-9670.2017.v2.i1.a21.

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A wide variety of community stakeholders have an interest in ensuring that sign codes are crafted in such a way as to allow for the free flow of speech while preserving community character. These stakeholders may include businesses, sign companies, graphic designers, historic preservationists, traffic safety specialists, environmental quality advocates, and chamber of commerce types, among others. Including interested parties in efforts to develop and revise sign codes can help ensure that the resulting regulation embraces the best available technologies and business practices of the time in an effort to promote the economic vitality of local business districts. This report seeks to: (a) explore best practices in citizen participation practices revolving around the development or amendment of sign codes; (b) to evaluate the experiences of communities who have revised their sign codes in the last 10 years; and (c) to establish best practices for improving the sign code development process.

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45

Manvi, Sunilkumar S., and Pallapa Venkataram. "An Agent-Based Best Effort Routing Technique for Load Balancing." Informatica 17, no. 3 (January 1, 2006): 407–26. http://dx.doi.org/10.15388/informatica.2006.146.

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46

Novitasari, Dinda, Imam Cholissodin, and Wayan Firdaus Mahmudy. "Optimizing SVR using Local Best PSO for Software Effort Estimation." Journal of Information Technology and Computer Science 1, no. 1 (April 16, 2016): 28. http://dx.doi.org/10.25126/jitecs.2016117.

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Abstract. In the software industry world, it’s known to fulfill the tremendous demand. Therefore, estimating effort is needed to optimize the accuracy of the results, because it has the weakness in the personal analysis of experts who tend to be less objective. SVR is one of clever algorithm as machine learning methods that can be used. There are two problems when applying it; select features and find optimal parameter value. This paper proposed local best PSO-SVR to solve the problem. The result of experiment showed that the proposed model outperforms PSO-SVR and T-SVR in accuracy. Keywords: Optimization, SVR, Optimal Parameter, Feature Selection, Local Best PSO, Software Effort Estimation

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47

Turk, Tomaž, and Borka Jerman-Blažič. "Users' responsiveness in the price-controlled best-effort QoS model." Computer Communications 24, no. 15-16 (October 2001): 1637–47. http://dx.doi.org/10.1016/s0140-3664(01)00314-0.

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48

Stanojevi, Rade, and Robert Shorten. "Fully decentralized emulation of best-effort and processor sharing queues." ACM SIGMETRICS Performance Evaluation Review 36, no. 1 (June 12, 2008): 383–94. http://dx.doi.org/10.1145/1384529.1375501.

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49

Peha, Jon M. "Dynamic pricing and congestion control for best-effort ATM services." Computer Networks 32, no. 3 (March 2000): 333–45. http://dx.doi.org/10.1016/s1389-1286(00)00003-7.

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50

Shajahan, B., and Srinivasan Alavandar. "Congestion Controller for Best Effort Networks Using Fuzzy Inference System." Journal of Computational and Theoretical Nanoscience 15, no. 1 (January 1, 2018): 40–46. http://dx.doi.org/10.1166/jctn.2018.7053.

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