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Journal articles on the topic 'Corrupted'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Choi, Charles. "Corrupted Clones." Scientific American 288, no. 6 (2003): 29. http://dx.doi.org/10.1038/scientificamerican0603-29a.

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2

Mercuri, Rebecca. "Corrupted polling." Communications of the ACM 36, no. 11 (1993): 122. http://dx.doi.org/10.1145/163359.163380.

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3

Beckerman, Wilfred. "Corrupted science?" Nature 369, no. 6476 (1994): 109. http://dx.doi.org/10.1038/369109a0.

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4

Tien, Lee, and Jeremy Gillula. "Corrupted code." New Scientist 228, no. 3051 (2015): 26–27. http://dx.doi.org/10.1016/s0262-4079(15)31753-x.

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5

Posner, Michael V. "Corrupted by money?" Nature 382, no. 6587 (1996): 123–24. http://dx.doi.org/10.1038/382123a0.

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6

Fuhrer, Michael S., and Shaffique Adam. "Carbon conductor corrupted." Nature 458, no. 7234 (2009): 38–39. http://dx.doi.org/10.1038/458038a.

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7

O'Gorman, Ellen. "No Place Like Rome: Identity and Difference in the Germania of Tacitus." Ramus 22, no. 2 (1993): 135–54. http://dx.doi.org/10.1017/s0048671x00002484.

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The Germania, as its full title de origine et situ Germanorum implies, is about Rome. This is clearest from passages couched almost entirely in negative terms, as in chapter 19— ergo saepta pudicitia agunt, nullis spectaculorum illecebris, nullis conviviorum irritationibus corruptae. litterarum secreta viri pariter ac feminae ignorant…nemo enim illic vitia ridet, nee corrumpere et corrumpi saeculum vocatur.(19.1-3)Therefore they live within the confines of chastity, uncorrupted by the enticements of the spectacle or the excitements of the banquet. Women and men alike are unaware of the use of

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8

Lichtman, Richard. "Cry the Corrupted Country." Critique 36, no. 1 (2008): 141–54. http://dx.doi.org/10.1080/03017600801892797.

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9

Molette. "When conflicted becomes corrupted." Journal of Cell Science 124, no. 24 (2011): 4133–35. http://dx.doi.org/10.1242/jcs.103077.

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10

Crotty, T. P. "The corrupted feedback hypothesis." Medical Hypotheses 61, no. 5-6 (2003): 605–16. http://dx.doi.org/10.1016/s0306-9877(03)00243-3.

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11

Mlađenović, Nikola. "Corrupted tragedy, alienated utopia." Kultura, no. 159 (2018): 261–80. http://dx.doi.org/10.5937/kultura1859261m.

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12

Amsterdam, Jay D., Leemon B. McHenry, Jon N. Jureidini, and Katarzyna Cyranka. "Industry-corrupted psychiatric trials." Psychiatria Polska 51, no. 6 (2017): 993–1008. http://dx.doi.org/10.12740/pp/80136.

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13

Pratt, Philip C. "Was the literature “corrupted”?" American Journal of Industrial Medicine 22, no. 4 (1992): 609–11. http://dx.doi.org/10.1002/ajim.4700220416.

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14

Deng, Hongyao, Qingxin Zhu, Xiuli Song, and Jinsong Tao. "A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal." Computational Intelligence and Neuroscience 2017 (2017): 1–20. http://dx.doi.org/10.1155/2017/2024396.

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Impulsive noise removal usually employs median filtering, switching median filtering, the total variation L1 method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is

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15

Bateman, Vesta, and Ronald Merritt. "Validation of Pyroshock Data." Journal of the IEST 55, no. 1 (2012): 40–56. http://dx.doi.org/10.17764/jiet.55.1.2q4650xqt7j0k506.

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This paper presents a comprehensive description of pyroshock, the interpretation of pyroshock data, and the validation of pyroshock data. Recent events in the pyroshock testing community show that corrupted pyroshock data are still being acquired at government agencies and private companies. A large part of this paper is devoted to acquisition and analysis of pyroshock data because proper time-history data acquisition and, consequently, test specification development are common industry problems. To avoid corrupted pyroshock data and thus corrupted pyroshock specifications, recommended practic

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16

Otáhal, Tomáš, Milan Palát, and Petr Wawrosz. "What is the Contribution of the Theory of Redistribution Systems to the Theory of Corruption?" Review of Economic Perspectives 13, no. 2 (2013): 92–107. http://dx.doi.org/10.2478/revecp-2013-0003.

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Abstract Scholars making economic policy recommendations to resolve corruption problem use several approaches, the most dominant of which are the principal-agent and rent-seeking theories. In this paper, we argue that the principal-agent theory has problems to account for the environment in which the agents offering and accepting corruption operate, and explain the importance of agents for survival of their environment. The rent-seeking theory, on the other hand, finds it difficult to establish socially effective legislation and ways to determine the barriers to entry that motivate agents to b

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17

Deng, Hongyao, Qingxin Zhu, and Xiuli Song. "Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images." Journal of Sensors 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/7635641.

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Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation L1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i) each channel in a color image is contaminated independently, and contaminative components are independent and identically distribut

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18

Broniatowski, Michel, Jana Jurečková, Ashok Moses, and Emilie Miranda. "Composite Tests under Corrupted Data." Entropy 21, no. 1 (2019): 63. http://dx.doi.org/10.3390/e21010063.

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This paper focuses on test procedures under corrupted data. We assume that the observations Z i are mismeasured, due to the presence of measurement errors. Thus, instead of Z i for i = 1 , … , n, we observe X i = Z i + δ V i, with an unknown parameter δ and an unobservable random variable V i. It is assumed that the random variables Z i are i.i.d., as are the X i and the V i. The test procedure aims at deciding between two simple hyptheses pertaining to the density of the variable Z i, namely f 0 and g 0. In this setting, the density of the V i is supposed to be known. The procedure which we p

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19

Lacaze, B., and C. Mailhes. "Interlaced Sampling Corrupted by Noise." Sampling Theory in Signal and Image Processing 1, no. 3 (2002): 185–205. http://dx.doi.org/10.1007/bf03549378.

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20

Melnik, Gil, Yuval Yekutieli, and Andrei Sharf. "Deep Segmentation of Corrupted Glyphs." Journal on Computing and Cultural Heritage 15, no. 1 (2022): 1–24. http://dx.doi.org/10.1145/3465629.

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Historical documents and archaeological artifacts are hard to process due to natural degradation, fading, spills, tears, overlaid data,, and so on. In this work, we focus on the task of recovering characters and symbols from images of corrupted archaeological artifacts where data is partially erased, occluded, or overwritten by other data. Such phenomena can be widely observed in image datasets of palimpsests and petroglyphs consisting of erased, overwritten, and in general heavily degraded data. Segmentation and binarization are typically applied to such images to detect and recover character

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21

Schneider-Poetsch, Tilman, Takeo Usui, Daisuke Kaida, and Minoru Yoshida. "Garbled messages and corrupted translations." Nature Chemical Biology 6, no. 3 (2010): 189–98. http://dx.doi.org/10.1038/nchembio.326.

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22

Apffelstaedt, Arno, and Jana Freundt. "Corrupted Votes and Rule Compliance." American Economic Journal: Microeconomics 16, no. 4 (2024): 440–74. http://dx.doi.org/10.1257/mic.20200038.

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Allegations of voter fraud accompany many real-world elections. How does electoral malpractice affect the acceptance of elected institutions? Using an online experiment in which participants distribute income according to majority-elected rules, we show that those who experience vote buying or voter disenfranchisement during the election are subsequently less likely to voluntarily comply with a rule. On average, the detrimental impact of electoral malpractice on compliance is of the same magnitude as removing the election altogether and imposing a rule exogenously. Our experiment shows how cor

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23

Studer, Christoph, Patrick Kuppinger, Graeme Pope, and Helmut Bolcskei. "Recovery of Sparsely Corrupted Signals." IEEE Transactions on Information Theory 58, no. 5 (2012): 3115–30. http://dx.doi.org/10.1109/tit.2011.2179701.

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24

Hart, Andrew, and Heinrich Matzinger. "Markers for error-corrupted observations." Stochastic Processes and their Applications 116, no. 5 (2006): 807–29. http://dx.doi.org/10.1016/j.spa.2005.11.012.

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25

Wang, Yulin, Rui Huang, Gao Huang, Shiji Song, and Cheng Wu. "Collaborative learning with corrupted labels." Neural Networks 125 (May 2020): 205–13. http://dx.doi.org/10.1016/j.neunet.2020.02.010.

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26

LEPKOWSKI, WIL. "HAS 'BIG MONEY' CORRUPTED SCIENCE?" Chemical & Engineering News 79, no. 44 (2001): 59–60. http://dx.doi.org/10.1021/cen-v079n044.p059.

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27

Costandi, Moheb. "Evidence-based justice: Corrupted memory." Nature 500, no. 7462 (2013): 268–70. http://dx.doi.org/10.1038/500268a.

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28

Lelis, Levi, Richard Valenzano, Gabriel Nazar, and Roni Stern. "Searching with a Corrupted Heuristic." Proceedings of the International Symposium on Combinatorial Search 7, no. 1 (2021): 63–71. http://dx.doi.org/10.1609/socs.v7i1.18394.

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Memory-based heuristics are a popular and effective class of admissible heuristic functions. However, corruptions to memory they use may cause these heuristics to become inadmissible. Corruption can be caused by the physical environment due to radiation and network errors, or it can be introduced voluntarily in order to decrease energy consumption. We introduce memory error correction schemes that do not require additional memory and exploit knowledge about the behavior of consistent heuristics. This is in contrast with error correcting code approaches which can limit the amount of corruption

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29

Ahamed, Bulbul, Rasel Ahmed, Khaled Mahmud, and Mohammad Nurul Huda. "Robust Phoneme Recognizer at Noise Corrupted Acoustic Environment." Scholars Journal of Engineering and Technology 4, no. 7 (2016): 308–11. http://dx.doi.org/10.21276/sjet.2016.4.7.3.

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30

Khompurngson, Kannika, and Boriboon Novaprateep. "Hypercircle Inequality for Partially-Corrupted Data." Annals of Functional Analysis 6, no. 1 (2015): 95–108. http://dx.doi.org/10.15352/afa/06-1-8.

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31

Karim, Kelvin. "Corrupted Clothier fits up Allitt staff." Nursing Standard 8, no. 22 (1994): 41. http://dx.doi.org/10.7748/ns.8.22.41.s42.

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32

Chakravarti, Srinjay. "Hrishikesh: A Poem on Corrupted Landscape." eTropic: electronic journal of studies in the Tropics 21, no. 1 (2022): 388–91. http://dx.doi.org/10.25120/etropic.21.1.2022.3845.

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This poem on the pilgrimage center of Hrishikesh set in a humid subtropical niche of the scenic Uttarakhand state, aims to capture the corruption of its cultural, religious and natural landscapes. Here, modernity — with its concomitant technologism — jostles for space with Hindu leitmotifs and traditions, causing pollution, ecological damage and environmental degradation. These are outcomes not just of distorted economic policies and skewed technological and developmental paradigms, but also the residuum of religious rituals, pollutants and garbage dumped into the holy Ganges. Named after a fo

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33

Sun, Zhongxing, Wei Cui, and Yulong Liu. "Quantized Corrupted Sensing With Random Dithering." IEEE Transactions on Signal Processing 70 (2022): 600–615. http://dx.doi.org/10.1109/tsp.2022.3141884.

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34

Sloan, Gary. "Twain's THE MAN THAT CORRUPTED HADLEYBURG." Explicator 58, no. 2 (2000): 83–85. http://dx.doi.org/10.1080/00144940009597018.

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35

Church, Joseph. "Twain’s the Man That Corrupted Hadleyburg." Explicator 49, no. 2 (1991): 94–97. http://dx.doi.org/10.1080/00144940.1991.11484019.

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36

Schneider-Poetsch, Tilman, Takeo Usui, Daisuke Kaida, and Minoru Yoshida. "Erratum: Garbled messages and corrupted translations." Nature Chemical Biology 6, no. 4 (2010): 306. http://dx.doi.org/10.1038/nchembio0410-306e.

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37

von Frank, Albert J. "The Man That Corrupted Sleepy Hollow." Studies in American Fiction 15, no. 2 (1987): 129–43. http://dx.doi.org/10.1353/saf.1987.0002.

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38

Brown, Alasdair, and Fuyu Yang. "Have Betting Exchanges Corrupted Horse Racing?" Journal of Sports Economics 18, no. 7 (2015): 673–97. http://dx.doi.org/10.1177/1527002515595267.

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39

Lee, T. C. M. "Segmenting images corrupted by correlated noise." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 5 (1998): 481–92. http://dx.doi.org/10.1109/34.682178.

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40

Chen, Jinchi, and Yulong Liu. "Data-Time Tradeoffs for Corrupted Sensing." IEEE Signal Processing Letters 25, no. 7 (2018): 941–45. http://dx.doi.org/10.1109/lsp.2018.2833428.

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41

Li, Teng, Yong Dou, Xinwang Liu, Yang Zhao, and Qi Lv. "Multiple kernel clustering with corrupted kernels." Neurocomputing 267 (December 2017): 447–54. http://dx.doi.org/10.1016/j.neucom.2017.06.044.

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42

Bagautdinova, N. G., I. V. Goncharova, E. Y. Shurkina, A. V. Sarkin, B. A. Averyanov, and A. A. Svirina. "Entrepreneurial Development in a Corrupted Environment." Procedia Economics and Finance 5 (2013): 73–82. http://dx.doi.org/10.1016/s2212-5671(13)00012-9.

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43

Wang, Ruxin, Tongliang Liu, and Dacheng Tao. "Multiclass Learning With Partially Corrupted Labels." IEEE Transactions on Neural Networks and Learning Systems 29, no. 6 (2018): 2568–80. http://dx.doi.org/10.1109/tnnls.2017.2699783.

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44

Fan, Haiyan, and Gangyao Kuang. "Recovery of Corrupted Low-Rank Tensors." Applied Mathematics 08, no. 02 (2017): 229–44. http://dx.doi.org/10.4236/am.2017.82019.

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45

Caro, Pedro, and Andoni Garcia. "The Calderón problem with corrupted data." Inverse Problems 33, no. 8 (2017): 085001. http://dx.doi.org/10.1088/1361-6420/aa7425.

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46

Yao, C., X. Yang, and L. Chen. "Unified framework for colourising corrupted image." IET Image Processing 6, no. 4 (2012): 345. http://dx.doi.org/10.1049/iet-ipr.2010.0249.

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47

Ellis, John M. "How Preferences Have Corrupted Higher Education." Academic Questions 21, no. 3 (2008): 265–74. http://dx.doi.org/10.1007/s12129-008-9062-x.

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48

Manjunath, G., S. Sivaji Ganesh, and G. V. Anand. "Denoising signals corrupted by chaotic noise." Communications in Nonlinear Science and Numerical Simulation 15, no. 12 (2010): 3988–97. http://dx.doi.org/10.1016/j.cnsns.2010.01.015.

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49

Badgainya, Shruti, Prof Pankaj Sahu, and Prof Vipul Awasthi. "Image Denoising by OWT for Gaussian Noise Corrupted Images." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 2477–84. http://dx.doi.org/10.31142/ijtsrd18337.

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50

Seong, Jin-Taek. "Bounds on Performance for Recovery of Corrupted Labels in Supervised Learning: A Finite Query-Testing Approach." Mathematics 11, no. 17 (2023): 3636. http://dx.doi.org/10.3390/math11173636.

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Label corruption leads to a significant challenge in supervised learning, particularly in deep neural networks. This paper considers recovering a small corrupted subset of data samples which are typically caused by non-expert sources, such as automatic classifiers. Our aim is to recover the corrupted data samples by exploiting a finite query-testing system as an additional expert. The task involves identifying the corrupted data samples with minimal expert queries and finding them to their true label values. The proposed query-testing system uses a random selection of a subset of data samples

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