Journal articles on the topic 'Rage Against The Machine'

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1

Bringsjord, Selmer, and Joe Johnson. "Rage against the machine." Philosophers' Magazine, no. 57 (2012): 90–95. http://dx.doi.org/10.5840/tpm20125755.

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Thornton, Stephanie. "Rage against the machine." 5 to 7 Educator 2009, no. 50 (February 2009): 14–15. http://dx.doi.org/10.12968/ftse.2009.8.2.37716.

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Crown, Hannah. "Rage against the machine." Nursery World 2016, Sup19 (September 18, 2016): 4–6. http://dx.doi.org/10.12968/nuwa.2016.sup19.4.

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4

Bonn, Dorothy. "RAGE against the cancer machine." Lancet 355, no. 9217 (May 2000): 1795. http://dx.doi.org/10.1016/s0140-6736(05)73063-4.

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Rasmussen, Keith G. "Rage Against the (ECT) Machine." Journal of ECT 31, no. 1 (March 2015): 1–2. http://dx.doi.org/10.1097/yct.0000000000000171.

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6

Smyth,, D., R. Barker, and T. Belcher. "Rage against the (translation) machine." Journal of Intellectual Property Law & Practice 10, no. 3 (January 23, 2015): 153–54. http://dx.doi.org/10.1093/jiplp/jpu257.

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Bezerra, Arthur Coelho, and Marco Antônio de Almeida. "Rage against the machine learning." Brazilian Journal of Information Science 14, no. 2 Abr-Jun (June 30, 2020): 06–23. http://dx.doi.org/10.36311/1981-1640.2020.v14n2.02.p6.

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Before being an exaltation to Luddites (the English workers from the 19th century who actually destroyed textile machinery as a form of protest) or to some sort of technophobic movement, the provocative pun contained in the title of this article carries a methodological proposal, in the field of critical theory of information, to build a diagnosis about the algorithmic filtering of information, which reveals itself to be a structural characteristic of the new regime of information that brings challenges to human emancipation. Our analysis starts from the concept of mediation to problematize the belief, widespread in much of contemporary society, that the use of machine learning and deep learning techniques for algorithmic filtering of big data will provide answers and solutions to all our questions and problems. We will argue that the algorithmic mediation of information on the internet, which is responsible for deciding which information we will have access to and which will remain invisible, is operated according to the economic interests of the companies that control the platforms we visit on the internet, acting as obstacle to the prospects of informational diversity and autonomy that are fundamental in free and democratic societies.
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8

Lamy, Jérôme. "Christophe Levaux, Rage Against The Machine." Volume !, no. 15 : 1 (December 5, 2018): 177–79. http://dx.doi.org/10.4000/volume.6006.

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9

Erasmus, David B., Si M. Pham, and Kevin P. Landolfo. "Commentary: Rage against the machine (ventilator that is)." Journal of Thoracic and Cardiovascular Surgery 160, no. 5 (November 2020): 1397–98. http://dx.doi.org/10.1016/j.jtcvs.2020.03.070.

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10

Mason, Chris, and John Simmons. "Rage against the machine: moral anger in whistleblowing." Qualitative Research in Organizations and Management: An International Journal 14, no. 3 (August 29, 2019): 337–55. http://dx.doi.org/10.1108/qrom-10-2017-1572.

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Purpose The purpose of this paper is to offer a theoretical framework of whistleblowing that gives due recognition to the emotional and reflexive processes that underpin it. Modes of anger are integrated into the model based on a reading of Geddes and Callister (2007), and developed by Lindebaum and Geddes (2016) work on moral anger. Design/methodology/approach The model is derived by interrogation of the extant literature on whistleblowing with due recognition accorded to emotional and reflexive dimensions that have been underrepresented in previous research. The model was tested by a qualitative study that uses memoir analysis to interrogate a board level whistle-blower’s account of the complex, traumatic and like-changing nature of his experience. Findings The paper identifies key stages in whistle-blower thinking before, during and subsequent to a decision to expose corporate wrongdoing. It demonstrates how emotional and reflexive processes influence a whistle-blower’s mode of anger expression, and how different perspectives by the whistle-blower and the focal organisation may view this expression as moral or deviant anger. Research limitations/implications The complexity of the whistleblowing process, together with possible alternative perspectives of it, makes identifying every influencing variable extremely challenging. Also, reliance on a whistle-blower’s own account of his experience means that recall may be partial or self-serving. The model can be used to analyse other whistle-blower accounts of their experience, and further confirm its applicability. Originality/value This is the first application of memoir analysis to a whistle-blower’s account of his experience that relates modes of anger expression to stages in the whistleblowing episode. It addresses a significant imbalance in whistleblowing research that hitherto has emphasised rationality in whistle-blower decision making and downplayed the influence of reflexivity and emotion.
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Maurer, Yael. "Rage against the machine: Cyberspace narratives in Rushdie’sFury." Journal of Commonwealth Literature 47, no. 1 (March 2012): 121–35. http://dx.doi.org/10.1177/0021989411425480.

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Gogoll, Jan, and Matthias Uhl. "Rage against the machine: Automation in the moral domain." Journal of Behavioral and Experimental Economics 74 (June 2018): 97–103. http://dx.doi.org/10.1016/j.socec.2018.04.003.

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13

Moon, Stephanie L., and Jeffrey Wilusz. "Cytoplasmic Viruses: Rage against the (Cellular RNA Decay) Machine." PLoS Pathogens 9, no. 12 (December 5, 2013): e1003762. http://dx.doi.org/10.1371/journal.ppat.1003762.

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14

Weeks, Liam. "Rage Against the Machine: Who is the Independent Voter?" Irish Political Studies 26, no. 1 (February 2011): 19–43. http://dx.doi.org/10.1080/07907184.2011.531104.

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15

Goodwin, Nastacia L., Simon R. O. Nilsson, and Sam A. Golden. "Rage Against the Machine: Advancing the study of aggression ethology via machine learning." Psychopharmacology 237, no. 9 (July 9, 2020): 2569–88. http://dx.doi.org/10.1007/s00213-020-05577-x.

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16

Kariko, Abdul Azis Turhan. "Sleep Now in The Fire: An Analysis of A Song by Rage Against The Machine Using Marxism." Humaniora 2, no. 2 (October 31, 2011): 1151. http://dx.doi.org/10.21512/humaniora.v2i2.3165.

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Rage against the Machine is known for their politics as well as their music, which the later helped creating an aggressive-heavy rock-rap genre. Article presents a song by Rage against the Machine band related to ideological movement in America, titled Sleep Now in the Fire. This effort brings an understanding ofideology that is embraced by the band. The method is through literature study. Presentation begins with a short biography of the band, theoretical concepts, and analysis of the song as well its music video. It is concluded that the song represents ideological criticism toward capitalism using Marxism. Both of these lyric and music video represents Marxism as it shares the same movement to fight capitalism—which in this case, Rage against the Machine is using their music, lyric, and video to fight the crime against humanity and cultural imperialism.
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17

Green, Andrew. "Rage Against The Machine, Zapatismo, and the aesthetics of anger." Popular Music 34, no. 3 (September 8, 2015): 390–407. http://dx.doi.org/10.1017/s0261143015000331.

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AbstractThis article explores the ways in which anger may travel across different musical and extra-musical contexts, looking in particular at the rap metal band Rage Against The Machine (RATM). Focusing on this band's appropriation of expressions of anger found in a documentary film about the Zapatistas in Mexico, it suggests that RATM can be read as a politico-emotional project, channelling anger towards political resistance which is then performed through, and upon, the body. Just as performances of anger need discourse to be sustainable, so music can simultaneously entrain the listener into a certain modality of feeling and expressing, and embed this modality in a discursive rationale. However, anger is a deeply ambiguous emotion, and cannot be easily focused; it may transcend the constraints some seek to place on it, and travel quickly between discursive contexts. Such slippages mark the limits of anger, as well as its power.
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18

Solak, Nevin, John T. Jost, Nebi Sümer, and Gerald L. Clore. "Rage Against the Machine: The Case for System-Level Emotions." Social and Personality Psychology Compass 6, no. 9 (September 2012): 674–90. http://dx.doi.org/10.1111/j.1751-9004.2012.00456.x.

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19

Yang, Charles. "Rage against the machine: Evaluation metrics in the 21st century." Language Acquisition 24, no. 2 (March 17, 2017): 100–125. http://dx.doi.org/10.1080/10489223.2016.1274318.

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20

Caprettini, Bruno, and Hans-Joachim Voth. "Rage against the Machines: Labor-Saving Technology and Unrest in Industrializing England." American Economic Review: Insights 2, no. 3 (September 1, 2020): 305–20. http://dx.doi.org/10.1257/aeri.20190385.

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Can new technology cause social instability and unrest? We examine the famous “Captain Swing” riots in 1830s England. Newly collected data on threshing machine diffusion shows that labor-saving technology was associated with more riots. We instrument technology adoption with the share of heavy soils in a parish: IV estimates demonstrate that threshing machines were an important cause of unrest. Where alternative employment opportunities softened the blow of new technology, there was less rioting. Conversely, where enclosures had impoverished workers, the effect of threshing machines on rioting was amplified. (JEL J24, L16, N13, N33, N53, O33, Q16)
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21

Storms, Stijn, Peggy Valcke, and Els Kindt. "Rage against the machine: does machine-to-machine communication fall within the scope of the confidentiality principle?" International Journal of Law and Information Technology 27, no. 4 (2019): 372–408. http://dx.doi.org/10.1093/ijlit/eaz012.

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Abstract In this paper we confront the technological notion of machine-to-machine communication (M2M) to the legal principle of confidentiality of communications (art. 5.1 ePrivacy Directive) in order to determine whether M2M is a protected communication. After demonstrating the answer is not straightforwardly found in the legal text we turn our attention to other aspects which can have an impact on the matter. We build up our case by considering four different levels. At the first level we ask whether the concepts used in the legal definition of ‘communication’ allow for the inclusion of M2M. At the second level we involve the history of the ePrivacy Directive in the discussion. At the third level the values behind the confidentiality principle come into play. Lastly at the fourth level we look at the issue from a practical stance. The resulting picture points towards the inclusion of M2M in the scope of the confidentiality principle.
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22

Teoh, Eric R., and David G. Kidd. "Rage against the machine? Google's self-driving cars versus human drivers." Journal of Safety Research 63 (December 2017): 57–60. http://dx.doi.org/10.1016/j.jsr.2017.08.008.

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23

Johnson, Nicola F., David Macdonald, and Tara Brabazon. "Rage against the Machine? Symbolic Violence in E-Learning Supported Tertiary Education." E-Learning and Digital Media 5, no. 3 (January 2008): 275–83. http://dx.doi.org/10.2304/elea.2008.5.3.275.

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24

Duersch, Peter, Albert Kolb, Jörg Oechssler, and Burkhard C. Schipper. "Rage against the machines: how subjects play against learning algorithms." Economic Theory 43, no. 3 (February 19, 2009): 407–30. http://dx.doi.org/10.1007/s00199-009-0446-0.

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25

Williams, John P. "Rage Against the Machine: Berkeley 1964 and the Birth of the Free Speech Movement." Perspectives on Global Development and Technology 17, no. 1-2 (February 13, 2018): 158–72. http://dx.doi.org/10.1163/15691497-12341472.

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Abstract This article examines the origins and contributions of the Freedom of Speech Movement (fsm) at the University of California, Berkeley (September-December 1964) that led to widespread social activism on other college and university campuses throughout the us. This article highlights the role of Mario Savio and other participants in the fsm while linking these efforts to the civil rights movements of the late 1950s and early 1960s. The essence of the fsm and its contribution to social activism by middle-class college and university students can be seen in the primary sources provided by Free Speech Movement Digital Archives.
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26

Lyall, Jason, and Isaiah Wilson. "Rage Against the Machines: Explaining Outcomes in Counterinsurgency Wars." International Organization 63, no. 1 (January 2009): 67–106. http://dx.doi.org/10.1017/s0020818309090031.

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AbstractDuring the nineteenth century, states routinely defeated insurgent foes. Over the twentieth century, however, this pattern reversed itself, with states increasingly less likely to defeat insurgents or avoid meeting at least some of their demands. What accounts for this pattern of outcomes in counterinsurgency (COIN) wars? We argue that increasing mechanization within state militaries after World War I is primarily responsible for this shift. Unlike their nineteenth-century predecessors, modern militaries possess force structures that inhibit information collection among local populations. This not only complicates the process of sifting insurgents from noncombatants but increases the difficulty of selectively applying rewards and punishment among the fence-sitting population. Modern militaries may therefore inadvertently fuel, rather than deter, insurgencies. We test this argument with a new data set of 286 insurgencies (1800–2005) and a paired comparison of two U.S. Army divisions in Iraq (2003–2004). We find that higher levels of mechanization, along with external support for insurgents and the counterinsurgent's status as an occupier, are associated with an increased probability of state defeat. By contrast, we find only partial support for conventional power- and regime-based explanations, and no support for the view that rough terrain favors insurgent success.
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27

Gulsrud, Natalie Marie, Christopher M. Raymond, Rebecca L. Rutt, Anton Stahl Olafsson, Tobias Plieninger, Mattias Sandberg, Thomas H. Beery, and K. Ingemar Jönsson. "‘Rage against the machine’? The opportunities and risks concerning the automation of urban green infrastructure." Landscape and Urban Planning 180 (December 2018): 85–92. http://dx.doi.org/10.1016/j.landurbplan.2018.08.012.

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Alexander, Neta. "Rage against the Machine: Buffering, Noise, and Perpetual Anxiety in the Age of Connected Viewing." Cinema Journal 56, no. 2 (2017): 1–24. http://dx.doi.org/10.1353/cj.2017.0000.

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Adam, Marc T. P., Timm Teubner, and Henner Gimpel. "No Rage Against the Machine: How Computer Agents Mitigate Human Emotional Processes in Electronic Negotiations." Group Decision and Negotiation 27, no. 4 (May 25, 2018): 543–71. http://dx.doi.org/10.1007/s10726-018-9579-5.

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Mongillo, Gianluigi, Hanan Shteingart, and Yonatan Loewenstein. "Race Against the Machine [Further Thoughts]." Proceedings of the IEEE 102, no. 4 (April 2014): 542–43. http://dx.doi.org/10.1109/jproc.2014.2308599.

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Darbyshire, Philip. "'Rage against the machine?': nurses' and midwives' experiences of using Computerized Patient Information Systems for clinical information." Journal of Clinical Nursing 13, no. 1 (January 2004): 17–25. http://dx.doi.org/10.1046/j.1365-2702.2003.00823.x.

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32

Schwing, Mel Andrew. "Don’t rage against the machine: why AI may be the cure for the ‘moral hazard’ of party appointments." Arbitration International 36, no. 4 (November 4, 2020): 491–507. http://dx.doi.org/10.1093/arbint/aiaa033.

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Abstract In 2010, Jan Paulsson decried the use of party-appointed arbitrators in international arbitration as a ‘moral hazard' that threatened the legitimacy of arbitration as an impartial method of dispute resolution. He suggested a series of reforms, most notably allowing arbitral institutions to make all arbitrator appointments. Over the past decade, commentators have debated Paulsson's arguments and whether arbitrators should be chosen by parties or arbitral institutions, relying on an assumption that those two methods are the only ways by which arbitrators can be selected. This essay demonstrates that both approaches are fundamentally flawed, because they are subject to the self-interest and biases of human beings. Moreover, it explains how modern technology has produced a new way by which arbitrators can be selected--specifically, via artificial intelligence (AI)--that allows for parties to have input into the selection process but removes the issues that arise when parties select arbitrators directly. As this essay illustrates, using an AI to select arbitrators will allow arbitrators to truly be independent, ensure that arbitrators are selected for their merit and not for their connections, eliminate incentives for compromise awards and the use of dissents to communicate leanings to future appointing parties, and increase diversity in arbitrator appointments.
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33

Nagle, Joelle, and Rosamund Stooke. "Railways, rebellions and Rage Against The Machine: adolescents' interests and meaning-making in the creation of multimodal identity texts." Literacy 50, no. 3 (May 16, 2016): 158–65. http://dx.doi.org/10.1111/lit.12081.

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34

Kang, Yoseb. "When People Rage Against the Machines: Strategic Framing of Nascent Markets under Externalities." Academy of Management Proceedings 2021, no. 1 (August 2021): 14206. http://dx.doi.org/10.5465/ambpp.2021.14206abstract.

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35

Granqvist, Nina, and Juha Laurila. "Rage against Self-replicating Machines: Framing Science and Fiction in the US Nanotechnology Field." Organization Studies 32, no. 2 (February 2011): 253–80. http://dx.doi.org/10.1177/0170840610397476.

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36

Feiner, Zachary S., Alexander W. Latzka, Max H. Wolter, Lawrence D. Eslinger, and Gene R. Hatzenbeler. "Assessing the Rage Against the Machines: Do Ice Anglers’ Electronics Improve Catch and Harvest Rates?" Fisheries 45, no. 6 (May 11, 2020): 327–33. http://dx.doi.org/10.1002/fsh.10427.

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37

Slop. "Mr Slop and computers." Bulletin of the Royal College of Surgeons of England 97, no. 5 (May 2015): 233. http://dx.doi.org/10.1308/147363515x14272809070645.

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38

Prettner, Klaus, and Holger Strulik. "Innovation, automation, and inequality: Policy challenges in the race against the machine." Journal of Monetary Economics 116 (December 2020): 249–65. http://dx.doi.org/10.1016/j.jmoneco.2019.10.012.

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39

Müller, M., and P. Hrabě. "Overlay materials used for increasing lifetime of machine parts working under conditions of intensive abrasion." Research in Agricultural Engineering 59, No. 1 (March 6, 2013): 16–22. http://dx.doi.org/10.17221/64/2011-rae.

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We evaluated a degree of the machine part abrasive wear with secondary focus on their hardness. The paper states laboratory results of overlay systems from their wear resistance point of view. Laboratory experiments were carried out by two-body abrasion on bonded abrasive of a P120 granularity. The results proved an increased abrasive wear resistance of martensitic, ledeburitic and stellitic overlays against eleven different original products. The overlay UTP Ledurit 60 reached the optimum values. The GD-OES (Glow Discharge Optical Emission Spectroscopy) method proved the different chemical composition of the overlay from the stated chemical composition of the overlaying electrode.    
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40

Darbyshire, Philip. "To DeZarn's commentary on Darbyshire P (2004) 'Rage against the machine?': nurses' and midwives' experiences of using computerized patient information systems for clinical information." Journal of Clinical Nursing 15, no. 8 (August 2006): 1060–61. http://dx.doi.org/10.1111/j.1365-2702.2005.01421.x.

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41

DeZarn, Tonya L. "Darbyshire P (2004) ‘Rage against the machine?’: nurses’ and midwives’ experiences of using computerized patient information systems for clinical information.Journal of Clinical Nursing13, 17-25." Journal of Clinical Nursing 15, no. 2 (February 2006): 237. http://dx.doi.org/10.1111/j.1365-2702.2006.01180.x.

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42

Oduntan, O. B., O. A. Koya, and M. O. Faborode. "Design, fabrication and testing of a cassava pelletizer." Research in Agricultural Engineering 60, No. 4 (November 27, 2014): 148–54. http://dx.doi.org/10.17221/77/2012-rae.

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This paper reports on the design of fabrication and testing of a machine for cottage level production of pellets from cassava mash. The pelletizer consists of a barreled screw auger which compresses cassava mash against perforated end plate, through which the pellets are pelletized. The result derived from the calculated design parameters (shaft diameter, tensile stress, torque, screw length, volumetric capacity mass flow rate and power rating) were used for the fabrication. The testing of the pelletizer was determined in terms of throughput of the machine, against the moisture content of the mash (18, 20 and 22% w.b.), die size (4, 6 and 8 mm) and the auger speed (90, 100 and 120 rpm). Test results showed that the pellets with the best quality attributes were obtained from cassava mash at 18% moisture content (w.b.) through the 4 mm die at 90 rpm and a maximum throughput of 54 kg/h.
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43

Mann, Cindy. "Commentary on Darbyshire P (2004) ‘Rage against the machine?: nurses’ and midwives’ experiences of using computerized patient information systems for clinical information. Journal of Clinical Nursing 13, 17-25." Journal of Clinical Nursing 17, no. 15 (July 14, 2008): 2090–91. http://dx.doi.org/10.1111/j.1365-2702.2007.02104.x.

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Larmuseau, Michiel, Michael Sluydts, Koenraad Theuwissen, Lode Duprez, Tom Dhaene, and Stefaan Cottenier. "Race against the Machine: can deep learning recognize microstructures as well as the trained human eye?" Scripta Materialia 193 (March 2021): 33–37. http://dx.doi.org/10.1016/j.scriptamat.2020.10.026.

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Arnold, David, Will Dobbie, and Peter Hull. "Measuring Racial Discrimination in Algorithms." AEA Papers and Proceedings 111 (May 1, 2021): 49–54. http://dx.doi.org/10.1257/pandp.20211080.

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Algorithmic decision-making can lead to discrimination against legally protected groups, but measuring such discrimination is often hampered by a fundamental selection challenge. We develop new quasi-experimental tools to overcome this challenge and measure algorithmic discrimination in pretrial bail decisions. We show that the selection challenge reduces to the challenge of measuring four moments, which can be estimated by extrapolating quasi-experimental variation across as-good-as-randomly assigned decision-makers. Estimates from New York City show that both a sophisticated machine learning algorithm and a simpler regression model discriminate against Black defendants even though defendant race and ethnicity are not included in the training data.
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46

Mihailescu, Maria-Elena, Darius Mihai, Mihai Carabas, Mikołaj Komisarek, Marek Pawlicki, Witold Hołubowicz, and Rafał Kozik. "The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset." Sensors 21, no. 13 (June 24, 2021): 4319. http://dx.doi.org/10.3390/s21134319.

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Cybersecurity is an arms race, with both the security and the adversaries attempting to outsmart one another, coming up with new attacks, new ways to defend against those attacks, and again with new ways to circumvent those defences. This situation creates a constant need for novel, realistic cybersecurity datasets. This paper introduces the effects of using machine-learning-based intrusion detection methods in network traffic coming from a real-life architecture. The main contribution of this work is a dataset coming from a real-world, academic network. Real-life traffic was collected and, after performing a series of attacks, a dataset was assembled. The dataset contains 44 network features and an unbalanced distribution of classes. In this work, the capability of the dataset for formulating machine-learning-based models was experimentally evaluated. To investigate the stability of the obtained models, cross-validation was performed, and an array of detection metrics were reported. The gathered dataset is part of an effort to bring security against novel cyberthreats and was completed in the SIMARGL project.
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47

Yu, Helong, Kang Yuan, Wenshu Li, Nannan Zhao, Weibin Chen, Changcheng Huang, Huiling Chen, and Mingjing Wang. "Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis." Complexity 2021 (February 6, 2021): 1–17. http://dx.doi.org/10.1155/2021/6315010.

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An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. The model is mainly based on an improved butterfly optimizer algorithm- (BOA-) optimized kernel extreme learning machine (KELM) model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (IMFs) using the complete ensemble empirical mode decomposition based on adaptive noise (CEEMDAN). Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA-KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm (SSA) was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA-KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA-KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA-KELM model can be effectively used to diagnose faults of rolling bearings.
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48

Alfares, Mohammed, Ghanim Al-Daihani, and Jasim Baroon. "The impact of vibration response due to rolling bearing components waviness on the performance of grinding machine spindle system." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 233, no. 3 (April 10, 2019): 747–62. http://dx.doi.org/10.1177/1464419319838773.

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In this research, an analytical model based on a five-degrees-of-freedom nonlinear dynamical system was utilized to investigate the impact of vibration response due to inner race, outer race, and ball bearing waviness on the performance of grinding machine spindle system supported by two angular ball bearings. The waviness of rolling elements is modeled as a sinusoidal function is incorporated along the inner or outer races of bearing and ball surface. The elastic deflection and nonlinear contact force are calculated based on Hertzian contact model, while the shaft has five-degrees-of-freedom motions, three translational and two angular motions. The five–degrees-of-freedom nonlinear governing equations of the spindle system were developed taking into consideration the effect of outer race, inner race, and ball surface waviness, which are included in the kinematic constraints and force equilibrium equations of a ball. The developed model and its results are validated against results found in literature. Results by this paper indicate that the inner race waviness can significantly distort the tolerance and machining quality of grinding spindle-bearing systems more than does waviness in the outer race and ball elements. This is because the inner race waviness generates more complicated vibrations than the outer race and ball waviness. The most severe vibrations caused by the inner race waviness were found to occur when the waviness was of the order that is equal to multiple of number of balls in a bearing set plus or minus one ([Formula: see text]).
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49

Lepoutre, Maxime. "Rage inside the machine." Politics, Philosophy & Economics 17, no. 4 (March 29, 2018): 398–426. http://dx.doi.org/10.1177/1470594x18764613.

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According to an influential objection, which Martha Nussbaum has powerfully restated, expressing anger in democratic public discourse is counterproductive from the standpoint of justice. To resist this challenge, this article articulates a crucial yet underappreciated sense in which angry discourse is epistemically productive. Drawing on recent developments in the philosophy of emotion, which emphasize the distinctive phenomenology of emotion, I argue that conveying anger to one’s listeners is epistemically valuable in two respects: first, it can direct listeners’ attention to elusive morally relevant features of the situation; second, it enables them to register injustices that their existing evaluative categories are not yet suited to capturing. Thus, when employed skillfully, angry speech promotes a greater understanding of existing injustices. This epistemic role is indispensable in highly divided societies, where the injustices endured by some groups are often invisible to, or misunderstood by, other groups. Finally, I defuse the most forceful objections to this defense – that anger is likely to be manipulated, that it is epistemically misleading, and that my defense presupposes unrealistic levels of trust – partly by showing that they overlook the systemic character of democratic discourse.
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50

OTANI, Takushi. "Can we win the race against the machine? : The problem of the mechanical unemployment in the present and the near future." Journal of Information Processing and Management 56, no. 12 (2014): 878–80. http://dx.doi.org/10.1241/johokanri.56.878.

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