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Relevant bibliographies by topics / Rage Against The Machine

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Academic literature on the topic 'Rage Against The Machine'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Rage Against The Machine"

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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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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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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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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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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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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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Dissertations / Theses on the topic "Rage Against The Machine"

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Castillo, C��rdenas C��sar Ignacio. "La ideolog��a del rock pol��tico: Rage Against The Machine." Thesis, Universidad de las Am��ricas Puebla, 2011. http://catarina.udlap.mx/u_dl_a/tales/documentos/lri/castillo_c_ci/.

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Dearth, Davin. "Rage against the machine critical perceptions of American democracy through man vs. the institution /." Laramie, Wyo. : University of Wyoming, 2009. http://proquest.umi.com/pqdweb?did=1939207311&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Haylock, Bradley John, and brad@newethic org. "The Front Line is Everywhere: For a Critique of Radical Commodities." RMIT University. Global Studies, Social Science and Planning, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080213.095326.

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This dissertation addresses the phenomenon of 'radical commodities'-commercial products which advance an oppositional politics. Examples of such include the products of Rage Against The Machine, a 'revolutionary' rock band; Michael Moore, a best-selling author and award-winning documentary filmmaker; Naomi Klein, a journalist and author of the international bestseller No Logo; The Body Shop, a multinational manufacturer and retailer of 'natural' cosmetics and toiletries; Freitag, a company which manufactures bags, wallets and other fashionable accessories from recycled materials, and; the Adbusters Media Foundation, publisher of Adbusters magazine and producer of Blackspot shoes. Radical commodities are fundamentally paradoxical objects whose apparent ethic would appear to be at odds with the fact that they are commodities. This dissertation asks: can a commodity-object legitimately serve as a vehicle for social and political critique? It is reasoned that the problem of radical commodities is principally structural. Marx's seminal writings on the commodity accordingly represent the logical point of departure. The Marxian analysis illuminates not only the commodity-structure, but also the political problematic which emerges from that structure-for Marx, the commodity is a mechanism of exploitation. From an orthodox Marxist perspective, the idea of a radical commodity would therefore be most contradictory, or indeed impossible. It is argued, however, that the Marxian analysis is inconclusive. This dissertation traces a genealogy of analyses of the commodity, which variously advance or diverge from the orthodox Marxist position. From a perspective of the consumption of commodity-objects, the radical commodity would appear to be possible. Yet, the relationship between the commodity-structure and the capitalist ideology runs deep. The question of the radical commodity is therefore markedly more complex than it might initially appear. With regard to the ideological consequence of the commodity-structure, however, certain streams of post-Marxist analysis are themselves problematic, for they ultimately short-circuit historical critique and destabilise the very possibility of politics. In contrast, this dissertation seeks to reaffirm a place for politics and, in so doing, to establish the theoretical possibility of radical commodities. To contend that the idea of a radical commodity is not fundamentally contradictory, however, says nothing of the political potency of such objects. These are undoubtedly complex objects, whose peculiarities cannot be ascertained by abstract theorisation alone. For this reason, this dissertation also employs empirical analyses of a number of radical commodities. In sum, it is argued that the sphere of commodities should be admitted as a possible site for the expression or implementation of a radical politics, and thus that radical commodities should be understood as a legitimate vehicle for social and political critique, but that such objects are by no means free from contradiction, and that the political efficacy of these products is anything but guaranteed.

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Lawson, Cecil Bryant. "Leviathan's Rage: State Sovereignty and Crimes Against Humanity in the Late Twentieth Century." Amherst, Mass. : University of Massachusetts Amherst, 2009. http://scholarworks.umass.edu/open_access_dissertations/13/.

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Ahlstedt, Isabella. "Paint Against the Machine : Konstnärliga processer inom digitalt måleri." Thesis, Konstfack, IBIS - Institutionen för bild- och slöjdpedagogik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:konstfack:diva-7146.

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Denna studie undersöker hur måleriskt arbete fokuserat på kvalitéer utöver avbildande, funktion och skönhet kan tillgängliggöras och förstärkas inom det digitala måleriprogrammet Photoshop. En pedagogisk metod och den digitala miljön (”workspacet”) analyseras och optimeras för syftet. Diskurser inom digital bildkonst analyseras och synliggörs för att ytterligare möjliggöra en konstnärlig frihet på fältet. Arbetet syftar till att vara en grund för bildpedagogisk undervisning och uppgiftsbyggande. Studiens empiri genererades av ett småskaligt cykliskt måleriexperiment utfört i Photoshop. Studien finner att måleriskt arbete i Photoshop överlag kan tillgängliggöras och förstärkas genom att låta problemlösande och innoverande driva kunskapandet, samt ger ett förslag på hur ett specialanpassat måleriworkspace kan vara uppbyggt. Vidare finner den även att det är möjligt att öka tillgängligheten till och förstärka fokus på kvalitéer utöver avbildande, funktion och skönhet inom digitalt bildskapande genom att använda visuell diskursanalys som motståndspraktik.

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Gewecke, Hanne. "Rage Against the System or its Measures? : Polity and Policy related Euroscepticism in Times of Economic Crisis." Thesis, Uppsala universitet, Statsvetenskapliga institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-422937.

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This master’s thesis offers a descriptive investigation of how public Euroscepticism has changed during the economic crisis. By regarding Eurosceptic opinions as positions on a scale as well as differentiating between opposition towards policy (concrete decisions and measures) and polity (the EU as a political system), the results show that overall, European citizens have not only become negative towards how the EU handled the crisis in terms of policy output. Policy opposition also tends to spill over into the polity area, entailing increasing preferences for reducing EU competences and for leaving the union all together. The policy-polity distinction is a perspective on Euroscepticism as opposition, that has largely been set aside in previous research. Furthermore, the paper describes how support for the EU polity and its policies has changed in different member states through a cross country analysis. The results show that there is great and unexpected variation between countries that would be assumed to display a similar development in the light of previous research. In addition, there are similarities between countries that were expected to develop differently. This indicates a limited understanding in this research field of how national factors influence public attitudes towards the European Union. A topic that needs to be investigated further.

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Wood, Robert James. "Against the Chinese Room Argument /." Halifax, N.S. : Saint Mary's University, 2008.

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Rigaki, Maria. "Adversarial Deep Learning Against Intrusion Detection Classifiers." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64577.

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Traditional approaches in network intrusion detection follow a signature-based ap- proach, however the use of anomaly detection approaches based on machine learning techniques have been studied heavily for the past twenty years. The continuous change in the way attacks are appearing, the volume of attacks, as well as the improvements in the big data analytics space, make machine learning approaches more alluring than ever. The intention of this thesis is to show that using machine learning in the intrusion detection domain should be accompanied with an evaluation of its robustness against adversaries. Several adversarial techniques have emerged lately from the deep learning research, largely in the area of image classification. These techniques are based on the idea of introducing small changes in the original input data in order to make a machine learning model to misclassify it. This thesis follows a big data Analytics methodol- ogy and explores adversarial machine learning techniques that have emerged from the deep learning domain, against machine learning classifiers used for network intrusion detection. The study looks at several well known classifiers and studies their performance under attack over several metrics, such as accuracy, F1-score and receiver operating character- istic. The approach used assumes no knowledge of the original classifier and examines both general and targeted misclassification. The results show that using relatively sim- ple methods for generating adversarial samples it is possible to lower the detection accuracy of intrusion detection classifiers from 5% to 28%. Performance degradation is achieved using a methodology that is simpler than previous approaches and it re- quires only 6.25% change between the original and the adversarial sample, making it a candidate for a practical adversarial approach.

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Farjam, Mike [Verfasser], Oliver [Gutachter] Kirchkamp, and Willem F. G. [Gutachter] Haselager. "Game against the machine : interacting with artificial economic agents / Mike Farjam ; Gutachter: Oliver Kirchkamp, Willem Haselager." Jena : Friedrich-Schiller-Universität Jena, 2016. http://d-nb.info/117761121X/34.

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Siddiqui, Abdul Jabbar. "Securing Connected and Automated Surveillance Systems Against Network Intrusions and Adversarial Attacks." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42345.

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In the recent years, connected surveillance systems have been witnessing an unprecedented evolution owing to the advancements in internet of things and deep learning technologies. However, vulnerabilities to various kinds of attacks both at the cyber network-level and at the physical worldlevel are also rising. This poses danger not only to the devices but also to human life and property. The goal of this thesis is to enhance the security of an internet of things, focusing on connected video-based surveillance systems, by proposing multiple novel solutions to address security issues at the cyber network-level and to defend such systems at the physical world-level. In order to enhance security at the cyber network-level, this thesis designs and develops solutions to detect network intrusions in an internet of things such as surveillance cameras. The first solution is a novel method for network flow features transformation, named TempoCode. It introduces a temporal codebook-based encoding of flow features based on capturing the key patterns of benign traffic in a learnt temporal codebook. The second solution takes an unsupervised learning-based approach and proposes four methods to build efficient and adaptive ensembles of neural networks-based autoencoders for intrusion detection in internet of things such as surveillance cameras. To address the physical world-level attacks, this thesis studies, for the first time to the best of our knowledge, adversarial patches-based attacks against a convolutional neural network (CNN)- based surveillance system designed for vehicle make and model recognition (VMMR). The connected video-based surveillance systems that are based on deep learning models such as CNNs are highly vulnerable to adversarial machine learning-based attacks that could trick and fool the surveillance systems. In addition, this thesis proposes and evaluates a lightweight defense solution called SIHFR to mitigate the impact of such adversarial-patches on CNN-based VMMR systems, leveraging the symmetry in vehicles’ face images. The experimental evaluations on recent realistic intrusion detection datasets prove the effectiveness of the developed solutions, in comparison to state-of-the-art, in detecting intrusions of various types and for different devices. Moreover, using a real-world surveillance dataset, we demonstrate the effectiveness of the SIHFR defense method which does not require re-training of the target VMMR model and adds only a minimal overhead. The solutions designed and developed in this thesis shall pave the way forward for future studies to develop efficient intrusion detection systems and adversarial attacks mitigation methods for connected surveillance systems such as VMMR.

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Books on the topic "Rage Against The Machine"

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Rage Against the Machine (Musical group). Rage Against the Machine. New York: Sony Music Entertainment, 1992.

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Meyi, Sonwabo. Rage against the beast. Grahamstown, South Africa: Aerial, 2009.

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Evans, Dan. Rage Against the Meshugenah. New York: Penguin USA, Inc., 2009.

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Masterman, Becky. Rage against the dying. Leicester: Thorpe, 2016.

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Rage against the dying. Toronto, Ontario, Canada: Penguin, 2014.

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Siegel, Lee. Against the Machine. New York: Random House Publishing Group, 2008.

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Kellerman, Jonathan. Rage. New York: Random House Large Print, 2005.

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Kellerman, Jonathan. Rage. New York: Random House Publishing Group, 2005.

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Kellerman, Jonathan. Rage. London: Penguin Group UK, 2008.

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Kellerman, Jonathan. Rage. New York: Ballantine Books, 2005.

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Book chapters on the topic "Rage Against The Machine"

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Gunkel, David J. "Rage Against the Machine: Rethinking Education in the Face of Technological Unemployment." In Surviving the Machine Age, 147–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51165-8_10.

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Tamilarasi, P., and R. Uma Rani. "Predict the Crime Rate Against Women Using Machine Learning Classification Techniques." In Data Science and Its Applications, 295–313. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003102380-15.

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Specht, Felix, and Jens Otto. "Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks." In Machine Learning for Cyber Physical Systems, 103–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_11.

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AbstractCondition monitoring systems based on deep neural networks are used for system failure detection in cyber-physical production systems. However, deep neural networks are vulnerable to attacks with adversarial examples. Adversarial examples are manipulated inputs, e.g. sensor signals, are able to mislead a deep neural network into misclassification. A consequence of such an attack may be the manipulation of the physical production process of a cyber-physical production system without being recognized by the condition monitoring system. This can result in a serious threat for production systems and employees. This work introduces an approach named CyberProtect to prevent misclassification caused by adversarial example attacks. The approach generates adversarial examples for retraining a deep neural network which results in a hardened variant of the deep neural network. The hardened deep neural network sustains a significant better classification rate (82% compared to 20%) while under attack with adversarial examples, as shown by empirical results.

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Schrijvers, Peter. "Human Rage." In The GI War Against Japan, 207–25. London: Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1057/9780230505278_9.

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Cohan, Deborah J. "Rage and Activism." In Violence Against Black Bodies, 38–52. 1st edition. | New York, NY : Routledge, [2017]: Routledge, 2017. http://dx.doi.org/10.4324/9781315408705-4.

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Machin, Linda. "‘Rage, rage against the dying of the light’." In Elderly Care, 226–34. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4509-9_35.

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Michalczak, Rafał. "Animals’ Race Against the Machines." In Legal Personhood: Animals, Artificial Intelligence and the Unborn, 91–101. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53462-6_6.

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Kinkead-Weekes, Mark. "Rage against the Murrys: ‘Inexplicable’ or ‘Psychopathic’?" In D. H. Lawrence in Italy and England, 116–34. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1007/978-1-349-27073-6_7.

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Ericsson, Daniel. "Notes on a Fetishist War Machine." In Against Entrepreneurship, 37–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47937-4_3.

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Melissa Snarr, C. "Wage Against the Machine." In Spirit and Capital in an Age of Inequality, 173–89. 1 [edition]. | New York : Routledge, 2018.: Routledge, 2017. http://dx.doi.org/10.4324/9781315413532-10.

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Conference papers on the topic "Rage Against The Machine"

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Petsas, Thanasis, Giannis Voyatzis, Elias Athanasopoulos, Michalis Polychronakis, and Sotiris Ioannidis. "Rage against the virtual machine." In the Seventh European Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2592791.2592796.

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Chen, Shang-Wen, Tzu-Hsien Chuang, Chin-Wei Tien, and Chih-Wei Chen. "An Experience in Enhancing Machine Learning Classifier Against Low-Entropy Packed Malwares." In 8th International Conference on Computer Science and Information Technology (CoSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110406.

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Both benign applications and malwares would take packing for their different purposes to conceal the real part of the program processes. According to recent research reports, existing machine learning (ML) approach-based malware detection engines are difficult to effectively classify the packed malwares, especially when they are in low entropy packed. Recently, we counted and found that the ratio of low-entropy packed ransomware is extremely high. This would cause a high error rate of the result on currently used ML approaches. Thus, we propose a new method to extract entropy-related features and use a stack model to build up an ML malware engine to effectively detect low-entropy packed malwares. We evaluate our method by using over 15,000 malware samples collected from VirusTotal and compare the result to related researches. This experience reports our adopted model and features can significantly lower the error rate of low-entropy packed detection from 11% to 1%.

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Tamilarasi, P., and R. Uma Rani. "Diagnosis of Crime Rate against Women using k-fold Cross Validation through Machine Learning." In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2020. http://dx.doi.org/10.1109/iccmc48092.2020.iccmc-000193.

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Nakano, Y., and H. Takahara. "Countermeasure Against Regenerative Chatter in End Milling Operations With Vibration Absorbers." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37818.

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Chatter can result in the poor machined surface, tool wear and reduced product quality. Chatter is classified into the forced vibration and the self-excited vibration in perspective of the generation mechanism. It often happens that the self-excited chatter becomes problem practically because this causes heavy vibration. Regenerative chatter due to regenerative effect is one of the self-excited chatter and generated in the most cutting operations. Therefore, it is very important to quench or avoid regenerative chatter (hereafter, simply called chatter). It is well known that chatter can be avoided by selecting the optimal cutting conditions which are determined by using the stability lobe of chatter. The stability lobe of chatter represents the boundary between stable and unstable cuts as a function of spindle speed and depth of cut. However, it is difficult to predict the stability lobe of chatter perfectly because the prediction accuracy of it depends on the tool geometry, the vibration characteristics of the tool system and the machine tool and the material behavior of the workpiece. In contrast, it is made clear that the stability lobe of chatter has been elevated in the wide range of spindle speed by the vibration absorber in the turning operations. However, it should be noted that none of the previous work has actually applied the vibration absorbers to the rotating tool system in the machining center and examined the effect of the vibration absorbers on chatter in the end milling operations to the best of authors’ knowledge. In this paper, the effect of the vibration absorbers on regenerative chatter generated in the end milling operations is qualitatively evaluated by the stability analysis and the cutting test. It is made clear the relationship between the suppression effect of the vibration absorbers and the tuning parameters of them. It is shown that the greater improvement in the critical axial depth of cut is observed in the wide range of spindle speed by the properly tuned vibration absorbers.

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Cartwright, Mark, Matt Jones, and Hiroko Terasawa. "Rage in Conjunction with the Machine." In the 7th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1279740.1279785.

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Naveen Prakash, G. V., and H. V. Ravindra. "Monitoring of the Machine Elements in Drilling Machine Using Vibration Signals by FEM." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66753.

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Deterioration in the machine running conditions always produces a corresponding increase in the vibration level. By monitoring vibration level it is possible to obtain information about the machine condition. Here, drilling machine was considered for the analysis. Spindle bearings and gearbox are found to be the critical elements of the machine tools on which condition monitoring has to be concentrated. To analyze the condition of the existing machine elements, finite element method has been used. An attempt has been made by modeling drilling machine to find out the response of the structure for various defects in machine elements. The finite element model of the machine tool was developed by using finite element package ANSYS. Modal analysis has been carried out to determine the natural frequencies and mode shapes of the structure. Frequency response analysis was used to determine the response of the structure due to the unbalance forces. Transient response analysis was used to study the effect of assumed defects on the machine tool structure. A defect was assumed on the outer race of the spindle bearing and on the gear and responses were observed at the spindle bearings location. From the analysis for the assumed defect in the outer race of the bearing, it was observed that the vibration velocity increases with the increase in depth of defect and depends on location of the defect. From the vibration signature resulting due to assumed destructive pitting defect in all teeth of the driven gear, it was observed that the vibration velocity level decreases with increase in spindle speed. This is because, at lower speeds time taken by each tooth to mesh with its counterpart is more, and hence persistence of disturbing force due to defect in meshing gear is more at lower speeds. Experimentation was carried out on the drilling machine by using the instrument Machine Condition Tester T-30. The experimental data obtained were used to analyze the condition of the machine elements. The vibration velocity was measured on the spindle bearings location for different spindle speed. From the Experimental analysis it was observed that with the increase in spindle speed, the vibration velocity also increases. Finally, theoretical results were judged against the experimental results. Thus the present work shows that the FEM is a valuable tool in finding sources of undesirable vibrations from various defects present in the machine elements of the machine tool structure.

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Klumpp, John. "Bayesian Analysis of Energy and Count Rate Data for Detection of Low Count Rate Radioactive Sources." In ASME 2013 15th International Conference on Environmental Remediation and Radioactive Waste Management. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icem2013-96357.

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We propose a radiation detection system which generates its own discrete sampling distribution based on past measurements of background. The advantage to this approach is that it can take into account variations in background with respect to time, location, energy spectra, detector-specific characteristics (i.e. different efficiencies at different count rates and energies), etc. This would therefore be a “machine learning” approach, in which the algorithm updates and improves its characterization of background over time. The system would have a “learning mode,” in which it measures and analyzes background count rates, and a “detection mode,” in which it compares measurements from an unknown source against its unique background distribution. By characterizing and accounting for variations in the background, general purpose radiation detectors can be improved with little or no increase in cost. The statistical and computational techniques to perform this kind of analysis have already been developed. The necessary signal analysis can be accomplished using existing Bayesian algorithms which account for multiple channels, multiple detectors, and multiple time intervals. Furthermore, Bayesian machine-learning techniques have already been developed which, with trivial modifications, can generate appropriate decision thresholds based on the comparison of new measurements against a non-parametric sampling distribution.

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Gu, Patrick, and Xin Wang. "Single Machine Infinite Bus Power System Control With Resilient Extended Kalman Filter." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50289.

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The Extended Kalman Filter is commonly used as a non-linear estimator for engineering practices. However, the Extended Kalman Filter’s performance degrades or may even become unstable when the measurement data becomes inaccurate through random measurement failures, which is a widespread problem in many data-intensive control applications. To address this issue, the paper proposes an improved Extended Kalman Filter which is resilient against sensor failures. This work focuses on the Resilient Extended Kalman Filter’s derivation with its application to the single generator infinite bus power system. The sensor failure rate is modeled as a Binomial distribution with a known mean value. The performance of the proposed Resilient Extended Kalman Filter is compared with the traditional Extended Kalman Filter under various chances of sensor failures.

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Rodriguez, Arturo, V. M. Krushnarao Kotteda, Luis F. Rodriguez, Vinod Kumar, Arturo Schiaffino, and Zackery R. Nieto. "Machine Learning Approach to Predict the Flow Rate for an Immiscible Two-Phase Flow at Pore Scale for Enhanced Oil Recovery Application." In ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fedsm2018-83050.

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Due to global demand for energy, there is a need to maximize oil extraction from wet reservoir sedimentary formations, which implies the efficient extraction of oil at the pore scale. The approach involves pressurizing water into the wetting oil pore of the rock for displacing and extracting the oil. The two-phase flow is complicated because of the behavior of the fluid flow at the pore scale, and capillary quantities such as surface tension, viscosities, pressure drop, radius of the medium, and contact angle become important. In the present work, we use machine learning algorithms in TensorFlow to predict the volumetric flow rate for a given pressure drop, surface tension, viscosity and geometry of the pores. The TensorFlow software library was developed by the Google Brain team and is one of the most powerful tools for developing machine learning workflows. Machine learning models can be trained on data and then these models are used to make predictions. In this paper, the predicted values for a two-phase flow of various pore sizes and liquids are validated against the numerical and experimental results in the literature.

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Board, David B. "Gear Diagnosis by Stress Wave Analysis." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/ptg-48081.

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Stress Wave Analysis (SWAN) provides real-time measurement of friction and mechanical shock in operating machinery. This high frequency acoustic sensing technology filters out background levels of vibration and audible noise, and provides a graphic representation of machine health. By measuring shock and friction events, the SWAN technique is able to detect wear and damage at the earliest stages, and track the progression of a defect throughout the failure process. This is possible because as the damage progresses, the energy content of friction and shock events increases. This ‘stress wave energy’ is then measured and tracked against normal machine operating conditions. This paper describes SWAN theory of operation, as well as case histories on several diverse types of aircraft and industrial gears. It documents SWAN’s ability to: 1) accurately detect a broad range of discrepant conditions: and 2) to characterize the severity of damage. Examples include tooth wear, tooth spalling, tooth breakage, and web cracks.

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Reports on the topic "Rage Against The Machine"

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Tooley, Clif. The Machine Nexus: Institutional Bias Against a Capabilities-Based Force. Fort Belvoir, VA: Defense Technical Information Center, March 1997. http://dx.doi.org/10.21236/ada326894.

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Cary, Dakota, and Daniel Cebul. Destructive Cyber Operations and Machine Learning. Center for Security and Emerging Technology, November 2020. http://dx.doi.org/10.51593/2020ca003.

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Machine learning may provide cyber attackers with the means to execute more effective and more destructive attacks against industrial control systems. As new ML tools are developed, CSET discusses the ways in which attackers may deploy these tools and the most effective avenues for industrial system defenders to respond.

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Lohn, Andrew. Poison in the Well: Securing the Shared Resources of Machine Learning. Center for Security and Emerging Technology, June 2021. http://dx.doi.org/10.51593/2020ca013.

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Modern machine learning often relies on open-source datasets, pretrained models, and machine learning libraries from across the internet, but are those resources safe to use? Previously successful digital supply chain attacks against cyber infrastructure suggest the answer may be no. This report introduces policymakers to these emerging threats and provides recommendations for how to secure the machine learning supply chain.

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Griffin, Andrew, Sean Griffin, Kristofer Lasko, Megan Maloney, S. Blundell, Michael Collins, and Nicole Wayant. Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40182.

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Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.

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