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Academic literature on the topic 'Mystery Machine'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Mystery Machine"

1

Briones, Victorino, and Kathrin Muegge. "The ghosts in the machine: DNA methylation and the mystery of differentiation." Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1819, no. 7 (July 2012): 757–62. http://dx.doi.org/10.1016/j.bbagrm.2012.02.013.

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Lipunova, Galina V., Ivan E. Panchenko, and Vladimir M. Lipunov. "The radioastronomical “time machine” effect can help the solution of the gamma ray bursts mystery." New Astronomy 2, no. 6 (December 1997): 555–58. http://dx.doi.org/10.1016/s1384-1076(97)00042-0.

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Tye, Michael. "Blindsight, the Absent Qualia Hypothesis, and the Mystery of Consciousness." Royal Institute of Philosophy Supplement 34 (March 1993): 19–40. http://dx.doi.org/10.1017/s1358246100002447.

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One standard objection to the view that phenomenal experience is functionally determined is based upon what has come to be called ‘The Absent Qualia Hypothesis’, the idea that there could be a person or a machine that was functionally exactly like us but that felt or consciously experienced nothing at all (see Block, 1980; Block and Fodor, 1980; Campbell, 1980; Nagel, 1980). Advocates of this hypothesis typically maintain that we can easily imagine possible systems that meet the appropriate functional specifications but that intuitively lack any phenomenal consciousness. Ned Block (1980), for example, asks us to suppose that a billion Chinese people are each given a two-way radio with which to communicate with one another and with an artificial (brainless) body. The movements of the body are controlled by the radio signals, and the signals themselves are made in accordance with instructions the Chinese people receive from a vast display in the sky which is visible to all of them. The instructions are such that the participating Chinese people together realize whatever programs the functionalist supposes underlie human phenomenal experience.

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Barham, Jeremy. "‘The Ghost in the Machine’: Thomas Koschat and the volkstümlich in Mahler’s Fifth Symphony." Nineteenth-Century Music Review 15, no. 3 (July 2, 2018): 353–90. http://dx.doi.org/10.1017/s1479409818000186.

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This article offers a solution to a long-standing mystery surrounding the identity of a melody by Carinthian folkloric composer Thomas Koschat used by Mahler in his Fifth Symphony. It first places such musical reference in the broader scholarly context of Mahler and the volkstümlich. Evidence surrounding the chronology and sketches of the symphony as well as Mahler’s intersection with Koschat and the latter’s reception is assessed. Musical materials are analysed in order to identify the source of borrowing in Koschat’s Liederspiel Am Wörther See (1880), and to understand the key structural and expressive roles it plays in Mahler’s work. The article concludes by reflecting on the possible socio-cultural meaning and significance of this case of Mahlerian allusive practice.

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Smith, Erin Sweeney. "‘Conjuring some magic’: paganism and the musical and visual aesthetics of Florence + the Machine and Bat for Lashes." Popular Music 38, no. 01 (January 2019): 90–104. http://dx.doi.org/10.1017/s0261143018000739.

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AbstractMagic and mystery shroud Florence + the Machine's and Bat for Lashes's performances and interviews, which frequently frame the two groups’ frontwomen, Florence Welch and Natasha Khan, as ‘witchy’ or magical, or spur comparisons to Pagan aesthetics. This article investigates the unique ways the two bands use Pagan influences to speak to mainstream and Pagan audiences. Ultimately, the two groups set themselves apart from other bands through marketing that fuses commercialism with an esoteric religion. Florence + the Machine and Bat for Lashes create a ‘Pagan chic’ that draws in listeners through videos, instrumentation often featuring harp and tom-tom drums, fantasy-oriented lyrics, and stage props which use a Pagan lens to respond to current political events reminiscent of the counterculture of the 1960s and 1970s.

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Vivek Tammineedi, Venkata Satya, and V. N. Rajavarman. "A Novel Analysis of Advanced Visual Cryptography Techniques for Providing Security Against Web Attacks Using Support Vector Machine Technique." Journal of Computational and Theoretical Nanoscience 17, no. 5 (May 1, 2020): 2097–114. http://dx.doi.org/10.1166/jctn.2020.8855.

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In today’s internet applications such as some real time application services like core banking and other public service oriented application have been major issue in authentication of user specification. To perform online dictionary attacks, passwords have been used for security and authentication mechanism. Present days, hacking of databases on web oriented applications is unavoidable to access them easily. Data maintenance is a complex task in internet applications. To solve these type of problems in internet applications, in this paper, we proposed a novel Integrated and Dynamic CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) (I&D CAPTCHA), which is extension version of existing CAPTCHA that valuated third party human attacks in internet applications based Visual Cryptography approach to discuss about authentication problem in real time applications. There is more number of methods presented for security in advanced pictures for insurance from inventive uninvolved or dynamic assaults in system correspondence environment. Like insightful Visual Cryptographic (VC) is a cutting edge strategy, which is utilized to mystery picture safely impart furthermore keep up to privacy. To proceed with difficulties of security in advanced picture information sharing, so in this paper we break down various VC security instruments for computerized picture information offering to regard tomystery information secrecy. Our examination give effective security answers for relative mystery advanced picture information imparting to correspondence progressively environment. Security aspects are main concepts in present days because of increasing statistical data storage. In Artificial Intelligence (AI) oriented applications, it is very difficult in terms of protection to increasing new aspects in real time world. So we also plan a Novel and Advanced Security system to enable solution for basic AI problems in this paper. This framework mainly works based on Captcha as visual security passwords (CaRP); it is two way communication plan which means that, it is the combination of Captcha and visual security plan. Our approach mainly worked with image security with respect to selection of passwords based on random way. In this paper, we also propose AMODS, an adaptive system that periodically updates the detection model to detect the latest unknown attacks. We also propose an adaptive learning strategy, called SVM HYBRID, leveraged by our system to minimize manual work. Our system out performs existing web attack detectionmethods, with an F-value of 94.79% and FP rate of 0.09%. The total number of malicious queries obtained by SVM HYBRID is 2.78 times that by the popular Support Vector Machine Adaptive Learning (SVMAL) method. The malicious queries obtained can be used to update the Web Application Firewall (WAF) signature library.

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Zhou, Zhi-Hua, and Ji Feng. "Deep forest." National Science Review 6, no. 1 (October 8, 2018): 74–86. http://dx.doi.org/10.1093/nsr/nwy108.

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Abstract Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting machines, we conjecture that the success of deep neural networks owes much to three characteristics, i.e. layer-by-layer processing, in-model feature transformation and sufficient model complexity. On one hand, our conjecture may offer inspiration for theoretical understanding of deep learning; on the other hand, to verify the conjecture, we propose an approach that generates deep forest holding these characteristics. This is a decision-tree ensemble approach, with fewer hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to achieve excellent performance by using the same default setting. This study opens the door to deep learning based on non-differentiable modules without gradient-based adjustment, and exhibits the possibility of constructing deep models without backpropagation.

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Srivastava, Praveen Ranjan, Zuopeng (Justin) Zhang, and Prajwal Eachempati. "Deep Neural Network and Time Series Approach for Finance Systems." Journal of Organizational and End User Computing 33, no. 5 (September 2021): 204–26. http://dx.doi.org/10.4018/joeuc.20210901.oa10.

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The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.

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Chatterjee, Sankar, and Surya Yadav. "The Origin of Prebiotic Information System in the Peptide/RNA World: A Simulation Model of the Evolution of Translation and the Genetic Code." Life 9, no. 1 (March 1, 2019): 25. http://dx.doi.org/10.3390/life9010025.

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Information is the currency of life, but the origin of prebiotic information remains a mystery. We propose transitional pathways from the cosmic building blocks of life to the complex prebiotic organic chemistry that led to the origin of information systems. The prebiotic information system, specifically the genetic code, is segregated, linear, and digital, and it appeared before the emergence of DNA. In the peptide/RNA world, lipid membranes randomly encapsulated amino acids, RNA, and peptide molecules, which are drawn from the prebiotic soup, to initiate a molecular symbiosis inside the protocells. This endosymbiosis led to the hierarchical emergence of several requisite components of the translation machine: transfer RNAs (tRNAs), aminoacyl-tRNA synthetase (aaRS), messenger RNAs (mRNAs), ribosomes, and various enzymes. When assembled in the right order, the translation machine created proteins, a process that transferred information from mRNAs to assemble amino acids into polypeptide chains. This was the beginning of the prebiotic <i>information</i> age. The origin of the genetic code is enigmatic; herein, we propose an evolutionary explanation: the demand for a wide range of protein enzymes over peptides in the prebiotic reactions was the main selective pressure for the origin of information-directed protein synthesis. The molecular basis of the genetic code manifests itself in the interaction of aaRS and their cognate tRNAs. In the beginning, aminoacylated ribozymes used amino acids as a cofactor with the help of bridge peptides as a process for selection between amino acids and their cognate codons/anticodons. This process selects amino acids and RNA species for the next steps. The ribozymes would give rise to pre-tRNA and the bridge peptides to pre-aaRS. Later, variants would appear and evolution would produce different but specific aaRS-tRNA-amino acid combinations. Pre-tRNA designed and built pre-mRNA for the storage of information regarding its cognate amino acid. Each pre-mRNA strand became the storage device for the genetic information that encoded the amino acid sequences in triplet nucleotides. As information appeared in the digital languages of the codon within pre-mRNA and mRNA, and the genetic code for protein synthesis evolved, the prebiotic chemistry then became more organized and directional with the emergence of the translation and genetic code. The genetic code developed in three stages that are coincident with the refinement of the translation machines: the GNC code that was developed by the pre-tRNA/pre-aaRS /pre-mRNA machine, SNS code by the tRNA/aaRS/mRNA machine, and finally the universal genetic code by the tRNA/aaRS/mRNA/ribosome machine. We suggest the coevolution of translation machines and the genetic code. The emergence of the translation machines was the beginning of the Darwinian evolution, an interplay between information and its supporting structure. Our hypothesis provides the logical and incremental steps for the origin of the programmed protein synthesis. In order to better understand the prebiotic information system, we converted letter codons into numerical codons in the Universal Genetic Code Table. We have developed a software, called CATI (Codon-Amino Acid-Translator-Imitator), to translate randomly chosen numerical codons into corresponding amino acids and vice versa. This conversion has granted us insight into how the genetic code might have evolved in the peptide/RNA world. There is great potential in the application of numerical codons to bioinformatics, such as barcoding, DNA mining, or DNA fingerprinting. We constructed the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. While using AnyLogic software, we were able to simulate and visualize the entire evolution of the translation machines, amino acids, and the genetic code.

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Netsey, Edem K., Dr samuel Kakraba, Samuel M. Naandam, and Aayire C. Yadem. "A Mathematical Graph-Theoretic Model of Single Point Mutations Associated with Sickle Cell Anemia Disease." JOURNAL OF ADVANCES IN BIOTECHNOLOGY 9 (September 25, 2021): 1–14. http://dx.doi.org/10.24297/jbt.v9i.9109.

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Many diseases like cystic fibrosis and sickle cell anemia disease (SCD), among others, arise from single point mutations in the respective proteins. How a single point mutation might lead to a global devastating consequence on a protein remains an intellectual mystery. SCD is a genetic blood-related disorder resulting from mutations in the beta chain of the human hemoglobin protein (simply, β-globin), subsequently affecting the entire human body. Higher mortality and morbidity rates have been reported for patients with SCD, especially in sub-Saharan Africa. Clinical management of SCD often requires specialized interdisciplinary clinicians. SCD presents a major global burden, hence an improved understanding of how single point mutations in β-globin results in different phenotypes of SCD might offer insight into protein engineering, with potential therapeutic intervention in view. By use of mathematical modeling, we built a hierarchical (nested) graph-theoretic model for the β-globin. Subsequently, we quantified the network of interacting amino acid residues, representing them as molecular system of three distinct stages (levels) of interactions. Using our nested graph model, we studied the effect of virtual single point mutations in β-globin that results in varying phenotypes of SCD, visualized by unsupervised machine learning algorithm, the dendrogram.

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Books on the topic "Mystery Machine"

1

The mystery machine. New York: Margaret K. McElderry Books, 1995.

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2

(Firm), Duendes Del Sur, ed. Map in the mystery machine. New York: Scholastic, 2000.

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Hurt machine: A Moe Prager mystery. Cincinnati, Ohio: Tyrus Books, 2011.

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4

Christopher, Matt. The hockey machine. Boston: Little, Brown, 1986.

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The hockey machine. Chicago, IL: Norwood House Press, 2008.

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Christopher, Matt. The hockey machine. Boston: Little, Brown, 1986.

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7

Norizuki, Rintarō. Nokkusu mashin =: Knox's machine. Tōkyō: Kadokawa shoten, 2013.

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Chris, Chuckry, ed. Electropolis: The infernal machine : a Menlo Park mystery. Milwaukie, OR: Dark Horse Books, 2009.

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Motter, Dean. Electropolis: The infernal machine : a Menlo Park mystery. Milwaukie, OR: Dark Horse Books, 2009.

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Motter, Dean. Electropolis: The infernal machine : a Menlo Park mystery. Milwaukie, OR: Dark Horse Books, 2009.

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Book chapters on the topic "Mystery Machine"

1

Takeno, Junichi, and Yoshihiko Takeno. "The Mystery of the Defense Chain Mechanism of Constantinople." In History of Mechanism and Machine Science, 199–211. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4132-4_14.

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Arumugam, Mohanapriya, Nirmaladevi Ponnusamy, Sajitha Lulu Sudhakaran, Vino Sundararajan, and Pandjassarame Kangueane. "Mystery of HIV Drug Resistance: A Machine Learning Perspective." In Global Virology III: Virology in the 21st Century, 307–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29022-1_9.

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Egorova, Olga V. "A Mystery of One Havana Portrait: The First Steam Machine in Cuba." In History of Mechanism and Machine Science, 189–213. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1251-5_8.

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Johnson, Michael L. "Translation, Universals, Mystère." In Mind, Language, Machine, 149–56. London: Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-19404-9_25.

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Lai, Wallace Wai-Lok. "Underground Utilities Imaging and Diagnosis." In Urban Informatics, 415–38. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_24.

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AbstractThe invisible and congested world of underground utilities (UU) is an indispensable mystery to the general public because their existence is invisible until problems happen. Their growth aligns with the continuous development of cities and the ever-increasing demand for energy and quality of life. To satisfy a variety of modern requirements like emergency or routine repair, safe dig and excavation, monitoring, maintenance, and upscaling of the network, two basic tasks are always required. They are mapping and imaging (where?), and diagnosis (how healthy?). This chapter gives a review of the current state of the art of these two core topics, and their levels of expected survey accuracy, and looks forward to future trends of research and development (Sects. 24.1 and 24.2). From the point of view of physics, a large range of survey technologies is central to imaging and diagnosis, having originated from electromagnetic- and acoustic-based near-surface geophysical and nondestructive testing methods. To date, survey technologies have been further extended by multi-disciplinary task forces in various disciplines (Sect. 24.3). First, it involves sending and retrieving mechanical robots to survey the internal confined spaces of utilities using careful system control and seamless communication electronics. Secondly, the captured data and signals of various kinds are positioned, processed, and in the future, pattern-recognized with a database to robustly trace the location and diagnose the conditions of any particular type of utilities. Thirdly, such a pattern-recognized database of various types of defects can be regarded as a learning process through repeated validation in the laboratory, simulation, and ground-truthing in the field. This chapter is concluded by briefly introducing the human-factor or psychological and cognitive biases, which are in most cases neglected in any imaging and diagnostic work (Sect. 24.4). In short, the very challenging nature and large demand for utility imaging and diagnostics have been gradually evolving from the traditional visual inspection to a new era of multi-disciplinary surveying and engineering professions and even towards the psychological part of human–machine interaction.

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"1. THE MYSTERY OF PURPOSE." In Neither Ghost nor Machine, 3–16. Columbia University Press, 2017. http://dx.doi.org/10.7312/sher17332-002.

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"2. THE BIGGEST MYSTERY WE EVER IGNORE." In Neither Ghost nor Machine, 17–27. Columbia University Press, 2017. http://dx.doi.org/10.7312/sher17332-003.

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Smith, Adam T. "The Political Machine in the Late Bronze Age." In The Political Machine. Princeton University Press, 2015. http://dx.doi.org/10.23943/princeton/9780691163239.003.0006.

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In the Late Bronze Age, the polities in the South Caucasus developed a new assemblage directed toward transforming charismatic authority into formal sovereignty. This chapter examines the assembling of this political machine, which drew the civilization and war machines into an extensive apparatus of rule, one that resolved the paradox at the heart of the joint operation of both. This novel political machine did not supersede the war and civilization machines. Rather, the political machine cloaked their contradictions, allowing the relation of the one to the many to persist as a “mystery” of sovereignty. The political machine not only provided the instruments of judicial ordering and bureaucratic regulation but it also transformed the polity itself into an object of devotion, securing not simply the surrender of subjects but their active commitment to the reproduction of sovereignty.

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Gupta, Ankur, Sabyasachi Pramanik, Hung Thanh Bui, and Nicholas M. Ibenu. "Machine Learning and Deep Learning in Steganography and Steganalysis." In Multidisciplinary Approach to Modern Digital Steganography, 75–98. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7160-6.ch004.

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Steganography is the way to conceal data inside a normal (non-mystery) record to keep it from discovery. While encryption expects to conceal the substance of data, steganography means to shroud the presence of data. Information security is significant when touchy information is communicated over the internet. Steganography and steganalysis methods can take care of the issue of copyright, possession, and discovery of malevolent information. Steganography is to conceal mystery information without contortion, and steganalysis is to distinguish the presence of shrouded information. In this chapter, steganography and steganalysis methods are depicted along with AI structures to show that AI systems can be utilized to recognize the mystery information covered up in the picture utilizing steganography calculations.

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"The mystery of the machine that invents itself." In Programming.Architecture, 61–100. Routledge, 2010. http://dx.doi.org/10.4324/9780203841488-8.

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