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Relevant bibliographies by topics / Computer science Machine theory

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Academic literature on the topic 'Computer science Machine theory'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Computer science Machine theory"

1

Molina, Mario, and Filiz Garip. "Machine Learning for Sociology." Annual Review of Sociology 45, no. 1 (2019): 27–45. http://dx.doi.org/10.1146/annurev-soc-073117-041106.

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Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and sociology. We offer a brief introduction to this vast toolbox and illustrate its current uses in the social sciences, including distilling measures from new data sources, such as text and images; characterizing population heterogeneity; improving causal inference; and offering predictions to aid policy decisions and theory development. We argue that, in addition

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Whyman, Richard. "Physical Computational Complexity and First-order Logic." Fundamenta Informaticae 181, no. 2-3 (2021): 129–61. http://dx.doi.org/10.3233/fi-2021-2054.

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We present the concept of a theory machine, which is an atemporal computational formalism that is deployable within an arbitrary logical system. Theory machines are intended to capture computation on an arbitrary system, both physical and unphysical, including quantum computers, Blum-Shub-Smale machines, and infinite time Turing machines. We demonstrate that for finite problems, the computational power of any device characterisable by a finite first-order theory machine is equivalent to that of a Turing machine. Whereas for infinite problems, their computational power is equivalent to that of

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Chen, Yijia, Jörg Flum, and Martin Grohe. "Machine-based methods in parameterized complexity theory." Theoretical Computer Science 339, no. 2-3 (2005): 167–99. http://dx.doi.org/10.1016/j.tcs.2005.02.003.

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Blum, Manuel, and Lenore Blum. "A Theoretical Computer Science Perspective on Consciousness." Journal of Artificial Intelligence and Consciousness 08, no. 01 (2021): 1–42. http://dx.doi.org/10.1142/s2705078521500028.

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The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. This paper studies consciousness from the perspective of theoretical computer science. It formalizes the Global Workspace Theory (GWT) originated by the cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, and others. Our major contribution lies in the precise formal definition of a Conscious Turing Machine (CTM), also called a Conscious AI. We define the CTM in the spirit of Alan Turing’s simple yet powerful definiti

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Galinkin, Erick. "Review of The Foundations of Computability Theory (Second Edition) by Borut Robič." ACM SIGACT News 52, no. 2 (2021): 7–9. http://dx.doi.org/10.1145/3471469.3471472.

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Computability theory forms the foundation for much of theoretical computer science. Many of our great unsolved questions stem from the need to understand what problems can even be solved. The greatest question of computer science, P vs. NP, even sidesteps this entirely, asking instead how efficiently we can find solutions for the problems that we know are solvable. For many students both at the undergraduate and graduate level, a first exposure to computability theory follows a standard sequence on data structures and algorithms and students often marvel at the first results they see on undeci

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House, Brian. "Machine Listening." A Peer-Reviewed Journal About 6, no. 1 (2017): 16–24. http://dx.doi.org/10.7146/aprja.v6i1.116008.

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Beyond the sub-field of computer science concerned with the extraction of meaningful information from audio data, that term "machine listening" invokes the knotty questions of what it is to listen, what (if anything) separates listening by machines and by humans, and how listening is entangled with the materiality of the voice. The timely emergence of WaveNet is provocative regarding each of these—it is, perhaps more than anything else, a listening machine. Furthermore, it reveals the limits of a media materialist approach to sonicity, as exemplified by Wolfgang Ernst, when it comes to media t

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Resma, KS, GS Sharvani, and Ramasubbareddy Somula. "Optimization of cloud load balancing using fitness function and duopoly theory." International Journal of Intelligent Computing and Cybernetics 14, no. 2 (2021): 198–217. http://dx.doi.org/10.1108/ijicc-11-2020-0176.

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PurposeCurrent industrial scenario is largely dependent on cloud computing paradigms. On-demand services provided by cloud data centre are paid as per use. Hence, it is very important to make use of the allocated resources to the maximum. The resource utilization is highly dependent on the allocation of resources to the incoming request. The allocation of requests is done with respect to the physical machines present in the datacenter. While allocating the tasks to these physical machines, it needs to be allocated in such a way that no physical machine is underutilized or over loaded. To make

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Sokolov, I. A. "Theory and practice in artificial intelligence." Вестник Российской академии наук 89, no. 4 (2019): 365–70. http://dx.doi.org/10.31857/s0869-5873894365-370.

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Artificial Intelligence is an interdisciplinary field, and formed about 60 years ago as an interaction between mathematical methods, computer science, psychology, and linguistics. Artificial Intelligence is an experimental science and today features a number of internally designed theoretical methods: knowledge representation, modeling of reasoning and behavior, textual analysis, and data mining. Within the framework of Artificial Intelligence, novel scientific domains have arisen: non-monotonic logic, description logic, heuristic programming, expert systems, and knowledge-based software engin

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Németh, Zoltán, and Miklós Kuczmann. "State space modeling theory of induction machines." Pollack Periodica 15, no. 1 (2020): 124–35. http://dx.doi.org/10.1556/606.2020.15.1.12.

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Abstract The paper discusses the theoretical background of the state space modeling of induction machines. The main goal is to present the necessary equations of the induction machine and the topic of the state space modeling. Although the induction machine is a highly non-linear system, LPV/qLPV model can be formulated from these equations.

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Grier, D. A. "The great machine theory of history." IEEE Annals of the History of Computing 25, no. 3 (2003): 96–95. http://dx.doi.org/10.1109/mahc.2003.1226668.

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Dissertations / Theses on the topic "Computer science Machine theory"

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Kaelbling, Michael John. "Braced languages and a model of translation for context-free strings : theory and practice /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487584612166164.

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Yedidia, Adam. "A relatively small turing machine whose behavior is independent of set theory." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100680.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 79-80).<br>Since the definition of the Busy Beaver function by Radó in 1962, an interesting open question has been what the smallest value of n for which BB(n) is independent of ZFC. Is this n approximately 10, or closer to 1,000,000, or is it unfathomably large? In this thesis, I show that it is at most 340,943 by presenting an explicit description of a 340,943-state Turing machine Z with

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Wong, Shun Ha Sylvia. "An investigation into the use of argument structure and lexical mapping theory for machine translation." Thesis, University of Birmingham, 2000. http://etheses.bham.ac.uk//id/eprint/4656/.

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In recent work on the Lexical-Functional Grammar (LFG) formalism, argument structure (a-structure) and lexical mapping theory have been used to explain many linguistic behaviours across languages. It has been suggested that the combination of c-structure, f-structure and a-structure might form a suitable architecture for Universal Grammar. If this suggestion is valid, the LFG formalism would be a suitable linguistic model for Machine Translation (MT). This thesis reports on the investigations carried out on using a-structure and lexical mapping theory for aiding various sub-tasks in MT. The tw

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Kalyanasundaram, Subrahmanyam. "Turing machine algorithms and studies in quasi-randomness." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42808.

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Randomness is an invaluable resource in theoretical computer science. However, pure random bits are hard to obtain. Quasi-randomness is a tool that has been widely used in eliminating/reducing the randomness from randomized algorithms. In this thesis, we study some aspects of quasi-randomness in graphs. Specifically, we provide an algorithm and a lower bound for two different kinds of regularity lemmas. Our algorithm for FK-regularity is derived using a spectral characterization of quasi-randomness. We also use a similar spectral connection to also answer an open question about quasi-random to

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Menke, Joshua E. "Improving machine learning through oracle learning /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1726.pdf.

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Raitt, Lesley Anne. "Random generation of finite automata over the domain of the regular languages." Thesis, Stellenbosch : Stellenbosch University, 2006. http://hdl.handle.net/10019.1/19646.

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Thesis (MSc)--University of Stellenbosch, 2007.<br>ENGLISH ABSTRACT: The random generation of finite automata over the domain of their graph structures is a wellknown problem. However, random generation of finite automata over the domain of the regular languages has not been studied in such detail. Random generation algorithms designed for this domain would be useful for the investigation of the properties of the regular languages associated with the finite automata. We studied the existing enumerations and algorithms to randomly generate UDFAs and binary DFAs as they pertained to the do

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Wang, Sinong. "Coded Computation for Speeding up Distributed Machine Learning." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555336880521062.

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Altun, Gulsah. "Machine Learning and Graph Theory Approaches for Classification and Prediction of Protein Structure." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/cs_diss/31.

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Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widel

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Huysamen, Nico. "FATKID : a Finite Automaton Toolkit." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71684.

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Thesis (MSc)--Stellenbosch University, 2012<br>ENGLISH ABSTRACT: This thesis presents the FATKID Finite Automata Toolkit. While a lot of toolkits currently exist which can manipulate and process nite state automata, this toolkit was designed to e ectively and e ciently generate, manipulate and process large numbers of nite automata by distributing the work ow across machines and running the computations in parallel. Other toolkits do not currently provide this functionality. We show that this framework is user-friendly and extremely extensible. Furthermore we show that the system e

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Riggelsen, Carsten. "Approximation methods for efficient learning of Bayesian networks /." Amsterdam ; Washington, DC : IOS Press, 2008. http://www.loc.gov/catdir/toc/fy0804/2007942192.html.

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Books on the topic "Computer science Machine theory"

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Davies, E. R. Computer and machine vision: Theory, algorithms, practicalities. 4th ed. Elsevier, 2012.

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Singh, Arindama. Elements of computation theory. Springer, 2009.

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Ron, Sigal, and Weyuker Elaine J, eds. Computability, complexity, and languages: Fundamentals of theoretical computer science. 2nd ed. Academic Press, Harcourt, Brace, 1994.

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Theory of computation. Wiley, 1987.

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Theory of computation. Harper & Row, 1987.

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Sudkamp, Thomas A. Languages and machines: An introduction to the theory of computer science. 2nd ed. Addison-Wesley Pub., 1997.

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Languages and machines: An introduction to the theory of computer science. Addison-Wesley, 1988.

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Languages and machines: An introduction to the theory of computer science. 3rd ed. Pearson Addison-Wesley, 2006.

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FCT '85 (1985 Cottbus, Germany). Fundamentals of computation theory. Springer-Verlag, 1985.

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Guoying, Zhao, Cheng Li, Pietikäinen Matti, and SpringerLink (Online service), eds. Machine Learning for Vision-Based Motion Analysis: Theory and Techniques. Springer-Verlag London Limited, 2011.

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Book chapters on the topic "Computer science Machine theory"

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Li, Ming, and Paul Vitányi. "Computational machine learning in theory and praxis." In Computer Science Today. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0015264.

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Baptiste, Philippe, Jacques Carlier, Alexander Kononov, Maurice Queyranne, Sergey Sevastyanov, and Maxim Sviridenko. "Integrality Property in Preemptive Parallel Machine Scheduling." In Computer Science - Theory and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03351-3_6.

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Kohlhase, Michael, Felix Mance, and Florian Rabe. "A Universal Machine for Biform Theory Graphs." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39320-4_6.

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Harrison, John. "A Machine-Checked Theory of Floating Point Arithmetic." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48256-3_9.

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Damiani, Ernesto, and Claudio A. Ardagna. "Certified Machine-Learning Models." In SOFSEM 2020: Theory and Practice of Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38919-2_1.

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Zhang, Haoran, and Xiaodong Wang. "Nonlinear Systems Modeling and Control Using Support Vector Machine Technique." In Computer Science – Theory and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11753728_66.

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Rathod, Nakul, and Lillian N. Cassel. "Machine Learning in Building a Collection of Computer Science Course Syllabi." In Theory and Practice of Digital Libraries. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33290-6_38.

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Wu, Chunguo, Yanchun Liang, Xiaowei Yang, and Zhifeng Hao. "Equivalence of Classification and Regression Under Support Vector Machine Theory." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539902_160.

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Çapuni, Ilir, and Peter Gács. "A Turing Machine Resisting Isolated Bursts of Faults." In SOFSEM 2012: Theory and Practice of Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27660-6_14.

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Gajardo, Anahí, and Pierre Guillon. "Zigzags in Turing Machines." In Computer Science – Theory and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13182-0_11.

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Conference papers on the topic "Computer science Machine theory"

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Kuzlyakina, Valentina V., and Marina V. Nagaeva. "Computer-Aided Laboratory Work to “Mechanism and Machine Science” Course." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34609.

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Laboratory work is an important element in engineering training, which should correspond to up-to-date tendencies in computer-based technologies in design, production, maintenance and preserving mechanisms. Computer-based laboratory work consists of 16 assignments. Seven assignments are carried out on laboratory stands, the remainder are provided on PC, using programs like “Visual Structure Editor”, “DYNAMO”, “APM Win Machine”. The system Visual Structure Editor (VSE) is designed by specialists of “Machine Mechanics and Computer-Aided Design” department, of the Maritime State University named

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Liu, Dao-wen. "Modeling for Nonlinear Series Prediction based on the Support Vector Machine Theory." In 2012 National Conference on Information Technology and Computer Science. Atlantis Press, 2012. http://dx.doi.org/10.2991/citcs.2012.88.

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Tang, Bo, Yu Hou, Guangming Zou, and Huaiguang Liu. "Application of Computer Assisted Instruction in Teaching of Mechanisms and Machine Theory Course." In 2017 2nd International Conference on Education, Management Science and Economics (ICEMSE 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/icemse-17.2017.39.

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Huimin Zhao, Chaoying Xia, Yunkui Xiao, Jianmin Mei, and Xian Zhang. "Unstable engine vibration signal analysis using cyclostationarity and support vector machine theory." In 2009 2nd IEEE International Conference on Computer Science and Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/iccsit.2009.5234914.

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Dey, Somdip. "Securing Majority-Attack in Blockchain Using Machine Learning and Algorithmic Game Theory: A Proof of Work." In 2018 10th Computer Science and Electronic Engineering (CEEC). IEEE, 2018. http://dx.doi.org/10.1109/ceec.2018.8674185.

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Chen, Wei, Qing-xuan Jia, Han-xu Sun, and Si-cheng Nian. "Bearing Fault Diagnosis of Sorting Machine Induction Based on Improved Neural Network and Evidence Theory." In 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). Atlantis Press, 2013. http://dx.doi.org/10.2991/iccsee.2013.49.

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Lu, Ning, Jianzhong Zhou, Yaoyao He, and Ying Liu. "Simulated Annealing Theory Based Particle Swarm Optimization for Support Vector Machine Model in Short-Term Load Forecasting." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5366400.

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Yu, Xiuna, and Liyun Xing. "Research on the AC servo system of CNC machine tools based on sliding mode variable structure theory." In 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC). IEEE, 2011. http://dx.doi.org/10.1109/mec.2011.6025398.

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Río-Belver, Rosa María, Gaizka Garechana, Iñaki Bildosola, and Enara Zarrabeitia. "Evolution and scientific visualization of Machine learning field." In CARMA 2018 - 2nd International Conference on Advanced Research Methods and Analytics. Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/carma2018.2018.8329.

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This article provides a retrospective and understanding of the development of automatic learning methods. The beginnings are visualized as a discipline within Computer Sciences in the subcategory of Artificial Intelligence, its development and the current transfer of knowledge to other areas of Engineering and its industrial applications. Based on the publications about machine learning and its application contained in the Web of Science database, records from 1986 to 2017 are downloaded. After a description of the technological profile, a new approach is introduced to the classification of a

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Naifar, Omar, Ghada Boukettaya, and Abderrazak Ouali. "A new control technic for induction machine based on the Lyapunov theory." In 14th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA2013). IEEE, 2013. http://dx.doi.org/10.1109/sta.2013.6783101.

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