Zeitschriftenartikel: „Computer Science“

1

Shiflet, Angela B. „Computer science with the sciences“. ACM SIGCSE Bulletin 34, Nr. 4 (Dezember 2002): 40–43. http://dx.doi.org/10.1145/820127.820162.

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2

Giza, Piotr. „CREATIVITY IN COMPUTER SCIENCE“. Creativity Studies 14, Nr. 2 (09.11.2021): 444–60. http://dx.doi.org/10.3846/cs.2021.14699.

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The aim of this paper is to briefly explore creative thinking in computer science, and compare it to natural sciences, mathematics or engineering. It is also meant as polemics with some theses of the pioneer work under the same title by Daniel Saunders and Paul Thagard because I point to important motivations in computer science the authors do not mention, and give examples of the origins of problems they explicitly deny. Computer science is a very specific field for it relates the abstract, theoretical discipline – mathematics, on the one hand, and engineering, often concerned with very practical tasks of building computers, on the other. It is like engineering in that it is concerned with solving practical problems or implementing solutions, often with strongly financial reasons, e.g. increasing a company’s income. It is like mathematics in that is deals with abstract symbols, logical relations, algorithms, computability problems, etc. Saunders and Thagard analyse rich experimental material from historical and contemporary work in computer science and argue that, as opposed to natural sciences, computer science is not concerned with describing and explaining natural phenomena. Now, I argue that there is a field of research in artificial intelligence (which, in turn, is a branch of computer science), called machine discovery, where explanation of natural phenomena, finding experimental laws and explanatory models is the primary goal. This goal is achieved by constructing computer systems whose job is to simulate various processes involved in scientific discovery done by human researchers, and help them in making new discoveries. On the other hand, motivations that give rise to ingenious projects in computer science can be very strange and include curiosity, fun or attempts to be famous out of boring, stable life of a successful programmer in a big corporation. A good example is the phenomenon of open-source software, especially the development of the Linux operating system and its applications when, from economical point of view, Microsoft absolutely dominated the software market of personal computers.

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Fagan, Jody Condit, und David J. Gaines. „EBSCO Computers and Applied Sciences Complete and ProQuest Computer Science Collection“. Charleston Advisor 13, Nr. 1 (01.07.2011): 14–17. http://dx.doi.org/10.5260/chara.13.1.14.

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4

Chen, Sifei. „Application of Computer Science Technology in Computer Education“. Journal of Education and Culture Studies 8, Nr. 1 (19.02.2024): p65. http://dx.doi.org/10.22158/jecs.v8n1p65.

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The arrival of the era of big data, making the computer in all walks of life are widely used in the teaching process is also inseparable from the application of computers, some of the traditional teaching methods have also been replaced by computer technology, computer teaching has become the mainstream of the times, and has become a very effective means of teaching as well as teaching tools. The application and popularization of computers has brought great convenience to people's daily life, and has a great effect on the improvement of teaching quality. In the process of computer popularization, it is widely used in real life. And the computer as an education and learning tools, for computer education itself has great significance. In assisting teachers in the management of teaching and practical work at the same time, but also to provide students with richer learning resources and learning efficiency, so that the teaching and learning process is more concise and easy. The arrival of the era of big data, making the computer in all walks of life are widely used in the teaching process is also inseparable from the application of computers, some of the traditional teaching methods have also been replaced by computer technology, computerized teaching has become the mainstream of the times, and has become a very effective means of teaching as well as the teaching tools of the times. The application and popularization of computers has brought great convenience to people's daily life, and has a great effect on the improvement of teaching quality. Especially in the cultivation of talents in colleges and universities, the application of computers can have a great role in promoting the cultivation of innovation ability of college students. This article briefly analyzes the application of computer science and technology in computer education, starting from computer science and technology.

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5

Barron, D. W. „Two cultures: computer science and personal computers“. Endeavour 13, Nr. 1 (Januar 1989): 25–28. http://dx.doi.org/10.1016/0160-9327(89)90047-1.

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6

Chakraborty, Pinaki. „Computer, Computer Science, and Computational Thinking: Relationship between the Three Concepts“. Human Behavior and Emerging Technologies 2024 (28.03.2024): 1–6. http://dx.doi.org/10.1155/2024/5044787.

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Digital computers were invented in the 1940s. They are sophisticated and versatile machines whose functioning is grounded in elaborate theory. Advances in theory and the availability of computers helped computer science to develop as an academic discipline, and university departments for the same started coming up in the 1960s. Computer science covers all phenomenon related to computers and consists primarily of man-made laws governing building, programming, and using computers. Computational thinking is a way of thinking influenced by computers and computer science. There are two schools of thought on computational thinking. The first school sees computational thinking as the use of computers to explore the world, while the other sees computational thinking as the application of concepts from computer science to solve real-world problems. Scholars typically agree that computational thinking has four essential components, viz., abstraction, decomposition, algorithm design, and generalization. Computational thinking is often feted by computer scientists as a useful skill that can be used by anybody anywhere. However, it is necessary to find out ways for successfully using computational thinking in domains other than computer science before it can be declared a universal skill.

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7

Chu, Yul, und Jin Hwan Park. „An Effective Model for Computer System Building Projects in Computer Engineering and Computer Science“. International Journal of Information and Education Technology 5, Nr. 6 (2015): 432–36. http://dx.doi.org/10.7763/ijiet.2015.v5.544.

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8

Prokhorov, S. P. „THE FUNDAMENTAL CONTRIBUTION OF THE ACADEMY OF SCIENCES TO THE DEVELOPMENT OF RUSSIAN COMPUTER SCIENCE AND COMPUTER TECHNOLOGY“. Вестник Российской академии наук 93, Nr. 10 (01.10.2023): 980–88. http://dx.doi.org/10.31857/s0869587323100092.

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The history of the creation of the first Soviet universal electronic computers is an excellent example of the ability and readiness of the Academy of Sciences to be a leader in the creation and development of new areas of science and technology. All work, starting with the design of computers and ending with the stage of creating computers and software, was carried out at the institutes of the Academy of Sciences. On December 4, 1948, I.S. Brook and B.I. Rameev received a copyright certificate for the invention of an automatic digital machine. It was the first official document indicating the beginning of work on the creation of computers in the USSR. The first Soviet computer, the M-1, was created at the Power Energy Institute under the leadership of Corresponding Member I.S. Brook. At the Institute of Precise Mechanics and Computer Engineering, under the guidance of Academician S.A. Lebedev, the most successful series of Soviet computers, BESM, was produced. The first copies of all new models of computers before being launched into a series were subjected to comprehensive tests at the Institute of Applied Mathematics, whose director was Academician M.V. Keldysh. Academician S.L. Sobolev was the ancestor of the Russian school of programming. The article contains little-known information about the first steps of computer science in Russia. Some facts about the history of those years were hidden in the archives for a long time and are now published for the first time.

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9

Kadijevich, Djordje. „Gender Differences in Computer Attitude among Ninth-Grade Students“. Journal of Educational Computing Research 22, Nr. 2 (März 2000): 145–54. http://dx.doi.org/10.2190/k4u2-pwqg-re8l-uv90.

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A study of fifty-four female and fifty-nine male Gymnasium (high school) students from four ninth-grade classes oriented toward natural sciences and mathematics surveyed students' attitudes toward computers in relation to gender and computer experience and found that males showed a more positive attitude toward computers than females, even when computer experience was controlled. An additional study of fifty-one females and fifty-four males from the initial sample found that males did show more interest in computer science than females, but these differences could not explain gender differences in computer attitude.

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10

Jirari, Ahmed. „Future of Computer Science Profession“. International Journal of Scientific Research and Management 10, Nr. 04 (15.04.2022): 809–11. http://dx.doi.org/10.18535/ijsrm/v10i4.ec01.

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As society becomes more reliant on computers and the Internet, A computer science degree is a great way to start a career in IT. You can find many different roles that suit your interests and strengths. The best thing about computer science is that it teaches you to think logically and solve problems. Computer hardware and software technology quickly expand in power and complexity as this process unfolds. Because developments happen so quickly, predicting where the area of computer science will go in the next 10 to 20 years can be challenging. The area of computer science is expected to undergo significant changes shortly.

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11

Ismail, Lizah. „Computer Science and Applied Sciences Complete (EBSCOhost)“. Charleston Advisor 20, Nr. 1 (01.07.2018): 33–34. http://dx.doi.org/10.5260/chara.20.1.33.

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12

Marchal, Bruno. „Theoretical computer science and the natural sciences“. Physics of Life Reviews 2, Nr. 4 (Dezember 2005): 251–89. http://dx.doi.org/10.1016/j.plrev.2005.07.001.

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13

Nasution, Mahyuddin K. M., Rahmat Hidayat und Rahmad Syah. „Computer Science“. International Journal on Advanced Science, Engineering and Information Technology 12, Nr. 3 (26.06.2022): 1142. http://dx.doi.org/10.18517/ijaseit.12.3.14832.

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14

Vaughan, Christopher. „Computer Science“. Science News 134, Nr. 10 (03.09.1988): 156. http://dx.doi.org/10.2307/3972976.

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15

Cupper, Robert D. „Computer science“. ACM SIGCSE Bulletin 30, Nr. 1 (März 1998): 25–29. http://dx.doi.org/10.1145/274790.273151.

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16

Cohen, Avi, und Bruria Haberman. „Computer science“. ACM SIGCSE Bulletin 39, Nr. 4 (Dezember 2007): 65–69. http://dx.doi.org/10.1145/1345375.1345417.

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17

Lazowska, Ed. „Computer science“. ACM SIGCSE Bulletin 40, Nr. 1 (29.02.2008): 549. http://dx.doi.org/10.1145/1352322.1352321.

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18

French, C. „Computer Science“. Computer Journal 35, Nr. 4 (01.08.1992): 404. http://dx.doi.org/10.1093/comjnl/35.4.404.

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19

Hopcroft, John E. „Computer science“. Communications of the ACM 30, Nr. 3 (März 1987): 198–202. http://dx.doi.org/10.1145/214748.214750.

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20

Stewart, N. F. „Science and computer science“. ACM Computing Surveys 27, Nr. 1 (März 1995): 39–41. http://dx.doi.org/10.1145/214037.214088.

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21

Denning, Peter J. „Is computer science science?“ Communications of the ACM 48, Nr. 4 (April 2005): 27–31. http://dx.doi.org/10.1145/1053291.1053309.

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22

Shneiderman, B. „COMPUTER SCIENCE: Science 2.0“. Science 319, Nr. 5868 (07.03.2008): 1349–50. http://dx.doi.org/10.1126/science.1153539.

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23

Chubarikov, Vladimir Nikolaevich, Nikolai Nikolaevich Dobrovol’skii, Irina Yurievna Rebrova und Nikolai Mihailovich Dobrovol’skii. „Computer science, computer, computational complexity“. Chebyshevskii sbornik 22, Nr. 1 (2021): 520–36. http://dx.doi.org/10.22405/2226-8383-2021-22-1-520-536.

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24

Chen, Yaozhang. „Analysis of the Development of Computer Science and its Future Trend“. Applied and Computational Engineering 8, Nr. 1 (01.08.2023): 341–45. http://dx.doi.org/10.54254/2755-2721/8/20230180.

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Computer science is one of the most influential innovations of the last century, including data structures, computer and network design, modeling data and information processes, and artificial intelligence. With the development of computer science, more and more people begin to pay attention to the importance of computers. This paper tells the history of computer science, and introduces some frontier technology of computer science. Computers have greatly improved people's work and lifestyle, developed modern society, and become an indispensable part of people's lives. Computers have entered the era of artificial intelligence, which has a major impact on the development of human society.

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25

Prakash Mathur, Ravi. „Application of Vedic Mathematics in Computer Science“. International Journal of Science and Research (IJSR) 13, Nr. 2 (05.02.2024): 990–93. http://dx.doi.org/10.21275/sr24206134323.

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26

Yuldashovna, Yusupova Gulchehra. „MODELING COMPETENCIES FOR FUTURE COMPUTER SCIENCE TEACHERS“. American Journal of Social Science and Education Innovations 6, Nr. 3 (01.03.2024): 27–31. http://dx.doi.org/10.37547/tajssei/volume06issue03-04.

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This article explores the improvement of modeling skills among future computer science teachers. The current methods of forming these skills are analyzed, and the need for their improvement is justified. The research methods and the results of the experiment on the introduction of a new model of modeling training are described. The article concludes with conclusions and suggestions for further improving the training of future computer science teachers in the field of modeling.

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27

Stevenson, D. E. „Science, computational science, and computer science“. Communications of the ACM 37, Nr. 12 (Dezember 1994): 85–96. http://dx.doi.org/10.1145/198366.198386.

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28

Ploeger, Lennert S. „Computer Science Meets Medical Science“. Analytical Cellular Pathology 28, Nr. 3 (01.01.2006): 69–70. http://dx.doi.org/10.1155/2006/312128.

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29

Frenkel, Karen A. „Computer Science meets environmental science“. Communications of the ACM 52, Nr. 9 (September 2009): 23. http://dx.doi.org/10.1145/1562164.1562174.

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30

Kumar, Deepak. „Data science overtakes computer science?“ ACM Inroads 3, Nr. 3 (September 2012): 18–19. http://dx.doi.org/10.1145/2339055.2339060.

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31

Denning, Peter J. „The science in computer science“. Communications of the ACM 56, Nr. 5 (Mai 2013): 35–38. http://dx.doi.org/10.1145/2447976.2447988.

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32

Kalia, Lipika Priyadarshini. „Computer Science is Cognitive Science“. Advanced Science Letters 22, Nr. 2 (01.02.2016): 538–39. http://dx.doi.org/10.1166/asl.2016.6851.

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33

Snow, Steven G., und James Hearne. „Computer Science With Political Science“. Social Science Computer Review 15, Nr. 3 (Oktober 1997): 288–91. http://dx.doi.org/10.1177/089443939701500306.

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34

Bell, Tim. „Ubiquity symposium: The science in computer science: unplugging computer science to find the science“. Ubiquity 2014, March (März 2014): 1–6. http://dx.doi.org/10.1145/2590528.2590531.

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35

Stacewicz, Paweł. „From Computer Science to the Informational Worldview. Philosophical Interpretations of Some Computer Science Concepts“. Foundations of Computing and Decision Sciences 44, Nr. 1 (01.03.2019): 27–43. http://dx.doi.org/10.2478/fcds-2019-0003.

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AbstractIn this article I defend the thesis that modern computer science has a significant philosophical potential, which is expressed in a form of worldview, called here informational worldview (IVW). It includes such theses like: a) each being contains a certain informational content (which may be revealed by computer science concepts, such as code or algorithm), b) the mind is an information processing system (which should be modeled by means of data processing systems), c) cognition is a type of computation. These (pre)philosophical theses are accepted in many sciences (e.g. in cognitive science), and this is both an expression and strengthening of the IWV. After a general discussion of the relations between philosophy, particular sciences and the worldview, and then the presentation of the basic assumptions and theses of the IWV, I analyze a certain specification of thesis b) expressed in the statement that “the mind is the Turing machine”. I distinguish three concepts of mind (static, variable and minimal) and explain how each of them is connected with the concept of the Turing machine.

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36

AKL, SELIM G. „THREE COUNTEREXAMPLES TO DISPEL THE MYTH OF THE UNIVERSAL COMPUTER“. Parallel Processing Letters 16, Nr. 03 (September 2006): 381–403. http://dx.doi.org/10.1142/s012962640600271x.

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It is shown that the concept of a Universal Computer cannot be realized. Specifically, instances of a computable function [Formula: see text] are exhibited that cannot be computed on any machine [Formula: see text] that is capable of only a finite and fixed number of operations per step. This remains true even if the machine [Formula: see text] is endowed with an infinite memory and the ability to communicate with the outside world while it is attempting to compute [Formula: see text]. It also remains true if, in addition, [Formula: see text] is given an indefinite amount of time to compute [Formula: see text]. This result applies not only to idealized models of computation, such as the Turing Machine and the like, but also to all known general-purpose computers, including existing conventional computers (both sequential and parallel), as well as contemplated unconventional ones such as biological and quantum computers. Even accelerating machines (that is, machines that increase their speed at every step) cannot be universal.

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37

Berque, Dave, Terri Bonebright und Michael Whitesell. „Using pen-based computers across the computer science curriculum“. ACM SIGCSE Bulletin 36, Nr. 1 (März 2004): 61–65. http://dx.doi.org/10.1145/1028174.971324.

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38

Addison, Paul. „Teaching computer science without a computer“. ACM SIGCSE Bulletin 29, Nr. 4 (Dezember 1997): 30–33. http://dx.doi.org/10.1145/271125.271149.

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39

Orwant, Carol J. „Computer ethics—part of computer science!“ ACM SIGCAS Computers and Society 21, Nr. 2-4 (November 1991): 40–45. http://dx.doi.org/10.1145/122652.122659.

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40

Zhao, Jinghai. „Modern Application of Computer Science and Technology“. Lifelong Education 9, Nr. 6 (28.09.2020): 154. http://dx.doi.org/10.18282/le.v9i6.1329.

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In the current era of rapid development of science and technology, computer technology has been used in various fields and has become an indispensable part of people’s lives. Nowadays, the quality of life has improved significantly, and at the same time, people are pursuing higher and higher goals. In order to meet the ideal living conditions that people are pursuing, relevant computer personnel are also working hard to explore modern applications of computers and technology. The connection makes the computer a symbol of the development of the times. At this stage in China, since computer technology is not yet mature, this article discusses the problems in the modern application of computer science and technology and the methods to solve the existing problems.

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41

Agarwal, Shresth. „THE IMPACT OF COMPUTER SCIENCE ON EDUCATION“. International Journal of Social Science and Economic Research 07, Nr. 12 (2022): 4153–60. http://dx.doi.org/10.46609/ijsser.2022.v07i12.021.

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Computer science and its different applications have drastically revolutionized the whole world. Computer technology is an essential part in every walk of life. Education system is one of the most developed sectors witnessing the utilization of technology as a magical wand. Teaching and learning methods with the emergence of computers has converted dreams into reality. The Internet, virtual reality, and artificial intelligence are the new ways of learning. This paper focuses on the emergence of the traditional education system and the impact of computer science in both positive and negative ways.

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42

DeReamer, Sharon. „Teaching computer science“. ACM SIGSOFT Software Engineering Notes 35, Nr. 1 (25.01.2010): 31–34. http://dx.doi.org/10.1145/1668862.1668880.

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43

West, Dave. „Hermeneutic computer science“. Communications of the ACM 40, Nr. 4 (April 1997): 115–16. http://dx.doi.org/10.1145/248448.248467.

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44

Kearse, Iretta B., und Charles R. Hardnett. „Computer science olympiad“. ACM SIGCSE Bulletin 40, Nr. 1 (29.02.2008): 92–96. http://dx.doi.org/10.1145/1352322.1352167.

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45

Allan, V. H., und M. V. Kolesar. „Teaching computer science“. ACM SIGCUE Outlook 25, Nr. 1-2 (Januar 1997): 2–10. http://dx.doi.org/10.1145/274375.274376.

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46

Freeman, Peter A. „Effective computer science“. ACM Computing Surveys 27, Nr. 1 (März 1995): 27–29. http://dx.doi.org/10.1145/214037.214048.

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47

Denning, Peter J., und Andrew McGettrick. „Recentering computer science“. Communications of the ACM 48, Nr. 11 (November 2005): 15–19. http://dx.doi.org/10.1145/1096000.1096018.

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48

Goode, Joanna, und Jane Margolis. „Exploring Computer Science“. ACM Transactions on Computing Education 11, Nr. 2 (Juli 2011): 1–16. http://dx.doi.org/10.1145/1993069.1993076.

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49

DePalma, Nick, und David Robert. „Creativity + Computer Science“. XRDS: Crossroads, The ACM Magazine for Students 19, Nr. 4 (Juni 2013): 11. http://dx.doi.org/10.1145/2460436.2460439.

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50

Rieffel, Eleanor. „Quantum computer science“. ACM SIGACT News 41, Nr. 3 (03.09.2010): 39–44. http://dx.doi.org/10.1145/1855118.1855127.

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Zeitschriftenartikel zum Thema „Computer Science“

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