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Artykuły w czasopismach na temat „Programming”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

El-Zakhem, Imad H. "Socratic Programming: An Innovative Programming Learning Method." International Journal of Information and Education Technology 6, no. 3 (2016): 247–50. http://dx.doi.org/10.7763/ijiet.2016.v6.694.

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COLLIS, D. "Programming Programming." Science 254, no. 5031 (1991): 589–90. http://dx.doi.org/10.1126/science.254.5031.589.

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Wheatman, Martin. "Programming Without Programming." ITNOW 60, no. 1 (2018): 56–57. http://dx.doi.org/10.1093/itnow/bwy025.

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Roque Hernández, Ramón Ventura, Sergio Armando Guerra Moya, and Frida Carmina Caballero Rico. "Acceptance and Assessment in Student Pair-Programming: A Case Study." International Journal of Emerging Technologies in Learning (iJET) 16, no. 09 (2021): 4. http://dx.doi.org/10.3991/ijet.v16i09.18693.

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This study analyzes pair programming's acceptance and assessment in the university setting considering participants' gender, previous programming ex-perience, and programming enjoyment. The sample included 80 students from three different sections enrolled in a basic programming course. We used a questionnaire to collect data after the pair programming practices. For data analysis, we used SPSS 24, and Mann-Whitney, Kruskal-Wallis, and Jonckheere-Terpstra statistical techniques. Descriptive and comparative re-sults showed a significant increasing monotonic trend in the acceptance of pair progr

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Shukla, Abhishek. "Bridging the Gap between Event-Based Programming and Functional Programming." International Journal of Science and Research (IJSR) 11, no. 1 (2022): 1595–98. http://dx.doi.org/10.21275/sr231116134821.

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6

Сальков and Nikolay Sal'kov. "Graph-analytic Solution of Some Special Problems of Quadratic Programming." Geometry & Graphics 2, no. 1 (2014): 3–8. http://dx.doi.org/10.12737/3842.

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Quadratic programming problems are one of special cases of mathematical programming problems. Mathematical programming problems solution is of great importance, because these problems are those of optimizing of solution related to presented issues from multitude of possible ones. The mathematical programming problems are linear, nonlinear, dynamic and others. It is suggested to consider a graph-analytic solution of quadratic programming’s special problems, which, taken together, constitute the quadratic programming problems for two and three vari

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7

Voronkov, A. A. "Logic programming and ?-programming." Cybernetics 25, no. 1 (1989): 83–91. http://dx.doi.org/10.1007/bf01074888.

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HUANG, HONG-ZHONG, ZHI-GANG TIAN, and YING-KUI GU. "RELIABILITY AND REDUNDANCY APPORTIONMENT OPTIMIZATION USING INTERACTIVE PHYSICAL PROGRAMMING." International Journal of Reliability, Quality and Safety Engineering 11, no. 03 (2004): 213–22. http://dx.doi.org/10.1142/s0218539304001476.

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In this paper, a new multiobjective optimization approach named interactive physical programming is proposed and used to solve the reliability and redundancy apportionment optimization problem. Interactive physical programming extends physical programming6 to an interactive framework. After the designer specifies which objectives need to be improved and which objectives can be sacrificed, interactive physical programming can obtain the Pareto solutions satisfying such improving preferences. It has good convergence performance, and can obtain satisfactory design in the end. Interactive physical

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9

Brustad, T. F. "Innføring av studentaktive læringsmetoder for å utvide studentenes forståelse av programmering." Nordic Journal of STEM Education 9, no. 1 (2025): 133–43. https://doi.org/10.5324/njsteme.v9i1.6294.

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Mange studenter forbinder programmering med det å skrive kode i et programmeringsspråk, men programmeringsprosessen er mye mer enn koding. Programmering er en måte å tenke, dekomponere, og løse problemer på, hvor det som regel ikke finnes ett fasitsvar. I undervisning i programmeringsfag kan det av og til være vanskelig å få studentene til å planlegge og reflektere rundt de valgene de tar når de koder, og de ender ofte opp med å velge første løsning de finner. I dette prosjektet er det gjennomført en pilotstudie hvor to ulike studentaktive læringsmetoder er implementert i et programmeringsfag.

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10

Rodríguez, Arturo, and Joaquín Trigueros. "Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming." Estudios de Administración 15, no. 2 (2020): 47. http://dx.doi.org/10.5354/0719-0816.2008.56413.

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In this study we examine different methodologies to estimate earnings. More specifically, we evaluate the viability of Genetic Programming as both a forecasting model estimator and a forecast-combining methodology. When we compare the performance of traditional mechanical forecasting (ARIMA) models and models developed using Genetic Programming we observe that Genetic Programming can be used to create time-series models for quarterly earnings as accurate as the traditional linear models. Genetic Programming can also effectively combine forecasts. However, Genetic Programming's forecast combina

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11

Gilmore, D. J., and T. R. G. Green. "Programming Plans and Programming Expertise." Quarterly Journal of Experimental Psychology Section A 40, no. 3 (1988): 423–42. http://dx.doi.org/10.1080/02724988843000005.

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This paper addresses issues of the nature of expertise in programming and asks whether “programming plans” represent the underlying deep structure of a program. It reports an experiment that investigated the effect, on experienced programmers, of highlighting the plan structure of a computer program, while they were performing both plan-related and unrelated tasks. The effect was examined in both Pascal and BASIC. For Pascal programmers, perceptual cues to the plan structure were useful only for plan-related tasks, but the same cues were of no benefit to experienced BASIC programmers in any of

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12

Goodell, Howie, Sarah Kuhn, David Maulsby, and Carol Traynor. "End user programming/informal programming." ACM SIGCHI Bulletin 31, no. 4 (1999): 17–21. http://dx.doi.org/10.1145/339290.339294.

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13

PUGET, JEAN-FRANÇOIS, and IRVIN LUSTIG. "Constraint programming and maths programming." Knowledge Engineering Review 16, no. 1 (2001): 5–23. http://dx.doi.org/10.1017/s0269888901000042.

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Maths programming (MP) and constraint programming (CP) are two techniques that are able to solve difficult industrial optimisation problems. The purpose of this paper is to compare them from an algorithmic and a modelling point of view. Algorithmic principles of each approach are described and contrasted. Some ways of combining both techniques are also introduced.

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14

Elving, Pernille Risør, and Thomas Ryberg. "Pædagogiske spændingsfelter ved inddragelse af programmering i grundskolen." Tidsskriftet Læring og Medier (LOM) 11, no. 19 (2019): 21. http://dx.doi.org/10.7146/lom.v11i19.103100.

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Abstract (dansk)I denne artikel undersøges, hvordan programmering benyttes i en pædagogisk praksis i grundskolen, samt hvilket perspektiv faglærerne har på at inddrage programmering som fagligt element i deres undervisning. Artiklen bygger på en empirisk undersøgelse af tre skolers inddragelse af programmering i undervisningen på mellemtrinnet under Coding Class-forløb i Vejle Kommune, hvor fire faglærere blev interviewet omkring deres erfaringer med forløbet. Analysen er struktureret omkring en række spændingsfelter, der omhandler brugen af programmering som enten et fagspecifikt eller tværfa

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15

Jahagirdar, Ashok. "Dynamic Programming vs. Recursive Programming: A Comparative Analysis of Efficiency and Applicability." International Journal of Science and Research (IJSR) 14, no. 2 (2025): 1282–84. https://doi.org/10.21275/sr25221083610.

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16

Gharib, Mona, and Amr I. Hassan Amr I. Hassan. "Stable Answer Set Programming." Indian Journal of Applied Research 3, no. 8 (2011): 78–88. http://dx.doi.org/10.15373/2249555x/aug2013/220.

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17

S.R, Srividhya, and Pothumani S. "Programming for Deconstructing Extreme." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (2019): 840–46. http://dx.doi.org/10.5373/jardcs/v11/20192641.

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18

Thompson, Carla J., and Joyce S. Friske. "Programming." Journal of Research on Computing in Education 20, no. 4 (1988): 367–74. http://dx.doi.org/10.1080/08886504.1988.10781851.

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19

Cooper, Stephen, and Wanda Dann. "Programming." ACM Inroads 6, no. 1 (2015): 50–54. http://dx.doi.org/10.1145/2723169.

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20

Bergin, Susan, and Ronan Reilly. "Programming." ACM SIGCSE Bulletin 37, no. 1 (2005): 411–15. http://dx.doi.org/10.1145/1047124.1047480.

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21

Lukkarinen, Aleksi, Lauri Malmi, and Lassi Haaranen. "Event-driven Programming in Programming Education." ACM Transactions on Computing Education 21, no. 1 (2021): 1–31. http://dx.doi.org/10.1145/3423956.

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During the past two decades, event-driven programming (EDP) has emerged as a central and almost ubiquitous concept in modern software development: Graphical user interfaces are self-evident in most mobile and web-based applications, as well as in many embedded systems, and they are most often based on reacting to events. To facilitate both teaching practice and research in programming education, this mapping review seeks to give an overview of the related knowledge that is already available in conference papers and journal articles. Starting from early works of the 1990s, we identified 105 pap

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22

Visnovitz, Márton. "Classical Programming Topics with Functional Programming." Central-European Journal of New Technologies in Research, Education and Practice 2, no. 2 (2020): 41–55. http://dx.doi.org/10.36427/cejntrep.2.2.965.

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23

B. Hammert, William, Ryo Kataoka, Ecaterina Vasenina, Adam H. Ibrahim, and Samuel L. Buckner. "Is “periodization programming” periodization or programming?" Journal of Trainology 10, no. 2 (2021): 20–24. http://dx.doi.org/10.17338/trainology.10.2_20.

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24

Cohen, Jacques. "Logic programming and constraint logic programming." ACM Computing Surveys 28, no. 1 (1996): 257–59. http://dx.doi.org/10.1145/234313.234416.

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25

Lageweg, B. J., J. K. Lenstra, A. H. G. RinnooyKan, L. Stougie, and A. H. G. Rinnooy Kan. "STOCHASTIC INTEGER PROGRAMMING BY DYNAMIC PROGRAMMING." Statistica Neerlandica 39, no. 2 (1985): 97–113. http://dx.doi.org/10.1111/j.1467-9574.1985.tb01131.x.

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26

Müller, Bernd. "Is object-oriented programming structured programming?" ACM SIGPLAN Notices 28, no. 9 (1993): 57–66. http://dx.doi.org/10.1145/165364.165385.

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27

KOWALSKI, ROBERT, and FARIBA SADRI. "Programming in logic without logic programming." Theory and Practice of Logic Programming 16, no. 3 (2016): 269–95. http://dx.doi.org/10.1017/s1471068416000041.

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AbstractIn previous work, we proposed a logic-based framework in which computation is the execution of actions in an attempt to make reactive rules of the form if antecedent then consequent true in a canonical model of a logic program determined by an initial state, sequence of events, and the resulting sequence of subsequent states. In this model-theoretic semantics, reactive rules are the driving force, and logic programs play only a supporting role. In the canonical model, states, actions, and other events are represented with timestamps. But in the operational semantics (OS), for the sake

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28

AZUMA, Yoshitaka, and Kin-ichi INAGAKI. "NC Programming by LANC Programming Language." Proceedings of The Computational Mechanics Conference 2016.29 (2016): 4_101. http://dx.doi.org/10.1299/jsmecmd.2016.29.4_101.

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29

Konrad Joschika, Thomas. "Programming system for programming hearing aids." Journal of the Acoustical Society of America 119, no. 6 (2006): 3523. http://dx.doi.org/10.1121/1.2212606.

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30

Fong, Abraham P., and Stephen J. Tapscott. "Skeletal muscle programming and re-programming." Current Opinion in Genetics & Development 23, no. 5 (2013): 568–73. http://dx.doi.org/10.1016/j.gde.2013.05.002.

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31

Iwamoto, S. "From Dynamic Programming to Bynamic Programming." Journal of Mathematical Analysis and Applications 177, no. 1 (1993): 56–74. http://dx.doi.org/10.1006/jmaa.1993.1243.

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32

Pal, B. B., and I. Basu. "A Goal Programming Method for Solving Fractional Programming Problems via Dynamic Programming." Optimization 35, no. 2 (1995): 145–57. http://dx.doi.org/10.1080/02331939508844136.

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33

Han, Jinmann, Zhoujing Zhoujing, and Chungmin Joo. "Changes of Programming of General Programming Channels." Journal of the Korea Contents Association 16, no. 12 (2016): 258–66. http://dx.doi.org/10.5392/jkca.2016.16.12.258.

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34

Guzdial, Mark, and Susan Landau. "Programming programming languages, and analyzing Facebook's failure." Communications of the ACM 61, no. 6 (2018): 8–9. http://dx.doi.org/10.1145/3204443.

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Belmer, S. V. "Special problems of food programming: foetal programming." Voprosy detskoj dietologii 14, no. 1 (2016): 26–31. http://dx.doi.org/10.20953/1727-5784-2016-1-26-31.

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Klamroth, Kathrin, Jørgen Tind, and Sibylle Zust. "Integer Programming Duality in Multiple Objective Programming." Journal of Global Optimization 29, no. 1 (2004): 1–18. http://dx.doi.org/10.1023/b:jogo.0000035000.06101.07.

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37

Dempe, Stephan, and Patrick Mehlitz. "Semivectorial bilevel programming versus scalar bilevel programming." Optimization 69, no. 4 (2019): 657–79. http://dx.doi.org/10.1080/02331934.2019.1625900.

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Calloni, Ben A., and Donald J. Bagert. "ICONIC programming in BACCII vs. textual programming." ACM SIGCSE Bulletin 26, no. 1 (1994): 188–92. http://dx.doi.org/10.1145/191033.191103.

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Morgenstern, Jamie, and Daniel R. Licata. "Security-typed programming within dependently typed programming." ACM SIGPLAN Notices 45, no. 9 (2010): 169–80. http://dx.doi.org/10.1145/1932681.1863569.

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Cai, Yongyang, Kenneth L. Judd, Thomas S. Lontzek, Valentina Michelangeli, and Che-Lin Su. "A NONLINEAR PROGRAMMING METHOD FOR DYNAMIC PROGRAMMING." Macroeconomic Dynamics 21, no. 2 (2016): 336–61. http://dx.doi.org/10.1017/s1365100515000528.

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A nonlinear programming formulation is introduced to solve infinite-horizon dynamic programming problems. This extends the linear approach to dynamic programming by using ideas from approximation theory to approximate value functions. Our numerical results show that this nonlinear programming is efficient and accurate, and avoids inefficient discretization.

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APT, KRZYSZTOF R., and ERIC MONFROY. "Constraint programming viewed as rule-based programming." Theory and Practice of Logic Programming 1, no. 6 (2001): 713–50. http://dx.doi.org/10.1017/s1471068401000072.

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We study here a natural situation when constraint programming can be entirely reduced to rule-based programming. To this end we explain first how one can compute on constraint satisfaction problems using rules represented by simple first-order formulas. Then we consider constraint satisfaction problems that are based on predefined, explicitly given constraints. To solve them we first derive rules from these explicitly given constraints and limit the computation process to a repeated application of these rules, combined with labeling. We consider two types of rule here. The first type, that we

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42

Chambers, John M. "Object-Oriented Programming, Functional Programming and R." Statistical Science 29, no. 2 (2014): 167–80. http://dx.doi.org/10.1214/13-sts452.

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Van Hulle, M. M. "A goal programming network for linear programming." Biological Cybernetics 65, no. 4 (1991): 243–52. http://dx.doi.org/10.1007/bf00206222.

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Repenning, Alexander, and Corrina Perrone. "Programming by example: programming by analogous examples." Communications of the ACM 43, no. 3 (2000): 90–97. http://dx.doi.org/10.1145/330534.330546.

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Xu, Z. K., and S. C. Fang. "Unconstrained convex programming approach to linear programming." Journal of Optimization Theory and Applications 86, no. 3 (1995): 745–52. http://dx.doi.org/10.1007/bf02192167.

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46

Myasnikov, K. P., S. A. Mikaeva, and Yu A. Zhuravleva. "MICROCONTROLLERS PROGRAMMING BASED ON THE PROGRAMMING LANGUAGE." Spravochnik. Inzhenernyi zhurnal, no. 315 (June 2023): 52–56. http://dx.doi.org/10.14489/hb.2023.06.pp.052-056.

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The relevance of the work is due to the need to develop controllers who encrypted information. The work was implemented in the work of encryption and decryption according to GOST 3412–2015, GOST 3413–2015. Programming was carried out on the basis of the ATMEGA 32A controller in the SI programming language. The program is made inside the microcontroller and interacts with the components of the circuit using PIN-S, thanks to which you can read and enter information. In the implementation of encryption and decryption algorithms, the Magma encryption algorithm, 4 types of transformations are used.

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47

SAHNI, PRIYANKA, and PUSHPENDRA KUMAR VASHISHTHA. "An Overview On Bilevel Programming." International Journal of Scientific Research 3, no. 6 (2012): 35–36. http://dx.doi.org/10.15373/22778179/june2014/173.

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B, Sundarraj. "Hope: Simulation of Evolutionary Programming." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (2019): 1221–27. http://dx.doi.org/10.5373/jardcs/v11/20192694.

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K, Kwon. "Exception Handling in Logic Programming." Advances in Robotic Technology 1, no. 1 (2023): 1–3. http://dx.doi.org/10.23880/art-16000104.

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One problem on logic programming is to express exception handling. We argue that this problem can be solved by adopting linear logic and prioritized-choice disjunctive goal formulas (PCD) of the form G G 0 *1 ⊕ where G0, G1 are goals. These goals have the following intended semantics: sequentially choose the first true goal GI and execute GI where i (= 0 or 1), discarding the rest if any.

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50

LACHHWANI, KAILASH. "FUZZY GOAL PROGRAMMING VS ORDINARY FUZZY PROGRAMMING APPROACH FOR MULTI OBJECTIVE PROGRAMMING PROBLEM." International Journal of Modern Physics: Conference Series 22 (January 2013): 757–61. http://dx.doi.org/10.1142/s2010194513010982.

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This paper presents the comparison between two solution methodologies Fuzzy Goal Programming (FGP) and ordinary Fuzzy Programming (FP) for multiobjective programming problem. Ordinary fuzzy programming approach is used to develop the solution algorithm for multiobjective functions which works for the minimization of the perpendicular distances between the parallel hyper planes at the optimum points of the objective functions. Suitable membership function is defined as the supremum perpendicular distance and a compromise optimum solution is obtained as a result of minimization of supremum perpe

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