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Journal articles on the topic 'Programming'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 August 2024

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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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2

COLLIS, D. "Programming Programming." Science 254, no. 5031 (October 25, 1991): 589–90. http://dx.doi.org/10.1126/science.254.5031.589.

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3

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

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4

Shukla, Abhishek. "Bridging the Gap between Event-Based Programming and Functional Programming." International Journal of Science and Research (IJSR) 11, no. 1 (January 5, 2022): 1595–98. http://dx.doi.org/10.21275/sr231116134821.

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5

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 (May 4, 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 programming as students' preference toward programming increased (standardized statistic = 3.20, p = 0.00, Kendall's τb = 0.30, p = 0.001). Also, pair programming was positively accepted and assessed even by students who reported a low level of programming enjoyment. There were no other statistically significant results.

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6

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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7

Сальков and Nikolay Sal'kov. "Graph-analytic Solution of Some Special Problems of Quadratic Programming." Geometry & Graphics 2, no. 1 (March 3, 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 variables. A total of eight special problems have been considered.

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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 (September 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 programming has been successfully applied to a reliability and redundancy apportionment optimization problem. It provides a new effective approach for reliability optimization.

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9

Gilmore, D. J., and T. R. G. Green. "Programming Plans and Programming Expertise." Quarterly Journal of Experimental Psychology Section A 40, no. 3 (August 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 the tasks. These results suggest that the actual content of programming plans does not generalise across different languages, although it is possible that the BASIC programmers can use other plans. From these results a more detailed description of programming plans and their role in programming expertise can be developed. The fact that BASIC programmers were not sensitive to the same plans as Pascal programmers implies that plans cannot represent the underlying deep structure of the programming problem.

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10

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

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11

PUGET, JEAN-FRANÇOIS, and IRVIN LUSTIG. "Constraint programming and maths programming." Knowledge Engineering Review 16, no. 1 (March 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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12

Rodríguez, Arturo, and Joaquín Trigueros. "Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming." Estudios de Administración 15, no. 2 (February 4, 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 combinations are sometimes unable to improve on Value Line. Moreover, simple averaging of forecasts results in better predictive accuracy than Genetic Programming-combining of forecasts. Hence, as implemented in this study, Genetic Programming is not superior to traditional methodologies in either forecasting or forecast combining of quarterly earnings.

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13

Gharib, Mona, and Amr I. Hassan Amr I. Hassan. "Stable Answer Set Programming." Indian Journal of Applied Research 3, no. 8 (October 1, 2011): 78–88. http://dx.doi.org/10.15373/2249555x/aug2013/220.

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14

S.R, Srividhya, and Pothumani S. "Programming for Deconstructing Extreme." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 840–46. http://dx.doi.org/10.5373/jardcs/v11/20192641.

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15

Elving, Pernille Risør, and Thomas Ryberg. "Pædagogiske spændingsfelter ved inddragelse af programmering i grundskolen." Tidsskriftet Læring og Medier (LOM) 11, no. 19 (January 8, 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 tværfagligt element, samt til understøttelse af elevernes faglige udbytte eller deres interesse, og slutteligt lærernes tilgang til deres egen rolle i klasseværelset. Lærerne oplevede alle højt engagement og fordybelse med programmeringsaktiviteterne blandt eleverne, men flere udtrykte behov for større kontrol med elevernes fagspecifikke udbytte og oplevede begrænsninger omkring egne tekniske kompetencer. På baggrund af analysen opstilles en række konkrete pædagogiske overvejelser, der udgør opmærksomhedspunkter til fremtidig planlægning og inddragelse af programmering i undervisningsforløb samt overvejelser omkring lærerens rolle i sådanne forløb. Abstract (engelsk)This article examines how programming is used as a pedagogical practice in K-12 and what perspective teachers have on implementing programming as a subject-specific element in their teaching. The article is based on an empirical study of three schools' use of programming during Coding Class in Vejle Kommune, where four teachers were interviewed about their experiences with the project. The analysis is structured around a number of challenges: including whether programming should be used as a subject-specific or interdisciplinary element and whether the goal is to enable development of student’s subject-specific learning or support their interests and finally, how teachers approach their own role in the classroom. All the teachers in the study experienced high levels of engagement and immersion in the programming activities among the students but several of the teachers lacked control over the students’ learning outcome and experienced limitations in their own technical skills. Based on the analysis, a number of concrete pedagogical considerations are presented, which can form the basis for planning and incorporating future programming activities in K-12.

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16

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

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17

Cooper, Stephen, and Wanda Dann. "Programming." ACM Inroads 6, no. 1 (February 9, 2015): 50–54. http://dx.doi.org/10.1145/2723169.

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18

Bergin, Susan, and Ronan Reilly. "Programming." ACM SIGCSE Bulletin 37, no. 1 (February 23, 2005): 411–15. http://dx.doi.org/10.1145/1047124.1047480.

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19

Lukkarinen, Aleksi, Lauri Malmi, and Lassi Haaranen. "Event-driven Programming in Programming Education." ACM Transactions on Computing Education 21, no. 1 (March 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 papers that address teaching practices, present learning resources, software tools or libraries to support learning, and empirical studies related to EDP. We summarize the publications, their main content, and findings. While most studies focus on bachelor’s level education in universities, there has been substantial work in K-12 level, as well. Few courses address EDP as their main content—rather it is most often integrated with CS1, CS2, or computer graphics courses. The most common programming languages and environments addressed are Java, App Inventor, and Scratch. Moreover, very little of deliberate experimental scientific research has been carried out to explicitly address teaching and learning EDP. Consequently, while so-called experience reports, tool papers, and anecdotal evidence have been published, this theme offers a wide arena for empirical research in the future. At the end of the article, we suggest a number of directions for future research.

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20

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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21

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 (September 23, 2021): 20–24. http://dx.doi.org/10.17338/trainology.10.2_20.

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22

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

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23

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 (June 1985): 97–113. http://dx.doi.org/10.1111/j.1467-9574.1985.tb01131.x.

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24

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

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25

KOWALSKI, ROBERT, and FARIBA SADRI. "Programming in logic without logic programming." Theory and Practice of Logic Programming 16, no. 3 (March 16, 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 of efficiency, timestamps are omitted and only the current state is maintained. State transitions are performed reactively by executing actions to make the consequents of rules true whenever the antecedents become true. This OS is sound, but incomplete. It cannot make reactive rules true by preventing their antecedents from becoming true, or by proactively making their consequents true before their antecedents become true. In this paper, we characterize the notion of reactive model, and prove that the OS can generate all and only such models. In order to focus on the main issues, we omit the logic programming component of the framework.

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26

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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27

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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28

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

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29

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

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30

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

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31

SAHNI, PRIYANKA, and PUSHPENDRA KUMAR VASHISHTHA. "An Overview On Bilevel Programming." International Journal of Scientific Research 3, no. 6 (June 1, 2012): 35–36. http://dx.doi.org/10.15373/22778179/june2014/173.

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32

B, Sundarraj. "Hope: Simulation of Evolutionary Programming." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 1221–27. http://dx.doi.org/10.5373/jardcs/v11/20192694.

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33

K, Kwon. "Exception Handling in Logic Programming." Advances in Robotic Technology 1, no. 1 (October 2, 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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34

Han, Jinmann, Zhoujing Zhoujing, and Chungmin Joo. "Changes of Programming of General Programming Channels." Journal of the Korea Contents Association 16, no. 12 (December 28, 2016): 258–66. http://dx.doi.org/10.5392/jkca.2016.16.12.258.

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35

Guzdial, Mark, and Susan Landau. "Programming programming languages, and analyzing Facebook's failure." Communications of the ACM 61, no. 6 (May 23, 2018): 8–9. http://dx.doi.org/10.1145/3204443.

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36

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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37

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

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38

Dempe, Stephan, and Patrick Mehlitz. "Semivectorial bilevel programming versus scalar bilevel programming." Optimization 69, no. 4 (June 13, 2019): 657–79. http://dx.doi.org/10.1080/02331934.2019.1625900.

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39

Calloni, Ben A., and Donald J. Bagert. "ICONIC programming in BACCII vs. textual programming." ACM SIGCSE Bulletin 26, no. 1 (March 12, 1994): 188–92. http://dx.doi.org/10.1145/191033.191103.

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40

Morgenstern, Jamie, and Daniel R. Licata. "Security-typed programming within dependently typed programming." ACM SIGPLAN Notices 45, no. 9 (September 27, 2010): 169–80. http://dx.doi.org/10.1145/1932681.1863569.

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41

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 (January 18, 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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42

APT, KRZYSZTOF R., and ERIC MONFROY. "Constraint programming viewed as rule-based programming." Theory and Practice of Logic Programming 1, no. 6 (November 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 call equality rules, leads to a new notion of local consistency, called rule consistency that turns out to be weaker than arc consistency for constraints of arbitrary arity (called hyper-arc consistency in Marriott & Stuckey (1998)). For Boolean constraints rule consistency coincides with the closure under the well-known propagation rules for Boolean constraints. The second type of rules, that we call membership rules, yields a rule-based characterization of arc consistency. To show feasibility of this rule-based approach to constraint programming, we show how both types of rules can be automatically generated, as CHR rules of Frühwirth (1995). This yields an implementation of this approach to programming by means of constraint logic programming. We illustrate the usefulness of this approach to constraint programming by discussing various examples, including Boolean constraints, two typical examples of many valued logics, constraints dealing with Waltz's language for describing polyhedral scenes, and Allen's qualitative approach to temporal logic.

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43

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

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44

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

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45

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

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46

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

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47

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. The controller proposed in the work in its physical implementation has small dimensions, it is quite convenient to use it for quick encryptions, short messages that are urgently required to convey that it is its advantages. For rapid encryption of large volumes of data, the microcontroller can be connected to a given electronic circuit and adjusted the interaction of the output input, which expands its functional purpose.

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48

Majeed, Amir Sabir, and Fadhil Salman Abed. "A Proposed Method to Solve Quadratic Fractional Programming Problem by Converting to Double Linear Programming." Journal of Zankoy Sulaimani - Part A 19, no. 1 (June 5, 2016): 239–49. http://dx.doi.org/10.17656/jzs.10602.

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49

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 perpendicular distance. Whereas, In the FGP model formulation, firstly the objectives are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them and suitable membership function is defined for each objectives. Then achievement of the highest membership value of each of fuzzy goals is formulated by minimizing the negative deviational variables.

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50

Xue Wang, Xue Wang, Yong Wang Xue Wang, Fei Yang Yong Wang, Wenge Le Fei Yang, and Shouhang Wang Wenge Le. "Measuring Programming Ability for Novice Programmers." 網際網路技術學刊 23, no. 3 (May 2022): 573–81. http://dx.doi.org/10.53106/160792642022052303015.

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<p>Coding is a key activity in the software development process and a programmer’s programming ability determines the software quality. Different from professional programmers, novice programmers usually refers to programmers who have learned a programming language for about three years. At this stage, measuring their programming ability is of great significance to improve their programming abilities. In previous work, researchers have proposed a variety of ways to measure programming ability for professional programmers. We set out to find out the best way to measure novice programming ability. We first exacted a questionnaire from published comprehension experiments for measuring programming ability. Then, we performed control experiments to compare the answers to the questionnaire with their performance. We found that module number and the number of programming-related websites visited seem to be a reliable way to measure programming ability for novice programmers. Furthermore, we perform exploratory factor analysis to generate a model to verify the effectiveness of our findings.</p> <p> </p>

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