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Journal articles on the topic 'Computer Science (miscellaneous)'

Author: Grafiati

Published: 9 July 2021

Last updated: 29 July 2025

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1

Fekete, Anett, and Zoltán Porkoláb. "Field Experiment of the Memory Retention of Programmers Regarding Source Code." Studia Universitatis Babeș-Bolyai Informatica 68, no. 1 (2023): 71–82. http://dx.doi.org/10.24193/subbi.2023.1.05.

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"Program comprehension is a continuously important topic in computer science since the spread of personal computers, and several program comprehension models have been identified as possible directions of active code comprehension. There has been little research on how much programmers remember the code they have once written. We conducted two experiments with a group of Computer Science MSc students. In the first experiment, we examined the code comprehension strategies of the participants. The students were given a task to implement a minor feature in a relatively small C++ project. In the s

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2

Czerbo, D. B. "Handbook of theoretical computer science." Artificial Intelligence in Medicine 4, no. 4 (1992): 309. http://dx.doi.org/10.1016/0933-3657(92)90044-p.

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3

Priss, Uta. "Facet-like Structures in Computer Science." Axiomathes 18, no. 2 (2008): 243–55. http://dx.doi.org/10.1007/s10516-007-9023-y.

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4

McCluer, Forrest. "Computer Virus Sculptures and the Science That Inspired Them." Leonardo 47, no. 3 (2014): 206–12. http://dx.doi.org/10.1162/leon_a_00704.

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This article explores the remarkable shape and structure of biological viruses through the lens of sculptures composed of parts from discarded computers. The science that inspired these sculptures is briefly reviewed. Collectively, these artworks are called the Computer Virus Sculpture series.

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5

Novak, Robert. "Computer Applications in Teaching Environmental Science." Bulletin of Science, Technology & Society 10, no. 4 (1990): 228–32. http://dx.doi.org/10.1177/027046769001000407.

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6

Murata, Ken T., Shinichi Watari, Tsutomu Nagatsuma, et al. "A Science Cloud for Data Intensive Sciences." Data Science Journal 12 (2013): WDS139—WDS146. http://dx.doi.org/10.2481/dsj.wds-024.

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7

Fox, John N., Kenneth E. Hershman, and Terry L. Peard. "Exploring Science with a Computer." American Biology Teacher 57, no. 1 (1995): 8–12. http://dx.doi.org/10.2307/4449906.

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8

Faust, Don. "What is mathematics? Another example: computer science." International Journal of Mathematical Education in Science and Technology 20, no. 2 (1989): 307–15. http://dx.doi.org/10.1080/0020739890200211.

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9

Fatima, Arooba, Ahmed Alamer, and Muhammad Javaid. "Computing Sharp Bounds of Metric Based Fractional Dimensions for the Sierpinski Networks." Mathematics 10, no. 22 (2022): 4332. http://dx.doi.org/10.3390/math10224332.

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The concept of metric dimension is widely applied to solve various problems in the different fields of computer science and chemistry, such as computer networking, integer programming, robot navigation, and the formation of chemical structuring. In this article, the local fractional metric dimension (LFMD) of the cycle-based Sierpinski networks is computed with the help of its local resolving neighborhoods of all the adjacent pairs of vertices. In addition, the boundedness of LFMD is also examined as the order of the Sierpinski networks approaches infinity.

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10

López-Ramírez, María, and Oscar Valero. "Qualitative versus quantitative fixed point techniques in computer science." Quaestiones Mathematicae 41, no. 1 (2017): 115–27. http://dx.doi.org/10.2989/16073606.2017.1373712.

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11

Manuck, Richard. "The Stanford Computer Science Technical Reports Collection." Issues in Science and Technology Librarianship, no. 3 (August 15, 1992): 460–675. http://dx.doi.org/10.29173/istl1975.

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12

Concas, Anna, Lothar Reichel, Giuseppe Rodriguez, and Yunzi Zhang. "Iterative Methods for the Computation of the Perron Vector of Adjacency Matrices." Mathematics 9, no. 13 (2021): 1522. http://dx.doi.org/10.3390/math9131522.

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The power method is commonly applied to compute the Perron vector of large adjacency matrices. Blondel et al. [SIAM Rev. 46, 2004] investigated its performance when the adjacency matrix has multiple eigenvalues of the same magnitude. It is well known that the Lanczos method typically requires fewer iterations than the power method to determine eigenvectors with the desired accuracy. However, the Lanczos method demands more computer storage, which may make it impractical to apply to very large problems. The present paper adapts the analysis by Blondel et al. to the Lanczos and restarted Lanczos

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13

Sullivan, Amanda, and Marina Umashi Bers. "Computer Science Education in Early Childhood: The Case of ScratchJr." Journal of Information Technology Education: Innovations in Practice 18 (2019): 113–38. http://dx.doi.org/10.28945/4437.

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Aim/Purpose: This paper aims to explore whether having state Computer Science standards in place will increase young children’s exposure to coding and powerful ideas from computer science in the early years. Background: Computer science education in the K-2 educational segment is receiving a growing amount of attention as national and state educational frameworks are emerging. By focusing on the app ScratchJr, the most popular free introductory block-based programming language for early childhood, this paper explores if there is a relationship between the presence of state frameworks and Scrat

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14

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 21, no. 4 (1990): 480–87. http://dx.doi.org/10.1177/104687819002100415.

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15

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 21, no. 1 (1990): 99–108. http://dx.doi.org/10.1177/1046878190211016.

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16

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 21, no. 2 (1990): 216–36. http://dx.doi.org/10.1177/1046878190212013.

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17

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 21, no. 3 (1990): 339–56. http://dx.doi.org/10.1177/1046878190213019.

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18

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 22, no. 1 (1991): 121–26. http://dx.doi.org/10.1177/1046878191221017.

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19

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 22, no. 2 (1991): 263–71. http://dx.doi.org/10.1177/1046878191222013.

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20

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 24, no. 4 (1993): 525–27. http://dx.doi.org/10.1177/1046878193244012.

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21

Crookail, David. "Miscellaneous Reviews." Simulation & Gaming 25, no. 1 (1994): 139–47. http://dx.doi.org/10.1177/1046878194251017.

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22

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 25, no. 2 (1994): 300–317. http://dx.doi.org/10.1177/1046878194252025.

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23

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 25, no. 4 (1994): 564–71. http://dx.doi.org/10.1177/1046878194254016.

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24

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 26, no. 4 (1995): 527–30. http://dx.doi.org/10.1177/1046878195264015.

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25

Crookall, David. "Miscellaneous Reviews." Simulation & Gaming 28, no. 1 (1997): 126–29. http://dx.doi.org/10.1177/1046878197281013.

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26

Zareena, S. Jainab. "AMCAT Utility to Engineering and Computer Science Students." Bulletin of Science, Technology & Society 40, no. 3-4 (2020): 54–58. http://dx.doi.org/10.1177/02704676211003811.

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Aspiring Minds Computer Adaptive Test (AMCAT) is a computer adaptive test used widely in India to assess the employability skill level of engineering students. Prefinal and final year students belonging to different streams take up this online test. The present study investigates the perception of students regarding the conduct of AMCAT. Furthermore, the study explores the benefits that they obtain through the test. The comparison is made among the students belonging to the two different streams, engineering and computer science. The conclusion drawn facilitates the readers to understand the u

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27

PAUL, THIERRY. "3-body, classical–quantum, discrete–continuous." Mathematical Structures in Computer Science 17, no. 2 (2007): 173–75. http://dx.doi.org/10.1017/s0960129507005932.

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This special issue of Mathematical Structures in Computer Science contains several contributions to the Workshop3 corps, classique-quantique, discret-continu.The Workshop was held at the Ecole Normale Supérieure, Paris on 28–29 September 2004 in the framework of the Collectif Histoire Philosophie Sciences. The Workshop was organised by Giuseppe Longo and Thierry Paul with the aim of bringing together mathematicians, physicists, computer scientists and philosophers.

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28

Sanvitale, Ginevra. "The computer is female: Brilliant and visionary heroines who made computer science history by Carla Petrocelli." Technology and Culture 62, no. 3 (2021): 925–26. http://dx.doi.org/10.1353/tech.2021.0119.

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29

Putnik, Zoran, Ivana Štajner-Papuga, Mirjana Ivanović, Zoran Budimac, and Katerina Zdravkova. "Gender related correlations of computer science students." Computers in Human Behavior 69 (April 2017): 91–97. http://dx.doi.org/10.1016/j.chb.2016.12.009.

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30

English, John, and Tammy English. "Experiences of Using Automated Assessment in Computer Science Courses." Journal of Information Technology Education: Innovations in Practice 14 (2015): 237–54. http://dx.doi.org/10.28945/2304.

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In this paper we discuss the use of automated assessment in a variety of computer science courses that have been taught at Israel Academic College by the authors. The course assignments were assessed entirely automatically using Checkpoint, a web-based automated assessment framework. The assignments all used free-text questions (where the students type in their own answers). Students were allowed to correct errors based on feedback provided by the system and resubmit their answers. A total of 141 students were surveyed to assess their opinions of this approach, and we analysed their responses.

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31

Beyer, Sylvia. "PREDICTORS OF FEMALE AND MALE COMPUTER SCIENCE STUDENTS' GRADES." Journal of Women and Minorities in Science and Engineering 14, no. 4 (2008): 377–409. http://dx.doi.org/10.1615/jwomenminorscieneng.v14.i4.30.

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32

Suranchiyeva, Zinagul, Bektas Bostanov, Serik Kenesbayev, Salamat Idrissov, and Kuralay Turganbay. "Unveiling the Digital Equation Through Innovative Approaches for Teaching Discrete Mathematics to Future Computer Science Educators." Journal of Information Technology Education: Innovations in Practice 22 (2023): 215–34. http://dx.doi.org/10.28945/5208.

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Aim/Purpose: This study seeks to present a learning model of discrete mathematics elements, elucidate the content of teaching, and validate the effectiveness of this learning in a digital education context. Background: Teaching discrete mathematics in the realm of digital education poses challenges, particularly in crafting the optimal model, content, tools, and methods tailored for aspiring computer science teachers. The study draws from both a comprehensive review of relevant literature and the synthesis of the authors’ pedagogical experiences. Methodology: The research utilized a system-act

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33

Bircan, Tuba, and Almila Alkim Akdag Salah. "A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences." Mathematics 10, no. 23 (2022): 4398. http://dx.doi.org/10.3390/math10234398.

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The use of Artificial Intelligence (AI) and Big Data analysis algorithms is complementary to theory-driven analysis approaches and becoming more popular also in social sciences. This paper describes the use of Big Data and computational approaches in social sciences by bibliometric analyses of articles indexed between 2015 and 2020 in Social Sciences Citation Index (SSCI) of the Web of Science repository. We have analysed especially the recent research direction called Computational Social Sciences (CSS) that bridges computer analytical approaches with social science challenges, generating new

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34

Saxena, Pawan, Sanjay Kumar Singh, and Gopal Gupta. "Achieving Effective Learning Outcomes through the Use of Analogies in Teaching Computer Science." Mathematics 11, no. 15 (2023): 3340. http://dx.doi.org/10.3390/math11153340.

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Analogy-based learning methods map the concept being learned to a concept well understood by the learner. An analogy is primarily useful when learners do not know the topic being studied. Computer science is an area where the concepts exhibit a high level of abstraction and, hence, are hard for students to comprehend. The use of analogies in instruction can significantly reduce the cognitive load a student faces in learning abstract computer science concepts. The role of analogies in helping students learn computer science topics has not been explored adequately. This paper presents our effort

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35

Francis, Akhil, Ephrata Zelleke, Ziyue Zhang, Alexander F. Kemper, and James K. Freericks. "Determining Ground-State Phase Diagrams on Quantum Computers via a Generalized Application of Adiabatic State Preparation." Symmetry 14, no. 4 (2022): 809. http://dx.doi.org/10.3390/sym14040809.

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Quantum phase transitions materialize as level crossings in the ground-state energy when the parameters of the Hamiltonian are varied. The resulting ground-state phase diagrams are straightforward to determine by exact diagonalization on classical computers, but are challenging on quantum computers because of the accuracy needed and the near degeneracy of the competing states close to the level crossings. On the other hand, classical computers are limited to small system sizes, which quantum computers may help overcome. In this work, we use a local adiabatic ramp for state preparation to allow

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36

Arteta Albert, Alberto, Ernesto Díaz-Flores, Luis Fernando de Mingo López, and Nuria Gómez Blas. "An In Vivo Proposal of Cell Computing Inspired by Membrane Computing." Processes 9, no. 3 (2021): 511. http://dx.doi.org/10.3390/pr9030511.

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Intractable problems are challenging and not uncommon in Computer Science. The computing generation we are living in forces us to look for an alternative way of computing, as current computers are facing limitations when dealing with complex problems and bigger input data. Physics and Biology offer great alternatives to solve these problems that traditional computers cannot. Models like Quantum Computing and cell computing are emerging as possible solutions to the current problems the conventional computers are facing. This proposal describes an in vivo framework inspired by membrane computing

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37

Mansoor, Asif, Muhammad Waleed Usman, Noreen Jamil, and M. Asif Naeem. "Deep Learning Algorithm for Brain-Computer Interface." Scientific Programming 2020 (August 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/5762149.

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Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorith

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38

Beck, Kimberly, Jessica F. Shumway, Umar Shehzad, Jody Clarke-Midura, and Mimi Recker. "Facilitating Mathematics and Computer Science Connections: A Cross Curricular Approach." International Journal of Education in Mathematics, Science and Technology 12, no. 1 (2023): 85–98. http://dx.doi.org/10.46328/ijemst.3104.

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In the United States, school curricula are often created and taught with distinct boundaries between disciplines. This division between curricular areas may serve as a hindrance to students’ long-term learning and their ability to generalize. In contrast, cross-curricular pedagogy provides a way for students to think beyond the classroom walls and make important connections across disciplines. The purpose of this paper is a theoretical reflection on our use of Expansive Framing in our design of lessons across learning environments within the school. We provide a narrative account of our early

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39

Gram, Chr. "Computer science: A challenge to mathematics, or vice‐versa?" International Journal of Mathematical Education in Science and Technology 17, no. 5 (1986): 535–47. http://dx.doi.org/10.1080/0020739860170502.

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40

Fung, P., T. O'Shea, D. Goldson, S. Reeves, and R. Bornat. "Computer science students perceptions of learning formal reasoning methods." International Journal of Mathematical Education in Science and Technology 24, no. 5 (1993): 749–59. http://dx.doi.org/10.1080/0020739930240516.

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41

CORRADINI, ANDREA, MARINA LENISA, and UGO MONTANARI. "Preface to Special Issue: Coalgebraic methods in computer science." Mathematical Structures in Computer Science 13, no. 2 (2003): 199. http://dx.doi.org/10.1017/s0960129502003870.

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42

Amman, Hans M. "Computer Science in Economics and Management: A New Discipline!" Computer Science in Economics and Management 1, no. 1 (1988): 1. http://dx.doi.org/10.1007/bf00435198.

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43

Gierl, Mark J., and Jiawen Zhou. "Computer Adaptive-Attribute Testing." Zeitschrift für Psychologie / Journal of Psychology 216, no. 1 (2008): 29–39. http://dx.doi.org/10.1027/0044-3409.216.1.29.

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The influence of interdisciplinary forces stemming from developments in cognitive science, mathematical statistics, educational psychology, and computing science are beginning to appear in educational and psychological assessment. Computer adaptive-attribute testing (CA-AT) is one example. The concepts and procedures in CA-AT can be found at the intersection between computer adaptive testing and cognitive diagnostic assessment. CA-AT allows us to fuse the administrative benefits of computer adaptive testing with the psychological benefits of cognitive diagnostic assessment to produce an innova

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44

Freiermuth, Mark R. "Connecting with Computer Science Students by Building Bridges." Simulation & Gaming 33, no. 3 (2002): 299–315. http://dx.doi.org/10.1177/104687810203300305.

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45

Hua, Zhongyun, and Yushu Zhang. "Preface to the Special Issue on “Mathematical Methods for Computer Science”." Mathematics 11, no. 16 (2023): 3608. http://dx.doi.org/10.3390/math11163608.

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46

Clubb, Jerome M. "Computer Technology and the Source Materials of Social Science History." Social Science History 10, no. 2 (1986): 97–114. http://dx.doi.org/10.1017/s0145553200015352.

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In his 1978 Presidential Address to this Association, Allan Bogue urged us to direct our attention to problems associated with the use and development of computer-readable source material (Bogue, 1979). My remarks are in a similar vein. They are limited, however, to only one of the categories of source material that Bogue discussed: information that is originally recorded and stored in computer-readable form. In this area problems have become substantially larger and more pressing than they seemed in 1978, although possible means to their amelioration are now also becoming more apparent. The p

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47

Rahn, Jennifer, Dana Willner, James Deverick, Peter Kemper, and Margaret Saha. "Incorporating Computer Programming & Data Science into a Guided Inquiry-Based Undergraduate Ecology Lab." American Biology Teacher 81, no. 9 (2019): 649–57. http://dx.doi.org/10.1525/abt.2019.81.9.649.

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The biological sciences are becoming increasingly reliant on computer science and associated technologies to quickly and efficiently analyze and interpret complex data sets. Introducing students to data analysis techniques is a critical part of their development as well-rounded, scientifically literate citizens. As part of a collaborative effort between the Biology and Computer Science departments at William & Mary, we sought to develop laboratory exercises that would introduce basic ideas of data analysis while also exposing students to Python, a commonly used computer programming languag

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48

Clubb, Jerome M. "Computer Technology and the Source Materials of Social Science History." Social Science History 10, no. 2 (1986): 97. http://dx.doi.org/10.2307/1170859.

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49

Larsen, Elizabeth A., and Margaret L. Stubbs. "INCREASING DIVERSITY IN COMPUTER SCIENCE: ACKNOWLEDGING, YET MOVING BEYOND, GENDER." Journal of Women and Minorities in Science and Engineering 11, no. 2 (2005): 139–70. http://dx.doi.org/10.1615/jwomenminorscieneng.v11.i2.20.

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50

Radicella, Sandro M. "New Ways to Modelling and Predicting Ionosphere Variables." Atmosphere 14, no. 12 (2023): 1788. http://dx.doi.org/10.3390/atmos14121788.

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The new way of thinking science from Newtonian determinism to nonlinear unpredictability and the dawn of advanced computer science and technology can be summarized in the words of the theoretical physicist Michel Baranger, who, in 2000, said in a conference: “Twenty-first-century theoretical physics is coming out of the chaos revolution; it will be about complexity and its principal tool will be the computer.”. This can be extended to natural sciences in general. Modelling and predicting ionosphere variables have been considered since many decades as a paramount objective of research by scient

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