Graph Coloring Problems in Modern Computer Science

Graph coloring is one of the most important concepts in graph theory and is used in many real time applications in computer science. The main aim of this paper is to present the importance of graph coloring ideas in various areas of compute applications for researches that they can use graph coloring concepts for the research. Graph coloring used in various research areas of computer science such data mining, image segmentation, clustering, image capturing, networking etc. This papers mainly focused on important applications such as Guarding an Art Gallery, Physical layout segmentation, Round-Robin Sports Scheduling, Aircraft scheduling, Biprocessor tasks, Frequency assignment, Final Exam Timetabling as a Grouping Problem, Map coloring and GSM mobile phone networks, and Student Time Table. In this paper we review several variants of graph colouring, such as precolouring extension, list colouring, multicolouring, minimum sum colouring, and discuss their applications in scheduling. A very important graph parameter is the chromatic number. Presently, graph coloring plays an important role in several real-world applications and still engages exciting research.

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A study on cubic graphs with novel application

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-182929 ◽

2021 ◽

Vol 40 (1) ◽

pp. 89-101 ◽

Cited By ~ 1

Author(s):

Hossein Rashmanlou ◽

G. Muhiuddin ◽

SK Amanathulla ◽

F. Mofidnakhaei ◽

Madhumangal Pal

Keyword(s):

Data Mining ◽

Image Segmentation ◽

Computer Science ◽

Cubic Graphs ◽

Theoretical Concepts ◽

Research Areas ◽

Strong Product ◽

Fuzzy Graphs ◽

Degree Of A Vertex ◽

Image Capturing

Theoretical concepts of graphs are highly utilized by computer science applications. Especially in research areas of computer science such as data mining, image segmentation, clustering, image capturing and networking. The cubic graphs are more flexible and compatible than fuzzy graphs due to the fact that they have many applications in networks. In this paper, we define the direct product, strong product, and degree of a vertex in cubic graphs and investigate some of their properties. Likewise, we introduce the notion of complete cubic graphs and present some properties of self complementary cubic graphs. Finally, We present fuzzy cubic organizational model as an example of cubic digraph in decision support system.

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Neural alignment predicts learning outcomes in students taking an introduction to computer science course

Nature Communications ◽

10.1038/s41467-021-22202-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Meir Meshulam ◽

Liat Hasenfratz ◽

Hanna Hillman ◽

Yun-Fei Liu ◽

Mai Nguyen ◽

...

Keyword(s):

Computer Science ◽

Learning Outcomes ◽

Brain Activity ◽

Activity Patterns ◽

Real Life ◽

Final Exam ◽

Neural Representations ◽

Real Life Setting ◽

The Right ◽

Mri Study

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

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CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics

International Journal on Digital Libraries ◽

10.1007/s00799-021-00305-y ◽

2021 ◽

Author(s):

Angelo Salatino ◽

Francesco Osborne ◽

Enrico Motta

Keyword(s):

Computer Science ◽

Digital Libraries ◽

Gold Standard ◽

Research Trends ◽

Alternative Methods ◽

Relevant Research ◽

Research Topics ◽

Research Papers ◽

Research Areas ◽

Unsupervised Approach

AbstractClassifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

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Computer science

10.12737/1069776 ◽

2021 ◽

Author(s):

Vladimir Yashin ◽

Anna Kolodenkova

Keyword(s):

Computer Science ◽

Information Technologies ◽

Computer Software ◽

Theoretical Computer Science ◽

Educational Institutions ◽

Federal State ◽

University Teachers ◽

Modern Computer ◽

Information Models ◽

State Educational

The book describes the main topics of modern computer science: branch of theoretical computer science, associated with the analysis of different information models; section of computer technology, dedicated to the development of common principles of computer systems; section of programming devoted to the principles of algorithms and computer software. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying information technologies in the framework of the discipline "Informatics", graduate students, university teachers and anyone interested in modern information technologies.

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ENIAC and Its Contemporaries Meet the “Stored Program Concept”

ENIAC in Action ◽

10.7551/mitpress/9780262033985.003.0012 ◽

2016 ◽

Author(s):

Thomas Haigh ◽

Mark Priestley ◽

Crispin Rope

Keyword(s):

Computer Science ◽

Theoretical Computer Science ◽

Theoretical Computer ◽

Modern Computer ◽

Initial Implementation ◽

Previous Discussion ◽

Actual Use ◽

Desk Calculator ◽

The Senses ◽

Over Time

Having explored ENIAC’s actual use and the programs it ran the authors shift to a more abstract analytical level. Previous discussion of the invention of the modern computer has focused on the “stored program concept” as the crucial innovation setting modern computers apart from their more limited predecessors. The authors explore the origins of this phrase and its changing meaning over time. They look in detail at a 1944 document produced by J. Presper Eckert and sometimes claimed as a first statement of this concept, showing that it actually describes an electronic desk calculator. The authors summarize ENIAC’s capabilities after conversion and to compare these on both practical and theoretical levels with the 1945 EDVAC design and with several other early computers. This supports a balanced appraisal of the senses in which the converted ENIAC did and did not constitute an initial implementation of the key ideas from the 1945 design. The chapter argues for an appraisal of early computers better grounded in the historical realities of documented use, and against a widespread fixation on the notion of “universality” based on a school of theoretical computer science that gained prominence years later.

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The Universal Machine

The Digital Mind ◽

10.7551/mitpress/9780262036030.003.0004 ◽

2017 ◽

Author(s):

Arlindo Oliveira

Keyword(s):

Computer Science ◽

Turing Machine ◽

Universal Machine ◽

Alan Turing ◽

Modern Computer ◽

Basic Concepts ◽

The Church

This chapter covers the development of computing, from its origins, with the analytical engine, to modern computer science. Babbage and Ada Lovelace’s contributions to the science of computing led, in time, to the idea of universal computers, proposed by Alan Turing. These universal computers, proposed by Turing, are conceptual devices that can compute anything that can possibly be computed. The basic concepts created by Turing and Church were further developed to create the edifice of modern computer science and, in particular, the concepts of algorithms, computability, and complexity, covered in this chapter. The chapter ends describing the Church-Turing thesis, which states that anything that can be computed can be computed by a Turing machine.

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Stochastic Learning for SAT- Encoded Graph Coloring Problems

International Journal of Applied Metaheuristic Computing ◽

10.4018/jamc.2010070101 ◽

2010 ◽

Vol 1 (3) ◽

pp. 1-19 ◽

Cited By ~ 6

Author(s):

Noureddine Bouhmala ◽

Ole-Christoffer Granmo

Keyword(s):

Random Walk ◽

Graph Coloring ◽

Learning Automata ◽

Frequency Assignment ◽

Random Walk Algorithm ◽

Stochastic Learning ◽

Graph Coloring Problem ◽

Sat Encoding ◽

Test Sets ◽

Walk Algorithm

The graph coloring problem (GCP) is a widely studied combinatorial optimization problem due to its numerous applications in many areas, including time tabling, frequency assignment, and register allocation. The need for more efficient algorithms has led to the development of several GC solvers. In this paper, the authors introduce a team of Finite Learning Automata, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP. The authors present an experimental analysis of the new algorithm’s performance compared to the random walk technique, using a benchmark set containing SAT-encoding graph coloring test sets.

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