MétaCan
Menu
Back to cohort
Record W2058046828 · doi:10.4236/jsea.2012.53017

Optimization in Computer Engineering—Theory and Applications: Book Review of Chapter 8—Applying Graph Coloring to Frequency Assignment

2012· article· en· W2058046828 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Software Engineering and Applications · 2012
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFrequency assignmentComputer scienceGRASPGraph coloringSimple (philosophy)Theoretical computer scienceGraphAlgorithmSoftware engineeringTelecommunications

Abstract

fetched live from OpenAlex

The chapter gives a concise and clear presentation of the basic concepts and the variants of the graph coloring problem. It discusses the link between these variants. It uses a very simple and clear language that is at the grasp of an average engineering or science undergraduate student. It then focuses on discussing the applicability of the graph coloring problem to Frequency Assignment Problem (FAP). It precisely selects a special case of this problem: the Fixed Channel Assignment (FCA). The chapter gives a clear explanation of the common application domains of the Frequency Assignment problem such as radio and television transmission, military applications needs, satellite communication, and frequency planning of WLANs. The transition from FAP to graph coloring problem is well explained by using simple examples and graphical illustrations. The empirical assessment of the efficiency of the applied algorithm on FAP instance and random graphs is presented by giving the process, and the results. The implementation is carried using the Budapest Complexity Analysis Toolkit (BCAT). The content of the chapter is expected to age gracefully. It tackles a problem that is relevant today and will remain pertinent for many years to come. The title of the chapter is accurate. It captures the method and the subject that is tackled in the chapter. The examples are simple and easy to follow and the illustrations are appropriate and well executed. The chapter is not only written is a clear natural language but also complying with technical accuracy, which makes its content accessible and suitable to a variety of readers. A reader with a basic background in optimization has access to the material presented in the chapter. At the same time, an expert can find very interesting the empirical assessment of the complexity of solving FAPs. The chapter would benefit from a better explanation for the rational of the empirical study. Explaining the reasons for focussing the study on increasing edge density, increasing the number of vertices, and on increasing the number of colors would help a non-expert reader understand its rational. In general the chapter is a wellarticulate piece of work and presents a lasting contribution to the field that is accessible to a wide audience.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.221
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it