dc.creator | Acher, Mathieu | es |
dc.creator | Temple, Paul | es |
dc.creator | Jézéquel, Jean-Marc | es |
dc.creator | Galindo Duarte, José Ángel | es |
dc.creator | Martínez, Jabier | es |
dc.creator | Ziadi, Tewfik | es |
dc.date.accessioned | 2022-11-25T11:50:07Z | |
dc.date.available | 2022-11-25T11:50:07Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Acher, M., Temple, P., Jézéquel, J., Galindo Duarte, J.Á., Martínez, J. y Ziadi, T. (2018). VaryLATEX: Learning Paper Variants That Meet Constraints. En VAMOS 2018: 12th International Workshop on Variability Modelling of Software-Intensive Systems (83-88), Madrid, España: ACM: Association for Computing Machinery. | |
dc.identifier.isbn | 978-1-4503-5398-4 | es |
dc.identifier.uri | https://hdl.handle.net/11441/139791 | |
dc.description.abstract | How to submit a research paper, a technical report, a grant pro posal, or a curriculum vitae that respect imposed constraints such
as formatting instructions and page limits? It is a challenging task,
especially when coping with time pressure. In this work, we present
VaryLATEX, a solution based on variability, constraint program ming, and machine learning techniques for documents written
in LATEX to meet constraints and deliver on time. Users simply have
to annotate LATEX source files with variability information, e.g.,
(de)activating portions of text, tuning figures’ sizes, or tweaking
line spacing. Then, a fully automated procedure learns constraints
among Boolean and numerical values for avoiding non-acceptable
paper variants, and finally, users can further configure their papers
(e.g., aesthetic considerations) or pick a (random) paper variant that
meets constraints, e.g., page limits. We describe our implementation
and report the results of two experiences with VaryLATEX. | es |
dc.description.sponsorship | Agence Nationale de la Recherche ANR-17-CE25- 0010-01 (VaryVary) | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2015-70560-R (BELI) | es |
dc.description.sponsorship | Junta de Andalucía P12-TIC-1867 (COPAS) | es |
dc.format | application/pdf | es |
dc.format.extent | 6 | es |
dc.language.iso | eng | es |
dc.publisher | ACM: Association for Computing Machinery | es |
dc.relation.ispartof | VAMOS 2018: 12th International Workshop on Variability Modelling of Software-Intensive Systems (2018), pp. 83-88. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | LATEX | es |
dc.subject | Technical writing | es |
dc.subject | Machine Learning | es |
dc.subject | Constraint programming | es |
dc.subject | Variability modelling | es |
dc.subject | Generators | es |
dc.title | VaryLATEX: Learning Paper Variants That Meet Constraints | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | ANR-17-CE25-0010-01 (VaryVary) | es |
dc.relation.projectID | TIN2015-70560-R (BELI) | es |
dc.relation.projectID | P12-TIC-1867 (COPAS) | es |
dc.relation.publisherversion | https://dl.acm.org/doi/10.1145/3168365.3168372 | es |
dc.identifier.doi | 10.1145/3168365.3168372 | es |
dc.contributor.group | Universidad de Sevilla. TIC-258: Data-centric Computing Research Hub | es |
dc.publication.initialPage | 83 | es |
dc.publication.endPage | 88 | es |
dc.eventtitle | VAMOS 2018: 12th International Workshop on Variability Modelling of Software-Intensive Systems | es |
dc.eventinstitution | Madrid, España | es |
dc.relation.publicationplace | New York, USA | es |
dc.contributor.funder | Agence Nationale de la Recherche. France | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Junta de Andalucía | es |