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Josep Argelich

dblp:37/2912 · DBLP profile ↗
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15ranked-venue papers
9as first author
0since 2021 · last 2020
0000-0003-4089-6422ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 15 · 9 first-authorTheory of computation · 6 · 6 first-authorSoftware engineering, systems software and programming languages · 3 · 2 first-authorDatabases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Mathematical optimization · 67% Computational complexity · 33%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization
multilevel optimization
0.112009
On Solving Boolean Multilevel Optimization Problemse · IJCAI 2009
Mathematical optimization
multi-objective optimization
0.112009
On Solving Boolean Multilevel Optimization Problemse · IJCAI 2009
YearPublicationVenuePosition
2020 Measuring user relevance in online debates through an argumentative model
abstract
Online debating forums are important social media for people to voice their opinions and engage in debates with each other. Measuring user relevance on these forums can be useful to identify different user profiles or behaviors in online debates, for example, users that tend to participate at the beginning of a debate and whose comments trigger participation, or users that post relevant comments but are not replied too much. To help users to distinguish such different user profiles, we propose graded measures based on users’ influence, the controversy that they generate throughout the debates, their contribution to the polarization of the debates, and their social acceptance, that we extract by analyzing the debates in which the users participate. Our approach is based on an argumentation-based analysis that represents a debate as a valued argumentation framework, in which comments of a debate are arguments, the attack relation between arguments models disagreement between comments, and values for arguments represent the overall support of users for comments. Finally, we test our measures with a sample of users from Reddit debates, identifying four main groups of users, from users with almost no impact on the debate to very active ones with decisive comments for the outcome of the debate.
Teresa Alsinet, Josep Argelich, Ramón Béjar, Santi Martínez
Pattern Recognit. Lett.2
2019 A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions
Teresa Alsinet, Josep Argelich, Ramón Béjar, Joel Cemeli
Soft Comput.2
2018 A Probabilistic Author-Centered Model for Twitter Discussions
Teresa Alsinet, Josep Argelich, Ramón Béjar, Francesc Esteva, Lluís Godo
IPMU (2)2
2018 An argumentative approach for discovering relevant opinions in Twitter with probabilistic valued relationships
Teresa Alsinet, Josep Argelich, Ramón Béjar, Cèsar Fernández 0001, Carles Mateu, Jordi Planes
Pattern Recognit. Lett.2
2017 Weighted argumentation for analysis of discussions in Twitter
Teresa Alsinet, Josep Argelich, Ramón Béjar, Cèsar Fernández 0001, Carles Mateu, Jordi Planes
Int. J. Approx. Reason.2
2013 MinSAT versus MaxSAT for Optimization Problems
Josep Argelich, Chu Min Li 0001, Felip Manyà
CP1
2012 A New Encoding from MinSAT into MaxSAT
Chu Min Li 0001, Felip Manyà, Josep Argelich
CP4
2011 Analyzing the Instances of the MaxSAT Evaluation
Josep Argelich, Chu Min Li 0001, Felip Manyà, Jordi Planes
SAT1
2009 On Solving Boolean Multilevel Optimization Problemse
Josep Argelich, Inês Lynce, João Marques-Silva 0001
IJCAI1
2009 Sequential Encodings from Max-CSP into Partial Max-SAT
Josep Argelich, Alba Cabiscol, Inês Lynce, Felip Manyà
SAT1
2008 Modelling Max-CSP as Partial Max-SAT
Josep Argelich, Alba Cabiscol, Inês Lynce, Felip Manyà
SAT1
2008 A Preprocessor for Max-SAT Solvers
Josep Argelich, Chu Min Li 0001, Felip Manyà
SAT1
2007 Partial Max-SAT Solvers with Clause Learning
Josep Argelich, Felip Manyà
SAT1
2005 Solving Over-Constrained Problems with SAT
Josep Argelich, Felip Manyà
CP1
2005 Solving Over-Constrained Problems with SAT Technology
Josep Argelich, Felip Manyà
SAT1