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Carles Mateu
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2020 – today
- 2023
- [c23]Marc Ribalta, Josep Pueyo-Ros, Lluís Corominas, Ramón Béjar, Carles Mateu, Edgar Rubión:
Explainable Machine Learning Models for Predicting COVID-19 Cases in Catalonia Based on Wastewater Monitoring Data. CCIA 2023: 262-266 - 2022
- [j16]Daniel Gibert
, Matt Fredrikson
Enhancing the insertion of NOP instructions to obfuscate malware via deep reinforcement learning. Comput. Secur. 113: 102543 (2022) - [j15]Daniel Gibert
Fusing feature engineering and deep learning: A case study for malware classification. Expert Syst. Appl. 207: 117957 (2022) - [i2]Daniel Gibert
Fusing Feature Engineering and Deep Learning: A Case Study for Malware Classification. CoRR abs/2206.05735 (2022) - 2021
- [j14]Daniel Gibert
Auditing static machine learning anti-Malware tools against metamorphic attacks. Comput. Secur. 102: 102159 (2021) - [i1]Daniel Gibert, Matt Fredrikson
Enhancing the Insertion of NOP Instructions to Obfuscate Malware via Deep Reinforcement Learning. CoRR abs/2111.09626 (2021) - 2020
- [j13]Daniel Gibert
HYDRA: A multimodal deep learning framework for malware classification. Comput. Secur. 95: 101873 (2020) - [j12]Daniel Gibert
The rise of machine learning for detection and classification of malware: Research developments, trends and challenges. J. Netw. Comput. Appl. 153: 102526 (2020) - [c22]Daniel Gibert
Orthrus: A Bimodal Learning Architecture for Malware Classification. IJCNN 2020: 1-8
2010 – 2019
- 2019
- [j11]Daniel Gibert
Using convolutional neural networks for classification of malware represented as images. J. Comput. Virol. Hacking Tech. 15(1): 15-28 (2019) - [c21]Daniel Gibert
An Android Malware Detection Framework Using Graph Embeddings and Convolutional Neural Networks. CCIA 2019: 45-53 - [c20]Daniel Gibert
A Hierarchical Convolutional Neural Network for Malware Classification. IJCNN 2019: 1-8 - 2018
- [j10]Teresa Alsinet
An argumentative approach for discovering relevant opinions in Twitter with probabilistic valued relationships. Pattern Recognit. Lett. 105: 191-199 (2018) - [c19]Daniel Gibert
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks. AAAI 2018: 7759-7764 - [c18]Daniel Gibert
An End-to-End Deep Learning Architecture for Classification of Malware's Binary Content. ICANN (3) 2018: 383-391 - 2017
- [j9]Teresa Alsinet
Weighted argumentation for analysis of discussions in Twitter. Int. J. Approx. Reason. 85: 21-35 (2017) - [c17]Daniel Gibert
Convolutional Neural Networks for Classification of Malware Assembly Code. CCIA 2017: 221-226 - 2016
- [j8]Josep Argelich
On the performance of MaxSAT and MinSAT solvers on 2SAT-MaxOnes. Ann. Math. Artif. Intell. 77(1-2): 43-66 (2016) - [j7]Cèsar Fernández, Carles Mateu
A predictor model for the composting process on an industrial scale based on Markov processes. Environ. Model. Softw. 79: 156-166 (2016) - [c16]Teresa Alsinet
Support Analysis of Weighted Discussions in Twitter. CCIA 2016: 197-206 - 2015
- [j6]Teresa Alsinet
Solving the Routing and Wavelength Assignment problem with conflict-driven ASP solvers. AI Commun. 28(1): 21-34 (2015) - [j5]Cèsar Fernández, Felip Manyà
Approximate dynamic programming for automated vacuum waste collection systems. Environ. Model. Softw. 67: 128-137 (2015) - 2014
- [j4]Cèsar Fernández, Felip Manyà
Modeling energy consumption in automated vacuum waste collection systems. Environ. Model. Softw. 56: 63-73 (2014) - 2013
- [j3]Carlos Ansótegui
On the hardness of solving edge matching puzzles as SAT or CSP problems. Constraints An Int. J. 18(1): 7-37 (2013) - [c15]Ramón Béjar
Solving Routing and Wavelength Assignment Problem with Conflict-Driven ASP Solvers. CCIA 2013: 60-63 - 2012
- [j2]Ramón Béjar
The Sudoku completion problem with rectangular hole pattern is NP-complete. Discret. Math. 312(22): 3306-3315 (2012) - [c14]Ramón Béjar, Cèsar Fernández, Carles Mateu, Felip Manyà, Francina Sole-Mauri, David Vidal:
The Automated Vacuum Waste Collection Optimization Problem. AAAI 2012: 264-266 - [c13]Ramón Béjar
Optimizing Energy Consumption in Automated Vacuum Waste Collection Systems. ICTAI 2012: 291-298 - 2011
- [j1]Carlos Ansótegui
Generating highly balanced sudoku problems as hard problems. J. Heuristics 17(5): 589-614 (2011) - [c12]Josep Argelich
On 2SAT-MaxOnes with Unbalanced Polarity: from Easy Problems to Hard MaxClique Problems. CCIA 2011: 21-30 - [c11]Ramón Béjar
Towards an Efficient Use of Resources in All-Optical Networks. CCIA 2011: 61-70 - 2010
- [c10]Carlos Ansótegui
Solving Pseudo-Boolean Modularity Constraints. ECAI 2010: 867-872
2000 – 2009
- 2009
- [b1]Carles Mateu:
CSP problems as algorithmic benchmarks: measures, methods and models. University of Lleida, Spain, 2009 - [c9]Ramón Béjar
Bounding the Phase Transition on Edge Matching Puzzles. ISMVL 2009: 80-85 - 2008
- [c8]Carlos Ansótegui, Ramón Béjar, Cèsar Fernández, Carles Mateu:
Generating Hard SAT/CSP Instances Using Expander Graphs. AAAI 2008: 1442-1443 - [c7]Carlos Ansótegui
How Hard is a Commercial Puzzle: the Eternity II Challenge. CCIA 2008: 99-108 - [c6]Carlos Ansótegui
From High Girth Graphs to Hard Instances. CP 2008: 298-312 - [c5]Carlos Ansótegui
Edge Matching Puzzles as Hard SAT/CSP Benchmarks. CP 2008: 560-565 - [c4]Carlos Ansótegui, Ramón Béjar, Cèsar Fernández, Carles Mateu:
Hard SAT and CSP instances with Expander Graphs. ISAIM 2008 - 2007
- [c3]Carlos Ansótegui, Ramón Béjar, Cèsar Fernández, Carles Mateu:
On Balanced CSPs with High Treewidth. AAAI 2007: 161-166 - 2006
- [c2]Carlos Ansótegui, Ramón Béjar, Cèsar Fernández, Carla P. Gomes, Carles Mateu:
The Impact of Balancing on Problem Hardness in a Highly Structured Domain. AAAI 2006: 10-15 - 2005
- [c1]Ramón Béjar, Cèsar Fernández, Carles Mateu
Statistical Modelling of CSP Solving Algorithms Performance. CP 2005: 861
Coauthor Index
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