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[EN] Finite State Controllers (FSCs) are an effective way to compactly represent sequential plans. By imposing appropriate conditions on transitions, FSCs can also represent generalized plans (plans that solve a range of planning problems from a given domain). In this paper we introduce the concept of hierarchical FSCs for planning by allowing controllers to call other controllers. This call mechanism allows hierarchical FSCs to represent generalized plans more compactly than individual FSCs, to compute controllers in a modular fashion or even more, to compute recursive controllers. The paper introduces a classical planning compilation for computing hierarchical FSCs that solve challenging generalized planning tasks. The compilation takes as input a finite set of classical planning problems from a given domain. The output of the compilation is a single classical planning problem whose solution induces: (1) a hierarchical FSC and (2), the corresponding validation of that controller on the input classical planning problems. ; This work is partially supported by grant TIN2015-67959 and the Maria de Maeztu Units of Excellence Programme MDM-2015-0502, MEC, Spain. Sergio Jimenez is supported by the RYC15/18009, program funded by the Spanish government. ; Segovia-Aguas, J.; Jiménez-Celorrio, S.; Jonsson, A. (2018). Computing Hierarchical Finite State Controllers with Classical Planning. Journal of Artificial Intelligence Research. 62:755-797. https://doi.org/10.1613/jair.1.11227 ; S ; 755 ; 797 ; 62

[EN] Generalized planning aims at computing an algorithm-like structure (generalized plan) that solves a set of multiple planning instances. In this paper we define negative examples for generalized planning as planning instances that must not be solved by a generalized plan. With this regard the paper extends the notion of validation of a generalized plan as the problem of verifying that a given generalized plan solves the set of input positives instances while it fails to solve a given input set of negative examples. This notion of plan validation allows us to define quantitative metrics to asses the generalization capacity of generalized plans. The paper also shows how to incorporate this new notion of plan validation into a compilation for plan synthesis that takes both positive and negative instances as input. Experiments show that incorporating negative examples can accelerate plan synthesis in several domains and leverage quantitative metrics to evaluate the generalization capacity of the synthesized plans. ; The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 731761, IMAGINE; and it is partially supported by grant TIN-2015- 67959 and the Maria de Maeztu Units of Excellence Programme MDM-2015-0502, MEC, Spain. Sergio Jimenez is supported by the Ramon y Cajal program, RYC-2015- 18009, the Spanish MINECO project TIN2017-88476-C2- 1-R, and the generalitat valenciana project GV/2019/082. Anders Jonsson is partially supported by the Spanish grants TIN2015-67959 and PCIN-2017-082. ; Segovia-Aguas, J.; Jiménez-Celorrio, S.; Jonsson, A. (2020). Generalized Planning with Positive and Negative Examples. 9949-9956. https://doi.org/10.1609/aaai.v34i06.6550 ; S ; 9949 ; 9956

This is the author's version of a work that was accepted for publication in Journal of Computational and Applied Mathematics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Computational and Applied Mathematics 318 (2017) 59–68. DOI 10.1016/j.cam.2016.11.048. ; The Non-Negative Matrix Factorization (NNMF) is a recent numerical tool that, given a nonnegative data matrix, tries to obtain its factorization as the approximate product of two nonnegative matrices. Nowadays, this factorization is being used in many science fields; in some of these fields, real-time computation of the NNMF is required. In some scenarios, all data is not initially available and when new data (as new rows or columns) becomes available the NNMF must be recomputed. Recomputing the whole factorization every time is very costly and not suitable for real time applications. In this paper we propose several algorithms to update the NNMF factorization taking advantage of the previously computed factorizations, with similar error and lower computational cost. © 2016 Elsevier B.V. All rights reserved. ; This work has been partially supported by EU together with Spanish Government through TEC2015-67387-C4-1-R (MINECO/FEDER), by Generalitat Valenciana through PROMETEOII/2014/003 and by Programa de FPU del Ministerio de Educacion, Cultura y Deporte FPU13/03828 (Spain). We want to thank Dr. Pedro Vera and his team (University of Jaen) for providing us with their music analysis software. ; San Juan Sebastián, P.; Vidal Maciá, AM.; García Mollá, VM. (2016). Updating/downdating the NonNegative Matrix Factorization. Journal of Computational and Applied Mathematics. 318:59-68. https://doi.org/10.1016/j.cam.2016.11.048 ; S ; 59 ; 68 ; 318

ACL materials are Copyright © 1963¿2021 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. ; [EN] This paper describes the participation of the MLLP research group of the Universitat Politècnica de València in the WMT 2019 News Translation Shared Task. In this edition, we have submitted systems for the German ¿ English and German ¿ French language pairs, participating in both directions of each pair. Our submitted systems, based on the Transformer architecture, make ample use of data filtering, synthetic data and domain adaptation through fine-tuning. ; The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 761758 (X5gon); the Government of Spain¿s research project Multisub, ref. RTI2018-094879-B-I00 (MCIU/AEI/FEDER, EU); and the Universitat Politecnica de València's PAID-01-17 R&D support programme. ; Iranzo-Sánchez, J.; Garcés Díaz-Munío, G.; Civera Saiz, J.; Juan, A. (2019). The MLLP-UPV Supervised Machine Translation Systems for WMT19 News Translation Task. The Association for Computational Linguistics. 218-224. https://doi.org/10.18653/v1/W19-5320 ; S ; 218 ; 224

[EN] We introduce a demonstration of our system, which implements online learning for neural machine translation in a production environment. These techniques allow the system to continuously learn from the corrections provided by the translators. We implemented an end-to-end platform integrating our machine translation servers to one of the most common user interfaces for professional translators: SDL Trados Studio. Our objective was to save post-editing effort as the machine is continuously learning from human choices and adapting the models to a specific domain or user style. ; The research leading to these results has received funding from the Spanish Centre for Technological and Industrial Development (Centro para el Desarrollo Tecnologico Industrial) (CDTI) and ¿ the European Union through Programa Operativo de Crecimiento Inteligente (Project IDI20170964). We gratefully acknowledge the support of NVIDIA Corporation with the donation of a GPU used for part of this research. ; Domingo-Ballester, M.; García-Martínez, M.; Estela, A.; Bié, L.; Helle, A.; Peris, Á.; Casacuberta Nolla, F. (2019). Demonstration of a Neural Machine Translation System with Online Learning for Translators. Association for Computational Linguistics. 70-74. http://hdl.handle.net/10251/180931 ; S ; 70 ; 74

[EN] This paper introduces a platform to support serverless computing for scalable event-driven data processing that features a multi-level elasticity approach combined with virtualization of GPUs. The platform supports the execution of applications based on Docker containers in response to file uploads to a data storage in order to perform the data processing in parallel. This is managed by an elastic Kubernetes cluster whose size automatically grows and shrinks depending on the number of files to be processed. To accelerate the processing time of each file, several approaches involving virtualized access to GPUs, either locally or remote, have been evaluated. A use case that involves the inference based on deep learning techniques on transtoracic echocardiography imaging has been carried out to assess the benefits and limitations of the platform. The results indicate that the combination of serverless computing and GPU virtualization introduce an efficient and cost-effective event-driven accelerated computing approach that can be applied for a wide variety of scientific applications. ; The work presented in this article has been partially funded by a research grant from the regional government of the Comunitat Valenciana (Spain), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020, with reference IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine). The authors would also like to thank the Spanish "Ministerio de Economia, Industria y Competitividad" for the project "BigCLOE" with reference number TIN2016-79951-R and the project ATMOSPHERE, funded jointly by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154 and the Brazilian Ministerio de Ciencia, Tecnologia e Inovacao (MCI-I), number 51119. D.M.N would like to thank the "Generalitat Valenciana, Spain" for the grant GrisoliaP/2017/071. ; Naranjo-Delgado, DM.; Risco, S.; ...

[EN] This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity. ; The authors would like to thank the European Union for the project "Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum" (AI-SPRINT), with code 101016577, funded under the H2020 Framework Programme and also the regional government of the Comunitat Valenciana (Spain) for the project IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020. ; Risco, S.; Moltó, G.; Naranjo-Delgado, DM.; Blanquer Espert, I. (2021). Serverless Workflows for Containerised Applications in the Cloud Continuum. Journal of Grid Computing. 19(3):1-18. https://doi.org/10.1007/s10723-021-09570-2 ; S ; 1 ; 18 ; 19 ; 3 ...

[EN] This paper describes the development of applications in the frame of the ATMOSPHERE platform. ATMOSPHERE provides means for developing container-based applications over a federated cloud offering measurin he trustworthiness of the applications. In this paper we show the design of a transcontinental application in the frame of medical imaging that keeps the data at one end and uses the processing capabilities of the resources available at the other end. The applications are described using TOSCA blueprints and the federation of IaaS resources is performed by the Fogbow middleware. Privacy guarantees are provided by means of SCONE and intensive computing resources are integrated through the use of GPUs directly mounted on the containers. ; The work in this article has been co-funded by project ATMOSPHERE, funded jointly by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154 and the Brazilian Ministerio de Ci ¿ encia, Tecnologia e ¿ Inovac¿ao (MCTI), number 51119. ¿ The authors also want to acknowledge the research grant from the regional government of the Comunitat Valenciana (Spain), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014- 2020, with reference IDIFEDER/2018/032 (HighPerformance Algorithms for the Modelling, Simulation and early Detection of diseases in Personalized Medicine). ; Blanquer Espert, I.; Alberich-Bayarri, Á.; García-Castro, F.; Teodoro, G.; Meirelles, A.; Nascimento, B.; Meira Jr., W. (2019). Medical Imaging Processing Architecture on ATMOSPHERE Federated Platform. ScitePress. 589-594. http://hdl.handle.net/10251/181077 ; S ; 589 ; 594

(c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. ; [EN] The wide adoption of microservices architectures has introduced an unprecedented granularisation of computing that requires the coordinated execution of multiple containers with diverse lifetimes and with potentially different auto-scaling requirements. These applications are managed by means of container orchestration platforms and existing centralised approaches for auto-scaling face challenges when used for the timely adaptation of the elasticity required for the different application components. This paper studies the impact of integrating bio-inspired approaches for dynamic distributed auto-scaling on container orchestration platforms. With a focus on running self-managed containers, we compare alternative configuration options for the container life cycle. The performance of the proposed models is validated through simulations subjected to both synthetic and real-world workloads. Also, multiple scaling options are assessed with the purpose of identifying exceptional cases and improvement areas. Furthermore, a nontraditional metric for scaling measurement is introduced to substitute classic analytical approaches. We found out connections for two related worlds (biological systems and software container elasticity procedures) and we open a new research area in software containers that features potential self-guided container elasticity activities. ; This work was supported by the Ministerio de Economía, Industria y Competitividad, Spanish Government, for the Project BigCLOE under Grant TIN2016-79951-R ; Herrera, J.; Moltó, G. (2020). Toward Bio-Inspired Auto-Scaling Algorithms: An Elasticity Approach for Container Orchestration Platforms. IEEE Access. ...

[EN] Border tracking in binary images is an important kernel for many applications. There are very efficient sequential algorithms, most notably, the algorithm proposed by Suzuki et al., which has been implemented for CPUs in well-known libraries. However, under some circumstances, it would be advantageous to perform the border tracking in GPUs as efficiently as possible. In this paper, we propose a parallel version of the Suzuki algorithm that is designed to be executed in GPUs and implemented in CUDA. The proposed algorithm is based on splitting the image into small rectangles. Then, a thread is launched for each rectangle, which tracks the borders in its associated rectangle. The final step is to perform the connection of the borders belonging to several rectangles. The parallel algorithm has been compared with a state-of-the-art sequential CPU version, using two different CPUs and two different GPUs for the evaluation. The computing times obtained show that in these experiments with the GPUs and CPUs that we had available, the proposed parallel algorithm running in the fastest GPU is more than 10 times faster than the sequential CPU routine running in the fastest CPU. ; This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities and the European Union through Grant RTI2018-098085-BC41 (MCUI/AEI/FEDER) and by GVA through PROMETEO/2019/109. The authors would also like to thank the AUTIS, S.L. company for their support of this work. ; García Mollá, VM.; Alonso-Jordá, P.; García-Laguía, R. (2020). Parallel border tracking in binary images using GPUs. The Journal of Supercomputing. https://doi.org/10.1007/s11227-021-04260-y ; S ; Leinio A, Lellis L, Cappabianco F (2019) Interactive border contour with automatic tracking algorithm selection for medical images: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19–22, 2018, Proceedings, 2019, pp 748–756. https://doi.org/10.1007/978-3-030-13469-3_87 ; Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour ...

Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia Artificial ; This work has been partially funded by the FCT (Fundaçao para a Ciencia e a Tecnologia) Scholarship SFRH/BD/22359/2995, POPH/FSE (Programa Operacional Potencial Humano and Fundo Social Europeu) as well as the Andalusian Regional Government project P09-TIC-04813.

[EN] Causality is a fundamental part of reasoning to model the physics of an application domain, to understand the behaviour of an agent or to identify the relationship between two entities. Causality occurs when an action is taken and may also occur when two happenings come undeniably together. The study of causal inference aims at uncovering causal dependencies among observed data and to come up with automated methods to find such dependencies. While there exist a broad range of principles and approaches involved in causal inference, in this position paper we argue that it is possible to unify different causality views under a common framework of symbolic learning. ; This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jimenez by the RYC15/18009, both programs funded by the Spanish government. ; Onaindia De La Rivaherrera, E.; Aineto, D.; Jiménez-Celorrio, S. (2018). A common framework for learning causality. Progress in Artificial Intelligence. 7(4):351-357. https://doi.org/10.1007/s13748-018-0151-y ; S ; 351 ; 357 ; 7 ; 4 ; Aineto, D., Jiménez, S., Onaindia, E.: Learning STRIPS action models with classical planning. In: International Conference on Automated Planning and Scheduling, ICAPS-18 (2018) ; Amir, E., Chang, A.: Learning partially observable deterministic action models. J. Artif. Intell. Res. 33, 349–402 (2008) ; Asai, M., Fukunaga, A.: Classical planning in deep latent space: bridging the subsymbolic–symbolic boundary. In: National Conference on Artificial Intelligence, AAAI-18 (2018) ; Cresswell, S.N., McCluskey, T.L., West, M.M.: Acquiring planning domain models using LOCM. Knowl. Eng. Rev. 28(02), 195–213 (2013) ; Ebert-Uphoff, I.: Two applications of causal discovery in climate science. In: Workshop Case Studies of Causal Discovery with Model Search (2013) ; Ebert-Uphoff, I., Deng, Y.: Causal discovery from spatio-temporal data with applications to climate science. In: 13th International Conference on Machine ...

Norms are used in open Multi-Agent Systems asa formal specification of deontic statements aimed at regulating the actions of agents and the interactions among them. In this paper, we propose a set of services facilitating the development of both non-normative and normative agents for norm-governed MAS. Specifically, we propose to provide agents with norm reasoning services. These services will help agent designers/developers to programme agents that consider norm reasoning without having to implement the needed mechanisms to reason about norms by themselves. This article shows how these services perform as well as the results of the experiments that we conducted to evaluate their performance. ; Natalia Criado was awarded a FPU scholarship AP-2007-01256 by the spanish government. This work has also been partially funded by grants CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2009-13839-C03-01. This research has also been partially funded by Valencian Prometeo project 2008/051. ; Criado Pacheco, N.; Such Aparicio, JM.; Botti Navarro, VJ. (2014). Norm reasoning services. Information Systems Frontiers. 16(2):201-223. https://doi.org/10.1007/s10796-013-9444-7 ; S ; 201 ; 223 ; 16 ; 2 ; Andrighetto G., Campenni M., Conte R., Cecconi F. (2008). Conformity in multiple contexts: imitation vs. norm recognition. In World congress on social simulation (pp. 14–17). ; Artikis A., & Pitt J. (2001). A formal model of open agent societies. In Proceeding of the international conference on autonomous agents and multiagent systems (AAMAS) (pp. 192–193). ; Bordini R., Hübner J., Wooldridge M. (2008). Programming multi-agent systems in AgentSpeak using Jason (Vol. 8). Wiley-Interscience. ; Broersen J., Dastani M., Hulstijn J., Huang Z., van der Torre, L (2001). The boid architecture: conflicts between beliefs, obligations, intentions and desires. In Proceedings of the fifth international conference on autonomous agents (pp. 9–16). ACM. ; Burdalo L., Garcia-Fornes A., Julian V., Terrasa A. (2011). TRAMMAS: a tracing model for ...

The mWater scenario requires the expression and use of regulations of different sorts: from actual laws and regulations issued by governments, to policies and local regulations issued by basin managers, to social norms that prevail in a given community of users. Some will be regimented as part of the electronic institutional framework specification, but others need to be expressed in a declarative form so that one may reason about them, both off- and on-line, both at design and at run time, and both from the institutional (or legislative) perspective and the agent's individual perspective. Issues that are relevant in this respect range from the choice of expressive formalism to the decision-making strategies that agents might use to comply or disobey regulations. Thus, structural aspects like governance, dynamics of norms (also from the legislative and execution perspectives) as well as criteria to evaluate the effectiveness of norms may and need to be explored in the demonstrator. ; Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Igual, F.; Noriega, P.; Igual (2013). mWater Analysis. http://hdl.handle.net/10251/32106

[EN] This paper presents FAMA, a novel approach for learning STRIPS action models from observations of plan executions that compiles the learning task into a classical planning task. Unlike all existing learning systems, FAMA is able to learn when the actions of the plan executions are partially or totally unobservable and information on intermediate states is partially provided. This flexibility makes FAMA an ideal learning approach in domains where only sensoring data are accessible. Additionally, we leverage the compilation scheme and extend it to come up with an evaluation method that allows us to assess the quality of a learned model syntactically, that is, with respect to the actual model; and, semantically, that is, with respect to a set of observations of plan executions. We also show that the extended compilation scheme can be used to lay the foundations of a framework for action model comparison. FAMA is exhaustively evaluated over a wide range of IPC domains and its performance is compared to ARMS, a state-of-the-art benchmark in action model learning. (C) 2019 Elsevier B.V. All rights reserved. ; This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jimenez by the RYC15/18009, both programs funded by the Spanish government. ; Aineto, D.; Jiménez-Celorrio, S.; Onaindia De La Rivaherrera, E. (2019). Learning action models with minimal observability. Artificial Intelligence. 275:104-137. https://doi.org/10.1016/j.artint.2019.05.003 ; S ; 104 ; 137 ; 275