Link prediction in very large directed graphs: Exploiting hierarchical properties in parallel

Link prediction is a link mining task that tries to find new edges within a given graph. Among the targets of link prediction there is large directed graphs, which are frequent structures nowadays. The typical sparsity of large graphs demands of high precision predictions in order to obtain usable results. However, the size of those graphs only permits the execution of scalable algorithms. As a trade-off between those two problems we recently proposed a link prediction algorithm for directed graphs that exploits hierarchical properties. The algorithm can be classified as a local score, which entails scalability. Unlike the rest of local scores, our proposal assumes the existence of an underlying model for the data which allows it to produce predictions with a higher precision. We test the validity of its hierarchical assumptions on two clearly hierarchical data sets, one of them based on RDF. Then we test it on a non-hierarchical data set based on Wikipedia to demonstrate its broad applicability. Given the computational complexity of link prediction in very large graphs we also introduce some general recommendations useful to make of link prediction an efficiently parallelized problem.Peer ReviewedPostprint (published version

Hierarchical inference applied to Cyc

Hierarchical graphs are a frequent solution for capturing symbolic data due the importance of hierarchies for defining knowledge. In these graphs, relations among elements may contain large portions of the element’s semantics. However, knowledge discovery based on analyzing the patterns of hierarchical relations is rarely used. We outline four inference based algorithms exploiting semantic properties of hierarchically represented knowledge for producing new links, and test one of them on a generalization of Cyc’s KB. Finally, we argue why such algorithms can be useful for unsupervised learning and supervised analysis of a KBPeer ReviewedPostprint (author’s final draft

A visual embedding for the unsupervised extraction of abstract semantics

Vector-space word representations obtained from neural network models have been shown to enable semantic operations based on vector arithmetic. In this paper, we explore the existence of similar information on vector representations of images. For that purpose we define a methodology to obtain large, sparse vector representations of image classes, and generate vectors through the state-of-the-art deep learning architecture GoogLeNet for 20 K images obtained from ImageNet. We first evaluate the resultant vector-space semantics through its correlation with WordNet distances, and find vector distances to be strongly correlated with linguistic semantics. We then explore the location of images within the vector space, finding elements close in WordNet to be clustered together, regardless of significant visual variances (e.g., 118 dog types). More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge (e.g., living things). Afterwards, we consider vector arithmetics. Although we are unable to obtain meaningful results on this regard, we discuss the various problem we encountered, and how we consider to solve them. Finally, we discuss the impact of our research for cognitive systems, focusing on the role of the architecture being used.This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051), and by the Core Research for Evolutional Science and Technology (CREST) program of Japan Science and Technology Agency (JST).Peer ReviewedPostprint (published version

Assessing biases through visual contexts

Bias detection in the computer vision field is a necessary task, to achieve fair models. These biases are usually due to undesirable correlations present in the data and learned by the model. Although explainability can be a way to gain insights into model behavior, reviewing explanations is not straightforward. This work proposes a methodology to analyze the model biases without using explainability. By doing so, we reduce the potential noise arising from explainability methods, and we minimize human noise during the analysis of explanations. The proposed methodology combines images of the original distribution with images of potential context biases and analyzes the effect produced in the model’s output. For this work, we first presented and released three new datasets generated by diffusion models. Next, we used the proposed methodology to analyze the context impact on the model’s prediction. Finally, we verified the reliability of the proposed methodology and the consistency of its results. We hope this tool will help practitioners to detect and mitigate potential biases, allowing them to obtain more reliable models.This work received funding from the European Union’s H2020-INFRAIA-2019-1 program under the Grant Agreement n.871042 (SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics) and from the HORIZON-INFRA-2021-DEV-02 program under the Grant Agreement n.101079043 (SoBigData RI Preparatory Phase Project). Additionally, this work was supported by the Departament de Recerca i Universitats of the Generalitat de Catalunya, under the Industrial Doctorate Grant DI 2018-100.Peer ReviewedPostprint (published version

Situated agents and humans in social interaction for elderly healthcare: the case of COAALAS

Assistive Technologies (AT) are an application area where several Artificial Intelligence techniques and tools have been successfully applied to support elder or impeded people on their daily activities. However, approaches to AT tend to center in the user-tool interaction, neglecting the user's connection with its social environment (such as care-takers, relatives and health professionals) and the possibility to monitor undesired behaviour providing both adaptation to a dynamic environment and early response to potentially dangerous situations. In previous work we have presented Coaalas, an intelligent social and norm-aware device for elder people that is able to autonomously organize, reorganize and interact with the different actors involved in elder-care, either human actors or other devices. In this paper we put our work into context, by first examining what are the desirable properties of such a system, analysing the state of the art on the relevant topics, and verifying the validity of our proposal.Postprint (author’s final draft

Towards a goal-oriented agent-based simulation framework for high-performance computing

Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning capabilities (at the expense of a limited number of agents per simulation). This paper describes a first attempt at putting goal-oriented agents into large agentbased (micro-)simulations. We discuss a model for goal-oriented agents in HighPerformance Computing (HPC) and then briefly discuss its implementation in PyCOMPSs (a library that eases the parallelisation of tasks) to build such a platform that benefits from a large number of agents with the capacity to execute complex cognitive agents.Peer ReviewedPostprint (author's final draft

Identification of circulating microRNA profiles associated with pulmonary function and radiologic features in survivors of SARS-CoV-2-induced ARDS

ABSTRACT There is a limited understanding of the pathophysiology of postacute pulmonary sequelae in severe COVID-19. The aim of current study was to define the circulating microRNA (miRNA) profiles associated with pulmonary function and radiologic features in survivors of SARS-CoV-2-induced ARDS. The study included patients who developed ARDS secondary to SARS-CoV-2 infection (n=167) and a group of infected patients who did not develop ARDS (n=33). Patients were evaluated 3 months after hospital discharge. The follow-up included a complete pulmonary evaluation and chest computed tomography. Plasma miRNA profiling was performed using RT-qPCR. Random forest was used to construct miRNA signatures associated with lung diffusing capacity for carbon monoxide (DLCO) and total severity score (TSS). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted. DLCO<80% predicted was observed in 81.8% of the patients. TSS showed a median [P25;P75] of 5 [2;8]. The miRNA model associated with DLCO comprised miR-17-5p, miR-27a-3p, miR-126-3p, miR-146a-5p and miR-495-3p. Concerning radiologic features, a miRNA signature composed by miR-9-5p, miR-21-5p, miR-24-3p and miR-221-3p correlated with TSS values. These associations were not observed in the non-ARDS group. KEGG pathway and GO enrichment analyses provided evidence of molecular mechanisms related not only to profibrotic or anti-inflammatory states but also to cell death, immune response, hypoxia, vascularization, coagulation and viral infection. In conclusion, diffusing capacity and radiological features in survivors from SARS-CoV-2-induced ARDS are associated with specific miRNA profiles. These findings provide novel insights into the possible molecular pathways underlying the pathogenesis of pulmonary sequelae. Trial registration: ClinicalTrials.gov identifier: NCT04457505.. Trial registration: ISRCTN.org identifier: ISRCTN16865246..This work is supported by Instituto de Salud Carlos III (COV20/00110), co-funded by European Regional Development Fund (ERDF)/“A way to make Europe”. CIBERES is an initiative of the Instituto de Salud Carlos III. CIBERES is an initiative of the Instituto de Salud Carlos III. Suported by: Programa de donaciones "estar preparados" UNESPA (Madrid, Spain) and Fundación Francisco Soria Melguizo (Madrid, Spain).. Finançat per La Fundació La Marató de TV3, projecte amb codi 202108-30/-31. COVIDPONENT is funded by Institut Català de la Salut and Gestió de Serveis Sanitaris. MM is the recipient of a predoctoral fellowship (PFIS: FI21/00187) from Instituto de Salud Carlos III. MCGH is the recipient of a predoctoral fellowship from “University of Lleida”. DdGC has received financial support from Instituto de Salud Carlos III (Miguel Servet 2020: CP20/00041), co-funded by the European Social Fund (ESF)/“Investing in your future”. ENL and GL were funded by COVID1005 and ACT210085 from National Agency of Investigation & Development (ANID), Chile."Article signat per 27 autors/es: María C. García-Hidalgo, Jessica González, Iván D. Benítez, Paola Carmona, Sally Santisteve, Manel Pérez-Pons, Anna Moncusí-Moix, Clara Gort-Paniello, Fátima Rodríguez-Jara, Marta Molinero, Thalia Belmonte, Gerard Torres, Gonzalo Labarca, Estefania Nova-Lamperti, Jesús Caballero, Jesús F. Bermejo-Martin, Adrián Ceccato, Laia Fernández-Barat, Ricard Ferrer, Dario Garcia-Gasulla, Rosario Menéndez, Ana Motos ,Oscar Peñuelas, Jordi Riera, Antoni Torres, Ferran Barbé, David de Gonzalo-Calvo & on behalf of the CIBERESUCICOVID Project (COV20/00110 ISCIII)"Postprint (published version

Applying COAALAS to SPiDer

Artificial Intelligence techniques and tools have been applied to Assistive Technologies (AT) in order to support elder or impeded people on their daily activities. A common application are intelligent pill dispensers and reminders that help the patient comply with his medication. This has become even more important, as patients suffering from multiple pathologies are prescribed cocktails of drugs that require strict compliance in order to achieve a successful treatment. Existing intelligent pill dispensers tend to focus in the user-tool interaction, neglecting user’s connection with its social environment and the possibility to monitor patient’s behaviour, effectively adapting to a dynamic environment and providing early response to potentially dangerous situations by detecting unexpected or undesired patterns of behaviour. In previous work we have presented COAALAS, an intelligent social and norm-aware device for elder people that is able to autonomously organize, reorganize and interact with the different actors involved in elder-care, either human actors or other devices. In this paper, we present SPiDer an intelligent pill dispenser integrated with the COAALAS architecture.Peer ReviewedPostprint (author's final draft

An operational approach for implementing normative agents in urban wastewater systems

Las políticas de gestión de la calidad del agua a nivel de cuenca hidrográfica son especialmente importantes para la prevención y/o reducción de la polución originada por el hombre en el medio ambiente. Los efluentes industriales son un elemento prioritario particularmente en los Sistemas Urbanos de Aguas Residuales (SUAR) que reciben mezcladas las aguas residuales provenientes de viviendas particulares y de industrias, así como el agua de lluvia. En este artículo, presentamos un análisis y una implementación de agentes normativos que capturan las regulaciones específicas de las políticas Catalanas de prevención de la polución. La implementación de los agentes normativos está basada en el Cálculo de Situaciones. // Water quality management policies on a river basin scale are of special importance in order to prevent and/or reduce environmental pollution caused by human sources. Industrial effluents are a priority issue particularly in Urban Wastewater Systems (UWS) that receive mixed household and industrial wastewaters, apart from rainfall water. In this paper, we present an analysis and implementation of normative agents that capture concrete regulations of the Catalan pollution- prevention policies. The implementation of the normative agents is based on Situation Calculus.Peer ReviewedPostprint (published version

An out-of-the-box full-network embedding for convolutional neural networks

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.Features extracted through transfer learning can be used to exploit deep learning representations in contexts where there are very few training samples, where there are limited computational resources, or when the tuning of hyper-parameters needed for training deep neural networks is unfeasible. In this paper we propose a novel feature extraction embedding called full-network embedding. This embedding is based on two main points. First, the use of all layers in the network, integrating activations from different levels of information and from different types of layers (i.e., convolutional and fully connected). Second, the contextualisation and leverage of information based on a novel three-valued discretisation method. The former provides extra information useful to extend the characterisation of data, while the later reduces noise and regularises the embedding space. Significantly, this also reduces the computational cost of processing the resultant representations. The proposed method is shown to outperform single layer embeddings on several image classification tasks, while also being more robust to the choice of the pre-trained model used as transfer source.This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051), and by the Core Research for Evolutional Science and Technology (CREST) program of Japan Science and Technology Agency (JST).Peer ReviewedPostprint (author's final draft