DEArt: Dataset of European Art

Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations. Our results show that object detectors for the cultural heritage domain can achieve a level of precision comparable to state-of-art models for generic images via transfer learning.Comment: VISART VI. Workshop at the European Conference of Computer Vision (ECCV

Core-guided minimal correction set and core enumeration

A set of constraints is unsatisfiable if there is no solution that satisfies these constraints. To analyse unsatisfiable problems, the user needs to understand where inconsistencies come from and how they can be repaired. Minimal unsatisfiable cores and correction sets are important subsets of constraints that enable such analysis. In this work, we propose a new algorithm for extracting minimal unsatisfiable cores and correction sets simultaneously. Building on top of the relaxation and strengthening framework, we introduce novel techniques for extracting these sets. Our new solver significantly outperforms several state of the art algorithms on common benchmarks when it comes to extracting correction sets and compares favorably on core extraction.Peer ReviewedPostprint (published version

Towards efficient large scale epidemiological simulations in EpiGraph

The work we present in this paper focuses on understanding the propagation of flu-like infectious outbreaks between geographically distant regions due to the movement of people outside their base location. Our approach incorporates geographic location and a transportation model into our existing region-based, closed-world EpiGraph simulator to model a more realistic movement of the virus between different geographic areas. This paper describes the MPI-based implementation of this simulator, including several optimization techniques such as a novel approach for mapping processes onto available processing elements based on the temporal distribution of process loads. We present an extensive evaluation of EpiGraph in terms of its ability to simulate large-scale scenarios, as well as from a performance perspective.We would like to acknowledge the assistance provided by David del Río Astorga and Alberto Martín Cajal. This work has been partially supported by the Spanish Ministry of Science TIN2010-16497, 2010.Peer ReviewedPostprint (author's final draft

Enhancing the performance of malleable MPI applications by using performance-aware dynamic reconfiguration

The work in this paper focuses on providing malleability to MPI applications by using a novel performance-aware dynamic reconfiguration technique. This paper describes the design and implementation of Flex-MPI, an MPI library extension which can automatically monitor and predict the performance of applications, balance and redistribute the workload, and reconfigure the application at runtime by changing the number of processes. Unlike existent approaches, our reconfiguring policy is guided by user-defined performance criteria. We focus on iterative SPMD programs, a class of applications with critical mass within the scientific community. Extensive experiments show that Flex-MPI can improve the performance, parallel efficiency, and cost-efficiency of MPI programs with a minimal effort from the programmer.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the project TIN2013- 41350-P, Scalable Data Management Techniques for High-End Computing Systems, and EU under the COST Program Action IC1305, Network for Sustainable Ultrascale Computing (NESUS)Peer ReviewedPostprint (author's final draft

Leveraging social networks for understanding the evolution of epidemics

<p>Abstract</p> <p>Background</p> <p>To understand how infectious agents disseminate throughout a population it is essential to capture the social model in a realistic manner. This paper presents a novel approach to modeling the propagation of the influenza virus throughout a realistic interconnection network based on actual individual interactions which we extract from online social networks. The advantage is that these networks can be extracted from existing sources which faithfully record interactions between people in their natural environment. We additionally allow modeling the characteristics of each individual as well as customizing his daily interaction patterns by making them time-dependent. Our purpose is to understand how the infection spreads depending on the structure of the contact network and the individuals who introduce the infection in the population. This would help public health authorities to respond more efficiently to epidemics.</p> <p>Results</p> <p>We implement a scalable, fully distributed simulator and validate the epidemic model by comparing the simulation results against the data in the 2004-2005 New York State Department of Health Report (NYSDOH), with similar temporal distribution results for the number of infected individuals. We analyze the impact of different types of connection models on the virus propagation. Lastly, we analyze and compare the effects of adopting several different vaccination policies, some of them based on individual characteristics -such as age- while others targeting the super-connectors in the social model.</p> <p>Conclusions</p> <p>This paper presents an approach to modeling the propagation of the influenza virus via a realistic social model based on actual individual interactions extracted from online social networks. We implemented a scalable, fully distributed simulator and we analyzed both the dissemination of the infection and the effect of different vaccination policies on the progress of the epidemics. The epidemic values predicted by our simulator match real data from NYSDOH. Our results show that our simulator can be a useful tool in understanding the differences in the evolution of an epidemic within populations with different characteristics and can provide guidance with regard to which, and how many, individuals should be vaccinated to slow down the virus propagation and reduce the number of infections.</p

QUARQ: QUick approximate and relaxed querying

Executing queries over Linked Open Data (LOD) is a complex task. The total number of sources triggered by a single query cannot be known in advance, nor the reasoning complexity applied to each source. In order to avoid this uncertainty, practitioners download full replicas of the open data and build applications on top of the datasets in a controlled environment. With this centralized approach, they lose dynamic data changes, and often they cannot account for the inference capabilities defined in the associated ontologies. In this work, we explore the feasibility of predicting the performance of Flexible Querying over Linked Open Data [1]. Concretely, we propose QUARQ: QUick Approximate and Relaxed Querying, a tool that using ML provides intelligence to the process of generating alternative queries that run more efficiently than the original ones. With this tool, we propose avoiding the use of replicated Linked Data by seizing the shareable nature of Linked Data and eluding the impracticality of maintaining copies up-to-date or the need to work with outdated data

Automated metadata annotation: What is and is not possible with machine learning

Automated metadata annotation is only as good as training dataset, or rules that are available for the domain. It's important to learn what type of data content a pre-trained machine learning algorithm has been trained on to understand its limitations and potential biases. Consider what type of content is readily available to train an algorithm—what's popular and what's available. However, scholarly and historical content is often not available in consumable, homogenized, and interoperable formats at the large volume that is required for machine learning. There are exceptions such as science and medicine, where large, well documented collections are available. This paper presents the current state of automated metadata annotation in cultural heritage and research data, discusses challenges identified from use cases, and proposes solutions.Peer ReviewedPostprint (published version