Art painting Data Collection

The classification of Art painting images is a computer vision applications that isgrowing considerably. The goal of this technology, is to classify an art paintingimage automatically, in terms of artistic style, technique used, or its author. For thispurpose, the image is analyzed extracting some visual features. Many articlesrelated with these problems have been issued, but in general the proposed solutionsare focused in a very specific field. In particular, algorithms are tested using imagesat different resolutions, acquired under different illumination conditions. Thatmakes complicate the performance comparison of the different methods. In thiscontext, it will be very interesting to construct a public art image database, in orderto compare all the existing algorithms under the same conditions. This paperpresents a large art image database, with their corresponding labels according to thefollowing characteristics: title, author, style and technique. Furthermore, a tool thatmanages this database have been developed, and it can be used to extract differentvisual features for any selected image. This data can be exported to a file in CSVformat, allowing researchers to analyze the data with other tools. During the datacollection, the tool stores the elapsed time in the calculation. Thus, this tool alsoallows to compare the efficiency, in computation time, of different mathematicalprocedures for extracting image data.

Dades obertes enllaçades (Linked Open Data) en el món de l'educació : Una visió de l'estat de l'art d'aquesta tecnologia aplicada a l'ensenyament i l'aprenentatge

En el present treball hem tractat d'aportar una visió actual del món de les dades obertes enllaçades en l'àmbit de l'educació. Hem revisat tant les aplicacions que van dirigides a implementar aquestes tecnologies en els repositoris de dades existents (pàgines web, repositoris d'objectes educacionals, repositoris de cursos i programes educatius) com a ser suport de nous paradigmes dins del món de l'educació.

Fedora 4 as a Shared Linked Data repository for the Art Institute of Chicago Collections

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Search for charged Higgs bosons through the violation of lepton universality in t art events using collision data at sqrts=7 TeV with the AT experiment

In several extensions of the Standard Model, the top quark can decay into a bottom quark and a light charged Higgs boson H+, t -> bH(+), in addition to the Standard Model decay t -> bW. Since W bosons decay to the three lepton generations equally, while H+ may predominantly decay into tau nu, charged Higgs bosons can be searched for using the violation of lepton universality in top quark decays. The analysis in this paper is based on 4.6 fb(-1) of proton-proton collision data at root s = 7 TeV collected by the ATLAS experiment at the Large Hadron Collider. Signatures containing leptons (e or mu) and/or a hadronically decaying tau (tau(had)) are used. Event yield ratios between e+ tau(had) and e + mu, as well as between mu + tau(had) and mu + e, final states are measured in the data and compared to predictions from simulations. This ratio-based method reduces the impact of systematic uncertainties in the analysis. No significant deviation from the Standard Model predictions is observed. With the assumption that the branching fraction B(H+ -> tau nu) is 100%, upper limits in the range 3.2%-4.4% can be placed on the branching fraction B(t -> bH(+)) for charged Higgs boson masses m(H+) in the range 90-140GeV. After combination with results from a search for charged Higgs bosons in t (t) over bar decays using the tau(had) + jets final state, upper limits on B(t -> bH(+)) can be set in the range 0.8%-3.4%, for m(H+) in the range 90-160GeV.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Comprehensive security in data processing systems : the state of the art /

"UIUCDCS-R-75-725"

The state of the art in fuzzy data envelopment analysis

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. This chapter provides a taxonomy and review of the fuzzy DEA (FDEA) methods. We present a classification scheme with six categories, namely, the tolerance approach, the α-level based approach, the fuzzy ranking approach, the possibility approach, the fuzzy arithmetic, and the fuzzy random/type-2 fuzzy set. We discuss each classification scheme and group the FDEA papers published in the literature over the past 30 years. © 2014 Springer-Verlag Berlin Heidelberg.

Data Mining for Browsing Patterns in Weblog Data by Art Neural Networks

Categorising visitors based on their interaction with a website is a key problem in Web content usage. The clickstreams generated by various users often follow distinct patterns, the knowledge of which may help in providing customised content. This paper proposes an approach to clustering weblog data, based on ART2 neural networks. Due to the characteristics of the ART2 neural network model, the proposed approach can be used for unsupervised and self-learning data mining, which makes it adaptable to dynamically changing websites.

Heat flow from the Southeast Indian Ridge flanks between 80°E and 140°E: Data review and analysis - art. no. B01101

We analyze available heat flow data from the flanks of the Southeast Indian Ridge adjacent to or within the Australian-Antarctic Discordance (AAD), an area with patchy sediment cover and highly fractured seafloor as dissected by ridge- and fracture-parallel faults. The data set includes 23 new data points collected along a 14-Ma old isochron and 19 existing measurements from the 20- to 24-Ma old crust. Most sites of measurements exhibit low heat flux (from 2 to 50 mW m(-2)) with near-linear temperature-depth profiles except at a few sites, where recent bottom water temperature change may have caused nonlinearity toward the sediment surface. Because the igneous basement is expected to outcrop a short distance away from any measurement site, we hypothesize that horizontally channelized water circulation within the uppermost crust is the primary process for the widespread low heat flow values. The process may be further influenced by vertical fluid flow along numerous fault zones that crisscross the AAD seafloor. Systematic measurements along and across the fault zones of interest as well as seismic profiling for sediment distribution are required to confirm this possible, suspected effect.

Multivalent reuse of web data about temporary art exhibitions: The Exhibitium Project

Our proposal aims to display the analysis techniques, methodologies as well as the most relevant results expected within the Exhibitium project framework (http://www.exhibitium.com). Awarded by the BBVA Foundation, the Exhibitium project is being developed by an international consortium of several research groups . Its main purpose is to build a comprehensive and structured data repository about temporary art exhibitions, captured from the web, to make them useful and reusable in various domains through open and interoperable data systems.

Contaminated sites and brownfield management State of art in Brazil and in Germany

Purpose - The aim of this paper is to briefly present aspects of public brownfield management policies from Brazilian and German points of view. Design methodology approach - The data collection method combined literature and documental research. The bibliography included Brazilian and German literature about brownfield management. The documental research includes Brazilian and German legislation and official documents published by CETESB, the Environmental Company of the State of São Paulo, Brazil. Furthermore, publications of German governmental research institutions have been integrated in the paper. Findings - In Brazil, despite the lack of a federal public policy, the State of São Paulo has approved specific rules to deal with contaminated sites. Topics that could be targets of scientific studies have been identified. Experiences in Germany show that it is essential to have political will and cooperation between the different political levels and technical disciplines. Partnerships between German and Brazilian universities would be welcome as there is a wide range of opportunities for academic post-graduation studies and research focusing on human resources capacitation in environmental management. Originality value - The paper makes an original contribution of exploring an area (brownfield management) that is at the forefront of discussion in academe and industry

E-Learning Used in a Training Course on Atraumatic Restorative Treatment (ART) for Brazilian Dentists

The purpose of this study was to assess the benefits of using e-learning resources in a dental training course on Atraumatic Restorative Treatment (ART). This e-course was given in a DVD format, which presented the ART technique and philosophy. The participants were twenty-four dentists from the Brazilian public health system. Prior to receiving the DVD, the dentists answered a questionnaire regarding their personal data, previous knowledge about ART, and general interest in training courses. The dentists also participated in an assessment process consisting of a test applied before and after the course. A single researcher corrected the tests, and intraexaminer reproducibility was calculated (kappa=0.89). Paired t-tests were carried out to compare the means between the assessments, showing a significant improvement in the performance of the subjects on the test taken after the course (p<0.05). A linear regression model was used with the difference between the means as the outcome. A greater improvement on the test results was observed among female dentists (p=0.034), dentists working for a shorter period of time in the public health system (p=0.042), and dentists who used the ART technique only for urgent and/or temporary treatment (p=0.010). In conclusion, e-learning has the potential of improving the knowledge that dentists working in the public health system have about ART, especially those with less clinical experience and less knowledge about the subject.