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Bias is the human tendency to favor one side of a discussion in argumentation, lacking neutrality and balance. Determining user biases is key to applications that process, interpret, and recommend content generated by those users in social media platforms. This paper addresses the problem of determining (in a supervised way) biases of microbloggers from the stream of messages. In this paper, we evaluate the use of a new criterion to identify user bias in social media systems: the temporal locality among users that have similar bias, i.e., the fact that people having similar biases express at about the same time. We show that this remarkable property indeed holds in somedomains discussed in Twitter and may be explained mainly by the real-time use of the microblogging platform, i.e., users with similar biases react altogether to the outcome of events that are in accordance with their opinion (e.g., their favorite soccer teams scores a goal). Besides the precision of the computed biases, our proposal presents two major advantages that are consequences of not considering content at all (only temporal information is used). First, it is very efficient, i.e., a modest hardware can process on the fly the whole stream of messages about a populartopic commented in Twitter. Second, we believe that it may be applied to a wide range of domains regardless the language in which the messages are written. The experimental section of this paper reports the efficient learning of precise biases in both sportive and political contexts where the numerous messages are either written in English or in Portuguese.

High-performance computing (HPC) and massive data processing (Big Data) are two trends that are beginning to converge. In that process, aspects of hardware architectures, systems support and programming paradigms are being revisited from both perspectives. This paper presents our experience on this path of convergence with the proposal of a framework that addresses some of the programming issues derived from such integration. Our contribution is the development of an integrated environment that integretes (i) COMPSs, a programming framework for the development and execution of parallel applications for distributed infrastructures; (ii) Lemonade, a data mining and analysis tool; and (iii) HDFS, the most widely used distributed file system for Big Data systems. To validate our framework, we used Lemonade to create COMPSs applications that access data through HDFS, and compared them with equivalent applications built with Spark, a popular Big Data framework. The results show that the HDFS integration benefits COMPSs by simplifying data access and by rearranging data transfer, reducing execution time. The integration with Lemonade facilitates COMPSs's use and may help its popularization in the Data Science community, by providing efficient algorithm implementations for experts from the data domain that want to develop applications with a higher level abstraction. ; Funding This work was partially funded by Fapemig, CAPES, CNPq, MCT/CNPq-InWeb, FAPEMIG-PRONEX-MASWeb (APQ-01400-14), and by the collaboration between Brazilian MCT/RNP and the European Union Horizon 2020 research and innovation programme under grants 690116 (EUBra-BIGSEA) and 777154 (EUBra Atmosphere). ; Peer Reviewed ; Postprint (published version)

[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