A social network for supporting end-users in the composition of services: definition and proof of concept

[EN] Nowadays, end users are surrounded by plenty of services that are somehow supporting their daily routines and activities. Involving end users into the process of service creation can allow end users to benefit from a cheaper, faster, and better service provisioning. Even though we can already find tools that face this challenge, they consider end users as isolate individuals. In this paper, we investigate how social networks can be used to improve the composition of services by end users. To do so, we propose a graph-based definition of a social structure, and analyse how social connections can be exploited to both facilitate end users to discover services through browsing these connections, and recommend services to end users during the composition activity. As proof of concept, we implement and evaluate the proposed social network in the context of EUCalipTool, a mobile end-user environment for composing services.This work has been developed with the financial support of the Spanish State Research Agency under the Project TIN2017-84094-R and co-financed with ERDF.Valderas, P.; Torres Bosch, MV.; Pelechano Ferragud, V. (2020). A social network for supporting end-users in the composition of services: definition and proof of concept. Computing. 102(8):1909-1940. https://doi.org/10.1007/s00607-020-00796-8S190919401028Yu J, Sheng QZ, Han J, Wu Y, Liu C (2012) A semantically enhanced service repository for user-centric service discovery and management. Data Knowl Eng 72:202–218Daniel F, Casati F, Benatallah B, Shan M-C (2009) Hosted universal composition: models, languages and infrastructure in mashart. In: International conference on conceptual modeling. 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Supporting a Hybrid Composition of Microservices. The EUCalipTool Platform

[EN] To provide complex and elaborated functionalities, Microservices may cooperate with each other either by following a centralized (orchestration) or decentralized (choreography) approach. It seems that the decentralized nature of microservices makes the choreography approach more appropriate to achieve such cooperation, where lighter solutions based on events and message queues are used. However, orchestration through the usage of a process model facilitates the analysis of the composition when this is modified. To benefit from the goodness of these two approaches, this paper presents a hybrid solution based on the choreography of business process pieces that are obtained from a previously defined description of the complete microservice composition. To support this solution, the EUCalipTool platform is presented.This work has been developed with the financial support of the Spanish State Research Agency under the project TIN2017-84094-R and co-financed with ERDF.Valderas, P.; Torres Bosch, MV.; Pelechano Ferragud, V. (2020). Supporting a Hybrid Composition of Microservices. The EUCalipTool Platform. Journal of Software Engineering Research and Development. 8(1):1-14. https://doi.org/10.5753/jserd.2020.457S1148

Software Knowledge Representation to Understand Software Systems

A software development process involves numerous persons, including customers, domain experts, software engineers, managers, evaluators and certifiers. Together, they produce some software that satisfies its requirements and its quality criteria at a certain point in time. This software contains faults and flaws of different levels of severity and at different phases of its production (specification, design, etc.) so maintenance is needed in order to correct it. Perfective and adaptive maintenance is also needed to cope with changes in the environment or with new requirements, e.g. new functionalities. In this work, we introduce the Persistent Knowledge Monitor (PKM), which is being developed within the DECODER H2020 project for handling (i.e. storing, retrieving, merging and checking for consistency) all kinds of knowledge and information related to a software project. The PKM will be part of a platform capable of taking advantage of all the artefacts available in a software ecosystem, not only the source code, but also its version control system, abstract specifications, informal documents or reports, etc. for representing the software knowledge and improving the workflow of software developers.This work has been developed with the financial support of the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 824231 and the Spanish State Research Agency under the project TIN2017-84094-R and co-financed with ERDF.Torres Bosch, MV.; Gil Pascual, M.; Pelechano Ferragud, V. (2019). Software Knowledge Representation to Understand Software Systems. Springer. 137-144. https://doi.org/10.1007/978-3-030-35333-9_10S13714

DECODER - DEveloper COmpanion for Documented and annotatEd code Reference

Software is everywhere and the productivity of Software Engineers has increased radically with the advent of new specifications, design and programming paradigms and languages. The main objective of the DECODER project is to introduce radical solutions to increase productivity by increasing the abstraction level, at specification stage, using requirements engineering techniques to integrate more complete specifications into the development process, and formal methods to reduce the time and efforts for integration testing. DECODER project will develop a methodology and tools to improve the productivity of the software development process for medium-criticality applications in the domains of IoT, Cloud Computing, and Operating Systems by combining Natural Language Processing techniques, modelling techniques and Formal Methods. A radical improvement is expected from the management and transformation of informal data into material (herein called knowledge ) that can be assimilated by any party involved in a development process. The project expects an average benefit of 20% in terms of efforts on several use cases belonging to the beforehand mentioned domains and will provide recommendations on how to generalize the approach to other medium-critical domains.This work has been developed with the financial support of the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 824231 and the Spanish State Research Agency under the project TIN2017-84094-R and co-financed with ERDF.Torres Bosch, MV.; Gil Pascual, M.; Pelechano Ferragud, V. (2019). DECODER - DEveloper COmpanion for Documented and annotatEd code Reference. Springer. 596-601. https://doi.org/10.1007/978-3-030-35333-9_44S59660

DECODER - DEveloper COmpanion for Documented and annotatEd code Reference

This work has been developed with the financial support of the European Union's Horizon 2020 research and innovation programme under grant agreement No. 824231Gil Pascual, M.; Pastor-Ricós, F.; Torres Bosch, MV.; Vos, TE. (2020). DECODER - DEveloper COmpanion for Documented and annotatEd code Reference. Springer. 643-644. http://hdl.handle.net/10251/178910S64364

Modeling of IoT devices in Business Processes: A Systematic Mapping Study

[EN] The Internet of Things (IoT) enables to connect the physical world to digital business processes (BP). By using the IoT, a BP can, e.g.: 1) take into account real-world data to take more informed business decisions, and 2) automate and/or improve BP tasks. To achieve these benefits, the integration of IoT and BPs needs to be successful. The first step to this end is to support the modeling of IoT-enhanced BPs. Although numerous researchers have studied this subject, it is unclear what is the current state of the art in terms of current modeling solutions and gaps. In this work, we carry out a Systematic Mapping Study (SMS) to find out how current solutions are modelling IoT into business processes. After studying 600 papers, we identified and analyzed in depth a total of 36 different solutions. In addition, we report on some important issues that should be addressed in the near future, such as, for instance the lack of standardization.This research has been funded by Internal Funds KU Leuven (Interne Fondsen KU Leuven) and the financial support of the Spanish State Research Agency under the project TIN2017-84094-R and co-financed with ERDF.Torres Bosch, MV.; Serral, E.; Valderas, P.; Pelechano Ferragud, V.; Grefen, P. (2020). Modeling of IoT devices in Business Processes: A Systematic Mapping Study. IEEE. 221-230. https://doi.org/10.1109/CBI49978.2020.00031S22123

Extracting Knowledge from Software Artefacts to Assist Software Project Stakeholders

[EN] Software development methods should foster the exploitation of artefacts from existing code bases in order to improve software development productivity. These artefacts are commonly stored in repositories from which extracting knowledge is very difficult for several reasons, i.e., the stored data is represented in a wide variety of formats or is not usually linked properly to all the related artefacts. In this work, we address the challenge of extracting knowledge from different artefacts that can be produced within a software project. To this end, we present a Persistent Knowledge Monitor (PKM) for handling several kinds of knowledge and information related to a software project. The PKM bases on the JSON format to structure and store the different artefacts. By using a common representation format, we are able to extract knowledge more easily. Also, we provide a query language for searching and reasoning on the stored data.This work has been developed with the financial support of the European Union's Horizon 2020 research and innovation programme under grant agreement No. 824231 and the Spanish State Research Agency under the project TIN2017-84094-R and co-financed with ERDFGil Pascual, M.; Torres Bosch, MV.; Albert Albiol, M.; Pelechano Ferragud, V. (2021). Extracting Knowledge from Software Artefacts to Assist Software Project Stakeholders. Universitat Politècnica de València. 1-12. http://hdl.handle.net/10251/18147811

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

Famílies botàniques de plantes medicinals

Facultat de Farmàcia, Universitat de Barcelona. Ensenyament: Grau de Farmàcia, Assignatura: Botànica Farmacèutica, Curs: 2013-2014, Coordinadors: Joan Simon, Cèsar Blanché i Maria Bosch.Els materials que aquí es presenten són els recull de 175 treballs d’una família botànica d’interès medicinal realitzats de manera individual. Els treballs han estat realitzat per la totalitat dels estudiants dels grups M-2 i M-3 de l’assignatura Botànica Farmacèutica durant els mesos d’abril i maig del curs 2013-14. Tots els treballs s’han dut a terme a través de la plataforma de GoogleDocs i han estat tutoritzats pel professor de l’assignatura i revisats i finalment co-avaluats entre els propis estudiants. L’objectiu principal de l’activitat ha estat fomentar l’aprenentatge autònom i col·laboratiu en Botànica farmacèutica

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges