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VirtualWorlds (VWs) are popular tools for teaching/learning in the twenty-first century classroom. The challenge remains however, to provide the means by which teachers could sustainably analyse and assess the performance of large groups of students in such environments. Unfortunately, external game features such as game scores and play duration have turned out to be unfair in some assessments. In this context, a case study was carried out in a foreign language course, illustrating how teachers could easily retrieve a number of performance indicators from VW-interaction logs and harness them to conduct a fine-grained analysis of students’ performance, while facilitating at the same time valuable tools for their assessment. Objective performance indicators in a server database were made accessible using an end-user development programming language. This way, a range of data visualisation methods could be employed to contrast different assumptions regarding learner performance when playing a VW-based game, which was designed to help CEFR A1 level students to learn German. This way, factors such as randomisation of game tasks, which could negatively affect learner performance, were alleviated., This research was funded by Spanish National Research Agency (AEI), through the projects VISAIGLE (TIN2017-85797-R) and DeepBio (TIN2017-85727-C4-3-P) with ERDF funds

The World Wide Web has become a common platform for interactive software development. Most web applications feature custom user interfaces used by millions of people every day. Information architecture addresses the structural design of information to build quality web applications with improved usability of content, navigation, and findability. One of the most frequently utilized information architecture methods is card sorting—an affordable, user-centered approach for eliciting and evaluating categories and navigable items. Card sorting facilitates decision-making during the development process based on users’ mental models of a given application domain. However, although the qualitative analysis of card sorts has become common practice in information architecture, the quantitative analysis of card sorting is less widely applied. The reason for this gap is that quantitative analysis often requires the use of customized techniques to extract meaningful information for decision-making. To facilitate this process and support the structuring of information, we propose a methodology for the quantitative analysis of card-sorting results in this paper. The suggested approach can be systematically applied to provide clues and support for decisions. These might significantly impact the design and, thus, the final quality of the web application. Therefore, the approach includes proper goodness values that enable comparisons among the results of the methods and techniques used and ensure the suitability of the analyses performed. Two publicly available datasets were used to demonstrate the key issues related to the interpretation of card sorting results and the overall suitability and validity of the proposed methodology, This work was partially supported by the Spanish Government [grant number RTI2018-095255-B-I00]; and the Madrid Research Council [Grant Number P2018/TCS-4314]

This paper presents a new class of multiple-follower bilevel problems and a heuristic approach to solving them. In this new class of problems, the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach. This approach uses Monte Carlo simulation and k-medoids clustering to reduce the bilevel problem to a single level, which can then be solved using integer programming techniques. The examples presented show that our approach produces better solutions and scales up better than the other approaches in the literature. Furthermore, for large problems, we combine our approach with the use of self-organising maps in place of k-medoids clustering, which significantly reduces the clustering times. Finally, we apply our approach to a real-life cutting stock problem. Here a forest harvesting problem is reformulated as a multiple-follower bilevel problem and solved using our approach, This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289

© ACM 2021. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Software Engineering and Methodology, http://dx.doi.org/10.1145/10.1145/3428076, Models are the central assets in model-driven engineering (MDE), as they are actively used in all phases of software development. Models are built using metamodel-based languages, and so objects in models are typed by a metamodel class. This typing is static, established at creation time, and cannot be changed later. Therefore, objects in MDE are closed and fixed with respect to the class they conform to, the fields they have, and the well-formedness constraints they must comply with. This hampers many MDE activities, like the reuse of model-related artefacts such as transformations, the opportunistic or dynamic combination of metamodels, or the dynamic reconfiguration of models. To alleviate this rigidity, we propose making model objects open so that they can acquire or drop so-called facets. These contribute with a type, fields and constraints to the objects holding them. Facets are defined by regular metamodels, hence being a lightweight extension of standard metamodelling. Facet metamodels may declare usage interfaces, as well as laws that govern the assignment of facets to objects (or classes). This article describes our proposal, reporting on a theory, analysis techniques, and an implementation. The benefits of the approach are validated on the basis of five case studies dealing with annotation models, transformation reuse, multi-view modelling, multi-level modelling, and language product lines, Work partially funded by the R&D programme of the Madrid Region (project FORTE, S2018/TCS-4314) and the Spanish Ministry of Science (project MASSIVE, RTI2018-095255-B-I00)

To train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for the automatic segmentation of ventricular volume in preterm infants with post haemorrhagic ventricular dilatation (PHVD). We trained a 2D convolutional neural network (CNN) for automatic segmentation ventricular volume from 3D US of preterm infants with PHVD. The method was validated with the Dice similarity coefficient (DSC) and the intra-class coefficient (ICC) compared to manual segmentation. The mean birth weight of the included patients was 1233.1 g (SD 309.4) and mean gestational age was 28.1 weeks (SD 1.6). A total of 152 serial 3D US from 10 preterm infants with PHVD were analysed. 230 ventricles were manually segmented. Of these, 108 were used for training a 2D CNN and 122 for validating the methodology for automatic segmentation. The global agreement for manual versus automated measures in the validation data (n = 122) was excellent with an ICC of 0.944 (0.874–0.971). The Dice similarity coefficient was 0.8 (± 0.01). 3D US based ventricular volume estimation through an automatic segmentation software developed through deep learning improves the accuracy and reduces the processing time needed for manual segmentation using VOCAL. 3D US should be considered a promising tool to help deepen our current understanding of the complex evolution of PHVD.

© 2020 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, While being one of the first and most elegant tools for dimensionality reduction, Fisher linear discriminant analysis (FLDA) is not currently considered among the top methods for feature extraction or classification. In this paper, we will review two recent approaches to FLDA, namely, least squares Fisher discriminant analysis (LSFDA) and regularized kernel FDA (RKFDA) and propose deep FDA (DFDA), a straightforward nonlinear extension of LSFDA that takes advantage of the recent advances on deep neural networks. We will compare the performance of RKFDA and DFDA on a large number of two-class and multiclass problems, many of them involving class-imbalanced data sets and some having quite large sample sizes; we will use, for this, the areas under the receiver operating characteristics (ROCs) curve of the classifiers considered. As we shall see, the classification performance of both methods is often very similar and particularly good on imbalanced problems, but building DFDA models is considerably much faster than doing so for RKFDA, particularly in problems with quite large sample sizes

Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results. © 2021 The Author(s), This work was supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund (ERDF) under research projects CoSmart and FAME (ref. TIN2017–83964-R and RTI2018–093608-B-C33), and by regional projects (ref. CEIJ-C01.1 and CEIJ-C01.2) coordinated from UAL-UCA and funded by Campus de Excelencia Internacional del Mar (CEIMAR) consortium. We would like to thank J. M. Pérez Sánchez, A. M. Garrido López and R. J. Catalán Alonso from the Autoridad Portuaria de la Bahía de Cádiz, Spain, for sharing valuable knowledge on port management and environment and collaborating with us in this work.

Precision Agriculture (PA) and Agriculture 4.0 (A4.0) have been widely discussed as a medium to address the challenges related to agricultural production. In this research, we present a Systematic Literature Review (SLR) supported by a Bibliometric Performance and Network Analysis (BPNA) of the use of A4.0 technologies and PA techniques in the coffee sector. To perform the SLR, 87 documents published since 2011 were extracted from the Scopus and Web of Science databases and processed through the Preferred Reporting Items for Systematic reviews and Meta-Analyzes (PRISMA) protocol. The BPNA was carried out to identify the strategic themes in the field of study. The results present 23 clusters with different levels of development and maturity. We also discovered and presented the thematic network structure of the most used A4.0 technologies in the coffee sector. Our findings shows that Internet of Things, Machine Learning and geostatistics are the most used technologies in the coffee sector, we also present the main challenges and trends related to technological adoption in coffee systems. We believe that the demonstrated results have the potential to be considered by researchers in future works and decision making related to the field of study.

Context: As a result of the growth of non-developer users of OSS applications, usability has over the last ten years begun to attract the interest of the open source software (OSS) community. The OSS community has some special characteristics (such as worldwide geographical distribution of both users and developers and missing resources) which are an obstacle to the direct adoption of many usability techniques as specified in the human-computer interaction (HCI) field. Objective: The aim of this research is to adapt and evaluate the feasibility of applying four usability techniques: user profiles, personas, direct observation and post-test information to four OSS projects from the viewpoint of the development team. Method: The applied research method was a multiple case study of the following OSS projects: Quite Universal Circuit Simulator, PSeInt, FreeMind and OpenOffice Writer. Results: We formalized the application procedure of each of the adapted usability techniques. We found that either there were no procedures for adopting usability techniques in OSS or they were not fully systematized. Additionally, we identified the adverse conditions that are an obstacle to their adoption in OSS and propose the special adaptations required to overcome the obstacles. To avoid some of the adverse conditions, we created web artefacts (online survey, wiki and forum) that are very popular in the OSS field. Conclusion: It is necessary to adapt usability techniques for application in OSS projects considering their idiosyncrasy. Additionally, we found that there are obstacles (for example, number of participant users, biased information provided by developers) to the application of the techniques. Despite these obstacles, it is feasible to apply the adapted techniques in OSS projects, This research was funded by the Secretariat of Higher Education, Science, Technology and Innovation (SENESCYT) of the Government of Ecuador as part of an academic scholarship granted for postgradu- ate training, and Quevedo State Technical University through doctoral scholarships for university professors. Also this research was funded by the Spanish Ministry of Education, Culture and Sports FLEXOR ( TIN2014-52129-R ) and TIN2014-60490-P projects and the eMadrid- CM project ( S2013/ICE-2715 ). Finally, this research received funding from the University of Atacama “DIUDA 22316 ”project

Port state control inspections implemented under the Paris Memorandum of Understanding (MoU) have become known as one of the best instruments for maritime administrations in European Union (EU) Member States to ensure that the ships docked in their ports comply with all maritime safety requirements. This paper focuses on the analysis of all inspections made between 2013 and 2018 in the top ten EU ports incorporated in the Paris MoU (17,880 inspections). The methodology consists of a multivariate statistical information system (STATIS) analysis using the inspected ship's characteristics as explanatory variables. The variables used describe both the inspected ships (classification society, flag, age and gross tonnage) and the inspection (type of inspection and number of deficiencies), yielding a dataset with more than 600,000 elements in the data matrix. The most important results are that the classifications obtained match the performance lists published annually by the Paris MoU and the classification societies. Therefore, the approach is a potentially valid classification method and would then be useful to maritime authorities as an additional indicator of a ship's risk profile to decide inspection priorities and as a tool to measure the evolution in the risk profile of the flag over time., This research was funded by University of Cadiz.

With the advent of smart cities (SCs), governance has been placed at the core of the debate on how to create public value and achieve a high quality of life in urban environments. In particular, given that public value is rooted in democratic theory and new technologies that promote networking spaces have emerged, citizen participation represents one of the principal instruments to make government open and close to the citizenry needs. Participation in urban governance has undergone a great development: from the first postmodernist ideals of countering expert dominance to today’s focus on learning and social innovation, where citizen participation is conceptualized as co-creation and co-production. Despite this development, there is a lack of research to know how this new governance context is taking place in the SC arena. Addressing this situation, in this article, we present an exhaustive survey of the research literature and a deep study of the experience in participative initiatives followed by SCs in Europe. Through an analysis of 149 SC initiatives from 76 European cities, we provide interesting insights about how participatory models have been introduced in the different areas and dimensions of the cities, how citizen engagement is promoted in SC initiatives, and whether the so-called creative SCs are those with a higher number of projects governed in a participatory way, This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P), and the Spanish Ministry of Culture and Sport (CAS18/00035)

© 2021 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, Chatbots are programs that supply services to users via conversation in natural language, acting as virtual assistants within social networks or web applications. Here, we review the most representative chatbot development tools with a focus on technical and managerial aspects, This work was partially funded by the R&D program of the Madrid Region (project FORTE, S2018/TCS4314), and the Spanish Ministry of Science (project MASSIVE, RTI2018-095255-B-I00)

The COVID-19 pandemic has caused a profound change in health organizations at both the primary and hospital care levels. This cross-sectional study aims to investigate the impact of the COVID-19 pandemic in the annual rate of new cancer diagnosis in two university-affiliated hospitals. This study includes all the patients with a pathological diagnosis of cancer attended in two hospitals in Málaga (Spain) during the first year of pandemic. This study population was compared with the patients diagnosed during the previous year 2019. To analyze whether the possible differences in the annual rate of diagnoses were due to the pandemic or to other causes, the patients diagnosed during 2018 and 2017 were also compared. There were 2340 new cancer diagnosis compared to 2825 patients in 2019 which represented a decrease of −17.2% (p = 0.0001). Differences in the number of cancer patients diagnosed between 2018 and 2019 (2840 new cases, 0.5% increase) or 2017 and 2019 (2909 new cases, 3% increase) were not statistically significant. The highest number of patients lost from diagnosis in 2020 was in breast cancer (−26.1%), colorectal neoplasms (−16.9%), and head and neck tumors (−19.8%). The study of incidence rates throughout the first year of the COVID-19 pandemic shows that the diagnosis of new cancer patients has been significantly impaired. Health systems must take the necessary measures to restore pre-pandemic diagnostic procedures and to recover lost patients who have not been diagnosed.

The Internet of Things (IoT) paradigm keeps growing, and many different IoT devices, such as smartphones and smart appliances, are extensively used in smart industries and smart cities. The benefits of this paradigm are obvious, but these IoT environments have brought with them new challenges, such as detecting and combating cybersecurity attacks against cyber-physical systems. This paper addresses the real-time detection of security attacks in these IoT systems through the combined used of Machine Learning (ML) techniques and Complex Event Processing (CEP). In this regard, in the past we proposed an intelligent architecture that integrates ML with CEP, and which permits the definition of event patterns for the real-time detection of not only specific IoT security attacks, but also novel attacks that have not previously been defined. Our current concern, and the main objective of this paper, is to ensure that the architecture is not necessarily linked to specific vendor technologies and that it can be implemented with other vendor technologies while maintaining its correct functionality. We also set out to evaluate and compare the performance and benefits of alternative implementations. This is why the proposed architecture has been implemented by using technologies from different vendors: firstly, the Mule Enterprise Service Bus (ESB) together with the Esper CEP engine; and secondly, the WSO2 ESB with the Siddhi CEP engine. Both implementations have been tested in terms of performance and stress, and they are compared and discussed in this paper. The results obtained demonstrate that both implementations are suitable and effective, but also that there are notable differences between them: the Mule-based architecture is faster when the architecture makes use of two message broker topics and compares different types of events, while the WSO2-based one is faster when there is a single topic and one event type, and the system has a heavy workload., This work was supported by the Spanish Ministry of Science, Innovation and Universities and the European Union FEDER Funds [grant numbers FPU 17/02007, RTI2018-093608-B-C33, RTI2018-098156-B-C52 and RED2018-102654-T] . This work was also supported by the JCCM [grant number SB-PLY/17/180501/000353] and the Research Plan from the University of Cadiz and Grupo Energetico de Puerto Real S.A. under project GANGES [grant number IRTP03' UCA] . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Sharing patient datasets curated by health institutions is critical for the advance of monitoring, surveillance and research. However, patient data is sensitive data and it can only be released under certain conditions and with previous explicit consent. Privacy preserving data sharing provides techniques to distribute datasets minimizing the risk of identification of patients. However, the sharing of datasets is typically done without considering the needs or requests of data consumers. Blockchain technologies provide an opportunity to gather those requests and share and assemble datasets using privacy-preserving methods as data and requirements on anonymity match. The architecture and design of such a solution is described, assuming an underlying permissioned blockchain network where providers such as healthcare institutions deal with consent, patient preferences and anonymity guarantees, playing a mediator role to a network of organizations.

Competitions are being widely used to motivate students in diverse learning processes, including those in computer programming. This paper presents a methodology for designing and assessing competitive learning scenarios that allow students to develop three different coding skills: the ability to compete against unknown competitors, the ability to compete against known competitors and the ability to compete against refined versions of known competitors. The proposal is based on peer code review, implemented as an improvement cycle after the dissemination of the code among participants. A case study evaluating the methodology was conducted with two cohorts of students in an undergraduate course. The analysis of the obtained grades suggests that while performance after our assistance was improved, students could still fail or succeed independently of the assistance. Complementary data from student questionnaires and supervisor observations are aligned with this finding. As a conclusion, the evidence supports the validity of the methodology. Additionally, several guidelines based on the experience are provided to transfer the proposal to other environments., This research was funded by Spanish National Research Agency (AEI), through the project VISAIGLE (TIN2017-85797-R) with ERDF funds.

Cloud computing has emerged as a cutting-edge technology which is widely used by both private and public institutions, since it eliminates the capital expense of buying, maintaining, and setting up both hardware and software. Clients pay for the services they use, under the so-called Service Level Agreements (SLAs), which are the contracts that establish the terms and costs of the services. In this paper, we propose the CloudCost UML profile, which allows the modeling of cloud architectures and the users’ behavior when they interact with the cloud to request resources. We then investigate how to increase the profits of cloud infrastructures by using price schemes. For this purpose, we distinguish between two types of users in the SLAs: regular and high-priority users. Regular users do not require a continuous service, so they can wait to be attended to. In contrast, high-priority users require a constant and immediate service, so they pay a greater price for their services. In addition, a computer-aided design tool, called MSCC (Modeling SLAs Cost Cloud), has been implemented to support the CloudCost profile, which enables the creation of specific cloud scenarios, as well as their edition and validation. Finally, we present a complete case study to illustrate the applicability of the CloudCost profile, thus making it possible to draw conclusions about how to increase the profits of the cloud infrastructures studied by adjusting the different cloud parameters and the resource configuration., This work was supported by the Spanish Ministry of Science and Innovation (co-financed by European Union FEDER funds) project “FAME (Formal modeling and advanced testing methods. Applications to medicine and computing systems) and MASSIVE (Engineering adaptive software by and for the people in a highly connected world)”, references RTI2018-093608-B-C32 and RTI2018-095255-B-I00. There was also support from the Junta de Comunidades de Castilla-La Mancha project SBPLY/17/180501/000276/ 01 (cofunded with FEDER funds, EU), the Region of Madrid (grant number FORTE-CM, S2018/TCS-4314), and the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with the Complutense University as part of the Program to Stimulate Research for Young Doctors in the context of the V PRICIT (Regional Programme of Research and Technological Innovation) under grant PR65/19-22452

Mutation testing is becoming increasingly widely used to evaluate the quality of test suites, especially to test programs coded in widely used programming languages in the industry. Mutation tools have arisen to automate the technique in different languages, including C++. With the increasing use of this technique, new mutation operators modeling possible faults often emerge to improve its abilities and adapt the tools to new advanced features. In this work, mutation operators for the new C++ standards, defined in previous work, are implemented and applied to generate and execute mutants in real programs. With this study, the MuCPP mutation tool is updated with the inclusion of these new operators. In addition, the improvements suggested in the definition of those operators can be finally tested, and conclusions about their utility in practice can be drawn. The implemented operators are checked on a set of four C++ programs that use these advanced features. The results show significant differences with the previous manual analysis: the number of invalid mutants was reduced by 64%, and we found fewer alive mutants (88%) and an increase in dead mutants (31%). In summary, both the number of mutants incorrectly classified in the previous manual analysis and the number of mutants generated (particularly equivalent mutants) have been reduced.

Testing is a crucial activity in the development of software systems. With the increasing complexity of software projects, the industry requires incorporating graduates with adequate testing skills and preparation in this field. A challenge in software testing education is to make students perceive the benefits of writing tests and assess their quality with advanced testing techniques. In this paper, we present an experience integrating both mutation testing and self/peer assessment –two of the most used techniques to that end in the past– into a software testing course during three years. This experience allowed us to analyze the effect of applying these strategies on the students’ perception of their manually-written test suites. Noticeably, the computation of the mutation score significantly undermined the initial expectations they had on the developed test suites. Also, the application of peer testing helped them estimate the relative quality of two comparable test suites, as we found a notable correspondence with their respective mutation coverage. Besides, a more in-depth analysis revealed that the students' test suites with more test cases did not always achieve the highest scores, that they found more readable their own tests, and that they tended to cover the basic operations while forgetting about more advanced features. An opinion survey confirmed the impact that the use of mutants had on their perception about testing, and they mostly supported paying a higher level of attention to testing concepts in software engineering degree plans., The work was partially funded by the European Commission (FEDER), the Spanish Ministry of Science, Innovation and Universities under the project FAME (RTI2018-093608-B-C33), the European project ASSETs (612678-EPP-1-2019-1-IT-EPPKA2-SSA-B), and the University of Cádiz.

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases., This research was funded by MICINN (Ministerio de Ciencia e Innovación-Spain), grant number RTI2018-098160-B-I00, and grant “Ayuda para Estancias en Centros de Investigación del Programa de Fomento e Impulso de la actividad Investigadora de la Universidad de Cádiz”.

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids "reinventing the wheel," but also presents a lightweight solution to otherwise computationally heavy problems., This research was funded by the research support program of Fb2, Frankfurt University of Applied Sciences. The research of D.G.-U. has been supported in part by the Spanish MICINN under grants PGC2018-096504-B-C33 and RTI2018-100754-B-I00, the European Union under the 2014-2020 ERDF Operational Programme and the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia (project FEDER-UCA18-108393). The research of I.M.-B. has been supported in part by the European Commission (ERDF), the Spanish Ministry of Science, Innovation and Universities [RTI2018-093608-BC33].

Test generation is a costly but necessary testing activity to increase the quality of software projects. Automated testing tools based on evolutionary computation principles constitute an appealing modern approach to support testing tasks. However, these tools still find difficulties to detect certain types of plausible faults in real-world projects. Besides, recent studies have shown that, in general, automatically-generated tests do not resemble those manually written and, consequently, testers are reluctant to adopt them. We observe two key issues, namely the opacity of the process and the lack of cooperation with the tester, currently hampering the acceptance of automated results. Based on these findings, we explore in this paper how the interaction between current tools and expert testers would help address the test case generation problem. More specifically, we identify a number of interaction opportunities related to the object-oriented test case design driven to boost their readability and detection power. Using EvoSuite as base implementation, we present a proof of concept focused on the possibility to integrate readability assessment of the most promising test suites into a genetic algorithm., Work partially funded by the European Commission (ERDF), the Spanish Ministry of Science, Innovation and Universities [RTI2018-093608-BC33 and RED2018-102472-T], the University of Cordoba (Plan Propio - mod. 2.4), and the University of Malaga

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicity, This work was supported by projects: PRIMA (H2020-MSCA-ITN-2019-860315), TRESPASS-ETN(H2020-MSCA-ITN-2019-860813), IDEA-FAST (IMI2-2018-15-853981), BIBECA(RTI2018-101248-B-I00MINECO/FEDER), RTI2018-095232-B-C22MINECO, PLeNTaS project PID2019-111430RBI00MINECO; and also by Pays de la Loire Region through RFI Atlanstic 2020

Despite the benefits of adopting IT Service Management (ITSM) reference models, such initiatives do not always produce the expected results. The research literature in this area concludes that motivation, engagement, skills, experience, performance and willingness to change of the personnel involved are among the critical factors for an effective ITSM implementation. Gamification has the capability to improve people’s motivation and engagement and to drive people’s behavior to meet the objectives set. Besides, gamification is widely used in learning systems for increasing students’ skills and competences. In the last years, many researchers have added gamification to their process improvement initiatives to increase the motivation and engagement of process participants and to address their behavior throughout the process. Thus, we consider that adopting gamification in ITSM processes can be an interesting area of study. In this paper, we conducted a systematic mapping study to analyze the actual state of research in the field of ITSM gamification and identify the key challenges that justify future research. The results of our study highlight the positive impact of adopting gamification in ITSM processes and that ITSM gamification is a novel an attractive research area with many action possibilities.

Gamification has rapidly emerged as one of the favorite persuasive technologies widely used with the aim of promoting a positive change in the user’s behavior by means of including game-like elements in non-game contexts. As a research discipline, gamification is growing fast, maturing from basic and fundamental questions such as what and why gamify to more mature ones such as how to gamify, when and when not, and still facing empirical and theoretical challenges to prove the effects of its practice and consolidate the principles that guide meaningful gamification designs. The purpose of this paper is to conduct a bibliometric study to describe how gamification as a scientific discipline is structured and how it has evolved over time. To do this, we make use of bibliometric performance analysis and science mapping methods to display and analyze the intellectual, conceptual and social network structures of gamification research, as well as the evolution and dynamical aspects of the discipline. The results reveal the research fronts and intellectual structures of the field, the internal relationships among articles, authors and keywords, the existing networks of collaboration, the emerging trends, the hot topics, and the most influential authors, publications and sources. Together, they picture the intellectual landscape of gamification as a scientific field that will be useful for junior and senior researchers, practitioners, funding agencies and policymakers.

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., Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing - - image quality which may be due to a change of acquisition devices and/or device capturing configurations, we observe that the top performing fusion algorithms are those that exploit automatically derived quality measurements. Our evaluation also suggests that while using all the available biometric sensors can definitely increase the fusion performance, this comes at the expense of increased cost in terms of acquisition time, computation time, the physical cost of hardware, and its maintenance cost. As demonstrated in our experiments, a promising solution which minimizes the composite cost is sequential fusion, where a fusion algorithm sequentially uses match scores until a desired confidence is reached, or until all the match scores are exhausted, before outputting the final combined score., This evaluation was made possible thanks to the EU funding from the following projects: BioSecure (www.biosecure.info) and Mobio (www.mobioproject.org). The participating teams are supported by their respective national fund bodies: the Dutch BSIK/BRICKS project and the Spanish project TEC2006-13141-C03-03.

Agile development processes are increasing their consideration of usability by integrating various user‐centered design techniques throughout development. One such technique is Personas, which proposes the creation of fictitious users with real preferences to drive application design. Since applying this technique conflicts with the time constraints of agile development, Personas has been adapted over the years. Our objective is to determine the adoption level and type of integration, as well as to propose improvements to the Personas technique for agile development. A systematic mapping study was performed, retrieving 28 articles grouped by agile methodology type. We found some common integration strategies regardless of the specific agile approach, along with some frequent problems, mainly related to Persona modelling and context representation. Based on these limitations, we propose an adaptation to the technique in order to reduce the creation time for a preliminary persona. The number of publications dealing with Personas and agile development is increasing, which reveals a growing interest in the application of this technique to develop usable agile software, This research was funded by the Spanish Ministry of Science, Innovation and Universities research grant PGC2018-097265-B-I00, MASSIVE project (RTI2018-095255-B-I00) and by EIT-Health, grant number 19091 (POSITIVE project). This research was also supported by the Madrid Region R&D programme (project FORTE, P2018/TCS-4314)

The Internet of Things (IoT) has grown significantly in popularity, accompanied by increased capacity and lower cost of communications, and overwhelming development of technologies. At the same time, big data and realtime data analysis have taken on great importance and have been accompanied by unprecedented interest in sharing data among citizens, public administrations and other organisms, giving rise to what is known as the Collaborative Internet of Things. This growth in data and infrastructure must be accompanied by a software architecture that allows its exploitation. Although there are various proposals focused on the exploitation of the IoT at edge, fog and/or cloud levels, it is not easy to find a software solution that exploits the three tiers together, taking maximum advantage not only of the analysis of contextual and situational data at each tier, but also of two-way communications between adjacent ones. In this paper, we propose an architecture that solves these deficiencies by proposing novel technologies which are appropriate for managing the resources of each tier: edge, fog and cloud. In addition, the fact that two-way communications along the three tiers of the architecture is allowed considerably enriches the contextual and situational information in each layer, and substantially assists decision making in real time. The paper illustrates the proposed software architecture through a case study of respiratory disease surveillance in hospitals. As a result, the proposed architecture permits efficient communications between the different tiers responding to the needs of these types of IoT scenarios., This work was partially supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund (ERDF) under project FAME [RTI2018-093608-B-C33] and excellence network RCIS [RED2018-102654-T]. We also thank Carlos Llamas Jaén for his support with the setting up of the performance evaluation tests.

Systematic reviews are powerful methods used to determine the state-of-the-art in a given field from existing studies and literature. They are critical but time-consuming in research and decision making for various disciplines. When conducting a review, a large volume of data is usually generated from relevant studies. Computer-based tools are often used to manage such data and to support the systematic review process. This paper describes a comprehensive analysis to gather the required features of a systematic review tool, in order to support the complete evidence synthesis process. We propose a framework, elaborated by consulting experts in different knowledge areas, to evaluate significant features and thus reinforce existing tool capabilities. The framework will be used to enhance the currently available functionality of CloudSERA, a cloud-based systematic review tool focused on Computer Science, to implement evidence-based systematic review processes in other disciplines., This research was funded by the Spanish Research Agency (Agencia Estatal de Investigacion) with ERDF funds grant number TIN2017-85797-R (VISAIGLE project). The research stay of T. Person in the SFU was funded by Erasmus+ KA107 grant number 2017-1-ES01-KA107-037422. The APC was funded by the VISAIGLE project.

Modern Deep Neuronal Network backends allow a great flexibility to define network architectures. This allows for multiple outputs with their specific losses which can make them more suitable for particular goals. In this work we shall explore this possibility for classification networks which will combine the categorical cross-entropy loss, typical of softmax probabilistic outputs, the categorical hinge loss, which extends the hinge loss standard on SVMs, and a novel Fisher loss which seeks to concentrate class members near their centroids while keeping these apart, The authors acknowledge financial support from the European Regional Development Fund and the Spanish State Research Agency of the Ministry of Economy, Industry, and Competitiveness under the project PID2019-106827GB-I00. They also thank the UAM–ADIC Chair for Data Science and Machine Learning and gratefully acknowledge the use of the facilities of Centro de Computaci´on Cient´ıfica (CCC) at UAM

Given the impact of renewable sources in the overall energy production, accurate predictions are becoming essential, with machine learning becoming a very important tool in this context. In many situations, the prediction problem can be divided into several tasks, more or less related between them but each with its own particularities. Multitask learning (MTL) aims to exploit this structure, training several models at the same time to improve on the results achievable either by a common model or by task-specific models. In this paper, we show how an MTL approach based on support vector regression can be applied to the prediction of photovoltaic and wind energy, problems where tasks can be defined according to different criteria. As shown experimentally with three different datasets, the MTL approach clearly outperforms the results of the common and specific models for photovoltaic energy, and are at the very least quite competitive for wind energy, The authors acknowledge financial support from Spain’s grants TIN2016-76406-P and PID2019-106827GB- I00/AEI/10.13039/501100011033. Work supported also by The first author is also supported by a FPU–UAM grant

© ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in International Conference on Generative Programming: Concepts and Experiences, https://doi.org/10.1145/3425898.3426957, Model-based development approaches, such as Model-Driven Engineering (MDE), heavily rely on the use of modelling languages to achieve and automate software development tasks. To enable the definition of model variants (e.g., supporting the compact description of system families), one solution is to combine MDE with Software Product Lines. However, this is technically costly as it requires adapting many MDE artefacts associated to the modelling language -- especially the meta-models and graphical environments. To alleviate this situation, we propose a method for the automated injection of variability into graphical modelling languages. Given the meta-model and graphical environment of a particular language, our approach permits configuring the allowed model variability, and the graphical environment is automatically adapted to enable creating models with variability. Our solution is implemented atop the Eclipse Modeling Framework and Sirius, and synthesizes adapted graphical editors integrated with FeatureIDE, Work funded by the R&D programme of Madrid (S2018/TCS4314), the Spanish Ministry of Science (RTI2018-095255-BI00), and the Austrian Science Fund (P 30525-N31)

Esta versión del artículo ha sido aceptada para su publicación, después de la revisión por pares (cuando corresponda) y está sujeta a los términos de uso de AM de Springer Nature, pero no es la Versión de Registro y no refleja mejoras posteriores a la aceptación, ni ninguna corrección. La versión del registro está disponible en línea en: https://doi.org/10.1007/978-3-030-62522-1_15, Chatbots are software services accessed via conversation in natural language. They are increasingly used to help in all kinds of procedures like booking flights, querying visa information or assigning tasks to developers. They can be embedded in webs and social networks, and be used from mobile devices without installing dedicated apps. While many frameworks and platforms have emerged for their development, identifying the most appropriate one for building a particular chatbot requires a high investment of time. Moreover, some of them are closed – resulting in customer lock-in – or require deep technical knowledge. To tackle these issues, we propose a model-driven engineering approach to chatbot development. It comprises a neutral meta-model and a domainspecific language (DSL) for chatbot description; code generators and parsers for several chatbot platforms; and a platform recommender. Our approach supports forward and reverse engineering, and model-based analysis. We demonstrate its feasibility presenting a prototype tool and an evaluation based on migrating third party Dialogflow bots to Rasa, Work funded by the Spanish Ministry of Science (RTI2018095255-B-I00) and the R&D programme of Madrid (P2018/TCS-4314)

© ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in {Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, https://doi.org/10.1145/3417990.3420200, Low-code development platforms allow users with a low technical background to build complete software solutions, typically by means of graphical user interfaces, diagrams or declarative languages. In these platforms, recommender systems play an important role as they can provide users with relevant, personalised suggestions generated according to previously developed software solutions. However, developing recommender systems requires a high investment of time as it implies the selection and implementation of a suitable recommendation method, its configuration for the problem and domain at hand, and its evaluation to assess the accuracy of its recommendations. To alleviate these problems, in this paper, we present the first steps towards a generic model-driven framework capable of generating ad-hoc, task-oriented recommender systems for their integration on low-code platforms. As a proof of concept, we present some preliminary results obtained from an offline evaluation of our framework on three datasets of class diagrams. The results show that the proposed framework is capable of providing relevant recommendations in the given context, This project has received funding from the EU Horizon 2020 research and innovation programme under the Marie SkłodowskaCurie grant agreement No 813884, the Spanish Ministry of Science (RTI2018-095255-B-I00) and the R&D programme of Madrid (P2018/TCS-4314)

This study analyzes the effects of COVID-19 confinement on the autonomous learning performance of students in higher education. Using a field experiment with 458 students from three different subjects at Universidad Autónoma de Madrid (Spain), we study the differences in assessments by dividing students into two groups. The first group (control) corresponds to academic years 2017/2018 and 2018/2019. The second group (experimental) corresponds to students from 2019/2020, which is the group of students that had their face-to-face activities interrupted because of the confinement. The results show that there is a significant positive effect of the COVID-19 confinement on students' performance. This effect is also significant in activities that did not change their format when performed after the confinement. We find that this effect is significant both in subjects that increased the number of assessment activities and subjects that did not change the student workload. Additionally, an analysis of students' learning strategies before confinement shows that students did not study on a continuous basis. Based on these results, we conclude that COVID-19 confinement changed students' learning strategies to a more continuous habit, improving their efficiency. For these reasons, better scores in students' assessment are expected due to COVID-19 confinement that can be explained by an improvement in their learning performance, This research was funded by ADeAPTIVE (Advanced Design of e-Learning Applications Personalizing Teaching to Improve Virtual Education) project with the support of the Erasmus + programme of the European Union (grant number 2017-1-ES01-KA203-038266). This study was also funded by ACCIO´, Spain (Pla d’Actuacio´ de Centres Tecnològics 2019) under the project Augmented Workplace. This study was also funded by the Fondo Supera COVID-19 (Project: Development of tools for the assessment in higher education in the COVID-19 confinement

As a result of the growth of non-developer users of OSS applications, usability has over the last ten years begun to attract the interest of the OSScommunity. The OSS community has some special characteristics which are an obstacle to the direct adoption of many usability techniques as specified in the HCIfield. The aim of this research is to adapt and evaluate the feasibility of applying the Personas usability technique to one OSS project from the viewpoint of the development team. The applied research method was a case study of the following OSS project LibreOffice-Writer. We formalized the application procedure of the adapted usability technique. We found that either there were no procedures for adopting usability technique in OSS or theywere not fully systematized. Additionally, we identified the adverse conditions that are an obstacle to their adoption in OSS and propose the special adaptations required to overcome the obstacles. To avoid some of the adverse conditions, we created web artefacts (e.g.wiki) that are very popular in the OSS field. Additionally, we found that there are obstacles to the application of the technique. Despite these obstacles, it is feasible to apply the adapted Personas technique in OSS project, Work funded by FEDER/Spanish Ministry of Science and Innovation –Research State Agency: project MASSIVE, RTI2018-095255-B-I00, the R&D programme of Madrid (project FORTE, P2018/TCS-4314), and project PGC2018-097265-B-I00, funded by: FEDER/Spanish Ministry of Science and Innovation –Research State Agency. Also, this research was funded by the Secretariat of Higher Education, Science, Technology andInnovation (SENESCYT) of the Government of Ecuador aspart of an academic scholarship grantedfor postgraduate training, and Quevedo State Technical University through doctoral scholarships for university professors. Finally, this research received funding from the University of Atacama “DIUDA 22316” project

In this study, we explore a 100 years of Work and Organizational Psychology (WOP). To do this, we carry out a bibliometric performance and network analysis (BPNA) to understand the evolution structure and the most important themes in the field of study. To perform the BNPA, 8,966 documents published since 1919 were exported from the Web of Science and Scopus databases. The SciMAT software was used to process data and to create the evolution structure, the strategic diagram, and the thematic network structure of the strategic themes of the field of WOP. We identified 29 strategic clusters and discuss the most important themes (motor themes) and their relationship with other clusters. This research presents the complete evolution of the field of study, identifying emerging themes and others with a high degree of development. We hope that this work will support researchers and future research in the field of WOP.

Among other conceptualizations, smart cities have been defined as functional urban areas articulated by the use of Information and Communication Technologies (ICT) and modern infrastructures to face city problems in efficient and sustainable ways. Within ICT, recommender systems are strong tools that filter relevant information, upgrading the relations between stakeholders in the polity and civil society, and assisting in decision making tasks through technological platforms. There are scientific articles covering recommendation approaches in smart city applications, and there are recommendation solutions implemented in real world smart city initiatives. However, to the best of our knowledge, there is not a comprehensive review of the state of the art on recommender systems for smart cities. For this reason, in this paper we present a taxonomy of smart city features, dimensions, actions and goals, and, according to these variables, we survey the existing literature on recommender systems. As a result of our survey, we do not only identify and analyze main research trends, but also show current opportunities and challenges where personalized recommendations could be exploited as solutions for citizens, firms and public administrations, This work was supported by the Spanish Ministries of Economy, Spain, Industry and Competitiveness, Spain (TIN2016-80630- P), and Science, Innovation and Universities, Spain (CAS18/00035)

© 2020 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., The description of families of production systems usually relies on the use of variability modelling. This aspect of modelling is gaining increasing interest with the emergence of Industry 4.0 to facilitate the product development as new requirements appear. As a consequence, there are several emerging modelling techniques able to apply variability in different domains. In this paper, we introduce an approach to establish product system families in AutomationML. Our approach is based on the definition of feature models describing the variability space, and on the assignment of presence conditions to AutomationML model elements. These conditions (de-)select the model elements depending on the chosen configuration. This way, it is possible to model a large set of model variants in a compact way using one single model. To realize our approach, we started from an existing EMF-based AutomationML workbench providing graphical modelling support. From these artifacts, we synthesized an extended graphical modelling editor with variability support, integrated with FeatureIDE. Furthermore, we validated our approach by creating and managing a production system family encompassing six scenarios of the Pick and Place Unit Industry 4.0 demonstrator

Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users with accurate predictions and novel recommendations of items. Here we propose, DeepMF, a novel collaborative filtering method that combines the Deep Learning paradigm with Matrix Factorization (MF) to improve the quality of both predictions and recommendations made to the user. Specifically, DeepMF performs successive refinements of a MF model with a layered architecture that uses the acquired knowledge in a layer as input for subsequent layers. Experimental results showed that the quality of both the predictions and recommendations of DeepMF overcome the baselines., This work has been supported by Spanish Ministry of Science and Education and Competitivity (MINECO) and European Regional Development Fund (FEDER) under grants TIN2017-85727-C4-3-P (DeepBio).

Event-related potentials (ERPs) are important neurophysiological markers widely used in scientific, medical and engineering contexts. Proper ERP detection contributes to widening the scope of use and, in general, improving functionality. The morphology and latency of ERPs are variable among subject sessions, which complicates their detection. Although variability is an intrinsic feature of neuronal activity, it can be addressed with novel views on ERP detection techniques. In this paper, we propose an agile method for characterizing and thus detecting variable ERPs, which keeps track of their temporal and spatial information through the continuous measurement of the area under the curve in ERP components. We illustrate the usefulness of the proposed ERP characterization for electrode selection in brain-computer interfaces (BCIs) and compare the results with other standard methods. We assess ERP classification for BCI use with Bayesian linear discriminant analysis (BLDA) and cross-validation. We also evaluate performance with both the information transfer rate and BCI utility. The results of our validation tests show that this characterization helps to take advantage of the information on the evolution of positive and negative ERP components and, therefore, to efficiently select electrodes for optimized ERP detection. The proposed method improves the classification accuracy and bitrate of all sets of electrodes analyzed. Furthermore, the method is robust between different day sessions. Our work contributes to the efficient detection of ERPs, manages inter- and intrasubject variability, decreases the computational cost of classic detection methods and contributes to promoting low-cost personalized brain-computer interfaces., This work was supported by the Predoctoral Research Grants of the Ecuador Government through of the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT) under Grant 2015-AR2Q9086, and by the Ministerio de Ciencia, Innovación y Universidades/FEDER under the Spanish Government Grants: TIN2017-84452-R, DPI2015-65833-P and PGC2018-095895-B-I00.

Context: The growth in the number of non-technical open source software (OSS) application users and the escalating use of these applications have redoubled the need for, and interest in, developing usable OSS. OSS communities are unclear about which techniques to use in each development process activity. Objective: The aim of our research is to adapt a usability technique (visual brainstorming) to an OSS project and evaluate the feasibility of its application. Method: We used the case study research method to investigate technique application and participation in a project. To do this, we participated as volunteers in the HistoryCal project. Results: We identified adverse conditions that were an obstacle to technique application (like it was not easy to recruit OSS users to participate) and modified the technique to make it applicable. Conclusion: We conclude that these changes were helpful for applying the technique using web artifacts like blogs, This research was funded by the SENESCYT , Quevedo State Tech- nical University, TIN2014-52129-R and TIN2014-60490-P projects and the e-Madrid project (S2013/ICE-2715)

The use of collaborative tools has notably increased recently. It is common to see distinct users that need to work simultaneously on shared documents. In most cases, large companies provide tools whose implementations have been a very complicated and expensive task. Likewise, their platform deployment requirements should be robust hardware infrastructures. It becomes even more critical when their main target is to reach scalability and highavailability. Therefore, this study aims to design and implement a microservices-based collaborative architecture using assembled containers in the cloud, enabling them to deploy Etherpad instances to guarantee high availability. To ensure such a task, we developed and optimized a central management system that creates Etherpad instances and continuously interacts with other Etherpad tools running on Docker containers. This design goes from the monolithic Etherpad instantiation and handling towards a service architecture, where every Etherpad is offered as a microservice. Furthermore, the management system follows (implements) the Observer, Factory Method, Proxy, and Service Layerpopular design patterns. This allows users to gain more privacy through access to validations and shared resources. Our results indicate both the correct operation in the automation of containers’ creation for new users who register in the system and quantifiable improvement in performance., The funding of this research is provided by the Mobility Regulation of the Universidad de las Fuerzas Armadas ESPE, from Sangolquí, Ecuador

Over the last years, the Internet of Things has fostered a growing interest in context-aware mobile applications; this fact is mainly due to highly favoring information provision from multiple Internetconnected devices. To identify user context, these applications collect information from the user and his/her environment and typically lter app information, so that the user receives only the interesting and relevant information. However, such a task usually implies further resource consumption on user mobile devices, not only regarding battery usage but also in terms of network traf c. Accordingly, although context-aware applications can improve user experiences in their daily lives, they must ensure the maintenance of lowlevel resource consumption; otherwise, the applications are promptly replaced by less consuming ones, and therefore, removed from the mobile market. In this paper, we evaluate and discuss several architectural styles for context-aware mobile applications, as well as, providing a set of guidelines to decide on the right architecture for a particular app depending on its characteristics. The use of such guidelines when choosing the right architectural style can strongly in uence the resource consumption of context-aware mobile applications. Following these guidelines, user satisfaction of a context-aware mobile application may be improved, thus guaranteeing the app success.

© Owner/Author 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in EASE '20: Proceedings of the Evaluation and Assessment in Software Engineering, https://doi.org/10.1145/3383219.3383246, Modelling is a fundamental activity in software engineering, which is often performed in collaboration. For this purpose, on-line tools running on the cloud are frequently used. However, recent advances in Natural Language Processing have fostered the emergence of chatbots, which are increasingly used for all sorts of software engineering tasks, including modelling. To evaluate to what extent chatbots are suitable for collaborative modelling, we conducted an experimental study with 54 participants, to evaluate the usability of a modelling chatbot called SOCIO, comparing it with the on-line tool Creately. We employed a within-subjects cross-over design of 2 sequences and 2 periods. Usability was determined by attributes of efficiency, effectiveness, satisfaction and quality of the results. We found that SOCIO saved time and reduced communication effort over Creately. SOCIO satisfied users to a greater extent than Creately, while in effectiveness results were similar. With respect to diagram quality, SOCIO outperformed Creately in terms of precision, while solutions with Creately had better recall and perceived success. However, in terms of accuracy and error scores, both tools were similar, Work funded by the Spanish Ministry of Science (project MASSIVE, RTI2018-095255-B-I00) and the R&D programme of Madrid (project FORTE, P2018/TCS-4314)

Technology evolves quickly. Low-cost and ready-to-connect devices are designed to provide new services and applications. Smart grids or smart health care systems are some examples of these applications. In this totally connected scenario, some security issues arise due to the large number of devices and communications. In this way, new solutions for monitoring and detecting security events are needed to address new challenges brought about by this scenario, among others, the real-time requirement allowing quick security event detection and, consequently, quick response to attacks. In this sense, Intrusion Detection Systems are widely used though their evaluation often relies on the use of predefined network datasets that limit their application in real environments. In this work, a real-time and ready-to-use tool for monitoring and detecting security events is introduced. The Multivariate Statistical Network Monitoring–Sensor is based on the Multivariate Statistical Network Monitoring methodology and provides an alternative way for evaluating Multivariate Statistical Network Monitoring–based Intrusion Detection System solutions. Experimental results based on the detection of well-known attacks in hierarchical network systems prove the suitability of this tool for complex scenarios, such as those found in smart cities or Internet of Things ecosystems., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by Spanish MINECO (Ministerio de Economı´a y Competitividad) through projects TIN2014-60346-R, TIN2017-83494-R, and FEDER funds.

This paper presents a novel design of an eight-port directional coupler with a very compact structure and simple manufacturing, working in the Ku frequency band. One of the main goals of the design was to ease the manufacturing with a simple structure: the coupler consisted of four rectangular waveguide input ports, four rectangular waveguide output ports, and a central coupling region with only H-plane variation. A prototype was fabricated using additive manufacturing, with a combination of 3D printing and silver coating metallization. The obtained performance showed a theoretical bandwidth of 6.6% with 20 dB return loss for the input/output ports. Good agreement between simulations and measurements was obtained, validating the proposed coupler as a good trade-off for low cost 3D printing, compactness, and high performance for systems requiring a high number of ports as in phase arrays or Butler matrices., This work was supported by the Spanish Government (Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional: AEI/FEDER, UE) under Grants TEC2016-76070-C3-1/2-R

We undertake a detailed examination of the steps that make up offline experiments for recommender system evaluation, including the manner in which the available ratings are filtered and split into training and test; the selection of a subset of the available users for the evaluation; the choice of strategy to handle the background effects that arise when the system is unable to provide scores for some items or users; the use of either full or condensed output lists for the purposes of scoring; scoring methods themselves, including alternative top-weighted mechanisms for condensed rankings; and the application of statistical testing on a weighted-by-user or weighted-by-volume basis as a mechanism for providing confidence in measured outcomes. We carry out experiments that illustrate the impact that each of these choice points can have on the usefulness of an end-to-end system evaluation, and provide examples of possible pitfalls. In particular, we show that varying the split between training and test data, or changing the evaluation metric, or how target items are selected, or how empty recommendations are dealt with, can give rise to comparisons that are vulnerable to misinterpretation, and may lead to different or even opposite outcomes, depending on the exact combination of settings used, The frst two authors were funded in part by Grant TIN2016-80630-P from the Spanish Ministry of Science, Innovation and Universities

Presently, we are living in a hyper-connected world where millions of heterogeneous devices are continuously sharing information in different application contexts for wellness, improving communications, digital businesses, etc. However, the bigger the number of devices and connections are, the higher the risk of security threats in this scenario. To counteract against malicious behaviours and preserve essential security services, Network Intrusion Detection Systems (NIDSs) are the most widely used defence line in communications networks. Nevertheless, there is no standard methodology to evaluate and fairly compare NIDSs. Most of the proposals elude mentioning crucial steps regarding NIDSs validation that make their comparison hard or even impossible. This work firstly includes a comprehensive study of recent NIDSs based on machine learning approaches, concluding that almost all of them do not accomplish with what authors of this paper consider mandatory steps for a reliable comparison and evaluation of NIDSs. Secondly, a structured methodology is proposed and assessed on the UGR’16 dataset to test its suitability for addressing network attack detection problems. The guideline and steps recommended will definitively help the research community to fairly assess NIDSs, although the definitive framework is not a trivial task and, therefore, some extra effort should still be made to improve its understandability and usability further., The authors would like to acknowledge the Spanish Ministerio de Ciencia, Innovación y Universidades and ERDF for the support provided under contracts RTI2018-100754-B-I00 (iSUN) and RTI2018-098160-B-I00 (DEEPAPFORE).

The availability of datasets providing information on the spatial and temporal evolution of greenhouse gas concentrations is of high relevance for the development of reliable climate simulations. However, current gas detection technologies do not allow for obtaining high-quality data at intermediate spatial scales with high temporal resolution. In this regard the deployment of a wireless gas sensor network equipped with in situ gas analysers may be a suitable approach. Here we present a novel, non-dispersive infrared absorption spectroscopy (NDIR) device that can possibly act as a central building block of a sensor node to provide high-quality data of carbon dioxide (CO2) concentrations under field conditions at a high measurement rate. Employing a gas-based, photoacoustic detector we demonstrate that miniaturized, low-cost, and low-power consuming CO2 sensors may be built. The performance is equal to that of standard NDIR devices but at a much reduced optical path length. Because of the spectral properties of the photoacoustic detector, no cross-sensitivities to humidity exist, Stefan Palzer acknowledges funding from the Community of Madrid under grant number 2016-T1/AMB- 1695. Benedikt Bierer was supported by scholarship funds from the State Graduate Funding Program of Baden-Württemberg (DENE).