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This paper proposes the minimization of α-divergences for approximate inference in the context of deep Gaussian processes (DGPs). The proposed method can be considered as a generalization of variational inference (VI) and expectation propagation (EP), two previously used methods for approximate inference in DGPs. Both VI and EP are based on the minimization of the Kullback-Leibler divergence. The proposed method is based on a scalable version of power expectation propagation, a method that introduces an extra parameter α that specifies the targeted α-divergence to be optimized. In particular, such a method can recover the VI solution when α → 0 and the EP solution when α → 1. An exhaustive experimental evaluation shows that the minimization of α-divergences via the proposed method is feasible in DGPs and that choosing intermediate values of the α parameter between 0 and 1 can give better results in some problems. This means that one can improve the results of VI and EP when training DGPs. Importantly, the proposed method allows for stochastic optimization techniques, making it able to address datasets with several millions of instances, The authors gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at Universidad Autónoma de Madrid. The authors also acknowledge financial support from Spanish Plan Nacional I+D+i, Ministerio de Ciencia e Innovación, grant PID2019-106827GB-I00 / AEI / 10.13039/501100011033

Recommender systems are widely adopted as an increasing research and development area, since they provide users with diverse and useful information tailored to their needs. Several strategies have been proposed, and in most of them some concept of similarity is used as a core part of the approach, either between items or between users. At the same time, Siamese Neural Networks are being used to capture the similarity of items in the image domain, as they are defined as a subtype of Artificial Neural Networks built with (at least two) identical networks that share their weights. In this review, we study the proposals done in the intersection of these two fields, that is, how Siamese Networks are being used for recommendation. We propose a classification that considers different recommendation problems and algorithmic approaches. Some research directions are pointed out to encourage future research. To the best of our knowledge, this paper is the first comprehensive survey that focuses on the usage of Siamese Neural Networks for Recommender Systems, This work has been funded by the Ministerio de Ciencia e Innovación (reference PID2019-108965GB-I00). The authors thank the reviewers for their thoughtful comments and suggestions

71 p. – Bibliogr.: p. 55-57 En este documento se muestra cómo se ha ido desarrollando el Trabajo de Fin de Grado de la autora. El proyecto consiste en añadir funcionalidades y especificaciones a un área de RAMSÉS, aplicación web de simulación de máquinas abstractas. Es una más completa herramienta de uso académico para la asignatura de segundo curso Lenguajes, Computación y Sistemas Inteligentes, cubriendo la funcionalidad de una herramienta ya existente, JFLAP. En el caso de este proyecto en cuestión se ha desarrollado la opción de importar cualquier fichero JFLAP o JSON a la base de datos. De igual manera se ha desarrollado la opción de exportar cualquier autómata a JFLAP o JSON. Asimismo, se han añadido funcionalidades para etiquetar máquinas abstractas en la base de datos permitiendo asociarles información académica, como pueden ser colecciones de ejercicios o preguntas de exámenes. También se ha desarrollado un mecanismo adecuado para que RAMSÉS sea una herramienta multi-idioma. Por último, se ha desarrollado un procedimiento para la realización de copias de seguridad de RAMSÉS para proteger las actividades académicas a pérdidas de información asociadas a incidentes de seguridad. Cabe destacar que este proyecto está realizado en gran parte en el lenguaje de programación JavaScript tanto en la parte cliente como en la de servidor, apoyado en tecnologías como Node.js, Git, JSON, SVG, CSS3 y HTML5, junto con MySQL para el almacenamiento y recuperación de datos. Por otro lado, se ha realizado la puesta en producción de la aplicación mediante máquinas virtuales en un servidor, mediante Docker y Proxmox. En estos momentos RAMSÉS está en producción en el servidor del departamento de LSI siendo accesible desde la Web.

Context: Testing is essential to improve the correctness of software systems. Metamorphic testing (MT) is an approach especially suited when the system under test lacks oracles, or they are expensive to compute. However, building an MT environment for a particular domain (e.g., cloud simulation, model transformation, machine learning) requires substantial effort. Objective: Our goal is to facilitate the construction of MT environments for specific domains. Method: We propose a model-driven engineering approach to automate the construction of MT environments. Starting from a meta-model capturing the domain concepts, and a description of the domain execution environment, our approach produces an MT environment featuring comprehensive support for the MT process. This includes the definition of domain-specific metamorphic relations, their evaluation, detailed reporting of the testing results, and the automated search-based generation of follow-up test cases. Results: Our method is supported by an extensible platform for Eclipse, called GOTTEN. We demonstrate its effectiveness by creating an MT environment for simulation-based testing of data centres and comparing with existing tools; its suitability to conduct MT processes by replicating previous experiments; and its generality by building another MT environment for video streaming APIs. Conclusion: GOTTEN is the first platform targeted at reducing the development effort of domain-specific MT environments. The environments created with GOTTEN facilitate the specification of metamorphic relations, their evaluation, and the generation of new test cases, Work supported by the Madrid government, Spain and the Complutense University (grant PR65/19-22452), the Spanish Ministry of Science (PID2021-122270OB-I00), and the Madrid region, Spain (P2018/TCS-4314, S2018/TCS-4339)

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]

Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research, This work has been funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 813884 (Lowcomote [134]), by the Spanish Ministry of Science (projects MASSIVE, RTI2018-095255-B-I00, and FIT, PID2019-108965GB-I00) and by the R&D programme of Madrid (Project FORTE, P2018/TCS-4314

There is an increasing demand nowadays for low-code development platforms (LCDPs). As they rely heavily on graphical languages rather than writing code, these platforms enable citizen developers to participate in software development. However, creating new LCDPs is very costly, since it requires building support for graphical modelling and its integration with services like model validation, recommendation systems, or code generation. While Model-driven Engineering (MDE) has developed technologies to create these components, most of them are not cloud-based, as required by LCDPs. In particular, a cloud-based graphical workbench capable of providing the scalability required by industrial applications and adequately supporting technological heterogeneity is currently missing. To fill this gap we introduce Dandelion, a cloud-based graphical language workbench for LCDPs built following an MDE approach. The tool handles model heterogeneity by using a harmonising meta-model to uniformly represent models from diverse technologies, and supports a customisable level of conformance between models and meta-models. Scalability is addressed by persisting models in a distributed, highly flexible database whose infrastructure is designed to conform to the harmonising meta-model, thus favouring model retrieval. Additionally, a customisable scalability component is introduced for lazy model loading. This paper describes the concepts and principles behind the tool design and reports on an evaluation on large synthetic process mining models, and on domain-specific languages and large industrial models used within the UGROUND company, showing promising results, This project has been funded by the EU Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813884, the Spanish Ministry of Science (PID2021-122270OBI00), and the R&D programme of Madrid (P2018/TCS-4314)

Because of their level of abstraction, Domain-Specific Languages (DSLs) enable building applications that ease software implementation. In the context of web applications, we can find a lot of technologies and programming languages for server-side applications that provide fast, robust, and flexible solutions, whereas those for client-side applications are limited, and mostly restricted to directly use JavaScript, HTML5, CSS3, JSON and XML. This article presents a novel approach to creating DSL-based web applications using JSON grammar (JSON-DSL) for both, the server and client side. The approach includes an evaluation engine, a programming model and an integrated web development environment that support it. The evaluation engine allows the execution of the elements created with the programming model. For its part, the programming model allows the definition and specification of JSON-DSLs, the implementation of JavaScript components, the use of JavaScript templates provided by the engine, the use of link connectors to heterogeneous information sources, and the integration with other widgets, web components and JavaScript frameworks. To validate the strength and capacity of our approach, we have developed four case studies that use the integrated web development environment to apply the programming model and check the results within the evaluation engine

© 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)

© 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

In this paper we present an indexing method for probably approximately correct nearest neighbor queries in high dimensional spaces capable of improving the performance of any index whose performance degrades with the increased dimensionality of the query space. The basic idea of the method is quite simple: we use SVD to concentrate the variance of the inter-element distance in a lower dimensional space, Ξ. We do a nearest neighbor query in this space and then we “peek” forward from the nearest neighbor by gathering all the elements whose distance from the query is less than dΞ(1+ζσΞ2), where dΞ is the distance from the nearest neighbor in Ξ, σΞ2 is the variance of the data in Ξ, and ζ a parameter. All the data thus collected form a tentative set T, in which we do a scan using the complete feature space to find the point closest to the query. The advantages of the method are that (1) it can be built on top of virtually any indexing method and (2) we can build a model of the distribution of the error precise enough to allow designing a compromise between error and speed. We show the improvement that we can obtain using data from the SUN data base, Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

© 2022 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., Software engineering uses models throughout most phases of the development process. Models are defined using modelling languages. To make these languages applicable to a wider set of scenarios and customizable to specific needs, researchers have proposed using product lines to specify modelling language variants. However, there is currently a lack of efficient techniques for ensuring correctness with respect to properties of the models accepted by a set of language variants. This may prevent detecting problematic combinations of language variants that produce undesired effects at the model level. To attack this problem, we first present a classification of instantiability properties for language product lines. Then, we propose a novel approach to lifting the satisfiability checking of model properties of individual language variants, to the product line level. Finally, we report on an implementation of our proposal in the Merlin tool, and demonstrate the efficiency gains of our lifted analysis method compared to an enumerative analysis of each individual language variant, This work has been funded by the Spanish Ministry of Science (RTI2018-095255-B-I00), the R&D programme of Madrid (P2018/TCS-4314), and by NSERC. We thank the anonymous referees for their useful comments

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.

Petri nets are commonly used to represent concurrent systems. However, they lack support for modelling and analysing system families, like variants of controllers, different variations of a process model, or the possible configurations of a flexible assembly line. To facilitate modelling potentially large collections of similar systems, in this paper, we enrich Petri nets with variability mechanisms based on product line engineering. Moreover, we present methods for the efficient analysis of the place and transition invariants in all defined versions of a Petri net. Efficiency is achieved by analysing the system family as a whole, instead of analysing each possible net variant separately. For this purpose, we lift the notion of incidence matrix to the product line level, and rely on constraint solving techniques. We present tool support and evaluate the benefits of our techniques on synthetic and realistic examples, achieving in some cases speed-ups of two orders of magnitude with respect to analysing each net variant separately, This work has been funded by the Spanish Ministry of Science (PID2021-122270OB-I00) and the R&D programme of Madrid (P2018/TCS-4314)

Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, the combination of their decisions is beneficial and could be superior to homogeneous ensembles. In this paper, a family of heterogeneous ensembles is built by pooling classifiers from M homogeneous ensembles of different types of size T. Depending on the fraction of base classifiers of each type, a particular heterogeneous combination in this family is represented by a point in a regular simplex in M dimensions. The M vertices of this simplex represent the different homogeneous ensembles. A displacement away from one of these vertices effects a smooth transformation of the corresponding homogeneous ensemble into a heterogeneous one. The optimal composition of such heterogeneous ensemble can be determined using cross-validation or, if bootstrap samples are used to build the individual classifiers, out-of-bag data. The proposed heterogeneous ensemble building strategy, composed of neural networks, SVMs, and random trees (i.e. from a standard random forest), is analyzed in a comprehensive empirical analysis and compared to a benchmark of other heterogeneous and homogeneous ensembles. The achieved results illustrate the gains that can be achieved by the proposed ensemble creation method with respect to both homogeneous ensembles and to the tested heterogeneous building strategy at a fraction of the training cost., The authors acknowledge financial support from PID2019-106827GB-I00/AEI/10.13039/501100011033

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

© 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)

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.

Modelling is an essential activity in software engineering. Ittypically involves two meta-levels: one includes meta-models thatdescribe modelling languages, and the other contains models built byinstantiating those meta-models. Multi-level modelling generalizes this approach by allowing models to span an arbitrarynumber of meta-levels. A scenario that profits from multi-levelmodelling is the definition of language families that can bespecialized (e.g., for different domains) by successive refinementsat subsequent meta-levels, hence promoting language reuse. Thisenables an open set of variability options given by allpossible specializations of the language family. However,multi-level modelling lacks the ability to express closed variability regarding the availability of language primitives or thepossibility to opt between alternative primitive realizations. Thislimits the reuse opportunities of a language family. To improve thissituation, we propose a novel combination of product lines withmulti-level modelling to cover both open and closed variability. Ourproposal is backed by a formal theory that guarantees correctness,enables top-down and bottom-up language variability design, and isimplemented atop the MetaDepth multi-level modelling tool, This work has been funded by the SpanishMinistry of Science (project MASSIVE, RTI2018-095255- B-I00), by the R&D programme of Madrid (project FORTE, P2018/TCS-4314), and the Universidad Autónoma de Madrid

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 objective of exposing girls to Computer Science as a career option has led to research directed towards gaming activities for girls. These activities include both game play and game design. Research about gaming activities for increasing girls’ interest in computer science has gained much attention over the past few years and has resulted in a number of contributions. We follow up with an overview of the status of research through a Systematic Literature Review. We investigate the relation between the various game playing or designing activities and their impact on girls’ perception of Computer Science as a career choice. We further present the design consideration for the games and related activities to potentially improve the perception of girls towards a Computer Science career. The applied method is a Systematic Literature Review through which we investigate which contributions were made, which knowledge areas were most explored, and which research facets have been used. We identify 25 papers to distill a common understanding of the state-of-the-art. Specifically, we investigate the effects that the game play/design activities had on girls’ perception about Computer Science; and what are the key design factors to be kept in mind while designing a serious game to improve girls’ perception about Computer Science. The results of this systematic literature review show that game playing or designing could indeed improve how girls perceive having a career in CS. The key aspects that such activities require are personalizing, opportunity for collaboration and the presence of a female lead character, This work has been done during the tenure of an ERCIM Alain Bensoussan fellowship

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.