Search

We present MESMOC+, an improved version of Max-value Entropy search for Multi-Objective Bayesian optimization with Constraints (MESMOC). MESMOC+ can be used to solve constrained multi-objective problems when the objectives and the constraints are expensive to evaluate. It is based on minimizing the entropy of the solution of the optimization problem in function space (i.e., the Pareto front) to guide the search for the optimum. The cost of MESMOC+ is linear in the number of objectives and constraints. Furthermore, it is often significantly smaller than the cost of alternative methods based on minimizing the entropy of the Pareto set. The reason for this is that it is easier to approximate the required computations in MESMOC+. Moreover, MESMOC+’s acquisition function is expressed as the sum of one acquisition per each black-box (objective or constraint). Therefore, it can be used in a decoupled evaluation setting in which it is chosen not only the next input location to evaluate, but also which black-box to evaluate there. We compare MESMOC+ with related methods in synthetic, benchmark and real optimization problems. These experiments show that MESMOC+ has similar performance to that of state-of-the-art acquisitions based on entropy search, but it is faster to execute and simpler to implement. Moreover, our experiments also show that MESMOC+ is more robust with respect to the number of samples of the Pareto front, The authors acknowledge financial support from Spanish Plan Nacional I + D+i, grant PID2019-106827 GB-I00/ AEI/ 10.13039/501100011033

Biohybrid circuits of interacting living and model neurons are an advantageous means to study neural dynamics and to assess the role of specific neuron and network properties in the nervous system. Hybrid networks are also a necessary step to build effective artificial intelligence and brain hybridization. In this work, we deal with the automatized online and offline adaptation, exploration and parameter mapping to achieve a target dynamics in hybrid circuits and, in particular, those that yield dynamical invariants between living and model neurons. We address dynamical invariants that form robust cycle-by-cycle relationships between the intervals that build neural sequences from such interaction. Our methodology first attains automated adaptation of model neurons to work in the same amplitude regime and time scale of living neurons. Then, we address the automatized exploration and mapping of the synapse parameter space that lead to a specific dynamical invariant target. Our approach uses multiple configurations and parallel computing from electrophysiological recordings of living neurons to build full mappings, and genetic algorithms to achieve an instance of the target dynamics for the hybrid circuit in a short time. We illustrate and validate such strategy in the context of the study of functional sequences in neural rhythms, which can be easily generalized for any variety of hybrid circuit configuration. This approach facilitates both the building of hybrid circuits and the accomplishment of their scientific goal, This research was supported by grants AEI/FEDER PID2021-122347NB-100, PGC2018-095895-B-I00, and PID2020- 114867RB-I00 (funded by MCIN/AEI/10.13039/501100011033 and ERDF - "A way of making Europe”)

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)

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.

We introduce a formalism based on a continuous time approximation to study the characteristics of random walks with jumps to random locations of the networks (Pagerank random walks). We find that the diffusion of the occupancy probability has a dynamics that exponentially “forgets” the initial conditions and settles to a steady state that depends only on the characteristics of the network. In the special case in which the walk begins from a single node, we show that the largest eigenvalue of the transition value (λ1=1) does not contribute to the dynamic and that the probability is constant in the direction of the corresponding eigenvector. We study the process of visiting new nodes, which we find to have a dynamic similar to that of the occupancy probability. Finally, we determine the average transit time between nodes 〈T〉. We show that, for suitable values of the walk parameters, Pagerank walks behave similarly to Lèvy walks in terms of unique nodes visited and average transit time, while requiring much less information about the graph, Simone Santini was supported in part by the Spanish Ministerio de Ciencia e Innovación under the grant N. PID2019-108965GB-I00 Más allá de la recomendación estática: equidad, interacción y transparencia

The presence of preferred orientations in single particle analysis (SPA) by cryo-Electron Microscopy (cryoEM) is currently one of the hurdles preventing many structural analyses from yielding high-resolution structures. Although the existence of preferred orientations is mostly related to the grid preparation, in this technical note, we show that some image processing algorithms used for angular assignment and three-dimensional (3D) reconstruction are more robust than others to these detrimental conditions. We exemplify this argument with three different data sets in which the presence of preferred orientations hindered achieving a 3D reconstruction without artifacts or, even worse, a 3D reconstruction could never be achieved, We acknowledge support from “la Caixa” Foundation (Fellowship LCF/BQ/DI18/11660021. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 713673. We also thank the financial support from the Spanish Ministry of Economy and Competitiveness through Grants BIO2016-76400-R(AEI/FEDER, UE) and SEV 2017-0712, the “Comunidad Autónoma de Madrid” through Grant: S2017/BMD-3817, Instituto de Salud Carlos III, PT17/ 0009/0010 (ISCIII-SGEFI/ERDF), European Union (EU) and Horizon 2020 through grants: CORBEL (INFRADEV-1-2014-1, Proposal: 654248), INSTRUCT-ULTRA (INFRADEV-03-2016-2017, Proposal: 731005), EOSC Life (INFRAEOSC-04-2018, Proposal: 824087), High- ResCells (ERC-2018-SyG, Proposal: 810057), IMpaCT (WIDESPREAD-03-2018 – Proposal: 857203), EOSC-Synergy (EINFRA-EOSC-5, Proposal: 857647), and iNEXT-Discovery (Proposal: 871037). The authors acknowledge the support and the use of resources of Instruct, a Landmark ESFRI project

Suggesting new venues to be visited by a user in a specific city remains an interesting but challenging problem, partly because of the inherent high sparsity of the data available in location-based social networks (LSBNs). At the same time, in traditional recommender systems, in order to improve their performance in these sparse situations, different techniques have been proposed mainly by augmenting and aggregating the data available in different domains. In this paper, we address the problem of venue recommendation from a novel perspective: we propose two strategies to select a set of candidate cities in order to use their information when performing recommendations for the users in a specific (target) city. In this context, we categorize users into two different groups (tourists and locals) according to their movement patterns and analyze the potential biases in the recommendations received by each of these groups. We provide an experimental comparison of several recommendation algorithms in a temporal split, where we analyze two strategies to select cities and augment the available data: based on the number of interactions and based on the distance with respect to the target city. Our results show that, in general, extending the available data by proximity increases the performance of the majority of the tested recommenders in terms of relevance and coverage, with almost no change in novelty and diversity. We have found that those users belonging to the tourist group tend to obtain better results in terms of relevance. Furthermore, in general, tourists consistently exhibit different performance by some families of recommenders for other evaluation dimensions, evidencing a popularity bias in user behavior and raising potential fairness issues regarding the quality of the received recommendations. We investigate these aspects and provide methods to better understand the problem. We expect these results could provide readers with an overall picture of what can be achieved in a real-world environment, This work has been co-funded by the European Social Fund (ESF) within the 2017 call for predoctoral contracts, the Ministerio de Ciencia e Innovación (ref: PID2019-108965GB-I00), and by the project ‘‘AmerMad-CM: America in Madrid. Interconnected heritage and impact on tourism in the Community of Madrid’’ (ref: H2019/HUM-5694), co-financed by the Community of Madrid and the European Social Fund (ESF)

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.

Automatic story generation systems usually deliver suspense by including an adverse outcome in the narrative, in the assumption that the adversity will trigger a certain set of emotions that can be categorized as suspenseful. However, existing systems do not implement solutions relying on predictive models of the impact of the outcome on readers. A formulation of the emotional effects of the outcome would allow storytelling systems to perform a better measure of suspense and discriminate among potential outcomes based on the emotional impact. This paper reports on a computational model of the effect of different outcomes on the perceived suspense. A preliminary analysis to identify and evaluate the affective responses to a set of outcomes commonly used in suspense was carried out. Then, a study was run to quantify and compare suspense and affective responses evoked by the set of outcomes. Next, a predictive model relying on the analyzed data was computed, and an evolutionary algorithm for automatically choosing the best outcome was implemented. The system was tested against human subjects’ reported suspense and electromyography responses to the addition of the generated outcomes to narrative passages. The results show a high correlation between the predicted impact of the computed outcome and the reported suspense.

Kernel-based techniques have become a common way for describing the local and global relationships of data samples that are generated in real-world processes. In this research, we focus on a multi-scale kernel based technique named Auto-adaptive Laplacian Pyramids (ALP). This method can be useful for function approximation and interpolation. ALP is an extension of the standard Laplacian Pyramids model that incorporates a modified Leave-One-Out Cross Validation procedure, which makes the method stable and automatic in terms of parameters selection without extra cost. This paper introduces a new algorithm that extends ALP to fit datasets that are non-uniformly distributed. In particular, the optimal stopping criterion will be point-dependent with respect to the local noise level and the sample rate. Experimental results over real datasets highlight the advantages of the proposed multi-scale technique for modeling and learning complex, high dimensional data, They wish to thank Prof. Ronald R. Coifman for helpful remarks. They 525 also gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at Universidad Autónoma de Madrid. Funding: This work was supported by Spanish grants of the Ministerio de Ciencia, Innovación y Universidades [grant numbers: TIN2013-42351-P, TIN2015-70308-REDT, TIN2016-76406-P]; project CASI-CAM-CM supported by Madri+d 530 [grant number: S2013/ICE-2845]; project FACIL supported by Fundación BBVA (2016); and the UAM–ADIC Chair for Data Science and Machine Learning

Modeling the temporal context efficiently and effectively is essential to provide useful recommendations to users. In this work, we focus on improving neighborhood-based approaches where we integrate three different mechanisms to exploit temporal information. We first present an improved version of a similarity metric between users using a temporal decay function, then, we propose an adaptation of the Longest Common Subsequence algorithm to be used as a time-aware similarity metric, and we also redefine the neighborhood-based recommenders to be interpreted as ranking fusion techniques where the neighbor interaction sequence can be exploited by considering the last common interaction between the neighbor and the user. We demonstrate the effectiveness of these approaches by comparing them with other state-of-the-art recommender systems such as Matrix Factorization, Neural Networks, and Markov Chains under two realistic time-aware evaluation methodologies (per user and community-based). We use several evaluation metrics to measure both the quality of the recommendations – in terms of ranking relevance – and their temporal novelty or freshness. According to the obtained results, our proposals are highly competitive and obtain better results than the rest of the analyzed algorithms, producing improvements under the two evaluation dimensions tested consistently through three real-world datasets, This work has been funded by the Ministerio de Ciencia, Innovación y Universidades (reference TIN2016-80630-P)

The interaction between synaptic and intrinsic dynamics can efficiently shape neuronal input-output relationships in response to temporally structured spike trains. We use a neuron model with subthresh-old oscillations receiving inputs through a synapse with short-term depression and facilitation to show that the combination of intrinsic subthreshold and synaptic dynamics leads to channel-specific nontrivial responses and recognition of specific temporal structures. Our study employs the Generalized Integrate and-Fire (GIF) model, which can be subjected to analytical characterization. We map the temporal structure of spike input trains to the type of spike response, and show how the emergence of nontrivial input- output preferences is modulated by intrinsic and synaptic parameters in a synergistic manner. We demonstrate that these temporal input discrimination properties are robust to noise and to variations in synaptic strength. Furthermore, we also illustrate the presence of these input-output relationships in conductance-based models. Our results suggest a widespread computationally economic and easily tunable mechanism for temporal information discrimination in single neurons. (c) 2020 Elsevier B.V. All rights reserved., This work was supported AEI/FEDER grants FIS2017-84256-P (JJT) and PGC2018-095895-B-I00, DPI2015-65833-P (RL & PV).

Currently, using conventional techniques for checking and optimising the energy consumption in cloud systems is unpractical, due to the massive computational resources required. An appropriate test suite focusing on the parts of the cloud to be tested must be efficiently synthesised and executed, while the correctness of the test results must be checked. Additionally, alternative cloud configurations that optimise the energetic consumption of the cloud must be generated and analysed accordingly, which is challenging. To solve these issues we present MT-EA4Cloud, a formal approach to check the correctness – from an energy-aware point of view – of cloud systems and optimise their energy consumption. To make the checking of energy consumption practical, MT-EA4Cloud combines metamorphic testing, evolutionary algorithms and simulation. Metamorphic testing allows to formally model the underlying cloud infrastructure in the form of metamorphic relations. We use metamorphic testing to alleviate both the reliable test set problem, generating appropriate test suites focused on the features reflected in the metamorphic relations, and the oracle problem, using the metamorphic relations to check the generated results automatically. MT-EA4Cloud uses evolutionary algorithms to efficiently guide the search for optimising the energetic consumption of cloud systems, which can be calculated using different cloud simulators, This work was supported by the Spanish MINECO/FEDER projects DArDOS, FAME and MASSIVE under Grants TIN2015-65845-C3-1-R, RTI2018-093608-B-C31 and RTI2018-095255- B-I00, and the Comunidad de Madrid project FORTE-CM under grant S2018/TCS-4314. The first author is also supported by the Universidad Complutense de Madrid Santander Universidades grant (CT17/17-CT18/17)

Resolution (global and local) is one of the most reported metrics of quality measurement in Single Particle Analysis (SPA). However, in electron tomography, the situation is different and its computation is not straightforward. Typically, resolution estimation is global and, therefore, reduces the assessment of a whole tomogram to a single number. However, it is known that tomogram quality is spatially variant. Still, up to our knowledge, a method to estimate local quality metrics in tomography is lacking. This work introduces MonoTomo, a method developed to estimate locally in a tomogram the highest reliable frequency component, expressed as a form of local resolution. The fundamentals lie in a local analysis of the density map via monogenic signals, which, in analogy to MonoRes, allows for local estimations. Results with experimental data show that the local resolution range that MonoTomo casts agrees with reported resolution values for experimental data sets, with the advantage of providing a local estimation. A range of applications of MonoTomo are suggested for further exploration., The author would like to express their gratitude to Dr. J.J. Fernandez for his interest, following and advice on this work. The authors would like to acknowledge economical support from The Spanish Ministry of Economy and Competitiveness through Grants BIO2016-76400-R(AEI/FEDER, UE), the “Comunidad Autónoma de Madrid” through Grant: S2017/BMD-3817, and the European Union (EU) and Horizon 2020 through grant EOSCpilot (INFRADEV-04-2016, Proposal: 739563), INSTRUCT - ULTRA (INFRADEV-03-2016-2017, Proposal: 731005), EOSC-LIFE (INFRAEOSC-04-2018, Proposal: 824087) and HighResCells (ERC-2018-SyG, Proposal: 810057). The authors acknowledge the support and the use of resources of Instruct-ERIC.

Modelling is a core activity in software development paradigms like Model-driven Engineering (MDE). Therefore, the quality of (meta-)models is crucial for the success of software projects. However, many times, modelling becomes a purely manual activity, which does not take advantage of information embedded in heterogeneous information sources, such as XML documents, ontologies, or other models and meta-models. In order to improve this situation, we present EXTREMO, an Eclipse plugin aimed at gathering the information stored in heterogeneous sources in a common data model, to facilitate the reuse of information chunks in the model being built. The tool covers the steps needed to incorporate this knowledge within an external modelling tool, supporting the uniform query of the heterogeneous sources and the evaluation of constraints. Flexibility of the main features (e.g., supported data formats, queries)is achieved by means of extensible mechanisms. To illustrate the usefulness of EXTREMO, we describe a practical case study in the financial domain and evaluate its performance and scalability, This work was partially supported by the Ministry of Education of Spain (FPU grant FPU13/02698), the Spanish Ministry of Science (RTI2018-095255-B-I00), and the R&D programme of the Madrid Region (S2018/TCS-4314)

Most user-centered assessment activities for ensuring usability are principally focused on performing formative evaluations, enrolling users to complete different tasks and thus obtaining indicators such as effectiveness and efficiency. However, when considering broader scenarios, such as in User Experience (UX) assessments, user perceived satisfaction (or perceived usability) is even much more relevant. There are different methods for measuring user perception, however most of them are mainly qualitative and based on individual assessments, providing little specific support to carry out comparisons–i.e., benchmarking on user-perceived usability. In this paper, we propose a quantitative metric to achieve comparative evaluations of usability perception based on Reaction Cards, a popular method for obtaining the user's subjective satisfaction in UX assessments. The metric was developed through an empirical study. Additionally, it has been validated with usability experts. Besides, we provide a supporting tool based on the developed metric, featuring a framework to store historical evaluations in order to obtain charts and benchmark levels for comparing perceived usability from different artifacts such as software products, applications categories, services, mockups, prototypes and so on. Furthermore, an evaluation involving usability professionals was achieved, providing satisfactory results to answer research questions, thus demonstrating the suitability of the approach proposed, This work was partially supported by the Spanish Government[grant number TIN2014-52129-R]; and the Madrid Research Council [grant number S2013/ICE-2715]

This work proposes a new algorithm for training a re-weighted ℓ2 Support Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Candès et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In particular, the margin required for each training vector is set independently, defining a new weighted SVM model. These weights are selected to be binary, and they are automatically adapted during the training of the model, resulting in a variation of the Frank–Wolfe optimization algorithm with essentially the same computational complexity as the original algorithm. As shown experimentally, this algorithm is computationally cheaper to apply since it requires less iterations to converge, and it produces models with a sparser representation in terms of support vectors and which are more stable with respect to the selection of the regularization hyper-parameter, The authors would like to thank the following organizations. • EU: The research leading to these results has received funding from the European Research Council under the European Ue- DATADRIVE-B (290923). This paper reflects only the authors’ views, the Union is not liable for any use that may be made of the contained information. • Research Council KUL: GOA/10/09 MaNet, CoE PFV/10/002 (OPTEC), BIL12/11T

Context: Search-based techniques have been applied to almost all areas in software engineering, especially to software testing, seeking to solve hard optimization problems. However, the problem of selecting mutants to improve the test suite at a lower cost has not been explored to the same extent as other problems, such as mutant selection for test suite evaluation or test data generation. Objective: In this paper, we apply search-based mutant selection to enhance the quality of test suites efficiently. Namely, we use the technique known as Evolutionary Mutation Testing (EMT), which allows reducing the number of mutants while preserving the power to refine the test suite. Despite reported benefits of its application, the existing empirical results were derived from a limited number of case studies, a particular set of mutation operators and a vague measure, which currently makes it difficult to determine the real performance of this technique. Method: This paper addresses the shortcomings of previous studies, providing a new methodology to evaluate EMT on the basis of the actual improvement of the test suite achieved by using the evolutionary strategy. We make use of that methodology in new experiments with a carefully selected set of real-world C++ case studies. Results: EMT shows a good performance for most case studies and levels of demand of test suite improvement (around 45% less mutants than random selection in the best case). The results reveal that even a reduced subset of mutants selected with EMT can serve to increase confidence in the test suite, especially in programs with a large set of mutants. Conclusions: These results support the use of search-based techniques to solve the problem of mutant selection for a more efficient test suite refinement. Additionally, we identify some aspects that could foreseeably help enhance EMT.

Many important linear sparse models have at its core the Lasso problem, for which the GLMNet algorithm is often considered as the current state of the art. Recently M. Jaggi has observed that Constrained Lasso (CL) can be reduced to an SVM-like problem, for which the LIBSVM library provides very efficient algorithms. This suggests that it could also be used advantageously to solve CL. In this work we will refine Jaggi’s arguments to reduce CL as well as constrained Elastic Net to a Nearest Point Problem, which in turn can be rewritten as an appropriate ν-SVM problem solvable by LIBSVM. We will also show experimentally that the well-known LIBSVM library results in a faster convergence than GLMNet for small problems and also, if properly adapted, for larger ones. Screening is another ingredient to speed up solving Lasso. Shrinking can be seen as the simpler alternative of SVM to screening and we will discuss how it also may in some cases reduce the cost of an SVM-based CL solution, With partial support from Spanish government grants TIN2013-42351-P, TIN2016-76406-P, TIN2015-70308-REDT and S2013/ICE-2845 CASI-CAM-CM; work also supported by project FACIL–Ayudas Fundación BBVA a Equipos de Investigación Científica 2016 and the UAM–ADIC Chair for Data Science and Machine Learning. The first author is also supported by the FPU–MEC grant AP-2012-5163. We gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM and thank Red Eléctrica de España for kindly supplying wind energy data

Performance bugs are common, costly, and elusive. Performance tests aim to detect performance bugs by running the program with specific inputs and determining whether the observed behaviour is acceptable. There not exist mechanisms, however, to assess the effectiveness of performance tests. Mutation testing is a technique to evaluate and enhance functional test suites by seeding artificial faults in the program under test. In this new idea paper, we explore the applicability of mutation testing to assess and improve performance tests. This novel approach is motivated with examples and open questions.

Model-Driven Engineering (MDE) uses models as its main assets in the software development process. The structure of a model is described through a metamodel. Even though modelling and meta-modelling are recurrent activities in MDE and a vast amount of MDE tools exist nowadays, they are tasks typically performed in an unassisted way. Usually, these tools cannot extract useful knowledge available in heterogeneous information sources like XML, RDF, CSV or other models and meta-models. We propose an approach to provide modelling and meta-modelling assistance. The approach gathers heterogeneous information sources in various technological spaces, and represents them uniformly in a common data model. This enables their uniform querying, by means of an extensible mechanism, which can make use of services, e.g., for synonym search and word sense analysis. The query results can then be easily incorporated into the (meta-)model being built. The approach has been realized in the Extremo tool, developed as an Eclipse plugin. Extremo has been validated in the context of two domains { production systems and process modelling { taking into account a large and complex industrial standard for classi cation and product description. Further validation results indicate that the integration of Extremo in various modelling environments can be achieved with low e ort, and that the tool is able to handle information from most existing technological spaces, This work was supported by the Ministry of Education of 1256 Spain (FPU grant FPU13/02698); the Spanish MINECO (TIN2014-52129-R);1257 the R&D programme of the Madrid Region (S2013/ICE-3006); the Austrian 1258 agency for international mobility and cooperation in education, science and re1259 search (OeAD) by funds from the Austrian Federal Ministry of Science, Research 1260 and Economy - BMWFW (ICM-2016-04969)

Internet of Things (IoT) has radically transformed the world; currently, every device can be connected to the Internet and provide valuable information for decision-making. In spite of the fast evolution of technologies accompanying the grow of IoT, we are still faced with the challenge of providing a service oriented architecture, which facilitates the inclusion of data coming together from several IoT devices, data delivery among a system's agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architectures still pose a challenge, in spite of being key requirements in order to get stronger IoT architectures. To face this challenge, we propose a COLLaborative ConText Aware Service Oriented Architecture (COLLECT), which facilitates both the integration of IoT heterogeneous domain context data — through the use of a light message broker — and easy data delivery among several agents and collaborative participants in the system — making use of an enterprise service bus —. In addition, this architecture provides real-time data processing thanks to the use of a complex event processing engine as well as services and intelligent decision-making procedures to users according to the needs of the domain in question. As a result, COLLECT has a great impact on context-aware decentralized and collaborative reasoning for IoT, promoting context-aware intelligent decision making in such scope. Since context-awareness is key for a wide range of recommender and intelligent systems, the presented novel solution improves decision making in a large number of fields where such systems require to promptly process a variety of ubiquitous collaborative and context-aware data.

Context: Mutation testing has been mainly analyzed regarding traditional mutation operators involving structured programming constructs common in mainstream languages, but mutations at the class level have not been assessed to the same extent. This fact is noteworthy in the case of C + + , despite being one of the most relevant languages including object-oriented features. Objective: This paper provides a complete evaluation of class operators for the C + + programming language. MuCPP, a new system devoted to the application of mutation testing to this language, was developed to this end. This mutation system implements class mutation operators in a robust way, dealing with the inherent complexity of the language. Method:MuCPP generates the mutants by traversing the abstract syntax tree of each translation unit with the Clang API, and stores mutants as branches in the Git version control system. The tool is able to detect duplicate mutants, avoid system headers, and drive the compilation process. Then, MuCPP is used to conduct experiments with several open-source C + + programs. Results: The improvement rules listed in this paper to reduce unproductive class mutants have a significant impact in the computational cost of the technique. We also calculate the quantity and distribution of mutants generated with class operators, which generate far fewer mutants than their traditional counterparts. Conclusions: We show that the tests accompanying these programs cannot detect faults related to particular object-oriented features of C + + . In order to increase the mutation score, we create new test scenarios to kill the surviving class mutants for all the applications. The results confirm that, while traditional mutation operators are still needed, class operators can complement them and help testers further improve the test suite.

Organizations all around the world need to manage huge amounts of data from heterogeneous sources every day in order to conduct decision making processes. This requires them to infer what the value of such data is for the business in question through data analysis as well as acting promptly for critical or relevant situations. Complex Event Processing (CEP) is a technology that helps tackle this issue by detecting event patterns in real time. However, this technology forces domain experts to define these patterns indicating such situations and the appropriate actions to be executed in their information systems, generally based on Service-Oriented Architectures (SOAs). In particular, these users face the incommodity of implementing these patterns manually or by using editors which are not user-friendly enough. To deal with this problem, a model-driven solution for real-time decision making in event-driven SOAs is proposed and conducted in this paper. This approach allows the integration of CEP with this architecture type as well as defining CEP domain and event pattern through a graphical and intuitive editor, which also permits automatic code generation. Moreover, the solution is evaluated and its benefits are discussed. As a result, we can assert this is a novel solution for bringing CEP technology closer to any user, positively impacting on business decision making processes.

Complex event processing (CEP) is a cutting-edge technology that allows the analysis and correlation of large volumes of data with the aim of detecting complex and meaningful events through the use of event patterns, as well as permitting the inference of valuable knowledge for end users. Despite the great advantages that CEP can bring to expert or intelligent business systems, it poses a substantial challenge to their users, who are business experts but do not have the necessary knowledge and experience using this technology. The main problem these users have to face is precisely hand-writing the code for event pattern definition, which requires them to implement the conditions to be met to detect relevant situations for the domain in question by using a particular event processing language (EPL). In order to respond to this need, in this paper we propose both a graphical domain-specific modeling language (DSML) for facilitating CEP domain definitions by domain experts, and a graphical DSML for event pattern definition by non-technological users. The proposed languages provide high expressiveness and flexibility and are independent of event patterns and actions’ implementation code. This way, domain experts can define the relevant event types and patterns within their business domain, without having to be experts on EPL programming, nor on other complicated computer science technological issues, beyond an understandable and intuitive graphical definition. Furthermore, with these DSMLs, users will also be able to define the actions to be automatically taken once a pattern is detected in the system. Further benefits of these DSMLs are evaluated and discussed in depth in this paper.

Complex Event Processing (CEP) is an emerging technology which allows us to efficiently process and correlate huge amounts of data in order to discover relevant or critical situations of interest (complex events) for a specific domain. This technology requires domain experts to define complex event patterns, where the conditions to be detected are specified by means of event processing languages. However, these experts face the handicap of defining such patterns with editors which are not user-friendly enough. To solve this problem, a model-driven approach for facilitating user-friendly design of complex event patterns is proposed and developed in this paper. Besides, the proposal has been applied to different domains and several event processing languages have been compared. As a result, we can affirm that the presented approach is independent both of the domain where CEP technology has to be applied to and of the concrete event processing language required for defining event patterns.

This is the author’s version of a work that was accepted for publication in Biosystems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Biosystems, 111, 2, (2013) DOI: 10.1016/j.biosystems.2012.12.004, This paper describes the use of grammatical evolution to obtain an ecology of artificial beings associated with mathematical functions, whose fitness is also defined mathematically. The system allows “parasite” species and “parasites of parasites” to develop, and supports the simultaneous evolution of several ecological niches. The use of standard measurements makes it possible to explore the influence of the number of niches or the presence of parasites on “biological” diversity and similar functions. Our results suggest that some of the features of biological evolution depend more on the genetic substrate and natural selection than on the actual phenotypic expression of that substrate.

This is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence,19, 2 (2006) DOI: 10.1016/j.engappai.2005.07.002, SkeletonAgent is an agent framework whose main feature is to integrate different artificial intelligent skills, like planning or learning, to obtain new behaviours in a multi agent environment. This framework has been previously instantiated in a deliberative domain (electronic tourism), where planning was used to integrate Web information in a tourist plan. RoboSkeleton results from the instantiation of the same framework, SkeletonAgent, in a very different domain, the robot soccer. This paper shows how this architecture is used to obtain collaborative behaviours in a reactive domain. The paper describes how the different modules of the architecture for the robot soccer agents are designed, directly showing the flexibility of our framework

This is the author’s version of a work that was accepted for publication in Biosystems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Biosystems, 66, 1-2, (2002) DOI: 10.1016/S0303-2647(02)00018-7, This paper presents simulation results of artificial foraging agent communities. The goal of each agent in the community is to find food. Once a food source is found, agents eat portions of it and carry some other portions to the nest (in a manner similar to ants) until the food is depleted. Agents may also communicate food positions when they are near each other. They are given a set of genes that control several characteristics, such as their activity, memory, scepticism, lying, etc. These genes are recombined and propagated by means of sexual reproduction. When one nest is superpopulated with agents, it can break in two nests. Agents can communicate only with those belonging to the same nest, which gives rise to emergent situations of competition and cooperation between the agents in the same nest, as well as competition between different nests. Other emergent phenomena such as the propagation of rumours are also studied.

This is the author’s version of a work that was accepted for publication in Computers & Graphics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Graphics 26, 1, (2002) DOI: 10.1016/S0097-8493(01)00162-5, L Systems have proved their expressive power. They have been used to represent the class of the initiator/iterator fractal curves (such as Sierpinski's gasket and von Koch's snowflake curve). Parametric L Systems, introduced by Prusinkiewicz and Lindenmayer, link real valued parameters to the symbols. In this paper, parametric 0L systems are extended to n dimensions and used to represent a different class of classic fractals that includes objects such the Mandelbrot and Julia sets, or Pickover’s biomorphs.