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Outlier detection, i.e., the task of detecting points that are markedly different from the data sample, is an important challenge in machine learning. When a model is built, these special points can skew the model training and result in less accurate predictions. Due to this fact, it is important to identify and remove them before building any supervised model and this is often the first step when dealing with a machine learning problem. Nowadays, there exists a very large number of outlier detector algorithms that provide good results, but their main drawbacks are their unsupervised nature together with the hyperparameters that must be properly set for obtaining good performance. In this work, a new supervised outlier estimator is proposed. This is done by pipelining an outlier detector with a following a supervised model, in such a way that the targets of the later supervise how all the hyperparameters involved in the outlier detector are optimally selected. This pipeline-based approach makes it very easy to combine different outlier detectors with different classifiers and regressors. In the experiments done, nine relevant outlier detectors have been combined with three regressors over eight regression problems as well as with two classifiers over another eight binary and multi-class classification problems. The usefulness of the proposal as an objective and automatic way to optimally determine detector hyperparameters has been proven and the effectiveness of the nine outlier detectors has also been analyzed and compared, 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 projects TIN2016-76406-P (AEI/FEDER, UE) and 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 Computación Científica (CCC) at UAM

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

Lecturers are often reluctant to set examinations online because of the potential problems of fraudulent behaviour from their students. This concern has increased during the coronavirus pandemic because courses that were previously designed to be taken face-to-face have to be conducted online. The courses have had to be redesigned, including seminars, laboratory sessions and evaluation activities. This has brought lecturers and students into conflict because, according to the students, the activities and examinations that have been redesigned to avoid cheating are also harder. The lecturers' concern is that students can collaborate in taking examinations that must be taken individually without the lecturers being able to do anything to prevent it, i.e. fraudulent collaboration. This research proposes a process model to obtain evidence of students who attempt to fraudulently collaborate, based on the information in the learning environment logs. It is automated in a software tool that checks how the students took the examinations and the grades that they obtained. It is applied in a case study with more than 100 undergraduate students. The results are positive and its use allowed lecturers to detect evidence of fraudulent collaboration by several clusters of students from their submission timestamps and the grades obtained., This research was funded by Spanish National Research Agency (AEI), through the project VISAIGLE (TIN2017-85797-R) with ERDF funds.

Autonomous robotic search problems deal with different levels of uncertainty. When uncertainty is low, deterministic strategies employing available knowledge result in most effective searches. However, there are domains where uncertainty is always high since information about robot location, environment boundaries or precise reference points is unattainable, e.g., in cave, deep ocean, planetary exploration, or upon sensor or communications impairment. Furthermore, latency regarding when search targets move, appear or disappear add to uncertainty sources. Here we study intrinsic and environmental factors that affect low-informed robotic search based on diffusive Brownian, naive ballistic, and superdiffusive strategies (Lévy walks), and in particular, the effectiveness of their random exploration. Representative strategies were evaluated considering both intrinsic (motion drift, energy or memory limitations) and extrinsic factors (obstacles and search boundaries). Our results point towards minimum-knowledge based modulation approaches that can adjust distinct spatial and temporal aspects of random exploration to lead to effective autonomous search under uncertainty, This work was supported in part by Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER), under Grants PGC2018-095895-B-I00, TIN2017-84452-R, and PID2020-114867RB-I00

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]

Quite often a machine learning problem lends itself to be split in several well-defined subproblems, or tasks. The goal of Multi-Task Learning (MTL) is to leverage the joint learning of the problem from two different perspectives: on the one hand, a single, overall model, and on the other hand task-specific models. In this way, the found solution by MTL may be better than those of either the common or the task-specific models. Starting with the work of Evgeniou et al., support vector machines (SVMs) have lent themselves naturally to this approach. This paper proposes a convex formulation of MTL for the L1-, L2- and LS-SVM models that results in dual problems quite similar to the single-task ones, but with multi-task kernels; in turn, this makes possible to train the convex MTL models using standard solvers. As an alternative approach, the direct optimal combination of the already trained common and task-specific models can also be considered. In this paper, a procedure to compute the optimal combining parameter with respect to four different error functions is derived. As shown experimentally, the proposed convex MTL approach performs generally better than the alternative optimal convex combination, and both of them are better than the straight use of either common or task-specific models, With partial support from Spain’s grant TIN2016-76406-P. Work supported also by the UAM–ADIC Chair for Data Science and Machine Learning.

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)

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11257-020-09285-1, One common characteristic of research works focused on fairness evaluation (in machine learning) is that they call for some form of parity (equality) either in treatment—meaning they ignore the information about users’ memberships in protected classes during training—or in impact—by enforcing proportional beneficial outcomes to users in different protected classes. In the recommender systems community, fairness has been studied with respect to both users’ and items’ memberships in protected classes defined by some sensitive attributes (e.g., gender or race for users, revenue in a multi-stakeholder setting for items). Again here, the concept has been commonly interpreted as some form of equality—i.e., the degree to which the system is meeting the information needs of all its users in an equal sense. In this work, we propose a probabilistic framework based on generalized cross entropy (GCE) to measure fairness of a given recommendation model. The framework comes with a suite of advantages: first, it allows the system designer to define and measure fairness for both users and items and can be applied to any classification task; second, it can incorporate various notions of fairness as it does not rely on specific and predefined probability distributions and they can be defined at design time; finally, in its design it uses a gain factor, which can be flexibly defined to contemplate different accuracy-related metrics to measure fairness upon decision-support metrics (e.g., precision, recall) or rank-based measures (e.g., NDCG, MAP). An experimental evaluation on four real-world datasets shows the nuances captured by our proposed metric regarding fairness on different user and item attributes, where nearest-neighbor recommenders tend to obtain good results under equality constraints. We observed that when the users are clustered based on both their interaction with the system and other sensitive attributes, such as age or gender, algorithms with similar performance values get different behaviors with respect to user fairness due to the different way they process data for each user cluster, The authors thank the reviewers for their thoughtful comments and suggestions. This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades (Reference: 123496 Y. Deldjoo et al. PID2019-108965GB-I00) and in part by the Center for Intelligent Information Retrieval. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors

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.

Nowadays, having a mechanism to guarantee the traceability of the information and to monitor the evolution of the news from its origin, and having elements to know the reputation and credibility of the media, analyze the news as well as its evolution and possible manipulation, etc. is becoming increasingly significant. Transparency in journalism is currently a key element in performing serious and rigorous journalism. End-users and factchecking agencies need to be able to check and verify the information published in different media. This transparency principle enables the tracking of news stories and allows direct access to the source of essential content to contrast the information it contains and to know whether it has been manipulated. Additionally, the traceability of news constitutes another instrument in the fight against the lack of credibility, the manipulation of information, misinformation campaigns and the propagation of fake news. This article aims to show how to use Blockchain to facilitate the tracking and traceability of news so that it can provide support to the automatic indexing and extraction of relevant information from newspaper articles to facilitate the monitoring of the news story and allows users to verify the veracity of what they are reading, This research work has been funded by the Madrid Regional Government through the project e-Madrid-CM (P2018/TCS-4307). The e-Madrid-CM project is also co-financed by the Structural Funds (FSE and FEDER)

Social media platforms have evolved into an online representation of our social interactions. We may use the resources they provide to analyze phenomena that occur within them, such as the development and viralization of offensive and hostile content. In today’s polarized world, the escalating nature of this behavior is cause for concern in modern society. This research includes an in-depth examination of previous efforts and strategies for detecting and preventing hateful content on the social network Twitter, as well as a novel classification approach based on users’ profiles, related social environment and generated tweets. This paper’s contribution is threefold: (i) an improvement in the performance of the HaterNet algorithm, an expert system developed in collaboration with the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security (Ministry of the Interior) that is capable of identifying and monitoring the evolution of hate speech on Twitter using an LTSM + MLP neural network architecture. To that end, a model based on BERT, HaterBERT, has been created and tested using HaterNet’s public dataset, providing results that show a significant improvement; (ii) A methodology to create a user database in the form of a relational network to infer textual and centrality features. This contribution, SocialGraph, has been independently tested with various traditional Machine Learning and Deep Learning algorithms, demonstrating its usefulness in spotting haters; (iii) a final model, SocialHaterBERT, that integrates the previous two approaches by analyzing features other than those inherent in the text. Experiment results reveal that this last contribution greatly improves outcomes, establishing a new field of study that transcends textual boundaries, paving the way for future research in coupled models from a diachronic and dynamic perspective, The research of Quijano-Sánchez was conducted with financial support from the Spanish Ministry of Science and Innovation, grant PID2019-108965GBI00. The research of Liberatore is partially funded by the European Commission’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie, grant number MSCA-RISE 691161 (GEOSAFE), and the Government of Spain, grant MTM2015-65803-R. All the financial support is gratefully acknowledged

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

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].

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

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)

Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying di erent types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using di erent sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include di erent parameters to control the e - ciency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent ( -matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm. We evaluate our approach with several state-of-the-art recommendation algorithms using di erent evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach o ers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited, This article has been co-funded by the European Social Fund (ESF) within the 2017 call for predoctoral contracts and the Spanish Ministry of Economy, Industry and Competitiveness (project reference: TIN2016-80630-P)

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).

Recommender Systems have to deal with a wide variety of users and user types that express their preferences in di erent ways. This di erence in user behavior can have a profound impact on the performance of the recommender system. Users receive better (or worse) recommendations depending on the quantity and the quality of the information the system knows about them. Speci cally, the inconsistencies in users' preferences impose a lower bound on the error the system may achieve when predicting ratings for one particular user { this is referred to as the magic barrier. In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are a icted with inconsistencies { noise. Furthermore, we propose a measure of the consistency of user ratings (rating coherence) that predicts the performance of recommendation methods. More speci cally, we show that user coherence is correlated with the magic barrier; we exploit this correlation to discriminate between easy users (those with a lower magic barrier) and di cult ones (those with a higher magic barrier). We report experiments where the recommendation error for the more coherent users is lower than that of the less coherent ones. We further validate these results by using two public datasets, where the necessary data to identify the magic barrier is not available, in which we obtain similar performance improvements, This research was in part supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P)

Recently, very thin AlAs capping layers (CLs) have been proposed as a useful tool to increase the performance of InAs/GaAs quantum dot (QDs) devices. However, the structure of QDs after AlAs deposition remains poorly understood and the mechanisms to explain it are often contradictory. In this work, the structural and compositional changes of InAs QDs using different AlAs CL thicknesses have been studied by state-of-the-art STEM-related techniques. First, the heights and In contents of InAs QDs progressively increase with the CL thickness, demonstrating that the AlAs capping produces a strong shielding effect against the decomposition of QDs. However, QD populations for CL thicknesses above 5 ML split into a bimodal distribution in which smaller lenticular QDs cohabit with bigger truncated pyramids. Second, the actual Al contents around the QDs are well below the nominal design, but increasing for thicker CLs. Its distribution is initially non-uniform, tending to accumulate on the flanks of the QDs to the detriment of the apex. Only for thicknesses above 2 ML the Al contents around the QDs start to be similar to those in the regions between the QDs, behaving as a continuous film without irregularities from 5 ML onwards. © 2021 The Author(s), The work has been co-financed by the Spanish National Research Agency (AEI projects MAT2016-77491-C2-2-R and PID2019-106088RB-C33), Regional Government of Andalusia (project FEDER-UCA18-108319) and the European Regional Development Fund (ERDF).

Since Massive Open Online Courses (MOOCs) became a global phenomenon in 2012, there has been constant evolution in the way Higher Education Institutions (HEIs) make sense of them. HEIs embracing MOOCs have dedicated a variety of human resource to this venture. Only in a minority of cases, staff have been appointed exclusively to this role. In all other cases, MOOC related tasks have been allocated to professionals who were already performing other educational tasks. This article contains a study that captures the experiences of these professionals in a Spanish university and a British university, as relates to their involvement in MOOCs. Interviews and group sessions were conducted to ascertain the influence of MOOCs in their practice, and in their opinions about the role of MOOCs in their institutions. The results seem to suggest that participants have positive attitudes towards incorporating MOOCs at the university, although they demand a serious bet for this educational approach from the strategic decision makers in the institutions., Work partially funded by the Madrid Regional Government from grant no. S2013/ICE-2715, the Spanish Ministry of Economy and Competitiveness project Flexor (TIN2014-52129-R)

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11063-017-9613-7, Deep Learning models are recently receiving a large attention because of their very powerful modeling abilities, particularly on inputs that have a intrinsic one- or two-dimensional structure that can be captured and exploited by convolutional layers. In this work we will apply Deep Neural Networks (DNNs) in two problems, wind energy and daily solar radiation prediction, whose inputs, derived from Numerical Weather Prediction systems, have a clear spatial structure. As we shall see, the predictions of single deep models and, more so, of DNN ensembles can improve on those of Support Vector Regression, a Machine Learning method that can be considered the state of the art for regression, With partial support from Spain’s Grants TIN2013-42351-P (MINECO), S2013/ICE2845 CASI-CAM-CM (Comunidad de Madrid), FACIL (Ayudas Fundación BBVA a Equipos de Investigación Científica 2016) and the UAM–ADIC Chair for Data Science and Machine Learning. The second author is also kindly supported by the FPU-MEC Grant AP-2012-5163. The authors gratefully acknowledge access to the MARS repository granted by the ECMWF, the use of the facilities of Centro de Computación Científica (CCC) at UAM and thank Red Eléctrica de España for kindly supplying wind energy production data and to Sotavento for making their production data publicly available.

Visible-Short Wave Near Infrared (VIS + SW &minus, NIR) spectroscopy is a real alternative to break down the next barrier in precision viticulture allowing a reliable monitoring of grape composition within the vineyard to facilitate the decision-making process dealing with grape quality sorting and harvest scheduling, for example. On-the-go spectral measurements of grape clusters were acquired in the field using a VIS + SW &minus, NIR spectrometer, operating in the 570&ndash, 990 nm spectral range, from a motorized platform moving at 5 km/h. Spectral measurements were acquired along four dates during grape ripening in 2017 on the east side of the canopy, which had been partially defoliated at cluster closure. Over the whole measuring season, a total of 144 experimental blocks were monitored, sampled and their fruit analyzed for total soluble solids (TSS), anthocyanin and total polyphenols concentrations using standard, wet chemistry reference methods. Partial Least Squares (PLS) regression was used as the algorithm for training the grape composition parameters&rsquo, prediction models. The best cross-validation and external validation (prediction) models yielded determination coefficients of cross-validation (R2cv) and prediction (R2P) of 0.92 and 0.95 for TSS, R2cv = 0.75, and R2p = 0.79 for anthocyanins, and R2cv = 0.42 and R2p = 0.43 for total polyphenols. The vineyard variability maps generated for the different dates using this technology illustrate the capability to monitor the spatiotemporal dynamics and distribution of total soluble solids, anthocyanins and total polyphenols along grape ripening in a commercial vineyard.

Objectives This paper focuses on the issue of intimate partner violence and, specifically, on the distribution of femicides over time and the existence of copycat effects. This is the subject of an ongoing debate often triggered by the social alarm following multiple intimate partner homicides (IPHs) occurring in a short span of time. The aim of this research is to study the evolution of IPHs and provide a far-reaching answer by rigorously analyzing and searching for patterns in data on femicides. Methods The study analyzes an official dataset, provided by the system VioGén of the Secretaría de Estado de Seguridad (Spanish State Secretariat for Security), including all the femicides occurred in Spain in 2007-2017. A statistical methodology to identify temporal interdepen-dencies in count time series is proposed and applied to the dataset. The same methodology can be applied to other contexts. Results There has been a decreasing trend in the number of femicides per year. No interdependen-cies among the temporal distribution of femicides are observed. Therefore, according to data, the existence of copycat effect in femicides cannot be claimed. Conclusions Around 2011 there was a clear change in the average number of femicides which has not picked up. Results allow for an informed answer to the debate on copycat effect in Spanish femicides. The planning of femicides prevention activities should not be a reaction to a perceived increase in their occurrence. As a copycat effect is not detected in the studied time period, there is no evidence supporting the need to censor media reports on femicides., The work by Torrecilla has been partially supported by Spanish Grant MTM2016-78751-P. The research of Liberatore has been supported by the Government of Spain, grant MTM2015-65803-R, and by the Government of Madrid, grant S2013/ICE-2845

[EN] Nowadays, there are a large number of unmanned aircraft on the market that can be commanded with high-level orders to perform complex tasks almost automatically, such as mapping crop fields. We can ask ourselves if it would be possible to coordinate a group of these robots to perform those same tasks more quickly, flexibly and robustly. In this work, we summarize the tasks that have been studied to be solved with systems composed by groups of unmanned aircraft and the algorithms used, as well as the methods and strategies on which they are based. Although the future of these systems is promising, there are certain legislative and technical obstacles that stop their implementation in a generalized way., [ES] A día de hoy, existen en el mercado una gran cantidad de aeronaves sin piloto que pueden ser comandadas con ordenes de alto nivel para realizar tareas complejas de forma casi automatica, como por ejemplo el mapeo de explotaciones agrícolas. De forma natural, nos podemos preguntar si sería posible coordinar a un grupo de estos robots para realizar esas mismas tareas de forma más rápida, flexible y robusta. En este trabajo se repasan las tareas que se han planteado resolver con sistemas compuestos por grupos de aeronaves no tripuladas y los algoritmos empleados, así como los metodos y estrategias en los que están basados. Aunque el futuro de estos sistemas es prometedor, existen ciertos obstaculos legislativos y técnicos que frenan su implantación de forma generalizada., Las investigaciones que han dado como resultado este trabajo han sido financiadas por RoboCity2030-DIH-CM, 426 Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, financiadas por los Programas de Actividades I+D en la Comunidad Madrid, y por el proyecto TASAR (Team of Advanced Search And Rescue Robots), PID2019-105808RB-I00, financiado por el Ministerio de Ciencia e Innovacion (Gobierno de España).

This is the author’s version of a work that was accepted for publication in Information Systems. 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 Information Systems, VOL 62, (2016)] DOI 10.1016/j.is.2016.06.008, Meta-models play a cornerstone role in Model-Driven Engineering as they are used to define the abstract syntax of modelling languages, and so models and all sorts of model transformations depend on them. However, there are scarce tools and methods supporting their Validation and Verification (V&V), which are essential activities for the proper engineering of meta-models. In order to fill this gap, we propose two complementary meta-model V&V languages. The first one has similar philosophy to the xUnit framework, as it enables the definition of meta-model unit test suites comprising model fragments and assertions on their (in-)correctness. The second one is directed to express and verify expected properties of a meta-model, including domain and design properties, quality criteria and platform-specific requirements. As a proof of concept, we have developed tooling for both languages in the Eclipse platform, and illustrate its use within an example-driven approach for meta-model construction. The expressiveness of our languages is demonstrated by their application to build a library of meta-model quality issues, which has been evaluated over the ATL zoo of meta-models and some OMG specifications. The results show that integrated support for meta-model V&V (as the one we propose here) is urgently needed in meta-modelling environments., This work has been funded by the Spanish Ministry of Economy and Competitivity with project “Flexor” (TIN2014-52129-R), the region of Madrid with project “SICOMORO-CM” (S2013/ICE-3006), and the EU commission with project “MONDO” (FP7- ICT-2013-10, #611125).

The study of tumor growth biology with computer-based models is currently an area of active research. Different simulation techniques can be used to describe the complexity of any real tumor behavior, among these, "cellular automata"-based simulations provide an accurate tumor growth graphical representation while, at the same time, keep simpler the implementation of the automata as computer programs. Several authors have recently published relevant proposals, based on the latter approach, to solve tumor growth representation problem through the development of some strategies for accelerating the simulation model. These strategies achieve computational performance of cellular-models representation by the appropriate selection of data types, and the clever use of supporting data structures. However, as of today, multithreaded processing techniques and multicore processors have not been used to program cellular growth models with generality. This paper presents a new model that incorporates parallel programming for multi and manycore processors, and implements any synchronization requirement necessary to implement the solution. The proposed parallel model has been proved using Java and C++ program implementations on two different platforms: chipset Intel i5-4440 and one node of 16-processors cluster of our university. The improvement resulting from the introduction of parallelism into the model is analyzed in this paper, comparing it with the standard sequential simulation model currently used by researchers in mathematical oncology.

Blockchain provides an immutable distributed ledger for storing transactions. One of the challenges of blockchain is the particular processing of dynamic queries due to accumulating costs. Complex Event Processing (CEP) provides efficient and effective support for this in a way, however, that is difficult to integrate with blockchain. This paper addresses the research challenges of integrating blockchain with CEP. More specifically, we envision an effective development environment in which (i) event-driven smart contracts are modeled in a graphical way, which are, in turn, (ii) automatically transformed into complementary code that is deployed in both a CEP engine and a blockchain network, and then (iii) executed on off-chain CEP applications which, connected to different data sources and sinks, automatically invoke smart contracts when event pattern conditions are met. We follow a classic systems engineering approach for defining the concepts of our system, called CEPchain, which addresses the described requirements. CEPchain was evaluated using a real-world case study for vaccine delivery, which requires an unbroken cold chain. The results demonstrate that our approach can be applied without requiring experts on event processing and smart contract languages. Our contribution simplifies the design of integrated CEP and blockchain functionality by hiding implementation details and supporting efficient deployment., This work was supported by the Spanish Ministry of Science and Innovation under the ‘‘Estancias de movilidad en el extranjero José Castillejo para jóvenes doctores’’ program [grant number CAS19/00241], and the Spanish Ministry of Science and Innovation and the European Regional Development Funds under project FAME [grant number RTI2018-093608-B-C33]. The authors would like to thank Orlenys López-Pintado for his help with the Caterpillar tool and his insightful comments. Juan Boubeta-Puig would also like to thank the Institute for Information Business for their hospitality when visiting them at the Vienna University of Economics and Business, Austria, where part of this work was developed.

Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry to apply different types of robots in the context of firefighting missions. Finally, all this information is used to propose a concept of operation for the comprehensive application of drone swarms in firefighting. The proposed system is a fleet of quadcopters that individually are only able to visit waypoints and use payloads, but collectively can perform tasks of surveillance, mapping, monitoring, etc. Three operator roles are defined, each one with different access to information and functions in the mission: Mission commander, team leaders, and team members. These operators take advantage of virtual and augmented reality interfaces to intuitively get the information of the scenario and, in the case of the mission commander, control the drone swarm, This research received no external funding

An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a three-step procedure, combining the strengths of linear and non-linear models and the capability of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages (SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of each cluster. These values are finally used as inputs of a support vector regression (SVR) model together with the historical data of the cluster. The final prediction result integrates the prediction results of each cluster. The experimental results showed that the proposed model provided accurate prediction results and outperforms the rest of the models tested. The proposed model can be used as an automatic decision-making tool by seaport management due to its capacity to plan resources in advance, avoiding congestion and time delays.

This paper describes the design and validation of a game based on a platform for easy deployment of collaborative educational games, named BECO Games platform. As an example of its potential, a learning experience for an Economics subject was created through a collaborative game to understand the concept of common goods. The effectiveness of the game was tested by comparing the performance of Bachelor students who used the platform and those who did not (137 students vs. 92 students). In addition, it was controlled that in previous years when students played the game through forums and an Excel sheet, these differences did not exist. Results indicate that the performance differences between students who participated in the online game and those who did not were greater than in previous years. In addition, a satisfaction survey was delivered to the students to understand their impressions better. This survey assessed student opinion about the platform, about the educational experience, and about their behavior during the game.

Decisions concerning crucial and complicated problems are seldom made by a single person. Instead, they require the cooperation of a group of experts in which each participant has their own individual opinions, motivations, background, and interests regarding the existing alternatives. In the last 30 years, much research has been undertaken to provide automated assistance to reach a consensual solution supported by most of the group members. Artificial intelligence techniques are commonly applied to tackle critical group decision-making difficulties. For instance, experts’ preferences are often vague and imprecise; hence, their opinions are combined using fuzzy linguistic approaches. This paper reports a bibliometric analysis of the ample literature published in this regard. In particular, our analysis: (i) shows the impact and upswing publication trend on this topic; (ii) identifies the most productive authors, institutions, and countries; (iii) discusses authors’ and journals’ productivity patterns; and (iv) recognizes the most relevant research topics and how the interest on them has evolved over the years.

The use of the photoacoustic effect to gauge the concentration of gases is an attractive alternative in the realm of optical detection methods. Even though the effect has been applied for gas sensing for almost a century, its potential for ultra-sensitive and miniaturized devices is still not fully explored. This review article revisits two fundamentally different setups commonly used to build photoacoustic-based gas sensors and presents some distinguished results in terms of sensitivity, ultra-low detection limits, and miniaturization. The review contrasts the two setups in terms of the respective possibilities to tune the selectivity, sensitivity, and potential for miniaturization., S.P. acknowledges funding from the Community of Madrid under grant number 2016-T1/AMB-1695.

Electron microscopy of macromolecular structures is an approach that is in increasing demand in the field of structural biology. The automation of image acquisition has greatly increased the potential throughput of electron microscopy. Here, the focus is on the possibilities in Scipion to implement flexible and robust image-processing workflows that allow the electron-microscope operator and the user to monitor the quality of image acquisition, assessing very simple acquisition measures or obtaining a first estimate of the initial volume, or the data resolution and heterogeneity, without any need for programming skills. These workflows can implement intelligent automatic decisions and they can warn the user of possible acquisition failures. These concepts are illustrated by analysis of the well known 2.2 angstrom resolution beta-galactosidase data set., Spanish Ministry of Economy and Competitiveness through the BIO2016-76400-R (AEI/FEDER, UE) grant, the Comunidad Auto ´noma de Madrid through grant S2017/BMD3817, the Instituto de Salud Carlos III (PT17/0009/0010), the European Union (EU) and Horizon 2020 through the CORBEL

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

This is the author’s version of a work that was accepted for publication in Applied Soft Computing. 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 Applied Soft Computing, Vol 36 (2015) DOI 10.1016/j.asoc.2015.06.053, A memetic approach that combines a genetic algorithm (GA) and quadratic programming is used to address the problem of optimal portfolio selection with cardinality constraints and piecewise linear transaction costs. The framework used is an extension of the standard Markowitz mean–variance model that incorporates realistic constraints, such as upper and lower bounds for investment in individual assets and/or groups of assets, and minimum trading restrictions. The inclusion of constraints that limit the number of assets in the final portfolio and piecewise linear transaction costs transforms the selection of optimal portfolios into a mixed-integer quadratic problem, which cannot be solved by standard optimization techniques. We propose to use a genetic algorithm in which the candidate portfolios are encoded using a set representation to handle the combinatorial aspect of the optimization problem. Besides specifying which assets are included in the portfolio, this representation includes attributes that encode the trading operation (sell/hold/buy) performed when the portfolio is rebalanced. The results of this hybrid method are benchmarked against a range of investment strategies (passive management, the equally weighted portfolio, the minimum variance portfolio, optimal portfolios without cardinality constraints, ignoring transaction costs or obtained with L1 regularization) using publicly available data. The transaction costs and the cardinality constraints provide regularization mechanisms that generally improve the out-of-sample performance of the selected portfolios.

This is the author’s version of a work that was accepted for publication in Neurocomputing. 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 Neurocomputing, VOL 163, (2015) DOI 10.1016/j.neucom.2014.08.090, The growing interest in big data problems implies the need for unsupervised methods for data visualization and dimensionality reduction. Diffusion Maps (DM) is a recent technique that can capture the lower dimensional geometric structure underlying the sample patterns in a way which can be made to be independent of the sampling distribution. Moreover, DM allows us to define an embedding whose Euclidean metric relates to the sample's intrinsic one which, in turn, enables a principled application of k-means clustering. In this work we give a self-contained review of DM and discuss two methods to compute the DM embedding coordinates to new out-of-sample data. Then, we will apply them on two meteorological data problems that involve time and spatial compression of numerical weather forecasts and show how DM is capable to, first, greatly reduce the initial dimension while still capturing relevant information in the original data and, also, how the sample-derived DM embedding coordinates can be extended to new patterns., The authors acknowledge partial support from Spain's grant TIN2010-21575-C02-01 and the UAM{ADIC Chair for Machine Learning. The first author is also supported by an FPI{UAM grant and kindly thanks the Applied Mathematics Department of Yale University for receiving her during her visits.

© 2015 by MDPI (http://www.mdpi.org). Reproduction is permitted for noncommercial purposes., In this paper, we present Virtual Touch, a toolkit that allows the development of educational activities through a mixed reality environment such that, using various tangible elements, the interconnection of a virtual world with the real world is enabled. The main goal of Virtual Touch is to facilitate the installation, configuration and programming of different types of technologies, abstracting the creator of educational applications from the technical details involving the use of tangible interfaces and virtual worlds. Therefore, it is specially designed to enable teachers to themselves create educational activities for their students in a simple way, taking into account that teachers generally lack advanced knowledge in computer programming and electronics. The toolkit has been used to develop various educational applications that have been tested in two secondary education high schools in Spain., We want to thank the IES Ernest Lluch and the IES Joan Coromines for all their support during the experience. The work reported in this paper was partially funded by the Spanish Research plan (project TIN2013-44586-R) and by the Madrid Research Plan (project S2013/ICE-2715).

This is the author’s version of a work that was accepted for publication in Neurocomputing. 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 Neurocomputing, VOL 160 (2015) DOI 10.1016/j.neucom.2014.12.086, Subsampling is used to generate bagging ensembles that are accurate and robust to class-label noise. The effect of using smaller bootstrap samples to train the base learners is to make the ensemble more diverse. As a result, the classification margins tend to decrease. In spite of having small margins, these ensembles can be robust to class-label noise. The validity of these observations is illustrated in a wide range of synthetic and real-world classification tasks. In the problems investigated, subsampling significantly outperforms standard bagging for different amounts of class-label noise. By contrast, the effectiveness of subsampling in random forest is problem dependent. In these types of ensembles the best overall accuracy is obtained when the random trees are built on bootstrap samples of the same size as the original training data. Nevertheless, subsampling becomes more effective as the amount of class-label noise increases., The authors acknowledge financial support from Spanish Plan Nacional I+D+i Grant TIN2013-42351-P and from Comunidad de Madrid Grant S2013/ICE-2845 CASI-CAM-CM.

© 2015 by MDPI (http://www.mdpi.org). Reproduction is permitted for noncommercial purposes., This paper presents a proof of concept from which the metaphor of fair trade is validated as an alternative to manage the private information of users. Our privacy solution deals with user's privacy as a tradable good for obtaining environmental services. Thus, users gain access to more valuable services as they share more personal information. This strategy, combined with optimistic access control and transaction registry mechanisms, enhances users' confidence in the system while encouraging them to share their information, with the consequent benefit for the community. The study results are promising considering the user responses regarding the usefulness, ease of use, information classification and perception of control with the mechanisms proposed by the metaphor., The work reported in this paper was partially funded by the Spanish “Ministerio de Economía y Competitividad” (Project TIN2013-44586-R) and by the “Comunidad Autonoma de Madrid” (Project S2013/ICE-2715).

Electronic version of an article published as International Journal of Software Engineering and Knowledge Engineering, Volume 25, Issue 04, May 2015, 727 http://dx.doi.org/10.1142/S0218194015500084 © World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijseke, Usability is a software system quality attribute. Although software engineers originally considered usability to be related exclusively to the user interface, it was later found to affect the core functionality of software applications. As of then, proposals for addressing usability at different stages of the software development cycle were researched. The objective of this paper is to present three reusable solutions at detailed design and programming level in order to effectively implement the Abort Operation, Progress Feedback and Preferences usability functionalities in web applications. To do this, an inductive research method was applied. We developed three web applications including the above usability functionalities as case studies. We looked for commonalities across the implementations in order to induce a general solution. The elements common to all three developed applications include: application scenarios, functionalities, responsibilities, classes, methods, attributes and code snippets. The findings were specified as an implementation-oriented design pattern and as programming patterns in three languages. Additional case studies were conducted in order to validate the proposed solution. The independent developers used the patterns to implement different applications for each case study. As a result, we found that solutions specified as patterns can be reused to develop web applications., This work has been funded by the Spanish Ministry of Science and Innovation “Tecnologías para la Replicación y Síntesis de Experimentos en IS” (TIN2011-23216) and “Go Lite” (TIN2011-24139) projects.

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

Open Access funded by Medical Research Council, The MRC binary file format is widely used in the three-dimensional electron microscopy field for storing image and volume data. Files contain a header which describes the kind of data held, together with other important metadata. In response to advances in electron microscopy techniques, a number of variants to the file format have emerged which contain useful additional data, but which limit interoperability between different software packages. Following extensive discussions, the authors, who represent leading software packages in the field, propose a set of extensions to the MRC format standard designed to accommodate these variants, while restoring interoperability. The MRC format is equivalent to the map format used in the CCP4 suite for macromolecular crystallography, and the proposal also maintains interoperability with crystallography software. This Technical Note describes the proposed extensions, and serves as a reference for the standard., We thank Chris Booth and Steffen Meyer from Gatan Inc. for clarifying the format definition used by Digital Micrograph. Acknowledgement for support from National Institute of Health, USA includes: NIGMS grant P41GM103310 (AC and SD), NIBIB grant 5R01-EB005027 (DM), and R01GM080139 (SJL). RH and MW would like to thank the UK Medical Research Council for the award of Partnership Grant MR/J000825/1 to support the establishment of CCP-EM. RH and JS are also supported by MRC grant U105184322.

The final publication is available at Springer via http://dx.doi.org/10.1007/s00165-014-0307-x, Metamodelling is one of the pillars of model-driven engineering, used for language engineering and domain modelling. Even though metamodelling is traditionally based on a two-metalevel approach, several researchers have pointed out limitations of this solution and proposed an alternative deep (also called multi-level) approach to obtain simpler system specifications. However, this approach currently lacks a formalisation that can be used to explain fundamental concepts such as deep characterisation, double linguistic/ontological typing and linguistic extension. This paper provides such a formalisation based on the Diagram Predicate Framework, and discusses its practical realisation in the metaDepth tool., This work was partially funded by the SpanishMinistry of Economy and Competitiveness (project “Go Lite” TIN2011- 24139).

The final publication is available at Springer via http://dx.doi.org/10.1007/s40558-014-0004-x, Place is a notion closely linked with the wealth of human experience, and invested by values, attitudes, and cultural influences. In particular, many places are strongly related to music, which contributes to shaping the perception and meaning of a place. In this paper we propose a computational approach to identify musicians and music suited for a place of interest (POI)––which is based on a knowledge-based framework built upon the DBpedia ontology––and a graph-based algorithm that scores musicians with respect to their semantic relatedness with a POI and suggests the top scoring ones. Through empirical experiments we show that users appreciate and judge the musician recommendations generated by the proposed approach as valuable, and perceive compositions of the suggested musicians as suited for the POIs., This work was supported by the Spanish Government (TIN201128538C02) and the Regional Government of Madrid (S2009TIC1542).

Requirements prioritization is an important issue that determines the way requirements are selected and processed in software projects. There already exist specific methods to classify and prioritize requirements, most of them based on quantitative measures. However, most of existing approaches do not consider collisions, which are an important concern in large-scale requirements sets and, more specifically, in agile development processes where requirements have to be uniquely selected for each software increment. In this paper, we propose QMPSR (Qualitative Method for Prioritizing Software Requirements), an approach that features the prioritization of requirements by considering qualitative elements that are related to the project's priorities. Our approach highlights a prioritization method that has proven to reduce collisions in software requirements rankings. Furthermore, QMPSR improves accuracy in classification when facing large-scale requirements sets, featuring no scalability problems as the number of requirements increases. We formally introduce QMPSR and then define prioritization effort and collision metrics to carry out comprehensive experiments involving different sets of requirements, comparing our approach with well-known existing prioritization methods. The experiments have provided satisfactory results, overcoming existing approaches and ensuring scalability, 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]

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet&rsquo, s word, emoji, and expression tokens&rsquo, embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literature.

This is the author’s version of a work that was accepted for publication in Pattern Recognition. 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 Pattern Recognition 46.5 (2013): 1323 – 1336, DOI: 10.1016/j.patcog.2012.10.021, We propose to determine the size of a parallel ensemble by estimating the minimum number of classifiers that are required to obtain stable aggregate predictions. Assuming that majority voting is used, a statistical description of the convergence of the ensemble prediction to its asymptotic (infinite size) limit is given. The analysis of the voting process shows that for most test instances the ensemble prediction stabilizes after only a few classifiers are polled. By contrast, a small but non-negligible fraction of these instances require large numbers of classifier queries to reach stable predictions. Specifically, the fraction of instances whose stable predictions require more than T classifiers for T ≫ 1 has a universal form and is proportional to T−1/2. The ensemble size is determined as the minimum number of classifiers that are needed to estimate the infinite ensemble prediction at an average confidence level , close to one. This approach differs from previous proposals, which are based on determining the size for which the prediction error (not the predictions themselves) stabilizes. In particular, it does not require estimates of the generalization performance of the ensemble, which can be unreliable. It has general validity because it is based solely on the statistical description of the convergence of majority voting to its asymptotic limit. Extensive experiments using representative parallel ensembles (bagging and random forest) illustrate the application of the proposed framework in a wide range of classification problems. These experiments show that the optimal ensemble size is very sensitive to the particular classification problem considered., The authors acknowledge financial support from the Spanish Dirección General de Investigación, project TIN2010-21575-C02-02.

The final publication is available at Springer via http://dx.doi.org/10.1007/s10838-013-9215-7, A preliminary (and extended) version of this article is available at arXiv under the title ‘‘Is the Multiverse Hypothesis capable of explaining the Fine Tuning of Nature Laws and Constants? The Case of Cellular Automata‘‘. See: http://arxiv.org/abs/1105.4278, The objective of this paper is analyzing to which extent the multiverse hypothesis provides a real explanation of the peculiarities of the laws and constants in our universe. First we argue in favor of the thesis that all multiverses except Tegmark’s “mathematical multiverse” are too small to explain the fine tuning, so that they merely shift the problem up one level. But the “mathematical multiverse" is surely too large. To prove this assessment, we have performed a number of experiments with cellular automata of complex behavior, which can be considered as universes in the mathematical multiverse. The analogy between what happens in some automata (in particular Conway’s “Game of Life") and the real world is very strong. But if the results of our experiments can be extrapolated to our universe, we should expect to inhabit—in the context of the multiverse—a world in which at least some of the laws and constants of nature should show a certain time dependence. Actually, the probability of our existence in a world such as ours would be mathematically equal to zero. In consequence, the results presented in this paper can be considered as an inkling that the hypothesis of the multiverse, whatever its type, does not offer an adequate explanation for the peculiarities of the physical laws in our world., This work has been partially sponsored by the Spanish MICINN projects TIN2011-28260-C03-02 and RYC-2009-04042.

This is the author’s version of a work that was accepted for publication in Pattern Recognition. 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 Pattern Recognition, Vol. 45, Iss. 12, (2012) DOI: 10.1016/j.patcog.2012.06.002, The increasing size and dimensionality of real-world datasets make it necessary to design efficient algorithms not only in the training process but also in the prediction phase. In applications such as credit card fraud detection, the classifier needs to predict an event in 10 ms at most. In these environments the speed of the prediction constraints heavily outweighs the training costs. We propose a new classification method, called a Hierarchical Linear Support Vector Machine (H-LSVM), based on the construction of an oblique decision tree in which the node split is obtained as a Linear Support Vector Machine. Although other methods have been proposed to break the data space down in subregions to speed up Support Vector Machines, the H-LSVM algorithm represents a very simple and efficient model in training but mainly in prediction for large-scale datasets. Only a few hyperplanes need to be evaluated in the prediction step, no kernel computation is required and the tree structure makes parallelization possible. In experiments with medium and large datasets, the H-LSVM reduces the prediction cost considerably while achieving classification results closer to the non-linear SVM than that of the linear case., The authors would like to thank the anonymous reviewers for their comments that help improve the manuscript. I.R.-L. is supported by an FPU Grant from Universidad Autónoma de Madrid, and partially supported by the Universidad Autónoma de Madrid-IIC Chair and TIN2010-21575-C02-01. R.H. acknowledges partial support by ONRN00014-07-1-0741, USARIEM-W81XWH-10-C-0040 (ELINTRIX) and JPL-2012-1455933.