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The idea of computable aggregation operators was introduced as a generalization of aggregation operators, allowing the replacement of the mathematical function usually considered for aggregation, by a program that performs the aggregation process. There are different reasons to justify this extension. One of them is the interest in exploring some computational properties not directly related to the aggregation itself but to its implementation (complexity, recursivity, parallelisation, etc). Another reason, the one driving to the present paper, is the need to define a framework where the quite common process of first sampling (over a large data set) and then aggregating the sample, could be analysed as a formal aggregation process. This process does not match with the idea of an aggregation function, due to its non-deterministic nature, but could easily be adapted to that of a (non-deterministic) computable aggregation. The idea of non-deterministic aggregation requires the extension of the concept of monotonicity (a key aspect of aggregation operators) to this new framework. The present paper will explore this kind of non-deterministic aggregation processes, first from an empirical point of view and then in terms of populations, adapting the idea of monotonicity to both of them and finally defining a common framework for its analysis.

Pedestrian structures regularly exhibit excessive vibrations when they are dynamically excited by human activities. Traditional modeling approaches neglect, due to the lack of suitable data, the temporal and spatial kinematic changes (such as the step length, step width, pace frequency and horizontal speed) when pedestrians are walking on a vibrating surface in the vertical direction such as slabs, footbridges, or stairs. Although several experimental studies have quantified the variability of human gait, most of these were conducted overground. These procedures do not fully account for the interaction between the pedestrian and an oscillating surface, compromising the comfort in service conditions for their occupants. In this paper, the spatio-temporal gait parameters were measured when six adults were instructed to walk on the beam and on the ground. The kinematic parameters were compared among the individual subjects to evaluate their variability associated with the vertical acceleration influence. Two gait modes were examined in this study, metronomic walk and self-selected pace. The results show that the walking locomotion parameters such as step length, step width, pace frequency, and pedestrian horizontal speed were found to have substantial differences on the ground as compared with the structural vibration under different walking modes. The findings here clearly demonstrate that gait and bridge kinematics interact, and continued research is warranted with larger numbers of participants under a wider range of structural conditions.

In this work we introduce the notion of the weighted graph associated with a fuzzy measure. Having a finite set of elements between which there exists an affinity fuzzy relation, we propose the definition of a group based on that affinity fuzzy relation between the individuals. Then, we propose an algorithm based on the Louvain’s method to deal with community detection problems with additional information independent of the graph. We also provide a particular method to solve community detection problems over extended fuzzy graphs. Finally, we test the performance of our proposal by means of some detailed computational tests calculated in several benchmark models.

In this paper we formally define the hierarchical clustering network problem (HCNP) as the problem to find a good hierarchical partition of a network. This new problem focuses on the dynamic process of the clustering rather than on the final picture of the clustering process. To address it, we introduce a new hierarchical clustering algorithm in networks, based on a new shortest path betweenness measure. To calculate it, the communication between each pair of nodes is weighed by the importance of the nodes that establish this communication. The weights or importance associated to each pair of nodes are calculated as the Shapley value of a game, named as the linear modularity game. This new measure, (the node-game shortest path betweenness measure), is used to obtain a hierarchical partition of the network by eliminating the link with the highest value. To evaluate the performance of our algorithm, we introduce several criteria that allow us to compare different dendrograms of a network from two point of view: modularity and homogeneity. Finally, we propose a faster algorithm based on a simplification of the node-game shortest path betweenness measure, whose order is quadratic on sparse networks. This fast version is competitive from a computational point of view with other hierarchical fast algorithms, and, in general, it provides better results.

In an increasingly globalized market, the relationship between the customer and the brand goes beyond the purchasing process. It is very important to understand the customer, to know their needs and to propose actions to increase the value of the brand for them. In the literature, there are several models capable of determining and segmenting customers according to variables dependent on the purchasing process. However, we have not found any study that applies to the business case of classifying customers according to their relationship with the contact centre. In this paper, we establish a working model that allows us to define the value of the customer in the process of interaction with the contact centre, so that we can propose actions both in the sales phase and during the post-sales service, so that the value and perception of the brand is increased. In this model, we propose using the value of recency, frequency, importance and duration of customer interactions with the post-sales service, thus obtaining a ranking, and grouping of customers to help establish personalized communication strategies. We have verified this model by presenting a business case applied to the telecom sector.

This article presents a differential evolution (DE) algorithm that can be used to plan public lighting for streets, roadways, and freeways and maximize the energy efficiency of the installation. The algorithm was applied to a model based on new relationships between the energy efficiency of street lighting systems and geometric parameters such as street width, luminaire height, and distance between poles. These relationships were derived from the regression analysis of a large sample of outputs. The results of this algorithm consisted of the luminaire arrangement (one-sided, two-sided staggered, and two-sided coupled), luminaire height, luminaire type, and pole spacing for the most energy-efficient installation. The input of the algorithm was the lighting class or illuminance level, street width, as well as various other luminaire parameters. When these results were compared with those of DIALux, the performance of this new method was found to be extremely satisfactory. Furthermore, the constraints applied guaranteed compliance with the recommendations of the International Commission on Illumination.

In this paper, we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the risk of polarization. Particularly, we are interested in dealing with the problem of taking into account the polarization of nodes in the community detection problem. Adding this type of information to the community detection problem makes it more realistic, as a community is more likely to be defined if the corresponding elements are willing to maintain a peaceful dialogue. The polarization capacity is modeled by a fuzzy measure based on the JDJpol measure of polarization related to two poles. We also present an efficient algorithm for finding groups whose elements are no polarized. Hereafter, we work in a real case. It is a network obtained from Twitter, concerning the political position against the Spanish government taken by several influential users. We analyze how the partitions obtained change when some additional information related to how polarized that society is, is added to the problem.

The dynamic interaction between a bridge and a moving train has been widely studied. However, there is a significant gap in our understanding of how the presence of isolation bearings influences the dynamic response, especially when an earthquake occurs. Here, we formulate a coupled model of a train-bridge-bearing system to examine the bearings’ dynamic effects on the system responses. In the analysis, the train is modeled as a moving oscillator, the bridge is a one span simply supported beam and one isolation bearing is installed under each support of the bridge. A mathematical model using fractional derivatives is used to capture the viscoelastic properties of the bearings. The vertical response is the focus of this investigation. Dynamic substructuring is used in modeling to efficiently capture the coupled dynamics of the entire system. Illustrative numerical simulations are carried out to examine the effects of the bearings. The results demonstrate that although the presence of bearings typically decreases the bridge seismic responses, there is a potential to increase the bridge response induced by the moving train.

Biodiversity monitoring through bioacoustics is gaining momentum as a way to promote conservation in highly biodiverse countries. However, in these regions, bioacoustic analysis is a complex task due to the diversity of overlapping sound sources coming to the recording microphones at the same time. Moreover, commonly, this highly subjective classification is performed manually by expert biologists trained for identifying different species by their particular sound. This cumbersome task is performed with non-specialized software. Here, we propose a visualization interface based on an interaction design process through a user-centered design philosophy.

In this paper we introduce the concept of multiple bipolar fuzzy measures as a generalization of a bipolar fuzzy measure. We also propose a new definition of a group, which is based on the multidimensional bipolar fuzzy relations of its elements. Taking into account this information, we provide a novel procedure (based on the well-known Louvain algorithm) to deal with community detection problems. This new method considers the multidimensional bipolar information provided by multiple bipolar fuzzy measures, as well as the information provided by a graph. We also give some detailed computational tests, obtained from the application of this algorithm in several benchmark models.

In this paper, we present our current work towards developing a context aware visual system with capabilities to generate knowledge using an adaptive cognitive model. Our goal is to assist people in their daily routines using the acquired knowledge in combination with a set of machine learning tools to provide prediction and individual routine understanding. This is useful in applications such as assistance to individuals with Alzheimer by helping them to maintain a daily routine based on historical data. The proposed cognitive model is based on simple exponential smoothing technique and provides real time detection of objects and basic relations in the scene. To fulfill these objectives we propose the integration of machine learning tools and memory based knowledge representation. 2019/UEM60 No data JCR 2021 No data SJR 2021 No data IDR 2021 UEM

In this paper we propose a new framework for community detection problems. The starting point is a n-vector which defines some evidence about the elements of a finite set. This vector is used to build an interaction measure between the n elements of the set to which it refers. This interaction measure is represented by a Sugeno λ-measure to which we make it being also a fuzzy measure. Then, we obtain the weighted graph associated with this new capacity measure. To carry on with it, we make use of the Shapley value. We also introduce the notion of extended vector fuzzy graph, which relates a graph with the capacity measure introduced in this work. Finally, we use a community detection method, based on Louvain algorithm, to search a cluster structure in the weighted graph. This partition is based on the relations among the individuals obtained from the initial vector. Let us note that in the case that there exist some connections among the elements, apart from their affinity, we can combine this extra information with that given by the vector, in order to obtain groups with highly-knit elements among which there are strong relations.

Base isolation is a powerful technique to prevent damage in low- and medium-rise structures during an earthquake. Nowadays, the extensive use of high-damping viscoelastic (VE) materials in ...

This paper reports on the design and evaluation of drum rhythm spaces as interactive bi-dimensional maps used for the visualisation, retrieval and generation of drum patterns. We carry out two experiments exploring human processing of polyphonic drum patterns concluding with a list of descriptors that significantly influence similarity sensations. These features are used to build spaces based on drum pattern collections, where patterns are organised by similarity, modelled according to human perception. A drum-interpolation algorithm is introduced (and evaluated) to enhance rhythm space functionality by means of patterns that bound it, converting a discrete space to a continuous generative one. This research has been partially supported by the EU funded GiantSteps project (http://www.giantsteps-project.eu) (FP7-ICT-2013-10 Grant agreement nr 610591).

Text mining and topic identification models are becoming increasingly relevant to extract value from the huge amount of unstructured textual information that companies obtain from their users and clients nowadays. Soft approaches to these problems are also gaining relevance, as in some contexts it may be unrealistic to assume that any document has to be associated to a single topic without any further consideration of the involved uncertainties. However, there is an almost total lack of reference documents allowing a proper assessment of the performance of soft classifiers in such soft topic identification tasks. To address this lack, in this paper a method is proposed that generates topic identification reference documents with a soft but objective nature, and which proceeds by combining, in random but known proportions, phrases of existing documents dealing with different topics. We also provide a computational study illustrating the application of the proposed method on a well-known benchmark for topic identification, as well as showing the possibility of carrying out an informative evaluation of soft classifiers in the context of soft topic identification.

Most item-shopping websites give people the opportunity to express their thoughts and opinions on items available for purchasing. This information often includes both ratings and text reviews expressing somehow their tastes and can be used to predict their future opinions on items not yet reviewed. Whereas most recommendation systems have focused exclusively on ranking the items based on rating predictions or user-modeling approaches, we propose an adapted recommendation system based on the prediction of opinion keywords assigned to different item characteristics and their sentiment strength scores. This proposal makes use of natural language processing (NLP) tools for analyzing the text reviews and is based on the assumption that there exist common user tastes which can be represented by latent review topics models. This approach has two main advantages: is able to predict interpretable textual keywords and its associated sentiment (positive/negative) which will help to elaborate a more precise recommendation and justify it, and allows the use of different dictionary sizes to balance performance and user opinion interpretability. To prove the feasibility of the adapted recommendation system, we have tested the capabilities of our method to predict the sentiment strength score of item characteristics not previously reviewed. The experimental results have been performed with real datasets and the obtained F1 score ranges from 66% to 77% depending on the dataset used. Moreover, the results show that the method can generalize well and can be applied to combined domain independent datasets. UEM E-modelo "E-Modelo Extracción de modelos de usuario y predicción de opinión a partir de textos Spanish Ministry of Economy and Competitiveness (MTM2014-57158-R) 2.679 JCR (2020) Q2, 38/91 Engineering, Multidisciplinary 0.435 SJR (2020) Q2, 366/2196 Computer Science Applications No data IDR 2019 UEM

The measurement of polarization has been studied over the last thirty years. Despite the different applied approaches, since polarization concept is complex, we find a lack of consensus about how it should be measured. This paper proposes a new approach to the measurement of the polarization phenomenon based on fuzzy set. Fuzzy approach provides a new perspective whose elements admit degrees of membership. Since reality is not black and white, a polarization measure should include this key characteristic. For this purpose we analyze polarization metric properties and develop a new risk of polarization measure using aggregation operators and overlapping functions. We simulate a sample of \(N = 391315\) cases across a 5-likert-scale with different distributions to test our measure. Other polarization measures were applied to compare situations where fuzzy set approach offers different results, where membership functions have proved to play an essential role in the measurement. Finally, we want to highlight the new and potential contribution of fuzzy set approach to the polarization measurement which opens a new field to research on.

Traditionally, the term aggregation is associated with an aggregation function, implicitly assuming that any aggregation process can be represented by a function. However, the concept of computable aggregation considers that the core of the aggregation processes is the program that enables it. This new concept of aggregation introduces the scenario where the aggregation can even be non-deterministic. In this work, this new class of aggregation is formally defined, and some desirable properties related with consistency, robustness and monotonicity are proposed.

Community detection problems are one of the hottest disciplines in social network analysis. Nevertheless, most of the related algorithms are specific for non-directed networks, or are based on a density concept of group. In this paper, we deal with a new concept of community for directed networks that is based on the classical flow concept. A community is strong and cohesive if their members can communicate among them. With the aim of dealing with the identification of this new class of groups, in this work, we propose the use of fuzzy measures to represent the flow capacity of a group. We also provide a competitive community detection algorithm that focus on the identification of these new class of flow-based community.

Traditionally, the edge detection process requires one final step that is known as scaling. This is done to decide, pixel by pixel, if these will be selected as final edge or not. This can be considered as a local evaluation method that presents practical problems, since the edge candidate pixels should not be considered as independent. In this article, we propose a strategy to solve these problems through connecting pixels that form arcs, that we have called segments. To accomplish this, our edge detection algorithm is based on a more global evaluation inspired by actual human vision. Our paper further develops ideas first proposed in Venkatesh and Rosin (Graph Models Image Process 57(2):146–160, 1995). These segments contain visual features similar to those used by humans, which lead to better comparative results against humans. In order to select the relevant segments to be retained, we use fuzzy clustering techniques. Finally, this paper shows that this fuzzy clustering of segments presents a higher performance compared to other standard edge detection algorithms.

In this paper, different ways of aggregating color information in edge extraction task are explored. One of these approaches is based on a previous transformation of the RGB images into a new color space of 8 dimensions. This increases the number of dimensions, making differences between colors easier to detect, and in this way improving the performance of edge detection task. Sobel and Canny algorithms are employed over the RGB images and its transformed version of 8 dimensions -that we have named Super8 image-. The set of employed images is the one from Berkeley’s dataset. In order to evaluate the performance, precision, recall and F measure are computed. The way of aggregating the color information is showed to be relevant. For some well-known algorithms, the new 8 dimension color space overtakes RGB’s for edge detection problem.

Most supervised classification algorithms produce a soft score (either a probability, a fuzzy degree, a possibility, a cost, etc.) assessing the strength of the association between items and classes. After that, each item is assigned to the class with the highest soft score. In this paper, we show that this last step can be improved through alternative procedures more sensible to the available soft information. To this aim, we propose a general fuzzy bipolar approach that enables learning how to take advantage of the soft information provided by many classification algorithms in order to enhance the generalization power and accuracy of the classifiers. To show the suitability of the proposed approach, we also present some computational experiences for binary classification problems, in which its application to some well-known classifiers as random forest, classification trees and neural networks produces a statistically significant improvement in the performance of the classifiers.

The problem of missing data is common in real-world applications of supervised machine learning such as classification and regression. Such data often gives rise to the need for functions defined for varying dimension. Here we propose optimization methods for learning the weights of quasi-arithmetic means in the context of data with missing values. We investigate some alternative approaches depending on the number of variables that have missing values and show results for several numerical experiments.

Community detection problems are one of the most important topics in social network analysis. Most of the algorithms and techniques that find communities in a network, model and represent is as something crisp. However, there exist many real situations in which fuzzy uncertainty appears in a natural way when the network is modeled. In this work, we present a modification of the well-known Louvain algorithm for crisp network that allows us to deal with fuzzy information in the network. In particular, we incorporate to the classical Louvain algorithm the use of fuzzy measures for the nodes of the graph. We also incorporate to the classical method the use of Shapley value to measure the importance of each node. We define the affinity among a pair of nodes as how each node of the pair is affected by the absence of the other one.

Traditionally, the last step of edge detection algorithms, which is called scaling-evaluation, produces the final output classifying each pixel as edge or nonedge. This last step is usually done based on local evaluation methods. The local evaluation makes this classification based on measures obtained for every pixel. By contrast, in this work, we propose a global evaluation approach based on the idea of edge list to produce a solution that suits more with the human perception. In particular, we propose a new evaluation method that can be combined with any classical edge detection algorithm in an easy way to produce a novel edge detection algorithm. The new global evaluation method is divided in four steps: in first place we build the edge lists, that we have called edge segments. In second place we extract the characteristics associated to each segment: length, intensity, location, and so on. In the third step we learn the characteristics that make a segment good enough to become an edge. At the fourth step, we apply the classification task. In this work we have built the ground truth of edge list necessary for the supervised classification. Finally, we test the effectiveness of this algorithm against other classical algorithms based on local evaluation approach.

This paper examines the ambiguity of subjective judgments, which are represented by a system of pairwise preferences over a given set of alternatives. Such preferences are valued with respect to a set of reasons, in favor and against the alternatives, establishing a complete judgment, or viewpoint, on how to solve the decision problem. Hence, viewpoints entail particular decisions coming from the system of preferences, where the preference-based reasoning of a given viewpoint holds according to its soundness or coherence. Here we explore such a coherence under the frame of ambiguity measures, aiming at learning viewpoints with highest preference-score and minimum ambiguity. We extend existing measures of ambiguity into a multi-dimensional fuzzy setting, and suggest some future lines of research towards measuring the coherence or (ir)rationality of viewpoints, exploring the use of information measures in the context of preference learning.

Most edge detection algorithms deal only with grayscale images, and the way of adapting them to use with RGB images is an open problem. In this work, we explore different ways of aggregating the color information of a RGB image for edges extraction, and this is made by means of well-known edge detection algorithms. In this research, it is been used the set of images from Berkeley. In order to evaluate the algorithm’s performance, F measure is computed. The way that color information -the different channels- is aggregated is proved to be relevant for the edge detection task. Moreover, post-aggregation of channels performed significatively better than the classic approach (pre-aggregation of channels).

In this paper, inspired by the Zadeh approach to the fuzzy connectives in fuzzy set theory and by some applications, we introduce and study set-based extended functions on different universes. After presenting some results for set-based extended functions on a general universe, we focus our investigation on set-based extended functions on some particular universes, including lattices and (bounded) chains. A special attention is devoted to characterization of set-based extended aggregation functions on the unit interval [0, 1].

Recently it has been shown that building networks from time series allows to study complex systems to characterize them when they go through a phase transition. This give us the opportunity to study this systems from a entire new point of view. In the present work we have used the natural and horizontal visualization algorithms to built networks of two popular models, which present phase transitions: the Ising model and the Kuramoto model. By measuring some topological quantities as the average degree, or the clustering coefficient, it was found that the networks retain the capability of detecting the phase transition of the system. From our results it is possible to establish that both visibility algorithms are capable of detecting the critical control parameter, as in every quantity analyzed (the average degree, the average path and the clustering coefficient) there is a minimum or a maximum value. In the case of the natural visualization algorithm, the average path results are much more noisy than in the other quantities in the study. Specially for the Kuramoto Model, which in this case does not allow a detection of the critical point at plain sight as for the other quantities. The horizontal visualization algorithm has proven to be more explicit in every quantity, as every one of them show a clear change of behavior before and after the critical point of the transition.

This article describes an innovative method for measuring lighting levels and other lighting parameters through the use of aerial imagery of towns and cities. Combined with electricity consumption data from smart electricity meters, it was possible to measure the energy efficiency of public lighting installations. The results of this study also confirmed that lighting measurements, installation material, luminaire position, and electricity consumption data can be easily integrated into geographic information systems (GIS). The main advantage of this new methodology is that it provides information about lighting installations in large areas in less time than more conventional procedures. It is thus a more effective way of obtaining the data required to calculate the energy efficiency of lighting levels and electricity consumption. There is even the possibility of generating street lighting maps that provide local administrations with up-to-date information regarding the status of public lighting installations in their city. In this way, modifications or improvements can be made to achieve greater energy savings and, if necessary, to correct the distribution or configuration of public lighting systems to make them more efficient and sustainable. This research studied levels of street lighting and calculated the energy efficiency in various streets of Deifontes (Granada), through the use of aerial imagery.

The aim of this work is to posit a model to evaluate the efficiency of a system of urban public libraries and to examine the impact of certain contextual variables on the level of performance. We take the System of Public Libraries in the city of Medellin (Colombia) as a case study and consider a production function which displays three main characteristics. First, it is a complete production function which spans the different activities undertaken by these institutions, not only the one that identifies it with its function as a repository of knowledge. Second, there is the production function in stages, which allows us to distinguish between the various activities controlled by management from those coproduced with users, together with the link between the two. The third is a production function which takes into account temporal interdependence relations by identifying quasi-fixed inputs that remain for the provision of the service over time. This then allows us to analyse how efficiency evolves during the period in question. Efficiency evaluation is carried out by employing a dynamic-network-DEA model and we also apply truncated bootstrap regression to estimate the effect of certain contextual variables on library efficiency. The results evidence a growing trend in the efficiency indices, with values that are slightly more favourable in the second stage of service provision than in the stage focusing on managing the cultural programme. Factors such as the level of education, population density, youthfulness, and safety are seen to positively affect library performance, particularly in the second stage vis-a-vis the public.

In this position paper we propose a consistent and unifying view to all those basic knowledge representation models that are based on the existence of two somehow opposite fuzzy concepts. A number of these basic models can be found in fuzzy logic and multi-valued logic literature. Here it is claimed that it is the semantic relationship between two paired concepts what determines the emergence of different types of neutrality, namely indeterminacy, ambivalence and conflict, widely used under different frameworks (possibly under different names). It will be shown the potential relevance of paired structures, generated from two paired concepts together with their associated neutrality, all of them to be modeled as fuzzy sets. In this way, paired structures can be viewed as a standard basic model from which different models arise. This unifying view should therefore allow a deeper analysis of the relationships between several existing knowledge representation formalisms, providing a basis from which more expressive models can be later developed., This research has been partially supported by the Government of Spain (grants TIN2015-66471-P and TIN2013-40765-P), the Government of Madrid (grant S2013/ICCE-2845) and the UCM (Research Group 910149).

A yearning for visually appealing bridges that are longer and more slender is driving advances in construction materials and techniques, alongside the necessary improvements in design guidelines and standards. However, lighter and more slender structures are also more susceptible to vibration. Certain pedestrian structures do frequently exhibit excessive vibrations when they are dynamically excited by walking pedestrians. To improve pedestrian-structure design procedures, uncertainties in the human biodynamic subsystem should be systematically included, and should represent a suitable range of possible body characteristics. Variations in the representation of the human body are intended to capture differences in physical characteristics while that human is walking on a lively surface. Here these variations are described as parametric uncertainties, while the structure is assumed to be known and deterministic. A time-varying and coupled model of the pedestrian-structure system is adopted, and the corresponding perturbations are defined as a set of structured uncertainties describing imprecise biodynamic parameters that can adopt any value within their proposed intervals. This parametric model with structured uncertainty is then used to obtain the range of biodynamic parameters for a variety of pedestrians by comparing the predicted structural responses against the experimental data. Experimental results are used to determined a set of intervals of biodynamic parameters such as mass, damping, stiffness, and pace frequency for a walking pedestrian. The simulation results show that the developed human-structure model with associated parameter intervals is able to obtain realistic serviceability demands by predicting the range of structural responses for a wide range of pedestrians.

This paper presents the problem definition and guidelines for a benchmark control problem in real-time hybrid simulation for a seismically excited building, to appear in a Special Issue of Mechanical Systems and Signal Processing. Benchmark problems have been especially useful in enabling a community of researchers to leap forward on a given topic, distill the lessons learned, and identify the capabilities and limitations of various approaches. The focus here is on the design of an effective transfer system displacement tracking controller which is a commonly used approach for ensuring that interface conditions between numerical and experimental substructures are satisfied. In this study, a laboratory model of a three-story steel frame is considered as the reference structure. Realistic numerical models are developed and provided to represent the numerical and experimental substructures and the transfer system, which is comprised of hydraulic actuation, sensing instrumentation, and control implementation hardware. Experimental components are identified and provided as Simulink models, which are executed in real-time using Simulink Desktop Real-Time capability to enable realistic virtual real-time hybrid simulation. The task of each participant of the Special Issue is to design, evaluate, and report on their proposed controller approaches using the numerical models and computational codes provided. Such approaches will be assessed for robustness and performance using the provided tools. This benchmark problem is expected to further the understanding of the relative merits, as well as provide a clear basis for evaluating the performance of various control approaches and algorithms for RTHS. To illustrate some of the design challenges, a sample control strategy employing a proportional-integral (PI) controller is included, in addition to the built-in control loop of the transfer system.

It is common to find index construction methods based on crisp techniques and linear models. However, these procedures are not always adequate to capture the essential model of an index. Index development can be approached as a nested or hierarchical aggregation process, where part of the data can have a different level of importance, and the other part can be unstructured. In addition, the information can be studied under a fuzzy approach. This paper proposes an index construction method using the notion of consistency in aggregation operator families to ensure robustness, in addition to considering a hierarchical structure with different level of importance of input information. Two applications are presented, a measure of social and advanced uses of the Internet considering attitudinal, demographic, social and technological factors, and a measure of the child’s family environment considering different aspects.

Comunicació presentada a: The 13th International Symposium on Computer Music Multidisciplinary Research CMMR 201, celebrat del 25 al 28 de setembre de 2017 a Matosinhos, Portugal. This paper presents two experiments carried out to find rhythm descriptors that allow the organization of drum patterns in spaces resembling subjects similarity sensations. We revisit rhythm spaces published by Alf Gabrielsson in 1973, based on subject similarity ratings of drum rhythms from an early drum machine, and construct a new rhythm space based on similarity judgments using contemporary electronic dance music (EDM) patterns. We observe how a specific set of descriptors can be used to reconstruct both Gabrielsson’s and the new EDM space, suggesting the descriptors capture drum similarity sensations in very different contexts. The set of descriptors and the methods employed are explained with detail and the possibility of having method for organizing rhythm patterns automatically is discussed. This research has been partially supported by the EU funded GiantSteps project (http://www.giantsteps-project.eu) (FP7-ICT-2013-10 Grant agreement nr 610591).

Capturing aerial images by Unmanned Aerial Vehicles (UAV) allows gathering a general view of an agricultural site together with a detailed exploration of its relevant aspects for operational actions. Here we explore the challenging task of detecting cirsium arvense, a thistle-weed species, from aerial images of barley-cereal crops taken from 50 m above the ground, with the purpose of applying herbicide for site-specific weed treatment. The methods for automatic detection are based on object-based annotations, pointing out the RGB attributes of the Weed or Cereal classes for an entire group of pixels, referring to a crop area which will have to be treated if it is classified as being of the Weed class. In this way, an annotation belongs to the Weed class if more than half of its area is known to be covered by thistle weeds. Hence, based on object and pixel-level analysis, we compare the use of k-Nearest Neighbours (k-NN) and (feed-forward, one-hidden layer) neural networks, obtaining the best results for weed detection based on pixel-level analysis, based on a soft measure given by the proportion of predicted weed pixels per object, with a global accuracy of over 98%.

Edge detection problems try to identify those pixels that represent the boundaries of the objects in an image. The process for getting a solution is usually organized in several steps, producing at the end a set of pixels that could be edges (candidates to be edges). These pixels are then classified based on some local evaluation method, taking into account the measurements obtained in each pixel. In this paper, we propose a global evaluation method based on the idea of edge list to produce a solution. In particular, we propose an algorithm divided in four steps: in first place we build the edge list (that we have called segments); in second place we extract the characteristics associated to each segment (length, intensity, location,…); in the third step we learn which are the characteristics that make a segment good enough to be a boundary; finally, in the fourth place, we apply the classification task. In this work we have built the ground truth of edge list necessary for the supervised classification. Finally we test the effectiveness of this algorithm against other classical approaches.

The aim of supervised classification algorithms is to assign objects/items to known classes. Before carrying out the final assignment, many classification algorithms obtain a soft score (probability, fuzzy, possibility, cost...) between each item and each class. In order to improve this final decision, we build a bipolar probabilistic model that considers some extra information about the dissimilarity structure between the classes. We present here some improvements for several supervised classification algorithms such as random forest, decision trees and neural networks for binary classification problems.

Edge detection comprises different stages that go from adaptation of the original image -conditioning- to the selection of the definitive edges. This last step, known as scaling, requires the application of a thresholding process over the gradients of luminosity values of the pixels. Traditionally, this is made through a local evaluation process that works pixel by pixel. As the edge candidate pixels are not independent, a wider strategy suggests the use of a more global evaluation. In this sense, this approach resembles more the human vision. This paper further develops ideas related to edge lists, first proposed in 1995 [1]. This paper will refer to edge lists as edge segments. These segments contain visual features similar to the ones that humans use, which might lead to better comparative results. In this paper we propose using clustering techniques to differentiate the appropriate segments or true segments from the false ones, and we introduce an algorithm that uses fuzzy clustering techniques. Finally, this paper shows that this fuzzy clustering over the segments performs at least as well as other standard algorithms used for edge detection.

In this paper we discuss about graph approach in image segmentation. In first place, some main image processing techniques are classified based upon the output these methods provide. Then, a fuzzy image segmentation definition is presented because in the literature review was found that it was not clearly defined. This definition of fuzzy image segmentation is then related to a hierarchical image segmentation procedure, so this concept is also formally defined in this work. As every output of an image processing algorithm has to be evaluated, then a method to evaluate a hierarchical segmentation output is proposed in order to later propose a method to evaluate a fuzzy image segmentation output. Computational experiences point to some advantages of the proposed hierarchical image segmentation procedure over other algorithms.

We present an elastic interface designed to capture coarse and continuous movements that are characteristics of artistic, creative and performance processes. We tested our prototype and compared its performance with other two more traditional interfaces. Experimental results show that our interface is outperformed in simple tasks. However, for more complex scenarios, its performance is similar to the other interfaces both in response time and in precision error. The results suggest that our prototype is suitable for artistic composition and performance.

The rapid proliferation of social network services (SNS) gives people the opportunity to express their thoughts, opinions, and tastes on a wide variety of subjects such as movies or commercial items. Most item shopping websites currently provide SNS systems to collect users’ opinions, including rating and text reviews. In this context, user modeling and hyper-personalization of contents reduce information overload and improve both the efficiency of the marketing process and the user’s overall satisfaction. As is well known, users’ behavior is usually subject to sparsity and their preferences remain hidden in a latent subspace. A majority of recommendation systems focus on ranking the items by describing this subspace appropriately but neglect to properly justify why they should be recommended based on the user’s opinion. In this paper, we intend to extract the intrinsic opinion subspace from users’ text reviews –by means of collaborative filtering techniques– in order to capture their tastes and predict their future opinions on items not yet reviewed. We will show how users’ reviews can be predicted by using a set of words related to their opinions. 2016/UEM16 No data (2017) UEM

Currently, construction tendencies tend to design flexible structures, such as footbridges, slabs and grandstands, due to the increased resistance of new materials and the regulation gap in building codes. These flexible structures are susceptible of excessive vibrations induced by human activities, producing serviceability failures and, sometimes, collapse. It is essential to improve the understanding of the effect of anthropic loads over the structures to mitigate serviceability problems and the risk of structural damage and, therefore, economic and human loses.