The Public University of Navarre joined with Navarre Government has created the Women, Science and Technology Chair. This chair arises due to the plummeting tendency of the percentage of women in STEM degrees with the aim of reversing this trend. The programme of activities is defined throughout this contribution by six activities: a Theatre Play, a Poster Award on Final Degree/Masters Project, The 1st Week of Women, Science and Technology, the Promotion of Technical Degrees in schools and high-schools, a Workshop about Gender Stereotypes and the Fostering of Women among Science and Environment. Each activity gained great success and the preset goals were highly accomplished, especially, the 1st Week of Women, Science and Technology activity. The latter achieved a great success both in participation and in repercussion, contributing to visualize the role of women in science and technology.

The number of women in scientific and technological careers is quite small compared to the number of men in the same careers. To equalize these numbers, the Public University of Navarra started a new initiative in 2017. The goal of this project is to make more-women in STEM visible, in order to serve as example for the young girls. It is brought to life by means of a theatre play called “Yo quiero ser científica” (translated as “I want to be a female scientist”). This play is divided into two parts: in the first one 9 teachers perform the role of 9 historical female scientists, by means of 9 monologues; in the second part, the same 9 teachers tell their experience as contemporary scientists. The feedback obtained through surveys, a contest and a final debate with the attendees show that this kind on initiatives are really necessary.

In this work we study how the outliers can distort a partitional clustering process. We present a new algorithm to avoid this distortion. It is based on the minimization of a new objective functions, which is an extension of the one of the Fuzzy Clusters Means algorithm. The main novelty is the use of interval values to calculate the membership degrees of each datum to each cluster. We show the performance of our proposal over different datasets and we present its advantages in image segmentation.

In this work we investigate a new family of fusion functions called internal fusion functions. The main characteristic of these functions is the fact that the output always corresponds to some of the given inputs. We propose a construction method and we study whether internal functions constructed in this way also satisfy properties of aggregation functions Finally, we apply internal functions in an example of a multi-class problem, where a set of matrices must be combine into a single representative collective matrix in order to obtain better classification rates.

In this paper we study interval-valued fuzzy relations. We consider preference relations, i.e. a triplet consisting of strict preference, indifference and incomparability which are defined with the use of a fuzzy negation. We analyze the preservation of the fuzzy negation based reciprocity property of interval-valued fuzzy relations by aggregation functions and by some basic interval-valued fuzzy relations. We use diverse representa-tions of aggregation functions. We also consider the connection between N-reciprocal relations and transitivity properties. We provide a numerical example where the final alternative is chosen with the use of generalized voting method, where admissible linear orders for intervals are applied.

The final goal of this study is to adapt the concept of fuzzy entropy of De Luca and Termini to deal with type-2 fuzzy sets. We denote this concept type-2 fuzzy entropy set. However, the construction of the notion of entropy measure on an infinite set, such us [0,1], is not effortless. For this reason, we first introduce the concept of quasi-entropy of a fuzzy set on the universe [0,1]. Furthermore, whenever the membership function of the considered fuzzy set in the universe [0,1] is continuous, we prove that the quasi-entropy of that set is a fuzzy entropy in the sense of De Luca and Termini. Finally, we present an illustrative example, where we use type-2 fuzzy entropy sets instead of fuzzy entropies in a classical fuzzy algorithm.

In this paper we axiomatically define weak homogeneity of a fuzzy subset, which means that its membership function fulfills at least the minimum properties required to represent the homogeneity of a region. We also provide several construction methods based on the homogeneity of an interval. Besides, we show an illustrative example of these functions applied to the problem of image segmentation.

In image processing, particularly in image reduction, averaging aggregation functions play an important role. In this work we study the aggregation of color values (RGB) and we present an image reduction algorithm for RGB color images. For this purpose, we define and study aggregation functions and penalty functions in product lattices. We show how the arithmetic mean and the median can be obtained by minimizing specific penalty functions. Moreover, we study other penalty functions and we show that, in general, aggregation functions on product lattices do not coincide with the cartesian product of the corresponding aggregation functions. Finally, we make an experimental study where we test our reduction algorithm and we analyze the stability of the penalty functions in images affected by noise.

The multiscale techniques for edge detection represent an effort to combine the spatial accuracy of small-scale methods with the ability to deal with spurious responses inherent to the large scale ones. In this work we introduce a multiscale extension of the Sobel method for edge detection based on Gaussian smoothing and fine-to-coarse edge tracking. We include examples illustrating the procedure and its results, as well as some quantitative measurements of the improvement obtained with the multiscale approach with respect to the original one.

In this paper, a simple and effective image-magnification algorithm based on intervals is proposed. A low-resolution image is magnified to form a high-resolution image using a block-expanding method. Our proposed method associates each pixel with an interval obtained by a weighted aggregation of the pixels in its neighborhood. From the interval and with a linear K(α) operator, we obtain the magnified image. Experimental results show that our algorithm provides a magnified image with better quality (peak signal-to-noise ratio) than several existing methods.

The edge detection process can be broken down into four basic transformations, modifying the image from the original presentation to the final edges one. The adoption of this framework makes the process far more understandable, and offers an starting point for the combination and comparison of different edge detection methods. In this work we analyze the role of the third of the transformations, the blending, where the edge features are combined to obtain the edginess values. This work studies the use of quasi-aritmethic means for the combination of the edge features. Moreover, we show results obtained with different operators on real images, in order to illustrate the importance of the blending phase in the edge detection process. Results will show the impact of the function selection in the final results.

In this work an ignorance-based fuzzy clustering algorithm is presented. The algorithm is based on the entropy-based clustering algorithm proposed by Yao et al.[14]In our proposal, we calculate the total ignorance instead of using the entropy at each data point to select the cluster centers. The experimental results show that the ignorance-based clustering improves the data classification made by the EFC in image segmentation.

In this work an ignorance-based fuzzy clustering algorithm is presented. The algorithm is based on the entropy-based clustering algorithm proposed by Yao et al.. In our proposal, we calculate the total ignorance instead of using the entropy at each data point to select the data point as the first cluster center. The experimental results show that the ignorance-based clustering improves the data classification made by the EFC in image segmentation.