Explainable Hopfield Neural Networks Using an Automatic Video-Generation System

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. They can be applied to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video-generation systems to explain its execution. This work constitutes a novel approach to obtain explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. We apply our approach to creating a complete explainer video about the execution of HNNs on a small recognition problem. Finally, several aspects of the videos generated are evaluated (quality, content, motivation and design/presentation).University of the Bio-Bio. Vicerrectoria de Investigacion. Facultad de Ciencias Empresariales. Departamento de Sistemas de Informacion

Preferences in discrete multi-adjoint formal concept analysis

Multi-adjoint concept lattice theory is a general fuzzy approach of formal concept analysis, which has diverse interesting properties. One of them is that it is possible to provide different degrees of preference among the set of objects/attributes. This paper studies a family of implications, based on the divisible discrete t-norms and in the Miller’s law, which can be associated with a qualitative range of preference degrees to be considered in the applications by non-expert users of the FCA framework.Partially supported by the 2014–2020 ERDF Operational Programme in collaboration with the State Research Agency (AEI) in projects PID2019-108991GB-I00 and PID2022-137620NB-I00, with the Ecological and Digital Transition Projects 2021 of the Ministry of Science and Innovation in project TED2021-129748B-I00, and with the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia in project FEDER-UCA18-108612, and by the European Cooperation in Science & Technology (COST) Action CA1712

A Novel Cause-Effect Variable Analysis in Enterprise Architecture by Fuzzy Logic Techniques

In this paper, or present a new integration approach for managing Information Technology variables within enterprise architecture in an integrated way. Additionially, a novel method based on fuzzy logic for cause-effect variable analysis is proposed as a useful support decision-making tool for companies in order to lmow the main actions they must perform for increasing their benefits. This is employed to assess the Integration Management System in Enterprises, based on Enterprise Architecture and Information Technology. We show as fuzzy logic plays an important role M this area due to these variables can be affected for multifactorial elements iinpregnated with uncertainty. The knowledge given by the experts is translated into dependence rules, Which have also been analyzed from a fuzzy point of view using a combination of two fuzzy techniques, namely, fuzzy relation equation theory and fuzzy graph. Firstly, fuzzy dependence rules are computed froth fuzzy relation equations and, secondly an analysis based on incidence subgraph is performed. The resulLisa strategic plan automatically generated from the data captured of each enterprise in which the most import variables to be improved are detailed. (C) 2020 The Authors. Published by Atlantis Press SARI.

Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language

In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this field, the main work has been done for English language texts, mainly using Machine Learning (ML) approaches, Lexicon approaches to a lesser extent, and very few works using hybrid approaches. In these, Lexicons and Machine Learning algorithms are used, such as counting the number of bad words in a sentence using a Lexicon of bad words, which serves as an input feature for classification algorithms. This research aims at contributing towards detecting aggressiveness in Spanish language texts by creating different models that combine the Lexicons and ML approach. Twenty-two models that combine techniques and algorithms from both approaches are proposed, and for their application, certain hyperparameters are adjusted in the training datasets of the corpora, to obtain the best results in the test datasets. Three Spanish language corpora are used in the evaluation: Chilean, Mexican, and Chilean-Mexican corpora. The results indicate that hybrid models obtain the best results in the 3 corpora, over implemented models that do not use Lexicons. This shows that by mixing approaches, aggressiveness detection improves. Finally, a web application is developed that gives applicability to each model by classifying tweets, allowing evaluating the performance of models with external corpus and receiving feedback on the prediction of each one for future research. In addition, an API is available that can be integrated into technological tools for parental control, online plugins for writing analysis in social networks, and educational tools, among others

On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications

In this paper we analyse the benefits of incorporating interval-valued fuzzy sets into the Bousi-Prolog system. A syntax, declarative semantics and im- plementation for this extension is presented and formalised. We show, by using potential applications, that fuzzy logic programming frameworks enhanced with them can correctly work together with lexical resources and ontologies in order to improve their capabilities for knowledge representation and reasoning

COVID-19 Impact: A Case Study at the School of Agricultural Engineering and Environment of the Universitat Politècnica de València

[EN] To study the first impact of the COVID-19 crisis on the results obtained by students belonging to the School of Agricultural Engineering and Environment at the Universitat Politecnica de Valencia (Spain), the average of the marks corresponding to three academic years (2016-2019) was compared to those obtained in 2019-2020 for a total of four bachelor's degrees and two semesters. Our results suggest a positive effect on the marks obtained during the activation of emergency remote teaching during the spring semester of 2019-2020 in three out of the four degrees, with these differences being significant for the whole study. Moreover, just at the end of that period, instructors and students were surveyed regarding teaching methodologies, evaluation modalities, and difficulties found throughout the process of adapting to distance teaching. Our results allow us to sensibly think about that exceptional situation in order to propose a set of counterweighting measures which could improve the implementation of distance learning in engineering colleges.Clemente Polo, G.; Garcia-Prats, A.; Lisón, P.; Rubio Michavila, C.; Vidal-Puig, S.; Ricarte Benedito, B.; Estruch-Guitart, V.... (2022). COVID-19 Impact: A Case Study at the School of Agricultural Engineering and Environment of the Universitat Politècnica de València. Sustainability. 14(17):1-14. https://doi.org/10.3390/su141710607114141

Distance Learning In Time Of Crisis: A Case Study At The School Of Agricultural Engineering And Environment Of Universitat Politècnica De València

[EN] Higher education is continuously evolving to keep up with the challenges posed by the introduction of information and communication technologies (ICT) to education. In this sense, distance learning is booming, with an increasing number of higher education students taking advantage of the flexibility remote learning provides. The School of Agricultural Engineering and Environment (ETSIAMN) of Universitat Politècnica de València (UPV) has been gradually incorporating ICT tools in its bachelor and master degrees for the last two decades. As a result, many college students and university instructors are familiar with ICT techniques. However, the unprecedented COVID-19 crisis has put distance learning in the spotlight like never before, forcing students, faculty, and staff to adapt to the new situation with hardly any preparation time. For that reason, it is convenient to analyse in depth the results and impact of the teaching and evaluation methodologies developed and applied during this critical period, as a way to detect and amend potential inefficiencies in the learning process. The specific goal of this study was to analyse the teaching period during the COVID-19 crisis in ETSIAMN, which covered the spring semester of the academic year 2019-2020. To this purpose, 114 instructors and 274 students were surveyed in July 2020, belonging to four bachelor degrees (agricultural and biological engineering; forestry engineering; food engineering; and biotechnology), and three master degrees (agricultural and biological engineering; forestry engineering, and oenology). Regarding the experimental design for the survey, three main blocks were identified: the first block corresponds to teaching methodologies, comparing students and faculty preferences for distance lecturing; the second block focuses on evaluation modalities and exam configurations; and the final block centers on the difficulties found by both students and lecturers along the adaptation process from conventional to distance teaching. Results showed that instructors and students preferred a combination of live streaming with recorded lectures, being multiple choice the favourite examination type, although many students rated first a project-based evaluation. Overall, students rejected tests with no possibilities to go back on already answered questions, and instructors mostly preferred limiting the time to complete the on-line tests. The lack of motivation was the main barrier encountered by students to achieve an effective learning. Finally, a set of counterweighting measures to improve and promote the successful implementation of distance learning in engineering colleges is proposed.Clemente Polo, G.; Garcia-Prats, A.; Lisón, P.; Rubio Michavila, C.; Ricarte Benedito, B.; Estruch-Guitart, V.; Fenollosa Ribera, ML.... (2020). Distance Learning In Time Of Crisis: A Case Study At The School Of Agricultural Engineering And Environment Of Universitat Politècnica De València. IATED Academy. 3938-3945. https://doi.org/10.21125/iceri.2020.0889S3938394

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Diseño e implementación de un lenguaje de programación lógica borrosa con unificación débil.

Con el objetivo de facilitar el desarrollo de tales aplicaciones y dar solución a los problemas que la Programación Lógica tiene a este respecto, ha surgido el interés por diseñar lenguajes declarativos (en particular, lenguajes lógicos) que incluyan características de la lógica borrosa e incorporen entre sus recursos expresivos la posibilidad de tratar la información imprecisa de forma natural. Este intento ha dado lugar a un área de investigación denominada Programación Lógica Borrosa, en la cual se enmarca nuestro trabajo. Dentro de la Programación Lógica Borrosa, fijamos nuestra atención en las propuestas que extienden el algoritmo de unificación, en particular, en propuestas que sustituyen el algoritmo de unificación sintáctico por uno borroso basado en relaciones de similaridad. A esta nuevo tipo de unificación se le denomina Unificación Débil y al procedimiento de resolución que lo soporta Resolución SLD Débil. En este trabajo diseñamos e implementamos un lenguaje de programación lógica basado en Unificación Débil que denominamos Bousi~Prolog. Como parte del diseño proponemos una nueva semántica declarativa para un subconjunto puro del lenguaje, demostrando su corrección. Además, con el objetivo de dar solución a algunas limitaciones de los lenguajes basados en similaridad, introducimos un nuevo modelo de unificación débil basada en relaciones de proximidad, que denominamos Unificación basada en Proximidad, demostrando la corrección y completitud del mismo. Adicionalmente, diseñamos e implementamos un nuevo y eficiente algoritmo de unificación de conjuntos borrosos, que nos permite incluir de forma natural el tipo de dato variable lingüística en nuestro lenguaje. Para que el lenguaje diseñado, pueda ser empleado en contextos reales analizamos la implementación de este lenguaje sobre una arquitectura basada en la Máquina Abstracta de Warren (WAM), en concreto, adaptamos la WAM para que pueda ejecutar programas Bousi~Prolog, como resultado obtenemos una WAM que permite el manejo de relaciones borrosas y que denominamos por motivos históricos WAM basada en Similaridad (SWAM). Para que esta máquina sea utilizable se ha implementado un compilador que transforma programas Bousi~Prolog en código SWAM y un entorno de desarrollo que facilita el desarrollo de estos programas. Por último mostramos la utilidad del lenguaje diseñado mediante el análisis e implementación de algunos pequeños ejemplos extraídos de diferentes campos de aplicación, como son: Bases de Datos Flexibles, Sistemas Basados en el Conocimiento, Recuperación de Información y Razonamiento Aproximado