Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

Fuzzy systems have been used widely thanks to their ability to successfully solve a wide range of problems in different application fields. However, their replication and application require a high level of knowledge and experience. Furthermore, few researchers publish the software and/or source code associated with their proposals, which is a major obstacle to scientific progress in other disciplines and in industry. In recent years, most fuzzy system software has been developed in order to facilitate the use of fuzzy systems. Some software is commercially distributed, but most software is available as free and open-source software, reducing such obstacles and providing many advantages: quicker detection of errors, innovative applications, faster adoption of fuzzy systems, etc. In this paper, we present an overview of freely available and open-source fuzzy systems software in order to provide a well-established framework that helps researchers to find existing proposals easily and to develop well-founded future work. To accomplish this, we propose a two-level taxonomy, and we describe the main contributions related to each field. Moreover, we provide a snapshot of the status of the publications in this field according to the ISI Web of Knowledge. Finally, some considerations regarding recent trends and potential research directions are presentedThis work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grants TIN2014-56633-C3-3-R and TIN2014-57251-P, the Andalusian Government under Grants P10-TIC-6858 and P11-TIC-7765, and the GENIL program of the CEI BioTIC GRANADA under Grant PYR-2014-2S

Exploring the balance between interpretability and performance with carefully designed constrainable Neural Additive Models

The interpretability of an intelligent model automatically derived from data is a property that can be acted upon with a set of structural constraints that such a model should adhere to. Often these are in contrast with the task objective and it is not straightforward how to explore the balance between model interpretability and performance. In order to allow an interested user to jointly optimise performance and interpretability, we propose a new formulation of Neural Additive Models (NAM) which can be subject to a number of constraints. Accordingly, our approach produces a new model that is called Constrainable NAM (or just CNAM in short) and it allows the specification of different regularisation terms. CNAM is differentiable and is built in such a way that it can be initialised as a solution of an efficient tree-based GAM solver (e.g., Explainable Boosting Machines). From this local optimum the model can then explore solutions with different interpretability-performance tradeoffs according to different definitions of both interpretability and performance. We empirically benchmark the model on 56 datasets against 12 models and observe that on average the proposed CNAM model ranks on the Pareto front of optimal solutions, i.e., models generated by CNAM exhibit a good balance between interpretability and performance. Moreover, we provide two illustrative examples which are aimed to show step by step how CNAM works well for solving classification tasks, but also how it can yield insights when considering regression tasks

Hierarchical approach to enhancing topology-based WiFi indoor localization in large environments

Traditionally, WiFi has been used for indoors localization purposes due to its important advantages. There are WiFi access points in most buildings and measuring WiFi signal is free of charge even for private WiFi networks. Unfortunately, it also has some disadvantages: when extending WiFi-based localization systems to large environments their accuracy decreases. This has been previously solved by manually dividing the environment into zones. In this paper, an automatic partition of the environment is proposed to increase the localization accuracy in large environments. To do so, a hierarchical partition of the environment is performed using K-Means and the Calinski-Harabasz Index. Then, different classification techniques have been compared to achieve high localization rates. The new approach is tested in a real environment with more than 200 access points and 133 topological positions, obtaining an overall increase in the accuracy of approximately 10% and reducing the error to the real position to 2.45 metres.Ministerio de Ciencia e InnovaciónUniversidad de AlcaláPrincipado de Asturia

Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort

Abstract Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effortThis work has been funded by TIN2014-56633-C3-3-R (ABS4SOWproject) from the Ministerio de Economía y Competitividad and the University of Alcalá Postdoctoral Research program (30400M000.541A.640.17)S

Fuzzy classifier ensembles for hierarchical WiFi-based semantic indoor localization

The number of applications for smartphones and tablets is growing exponentially in the last years. Many of these applications are supported by the so-called Location Based Services, which are expected to provide reliable real-time localization anytime and anywhere, no matter either outdoors or indoors. Even though outdoors world-wide localization has been successfully developed through the well-known Global Navigation Satellite System technology, its counterpart large-scale deployment indoors is not available yet. In previous work, we have already introduced a novel technology for indoor localization supported by a WiFi fingerprint approach. In this paper, we describe how to enhance such approach through the combination of hierarchical localization and fuzzy classifier ensembles. It has been tested and validated at the University of Edinburgh, yielding promising results.Ministerio de Economía y CompetitividadXunta de Galici

Aplicando Gamificación con Kahoot en el desarrollo de competencias

En el escenario del Espacio Europeo de Educación Superior (EEES) se plantean nuevas metodologías, tanto de evaluación como de aprendizaje, como alternativa a la clase magistral con el fin de situar al alumno como elemento activo del proceso de enseñanza-aprendizaje. En este contexto el sistema educativo se ha centrado en el aprendizaje y el papel activo de los estudiantes, así como en la integración de las Tecnologías de la Información y la Comunicación (TIC) [1]. Un ejemplo de integración de las TIC en el sistema educativo es el concepto de aprendizaje electrónico móvil (m-learning en inglés), el cual se puede asociar a una metodología de enseñanza y aprendizaje que facilita la construcción del conocimiento, la resolución de problemas y el desarrollo de competencias de manera autónoma y ubicua, gracias a la mediación de dispositivos móviles portables con conexión a Internet tales como teléfonos móviles, tabletas, portátiles, werables, etc. [2]. En este sentido, el aprendizaje móvil plantea una nueva conceptualización de los modelos tradicionales de uso y aplicación de las tecnologías, una realidad con un plazo de adopción inmediato según el último informe NMC Horizon Report: 2017 Higher Education Edition [3].Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Genetic polymorphisms of the wint receptor LRP5 are differentially associated with trochanteric and cervical hip fractures

Producción CientíficaPurpose. Epidemiological studies suggest that cervical and trochanteric hip fractures have different pathogenesis. We planned to test the hypothesis that genetic factors have different influences on both types of fractures. Methods. Ten polymorphisms of genes known to play an important role in skeletal homeostasis (estrogen receptor alpha [ESR1], aromatase [CYP19A1], type I collagen [COL1A1], and lipoprotein receptor-related protein 5 [LRP5]) were analyzed in 471 Spanish patients with fragility hip fractures. Results. Two polymorphisms of the LRP5 gene (rs7116604 and rs3781600) were associated with the type of fracture (p-value 0.0085 and 0.0047, respectively). The presence of rare alleles at each locus was associated with trochanteric fractures over cervical fractures (OR 1.7 in individuals with at least one rare allele at rs7116604 or rs3781600 loci, in comparison with the common homozygotes). Considering individuals bearing the four common alleles as reference, the OR for trochanteric fractures was 1.6 in those with 1 or 2 rare alleles, and 7.5 in those with 3 or 4 rare alleles (p-value for trend 0.0074), which is consistent with an allele-dosage effect. There were no significant differences in the frequency distributions of the ESR1, CYP19A1 and COL1A1 genotypes between trochanteric and cervical fractures in either the original group or in an extended group of 818 patients. Conclusions. These results suggest LRP5 alleles influence the type of hip fractures. They support the view that different genetic factors are involved in cervical and trochanteric fractures, which should be taken into consideration in future genetic association studies

An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis

Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitusThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science, ICT and Future Planning)-NRF-2017R1A2B2012337)S

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease riskThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2020R1A2B5B02002478). In addition, Dr. Jose M. Alonso is Ramon y Cajal Researcher (RYC-2016-19802), and its research is supported by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-B-I00, TIN2017-84796-C2-1-R, TIN2017-90773-REDT, and RED2018-102641-T) and the Galician Ministry of Education, University and Professional Training (grants ED431F 2018/02, ED431C 2018/29, ED431G/08, and ED431G2019/04), with all grants co-funded by the European Regional Development Fund (ERDF/FEDER program)S