Tutorials will be held on July 9, 2017. Traditionally, tutorials attract a broad range of audiences, including professionals, researchers from academia, students, and practitioners, who wish to enhance their knowledge in the selected tutorial topic. Tutorials offer a unique opportunity to disseminate in-depth information on specific topics in fuzzy sets and systems, soft computing/computational intelligence, and related areas. 
In the following, the details of tutorials which will be held at FUZZ-IEEE 2017 including the name, the description, the presenters and their biographies (by clicking on the photos) are given.

  • Dr. José Maria Alonso
  • University of Santiago de Compostela, Spain

Software for Fuzzy Computing

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 requires 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 regarding both academy and industry. In recent years, most fuzzy system software (FSS) 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. As advised by the Task Force on Fuzzy Systems Software in the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society, researchers and developers should think carefully about some critical considerations (interoperability, novelty, usability, and relevance) when publishing a new software. 
Moreover, the Standards Committee of the IEEE Computational Intelligence Society has recently sponsored a new standard fuzzy language, namely IEEE Std. 1855TM –2016, which exploits the benefits offered by the eXtensible Markup Language (XML) specifications and related tools in order to make easier modeling fuzzy logic systems in a human-readable and hardware independent way. This tutorial is aimed at bridging the gap in designing fuzzy systems by providing its attendees with a systematic and complete description of state-of-the art of fuzzy system software. 
This tutorial is made up of two main parts: 
1) Fuzzy Systems Software Overview: Taxonomy, Current Research Trends and Prospects 
2) Description of the IEEE Standard for Fuzzy Markup Language 
In the first part of this tutorial, J. Alcala-Fdez and Jose M. Alonso (Chair and Vice-chair of the Task Force on Fuzzy Systems Software in the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society) will present an overview of freely available and open source FSS in order to provide a well-established framework that helps researchers to find existing proposals easily and to develop well founded future work. They will first introduce a two-level taxonomy and they will briefly discuss some of the main contributions related to each field (Fuzzy Sets Theory, Control, Decision-Making, Software Engineering, etc.). Then, they will provide a snapshot of the status of the publications and citations in this field according to the Thomson Reuters Web of Science, formerly widely known as Institute for Scientific Information (ISI) Web of Knowledge. Then, they will sketch some critical considerations (interoperability, novelty, usability, and relevance) regarding recent trends and potential research directions. 
In the second part of the tutorial, G. Acampora and A. Vitiello will introduce the new IEEE Std. 1855TM recently published by IEEE. The Standards Committee of the IEEE Computational Intelligence has published a new standard fuzzy language which exploits the benefits offered by the eXtensible Markup Language (XML) specifications and related tools in order to make easier modeling fuzzy logic systems in a human-readable and hardware independent way. The goal of this part of the tutorial is 1) to show how IEEE 1855 is used to model different kinds of fuzzy systems, and 2) how it can be integrated in different software architecture to enable systems’ designers to embed fuzzy reasoning in their frameworks. 
Bibliographical References 
1) J. Alcalá-Fdez and J.M. Alonso, A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends and Prospects, IEEE Transactions on Fuzzy Systems, 24(1):40-56, 2016, DOI:10.1109/TFUZZ.2015.2426212. Complementary material is available online at http://sci2s.ugr.es/fss 
2) IEEE-SA Standards Board, IEEE Std. 1855TM –2016, IEEE Standard for Fuzzy Markup Language, IEEE Computational Intelligence Society, sponsored by the IEEE Standards Committee, 2016.

  • Dr. Isaac Triguero
  • University of Nottingham, UK

Fuzzy Models for Data Science and Big Data

In the era of the information technology, the problem of managing Big Data applications is becoming the main focus of attention in a wide variety of disciplines such as science, business, industry, and so on. Data and the ability to process and extract knowledge from it are the “new gold” in the digital economy in which we move. As a result, it has emerged an area called Data Science. It collects all scenarios in which data has a starring role with the aim of turning it into knowledge. Data Science encompasses the areas known as machine learning, data mining, social mining, Big Data, and so on. 
Addressing Big Data becomes a very interesting and challenging task where we must consider new paradigms to develop scalable algorithms. The MapReduce framework, introduced by Google, allows us to carry out the processing of these large amounts of information. Its open source implementation, named Hadoop, led the development of a popular platform with a wide use. Recently, new alternatives to the standard Hadoop-MapReduce framework have arisen to improve the performance in this scenario, being the most relevant one the Apache Spark project.The MapReduce framework implies that existing algorithms have to be redesigned or that new ones need to be developed in order to take advantage of their capabilities in the big data context. 
The challenges posed by real-world Big Data problems are manifold. Apart from the straightforward computational complexity, researchers in this field must deal with vague, imprecise or uncertain data. Among different techniques for the data modeling, those based on fuzzy sets and fuzzy logic are a valuable tool for developing robust solutions. In this tutorial, we will first provide a gentle introduction to the problem of Big Data as well as the presentation of recent technologies (Hadoop ecosystem, Spark). Then, we will dive into the field of Big Data analytics, introducing machine learning libraries such as Mahout and MLlib. 
Afterward, we will go across the topic of fuzzy modeling in the Big Data context. We start by introducing the features and design for the most recent approaches in this field. We aim at defining the direction for the design of powerful algorithms based on fuzzy systems, and how the information extracted with these models can be useful for the experts. 
The last part of this tutorial will dig into the concept of Data Science. To do so, we will shortly introduce non-classical problems that have acquired much relevance in the last years, such as multi-label problems, transfer learning, multi-view learning, and so on, discussing the usefulness on the use of fuzzy modeling. Additionally, we will focus on the interpretability to raise some questions to reflect the comprehensibility, beyond the dialectical interpretability-accuracy debate, looking for models performing well, understandable and simple. Finally, we analyze the future opportunities that fuzzy modeling may have with respect to Data Science in terms of the knowledge represented by fuzzy and linguistic rules. 
Table of contents and estimated duration: 
– A gentle introduction to big data 
– Big Data Analytics 
– Fuzzy Modeling for big data 
– Data Science: New scenarios and Non-classical problems 
– Fuzzy Models in Data Science – Beyond interpretability vs accuracy trade-off 
– Fuzzy Models in Data Science: Opportunities