12th International Symposium on Intelligent Distributed Computing
IDC 2018
15-17 October 2018, Bilbao, Spain

Sponsored by:

Latest news:

September 12th, 2018:
Early registration fees will be maintained until the celebration of the conference

September 4th, 2018:
Program is available

June 28th, 2018:
Registration is open

June 4th, 2018:
Updated notification deadline: June 5th, 2018

May 1st, 2018:
Submission deadline extended: May 15th, 2018 (no more extensions will be granted)

April 9, 2018:
Submission deadline extended: May 1st, 2018.

March 26, 2018:
New tutorial: ANDROPYTOOL.

March 6, 2018:
Confirmed Special Issue on Applied Soft Computing.

March 6, 2018:
New invited speaker: Albert Bifet.

January 19, 2018:
New Accepted Workshop: ML-PdM.

December 13, 2017:
New tutorial: JMETALSP.

December 13, 2017:
New tutorial: KMBD.

December 7, 2017:
New invited speakers: Francisco Herrera and Eleni I. Vlahogianni.

December 5, 2017:
New Accepted Workshop: COMPSUS.

November 15, 2017:
New Accepted Workshop: INDILOG.

November 6, 2017:
Confirmed Special Issue on Future Generation Computer Systems.

October 30, 2017:
Definitive conference dates published.

October 25, 2017:
Tentative conference dates published.

October 20, 2017:
First CFP published.

October 19, 2017:
Invited speakers: Jose A. Lozano and David Camacho.

October 18, 2017:
IDC 2018 web site was launched.

Invited Speakers

José A. Lozano

Full Professor, University of the Basque Country (UPV-EHU), Spain
Research Professor, Basque Center for Applied Mathematics (BCAM), Spain


Jose A. Lozano received the PhD in Computer Science in 1998 and currently is Full Professor in the Department of Computer Science and Artificial Intelligence at the University of the Basque Country UPV/EHU (Spain), where he leads since 2005 the Intelligent Systems Group. His research interests ranged over several areas of computer science, particularly metaheuristic optimization, probabilistic graphical models and machine learning and their application to problems in biomedicine, bioinformatics, ecology and risk analysis, to name a few. Prof. Lozano has published more than 100 ISI journal papers receiving his works more than 9500 citations in Google Scholar. He is currently an associate editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Evolutionary Computation and a member of the editorial board of Evolutionary Computation, Memetic Computing and several other journals on computational intelligence.

Title: Recent Advances on Time Series Data Mining


Time series have gained much interest in the last decade. They appear naturally in industrial, medical or economical environments to name a few. Time series mining refers to the activities related with the extraction of knowledge from time series databases. Particularly, typical machine learning activities such as supervised classification or clustering are carried out from this kind of data. Furthermore, the timely nature of the data allows considering new problems. An example is the early classification of time series, where the objective is to classify the series as early as possible a before its end. In this talk we will give an overview of recent advances in the field, dealing with topics such as streaming time series, multi-variate time series, outliers, etc.

David Camacho

Associate Professor, Universidad Autónoma de Madrid, Spain


David Camacho is currently working as Associate Professor in the Computer Science Department at Universidad Autónoma de Madrid (Spain). He is the founder and head of the Applied Intelligence & Data Analysis Group (http://aida.ii.uam.es). He received a Ph.D. in Computer Science (2001) from Universidad Carlos III de Madrid, and a B.S. in Physics (1994) from Universidad Complutense de Madrid. He has published more than 150 journals, books, and conference papers. His research interests include Data Mining (Clustering), Computational Intelligence (mainly in Evolutionary Computation: GA & GP, Swarm Intelligence: ACO algorithms), Multi- Agent Systems & DSS (Unmanned Air Vehicles) and Videogames.

Title: Bioinspired Social Network Analysis: a case study on radical networks


Social Mining and Community Finding methods can be seen as a particular application domain of Big Data and Data Mining areas. The interest in Community Finding Problems on Social Networks (SN) have experienced an increasing attention over the last years due to the straigthforward access to the information stored in these sources, which can be done through APIs or bots. Once the information is gathered and preprocessed, is theoretically simple to apply different kind of algorithms to analyse the knowledge structure, extracting patterns that can be later used in Decision Support Systems. However, when this kind of algorithms are applied some several especific application domains in Social Networks, as Radical Networks, some problems related to both, the amount of available, and the quality, of information, can be a challenge to the application of traditional Machine Learning-based methods. This keynote will show how Evolutionary strategies, and other bio-inspired methods, have been successfully applied to tackle the community finding problem in social networks. The keynote will briefly analyse the main challenges and problems related to small SN (networks with low amount of trustworthy data), and how these methods can be applied to extract patterns from static SN (where no modifications are allowed during the application of the algorithm), or dynamic SN (where it's allowed to use temporal information about the evolution of the network). The keynote will be applied over the detection of radical networks, where the interactions and flow information between the users of the network are used to promote, disseminate and make proselitism of radical ideologies.

Francisco Herrera

Full Professor, Universidad de Granada, Spain


Francisco Herrera (SM'15) received his M.Sc. in Mathematics in 1988 and Ph.D. in Mathematics in 1991, both from the University of Granada, Spain. He is currently a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada. He has been the supervisor of 42 Ph.D. students. He has published more than 370 journal papers that have received more than 58000 citations (Scholar Google, H-index 120). He is coauthor of the books "Genetic Fuzzy Systems" (World Scientific, 2001) and "Data Preprocessing in Data Mining" (Springer, 2015), "The 2-tuple Linguistic Model. Computing with Words in Decision Making" (Springer, 2015), "Multilabel Classification. Problem analysis, metrics and techniques" (Springer, 2016), "Multiple Instance Learning. Foundations and Algorithms" (Springer, 2016). He currently acts as Editor in Chief of the international journals "Information Fusion" (Elsevier) and “Progress in Artificial Intelligence (Springer). He acts as editorial member of a dozen of journals.
He received the following honors and awards: ECCAI Fellow 2009, IFSA Fellow 2013, 2010 Spanish National Award on Computer Science ARITMEL to the "Spanish Engineer on Computer Science", International Cajastur "Mamdani" Prize for Soft Computing (Fourth Edition, 2010), IEEE Transactions on Fuzzy System Outstanding 2008 and 2012 Paper Award (bestowed in 2011 and 2015 respectively), 2011 Lotfi A. Zadeh Prize Best paper Award of the International Fuzzy Systems Association, 2013 AEPIA Award to a scientific career in Artificial Intelligence, and 2014 XV Andalucía Research Prize Maimónides (by the regional government of Andalucía), 2017 Security Forum I+D+I Prize, and 2017 Andalucía Medal (by the regional government of Andalucía). He has been selected as a Highly Cited Researcher http://highlycited.com/ (in the fields of Computer Science and Engineering, respectively, 2014 to present, Clarivate Analytics).
His current research interests include among others, soft computing (including fuzzy modeling, evolutionary algorithms and deep learning), computing with words, information fusion and decision making, and data science (including data preprocessing, prediction and big data).

Title: Quality data for Enriched Deep Learning models: Preprocessing and data augmentation


In the last years, deep learning methods and particularly Convolutional Neural Networks (CNNs) have exhibited excellent accuracies in many image and pattern classification problems, among others. To get quality data is the foundation for god data analytics in general, and it is also very important for getting a good deep learning model. Quality data requires a deep data preprocessing analysis to adapt the data to fulfill the input demands of each learning algorithm. Data preprocessing is an essential part of any data mining process. In some cases, it focuses on correcting the deficiencies that may damage the learning process, such as omissions, noise and outliers, among others. In contrast to the classical classification models, the high abstraction capacity of CNNs allows them to work on the original high dimensional space, which reduces the need for manually preparing the input. However, a suitable preprocessing is still important to improve the quality of the result. One of the most used preprocessing techniques with CNNs is data augmentation for small image datasets, which increases the volume of the training dataset by applying several transformations to the original input, decreasing the of the training to noise and overfitting. In this talk we present the connection between deep learning and data preprocessing throughout all families of methods used to improve the deep learning capabilities, including a short overview of the state-of-the-art and some applications.

Eleni I. Vlahogianni

Assistant Professor, National Technical University of Athens, Greece


Eleni I. Vlahogianni is an Assistant Professor at the Department of Transportation Planning and Engineering of the National Technical University of Athens. She hold a diploma in Civil Engineering and a Ph.D. from the National Technical University of Athens, Greece (NTUA) (specializing in Traffic Operations). She was a Visiting Scholar at the Institute of Transportation Studies of the University of California, Berkeley (US) working on the development of an Arterial Performance Measure System (APeMS). Her primary research field is traffic flow analysis, modeling and forecasting. Other research fields include mobility modeling, driving analytics, ICT applications to transportation, Intelligent Transportation Systems, traffic management, advanced technologies for monitoring transportation infrastructures. She has a strong algorithmic background, which includes nonlinear dynamics, applied statistical modeling, data mining and machine learning techniques and computational intelligence. Her professional and research experience includes projects and consultancies, in a national and European level focusing on urban mobility and traffic flow management, public transport and traffic safety. Moreover, she is an author of more than 50 articles in international peer reviewed journals and more than 60 publications in international conference proceedings with over 2000 citations. She serves as Reviewer in 58 journals including Chaos, Solitons & Fractals, Physica D, European Physical Journal, Transportation Research Part Α, Β and C, ΙΕΕΕ Transactions οn Intelligent Transport Systems, Physica A: Statistical Mechanics and its Applications, ASCE Journal of Transportation Systems. She is Associate Editor of the Transportation Research Part C: Emerging Technologies and the International Journal of Transportation Science and Technology. She serves as a Member of the International Committee on Artificial Intelligence and Advanced Computing Applications of the Transportation Research Board.

Title: When Data Science meets Transportation


With the overwhelming amount of data being gathered worldwide, the transportation sector is faced with several modeling challenges. New modeling paradigms based on Data Science that take advantage of the advent of large datasets have been systematically proposed and most times implemented. This talk attempts to present the major challenges and opportunities stemming from data science applications for personalized policy making and network level operations management through the analysis of two case studies: smartphone driving analytics and short term macroscopic traffic forecasting.

Albert Bifet

Full Professor, Telecom ParisTech, France


Albert Bifet is Full Professor at Telecom ParisTech, Head of the Data, Intelligence and Graphs (DIG) Group, and Honorary Research Associate at the WEKA Machine Learning Group at University of Waikato. Previously he worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, University of Waikato and UPC BarcelonaTech. He is the co-author of a book on Machine Learning from Data Streams. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of BigMine (2017-2012), and ACM SAC Data Streams Track (2018-2012).

Title: Massive Online Analytics for the Internet of Things


Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. In this talk, I will present an overview of data stream mining, and I will introduce some popular open source tools for data stream mining.