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

Sponsored by:

Latest news:

October 14th, 2018:
Slides for the opening talk available.

October 13th, 2018:
Social Program available (further details to be given during the conference opening)

October 11th, 2018:
New invited speaker: Josu Ceberio, in replacement of Prof. Herrera (apologies on his behalf due to illness)

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: Beyond supervised and unsupervised scenarios: Weakly supervised classification


The literature around machine learning has recently seen many problems that depart from the standard supervised classification problem. In these problems the common structure of a supervised dataset where there is a label associated to each instance is broken: an instance can have several labels, a label is assigned to a subset of instances, etc. These problems present different degrees of uncertainty in learning but also in prediction. In this talk we will provide a taxonomy of non-standard supervised classification problems illustrating each of them with case examples. We will also elaborate on how to learn classifiers in these scenarios, and give a particular successful example.

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.

Josu Ceberio

Assistant Professor, University of the Basque Country (UPV-EHU), Spain


Josu Ceberio received the bachelor's degree in Computer Science from the University of the Basque Country (UPV/EHU) in 2007, and two years later he took the master’s degree in Computer Science from the same university. Since 2010, Josu has been member of the Intelligent Systems Group where he obtained, in 2014, the PhD in Computer Science with the thesis entitled “Solving Permutation-based Combinatorial Optimization Problems with Estimation of Distribution Algorithms and Extensions Thereof”.
Since 2017, Josu Ceberio is lecturer in the department of Computer Science and Artificial Intelligence in UPV/EHU. His main research interest include the combinatorial optimization, evolutionary computation and machine learning, with especial dedication to the development of estimation of distribution algorithms. Not limited to that, in the last year, he began investigating the structure of permutation-based problems from the perspective of the generation of difficulty under the scope of local search algorithms.

Title: Permutation-based Combinatorial Optimization Problems under the Microscope


Since the publication of the Travelling Salesman Problem, permutation-based problems have been recurrently investigated by the operations research and artificial intelligence community. Motivated by the complexity of solving them up to optimality (NP-hard in most of the cases), a large amount of papers has been written in which a variety of exact, heuristic and meta-heuristic algorithms have been proposed. However, as claimed by Simon and Newell in 1958, when optimizing a problem it is recommendable (if not mandatory) to, first, gain intuition, insight and knowledge related to the structure of the problem. The development of the algorithm should be carried out afterwards. Inspired by that idea, in this talk, we show that, taking permutation-based problems as basis, the idea of Simon and Newell is still valid, and is, in fact, the path to follow when designing efficient algorithms. To that end, during the talk, we will study three examples of problems separately, and present three different approaches to optimize them: Estimation of Distribution Algorithms, Local Search based algorithms and Multi-objectivization. In the three cases, the scientific contribution comes from the understanding of the problem. The talk will conclude by describing the large amount of aspects that are still pending with regard to this type of problems.

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.