Multi-objective Optimization – Computational Intelligence

Interest in Multi-Objective Optimization (MOP) has been notably increasing since Multi-Objective Optimization Evolutionary Algorithms (MOEAs) proved their ability to solve optimization problems with several conflicting objective functions, even when many objective functions are simultaneously considered. This talk will mathematically present a general MOP as well as different alternatives and algorithms to solve a MOP, analyzing its application to several different areas of science, engineering, economy, industry and even social areas. Practical examples will illustrate the main concepts, summarizing the start of the art.


Benjamín Barán
PhD in Systems and Computer Engineering (Federal University of Rio de Janeiro – Brazil, 1993), Master in Electrical Engineering (Northeastern University, Boston – United States, 1987), Electronic Engineer (Asunción National University, Paraguay – 1983), with a widely scientific and academic experience at several universities in three continents, he has published more than a hundred scientific articles, having been awarded a dozen awards and recognitions as: The Honor of Merit Latin American Computer Science in 2013, The Panamerican Prize in Scientific Computing in 2012, The National Prize of Sciences of Paraguay – 1996, The Andrés Barbero – 1982 Prize of the Scientific Society of Paraguay, and moreover, he received a Doctorate Honoris Causa by the National University of the East in 2012. He is a member of the Honorary Scientific Commission (category III) of the National Program of Incentive to Researchers – PRONII of CONACYT. For more than two decades he has served as president of the consultancy Barán y Asociados – CBA, leading large projects in computing and communications from both the public and the private sector, including consulting works for agencies Internationals such as ITU, UNESCO, UNDP, OAS, World Bank and IDB. His main research areas are: cloud computing, multi objective optimization problems, bio-inspired algorithms, communications networks, applications to engineering and quantum computing.

What can we do in education with less “friction”? – Learning Analytics

We already know, Information and Communication Technologies (ICT) are disrupting the established order in the Industrial Age. All processes (business, personal, conceptual, of all kinds) are transformed, business models are modified and new ones are identified. The instantaneous and massive communication capacity eliminates many of the frictions and stiffness of the Industrial Age.
How does the exponential evolution of ICT affect teaching-learning processes?
We see how MOOCs (massive open online courses) expand the scope of the recipients of education with little effort and how learning analytics allows us to go into details of the educational process never before observed. But this only represents in principle what is possible. In the presentation, we will reflect on the opportunities that lie before us,but also on the challenges, uncertainties and difficulties of managing change.


Carlos Delgado Kloos
He is a Telecommunications Engineer from the Polytechnic University of Madrid, Spain and PhD in Computer Science from the Technical University of Munich, Germany. He is professor of telematics engineering at the Carlos III University of Madrid where He is also director of the GAST Research Group and Director of the UNESCO chair on “Scalable Digital education for all”. He is also vice-rector of strategy and Digital education at his university. He coordinates the eMadrid network on educational technology in the community of Madrid and is the Spanish representative in the TC3 Committee on Education of IFIP. He has led a multitude of research projects at both the European, national and bilateral levels. He has been the manager of the National I+D Program in ICT in the ministry. He has carried out research stays in universities such as Harvard, MIT, Munich and others. The number of scientific contributions in national and international conferences or magazines exceeds 400. In addition, he has written a book and co-edited more than a dozen.

Interpretability in classifiers – Data Science

Recent technological advances rely on accurate decision support systems that have been constructed as black boxes. That is, the system’s internal logic is not available to the user, either for financial reasons or due to the complexity of system. This lack of explanation can lead to technical, ethical, and legal issues. For example, if the control module of a self-driving car failed at detecting a pedestrian, it becomes crucial to know why the system erred. In some other cases, the decision system may reflect unacceptable biases that can generate distrust. Recently, the European Parliament adopted the General Data Protection Regulation (GDPR), a law that for the first time in history stipulates the right for all individuals to obtain comprehensible explanations of the logic involved when automated decision making takes place. For all these reasons, multiple research approaches provide comprehensible explanations for traditionally accurate but black-box-like machine learning classifiers such as neural networks, random forests, and support vector machines. In this talk I will survey the most relevant research efforts in “opening a black-box classifier” and I will explain why it is challenging task (the trade-off between interpretability and accuracy).


Luis Galárraga
He works as a full-time researcher at Inria (Institut National de Recherche en Informatique et Automatique) in Rennes, France. His research interests comprise the semantic Web, knowledge representation, and rule mining. Luis did his bachelor studies in Computer Engineering at ESPOL (Escuela Superior Politécnica del Litoral). He has a master’s degree in Computer Science from the University of Saarland in Saarbrücken, Germany. It was in Saarbrücken where Luis earned an IMPRS scholarship to start his doctoral studies at the Max Planck Institute for Informatics. One year later, Luis’ research group moved to Télécom ParisTech in Paris, France, where Luis obtained his doctoral degree for the disertation “Rule Mining in Knowledge Bases” in September 2016. This disertation was awarded the best thesis of the year 2016 in the domain of knowledge management by the French conference EGC (Conférence Extraction et Gestion de Connaissances). Moreover, his work was recently awarded a honorable mention in the Jim Gray Dissertation Award in the ACM SIGMOD conference. SIGMOD is one of the most important conferences in the domain of databases.

Software Product Lines – Software Engineering

One of the most important features of the software is the ability to adapt to different scenarios. Recently, the variability of the software is studied as an essential element in different domains that vary from the product lines software to cloud systems. These types of systems are known as high variability systems. High variability systems are software systems that, due to their nature, manage a large number of variable software artifacts. Feature models emerged to represent common and variable parts of high variability systems. In addition, the high number of configurations in a high variability system preclude manual analysis. In order to save this disadvantage, different researchers have proposed the use of computer mechanisms and tools. This is known as automatic analysis of feature models. Recently, the analysis has been applied to a variety of high-variability systems such as cloud computing systems or Linux kernel management. In this talk, we explore the different trends in automatic analysis of feature models and show how to improve solutions in real high variability systems such as the Android ecosystem and the generation systems of Video-Sequences.


José A. Galindo
He has developed his professional activity working both in the private company and in the public university in the United States, France, and Spain. His research areas are the product lines software and the configuration of products. He obtained a Ph.D. from the University of Seville and the University of Rennes 1 in March 2015 receiving the extraordinary prize of doctorate by the University of Seville and the award to the best national thesis by SISTEDES. He has developed his post-doctoral research activity in INRIA, France. Currently working as a researcher Juan de la Cierva at the University of Seville where he continues his line of research on configuration, testing, and evolution of highly configurable systems.

Understanding Confusion in Code Reviews – Software Engineering

Code reviews are an important mechanism for software quality assurance. However, performing code reviews can be difficult. Several studies show that code reviewers often do not understand or are confused about the change under review and its context.
In this work, We try to understand such phenomenon of confusion in code reviews. First we create a framework for confusion identification in code reviews’ comments. Then we investigate the reasons, impacts and how developers overcome confusion. And lastly, we deepen our study to investigate the intention of developers questions to understand how and what developers ask when they are confused.


Felipe Ebert
I’m a researcher from the Informatics Center (CIn), Federal University of Pernambuco (UFPE) under supervision of Prof. Fernando Castor and Prof. Alexander Serebrenik. My research interests are related to how software sytems and developers interact with each other. I’m interested in both technical and social aspects of software maintenance, specifically code reviews, mining software repositories, and also social development aspects.

Soft Computing & e-government at Ecuador. Challenges and opportunities

Nowadays, several governmental institutions around the world faced new challenges in order to improve their services and increase citizen’s engagement. Artificial Intelligence (AI) and Soft Computing (SC) approaches, have given some insights and positives outcomes. E-Government maturity models proposed by international organizations pushing toward citizens and governmental institutions working together. In this sense, governmental institutions at Ecuador show several efforts in E-government field, however early efforts with AI & SC approaches have been evidenced. This presentation explores Challenges and opportunities that governmental institutions at Ecuador should considered in the short and middle term with AI & SC approaches.


Jaime Meza
PhD in Project and Systems Engineering with mention of International Doctor and Cum Louden of excellence from the Polytechnic University of Catalonia, Spain; Master in Business Administration (MBA) and Computer Engineer. Postdoctoral researcher affiliated to University of Fribourg-Switzerland, and external researcher of Technical University of Manabí – Ecuador. Staff member of IEEE eGovernment STC. His research interests are Collective Intelligence, Soft Computing, recommender systems, and models of collaborative cognition as a way to improve public services and Higher Education. His research projects seek the reduction of tax evasion-fraud and, the improvement of tax collection using artificial intelligence systems and advanced analytical techniques. In addition, collaborative spatial urban planning supported by cognitive systems and recommendations. He has been a teacher for more than 10 years in multiple universities at Ecuador, as well as a guess professor at the Polytechnic University of Catalonia-Spain and the University of Freiburg-Switzerland. In the professional field, he has held various management positions (project manager, chair and IT advisor, auditor, etc.) in companies and government institutions at the local and national levels

Level Set Method and its Applications to Simulation – Applied Computing

The Level set Method is a numerical technique that allows us to track dynamic interfaces or boundaries. Interestingly, nature has numerous examples of this kind of phenomena. Thus, science and engineering applications have been developed over the years to exploit this technique. This talk presents the theoretical and mathematical construction, implementation ideas, and some applications to the simulation of fluids and biological objects.


Israel Pineda
He earned his Ph.D. and Master degrees in Computer Science and Engineering from Chonbuk National University, South Korea. He has a Bachelor of Engineering in Computer Systems with a minor in Artificial Intelligence and Robotics from Salesian Polytechnic University, Ecuador. Previously, he worked at the Computer Graphics and Virtual Reality Laboratory of Chonbuk National University where he developed methods to optimize the simulation of fluids and leaves for computer graphics purposes. He has experience in the software industry as a Software Engineer and as a Software Architect. He has participated as a committee member for several academic events. Currently, he is a full-time professor at the Department of Information Systems at Ecuador Metropolitan University where he is a permanent member of the scientific committee. His main research areas include numerical simulation, computer vision, computer graphics, mathematical modeling, and scientific computing.