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Keynotes

Functional brain connectivity measured with non-invasive brain-computer interfaces

Dra. Dania Gutiérrez Ruiz
Centro de Investigación y de Estudios Avanzados (Cinvestav), Unidad Monterrey.
Personal webpage

Abstract

Functional brain connectivity is a popular technique for investigating neural organization in both healthy subjects and patients with mental illness, and such connectivity is usually measured with functional magnetic resonance imaging (fMRI). Recently, brain-computer interfaces (BCI) based on non-invasive measurements, such as electroencephalography (EEG), are allowing to acquire brain activity related to different mental tasks. It is through the analysis of such EEG measurements that we try to assess the functional brain networks during the different mental tasks. Hence, in this presentation we will review some of the metrics that our laboratory has explored so far in order to make that assessment. In particular, I will mainly talk about the partial directed coherence (PDC), which was recently introduced as a linear frequency-domain quantifier of the multivariate relationship between simultaneously observed EEG data. Therefore, we use the PDC to explain functional connectivitites observed in typical BCI experiments, which are in agreement with those previously observed in fMRI, but with the advantage of having a less elaborated experimental setting.

About the speaker

Dania Gutiérrez received the B.Sc. degree in Electrical Engineering from the National Autonomous University of Mexico (UNAM) in 1997, the M.Sc. degree in Electrical Engineering and Computer Sciences, as well as the Ph.D. degree in Bioengineering from the University of Illinois at Chicago (UIC), in 2000 and 2005, respectively. Her studies in the United States were sponsored by the Fulbright Program. From March 2005 to May 2006, she held a postdoctoral fellowship at the Department of Computer Systems Engineering and Automation, Institute of Research in Applied Mathematics and Systems (IIMAS), UNAM. In June 2006, she joined the Center for Research and Advanced Studies (Cinvestav) at Monterrey, Mexico. There, she is an Associate Professor in the area of bioengineering, as well as the current Academic Secretary. In 2015, Dr. Gutiérrez was a visiting professor at the Nicolaus Copernicus University at Toruń, Poland. Dr. Gutiérrez’s area of expertise is statistical signal processing and its applications to biomedicine. She is currently working in projects related to human-machine interfaces, neurocognition, bioelectromagnetism, and biomechanics. Dr. Gutiérrez is also an activist fighting for the rights of the LGBT community in Mexico.

Spiking neural models and its application in pattern recognition

Dr. Roberto A. Vázquez
Universidad La Salle México

Google Scholar
Researchgate
Scopus
Orcid

Abstract

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. These models have been called the 3rd generation of artificial neural networks. This kind of models has been applied in a wide range of areas, mainly from the field of computational neurosciences, however, its application in the field of artificial intelligence is practically new. Spiking neurons are neural models that try to simulate the behavior of biological neurons when they are excited with an input current (input pattern) during a certain period time. Instead of generating a response in its output every iteration, as classical neurons do, this model generates a response (spikes or spike train) only when the model reaches a specific threshold. This response could be coded into a firing rate and perform a pattern classification task according to the firing rate generated with the input current. To perform a classification task the model ought to exhibit the next behavior: patterns from the same class must generate similar firing rates and patterns from other classes have to generate firing rates sufficiently dissimilar to differentiate among the classes. In this talk, we describe a methodology for apply a spiking neural model in pattern recognition problems as well as the advantage against classical neural models.

About the speaker

Roberto A. Vazquez received his Ph.D. degree at the Center for Computing Research, National Polytechnic Institute (CIC-IPN), Mexico City, Mexico, 2009. He was published, as author or coauthor, several journal articles, book chapters and proceedings on the fields of Neurocomputing, Computational Neuroscience, Evolutionary Computation, Swarm Intelligence, Pattern Recognition and Image Analysis. Since 2010 he has developed a research field related to the application of spiking neural models in pattern recognitions and their results have been published in several journals, book chapters and proceeding. Actually, he is the Head of Research and Professor- Researcher at La Salle University, Mexico City, Mexico and member of the National Research System of Mexico, level I and he has and H-Index 17, according to google scholar.

Towards Evidence Based Education and Learning with Learning Analytics

Dr. Hiroaki Ogata
Kyoto University

Abstract

The multi-disciplinary research approach of Learning Analytics (LA) has provided methods to understand learning logs collected during varied teaching-learning activities and potentially enrich such experiences. This talk will explain how technology can help to extract evidence of effective teaching-learning practices by applying the knowledge base of LA and developing novel techniques. It focuses discussions on realizing a technology-enhanced evidence-based education and learning (TEEL) system. This talk will propose the Learning Evidence Analytics Framework (LEAF) and draw a research road-map of educational big data-driven evidence-based education system. Teachers can refine their instructional practices, learners can enhance learning experiences and researchers can study the dynamics of the teaching-learning process with it.

About the speaker

Hiroaki Ogata is a Professor at the Academic Center for Computing and Media Studies, and the Graduate School of Informatics, Kyoto University, and an associate member of Science Council of Japan. His research includes Computer Supported Ubiquitous and Mobile Learning, CSCL, CALL, and Learning Analytics. He has published more than 300 peer-reviewed papers including SSCI Journals and international conferences. He has received several Best Paper Awards, and gave keynote lectures in several countries. He is an associate editor of IEEE Transactions on Learning Technologies. RPTEL and IJMLO, and also an editorial board member of IJCSCL, IJAIED and JLA. He is an EC member of SOLAR and APSCE societies.

Decentralized event-triggered leader-following consensus of VTOL-UAVs

Dr. José Fermi Guerrero-Castellanos
Benemérita Universidad Autónoma de Puebla

Abstract

Motivated by applications in physics, biology and engineering the study of consensus and cooperative control of dynamic systems has become an important topic in engineering and control theory. Roughly speaking, consensus means to reach an agreement regarding a certain quantity of interest that depends on the state of all agents. Recently, published works addressed resource aware implementations of the control law using event-triggered sampling, where the control law is event-driven. Such a paradigm calls for resources whenever they are indeed necessary. In the context of cooperative control (centralized or decentralized), the event-based paradigm appears as a mean to reduce the communication bandwidth in the network since, contrary to the classical scheme, an event-triggered control invokes a communication between the different agents only when a certain condition is satisfied. In this talk one presents the development of an event-based collaborative control applied to the problem of consensus and formation of a group of VTOL-UAVs (Vertical Take-off and Landing, Unmanned Aerial Vehicles). Assuming that the vehicle network exchange information through a directed and strongly connected graph, a decentralized control law is designed, and an event-based algorithm is developed. Each VTOL-UAV decides, based on the difference of its current state and its latest broadcast state, when it has to send a new value to its neighbors. The stability of the complete system is carried out in the Lyapunov sense together with the ISS (Input-to-State Stability) approach. Numerical simulations and real-time experimentations validate the theoretical analysis. Finally, an overview about our work at BUAP in the framework of UAVs will be presented.

About the speaker

José Fermi Guerrero-Castellanos received a B.S. degree in electronic science, from the Autonomous University of Puebla (BUAP), México in 2002 and the M.Sc and Ph.D degree in Automatic Control from the Grenoble Institute of Technology and Joseph Fourier University, Grenoble, France, in 2004 and 2008, respectively. Between January and June 2008, he was a Postdoctoral Researcher at GIPSA-Lab Laboratory, Grenoble, France. After spending one year at the University Polytechnic of Puebla, Mexico as an assistant Professor, he joined in 2009 the Faculty of Electronics at BUAP as a full professor, where he established and directs the Control and Cyber-Physical Systems Laboratory. In 2016 he was a visiting research professor at the Laboratory of Image, Signal and Intelligent System (LISSI) – University of Paris-Est Créteil (UPEC). Between August 2013 and December 2017 he was the head of Renewable Energy Engineering at BUAP. He is a Member of Mexican Academy of Science (AMC), Mexican Association on Automatic Control (AMCA), IEEE and Member of the National System of Researchers (Researcher Level I), Mexico. His research interests include guidance and control of autonomous systems, event-triggered control, wearable robots and control of renewable energy systems.

From Author to Behavioral Profiling in Social Media

Dr. Manuel Montes y Gómez
Laboratorio de Tecnologias del Lenguaje
Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)

Email

Abstract

Everyday the Internet witnesses a great amount of people activity in many different social media sites. They allow us to share –personal– information very easily and with it they offer innumerable new opportunities for business intelligence, health monitoring and personalization of applications. However, at the same time, and as a consequence of the anonymity they provide, they also represent a major threat to users who are exposed to a number of risks and potential attacks.
This talk aims to resume some ideas we have developed in the previous five years towards the profiling of social media users and the detection of some of such attacks. Firstly, it will describe the author profiling task and present a new feature selection and weighting approach especially suited for it. Secondly, it will show the application of this approach for the detection of depressed users. Thirdly, it will present a new representation based on the idea of the emotions flow for the detection of depressed and aggressive users. The talk will conclude with a brief overview of our ongoing work in these tasks.

About the speaker

Manuel Montes obtained his Ph.D. in Computer Science from the Computing Research Center (CIC) of the National Polytechnic Institute (IPN) of Mexico in 2002. Currently, he is researcher at the National Institute of Astrophysics, Optics and Electronics (INAOE), located in Puebla, Mexico. His research is on automatic text processing. He is author of more than 200 journal and conference papers in the fields of information retrieval, text mining and authorship analysis, which have earned him the recognition of National Researcher Level II (SNI II) and be a member of the Mexican Academy of Sciences (AMC).