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Introducción

El Programa de Doctorado en Informática  de la Universidad de Almería se propone como objetivo ofertar cursos de formación específicos para sus estudiantes de doctorado en las líneas de investigación del programa. 

Por este motivo, la Comisión Académica del Doctorado en Informática se complace en anunciar el presente Programa de Actividades Formativas para el año 2020 El programa está especialmente destinado a los estudiantes del Doctorado en Informática de la Universidad de Almería, aunque queda abierto a cualquier persona con intereses científicos en los citados campos de la Informática.

El programa contará con la participación de ponentes de gran prestigio internacional en el ámbito de la Informática.

Se otorgará un "Certificado de aprovechamiento" con el listado de los cursos y número de horas totales asistidas, siempre y cuando se acredite al menos un 80% de la asistencia en cada uno de los cursos asistidos, además de las actividades requeridas por el ponente en cada curso (si las hay). El certificado se emitirá a final de año, al terminar el programa formativo completo. 

Programa

Abril/Mayo 2019

Advanced techniques for management of big spatial data

Dr. Michael Vassilakopoulos, University of Thessaly, Department of Electrical and Comp. Eng., Volos, Greece.

Duración: 10 horas

Lugar: Sala de Grados (2da planta del edificio CITE-III, Universidad de Almería)

Horario (3 días): 29 y 30 abril (16:00-19:00) y 2 de mayo (16:00-20:00)

Modalidad: Seminario (es necesario registro)

Resumen: We are living in the era of Big Data. Spatial and Spatiotemporal Data are not an exception. Mobile apps, cars, GPS devices, UAVs, ships, airplanes, space telescopes, medical devices and IoT devices are generating explosive amounts of data with spatial characteristics. Web apps and social networking systems also store vast amounts of geo-located information, like geo-located tweets, or captured mobile users' locations. Modeling, storing, querying and analyzing big spatial and spatiotemporal data is an active area of basic and applied research with many challenges. Parallel and distributed frameworks utilizing cloud infrastructures are being created and extended for novel big spatial data management solutions. Solid-State Drives (SSDs) are secondary storage devices that exhibit higher (especially read) performance than Hard Disk Drives and nowadays are being used in database systems, although their exploitation for processing of spatial data is a rather recent area of research. Moreover, the large amounts of available RAM in today's computing systems permit to accelerate spatial query processing by transferring data from secondary to main memory in large chunks and processing each chunk by taking advantage of efficient algorithmic techniques.

Short-Bio: Department of Electrical and Computer Engineering, Volos, Greece, Computer Engineering and Informatics from the University of Patras (Greece) and a PhD in Computer Science from the Department of Electrical and Computer Engineering of the Aristotle University of Thessaloniki (Greece). He has participated in/coordinated several RTD projects related to Databases, GIS, WWW, Information Systems and Employment. His research interests include databases, data structures, algorithms, data mining, employment analysis, information systems, GIS and current trends of data management.

[ Descargar material del curso ] [ Fotos ]

Blockchain technology: fundamentals, current status, applications and future perspectives

Dr. Ernestas Filatovas profesor de la Universidad de Vilnius, Lituania

Lugar: Sala de Grados (2da planta del edificio CITE-III, Universidad de Almería)

Horario: 3 de mayo a las 10:30h

Modalidad: Charla (no es necesario registro)

Resumen: Blockchain, the foundation of Bitcoin, has recently received extensive attention. October 31, 2018, marked the tenth anniversary of the release of the Bitcoin white paper. In this talk, I review the blockchain evolution, starting from prehistory, following with the introductory to the essential blockchain concepts, providing details of a wide range of applications, including currently facing challenges and possible future directions.

The content of this talk has been broken down into four following parts:

1. Blockchain fundamentals – in the first part of this talk, I provide a detailed overview of evolution and essential concepts of blockchain technology.

2. Blockchain platforms – in the second part I give a broader understanding of the blockchain ecosystem. Here I also introduce a smart contract – the computational element of the blockchain technology which allows implementing user-defined operations that are not possible through plain cryptocurrency protocols.

3. Applications of blockchain – in the third part I explore different blockchain applications, especially such where the blockchain technology could become a powerful tool for improving existing business solutions. In this talk, particular emphasis is given to blockchain-based applications currently being under developed with our business partners from medicine, e-commerce, publishing, and the finance – Bank of Lithuania (LBChain project).

4. Challenges and future perspective – in the last part, I discuss the biggest facing challenges today: slow transaction speeds, scalability, interoperability, energy consumption (cost), lack of regulation, etc.

[ Descargar material del curso ]

Methods for signal analysis

Dr. Povilas Treigys profesor de la Universidad de Vilnius, Lituania

Lugar: Sala de Grados (2da planta del edificio CITE-III, Universidad de Almería)

Horario: 10 de mayo a las 10:00h

Modalidad: Charla (no es necesario registro)

Resumen: An introduction to Lithuania and Vilnius University, the Institute of Data Science and Digital Technologies, and the group of Signal and Image Analysis. A short introduction to areas of expertise in speech and image signal analysis. Further, the talk will switch to the deep learning based methods our group is currently working on:

· Histological whole slide image (WSI) analysis for tumour detection;

· WSI nuclei segmentation;

· WSI collagen framework features extraction and analysis for human survival;

· Speech signal feature space investigation for uttered word recognition;

· The application of SOM and the LSTM to vessel type classification and abnormal behaviour detection.

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Jornada sobre Transición del sector eléctrico español hacia un mercado mayoritariamente renovable en 2030

Fecha del evento: Jueves 16 de mayo de 2019 de 9:00 a 13:00

Lugar: Sala de Grados del Edificio de Gobierno y Paraninfo de la Universidad de Almería

Todos los asistentes que hayan formalizado su inscripción hasta el 15 de mayo, recibirán un certificado de asistencia

Modalidad: Jornada (es necesario hacer registro en el enlace de abajo)

[ ENLACE AL REGISTRO ] [ VER CARTEL ]

Junio 2019

Build your own Optimization Models: a practical course on AMPL

Dr. Boglárka G.-Tóth (Univ. Szeged, Hungría)

Fechas: 3 al 7 de junio de 2019

Duración: 10 horas

Lugar: Laboratorio 2.02. CITE III

Horario: 10:00 a 12:00 horas

Modalidad: Seminario (es necesario registro)

Recursos opcionales: Portatil con linux y AMPL

Resumen: This course intends to give a practical guide to solve optimization problems in general. The students will learn how to model many classes of problems, understand what makes a problem easy or difficult to solve, and which are the suitable solvers for a given problem. Starting at the very basic concepts of modeling, we will touch advanced topics like constraint and column generations, decompositions, etc., always solving a practical problem for the easier understanding. All problems will be modeled using the AMPL mathematical modeling language by the students.

Short-Bio: Boglárka G.-Tóth is a senior research fellow at the Department of Computational Optimization at the University of Szeged (Hungary) granted by the National Research, Development and Innovation Office in Hungary. She received her PhD degree in Informatics from the University of Almería in 2007. She is currently involved in research projects in the areas of facility location, global optimization and High-Performance Computing.

[ Descargar material del curso ]

Optimal Control Problems and Pontryagin’s Maximum Principle

Dr. Prof. João Miranda Lemos (Instituto Superior Técnico, Universidade de Lisboa)

Fechas: 6 y 7 de junio de 2019

Duración: 10 horas

Lugar: AULA 11 DE INFORMÁTICA (planta 1ª - CITE-III)

Modalidad y estructura de las sesiones (es necesario registro):

Horario: Jueves 6 de junio: 10:00-13:00 + 17:00-19:00 (seminarios) + 19:00-20:00 (discusiones con alumnos de doctorado).

Viernes 7 de junio: 10:00-12:00 (seminario) + 12:00-14:00 (discusiones con alumnos de doctorado).

Resumen: The seminar provides an introduction to the solution of optimal control (OC) problems using Pontryagin’s Maximum Principle. The background assumed is the one common to Ph. D. students of engineering. The idea is to present some basic principles and then propose a sequence of problems to the audience too make them involved in learning the basics of this issue guided by the professor. The problems will have an “application” content (such as biological reactors, cancer therapy, water reservoir management, mobile robots …) although they will be simple enough to be solved with pencil and paper. Sometime will also be devoted to numerical methods and experiments using MATLAB. Even for students that are not from the control area, this may be interesting, because they end up by mastering a tool that can be useful in many contexts.

Short-Bio: He is Scientific Coordinator of the INESC-ID research group on Control of Dynamic Systems. His main scientific interests are in control applications to solar thermal energy and biomedical systems, predictive adaptive control and system identification. He organizes and teches courses related to the scientific area of Systems, Decision and Control, for master students of Electrical and Computer Engineering and Aerospace Engineering.

[ Descargar material del curso ]

Septiembre 2019

Avances en inteligencia ambiental. La adopción desde una perspectiva de impacto

Dra. Macarena Espinilla Estévez, profesora titular del área de Arquitectura y Tecnología de Computadores de la Universidad de Jaén.

Fechas: 23 y 24 de septiembre de 2019

Duración: 10 horas

Lugar: Laboratorio 2.02 del edificio CITE III

Horario: Lunes 23, de 10:00 a 14:00 (4h) y de 16:00 a 19:00 (3h) y Martes 24, de 10:00 a 13:00 (3 h)

Modalidad: Seminario (es necesario registro)

Resumen: Durante el curso se expondrán los conceptos de Internet de las Cosas e Inteligencia Ambiental y su adopción para la monetización en múltiples sectores como son el turismo, la telemedicina, las ciudades inteligentes o la agricultura de precisión. Desde una perspectiva teórica y práctica se evidenciará el impacto de la inteligencia ambiental en múltiples sectores junto con los retos y desafíos que deben ser afrontados. En la perspectiva teórica, se analizarán dispositivos inteligentes de diferente índole y su aplicación en múltiples campos, presentando qué tipo de información se puede obtener de ellos y su procesamiento de manera inteligente. En la perspectiva práctica, se realizarán diversas actividades con las placas de desarrollo NodeMCU y múltiples sensores (presencia, interrupción, leds, etc.) con el fin de realizar un proyecto de Smart cities basado en un parking inteligente.

Short-Bio: Macarena Espinilla received the M.Sc. and PhD degrees in Computer Sciences from the University of Jáen (UJA), Jáen, Spain, in 2006 and 2009, respectively. She led the development of the UJAmI Smart Lab of the CEATIC (Advanced Studies Centre in Information and Communication Technologies and Engineering) in the University of Jaén from its conception. She is currently an Associate Professor with the Department of Computer Science, University of Jaén. She is currently a member of the CEATIC’s steering committee and the head of the project entitled “Real-Time Monitoring in Home-Based Cardiac Rehabilitation Using Wrist-Worn Heart Rate Devices” from the Council of Health for the Andalucian Health Service as well as the head of the UJA partner of MSCA-RISE-2016 - Research and Innovation Staff Exchange entitled “The use of computational techniques to Improve compliance to reminders within smart environments”. Her research interests include ambient-assisted living, activity recognition, mobile and ubiquitous health, ambient intelligence, group decision making, decision support systems, intelligence systems, fuzzy logic, and linguistic modelling.

[ Descargar material del curso ]

Octubre 2019

Una introducción práctica a la computación cuántica

Dr. Elías Fernández-Combarro Álvarez (Univ. Oviedo, ES)

Fechas: 1 y 2 de octubre de 2019

Duración: 10 horas

Lugar: Laboratorio de Redes del Departamento de Informática

Horario: Dia 1 de 10:00 a 14:00 y 16:00 a 17:00, Dia 2 de 11:00 a 14:00

Modalidad: Seminario (es necesario registro)

Resumen: En este curso se introducirán los elementos fundamentales de la computación cuántica, así como los principales algoritmos cuánticos. Se estudiarán los conceptos de Qubit, puerta cuántica y medida y cómo se articulan para el diseño de algoritmos que superan a los métodos clásicos en ciertas tareas como la búsqueda no estructurada (algoritmo de Grover) o la factorización (algoritmo de Shor). También se estudiará la aplicación de los ordenadores cuánticos para la resolución de problemas de optimización tanto desde el punto de vista de la computación cuántica adiabática (modelo de Ising y problemas QUBO) como desde el paradigma de la computación cuántica basada en circuitos (Quantum Approximate Optimization Algorithm). En todo momento se pondrá énfasis en las aplicaciones prácticas de los conceptos estudiados, desarrollando prototipos de los algoritmos en lenguajes de programación cuántica de alto y bajo nivel y ejecutándolos tanto en simuladores como en ordenadores cuánticos reales.

Short-Bio: Elías F. Combarro received a B.S. degree in mathematics (1997), an M.S. degree in computer science (2002), and a PhD. In mathematics (2001) from the University of Oviedo (Spain), where he currently is an Associate Professor at the Computer Science Department. He has authored more than 30 research papers in topics such as computability theory, the theory of fuzzy measures, the computational classification of semifields and text categorization. His current research focuses mainly on quantum computing and its applications to algebraic and optimization problems.

[ Descargar material del curso ]

Blockchain integration with High Performance Computing

Dr. Ernestas Filatovas, Vilnius Univ., I. of Data Sci. & Digital Tech., Blockchain Tech. Group (Lituania).

Fechas: 14, 15 y 17 de octubre de 2019

Duración: 10 horas

Lugar: Laboratorio de Redes del Departamento de Informática (provisional)

Horario: Lunes y Jueves de 10:00 a 14:00 (14 y 17 de octubre) Lab SmartHome / Martes de 11:00 a 14:00 (15 de octubre) Lab de Redes

Modalidad: Seminario (es necesario registro)

Resumen: Blockchain, the foundation of Bitcoin, has recently received extensive attention. The decentralized infrastructure of blockchain and its principal features as immutability, transparency, security, and openness give many new opportunities and for a wide range of practical applications. In this course, the essential concepts of blockchain technology and fundamentals will be introduced. Popular blockchain platforms and the concepts of smart contracts that allow running decentralized applications will be presented. Further, blockchain use cases and popular blockchain-based applications in finance, logistics, health, IoT, etc. will we discussed. In this course, great attention will be given to exploration and investigation of blockchain-based networks for resource sharing and distributed computing such as GOLEM, SONM, IEXEC, and GRIDCOIN for BOINC. Finally, the biggest facing challenges today: slow transaction speeds, scalability, interoperability, energy consumption (cost) and their solutions to improve the performance of blockchain as sharing, sidechains, energy efficient consensus algorithms alternative to mining will be discussed.

Short-Bio: Ernestas Filatovas received the PhD in Informatics Engineering from the Vilnius University in 2012, Lithuania. He is currently a senior researcher at Vilnius University. His main research interests include blockchain technologies, multiobjective and global optimization, high-performance computing. He is a co-founder of Blockchain Technologies Group at Institute of Data Science and Digital Technologies of Vilnius University. He is currently involved in research and R&D projects in the aforementioned research areas (e.g., “Development of e-commerce platform based on blockchain technologies,” “Development and applications of bilevel optimization algorithms”).

[ Descargar material del curso ]

Advances in PID Control

Dr. Prof. Tore Hägglund (Lund University, Sweden).

Fechas: 22 y 23 de octubre de 2019

Duración: 10 horas

Lugar: Sala de Grados del edificio CITE-III

Horario: 22 de octubre de 10:00 a 13:30 (Seminarios) - de 16:30 a 18:00 (discusiones con alumnos de doctorado) / 23 de octubre de 10:00 a 13:30 (Seminarios) - de 16:30 a 18:00 (discusiones con alumnos de doctorado)

Modalidad: Seminario (es necesario registro)

Resumen: La actividad formativa incluirá la descripción y retos en investigación sobre diferentes técnicas muy novedosas sobre el control PID que es la técnica de control más utilizada tanto en el mundo académico como en el mundo industrial. Algunos de los temas a tratar son: Nuevos métodos de sintonización de controladores PID. Nuevas estrategias de Ratio Control. Nuevas reglas de sintonización de control anticipativo. Control en cascada. Control de Gama Partida.

Short-Bio: He was with ABB, Lund, Sweden, from 1985 to 1989, where he was involved in the development of industrial adaptive controllers. His research is still performed in collaboration with the industry. He has authored or co-authored several books, e.g., Advanced PID Control. His current research interests include process control, tuning and adaptation, and supervision and detection.

[ Descargar material del curso ]

Noviembre 2019

Applying Data Engineering in Software Teams: Software Analytics

Dr. Silverio Martínez-Fernández, Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern (Alemania)

Dr. Petar Jovanovic, Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona (España)

Fechas: 5 y 6 de noviembre

Duración: 10 horas

Lugar: Laboratorio ADS del Departamento de Informática, 1ra planta del edificio CITE-III

Horario: 5 de noviembre (martes) de 10:00 a 14:00 horas, 6 de noviembre (miércoles) de 10:00 a 14:00 horas y de 16:00 a 18:00 horas (discusiones con alumnos de doctorado)

Modalidad: Seminario (es necesario registro)

Resumen/Abstract:

Currently, in any domain, to make informative decisions, organizations need to obtain valuable insights and actionable knowledge from a variety of available up-to-date data. In this course, we will talk about how a specific domain like software engineering, can benefit from current advances in Big Data management to obtain valuable insights. Therefore, this course is organized in two parts: 1) Software analytics, and 2) Big Data management.

The software development process produces various types of data such as source code, bug reports, check-in histories, and test cases. The data sets not only include data from the development (e.g., GitHub with over 14 million projects), but also millions of data points produced per second about the usage of software (e.g., Facebook or eBay ecosystems). All this data can be exploited with “software analytics”, which is about using data-driven approaches to obtain insightful and actionable information at the right time to help software practitioners with their data-related tasks. The first part of this course explores both the state-of-the-art and state-of-the-practice on software analytics, and includes practical examples (in which the participants will get familiar to Apache Kafka, and Elastic) within the Q-Rapids framework (https://github.com/q-rapids).

The variety and volume of domain data, potentially useful for the analysis creates an additional burden for data analysts. They have to understand and select the right data, transform them into unified formats, and find the ways to integrate them, yielding cross-analysis to obtain valuable and contextualized knowledge. The additional challenge, in comparison to the traditional DBMS systems, is that Big Data technology stack is dispersed and yet without integrated solutions, meaning that given a use case at hand, the users need to select best fitting technologies to their problem, and optimize the data processing flows accordingly. In the second part of this course, we will first overview the most important tools from the Big Data technology stack, both for data storage (Hadoop/HDFS, HBase) and data processing (MapReduce, Spark/SparkSQL/Spark Streaming), as well as some data modelling paradigms that arise to facilitate the challenges of big data management (key/value, document stores, graphs). Finally, we will see some in-practice and research solutions for Big Data architectures that pave the way towards the integrated Big Data Management Systems that will remove part of the burden from data analysts.

Short-Bio:

Silverio Martínez-Fernández received the PhD degree in Computing from the Universitat Politècnica de Catalunya (UPC-BarcelonaTech), Spain. He was a Post-Doctoral Fellow of the European Research Consortium for Informatics and Mathematics (2 years). From 2018, he is a senior researcher at Fraunhofer IESE, Germany. His research interest includes software engineering, software reference architectures, and data-driven development. Dr. Martínez-Fernández has been PC co-chair in venues like PROFES, CESI@ICSE and QuASD@PROFES, and has been PC member on conferences tracks like ESEM, ICSME, ECSA, and CIbSE. Recently, he has been acting as “Evaluation” Work Package leader in the H2020 Q-Rapids project. More info at http://www.essi.upc.edu/~smartinez

Petar Jovanovic obtained his PhD from Universitat Politècnica de Catalunya and Université libre de Bruxelles in 2016. Currently, he is a postdoctoral research and teaching assistant at UPC. His research areas are Business Intelligence and Big Data, with special focus on big data management and data flow optimization. He has published articles, in peer reviewed international journals (such as TKDE, IS, Fundamenta Informaticae), and conferences (such as ICDE, SIGMOD, EDBT, ADBIS, ER). He currently serves as committee member of several international conferences and journals. He has also participated in projects with organizations such as World Health Organization, HP Labs Palo Alto, etc. He teaches courses in the local Master’s Programme at Facultat d’Informatica de Barcelona, EM Joint Master in BDMA, and UPC School postgraduate programme. More info at http://www.essi.upc.edu/~petar/

[ Descargar material del curso ]

Data Analysis in Software Engineering using R

José Javier Dolado Cosín. Facultad de Informática (Donostia, San Sebastián). Universidad del País Vasco/Euskal Herriko Unibertsitatea.

Fechas: 7 y 8 de noviembre de 2019

Duración: 10 horas

Lugar: Aula de informática 8 y 7

Horario: 9:00 a 14:00 horas

Modalidad: Seminario (es necesario registro)

Resumen: This course provides an introduction to the analysis of data of software engineering projects in a hands-on manner. We will discuss the need for performing data analysis in software engineering projects and the areas in which data analysis can be applied. We will show how the tasks of data analysis can be carried out using the R language. We will do exploratory data analysis before applying basic machine learning algorithms.

Short-Bio: Doctor in Informatics at the University of the Basque Country in 1989. Full Professor at the Department of Computer Languages and Systems. Some teaching and research interests are AI Advanced Techniques, Software Project Dynamics and Complexity Software Metrics, Software Metrics and Software Engineering, System Dynamics, Qualitative Reasoning and Simulation, Complex Systems.

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Control Predictivo Centralizado, Distribuido y Coalicional en sistemas ciberfísicos

Dr. José María Maestre (Univ. Sevilla, ES)

Fechas: 13 al 15 de noviembre de 2019

Duración: 10 horas

Lugar: Sala de Grados del edificio CITE III

Horario: día 13 de 16:00 a 18:00 - día 14 de 10:00 a 13:00 y 16:00 a 18:00 - día 15 de 10:00 a 13:00

Modalidad: Seminario (es necesario registro)

Resumen: El objetivo de este seminario es presentar una introducción al MPC y a algunos de sus desarrollos y retos actuales, especialmente a aquellos relacionados con su aplicación a sistemas de gran escala y distribuidos. En este sentido, se hará hincapié en el control coalicional, en el que se agrupan de forma dinámica los controladores del sistema en conjuntos débilmente acoplados para mejorar el rendimiento con baja carga de comunicación. Contenidos: Introducción; Implementación a bajo nivel de MPC; Sistemas ciberfísicos: control en canales de riego; MPC distribuido y coalicional; Otros temas y conclusiones: control estocástico, ciberseguridad.

Short-Bio: Doctorate in robotics and automatic control, recognized with the Extraordinary Prize by the University of Seville, and Master degree in domotics from the Polytechnic University of Madrid and in telecommunications economics from the National University of Distance Education. His research activity is focused on the control of distributed systems and the integration of service robots in the digital home.

[ Descargar material del curso ]

Ciberseguridad en Industria 4.0

Prof. Dr. Manuel Domínguez, Universidad de León (España)

Fechas: 20 y 21 de noviembre de 2019

Duración: 10 horas

Lugar: Sala de grados del Aulario IV

Horario: (1) Miércoles 20/11/19 de 09:00 a 13:30 Parte I del curso / (2) Miércoles 20/11/19 de 15:00 a 17:00 reuniones con alumno de doctorado interesados en el tema y tengan dudas y consultas / (3) Jueves 21/11/19 de 09:00 a 13:30 Parte II del curso

Modalidad: Seminario (es necesario registro)

Resumen: Según el Instituto Nacional de Ciberseguridad (INCIBE), la ciberseguridad Industrial es el conjunto de prácticas, procesos y tecnologías, diseñadas para gestionar el riesgo del ciberespacio derivado del uso, procesamiento, almacenamiento y transmisión de información utilizada en las organizaciones e infraestructuras industriales, utilizando las perspectivas de personas, procesos y tecnologías. La industria es un sector con ciertas particularidades y por esta razón, las medidas de seguridad necesarias para protegerlos son distintas. En este curso, se describirán los conceptos fundamentales, el estado actual, estándares y técnicas y recomendaciones para mantener segura una instalación industrial y/o crítica.

Short-Bio: Professor in the Area of Systems Engineering and Automatic Control at the University of León and a researcher responsible for the SUPPRESS group (Supervision, Control and Automation) of that University. He is also responsible for the Education Group of the Spanish Automatic Control Committee, being a reference in the development of virtual and remote laboratories of Automatic and in cybersecurity in Industry 4.0.

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Vehículos autónomos inteligentes

Prof. Dr. Arturo de la Escalera Hueso (Univ. Carlos III de Madrid, ES)

Fechas: 28 y 29 de noviembre de 2019

Duración: 10 horas

Lugar: Sala de Grados y sala de reuniones de la ESI

Horario: Jueves 16:00 - 20:00 y Viernes 9:30 - 13:30

Modalidad: Seminario (es necesario registro)

Resumen: La actividad formativa incluirá la descripción y retos en investigación en el desarrollo de vehículos autónomos inteligentes, entre los que interesan múltiples aspectos: Sensores de los Vehículos Inteligentes; Sistema de Reconocimiento de Señales de Tráfico; Sistema de Detección de Peatones; Sistema de Control de Velocidad Variable; Sistema de Control del Conductor; Vehículos Autónomos

Short-Bio: Professor of Robotics and Automation at Intelligent Systems Laboratory (Carlos III University). He received his Ph.D. in Automation from the Universidad Carlos III of Madrid in 1997. His research interest focus on Computer Vision, Image Processing and Real-Time Systems applied to Intelligent Transportation Systems. He is member of the Editorial Board of the ISNR Robotics, Journal of Physical Agents and Securitas Vialis.

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Diciembre 2019

Procesamiento de Big Data en tiempo real para la detección de situaciones de interés en ciudades inteligentes

Dr. Juan Boubeta-Puig, Universidad de Cádiz.

Fechas: 4 y 5 de diciembre de 2019

Duración: 10 horas

Lugar: Aula 11 de informática del edificio CITE-III

Horario: 10:00 a 14:00 (miércoles y jueves). El jueves de 16:00 a 18:00 sesión de dudas, preguntas y posibles colaboraciones con estudiantes de doctorado interesados en la materia.

Modalidad: Seminario (es necesario registro)

Resumen: El Procesamiento de Eventos Complejos (Complex Event Processing, CEP), es una tecnología novedosa que permite procesar, correlacionar y analizar Big Data en tiempo real con el fin de detectar automáticamente situaciones de interés en un dominio en particular. Para ello se requiere definir e implementar unos patrones de eventos en donde se indiquen las condiciones que deberán cumplirse para detectar dichas situaciones. Uno de los grandes beneficios de esta tecnología, y por lo que está suscitando tanto interés a nivel nacional e internacional tanto en universidades, empresas y sector público, es su capacidad de reaccionar rápidamente ante situaciones críticas y tomar acciones que den respuesta automáticamente. La integración de CEP con arquitecturas orientadas a servicios y dirigidas por eventos (Event-Driven Service-Oriented Architecture, SOA 2.0) hace posible, por un lado, que la información heterogénea que se necesite procesar en tiempo real provenga de fuentes diversas, tales como plataformas de Internet de las Cosas (Internet of Things, IoT), dispositivos ciberfísicos y ciudades inteligentes. En este curso se introducirán los fundamentos de esta tecnología y varios casos prácticos para detectar situaciones en ciudades inteligentes.

Short-Bio: Degree in Computer Systems Management and PhD in Computer Science from the University of Cádiz (UCA), Spain. Since 2009, he is working as an Assistant Professor in the Department of Computer Science and Engineering at UCA. His research focuses on the integration of complex event processing in event-driven service-oriented architectures, the Internet of Things, and model-driven development of advanced user interfaces.

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Interactions between Group Theory, Cyber Security, Artificial Intelligence, and Quantum Computation

Prof. Dr. Delaram Kahrobaei, Universidad de York

Lugar: seminario de álgebra del Departamento

Fecha: 11 de diciembre miércoles

Horario: 12:00 horas

Modalidad: Charla invitada (no es necesario registro)

Resumen: In this talk, I explore how group theory playing a crucial role in cyber security and quantum computation. At the same time, how computer science for example machine learning algorithms and computational complexity could help group theorists so tackle their open problems, as such this could help with cryptanalysis of the proposed primitives. Symmetry is present in all forms in the natural and biological structures as well as man-made environments. Computational symmetry applies group-theory to create algorithms that model and analyze symmetry in real data set. The use of symmetry groups in optimizing the formulation of signal processing and machine learning algorithms can greatly enhance the impact of these algorithms in many fields of science and engineering where highly complex symmetries exist. At the same time, Machine Learning techniques could help with solving long standing group theoretic problems. Graph theoretic problems have been of interest of theoretical computer scientists for many years, especially the computational complexity problems for such algorithmic problems. Such studies have been fruitful for one of the millennium problems (P vs NP) of the Clay Math Institute. Since graph groups are uniquely defined by a finite simplicial graph and vice versa, it is clear that there is a natural connection between algorithmic graph theoretic problems and group theoretic problems for graph groups. Since the graph theoretic problems have been of central importance in complexity theory, it is natural to consider some of these graph theoretic problems via their equivalent formulation as group theoretic problems about graph groups. In the past couple of decades many groups have been proposed for cryptography, for instance: polycyclic groups for public-key exchanges, digital signatures, secret sharing schemes (Eick, Kahrobaei), hyperbolic groups for private key encryption (Chatterji-Kahrobaei), p-groups for multilinear maps (Kahrobaei, Tortora, Tota) among others. Most of the current cryptosystems are based on number theoretic problems such discrete logarithm problem (DLP) for example Diffie-Hellman key-exchange. Recently there has been some natural connections between algorithmic number theoretic and algorithmic group theoretic problems. For example, it has been shown that for a different subfamily of metabelian groups the conjugacy search problem reduces to the DLP. One goal of cryptography, as it relates to complexity theory, is to analyze the complexity assumptions used as the basis for various cryptographic protocols and schemes. A central question is determining how to generate intractible instances of these problems upon which to implement an actual cryptographic scheme.

Fotos

Lugar de celebración

Campus universitario de La Cañada, Universidad de Almería

Para cada curso ver el lugar de celebración.

Organización

Organiza la Comisión Académica del Doctorado en Informática y el Departamento de Informática de la UAL en colaboración con la Escuela Internacional de Doctorado.