SADIO Taller Virtual de Analitica en Grafos

May 21st, 2021 Daniela Lopez de Luise

                                    “en la senda de Sadosky”

Estimados,

Les acercamos información sobre el “Curso-Taller de Analítica en Grafos” que se dictará virtualmente en el campus de SADIO.
 
Esperamos que sea de su interés y agradecemos la difusión que pueda darle. 
 
Saludos cordiales.
 
¡Seguinos en nuestras redes sociales para enterarte de más novedades! 
 
        
Curso – Taller de Analítica en Grafos
 

Docentes: Vanina Beraudo, Néstor Coppolillo y Eduargo Poggi.
 
Fecha de inicio: 14 de Junio de 2021
 
Descripción: 
La Analítica en Grafos como subdisciplina de Minería de Datos ha incrementado su valor en los últimos tiempos acompañando el crecimiento de análisis de redes sociales. Sin embargo, su utilización y potencial puede aplicarse en una gran variedad de temáticas. El estudio de los grafos tiene una trayectoria importante que combinada con la potencialidad de las herramientas propias de la minería puede ofrecer resultados sumamente interesantes.
 
Objetivos:
  • Apropiar los conceptos básicos de grafos y practicar con herramientas genéricas de persistencia y análisis de grafos.
  • Investigar el enriquecimiento de las técnicas tradicionales de Minería de Datos con el uso de indicadores propios de grafos y aplicarlas en casos de estudio.
  • Investigar nuevas aplicaciones de Minería en Datos en estructuras de grafos.
 
Dirigido a: Profesionales que quieran profundizar en las técnicas modernas de persistencia y análisis de datos: científicos de datos, informáticos, estadísticos, actuarios, sociólogos, politólogos, físicos, biólogos, matemáticos, médicos sanitaristas, etc.
 
Requisitos: Para participar en este curso es deseable conocimientos básicos de: Lenguajes de consulta a base de datos. Programación, idealmente Python en entorno Anaconda. Algoritmos básicos de Aprendizaje Automático como Árboles de Decisión, Bayesianos y KNN.
 
Modalidad: El curso tendrá una fuerte orientación práctica. Se brindará a los participantes herramientas, guías y prácticas para aplicar los conceptos básicos con software específico y datos de interés general.
El curso está armado en 8 clases sincrónicas de 2 horas cada una para las exposiciones.
Cada participante deberá dedicar además una carga horaria semejante para el desarrollo de las prácticas.
Se aportará bibliografía general y específica para el estudio de los distintos temas y datasets para el desarrollo de los ejercicios.
La evaluación estará basada en la exposición, compleción y calidad del informe desarrollado sobre un Trabajo Práctico Integrador (TPI). 
 
Duración: 4 semanas
 
Días y horarios: Lunes y Jueves de 17.30 a 19.30. Dos clases sincrónicas por semana de 2 horas cada una. 
 
Temario: 
  • Conceptos básicos de grafos: indicadores de relación; patrones en grafos; búsquedas en grafos; implementación de bases de persistencia orientadas a grafos.
  • Aplicación de indicadores de relaciones en prácticas de minería de datos: gracias a la generación de indicadores de relación a partir de una representación de entidades en una base de grafos enriquecer las prácticas tradicionales de minería de datos.
  • Aplicación de nuevas técnicas de Minería de Grafos para diversos problemas como  segmentación y reconocimiento de patrones en diversas temáticas como, por ejemplo: lenguaje natural, redes sociales, detección de relaciones particulares en diferentes negocios, etc. 

No se incluye en el temario: 

  • Técnicas de modelado de grafos.
  • Técnicas de uso de base de datos orientadas a grafos como medio de persistencia de sistemas transaccionales.
 
Programa detallado click aquí
 
Aranceles (en pesos argentinos)
Inscripción temprana (hasta el 07/06/2021): $18.000
Inscripción tardía (desde el 08/06/2021): $19.800
Descuento para socios de SADIO 50% 
 
Formulario de inscripción: https://tinyurl.com/pzue8nyf
 
Medios de pago disponibles:
* Pago por Transferencias Bancarias (solo para residentes en Argentina) a:
SADIO (CUIT 30-64931218-0)
BBVA – Sucursal 330 Tribunales
Cta. Cte. Pesos: 502/7
CBU: 0170330420000000050276
 
*Pago con Tarjeta de crédito/débito (Visa, Master o Cabal).
Solicitar el botón de pago correspondiente a informacion@sadio.org.ar
 
Antecedentes de los docentes:
Vanina Beraudo: Integra el equipo de Analítica de Datos de AFIP. También se ha desempeñado como especialista en Minería de Datos en la Subdirección Gral. de Sistemas y Telecomunicaciones de la misma institución. Ha realizado diversas presentaciones sobre aspectos de Minería de Datos en el ámbito fiscal, así como participa como docente del curso de Minería de Datos para el personal de AFIP.
Magister en Explotación de Datos y Descubrimiento del Conocimiento UBA – FCEN. Licenciada en Sistemas de Información por UN Luján.
 
Néstor Coppolillo: Tiene experiencia profesional en proyectos orientados a la gestión de las bases de datos y Data Mining en el ámbito del sector público. Es docente de posgrado en Ciencia de Datos y trabaja en la generación de contenidos de una especialización en Data Science. Así también participa como docente en distintos cursos de Data Mining ofrecidos en instituciones públicas y privadas.
Es Licenciado en Ciencias de la Computación por la UBA (FCEN), especialista en Explotación de Datos y Descubrimiento del Conocimiento por la UBA.
 
Eduardo Poggi: Cuenta con 40 años de experiencia profesional en proyectos de Tecnología de la Información fundamentalmente orientada al sector público latinoamericano. En la última década se orientó a la gestión de datos públicos: Datos Abiertos, Interoperabilidad y Ciencia de Datos. Acredita unos 25 años de docencia de grado y posgrado en: Aprendizaje Automático, Minería de Datos y gestión de TI Pública en general. Actualmente se desempeña como asesor en la Dirección de Analítica de Datos de la AFIP de Argentina, docente de posgrado y consultor internacional.
Es licenciado en Ciencias de la Computación por la UBA (FCEN), magister en Administración y Políticas Públicas y posee una especialización en Negocios y Tecnología por la Universidad de San Andrés; ambas de Argentina.

 

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VISMAC2020 PhD summer school – REGISTRATION IS NOW OPEN!

May 21st, 2021 Daniela Lopez de Luise
V I S M A C (VISione delle MACchine)
[in English, “Machine Vision”]

International Summer School
September 21st – 24th, 2021, Palermo, Italy

https://math.unipa.it/vismac2020
____________________________________________

=== UPDATES regarding COVID-19 (coronavirus) ===

VISMAC2020 is half a year away and hopefully the COVID-19 emergency will
be over by then. We would like to thank all those who have already
expressed interest in attending the VISMAC summer school. The school is
now set to be held from the 21st to the 24th of September 2021, either
as a full online event or in mixed online / live mode, depending on the
developments related to COVID-19 pandemic.

Please, feel free to express your interest by emailing us: we will add
you to our mailing list and keep you promptly informed of any new
development.

Take care of yourselves, and see you soon.

=== Aim & Scope ===

The international summer school VISMAC “VISione delle MACchine” (in
English, “Machine Vision”) is organized every two years by the
“Associazione Italiana per la ricerca in Computer Vision, Pattern
recognition e machine Learning” (CVPL – ex-GIRPR) affiliated to
International Association for Pattern Recognition (IAPR). It represents
a stimulating opportunity for doctoral students, young researchers from
universities, research institutions and industry. The primary objective
of the Summer School is to provide a common scientific and cultural
background on the subjects of computer vision and pattern recognition.

This edition of VISMAC will mainly focus on four renowned research
topics: Bio-imaging, Automotive, Cultural Heritage, Image forensics.

=== List of Speakers ===

Bio-imaging
– Carlo Sansone, UNINA Federico II
– Elena Casiraghi, UNIMI
– Paolo Soda, UCBM
– Francesco Tortorella, UNISA
– Joseph Stancanello, Elekta

Automotive
– Sergio Saponara, UNIPI
– Roberto Vezzani, UNIMORE
– Alessandro Rizzi, UNIMI
– Alberto Broggi, VisLab/Ambarella

Cultural Heritage
– Gabriele Guidi, POLIMI
– Carlo Colombo, UNIFI
– Andrea Fusiello, UNIUD
– Francesca Odone, UNIGE
– Fabio Remondino, FBK Trento
– Alessandro Dal Colle, Klain Robotics

Image forensics
– Francesco De Natale, UNITN
– Gian Luca Marcialis, UNICA
– Luisa Verdoliva, UNINA Federico II
– Jerian Martino, Amped Software

=== Program ===

Book of abstracts can be downloaded here:
https://math.unipa.it/vismac2020/resources/VISMAC2020-2021-BoA-Onine.pdf

=== Registration ===

School registrations are limited to forty participants, on a FIFS basis.
The registration fee is 100 Euro.

Accepted students can submit a poster to present their research
activity. The best poster selected by the school committee will receive
a prize sponsored by CVPL.

Registration form and info are available here:
https://math.unipa.it/vismac2020/markdown-2/

=== Scientific Committee ===

– Domenico Tegolo, UNIPA
– Cesare Valenti, UNIPA
– Roberto Pirrone, UNIPA
– Filippo Stanco, UNICT

=== Local Committee ===

– Marco E. Tabacchi, UNIPA
– Fabio Bellavia, UNIPA

=== Sponsors ===

– CVPL (ex-GIRPR) – Associazione Italiana per la ricerca in Computer
Vision, Pattern recognition e machine Learning
– Universita' degli Studi di Palermo
– Universita' degli Studi di Catania
– CITC – Centro Interdipartimentale di Tecnologie della Conoscenza,
Universita' degli Studi di Palermo
– DMI – Dipartimento di Matematica e Informatica, Universita' degli
Studi di Palermo
____________________________________________

Contacts
https://math.unipa.it/vismac2020
vismac2020@gmail.com

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ICCSI 2021 Publicity for Tutorials/Workshops

May 21st, 2021 Daniela Lopez de Luise
Dear Colleagues,

It is our pleasure to invite you to organize special sessions/tracks and/or workshops/tutorials for the 2021 International Conference on Cyber-physical Social Intelligence (ICCSI2021, https://iccsi2021.agist.org , December 18 – 20, 2021, Beijing, China). Please contact us and submit your proposal as soon as possible to benefit the visibility of the conference website in addition to your own effort of attracting papers for your sessions/tracks. The deadline for special sessions/tracks and/or workshops/tutorials proposals is June 13th, 2021.

ICCSI 2021 will provides an international forum for researchers and practitioners to share innovative technical and scientific developments, findings, and visions in all aspects of machine intelligence, human intelligence, and cyber-physical-social intelligence rendered by various interactions in cyberspace, physical space, and social space. It will feature tutorials and workshops, a technical program of presentations, keynote lectures and social events. It is a great opportunity for academics, researchers, and industrial players to network together, present research progress and address new challenges

Please refer to the attached CFP for important dates and a wider topic illustration.

Best regards,

ICCSI 2021 Special Session Co-Chairs

Mounim A. El Yacoubi, Telecom SudParis, FR        mounim.el_yacoubi@telecom-sudparis.eu
Xuemin Chen, Texas Southern Univ., USA             xuemin.chen@tsu.edu

 

pdf icon CFP-ICCSI2021.pdf
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The 7th Int. Conf. on Machine Learning, Optimization & Data Science – LOD 2021, October 5-8, 2021 – Grasmere, Lake District, England – UK – Paper Submission Deadline: May 31

May 21st, 2021 Daniela Lopez de Luise
The 7th International Conference on Machine Learning, Optimization, and Data Science – LOD 2021 – October 5-8, 2021 – Grasmere, Lake District, England – UK
LOD 2021, An Interdisciplinary Conference: Machine Learning, Optimization, Big Data & Artificial Intelligence without Borders
PAPERS SUBMISSION: 
All papers must be submitted using EasyChair:
Paper Submission deadline: Monday May 31, 2021 (Anywhere on Earth)
Any questions regarding the submission process can be sent to conference organizers: lod@icas.cc
PAPER FORMAT:
Please prepare your paper in English using the Springer Nature – Lecture Notes in Computer Science (LNCS) template, which is available here. Papers must be submitted in PDF.
TYPES OF SUBMISSIONS:
When submitting a paper to LOD 2021, authors are required to select one of the following four types of papers:
* long paper: original novel and unpublished work (max. 15 pages in Springer LNCS format);
* short paper: an extended abstract of novel work (max. 5 pages);
* work for oral presentation only (no page restriction; any format). For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the conference;
* abstract for poster presentation only (max 2 pages; any format). The poster format for the presentation is A0 (118.9 cm high and 84.1 cm wide, respectively 46.8 x 33.1 inch). For research work which is relevant and which may solicit fruitful discussion at the conference.
Each paper submitted will be rigorously evaluated. The evaluation will ensure the high interest and expertise of reviewers. Following the tradition of LOD, we expect high-quality papers in terms of their scientific contribution, rigor, correctness, novelty, clarity, quality of presentation and reproducibility of experiments.
Accepted papers must contain significant novel results. Results can be either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact.
It is also possible to present the talk virtually (Zoom).
KEYNOTE SPEAKERS:
* Ioannis Antonoglou, DeepMind, UK
  Topics:  AlphaGO, Model-Based Reinforcement Learning
  Title: TBA
* Paige Bailey, Microsoft, USA
  Topics: TensorFlow 2.0, Data Analysis, Machine Learning
  Title: Machine Learning with TF 2.x and JAX
* Roberto Cipolla, University of Cambridge, UK
  Topics: Computer Vision, Machine Learning
* Panos Pardalos, University of Florida, USA
  Topics: Optimization, Complex Networks & Data Science
  Title: TBA
* Verena Rieser, Heriot Watt University, UK
  Topics: Natural Language Processing, Conversational AI, Spoken Dialogue Systems, Dialog, Natural Language Generation
  Title: Advances and Challenges in Conversational AI
 
ACAIN 2021 Keynote Speakers
* Timothy Behrens, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
  Topics: Computational Neuroscience, Behavioral Neuroscience, Decision Making, Learning Brain Connectivity
* Matthew Botvinick, DeepMind, UK
Topics: Artificial Intelligence, Neuroscience, Cognitive Psychology, Cognitive Science
* Claudia Clopath, Computational Neuroscience Lab, Dept of Bioengineering, Imperial College London, UK
  Topics: Computational Neuroscience
* Ila Fiete, MIT, USA 
  Topics: Theoretical neuroscience, Computational neuroscience, Neural coding
* Karl Friston, Institute of Neurology, University College London, UK & Wellcome Trust Centre for Neuroimaging
  Topics: Neuroscience
* Timothy Lillicrap, Google DeepMind & UCL, UK
  Topics: Computational Neuroscience
* Rosalyn Moran, Department of Neuroimaging, King’s College London, UK
  Topics: Computational Neuroscience 
* Maneesh Sahani, Gatsby Computational Neuroscience Unit, University College London, UK
  Topics: Theoretical Neuroscience,  Machine Learning
* Jane Wang, DeepMind, UK
  Topics: neural networks, cognitive neuroscience,  meta-learning, deep reinforcement learning
TUTORIAL SPEAKER(S):
* “Introduction to PyTorch” (4 hours), Thomas Viehmann, MathInf GmbH, Germany
More Tutorial Speakers Coming soon!
PAST LOD KEYNOTE SPEAKERS:
Pierre Baldi, University of California Irvine, USA
Yoshua Bengio, Head of the Montreal Institute for Learning Algorithms (MILA) & University of Montreal, Canada
Bettina Berendt, TU Berlin, Germany & KU Leuven, Belgium, and Weizenbaum Institute for the Networked Society, Germany
Jörg Bornschein, DeepMind, London, UK
Michael Bronstein, Imperial College London, UK
Nello Cristianini, University of Bristol, UK
Peter Flach, University of Bristol, UK, and EiC of the Machine Learning Journal
Marco Gori, University of Siena, Italy
Arthur Gretton, UCL, UK
Arthur Guez, Google DeepMind, Montreal, UK
Yi-Ke Guo, Imperial College London, UK
George Karypis, University of Minnesota, USA
Vipin Kumar, University of Minnesota, USA
Marta Kwiatkowska, University of Oxford, UK
Angelo Lucia, University of Rhode Island, USA
George Michailidis, University of Florida, USA
Kaisa Miettinen, University of Jyväskylä, Finland
Stephen Muggleton, Imperial College London, UK
Panos Pardalos, University of Florida, USA
Jan Peters, Technische Universitaet Darmstadt & Max-Planck Institute for Intelligent Systems, Germany
Tomaso Poggio, MIT, USA
Andrey Raygorodsky, Moscow Institute of Physics and Technology, Russia
Mauricio G. C. Resende, Amazon.com Research and University of Washington Seattle, Washington, USA
Raniero Romagnoli, CTO Almawave, Italy
Ruslan Salakhutdinov, Carnegie Mellon University, USA, and AI Research at Apple
Maria Schuld, Xanadu & University of KwaZulu-Natal, South Africa
My Thai, University of Florida, USA
Richard E. Turner, Department of Engineering, University of Cambridge, UK
Ruth Urner, York University, Toronto, Canada
Isabel Valera, Saarland University, Saarbrücken & Max Planck Institute for Intelligent Systems, Tübingen, Germany
SPECIAL SESSIONS:
*) Special Session on “Data Science for Sustainable Cities”
Chairs: Alberto Castellini, Alessandro Farinelli, Giuseppe Nicosia, Varun Ojha
The amount of data generated nowadays by society, city infrastructures, and digital technologies around us is astonishing. The analysis, modeling and knowledge extraction of/from these data is a key asset for understanding urban environments and improving the efficiency of urban mobility,  air quality and other forms of sustainability. This special session provides a platform to share high-quality research ideas related to data science methods and technologies for urban environments, a topic of crucial importance for many Sustainable Development Goals (i.e., SDG 7 on Sustainable Energy and SDG 11 on Sustainable Cities and Communities). Another important goal is to establish a meeting point for researchers in academia and industry who develop methodologies and technologies for data science, machine learning and artificial intelligence with specific applications in smart and sustainable cities.
Analytics for smart growth and effective infrastructure
A selection of the best papers accepted for the presentation at the special session will be invited to submit an extended version for publication on Frontiers in Sustainable Cities (https://www.frontiersin.org/journals/sustainable-cities#)
*) Special Session on AI for Sustainability
We welcome  contributions on AI for Sustainable Development, AI for Sustainable Urban Mobility, AI for Food Security, AI to fight Deforestation, cutting-edge technology AI to create Inclusive and Sustainable development that leaves no one behind.
*) Special Session on AI to help to fight Climate Change
AI is a new tool to help us better manage the impacts of climate change and protect the planet. AI can be a “game-changer” for climate change and environmental issues.
AI refers to computer systems that “can sense their environment, think, learn, and act in response to what they sense and their programmed objectives,”
World Economic Forum report, Harnessing Artificial Intelligence for the Earth.
We accept papers/short papers/talks at the intersection of climate change, AI, machine learning and data science. AI, Machine Learning and Data Science  can be invaluable tools both in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change.
We invite submissions  using AI, Machine Learning and/or Data Science to address problems in climate mitigation/adaptation including but not limited to the following topics:
* Industrial Session
Chairs: Giovanni Giuffrida – Neodata.
* Special Session on Explainable Artificial Intelligence
Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.
* Special Session on Multi-Objective Optimization (MOO) & Multi Criteria Decision Aiding (MCDA)
* The 7 Special Sessions on Machine Learning
Multi-Task Learning
Reinforcement Learning
Deep Learning
Generative Adversarial Networks
Deep Neuroevolution
Networks with Memory
Learning from Less Data and Building Smaller Models
* The 7 Special Session on Data Science and Artificial Intelligence
Simulation Environments to understand how AI Systems Learn
Chatbots and Conversational Agents
Data Science at Scale & Data in the Cloud
Urban Informatics & Data-Driven Modelling of Complex Systems
Data-centric Engineering
Data Security, Traceability of Information & GDPR
Economic Data Science
BEST PAPER AWARD:
Springer sponsors the LOD 2021 Best Paper Award with a cash prize of 1,000 Euro.
PROGRAM COMMITTEE:
500+ confirmed PC members! 
VENUE:
“ESCAPE THE HURRYING WORLD – The loveliest spot that man hath ever found…”
Escape to the Lake District, England – a UNESCO World Heritage site – and you’ll find it’s easy to share William Wordsworth’s delight in the area. 
The Wordsworth Hotel & Spa (****)
Address: Grasmere, Ambleside, Lake District, Cumbria, LA22 9SW, England, UK
Phone: +44-1539-435592
ACCOMMODATION:
ACTIVITIES:
Best WALKS in the Lake District National Park:
LOD 2021 POSTER:
Submit your research work today!
See you in the beautiful Lake District – UK in October!
Best regards, 
  LOD 2021 Organizing Committee
LOD 2021 NEWS:
Past Editions
LOD 2020, The Sixth International Conference on Machine Learning, Optimization and Big Data
Certosa di Pontignano – Siena – Tuscany – Italy. Nature Springer – LNCS volumes 12565 and 12566.
LOD 2019, The Fifth International Conference on Machine Learning, Optimization and Big Data
Certosa di Pontignano – Siena – Tuscany – Italy.
Nature Springer – LNCS volume 11943.
LOD 2018, The Fourth International Conference on Machine Learning, Optimization and Big Data
Volterra – Tuscany – Italy. Nature Springer – LNCS volume 11331.
MOD 2017, The Third International Conference on Machine Learning, Optimization and Big Data
Volterra – Tuscany – Italy. Springer – LNCS volume 10710.
MOD 2016,  The Second International Workshop on Machine learning, Optimization and big Data
Volterra – Tuscany – Italy. Springer – LNCS volume 10122.
MOD 2015, International Workshop on Machine learning, Optimization and big Data
Taormina – Sicily – Italy. Springer – LNCS volume 9432.
Past Keynote Speakers:
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Training Gratuito ZK | Soluciones de Control de Acceso: reconocimiento facial para la nueva normalidad

May 21st, 2021 Daniela Lopez de Luise

libre y grauito – requiere inscripción
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