IWANN’21 special session on convolutional NNs

April 29th, 2021 Daniela Lopez de Luise

*2021 International Work-Conference on Artificial Neural Networks*

*IWANN-2021*

*June, 16th-18th, 2021. Online*

*http://iwann.uma.es/ <http://iwann.uma.es/>***

It is our great pleasure to announce again you the 16th International
Work-Conference on Artificial Neural Networks, which will take place in
June, 16th-18th, 2021. Please, find further information about our
conference at the website: http://iwann.uma.es/ <http://iwann.uma.es/>.

The specific circumstances we are living now and in the near future
require us to be very cautious in making decisions especially in
organizing events involving numerous people. The safety and security of
our participants in the next edition of IWANN 2021 is always on top of
the agenda.

Due to the current situation of the pandemic, we consider that the most
convenient decision is that IWANN 2021 will be an online conference. It
is a painful decision, but we believe that it is the most recommended
from a health point of view.

We will maintain the spirit of work and collaboration that has always
distinguished the past editions of IWANN, and for this, we will have a
professional online platform, which allows the presentation of
communications and the interaction of the speakers in an optimal way.

We are convinced that the scientific-technical quality of this future
edition of IWANN will be a success, and for this reason we request your
collaboration and active participation in it.

We will continue with the structure of previous editions (Plenary
Sessions, Special Sessions, Tutorials on relevant topics and Open
Discussion forum), favouring the connection/interaction among attendees
to facilitate the debate

We hope that the changes introduced will be well received and that we
will be able to collaborate closely to make this IWANN-2021 edition a
success.

*IMPORTANT DATES*

Submission of papers/abstract by authors: May 10th, 2021.

Notification of provisional acceptance: May 31st, 2021.

Early Registration: June 10th, 2021.

IWANN ONLINE CONFERENCE: June 16th-18th, 2021.

Please, if you have problem with these deadlines, contact: IWANN@ugr.es
<mailto:IWANN@ugr.es>

**

*TOPICS*

The topics of interest include, but are not limited to:

1.- Mathematical and theoretical methods in computational intelligence:

2.- Deep Learning

3.- Learning and adaptation

4.- Emulation of cognitive functions.

5.- Bio-inspired systems and neuro-engineering.

6.- Advanced topics in computational intelligence.

7.- Applications in artificial intelligence.

*Paper publication:*

All accepted papers will be published in the conference proceedings,
under ISSN references (LNCS). Full papers contributions will be sent to
be indexed in the ISI Conference Proceedings Citation Index (Thomson
Reuters) and the DBLP database (LNCS).

*Special issues:*

A list of papers with very high quality will be selected to be extended
and submitted in different special issues. More information in the web page.

It will be a pleasure if you can actively participate in IWANN 2021
conference. Thanks for your attention, and we hope your active
participation during the IWANN 2021 event.

Please, feel free to contact us for any further question or remark.

Sincerely yours,

*Andreu Catala, Gonzalo Joya, Ignacio Rojas*

*____________________________________________*

*IWANN 2021. Conference chairs.*

*16th International Work-Conference on Artificial Neural Networks- IWANN
2021.*

*June 16th-18th, 2021. Online: **http://iwann.uma.es/
<http://iwann.uma.es/>***

*____________________________________________*

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First Workshop on “When Graph Signal Processing meets Computer Vision” at ICCV 2021

April 29th, 2021 Daniela Lopez de Luise

First Workshop on “When Graph Signal Processing meets Computer Vision”

https://gsp-cv.univ-lr.fr/gspcv-21/

Graph signal processing (GSP) [is the study of computational tools to process and analyze data residing on irregular correlation structures described by graphs. Early GSP researchers explored low-dimensional representations of high-dimensional data via spectral graph theory—mathematical analysis of eigen-structures of the adjacency and graph Laplacian matrices. Researchers first developed algorithms for low-level tasks such as signal compression, wavelet decomposition, filter banks on graphs, regression, and denoising, motivated by data collected from distributed sensor networks. Soon, researchers widened their scope and studied GSP techniques for image applications (image filtering, segmentation) and computer graphics.. More recently, GSP tools were extended to video processing tasks such as moving object segmentation, demonstrating its potential in a wide range of computer vision problems. More generally, GSP has been found effective in image processing tasks (image restoration and denoising, image composition, image alignment and rectification, multi-focus image fusion, etc), video processing tasks (tracking, motion saliency, video coding, background/foreground separation, etc.), and 3D imaging tasks (point cloud processing, 3D motion recovery, etc). Moreover, GSP can potentially influence the development of Graph Convolutional Networks from a theoretical standpoint.

However, designing GSP algorithms for specific computer vision tasks has several practical challenges such as spatio-temporal constraints, time-varying models and real-time implementations. Indeed, the computational complexity of many existing GSP algorithms at present for very large graphs is currently one limitation. In semi-supervised learning, GSP-based classifiers provide clear interpretations from a graph spectral perspective when propagating label information from known to unknown nodes. However, centralized graph spectral algorithms are slow and no fast distributed graph labeling algorithms are known to perform well. In that sense, research is required in the development of fast GSP tools to be competitive against deep learning methods.

The goals of this workshop are thus three-fold: 1) designing GSP methods for computer vision applications; 2) proposing new adaptive and incremental algorithms that reach the requirements of real-time applications; and 3) proposing robust and interpretable algorithms to handle the key challenges in computer vision applications.

Papers are solicited to address graph signal processing to be applied in computer vision, including but not limited to the followings:

Sampling and Recovery of Graph Signals

Statistical Graph Signal Processing

Non-linear Graph Signal Processing

Signals in high-order Graphs

Graph-based Image Restoration

Graph-based  Image Filtering

Graph-based  Segmentation and  Classification

Graph-based Image and Video Processing

Graph Convolutional Networks.(GNNs)

Interpretable/Explainable GNNs

Unsupervised/Self-Supervised GNNs

GSP-based Graph Learning in GNNs

Timeline

Full Paper Submission Deadline:    July 13, 2021 (for papers not submitted at ICCV,  July 25, 2021 (for papers that are awaiting for ICCV decisions)

Decisions to Authors: July 31, 2021

Camera-ready Deadline:  August 17, 2021

Main organizers

Thierry Bouwmans, Associate Professor, Laboratoire MIA, Univ. La Rochelle, France.

Gene Cheung,  Associate Professor, Department of EECS, York University, Toronto, Canada.

Wei Hu, Wangxuan Institute of Computer Technology Peking University, Beijing, China.

Yuichi Tanaka, Tokyo University of Agriculture and Technology, Japan.

Laura Toni, University College London, UK.

 

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IEEE International Conference on ICT solutions for eHealth (ICTS4eHealth) – Athens, Greece, September 5 – 8, 2021

April 29th, 2021 Daniela Lopez de Luise
IEEE International Conference on ICT Solutions for e-Health
ICTS4eHealth 2021

Athens, Greece, September 5-8, 2021

www.icts4ehealth.icar.cnr.it

in conjunction with the Twenty-Sixth IEEE Symposium on Computers and
Communications

=======================================================

MISSION:

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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: April 29

April 29th, 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: Thursday April 29, 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

 

* 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

 

More Keynote Speakers Coming soon!

 

 

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

Vincenzo Sciacca, Almawave, Italy

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! Breaking the record of the last edition of LOD!

 

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:

 

 

Submit your research work today!

 

 

 

See you in the beautiful Lake District – UK in October!

 

 

Best regards, 

  LOD 2021 Organizing Committee

 

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IEEE WCNEE 2021 call for papers – deadline: May 21, 2021

April 28th, 2021 Daniela Lopez de Luise

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