Dear colleagues,
We are happy to announce that the University of Basel will offer a free online course on probabilistic morphable models, starting on 1. March 2021.
Probabilistic Morphable Models are an approach for model-based image analysis based on the analysis-by-synthesis paradigm. The theoretical basis consists of two main concepts:
- Gaussian processes modelling shape variations within a population
- Bayesian inference with Markov Chain Monte Carlo methods for model fitting.
The theoretical concepts will be illustrated and applied in practical examples and projects from the domain of medical image analysis and face modelling, using the open source software Scalismo (https://scalismo.org).
This course is composed of two parts:
- An open online course on “Statistical Shape Modelling” , starting 1. March 2021 on FutureLearn
https://www.futurelearn.com/courses/statistical-shape-modelling
- A follow-up open online course on “Probabilistic fitting“, starting 27. April 2021
https://shapemodelling.cs.unibas.ch/pmm2021
The course is free of charge. Please visit https://shapemodelling.cs.unibas.ch/pmm2021/ for more information.
Best regards,
Marcel Lüthi
Journal special issue AI for CAV
March 2nd, 2021 
Daniela Lopez de Luise Special Issue “Artificial Intelligence for Connected and Automated Vehicles”
Applied Sciences (MDPI, ISSN 2076-3417, IF 2.47).
Deadline for manuscript submissions: 10 November 2021
Dear Colleagues,
Connected and automated vehicles (CAVs) will provide greater transport convenience and interconnectivity, increase mobility options, and reduce traffic accidents, congestion and emissions by exploiting Artificial Intelligence and communication technologies. At the same time, major barriers towards the public deployment of CAVs and the realization of smart cities exist, including the safety evaluation and validation of Artificial Intelligence-based vehicle functions. This Special Issue aims to bring together recent advances in methods and tools in the areas of machine learning, deep learning and computer vision, knowledge discovery, forecasting, as well as testing and validation to make connected and automated vehicles efficient and safe. We particularly invite contributions that identify and provide insight into the limitations of Artificial Intelligence-based techniques for connected and automated vehicles and/or advance the state of the art by breaking existing limitations.
Guest Editors
Prof. Stratis Kanarachos
Prof. Aristotelis Naniopoulos
Dr. Dimitrios Nalmpantis
Prof. Vasile Palade
Dr. Islam Babaev
Keywords
Machine Learning
Deep Neural Networks
Computer Vision
Connected Vehicles
Automated Vehicles
CAV testing and validation
Big Data
Smart Cities
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Charla informativa del Programa de actualización en Derecho Informático
March 2nd, 2021 Daniela Lopez de Luise | Comunicación académica de la Facultad de Derecho de la Universidad de Buenos Aires |
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Nuevo Webinar en vivo: Aprendizaje a distancia y laboratorios virtuales con MATLAB y Simulink
March 2nd, 2021 Daniela Lopez de Luise
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CallParticipation: ImageCLEF Social Media User Data Awareness Task
March 2nd, 2021 Daniela Lopez de Luise ImageCLEFaware
https://www.imageclef.org/2021/aware
*** CALL FOR PARTICIPATION ***
Images constitute a large part of the content shared on social networks. Their disclosure is often related to a particular context and users are often unaware of the fact that, depending on their privacy status, images can be accessible to third parties and be used for purposes which were initially unforeseen. For instance, it is common practice for employers to search information about their future employees, online.
Most existing approaches which propose feedback about shared data focus on inferring user characteristics, and their practical utility is rather limited. We hypothesize that user feedback would be more efficient if conveyed through the real-life effects of data sharing.
The objective of the task is to automatically score user photographic profiles in a series of situations with strong impact on her/his life.
*** TASK ***
Given a set of social media user profiles, participants will propose machine learning techniques which provide a ranking of these in various (unaware) usage situations.
*** DATA SET ***
A data set of 500 user profiles with 100 photos per profile was created and annotated with an appeal score for a series of real-life situations via crowdsourcing. User profiles are created by repurposing a subset of the YFCC100M dataset. In accordance with GDPR, data minimization is applied, and participants receive only the information necessary to carry out the task in an anonymized form. Resources
include: (i) anonymized visual concept ratings for each situation modeled; (ii) automatically extracted predictions for the images that compose the profiles.
*** METRICS ***
The correlation with the ground truth will be measured using Pearson's correlation coefficient. The final score of each participating team will be obtained by averaging correlations obtained for individual situations.
*** IMPORTANT DATES ***
– Task registration opens: November 16, 2020
– Development data release: February 15, 2021
– Test data release: March 15, 2021
– Run submission: May 10, 2021
– Working notes submission: May 28, 2021
– CLEF 2021 conference: September 21-24, Bucharest, Romania
*** REGISTER ***
https://www.imageclef.org/2021#registration
*** OVERALL COORDINATION ***
Adrian Popescu, CEA LIST, France
Jérôme Deshayes-Chossart, CEA LIST, France Bogdan Ionescu, University Politehnica of Bucharest, Romania
On behalf of the Organizers,
Bogdan Ionescu
https://www.AIMultimediaLab.ro/