Live e-Lecture by Prof. Jens Kober: “Robots Learning (Through) Interactions”, 5th April 2022 17:00-18:00 CET. Upcoming AIDA AI excellence lectures

March 30th, 2022 Daniela Lopez de Luise

 

Prof. Jens Kober (TU Delft, Netherlands), a prominent AI & Robotics researcher internationally, will deliver the e-lecture:

‘Robots Learning (Through) Interactions’, on Tuesday 5th April 2022 17:00-18:00 CET (8:00-9:00 am PST), (12:00 am-1:00am CST),

see details in: http://www.i-aida.org/ai-lectures/

You can join for free using the zoom link: https://authgr.zoom.us/s/92075053612 & Passcode: 148148

 

The International AI Doctoral Academy (AIDA), a joint initiative of the European R&D projects AI4Media, ELISE, Humane AI Net, TAILOR, VISION, currently in the process of formation,

is very pleased to offer you top quality scientific lectures on several current hot AI topics.

 

Lectures will be offered alternatingly by:

Top highly-cited senior AI scientists internationally or

Young AI scientists with promise of excellence (AI sprint lectures)

 

Lectures are typically held once per week, Tuesdays 17:00-18:00 CET (8:00-9:00 am PST), (12:00 am-1:00am CST).  Attendance is free.

 

Other upcoming lectures:

1. Prof. Jan Peters (Technische Universitaet Darmstadt, Germany), “Robot Learning”, 10th May 2022 17:00 – 18:00 CET.

2. Prof. Luc De Raedt (KU Leuven, Belgium), “Probabilistic Logics to Neuro-Symbolic Artificial Intelligence”, 7th June 2022 17:00 – 18:00 CET.

More lecture infos in: http://www.i-aida.org/ai-lectures/

 

These lectures are disseminated through multiple channels and email lists (we apologize if you received it through various channels).

If you want to stay informed on future lectures, you can register in the email lists AIDA email list and CVML email list.

 

Best regards

Profs. M. Chetouani, P. Flach, B. O’Sullivan, I. Pitas, N. Sebe, J. Stefanowski

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Summer School on Surgical Data Science, July 18-22, Strasbourg, France

March 30th, 2022 Daniela Lopez de Luise

We are very happy to announce the 1st Surgical Data Science (SDS) Summer School to be held in Strasbourg, France, from 18th – 22nd July 2022.

Recognizing the surge in Surgical Data Science research and the need for effective clinical translation, the SDS summer school aims to promote research and innovation of clinical value by educating clinicians and computer scientists on respective contexts, needs, tools and methodologies.

This first school organized by the Institute of Image-Guided Surgery, IHU-Strasbourg, and the University of Strasbourg will be focusing on endoscopic video analysis. Leading clinical and computers science experts from top institutions will cover subjects related to the clinical use of endoscopy and endoscopic video analysis, spanning from endoscopic instruments and data annotations all the way to advanced deep learning algorithms and the design of clinical translation studies.

Short online lectures on fundamentals of endoscopy and computer science will be freely accessible starting from 15th of March 2022 at https://edu4sds.eve-evolving-education.eu/. Upon completing these fundamentals, prospective participants can apply to the onsite summer school consisting of a series of lectures, hands-on labs, and group projects. Twenty selected computer scientists and clinicians will have the opportunity join us at Institute of Image-Guided Surgery, IHU-Strasbourg, from 18th – 22nd July 2022 to work in a truly multidisciplinary environment with unique clinical and computer science resources to come up with novel ideas and data science solutions of clinical value.

We invite early-career computer science and clinical researchers who are interested in diving into the exciting world of Surgical Data Science to visit http://edu4sds.org/, review the program and learn more about the registration.

Applications close on 25th April 2022. Places are limited so early application is advised.

Best wishes,

The Surgical Data Science Summer School organizing committee

Pietro Mascagni, Alexandros Karargyris, Vinkle Srivastav, Silvana Perretta, Nicolas Padoy

 

A picture containing text, tableware, plate, dishware Description automatically generated

 

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CFP Special Issue on Socially Acceptable Robot Behavior: Approaches for Learning, Adaptation and Evaluation

March 30th, 2022 Daniela Lopez de Luise
Special Issue on

Socially Acceptable Robot Behavior: Approaches for Learning, Adaptation and Evaluation

in Interaction Studies

I. Aim and Scope

A key factor for the acceptance of robots as regular partners in human-centered environments is the appropriateness and predictability of their behavior. The behavior of human-human interactions is governed by customary rules that define how people should behave in different situations, thereby governing their expectations. Socially compliant behavior is usually rewarded by group acceptance, while non-compliant behavior might have consequences including isolation from a social group. Making robots able to understand human social norms allows for improving the naturalness and effectiveness of human-robot interaction and collaboration. Since social norms can differ greatly between different cultures and social groups, it is essential that robots are able to learn and adapt their behavior based on feedback and observations from the environment.

This special issue in Interaction Studies aims to attract the latest research aiming at learning, producing, and evaluating human-aware robot behavior, thereby, following the recent RO-MAN 2021 Workshop on Robot Behavior Adaptation to Human Social Norms (TSAR) in providing a venue to discuss the limitations of the current approaches and future directions towards intelligent human-aware robot behaviors.

II. Submission

  1. Before submitting, please check the official journal guidelines.
  2. For paper submission, please use the online submission system.
  3. After logging into the submission system, please click on “Submit a manuscript” and select “Original article”.
  4. Please ensure that you select “Special Issue: Socially Acceptable Robot Behavior” under “General information”.

    The primary list of topics covers the following points (but not limited to):

  • Human-human vs human-robot social norms
  • Influence of cultural and social background on robot behavior perception
  • Learning of socially accepted behavior
  • Behavior adaptation based on social feedback
  • Transfer learning of social norms experience
  • The role of robot appearance on applied social norms
  • Perception of socially normative robot behavior
  • Human-aware collaboration and navigation
  • Social norms and trust in human-robot interaction
  • Representation and modeling techniques for social norms
  • Metrics and evaluation criteria for socially compliant robot behavior

III. Timeline

  1. Deadline for paper submission: March 31, 2022
  2. First notification for authors: June 15, 2022
  3. Deadline for revised papers submission: July 31, 2022
  4. Final notification for authors: September 15, 2022
  5. Deadline for submission of camera-ready manuscripts: October 15, 2022

    Please note that these deadlines are only indicative and that all submitted papers will be reviewed as soon as they are received.

IV. Guest Editors

  1. Oliver Roesler – Vrije Universiteit Brussel – Belgium
  2. Elahe Bagheri – Vrije Universiteit Brussel – Belgium
  3. Amir Aly – University of Plymouth – UK
  4. Silvia Rossi – University of Naples Federico II – Italy
  5. Rachid Alami – CNRS-LAAS – France
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Bhavani Thuraisingham (the University of Texas at Dallas) – Distinguished Lecture, Trusting AI with Our Cybersecurity

March 30th, 2022 Daniela Lopez de Luise

Web version
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EAI Insider


This newsletter features:

1.  Distinguished Lecture by Bhavani Thuraisingham (The University of Texas at Dallas (UTD))

2. Trusting AI with Our Cybersecurity by Prof. Mohammed M. Alani 

presenting eai distinguished lecture

Bhavani Thuraisingham | the University of Texas at Dallas (UTD)
Bhavani Thuraisingham is the Founders Chair Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute at the University of Texas at Dallas (UTD). 

Prof. Thuraisingham's research interests are on integrating cyber security and artificial intelligence/data science.

At SecureComm 2021 she talked about Integrating Cyber Security and Data Science/Artificial Intelligence with Applications in the Internet of Transportation Systems in her EAI Distinguished Lecture.
▶️Watch the lecture

Discover over 60 more presentations on Security and Privacy in Communication Networks from EAI SecureComm 2021 conference.

EAI RESEARCH Academy

In this edition, we present to you the article of our featured author Prof. Mohammed M. Alani from Seneca College of Applied Arts and Technology, Toronto, Canada. 

>Trusting AI with Our Cybersecurity
by Prof. Mohammed M. Alani 
Areas such as intrusion detection, malware detection, and phishing detection can be conquered by well-implemented machine learning-based solutions. However, we need to set up the right environment for these systems to succeed. Discover whether machine-learning models are capable of capturing sophisticated and unknown cyber-attacks in the article.
Read the article

Feel like publishing your own article, but didn't find the right platform? You can submit articles to share your vision of the future alongside emerging technologies via EAI Blog. Find out more in Submission Guideline. 

EAI is a non-profit organization with free membership and the largest open professional society. At EAI we are proud to empower our members to advance their career through online community participation. With tens of thousands of members from over 170 countries and hundreds of thousands of users EAI provides a leading global platform for research collaboration, fair evaluation, and transparent recognition.

Thank you for being a valuable part of our community. 

Stay safe,

Your EAI team
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The Deep Video Understanding Grand Challenge at ACM Multimedia 2022

March 30th, 2022 Daniela Lopez de Luise
Call For Participation


Challenge Website: https://sites.google.com/view/dvuchallenge2022

Deep video understanding is a difficult task which requires systems to develop a deep analysis and understanding of the relationships between different entities in video, to use known information to reason about other, more hidden information, and to populate a knowledge graph (KG) representation with all acquired information. To work on this task, a system should take into consideration all available modalities (speech, image/video, and in some cases text). 

The aim of this challenge series is to push the limits of multimodal extraction, fusion, and analysis techniques to address the problem of analyzing long duration videos holistically and extracting useful knowledge to utilize it in solving different types of queries. The target knowledge includes both visual and non-visual elements. As videos and multimedia data are getting more and more popular and usable by users in different domains and contexts, the research, approaches and techniques we aim to be applied in this Grand Challenge will be very relevant in the coming years and near future.

Challenge Overview:

Interested participants are invited to apply their approaches and methods on an extended novel Deep Video Understanding (DVU) dataset being made available by the challenge organizers. The dataset is split into a development data of 14 movies from the 2020-2021 versions of this challenge with Creative Commons licenses, and a new set of 10 movies licensed from KinoLorberEdu platform. 4 new movies out of the 10 will be added to the 14 movies, while 6 will be chosen as the testing data in 2022. The development data includes: original while videos, segmented scene shots, image examples of main characters and locations, movie-level KG representation of the relationships between main characters, relationships between characters key-locations, scene-level KG representation of each scene in a movie (location type, characters, interactions between them, order of interactions, sentiment of scene, and a short textual summary), and a global shared ontology of locations, relationships (family, social, work), interactions and sentiments. 

The organizers will support evaluation and scoring for a hybrid of main query types, at the overall movie level and at the individual scene level distributed with the dataset. Participants will be given the choice to submit results for either the movie-level or scene-level queries, or both. And for each category, queries are grouped for more flexible submission options :

More details are here on queries and dataset: https://sites.google.com/view/dvuchallenge2022/home/datasets-queries

Example Question types at Overall Movie Level:

Example Question types at Individual Scene Level:

A new addition to 2022 challenge is that systems will be asked to submit with their results for some queries a temporal segment from the movie or scene (e.g. using starting/ending timestamps) to act as an evidence for their answers. This requirement will be evaluated independently from the main scoring method and its objective is to demonstrate if systems can explain their results and if they are submitting their answers for the correct reasons.

Important Dates:

  • DVU development data release: Available from This URL

  • Testing dataset release : TBD (Coming Soon)

  • Testing queries release: TBD

  • Run submissions due to organizers: TBD

  • Paper submission deadline: TBD

  • Results released back to participants: TBD

  • Notification to authors: TBD

  • camera-ready submission: July 24th, 2022

  • ACM Multimedia dates: October 10 – 14, 2022

We hope you can join the challenge. For any questions please email the organizers directly:
Best Wishes
The DVU Grand Challenge Team
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