Call for Papers
Graph Models for Learning and Recognition (GMLR) Track
The 38th ACM Symposium on Applied Computing (SAC 2023)
March 27 – April 2, 2023, Tallinn, Estonia
http://phuselab.di.unimi.it/GMLR2023
Important Dates
===============
Submission of regular papers:
October 1, 2022October 15, 2022Notification of acceptance/rejection: November 19, 2022
Camera-ready copies of accepted papers: December 6, 2022
SAC Conference: March 27 – April 2, 2023
Motivations and topics
======================
The ACM Symposium on Applied Computing (SAC 2023) has been a primary gathering
forum for applied computer scientists, computer engineers, software engineers,
and application developers from around the world. SAC 2023 is sponsored by the
ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in
Tallinn, Estonia. The technical track on Graph Models for Learning and
Recognition (GMLR) is the second edition and is organized within SAC 2023.
Graphs have gained a lot of attention in the pattern recognition community
thanks to their ability to encode both topological and semantic information.
Despite their invaluable descriptive power, their arbitrarily complex
structured nature poses serious challenges when they are involved in learning
systems. Some (but not all) of challenging concerns are: a non-unique
representation of data, heterogeneous attributes (symbolic, numeric, etc.),
and so on.
In recent years, due to their widespread applications, graph-based learning
algorithms have gained much research interest. Encouraged by the success of
CNNs, a wide variety of methods have redefined the notion of convolution and
related operations on graphs. These new approaches have in general enabled
effective training and achieved in many cases better performances than
competitors, though at the detriment of computational costs.
Typical examples of applications dealing with graph-based representation are:
scene graph generation, point clouds classification, and action recognition in
computer vision; text classification, inter-relations of documents or words to
infer document labels in natural language processing; forecasting traffic
speed, volume or the density of roads in traffic networks, whereas in
chemistry researchers apply graph-based algorithms to study the graph
structure of molecules/compounds.
This track intends to focus on all aspects of graph-based representations and
models for learning and recognition tasks. GMLR spans, but is not limited to,
the following topics:
● Graph Neural Networks: theory and applications
● Deep learning on graphs
● Graph or knowledge representational learning
● Graphs in pattern recognition
● Graph databases and linked data in AI
● Benchmarks for GNN
● Dynamic, spatial and temporal graphs
● Graph methods in computer vision
● Human behavior and scene understanding
● Social networks analysis
● Data fusion methods in GNN
● Efficient and parallel computation for graph learning algorithms
● Reasoning over knowledge-graphs
● Interactivity, explainability and trust in graph-based learning
● Probabilistic graphical models
● Biomedical data analytics on graphs
Authors of selected top papers of this track will be asked to publish an
extended version in a Special Issue of a high-impact Journal (the journal
will be announced later).
Track Chairs
============
Donatello Conte (University of Tours)
Alessandro D'Amelio (University of Milan)
Giuliano Grossi (University of Milan)
Raffaella Lanzarotti (University of Milan)
Jianyi Lin (Università Cattolica del Sacro Cuore)
Scientific Program Committee
============================
Annalisa Barla (University of Genoa)
Davide Boscaini (Bruno Kessler Foundation)
Vittorio Cuculo (University of Milan)
Samuel Feng (Sorbonne University)
Gabriele Gianini (University of Milan)
Alessio Micheli (University of Pisa)
Carlos Oliver (ETH Zürich)
Maurice Pagnucco (University of New South Wales)
Ryan A. Rossi (Adobe Research)
Jean-Yves Ramel (University of Tours)
(others to be confirmed)
Submission Guidelines
=====================
Authors are invited to submit original and unpublished papers of research
and applications for this track. The author(s) name(s) and address(es) must
not appear in the body of the paper, and self-reference should be in the
third person. This is to facilitate double-blind review. Please, visit the
website for more information about submission.
SAC No-Show Policy
==================
Paper registration is required, allowing the inclusion of the paper/poster
in the conference proceedings. An author or a proxy attending SAC MUST
present the paper. This is a requirement for the paper/poster to be included
in the ACM digital library. No-show of registered papers and posters will
result in excluding them from the ACM digital library.
2nd Edition of Graph Models for Learning and Recognition (GMLR) Track at 37th ACM-SAC 2022 in Brno, Czech Republic
October 4th, 2022 
Daniela Lopez de Luise Webinar by Dr. Hany Farid on Combating Deep Fakes
October 4th, 2022 Daniela Lopez de Luise webinar by Prof. Hany Farid on “Combating Deep Fakes”. Detail on the
webinar are given below:
Title: Combatting Deep Fakes
Speaker: Prof. Hany Farid , University of California, Berkeley, USA
When: 12 October 2022, at 10am PT (1 pm EST, 7pm CEST)
Where: Online (Zoom)
Registration: (free, but required):
https://us06web.zoom.us/webinar/register/WN_516wKq4yR_yp1z9Z6g5Bgw
*** Talk Summary ***
In the early days of the Russian invasion of Ukraine, President
Zelenskyy warned the world that Russia's digital disinformation
machinery would create a deep fake of him admitting defeat. By mid-March
of 2022, a deep fake of Zelenskyy appeared with just this message. This
video was eventually debunked, but not before it spread across social
media and appeared briefly on Ukrainian television. Three months later,
the mayors of Berlin, Madrid, and Vienna collectively spoke for nearly
30 minutes with a deep-fake version of Kiev Mayor Klitschko, before
realizing they were being duped. In addition to adding jet fuel to
disinformation campaigns, this new breed of synthetic media also makes
it easier to deny reality — the so-called liar's dividend — as seen by
the recent baseless claim that video addresses by President Biden are
deep fakes deployed to conceal his death. I will discuss how deep fakes
are made, how they are being weaponized, and how they can be detected.
*** About the Speaker ***
Dr. Hany Farid is a Professor at the University of California, Berkeley
with a joint appointment in Electrical Engineering & Computer Sciences
and the School of Information. His research focuses on digital
forensics, forensic science, misinformation, image analysis, and human
perception. He received his undergraduate degree in Computer Science and
Applied Mathematics from the University of Rochester in 1989, and his
Ph.D. in Computer Science from the University of Pennsylvania in 1997.
Following a two-year post-doctoral fellowship in Brain and Cognitive
Sciences at MIT, he joined the faculty at Dartmouth College in 1999
where he remained until 2019. He is the recipient of an Alfred P. Sloan
Fellowship, a John Simon Guggenheim Fellowship, and a Fellow of the
National Academy of Inventors.
For more information, visit:
https://ieee-biometrics.org/index.php/activities/webinars
DeepLearn 2023 Winter: early registration October 24
October 4th, 2022 Daniela Lopez de Luise DeepLearn 2023 Winter
Bournemouth, UK
January 16-20, 2023
https://irdta.eu/deeplearn/2023wi/
***********
Co-organized by:
Department of Computing and Informatics
Bournemouth University
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
******************************************************************
Early registration: October 24, 2022
******************************************************************
SCOPE:
DeepLearn 2022 Winter will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Guimarães, Las Palmas de Gran Canaria and Luleå.
Deep learning is a branch of artificial intelligence covering a spectrum of current exciting research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, health informatics, medical image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, biometrics, communications, climate sciences, bioinformatics, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience.
Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely.
An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles.
ADDRESSED TO:
Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2023 Winter is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators.
VENUE:
DeepLearn 2023 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be:
Talbot Campus
Bournemouth University
https://www.bournemouth.ac.uk/about/contact-us/directions-maps/directions-our-talbot-campus
STRUCTURE:
3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
Full live online participation will be possible. However, the organizers highlight the importance of face to face interaction and networking in this kind of research training event.
KEYNOTE SPEAKERS:
Yi Ma (University of California, Berkeley), CTRL: Closed-Loop Data Transcription via Rate Reduction
Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks
Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills
PROFESSORS AND COURSES:
Mohammed Bennamoun (University of Western Australia), [intermediate/advanced] Deep Learning for 3D Vision
Matias Carrasco Kind (University of Illinois, Urbana-Champaign), [intermediate] Anomaly Detection
Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning
Seungjin Choi (Intellicode), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces
Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare
Luc De Raedt (KU Leuven), [introductory/intermediate] From Statistical Relational to Neuro-Symbolic Artificial Intelligence
Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning
João Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities
Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology
Zhiting Hu (University of California, San Diego) & Eric P. Xing (Carnegie Mellon University), [intermediate/advanced] A “Standard Model” for Machine Learning with All Experiences
Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances
Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models
Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond
David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning
Abdelrahman Mohamed (Meta), [intermediate/advanced] Speech Representation Learning for Recognition and Generation
Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How?
Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning
Bracha Shapira (Ben-Gurion University of the Negev), [introductory/intermediate] Recommender Systems
Richa Singh (Indian Institute of Technology Jodhpur), [introductory/intermediate] Trusted AI
Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning
Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks
Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning
Bram van Ginneken (Radboud University Medical Center), [introductory/intermediate] Deep Learning for Medical Image Analysis
Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis
OPEN SESSION:
An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david@irdta.eu by January 8, 2023.
INDUSTRIAL SESSION:
A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david@irdta.eu by January 8, 2023.
EMPLOYER SESSION:
Organizations searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david@irdta.eu by January 8, 2023.
ORGANIZING COMMITTEE:
Rashid Bakirov (Bournemouth, local co-chair)
Marcin Budka (Bournemouth)
Vegard Engen (Bournemouth)
Nan Jiang (Bournemouth, local co-chair)
Carlos Martín-Vide (Tarragona, program chair)
Sara Morales (Brussels)
David Silva (London, organization chair)
REGISTRATION:
It has to be done at
https://irdta.eu/deeplearn/2023wi/registration/
The selection of 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish.
Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will have got exhausted. It is highly recommended to register prior to the event.
FEES:
Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. The fees for on site and for online participation are the same.
ACCOMMODATION:
Accommodation suggestions are available at
https://irdta.eu/deeplearn/2023wi/accommodation/
CERTIFICATE:
A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.
QUESTIONS AND FURTHER INFORMATION:
ACKNOWLEDGMENTS:
Bournemouth University
Rovira i Virgili University
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
ACM Journal on Autonomous Transportation Systems (JATS)
October 4th, 2022 Daniela Lopez de Luise ACM Launches Journal on Autonomous Transportation Systems
The potential of autonomous cars, buses and trucks to revolutionize transportation and society is one of the most anticipated subjects in technology. Several companies have already developed working prototypes of autonomous vehicles and billions of dollars have been invested in research and development. At the same time, important challenges remain in the effort to bring autonomous transportation to the general public.
ACM, the Association for Computing Machinery, announced it is now accepting submissions for a new publication, the ACM Journal on Autonomous Transportation Systems (JATS). In launching the new journal, the ACM JATS editorial team recognizes that the area of autonomous transportation systems is at a critical point where issues related to data, models, computation, and scale are increasingly important. Based on the significant growth in research activity around autonomous transportation in areas including computer science, electrical engineering, civil engineering, sensor technology, and artificial intelligence, the new journal is envisioned as a timely addition to the field.
The ACM Journal on Autonomous Transportation Systems aims to cover the topics in design, analysis, and control of autonomous transportation systems. The expected topics of interest include, but are not limited to:
- Data Science in autonomous transportation systems
- Communication and real-time control in connected transportation systems
- Smart traffic analysis, management, and control solutions
- Public transit planning and operation
- Algorithm design for autonomous transportation systems
- Mathematical modelling of traffic flow
- Computation in transportation networks
- Algorithms for urban and inter-city logistics systems
- The latest advances in unmanned aerial systems
For more details, see https://www.acm.org/media-center/2022/september/jats-launch
Call for workshop papers
October 4th, 2022 Daniela Lopez de Luise 2023, to be held in Hawaii, USA in January 2023, will host 13 workshops
on different topics with paper submission deadlines in October and
November 2022. Accepted papers will be published as part of the WACV
2023 workshop proceedings and will appear in IEEE Xplore.
Many of the workshops also include challenges.
This year's workshops include:
+ 2nd Workshop on Video/Audio Quality in Computer Vision
Organizers: Yarong Feng; Zongyi Liu; Kevin Bowyer; Larry Davis
+ Real-World Surveillance: Applications and Challenges
Organizers: Kamal Nasrollahi; Sergio Escalera; Radu Tudor Ionescu;
Fahad Shahbaz Khan; Thomas B. Moeslund; Anthony Hoogs; Shmuel Peleg;
Mubarak Shah
+ Workshop on Fine-grained Activity Recognition
Organizers: Jeffrey Byrne; Jonathan Fiscus; Yooyoung Li; Hilde
Kuehne; Yogesh Rawat; Mubarak Shah; Alexander Hauptmann; Rama Chellappa
+ 2nd Workshop on Computer Vision for Winter Sports (CV4WS)
Organizers: Matteo Dunnhofer; Nicola Conci; Christian Micheloni
+ 1st Workshop on Maritime Computer Vision
Organizers: Benjamin Kiefer; Fabio Poiesi; Matej Kristan; Janez
Perš; Fabio Andrade; Alexandre Bernardino; Matthew D Dawkins; Jenni
Raitoharju
+ Monocular Depth Estimation Challenge
Organizers: Jaime Spencer Martin; Stella Qian; Chris Russell; Simon
Hadfield; Erich Graf; Andrew Schofield; Richard Bowden
+ 4th Annual Workshop on Demographic Variations in Performance of
Biometric Algorithms
Organizers: Nisha Srinivas; Michael King; Arun Ross; Kevin Bowyer;
Karl Ricanek
+ Workshop on Pretraining Large Vision and Multimodal Models
Organizers: Emily Webber; Fouad Bousetouane; Arjun Balasubramanian;
Anish Mohan; Corey Barrett
+ 3rd Workshop on Explainable & Interpretable Artificial Intelligence
for Biometrics – xAI4Biometrics
Organizers: Ana F. Sequeira; Jaime S. Cardoso; Adam Czajka; Hugo
Proenca; João Pinto; Kiran Raja; Naser Damer; Pedro C. Neto; Tiago FS
Gonçalves; Sara P. Oliveira; Isabel Rio-Torto; Peter Eisert; Cynthia Rudin
+ First Workshop on Photorealistic Image and Environment Synthesis for
Computer Vision (PIES-CV)
Organizers: Nicholas R Gans; Ryan McMahan
+ 2nd Workshop on Dealing with the Novelty in Open Worlds
Organizers: Pulkit Kumar; Anubhav Gupta; Shu Kong; Dawei Du;
Christopher Funk; Walter Scheirer; Terrance E Boult; Carl Vondrick;
Abhinav Shrivastava
+ Long-Range Recognition
Organizers: Scott McCloskey; Terrance E Boult; Patrick Flynn; Rama
Chellappa; Vishal Patel; Benjamin Riggan
+ Workshop On Manipulation, Adversarial and Presentation Attacks In
Biometrics
Organizers: Kiran Raja; Naser Damer; Raghavendra Ramachandra; Julian
Fierrez
For details on the submission procedure, deadlines and other information
please visit:
https://wacv2023.thecvf.com/node/138