2nd CfP for Graph Models for Learning and Recognition (GMLR) Track at 37th ACM-SAC 2022 in Brno, Czech Republic

October 5th, 2021 Daniela Lopez de Luise

 

Call for Papers

 

                 Graph Models for Learning and Recognition (GMLR) Track

               The 37th ACM Symposium on Applied Computing (SAC 2022)

                           April 25-29, 2022, Brno, Czech Republic

                              http://phuselab.di.unimi.it/GMLR2022

 

Important Dates

 

Submission of regular papers:    October 15, 2021

Notification of acceptance/rejection:     December 10, 2021

Camera-ready copies of accepted papers: December 21, 2021

SAC Conference:              April 25 – 29, 2022

 

Scientific Program Committee (in progress)

 

Davide Boscaini

Federico Castelletti

Vittorio Cuculo

Alessandro D’Amelio

Gabriele Gianini

Alessio Micheli

Ryan A. Rossi

Carlo Vercellis

Naoufel Werghi

 

Motivations and topics

 

The ACM Symposium on Applied Computing (SAC 2022) has been a primary gathering forum

for applied computer scientists, computer engineers, software engineers, and application

developers from around the world. SAC 2022 is sponsored by the ACM Special Interest Group

on Applied Computing (SIGAPP), and will be held in Brno, Czech Republic. The technical track on

Graph Models for Learning and Recognition (GMLR) is the first edition and is organized within

SAC 2022.

Graphs have gained a lot of attention in the pattern recognition community thanks to their

ability to encode both topological and semantic information. Encouraged by the success of CNNs,

a wide variety of methods have redefined the notion of convolution for 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 representationa 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 Journal (the journal will be announced soon).

 

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.

 

Track Chairs

 

Donatello Conte (University of Tours)

Giuliano Grossi (University of Milan)

Raffaella Lanzarotti (University of Milan)

Jianyi Lin (Università Cattolica del Sacro Cuore)

Jean-Yves Ramel (University of Tours)

 

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Short course (virtual & free) on “Representation Learning and Disentanglement in Computer Vision and Medical Imaging” Wed 6 October 2021

October 5th, 2021 Daniela Lopez de Luise
“Representation Learning and Disentanglement in Computer Vision and Medical Imaging”

Organized by: Dept. of Information Engineering (DINFO), University of Florence
Date: Wednesday October 6, 2021, 15:00 to 19:00 Italy time.

Lecturer:  Prof. Sotirios A. Tsaftaris
           Canon Medical/Royal Academy of Engineering Research Chair in Healthcare AI
           Chair in Machine Learning and Computer Vision at the University of Edinburgh (UK)

Google Meet Link: meet.google.com/xhc-cmeo-buz

SCHEDULE

PART 1: Learning representations (15:00 to 17:00)

The deep learning (DL) paradigm has been widely adopted in almost all domains of image analysis
as an alternative to traditional handcrafted techniques. However, the majority of deep neural
networks rely on the existence of significant amounts of training data that are not always
readily available.
– Why does modern machine learning require such large amounts of information/supervision?
– How do neural networks learn representations and what is representation learning?
– How representation learning relates to causality and the notion of generating factors?
– How disentangled representations related to generating factors and what are the standard
  methods to learn disentangled representations?

PART 2: Applications of disentangled representations in computer vision and medical imaging (17:15 to 19:00)

We discuss possible applications in computer vision and the medical imaging field and existing
open-ended challenges.
– What have been instrumental models solving image to image, segmentation and other tasks in computer vision?
– What have been ground breaking approaches in medical image analysis that can address challenges
  of data scarcity in medical imaging?
– What open challenges remain in the field of disentanglement?

Accompanying notes: https://arxiv.org/abs/2108.12043

BIO – Prof. Sotirios A. Tsaftaris, or Sotos, is currently the Canon Medical/Royal Academy of Engineering
Research Chair in Healthcare AI, and Chair (Full Professor) in Machine Learning and Computer Vision
at the University of Edinburgh (UK). He is also a Turing Fellow with the Alan Turing Institute. Previously
he held faculty positions with IMT Institute for Advanced Studies Lucca (Italy) and Northwestern University (USA).
He has published extensively, particularly in interdisciplinary fields, with more than 180 journal and conference
papers in his active record. His research interests are machine learning, computer vision, image analysis and processing.  

pdf icon Tsatftaris_short-course.pdf
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Understanding Social Behavior in Dyadic and Small Group Interactions (PMLR)

October 5th, 2021 Daniela Lopez de Luise

Call for papers/brief book chapters: 

Understanding Social Behavior in Dyadic and Small Group Interactions 

(Proceedings of Machine Learning Research – PMLR: https://proceedings.mlr.press/)

Description: Human interaction has been a central topic in psychology and social sciences, aiming at explaining the complex underlying mechanisms of communication with respect to cognitive, affective and behavioral perspectives. From a computational point of view, research in dyadic and small group interactions enables the development of automatic approaches for detection, understanding, modeling and synthesis of individual and interpersonal social signals and dynamics. Many human-centered applications for good (e.g., early diagnosis and intervention, augmented telepresence and personalized assistive agents) depend on devising solutions for such tasks. 

Verbal and nonverbal communication channels are used in dyadic and small group interactions to convey our goals and intentions while building a common ground. During interactions, people influence each other based on the cues they perceive. However, the way we perceive, interpret, react, and adapt to them depends on a myriad of factors (e.g., our personal characteristics, either stable or transient; the relationship and shared history between individuals; the characteristics of the situation and task at hand; societal norms; and environmental factors). To analyze individual behaviors during a conversation, the joint modeling of participants is required due to the existing dyadic or group interdependencies. While these aspects are usually contemplated in non-computational dyadic research, context- and interlocutor-aware computational approaches are still scarce, largely due to the lack of datasets providing contextual metadata in different situations and populations. 

Topics and Motivation: In line with these, we would like to bring together researchers in the field and from related disciplines to discuss the advances and new challenges on the topic of dyadic and small group interactions. We want to put a spotlight on the strengths and limitations of the existing approaches, and define the future directions of the field. In this context, we accept papers/brief book chapters in form of tutorials, surveys, and/or novel technical/scientific contributions addressing the issues related to, but not limited to, these topics:

  • Detection, understanding, modeling, prediction and synthesis of individual and interpersonal social signals and dynamics;

  • Verbal / nonverbal communication analysis in dyadic and small groups;

  • Contextual analysis in dyadic and small groups;

  • Datasets, annotation protocols and bias discovering/mitigation methods in dyadic and small groups;

  • Interpretability / Explainability in dyadic and small groups;

Researchers that express their interest in contributing to the PMLR proceedings by October 1st may be invited to present a snippet of their research during the ICCV’21 Understanding Social Behavior in Dyadic and Small Group Interactions (DYAD) Workshop (https://chalearnlap.cvc.uab.cat/workshop/44/description/) if they wish to in order to increase visibility of their research and possibly foster multidisciplinary collaboration.

Important dates:

  • Title and abstract (optional but strongly encouraged): October 1st, 2021 

  • ICCV’21 DYAD workshop: October 16th, 2021

  • Paper submission: November 30th, 2021 (extended)

  • Author notification: mid-January 2022 (tentative)

  • Camera-ready (PMLR): mid-February 2022 (tentative) 

Submission of intent (title and abstract) by email: sergio@maia.ub.es

Paper submission via CMT platform: https://cmt3.research.microsoft.com/PLMRDYAD2021

More info at: https://chalearnlap.cvc.uab.cat/workshop/44/schedule/ (PMLR track)

ORGANIZATION and CONTACT*

Sergio Escalera*, Computer Vision Center (CVC) and University of Barcelona, Spain <sergio.escalera.guerrero@gmail.com> 

Cristina Palmero*, Computer Vision Center (CVC) and University of Barcelona, Spain <c.palmero.cantarino@gmail.com> 

Wei-Wei Tu, 4Paradigm Inc., China

Albert Clapés, Aalborg University (AAU), Denmark, and Computer Vision Center (CVC) , Spain

Julio C. S. Jacques Junior, Computer Vision Center (CVC/UAB), Spain

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CFP – International Conference on Optimization and Learning (Sicily, Italy)

October 5th, 2021 Daniela Lopez de Luise
****************************************************************************************
                          OLA'2022
          International Conference on Optimization and Learning
                          18-20 July 2022
                      Syracuse (Sicilia), Italy
                http://ola2022.sciencesconf.org/
                    SCOPUS Springer Proceedings
****************************************************************************************

OLA is a conference focusing on the future challenges of optimization and learning methods and their applications. The conference OLA'2022 will provide an opportunity to the international research community in optimization and learning to discuss recent research results and to develop new ideas and collaborations in a friendly and relaxed atmosphere.

OLA'2022 welcomes presentations that cover any aspects of optimization and learning research such as big optimization and learning, optimization for learning, learning for optimization, optimization and learning under uncertainty, deep learning, new high-impact applications, parameter tuning, 4th industrial revolution, computer vision, hybridization issues, optimization-simulation, meta-modeling, high-performance computing, parallel and distributed optimization and learning, surrogate modeling, multi-objective optimization …

Submission papers: We will accept two different types of submissions:
–       S1: Extended abstracts of work-in-progress and position papers of a maximum of 3 pages
–       S2: Original research contributions of a maximum of 10 pages

Important dates:
===============

Invited session organization  Dec 20, 2021
Paper submission deadline     Jan 28, 2022
Notification of acceptance    March 25, 2022

Proceedings: Accepted papers in categories S1 and S2 will be published in the proceedings. A SCOPUS and DBLP indexed Springer book will be published for accepted long papers. Proceedings will be available at the conference.

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Natural Computation Special Issue – Visualisation in Evolutionary Computation

October 5th, 2021 Daniela Lopez de Luise
Special Issue of Springer's Natural Computation – Visualisation in Evolutionary Computation

Scope and objectives

Visualisation is a crucial tool in evolutionary computation (EC), enabling vital insight and understanding about algorithm operation and problem landscapes, as well as enabling decision makers to explore solution sets comprising large numbers of conflicting objectives. In addition to visualising the solutions generated by an EC process, the processes themselves can be visualised to provide algorithm users with information about how successful their optimisers are. Advances in animation and the prevalence of digital displays, along with improvements in processing power, mean that it is possible to use visualisation methods to illustrate aspects of an algorithm’s performance in real time.
The focus of this special issue is to highlight the current state-of-the-art in visualisation research within evolutionary computation. In addition to including extensions of papers from GECCO’s “Visualisation in Genetic and Evolutionary Computation” workshop (VizGEC 2021) we are soliciting novel contributions from the wider community.
Topics
Topics of interest for this special issue include (but are not limited to) the following:
  • visualisation of the evolution of a synthetic population;
  • visualisation of algorithm operation;
  • visualisation of problem landscapes;
  • visualisation of multi-objective trade-off sets;
  • the use of genetic and evolutionary techniques for visualising data;
  • novel technologies for visualisation within genetic and evolutionary computation;
  • visualisation to facilitate interactive evolution;
  • visualisation in real-world applications.
Submission guidelines

Submitted manuscripts must not have been published or simultaneously submitted elsewhere. For extended papers, at least 30% additional material beyond that in the published proceedings is expected. Each paper (extended or not) will receive thorough peer reviews and evaluation. Papers will be selected based on their originality, scientific and technical quality of the contributions, organisation and presentation and relevance to the special issue. When submitting your manuscript please select “SI: VizGEC” as the article type. In order to ensure timely publication manuscripts must be submitted before 19 December 2021.
Please ensure you read the Guide for Authors before writing your manuscript. Manuscripts should be formatted according to the journal's formatting instructions. The Guide for Authors and link to submit your manuscript is on the journal’s home page, https://www.springer.com/journal/11047.
Important dates
  • Paper submission due: 19 December 2021
  • First-round acceptance decision notification: 3 March 2022
  • First revision submission due: 1 May 2022
  • Second-round acceptance decision notification: 1 June 2022
  • Notification of final decision: 1 August 2022
Guest editors

Dr David Walker
University of Plymouth, UK
Prof. Richard Everson
University of Exeter, UK
Prof. Neil Vaughan
University of Exeter, UK
Dr Rui Wang
Uber AI, USA

Email: ruiwang@uber.com

Dr David Walker
Lecturer in Computer Science
UG Computing Programmes Manager
University of Plymouth

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