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

 

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)

 

Scientific Program Committee (in progress)

 

Davide Boscaini (Fondazione Bruno Kessler)

Ryan A. Rossi     (Adobe Research)

…

 

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

 

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.

 

CALL FOR PAPERS

 

The Fifth International Econometric Conference of Vietnam – ECONVN2022

Ho Chi Minh City, Vietnam, January 10-12, 2022

 

http://hcm-hn.conference-econ-buh-bav-rist.vn/

 

in hybrid mode

 

Main theme: Financial Econometrics: Bayesian Analysis, Quantum Uncertainty,

and Related Topics

 

* The first ECONVN2018 was focused on the main theme “Econometrics for

Financial Applications”; selected papers were published in the Springer

Series in Computational Intelligence, # 760, 2018.

 

* The main theme of the second conference ECONVN2019 was “Beyond Traditional

Probabilistic Methods in Economics”; selected papers were published in the

Springer Series in Computational Intelligence, # 809, 2019.

 

* The main theme of the third conference ECONVN2020 was “Data Science for

Financial Econometrics”; selected papers were published in the Springer

Series in Computational Intelligence, # 898, 2020.

 

* The main theme of the fourth conference ECONVN2021 was “Prediction and

Causality in Econometrics and Related Topics”; selected papers will also appear

in a volume published by Springer.

 

The main theme of the fifth conference is “Financial Econometrics: Bayesian

Analysis, Quantum Uncertainty, and Related Topics”. This theme is motivated

by the need to go beyond traditional frequency-based probabilistic

techniques, the need that comes on several levels. On the level of

macro-economics, this need comes from the fact that in many practical

situations, we do not have anough data to reliably deduce frequencies. So, it

is necessary to supplement the data with expert estimates — and in the

probabilistic framework, this means using Bayesian approach. On the other

hand, on the level of micro-economics, where we study individuals that form

the economics milieu, behavioral economics has shown that individuals'

behavior cannot be accurately described by the traditional probabilistic

models, we need to use more general techniques — and quantum ideas are an

example of such general techniques that have been successfully used to

describe this behavior. So, this conference aims at bringing together

researchers in Econometrics and Finance for an opportunity to present and

discuss theoretical and applied issues related to Bayesian and quantum

approaches, as well as to foster research collaboration. While the theme is

the focus of the conference, all other research in economics, especially in

finance and banking, are welcome.

 

Venue: The Banking University HCMC (BUH), 36 Ton That Dam Street, District 1,

Ho Chi Minh City, Vietnam

 

Conference Dates: January 10-12, 2022

 

Conference website: http://hcm-hn.conference-econ-buh-bav-rist.vn

 

Point of contact: vietnam.buh.econvn@gmail.com                              

 

Tel: (84) 28.382.117.06

 

The Conference welcomes research contributions to all aspects of Econometrics

and Finance, with special emphasis on, but not limited to different techniques:

 

* Bayesian Analysis

 

* Quantum Uncertainty

 

* Predictive Modeling

 

* Causal Inference

 

* Machine Learning

 

* Intelligent Data Analysis

 

* Big Data Techniques

 

and their applications to:

 

* Macroeconomics and international economics

 

* Money market and capital market

 

* Public and corporate finance

 

Publications: Accepted papers that satisfy Springer criteria for

innovativeness will be published in a Springer volume and included in Scopus.

Accepted papers which are not accepted for a Springer volume will be

published in Proceedings published by a Vietnamese publishing house.

 

Important Dates:    

 

* Paper submission deadline: September 15, 2021

 

* Notification: October 1, 2021

 

* Camera-Ready Manuscript: October 31, 2021

 

Instructions for submissions:   

 

* Get your account on EasyChair (then click on Conferences, on ECONVN2022)

and submit your PDF files to EasyChair.

 

Link: https://easychair.org/conferences/?conf=econvn2022

 

* Make sure your PDF files have Authors' EMAILS on them for correspondence.

 

Registration fee            

 

1. General presenters USD 200 (non-residents)/VND 6,900,000 (residents)

 

2. Student presenters USD 100 (non-residents)/VND 3,450,000 (residents)

 

3. General participants USD 100/VND 2,300,000

 

4. Student participants USD 30/VND 1,000,000

 

Papers published in the proceedings published by a Vietnamese publisher need

an additional publication fee of 3,000,000 VND/paper

 

Registration fee includes conference bag, refreshments, and lunches.

 

Information is subject to change without notice

 

Plenary invited speakers (Tentative)

 

* Thomas Augustin (Germany)

 

* Matt Briggs (USA)

 

* Michael Eichler (Netherlands)

 

* Rakesh Gupta (Australia)

 

* Daniel S. Hain (Denmark)

 

* John Harding (USA)

 

* Emmanuel Haven (Canada)

 

* Polina Khrennikova (UK)

 

* Vladik Kreinovich (USA)

 

* Poom Kumam (Thailand)

 

* Hung T. Nguyen (USA and Thailand)

 

* Mark Schaffer (UK)

 

* Tonghui Wang (USA)

 

* Woraphon Yamaka (Thailand)

 

Organizing committee

 

Chair: Nguyen Ngoc Thach

 

Vice-Chair: Ha Van Dung

 

Secretary: Le Hoang Anh

 

Secretary assistant: Vo Thi Thuy Kieu

 

Scientific committee

 

Chair: Hung T. Nguyen (USA & Thailand)

 

Members:

 

* Bernadette Bouchon-Meunier (France) 

 

* William Briggs (USA)

 

* Bernard De Baets (Belgium)

 

* Emmanuel Haven (Canada)

 

* Duan Jin-Chuan (Singapore)

 

* Janusz Kacprzyk (Poland)

 

* Polina Khrennikova (UK)

 

* Vladik Kreinovich (USA)

 

* Poom Kumam (Thailand)

 

* Tran T. Lanh (USA)

 

* Nguyen Duc Trung (Vietnam)

 

* Nguyen Ngoc Thach (Vietnam)

 

* M. E. Shaffer (UK)  

 

* Tonghui Wang (USA)  

 

* Woraphon Yamaka (Thailand)     

 

BANKING UNIVERSITY HCMC(BUH)

 

Institute for Research Science and Banking Technology