DAFUSAI at ECAI 2024
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Graçaliz Dimuro
May 2, 2024, 7:24:50 AM5/2/24
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DAFUSAI 2024
2nd WORKSHOP ON DATA FUSION FOR ARTIFICIAL INTELLIGENCE
at ECAI 2024
The 2ND Workshop on DATA FUSION FOR ARTIFICIAL INTELLIGENCE will
occur at 27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI
2024), as a pre-conference event, which will be on Saturday or/and
Sunday, 19 and 20 October 2024.
Summary: The workshop is open to both theoretical and applied works,
as long as the focus is on the analysis of the functions used for
information fusion.
Description: Data fusion is crucial in almost every application of
artificial intelligence. Classification, image processing,
decision-making, sentimental analysis, big data applications, neural
networks, and deep learning require collecting and fusing data in
appropriate ways to solve specific problems. For this reason, a huge
effort is devoted to the development and analysis of data fusion
methods.
Aggregation functions are one of the most widely used methods in this
sense. They are defined as monotone functions with appropriate
boundary conditions and include, among others, most of the means or
functions such as the product, the minimum, or the maximum. However,
in recent years it has been shown that the concept of aggregation
function can be too restrictive, as it does not cover some examples
that can provide good results in particular applications, as is the
case of the mode. Furthermore, some data fusion functions are more
general than aggregation functions. For example, the so-called
pre-aggregation functions have been proposed to deal with problems
ranging from classification to the computational brain, with promising
results.
Topics of interest: In this workshop, we intend to review the most
recent developments in the field of data fusion with uncertainty,
including, but not limited to:
- Theoretical results in aggregation functions, pre-aggregation
functions, and fusion functions with other kinds of weaker
monotonicity;
- Theoretical results in the controlling of the uncertainty in
interval-valued data fusion;
- Applications in deep learning and adaptative neuro-fuzzy systems,
decision making (including, e.g. multi-criteria decision making),
image processing, classification and multi-label classification,
machine learning, data stream clustering, sentimental analysis, and
data flow prediction.
Workshop Style:
Proceedings: The organizers will provide the proceedings by collecting
submitted papers. The proceedings will be available online. The
proceedings of this workshop will not be included in the proceedings
of ECAI 2024.
Paper Format: Researchers are invited to submit an extended abstract
(in PDF) of up to 4 pages (written in English). This extended abstract
should include the title, authors, affiliation, a short standard
abstract, keywords, the body of the extended abstract, and references.
In addition to regular paper submissions, we will consider accepting
papers rejected from the main conference, doing so purely based on the
reviews written for the main conference.
Overleaf template:
https://www.overleaf.com/latex/templates/springer-nature-latex-template/myxmhdsbzkyd
Submissions: https://chairingtool.com/conferences/DATAFUSAI24/MainTrack
Important dates:
Extended Abstracts: May 15th, 2024
Notification Acceptation: June 15th, 2024
Camera-ready extended abstract: June 22nd, 2024
Organizers:
Cedric Marco-Detchart (Universtat Politècnica de València)
Graçaliz Dimuro (Universidade Federal de Rio Grande, Brazil)
Helida Santos (Universidade Federal de Rio Grande, Brazil)
Humberto Bustince (Universidad Publica de Navarra. Spain)
Javier Fernadez (Universidade Federal de Rio Grande, Brazil)
Tiago Asmus (Universidade Federal de Rio Grande, Brazil)
Contact: grac...@gmail.com, gracali...@furg.br, hel...@furg.br
Graçaliz Pereira Dimuro
IFSA Secretary
Centro de Ciências Computacionais - C3
Programa de Pós-Graduação em Modelagem Computacional - PPGMC
Programa de Pós-Graduação em Computação - PPGCOMP
Universidade Federal do Rio Grande - FURG
Av. Itália km 08 Campus Carreiros
96201-900 - Rio Grande, RS, Brazil
emails: grac...@gmail.com or gracali...@furg.br
2nd WORKSHOP ON DATA FUSION FOR ARTIFICIAL INTELLIGENCE
at ECAI 2024
The 2ND Workshop on DATA FUSION FOR ARTIFICIAL INTELLIGENCE will
occur at 27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI
2024), as a pre-conference event, which will be on Saturday or/and
Sunday, 19 and 20 October 2024.
Summary: The workshop is open to both theoretical and applied works,
as long as the focus is on the analysis of the functions used for
information fusion.
Description: Data fusion is crucial in almost every application of
artificial intelligence. Classification, image processing,
decision-making, sentimental analysis, big data applications, neural
networks, and deep learning require collecting and fusing data in
appropriate ways to solve specific problems. For this reason, a huge
effort is devoted to the development and analysis of data fusion
methods.
Aggregation functions are one of the most widely used methods in this
sense. They are defined as monotone functions with appropriate
boundary conditions and include, among others, most of the means or
functions such as the product, the minimum, or the maximum. However,
in recent years it has been shown that the concept of aggregation
function can be too restrictive, as it does not cover some examples
that can provide good results in particular applications, as is the
case of the mode. Furthermore, some data fusion functions are more
general than aggregation functions. For example, the so-called
pre-aggregation functions have been proposed to deal with problems
ranging from classification to the computational brain, with promising
results.
Topics of interest: In this workshop, we intend to review the most
recent developments in the field of data fusion with uncertainty,
including, but not limited to:
- Theoretical results in aggregation functions, pre-aggregation
functions, and fusion functions with other kinds of weaker
monotonicity;
- Theoretical results in the controlling of the uncertainty in
interval-valued data fusion;
- Applications in deep learning and adaptative neuro-fuzzy systems,
decision making (including, e.g. multi-criteria decision making),
image processing, classification and multi-label classification,
machine learning, data stream clustering, sentimental analysis, and
data flow prediction.
Workshop Style:
Proceedings: The organizers will provide the proceedings by collecting
submitted papers. The proceedings will be available online. The
proceedings of this workshop will not be included in the proceedings
of ECAI 2024.
Paper Format: Researchers are invited to submit an extended abstract
(in PDF) of up to 4 pages (written in English). This extended abstract
should include the title, authors, affiliation, a short standard
abstract, keywords, the body of the extended abstract, and references.
In addition to regular paper submissions, we will consider accepting
papers rejected from the main conference, doing so purely based on the
reviews written for the main conference.
Overleaf template:
https://www.overleaf.com/latex/templates/springer-nature-latex-template/myxmhdsbzkyd
Submissions: https://chairingtool.com/conferences/DATAFUSAI24/MainTrack
Important dates:
Extended Abstracts: May 15th, 2024
Notification Acceptation: June 15th, 2024
Camera-ready extended abstract: June 22nd, 2024
Organizers:
Cedric Marco-Detchart (Universtat Politècnica de València)
Graçaliz Dimuro (Universidade Federal de Rio Grande, Brazil)
Helida Santos (Universidade Federal de Rio Grande, Brazil)
Humberto Bustince (Universidad Publica de Navarra. Spain)
Javier Fernadez (Universidade Federal de Rio Grande, Brazil)
Tiago Asmus (Universidade Federal de Rio Grande, Brazil)
Contact: grac...@gmail.com, gracali...@furg.br, hel...@furg.br
Graçaliz Pereira Dimuro
IFSA Secretary
Centro de Ciências Computacionais - C3
Programa de Pós-Graduação em Modelagem Computacional - PPGMC
Programa de Pós-Graduação em Computação - PPGCOMP
Universidade Federal do Rio Grande - FURG
Av. Itália km 08 Campus Carreiros
96201-900 - Rio Grande, RS, Brazil
emails: grac...@gmail.com or gracali...@furg.br
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