Hello,
we decided to extend the deadline to April 5th. regards,
vicenc
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CALL FOR PAPERS
18th Modeling Decisions for Artificial Intelligence
MDAI 2021, online,
and, if covid permits, hybrid:
Umea, Sweden, September 27 - 30, 2021
http://www.mdai.cat/mdai2021
Proceedings: LNAI; CORE-B conference; Deadline: March 22nd
The conference is on different facets of decision processes in a
broad sense. This includes model building and all kind of
mathematical tools for data aggregation, information fusion, and
decision making; tools to help decision in data science problems
(including e.g., statistical and machine learning algorithms as
well as data visualization tools); and algorithms for data
privacy and transparency-aware methods so that data processing
processes and decisions made from them are fair, transparent,
explainable and avoid unnecessary disclosure of sensitive
information.
The MDAI conference includes tracks on the topics of (i) data
science, (ii) machine learning, (iii) data privacy, (iv)
aggregation funcions, (v) human decision making, and (vi) graphs
and (social) networks, (vii) recommendation and search. The
conference has been since 2004 a forum for researchers to discuss
last results into these areas of research.
Previous conferences were celebrated in Barcelona (2004, 2013),
Tsukuba (2005), Tarragona (2006), Kitakyushu (2007), Sabadell
(2008), Awaji Island (2009), Perpinya (2010), Changsha (2011),
Girona (2012), Tokyo (2014), Skovde (2015), St Julia de Loria
(2016), Kitakyushu (2017), Mallorca (2018), Milan (2019),
cancelled due to COVID (2020).
MDAI is rated as a CORE B conference by the Computing Research
and Education Association of Australasia - CORE.
*Important Dates*
LNAI Submission deadline: April 5th, 2021 EXTENDED
LNAI Acceptance notification: May, 10th, 2021
Final version of LNAI accepted papers: June 10th, 2021
Early registration: June 10th, 2021
Conference: September 27 -30, 2021
*Submission and Publication*
Original technical contributions are sought. Contributions will
be selected on the basis of their quality. Papers should not
exceed 12 pages in total (using LNCS/LNAI style). Proceedings
with accepted papers will be published in the LNAI/LNCS series
(Springer-Verlag).
We will also publish additional proceedings in a USB memory with
a later
deadline.
*Tracks*
Data Science track. Data science is the science of data. Its goal
is to explain processes and objects through the available data.
The explanation is expected to be objective and suitable to make
predictions. The ultimate goal of the explanations is to make
informed decisions based on the knowledge extracted from the
data. Original contributions on methods, models, and tools for
data science are sought.
Machine learning track. Algorithms and methods building models
that are fair, transparent, explainable and that avoid
unnecessary disclosure of sensitive information.
Data privacy track. Privacy-preserving data mining, privacy
enhancing technologies, and statistical disclosure control
provide tools to avoid disclosure, and/or have a good balance
between disclosure risk and data utility and security. Original
contributions on aspects related to data privacy are sought.
Aggregation functions. Functions to aggregate data appear in
several contexts. They are used for decision making and
information fusion. Data science and artificial intelligence
systems need these functions to summarize information, improve
data quality and help in decision processes. Original
contributions on aggregation functions and their applications are
sought.
Human decision making. Decision making is a pervasive problem in
intelligent systems, and decisions are to be made in scenarios
where uncertainty is common. Most mathematical models for
decision making under risk and uncertainty provide optimal
decisions under certain constraints. Experience and studies show
that these rational decision making models diverge from the
typical approach human use to make decisions.
Graphs and (social) networks track. Graphs are often a convenient
way to represent data. Social networks is a paradigmatic case.
Algorithms and functions to process graphs and to extract
information and knowledge from them are of high relevance in data
science. Original contributions on graph analysis are sought.
Recommendation and search track. Searching and recommending
online information/items to users deals with both the
subjectivity related to the user's needs and the uncertainty and
vagueness that characterize the retrieval process, in particular
on the Web and on social media where huge amounts of new contents
are generated every day. For these reasons, original
contributions on search and recommendation algorithms and
applications are sought.
*MDAI 2021 Organization*
General chair:
Vicenc Torra (Umea University, Sweden)
Program co-chairs:
Vicenc Torra (Umea University, Sweden)
Yasuo Narukawa (Tamagawa University, Japan)
AB, PC, local organizing committee and additional information:
http://www.mdai.cat/mdai2021
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