Let me forward this. The conference could be of interest for many
of us.
-------- Weitergeleitete Nachricht --------
Apologies if you receive multiple copies.
We encourage PC members to consider submitting papers and kindly
ask them to forward this call to interested parties.
**** We want to reassure authors that we are monitoring the
coronavirus situation and exploring contingency plans for a
virtual conference should there a physical conference not be
possible. Regardless, the proceedings will go ahead and will be
published this year. ****
===========================================================================================
Call for Papers
===========================================================================================
The 14th International Conference on Scalable Uncertainty
Management (SUM 2020) will be held in Bolzano, Italy from
September 23-25, 2020. See:
https://sum2020.inf.unibz.it/
The conference will be taking places as part of the Bolzano Summer
of Knowledge event, see:
https://summerofknowledge.inf.unibz.it/
===========================================================================================
Description
===========================================================================================
Established in 2007, the SUM conferences are annual events which
aim to gather researchers with a common interest in managing and
analyzing imperfect information from a wide range of fields, such
as Artificial Intelligence and Machine Learning, Databases,
Information Retrieval and Data Mining, the Semantic Web and Risk
Analysis, and with the aim of fostering collaboration and
cross-fertilization of ideas from the different communities. An
originality of the SUM conferences is their care for dedicating a
large space of their program to tutorials covering a wide range of
topics related to uncertainty management. Each tutorial provides a
survey of one of the research areas in the scope of the
conference.
===========================================================================================
Topics of Interest
===========================================================================================
We solicit papers on the management of large amounts of complex
kinds of uncertain, incomplete, or inconsistent information. We
are particularly interested in papers that focus on bridging gaps,
for instance between different communities, between numerical and
symbolic approaches, or between theory and practice. Topics of
interest include (but are not limited to):
Imperfect information in databases
- Methods for modeling, indexing, and querying uncertain databases
- Top-k queries, skyline query processing, and ranking
- Approximate, fuzzy query processing
- Uncertainty in data integration and exchange
- Uncertainty and imprecision in geographic information systems
- Probabilistic databases and possibilistic databases?
- Data provenance and trust
- Data summarization
- Very large datasets
Imperfect information in information retrieval and semantic web
applications
- Approximate schema and ontology matching
- Uncertainty in description logics and logic programming
- Learning to rank, personalization, and user preferences
- Probabilistic language models
- Combining vector-space models with symbolic representations
- Inductive reasoning for the semantic web
Imperfect information in artificial intelligence
- Statistical relational learning, graphical models, probabilistic
inference
Argumentation, defeasible reasoning, belief revision
- Weighted logics for managing uncertainty
- Reasoning with imprecise probability, Dempster-Shafer theory,
possibility theory
- Approximate reasoning, similarity-based reasoning, analogical
reasoning
- Planning under uncertainty, reasoning about actions, spatial and
temporal reasoning
- Incomplete preference specifications
- Learning from data
Risk analysis
- Aleatory vs. epistemic uncertainty
- Uncertainty elicitation methods
- Uncertainty propagation methods
- Decision analysis methods
- Tools for synthesizing results
===========================================================================================
Submission Guidelines
===========================================================================================
SUM 2020 solicits original papers in the following three
categories:
- Long papers (14 pages): technical papers reporting original
research or survey papers
- Short papers (8 pages): papers reporting promising
work-in-progress, system descriptions, position papers on
controversial issues, or survey papers providing a synthesis of
some current research trends
- Extended abstracts (2 pages) of recently published work in a
relevant journal or top-tier conference
All SUM submissions must be formatted according to the LNCS/LNAI
guidelines:
https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
Papers should be submitted via
EasyChair:
https://easychair.org/conferences/?conf=sum2020
===========================================================================================
Dates
===========================================================================================
All Deadlines are 23:59 Central European Time.
Submission deadline: May 4th, 2020
Notification: June 17th, 2020
Camera-ready copies due: July 1st, 2020
Conference: Sept. 23rd-25th, 2020
===========================================================================================
Publication
===========================================================================================
Accepted long (14 pages) and short papers (8 pages) will be
published by Springer in the Lecture Notes in Artificial
Intelligence (LNAI) series. Authors of an accepted long or short
paper will be expected to sign copyright release forms, and one
author is expected to give a presentation at the conference.
Authors of accepted abstracts (2 pages) will be expected to
present their work during the conference, but the extended
abstracts will not be published in the LNCS/LNAI proceedings (they
will be made available in a separate booklet)
===========================================================================================
Organization
===========================================================================================
Jesse Davis (KU Leuven), PC Co Chair
Karim Tabia (Artois University), PC Co Chair
Rafael Penaloza Nyssen (University of Milano-Bicocca), Local Chair
===========================================================================================
PC Members
===========================================================================================
Alessandro Antonucci, IDSIA
Nahla Ben Amor, Institut Sup��rieur de Gestion de Tunis
Salem Benferhat, Cril CNRS UMR8188, Universit�� d���Artois
Leopoldo Bertossi, Adolfo Ib����ez University (Santiago, Chile)
Fernando Bobillo, University of Zaragoza
Imen Boukhris, LARODEC ��� Universit�� de Tunis- ISG Tunis
Davide Ciucci, Universit�� di Milano-Bicocca
Thierry Denoeux, Universit�� de Technologie de Compi��gne
S��bastien Destercke, CNRS UMR Heudiasyc
Zied Elouedi, Institut Sup��rieur de Gestion de Tunis
Rainer Gemulla, Universit��t Mannheim
Lluis Godo, Artificial Intelligence Research Institute, IIIA ���
CSIC
John Grant, Towson University
Manuel G��mez-Olmedo, University of Granada
Arjen Hommersom, Open University of the Netherlands
Angelika Kimmig, Cardiff University
Eric Lefevre, Universit�� d���Artois
Philippe Leray, LS2N/DUKe ��� Nantes University
Sebastian Link, The University of Auckland
Thomas Lukasiewicz, University of Oxford
Silviu Maniu, Universite Paris-Sud
Serafin Moral, University of Granada
Francesco Parisi, DIMES ��� University of Calabria
Nico Potyka, Universitaet Osnabrueck, IKW
Henri Prade, IRIT ��� CNRS
Andrea Pugliese, University of Calabria
Benjamin Quost, HeuDiaSyC laboratory, University of Technology of
Compi��gne
Steven Schockaert, Cardiff University
Umberto Straccia, ISTI-CNR
Andrea Tettamanzi, Univ. Nice Sophia Antipolis
Matthias Thimm, Universit��t Koblenz-Landau
Barbara Vantaggi, Universita��� La Sapienza of Rome
Maurice van Keulen, University of Twente
--
Prof. dr. Jesse Davis
Machine Learning Group & DTAI Sports Analytics Lab
Department of Computer Science
KU Leuven
Belgium
@jessejdavis1
https://people.cs.kuleuven.be/~jesse.davis/
https://dtai.cs.kuleuven.be/sports/
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