Paper submission deadline for the BNAIC/BNLEARN 2021 conference is now extended 
to September 10, 2021!
We look forward to your submissions!

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CALL FOR PAPERS
33rd Benelux Conference on Artificial Intelligence and 30th Belgian Dutch 
Conference on Machine Learning (BNAIC/BNLEARN 2021)

10 - 12 November, 2021
Belval, Esch-sur-Alzette, Luxembourg
https://bnaic2021.uni.lu

The 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian 
Dutch Conference on Machine Learning (BNAIC/BNLEARN 2021) are organised as a 
joint conference by the University of Luxembourg, under the auspices of the 
Faculty of Science, Technology and Medicine (FSTM) and the Interdisciplinary 
Lab for Intelligent and Adaptive Systems (ILIAS), and the IT for Innovative 
Services (ITIS) research department of the Luxembourg Institute of Science and 
Technology.

BNAIC/BENELEARN 2021 will be held in a hybrid online/onsite format and will 
provide ample opportunity for interaction between academics and businesses: 
academics are also encouraged to join the business sessions, and vice versa.

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SUBMISSION INFORMATION
Researchers are invited to submit unpublished original research on all aspects 
of Artificial Intelligence and Machine Learning. Additionally, high-quality 
research results already published at international AI/ML conferences or 
journals are also welcome as extended abstracts. Four types of submissions are 
invited:

Type A: Regular papers
Papers presenting original work that advances Artificial Intelligence and 
Machine Learning. Position and review papers are also welcomed. These 
contributions should address a well-developed body of research, an important 
new area, or a promising new topic, and provide a big picture view. Type A 
papers can be long (>= 12 pages, including references and appendices) or short 
(>12 pages, including references and appendices). Contributions will be 
reviewed on the basis of their overall quality and relevance.

Type B: Encore abstracts
Abstracts of already published work that has been accepted in 2021 to any AI/ML 
conference or journal. Authors are invited to submit the author version of 
their officially published paper together with a 2-page abstract (excluding 
references). Authors may submit at most one type B paper of which they are the 
corresponding author.

Type C: Posters and demonstrations
Posters and demonstration abstracts. Proposals should be submitted as a 2-page 
(excluding references) abstract. Demonstrations should also submit a short 
video illustrating the working of the system (not exceeding 15 minutes). Any 
system requirements should also be mentioned in the submission. Posters and 
demonstrations will be evaluated based on their originality and innovative 
character, the technology deployed, the purpose of the systems in interaction 
with users and/or other systems, and their economic and/or societal potential.

Type D: Thesis abstracts
Abstracts of graduation reports. Bachelor and Master students are invited to 
submit a 2-page abstract (excluding references) of their completed 
AI/ML-related thesis. Supervisors should be listed. The thesis should have been 
accepted after June 1, 2020. Submissions will be judged based on their 
originality and relevance for the conference.

All submissions should not be anonymous, i.e. they must include all author 
names and their affiliations.

PRESENTATION
Type A, B, and D papers can be accepted for either oral or poster presentation.

PRIZES
Just like past years, there will be prizes for the best paper (type A), best 
poster and demonstration (type C), and best thesis (type D).
The best paper will be automatically nominated to the yearly special issue of 
AI Communications on Best of AI Research in Europe.

PREPROCEEDINGS & POSTPROCEEDINGS
Accepted contributions within all four categories will be included in the 
online conference proceedings. All contributions should be written in English, 
using the Springer CCIS/LNCS format (see 
https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines)
 and submitted electronically via EasyChair: 
https://easychair.org/conferences/?conf=bnaicbenelearn2021

Submission implies willingness of at least one author to register for 
BNAIC/BENELEARN 2021 and present the paper. For each paper, a separate author 
registration is required.
Selected Type A long papers will be invited to submit to the postproceedings 
published in Springer’s CCIS series (https://www.springer.com/series/7899).

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IMPORTANT DATES
Paper submission deadline: September 10, 2021
Author notification: October 8, 2021
Camera ready submission deadline: October 15, 2021
All deadlines are at 23:59, AoE time zone: https://time.is/Anywhere_on_Earth
Conference: November 10-12, 2021

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TOPICS OF INTEREST
This year we encourage authors to submit academic work on the intersection of:

AI & Arts
The town where the conference will take place, Esch sur Alzette, will be the 
Cultural Capital of Europe in 2022. For this event the University of Luxembourg 
will create an AI & Art Pavilion, which aims to reflect on AI and the future of 
art including AI generated painting, AI painting style transfer, AI & Music, 
etc. In this emerging and hot topic, we expect papers exploring the relations 
between AI and Art from various points of view from using AI as a technology 
for art production to art production questioning the place of AI in our 
societies.

AI & Law
The application of AI tools in and for the legal domain is a manifold and 
continuously enriching area with quickly increasing interest from and 
involvement of both the legal professionals and AI researchers. The theoretical 
foundations and applications in AI & Law don’t only aim at modeling legal 
reasoning, providing analysis of trends and making legal tasks easier and more 
efficient, but also at providing foundations for law-abiding artificial agents. 
The topics range from rule-based reasoning, case-based reasoning, and formal 
legal ontologies, through computational legal argumentation, theory 
construction and legal deontics, until ML for legal analytics and RegTech.

AI & Ethics
The significant impact of AI, machine learning and robotics on society and the 
development of humanity is unquestionable. Its nature, controllability, tools, 
dangers and potential constraints have been subject to hot debates notably when 
AI is used in applications with sensitive ethical consequences (e-health, 
surveillance, human resources, micro-finance, etc.) since this raises concern 
about its fairness, accountability, and transparency. Thus, especially with the 
recent debates about user privacy and the Covid Tracking apps, this topic will 
remain a hot topic throughout the year of 2021.

AI & Systems
Over the last decade, computing became consistently ubiquitous and pervasive in 
all aspects of our private and professional lives, ending up in a seamless 
integration of distributed computing power, software, data, sensors, and 
actuators interacting with each other and with humans ultimately making the 
concept of ambient intelligence a reality as Cyber-Physical Social Systems. AI 
is central in such systems both as the functional computation building blocks 
and as means to create natural and seamless interactions among humans and 
between humans and their smart physical environment. From applying AI to IoT 
systems, paradigms like cognitive computing emerged and have raised the 
interest of the AI community. In this specific track, contributions on the 
application of AI on systems ranging from classic IoT to advanced cognitive 
systems including human in the loop are expected.

A non-exhaustive list of topics includes:
Automated Machine Learning and meta-learning
Bayesian Learning
Case-based Learning
Causal Learning
Clustering
Computational Creativity
Computational Learning Theory
Computational Models of Human Learning
Data Mining
Data Visualisation
Deep Learning
Ensemble Methods
Evaluation Frameworks
Evolutionary Computation
Feature Selection and Dimensionality Reduction
Inductive Logic Programming
Interactive AI Methods and Applications
Kernel Methods
Knowledge Discovery in Databases
Learning and Ubiquitous Computing
Learning in Multi-Agent Systems
Learning from Big Data
Learning from User Interactions
Learning for Language and Speech
Media Mining and Text Analytics
ML and Information Theory
ML Applications in Industry
ML for Scientific Discovery
ML in Non-stationary Environments
ML with Expert-in-the-loop
Natural Language Processing / Natural Language Understanding
Neural Networks
Online Learning
Pattern Mining
Predictive Modeling
Ranking / Preference Learning / Information Retrieval
Reinforcement Learning
Representation Learning
Robot Learning
Social Networks
Statistical Learning
Structured Output Learning
Transfer and Adversarial Learning
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