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<p class="MsoNormal"><span style="color:black">***************</span><span lang="EN-US" style="color:black">****</span><o:p></o:p></p>
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<b><span style="color:black">CALL FOR PAPERS</span></b><o:p></o:p></p>
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<b><span style="color:black">33rd Benelux Conference on Artificial Intelligence and 30th Belgian Dutch Conference on Machine Learning (BNAIC/BNLEARN 2021)</span></b><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<span style="color:black">10 - 12 November, 2021</span><o:p></o:p></p>
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<span style="color:black">Belval, Esch-sur-Alzette, Luxembourg</span><o:p></o:p></p>
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<span style="color:black"><a href="https://bnaic2021.uni.lu" title="https://bnaic2021.uni.lu"><span style="color:#00004D">https://bnaic2021.uni.lu</span></a></span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<span style="color:black">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.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<span style="color:black">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.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">************************</span><span lang="EN-US" style="color:black">*****</span><o:p></o:p></p>
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<b><span style="color:black">SUBMISSION INFORMATION</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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:</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">Type A: Regular papers</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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 (10-15 pages, including references) or short (6-10 pages, including references). Contributions will be reviewed on the basis of their overall quality and
relevance.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">Type B: Encore abstracts</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">Abstracts of already published work that has been accepted in 2021 to any AI/ML conference. 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.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">Type C: Posters and demonstrations</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">Type D: Thesis abstracts</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">PRESENTATION</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">Type A, B, and D papers can be accepted for either oral or poster presentation.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">PRIZES</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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).</span><o:p></o:p></p>
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<span style="color:black">The best paper will be automatically nominated to the yearly special issue of AI Communications on Best of AI Research in Europe.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">PREPROCEEDINGS & POSTPROCEEDINGS</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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<span class="apple-converted-space"> </span><a href="https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines"><span style="color:#00004D">https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines</span></a>)
and submitted electronically via EasyChair:<span class="apple-converted-space"> </span><a href="https://easychair.org/conferences/?conf=bnaicbenelearn2021"><span style="color:#00004D">https://easychair.org/conferences/?conf=bnaicbenelearn2021</span></a></span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<span style="color:black">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.</span><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">Selected Type A long papers will be invited to submit to the postproceedings published in Springer’s CCIS series (<a href="https://www.springer.com/series/7899"><span style="color:#00004D">https://www.springer.com/series/7899</span></a>).</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">*******************</span><span lang="EN-US" style="color:black">**</span><o:p></o:p></p>
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<b><span style="color:black">IMPORTANT DATES</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">Paper submission deadline: September 3, 2021</span><o:p></o:p></p>
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<span style="color:black">Author notification: October 1, 2021</span><o:p></o:p></p>
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<span style="color:black">Camera ready submission deadline: October 15, 2021</span><o:p></o:p></p>
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<span style="color:black">All deadlines are at 23:59, AoE time zone:<span class="apple-converted-space"> </span><a href="https://time.is/Anywhere_on_Earth"><span style="color:#00004D">https://time.is/Anywhere_on_Earth</span></a></span><o:p></o:p></p>
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<span style="color:black">Conference: November 10-12, 2021</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">********************</span><span lang="EN-US" style="color:black">**</span><o:p></o:p></p>
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<b><span style="color:black">TOPICS OF INTEREST</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">This year we encourage authors to submit academic work on the intersection of:</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">AI & Arts</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">AI & Law</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">AI & Ethics</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">AI & Systems</span></b><o:p></o:p></p>
<p class="MsoNormal"><span style="color:black">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.</span><o:p></o:p></p>
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<span style="color:black"> </span><o:p></o:p></p>
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<b><span style="color:black">A non-exhaustive list of topics includes:</span></b><o:p></o:p></p>
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<span style="color:black">Automated Machine Learning and meta-learning</span><o:p></o:p></p>
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<span style="color:black">Bayesian Learning</span><o:p></o:p></p>
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<span style="color:black">Case-based Learning</span><o:p></o:p></p>
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<span style="color:black">Causal Learning</span><o:p></o:p></p>
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<span style="color:black">Clustering</span><o:p></o:p></p>
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<span style="color:black">Computational Creativity</span><o:p></o:p></p>
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<span style="color:black">Computational Learning Theory</span><o:p></o:p></p>
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<span style="color:black">Computational Models of Human Learning</span><o:p></o:p></p>
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<span style="color:black">Data Mining</span><o:p></o:p></p>
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<span style="color:black">Data Visualisation</span><o:p></o:p></p>
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<span style="color:black">Deep Learning</span><o:p></o:p></p>
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<span style="color:black">Ensemble Methods</span><o:p></o:p></p>
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<span style="color:black">Evaluation Frameworks</span><o:p></o:p></p>
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<span style="color:black">Evolutionary Computation</span><o:p></o:p></p>
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<span style="color:black">Feature Selection and Dimensionality Reduction</span><o:p></o:p></p>
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<span style="color:black">Inductive Logic Programming</span><o:p></o:p></p>
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<span style="color:black">Interactive AI Methods and Applications</span><o:p></o:p></p>
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<span style="color:black">Kernel Methods</span><o:p></o:p></p>
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<span style="color:black">Knowledge Discovery in Databases</span><o:p></o:p></p>
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<span style="color:black">Learning and Ubiquitous Computing</span><o:p></o:p></p>
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<span style="color:black">Learning in Multi-Agent Systems</span><o:p></o:p></p>
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<span style="color:black">Learning from Big Data</span><o:p></o:p></p>
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<span style="color:black">Learning from User Interactions</span><o:p></o:p></p>
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<span style="color:black">Learning for Language and Speech</span><o:p></o:p></p>
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<span style="color:black">Media Mining and Text Analytics</span><o:p></o:p></p>
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<span style="color:black">ML and Information Theory</span><o:p></o:p></p>
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<span style="color:black">ML Applications in Industry</span><o:p></o:p></p>
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<span style="color:black">ML for Scientific Discovery</span><o:p></o:p></p>
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<span style="color:black">ML in Non-stationary Environments</span><o:p></o:p></p>
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<span style="color:black">ML with Expert-in-the-loop</span><o:p></o:p></p>
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<span style="color:black">Natural Language Processing / Natural Language Understanding</span><o:p></o:p></p>
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<span style="color:black">Neural Networks</span><o:p></o:p></p>
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<span style="color:black">Online Learning</span><o:p></o:p></p>
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<span style="color:black">Pattern Mining</span><o:p></o:p></p>
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<span style="color:black">Predictive Modeling</span><o:p></o:p></p>
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<span style="color:black">Ranking / Preference Learning / Information Retrieval</span><o:p></o:p></p>
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<span style="color:black">Reinforcement Learning</span><o:p></o:p></p>
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<span style="color:black">Representation Learning</span><o:p></o:p></p>
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<span style="color:black">Robot Learning</span><o:p></o:p></p>
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<span style="color:black">Social Networks</span><o:p></o:p></p>
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<span style="color:black">Statistical Learning</span><o:p></o:p></p>
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<span style="color:black">Structured Output Learning</span><o:p></o:p></p>
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<span style="color:black">Transfer and Adversarial Learning</span><o:p></o:p></p>
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