2nd International Workshop on Industrial Machine Learning
In conjunction with ICPR 2022
https://sites.google.com/view/iml2022
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AIMS AND SCOPE
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With the advent of Industry 4.0 and Smart Manufacturing paradigms, data has become a valuable resource, and very often an asset, for every manufacturing company. Data from the market, from machines, from warehouses and many other sources are now cheaper than ever to be collected and stored. A study from Juniper Research has identified industrial internet of things (IIoT) as a key growth market over the next five years, accounting for an increase in the global number of IIoT connections from 17.7 billion in 2020 to 36.8 billion in 2025, representing an overall growth rate of 107%. With such an amount of data produced every second, classical data analysis approaches are not useful and only automated learning methods can be applied to produce value, a market estimated in more than 200B$ worldwide. Using machine learning techniques manufacturers can exploit data to significantly impact their bottom line by greatly improving production efficiency, product quality, and employee safety.
The introduction of ML to industry has many benefits that can result in advantages well beyond efficiency improvements, opening doors to new opportunities for both practitioners and researchers. Some direct applications of ML in manufacturing include predictive maintenance, supply chain management, logistics, quality control, human-robot interaction, process monitoring, anomaly detection and root cause analysis to name a few.
This workshop will ground on the successful story of the first edition, with 19 oral presentations and 3 invited talks, to draw attention to the importance of integrating ML technologies and ML-based solutions into the manufacturing domain, while addressing the challenges and barriers to meet the specific needs of this sector. Workshop participants will have the chance to discuss:
– needs and barriers for ML in manufacturing
– state-of-the-art in ML applications to manufacturing
– future research opportunities in this domain
TOPICS OF INTEREST
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This is an open call for papers, soliciting original contributions considering recent findings in theory, methodologies, and applications in the field of industrial machine learning. Position papers presenting industrial use cases and discussing potential solutions are welcome. Potential topics include, but are not limited to:
– Robustness-oriented learning algorithms
– Machine learning for robotics (e.g. learning from demonstration)
– Continuous and life-long learning for industrial applications
– Transfer learning and domain adaptation
– Anomaly detection and process monitoring
– ML applications to Predictive Maintenance
– ML applications to Supply Chain and Logistics
– ML applications to Quality Control
– ML for flexible manufacturing
– Deep Learning for industrial applications
– Learning from Big-Data
– Inference in real-time applications
– Machine Learning on Embedded and Edge computing hardware
All the contributions are expected to expose applications to the industrial sector, possibly with real world case studies. Position papers presenting new industrial systems and case studies, possibly reporting preliminary validation studies, are also encouraged.
IMPORTANT DATES
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Full Paper Submission: June 6, 2022
Notification of Acceptance: June 20, 2022
Camera-Ready Paper Due: June 30, 2022
Workshop date: August 21, 2022
In case of rejection from ICPR main conference, authors can submit their work to the IML workshop. Authors should address all ICPR reviewers' comments in the submitted paper and submit the ICPR reviews as supplementary material.
SUBMISSION GUIDELINES
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Papers must be prepared according to the ICPR guidelines. All papers will be reviewed by at least two reviewers with single-blind peer review policy. Papers will be selected based on relevance, significance and novelty of results, technical merit, and clarity of presentation. Papers will be published in ICPR proceedings.
All the papers must be submitted using CMT submission server: https://cmt3.research.microsoft.com/IML2022
ORGANIZING COMMITTEE
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Luigi Di Stefano, University of Bologna, Italy
Massimiliano Mancini, Univeristy of Tubingen, Germany
Vittorio Murino, University of Verona, Italy
Paolo Rota, University of Trento, Italy
Francesco Setti, University of Verona, Italy
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Call for papers [extended deadline]: Workshop on the Interactions between Analogical Reasoning and Machine Learning (IARML @ IJCAI-ECAI 2022)
May 17th, 2022 
Daniela Lopez de Luise ==================================
Workshop on the Interactions between Analogical Reasoning and Machine Learning (IARML @ IJCAI-ECAI 2022)
Call For Papers
Dates: July 23rd-25th, 2022
Location: Vienna, Austria
Website: https://iarml2022-ijcai-ecai.loria.fr
Important dates:
* May 20, 2022: Workshop Paper Due Date
* June 10, 2022: Notification of Paper Acceptance
* June 24, 2022: Camera-ready papers due
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Analogical reasoning is a remarkable capability of human reasoning, used to solve hard reasoning tasks. It consists in transferring knowledge from a source domain to a different, but somewhat similar, target domain by relying simultaneously on similarities and dissimilarities. In particular, analogical proportions, i.e., statements of the form “A is to B as C is to D”, are the basis of analogical inference.
Analogical inference is pertaining to case-based reasoning and it has contributed to multiple machine learning tasks such as classification, decision making, and automatic translation with competitive results. Moreover, analogical extrapolation can support dataset augmentation (analogical extension) for model learning, especially in environments with few labeled examples. Conversely, advanced neural techniques, such as representation learning, enabled efficient approaches to detecting and solving analogies in domains where symbolic approaches had shown their limits. However, recent approaches using deep learning architectures remain task and domain specific, and strongly rely on ad-hoc representations of objects, i.e., tailor made embeddings.
The purpose of this workshop is to bring together AI researchers at the cross roads of machine learning and knowledge representation and reasoning, who are interested by the various applications of analogical reasoning in machine learning or, conversely, of machine learning techniques to improve analogical reasoning. The IARML workshop aims at bridging gaps between different communities of AI researchers, including case-based reasoning, deep learning and neuro-symbolic machine learning.
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Themes and topics
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We invite submissions of research papers on all topics at the intersection of analogical reasoning and machine learning. Topics of interest include, but are not limited to:
Machine learning for analogical reasoning:
* Representation learning;
* Transfer learning;
* Neuro-symbolic models for analogical inference.
Analogical reasoning for machine learning:
* Classification using analogical reasoning;
* Recommendation using analogical reasoning;
* Case-Based Reasoning.
Applications:
* Analogical reasoning in visual domains;
* Analogical reasoning in Natural Language Processing;
* Analogical reasoning in healthcare;
* Analogies in software engineering.
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Submission
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We welcome contributions in the form of extended abstracts (up to two pages) and long papers (up to six pages plus 1 page for references). Submissions can describe either work in progress or mature work that has already been published at other research venues. Previously published work in whole or in part may be in the form of a resubmission of a previous paper, or in the form of a survey or position paper that overviews and cites a body of work. Submitted papers must be formatted according to IJCAI-ECAI 2022 guidelines, which can be downloaded: https://www.ijcai.org/authors_kit
All papers will be thoroughly reviewed. Overlength papers will be rejected without review. The reviewing process will be double-blind.
Submission link: https://cmt3.research.microsoft.com/IARML2022
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Proceedings
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Accepted papers will appear in the preproceedings published in HAL and made available at the workshop. Selected papers will be invited for publication in a CEUR-WS postproceedings.
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Confirmed keynote speakers
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* Kenneth Forbus (Northwestern University)
* Yves Lepage (Waseda University)
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Chairs
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* Miguel Couceiro (University of Lorraine, CNRS, LORIA, Miguel.couceiro@loria.fr )
* Pierre-Alexandre Murena (Aalto University, pierre-alexandre.murena@aalto.fi )
The organizers would be grateful if you could inform potentially interested participants of this conference.
International Workshop on Statistical Relational AI (StaRAI)
May 17th, 2022 Daniela Lopez de Luise DESCRIPTION
The purpose of the Statistical Relational AI (StarAI) workshop is to bring together researchers and practitioners from three fields: logical (or relational) AI/learning, probabilistic (or statistical) AI/learning and neural approaches for AI/learning with knowledge graphs and other structured data. These fields share many key features and often solve similar problems and tasks. Until recently, however, research in them has progressed independently with little or no interaction. The fields often use different terminology for the same concepts and, as a result, keeping up and understanding the results in the other field is cumbersome, thus slowing down research. Our long term goal is to change this by achieving synergy between logical, statistical and neural AI. As a stepping stone towards realising this big-picture view on AI, we are organizing the Tenth International Workshop on Statistical Relational AI at IJCAI 2022, July 22-23, 2022.
TOPICS
StarAI is currently provoking a lot of new research and has tremendous theoretical and practical implications. The focus of the workshop will be on general-purpose representation, reasoning and learning tools for StarAI as well as practical applications. Specifically, the workshop will encourage active participation from researchers in the following communities, and integration thereof: satisfiability, knowledge representation, constraint satisfaction and programming, (inductive) logic programming, graphical models and probabilistic reasoning, statistical learning, relational embeddings, neural-symbolic integration, graph mining and probabilistic databases. It will also actively involve researchers from more applied communities, such as natural language processing, information retrieval, vision, semantic web and robotics. We seek to invite researchers in all subfields of AI to attend the workshop and to explore together how to reach the goals imagined by the early AI pioneers.
FORMAT
StarAI will be a one day workshop with short paper presentations, a poster session, and three invited speakers.
ATTENDANCE: open to all
SUBMISSION REQUIREMENTS
Authors should submit either:
– a full paper reporting on novel technical contributions or work in progress (AAAI style, up to 7 pages excluding references),
– a short position paper (AAAI style, up to 2 pages excluding references),
– an already published work (verbatim, no page limit, citing original work)
in PDF format via EasyChair.
All submitted papers will be carefully peer-reviewed by multiple reviewers and low-quality or off-topic papers will be rejected.
SUBMISSION DEADLINE: May 13, 2022
SUBMIT TO
https://easychair.org/my/conference?conf=starai22
WORKSHOP COMMITTEE:
Sebastijan Dumančić (TU Delft)
Angelika Kimmig (KU Leuven)
David Poole (UBC)
Jay Pujara (USC)
WORKSHOP URL
http://www.starai.org
SUMAC’22: The 4th workshop on Structuring and Understanding of Multimedia heritAge Contents @ ACM Multimedia 2022
May 17th, 2022 Daniela Lopez de Luise THRI Special Issue on Artificial Intelligence for Human-Robot Interaction (AI-HRI)
May 17th, 2022 Daniela Lopez de Luise
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The application of Artificial Intelligence to Human-Robot Interaction domains has proven to be a powerfully effective mechanism for achieving robust, interactive, and autonomous systems with applications ranging from personalized tutors to smart manufacturing collaborators to healthcare assistants and nearly everything in between. Developing such systems often involves innovations and integrations between many diverse technical areas, including but not limited to task and motion planning, learning from demonstration, dialogue synthesis, activity recognition and prediction, human behavior modeling, and shared control. For this special issue we are soliciting high quality, original articles that present the design and/or evaluation of novel computational techniques and systems at the intersection of artificial intelligence and human-robot interaction. We aim to bring together a wide variety of articles to showcase the state-of-the-art in AI-HRI within a single issue of the world's leading journal of Human-Robot Interaction research.