We are delighted to announce the *2nd International Workshop on Active Inference IWAI2021*, in conjunction with the European Conference on Machine Learning (ECML/PKDD 2021) that will be held *VIRTUALLY* in September 2021 in Bilbao, Spain.
*CALL FOR PAPERS*
The 2nd International Workshop on Active Inference wants to bring together researchers on active inference as well as related research fields in order to discuss current trends, novel results, (real-world) applications, to what extent active inference can be used in modern machine learning settings, such as deep learning, and how it can be unified with the latest psychological and neurological insights.
Website: https://iwaiworkshop.github.io/
Twitter: @iwai_ws
*Important dates*
Workshop Date: September 13th or 17th, 2021 to be decided with the ECML conference
Abstract Submission Deadline: June 9th, 2021
Paper Submission Deadline: June 23rd, 2021
Acceptance Notification: July 28th, 2021
*Topics of interest*
Papers on all subjects and applications of active inference and related research areas are welcome.
– Active inference
– (Bayesian) surprise
– Cognitive robotics
– Control as inference
– Variational inference
– Computational neuroscience
– (Deep) generative models
– State-space models
– Representation learning
– Intrinsic motivation
– Intelligent systems
– Decision making in economics
– etc
*Paper submissions*
We welcome submissions of papers with up to 8 printed pages (excluding references) in LNCS format: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
Submissions will be evaluated according to their originality and relevance to the workshop, and should have an abstract of 60-100 words.
Contributions should be in PDF format and submitted via Easychair: https://easychair.org/conferences/?conf=iwai2021
In accordance with the main conference, will apply a double-blind review process (see also the double-blind reviewing process section below for further details). All papers need to be anonymized in the best of efforts. It is allowed to have a (non-anonymous) online pre-print. Reviewers will be asked not to search for them.
Previous edition: https://iwaiworkshop.github.io/2020.html
Previous proceedings: https://www.springer.com/gp/book/9783030649180
On behalf of the organizers,
Tim Verbelen, Daniela Cialfi, Maxwell Ramstead, Christopher Buckley and Pablo Lanillos
Journal of Smart Environments and Green Computing
May 5th, 2021 
Daniela Lopez de Luise Dear Friends, A new journal has been launched, Journal of Smart Environments and Green Computing (JSEGC), https://segcjournal.com
Its first issue appeared in March, as you can see from the list of papers in the inaugural issue, machine learning is one of the important techniques for solving the corresponding important class of problems
https://segcjournal.com/journal/volume_issues_archive/2021/1/72.
papers in the INAUGURAL ISSUE:
1. Zavadskas EK, Đalić I, Stević . Application of novel DEA-SWARA-WASPAS model for efficiency assessment of agricultural products. J Smart Environ Green Comput 2021;1:32-46.
http://dx.doi.org/10.20517/jsegc.2020.02
2. Soomro AM, Bharathy G, Biloria N, Prasad M. A review on motivational nudges for enhancing building energy conservation behavior. J Smart Environ Green Comput 2021;1:3-20.
http://dx.doi.org/10.20517/jsegc.2020.03
3. Marszałek A, Burczynski T. Forecasting day-ahead spot electricity prices using deep neural networks with attention mechanism. J Smart Environ Green Comput 2021;1:21–31.
http://dx.doi.org/10.20517/jsegc.2021.02
4. Chai Y, Zeng XJ. The development of green wireless mesh network: A survey. J Smart Environ Green Comput 2021;1:47-59. http://dx.doi.org/10.20517/jsegc.2020.05.
5. Lu Y, Karimi HR, Zhang N.Observer-based H∞ consensus for linear multi-agent systems subject to measurement osutliers. J Smart Environ Green Comput 2021;1:50-65.
Mujeres comprometidas con la calidad y continuidad del servicio.
May 5th, 2021 Daniela Lopez de Luise 
AIMLAI 2021 : 4th International Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
May 5th, 2021 Daniela Lopez de Luise We invite researchers working on interpretability and explainability in ML/AI, and related topics, to submit regular (8 pages, single column) or short (3 pages, single column) papers to the AIMLAI workshop that will be held virtually at ECML/PKDD 2021.
Website: https://project.inria.fr/aimlai/
Submission link: https://easychair.org/conferences/?conf=aimlaiecml21
Submission deadline: June 24th, 2021
The purpose of AIMLAI (Advances in Interpretable Machine Learning and Artificial Intelligence) is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining, machine learning, and artificial intelligence. AIMLAI is a workshop that seeks top-quality submissions addressing uncovered important issues related to explainable and interpretable data mining and machine learning models. Papers should present research results in any of the topics of interest for the workshop as well as application experiences, tools and promising preliminary ideas. AIMLAI asks for contributions from researchers, academia and industry, working on topics addressing these challenges primarily from a technical point of view, but also from a legal, ethical or sociological perspective. Besides the central topic of interpretable algorithms and explanation methods, we also welcome submissions that answer research questions like “how to measure and evaluate interpretability and explainability?” and “how to integrate humans in the machine learning pipeline for interpretability purposes?”. This year's edition of AIMLAI is open to two kinds of submissions: regular papers (8 pages, single column) presenting novel ideas, and extended abstracts (3 pages, single column) of already published works.
A non-exhaustive list of topics that are of interest for AIMLAI are the following:
- Interpretability and explanations in machine learning
- Machine learning models that are directly interpretable
- Explanation modules for black-box models (post-hoc interpretability)
- Methodology and formalization of interpretability
- Interpretability/complexity trade-off
- Formal measures of interpretability
- Methodological guidelines to evaluate interpretability
- User-centric interpretability
- Semantic interpretability: how to add semantics to explanations?
- Human-in-the-loop to construct and/or evaluate interpretable models
- Combining of ML models with infovis and man-machine interfaces
- Transparency in AI and ML
- Ethical aspects
- Legal aspects
- Fairness issues
While interpretability and explanations for classical supervised learning models are always welcome, ideas on techniques and definitions/formalizations of these concepts in unsupervised learning are particularly welcome.
* Submission Guidelines
Papers must be written in English and formatted according to the Springer LNCS guidelines. Regular papers must be 8 pages long maximum. Extended abstracts are restricted to a maximum of 3 pages. Overlength papers will be rejected without review (papers with smaller page margins and font sizes than specified in the author instructions and set in the style files will also be treated as overlength).
Authors who submit their work to AIMLAI 2021 commit themselves to present their paper at the workshop in case of acceptance. AIMLAI 2021 considers the author list submitted with the paper as final. No additions or deletions to this list may be made after paper submission, either during the review period, or in case of acceptance, at the final camera ready stage.
Condition for inclusion in the post-proceedings is that at least one of the co-authors has (virtually) presented the paper at the workshop. Pre-proceedings will be available online before the workshop. A special issue in a relevant international journal with extended versions of the selected papers is under consideration.
All papers for AIMLAI 2021 must be submitted by using the online submission system at https://easychair.org/conferences/?conf=aimlaiecmlpkdd21.
* Program Chairs
Adrien Bibal, University of Namur, Belgium
Tassadit Bouadi, University of Rennes/IRISA, France
Benoît Frénay, University of Namur, Belgium
Luis Galárraga, Inria/IRISA, France
José Oramas, University of Antwerp/imec-IDLab, Belgium
* Important dates
All dates are given in Central European Standard Time (CEST).
- Paper submission deadline: June 24th, 2021 at 11.59 pm
- Paper reviewing period: June 28th to July 14th, 2021
- Paper Notifications: July 16th, 2021
- Camera-ready deadline: July 30th, 2021
* Publication
All accepted papers will be published as post-proceedings.
* Venue
The workshop will be co-located with the conference ECML/PKDD 2021, which will be held online from the 13th to the 17th of September, 2021.
* Contact
All questions about submissions should be emailed to aimlaiecml21@easychair.org.
Deep Learning for Wellbeing Applications Leveraging Mobile Devices and Edge Computing
May 5th, 2021 Daniela Lopez de Luise HealthDL: Deep Learning for Wellbeing Applications Leveraging
Mobile Devices and Edge Computing
Workshop Chairs:
Yan Wang (Temple University)
Jerry Cheng (New York Institute of Technology)
The availability of affordable wearable Internet of Things (wIoT) and edge devices with embedded sensors has revolutionized intelligent health and wellness applications. Users often use wIoT and smartphones to collect medical data and send them to the cloud for further analysis. Edge-based solutions, where analysis and inference of such data are carried out on edge devices, have been proposed to address users' security and privacy concerns since users' sensitive data is not transferred to untrusted cloud servers for inferencing. However, resource constraints on the edge devices also pose challenges in using deep learning solutions. Research needs to be conducted to produce efficient system designs, algorithms, and deep learning models that can be deployed in edge devices. Such outcomes will enable better personalization of health-related solutions and enhance users' experience. Furthermore, thanks to the ever-improving voice recognition and synthesis schemes, many wearables and smartphone applications now rely on voice assistants to interact with users. Existing work has shown that such interactions can significantly improve users' experience but incur significant security and privacy issues. This workshop aims to fill the gap between deep learning for intelligent healthcare and power-constrained wIoT and edge and create impactful solutions to help in the well beings of users.
This workshop invites researchers from academia and industry to submit their current research for fostering academic-industry collaboration. The scope of this workshop includes but not limited to the following topics:
• E2E deep learning for smart health applications.
• Deep learning for sensing, analysis and interpretation of wIoT healthcare data
• Resource constrained deep learning schemes for smartphones and wIoT.
• Edge-based deep learning & AI for mental health
• Transfer learning and model compression for smart health applications
• Context-aware ubiquitous healthcare systems based on wearables, edge machine learning
• Emerging applications or sensors for personalized health and fitness
• User and device authentication for smartphones and wIoT
• Cutting edge technology for physiological sensing
Important Dates
Submission Deadline: May 7, 2021
Acceptance Notice: June 4, 2021
Camera-ready Deadline: June 11, 2021
Submission Specifications
The papers are limited to 6 pages including references. The formatting should adhere to the formatting requirements of ACM Mobisys submissions: https://www.sigmobile.org/mobisys/2020/submission
The papers should be submitted to the workshop submission site: https://healthdl21.hotcrp.com/
The workshop website: https://cis.temple.edu/~yanwang/healthdl2021/
Any questions regarding submission issues should be directed to y.wang@temple.edu