Special issue “Sensing, Perception, and Navigation in Space Robotics”

February 23rd, 2021 Daniela Lopez de Luise
EXTENDED DEADLINE: 30-June-2021

Submissions are welcome to the special issue on “Sensing, Perception, and Navigation in Space Robotics” of the journal Sensors (MDPI, ISSN 1424-8220, IF 3.031, SJR Q1).

Special Issue Website:
https://www.mdpi.com/journal/sensors/special_issues/rb_sensors

Guest Editors: Prof. Rodrigo Ventura

The particular topics of interest include, but are not limited to:

• New concepts, mission requirements, and specifications
• Effects of space environment on sensor devices
• Novel methods for sensor data processing, including computer vision,
estimation, machine learning, and artificial intelligence
• Innovative applications targeting space domain
• Experiments in laboratory or in space environment, including micro-
and hypergravity platforms (e.g., drop towers, parabolic flights,
centrifuge campaigns)
• Datasets of sensor data in a space environment or in micro- and
hypergravity platforms

Best regards,

Rodrigo Ventura

Institute for Systems and Robotics
Instituto Superior Técnico
Lisbon, Portugal

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JSC Special Issue “Algebraic Geometry and Machine Learning”

February 23rd, 2021 Daniela Lopez de Luise
JOURNAL OF SYMBOLIC COMPUTATION

https://www.journals.elsevier.com/journal-of-symbolic-computation
SPECIAL ISSUE on “Algebraic Geometry and Machine Learning”

CALL FOR PAPERS

Guest Editors:

Jonathan Hauenstein
University of Notre Dame, USA

Yang-Hui He
City, University of London and
University of Oxford, UK

Ilias Kotsireas
Wilfrid Laurier University, Canada

Dhagash Mehta
The Vanguard Group, USA

Tingting Tang
San Diego State University Imperial Valley Campus, USA

Important Dates:

Submission deadline: May 30, 2021
Author notification: November 2021
Camera ready: December 2021

Machine learning research, both from applied and theoretical sides, has exploded in the recent years. In the last few years, algebraic geometry techniques have also been rapidly progressing due to novel mathematical results as well as sophisticated symbolic and numerical methods which can solve various algebraic geometry problems with ever increasing complexities.
Many machine learning problems, for example, exploring optimization landscapes of deep learning and training various machine learning algorithms, can be seen as algebraic geometry problems. A general theoretical question of machine learning, particularly, deep learning, is explainability and interpretability of the model and results it produces. Research efforts have been invested into the theoretical understanding of machine learning (“Explainable Artificial Intelligence” (XAI) from DARPA and the “American AI Initiative”). Some of the recent research papers address these theoretical issues in a unique and rigorous way using algebraic geometry methods. Interestingly, various computational algebraic geometry problems can be posed and solved with machine learning.
On the other end, machine learning has been successfully employed to solve problems arising in both theoretical and computational algebraic geometry: machine learning has been shown to improve algorithms (both symbolic and numerical) to solve polynomial systems, approximate algebraic varieties and discriminant loci, as well as applications to the landscape of algebraic varieties of interest to theoretical physics, especially string theory etc. The neural networks in particular provide a novel representation of the data which may help symbolic as well as numerical computation further.
The literature on this emerging interaction of the two disciplines is scattered. In the proposed special issue, we aim to bring researchers from both the areas together and provide a serious peer-reviewed platform to present their interdisciplinary research.
To the best of our knowledge this will be a first ever special issue on this topic.

Paper Submission:

Papers should be submitted exclusively via EES (Elsevier Editorial System) at:
https://www.editorialmanager.com/jsco/
Please indicate that your submission is intended for the special issue “AG and ML”

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DEADLINE EXTENSION PRL special issue: Pattern Recognition-driven User Experiences (PRUE)

February 23rd, 2021 Daniela Lopez de Luise

PATTERN RECOGNITION LETTERS SPECIAL ISSUE: Pattern Recognition-driven User Experiences (PRUE)

Games, search engines, e-commerce, infotainment, and many other services allow users a high degree of personalization; this evolution creates new needs, changes habits, and raises expectations. At the same time, the availability of new instruments is noticeably changing the kind of experience the users expect. The strong immersivity and high degree of realism of VR, MR, and AR are freeing the UX from the classic screen borders, with voice and gestures adding naturalness to the experience and keeping high the sense of users’ involvement and immersion.

IoT ecosystems, smartwatches, digital assistants, and other devices, are instruments that may provide precious hints about users and usage contexts, if supported by the application of Pattern Recognition theories and techniques.
Aiming at improving efficiency, intelligence, and delight perceived by users, Pattern Recognition-driven User Experience leverages intelligent computing to dynamically adapt appearance and behaviour with automatic decision-making. Pattern Recognition offers the instruments to detect and “understand” context, user’s signals, intents, emotions, and  provides a set of disruptive methodologies for an effective personalization of the experience.

The purpose of this Special Issue is to investigate how concepts and theories related to Pattern Recognition can be applied to improve or create a fully novel User Experience, new opportunities, and open problems. The Special Issue aims at collecting and presenting new advances in the application of Pattern Recognition to (but not limited):

    • Emotion recognition and adaptive applications;
    • Speech recognition;
    • Pattern recognition for virtual, augmented and mixed reality;
    • Applications to mobile and embedded systems;
    • Natural language applications;
    • Design and evaluation of innovative interactive system;
    • Ambient intelligence;
    • Personalization of user experience.
 
   
IMPORTANT DATES:

Submission period:   DEADLINE EXTENSION – February 28, 2021
Final Manuscript due: 1st April 2021
Tentative publication date: End of 2021

SUBMISSION AND CFP HERE: https://www.journals.elsevier.com/pattern-recognition-letters/call-for-papers/pattern-recognition-driven-user-experiences

SPECIAL ISSUE EDITORS:

Andrea F. Abate, University of Salerno, Italy (abate@unisa.it)

Giovanni Motta, Google Inc, Mountain View (CA), US (giovannimotta@google.com)

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ISMAR 2021 – Call for Competition to provide Natural Social Interactions at the Hybrid Conference of the Future

February 23rd, 2021 Daniela Lopez de Luise

IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY Bari, Italy October 4 to 8, 2021 http://www.ismar21.org/

OVERVIEW
This is an open invitation to anyone interested in Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) to participate in the ISMAR Contest. The ISMAR Contest will be held during IEEE ISMAR 2021, the premier conference for AR and MR, October 4-8, 2021.

PURPOSE
The purpose of the ISMAR Contest is to stimulate innovative and creative solutions to facilitating the “Hybrid Conference of the Future” in which conference attendees can synchronously engage each other intellectually and socially in a natural manner, as if they were co-located in the same real-world venue, despite only some being physically present at the venue, while others attend remotely. The theme of the contest is “Natural Social Interactions.”

For this year’s ISMAR Contest, we expect the following functionalities of any solution:
        • The implemented solution facilitates natural social interactions among multiple remote attendees joining a conference from various global locations.
                • The solution must include software that ISMAR 2021 remote attendees can download, install, and use to engage in the social interactions.
                • The solution software must support at least one of the following types of devices: desktop/laptop, smartphone, tablet. However, ideal solutions will support all three and will be cross platform (i.e., Windows/MacOS/Linux for desktops and laptops, Android/iOS for smartphones and tablets).
        • The implemented solution preserves the natural social interactions among multiple attendees co-located at a conference in the same real-world space.
                • Contestants will be responsible for bringing the hardware for the co-located attendees to ISMAR 2021.
        • The implemented solution facilitates natural social interactions among the multiple remote attendees and the multiple attendees co-located at a conference in the real-world space.

Selected contestants will be required to provide a live demonstration of their solutions during ISMAR 2021, October 4-8, 2021. ISMAR 2021 is scheduled to be a hybrid conference; however, due to the current COVID-19 pandemic, the conference may become purely virtual. Contestants are expected to create solutions that can be demonstrated for either modality, i.e., hybrid or purely virtual (where all attendees are remote).

CONTEST ENTRY (Due July 19, 2021)
Teams interested in entering the ISMAR Contest should send a high-quality video (no more than 5 minutes) demonstrating their tentative solution, a brief description of the solution, a title for the solution, and a list of all team members via email to competition_chairs@ismar21.org by July 19, 2021. The Contest Chairs will review the contest entries and will notify teams whether their entries have been selected or not by July 26, 2021.

ABSTRACT SUBMISSION (Due August 6, 2021) Teams selected for the competition must submit an abstract (2 pages) that describes their solution and its innovations. This abstract must adhere to the ISMAR 2021 Conference Proceedings format: https://ismar21.org/call-for-papers/. Acceptable abstracts will be published in the conference proceedings of IEEE ISMAR 2021 and will be included in the IEEE Xplore digital library.

COMPETITION (October 4-8, 2021)
Teams selected for the competition will demonstrate their solutions live at IEEE ISMAR 2021. If ISMAR 2021 is held as a hybrid conference, teams can choose to either demonstrate their solutions at ISMAR 2021 in Bari, Italy or remotely. If ISMAR 2021 is held as a purely virtual conference, all teams will demonstrate their solutions remotely.

A panel of judges with no conflicts of interest will be chosen to judge the demonstrations and solutions. Additionally, IEEE ISMAR 2021 attendees will have the ability to vote for their favorite demonstrations and solutions. The Contest Chairs will use the feedback of the judges and the attendee votes to decide Grand Prize and Honorable Mention solutions.

PRIZES
The team with the Grand Prize solution will receive a $1500 reward and will be invited to submit an IEEE Transactions on Visualizations and Computer Graphics (TVCG) journal paper, which will be included in the IEEE ISMAR 2022 Journal Proceedings. In addition to describing the solution, this TVCG journal paper must also include a formal evaluation of the solution, which can be conducted after the 2021 Competition. The submission will undergo the same reviewing rigor as all other journal submissions.

The team with the Honorable Mention solution will receive a $500 reward for their solution.

CONTEST CHAIRS
Ferran Argelaguet, INRIA Rennes, France
Ryan P. McMahan, University of Central Florida, USA Voicu Popescu, Purdue University, USA

CONTACT
You can contact us at competition_chairs@ismar21.org if you have any questions.

Call updates will be posted on the ISMAR Competition web page https://ismar21.org/competition

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Elsevier Journal of “Environmental Modelling & Software” journal, special Issue on “Machine Learning Advances Environmental Science”

February 23rd, 2021 Daniela Lopez de Luise

Environmental Modelling & Software
Official Journal of the International Environmental Modelling & Software Society

Special Issue: Machine Learning Advances Environmental Science
==============================================================

|>> https://www.journals.elsevier.com/environmental-modelling-and-software/call-for-papers/machine-learning-advances<<|

============
Aim & Scope
============

Environmental data are growing steadily in volume, complexity and diversity to Big Data, mainly driven by advanced sensor technology. Machine Learning offers new techniques for unravelling complexity and knowledge discovery from Big Data in environmental sciences.

The aim of the SI is to provide a state-of-the-art survey of environmental research topics that can benefit from Machine Learning methods and techniques.

To this purpose, the SI welcomes papers on successful environmental applications of machine learning and pattern recognition techniques to diverse domains of environmental research, that demonstrate how Machine Learning improves our understanding of natural systems, socio-environmental interactions, or tackling the inherent complexity of environmental Big Data. Application domains may vary, and include for instance recognition of biodiversity in thermal, photo and acoustic images, natural hazards analysis and prediction, environmental remote sensing, estimation of environmental risks, prediction of the concentrations of pollutants in geographical areas, environmental threshold analysis and predictive modelling, estimation of Genetical Modified Organisms (GMO) effects on non-target species. Contributions are expected to have a strong methodological contribution to environmental sciences research, and applications of known methods in new case studies will not be considered.

The SI offers a place for Machine Learning and Environmental research communities to interact, and demonstrate the advances of Machine Learning for the Environmental Sciences. Prospective contributions should clearly indicate their contribution in tackling open problems in environmental research that still have not properly benefited from Machine Learning.

The SI is inspired by the first Workshop on Machine Learning Advances Environmental Science (MAES) held at International Conference on Pattern Recognition (ICPR) 2020, held on January 10-15, 2021.

Αuthors should consult the general author guidelines of the journal [1] and submit their articles through the Editorial Manager submission system [2].
When submitting the manuscript, select as article type “VSI-Mach.Learn.Adv.Env.Sc”.
[1]: https://www.elsevier.com/journals/environmental-modelling-and-software/1364-8152/guide-for-authors
[2]: https://www.editorialmanager.com/envsoft/default.aspx

==========
Timetable
==========

01 Feb 2021 – Open for submissions
01 July 2021 – ***Submission deadline***
July-August 2021 – Author notifications & revisions
September 2021 – Final editorial decisions
December 2021 – Publication

================
Editor-in-Chief
================

D.P. Ames, Brigham Young University, Provo, Utah, United States

==============
Guest Editors
==============

Ioannis N. Athanasiadis, Wageningen University and Research, The Netherlands
Francesco Camastra, University of Naples Parthenope, Italy
Friedrich Recknagel, University of Adelaide, Australia
Antonino Staiano, University of Naples Parthenope, Italy

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