Workshop on Mobility in the Evolving Internet Architecture 2022 | Sydney, Australia

July 6th, 2022 Daniela Lopez de Luise


MobiArch 2022  will be held jointly with MobiCom 2022 in Sydney, Australia on 21st Oct. Please note the extended deadline for MobiArch 2022 is 7th July, 2022 Furthermore, the Workshop is now allowing for remote paper presentations should the presenter be unable to attend in person.


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 Workshop on Mobility in the Evolving Internet Architecture (MobiArch) 2022
                                                     Sydney, AUSTRALIA
                          Homepage: https://mobiarch2022.github.io/call.html
                           Dates: In-person (21st Oct. '22)
                           Submission Deadline: 7th July 2022
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MobiArch 2022 (to be held jointly with ACM MobiCom 2022) welcomes submissions from both researchers and practitioners from academia and industry that explore the latest developments in mobile communication technologies, architectures and machine learning/artificial intelligence/security/privacy related issues in mobile networks. In addition, we encourage work-in-progress and position papers that describe highly original ideas, present new directions, or have the potential to generate insightful provocative discussion at the workshop.

The workshop solicits submissions covering aspects around architectural issues and system support for mobility in the evolving mobile Internet (e.g. terahertz and cell-free communication, integrated space-earth networks, massive IoT, edge computing, autonomous cars and systems, distributed artificial intelligence, security/privacy issues in mobile networks and network virtualization), including but are not limited to:

• Effects of COVID-19 Pandemic on mobile network architecture
• Future Internet and next-generation networking architectures
• Mobile computing and networking architectures with 5G and beyond 5G
• Joint communication, computing, networking and computing architecture design
• Software defined networking (SDN) and network function virtualization (NFV)
• Network virtualization in mobile Internet architecture
• Terahertz wireless communications
• Mobile internet architectures and technologies for connected autonomous vehicles and cooperative ITS
• Next generation heterogenous radio access networks
• Mobile internet architecture for massive and low-power IoT access and smart cities
• Mobile data sensing and fusion for big M2M data
• Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC)
• Information-Centric Networking for mobile networks and mobile computing
• Mobile network management and architecture design with data analysis and learning
• Location management, positioning, and data management for wireless and mobility
• Future Internet architecture for efficient mobility support
• Resource orchestration in next-generation networks
• Field trials, deployment and evaluation of innovative mobile Internet architectures
• Blockchain application and SD-RAN systems
• Distributed/Federated/Split learning in mobile networks
• Data privacy preservation in mobile networks
• Machine learning and artificial intelligence in mobile networks

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Important Dates:

Deadline for submissions: 7th July, 2022
Notification of acceptance: 7th August, 2022
Submission: https://easychair.org/conferences/?conf=mobiarch2022
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Please see https://mobiarch2022.github.io/index.html for detailed information.

CO-CHAIRS
•Lorenzo Bertizzolo

Stefano Secci (Cnam, Paris)
• Yipeng Zhou (Macquarie University)

Regards,
Sudipta Acharya
(On behalf of MobiArch'22 organizers,)

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Deadline Extension – CFP RO-MAN 2022 Workshop on Machine Learning for HRI: Bridging the Gap between Action and Perception

July 6th, 2022 Daniela Lopez de Luise

 

CALL FOR PAPERS

The full-day virtual workshop:

Machine Learning for HRI: Bridging the Gap between Action and Perception (ML-HRI)

In conjunction with the 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) – August 22, 2022   

Webpage: https://ml-hri2022.ivai.onl/

 

I. Aim and Scope

A key factor for the acceptance of robots as partners in complex and dynamic human-centered environments is their ability to continuously adapt their behavior. This includes learning the most appropriate behavior for each encountered situation based on its specific characteristics as perceived through the robots senors. To determine the correct actions the robot has to take into account prior experiences with the same agents, their current emotional and mental states, as well as their specific characteristics, e.g. personalities and preferences. Since every encountered situation is unique, the appropriate behavior cannot be hard-coded in advance but must be learned over time through interactions. Therefore, artificial agents need to be able to learn continuously what behaviors are most appropriate for certain situations and people based on feedback and observations received from the environment to enable more natural, enjoyful, and effective interactions between humans and robots.

This workshop aims to attract the latest research studies and expertise in human-robot interaction and machine learning at the intersection of rapidly growing communities, including social and cognitive robotics, machine learning, and artificial intelligence, to present novel approaches aiming at integrating and evaluating machine learning in HRI. Furthermore, it will provide a venue to discuss the limitations of the current approaches and future directions towards creating robots that utilize machine learning to improve their interaction with humans.

II. Keynote Speakers and Panelists

  1. Dorsa Sadigh – Stanford University – USA
  2. Oya Celiktutan – King's College London – UK
  3. Sean Andrist – Microsoft – USA
  4. Stefan Wermter – University of Hamburg – Germany

III. Submission

  1. For paper submission, use the following EasyChair web link: Paper Submission.
  2. Use the RO-MAN 2022 format: RO-MAN Papers Templates.
  3. Submitted papers should be 4-6 pages for regular papers and 2 pages for position papers.

    The primary list of topics covers the following points (but not limited to):

  • Autonomous robot behavior adaptation
  • Interactive learning approaches for HRI
  • Continual learning
  • Meta-learning
  • Transfer learning
  • Learning for multi-agent systems
  • User adaptation of interactive learning approaches
  • Architectures, frameworks, and tools for learning in HRI
  • Metrics and evaluation criteria for learning systems in HRI
  • Legal and ethical considerations for real-word deployment of learning approaches

IV. Important Dates

  1. Paper submission: June 17, 2022 July 15, 2022 (AoE)
  2. Notification of acceptance: August 1, 2022 August 7, 2022 (AoE)
  3. Camera ready: August 14, 2022 (AoE)
  4. Workshop: August 22, 2022

V. Organizers

  1. Oliver Roesler – IVAI – Germany
  2. Elahe Bagheri – IVAI – Germany
  3. Amir Aly – University of Plymouth – UK

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CALL for Workshop on Robust AI for High-Stakes Applications, collocated with KI 2022

July 6th, 2022 Daniela Lopez de Luise

Workshop on Robust AI for High-Stakes Applications (RAI), collocated with KI 2022 (https://ki2022.gi.de/)

Website: https://rai2022.sme.uni-bamberg.de/

 

Introduction

Robustness is widely understood as the property of some method, algorithm, or system to only decrease gradually in performance when assumptions about its input are decreasingly met. This renders robustness to be a crucial property for dependable and trustworthy applications of AI in open-world environments, in particular in high-stake applications in which human well-being is at risk. However, the usual definition of robustness raises several questions, including:

§  What are the performance measures for evaluating the decrease in performance, i.e., which shortcomings are acceptable and which are not?

§  How do we identify the degree to which assumptions about input characteristics are not met, in particular if assumptions are hard to specify?

Depending on the respective application area and technique considered, various approaches have been taken to measure or benchmark performance and abnormality of input characteristics. Sometimes, we may be facing unknown requirements on input data and only experiments reveal much later that an approach is not robust (1-pixel-attacks on CNN-based object classification being one infamous example).

There has been a lot of progress in AI over the past few years, with many successful examples in perception and reasoning, which has encouraged the integration of the resulting technologies into important and high-stakes real-world applications such as autonomous mobile systems (e.g., self-driving cars, autonomous drones, service robots) automated surgical assistants, electrical grid management systems, control of critical infrastructure, to name a few. However, for such an integration to constitute a beneficial socio-technical system, safety and reliability are key, and robustness is essential to avert potential catastrophic events due to unconsidered phenomena or situations. The aim of this workshop is to bring together researchers from basic or applied AI across all sub-fields of AI to discuss approaches and challenges for developing robust AI. In particular, we envisage a dialogue between the Machine Learning and the Symbolic AI communities for the benefit of critical real-world applications. Our aim is to foster exchange between the various AI sub-fields present at KI and to discuss future research directions.

 

Topics

Robustness refers to capability of coping with unforeseen phenomena or situations. Gearing AI towards robustness has always been an aim for open-world AI, and it becomes a pressing requirement as AI makes its way into control of high-stake applications. Robustness is addressed in many sub-fields of AI using various working definitions, and various measures. This workshop aims to bring together researchers from all sub-fields of AI working on robust methods.

In this workshop, we invite the research community in Artificial Intelligence to submit position statements and technical works related to the theme of Robust AI for High-Stakes Applications in order to develop a joint understanding of robustness in AI and to foster the exchange on robust AI. Topics of interest include:

Explainable Artificial Intelligence

§  Benchmarking, evaluation, and regularization

§  Regularization in Machine Learning

§  Robust optimization

§  Robust inference algorithms

§  Causal model learning

§  Neuro-symbolic integration; Logic as a referee

§  Anomaly detection

§  Open-world planning and decision-making

§  AI in socio-technological systems

The list above is by no means exhaustive, as the aim is to foster the debate around all aspects of the suggested theme.

 

Submission

Guidelines

We invite submissions of regular research papers (up to 12 pages in KI format), position papers (up to 6 pages), or abstracts of recently published papers (3 pages) on the topic of robustness. Accepted papers will be published as a collection of working papers. The workshop is also open to people who would like to attend without submitting a paper as discussion of the topic will play a major role. During the workshop, perspectives on proposing a special issue for the KI journal on robust AI will be discussed. Workshop submissions and camera-ready versions will be handled by EasyChair; the submission link is as follows: https://easychair.org/conferences/?conf=ki2022

Important Dates

July 15, 2022: Workshop Paper Due Date

August 5, 2022: Notification of Paper Acceptance

August 19, 2022: Camera-ready papers due

Note: all deadlines are Central European Time (CET), UTC +1, Paris, Brussels, Vienna.

 

Organizing Committee

Prof. Dr. Ulrich Furbach, University of Koblenz-Landau, Germany / wizAI solutions GmbH

Dr. Alexandra Kirsch, Independent Scientist

Dr. Michael Sioutis, University of Bamberg, Germany

Prof. Dr. Diedrich Wolter, University of Bamberg, Germany

 

Contact

All questions about submissions should be emailed to the workshop co-organizers

 

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CfP for ARIAL Workshop at ICDM 2022 – AI for Aging, Rehabilitation and Intelligent Assisted Living.

July 6th, 2022 Daniela Lopez de Luise
According to a United Nations’ report on World Population Aging (2020), the number of people in the world aged 60 or over is projected to grow to 1.5 billion by the year 2050. Aging can come with various complexities and challenges, such as decline in the physical, cognitive and mental health of a person. These changes affect a person’s everyday life, resulting in decreased social participation, lack of physical activity, and vulnerability to injury and disability that can be exacerbated by the occurrence of various acute health events, such as strokes, or long term illnesses.

The field of assistive technology amalgamates several multi-disciplinary areas including data mining, rehabilitation engineering, and clinical studies. The idea of assistive technology solutions is to promote independent, active and healthy aging with a specific focus on older adults, and those living with mild cognitive impairments.

The COVID-19 pandemic has highlighted the challenges encountered by vulnerable populations in terms of not getting adequate care, difficulties in access to healthcare services and lack of necessary support to stay independent and safe. Many clinical treatments and rehabilitation services have gone virtual due to strict social distancing guidelines that have added more complexity to supporting the older population.

Collecting and mining health data using assistive technology devices is a challenging task. Leveraging Artificial Intelligence (AI) techniques is essential to make advancements in the field of aging and rehabilitation. Building AI models on vast amounts of health data from older adults will facilitate independent assisted living, promote healthy living, and manage rehabilitation routines effectively.

** Call for Papers **

ARIAL@ICDM22 will be held in Orlando, FL, USA. In this workshop, we invite previously unpublished and novel submissions in the following areas (pertaining to aging and rehabilitation), but not limited to:

* Methods, protocols and challenges for multimodal data collection, data annotation, and data labeling with older adult populations.
* Development and deployment of long-term sensor-based monitoring systems.
* Methodologies for big data, large-scale data mining, including cloud and edge computing.
* Data cleaning, curation, sharing and harmonization.
* Data analytics and visualization techniques for healthcare data of older adults.
* Data mining challenges such as handling missing data, dealing with mixed, imbalanced, poorly labeled and noisy data.
* Techniques for tele-rehabilitation/virtual rehabilitation, telemedicine and remote monitoring.
* Audio/video, multimodal interaction for patient engagement, exercise monitoring and successful delivery of rehabilitation.
* Addressing privacy concerns of patient data, e.g., privacy-protecting sensing modalities, federated learning and differential privacy.
* Machine learning and Deep Learning algorithms to identify harmful, life-threatening, abnormal behaviors in older care settings.
* AI approaches for continuous streaming, monitoring and analysis of health, activity, contextual, and online data for older adults.
* Techniques for handling data biases, and other biases related to sex, gender, ethnicity and age (e.g., fair machine learning strategies).
* Data mining methods for measuring health indicators, progression of physical and cognitive health, e.g., frailty, dementia, social isolation, mobility, mental health, gait stability.
* AI approaches for data fusion from multi-modal sensor interaction and ensemble algorithm development (e.g., multi-view learning approaches).

** Important Dates **
Paper submissions: September 2, 2022 (anywhere in the world)
Paper notifications: September 23 , 2022
Camera-ready deadline for the final version of accepted papers: To be announced later
Paper Registration Date: To be announced later
Workshop date: To be announced later

** Submission Guidelines **
Please use this link (https://wi-lab.com/cyberchair/2022/icdm22/scripts/submit.php?subarea=S06&undisplay_detail=1) to submit your papers. All papers must be original and not simultaneously submitted to another journal or conference. We request you to submit Full papers – 5 pages (including references). The review process will be double blind; therefore, authors must not write their names, contact details or affiliations in their papers at the time of submission. The accepted papers will be published in IEEE Xplore with a DOI.

Please use the IEEE 2-column format for paper submission (https://www.ieee.org/conferences/publishing/templates.html).

Note: We are working with a journal to submit full-length extended papers of ARIAL workshop.

**Committees**
*Workshop Co-chairs*
Shehroz Khan, KITE, University Health Network, Canada.
Dinesh Jayagopi, IIIT, Bangalore, India.
Luca Romeo, University of Macerata, Italy.
Amir Ahmad, United Arab Emirate University, UAE.

*Organizing Committee*
Ali Abedi, KITE, University Health Network, Canada.
Elham Khodabandehloo, KITE, University Health Network, Canada.

*Program Committee*
Michele Bernardini, Marche Polytechnic University, Italy
Ladislau Bölöni, University of Central Florida, USA
Ryan Koh, Toronto Rehabilitation Institute, Canada
Riccardo Rosati, Marche Polytechnic University
José Zariffa, University of Toronto, Canada

*Venue*
ARIAL@ICDM2022 will be held in person at Hilton Orlando, 6001 Destination Pkwy, Orlando, Florida 32819, USA.

*Contact*
All questions about submissions should be emailed to shehroz.khan@utoronto.ca/Ali.Abedi@uhn.ca
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AI4Space Workshop at ECCV 2022

July 6th, 2022 Daniela Lopez de Luise

2nd Workshop on AI for Space in conjunction with ECCV 2022:

AI4Space focuses on the role of AI, particularly computer vision and machine learning, in helping to solve technical challenges related to space, from autonomous spacecraft, space mining, debris monitoring and mitigation, to answering fundamental questions about the universe. The workshop will highlight the space capabilities that draw from and/or overlap significantly with vision and learning research, outline the unique difficulties presented by space applications to vision and learning, and discuss recent advances towards overcoming those obstacles.

 

Website:

https://aiforspace.github.io/2022/

 

Call for Papers:

We solicit papers for AI4Space. Papers will be reviewed and accepted papers will be published in the proceedings of ECCV Workshops. Authors of accepted papers will also be invited to present at the workshop (in hybrid mode) at ECCV 2022, Tel-Aviv, late October 2022.

The general emphasis of AI4Space is vision and learning algorithms in off-Earth environments, including in the orbital region, surface and underground environments on other planetary bodies (e.g., the moon, Mars and asteroids), interplanetary space and solar system, and distant galaxies. Target application areas include autonomous spacecraft, space robotics, space traffic management, astronomy, astrobiology and cosmology. Emphasis is also placed on novel sensors and processing hardware for vision and learning in space, mitigating the challenges of the space environment towards vision and learning (e.g., solar radiation, extreme temperatures), and solving practical difficulties in vision and learning for space (e.g., lack of training data, unknown or partially known characteristics of operating environments).

 

A specific list of topics is as follows:
– Visual navigation for spacecraft operations
– Vision and learning for space robotics
– GPS-denied positioning on the moon and Mars
– Space debris monitoring and mitigation
– Vision and learning for astronomy, astrobiology and cosmology
– Novel sensors for space applications
– Processing hardware for vision and learning in space
– Mitigating challenges of the space environment to vision and learning
– Datasets, transfer learning and domain gap for space problems

 

Paper deadline:

11:59pm 15 July 2022

 

More details:

https://aiforspace.github.io/2022/

 

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