Call for papers IEEE J-BHI special issue: Trustworthy and Collaborative AI for Personalised Healthcare Through Edge-of-Things

September 7th, 2022 Daniela Lopez de Luise
we are recently organising a special issue on Trustworthy and
Collaborative AI for Personalised Healthcare Through Edge-of-Things in
IEEE Journal of Biomedical and Health Informatics (J-BHI) (Impact
Factor: 7.021).

We kindly invite you and your colleagues who are interested to
contribute an article. The special issue will highlight, but not be
limited to the following topics:
*Trustworthy AI models for health, medicine, biology, and biomedical
applications
*AI-driven Edge of Things infrastructure for healthcare
*Discussion of the trade-off between explainability and performance of
machine learning
*Development of model-specific or model-agnostic approaches for
explaining machine learning models
*Generation and detection of adversarial attacks for safety in AI
systems for personalised healthcare
*Federated Learning for data privacy in AI systems for personalised
healthcare
*Fairness and bias issues in AI systems for personalised healthcare
*Designing integrating virtual agents for healthcare usages
*Collaborative robots for healthcare usages

More details can be found in the following link:

https://www.embs.org/jbhi/special-issues-page/trustworthy-and-collaborative-ai-for-personalised-healthcare-through-edge-of-things/

Thanks you and best regards,

Zhao Ren

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CFP MDAI 2023, Deadline 15 December 2022

September 7th, 2022 Daniela Lopez de Luise
CALL FOR PAPERS

20th Modeling Decisions for Artificial Intelligence MDAI 2023, Umea, Sweden June 19-22, 2023
http://www.mdai.cat/mdai2023

Proceedings: LNAI; CORE-B conference; Deadline: December 15th

Decision processes in a broad sense, including model building and all kind of mathematical tools for data aggregation, information fusion, and decision making; tools to help decision in data science problems (including e.g., statistical and machine learning algorithms as well as data visualization tools); and algorithms for data privacy and transparency-aware methods so that data processing processes and decisions made from them are fair, transparent, explainable and avoid unnecessary disclosure of sensitive information.

Tracks on (i) data science, (ii) machine learning, (iii) data privacy, (iv) aggregation funcions, (v) human decision making, and (vi) graphs and (social) networks.

MDAI is rated as a CORE B conference by the Computing Research and Education Association of Australasia – CORE.

*Important Dates*

     LNAI Submission deadline: December 15th, 2022
     LNAI Acceptance notification: March 1st, 2023
     Final version of LNAI accepted papers: March 17th, 2023

     USB-only Submission deadline: April 30th, 2023
     USB Acceptance notification: May 20th, 2023

     Early registration: March 15th, 2023
     Conference: 19-22 June, 2023

*Submission and Publication*

Original technical contributions are sought. Contributions will be selected on the basis of their quality. Papers should not exceed 12 pages in total (using LNCS/LNAI style). Proceedings with accepted papers will be published in the LNAI/LNCS series (Springer-Verlag).

We publish additional proceedings in a volume (with ISBN) with a later deadline.

Program co-chairs:
     Vicenc Torra (Umea University, Sweden)
     Yasuo Narukawa (Tamagawa University, Japan)

AB, PC, local organizing committee and additional information:
     http://www.mdai.cat/mdai2023

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Call for Tutorial Proposals

September 7th, 2022 Daniela Lopez de Luise
CALL FOR TUTORIAL PROPOSALS – FG 2023

We invite proposals for tutorials to be organized in conjunction with
the 2023 IEEE Conference on Automatic Face and Gesture Recognition (FG
2023: https://hal.cse.msu.edu/fg2023/) in Waikoloa, Hawaii. The
tutorials should complement and enhance the scientific program of FG
2023 by providing authoritative and compreh7ensive overviews of growing
themes that are of sufficient relevance with respect to the
state-of-the-art and the conference topics.

Accepted tutorials will be held on either 4 January or 5 January 2023 in
the same venue as the FG 2023 main conference, at the Waikoloa Beach
Marriott Resort, Hawaii, USA.
We solicit proposals on any topic of interest to the FG community.
Interdisciplinary topics that could attract a significant cross-section
of the community are highly encouraged. We particularly welcome
tutorials which address advances in emerging areas not previously
covered in an FG related tutorial. Proposals should be submitted by 3
October 2022. Notifications will be circulated on 10 October 2022.

*** TUTORIAL PROPOSAL SUBMISSION ***

Tutorial proposals should be submitted through CMT and will be reviewed
and evaluated by the workshop and tutorial co-chairs, Tae-Kyun Kim,
Vitomir Štruc, and Lijun Yin. The CMT submission website is available
from: https://cmt3.research.microsoft.com/FGWT2023
A tutorial proposal should include the following information to
facilitate the decision process:

• Title
• Proposer’s contact information and short CV
• Names of any additional lecturers and short CV
• Tutorial description and description of relevance to the FG community
and an evaluation plan
• References and experience of the instructors with respect to the
proposed tutorial topic
• Planned length of the tutorial
• List of relevant tutorials recently presented in other conferences
• Requirements (e.g., facilities, internet access, etc.),
• Other useful information (e.g., estimated attendance, slides/notes
available, etc.).

The main conference will provide rooms, equipment, and coffee breaks for
the tutorials.

For your reference, the titles of the three tutorials held in
conjunction with the previous FG conferences were as follows:

•    Multi-view Face Representation
•    Remote Physiological Measurement from Images and Videos
•    From Deep Unsupervised to Supervised Models for Face Analysis
•    Statistical Methods for Affective Computing

*** Review process ***
Tutorial proposals will be evaluated on the basis of their estimated
benefit for the community and their fit within the tutorials program as
a whole. Factors to be considered include relevance, timeliness,
importance, and audience appeal; suitability for presentation in a half
or full day format; past experience and qualifications of the
instructors. Selection will also be based on the overall distribution of
topics, expected attendance, and specialties of the intended audiences.

CONTACT AND QUERIES
For additional information and queries regarding the workshop proposal
procedure, please contact the Workshop and Tutorial Co-chairs: Tae-Kyun
Kim (tk.kim@imperial.ac.uk). Vitomir Štruc (vitomir.struc@fe.uni-lj.si)
and Lijun Yin (lijun@cs.binghamton.edu).

*** Important Dates ***
Tutorial proposals due:         3 October 2022
Notification of acceptance:     10 October 2022
Workshops and tutorials:     4 January or 5 January 2023

*** Submission ***
https://cmt3.research.microsoft.com/FGWT2023

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DeepLearn 2023 Winter: early registration September 26

September 7th, 2022 Daniela Lopez de Luise
8th INTERNATIONAL SCHOOL ON DEEP LEARNING 
DeepLearn 2023 Winter
Bournemouth, UK
      
January 16-20, 2023   
      
***********
Co-organized by:
      Department of Computing and Informatics
Bournemouth University
 Institute for Research Development, Training and Advice – IRDTA
 
Brussels/London
******************************************************************
     Early registration: September 26, 2022
      
******************************************************************
      
SCOPE:
      
DeepLearn 2022 Winter will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Guimarães, Las Palmas de Gran Canaria and Luleå.
 
Deep learning is a branch of artificial intelligence covering a spectrum of current exciting research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, health informatics, medical image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, biometrics, communications, climate sciences, bioinformatics, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience.
Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely.
 
An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles.
 
ADDRESSED TO:
 
Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2023 Winter is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators.
      
VENUE:
DeepLearn 2023 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be:
 
Talbot Campus
      Bournemouth University
 
      
 
STRUCTURE:
3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
      
Full live online participation will be possible. However, the organizers highlight the importance of face to face interaction and networking in this kind of research training event.
 
      
KEYNOTE SPEAKERS:
       
Yi Ma (University of California, Berkeley), CTRL: Closed-Loop Data Transcription via Rate Reduction
      
Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks
            
Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills
      
PROFESSORS AND COURSES:
Mohammed Bennamoun (University of Western Australia), [intermediate/advanced] Deep Learning for 3D Vision
 
Matias Carrasco Kind (University of Illinois, Urbana-Champaign), [intermediate] Anomaly Detection
Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning
 
Seungjin Choi (Intellicode), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces
Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare
       
Luc De Raedt (KU Leuven), [introductory/intermediate] From Statistical Relational to Neuro-Symbolic Artificial Intelligence
 
Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning
 
João Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities
 
Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology
 
Zhiting Hu (University of California, San Diego) & Eric P. Xing (Carnegie Mellon University), [intermediate/advanced] A “Standard Model” for Machine Learning with All Experiences
      
Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances
      
Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models
      
Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond
 
David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning
 
Abdelrahman Mohamed (Meta), [intermediate/advanced] Speech Representation Learning for Recognition and Generation
      
Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How?
 
Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning
      
Bracha Shapira (Ben-Gurion University of the Negev), [introductory/intermediate] Recommender Systems
Richa Singh (Indian Institute of Technology Jodhpur), [introductory/intermediate] Trusted AI
      
Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning
      
 
Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks
 
Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning
 
 
Bram van Ginneken (Radboud University Medical Center), [introductory/intermediate] Deep Learning for Medical Image Analysis
 
Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis
 
OPEN SESSION:
 
       
An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david@irdta.eu by January 8, 2023.
 
 
 
INDUSTRIAL SESSION:
 
 
 
A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david@irdta.eu by January 8, 2023.
 
 
 
EMPLOYER SESSION:
 
 
 
Organizations searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david@irdta.eu by January 8, 2023.
 
 
 
ORGANIZING COMMITTEE:
 
 
 
Rashid Bakirov (Bournemouth, local co-chair)
 
Marcin Budka (Bournemouth)
 
Vegard Engen (Bournemouth)
 
Nan Jiang (Bournemouth, local co-chair)
 
Carlos Martín-Vide (Tarragona, program chair)
 
Sara Morales (Brussels)
 
David Silva (London, organization chair)
 
 
 
REGISTRATION:
 
 
 
It has to be done at
 
 
 
 
 
 
The selection of 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish.
 
 
 
Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will have got exhausted. It is highly recommended to register prior to the event.
 
 
 
FEES:
 
 
 
Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. The fees for on site and for online participation are the same.
 
 
 
ACCOMMODATION:
 
 
 
Accommodation suggestions are available at
 
 
 
 
 
 
CERTIFICATE:
 
 
 
A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.
 
 
 
QUESTIONS AND FURTHER INFORMATION:
 
 
 
 
 
 
ACKNOWLEDGMENTS:
 
 
 
Bournemouth University
 
 
 
Rovira i Virgili University
 
 
 
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
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Deadline Extended: Sept. 10: 2nd IEEE ICDM International Workshop on AI for Nudging and Personalization (WAIN-2022)

September 7th, 2022 Daniela Lopez de Luise

Call for Papers: 2nd IEEE ICDM International Workshop on AI for Nudging (WAIN-2022)

Co-located with the IEEE International Conference on Data Mining (ICDM)

 

Nudging has been widely used by decision makers and organizations (both government and private) to influence the behavior of target populations, and the concept of nudging is now being widely used in the digital world. Examples of digital nudging include emails from hospitals or public health officials encouraging individuals to get vaccinated, text messages from colleges to stressed-out students to advertise the availability of counseling services during exam weeks, marketing messages through various digital media, and user interfaces designed to guide people’s behavior in digital choice environments.  

 

The central idea behind nudging is to make small changes to the environments in which citizens make decisions to encourage better behaviors. Even though nudges have traditionally involved simple changes that are easy and inexpensive to implement, more complex and sustained behavior change requires more complex interventions, presenting new challenges for nudging in the virtual world. Though the concept of nudging has been popularized recently, nudges have been in use in various aspects of society for a long time, including in healthcare, public health policy, law, economics, politics, insurance, finance, and advertising. With increasing availability of big data from many scientific disciplines, artificial intelligence (AI), machine learning (ML), and data science (DS) technologies have vast potential to transform data-driven nudging and decision making. This workshop seeks to build a new community around AI for nudging and provide a platform for exploring the state of the art in AI/ML/DS based systems and applications of digital nudging.  

 

Adaptation of products and services to individual preferences, called Personalization, has been at the core of modern businesses to improve customer satisfaction. Modern business and digital systems coupled with artificial intelligence technologies are poised to enable personalization on a grand scale. Personalization is a key element behind many modern businesses such as Netflix, Facebook, and Amazon to increase their revenue and customer base. Modern businesses are tailoring content for individual users based on the social, economic, and cultural profiles mined from the data, as it is shown to increase revenue and attract new customers. Modern applications ranging from precision marketing to precision healthcare have shown a clear demand for personalized content.  

 

We invite contributions from researchers of any discipline who are developing AI/ML/DS technologies that impact human behavior based on nudging theory or personalization or behavioral science-based solutions. For example, in the context of public health communications, how can AI/ML be used to address the construction of a message incorporating nudges; how do you digitally nudge people towards better healthcare outcomes, better financial decisions, or improve productivity; or how can nudging be personalized? What are the key data, technology, privacy and ethical, adaptation, and scaling challenges in nudging and personalization? In addition to algorithmic and systems papers, case studies that shed light on the effectiveness of nudges and personalization at maximizing a specific outcome, how AI/ML based systems can nudge people to make better decisions, or how industry is developing and/or using nudging and personalization technology to influence behavior of consumers are of great interest to this workshop.  

 

Topics of interest include, but not limited to, the following:  

  • Theoretical foundations of nudging and personalization 
  • Data driven and evidence based approaches in nudging and personalization
  • Core AI/ML topics including multi-agents, federated learning, active learning, semi-supervised learning, multi-armed bandits, contextual bandits, reinforcement learning, deep learning, transfer learning 
  • Multi-modal data and model fusion 
  • Representation learning, and embeddings 
  • Learning from categorical and relational data 
  • Feature engineering 
  • Statistical models, A/B testing 
  • Privacy and Ethical issues in nudging and personalization 
  • Personalized nudging 
  • Challenges for AI in real-time nudging 
  • AI-driven interactions encoding behavior change solutions 
  • Nudging and personalization in conversational AI systems 
  • Evaluation strategies to measure impact and effectiveness of nudging and personalization
  • Applications: Healthcare, Precision Medicine, Energy, Environment, Transportation, Workforce, Education, Advertising, Government, Politics, Policy, Software Engineering 

Important Dates: 

Sept. 10, 2022: Paper submission 

Sep. 23, 2022: Acceptance notification 

Oct. 01, 2022: Camera-ready deadline and copyright form 

Nov. 28, 2022: Workshop 

 

Paper Submissions:  

This is an open call-for-papers. We invite both full papers (max 8 pages) describing mature work and short papers (max 4-5 pages) describing work-in-progress or case studies. Only original and high-quality papers formatted using the IEEE 2-column format (Latex Template), including the bibliography and any possible appendices will be considered for reviewing.

 

Proceedings:  

All submitted papers will be evaluated by 2-3 program committee members, and accepted papers will be included in an ICDM Workshop Proceedings volume, to be published by IEEE Computer Society Press and will be included in the IEEE Xplore Digital Library.  

 

Best Research/Application/Student Paper Awards:  

Best research, application, and student paper awards are sponsored by Lirio. The awards committee will select papers for these awards based on relevance, program committee reviews, and presentation quality.

 

Contact:  

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