ACM IUI Workshop – SOCIALIZE 2022

SOCIALIZE Workshop @ ACM IUI 2022

 

Second Workshop on SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE)

March 22, 2022, hosted online

 

Co-located  with the 27th ACM Conference on Intelligent User Interfaces (IUI) 2022 (https://iui.acm.org/2022/) March 22-25, 2022, Virtually Hosted by University of Helsinki, Finland

 

For any information: socialize2022@easychair.org

 

******************Important Dates*******************

 

Submission deadline: January 9, 2022

Authors notification: January 28, 2022

Camera-ready: February 18, 2022

Workshop: March 22, 2022

 

*********************Overview**********************

 

The SOCIALIZE workshop aims to bring together all those interested in the development of interactive techniques that may contribute to fostering the social and cultural inclusion of a broad range of users. More specifically, we intend to attract research that takes into account the interaction peculiarities typical of different realities, with a focus on disadvantaged and at-risk categories (e.g., refugees and migrants) and vulnerable groups (e.g., children, elderly, autistic, and disabled people). Among others, we are also interested in human-robot interaction techniques aimed at the development of social robots, that is, autonomous robots that interact with people by engaging in social-affective behaviors, abilities, and rules related to their collaborative role.

 

**********************Topics***********************

 

The main topics of the workshop are (but not limited to):

* Multi-cultural system design;

* Empirical studies on the impact of culture on systems;

* Cultural and social influence in recommender systems;

* Cultural and social aspects as contextual factors;

* Cultural and social influence in online learning platforms (e.g., MOOC);

* Methods for automatic assessment of social and cultural background from social media;

* Strategies for adapting systems to groups of users with different backgrounds;

* Cross-cultural analyses of trust in systems;

* Human-robot adaptation for cross-cultural users, including under-represented groups;

* Personalized Human-Robot Interaction (HRI);

* Adaptation strategies for social HRI;

* Machine learning for social robots;

* Emotion and personality detection in social HRI;

* Social robots as conversational recommender systems

* Social robots in the real world;

 

********************Submissions********************

 

Researchers and practitioners in academia or industry are encouraged to submit original manuscripts relevant to the audience of the workshop. Papers must comply with the standard ACM SIGCHI Paper Format (see https://iui.acm.org/2022/call_for_papers.html).

The contributions can be: Full papers (6 pages excluding references) and Short papers (4 pages excluding references), including substantial work-in-progress, perspective papers, and lessons learned; and Challenge, Position, and Demo papers (2 pages excluding references), describing research challenges in theory or practice, defining new promising research directions, and demonstrating innovative software prototypes.

 

Manuscripts must be submitted via Easychair (https://easychair.org/conferences/?conf=socialize2022) by January 9, 2022. All submissions will undergo a peer-review process. Reviewers will consider originality, significance, technical soundness, clarity, and relevance to the workshop’s topics. The reviewing process will be double-blind.

Authors of accepted papers will be invited to present their work during the workshop, and at least one author of each accepted paper must register for and (virtually) attend the workshop.

 

For further questions please contact the workshop organizers at <socialize2022@easychair.org>

 

********************Proceedings********************

 

Accepted papers  will be published as open access joint workshop proceedings via CEUR-WS.org

 

********************Organization********************

 

Fabio Gasparetti, Roma Tre University

Cristina Gena, University of Torino

Giuseppe Sansonetti, Roma Tre University

Marko Tkalčič, University of Primorska

Call for Participation: Winter School on Deep Learning: From Perceptrons to Transformers (Organized by The Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata)


Winter School

on

Deep Learning: From Perceptrons to Transformers

21st January – 12th March 2022 (Fridays and Saturdays)

Electronics and Communication Sciences Unit

Indian Statistical Institute, Kolkata

Call for Participation


The Objective: The Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata is organizing the Winter School on Deep Learning: From Perceptrons to Transformers. This winter school will focus heavily on imparting a hands-on experience towards developing a wide range of classical and advanced deep learning models, in addition to making the associated theory easy to understand. Participants will learn from the basics of machine learning to the advanced deep learning-based approaches with application to Computer Vision and Natural Language Processing. Theoretical lectures will be delivered by renowned professors and scientists (from ISI and other esteemed organizations) who have made significant contributions in their areas of research. The lectures will be supplemented by extremely detailed hands-on sessions instructed by post-docs and research scholars.

Course coverage: The winter school will have the following course structure (theory and associated hands-on)

  • Basics of Python

  • Basics of the Deep Learning Library: PyTorch

  • Essentials of Vector Calculus and Linear Algebra for Machine Learning

  • Conceptual Fundamentals of Machine Learning, Image Processing, Computer Vision, Natural Language Processing

  • Perceptrons and Backpropagation

  • Ingredients of Deep Learning: Gradient Descent, Batch Normalization, Regularization, Dropout

  • Convolutional Neural Networks (CNN), Convolutional Autoencoders

  • CNN for Object Classification, Detection, and Segmentation

  • Recurrent Neural Network, LSTM, Word Embedding

  • Attention Models and Transformer (BERT and Visual Transformer)

  • Deep Generative Models (GAN and VAE)

  • Weakly Supervised Deep Learning, Self-Supervised Learning

  • Meta-Learning and Few-Shot Learning

  • Deep Reinforcement Learning

  • Explainable Artificial Intelligence

  • Geometric Deep Learning

Mode of tutorials: Lectures and Hands-on sessions will be conducted in online mode only. All sessions will be on Fridays and Saturdays, and the recordings will be shared with all the participants.

Important Dates:

Submit Application on Website

Dec 28, 2021 – Jan 12, 2022


Notification to Selected Applicants

Jan 13, 2022


Registration

Jan 14 – Jan 17, 2022


Course Duration

Jan 21 – Mar 12, 2022

Who can apply?
Professionals from academia and industry, research/project scholars, masters and final-year bachelors students. Interested candidates must submit the online application (https://sites.google.com/view/wsdl2022/apply). Selected applicants will be informed to register for the school.
For application, registration fees and other details: www.sites.google.com/view/wsdl2022/

CFP – WCCI – FUZZ-IEEE 2022 Special Session on Advances in Deep Fuzzy Systems (FUZZ-SS-9)

 

 

Call for Papers:

 

2022 IEEE World Congress on Computational Intelligence (WCCI 2022)

WCCI – FUZZ-IEEE 2022 Special Session on Advances in Deep Fuzzy Systems

 

Organized by Uzay Kaymak, João Sousa and Alexander Gegov

 

 

Deep learning has gained significant attention within the computational intelligence community over the recent years. Its success has been mainly due to the increased capability of modern computers to collect, store and process large volumes of data. This has led to a substantial increase in the effectiveness and efficiency of data management. As a result, it has become possible to achieve high accuracy within a short time frame for some benchmark learning tasks such as object classification and image recognition. The most common implementation of deep learning has been through neural networks due to the ability of their layers to perform multiple functional composition as part of a multistage learning process. However, despite the significant advances in deep learning, there are also limitations. Effectiveness is usually adversely affected when the data is not well defined due to inherent noise, uncertainty, ambiguity, vagueness and incompleteness. This has an adverse impact on efficiency due to the necessity to refine the data by additional collection, analysis and cleaning. The reduced effectiveness and efficiency undermine the ability of deep learning to address some real-life tasks that are safety critical or time critical. Besides this, deep leaning has been used mainly in a passive manner for the purpose of observing the environment, but it almost has not been used in an active manner for the purpose of changing the environment. Finally, deep learning models often have poor transparency which makes them difficult for understanding, explanation and interpretation by non-technical users.

 

Deep fuzzy systems could address some of these problems and limitations. Deep learning can also be used for developing fuzzy systems that can solve more complicated tasks in more dynamic environments. DFS have been around in different forms and under different names such as hierarchical fuzzy systems (also known as fuzzy trees) and networked fuzzy systems (also known as fuzzy networks). DFS are well suited for performing multiple functional composition at both crisp and linguistic level. Moreover, they have the potential of handling effectively and efficiently data that is not well defined due to the ability of fuzzy logic to deal successfully with different types of uncertainty. Also, DFS can be used in both passive and active manner with regards to the environment due to their generic structure. Finally, these systems have a high level of transparency due to the ability of fuzzy rules to capture well the interactions between input and output variables.

 

This special session aims to bring together contributions on recent advances in deep fuzzy systems (DFS), both from a theoretical and a practical perspective. As such, the session will provide a good outlook on the state-of-the-art in this growing field for the fuzzy systems community.

 

The topics covered, include but are not limited to:

 

Theoretical methods

  • Hierarchical Fuzzy Systems
  • Fuzzy Networks
  • Chained Fuzzy Systems
  • Multistage Fuzzy Systems
  • Deep learning for Fuzzy Systems
  • Deep Fuzzy Learning Systems

Application areas

  • Object Classification
  • Image Recognition
  • Systems Control
  • Fault Detection
  • Decision Making

Case studies

  • Transport
  • Robotics
  • Business
  • Environment
  • Healthcare
  • Security
  • Energy

 

Important dates:

  • Full paper submission:                        January 31, 2022 (Strict deadline!)
  • Notification of paper acceptance:       April 26, 2022
  • Camera-ready paper submission:      May 23, 2022
  • Conference starts:                              July 18, 2022
  • Conference ends:                               July 23, 2022

 

Conference website:                     https://wcci2022.org/

Special Session website:              https://omarali0703.github.io/ieee-deepfuzzy-specialsession/

Information for authors:              https://wcci2022.org/submission/

Submission website:                      https://cmt3.research.microsoft.com/IEEEWCCI2022/

(Please select SS on Advances in Deep Fuzzy Systems (FUZZ-SS-9) as Main research topic when submitting.)

 

 

We look forward to see you in Padua!

 

Uzay Kaymak, João Sousa, Alex Gegov.

 

IEEE ARGENCON 2022 y novedades del IEEE Argentina

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IEEE AR 'e-notice'
Información para Socios IEEE de la Sección Argentina
30 de diciembre  de 2021
 
 
En este último envío de 2021, agradecemos a todos los que nos acompañaron y ayudaron a transitar por un año más,
y les extendemos nuestros mejores deseos para el 2022.
 
 
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IEEE ARGENCON 2022 – Primer llamado a envío de trabajos

Primer Llamado a Envío de Trabajos para IEEE ARGENCON 2022.

El Congreso se realizará del miércoles 7 al viernes 9 de septiembre de 2022, en formato híbrido (presencial y virtual) en San Juan, organizado por el Instituto de Energía Eléctrica (IEE, UNSJ / CONICET), con la colaboración de la Facultad de Ingeniería de la Universidad de Palermo.

El Pre-Congreso tendrá lugar el lunes 5 y martes 6 de septiembre, con diversas actividades, a definir.

Fechas importantes

* Envío de trabajos 04/04/2022
    Oportunamente se indicará el mecanismo para envío de los trabajos.

* Propuestas de tutoriales y talleres 25/04/2022 (*)

    Deberán contactar a los respectivos Chairs de Programa.
    La información estará disponible a partir de febrero de 2022.

Web: http://www.argencon.org.ar
Contacto: argencon@ieee.org.ar

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