[rede.APPIA] 11th International School on Deep Learning (and the Future of Artificial Intelligence).

Caros/as Colegas,

Divulgamos a summer school DeepLearn 2024 que terá o pólo do LIACC na Universidade da Maia como anfitriã este ano.
O pólo do LIACC na Universidade da Maia será o anfitrião da DeepLearn 2024, 11th International School on Deep Learning (and the Future of Artificial Intelligence), de 15 a 19 de Julho de 2024 (https://deeplearn.irdta.eu/2024/). Desde já contamos com keynote speakers e professores abordando temas e ministrando workshops tais como:

 

  • LLMs, text mining & machine reading comprehension
  • Deep Learning para linguagem e discurso
  • Multi Agent Teaming
  • Edge Machine Learning
  • AutoML
  • Graph Machine Learning
  • Sustentabilidade e AI
  • Explainable Artificial Intelligence (xAI)
  • Deep networks para visão computacional 3D
  • Aplicações para as áreas da saúde e medicina

Como novidade, haverá hackathons coordenados pelo Professor Sergei V. Gleyzer (http://sergeigleyzer.com/). Os desafios serão divulgados duas semanas antes do início do DeepLearn 2024, sendo os resultados divulgados no último dia.

 

A participação do mundo das empresas também é desejável e encorajada. Haverá períodos de 10 minutos para demonstrações e aplicações práticas do Deep Learning no mundo dos negócios. Estas participações deverão enviar uma expressão de interesse em 1 página para david@irdta.eu até 07 de Julho de 2024.

 

Há também uma open session, para apresentações de 5 minutos de trabalhos em desenvolvimento pelos participantes. Para tal, os interessados deverão enviar um resumo de meia-página contendo título, autores, e sumário da investigação para david@irdta.eu até 07 de Julho de 2024.

 

Os empregadores que procurem especialistas em deep learning terão um espaço para contactos individuais. Deverão produzir um pdf de 1 página com uma breve descrição da organização e dos perfis que procura para circulação por entre os participantes antes do evento. Este pdf deverá acompanhar uma expressão de interesse a a enviar para david@irdta.eu até 07 de Julho de 2024.

 

Há condições favoráveis para os afiliados à Universidade da Maia e Universidade do Porto.

 

Cumprimentos,

O Comité Organizador do DeepLearn 2024

 

José Paulo Marques dos Santos (Universidade da Maia, local chair)

Carlos Martín-Vide (Universitat Rovira i Virgili, program chair)

José Luís Reis (Universidade da Maia)

Luís Paulo Reis (Universidade do Porto)

Sara Morales (IRDTA, Bruxelas)

David Silva (IRDTA, Londres, organization chair)


https://deeplearn.irdta.eu/2024/

Best Regards/Cumprimentos
Luis Paulo Reis
—————————————————————————-
Associate Professor at FEUP – Faculty of Engineering, University of Porto
Director of LIACC – Artificial Intelligence and Computer Science Lab.
Co-Director of LIACD – First Degree on AI and DS (FCUP/FEUP)
Tel. +351 919455251 / skype: luis.paulo.reis
—————————————————————————-
“Don't close your eyes unless you can dream.
Don't open your eyes unless you can believe!”
—————————————————————————-

[rede.APPIA] LUHME: Language Understanding in the Human-Machine Era | Workshop at ECAI 2024

Call for Papers: Language Understanding in the Human-Machine Era (LUHME)

The LUHME 2024 workshop Language Understanding in the Human-Machine Era is part of the 27th European Conference on Artificial Intelligence, ECAI 2024 (https://www.ecai2024.eu/).

Workshop description
Large language models (LLMs) have revolutionized the development of interactional artificial intelligence (AI) systems by democratizing their use. These models have shown remarkable advancements in various applications such as conversational AI and machine translation, marking the undeniable advent of the human-machine era. However, despite their significant achievements, state-of-the-art systems still exhibit shortcomings in language understanding, raising questions about their true comprehension of human languages.
The concept of language understanding has always been contentious, as meaning-making depends not only on form and immediate meaning but also on context. Therefore, understanding natural language involves more than just parsing form and meaning; it requires access to grounding for true comprehension. Equipping language models with linguistics-grounded capabilities remains a complex task, given the importance of discourse, pragmatics, and social context in language understanding.
Understanding language is a doubly challenging task as it necessitates not only grasping the intrinsic capabilities of LLMs but also examining their impact and requirements in real-world applications. While LLMs have shown effectiveness in various applications, the lack of supporting theories raises concerns about ethical implications, particularly in applications involving human interaction.
The “Language Understanding in the Human-Machine Era” (LUHME) workshop aims to reignite the debate on the role of understanding in natural language use and its applications. It seeks to explore the necessity of language understanding in computational tasks like machine translation and natural language generation, as well as the contributions of language professionals in enhancing computational language understanding.

Topics of Interest
Topics of interest include, but are not limited to:
• Language understanding in LLMs
• Language grounding
• Psycholinguistic approaches to language understanding
• Discourse, pragmatics and language understanding
• Evaluation of language understanding
• Multi-modality and language understanding
• Socio-cultural aspects in understanding language
• Effects of language misunderstanding by computational models
• Manifestations of language understanding
• Distributional semantics and language understanding
• Linguistic theory and language understanding by machines
• Linguistic, world, and common sense knowledge in language understanding
• Machine translation and/or interpreting and language understanding
• Human vs. machine language understanding
• Role of language professionals in the LLMs era
• Understanding language and explainable AI

Ethics Statement
Research reported at ECAI and the LUHME workshop should avoid harm, be honest and trustworthy, fair and non-discriminatory, and respect privacy and intellectual property. Where relevant, authors can include in the main body of their paper, or on the reference page, a short ethics statement that addresses ethical issues regarding the research being reported and the broader ethical impact of the work. Reviewers will be asked to flag possible violations of relevant ethical principles. Such flagged submissions will be reviewed by a senior member of the programme committee. Authors may be required to revise their paper to include a discussion of possible ethical concerns and their mitigation.

Submission Instructions
Papers must be written in English, be prepared for double-blind review using the ECAI LaTeX template, and not exceed 7 pages (not including references). The ECAI LaTeX Template can be found at https://ecai2024.eu/download/ecai-template.zip. Papers should be submitted via OpenReview: https://openreview.net/group?id=eurai.org/ECAI/2024/Workshop/LUHME

Excessive use of typesetting tricks to make things fit is not permitted. Please do not modify the style files or layout parameters. You can resubmit any number of times until the submission deadline. The workshop papers will be published in the proceedings (further information will be provided soon).

Important Dates
• Paper submission: 31 May 2024
• Notification of acceptance: 15 July 2024
• Camera-ready papers: 31 July 2024
• LUHME workshop: 19 or 20 October 2024

Invited Speakers
• Alexander Koller, Saarland University
• Anders Søgaard, University of Copenhagen
• Melanie Mitchell, Santa Fe Institute

Organization
This workshop is jointly organized by the chairs of working groups 1 (Computational Linguistics) and 7 (Language work, language professionals) of the COST Action LITHME – Language in the Human-Machine Era.

Workshop Organizers
• Rui Sousa-Silva (University of Porto, Portugal)
• Henrique Lopes Cardoso (University of Porto, Portugal)
• Maarit Koponen (University of Eastern Finland, Finland)
• Antonio Pareja-Lora (Universidad de Alcalá, Spain)
• Márta Seresi (Eötvös Loránd University, Hungary)

Program Committee
• Aida Kostikova (Bielefeld University)
• Alex Lascarides (University of Edinburgh)
• Alípio Jorge (University of Porto)
• António Branco (University of Lisbon)
• Belinda Maia (University of Porto)
• Caroline Lehr (ZHAW School of Applied Linguistics)
• Diana Santos (Universitetet i Oslo)
• Efstathios Stamatatos (University of the Aegean)
• Ekaterina Lapshinova-Koltunski (University of Hildesheim)
• Eliot Bytyçi (Universiteti i Prishtinës “Hasan Prishtina”)
• Hanna Risku (University of Vienna)
• Jörg Tiedemann (University of Helsinki)
• Lynne Bowker (University of Ottawa)
• Nataša Pavlović (University of Zagreb)
• Paolo Rosso (Universitat Politècnica de València)
• Ran Zhang (Bielefeld University)
• Ruslan Mitkov (Lancaster University)
• Sule Yildirim Yayilgan (Norwegian University of Science and Technology)
• Tharindu Ranasinghe (Lancaster University)

For further information, please visit https://luhme.web.uah.es/ or contact rssilva@letras.up.pt 

[rede.APPIA] AmIA@EPIA2024 – CALL FOR PAPERS

[Apologies if you receive multiple copies of this CFP]

 

———————————- – – – – – – – – – – – – – – –

AmIA_Environments@EPIA2024 :: CALL FOR PAPERS

———————————- – – – – – – – – – – – – – – –

 

Special Track on Ambient Intelligence and Affective Environments (AmIA

Environments 2024)

23rd Portuguese Conference on Artificial Intelligence – EPIA 2024

 

EPIA 2024

23rd EPIA Conference on Artificial Intelligence

https://epia2024.pt/

 

September 3-6, 2024

Viana do Castelo, Portugal

 

 

———————————- – – – – – – – – – – – – – – –

 

Paper submission: April 30, 2024

 

———————————- – – – – – – – – – – – – – – –

 

Ambient Intelligence (AmI) is a paradigm emerging from Artificial

Intelligence (AI), where computers are used as proactive tools assisting

people with their day-to-day activities, making everyone’s life more

comfortable.

 

Affect and social behaviour plays an important role in the development

of Ambient Intelligent Environments. Consideration of aspects like

emotions, mood, personality traits, and attitudes in human-computer,

human-robot, and human-environment interaction, especially insofar as

they provide better or more “natural” support for humans. These

environments should be aware of the needs of people, customizing

requirements and forecasting behaviours.

 

AmI environments may be highly diverse, such as homes, offices, meeting

rooms, schools, hospitals, control centers, transport facilities,

tourist attractions, stores, sport installations, music devices, etc.

 

In the Thematic track on AmIA Environments we will create a

multi-disciplinary discussion forum that will bring together researchers

from the different fields addressed discussing issues in Artificial

Intelligence topics included in the Ambient Intelligence and affective

environments. Researchers are welcome to present both theoretical and

practical works as well as the lessons learned with their application in

the varied range of domains. Emphasis will be placed on the presentation

of concrete systems, discussion of implementation and development

challenges and sharing of conclusions achieved and relevant results.

 

———————————- – – – – – – – – – – – – – – –

CONTRIBUTIONS

———————————- – – – – – – – – – – – – – – –

 

In order to fulfill these objectives, submissions of substantial,

original and previously unpublished work are invited in all areas of

Ambient Intelligence and Affective environments. The topics of interest

include, but are not limited to:

– Applications

– Ambient Assisted Living

– Ubiquitous Computing

– Artificial Intelligence for AmI

– Intelligent Environments

– Pervasive Computing

– Context Aware Computing

– Agent & Multiagent Systems for AmI

– Mobile Computing

– Sentient Computing

– e-Health

– Mobile Health (mHealth)

– Context Modelling

– AmI for e-Learning

– On-line Dispute Resolution

– Memory Assistant

– Computational models of emotions

– Group Emotion

– Affect and learning

– Artificial characters

– Affect and emotion recognition

– Sensor-based Applications

 

———————————- – – – – – – – – – – – – – – –

SUBMISSION INSTRUCTIONS and PAPERS FORMAT

———————————- – – – – – – – – – – – – – – –

 

Submissions must be full technical papers on substantial, original, and

previously unpublished research. Papers can have a maximum length of 12

pages. All papers should be prepared according to the formatting

instructions of Springer LNCS series (Springer Lecture Notes in Computer

Science). Authors should omit their names from the submitted papers, and

should take reasonable care to avoid indirectly disclosing their identity.

 

All papers should be submitted in PDF format through the EPIA’2024

submission Website

(https://www.easychair.org/conferences/?conf=epia2024) selecting the

track AmIA – Ambient Intelligence and Affective Environments.

 

———————————- – – – – – – – – – – – – – – –

IMPORTANT DATES

———————————- – – – – – – – – – – – – – – –

 

Paper submission: April 30, 2024

Notification of paper acceptance: June 15, 2024

Camera-ready papers deadline: July 15, 2024

Conference dates: September 3-6, 2024

 

———————————- – – – – – – – – – – – – – – –

PUBLICATION

———————————- – – – – – – – – – – – – – – –

 

Accepted papers will be included in the conference proceedings (Springer

LNAI – Lecture Notes in Artificial Intelligence), provided that at least

one author is registered in EPIA 2024 by the early registration deadline.

Proceedings will be submitted for indexation by ISI Thomson, SCOPUS,

DBLP, EI-Compendex among several other scientific databases.

 

———————————- – – – – – – – – – – – – – – –

ORGANIZING COMMITTEE

———————————- – – – – – – – – – – – – – – –

 

Luís Conceição, Institute of Engineering – Polytechnic of Porto, Portugal (msc@isep.ipp.pt)

Paulo Novais, University of Minho, Portugal (pjon@di.uminho.pt)

Goreti Marreiros, Institute of Engineering – Polytechnic of Porto, Portugal (mgt@isep.ipp.pt)

Sara Rodriguez, University of Salamanca, Spain (srg@usal.es)

João Carneiro, Devoteam, Portugal (joao.carneiro@devoteam.com)

Peter Mikulecky, University of Hradec Kralove (peter.mikulecky@uhk.cz)

Best Regards,
Luís Conceição
Guest Assistant Professor
Department of Informatics Engineering
Research Fellow
GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development

ISEP | Instituto Superior de Engenharia do Porto
Rua Dr. António Bernardino de Almeida, 431
4249-015 Porto – PORTUGAL
tel. +351 228 340 500 | fax +351 228 321 159
mail@isep.ipp.pt | www.isep.ipp.pt

[rede.APPIA] DaSSWeb – Data Science and Statistics Webinar – 16 April – Matthias Templ – Imputation of missing values revisited

DaSSWeb – Data Science and Statistics Webinar

 

 

Tuesday, 16 April, 11:30 (WEST)

 

Speaker

Matthias Templ

FHNW University of Applied Sciences and Arts Northwestern Switzerland
School of Business 
Institute for Competitiveness and Communication

 

 

Title

Imputation of missing values revisited

 

 

Zoom link

https://videoconf-colibri.zoom.us/j/95200534911

 

Abstract

This presentation explores imputation techniques for missing values.

It discusses the concepts of common single and multiple imputation methods including modern methods of robust imputation,

imputation based on deep learning and imputation for complex data and it critically review some of the paradigms in imputation literature.

Additionally, the problems of outliers are discussed.

 

More information at

[rede.APPIA] [CfP] Extended Deadline – 3rd Workshop on Enhancing Generative Machine Learning with Evolutionary Computation at GECCO24

Dear Colleague(s),

Thus, below you will find the extended deadline call for papers for EGML-EC 2024 – The Third workshop on Enhancing Generative Machine Learning with Evolutionary Computation. 

Extended deadline: 12 April.

https://sites.google.com/view/egml-ec2024

Feel free to distribute, and thank you for your time.

Best regards,

The Workshop Chairs

Jamal Toutouh

Una-May O’Reilly

João Correia

Penousal Machado

Erik Hemberg

———————————————————————-

CALL FOR PAPERS

EGML-EC@GECCO-2024

3rd Workshop on Enhancing Generative Machine Learning with Evolutionary Computation

https://sites.google.com/view/egml-ec2024

Genetic and Evolutionary Computation Conference (GECCO'24)

Melbourne, Australia, July 14 to 18, 2024

.Overview and Scope

Generative Machine Learning has become a key field in machine learning and deep learning.  In recent years, this field of research has proposed many deep generative models (DGMs) that range from a broad family of methods such as large language models (LLMs), generative adversarial networks (GANs), variational autoencoders (VAEs), Transformers, autoregressive (AR) models and stable diffusion models (SD).  Although these methods have achieved state-of-the-art results in the generation of synthetic data of different types, such as images, speech, text, molecules, video, etc., Deep generative models are still difficult to train, optimize, and fine tune. 

There are still open problems, such as the vanishing gradient and mode collapse in DGMs, which limit their performance. Although there are strategies to minimize the effect of those problems, they remain fundamentally unsolved. In recent years, evolutionary computation (EC) and related bio-inspired techniques (e.g. particle swarm optimization) and in the form of Evolutionary Machine Learning approaches have been successfully applied to mitigate the problems that arise when training DGMs, leveraging the quality of the results to impressive levels. Among other approaches, these new solutions include LLM, GAN, VAE, AR, and SD training methods or fine tuning optimization based on evolutionary and coevolutionary algorithms, the combination of deep neuroevolution with training approaches, and the evolutionary exploration of latent space. 

The workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML-EC) aims to act as a medium for debate, exchange of knowledge and experience, and encourage collaboration for researchers focused on DGMs and the EC community. Bringing these two communities together will be essential for making significant advances in this research area. Thus, this workshop provides a critical forum for disseminating the experience on the topic of enhancing generative modeling with EC, presenting new and ongoing research in the field, and to attract new interest from our community.

.Topics of Interest

-Particular topics of interest are (not exclusively):

-Evolutionary prompt optimization for large language models

-Evolutionary operators based on large language models

-Evolutionary and co-evolutionary algorithms to train deep generative models

-EC-based optimization of hyper-parameters for deep generative models

-Neuroevolution applied to train deep generative architectures 

-Dynamic EC-based evolution of deep generative models training parameters

-Evolutionary latent space exploration (e.g. LVEs)

-Real-world applications of EC-based deep generative models solutions 

-Multi-criteria adversarial training of deep generative models

-Evolutionary generative adversarial learning models

-Software libraries and frameworks for deep generative models applying EC

  

All accepted papers of this workshop will be included in the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'24) Companion Volume.

 

.Important Dates

Submission opening: February 12, 2024

Submission deadline: April 12, 2024

Acceptance notification: May 3, 2024

Camera-ready and registration: May 10, 2024

Workshop date: TBC depending on GECCO program schedule (July 14 or 18, 2024)

 

.Instructions for Authors

We invite submissions of two types of paper:

·     Regular papers (limit 8 pages)

·     Short papers (limit 4 pages)

Papers should present original work that meets the high-quality standards of GECCO. Each paper will be rigorously evaluated in a review process. Accepted papers appear in the ACM digital library as part of the Companion Proceedings of GECCO. Each paper accepted needs to have at least one author registered by the author registration deadline. Papers must be submitted via the online submission system https://ssl.linklings.net/conferences/gecco/. Please refer to https://gecco-2024.sigevo.org/Paper-Submission-Instructions for more detailed instructions. 

As a published ACM author, you and your co-authors are subject to all ACM Publications Policies (https://www.acm.org/publications/policies/toc), including ACM's new Publications Policy on Research Involving Human Participants and Subjects (https://www.acm.org/publications/policies/research-involving-human-participants-and-subjects).

 

Workshop Chairs

 

·         Jamal Toutouh, Univ. of Málaga (ES) – MIT (USA), jamal@lcc.uma.es

·         Una-May O’Reilly, MIT (USA), unamay@csail.mit.edu

·         João Correia, University of Coimbra (PT), jncor@dei.uc.pt

·         Penousal Machado, University of Coimbra (PT), machado@dei.uc.pt

·         Erik Hemberg, MIT (USA), hembergerik@csail.mit.edu

 

More information at:

https://sites.google.com/view/egml-ec2024

Dear Colleague(s),

Thus, below you will find the extended deadline call for papers for EGML-EC 2024 – The Third workshop on Enhancing Generative Machine Learning with Evolutionary Computation. 

Extended deadline: 12 April.

https://sites.google.com/view/egml-ec2024

Feel free to distribute, and thank you for your time.

Best regards,

The Workshop Chairs

Jamal Toutouh

Una-May O’Reilly

João Correia

Penousal Machado

Erik Hemberg

———————————————————————-

CALL FOR PAPERS

EGML-EC@GECCO-2024

3rd Workshop on Enhancing Generative Machine Learning with Evolutionary Computation

https://sites.google.com/view/egml-ec2024

Genetic and Evolutionary Computation Conference (GECCO'24)

Melbourne, Australia, July 14 to 18, 2024

.Overview and Scope

Generative Machine Learning has become a key field in machine learning and deep learning.  In recent years, this field of research has proposed many deep generative models (DGMs) that range from a broad family of methods such as large language models (LLMs), generative adversarial networks (GANs), variational autoencoders (VAEs), Transformers, autoregressive (AR) models and stable diffusion models (SD).  Although these methods have achieved state-of-the-art results in the generation of synthetic data of different types, such as images, speech, text, molecules, video, etc., Deep generative models are still difficult to train, optimize, and fine tune. 

There are still open problems, such as the vanishing gradient and mode collapse in DGMs, which limit their performance. Although there are strategies to minimize the effect of those problems, they remain fundamentally unsolved. In recent years, evolutionary computation (EC) and related bio-inspired techniques (e.g. particle swarm optimization) and in the form of Evolutionary Machine Learning approaches have been successfully applied to mitigate the problems that arise when training DGMs, leveraging the quality of the results to impressive levels. Among other approaches, these new solutions include LLM, GAN, VAE, AR, and SD training methods or fine tuning optimization based on evolutionary and coevolutionary algorithms, the combination of deep neuroevolution with training approaches, and the evolutionary exploration of latent space. 

The workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML-EC) aims to act as a medium for debate, exchange of knowledge and experience, and encourage collaboration for researchers focused on DGMs and the EC community. Bringing these two communities together will be essential for making significant advances in this research area. Thus, this workshop provides a critical forum for disseminating the experience on the topic of enhancing generative modeling with EC, presenting new and ongoing research in the field, and to attract new interest from our community.

.Topics of Interest

-Particular topics of interest are (not exclusively):

-Evolutionary prompt optimization for large language models

-Evolutionary operators based on large language models

-Evolutionary and co-evolutionary algorithms to train deep generative models

-EC-based optimization of hyper-parameters for deep generative models

-Neuroevolution applied to train deep generative architectures 

-Dynamic EC-based evolution of deep generative models training parameters

-Evolutionary latent space exploration (e.g. LVEs)

-Real-world applications of EC-based deep generative models solutions 

-Multi-criteria adversarial training of deep generative models

-Evolutionary generative adversarial learning models

-Software libraries and frameworks for deep generative models applying EC

  

All accepted papers of this workshop will be included in the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'24) Companion Volume.

 

.Important Dates

Submission opening: February 12, 2024

Submission deadline: April 12, 2024

Acceptance notification: May 3, 2024

Camera-ready and registration: May 10, 2024

Workshop date: TBC depending on GECCO program schedule (July 14 or 18, 2024)

 

.Instructions for Authors

We invite submissions of two types of paper:

·     Regular papers (limit 8 pages)

·     Short papers (limit 4 pages)

Papers should present original work that meets the high-quality standards of GECCO. Each paper will be rigorously evaluated in a review process. Accepted papers appear in the ACM digital library as part of the Companion Proceedings of GECCO. Each paper accepted needs to have at least one author registered by the author registration deadline. Papers must be submitted via the online submission system https://ssl.linklings.net/conferences/gecco/. Please refer to https://gecco-2024.sigevo.org/Paper-Submission-Instructions for more detailed instructions. 

As a published ACM author, you and your co-authors are subject to all ACM Publications Policies (https://www.acm.org/publications/policies/toc), including ACM's new Publications Policy on Research Involving Human Participants and Subjects (https://www.acm.org/publications/policies/research-involving-human-participants-and-subjects).

 

Workshop Chairs

 

·         Jamal Toutouh, Univ. of Málaga (ES) – MIT (USA), jamal@lcc.uma.es

·         Una-May O’Reilly, MIT (USA), unamay@csail.mit.edu

·         João Correia, University of Coimbra (PT), jncor@dei.uc.pt

·         Penousal Machado, University of Coimbra (PT), machado@dei.uc.pt

·         Erik Hemberg, MIT (USA), hembergerik@csail.mit.edu

 

More information at:

https://sites.google.com/view/egml-ec2024

[rede.APPIA] AmIA@EPIA2024 – CALL FOR PAPERS

[Apologies if you receive multiple copies of this CFP]

 

———————————- – – – – – – – – – – – – – – –

AmIA_Environments@EPIA2024 :: CALL FOR PAPERS

———————————- – – – – – – – – – – – – – – –

 

Special Track on Ambient Intelligence and Affective Environments (AmIA

Environments 2024)

23rd Portuguese Conference on Artificial Intelligence – EPIA 2024

 

EPIA 2024

23rd EPIA Conference on Artificial Intelligence

https://epia2024.pt/

 

September 3-6, 2024

Viana do Castelo, Portugal

 

 

———————————- – – – – – – – – – – – – – – –

 

Paper submission: April 30, 2024

 

———————————- – – – – – – – – – – – – – – –

 

Ambient Intelligence (AmI) is a paradigm emerging from Artificial

Intelligence (AI), where computers are used as proactive tools assisting

people with their day-to-day activities, making everyone’s life more

comfortable.

 

Affect and social behaviour plays an important role in the development

of Ambient Intelligent Environments. Consideration of aspects like

emotions, mood, personality traits, and attitudes in human-computer,

human-robot, and human-environment interaction, especially insofar as

they provide better or more “natural” support for humans. These

environments should be aware of the needs of people, customizing

requirements and forecasting behaviours.

 

AmI environments may be highly diverse, such as homes, offices, meeting

rooms, schools, hospitals, control centers, transport facilities,

tourist attractions, stores, sport installations, music devices, etc.

 

In the Thematic track on AmIA Environments we will create a

multi-disciplinary discussion forum that will bring together researchers

from the different fields addressed discussing issues in Artificial

Intelligence topics included in the Ambient Intelligence and affective

environments. Researchers are welcome to present both theoretical and

practical works as well as the lessons learned with their application in

the varied range of domains. Emphasis will be placed on the presentation

of concrete systems, discussion of implementation and development

challenges and sharing of conclusions achieved and relevant results.

 

———————————- – – – – – – – – – – – – – – –

CONTRIBUTIONS

———————————- – – – – – – – – – – – – – – –

 

In order to fulfill these objectives, submissions of substantial,

original and previously unpublished work are invited in all areas of

Ambient Intelligence and Affective environments. The topics of interest

include, but are not limited to:

– Applications

– Ambient Assisted Living

– Ubiquitous Computing

– Artificial Intelligence for AmI

– Intelligent Environments

– Pervasive Computing

– Context Aware Computing

– Agent & Multiagent Systems for AmI

– Mobile Computing

– Sentient Computing

– e-Health

– Mobile Health (mHealth)

– Context Modelling

– AmI for e-Learning

– On-line Dispute Resolution

– Memory Assistant

– Computational models of emotions

– Group Emotion

– Affect and learning

– Artificial characters

– Affect and emotion recognition

– Sensor-based Applications

 

———————————- – – – – – – – – – – – – – – –

SUBMISSION INSTRUCTIONS and PAPERS FORMAT

———————————- – – – – – – – – – – – – – – –

 

Submissions must be full technical papers on substantial, original, and

previously unpublished research. Papers can have a maximum length of 12

pages. All papers should be prepared according to the formatting

instructions of Springer LNCS series (Springer Lecture Notes in Computer

Science). Authors should omit their names from the submitted papers, and

should take reasonable care to avoid indirectly disclosing their identity.

 

All papers should be submitted in PDF format through the EPIA’2024

submission Website

(https://www.easychair.org/conferences/?conf=epia2024) selecting the

track AmIA – Ambient Intelligence and Affective Environments.

 

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IMPORTANT DATES

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Paper submission: April 30, 2024

Notification of paper acceptance: June 15, 2024

Camera-ready papers deadline: July 15, 2024

Conference dates: September 3-6, 2024

 

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PUBLICATION

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Accepted papers will be included in the conference proceedings (Springer

LNAI – Lecture Notes in Artificial Intelligence), provided that at least

one author is registered in EPIA 2024 by the early registration deadline.

Proceedings will be submitted for indexation by ISI Thomson, SCOPUS,

DBLP, EI-Compendex among several other scientific databases.

 

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ORGANIZING COMMITTEE

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Luís Conceição, Institute of Engineering – Polytechnic of Porto, Portugal (msc@isep.ipp.pt)

Paulo Novais, University of Minho, Portugal (pjon@di.uminho.pt)

Goreti Marreiros, Institute of Engineering – Polytechnic of Porto, Portugal (mgt@isep.ipp.pt)

Sara Rodriguez, University of Salamanca, Spain (srg@usal.es)

João Carneiro, Devoteam, Portugal (joao.carneiro@devoteam.com)

Peter Mikulecky, University of Hradec Kralove (peter.mikulecky@uhk.cz)

Best Regards,
Luís Conceição
Guest Assistant Professor
Department of Informatics Engineering
Research Fellow
GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development

ISEP | Instituto Superior de Engenharia do Porto
Rua Dr. António Bernardino de Almeida, 431
4249-015 Porto – PORTUGAL
tel. +351 228 340 500 | fax +351 228 321 159
mail@isep.ipp.pt | www.isep.ipp.pt

[rede.APPIA] [CFP] AIPES – Artificial Intelligence in Power and Energy Systems Thematic Track -EPIA 2024

*** we apologize for multiple copies of this mail ***

Dear colleagues,

 

We would like to invite you to submit a paper to the Thematic Track on Artificial Intelligence in Power and Energy Systems (AIPES) of EPIA 2024, to be held Viana do Castelo, Portugal between September 3rd-6th, 2024.

 

Important Deadlines:

Deadline for full paper submission: 30th April, 2024

Notification of acceptance: 15th June, 2024

Camera-Ready papers: 15th July, 2024

Conference: 3-6 September 2024

 

Submissions:

AIPES welcomes full length papers (of up to 12 pages) and also short papers (up to 6 pages), demonstrating practical applications. All papers should be submitted in PDF format through the EPIA 2024 EasyChair submission page.

Authors should consult Springer’s authors’ guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made.

Accepted papers will be included in the conference proceedings (a volume of Springer’s LNAI-Lecture Notes in Artificial Intelligence), provided that at least one author is registered in EPIA 2024 by the early registration deadline. EPIA 2024 proceedings are indexed in Thomson Reuters ISI Web of Science, Scopus, DBLP and Google Scholar.

Each accepted paper must be presented by one of the authors in a track session.

 

Scope:

The Thematic Track on Artificial Intelligence in Power and Energy Systems aims at providing an advanced discussion forum on recent and innovative work on the application of artificial intelligence approaches in the field of power and energy systems, including agent-based systems, data-mining, machine learning methodologies, forecasting and optimization.

 

Submission Topics:

·       Agent-based Smart Grid Simulation

·       Big Data Applications for Energy Systems

·       Coalitions and Aggregations of Smart Grid and Market Players

·       Consumer Profiling

·       Context Aware Systems

·       Data-Mining Approaches in Smart Grids

·       Decision Support Approaches for Smart Grids

·       Demand Response Aggregation

·       Demand Response Integration in the Market

·       Demand Response Remuneration Methods

·       Electric vehicles

·       Electricity Market Modelling and Simulation

·       Electricity Market Negotiation Strategies

·       Energy Resource Management in Buildings

·       Information technology applications

·       Innovative Demand Response Models and Programs

·       Innovative Energy Tariffs

·       Integration of Electric Vehicles in the Power System

·       Intelligent Approaches for Microgrid Management

·       Intelligent Home Management Systems

·       Intelligent methods for Demand Management

·       Intelligent Resources Scheduling

·       Intelligent Supervisory Control Systems

·       Knowledge-based approaches for Power and Energy Systems

·       Load Forecast

·       Market Models for Variable Renewable Energy

·       Multi-Agent Applications for Smart Grids

·       Multi-Agent Systems in Power and Energy Systems

·       Other Artificial Intelligence-based Methods for Power and Energy Systems

·       Phasor Measurement Units Applications

·       Real-time simulation

·       Reliability, Protection and Network Security Methods

·       Renewable Energy Forecast using Computational Intelligence

·       Semantic communication and data

·       Smart Sensors and Advanced Metering Infrastructure

Thematic Track Organizers:

·       Zita Vale – Polytechnic of Porto (Portugal)

·       Tiago Pinto – Universidade de Trás-os-Montes e Alto Douro (Portugal)

·       Pedro Faria – Polytechnic of Porto (Portugal)

·       Bo Norregaard Jorgensen – University of Southern Denmark (Denmark)