[rede.APPIA] – Page 34

[rede.APPIA] CFP: Thematic Track on MultiAgent Systems: Theory and Applications (MASTA@EPIA 2021)

2021-03-062021-03-07 Ana Paula Cunha Rocha

************************************************* CALL FOR PAPERS **************************************************

MASTA 2021, 11th Thematic Track on MultiAgent Systems: Theory and Applications

@EPIA2021, 20th EPIA Conference on Artificial Intelligence

http://www.appia.pt/epia2021/multi-agent-systems-theory-and-applications-masta

September 7-9, 2021

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Important Dates

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Paper submission deadline: April 15, 2021

Paper acceptance notification: May 31, 2021

Camera-ready deadline: June 15, 2021

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Research on Multi-Agent Systems (MAS) has a vigorous, exciting tradition and has led to important theories and systems. However, new trends and concerns are still emerging and form the basis of current and future research. The 11th thematic track on “MultiAgent Systems: Theory and Applications”, to take place at EPIA 2020, and will provide a discussion forum on the most recent and innovative work in all areas of MAS.

The unifying focus of the thematic track will be on methodological aspects. Both theoretical and practical research should be situated in the context of existing or new methodologies. This will not preclude any specific topic, but preference will be given to research work that establishes some connection with the methodological aspects or to successful applications built upon some methodology.

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List of Topics

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Topics of interest include, but are not limited to:

Agent theories, architectures and models

Agent-based systems Interoperability

Agreement technologies

Applications of agents and MAS (industrial and commercial)

Artificial social systems

Automated negotiation and computational argumentation

Cognitive models, including emotions and philosophies

Communication: languages, semantics, protocols, and conversations

Cooperation, coordination and teamwork in MAS

Ethical and legal issues raised by autonomous agents and MAS

Formal methods for modelling agents and agent-based systems

Human-agent interaction

Learning in MAS

Multiagent evolution, emergent behavior and adaptation

Multiagent modelling and simulation

Scalability and performance of MAS

Societal and ethical issues: organizations, institutions, norms, socio-technical systems

Trust, reputation, privacy and security

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Submission and Reviewing

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All papers should be submitted in PDF format through the EPIA 2021 EasyChair submission page. Prospective authors should select the thematic track to which their paper is to be submitted. The papers should be prepared according to the Springer LNCS format, with a maximum of 12 pages. Submitted papers will be subject to a double-blind review process and will be peer-reviewed by at least three members of the respective track Program Committee. It is the responsibility of the authors to remove names and affiliations from the submitted papers, and to take reasonable care to assure anonymity during the review process.

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.

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Proceedings and Presentations

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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 2021 by the early registration deadline. EPIA 2021 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 the track session.

********************************* MASTA 2021 Committees *********************************

Organizing committee

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Ana Paula Rocha (arocha@fe.up.pt), FEUP / LIACC, Portugal

João Balsa (jbalsa@ciencias.ulisboa.pt), DI / FCUL, Portugal

Graçaliz Pereira Dimuro (gracalizdimuro@furg.br), Universidade Federal do Rio Grande, Brazil

Alberto Fernandez-Gil (alberto.fernandez@urjc.es), Universidad Rey Juan Carlos, Spain

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Steering Committee

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Eugénio Oliveira, FEUP / LIACC, Portugal

Hélder Coelho, FCUL, Portugal

João Balsa, FCUL, Portugal

Luís Paulo Reis, FEUP / LIACC, Portugal

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Program Committee

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Adriana Giret, Universitat Politècnica de València, Spain

Alberto Sardinha, University of Lisbon, Portugal

Alejandro Guerra-Hernández, Universidad Veracruzana, Mexico

Andrea Omicini, Alma Mater Studiorum–Università di Bologna, Italy

Antonio J. M. Castro, LIACC, University of Porto, Portugal

Carlos Carrascosa, GTI-IA DSIC Universidad Politecnica de Valencia, Spain

Carlos Martinho, University of Lisbon, Portugal

Daniel Castro Silva, FEUP-DEI / LIACC, Portugal

Dave De Jonge, IIIA-CSIC, Spain

Diana Adamatti, Universidade Federal do Rio Grande (FURG), Brazil

Francisco Grimaldo, Departament d'Informàtica – Universitat de València, Spain

Henrique Lopes Cardoso, University of Porto, Portugal

Javier Carbo, Univ. Carlos III of Madrid, Spain

Joao Leite, Universidade NOVA de Lisboa, Portugal

John-Jules Meyer, Utrecht University, The Netherlands

Jordi Sabater Mir, IIIA-CSIC, Spain

Jorge Gomez-Sanz, Universidad Complutense de Madrid, Spain

Juan Carlos Burguillo, University of Vigo, Spain

Juan Corchado, University of Salamanca, Spain

Lars Braubach, University of Hamburg, Germany

Luís Correia, Universidade de Lisboa, Portugal

Luis Macedo, University of Coimbra, Portugal

Luís Nunes, Iscte – Instituto Universitário de Lisboa, Portugal

Marin Lujak, IMT Lille Douai, France

Michael Ignaz Schumacher, University of Applied Sciences Western Switzerland, Switzerland

Paulo Leitao, Polythecnic Institute of Braganca, Portugal

Paulo Novais, University of Minho, Portugal

Rafael H. Bordini, Pontífica Universidade Católica do Rio Grande do Sul, Brazil

Ramon Hermoso, University of Zaragoza, Spain

Reyhan Aydogan, Delft University of Technology, Turkey

Rosa Vicari, Universidade Federal do Rio Grande do Sul, Brazil

Viviane Silva, IBM Research Brazil, Brazil

[rede.APPIA] CFPs: IEEE/ACM/ASA DSAA’2021

2021-03-012021-03-02 Carlos Manuel Abreu Gomes Ferreira

IEEE/ACM/ASA DSAA’2021
Porto, Portugal, 06-09 October 2021
Website: dsaa2021.dcc.fc.up.pt/
Submission website: cmt3.research.microsoft.com/DSAA2021
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Important Dates Research, Application and Tutorials: submission deadline: 23 May 2021 Research and Application notification: 25 Jul. 2021 Research and Application camera ready due: 8 Aug. 2021
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Highlights of DSAA’2021 *A strong interdisciplinary research program spanning the areas of data science, including statistics, machine learning, computing, and analytics. *Strong Journal, Research, and Applications tracks with reproducible and open results. *Industry track session with lightning results highlighting research advances and industry’s best practices. *Special sessions on the foundations and emerging areas for data science. *Tutorials on hot topics, and hands-on tutorials *Special panel on the trends and controversies of data science and analytics. *Strong cross-domain interactions among researchers and industry and government policymakers and practitioners. *Industry and research exhibits. *Financially sponsored by IEEE CIS, proceedings by IEEE Xplore and EI indexed. *Technically supported by ACM SIGKDD and ASA.
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Precaution of COVID-19 Due to ongoing uncertainty about future travel due to COVID-19, DSAA’2021 commits to allowing video presentations of accepted papers by authors who are unable to attend due to COVID-19 travel restrictions.
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About DSAA’2021 The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA’2021) will provide a premier forum that brings together researchers, industry and government practitioners, as well as developers and users in statistics, computing science, and intelligence science for the exchange of the latest theoretical developments in Data Science and Analytics and the best practice for a wide range of applications. The conference invites submission of papers describing innovative research on all aspects of data science and advanced analytics as well as application-oriented papers that make significant, original, and reproducible contributions to improving the practice of data science and analytics in real-world scenarios. DSAA’2021 is a multi-track conference consisting of a Journal Track, Research Track, Application Track, and Industry Track. DSAA’2021 will also feature a peer-reviewed Poster session whose purpose is to showcase recent and early-stage research developments on topics that are of interest to students and industry/government practitioners in data science and analytics.
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Research Track The Research Track solicits the latest, original, and significant contributions related to foundations and theoretical developments of Data Science and Advanced Analytics. Topics of interest include but are not limited to: *Data Science and Advanced Analytics Methods *Mathematics, Statistics for data science and analytics *Understanding data characteristics and complexities *Deep Learning *Data quality and misinformation *Bayesian learning, optimization, inference, and regularization *Infrastructures, and systems *Evaluation, explanation, visualization, and presentation *Fairness, accountability, and trustworthy Machine Learning *Survey and review The paper submission deadline for the Research Track is May 23, 2021 (no separate abstract deadline) and the acceptance notifications will be sent out by July 25, 2021.
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Applications Track The Application track solicits original, impactful, and actionable application results of Data Science and Advanced Analytics across various disciplines and domains, including business, government, healthcare and medical science, physical sciences, and social sciences. Submissions address a real problem on real-life data that is reproducible ideally through a public git repository, providing inspiring results to policymakers, end-users or practitioners or highlighting new practical challenges for researchers. Topics of interest include but are not limited to: *Domain-driven data science and analytics practice *Real-world applications and case studies *Operationalizable infrastructures, platforms, and tools *Deployment, management, and decision-making *System and software demonstrations *Social and economic impact modelling *Ethics, social issues, privacy, trust, and bias
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Industrial Track The IEEE DSAA’2021 Industry track aims to highlight the challenges of putting together production-grade software solutions to different business verticals based on data science. Our goal is to promote the debate, the exchange of ideas and/or collaborations on how to add business value to society through data science.
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Submission Submissions to the main conference including Research Track & Application Track are available from CMT: cmt3.research.microsoft.com/DSAA2021.
Carlos Ferreira
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] Fwd: Parceria | Champalimaud Research Symposium 2020

2021-03-012021-03-01 Henrique Lopes Cardoso

13.10.2021 – 15.10.2021

2021 Champalimaud Research Symposium
Dialogues on Neural and Machine Intelligence

Dear colleague,

On behalf of the organising committee we are pleased to announce the 2021 Champalimaud Research Symposium (CRS21) under the topic Dialogues on Neural and Machine Intelligence, from 13th to 15th of October 2021, which is now open for pre-registration.

CRS21 will focus on the interface of neuroscience, artificial intelligence and machine learning with the main goal of starting an interdisciplinary conversation about the deep conceptual problems that emerge when trying to understand how intelligent behaviour is generated in animals and machines.

We are delighted to announce that Professor Manuela Veloso, Professor Jim DiCarlo and Professor Anthony Zador have kindly accepted the invitation to be our keynote lecturers, heading a diverse list of 22 speakers. The Symposium will be structured as a single-track programme in which talks by a distinguished line-up of invited speakers, as well as speakers selected from submitted abstracts, are intertwined with poster sessions, where participants will be able to discuss and learn more about all the selected projects’ abstracts.

The event is being organised at the Champalimaud Centre for the Unknown, in a stunning setting on the waterfront in Lisbon, featuring a social programme which will give participants the chance to explore this beautiful and historic city. However, depending on the evolution of the pandemic, the organisation is also ready to implement a contingency plan in which the symposium will take place virtually or in a hybrid setting, promoting healthy behaviours that help to reduce spread.

If you are thinking about joining us, and in order to keep receiving news and updates about this unique event, we would like to invite you to visit our website and pre-register for the 2021 Champalimaud Research Symposium – Dialogues on Neural and Machine Intelligence.

We hope to see you in October!

With best wishes,

Joe Paton & Leopoldo Petreanu, Champalimaud Research
Jakob Macke, University of Tübingen
Chairs of the 2021 Champalimaud Research Symposium

Pre-register

INVITED SPEAKERS

[Keynote] Manuela Veloso, Carnegie Mellon University, United States of America
[Keynote] Jim Di Carlo, MIT McGovern Institute, United States of America
[Keynote] Anthony Zador, Cold Spring Harbor Laboratory, United States of America

Andreas Tolias, Baylor College of Medicine, United States of America
Blake Richards, McGill University, Canada
David Sussillo, Stanford University, United States of America
Ero Simoncelli, New York University, United States of America
Jane Wang, Deepmind, United Kingdom
Kim Stachenfeld, Deepmind, United Kingdom
Konrad Kording, University of Pennsylvania, United States of America
Matthias Bethge, University of Tübingen, Germany
Odelia Schwartz, University of Miami, United States of America
Terry Sejnowski, Salk Institute for Biological Studies, United States of America
Valerio Mante, ETH Zurich, Switzerland
Walter Senn, University of Bern, Switzerland
Yulia Sandamirskaya, University of Zurich and ETH Zurich, Switzerland
Jakob Macke, University of Tübingen, Germany
Ana Paiva, Instituto Superior Técnico, University of Lisbon, Portugal
Mihaela van der Schaar, University of Cambridge, United Kingdom
Peter Dayan, The Max Planck Institute for Biological Cybernetics and the University of Tübingen, Germany
Ida Momennejad, Microsoft Research, United States of America
Anna Kreshuk, EMBL Heidelberg, Germany

Our mailing address is:

Champalimaud Centre for the Unknown

Avenida Brasília

Champalimaud Foundation

Lisbon 1400-038

Portugal

Add us to your address book

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[rede.APPIA] Fwd: Convite

2021-02-282021-03-01 Henrique Lopes Cardoso

[rede.APPIA] DaSSWeb – Data Science and Statistics Webinar – 23 Fev – Anabela Carneiro

2021-02-182021-03-01 Maria Paula Brito Silva

DaSSWeb – Data Science and Statistics Webinar
Tuesday, 23 February, 14:30
Speaker: Anabela Carneiro (FEP, U.Porto and CEF.UP)
Title: Decomposition Methods in Economics to Assess which Covariates Matter
Zoom link: videoconf-colibri.zoom.us/j/81745596464
Abstract: It is very common in empirical research to estimate several regression models to check the robustness of the results or to evaluate how the estimate of the coefficient of interest changes as we add a set of covariates to a baseline model. For example, in explaining the sources of the gender wage gap, very often researchers estimate multiple wage equations in order to evaluate how the gender-dummy coefficient changes as individual and job characteristics are added to the model and then attribute this difference to the new set of variables included in the model. This approach is not exempt of criticism as the order in which covariates are added is not irrelevant. In this seminar, I will present a decomposition technique, proposed by Gelbach (2016), that appeals to the omitted variable bias formula to unambiguously disentangle the contribution of each covariate to the change in the estimate of the coefficient of the variable under scrutiny. This procedure was applied to matched employer-employee data in Portugal to decompose the sources of the wage losses of displaced workers (Raposo, Portugal & Carneiro, 2021).

DaSSWeb_Anabela_Carneiro.pdf