DaSSWeb – Data Science and Statistics Webinar
Tuesday 9 November, 14:30
Speaker: Victor Lobo NOVA-Information Management School & Escola Naval
Title: Cartograms, and how to obtain them using Self-Organizing Maps and controling their Magnification Effect.
Zoom link: videoconf-colibri.zoom.us/j/84889120945
<videoconf-colibri.zoom.us/j/87373848710> Abstract: Cartograms are geographic representations where the area of each region is distorted to as to be proportional to a given variable of interest. The most common ones are population cartograms, where regions with large populations are enlarged, squeezing the regions with less population. To obtain a good cartogram, not only each regions should occupy an area proportional to the variable of interest, but at the same time the necessary distortions should allow the users to still recognize the map. Several examples of cartograms shall be given, together with the algorithms that produce them. Finally, a method based on Self-Organizing Maps, named CartoSOM will be presented, together with a recent improvement based on a better estimation of the “Magnification Effect” of the SOM.
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DaSSWeb-Victor-Lobo.pdf

Caros colegas:
A UM-Cidades é uma plataforma da Universidade do Minho que pretende valorizar, transferir e aplicar conhecimento nos municípios e regiões, numa base de intercâmbio e valorização recíproca.
No âmbito da sua atividade, em 2021 está programado um ciclo de conferências debatendo temas da atualidade em domínios centrais das preocupações das cidades, em particular, e da sociedade, em geral.
*A segunda conferência, a ter lugar em 26 de novembro deste ano de 2021, será dedicada ao tema “Desafios Tecnológicos das Cidades”, na qual haverá a intervenção de oradores especialistas nesta temática: Miguel de Castro Neto (NOVA IMS) Zita Vale (ISEP/LASI) Rui José (UMinho) Rui Costa (Ubiwhere) António Cunha (CCDR-N)
A conferência está aberta à participação de todos, com confirmação de presença até ao _próximo dia 15_.
Abraço, Paulo Novais — Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para infos [at] appia [ponto] pt

00.-UMCidades-Desafios-Tecnologicos-26nov2021.pdf

CALL FOR PAPERS
IEEE Transactions on Neural Networks and Learning Systems
Special Issue on STREAM LEARNING
Deadline: 15 December 2021
Introduction
In recent years, machine learning from streaming data (called Stream Learning) has enjoyed tremendous growth and exhibited a wealth of development at both the conceptual and application levels. Stream Learning is highly visible in both the machine learning and data science fields and become a new hot direction in recent years. Research developments in Stream Learning include learning under concept drift detection (whether a drift occurs), understanding (where, when, and how a drift occurs), and adaptation (to actively or passively update models). Recently we have seen several new successful developments in Stream Learning such as massive stream learning algorithms; incremental and online learning for streaming data; and streaming data-based decision-making methods. These developments have demonstrated how Stream Learning technologies can contribute to the implementation of machine learning capability in dynamic systems. We have also witnessed compelling evidence of successful investigations on the use of Stream Learning to support business real-time prediction and decision making.
In light of these observations, it is instructive, vital, and timely to offer a unified view of the current trends and form a broad forum for the fundamental and applied research as well as the practical development of Stream Learning for improving machine learning, data science and practical decision support systems of business. This special issue aims at reporting the progress in fundamental principles; practical methodologies; efficient implementations; and applications of Stream Learning methods and related applications. The special issue also welcomes contributions in relation to data streams, incremental learning and reinforcement learning in data streaming situations.
Scope of the Special Issue
We invite submissions on all topics of Stream Learning, including but not limited to:
• Data stream prediction
• Concept drift detection, understanding and adaptation
• Recurrent concepts
• Experimental setup and Evaluation methods for stream learning
• Reinforcement learning on streaming data
• Streaming data-based real-time decision making
• Ensemble methods for stream learning
• Auto machine learning for stream algorithms
• Neural networks for big data streams
• Transfer learning for streaming data
• Real-world applications of stream learning
• Active learning for streaming data
• Online learning for streaming data
• Imbalance learning for streaming data
• Lifelong learning for streaming data
• Incremental learning for streaming data
• Continuous learning for streaming data
• Clustering for streaming data
• Audio/speech/music streams processing
• Stream learning benchmark datasets
• Multi-drift and multi-stream learning
• Stream processing platforms
Timeline
• Submission deadline: Dec 15, 2021
• Notification of first review: Feb 1, 2022
• Submission of revised manuscript: May 1, 2022
• Notification of final decision: July 1, 2022
Guest Editors
• Jie Lu (University of Technology Sydney, Australia)
• Joao Gama (University of Porto, Portugal)
• Xin Yao (Southern University of Science and Technology, China)
• Leandro Minku (University of Birmingham, UK)
Submission Instructions
– Read the Information for Authors at cis.ieee.org/tnnls
– Submit your manuscript at the TNNLS webpage (mc.manuscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript is submitted to this special issue. Early submissions are welcome.
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
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*ACM Symposium on Applied Computing *
The 37th ACM/SIGAPP Symposium on Applied Computing in Brno, Czech Republic
April 25 – April 29, 2022
www.sigapp.org/sac/sac2022/
*Data Streams Track *
abifet.github.io/SAC2022/

* IMPORTANT DATES *
1. Submission deadline (Extended): October 31, 2021
2. Notification deadline: December 10, 2021
3. Camera-ready deadline: December 21, 2021

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
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