Boa tarde,
Poderiam fazer o favor de divulgar o anúncio abaixo junto dos sócios da APPIA ?
Desde já muito obrigada !
Melhores cumprimentos,
Paula Brito
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A FEP-UP inicia um Ciclo de Seminários em Estatística e Data Science, em versão Webinar.
O ciclo, designado DaSSWeb – Data Science and Statistics Webinar – terá periodicidade quinzenal, às 3as feiras, via Zoom.
O primeiro seminário será apresentado pelo João Gama, dia 2 de Junho, pelas 11:30, com título “DATA SCIENCES FOR THE XXI CENTURY” Link disponível em : DaSSWeb_2Jun2020<sigarra.up.pt/fep/pt/noticias_geral.ver_noticia?p_nr=32889>
Todos estão convidados a participar !

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Paula Brito Tel. (direct): (+351) 220426473 Faculdade de Economia Tel. (central FEP): (+351) 2205571100 Universidade do Porto Tel. (internal line): 4573 Rua Dr. Roberto Frias Fax: (+351) 225505050 4200-464 Porto e-mail: mpbrito@fep.up.pt<mailto:mpbrito@fep.up.pt> PORTUGAL www.fep.up.pt/docentes/mpbrito<www.fep.up.pt/docentes/mpbrito>

Our apologies if you receive multiple copies of this CFP
——————————————————————————————————- The 21st International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2020) 4-6 november, Guimarães, Portugal
islab.di.uminho.pt/ideal2020
Submission Deadline – 5 June 2020 ——————————————————————————————————-
—————- Call for papers —————-
The International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) is an annual international conference dedicated to emerging and challenging topics in intelligent data analytics and associated learning systems and paradigms. After the hugely successful IDEAL 2018 in Madrid and its 20th edition in Manchester, IDEAL 2020 is going to Guimarães the birthplace of Portugal. Its main themes or topics include, but not limited to:
– Big Data Analytics – Machine Learning & Deep Learning – Data Mining – Information Retrieval and Management – Bio- and Neuro-Informatics – Bio-Inspired Models – Agents and Hybrid Intelligent Systems – Real-world Applications of Intelligent Techniques
—————————- Special Sessions —————————-
We are pleased to announce several Special Sessions lined up, which you can submit papers to, in addition to the main IDEAL 2020 track:
Special Session 1: Data Generation and Data Pre-processing in Machine Learning Special Session 2: Optimization and Machine Learning for Industry 4.0 Special Session 3: Practical Applications of Deep Learning Special Session 4: New trends and challenges on Social Networks Analysis Special Session 5: Machine Learning in Automatic Control Special Session 6: Emerging Trends in Machine Learning Special Session 7: Machine Learning, Law and Legal Industry Special Session 8: Data Recovery Approach to Clustering and Interpretability Special Session 9: Automated learning for industrial applications Special Session 10: Machine learning towards smarter multimodal systems
—————————- Workshops —————————-
We are pleased to announce two workshops lined up, which you can submit papers to, in addition to the main IDEAL 2020 track:
Workshop 1: Workshop on Machine Learning in Smart Mobility Workshop 2: 3rd Workshop on Methods for Interpretation of Industrial Event Logs
—————— Paper Submission —————— Authors are invited to submit their manuscripts (in pdf format) written in English by the deadline via the Easychair online submission system (easychair.org/conferences/?conf=ideal2020). Papers should be within 8 pages but must not exceed 12 pages and must comply with the format of Springer LNCS/LNAI Proceedings (see www.springer.com/lncs ). All accepted papers will be included in the conference proceedings, to be published by Springer in the LNCS series, indexed by EI.
—————— Important dates —————— Submission Deadline 5 June 2020
Notification of Acceptance 10 July 2020
Camera-Ready Copy Due 31 July 2020
Early Registration 31 July 2020
Conference Presentation 4-6 Nov. 2020
———– Special Issues ———–
Authors of selected papers from IDEAL 2020 will be invited to submit an extended and improved version to a special issue in different journals. – Neural Computing and Applications (JCR 2018: 4.664). – Ambient Intelligence and Humanized Computing (JCR 2018: 1.91). – Expert Systems (JCR 2018: 1.505).
———– Best Paper Awards ———–
A Best Paper and a Best Paper Application Awards will be given based on several aspects such as originality, development, demonstration/presentation, and application.
—————— Organizing Committee ——————
Honorary chair – Hujun Yin, University of Manchester, UK
General chairs – Paulo Novais, Universidade do Minho, Portugal – Cesar Analide, Universidade do Minho, Portugal – David Camacho, Universidad Politécnica de Madrid, Spain
Program chairs – Cesar Analide, Universidade do Minho, Portugal – David Camacho, Universidad Politécnica de Madrid, Spain – Hujun Yin, University of Manchester, UK – Paulo Novais, Universidade do Minho, Portugal
Special Session/ Workshop chairs – Susana Nascimento, Universidade Nova de Lisboa, Portugal – Antonio J. Tallón-Ballesteros, University of Seville, Spain
—————— Contact —————— For all general information concerning IDEAL 2020, contact: ideal2020.guimaraes@gmail.com

*** Apologies for cross-posting ***
Call for Papers
2nd ECML/PKDD 2020 Workshop on
IoT Streams for Data-Driven Predictive Maintenance

ECML-PKDD 2020, September 14 –18, 2020, Ghent-Belgium
abifet.wixsite.com/iotstream2020
——————————————————- Motivation and focus
Maintenance is a critical issue in the industrial context for preventing high costs and injuries. Various industries are moving more and more toward digitalization and collecting “big data” to enable or improve the accuracy of their predictions. At the same time, the emerging technologies of Industry 4.0 empowered data production and exchange, which leads to new concepts and methodologies for the exploitation of large datasets in maintenance. The intensive research effort in data-driven Predictive Maintenance (PdM) is producing encouraging results. Therefore, the main objective of this workshop is to raise awareness of research trends and promote interdisciplinary discussion in this field.
Data-driven predictive maintenance must deal with big streaming data and handle concept drift due to both changing external conditions, but also normal wear of the equipment. It requires combining multiple data sources, and the resulting datasets are often highly imbalanced. The knowledge about the systems is detailed, but in many scenarios, there is a large diversity in both model configurations, as well as their usage, additionally complicated by low data quality and high uncertainty in the labels. In particular, many recent advancements in supervised and unsupervised machine learning, representation learning, anomaly detection, visual analytics and similar areas can be showcased in this domain. Therefore, the overlap in research between machine learning and predictive maintenance continues to increase in recent years.
This event is an opportunity to bridge researchers and engineers to discuss emerging topics and key trends. The previous edition of the workshop at ECML 2019 has been very popular, and we are planning to continue this success in 2020.
———————————————————- Aim and scope
This workshop welcomes research papers using Data Mining and Machine Learning (Artificial Intelligence in general) to address the challenges and answer questions related to the problem of predictive maintenance. For example, when to perform maintenance actions, how to estimate components current and future status, which data should be used, what decision support tools should be developed for prognostic, how to improve the estimation accuracy of remaining useful life, and similar. It solicits original work, already completed or in progress. Position papers will also be considered. The scope of the workshop covers, but is not limited to, the following:
* Predictive and Prescriptive Maintenance
* Fault Detection and Diagnosis (FDD)
* Fault Isolation and Identification
* Anomaly Detection (AD)
* Estimation of Remaining Useful Life of Components, Machines, etc.
* Forecasting of Product and Process Quality
* Early Failure and Anomaly Detection and Analysis
* Automatic Process Optimization
* Self-healing and Self-correction
* Incremental and evolving (data-driven and hybrid) models for FDD and AD
* Self-adaptive time-series based models for prognostics and forecasting
* Adaptive signal processing techniques for FDD and forecasting
* Concept Drift issues in dynamic predictive maintenance systems
* Active learning and Design of Experiment (DoE) in dynamic predictive maintenance
* Industrial process monitoring and modelling
* Maintenance scheduling and on-demand maintenance planning
* Visual analytics and interactive Machine Learning
* Analysis of usage patterns
* Explainable AI for predictive maintenance
* …

It covers real-world applications such as:

* Manufacturing systems
* Transport systems (including roads, railways, aerospace and more)
* Energy and power systems and networks (wind turbines, solar plants and more)
* Smart management of energy demand/response
* Production Processes and Factories of the Future (FoF)
* Power generation and distribution systems
* Intrusion detection and cybersecurity
* Internet of Things
* Smart cities
* …
———————————————————- Submission and Review process
Regular and short papers presenting work completed or in progress are invited. Regular papers should not exceed 12 pages, while short papers are a maximum of 6 pages. Papers must be written in English and submitted in PDF format online via the Easychair submission interface easychair.org/conferences/?conf=iotstream2020.
Each submission will be evaluated on the basis of relevance, the significance of contribution, quality of presentation and technical quality by at least two members of the program committee. All accepted papers will be included in the workshop proceedings and will be publically available on the conference web site. At least one author of each accepted paper is required to attend the workshop to present.
———————————————————- Important dates

Workshop paper submission deadline: 11th of June 2020
Workshop paper acceptance notification: 20th of July 2020
Workshop paper camera-ready deadline: 27th of July 2020
Workshop Day: 14th of September 2020 (alternatively, 18th of September)

The exact schedule, including time slots, will be published on the official ECML website
———————————————————- Program Committee members (to be confirmed)
* Carlos Ferreira, LIAAD INESC Porto LA, ISEP, Portugal
* Edwin Lughofer, Johannes Kepler University of Linz, Austria
* Sylvie Charbonnier, Université Joseph Fourier-Grenoble, France
* David Camacho Fernandez, Universidad Politecnica de Madrid, Spain
* Bruno Sielly Jales Costa, IFRN, Natal, Brazil
* Fernando Gomide, University of Campinas, Brazil
* José A. Iglesias, Universidad Carlos III de Madrid, Spain
* Anthony Fleury, Mines-Douai, Institut Mines-Télécom, France
* Teng Teck Hou, Nanyang Technological University, Singapore
* Plamen Angelov, Lancaster University, UK
* Igor Skrjanc, University of Ljubljana, Slovenia
* Slawomir Nowaczyk, Halmstad University, Sweden
* Indre Zliobaite, University of Helsinki, Finland
* Elaine Faria, Univ. Uberlandia, Brazil
* Mykola Pechenizkiy, TU Eindonvhen, Netherlands
* Raquel Sebastião, Univ. Aveiro, Portugal
* Anders Holst, RISE SICS, Sweden
* Erik Frisk, Linköping University, Sweden
* Enrique Alba, University of Málaga, Spain
* Thorsteinn Rögnvaldsson, Halmstad University, Sweden
* Andreas Theissler, University of Applied Sciences Aalen, Germany
* Vivek Agarwal, Idaho National Laboratory, Idaho
* Manuel Roveri, Politecnico di Milano, Italy
* Yang Hu, Politecnico di Milano, Italy
* Rita Ribeiro, University of Porto, Porto, Portugal
———————————————————- Workshop Organizers
* Joao Gama, University of Porto, Porto, Portugal, jgama@fep.up.pt
* Albert Bifet, Telecom-ParisTech, Paris, France, albert.bifet@telecom-paristech.fr
* Moamar Sayed Mouchaweh, IMT Lille-Douai, Douai, France, moamar.sayed-mouchaweh@imt-lille-douai.fr
* Grzegorz J. Nalepa, Jagiellonian University, Krakow, Poland, gjn@gjn.re
* Sepideh Pashami, Halmstad University, Sweden, sepideh.pashami@hh.se
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

Summer School on Machine Learning and Big Data with Quantum Computing (SMBQ 2020)
Porto, Portugal, September 7-8, 2020
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Machine Learning (ML) is an Artificial Intelligence (AI) branch, that focuses on developing algorithms to teach how computers learn from data to make decisions or predictions. Deep Learning (DL) is part of a broader family of ML algorithms, that is based on artificial neural networks. Arguably, DL techniques demand for big amounts of data and, as such, they require huge computational resources and advanced processing techniques.
Cloud Computing is a well-known alternative to deal with big amounts of data, since its elasticity allows for an efficient scalability of huge computational resources, such as, data storage and processing power. On the other hand, Quantum Computing is an advanced processing technique, that uses the fundamentals of quantum mechanics to accelerate the process of solving highly complex problems.
SMBQ 2020 addresses the current trends in AI and in the computational techniques that deal with big data demands, together with, a powerful processing technique that will shape the future of computation.
During 2 days, from 7-8 September 2020, we will introduce concepts, discuss the current trends and provide direct practical experience in hands-on lessons.
For more information visit: smbq2020.dcc.fc.up.pt/
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Registration:
Attendance is free, but it is required that participants register in advance by filling a form until 15 August, 2020. Prior to the event, information regarding the access of the live sessions will be sent to all registrants via email.
The number of participants is limited, thus the registration process closes once the limit is reached. For any further information, please send a message to SMBQ2020.

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Contact Persons:
Carlos Ferreira, Polytechnic Institute of Porto, LIAAD – INESC TEC, E-mail: cgf@isep.ipp.pt Miguel Areias, University of Porto, CRACS – INESC TEC, E-mail: miguel-areias@dcc.fc.up.pt
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