Call for Papers
SoGood 2020 – 5th Workshop on Data Science for Social Good Affiliated with ECML-PKDD 2020, 14-18 September 2020,https://ecmlpkdd2020.net/
SoGodd2020: sites.google.com/view/ecmlpkddsogood2020/home
Dus to several requests, we have extended the submission deadline:
* Workshop paper/project submission deadline:*June 18th, 2020 (extended*) * Workshop paper/project acceptance notification :*July 2nd July 14th, 2020 (extended)* * Workshop paper camera-ready deadline: *July 21st, 2020*
Organizers:
* Ricard Gavaldà (UPC BarcelonaTech, Spain),gavalda@cs.upc.edu <mailto:gavalda@cs.upc.edu> * Irena Koprinska (University of Sydney, Australia),irena.koprinska@sydney.edu.au <mailto:irena.koprinska@sydney.edu.au> * João Gama (University of Porto, Portugal),jgama@fep.up.pt <mailto:jgama@fep.up.pt> * Rita Ribeiro (University of Porto, Portugal),rpribeiro@fc.up.pt <mailto:rpribeiro@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

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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2º seminário do ciclo DaSSWeb – Data Science and Statistics Webinar
Dia 16 de Junho, 14:30h
Oradora: Raquel Menezes, Universidade do Minho Título: CoronaSurveys – Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics
Link disponível em <sigarra.up.pt/fep/pt/noticias_geral.ver_noticia?p_nr=32969>
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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>

Workshop on BIG DATA & DEEP LEARNING in HIGH PERFORMANCE COMPUTING (BDL2020) (sbac2020.dcc.fc.up.pt/bdl2020/)
in conjunction with the IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2020) (sbac2020.dcc.fc.up.pt/)
September 9, 2020, Porto, Portugal
—————————————————————————- ** NEW ** BDL2020 will be held online (synchronous and/or asynchronous) —————————————————————————-
We are monitoring the Coronavirus disease (COVID-19) outbreak and following the recommendations/guidelines from the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC).
The safety of all conference participants is our main priority. In this perspective, regardless of the outbreak outcomes in September, we will make BDL2020 an online (synchronous and/or asynchronous) event and we will maintain the regular publication activities, i.e., accepted papers will be eligible for publication at the IEEE Xplore.
The Workshop fee is now 200 euros.
———————————— WORKSHOP ON BIG DATA & DEEP LEARNING IN HIGH PERFORMANCE COMPUTING ————————————
The number of very large data repositories (big data) is increasing in a rapid pace. Analysis of such repositories using the “traditional” sequential implementations of ML and emerging techniques, like deep learning, that model high-level abstractions in data by using multiple processing layers, requires expensive computational resources and long running times. Parallel or distributed computing are possible approaches that can make analysis of very large repositories and exploration of high-level representations feasible. Taking advantage of a parallel or a distributed execution of a ML/statistical system may: i) increase its speed; ii) learn hidden representations; iii) search a larger space and reach a better solution or; iv) increase the range of applications where it can be used (because it can process more data, for example). Parallel and distributed computing is therefore of high importance to extract knowledge from massive amounts of data and learn hidden representations.
The workshop will be concerned with the exchange of experience among academics, researchers and the industry whose work in big data and deep learning require high performance computing to achieve goals. Participants will present recently developed algorithms/systems, on going work and applications taking advantage of such parallel or distributed environments.
———————————— LIST OF TOPICS ————————————
All novel data-intensive computing techniques, data storage and integration schemes, and algorithms for cutting-edge high performance computing architectures which targets Big Data and Deep Learning are of interest to the workshop. Examples of topics include but not limited to: – parallel algorithms for data-intensive applications; – scalable data and text mining and information retrieval; – using Hadoop, MapReduce, Spark, Storm, Streaming to analyze Big Data; – energy-efficient data-intensive computing; – deep-learning with massive-scale datasets; – querying and visualization of large network datasets; – processing large-scale datasets on clusters of multicore and manycore processors, and accelerators; – heterogeneous computing for Big Data architectures; – Big Data in the Cloud; – processing and analyzing high-resolution images using high-performance computing; – using hybrid infrastructures for Big Data analysis. – New algorithms for parallel/distributed execution of ML systems; – applications of big data and deep learning to real-life problems.
———————————— KEY DATES ————————————
Deadline for paper submission: ***June 28, 2020***
Author notification: July 22, 2020
Camera-ready version of papers: July 25, 2020
———————————— SUBMISSION ————————————
We invite authors to submit original work to BDL. All papers will be peer reviewed and accepted papers will be published in IEEE Xplore.
Submissions must be in English, limited to 8 pages in the IEEE conference format (see www.ieee.org/conferences/publishing/templates.html)
All submissions should be made electronically through the EasyChair system: easychair.org/conferences/?conf=bdl2020
———————————— REGISTRATION ————————————
A full registration to the workshop and presentation are needed in order to have your paper included in the workshop proceedings.
The Workshop fee is 200 euros.
Registration system available in sbac2020.dcc.fc.up.pt/bdl2020/registration.html
———————————— ORGANIZATION ————————————
Carlos Ferreira (LIAAD – INESC TEC LA and Polytechnic Institute of Porto) João Gama (LIAAD – INESC TEC LA and University of Porto) Albert Bifet (Telecom ParisTech) Miguel Areias (CRACS – INESC TEC LA and University of Porto) Rui Camacho (LIAAD -INESC TEC LA and University of Porto)
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

*** 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