Summer School on Machine Learning and Big Data with Quantum Computing (SMBQ 2020)
Porto, Portugal, September 7-8, 2020
Web page: smbq2020.dcc.fc.up.pt/
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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.
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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.
smbq2020.dcc.fc.up.pt/#registration
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

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
BDL2020 will be held online (synchronous and/or asynchronous)
———————————— KEY DATES ————————————
Deadline for paper submission: ***June 28, 2020***
Author notification: July 22, 2020
Camera-ready version of papers: July 25, 2020

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

SBAC-PAD 2020
32nd IEEE International Symposium on Computer Architecture and High Performance Computing September 8-11, 2020 Porto, Portugal sbac2020@dcc.fc.up.pt
sbac2020.dcc.fc.up.pt/
www2.sbc.org.br/sbac/ (Historical acceptance rate).
—————————————————————————- ** NEW ** —————————————————————————- SBAC-PAD2020 deadline postponed to June 28, 2020 —————————————————————————-
Following the recommendations/guidelines from the World Health Organization (WHO), regarding the Coronavirus disease (COVID-19) outbreak, SBAC-PAD2020 will be converted to an online event.
However, the outbreak’s impact led to several requests for postponing significantly the deadlines. Following those requests, the SBAC-PAD2020’s organization decided to postpone the deadline for the abstract/paper submission to June 28, 2020.
Stay safe!
—————————————————————————- Aims and Scope —————————————————————————-
SBAC-PAD is an international symposium, started in 1987, which has continuously presented an overview of new developments, applications, and trends in parallel and distributed computing technologies. SBAC-PAD is open for faculty members, researchers, specialists and graduate students around the world.
In this edition, the symposium will be held at the University of Porto, Porto, Portugal. The city of Porto is famous for its Port wine and beautiful scenery, architecture and cultural events. More information about the conference can be found at sbac2020.dcc.fc.up.pt/
—————————————————————————- Paper Submission —————————————————————————-
Authors are invited to submit original manuscripts on a wide range of high-performance computing areas, including computer architecture, systems software, languages and compilers, algorithms and applications.
Topics of interest include (but are not limited to): -Application-specific systems -Architecture and Programming Support for Emerging Domains (Big Data, Deep Learning) -Benchmarking, performance measurements, and analysis -Cloud, Grid, cluster, and peer-to-peer systems -Embedded and pervasive systems -GPUs, FPGAs and other accelerator architectures -Languages, compilers, and tools for parallel and distributed programming -Modeling and simulation methodology -Operating systems and virtualization -Parallel and distributed systems, algorithms, and applications -Power and energy-efficient systems -Processor, cache, memory, storage, and network architecture -Real-world applications and case studies -Reconfigurable, resilient and fault-tolerant systems
Submissions must be in English, 8 pages maximum, following the IEEE conference formatting guidelines. To be published in the conference proceedings and to be eligible for publication at the IEEE Xplore, at least one of the authors must register at the full rate and present her/his work.
Authors may not use a single registration for multiple papers. Authors of selected papers will be invited to submit extended versions of their papers for publication on the Journal of Parallel and Distributed Computing.
Paper submission will be done through EasyChair: www.easychair.org/conferences/?conf=sbacpad2020
—————————————————————————- Important Dates —————————————————————————-
Abstract deadline: June 28, 2020 Paper deadline: June 28, 2020 Reviewing period: June 29 – July 15, 2020 Rebuttal period: July 15 – July 19, 2020 Author notification: July 22, 2020 Camera-ready submission: July 25, 2020
—————————————————————————- Organizing Committee —————————————————————————-
General Chairs Inês Dutra,ines@dcc.fc.up.pt (University of Porto, Portugal) Jorge Barbosa,jbarbosa@fe.up.pt (University of Porto, Portugal) Miguel Areias,miguel-areias@dcc.fc.up.pt (University of Porto, Portugal)
Program Co-chairs Jorge Barbosa (University of Porto, Portugal) Laurent Lefévre (Inria, ENS Lyon, University of Lyon, France) Lucia Drummond (Universidade Federal Fluminense, Brazil)
Track Chairs Computer Architecture Chair: José Moreira, IBM Thomas J. Watson Research Center, USA Edson Borin, University of Campinas, Brazil Felipe França, State University of Rio de Janeiro, Brazil Gabriel Falcão, University of Coimbra, Portugal Jairo Panetta, Aeronautics Institute of Technology, Brazil Jean-Luc Gaudiot, University of California, USA Jose Cano, University of Glasgow, UK Leandro Santiago, Federal Fluminense University, Brazil Lluc Alvarez, Barcelona Supercomputing Center, Spain Nuno Roma, University of Lisbon, Portugal Peter Hofstee, IBM Austin Research Laboratory, USA Rodolfo Azevedo, University of Campinas, Brazil Serif Yesil, University of Illinois, USA Wagner Meira, Federal University of Minas Gerais, Brazil
Networking and Distributed Systems Chair: Jesús Carretero, University Carlos III of Madrid, Spain Alexey Lastovetsky, University College Dublin, Ireland Angelos Bilas, FORTH-ICS and University of Crete, Greece Bruno Schulze, National Laboratory for Scientific Computing (LNCC), Brazil Carla Osthoff Barros, National Laboratory for Scientific Computing (LNCC), Brazil Domenico Talia, University of Calabria, Italy Emmanuel Jeannot, INRIA, France Jose Luis Gonzalez, Instituto Tecnológico de Ciudad Valles (ITV), México Leonel Sousa, Universidade de Lisboa, Portugal Marco Aldinucci, University of Torino, Italy Silvina Caino, University of Tennessee, USA
Parallel Applications and Algorithms Chair: Alba Melo, University of Brasília, Brazil Alfredo Goldman, University of São Paulo, Brazil Ananth Kalyanaraman, Washington State University, USA Anne Benoit, ENS Lyon‐LIP, France Antonio J. Peña, Barcelona Supercomputing Center, Spain Bertil Schmidt, University of Mainz, Germany Cristiana Bentes, State University of Rio de Janeiro, Brazil Cristina Boeres, Federal Fluminense University, Brazil Edson Caceres, Federal University of Mato Grosso do Sul, Brazil George Teodoro, Federal University of Minas Gerais, Brazil Gianfranco Bilardi, University of Padova, Italy Jose Nelson Amaral, University of Alberta, Canada Luciana Arantes, Université Pierre et Marie Curie-Paris, France Ricardo Rocha, University of Porto, Portugal Viktor Prasanna, University of Southern California, USA
Performance Evaluation Chair: Ariel Oleksiak, PoznaÅ„ Supercomputing and Networking Center, Poland Altino Sampaio, Instituto Politécnico do Porto, Portugal Aurélien Cavelan, University of Basel, Switzerland Frederic Suter, French National Institute of Nuclear and Particle Physics, France Georges Da Costa, Universite Toulouse III – Paul Sabatier, France Giovanni Agosta, Politecnico di Milano, Italy Hamid Arabnejad, Brunel University, UK Hongyang Sun, Vanderbilt University, USA Lucas Schnorr, Federal University of Rio Grande do Sul, Brazil Martin Schulz Technical University of Munich, Germany
System Software Chair: Jidong Zhai, Tsinghua University, China Ang Li, Pacific Northwest National Laboratory, USA Chi Zhou, Shenzhen University, China Christoph Kessler, Linköping University, Sweden Clemens Grelck, University of Amsterdam, Netherlands Dandan Song, Beijing Institute of Technology, China Dazhao Cheng, University of North Carolina at Charlotte, USA Feng Zhang, Renmin University of China, China Guangyu Sun, Peking University, China Haikun Liu, Huazhong University of Science and Technology, China Mingyu Gao, Tsinghua University, China Philippe Navaux, Federal University of Rio Grande do Sul, Brazil Pradeep Kumar, William & Mary, USA Quan Chen, Shanghai Jiaotong University, China Shanjiang Tang, Tianjin University, China Shigang Li, ETH Zurich, Switzerland Teng Yu, Tsinghua University, China Vinod Rebello, Universidade Federal Fluminense, Brazil Zeyi Wen, National University of Singapore, Singapore Zhaoguo Wang, Shanghai Jiaotong University, China
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

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

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

*** 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