Carlos Manuel Abreu Gomes Ferreira

[rede.APPIA] CFP: Special Issue on Foundations of Data Science – Machine Learning Journal

2020-03-142020-03-15 Carlos Manuel Abreu Gomes Ferreira

Special Issue on Foundations of Data Science – Machine Learning Journal
Data science is currently a very active topic with an extensive scope, both in terms of theory and applications. Machine Learning is one of its core foundational pillars. Simultaneously, Data Science applications provide important challenges that can often be addressed only with innovative Machine Learning algorithms and methodologies. This special issue focuses on the latest developments in Machine Learning foundations of data science, as well as on the synergy between data science and machine learning. We welcome new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science. Theoretically well-founded contributions and their real-world applications in laying new foundations for machine learning and data science are welcome.
This special issue solicits the attention of a broad research audience. Since it brings together a variety of foundational issues and real-world best practices, it is also relevant to practitioners and engineers interested in machine learning and data science.
Accepted papers will be presented at the IEEE DSAA conference in Porto, October 2021.
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Topics of Interest
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We welcome original research papers on all aspects of data science in relation to machine learning, including the following topics:
*Machine Learning Foundations of Data Science
Auto-ML
Fusion of information from disparate sources
Feature engineering, Feature embedding and data preprocessing
Learning from network data
Learning from data with domain knowledge
Reinforcement learning
Evaluation of Data Science systems
Risk analysis
Causality, learning causal models
Multiple inputs and outputs: multi-instance, multi-label, multi-target
Semi-supervised and weakly supervised learning
Data streaming and online learning
Deep Learning
*Emerging Applications
Autonomous systems
Analysis of Evolving Social Networks
Embedding methods for Graph Mining
Online Recommender Systems
Augmented Reality, Computer Vision
Real-Time Anomaly, Failure, image manipulation and fake detection
*Human Centric Data Science
Privacy preserving, Ethics, Transparency
Fairness, Explainability, and Algorithm Bias
Accountability and responsibility
Reproducibility, replicability and retractability
Green Data Sciences
*Infrastructures
IoT data analytics and Big Data
Large-scale processing and distributed/parallel computing;
Cloud computing
*Data Science for the Next Digital Frontier
in: Telecommunications and 5G
Retail,
Green Transportation
Finance, Blockchains, Cryptocurrencies
Manufacturing, Predictive Maintenance, Industry 4.0
Energy, Smart Grids, Renewable energies
Climate change and sustainable environment
Contributions must contain new, unpublished, original and fundamental work relating to the Machine Learning journal’s mission. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.
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Submission Instructions
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Submit manuscripts to: MACH.edmgr.com. Select “SI: Foundations of Data Science” as the article type. Papers must be prepared in accordance with the Journal guidelines: www.springer.com/journal/10994
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
All papers will be reviewed following standard reviewing procedures for the Journal.
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Key Dates
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Continuous submission/review process
Cutoff dates: 30 September, 30 December and 1st March
Last paper submission deadline: 1 March 2021
Paper acceptance: 1 June 2021
Camera-ready: 15 June 2021
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Guest Editors
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Alípio Jorge, University of Porto,
João Gama, University of Porto
Salvador García, University of Granada

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] CFP: Big Data & Deep Learning in HPC (IEEE Xplore) @Porto, Portugal

2020-02-172020-02-18 Carlos Manuel Abreu Gomes Ferreira

Workshop on BIG DATA & DEEP LEARNING in HIGH PERFORMANCE COMPUTING (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/)
Porto, Portugal
The city of Porto is famous for its Port wine and beautiful scenery, architecture and cultural events.
Portugal has again been awarded the best European Tourist Destination by the World Travel Awards, the Oscars equivalent in the field of tourism.
———————————— 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: May 25, 2020
Author notification: July 1, 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 300 euros.
Registration system available in sbac2020.dcc.fc.up.pt/bdl2020/registration.html
———————————— VENUE ————————————
Department of Computer Science, Faculty of Sciences, University of Porto
Rua do Campo Alegre 1021/1055 4169-007 Porto, Portugal
The city of Porto is famous for its Port wine and beautiful scenery, architecture and cultural events.
Portugal has again been awarded the best European Tourist Destination by the World Travel Awards, the Oscars equivalent in the field of tourism.
———————————— 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)

[rede.APPIA] Summer School on Machine Learning and Big Data with Quantum Computing @ Porto (Portugal), 7-8 September 2020

2020-02-142020-02-15 Carlos Manuel Abreu Gomes Ferreira

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, is about teaching computers how to 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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Venue:
Department of Computer Science, Faculty of Sciences, University of Porto
Rua do Campo Alegre 1021/1055 4169-007 Porto, Portugal
The city of Porto is famous for its Port wine and beautiful scenery, architecture and cultural events.
Portugal has again been awarded the best European Tourist Destination by the World Travel Awards, the Oscars equivalent in the field of tourism.
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Registration:
Registrations include attendance to all sessions, coffee breaks, lunches, wi-fi internet and access to a desktop during the hands-on sections. We have a limit on the number of registrations (60 attendees), please register early!
Early Fees: 150 euros
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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

[rede.APPIA] CFP: SBAC – PAD 2020 – IEEE International Symposium on Computer Architecture and High Performance Computing

2020-02-102020-02-11 Carlos Manuel Abreu Gomes Ferreira

SBAC-PAD 2020
Department of Computer Science, School of Sciences, University of Porto
Porto, Portugal
sbac2020.dcc.fc.up.pt <sbac2020.dcc.fc.up.pt/>
sbac2020@dcc.fc.up.pt <mailto:sbac2020@dcc.fc.up.pt>
Call for Papers
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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.
Authors are invited to submit manuscripts on a wide range of high-performance and distributed computing areas. Topics of interest include (but are not limited to):
*
Application-specific systems
*
Architecture and programming support for emerging domains (Big Data, Deep Learning, Machine learning, Cognitive Systems)
*
Benchmarking, performance measurements, and analysis
*
Cloud, cluster, and edge/fog computing systems
*
Embedded and pervasive systems
*
GPUs, FPGAs and 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
Paper Submission
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 accepted papers will be invited to submit extended versions of their papers for publication on the Journal of Parallel and Distributed Computing. The call will be open and will follow the normal journal submission procedure.
Important Dates
Abstract deadline:

*May 15th, 2020*
Paper deadline:

*May 22nd, 2020*
Reviewing period:

*May 24-Jun 21, 2020*
Author notification:

*June 26th, 2020*
Camera-ready submission:

*July 3rd, 2020*
Organizing Committee
General Chairs
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InêsDutra.ines@dcc.fc.up.pt <mailto:ines@dcc.fc.up.pt>(University of Porto, Portugal)
*
Jorge Barbosa,jbarbosa@fe.up.pt <mailto:jbarbosa@fe.up.pt>(University of Porto, Portugal)
*
Miguel Areias,miguel-areias@dcc.fc.up.pt <mailto:miguel-areias@dcc.fc.up.pt>(University of Porto, Portugal)
Program Co-chairs
*
Jorge Barbosa (University of Porto, Portugal)
*
Laurent Lefèvre (Inria, ENS Lyon, University of Lyon, France)
*
Lucia Drummond (Universidade Federal Fluminense, Brazil)
Track Chairs
*
/*Computer Architecture*///
o
*Chair*: José Moreira (IBM Thomas J. Watson Research Center, USA)
*
*Networking and Distributed Systems*
o
*Chair*: Jesús Carretero (University Carlos III of Madrid, Spain)
*
*Parallel Applications and Algorithms*
o
*Chair*: Alba Melo (Universidade de Brasília, Brazil)
*
*Performance Evaluation*
o
*Chair*: Ariel Oleksiak (Poznań Supercomputing and Networking Cente, Poland)
*
*System Software*
o
*Chair*: Jidong Zhai (Tsinghua University, China)
Publicity Chairs
*
Carlos Ferreira, Instituto Superior de Engenharia, Portugal
*
Miguel Areias, University of Porto, Portugal
*
Feng Zhang, Renmin University of China
Workshop Chairs
*
Miguel Areias, University of Porto, Portugal
*
Iván Carrera, Escuela Politécnica Nacional, Quito, Equador and University of Porto, Portugal
Publications Chairs
*
IEEE – Proceedings
o
Rui Camacho, University of Porto, Portugal
o
Iván Carrera, Escuela Politécnica Nacional, Quito, Equador and University of Porto, Portugal
*
JPDC – Special Issue
o
Jorge Barbosa, University of Porto, Portugal
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] CFP: DATA STREAMS TRACK – ACM SAC 2020 (Submission deadline: September 29, 2019)

2019-09-262019-09-27 Carlos Manuel Abreu Gomes Ferreira

*ACM Symposium on Applied Computing *
The 35th ACM/SIGAPP Symposium on Applied Computing in Brno, Czech Republic
March 30 – April 3, 2020
www.sigapp.org/sac/sac2020/
*Data Streams Track *
www.cs.waikato.ac.nz/~abifet/SAC2020/

*Call for Papers *
The rapid development in Big Data information science and technology in general and in growth complexity and volume of data in particular has introduced new challenges for the research community. Many sources produce data continuously. Examples include the Internet of Things (IoT), Smart Cities, Urban Computing, sensor networks, wireless networks, radio frequency identification, health-care devices and information systems, customer click streams, telephone records, multimedia data, scientific data, sets of retail chain transactions, etc. These sources are called data streams. A data stream is an ordered sequence of instances that can be read only once or a small number of times using limited computing and storage capabilities. These sources of data are characterized by being open-ended, flowing at high-speed, and generated by non stationary distributions.
*TOPICS OF INTEREST *
We are looking for original, unpublished work related to algorithms, methods and applications on big data streams and large scale machine learning. Topics include (but are not restricted) to:
* Real-Time Analytics
* Big Data Mining
* Data Stream Models
* Large Scale Machine Learning
* Languages for Stream Query
* Continuous Queries
* Clustering from Data Streams
* Decision Trees from Data Streams
* Association Rules from Data Streams
* Decision Rules from Data Streams
* Bayesian Networks from Data Streams
* Neural Networks for Data Streams
* Feature Selection from Data Streams
* Visualization Techniques for Data Streams
* Incremental on-line Learning Algorithms
* Single-Pass Algorithms
* Temporal, spatial, and spatio-temporal data mining
* Scalable Algorithms
* Real-Time and Real-World Applications using Stream data
* Distributed and Social Stream Mining
* Urban Computing, Smart Cities
* Internet of Things (IoT)
*IMPORTANT DATES (NEW!) *
1. Submission deadline: September 15 -> September 29
2. Notification deadline: November 10 -> November 24
3. Camera-ready deadline: November 25 -> December 9
*PAPER SUBMISSION GUIDELINES *
Papers should be submitted in PDF. Authors are invited to submit original papers in all topics related to data streams. All papers should be submitted in ACM 2-column camera ready format for publication in the symposium proceedings. ACM SAC follows a double blind review process. Consequently, the author(s) name(s) and address(s) must NOT appear in the body of the submitted paper, and self-references should be in the third person. This is to facilitate double blind review required by ACM. All submitted papers must include the paper identification number provided by the eCMS system when the paper is first registered. The number must appear on the front page, above the title of the paper. Each submitted paper will be fully refereed and undergo a blind review process by at least three referees. The conference proceedings will be published by ACM. The maximum number of pages allowed for the final papers is 6 pages. There is a set of templates to support the required paper format for a number of document preparation systems at: www.acm.org/sigs/pubs/proceed/template.html
Important notice:
1. Please submit your contribution via SAC 2020 Webpage. 2. Paper registration is required, allowing the inclusion of the paper, poster, or SRC abstract in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for including the work in the ACM/IEEE digital library. No-show of registered papers, posters, and SRC abstracts will result in excluding them from the ACM/IEEE digital library.
If you encounter any problems with your submission, please contact the Program Coordinator.

Carlos Ferreira
[http://www2.isep.ipp.pt/assinatura_email/EMAIL_ISEP.png]<www2.isep.ipp.pt/assinatura_email/> 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<www.isep.ipp.pt>

[rede.APPIA] CFP: DATA STREAMS TRACK – ACM SAC 2020 (Submission deadline: September 29, 2019)

2019-09-262019-09-27 Carlos Manuel Abreu Gomes Ferreira

[rede.APPIA] EXTENDED DEADLINE: DATA STREAMS TRACK – ACM SAC 2020 (Submission deadline: September 29, 2019)

2019-09-152019-09-16 Carlos Manuel Abreu Gomes Ferreira