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

———————————- – – – – – – – – – – – – – – – AmIA_Environments@EPIA2020 :: CALL FOR PAPERS ———————————- – – – – – – – – – – – – – – –
Special Track on Ambient Intelligence and Affective Environments (AmIA Environments 2020) 20th Portuguese Conference on Artificial Intelligence – EPIA 2020
7th-9th September, 2020 :: Lisbon, Portugal epia2020.inesc-id.pt/
———————————- – – – – – – – – – – – – – – – Submission deadline: 15th April, 2020 ———————————- – – – – – – – – – – – – – – –
Ambient Intelligence (AmI) is a paradigm emerging from Artificial Intelligence (AI), where computers are used as proactive tools assisting people with their day-to-day activities, making everyoneís life more comfortable.
Affect and social behaviour plays an important role in the development of Ambient Intelligent Environments. Consideration of aspects like emotions, mood, personality traits, and attitudes in human-computer, human-robot, and human-environment interaction, especially insofar as they provide better or more ìnaturalî support for humans. These environments should be aware of the needs of people, customizing requirements and forecasting behaviours.
AmI environments may be highly diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, transport facilities, tourist attractions, stores, sport installations, music devices, etc.
In the Thematic track on AmIA Environments we will create a multi-disciplinary discussion forum that will bring together researchers from the different fields addressed discussing issues in Artificial Intelligence topics included in the Ambient Intelligence and affective environments. Researchers are welcome to present both theoretical and practical works as well as the lessons learned with their application in the varied range of domains. Emphasis will be placed on the presentation of concrete systems, discussion of implementation and development challenges and sharing of conclusions achieved and relevant results.
———————————- – – – – – – – – – – – – – – – CONTRIBUTIONS ———————————- – – – – – – – – – – – – – – –
In order to fulfill these objectives, submissions of substantial, original and previously unpublished work are invited in all areas of Ambient Intelligence and Affective environments. The topics of interest include, but are not limited to: – Applications – Ambient Assisted Living – Ubiquitous Computing – Artificial Intelligence for AmI – Intelligent Environments – Pervasive Computing – Context Aware Computing – Agent & Multiagent Systems for AmI – Mobile Computing – Sentient Computing – e-Health – Context Modelling – AmI for e-Learning – On-line Dispute Resolution – Memory Assistant – Computational models of emotions – Group Emotion – Affect and learning – Artificial characters – Affect and emotion recognition
———————————- – – – – – – – – – – – – – – – SUBMISSION INSTRUCTIONS and PAPERS FORMAT ———————————- – – – – – – – – – – – – – – –
Submissions must be full technical papers on substantial, original, and previously unpublished research. Papers can have a maximum length of 12 pages. All papers should be prepared according to the formatting instructions of Springer LNCS series (Springer Lecture Notes in Computer Science). Authors should omit their names from the submitted papers, and should take reasonable care to avoid indirectly disclosing their identity.
All papers should be submitted in PDF format through the EPIAí2020 submission Website (www.easychair.org/conferences/?conf=epia2020) selecting the track Ambient Intelligence and Affective Environments.
———————————- – – – – – – – – – – – – – – – IMPORTANT DATES ———————————- – – – – – – – – – – – – – – –
– Paper submission deadline: April 15, 2020 – Paper acceptance notification: May 31, 2020 – Camera-ready deadline: June 15, 2020 – EPIA Conference: September 7-9, 2020(Lisboa, Portugal)
———————————- – – – – – – – – – – – – – – – PUBLICATION ———————————- – – – – – – – – – – – – – – –
Accepted papers will be included in the conference proceedings (Springer LNAI – Lecture Notes in Artificial Intelligence), provided that at least one author is registered in EPIA 2020 by the early registration deadline. Proceedings will be submitted for indexation by ISI Thomson, SCOPUS, DBLP, EI-Compendex among several other scientific databases.
———————————- – – – – – – – – – – – – – – – ORGANIZING COMMITTEE ———————————- – – – – – – – – – – – – – – –
Ana Almeida, Polytechnic of Porto, Portugal (amn@isep.ipp.pt) Sara Rodriguez, University of Salamanca, Spain (srg@usal.es) Goreti Marreiros, Polytechnic of Porto (mgt@isep.ipp.pt) Paulo Novais, University of Minho, Portugal (pjon@di.uminho.pt) Peter Mikulecky, University of Hradec Kralove (peter.mikulecky@uhk.cz)

Goreti Marreiros Professor Adjunto
Departamento de Engenharia Informática
[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>