• CFP: Special Issue on Foundations of Data Science - Machine Learning Jo

    From Carlos Ferreira@21:1/5 to All on Sat Mar 14 15:03:04 2020
    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.


    Topics of Interest


    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


    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


    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


    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.


    Submission Instructions


    Submit manuscripts to: http://MACH.edmgr.com. Select “SI: Foundations of Data Science” as the article type.
    Papers must be prepared in accordance with the Journal guidelines: https://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.


    Key Dates


    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


    Guest Editors


    Alípio Jorge, University of Porto,

    João Gama, University of Porto

    Salvador García, University of Granada

    --- SoupGate-Win32 v1.05
    * Origin: fsxNet Usenet Gateway (21:1/5)