DaSSWeb, Data Science and Statistics Webinar ‘GOOGLE TRENDS: A NEW DATA SOURCE FOR RESEARCH ON ECONOMICS?’ EDUARDO COSTA Economics Ph.D. student at FEP doctoral programme July, 14, 2020, 14:30
Join us here<videoconf-colibri.zoom.us/j/95182035541>.
Abstract: The “big data” advent raised interest in using new sources of data to improve, describe, and track economic activities. As a relatively new source of data, the Google Trends (GT), launched
by Google in 2006, is a tool that provides updated reports on the relative demand for keywords, search-terms or search-categories via an index that represents the proportion of all searches
on Google from 2004 onwards for a particular search-term, given a geographical area and period. This sort of data, which is still under scrutiny, allows cost reduction, has a higher-frequency,
and realtime availability compared to traditional surveys.
Scientific research that uses GT data focusing on economic indicators is still embryonic; however, the literature points to promising results when predicting economic indicators such as GDP,
unemployment, private consumption, and price index. This talk aims at presenting how to obtain data from Google Trends, the mechanisms and economic indicators most used by the scientific
community when incorporating the GT data, and the preliminary findings on modeling the unemployment for Portugal.

Short Bio: Eduardo Costa is an Economics Ph.D. student at the FEP doctoral programme, holds an undergraduate degree in Statistics and a master in Production Engineering. His experience includes over
18 years of working for financial, insurance, energy and publishing companies focused on data mining, analysis, predictive models and consumer segmentation.

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

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

———————————————— 1st Call for papers for the EvoStar conference
Please distribute (Apologies for cross-posting) ————————————————
EvoStar comprises of four co-located conferences run each spring at different locations throughout Europe. These events arose out of workshops originally developed by EvoNet, the Network of Excellence in Evolutionary Computing, established by the Information Societies Technology Programme of the European Commission, and they represent a continuity of research collaboration stretching back over 20 years.
EvoStar is organised by SPECIES, the Society for the Promotion of Evolutionary Computation in Europe and its Surroundings. This non-profit academic society is committed to promoting evolutionary algorithmic thinking, with the inspiration of parallel algorithms derived from natural processes. It provides a forum for information and exchange.
The four conferences include: – EuroGP 24th European Conference on Genetic Programming www.evostar.org/2021/eurogp/
– EvoApplications 24th European Conference on the Applications of Evolutionary and bio-inspired Computation www.evostar.org/2021/evoapps/
– EvoCOP 21st European Conference on Evolutionary Computation in Combinatorial Optimisation www.evostar.org/2021/evocop/
– EvoMUSART 10th International Conference (and 15th European event) on Evolutionary and Biologically Inspired Music, Sound, Art and Design www.evostar.org/2021/evomusart/
*** Important Dates, Venue and Publication ***
Submission Deadline: November 1, 2019 Conference: 7 to 9 April 2021. Venue: Seville, Spain All accepted papers will be printed in the proceedings published by Springer Verlag in the Lecture Notes in Computer Science (LNCS) series.
Please, check the website for more information: www.evostar.org/2021/
And follow us at: Facebook – www.facebook.com/evostarconf/ Twitter – twitter.com/EvostarConf/ Instagram – www.instagram.com/evostarconference/