CALL FOR PAPERS
IEEE Transactions on Neural Networks and Learning Systems
Special Issue on STREAM LEARNING
Deadline: 15 December 2021
Introduction
In recent years, machine learning from streaming data (called Stream Learning) has enjoyed tremendous growth and exhibited a wealth of development at both the conceptual and application levels. Stream Learning is highly visible in both the machine learning and data science fields and become a new hot direction in recent years. Research developments in Stream Learning include learning under concept drift detection (whether a drift occurs), understanding (where, when, and how a drift occurs), and adaptation (to actively or passively update models). Recently we have seen several new successful developments in Stream Learning such as massive stream learning algorithms; incremental and online learning for streaming data; and streaming data-based decision-making methods. These developments have demonstrated how Stream Learning technologies can contribute to the implementation of machine learning capability in dynamic systems. We have also witnessed compelling evidence of successful investigations on the use of Stream Learning to support business real-time prediction and decision making.
In light of these observations, it is instructive, vital, and timely to offer a unified view of the current trends and form a broad forum for the fundamental and applied research as well as the practical development of Stream Learning for improving machine learning, data science and practical decision support systems of business. This special issue aims at reporting the progress in fundamental principles; practical methodologies; efficient implementations; and applications of Stream Learning methods and related applications. The special issue also welcomes contributions in relation to data streams, incremental learning and reinforcement learning in data streaming situations.
Scope of the Special Issue
We invite submissions on all topics of Stream Learning, including but not limited to:
• Data stream prediction
• Concept drift detection, understanding and adaptation
• Recurrent concepts
• Experimental setup and Evaluation methods for stream learning
• Reinforcement learning on streaming data
• Streaming data-based real-time decision making
• Ensemble methods for stream learning
• Auto machine learning for stream algorithms
• Neural networks for big data streams
• Transfer learning for streaming data
• Real-world applications of stream learning
• Active learning for streaming data
• Online learning for streaming data
• Imbalance learning for streaming data
• Lifelong learning for streaming data
• Incremental learning for streaming data
• Continuous learning for streaming data
• Clustering for streaming data
• Audio/speech/music streams processing
• Stream learning benchmark datasets
• Multi-drift and multi-stream learning
• Stream processing platforms
Timeline
• Submission deadline: Dec 15, 2021
• Notification of first review: Feb 1, 2022
• Submission of revised manuscript: May 1, 2022
• Notification of final decision: July 1, 2022
Guest Editors
• Jie Lu (University of Technology Sydney, Australia)
• Joao Gama (University of Porto, Portugal)
• Xin Yao (Southern University of Science and Technology, China)
• Leandro Minku (University of Birmingham, UK)
Submission Instructions
– Read the Information for Authors at cis.ieee.org/tnnls
– Submit your manuscript at the TNNLS webpage (mc.manuscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript is submitted to this special issue. Early submissions are welcome.
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
— Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para infos [at] appia [ponto] pt

*ACM Symposium on Applied Computing *
The 37th ACM/SIGAPP Symposium on Applied Computing in Brno, Czech Republic
April 25 – April 29, 2022
www.sigapp.org/sac/sac2022/
*Data Streams Track *
abifet.github.io/SAC2022/

* IMPORTANT DATES *
1. Submission deadline (Extended): October 31, 2021
2. Notification deadline: December 10, 2021
3. Camera-ready deadline: December 21, 2021

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
— Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para infos [at] appia [ponto] pt

DaSSWeb – Data Science and Statistics Webinar
Tuesday 26 October, 14:30
Speaker: Nuno Moniz
Researcher INESC TEC Invited Professor @ Faculty of Sciences, University of Porto Invited Professor @ Faculty of Engineering, University of Porto
Title: Is there a free lunch in imbalanced learning?
Zoom link: videoconf-colibri.zoom.us/j/89767753105
<videoconf-colibri.zoom.us/j/87373848710> Abstract: The ability to predict rare events remains one of the most challenging tasks to solve in machine learning. For almost three decades, research in imbalanced learning has produced many strategies to help in this endeavour. The most popular – resampling strategies – work by creating new data sets where the original data is biased towards cases describing rare events having higher probability. Today, it would seem that both research and industry have widespread assumptions concerning which are the “best” or “worst” strategies. In this talk, we will set up a face-to-face between theory and practice. First, we will leverage the concept of no free lunch to analyse if we can assume there are resampling strategies more likely to be the best in solving imbalanced learning problems. Second, we will evaluate if data characteristics can help us automatically decide which strategies are most likely to produce the best outcome in unseen data.
— Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para infos [at] appia [ponto] pt

*ACM Symposium on Applied Computing *
The 37th ACM/SIGAPP Symposium on Applied Computing in Brno, Czech Republic
April 25 – April 29, 2022
www.sigapp.org/sac/sac2022/
*Data Streams Track *
abifet.github.io/SAC2022/

*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 *
1. Submission deadline (Extended): October 24, 2021
2. Notification deadline: December 10, 2021
3. Camera-ready deadline: December 21, 2021

*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 8 pages. There is a set of templates to support the required paper format for a number of document preparation systems at www.sigapp.org/sac/sac2022/authorkit.html
Important notice:
1. Please submit your contribution via SAC 2022 Webpage: www.softconf.com/m/sac2022/ 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
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
— Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para infos [at] appia [ponto] pt

*ACM Symposium on Applied Computing *
The 37th ACM/SIGAPP Symposium on Applied Computing in Brno, Czech Republic
April 25 – April 29, 2022
www.sigapp.org/sac/sac2022/
*Data Streams Track *
abifet.github.io/SAC2022/

*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 *
1. Submission deadline: October 15, 2021
2. Notification deadline: December 10, 2021
3. Camera-ready deadline: December 21, 2021

*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 8 pages. There is a set of templates to support the required paper format for a number of document preparation systems at www.sigapp.org/sac/sac2022/authorkit.html
Important notice:
1. Please submit your contribution via SAC 2022 Webpage: www.softconf.com/m/sac2022/ 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
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
— Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para infos [at] appia [ponto] pt