AIST 2020
Online, Russia,
October 15-16, 2020.

CALL FOR PAPERS

We invite you to submit a paper to AIST-2020, a scientific conference on Analysis of Images, Social Networks, and Texts. The conference is intended for researchers and practitioners interested in data science focusing on innovative applications of data mining and machine learning techniques to image processing, analysis of social networks, text processing, and other domains, such as economics or geographic information systems.

The previous conferences in 2012-2019 attracted a significant number of students, researchers, academics, and engineers working on the analysis of images, texts, and social networks. The broad scope of AIST makes it an event where researchers from different domains, exploiting various data analysis techniques, can meet and exchange ideas. The conference allows specialists from different fields to meet each other, present their work, and discuss both theoretical and practical aspects of their data analysis problems.

Past Conferences

Similar to the previous years, the conference proceedings will be published in the Springer’s in Lecture Notes in Computer Science (LNCS) series. You can take a look at the proceedings of the last three years to get an idea about what kind of papers are accepted and what kind of topics are discussed at the conference:

Tracks

The conference will feature six tracks:

  • Data Analysis and Machine Learning
  • Natural Language Processing
  • Social Network Analysis
  • Computer Vision
  • Theoretical Machine Learning and Optimization
  • Process Mining

The list of topics relevant to these tracks is including but is not limited to:

  • Analysis of images and videos
  • Computational linguistics
  • Core data mining and machine learning techniques
  • Data analysis in geoinformation systems
  • Deep learning applications
  • Discovering and analyzing processes using event data
  • Educational data mining
  • Game analytics
  • Machine learning and data mining for economics and social sciences
  • Natural language processing and applications
  • Optimization problems in complex networks
  • Optimization models in data science
  • Recommender systems and collaborative technologies
  • Semantic web and ontologies
  • Social network analysis

Important Dates

Abstract deadline July 1, 2020
Submission deadline July 15, 2020
Notification of acceptance September 15, 2020
Camera-ready papers due October 1, 2020
Conference dates October 15 – 16, 2020
All deadlines are 11.59 pm UTC -12h ("anywhere on Earth").

Publication

AIST-2020 proceedings will be published in Springer LNCS (Lecture Notes in Computer Science). The companion volume will be published in the satellite series CCIS (Communications in Computer and Information Science).

Venue

The conference will be held online with the support of Skolkovo Institute of Science and Technology (Skoltech).

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

Full papers describing completed research up to 12 pages of content with references. Posters describing ongoing research up to 6 pages of content with references. The papers must be formatted according to the Springer LNCS style. LaTeX template of the Springer LNCS is available at ShareLaTeX platform. We encourage you to use the LaTeX template instead of the Word template.

Papers should be submitted through the EasyChair conference management system. Submitted papers should provide sufficient detail to allow the Program Committee to assess the merits of the paper on the basis of technical quality, relevance to the conference topics, originality, significance, and clarity of presentation. Papers should present original work previously not published or concurrently submitted to another conference or journal. Each paper will be reviewed by at least three PC members. To ensure a fair assessment of the submissions, the review will be double-blind, so you need to make your paper anonymous (remove links to your personal pages, acknowledgements, affiliations, etc.).

Contact Information