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Special Sessions

Special sessions are very small and specialized events to be held during the conference as a set of oral and poster presentations that are highly specialized in some particular theme or consisting of the works of some particular international project. The goal of special sessions (minimum 4 papers; maximum 9) is to provide a focused discussion on innovative topics. All accepted papers will be published in a special section of the conference proceedings book, under an ISBN reference, and on digital support. All papers presented at the conference venue will be available at the SCITEPRESS Digital Library. SCITEPRESS is a member of CrossRef and every paper is given a DOI (Digital Object Identifier). The proceedings are submitted for indexation by Thomson Reuters Conference Proceedings Citation Index (ISI), DBLP, EI (Elsevier Engineering Village Index) and Scopus.


SPECIAL SESSIONS LIST

NLPinAI 2019Special Session on Natural Language Processing in Artificial Intelligence
Chair(s): Roussanka Loukanova

HAMT 2019Special Session on Human-centric Applications of Multi-agent Technologies
Chair(s): Yasushi Kambayashi

MALM 2019Special Session on Multimodal and Lifelong Machine Learning
Chair(s): Lambert Schomaker and Marco Wiering

Special Session on Natural Language Processing in Artificial Intelligence - NLPinAI 2019

Paper Submission: December 20, 2018
Authors Notification: January 7, 2019
Camera Ready and Registration: January 15, 2019


Chair

Roussanka Loukanova
Stockholm University
Sweden
e-mail
 
Scope

Computational and technological developments that incorporate natural language are proliferating. Adequate coverage encounters difficult problems related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Furthermore, agents (humans or computational systems) are information conveyors, interpreters, or participate as components of informational content. Generally, language processing depends on agents' knowledge, reasoning, perspectives, and interactions.


Special Session on Human-centric Applications of Multi-agent Technologies - HAMT 2019

Paper Submission: December 20, 2018
Authors Notification: January 7, 2019
Camera Ready and Registration: January 15, 2019


Chair

Yasushi Kambayashi
Nippon Institute of Technology
Japan
e-mail
 
Scope

Today, we are witnessing the advent of ubiquitous computing. Computers are not special machines, they are common apparatus. We are using computers without any special attention. Therefore computational technologies should become human centric; multi-agent systems are not exception. Human centric applications should be one of the mainstreams of multi-agent technologies. In this session, we would like to meet with various background research scientists as well as industry specialists to discuss how we can make various agent technologies, support the welfare of humanity. Through this session, we would like to foster any inspirations for designing and developing novel computational applications based on agent technologies as well as communication technologies such as Content-Centric Network technology to support human beings. 



Special Session on Multimodal and Lifelong Machine Learning  - MALM 2019

Paper Submission: December 20, 2018
Authors Notification: January 7, 2019
Camera Ready and Registration: January 15, 2019


Co-chairs

Lambert Schomaker
University of Groningen
Netherlands
e-mail
 
Marco Wiering
University of Groningen
Netherlands
e-mail
 
Scope

Modern machine learning has proven to be very successful in unimodal applications (image OR text OR sound). Furthermore, the successes are usually based on idealized training conditions with nicely packaged benchmark tests. The reality of historical-document retrieval is quite different. Optical character recognition is too limited to handle the variety of visual patterns in such image collections: text, graphics, tabular structures, doodles and diverse image problems make this a challenging playing field. Such systems start with zero labels, the labels change over time and the data is neither stationary nor ergodic. This session is intended for researchers who have picked up challenges in multimodal machine learning, possibly even in a time-varying context.


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