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Introduction to the Special Issue on Affective Services based on Representation Learning

Introduction to the Special Issue on Affective Services based on Representation Learning Introduction to the Special Issue on Affective Services based on Representation Learning With advances in machine learning and artificial intelligence, a considerable impact is brought to all aspects of people’s lifestyles in terms of work, social, and economy. Especially, representation learning, which is one of the most crucial roles of deep learning, is developing rapidly and has been applied to many areas. Representation learning is expected to discover useful features or representations from complex, redundant, and highly variable data, such as images, video, and sensory data. In particular, through representation learning, a machine is available to learn the features rather than use the features. However, technologists have largely ignored emotion and created an often frustrating experi- ence for people, in part because affect has been misunderstood and hard to measure. Emotion is fundamental to human experience, influencing cognition, perception, and everyday tasks such as learning, communication, and even rational decision-making. Although the advanced techniques have considerably provided a lot of intelligent services, it is not adequate to provide affective services, including various unique aspects, e.g., sentiment anal- ysis, emotion recognition, affective interaction, affective computing, and so on. Under the new 135e service paradigm, novel affective services and innovative applications http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing Communications and Applications (TOMCCAP) Association for Computing Machinery

Introduction to the Special Issue on Affective Services based on Representation Learning

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2022 Copyright held by the owner/author(s).
ISSN
1551-6857
eISSN
1551-6865
DOI
10.1145/3567836
Publisher site
See Article on Publisher Site

Abstract

Introduction to the Special Issue on Affective Services based on Representation Learning With advances in machine learning and artificial intelligence, a considerable impact is brought to all aspects of people’s lifestyles in terms of work, social, and economy. Especially, representation learning, which is one of the most crucial roles of deep learning, is developing rapidly and has been applied to many areas. Representation learning is expected to discover useful features or representations from complex, redundant, and highly variable data, such as images, video, and sensory data. In particular, through representation learning, a machine is available to learn the features rather than use the features. However, technologists have largely ignored emotion and created an often frustrating experi- ence for people, in part because affect has been misunderstood and hard to measure. Emotion is fundamental to human experience, influencing cognition, perception, and everyday tasks such as learning, communication, and even rational decision-making. Although the advanced techniques have considerably provided a lot of intelligent services, it is not adequate to provide affective services, including various unique aspects, e.g., sentiment anal- ysis, emotion recognition, affective interaction, affective computing, and so on. Under the new 135e service paradigm, novel affective services and innovative applications

Journal

ACM Transactions on Multimedia Computing Communications and Applications (TOMCCAP) – Association for Computing Machinery

Published: Feb 13, 2023

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