Few-shot Learning for Human-machine Interactions (FSL-HMI)

The widespread use of Web technologies, mobile technologies, and cloud computing have paved a new surge of ubiquitous data available for business, human, and societal research. Nowadays, people interact with the world via various Information and Communications Technology (ICT) channels, generating a variety of data that contain valuable insights into business opportunities, personal decisions, and public policies. Machine learning has become the common task of applications in various application scenarios, e.g., e-commerce, health, transport, security and forensics, sustainable resource management, emergency and crisis management to support intelligent analytics, predictions, and decision-making. It has proven highly successful in data-intensive applications and revolutionized human-machine interactions in many ways in modern society.

Essential to machine learning is to deal with a small dataset or few-shot learning, which aims to develop learning models that can generalize rapidly generalize from a few examples. Though challenging, few-shot learning has gained increasing popularity since inception and has mostly focused on the studies in general machine learning contexts. Meanwhile, traditional human-machine interactions research has primarily focused on interaction design and local adaptation for user-friendliness, ergonomics, or efficiency. The emerging topics such as brain-computer interface, multimodal user interfaces, and mobile personal assistants as new means of human-machine interactions are still in their infancies. Few-shot learning is especially important for such new types of human-machine interactions due to the difficulty of acquiring examples with supervised information due to privacy, safety, expense, or ethical concerns. Although the related research is relatively new, it promises a fertile ground for research and innovation.

This special issue aims at gathering the recent advances and novel contributions from academic researchers and industry practitioners in the vibrant topic of few-shot learning to achieve the full potential of human-machine interaction applications. It calls for innovative methodological, algorithmic, and computational methods that incorporate the most recent advances in data analytics, artificial intelligence, and interaction research to solve the theoretical and practical problems. It also requires reexamining the existing architectures, models, and techniques in machine learning and deep neural networks to address the challenges to advance state-of-the-art knowledge in this area.

Topics of Interest include but not limited to:

* Novel few-shot, one-shot, or zero-shot learning models and algorithms for sense-making of humans, systems, and their interactions
* Conceptual frameworks, computational design for few-shot learning or human-centric computing
* Methods that improve the learnability, efficiency, or usability of systems that interact with humans
* Techniques to address small datasets, e.g., data imputation/augmentation, generative models, reinforcement learning, active learning.
* Novel recommender systems in HCI related aspects 
* Trust, security/privacy, and performance evaluations for few-shot learning
* Interface or interaction designs based on few shot examples to enable humans to interact with computers in novel ways 
* Other technologies and applications that advocates a better understanding of or exploiting values from human-machine interactions

Important Dates

Submission period:				July 1 – July 20, 2021
First review notification:		 	October 31, 2021
Revision submission:				December 20, 2021
Second review/submission (if required):		April 30, 2022
Publication:					June 30, 2022

Review Process

The review process will follow the standard PRLetters scheme, meaning that each paper will be reviewed by (at least) 2 referees and that, in general, only two reviewing rounds will be possible, out of which major revision is possible only for the first round. A paper will be most possibly rejected if after the 2nd reviewing round still need major revision.

Submission Instructions

Prospective authors should upload their submissions during the submission period through the Elsevier online system (https://ees.elsevier.com/prletters), with the article type selected as “FSL-HMI.” All submissions should be prepared by adhering to the PRLetters guidelines by taking into account that VSI papers follow the same submission rules as regular articles. The submissions should be original and technically sound, and they should not have been published previously, nor be under consideration for publication elsewhere. If the submissions are extended works of previously published papers, the original works should be quoted in the References and a description of the changes that have been made should be provided. All templates for preparing the submissions are available on the journal web site (https://www.elsevier.com/journals/pattern-recognition-letters/0167-8655/guide-for-authors).

Guest Editors:

Xianzhi Wang
University of Technology Sydney

Lina Yao
University of New South Wales

Yu Zhang
Lehigh University

Jordi Solé-Casals
University of Vic - Central University of Catalonia