Impact Score 6.76
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:
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).
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 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
Xianzhi Wang (Managing Guest Editor)
Affiliation: School of Computer Science, University of Technology Sydney
Address: 81 Broadway, Ultimo, NSW 2007, Australia
Email: [email protected]
Telephone: +61 2 9514 5386
Bio: Xianzhi Wang is a lecturer and Decra Fellow at the School of Computer Science, University of Technology Sydney, Australia. He received his PhD degree from Harbin Institute of Technology, China in 2014. His research interests include Internet of Things, data mining, machine learning, recommender systems, and cybersecurity. His work has been published in top-tier journals and conferences, e.g., IEEE TNNLS, IEEE TMC, IEEE TSC, ACM TOIT, ICDM, KDD, AAAI, IJCAI, UbiComp, SIGIR, CIKM, ER, PAKDD, IJCNN, ICSOC, and ICWS. He worked at Singapore Management University, UNSW Sydney, University of Adelaide, respectively, during 2014-2018. He visited/interned at Arizona State University and IBM Research – China in 2010-2011 and 2013. He served as the guest editor for ACM Trans. on Sensor Networks, Journal of Big Data, and editorial board member for IJWET. He was the program co-chair for special tracks in EUSPN 2018, IEEE Mobile Service 2015, DPBA 2015, publicity chair for IUPT 2017, and panelist for Australian Computer Science Week 2019. He is the recipient of Australian Research Council Discovery Early Career Researcher Award 2017, IBM PhD Fellowship 2013, and Best Paper Award of China Computer Federation National Conference on Service Computing 2010. He has made three publications in PRLetters.
Google Scholar: https://scholar.google.com.au/citations?user=Xej6piMAAAAJ&hl=en
Affiliation: School of Computer Science and Engineering, University of New South Wales
Address: Barker St, Kensington, NSW 2052, Australia
Email: [email protected]
Telephone: +61 2 9385 6588
Bio: Lina Yao is a Senior Lecturer at the School of Computer Science and Engineering, University of New South Wales (UNSW), Australia. She received her PhD degree from the University of Adelaide in July 2014. Her research interests include developing novel theoretically sound and empirically useful intelligent systems and techniques (e.g., recommender systems, human activity recognition, Brain-computer Interface, and Internet of Things) to reinforce joining forces of human and AI systems. She is particularly interested in developing data-efficient and robust machine learning frameworks, models and algorithms to discover unknown patterns and learn actionable knowledge from heterogeneous, multimodal, dynamic, and sparse data. She has published over 120 peer-reviewed papers in prestigious journals and top international conferences in the areas of data mining, machine learning and intelligent systems, e.g., ACM CACM, ACM CUSR, IEEE TMC, ACM TIST, IEEE TKDE, ACM TKDD, ACM TOIT, PR, IEEE TNSRE, IEEE IIT, JBHI, NeurIPS, SIGKDD, IEEE ICDM, ACM Ubicomp, AAAI, SIGIR, IJCAI, and CIKM. She serves as the Associate Editor for ACM Transactions on Sensor Networks (TOSN), PC member for NeurIPS, SIGKDD, AAAI, IJCAI, ICDM, CIKM, ACM MM, PerCom, WoWMoM, and reviewer for several reputed journals, e.g., ACM TOIS, IEEE TKDE, ACM TKDD, ACM TOSN, IEEE TNNLS, IEEE TSC, IEEE TYCB, WWW, JBHI, TOIS, and IEEE IIT. She has made three publications in PRLetters.
Google Scholar: https://scholar.google.com.au/citations?user=EU3snBgAAAAJ&hl=en
Affiliation: Department of Bioengineering, Lehigh University
Address: Iaccoca Hall, 111 Research Drive, D325, Bethlehem, PA 18015, USA
Email: [email protected]
Telephone: +1 610 758 4091
Bio: Yu Zhang is an Assistant Professor of Bioengineering at Lehigh University, USA and a Research Fellow (2017-2020) at the Department of Psychiatry and Behavior Sciences, Stanford University, USA. Before joining Stanford, he also worked as a Research Fellow (2016-2017) at Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, USA. Since 2014, he has been a Visiting Scientist at RIKEN Brain Science Institute, Japan. From 2013 to 2016, he worked as an Assistant Professor and then Associate Professor in the Department of Automation at ECUST, China, where he got his PhD degree in Control Science and Engineering in 2013. From 2010 to 2012, he was a Research Associate with the RIKEN BSI, Japan. He is the author of over 100 technical papers published in prestigious Journals, e.g., Nature Biotechnology, Nature Human Behaviour, Proceedings of the IEEE, IEEE TCYB, IEEE TNNLS, IEEE TNSRE, and IEEE TBME. His research interests include computational neuroscience, brain network, pattern recognition, machine learning, signal processing, artificial intelligence, brain-computer interface, and medical imaging computing.
Affiliation: Department of Engineering, University of Vic - Central University of Catalonia
Address: Carrer de la Laura 13, 08500 Vic, Barcelona, Spain
Email: [email protected]
Telephone: +34 9 3881 5519
Bio: Jordi Solé-Casals currently holds a permanent position as a Full Professor of the Department of Engineering of the University of Vic – Central University of Catalonia and is the head of the Data and Signal Processing Research Group (DSP, UVic-UCC). He is also Visiting Scientist (2016 ~) at the Brain Mapping Unit of the Department of Psychiatry of the University of Cambridge (UK) and Visiting Scientist (2020 ~) at the College of Artificial Intelligence, Nankai University (China). He obtained the Ph.D. degree with European label in 2000, and the B.Sc. degree in Telecommunications in 1995, both from the Polytechnic University of Catalonia (UPC), Barcelona; and the B.Hum in 2010 from the Open University of Catalonia (UOC), Barcelona. In 1994 he joined the Department of Engineering of the University of Vic – Central University of Catalonia, where he was the Director (2010-2012). He was Visiting Research/Scientist with the GIPSA Lab. in Grenoble (France), the Lab. for Advanced Brain Signal Processing, BSI-RIKEN in Wako (Japan) and the Tensor Learning Team, at the RIKEN Center for Advanced Intelligence Project (AIP), Tokyo (Japan). Currently he continues the relationships with these laboratories. His research interests include signal processing specially in the biomedical field (EEG, fMRI, speech, handwritten, biometric applications), machine learning/deep learning and statistical modelling for applied sciences.