Special Issue "Recent Advances in Human-AI Interaction"
Deadline for manuscript submissions: 31 March 2021.
Interests: deep learning; machine learning; human–computer interaction; multi-modal interaction
Interests: ergonomics; UI/UX design and assessment; universal design; biomechanics; biomedical engineering
Interests: embedded system; wearable UI/UX; intelligent systems; machine learning
Interests: brain–computer Interface (BCI); neuromodulation; myoelectric control; deep learning; machine learning
Special Issues and Collections in MDPI journals
AI agents, such as chatbots, voice assistants, and social robots, are now becoming some of the most important and popular components that people can interact with in their daily lives. For successful design, implementation, and application of AI agents, researchers from various fields focus on addressing challenging issues, such as understanding users, sensing interaction contexts, detecting user intents, recognizing user emotions, and so on. Accordingly, the importance of basic building blocks of AI agents, such as multi-modal sensing, feature extraction and representation, and machine/deep learning technologies, is also increasing.
Despite the enormous efforts made in the past, providing a successful human-centered AI experience during human–AI interaction (HAII) is still a challenging ongoing task. The purpose of this Special Issue is to present and discuss novel ideas, research, applications, and results regarding HAII. It aims to bring together researchers from various fields to share their recent findings and developments in HAII, with a focus on technical aspects, and to explore future research directions.
The topics of interest include, but are not limited to:
- Hardware technologies for human–AI interaction
- Multi-modal (e.g., vision, speech, physiological signals) sensing for human–AI interaction
- Signal processing for human–AI interaction
- Feature extraction and representation for human–AI interaction
- Machine learning methodologies for human–AI interaction
- Deep learning methodologies for human–AI interaction
- Datasets and tools for human–AI interaction
- System architecture for human–AI interaction
- UI/UX for human–AI interaction
- Evaluation metrics for human–AI interaction
- Implementation of human–AI interaction
- Applications of human–AI interaction
Prof. Jin-Woo Jeong
Prof. Dr. Sang-Ho Kim
Prof. Dr. Wan-Su Lim
Dr. Han-Jeong Hwang
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Human–computer interaction (HCI)
- Human–AI interaction (HAII)
- User modeling and understanding
- Multi-modal interaction
- Multi-modal sensing
- Multi-modal signal processing
- Affective/cognitive computing and interaction
- Deep learning and machine learning