Special Issue "Feature Papers in Human-Computer Interaction"

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Human-Computer Interaction".

Deadline for manuscript submissions: 31 January 2023 | Viewed by 4005

Special Issue Editor

Prof. Dr. Long Jin
E-Mail Website
Guest Editor
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Interests: robotics; neural networks; intelligent computing; optimization; distributed control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the Special Issue, entitled Feature Papers in Human–Computer Interaction. This Special Issue is keen to receive and publish high-quality submissions on any subject relevant to human–computer interaction. The topics of this Special Issue includes, but are not limited to:

  • Social computing;
  • Knowledge-driven human–computer interaction;
  • Emotions and human–computer interaction;
  • Brain–computer interfaces;
  • Human-centered artificial intelligence;
  • Optometry and human vision simulation;
  • Digital design and fabrication.

We welcome researchers to submit original research articles, reviews, and case reports. For well-prepared papers and those approved for further publication, authors might be eligible for discounts for publication.

Prof. Dr. Long Jin
Guest Editor

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 submissions that pass pre-check are 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. Informatics is an international peer-reviewed open access quarterly 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 1600 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.

Keywords

  • robot
  • human–computer interaction
  • robot design and algorithm
  • man–machine interaction
  • human–robot interaction

Published Papers (3 papers)

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Research

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Article
Raising Awareness of Smartphone Overuse among University Students: A Persuasive Systems Approach
Informatics 2022, 9(1), 15; https://doi.org/10.3390/informatics9010015 - 23 Feb 2022
Viewed by 1059
Abstract
Smartphone overuse can lead to a series of physical, mental and social disturbances. This problem is more prevalent among young adults as compared to other demographic groups. Additionally, university students are already undergoing high cognitive loads and stress conditions; therefore, they are more [...] Read more.
Smartphone overuse can lead to a series of physical, mental and social disturbances. This problem is more prevalent among young adults as compared to other demographic groups. Additionally, university students are already undergoing high cognitive loads and stress conditions; therefore, they are more susceptible to smartphone addiction and its derived problems. In this paper, we present a novel approach where a conversational mobile agent uses persuasive messages exploring the reflective mind to raise users’ awareness of their usage and consequently induce reduction behaviors. We conducted a four-week study with 16 university students undergoing stressful conditions—a global lockdown during their semester—and evaluated the impact of the agent on smartphone usage reduction and the perceived usefulness of such an approach. Results show the efficacy of self-tracking in the behavior change process: 81% of the users reduced their usage time, and all of them mentioned that having a conversational agent alerting them about their usage was useful. Before this experiment, only 68% of them considered such an approach could be useful. In conclusion, users deemed it essential to have an engaging conversational agent on their smartphones, in terms of helping them become more aware of usage times. Full article
(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)
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Review

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Review
Risk Determination versus Risk Perception: A New Model of Reality for Human–Machine Autonomy
Informatics 2022, 9(2), 30; https://doi.org/10.3390/informatics9020030 - 24 Mar 2022
Cited by 1 | Viewed by 920
Abstract
We review the progress in developing a science of interdependence applied to the determinations and perceptions of risk for autonomous human–machine systems based on a case study of the Department of Defense’s (DoD) faulty determination of risk in a drone strike in Afghanistan; [...] Read more.
We review the progress in developing a science of interdependence applied to the determinations and perceptions of risk for autonomous human–machine systems based on a case study of the Department of Defense’s (DoD) faulty determination of risk in a drone strike in Afghanistan; the DoD’s assessment was rushed, suppressing alternative risk perceptions. We begin by contrasting the lack of success found in a case study from the commercial sphere (Facebook’s use of machine intelligence to find and categorize “hate speech”). Then, after the DoD case study, we draw a comparison with the Department of Energy’s (DOE) mismanagement of its military nuclear wastes that created health risks to the public, DOE employees, and the environment. The DOE recovered by defending its risk determinations and challenging risk perceptions in public. We apply this process to autonomous human–machine systems. The result from this review is a major discovery about the costly suppression of risk perceptions to best determine actual risks, whether for the military, business, or politics. For autonomous systems, we conclude that the determinations of actual risks need to be limited in scope as much as feasible; and that a process of free and open debate needs to be adopted that challenges the risk perceptions arising in situations facing uncertainty as the best, and possibly the only, path forward to a solution. Full article
(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)
Review
Human-Computer Interaction in Digital Mental Health
Informatics 2022, 9(1), 14; https://doi.org/10.3390/informatics9010014 - 22 Feb 2022
Cited by 1 | Viewed by 1680
Abstract
Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have [...] Read more.
Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance. Full article
(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)
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