How Trust is Defined and its use in Human-Human and Human-Machine Interaction

2020 ◽  
Vol 64 (1) ◽  
pp. 1150-1154
Author(s):  
A.D. Kaplan ◽  
T.T. Kessler ◽  
P.A. Hancock
Keyword(s):  
Critical Element ◽  
Human Machine Interaction ◽  
Qualitative And Quantitative ◽  
Empirical Measurement ◽  
Definition Of ◽  
Machine Interaction

Trust is a critical element in virtually all forms of interactions, including those between humans and machines. Yet aspects of trust do vary somewhat between definitions. The present work seeks to unify these disparate definitions of trust, comparing and contrasting between the major works. Overall, every definition of trust involves an individual in a position of vulnerability (the trustor) and a person on whom they must rely (the trustee) despite circumstances which may place the trustor in some kind of potential for harm. Such engagements are enacted in order to secure some form of gain from the trusting relationship. The ways in which these definitions influence empirical measurement (both qualitative and quantitative) are identified and elaborated on.

AF 447 as a Paradigmatic Accident

2021 ◽  
pp. 1-27
Author(s):  
Antonio Chialastri
Keyword(s):  
Complex Systems ◽  
Human Factors ◽  
Cognitive Demands ◽  
Human Machine Interaction ◽  
History Of ◽  
Definition Of ◽  
Machine Interaction

In this chapter, the author presents a human factors problem for automation: why, when, and how automation has been introduced in the aviation domain; what problems arise from different ways of operating; and the possible countermeasures to limit faulty interaction between humans and machines. This chapter is divided into parts: definition of automation, its advantages in ensuring safety in complex systems such as aviation; reasons for the introduction of on-board automation, with a quick glance at the history of accidents in aviation and the related safety paradigms; ergonomics: displays, tools, human-machine interaction emphasizing the cognitive demands in high tempo and complex flight situations; illustration of the AF 447 case, a crash happened in 2009, which causes are linked to faulty human-machine interaction.

scholarly journals Combining Big Data and Artificial Intelligence for Managing Collective Knowledge in Unpredictable Environment—Insights from the Chinese Case in Facing COVID-19

2020 ◽  
Author(s):  
Francesca Iandolo ◽  
Francesca Loia ◽  
Irene Fulco ◽  
Chiara Nespoli ◽  
Francesco Caputo
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Knowledge Management ◽  
Business Models ◽  
New Technologies ◽  
Human Machine Interaction ◽  
Decision Making Processes ◽  
Unpredictable Environment ◽  
Definition Of ◽  
Machine Interaction

AbstractThe increasing fluidity of social and business configurations made possible by the opportunities provided by the World Wide Web and the new technologies is questioning the validity of consolidated business models and managerial approaches. New rules are emerging and multiple changes are required to both individuals and organizations engaged in dynamic and unpredictable paths.In such a scenario, the paper aims at describing the potential role of big data and artificial intelligence in the path toward a collective approach to knowledge management. Thanks to the interpretative lens provided by systems thinking, a framework able to explain human-machine interaction is depicted and its contribution to the definition of a collective approach to knowledge management in unpredictable environment is traced.Reflections herein are briefly discussed with reference to the Chinese governmental approach for managing COVID-19 spread to emphasise the support that a technology-based collective approach to knowledge management can provide to decision-making processes in unpredictable environments.

AF 447 as a Paradigmatic Accident

2019 ◽  
pp. 166-192
Author(s):  
Antonio Chialastri
Keyword(s):  
Complex Systems ◽  
Human Factors ◽  
Cognitive Demands ◽  
Human Machine Interaction ◽  
History Of ◽  
Definition Of ◽  
Machine Interaction

In this chapter, the author presents a human factors problem for automation: why, when, and how automation has been introduced in the aviation domain; what problems arise from different ways of operating; and the possible countermeasures to limit faulty interaction between humans and machines. This chapter is divided into parts: definition of automation, its advantages in ensuring safety in complex systems such as aviation; reasons for the introduction of on-board automation, with a quick glance at the history of accidents in aviation and the related safety paradigms; ergonomics: displays, tools, human-machine interaction emphasizing the cognitive demands in high tempo and complex flight situations; illustration of the AF 447 case, a crash happened in 2009, which causes are linked to faulty human-machine interaction.

Identification of Physical Parameters in a Robotized IPC Device Interacting with Human

2014 ◽  
Vol 490-491 ◽  
pp. 1729-1733 ◽  
Author(s):  
Carlo Ferraresi ◽  
Hamidreza Hajimirzaalian ◽  
Daniela Maffiodo
Keyword(s):  
Intermittent Pneumatic Compression ◽  
Physical Parameters ◽  
Human Machine Interaction ◽  
Sports Activity ◽  
Simple Method ◽  
Performance Recovery ◽  
Pneumatic Compression Devices ◽  
Definition Of ◽  
And Control ◽  
Machine Interaction

Intermittent Pneumatic Compression devices are widely used for various therapies concerning the cardio-circulatory or lymphatic system, and also for performance recovery in sports activity. The development and setup of such devices are mainly based on empirical procedures, while few researches adopt an engineering approach based on mathematical modeling and identification. In this approach, the most critical point is the definition of parameters concerning the human-machine interaction. This paper proposes an original and simple method to identify such parameters, which allows to describe in effective way the main dynamic characteristics, fundamental for a correct design and control of the device.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7567
Author(s):  
Xiaochen Zhang ◽  
Lanxin Hui ◽  
Linchao Wei ◽  
Fuchuan Song ◽  
Fei Hu
Keyword(s):  
The Elderly ◽  
Research Field ◽  
Future Research ◽  
Human Machine Interaction ◽  
Research Directions ◽  
The Past ◽  
Power Wheelchairs ◽  
The Disabled ◽  
Future Research Directions ◽  
Machine Interaction

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

Holistic, Context-sensitive Human-machine Interaction for Automated Vehicles

ATZ worldwide ◽  
2021 ◽  
Vol 123 (3) ◽  
pp. 46-49
Author(s):  
Tobias Hesse ◽  
Michael Oehl ◽  
Uwe Drewitz ◽  
Meike Jipp
Keyword(s):  
Automated Vehicles ◽  
Human Machine Interaction ◽  
Context Sensitive ◽  
Machine Interaction

scholarly journals Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 834
Author(s):  
Magbool Alelyani ◽  
Sultan Alamri ◽  
Mohammed S. Alqahtani ◽  
Alamin Musa ◽  
Hajar Almater ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Vital Role ◽  
Human Machine Interaction ◽  
The Future ◽  
Level Of Knowledge ◽  
Electronic Questionnaires ◽  
Machine Interaction ◽  
Applications Of Ai

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

Sign in / Sign up

Export Citation Format

Share Document