scholarly journals Artificial sociality in the human-machine interaction

2021 ◽  
Vol 21 (2) ◽  
pp. 377-390
Author(s):  
V. Komarova ◽  
J. Lonska ◽  
V. Tumalavičius ◽  
A. Krasko
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Correlation Analysis ◽  
Empirical Study ◽  
Scientific Literature ◽  
Cultural Dimensions ◽  
Logical Analysis ◽  
Human Machine Interaction ◽  
Contemporary World ◽  
Machine Interaction

The article aims at clarifying the concept artificial sociality in the human-machine interaction by answering the question whether artificial sociality is a prerequisite or a result of this interaction. The authors conducted a logical analysis of the definitions of sociality and artificial sociality as presented in the scientific literature, and conducted an empirical study of artificial sociality in the human-machine interaction with three methods - comparison of means, correlation analysis and discriminant analysis. All three methods were used in the analysis of the same data: indicators of the potential of the human-machine interaction and G. Hofstedes six cultural dimensions. With these measurements of culture, the authors interpreted empirically the degree of its artificiality (based on the methodological assumption about the combination of natural and artificial in culture) which determines the development of artificial sociality. Based on the results of the application of three methods of statistical analysis, the authors conclude that in the contemporary world, there are both conditionally artificial cultures that are the most favourable for the development of artificial (algorithmic) sociality and conditionally natural cultures that hinder the development of artificial sociality. This type of sociality emerged under the development of writing and various methods of processing and storing information (catalogues, archives, etc.), i.e., long before the creation of machines. Artificial sociality is determined by the relative artificiality of culture, and is a prerequisite rather than a result of the human-machine interaction.

The Effects of Exercise Commitment Facilitated by Service Quality of Smartphone Exercise Applications on Continued Exercise Intention

2019 ◽  
Vol 118 (1) ◽  
pp. 14-19
Author(s):  
Boo-Gil Seok ◽  
Hyun-Suk Park
Keyword(s):  
College Students ◽  
Factor Analysis ◽  
Regression Analysis ◽  
Statistical Analysis ◽  
Correlation Analysis ◽  
Service Quality ◽  
Frequency Analysis ◽  
Primary Data ◽  
Exercise Intention

Background/Objectives: The purpose of this study is to examine the effects of exercise commitment facilitated by service quality of smartphone exercise Apps on continued exercise intention and provide primary data for developing and/or improving smartphone exercise Apps. Methods/Statistical analysis: A questionnaire survey was conducted amongst college students who have experiences in using exercise App(s) and regularly exercise. The questionnaire is composed of four parts asking about service quality, exercise commitment, continued exercise intention, which were measured with a 5-point Likert Scale, and demographics. Frequency analysis, factor analysis, correlation analysis, and regression analysis were carried out to analyze the obtained data with PASW 18.0.

Structural Relationships among Customer Delight, Exercise Commitment, and Psychological Happiness

2019 ◽  
Vol 118 (1) ◽  
pp. 8-13
Author(s):  
Boo-Gil Seok ◽  
Hyun-Suk Park
Keyword(s):  
College Students ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Confirmatory Factor Analysis ◽  
Correlation Analysis ◽  
Frequency Analysis ◽  
Structural Correlation ◽  
Structural Relationships ◽  
Confirmatory Factor ◽  
Customer Delight

Background/Objectives: The purpose of this study is to find out the structural relationships among customer delight, exercise commitment, and psychological happiness to contribute developing exercise Apps. Methods/Statistical analysis: A questionnaire survey was conducted and 160 college students who are familiar with mobile exercise applications participated. The data analyzed with frequency analysis, exploratory factor analysis, confirmatory factor analysis, correlation analysis, and structural correlation analysis. The validity and the reliability were obtained: customer delight (χ2=26.532, df=14, CFI=.985, TLI=.971, RMSEA=.075), exercise commitment (χ2=113.802, df=49, CFI=.956, TLI=.941, RMSEA=.091), and psychological happiness (χ2=15.338, df=8, CFI=.989, TLI=.980, RMSEA=.076, and Cronbach’s α=.906~.938).

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.

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