scholarly journals Applying Code Transform Model to Newly Generated Program for Improving Execution Performance

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Bao Rong Chang ◽  
Hsiu-Fen Tsai ◽  
Po-Wen Su
Keyword(s):  
Execution Time ◽  
Program Execution ◽  
Theoretical Estimation ◽  
Human Machine Interaction ◽  
Transform Method ◽  
Crucial Problem ◽  
Program Output ◽  
The Voice ◽  
Machine Interaction ◽  
Semantic Database

The existing programs inside the voice assistant machine prompt human-machine interaction in response to a request from a user. However, the crucial problem is that the machine often may not give a proper answer to the user or cannot work out the existing program execution efficiently. Therefore, this study proposes a novel transform method to replace the existing programs (called sample programs in this paper) inside the machine with newly generated programs through code transform model GPT-2 that can reasonably solve the problem mentioned above. In essence, this paper introduces a theoretical estimation in statistics to infer at least a number of generated programs as required so as to guarantee that the best one can be found within them. In addition, the proposed approach not only imitates a voice assistant system with filtering redundant keywords or adding new keywords to complete keyword retrieval in semantic database but also checks code similarity and verifies the conformity of the executive outputs between sample programs and newly generated programs. According to code checking and program output verification, the processes can expedite transform operations efficiently by removing the redundant generated programs and finding the best-performing generated program. As a result, the newly generated programs outperform the sample programs because the proposed approach reduces the number of code lines by 32.71% and lowers the program execution time by 24.34%, which is of great significance.

scholarly journals Voice as a Mouse Click: Usability and Effectiveness of Simplified Hands-Free Gaze-Voice Selection

2020 ◽  
Vol 10 (24) ◽  
pp. 8791
Author(s):  
Darisy G. Zhao ◽  
Nikita D. Karikov ◽  
Eugeny V. Melnichuk ◽  
Boris M. Velichkovsky ◽  
Sergei L. Shishkin
Keyword(s):  
Moving Objects ◽  
Human Machine Interaction ◽  
Pursuit Eye Movements ◽  
Mouse Click ◽  
Computer Mouse ◽  
The Difference ◽  
Low Performance ◽  
The Voice ◽  
Machine Interaction ◽  
Healthy Participants

Voice- and gaze-based hands-free input are increasingly used in human-machine interaction. Attempts to combine them into a hybrid technology typically employ the voice channel as an information-rich channel. Voice seems to be “overqualified” to serve simply as a substitute of a computer mouse click, to confirm selections made by gaze. It could be expected that the user would feel discomfort if they had to frequently make “clicks” using their voice, or easily get bored, which also could lead to low performance. To test this, we asked 23 healthy participants to select moving objects with smooth pursuit eye movements. Manual confirmation of selection was faster and rated as more convenient than voice-based confirmation. However, the difference was not high, especially when voice was used to pronounce objects’ numbers (speech recognition was not applied): Score of convenience (M ± SD) was 9.2 ± 1.1 for manual and 8.0 ± 2.1 for voice confirmation, and time spent per object was 1269 ± 265 ms and 1626 ± 331 ms, respectively. We conclude that “voice-as-click” can be used to confirm selection in gaze-based interaction with computers as a substitute for the computer mouse click when manual confirmation cannot be used.

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.

scholarly journals Human Factors Modelling Approach: Application to a Safety Device Supporting Crane Operations in Major Hazard Industries

2021 ◽  
Vol 13 (4) ◽  
pp. 2304
Author(s):  
Maria Francesca Milazzo ◽  
Giuseppa Ancione ◽  
Giancarlo Consolo
Keyword(s):  
Systematic Approach ◽  
New Technologies ◽  
Health And Safety ◽  
Safety Device ◽  
Human Machine Interaction ◽  
Safety And Health ◽  
Emotional Strain ◽  
Smart Safety ◽  
Machine Interaction ◽  
Modelling Approach

The European Directive on Safety and Health at Work and the following normatives have the scope to provide high levels of health and safety at work, based on some general principles managing activities and including the risk assessment to continuously improve processes and workplaces. However, the working area changes and brings new risks and challenges for workers. Several of them are associated with new technologies, which determine complex human–machine interactions, leading to an increased mental and emotional strain. To reduce these emerging risks, their understanding and assessment are important. Although great efforts have already been made, there is still a lack of conceptual frameworks for analytically assessing human–machine interaction. This paper proposes a systematic approach that, beyond including the classification in domains to explain the complexity of the human–machine interaction, accounts for the information processing of the human brain. Its validation is shown in a major accident hazard industry where a smart safety device supporting crane related operations is used. The investigation is based on the construction of a questionnaire for the collection of answers about the feeling of crane operators when using the device and the evaluation of the Cronbach’s alpha to measure of the reliability of the assessment.

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