User experience research on social robot application

PurposeSocial robot, a subtype of robots that is designed for the various interactive services for human, which must deliver superior user experience (UX) by expressing human-like social behavior or service and emotional sensitivity. This study develops a social robot app called the “Music Buddy” in ASUS Zenbo that provides a situational music based on the users' electroencephalogram (EEG) data. The research uses this app to explore its UX criteria and the prioritization of human robot interaction (HRI).Design/methodology/approachThe research methodologies include the both system development and decision analysis for the social robot. The first part is to design and develop a social robot app. The second part is to investigate the criteria of HRI through the Analytic Hierarchy Process (AHP) from UX aspects.FindingsIn view of the results of the AHP, the first-layer criteria consist of personalized function, easy-to-use the system and intelligent process. In terms of prioritization of multi-criteria, the overall ranking discloses the nine criteria in order including autonomy for robot, easy-to-use EEG device, accurate music preference, simple operations for brainwave device and easy-to-use applications, active music recommendation, automatic updates of music and easy-to-use robot as well as fast detection for emotion.Originality/valueThis research includes a self-developed social robot app and its UX research using AHP. This paper contributes to the improvement and innovation of the social robot design according to the results of UX research on HRI of social robot.

Can machine learning improve carbon storage? Synergies of deep learning, uncertainty quantification and intelligent process control

As we transition from fossil fuel to renewable energy, negative emission technologies, such ascarbon capture and storage (CCS), can help us reduce CO2 emissions. Effective CO2 storage requires: (1) detailed site characterization, (2) regular, integrated risk assessment, and (3) flexible design and operation. We believe that recent advances in machine learning coupled with uncertainty quantification and intelligent process control help us with these task and thus im-prove the efficiency and safety of subsurface CO2 storage.

Masked Face Recognition Using Deep Learning: A Review

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

Application of Information Digitalization Technology in Audit Process Through Intelligent Process Automation (IPA) Approach

Intelligent Process Automation (IPA) is a technology capable of organizing automation processes for structured, semi-structured, and unstructured data. In audit assignments, there are three types of assignments, namely structured, semi-structured and unstructured assignments. This study aims to identify the possibility of applying IPA (Intelligent Process Automation) in audit assignments. By using IPA technology, it is expected to be able to create efficiency and effectiveness in the audit process. As we know that audit assignments require time and high costs, so IPA technology is expected to be able to shorten audit time and costs without reducing the quality of services provided by auditors. This research is qualitative research using primary data. The respondents in this study are public accountants who work in the Public Accounting Firm (KAP). The research instrument consists of mapping structured, semi-structured, unstructured audit assignments; then identified each with the possibility of applying IPA (Intelligent Process Automation) in each audit process. Respondents in this study were forty-one. The results of the analysis using descriptive analysis and frequency analysis, and it was found that the use of automation of audit procedures at the audit planning stage was still rarely used. With the following details (1) structured assignment, still rarely used; (2) semi-structured assignment, still rarely used; (3) unstructured assignments, sometimes used. Meanwhile, the use of automation of audit procedures at the control and substantive testing stage as well as the overall conclusion (audit completion) stage is sometimes used, both structured, semi-structured, and unstructured assignments.

Selection of optimal forecasting models in the navigation safety decision support system

One of the most important components of the navigation safety decision support system (NS DSS) is the forecasting block. The results of his work have an impact on the type of control actions formed by the system. The effectiveness of this block depends on the forecasting methods used. The accuracy of the result of the forecasting block depends largely on the choice of the forecasting method.To develop reliable forecasts, it is necessary to determine forecasting methods, in relation to the specifics of the functioning of the NS DSS. Several ways of solving the problem of choosing the appropriate forecasting method in relation to the NS DSS are proposed. Two methods are demonstrated: Artificial neural networks (ANN) using precedents and Intelligent process analysis (IPA). Solving the problem of choosing the optimal method will guarantee obtaining a forecast with a certain level of accuracy, which will significantly increase the reliability of the forecast and, as a result, the effectiveness of the NS DSS. Solving the problem of choosing the optimal method will guarantee obtaining a forecast with a certain level of accuracy, which will significantly increase the reliability of the forecast and, as a result, the effectiveness of the NS DSS, which allows us to assert the relevance of this problem and further research.