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This study aims to realize natural and effort-saving motion behavior and improve effectiveness for different operators in human–robot force cooperation.

The parameter of admittance model is identified by deep deterministic policy gradient (DDPG) to realize human–robot force cooperation for different operators in this paper. The movement coupling problem of hybrid robot is solved by realizing position and pose drags. In DDPG, minimum jerk trajectory is selected as the reward objective function, and the variable prioritized experience replay is applied to balance the exploration and exploitation.

A series of simulations are implemented to validate the superiority and stability of DDPG. Furthermore, three sets of experiments involving mass parameter, damping parameter and DDPG are implemented, the effect of DDPG in real environment is validated and could meet the cooperation demand for different operators.

DDPG is applied in admittance model identification to realize human–robot force cooperation for different operators. And minimum jerk trajectory is introduced into reward objective to meet requirement of human arm free movements. The algorithm proposed in this paper could be further extended in the other operation task.

This research aims to explore digital feedback needs/preferences in online education during lockdown and the implications for post-pandemic education.

An empirical study approach was used to explore feedback needs and experiences from educational institutions in the Netherlands and Germany (N = 247) using a survey method.

The results showed that instruments supporting features for effortless interactivity are among the highly preferred options for giving/receiving feedback in online/hybrid classrooms, which are in addition also opted for post-pandemic education. The analysis also showed that, when communicating feedback digitally, more inclusive formats are preferred, e.g. informing learners about how they perform compared to peers. The increased need for comparative performance-oriented feedback, however, may affect students' goal orientations. In general, the results of this study suggest that while interactivity features of online instruments are key to ensuring social presence when using digital forms of feedback, balancing online with offline approaches should be recommended.

This research contributes to the gap in the scientific literature on feedback digitalization. Most of the existing research are in the domain of automated feedback generated by various learning environments, while literature on digital feedback in online classrooms, e.g. empirical studies on preferences for typology, formats and communication channels for digital feedback, to the best of the authors’ knowledge is largely lacking. The findings and recommendations of this study extend their relevance to post-pandemic education for which hybrid classroom is opted among the highly preferred formats by survey respondents.

The purpose of this paper is to analyze user behavior toward multi-screen services by employing neural networks to predict overall customer satisfaction and to prioritize the factors that influence customer intentions.

Multi-screen experiences require a new approach incorporating multiple methods. A proposed multi-state analytic approach in which the research model is tested using structural equation modeling was utilized. The results were then used as inputs for a neural network model to predict multi-screen adoption.

The findings indicate that multi-screen quality significantly influences usability, which subsequently affects the adoption of the technology.

The policy and managerial implications of multi-screen development are discussed based on the models of acceptance and diffusion.

The emergence of multi-screen services as well as the simultaneous and sequential engagement of users with multiple devices throughout the day challenges the ability of marketers to develop effective communication strategies.

This study provides an in-depth analysis and heuristic data regarding user drivers, market dynamics, and policy implications in the one-source multi-use ecosystem.

Lectures can be digitally recorded and replayed to provide multimedia revision material for students who attended the class and a substitute learning experience for students unable to attend. Deaf and hard of hearing people can find it difficult to follow speech through hearing alone or to take notes while they are lip‐reading or watching a sign‐language interpreter. Notetakers can only summarise what is being said while qualified sign language interpreters with a good understanding of the relevant higher education subject content are in very scarce supply. Synchronising the speech with text captions can ensure deaf students are not disadvantaged and assist all learners to search for relevant specific parts of the multimedia recording by means of the synchronised text. Real time stenography transcription is not normally available in UK higher education because of the shortage of stenographers wishing to work in universities. Captions are time consuming and expensive to create by hand and while Automatic Speech Recognition can be used to provide real time captioning directly from lecturers’ speech in classrooms it has proved difficult to obtain accuracy comparable to stenography. This paper describes the development of a system that enables editors to correct errors in the captions as they are created by Automatic Speech Recognition.

This research aims to explore how textual literacy and computational literacy can support each other and combine to create literacies with new critical possibilities. It describes the development of a Web application for interactive storytelling and analyzes how its use in a high-school classroom supported new rhetorical techniques and critical analysis of gender and race.

Three iterations of design-based research were used to develop a Web application for interactive storytelling, which combines writing with programming. A two-week study in a high-school sociology class was conducted to analyze how the Web application's textual and computational affordances support rhetorical strategies, which in turn support identity authorship and critical possibilities.

The results include a Web application for interactive storytelling and an analytical framework for analyzing how affordances of digital media can support literacy practices with unique critical possibilities. The final study showed how interactive stories can function as critical discourse models, simulations of social realities which support analysis of phenomena such as social positioning and the use of power.

Previous work has insufficiently spanned the fields of learning sciences and literacies, respectively emphasizing the mechanisms and the content of literacy practices. In focusing a design-based approach on critical awareness of identity, power and privilege, this research develops tools and theory for supporting critical computational literacies. This research envisions a literacy-based approach to K-12 computer science which could contribute to liberatory education.

As complex analysis of contact models is required in the traditional assembly strategy, it is still a challenge for a robot to complete the multiple peg-in-hole assembly tasks autonomously. This paper aims to enable the robot to complete the assembly tasks autonomously and more efficiently, with the strategies learned by reinforcement learning (RL), a learning-accelerated deep deterministic policy gradient (LADDPG) algorithm is proposed.

The multiple peg-in-hole assembly strategy is designed in two modules: an advanced planning module and a bottom control module. The advanced module is completed by the LADDPG agent, which is used to derive advanced commands based on geometric and environmental constraints, that is, the desired contact force. The bottom-level control module will drive the robot to complete the compliant assembly task through the adaptive impedance algorithm according to the command set issued by the advanced module. In addition, a set of safety assurance mechanisms is developed to safely train a collaborative robot to complete autonomous learning.

The method can complete the assembly tasks well through RL, and it can realize satisfactory compliance of the robot to the environment. Compared with the original DDPG algorithm, the average values of the instantaneous maximum contact force and contact torque during the assembly process are reduced by approximately 38% and 74%, respectively.

The entire algorithm can also be applied to other robots and the assembly strategy can be applied in the field of the automatic assembly.

A compliant assembly strategy based on the LADDPG algorithm is proposed to complete the automated multiple peg-in-hole assembly tasks.

The purpose of this paper is to examine spectator perceptions of service quality at the Formula One (F-1) event in Shanghai by means of Martilla and James’ Importance and Performance Analysis (IPA).

The items were plotted on the four IPA dimensions, including: Concentrate Here; Keep Up the Good Work; Lower Priority; and Possible Overkill.

The results indicated that staff courtesy seems to be the strength of the event. However, concession quality, food prices, and arena accessibility, including restrooms at the Shanghai F-1 event, did not meet spectators’ expectations. In addition, the results suggested that in any spectator sporting event, food consumption helps to create a positive experience. Therefore, IPA revealed that service quality related to concessions, food pricing, and arena accessibility at the Shanghai F-1 are in need of immediate improvement, whereas staff courtesy and race visuals were satisfactory.

The findings will help marketers prioritize certain services and offer strategic direction in effective service provision by highlighting management problems.

This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs).

The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control.

It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies.

In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions.

The authors propose the concept of “Autonomic Cycle for innovation processes,” which defines a set of tasks of data analysis, whose objective is to improve the innovation process in micro-, small and medium-sized enterprises (MSMEs).

The authors design autonomic cycles where each data analysis task interacts with each other and has different roles: some of them must observe the innovation process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the innovation process.

In this article, the authors identify three innovation sub-processes which can be applied to autonomic cycles, which allow interoperating the actors of innovation processes (data, people, things and services). These autonomic cycles define an innovation problem, specify innovation requirements, and finally, evaluate the results of the innovation process, respectively. Finally, the authors instance/apply the autonomic cycle of data analysis tasks to determine the innovation problem in the textile industry.

It is necessary to implement all autonomous cycles of data analysis tasks (ACODATs) in a real scenario to verify their functionalities. Also, it is important to determine the most important knowledge models required in the ACODAT for the definition of the innovation problem. Once determined this, it is necessary to define the relevant everything mining techniques required for their implementations, such as service and process mining tasks.

ACODAT for the definition of the innovation problem is essential in a process innovation because it allows the organization to identify opportunities for improvement.

The main contributions of this work are: For an innovation process is specified its ACODATs in order to manage it. A multidimensional data model for the management of an innovation process is defined, which stores the required information of the organization and of the context. The ACODAT for the definition of the innovation problem is detailed and instanced in the textile industry. The Artificial Intelligence (AI) techniques required for the ACODAT for the innovation problem definition are specified, in order to obtain the knowledge models (prediction and diagnosis) for the management of the innovation process for MSMEs of the textile industry.