Despite the fact that task-oriented conversation systems have received much attention from the dialogue research community, only a handful of them have been studied in a real-world manufacturing context using industrial robots. One stumbling block is the lack of a domain-specific discourse corpus for training these systems. Another difficulty is that earlier attempts to integrate natural language interfaces (such as chatbots) into the industrial sector have primarily focused on task completion rates. When designing a dialogue system for social robots, the user experience is prioritized above industrial robots. We provide the Industrial Robots Domain Wizard-of-Oz dataset (IRWoZ) to overcome these challenges, a fully-labeled discourse dataset covering four robotics domains. It delivers simulated discussions between shop floor workers and industrial robots, with over 401 dialogues, to promote language-assisted Human-Robot Interaction (HRI) in industrial settings. Small talk concepts and human-to-human conversation strategies are provided to support humanlike answer generation and give a more natural and adaptable dialogue environment to increase user experience and engagement. Finally, we propose and evaluate an end-to-end Task-oriented Dialogue for Industrial Robots (ToD4IR) using two types of pre-trained backbone models: GPT-2 and GPT-Neo, on the IRWoZ dataset. ToD4IR’s performance in a real manufacturing context was validated through a series of trials. Our experiments demonstrate that ToD4IR outperforms three downstream task-oriented dialogue tasks, i.e., dialogue state tracking, dialogue act generation, and response generation, on the IRWoZ dataset. Our source code of ToD4IR and the IRWoZ dataset is accessible at https://github.com/lcroy/ToD4IR for reproducible research.

The development of tourism intangible cultural heritage, by its very nature, is a process of social construction by stakeholders through their transaction, coordination, interest alienation and sharing the responsibility. Coordinating and balancing the interest conflict within every stakeholder to achieve the 'Pareto Optimality' is a major difficulty in the tourism intangible cultural heritage development. In another side, semantic web is a computer term that is known its efficiency in knowledge representation of concepts. In this paper, we applied semantic web to modelling the balance model of stakeholders of tourism intangible heritage in the data collecting process, including data acquisition from entire network combining with structural equation method and path analysis based on a big data platform and questionnaire design, which is the innovative attempt to integrate these two typical computer technologies into social scientific research. Further analysis of the final constructed model demonstrates the effectiveness of the proposed application.

Web service security is essential for SOA-based applications. Based on the analysis of the two existing authentications of REST-style Web services, Basic HTTP Authentication and HTTP Digest Authentication, we propose an extended UsernameToken-based approach for REST-style Web service. In this approach, the WS-Security UsernameToken and secondary password are added into the HTTP header. By this way, the approach allows service providers to define their own authentication which makes up for the disadvantages of the current security aspect of REST-style Web services, especially when Basic HTTP Authentication and HTTP Digest Authentication are not applicable. Analysis shows that the approach implements the REST-style Web service security effectively.

Enabling a flexible and natural human-robot interaction (HRI) for industrial robots is a critical yet challenging task that can be facilitated by the use of conversational artificial intelligence (AI). Prior research has concentrated on strengthening interactions through the deployment of social robots, while disregarding the capabilities required to boost the flexibility and user experience associated with human-robot collaboration (HRC) on manufacturing tasks. One of the main challenges is the lack of publicly available industrial-oriented dialogue datasets for the training of conversational AI. In this work, we present an industrial robot wizard-of-Oz dialoguing dataste (IRWoZ) focused on enabling HRC in manufacturing tasks. The dataset covers four domains: assembly, transportation, position, and relocation. It is created using the Wizard-of-Oz technique to be less noisy. We manually constructed, annotated and validated dialogue segments (e.g., intentions, slots, annotations), as well as the responses. Building upon the proposed dataset, we benchmark it on the state-of-the-art (SoTA) language models, generative pre-trained (GPT-2) models, on dialogue state tracking and response generation tasks. We expect that the IRWoZ dataset will facilitate exciting ongoing dialogue research and we provide it freely accessible at https://github.com/lcroy/ToD4IR/tree/main/dataset.