Intelligent Interaction Research Articles

Integrated and intelligent remote operation centres (I2ROCs): Assessing the human–machine requirements for 21st century mining operations

The futuristic view of smart mines is the attainment of automated self-governed mines without human intervention throughout the value chain. However, reality entails a hybrid environment where humans and machines must effectively work together, with a bias towards machines highly aiding human capabilities. The minerals industry will require systems, processes and strategies to navigate this hybrid environment and transitional period. In this work, we focus on the role that integrated remote operation centres (IROCs) will play in ensuring that mining companies continue to get value from technological advancements during the transformation period. A review of IROC implementations by leading mining companies revealed that most functions in IROCs are powered by low-level artificial intelligence, in which most capabilities are goal-oriented. In this situation, automated machines and processes are programmed to accomplish specific tasks without a two-way intelligent interaction with humans. An integrated and intelligent remote operation centre can provide additional benefits. We refer to such a facility as I2ROC. I2ROCs offer opportunities beyond the traditional command-and-control capabilities by providing analytical insights and the interaction between people and machines, which contributes to better decision-making, ultimately improving performance measurability. In this paper, several enablers of I2ROCs are identified, as well as drivers that will accelerate I2ROCs' attainment. The human element is critical in achieving the 21st Century mining model. As such, this paper takes a two-dimensional approach focusing on the role of humans and technology in the future mining model. Key factors are explored, such as the required skillset and how these skills can be enhanced. Specifically, we present essential elements defining I2ROCs and the critical requirements for a digitalised workforce fit for operation within I2ROCs. Our findings provide a comprehensive framework for multiple stakeholders to collaboratively pursue I2ROCs through effective education, training, and governance, fostering a more sustainable and resilient future.

Intelligent Interaction of Fluorine containing materials Based on NL2SQL

The data on the production and development of fluorine containing materials are characterized by a large amount of data and a high degree of dimensionality of physical and chemical property characterization indicators. The manual way of analyzing the data item by item not only has high interaction cost, but also is difficult to analyze and explore the data intuitively. In order to efficiently utilize the data, this paper firstly constructs a dataset of fluorine-containing materials and proposes the Mengzi-ITPT model based on it, which takes Mengzi as the encoder and uses the attention mechanism to enhance the representation of the listed information. Meanwhile, for the data characteristics of fluorine containing materials, the training strategy of ITPT is adopted to improve the accuracy of the model. The experimental results show that the accuracy of the Mengzi-ITPT model query reaches 86.9% when the model is trained under the fluorine-containing material dataset.

Redefining User Expectations: The Impact of Adjustable Social Autonomy in Human–Robot Interaction

To promote the acceptance of robots in society, it is crucial to design systems exhibiting adaptive behavior. This is particularly needed in various social domains (e.g., cultural heritage, healthcare, education). Despite significant advancements in adaptability within Human-Robot Interaction and Social Robotics, research in these fields has overlooked the essential task of analyzing the robot’s cognitive processes and their implications for intelligent interaction (e.g., adaptive behavior, personalization). This study investigates human users’ satisfaction when interacting with a robot whose decision-making process is guided by a computational cognitive model integrating the principles of adjustable social autonomy. We designed a within-subjects experimental study in the domain of Cultural Heritage, where users (e.g., museum visitors) interacted with the humanoid robot Nao. The robot’s task was to provide the user with a museum exhibition to visit. The robot adopted the delegated task by exerting some degree of discretion, which required different levels of autonomy in the task adoption, relying on its capability to have a theory of mind. The results indicated that as the robot’s level of autonomy in task adoption increased, user satisfaction with the robot decreased, whereas their satisfaction with the tour itself improved. Results highlight the potential of adjustable social autonomy as a paradigm for developing autonomous adaptive social robots that can improve user experiences in multiple HRI real domains.

Human and artificial intelligence interaction

Background and Aim of Study: The advent of artificial intelligence (AI) has changed our world forever. No matter what it is that we do, there will always be a place for AI in what we do. Controlling and managing this system of interactions is still within our power. However, the potential and the speed of developing AI-based information technology is so great that we may soon need to concede this primacy. The aim of the study: to justify whether artificial intelligence will become our assistant or, on the contrary, create problems; to identify what needs to be done to build a harmonious Human-AI System of interactions and relationships. Conclusions: It requires the development, ratification and implementation of laws that regulate the norms of interactions and relationships between humans and AI. The first steps have already been taken to legitimise AI-based Chatbots in scientific research and publications. This paper proposes an attribution for a product created by human without the involvement of AI. The use of this attribution helps to protect the individual’s right to their own activities.

Evaluation Model of Modern Network Teaching Quality Based on Artificial Intelligence E-Learning

Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing principles of improved decision tree algorithm, and the information gain of the algorithm determines the inheritance of information. The process of modern e-learning system is based on personalized teaching and humanized intelligent interaction. This paper theoretically analyzes the improvement performance of the existing e-learning system in teaching quality evaluation and shows a good classification effect. This model provides reference materials for the expansion of education and teaching and provides a feasible practical model for personalized teaching in online schools. The authors provide good educational conditions and environment for students and cultivate all-around talents for the society.

Electronic skin with biomimetic structures realizes excellent isothermal regulation

Simulating the various functions of the human body in electronic skin to give it a higher level of bionics is the basis for realizing intelligent interaction. Isothermal regulation is the key to realizing a healthy life of the body, yet only simple heating or cooling is achieved in electronic skin. Here, we propose a novel thermoregulating electronic skin (thermo-e-skin) with biomimetic structures, which mimics the human body thermoregulation mechanism by integrating a flexible thermoelectric device with a hydrogel composite material. Through the delicate balance of heat generation and dissipation, the thermo-e-skin maintained a stable surface temperature of 35 °C across a wide range of ambient temperatures from 10 °C to 45 °C for extended durations (e.g., 4.2 h at an ambient temperature of 40 °C). Additionally, the thermo-e-skin, equipped with water regeneration capabilities, exhibits exceptional cyclic performance, further demonstrating its practical potential for soft electronics, bionics, and prosthetics.

The Relationship between Human and Artificial Intelligence in Victor Pelevin's Novels “S.N.U.F.F.”, “iPhuck 10”, “TRANSHUMANISM Inc.”

The article analyzes V.O. Pelevin’s view on the human and artificial intelligence interaction and highlights the risks of digitalization that the writer identifies. The article also attempts to answer two questions: what are the permissible limits of the influence machines and networks exert on human life and future and is there a way out of global dependence on artificial intelligence? Pelevin presents a satirical portrait of modern society; draws attention to the ways of salvation from total machine dependence; shows prospects and risks of the XXI century “new humanism”. The research has revealed five main similarities which identify Pelevin’s idea of the relationship between man and artificial intelligence. The theme is presented at the plot, problem-thematic and stylistic levels. Special attention is paid to Pelevin’s moral and philosophical reflections. The research is based on three Pelevin’s novels: “S.N.U.F.F.”, “iPhuck 10”, “TRANSHUMANISM Inc.”.

Integration of 3D Scanning and Augmented Reality (AR) Technology in East Kalimantan Furniture Products in E-Commerce

The creative furniture industry in East Kalimantan faces the challenge of less effective online marketing in e-commerce. In 2018, there were more than 370 furniture entrepreneurs in the region, but only 37% of their total production managed to reach the market. According to projections from Markets and Markets, the market value of Augmented Reality (AR) in e-commerce is expected to grow by 34% between 2020 and 2025. To overcome this problem, the use of 3D object configuration visual technology and AR presents a better solution. promising solution. This technology can improve product presentation, create a more interactive and realistic shopping experience, and reduce the environmental impact of e-commerce due to the re-shipment of non-conforming products. The main objective of the research is to assess the effectiveness of product communication, the ease of the purchasing process, and the level of consumer trust in the product. This visual method of 3D object configuration combines 3D scanning to produce detailed and realistic 3D images. This research uses a sprint design method based on design thinking, which involves co-creation to refine ideas. Next, a prototype design was developed with a focus on aspects of the user interface and user experience, fostering intelligent interaction between users and their environment, thus contributing to the progress of the Smart City concept in IKN Nusantara.

Design of Highly Conductive Leather Based on Zirconium Ion Coordination for Health Protection in Severe Harsh Conditions

AbstractFlexible electric heaters with softness, comfort and heating performance have application prospects in the fields of life and health, medical security and so on. In this study, by taking advantage of the soft and comfortable properties of leather itself, collagen fibers and carbon nanotubes (CNTs) are composited through zirconium ion (Zr4+) coordination and complexation. The composition, microstructure and Joule heating performance of the prepared flexible conductive leather (CNTs@Zr@OTL) are investigated in detail. The characterisation results prove the (Zr4+) coordination effect and the successful preparation of CNTs@Zr@OTL. At the same time, it has a resistance of about 250 Ω, a shrinkage temperature (Ts) of about 75°C, mechanical property of about 8 MPa, 60 h strong biodegradability and Joule heating of about 65°C under 15 V DC voltage. The obtained product can be further designed as a flexible Joule heating heater (Jhh) with softness, comfort as well as conductivity, and excellent Joule heating performance, and its practical application effect in heating wrists, fingers, joints and car seats is explored, as well as the heating effect under the actual movement of human body such as squatting, sitting and walking. This work provides an idea and method for the intelligent interaction of flexible heaters.

A deepening study of philosophical cognitive logic based on intelligent interaction

Abstract This paper analyzes the research object of cognitive logic in the context of information technology and philosophical reflection and constructs a model of cognitive logic for contextual dynamics. Contextual recognition of philosophical cognitive logic is achieved through contextual sequence feature extraction, and realistic, intelligent interaction is analyzed through probabilistic potential semantic analysis, while philosophical cognitive logic deepening is achieved using PELP grammar. The analysis of the deepening model showed that the interaction degree of the mental dimension increased from 0.79 to 0.91, and the interaction degree of the thinking dimension increased from 0.88 to 0.97. Applying the model to learning, the difference between learning methods without the model was up to 15 points, and the deepening of philosophical cognitive logic based on intelligent interaction could improve thinking ability.

Female Gendering of Artificial Intelligence in Travel: A Social Interaction Perspective

ABSTRACT Implementing artificial intelligence enables the travel industry to improve efficiency, attract customers, and enhance their experiences. However, the typically biased social perceptions of humans are now applied to artificial intelligence, which has reinforced its female gendering in the industry. Nevertheless, the mechanism of its gendering process remains unclear. Guided by the gender theory, this study has explored how gendering is constructed through the social interaction perspective. Sixteen respondents who interacted with service robots or virtual assistants during their trip were recruited and interviewed via Zoom. Our findings suggest that the female gendering of artificial intelligence can be mapped from human-human to human-artificial intelligence interaction. Four main factors related to the social construction of the gendering process are identified. The findings contribute to understanding human-technology interaction and help tourism practitioners find a more cost-effective way to integrate human-technology collaboration strategies into their daily operations.

Minding the source: toward an integrative theory of human–machine communication

Abstract According to the computers are social actors (CASA) paradigm, a dominant theoretical framework for research on human–computer interaction, people treat computers as if they were people. Recent studies on human–machine communication (HMC) and human–artificial intelligence (AI) interaction, however, appear to focus on when and how people respond to machines differently than to human agents. To reconcile this apparent contradiction, this study reviews critically the two overarching theoretical explanations proposed and tested in each respective tradition, the mindlessness account and the machine heuristic. After elaborating on several conceptual and operational issues with each explanatory mechanism, an alternative theoretical model of HMC is proposed that integrates both research traditions and generates predictions that potentially deviate from the dual-process models. Lastly, it is discussed how recent developments in AI technology invite modifications to the current understanding of HMC and beyond.

When Service Quality is Enhanced by Human–Artificial Intelligence Interaction: An Examination of Anthropomorphism, Responsiveness from the Perspectives of Employees and Customers

This article reports on two distinct studies conducted in the context of employees and customers, respectively. The aim of Study 1 is to investigate the influence of anthropomorphism and responsiveness of food delivery people using artificial intelligence (AI) devices on their psychological safety and AI empathy. This study also evaluated the mediating impact of psychological safety and AI empathy with relation to anthropomorphism and responsiveness on AI service quality. In addition, Study 2 examines the impact of perceptions of AI service quality on customer engagement and customer satisfaction. Furthermore, in Study 2, we tested the moderating influence of AI usability on the relationship between AI service quality and customer engagement and on the relationship between AI service quality and customer satisfaction. In Study 1, we collected data from 312 food delivery boys/girls who used AI-based applications to offer e-food restaurant services. This study used a cross-sectional research design. We expand the scope of Study 1 by collecting data from 363 customers of the companies surveyed in Study 1 from eastern and southern parts of China. Study 2 used a time-delayed approach and collected data in two waves three months apart. The overall results of Study 1 and Study 2 support the proposed hypotheses. The results showed the direct and indirect relationship between independent variables (anthropomorphism and responsiveness) and service quality, customer engagement and customer satisfaction. Furthermore, the results indicated a significant role of mediators (psychological safety and AI empathy) in Study 1 and moderators (AI usability) in Study 2. This article provided valuable insights for practitioners and researchers concerned with the use and application of AI in deal with the service sector.

Computer Vision Interaction Design in Sustainable Urban Development: A Case Study of Roof Garden Landscape Plants in Marine Cities.

The rapid urbanization and the increasing need for sustainable development have led to the emergence of green roof landscapes in ocean cities. These rooftop gardens provide numerous environmental benefits and contribute to the overall well-being of urban dwellers. However, optimizing the design and interaction experience of green roof landscapes requires the integration of intelligent technologies. This paper explores the application of computer visual design techniques, specifically 3DMAX modeling and virtual reality, in the intelligent interaction design of green roof landscape plants in ocean cities. Designers can use computer visual design (3DMAX) and other technologies to interact intelligently with the roof landscape in order to improve landscape design. Through case studies, this indicated that computer vision is excellent for image processing of rooftop landscapes and also demonstrates a high degree of compatibility between computer vision and green rooftop landscape plant design in marine cities. This paper highlights the significance of intelligent interaction design and computer visual design techniques in optimizing the integration of green roof landscape plants in ocean cities. It emphasizes the potential of 3DMAX modeling and VR technology in creating immersive and engaging experiences for designers, users, and stakeholders alike. The findings contribute to the growing body of knowledge in the field of sustainable urban development and provide insights for designers, policymakers, and researchers seeking to enhance green roof landscapes in ocean cities. The dissertation highlights the potential of using computer vision design techniques to enhance the roof garden landscaping process and advocates for more efficient and effective ways to design, visualize, and improve rooftop gardens by utilizing software equipped with computer vision technology such as 3DMAX, ultimately contributing to the advancement of sustainable urban landscapes.

Joint Modelling of Slot Filling and Intent Detection in Constrained Resource Scenarios

In the era of pervasive smart devices, natural language understanding (NLU) holds a pivotal role for facilitating intelligent interactions and decision-making. Core to NLU are slot filling and intent recognition, essential tasks for comprehending user input. While joint modelling of these tasks has gained prominence, the challenges of realizing efficient joint models on resource-constrained devices have emerged as significant. These devices possess limited computational capacity and real-time requirements, necessitating lightweight and efficient models. In this study, we explore the design of a resource-efficient joint model for slot filling and intent recognition. Through leveraging BERT, , graph neural networks, and mask mechanisms, our model achieves the dual goals of semantic slot prediction and intent classification. We focus on model design, training, and real-time inference, aiming to contribute to the paradigm of resource-constrained natural language understanding. Our investigation demonstrates the efficacy of our approach, even when working with a reduced dataset, underscoring the model's applicability to real-world scenarios with limited resources.

All Creatures Great and Small: A Review and Typology of Employee-Animal Interactions

Employees interact with animals in a myriad of ways in the context of work. Herein, we seek to catalog this multiplex phenomenon in order to gain theoretical insights. Our article unfolds in four parts. First, we conduct an interdisciplinary review from which we develop a typology of four employee-animal interactions: working for, with, around, and on animals. Second, we outline the current research for each discrete category. Third, we consider key divergent experiences (e.g., the uniquely gendered nature of each) and convergent themes (e.g., all animal work is emotional and hierarchical) across the typology. Fourth, we supplement our review with two metasyntheses of other workplace team interactions—employee-employee and employee-machine/artificial intelligence interactions—to highlight how the study of human-animal interactions can address current conundrums in the organizational domain. We do so to demonstrate that considerations of employee-animal interactions can offer theoretical value to scholars, including those who may not have an inherent interest in the phenomenon. We complement theoretical extensions with suggestions for future research on core management topics, including humanizing workplaces, the future of work, and team collaboration.

Tremella polysaccharide-based conductive hydrogel with anti-freezing and self-healing ability for motion monitoring and intelligent interaction

The application of conductive hydrogels in flexible wearable devices has garnered significant attention. In this study, a self-healing, anti-freezing, and fire-resistant hydrogel strain sensor is successfully synthesized by incorporating sustainable natural biological materials, viz. Tremella polysaccharide and silk fiber, into a polyvinyl alcohol matrix with borax cross-linking. The resulting hydrogel exhibits excellent transparency, thermoplasticity, and remarkable mechanical properties, including a notable elongation (1107.3 %) and high self-healing rate (91.11 %) within 5 min, attributed to the dynamic cross-linking effect of hydrogen bonds and borax. A strain sensor based on the prepared hydrogel sensor can be used to accurately monitor diverse human movements, while maintaining exceptional sensing stability and durability under repeated strain cycles. Additionally, a hydrogel touch component is designed that can successfully interact with intelligent electronic devices, encompassing functions like clicking, writing, and drawing. These inherent advantages make the prepared hydrogel a promising candidate for applications in human health monitoring and intelligent electronic device interaction.

Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study

Assessment of computed tomography (CT)/magnetic resonance imaging Liver Imaging Reporting and Data System (LI-RADS) v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. We assessed the performance and added-value of a machine learning (ML)-based algorithm in assessing CT LI-RADS major features and categorisation of liver observations compared with qualitative assessment performed by a panel of radiologists. High-risk patients as per LI-RADS v2018 with pathologically proven liver lesions who underwent multiphase contrast-enhanced CT at diagnosis between January 2015 and March 2019 in seven centres in five countries were retrospectively included and randomly divided into a training set (n= 84 lesions) and a test set (n= 345 lesions). An ML algorithm was trained to classify non-rim arterial phase hyperenhancement, washout, and enhancing capsule as present, absent, or of uncertain presence. LI-RADS major features and categories were compared with qualitative assessment of two independent readers. The performance of a sequential use of the ML algorithm and independent readers were also evaluated in a triage and an add-on scenario in LR-3/4 lesions. The combined evaluation of three other senior readers was used as reference standard. A total of 318 patients bearing 429 lesions were included. Sensitivity and specificity for LR-5 in the test set were 0.67 (95% CI, 0.62-0.72) and 0.91 (95% CI, 0.87-0.96) respectively, with 242 (70.1%) lesions accurately categorised. Using the ML algorithm in a triage scenario improved the overall performance for LR-5. (0.86 and 0.93 sensitivity, 0.82 and 0.76 specificity, 78% and 82.3% accuracy for the two independent readers). Quantitative assessment of CT LI-RADS v2018 major features is feasible and diagnoses LR-5 observations with high performance especially in combination with the radiologist's visual analysis in patients at high-risk for HCC. Assessment of CT/MRI LI-RADS v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. Rather than replacing radiologists, our results highlight the potential benefit from the radiologist-artificial intelligence interaction in improving focal liver lesions characterisation by using the developed algorithm as a triage tool to the radiologist's visual analysis. Such an AI-enriched diagnostic pathway may help standardise and improve the quality of analysis of liver lesions in patients at high risk for HCC, especially in non-expert centres in liver imaging. It may also impact the clinical decision-making and guide the clinician in identifying the lesions to be biopsied, for instance in patients with multiple liver focal lesions.

Improving attention network to realize joint extraction for the construction of equipment knowledge graph

In the context of promoting autonomous decision, intelligent interaction, and concept recognition of the equipment, constructing an equipment Knowledge Graph (KG) is the key technology to facilitate this. In this article, we propose a method that combines design rules and automatic extraction to construct equipment KG from equipment manuals. We define that equipment KG is scheme-based and depicts scheme in the form of KG triples (a.k.a design rules, scheme layer of KG, or concept KG). Our contributions include designing an initial concept KG for the equipment domain and improving the BERT (Bidirectional Encoder Representations from Transformers) model to jointly extract knowledge from texts to enrich the KG. The BERT model was improved from internal calculations so that joint extraction could be achieved directly without extra additional parts. We applied our model to a standard dataset “SemEval2010 Task 8” and achieved the F1 score of 89.55 which demonstrates its rationality. We also established a dataset called “Equipment Manuals Corpus for KG” based on the concept KG and applied the joint model in the dataset to extract knowledge. The result was visualized in the form of a graph.

Application Prospect of Artificial Intelligence in Computer Network Technology

With the continuous development of artificial intelligence technology, its application prospects in computer network technology are gradually showing. In the introduction, the research points out the importance and prospects of artificial intelligence in computer networks, and then introduces the basic concepts and technologies of artificial intelligence in computer network technology from the aspects of machine learning technology, intelligent interaction technology and data analysis technology. In the application of enterprise network architecture, this study reveals the intelligent upgrading of network equipment such as routers and switches, as well as the way of using artificial intelligence in security defense and risk management. In terms of mobile Internet network technology, this study analyzes the deep integration of artificial intelligence and 5G mobile communication, as well as the improvement of network service quality and the artificial transformation of operation support technology. Finally, this paper draws the conclusion that artificial intelligence is becoming the strongest assistant in the field of computer network technology, and it is the direction of future network technology development.