Machine Collaboration Research Articles

COGNITIVE ARBITRAGE: THE OUTSOURCING OF INTELLIGENCE

Artificial intelligence (AI) is disrupting industry and potentially threatening to replace humans at work. In this article, we offer a strategy to ensure that executive decision-makers are given the tools to combine the best of human skills with AI, both preserving human dignity and enhancing organizational achievement. We propose a decision-making framework, the Arbitrage-Enhancement Decision Grid (AEDG), that enables organization leaders to determine the optimum human and intelligent machine collaboration to improve workforce performance. The framework recognizes the inevitable adoption of technology innovation, in conjunction with an organization’s need to balance human performance and competitive objectives. The authors then advance an actionable roadmap for developing human workforce and intelligent machine competencies and skills, the Human Resource-Artificial Intelligence Collaboration (HRAIC) framework that complements the decision-making outcomes of the AEDG.

Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era

The majority of the global population now resides in cities, and this trend continues to grow. In this context, the Internet of Things (IoT) is crucial in transforming existing urban areas into Smart Cities. However, IoT architectures mainly focus on machine-to-machine interactions, leaving human involvement aside. The Internet of Everything (IoE) includes human-to-human and human–machine collaboration, but the specifics of these interactions are still under-explored. As urban populations grow and IoT integrates into city infrastructure, efficient, collaborative architectures become crucial. In this work, we use the Rapid Review methodology to analyze collaboration in four prevalent computing architectures in the IoE paradigm, namely Edge Computing, Cloud Computing, Blockchain/Web Services, and Fog Computing. To analyze the collaboration, we use the 3C collaboration model, comprising communication, cooperation, and coordination. Our findings highlight the importance of Edge and Cloud Computing for enhancing collaborative coordination, focusing on efficiency and network optimization. Edge Computing supports real-time, low-latency processing at data sources, while Cloud Computing offers scalable resources for diverse workloads, optimizing coordination and productivity. Effective resource allocation and network configuration in these architectures are essential for cohesive IoT ecosystems. Therefore, this work offers a comparative analysis of four computing architectures, clarifying their capabilities and limitations. Smart Cities are a major beneficiary of these insights. This knowledge can help researchers and practitioners choose the best architecture for IoT and IoE environments. Additionally, by applying the 3C collaboration model, the article provides a framework for improving collaboration in IoT and IoE systems.

The Emerging Industrial Revolution and the Future of the Accountant in Nigeria

The study was inspired by the concern for the next industrial revolution of artificial general intelligence with respect to the career of the accountant, given the continuous and increasing automation of the accounting process and systems in the current industrial revolution. Consequently, the study adopted an ex-post facto design to fathom the career fortune of the human accountant in the emerging industrial era of artificial general intelligence, using ten years personnel cost and net assets data, from the annual accounts and reports of the 7 deposits money banks in Nigeria that had international authorization, as at 31st December, 2022. Linear regression analysis results from SPSS 20.0 disclosed that the effect of the deployment of artificial intelligence on the career of the accountant in the current industrial era is statistically low and insignificant. Accordingly, the study predictively concludes that the technological agenda of the emerging industrial revolution will not impair the job fortune of the accountant. However, the study recommends that accounting practitioners should proactively develop themselves with relevant soft skills, and deep learning, python and big data programmes in readiness for effective machine collaboration and synthesis, in the emerging industrial era.

On-body sensing technologies and signal processing techniques in addressing safety of human machine collaboration

AbstractSafety is a challenge in human machine collaboration despite of the advantages in achieving efficiency, cost reduction and productivity in a collaborative scenario between human and machine/robot. During collaboration with machines, the user might not be able to follow the collaborative tasks as expected due to the cognitive burden causing potential safety concerns such as collision. Addressing this challenge, the aim of this paper is to explore the potential of on-body sensing systems in study of user experience and the psychological condition during the collaboration between machines and human. As the psychological condition is reflected in physiological signals, sensing technologies and signal processing techniques to extract features from physiological signals are explored with applicability in human machine collaboration scenarios. An experiment is designed utilising an industrial collaborative robot arm while quantitative and qualitative data is gathered for this purpose exploring the problem to study user experience and impact of mental strain and cognitive workload on user performance and experience during human machine collaboration. Results show that an adaptive machine to user experience measured by on-body sensing systems during the collaboration has the potential to address safety in human machine collaboration while improving performance and user experience.

Ergonomic design of Human-Robot collaborative workstation in the Era of Industry 5.0

The increasing adoption of collaborative robots to support job execution in manufacturing has catalyzed companies’ attention to safety and well-being issues. Sharing the human-centric perspective and harmonious human–machine collaboration concepts emphasized by Industry 5.0, the design phase of a collaborative workstation must integrate both psychological and physical risk evaluations to provide a safe and inclusive work environment suitable for a diversified workforce. Accelerating the pre-deployment phase to quickly reconfigure workstation design and assess its impact on workload balancing and task sequencing during the deployment of assembly lines still represents a challenging task considering the available software tools. This research proposes a new mathematical model to accelerate the design of ergonomic human-robot collaborative workstations based on task alternatives and the combined consideration of postural assessment and fatigue analyses for each of them to design an ergo-friendly collaborative environment. Surface electromyography analysis is jointly adopted with postural risk assessment measured with inertial measurement units and developed by a digital ergonomic platform to determine the optimal workplace configuration for tools, equipment, and resources to promote physical well-being while considering station productivity. Experimental tests are performed to investigate arm muscles and postural risk assessment for different configurations of workstation design and collaborative human-robot job progression. Experimental results demonstrate the feasibility, and the advantages of the proposed approach compared to existing simulation software to quickly generate and assess alternative scenarios and find a trade-off between ergo-quality levels and system performance. The final discussion offers valuable information for decision-makers and practitioners to facilitate the integration of human factors throughout the early stages of ergo-friendly workspace design, while effectively managing the complexity generated by resource allocation and collaborative robots.

Printing the Future Layer by Layer: A Comprehensive Exploration of Additive Manufacturing in the Era of Industry 4.0

In the era of Industry 4.0, 3D printing, or additive manufacturing (AM), has revolutionized product design and manufacturing across various sectors. This review explores the evolution of 3D printing technology and its impact on industrial innovation, highlighting advancements in aeronautics, the automotive industry, and biomedicine. Various AM processes, such as binder jetting, direct energy deposition, and powder bed fusion, and materials like metals, polymers, ceramics, and composites, are discussed. Innovations like high-speed sintering, continuous liquid interface production, and bioprinting demonstrate ongoing advancements. The potential of 3D printing in personalized medical applications is emphasized due to its flexibility in geometry and materials. Despite progress, challenges like standardization, material quality, recycling, sustainability, and economic feasibility hinder widespread adoption. Overcoming these challenges is crucial for optimizing 3D printing technologies, ensuring high-quality, efficient, and affordable production. The review also addresses the future prospects of 4D and 5D printing technologies and their potential applications in various industries. This overview underscores 3D printing’s role in shaping the future of manufacturing within the context of Industry 5.0, emphasizing human–machine collaboration and sustainability.

AI meets academia: transforming systematic literature reviews

PurposeThis study synthesizes the role of artificial intelligence (AI) and automation in systematic literature reviews (SLRs), focusing in particular on efficiency, methodological quality and human–machine collaboration.Design/methodology/approachA systematic review methodology was applied, analyzing studies from Scopus and Web of Science databases to explore the use of AI and automation in SLRs. A final sample of 28 articles was selected through a rigorous and interdisciplinary screening process.FindingsOur analysis leads to seven themes: human and machine collaboration; efficiency and time savings with AI; methodological quality; analytical methods used in SLRs; analytical tools used in SLRs; SLR stages AI is utilized for and living systematic reviews. These themes highlight AI’s role in enhancing SLR efficiency and quality while emphasizing the critical role of human oversight.Research limitations/implicationsThe rapid advancement of AI technologies presents a challenge in capturing the current state of research, suggesting the need for ongoing evaluation and theory development on human–machine collaboration.Practical implicationsThe findings suggest the importance of continuously updating AI applications for SLRs and advocating for living systematic reviews to ensure relevance and utility in fast-evolving fields.Social implicationsIntegrating AI and automation in SLRs could democratize access to up-to-date research syntheses, informing policy and practice across various disciplines and redefining the researcher’s role in the digital age.Originality/valueThis review offers a unique synthesis of AI and automation contributions to SLRs, proposing a conceptual model emphasizing the synergy between human expertise and machine efficiency to improve methodological quality.

Research on the mechanism of human-machine security collaboration of miners considering automation trust

Safety in the coal mining industry is a long-standing issue. With the implementation of intelligent construction in coal mines, humans–machine collaboration is critical to accident prevention. We investigate the psychological dynamics among miners during intelligent construction by developing distinct evolutionary game models for miner security collaboration mechanisms, while also considering the level of trust in automation. We explore the impact of changes in automation trust on the human–machine security collaboration behaviour among miners as well as the regulatory strategies adopted by coal mining enterprises. Evidently, the current level of automation trust among miners is insufficient to ensure safety, and the safety management systems of coal mining enterprises are yet to reach a stable state. Examination of the relevant parameters using specific examples reveals that the optimal range for the level of automation trust among miners lies between 0.7 and 0.9, indicating a favourable coal mine safety production system. These conclusions provide a scientific foundation for effectively enhancing safety management in the context of intelligent construction in coal mines.

Herd behavior identification based on coevolution in human–machine collaborative multi-stage large group decision-making

As the scale of multi-stage large group decision-making (LGDM) continues to expand, the possibility of low-contribution individuals exhibiting herd behavior also increases, potentially leading to the phenomenon of “fishing in troubled waters.” This may obstruct the speed of consensus reaching while generating no valuable opinions, which is a topic worthy of exploration. Considering that humans are easily influenced by interests, the employment of machine intelligence to objectively identify herd behavior is more appropriate. In this context, a herd behavior identification method based on behavioral characteristics clustering from the perspective of human–machine collaboration is herein proposed. First, from the human side, an opinion–social network coevolution model is constructed to simulate the consensus reaching process (CRP) of the expert group. Then, the group is clustered into three subgroups in consideration of behavior that encompasses both opinion changes and trust relationship changes. Based on this, the low-contribution cluster with a herd behavior pattern can be optimized from the machine side. Through simulation experiments, it is verified that herd behavior management significantly accelerates the consensus-reaching speed under the premise of having minimal impact on the decision-making results. In general terms, this study is the first to propose the concept of herd behavior and provides a solution to manage it from a new perspective, which is suitable for application in multi-stage LGDM scenarios.

Industry 5.0's pillars and Lean Six Sigma: mapping the current interrelationship and future research directions

PurposeIn the evolving landscape of Industry 5.0 (I5.0), which emphasises sustainability, human-centricity and resilience, mapping the current interrelationship and future research directions, the role of Lean Six Sigma (LSS) methodology remains underexplored. Our study addresses this gap by examining the potential of LSS to support I5.0 while identifying areas for further investigation.Design/methodology/approachThis study's multifaceted approach, which includes systematic literature review (SLR), bibliographic network analysis (BNA) and expert validation (EV), provides a holistic exploration of the interaction between LSS and I5.0 as the basis for well-founded conclusions.FindingsThe analysis yields several valuable insights. Firstly, it demonstrates the absence of a direct link between LSS and I5.0. Secondly, the substantial body of literature analysed establishes connections between LSS and its pillars. Thirdly, the analysis identifies points of intersection, difference and similarity between LSS and I5.0, highlighting the potential of LSS to facilitate implementation of I5.0 through its proven methodologies, continuous improvement culture, risk management, error learning, human–machine collaboration and training and skill development.Originality/valueThis study pioneers the effort to realise the latent potential of LSS in the context of I5.0. Its systematic identification of the synergies between these paradigms fills a critical gap in the literature and gives policymakers, managers and researchers a guide for informed decision-making to maximise the benefits of I5.0 for individuals, companies, society and the planet.

Technology Trends Revolutionizing the World of Work- Auto Industry Perspective based on the SDG

Aim/Purpose: Disruptive technologies impact business cycles and work practices, paving the way for new-generation manufacturing trends. Organizations are constantly working to introduce products with a higher degree of innovation, catering to customer demands in the market. Background: The automobile industry plays a significant role in applying disruptive technologies while proving value to all stakeholders. The technological disruptions in the auto sector can potentially change the relationship between consumers and the automobile while impacting business trends and work practices. Methodology: Various scholarly research material was reviewed to understand the impact of technology disruptions on manufacturing methods, work practices, employee engagement, and skill development. Secondary data analysis was conducted for this qualitative and descriptive study. Contribution: This paper helps to understand the impact created by various technologies in the auto sector on employee retention—the role of man- Machine collaboration in creating job satisfaction among employees. Findings: Besides automation and digitalization, AI, Robotics, and Mechatronics play a crucial role in the sector's evolution. The study findings help us understand the impact of technology on manufacturing and business practices. Recommendation for Practitioners: Investment in training the workforce and embracing collaboration helps employees to benefit from the real world of work. Recommendation for Researcher: To ensure the relevance of research studies, practitioners and researchers should maintain a collaborative approach, using research insights to inform business strategies. Impact on Society: Analyze how technologies like collaborative robots (cobots) are integrated into manufacturing processes and their impact on job roles and efficiency. Technology continues to evolve, so societies shall proactively address challenges and leverage opportunities to take advantage of these innovations. It is the role of government policymakers, industry stakeholders, and educators to shape the auto industry's future in a way that maximizes the benefits to society from technological innovation. Future Research: The study's scope is to be done quantitatively by covering OEMs all over India. It discussed disruptive technology in general. Specific technological advancements can be analysed to dive deep into the phenomenon. A similar study could be undertaken in another industry besides the automobile.

Analysis of virtual standardized patients for assessing clinical fundamental skills of medical students: a prospective study

BackgroundHistory-taking is an essential clinical competency for qualified doctors. The limitations of the standardized patient (SP) in taking history can be addressed by the virtual standardized patient (VSP). This paper investigates the accuracy of virtual standardized patient simulators and evaluates the applicability of the improved system’s accuracy for diagnostic teaching support and performance assessment.MethodsData from the application of VSP to medical residents and students were gathered for this prospective study. In a human–machine collaboration mode, students completed exams involving taking SP histories while VSP provided real-time scoring. Every participant had VSP and SP scores. Lastly, using the voice and text records as a guide, the technicians will adjust the system’s intention recognition accuracy and speech recognition accuracy.ResultsThe research revealed significant differences in scoring across several iterations of VSP and SP (p < 0.001). Across various clinical cases, there were differences in application accuracy for different versions of VSP (p < 0.001). Among training groups, the diarrhea case showed significant differences in speech recognition accuracy (Z = -2.719, p = 0.007) and intent recognition accuracy (Z = -2.406, p = 0.016). Scoring and intent recognition accuracy improved significantly after system upgrades.ConclusionVSP has a comprehensive and detailed scoring system and demonstrates good scoring accuracy, which can be a valuable tool for history-taking training.

AI for industrial: automate the network design for 5G URLLC services

Fifth generation (5G) mobile networks enable ultra-reliable low-latency communication (URLLC) applications, ushering in an era of endless possibilities for 5G. URLLC supports emerging 5G services and applications with stringent requirements for latency and reliability. Factory automation (FA) is a URLLC application that automates and optimizes workflows and processes in factories. To accommodate diversified FA services, 5G networks employ the “network slicing” technique, which divides the network into slices tailored to different service requirements. Designing a sliced network and translating diversified service-level agreements (SLAs) into network attributes necessitates advanced automation techniques to enhance human–machine collaboration, increase efficiency, minimize manual errors, reduce operating costs, and, most importantly, provide adequate service quality economically and reliably. To apply autonomic computing to FA network design, new architectures and software components have been envisioned. These include information extraction, domain knowledge representation, rule-based reasoning, performance model calculation, and querying using simulators and neural networks (NNs), among others. This paper proposes an innovative approach to network slicing design using advanced automation methods. This approach can be easily extended to include new services or to integrate cutting-edge 5G techniques.

Citizen science games on the timeline of quantum games

This article provides an overview of existing quantum physics-related games, referred to as quantum games, that serve citizen science research in quantum physics. Additionally, we explore the connection between citizen science and quantum computer games, games played on quantum computers. The information presented is derived from academic references and supplemented by diverse sources, including social media publications, conference presentations and blog posts from research groups and developers associated with the presented games. We observe that the current landscape of quantum games is shaped by three distinct driving forces: the serious application of games, the evolution of quantum computers and open game development events such as Quantum Game Jams. Notably, citizen science plays an influential role in all three aspects. The article points to existing design guides for citizen science quantum games and views future prospects of citizen science projects and quantum games through collaborative endeavours, human–machine collaboration and open access quantum computers.

Designing a digital-twin based dashboard system for a flexible assembly line

The current dashboard system is limited of coordination, flexibility and scalability. This study proposed a digital-twin based dashboard system. The digital twin components are divided into four, namely the operator, screw machine, industrial robot and automatic optical inspection in a flexible GPU card assembly line. This study designs a protocol enabling digital twins to interact and operate the system collaboratively and flexibly. In addition, this study presents a landscape of digital twin dashboards to demonstrate the relationship between the digital twins and the physical modules, providing a use case for the future digital-twin based dashboard design. The proposed system is implemented in a flexible GPU card assembly line and constructed by using JMobile to build a real-time dashboard system capable of scalability. The proposed dashboard framework for the GPU card assembly line offers several advantages over existing dashboards in terms of productivity, quality, and accident reduction. This study contributes to a scalable real-time dashboard system framework by incorporating human–machine collaboration, laying the foundation for advanced dashboard system development.

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation

Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human–machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human–machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human–machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human–machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

Explainable AI decision support improves accuracy during telehealth strep throat screening

BackgroundArtificial intelligence-based (AI) clinical decision support systems (CDSS) using unconventional data, like smartphone-acquired images, promise transformational opportunities for telehealth; including remote diagnosis. Although such solutions’ potential remains largely untapped, providers’ trust and understanding are vital for effective adoption. This study examines how different human–AI interaction paradigms affect clinicians’ responses to an emerging AI CDSS for streptococcal pharyngitis (strep throat) detection from smartphone throat images.MethodsIn a randomized experiment, we tested explainable AI strategies using three AI-based CDSS prototypes for strep throat prediction. Participants received clinical vignettes via an online survey to predict the disease state and offer clinical recommendations. The first set included a validated CDSS prediction (Modified Centor Score) and the second introduced an explainable AI prototype randomly. We used linear models to assess explainable AI’s effect on clinicians’ accuracy, confirmatory testing rates, and perceived trust and understanding of the CDSS.ResultsThe study, involving 121 telehealth providers, shows that compared to using the Centor Score, AI-based CDSS can improve clinicians’ predictions. Despite higher agreement with AI, participants report lower trust in its advice than in the Centor Score, leading to more requests for in-person confirmatory testing.ConclusionsEffectively integrating AI is crucial in the telehealth-based diagnosis of infectious diseases, given the implications of antibiotic over-prescriptions. We demonstrate that AI-based CDSS can improve the accuracy of remote strep throat screening yet underscores the necessity to enhance human–machine collaboration, particularly in trust and intelligibility. This ensures providers and patients can capitalize on AI interventions and smartphones for virtual healthcare.

Enhancing high-school dropout identification: a collaborative approach integrating human and machine insights

Despite its proven success in various fields such as engineering, business, and healthcare, human–machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human–machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school dropout prediction serves as a case study for examining human–machine collaboration’s efficacy. Unlike previous research prioritizing high accuracy with immutable predictors, this study seeks to bridge gaps by identifying actionable factors for dropout prediction through a framework of human–machine collaboration. Utilizing a large dataset from the High School Longitudinal Study of 2009 (HSLS:09), two machine learning models were developed to predict 9th-grade students’ high school dropout history. Results indicated that the Random Forest algorithm outperformed the deep learning algorithm. Model explainability revealed the significance of actionable variables such as students’ GPA in the 9th grade, sense of school belonging, self-efficacy in mathematics and science, and immutable variables like socioeconomic status in predicting high school dropout history. The study concludes with discussions on the practical implications of human–machine partnerships for enhancing student success.

Human–Machine Collaboration in Language Education in the Age of Artificial Intelligence

Human–Machine Collaboration in Language Education in the Age of Artificial Intelligence

Evolution of Industrial Robots from the Perspective of the Metaverse: Integration of Virtual and Physical Realities and Human–Robot Collaboration

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the limitations of traditional industrial operations. This paper explores the application and evolution of metaverse technology in the field of industrial robotics, focusing on the realization of virtual–real integration and human–machine collaboration. It proposes a design framework for a virtual–real interaction system based on the ROS and WEB technologies, supporting robot connectivity, posture display, coordinate axis conversion, and cross-platform multi-robot loading. This paper emphasizes the study of two key technologies for the system: virtual–real model communication and virtual–real model transformation. A general communication mechanism is designed and implemented based on the ROS, using the ROS topic subscription to achieve connection and real-time data communication between physical robots and virtual models, and utilizing URDF model transformation technology for model invocation and display. Compared with traditional simulation software, i.e., KUKA Sim PRO (version 1.1) and RobotStudio (version 6.08), the system improves model loading by 45.58% and 24.72%, and the drive response by 41.50% and 28.75%. This system not only supports virtual simulation and training but also enables the operation of physical industrial robots, provides persistent data storage, and supports action reproduction and offline data analysis and decision making.