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Journal articles on the topic 'Human-machine collaboration'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Kennedy, Krista. "Designing for human-machine collaboration." Communication Design Quarterly 5, no. 4 (2018): 40–51. http://dx.doi.org/10.1145/3188387.3188391.

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2

A., Hanzis. "The Concept of Human-Machine Decision Making." Open Access Journal of Economic Research 2, no. 1 (2025): 01–05. https://doi.org/10.64030/3065-9035.02.01.05.

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Currently there is a range of applications of AI within decision-making, with increasing integration and collaboration mainly through AI systems augmenting human decision-making capabilities within a range of industries and sectors. AI is increasingly used for business decision support, although this tends to be focused more upon lower-level decisions routines instead of highstakes decisions necessitating human knowledge [1]. However, the rapid development of the use of AI predicts that this is likely to change [2]. The future of human-machine decision-making lies in optimizing the synergies b

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3

Xiong, Wei, Hongmiao Fan, Liang Ma, and Chen Wang. "Challenges of human—machine collaboration in risky decision-making." Frontiers of Engineering Management 9, no. 1 (2022): 89–103. http://dx.doi.org/10.1007/s42524-021-0182-0.

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AbstractThe purpose of this paper is to delineate the research challenges of human—machine collaboration in risky decision-making. Technological advances in machine intelligence have enabled a growing number of applications in human—machine collaborative decision-making. Therefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilities. In risky decision-making, a human decision-maker is vulnerable to cognitive biases when judging the possible outcomes of a risky event, whereas a machine decision-maker cannot handle new and dynamic contexts with inco

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4

Shruthi Ashok. "Human-Machine Collaboration in Semiconductor Processes." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 629–36. https://doi.org/10.32996/jcsts.2025.7.5.69.

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Human-machine collaboration represents an essential frontier for optimizing semiconductor fabrication processes, addressing unique challenges that require both human expertise and computational precision. This article explores the transformative integration of AI-powered systems, augmented reality interfaces, digital twins, and collaborative technologies in semiconductor manufacturing operations. AI-vision systems and cobots enhance precision handling while minimizing contamination risks, while augmented reality interfaces project process parameters and maintenance procedures directly into ope

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Jhaver, Shagun, Iris Birman, Eric Gilbert, and Amy Bruckman. "Human-Machine Collaboration for Content Regulation." ACM Transactions on Computer-Human Interaction 26, no. 5 (2019): 1–35. http://dx.doi.org/10.1145/3338243.

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6

Davenport, Tom, and Jim Euchner. "The Rise of Human-Machine Collaboration." Research-Technology Management 66, no. 1 (2022): 11–15. http://dx.doi.org/10.1080/08956308.2023.2142435.

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7

Stenhouse, Alan, Nicole Fisher, Brendan Lepschi, et al. "Improving Biological Collections Data through Human-AI Collaboration." Biodiversity Information Science and Standards 7 (September 11, 2023): e112488. https://doi.org/10.3897/biss.7.112488.

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Biological collections play a crucial role in our understanding of biodiversity and inform research in areas such as biosecurity, conservation, human health and climate change. In recent years, the digitisation of biological specimen collections has emerged as a vital mechanism for preserving and facilitating access to these invaluable scientific datasets. However, the growing volume of specimens and associated data presents significant challenges for curation and data management. By leveraging human-Artificial Intelligence (AI) collaborations, we aim to transform the way biological collection

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8

Mitterberger, Daniela. "Augmented human, extended machine." XRDS: Crossroads, The ACM Magazine for Students 29, no. 1 (2022): 48–53. http://dx.doi.org/10.1145/3558196.

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9

Haesevoets, Tessa, David De Cremer, Kim Dierckx, and Alain Van Hiel. "Human-machine collaboration in managerial decision making." Computers in Human Behavior 119 (June 2021): 106730. http://dx.doi.org/10.1016/j.chb.2021.106730.

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10

Alhaji, Basel, Janine Beecken, Rüdiger Ehlers, et al. "Engineering Human–Machine Teams for Trusted Collaboration." Big Data and Cognitive Computing 4, no. 4 (2020): 35. http://dx.doi.org/10.3390/bdcc4040035.

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The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive a

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Lochner, Michelle, and Lawrence Rudnick. "Astronomaly Protege: Discovery through Human-machine Collaboration." Astronomical Journal 169, no. 3 (2025): 121. https://doi.org/10.3847/1538-3881/ada14c.

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Abstract Modern telescopes generate catalogs of millions of objects with the potential for new scientific discoveries, but this is beyond what can be examined visually. Here we introduce ASTRONOMALY: PROTEGE, an extension of the general-purpose machine-learning-based active anomaly detection framework ASTRONOMALY. PROTEGE is designed to provide well-selected recommendations for visual inspection, based on a small amount of optimized human labeling. The resulting sample contains rare or unusual sources that are simultaneously as diverse as the human trainer chooses and of scientific interest to

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12

Goldstein, Ian M., Julie Lawrence, and Adam S. Miner. "Human-Machine Collaboration in Cancer and Beyond." JAMA Oncology 3, no. 10 (2017): 1303. http://dx.doi.org/10.1001/jamaoncol.2016.6413.

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13

Kumar, Deobojyoti. "Human-Machine Collaboration and Interaction in Industry5.0." Journal of Development Economics and Management Research Studies 12, no. 23 (2025): 41–47. https://doi.org/10.53422/jdms.2025.122304.

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This article reveals the real scenario of economic progress. Using real-time data from the Industry like Gartner in the coming decade the possibility of growth in AI and ML in the Indian Industry. The biggest concern in topics like human-machine collaboration and interaction in the industry is to acknowledge the psychological traits of the physical human beings towards the choice of interpretation on AI and ML regarding the preference of the industry.

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14

Chen, Rong, and Bo Guo. "Human-machine Collaborative Translation Based on Artificial Intelligence Technology." Scientific and Social Research 6, no. 12 (2024): 225–40. https://doi.org/10.26689/ssr.v6i12.9144.

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This paper is dedicated to an in-depth exploration of the human-machine collaborative translation, which specifically refers to the synergy between human translators and artificial intelligence tools. The paper systematically reports the merits and drawbacks emerging in the pre-translation, in-translation, and post-translation phases. To illustrate this, a real translation project is taken as an example to depict the detailed collaboration process. In the current translation field, the human-machine collaborative model has become a key research focus. Through this case study, several factors t

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15

Schwartz, David, Dov Te'Eni, and Inbal Yahav. "Reciprocal Human Machine Learning (RHML): Human-AI Collaboration based on theories of dyadic learning." Proceedings of the AAAI Symposium Series 1, no. 1 (2023): 94–97. http://dx.doi.org/10.1609/aaaiss.v1i1.27483.

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In this position paper we advocate a Reciprocal Human Machine Learning paradigm based on two theories of human-human learning behavior. Drawing from Jörg's theory of reciprocal learning in dyads and the Jewish tradition of Havruta - pair-based study, we suggest that human-machine collaboration based on these established human-human collaborative forms can achieve a rich and robust human-in-the-learning-loop (HITLL) framework in which both parties experience learning over time.

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16

Tan, Jeffrey Too Chuan, Feng Duan, Ryu Kato, and Tamio Arai. "Man-Machine Interface for Human-Robot Collaborative Cellular Manufacturing System." International Journal of Automation Technology 3, no. 6 (2009): 760–67. http://dx.doi.org/10.20965/ijat.2009.p0760.

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In a human-centered cellular manufacturing system, various machines, including robots, are implemented to support the human operator with the goal of improving overall productivity. In order to ensure the effectiveness of such a system, an effective man-machine interface (MMI) plays an important role in ensuring collaboration and safety, and providing assembly information support. Using a task analysis approach, the assembly operation is broken down into a hierarchical task structure and remodeled for collaboration. In the modeling, appropriate operation properties are extracted as assembly in

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17

Kaasinen, Eija, Anu-Hanna Anttila, Päivi Heikkilä, Jari Laarni, Hanna Koskinen, and Antti Väätänen. "Smooth and Resilient Human–Machine Teamwork as an Industry 5.0 Design Challenge." Sustainability 14, no. 5 (2022): 2773. http://dx.doi.org/10.3390/su14052773.

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Smart machine companions such as artificial intelligence (AI) assistants and collaborative robots are rapidly populating the factory floor. Future factory floor workers will work in teams that include both human co-workers and smart machine actors. The visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators. What kinds of approaches can help design these kinds of resilient human–machine teams and collaborations within them? In this paper, w

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18

Tian, Yaling, Ji Li, Junjie Dan, et al. "Extracting Technicians’ Skills for Human–Machine Collaboration in Aircraft Assembly." Biomimetics 8, no. 8 (2023): 604. http://dx.doi.org/10.3390/biomimetics8080604.

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Research on the efficiency and quality issues faced in aircraft assembly was conducted in this article. A new method of human–machine collaborative riveting was proposed, which combined the flexibility of manual collaboration with the precise control of automatic riveting. The research works include: (1) a theoretical model of pneumatic hammer riveting was established to clarify the principle and parameters of riveting process. (2) A smart bucking bar was designed to support the data collection and extraction of manual collaborative riveting process. (3) An automatic riveting experimental plat

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19

Souza, Débora, Gabriele Iwashima, Viviane Cunha Farias da Costa, Carlos Eduardo Barbosa, Jano Moreira de Souza, and Geraldo Zimbrão. "Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era." Future Internet 16, no. 12 (2024): 445. http://dx.doi.org/10.3390/fi16120445.

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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. I

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20

Saito, Hiroki. "Human-Machine Collaboration in Quantum Many-Body Problems." JPSJ News and Comments 17 (January 15, 2020): 06. http://dx.doi.org/10.7566/jpsjnc.17.06.

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21

Robinson, Caleb, Anthony Ortiz, Kolya Malkin, et al. "Human-Machine Collaboration for Fast Land Cover Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2509–17. http://dx.doi.org/10.1609/aaai.v34i03.5633.

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We propose incorporating human labelers in a model fine-tuning system that provides immediate user feedback. In our framework, human labelers can interactively query model predictions on unlabeled data, choose which data to label, and see the resulting effect on the model's predictions. This bi-directional feedback loop allows humans to learn how the model responds to new data. We implement this framework for fine-tuning high-resolution land cover segmentation models and compare human-selected points to points selected using standard active learning methods. Specifically, we fine-tune a deep n

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22

Domae, Yukiyasu, Mitsunori Tada, and Tamio Tanikawa. "Cyber Physical Systems and Human-Machine (Robot) Collaboration." Journal of the Robotics Society of Japan 37, no. 8 (2019): 683–86. http://dx.doi.org/10.7210/jrsj.37.683.

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23

Nakajima, Hiroshi, Yasunori Morishima, Ryota Yamada, et al. "Social Intelligence in a Human-Machine Collaboration System." Transactions of the Japanese Society for Artificial Intelligence 19 (2004): 184–96. http://dx.doi.org/10.1527/tjsai.19.184.

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24

Ahmad, Omer F., Danail Stoyanov, and Laurence B. Lovat. "Human-machine collaboration: bringing artificial intelligence into colonoscopy." Frontline Gastroenterology 10, no. 2 (2018): 198–99. http://dx.doi.org/10.1136/flgastro-2018-101047.

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25

McCaffrey, Tony, and Lee Spector. "An approach to human–machine collaboration in innovation." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, no. 1 (2017): 1–15. http://dx.doi.org/10.1017/s0890060416000524.

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AbstractIf a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of

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26

Saikia, Adity, and Shyamanta M. Hazarika. "cBDI: Towards an Architecture for Human–Machine Collaboration." International Journal of Social Robotics 9, no. 2 (2016): 211–30. http://dx.doi.org/10.1007/s12369-016-0383-6.

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27

Daugherty, Paul, and Jim Euchner. "Human + Machine: Collaboration in the Age of AI." Research-Technology Management 63, no. 2 (2020): 12–17. http://dx.doi.org/10.1080/08956308.2020.1707001.

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28

Scholl, Isabelle, Raymond U. Osarogiagbon, and Glyn Elwyn. "Human-Machine Collaboration—A New Form of Paternalism?" JAMA Oncology 4, no. 4 (2018): 589. http://dx.doi.org/10.1001/jamaoncol.2017.1507.

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29

Yuan, Philip F., and Keke Li. "Novel Bricks: A Scenario of Human–Machine Collaboration." Architectural Design 90, no. 5 (2020): 22–29. http://dx.doi.org/10.1002/ad.2607.

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30

Hanna, Gila, Brendan Larvor, and Xiaoheng Yan. "Human-Machine Collaboration in the Teaching of Proof." Journal of Humanistic Mathematics 13, no. 1 (2023): 99–117. http://dx.doi.org/10.5642/jhummath.azev3747.

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31

Yin, Cuizhi, Fan Bu, and Xia Liu. "Research on the Mechanism and Implementation Path of Enhancing Innovation Capability in Manufacturing Enterprises Based on Human-Machine Collaboration." Industry Science and Engineering 1, no. 1 (2024): 30–37. http://dx.doi.org/10.62381/i245105.

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In today's rapidly evolving business landscape, manufacturing enterprises face the challenge of enhancing their innovation capabilities to remain competitive. This research aims to investigate the mechanism and implementation path of enhancing innovation capability in manufacturing enterprises through human-machine collaboration. By examining the case of Xingyu Co., Ltd., this study explores the effects of human-machine collaboration on production efficiency, cost reduction, product quality improvement, and employee innovation potential. Additionally, it discusses the need for further research

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32

Smirnov, A. V., and T. V. Levashova. "Ontology of human-machine collaboration patterns for decision sup-port." Ontology of designing 14, no. 3 (2024): 421–39. http://dx.doi.org/10.18287/2223-9537-2024-14-3-421-439.

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In the process of human-machine collaboration, recurring problems often arise, prompting the use of collaboration patterns that offer ready-made solution schemes for similar recurring issues. The aim of the research is to develop models that facilitate the organization of human-machine collaboration while supporting decision-making based on collaboration patterns. The paper employs methods of conceptual, ontological, and scenario modeling. Based on the descriptions of collaboration patterns found in various problem areas, conceptual models were constructed representing five types of patterns:

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Zhu, Xiaojin, Bryan Gibson, and Timothy Rogers. "Co-Training as a Human Collaboration Policy." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 852–57. http://dx.doi.org/10.1609/aaai.v25i1.7945.

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We consider the task of human collaborative category learning, where two people work together to classify test items into appropriate categories based on what they learn from a training set. We propose a novel collaboration policy based on the Co-Training algorithm in machine learning, in which the two people play the role of the base learners. The policy restricts each learner's view of the data and limits their communication to only the exchange of their labelings on test items. In a series of empirical studies, we show that the Co-Training policy leads collaborators to jointly produce uniqu

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34

Rani, Sunanda, Dong Jining, Khadija Shoukat, Muhammad Usman Shoukat, and Saqib Ali Nawaz. "A Human–Machine Interaction Mechanism: Additive Manufacturing for Industry 5.0—Design and Management." Sustainability 16, no. 10 (2024): 4158. http://dx.doi.org/10.3390/su16104158.

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Industry 5.0 is an emerging value-driven manufacturing model in which human–machine interface-oriented intelligent manufacturing is one of the core concepts. Based on the theoretical human–cyber–physical system (HCPS), a reference framework for human–machine collaborative additive manufacturing for Industry 5.0 is proposed. This framework establishes a three-level product–economy–ecology model and explains the basic concept of human–machine collaborative additive manufacturing by considering the intrinsic characteristics and functional evolution of additive manufacturing technology. Key enabli

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35

Kaczmarek, Christopher. "Creative collaboration within heterogeneous human/intelligent agent teams." Technoetic Arts 19, no. 3 (2021): 269–81. http://dx.doi.org/10.1386/tear_00068_1.

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As we move towards a world that is using machine learning and nascent artificial intelligence to analyse and, in many ways, guide most aspects of our lives, new forms of heterogeneous collaborative teams that include human/intelligent machine agents will become not just possible, but an inevitable part of our shared world. The conscious participation of the arts in the conversation about, and development and implementation of, these new collaborative possibilities is crucial, as the arts serve as our best lens through which we can explore the full spectrum of potential possibilities and future

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36

Jose, John Anthony C., Meygen D. Cruz, Jefferson James U. Keh, Maverick Rivera, Edwin Sybingco, and Elmer P. Dadios. "Anno-Mate: Human–Machine Collaboration Features for Fast Annotation." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 4 (2021): 404–9. http://dx.doi.org/10.20965/jaciii.2021.p0404.

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Large annotated datasets are crucial for training deep machine learning models, but they are expensive and time-consuming to create. There are already numerous public datasets, but a vast amount of unlabeled data, especially video data, can still be annotated and leveraged to further improve the performance and accuracy of machine learning models. Therefore, it is essential to reduce the time and effort required to annotate a dataset to prevent bottlenecks in the development of this field. In this study, we propose Anno-Mate, a pair of features integrated into the Computer Vision Annotation To

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37

Komenda, Titanilla, and Wolfgang Leidholdt. "ema – a Software Tool for Planning Human-Machine-Collaboration." SNE Simulation Notes Europe 26, no. 1 (2016): 27–32. http://dx.doi.org/10.11128/sne.26.sw.10325.

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38

Krupáš, Maros, Mykyta Kot, Erik Kajáti, and Iveta Zolotová. "AI-Powered Obstacle Detection for Safer Human-Machine Collaboration." Acta Electrotechnica et Informatica 24, no. 3 (2024): 23–27. http://dx.doi.org/10.2478/aei-2024-0011.

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Abstract This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartpho

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39

Charissis, Vassilis, and Stylianos Papanastasiou. "Human–machine collaboration through vehicle head up display interface." Cognition, Technology & Work 12, no. 1 (2008): 41–50. http://dx.doi.org/10.1007/s10111-008-0117-0.

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40

Goldstein, Ian M., Julie Lawrence, and Adam S. Miner. "Human-Machine Collaboration—A New Form of Paternalism?—Reply." JAMA Oncology 4, no. 4 (2018): 589. http://dx.doi.org/10.1001/jamaoncol.2017.1519.

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41

Nakabo, Yoshihiro. "Safety of Machinery, Human Machine Collaboration Safety and AI." Journal of the Robotics Society of Japan 41, no. 9 (2023): 767–70. http://dx.doi.org/10.7210/jrsj.41.767.

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42

Nakada, Kazuki, Miwako Tsunematsu, Takuya Kihara, Takumu Hattori, Tatsuji Tokiwa, and Hiroshi Fukuda. "CISTEM Education with Robotic Platform: For Human-Human Synchrony and Human-Machine Synchrony." Journal of Robotics and Mechatronics 29, no. 6 (2017): 969–79. http://dx.doi.org/10.20965/jrm.2017.p0969.

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In Hiroshima Prefecture, an inter-university collaborative educational project of an interdisciplinary field: Clinical Informatics and Technology (the CIT program) has being implemented. As a part of the CIT program, we have been working on CISTEM (Clinical Information, Science, Technology, Engineering, Mathematics, and Medicine) education to establish a systematic educational curriculum for students (mainly undergraduate students) who will be responsible for medical engineering cooperation/collaboration in the future. In this paper, from the viewpoints of communication and control engineering

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43

Jiang, Yunan, and Jinyi Zhi. "Can Anthropomorphic Interfaces Improve the Ergonomics and Safety Performance of Human–Machine Collaboration in Multitasking Scenarios?—An Example of Human–Machine Co-Driving in High-Speed Trains." Biomimetics 10, no. 5 (2025): 307. https://doi.org/10.3390/biomimetics10050307.

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High-speed trains are some of the most important transportation vehicles requiring human–computer collaboration. This study investigated the effects of different types of icons on recognition performance and cognitive load during frequent observation and sudden takeover tasks in high-speed trains. The results of this study can be used to improve the efficiency of human–computer collaboration tasks and driving safety. In this study, 48 participants were selected for a simulated driving experiment on a high-speed train. The recognition reaction time, operation completion time, number of recognit

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Wang, Mohan, and Pin-Chao Liao. "Personality Assessment Based on Electroencephalography Signals during Hazard Recognition." Sustainability 15, no. 11 (2023): 8906. http://dx.doi.org/10.3390/su15118906.

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Hazard recognition assisted by human–machine collaboration (HMC) techniques can facilitate high productivity. Human–machine collaboration techniques promote safer working processes by reducing the interaction between humans and machines. Nevertheless, current HMC techniques acquire human characteristics through manual inputs to provide customized information, thereby increasing the need for an interactive interface. Herein, we propose an implicit electroencephalography (EEG)-based measurement system to automatically assess worker personalities, underpinning the development of human–machine col

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45

Flach, John M., and Peter A. Hancock. "An Ecological Approach to Human-Machine Systems." Proceedings of the Human Factors Society Annual Meeting 36, no. 14 (1992): 1056–58. http://dx.doi.org/10.1177/154193129203601409.

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Human Factors is unlike other traditional divisions of knowledge and is more than the mere haphazard interdisciplinary collaboration between psychology and engineering. As such, it requires a unique theoretical structure that reflects the opportunities and constraints intrinsic to emergent complex dynamical operational spaces derived from the interplay of human, machine, task, and environment.

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Yang, Jing. "Review of Research on Human-Machine Collaboration in Disassembly Line Balancing Problem." International Journal of Global Economics and Management 2, no. 3 (2024): 37–43. http://dx.doi.org/10.62051/ijgem.v2n3.04.

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This paper aims to explore the research on the human-machine collaboration in the disassembly line balancing problem, in response to the importance of dismantling and reusing waste products. Human-machine collaboration in disassembly, as a method combining human intelligence with machine power, has the potential to enhance disassembly efficiency, reduce resource wastage, and minimize human exposure to hazards. However, due to the NP-hard nature of this problem, traditional exact algorithms perform poorly when facing large-scale and complex problems. Therefore, this paper explores the applicati

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47

Liu, Ying, and Lei Shen. "Consolidating Human-AI Collaboration Research in Organizations: A Literature Review." Journal of Computer, Signal, and System Research 2, no. 1 (2025): 131–51. https://doi.org/10.71222/7dehvd30.

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The purpose of this study is to depict the value added by human-AI collaboration in organizations to collaboration system design by virtue of the studies reached by the literature review on different databases are examined. Web of Science content and covering the title of “human-AI collaboration” has been selected in this study. Research using bibliometric analysis has been conducted and it has been determined that the terms “human-AI collaboration” and “generative artificial intelligence” should be searched for simultaneously in each and every article published in the journal between the year

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48

Al-Sabbag, Zaid Abbas, Chul Min Yeum, and Sriram Narasimhan. "Enabling human–machine collaboration in infrastructure inspections through mixed reality." Advanced Engineering Informatics 53 (August 2022): 101709. http://dx.doi.org/10.1016/j.aei.2022.101709.

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Muin, Sifat, and Khalid M. Mosalam. "Human-machine collaboration framework for structural health monitoring and resiliency." Engineering Structures 235 (May 2021): 112084. http://dx.doi.org/10.1016/j.engstruct.2021.112084.

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Coetzer, Johannes, Jacques P. Swanepoel, and Robert Sabourin. "Optimal human-machine collaboration for enhanced cost-sensitive biometric authentication." SAIEE Africa Research Journal 112, no. 2 (2021): 110–19. http://dx.doi.org/10.23919/saiee.2021.9432899.

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