Academic literature on the topic 'Human-machine collaboration'

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Journal articles on the topic "Human-machine collaboration"

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Human-machine collaboration"

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Roy, Brandon C. (Brandon Cain). "Human-machine collaboration for rapid speech transcription." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41751.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.<br>Includes bibliographical references (p. 121-127).<br>Inexpensive storage and sensor technologies are yielding a new generation of massive multimedia datasets. The exponential growth in storage and processing power makes it possible to collect more data than ever before, yet without appropriate content annotation for search and analysis such corpora are of little use. While advances in data mining and machine learning have helped to automate some types of analy
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Kim, Been. "Interactive and interpretable machine learning models for human machine collaboration." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98680.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 135-143).<br>I envision a system that enables successful collaborations between humans and machine learning models by harnessing the relative strength to accomplish what neither can do alone. Machine learning techniques and humans have skills that complement each other - machine learning techniques are good at computation on data at the lowest level of granularity, whereas people are better at abstracting
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Wilson, Adam James. "Automatic improvisation a study in human/machine collaboration /." Diss., [La Jolla, Calif.] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3386761.

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Thesis (Ph. D.)--University of California, San Diego, 2009.<br>Title from 1st page of PDF file (viewed Feb. 9, 2010). Available via ProQuest Digital Dissertations. Vita. Includes sound files of examples in 44K stereo. and 96K formats. Includes bibliographical references: P. 143.
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SORIANO, PINTER JAUME. "Machine learning-based image processing for human-robot collaboration." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278899.

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Human-robot Collaboration as a new paradigm in manufacturing has already been a hot topic in both manufacturing science, production research, intelligent robotics, and computer science. Due to the boost of deep learning technologies in the nearly ten years, advanced information processing technologies bring the new possibility to human-robot Collaboration. Meanwhile, machine learning-based image processing such as convolutional neural network has become a powerful tool in dealing with problems like target recognizing and locating. This kind of technologies shows potentials on robotic manufactu
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Perez, Jorge (Jorge I. ). "Designing interaction for human-machine collaboration in multi-agent scheduling." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/106007.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 57-58).<br>In the field of multi-agent task scheduling, there are many algorithms that are capable of minimizing objective functions when the user is able to specify them. However, there is a need for systems and algorithms
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Aarno, Daniel. "Intention recognition in human machine collaborative systems." Licentiate thesis, KTH, Numerical Analysis and Computer Science, NADA, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4303.

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<p>Robotsystem har använts flitigt under de senaste årtiondena för att skapa automationslösningar i ett flertal områden. De flesta nuvarande automationslösningarna är begränsade av att uppgifterna de kan lösa måste vara repetitiva och förutsägbara. En av anledningarna till detta är att dagens robotsystem saknar förmåga att förstå och resonera om omvärlden. På grund av detta har forskare inom robotik och artificiell intelligens försökt att skapa intelligentare maskiner. Trots att stora framsteg har gjorts då det gäller att skapa robotar som kan fungera och interagera i en mänsklig miljö så finn
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Tidball, Brian Esley. "Designing Computer Agents with Facial Personality to Improve Human-Machine Collaboration." Wright State University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=wright1146857305.

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NUZZI, CRISTINA. "Towards true human-machine collaboration: The concept of Meta-Collaborative workstations and a first software prototype." Doctoral thesis, Università degli studi di Brescia, 2020. http://hdl.handle.net/11379/544100.

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Towards true human-machine collaboration: The concept of Meta-Collaborative workstations and a first software prototype.<br>Towards true human-machine collaboration: The concept of Meta-Collaborative workstations and a first software prototype.
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Prabhala, Sasanka V. "Designing Computer Agents with Personality to Improve Human-Machine Collaboration in Complex Systems." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1173299872.

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Ive, Julia. "Towards a Better Human-Machine Collaboration in Statistical Translation : Example of Systematic Medical Reviews." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS225/document.

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La traduction automatique (TA) a connu des progrès significatifs ces dernières années et continue de s'améliorer. La TA est utilisée aujourd'hui avec succès dans de nombreux contextes, y compris les environnements professionnels de traduction et les scénarios de production. Cependant, le processus de traduction requiert souvent des connaissances plus larges qu'extraites de corpus parallèles. Étant donné qu'une injection de connaissances humaines dans la TA est nécessaire, l'un des moyens possibles d'améliorer TA est d'assurer une collaboration optimisée entre l'humain et la machine. À cette fi
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Books on the topic "Human-machine collaboration"

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Bhattacharyya, Siddhartha, Mario Koeppen, Debashis De, and Vincenzo Piuri, eds. Intelligent Systems and Human Machine Collaboration. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8477-8.

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Goh, Dion H., Shu-Jiun Chen, and Suppawong Tuarob, eds. Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8085-7.

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Goh, Dion H., Shu-Jiun Chen, and Suppawong Tuarob, eds. Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8088-8.

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Intelligent Human-Machine Collaboration. National Academies Press, 2012. http://dx.doi.org/10.17226/13479.

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Chiang, Ethan N., Policy and Global Affairs, National Research Council, Patricia S. Wrightson, and Board on Global Science and Technology. Intelligent Human-Machine Collaboration: Summary of a Workshop. National Academies Press, 2012.

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Chiang, Ethan N., Policy and Global Affairs, National Research Council, Patricia S. Wrightson, and Board on Global Science and Technology. Intelligent Human-Machine Collaboration: Summary of a Workshop. National Academies Press, 2012.

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Chiang, Ethan N., Policy and Global Affairs, National Research Council, Patricia S. Wrightson, and Board on Global Science and Technology. Intelligent Human-Machine Collaboration: Summary of a Workshop. National Academies Press, 2012.

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Intelligent Human-Machine Collaboration: Summary of a Workshop. National Academies Press, 2012.

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Augmented Intelligence: The Business Power of Human-Machine Collaboration. Auerbach Publishers, Incorporated, 2019.

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Henry, Morris, Judith Hurwitz, Daniel Kirsch, and Candace Sidner. Augmented Intelligence: The Business Power of Human-Machine Collaboration. Auerbach Publishers, Incorporated, 2019.

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Book chapters on the topic "Human-machine collaboration"

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Krueger, Joerg. "Human-Machine Collaboration." In CIRP Encyclopedia of Production Engineering. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-35950-7_6623-4.

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Krüger, Jörg. "Human-Machine Collaboration." In CIRP Encyclopedia of Production Engineering. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-53120-4_6623.

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Krueger, Joerg. "Human-Machine Collaboration." In CIRP Encyclopedia of Production Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-20617-7_6623.

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Roddy, Stephen. "Creative Machine-Human Collaboration." In AI and the Future of Creative Work. Routledge, 2023. http://dx.doi.org/10.4324/9781003299875-2.

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Daga, Brijmohan, Nitish Pathak, Srushti Suraj Shah, Austin Bernard Fernandes, Jayden Jude Dsouza, and Gayatri Pramod Varma. "Human-Machine Collaboration in Business Process." In Advanced Digital Technologies in Financial and Business Management. Apple Academic Press, 2025. https://doi.org/10.1201/9781003558279-9.

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Razmerita, Liana, Armelle Brun, and Thierry Nabeth. "Collaboration in the Machine Age: Trustworthy Human-AI Collaboration." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93052-3_14.

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Landry, Steven J. "Directed Graphs for Task Analysis of Human-Machine Systems." In Systems Collaboration and Integration. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-44373-2_9.

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Nicoletti, Bernardo. "Supply Network 5.0 Human Automation Machine Collaboration." In Supply Network 5.0. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22032-6_4.

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Ren, Zhuoli, Zhiwen Yu, Hui Wang, Liang Wang, and Jiaqi Liu. "Human-Machine Collaboration Based Named Entity Recognition." In Computer Supported Cooperative Work and Social Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4546-5_27.

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Boutelier, Stefani, and Sarah J. Donovan. "AI + Nonet: Machine and Human Poetry Collaboration." In Teaching Poetry in a Digital World. Routledge, 2025. https://doi.org/10.4324/9781003581239-9.

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Conference papers on the topic "Human-machine collaboration"

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Smirnov, Alexander, and Tatiana Levashova. "Collaboration Patterns Ontology for Human-Machine Decision Support." In 16th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013057300003838.

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Guo, Liang, Junjie Zhang, Yanjing Zhang, Zhirong Peng, Zheng Zhao, and Weiwei Jiang. "Substation risk state detection method considering human-machine collaboration." In 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2024. https://doi.org/10.1109/iccasit62299.2024.10827952.

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Liu, Zhongtao, Parker Riley, Daniel Deutsch, et al. "Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data." In Proceedings of the Ninth Conference on Machine Translation. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.wmt-1.110.

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Malatji, Masike. "Evaluating Human-Machine Interaction Paradigms for Effective Human-Artificial Intelligence Collaboration in Cybersecurity." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10913015.

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Wang, Hua. "Human-Machine Collaboration in Healthcare Investment Integrating Large Language Models." In 2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP). IEEE, 2024. https://doi.org/10.1109/mlnlp63328.2024.10800279.

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Cheng, Luqi, Zijie Zhou, Zhangshuo Qi, and Guangming Xiong. "Low-Bandwidth Adaptive Human-Machine Collaboration Technology for Small Indoor Robots." In 2024 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2024. https://doi.org/10.1109/icus61736.2024.10839932.

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Wen, Sijie, Yongming Chen, Xinyu Pan, Weibin Zhuang, and Xinyu Li. "Enhancing Fault Troubleshooting through Human-Machine Collaboration: A Multi-Stage Reasoning Approach." In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711734.

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Lu, Yi-Fang, Kumar Shivam, Jen-Chung Hsiao, Che-Chien Chen, and Wei-Ming Chen. "Enhancing Human-Machine Collaboration Safety Through Personnel Behavior Detection and Separate Speed Monitoring." In 2024 International Conference on Advanced Robotics and Intelligent Systems (ARIS). IEEE, 2024. http://dx.doi.org/10.1109/aris62416.2024.10679966.

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Jayaraman, Suresh Kumaar, Reid Simmons, Aaron Steinfeld, and Henny Admoni. "Understanding Robot Minds: Leveraging Machine Teaching for Transparent Human-Robot Collaboration Across Diverse Groups." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801370.

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Ravindranath, Saurabh, Rahul Baburaj, Vineeth N. Balasubramanian, NageswaraRao Namburu, Sujit Gujar, and C. V. Jawahar. "Human-Machine Collaboration for Face Recognition." In CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD. ACM, 2020. http://dx.doi.org/10.1145/3371158.3371160.

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Reports on the topic "Human-machine collaboration"

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Pasupuleti, Murali Krishna. Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv525.

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Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences.
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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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Greitzer, F. L., R. V. Badalamente, and T. S. Stewart. Collaborative human-machine nuclear non-proliferation analysis. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10110684.

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Adegoke, Damilola, Natasha Chilambo, Adeoti Dipeolu, Ibrahim Machina, Ade Obafemi-Olopade, and Dolapo Yusuf. Public discourses and Engagement on Governance of Covid-19 in Ekiti State, Nigeria. African Leadership Center, King's College London, 2021. http://dx.doi.org/10.47697/lab.202101.

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Numerous studies have emerged so far on Covid-19 (SARS-CoV-2) across different disciplines. There is virtually no facet of human experience and relationships that have not been studied. In Nigeria, these studies include knowledge and attitude, risk perception, public perception of Covid-19 management, e-learning, palliatives, precautionary behaviours etc.,, Studies have also been carried out on public framing of Covid-19 discourses in Nigeria; these have explored both offline and online messaging and issues from the perspectives of citizens towards government’s policy responses such as palliat
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Veung, Naron, and Seyhah Ven. Exploring Insights into Vocational Skills Development and Industrial Transformation in Cambodia. Cambodia Development Resource Institute, 2021. https://doi.org/10.64202/wp.131.202110.

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Industrial development is central to Cambodia’s economic development as it plans to upgrade the country’s status to an upper-middle-income country by 2030 and to a high-income country by 2050. Equipping the workforce with the skills and competencies that match industrial needs requires a great deal of continuous efforts and resources. In this regard, this paper aims to look into the linkage between skills and transformation based on employers’ perspectives. It explores insights into how companies perceive and use the skills and qualifications of their employees to overcome changes in technolog
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AI and the Knowledge Economy: Transforming APO Members. Asian Productivity Organization, 2025. https://doi.org/10.61145/lsys8690.

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AI is becoming a key driver of the knowledge economy, accelerating innovation, automating routine tasks, and enabling deeper human–machine collaboration. This APO report explores how member economies are adopting AI, where gaps remain, and what strategies can support inclusive, responsible, and future-ready development. It also presents tailored policy options to support national strategies, strengthen digital ecosystems, and drive inclusive productivity growth to help ensure that all members can benefit from the AI-driven transformation.
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