The Future of Human-in-the-Loop Cyber-Physical Systems
By G. Schirner, Deniz Erdoğmuş, Kaushik R. Chowdhury, T. Padır. 2013
321 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Surgery
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Citations in the corpus
- On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, neutral): “ In order to adapt to the constantly changing and uncertain environment, they need to take into account not only the current application but also the operator’s preferences, intent, and past behavior [36]. Autonomous machine learning and artificial intelligence techniques have been applied to several”
- Toward New-Generation Intelligent Manufacturing (2018, background): “(2) Through the hybrid-augmented intelligence of ‘‘humans in the loop,” in-depth human-machine integration will fundamentally improve the capability of manufacturing systems to handle complex and uncertain problems, and will greatly optimize the performance of manufacturing systems [52,57].”
- Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry (2020, background): “Thereby, we provide some of the first behavioral insights into people ’ s reactions to different HITL systems, and complement a rich (technical) literature in AI research (Schirner et al., 2013).”
- Research Directions for the Internet of Things (2014, background): “See also [26] for additional descriptions of human-in-the-loop systems.”
- Mobile Crowd Sensing and Computing (2015, background): “Recently, there have been studies about having humans in the loop in cyber-physical systems [Schirner et al. 2013], which can measure the cognitive activity of a human through body and brain sensors and then use the “inferred” intent for physical environment controlling.”
- A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop (2017, background): “Other definitions speak also of a human-in-the-loop, but it is what we would call classic supervised approaches (Shyu et al., 1999), or in a total different meaning to put the human into physical feedback loops (Schirner et al., 2013).”
- Introduction to MAchine Learning & Knowledge Extraction (MAKE) (2017, background): “Other definitions speak also of a human-in-the-loop, but it is what we would call classic supervised approaches [45], or in a total different meaning to put the human into physical feedback loops [46].”
- A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism (2020, background): “This includes implementation of ideas around human in the loop or human on the loop (Dautenhahn, 2011; Schirner et al., 2013).”
- Mitigating Byzantine Attacks in Federated Learning (2020, methodology): “…clients that share corrupted data with the server can be detected and removed before the start of training through manual sample analysis [15], [16], for example by leveraging the current data labelling platforms (such as those provided by Google and Amazon) for matching the labels and…”
- A Survey of Human-in-the-loop for Machine Learning (2021, unknown): “While for the fluency, a new mechanism for interaction as well as new interface such as BCI [203, 207, 208, 209] have been proposed.”