Human-in-the-loopModel mergingLabel noiseWearable technologies enable continuous monitoring of various health metrics, such as physical activity, heart rate, sleep, and stress levels. A key challenge with wearable data is obtaining quality labels. Unlike modalities like video where the videos ...
Finally, we use the manual and automatically computed labels jointly to retrain the classifier in an active learning (AL) loop scheme. Experiments using a toy and a real-world application dataset show that our proposed combination of manual labeling supported by visualization of decision boundaries ...
Automated clinical coding (ACC) has emerged as a promising alternative to manual coding. This study proposes a novel human-in-the-loop (HITL) framework, CliniCoCo. Using deep learning capacities, CliniCoCo focuses on how such ACC systems and human coders
Humans In-The-Loop Ensure accountability, measurement, and governance by enabling traceability across distributed, autonomous AI systems. Behavure's Intelligent Approach Ingest, Enrich, Correlate, and Activate on Behavioral Data in Real-Time Ingest at the Edge ...
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Current temporal 3D human mesh and motion recovery methods, as well as most action classification algorithms, treat these shots as independent scenes, which reduce the rich potential of a film to a se- ries of short independent temporal sequences. Furthermore, shot changes often manifest in ...
Human-In-The-Loop ICT Information and Communication Technology ITS Intelligent Tutoring System LDA Latent Dirichlet Allocation NLP Natural Language Processing PLS Personalized Learning Space 1. Introduction A key goal of education is to foster the talents of students and to provide them with a holistic...
Although wearable robots are human-centered systems, not putting the human user in the control loop remains a major concern9. For instance, issues may arise if individuals wearing exoskeletons are unintentionally pulled or moved against their intentions. A lot of wearable robots mainly focus on the...
Here, we measured the transcriptional activity of DNA sequences that represent an ~100 times larger sequence space than the human genome using massively parallel reporter assays (MPRAs). Machine learning models revealed that transcription factors (TFs) generally act in an additive manner with weak ...
on query selection to \emph{minimize} the amount of data required for learning reward functions, we take an opposite approach: \emph{expanding} the pool of available data by viewing human-in-the-loop RL through the more flexible lens of multi-task learning. Motivated by the success of meta...