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AI systems developed for workplace well-being and Earth observation data collection

By PulseAugur Editorial · [2 sources] ·

Researchers have developed two distinct AI-driven systems for specialized data collection and analysis. The first, TrustmeWatcher, is an application designed for the TRUST-ME project that uses OS-level watchers to collect computer activity and synchronize it with self-reported data for well-being feedback. This system integrates activity review, data collection, privacy controls, and explainable AI (XAI) to help users understand prediction outputs. The second system, Deficit-Driven Stratified Acquisition (DDSA), is an algorithm for building a co-registered SAR-optical corpus for self-supervised Earth Observation. DDSA addresses the issue of under-sampling in existing datasets by casting corpus construction as a constrained maximum-coverage problem, ensuring balanced representation across different regions and seasons. The algorithm was measured on its product, STRATA-S12, demonstrating improved stratum balance and effective recovery of rejected acquisition attempts. AI

IMPACT These systems showcase advancements in AI for specialized data collection and analysis, potentially improving user well-being feedback and Earth observation data quality.

RANK_REASON Two distinct research papers detailing novel AI applications and algorithms are presented.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI systems developed for workplace well-being and Earth observation data collection

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Two distinct research papers detailing novel AI applications and algorithms are presented.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Chengyu Yu, Leon Jacopo Costa, Zoja An\v{z}ur, Mohan Li, Ga\v{s}per Slapni\v{c}ar, Daniil Kirilenko, Martin Gjoreski, Mitja Lu\v{s}trek, Marc Langheinrich ·

    TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback

    arXiv:2610.06657v2 Announce Type: replace Abstract: Workplace sensing studies combine long-running behaviour traces with self-reports, yet the tools that collect those data often sit apart from the interface that returns results. We present TrustmeWatcher, the application built f…

  2. arXiv cs.LG TIER_1 English(EN) · Christian Sanchez-Leon, Dongming Chen, Dongqi Wang ·

    Deficit-Driven Stratified Acquisition for Building STRATA-S12: An Open SAR-Optical Corpus for Self-Supervised Learning

    arXiv:2610.17680v2 Announce Type: replace-cross Abstract: Building a co-registered SAR-optical corpus for self-supervised Earth Observation is above all an acquisition problem, yet the main multi-modal datasets are collected opportunistically, with no per-stratum quota control an…

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