As eye-tracking becomes increasingly common in modern mobile devices, the potential for hands-free, gaze-based interaction grows, but current gesture sets are largely expert-designed and often misaligned with how users naturally move their eyes. To address this gap, we introduce a two-phase methodology for developing intuitive gaze gestures. First, four co-design workshops with 20 non-expert participants generated 102 initial concepts. Next, four gaze interaction experts reviewed and refined these into a set of 32 gestures. We found that non-experts, after a brief introduction, intuitively anchor gestures in familiar metaphors and develop a compositional grammar; i.e., activation (dwell) + action (gaze gesture or blink), to ensure intentionality and mitigate the classic Midas Touch problem. Experts prioritized gestures that are ergonomically sound, aligned with natural saccades, and reliably distinguishable. The resulting user-grounded, expert-validated gesture set, along with actionable design principles, provides a foundation for developing intuitive, hands-free interfaces for gaze-enabled devices.

Process-based learning is crucial for the transmission of intangible cultural heritage, especially in complex arts like Chinese calligraphy, where mastering techniques cannot be achieved by merely observing the final work. To explore the challenges faced in calligraphy heritage transmission, we conducted semi-structured interviews (N=8) as a formative study. Our findings indicate that the lack of calligraphy instructors and tools makes it difficult for students to master brush techniques, and teachers struggle to convey the intricate details and rhythm of brushwork. To address this, we collaborated with calligraphy instructors to develop an educational tool that integrates writing process capture and visualization, showcasing the writing rhythm, hand force, and brush posture. Through empirical studies conducted in multiple teaching workshops, we evaluated the system's effectiveness with teachers (N=4) and students (N=12). The results show that the tool significantly enhances teaching efficiency and aids learners in better understanding brush techniques.

Figure 1: We review and categorize VMIs aimed at enhancing context awareness.Our key contribution is a Macro-Micro-Macro level (whole-detail-whole) system design framework, providing actionable references from a Data Modality-Driven perspective: (1) Macro-level contextual factors: considerations for context understanding (Section 3); (2) Micro-level system foundations: input data modality (visual + other modalities), data integration stages, multimodal data processing and evaluation strategies (Sections 4, 5); (3) Macro-level design synthesis: application domains, design considerations and key challenges (Sections 6, 7).

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard China’s Fengyun-4A (FY-4A) geostationary (GEO) meteorological satellite provides high-spectral-resolution infrared (IR) observations for targeted observing areas with high temporal resolution. Due to the high uncertainties in radiative transfer modeling of cloudy radiances, it is challenging to take full advantage of the thermodynamic information from GIIRS in all-sky conditions. The Advanced Geostationary Radiation Imager (AGRI) onboard the same platform provides a variety of cloud products with high spatial resolution. A bias-corrected optimal cloud-clearing (BCOCC) approach is introduced to generate GIIRS cloud-cleared radiances (CCRs) with the help of AGRI-collocated clear radiances (CLRs). The bias correction (BC) scheme is based on the inter-comparisons between GIIRS and AGRI for each field-of-view (FOV) and different scene temperatures. The BC method ensures the radiometric consistency between GIIRS and AGRI. Evaluations of GIIRS CCRs show that the mean biases are 0.09, −0.06, and 0.06 K when compared with the three AGRI IR bands, B12, B13, and B14. In addition, BCOCC significantly increases the data yields of successful CCRs by three times that of the optimal cloud-clearing (OCC) approach without BC. For 15 days from September 16–30, 2021, around 37% more GIIRS partially cloudy footprints than clear sky are cloud cleared successfully. The CCRs can be assimilated as CLRs in numerical weather prediction (NWP) models without worrying about the cloud impact. This study provides evidence of the importance of placing an advanced hyperspectral IR sounder and imager onboard the same GEO platform for better quantitative applications.

As the first hyperspectral infrared sounder onboard a geostationary satellite, FengYun 4A (FY-4A) Geostationary Interferometric Infrared Sounder (GIIRS) plays an important role in high-impact weather forecasting with a high spatial and temporal resolution. Because hyperspectral infrared data are highly sensitive to clouds and these cloud-affected data are currently challenging to assimilate, cloud detection is a necessary step to assimilate hyperspectral infrared data better. FY-4A GIIRS has been operationally assimilated in the Chinese operational numerical weather prediction system CMA-GFS (China Meteorological Administration Global Forecast System), which uses an imager-assisted cloud detection method to detect the clear-sky field of view. This paper develops a new cloud detection method to detect clear channels for FY-4A GIIRS in CMA-GFS, extending the original method from two-dimensional to three-dimensional (3D) space. Compared to the original method, the 3D cloud detection method increases the number of observations to two to three times for the middle- and lower-tropospheric channels and three to four times for the upper-tropospheric channels. The additional observations are of the same quality as those retained by the original cloud detection method, and the averaged Observation minus Background (OMB) biases after bias correction for upper-tropospheric channels decreased 54% compared to the original. Experiments results indicate that this method successfully achieves the cloud detection in 3D for GIIRS data and shows the potential in improving data utilization. Also, the results show that 3D cloud detection has a slightly positive impact on temperature and wind forecasts.