Current touch-based interactions on earphones are limited by hygiene concerns and the small interaction surface. Recent works attempt to bypass these issues with mid-air gesture systems using active acoustic sensing. However, these signals may be audible and pose potential hearing risks. To address this, we propose FingerBar, a mid-air gesture recognition system for earphones that relies solely on microphones without active signal transmission. FingerBar leverages the distinctive friction sounds generated by finger gestures to achieve gesture recognition. We design a gesture filtering pipeline to maintain robustness against daily noise. An adversarial training strategy further enhances user-independent performance. From a set of 16 gestures, we identify the 7 most suitable for FingerBar based on user acceptability. Extensive evaluations demonstrate high accuracy and robustness. Furthermore, a user study confirms the practicality and acceptability of the system. Our findings highlight the promise of passive acoustic sensing as a user-friendly interaction modality for earphones.

With the rapid development of cellular networks, wireless base stations (WBSes) have become crucial infrastructure for mobile web systems. To ensure service quality, operators constantly monitor the operation status of WBSes and deploy anomaly detection methods to identify anomalies promptly. After the deployment of anomaly detection methods, operators periodically collect feedback, which holds significant value in improving anomaly detection performance. In real-world industrial environments, the frequency of false negative feedback is usually very low, and the newly generated data's distribution can differ significantly from that of the original training data. Therefore, the feedback-based performance improvement of the previously proposed methods is limited. In this paper, we propose AnoTuner, which incorporates a false negative augmentation mechanism to generate similar false negative feedback cases, effectively compensating for the low feedback frequency. Additionally, we introduce a Two-Stage Active Learning (TSAL) mechanism that minimizes data contamination issues caused by the difference between the distribution of feedback data and that of the training data. Experiments conducted on the real-world data collected from a top-tier global Internet Service Provider (ISP) demonstrate that the performance improvement of AnoTuner after feedback-based fine-tuning is significantly higher than that of the best baseline method.

The leaf area density (LAD) plays an important role in describing the vertical canopy structure. Light Detection and Ranging (LiDAR) is an active remote-sensing technology that has already been applied to canopy measurements. In this paper, the vertical profiles of the LAD of three different size plots (15 x 15 m, 10 x 10m, and 5 x 5 m) of forest canopy were estimated and compared based on a voxel-based model using airborne LiDAR data and terrestrial LiDAR data, respectively. The LAD profiles retrieved from airborne LiDAR data were different from those obtained by terrestrial LiDAR data. The height of the maximum LAD estimated from the airborne LiDAR data was significantly higher than that estimated from the terrestrial LiDAR data. In addition, the middle and lower parts of the forest canopy LAD were underestimated by airborne LiDAR while the upper part of the forest canopy LAD were underestimated by terrestrial LiDAR compared with the actual canopy vertical structure.