Accurate and quick relative-distance measurement is crucial for supporting various intelligent transparent services, such as multi-device collaboration, screen rotation, and multi-device mirroring. Unfortunately, current methods often rely on single-modality sensing, resulting in various limitations: BLE-based and WiFi-based methods suffer from coarse-grained estimation, and ultrasound-based approaches suffer from limited sensing range. In this work, we aim at designing a distance-measurement system that enjoys long range, high accuracy, and small delay. Our designed system, named BAIR, relies on low-energy Bluetooth (BLE), acoustic sensors, and inertial measurement units (IMU) equipped on commercial smartphones for fine-grained and real-time relative distance estimation. BAIR effectively aligns multiple sensory signals with different sampling rates via the improved Kalman filter technology. To mitigate IMU's integration errors, BAIR calculates the average velocity over a preceding period and uses this, alongside accumulated velocity data from the IMU, significantly improving distance prediction accuracy. We implemented our BAIR system on smartphones and conducted extensive experiments to evaluate its performance. Specifically, in static scenarios, BAIR achieves a mean average error (MAE) of 11 cm. In moving scenarios, the cumulative distribution function (CDF) values for 95%, 80%, and 50% are 31 cm, 13 cm, and 8 cm, respectively. The memory footprint of BAIR is 16.41 MB. We release a video demo on YouTube11https://youtu.be/7Fbmn4ALaI0.

Knowledge Graph (KG) embedding has become crucial for the task of link prediction. Recent work applies encoder-decoder models to tackle this problem, where an encoder is formulated as a graph neural network (GNN) and a decoder is represented by an embedding method. These approaches enforce embedding techniques with structure information. Unfortunately, existing GNN-based frameworks still confront 3 severe problems: low representational power, stacking in a flat way, and poor robustness to noise. In this work, we propose a novel multi-level graph neural network (M-GNN) to address the above challenges. We first identify an injective aggregate scheme and design a powerful GNN layer using multi-layer perceptrons (MLPs). Then, we define graph coarsening schemes for various kinds of relations, and stack GNN layers on a series of coarsened graphs, so as to model hierarchical structures. Furthermore, attention mechanisms are adopted so that our approach can make predictions accurately even on the noisy knowledge graph. Results on WN18 and FB15k datasets show that our approach is effective in the standard link prediction task, significantly and consistently outperforming competitive baselines. Furthermore, robustness analysis on FB15k-237 dataset demonstrates that our proposed M-GNN is highly robust to sparsity and noise.