Bluetooth-based location services have experienced significant growth over the past decades. RSSI-based techniques using beacons only provide meters-level accuracy. Angular-based approaches rely on customized antenna arrays, introducing high costs and limited usability. In 2020, Bluetooth Special Interest Group (Bluetooth SIG) released version 5.1, integrating Angle of Arrival (AoA) estimation to enable direction finding capabilities, which has the potential to improve localization across various fields, including logistics and industry. However, more than 4.1 billion devices (68% of the total) still do not support the direction finding feature. To address this issue and ensure backward compatibility, we proposed Bridge, a solution that leverages an additional trigger node (referred to as Trigger) to make the direction finding feature compatible with all Bluetooth devices without requiring modifications to existing hardware or firmware. The Trigger mimics communication behaviors with both locators and targets simultaneously by sending a nesting packet. Subsequently, processes and algorithms are delicately designed to estimate AoA. Bridge also supports large-scale deployment through dynamic packet flow switching, enabling it to handle concurrent targets and manage handover with a consistent operation pattern. We implemented and evaluated Bridge in real-world scenarios. The system achieved an average localization error of 33.4cm while extending the direction-finding feature to 10 target devices of different Bluetooth versions, indicating the effectiveness of Bridge.

BLE direction finding provides high-accuracy localization based on Angle-of-Arrival (AoA), but this feature is only available on BLE 5.1+ devices. Billions of existing Bluetooth devices are excluded from direction finding indoor localization systems. We present Bridge that enables direction finding for all Bluetooth versions without any hardware or firmware modifications. Bridge introduces a novel Trigger that mimics communication behaviors of both locators and targets, allowing the locator to extract AoA information from originally unsupported devices. We implement Bridge on COTS direction finding system and evaluate it on 10+ BLE devices, achieving a median localization error of 33.4cm.

Bluetooth Low Energy (BLE) direction finding, a feature introduced in BLE version 5.1, enables precise localization through Angle of Arrival (AoA) estimation. However, this advancement introduces new risk to BLE direction finding based localization system. Specifically, the AoA estimation based on phase sampling of constant-tone-extension (CTE) is susceptible to the signal injection attack. This paper presents SaDiF, a feasible spoofing attack mechanism to mislead the locators into mistaking the positioning result as a continuous path. By eavesdropping on BLE packets and injecting attack signals containing pre-designed disturbing phase shift, SaDiF subtly alters the AoA estimation without detection, thus interfere the localization results. Moreover, SaDiF address the challenges posed by hardware imperfections by proposing an injection timing optimization to improve attack robustness. Extensive experiments demonstrates the effectiveness of SaDiF in successfully attacking multiple BLE targets in real-time scenarios. In conclusion, our findings reveal critical security risks in BLE direction finding feature and provide insights into strengthening its defenses.

Data attribution methods, which quantify the influence of individual training data points on a machine learning model, have gained increasing popularity in data-centric applications in modern AI. Despite a recent surge of new methods developed in this space, the impact of hyperparameter tuning in these methods remains under-explored. In this work, we present the first large-scale empirical study to understand the hyperparameter sensitivity of common data attribution methods. Our results show that most methods are indeed sensitive to certain key hyperparameters. However, unlike typical machine learning algorithms---whose hyperparameters can be tuned using computationally-cheap validation metrics---evaluating data attribution performance often requires retraining models on subsets of training data, making such metrics prohibitively costly for hyperparameter tuning. This poses a critical open challenge for the practical application of data attribution methods. To address this challenge, we advocate for better theoretical understandings of hyperparameter behavior to inform efficient tuning strategies. As a case study, we provide a theoretical analysis of the regularization term that is critical in many variants of influence function methods. Building on this analysis, we propose a lightweight procedure for selecting the regularization value without model retraining, and validate its effectiveness across a range of standard data attribution benchmarks. Overall, our study identifies a fundamental yet overlooked challenge in the practical application of data attribution, and highlights the importance of careful discussion on hyperparameter selection in future method development.

This paper presents a drug development platform based on virtual screening technology. The platform integrates key components such as pocket prediction, molecular docking, molecular dynamics simulation, and ADMET evaluation to achieve an efficient and automated drug virtual screening process. The platform utilizes Docker for modular encapsulation, ensuring environment isolation and convenient deployment. It also provides standardized input-output formats and a task allocation system, enabling users to quickly deploy and customize the workflow. Experimental results demonstrate the effectiveness of the platform in identifying real drugs and evaluating virtual screening results, providing an efficient and reliable solution for drug development. The platform features easy deployment and migration, independent module execution, automated workflow implementation, personalized customization and replacement, task allocation for computationally intensive steps, and complex operations in molecular dynamics simulation.