Head orientation tracking has many potential applications in many fields,e.g., human-computer interaction, AR, and VR. In recent years, a large amount of existing work only focuses on the positioning of the user but ignore the tracking of the head orientation. Undoubtedly, with the information of the user's head orientation, many applications will have more opportunities for performance enhancement and better user experience. However, reviewing existing works regarding head tracking, the CV-based solutions have limited tracking angle range and privacy issues, the IMU-based solutions have accumulated errors, and the traditional microphone array-based solutions have low accuracy. Thus, none of these methods provides accurate and stable head orientation. In this paper, we propose EHeadTracker, an enhanced fine-grained head orientation tracking system based on head-mounted acoustic devices. This system recognizes rich head motions and achieves high-precision head direction tracking, while solving the problem of pivot point initialization. The experimental results show that the system can achieve an average error of 6 degrees in the head orientation tracking. To the best of our knowledge, EHeadTracker is the first system to use head-mounted acoustic devices to achieve head orientation tracking and has the highest accuracy in all current work.

Rotation is a fundamental form of motion and rotation speed measurement holds paramount importance for assessing the health and performance of machinery with rotating components. However, existing measurement systems often face challenges such as limited measurement distance, low accuracy, and complex installation or maintenance processes. In this paper, we propose RoLEX, a LoRa-based rotation speed measurement system for long-distance and contactless monitoring of rotating machinery in ubiquitous scenarios. RoLEX employs a novel Signal Selection method to eliminate chirp interference and adapt to varying rotation speeds, along with a Boost Sensing method to enhance sampling rates and an advanced feature processing algorithm for precise rotation speed estimation and tracking. Comprehensive experiments validate that RoLEX achieves a measurement distance of 50 m, approximately 17 times farther than the latest wireless rotation speed measurement systems. Moreover, RoLEX is robust to interference and obstructions (including through-wall scenarios) and achieves an average measurement error less than 0.69% across different rotation speeds (100 - 5100 Revolutions Per Minute). For tracking performance, RoLEX achieves a relative error less than 2.8% in 90% of cases. We also present a case study to highlight RoLEX's practical applicability in real-world scenarios.

Acoustic sensing for heartbeat monitoring has emerged as a prevailing research topic in wireless sensing. However, existing acoustic sensing systems face two limitations: a restricted sensing range and operation limited to a single user, impeding large-scale deployment of its applications. In this paper, we present DF-Sense, aDualForming based multi-user acousticSensingsystem for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namelyDualforming, which leverages constructive superposition across multiple subcarriers and microphones. To facilitateDualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method and a 2-D peak identification scheme to locate and identify multiple subjects with subtle motions. Additionally, we propose a phase change-based method to promptly identify body leaning and adaptively re-localize subjects, thereby avoiding the high computational cost. Finally, we propose an enhanced recursive least squares (RLS) filter to effectively reconstruct high-quality heartbeat waveforms from Channel Frequency Response (CFR) signals affected by limb movements. Experimental results show that DF-Sense achieves high precision measurement of instantaneous heart rates within a range of 10 m, sufficient for most daily space requirements, and can monitor heartbeat for up to 6 subjects in a 2-D space.

Acoustic ranging has been viewed as a promising Human-Computer Interaction (HCI) technology in many scenarios, such as Augmented Reality (AR)/Virtual Reality (VR) and smart appliances. Most ranging systems with distributed devices undergo an extra calibration process to remove the timing errors. However, the calibration process needs user intervention. Furthermore, it should assume that the clock drifts are linear and stable, which is disabled within tens of minutes. In this paper, we introduce a self-calibrated acoustic ranging system that achieves sub-millimeter accuracy on distributed asynchronous devices. Based on our theoretical timing model, we precisely cancel both the system delay and the nonlinear clock drift with carefully designed Orthogonal Frequency-Division Multiplexing (OFDM) ranging signals. Our synchronization scheme achieves a timing accuracy of 1.9 microseconds, which allows us to build large-scale virtual acoustic arrays. Based on such a calibration scheme, our localization system achieves a ranging error of$\rm{0.39}~mm$within three meters in real-world experiments.

Federated learning (FL) is a popular distributed machine learning framework in which clients aggregate models' parameters instead of sharing individual data.In FL, clients frequently communicate with the server under limited network bandwidth, raising the communication challenge.Multiple compression methods have been proposed to reduce the transmitted parameters.However, these techniques show that the federated performance degrades significantly with Non-IID (non-identically independently distributed) datasets.To address this issue, we propose an effective method called FedPSE, which solves the efficiency challenge of FL with heterogeneous data.FedPSE compresses the local updates on clients using Top-K sparsification and aggregates these updates on the server by element-wise aggregation.Then clients download the personalized sparse updates from the server to update their individual local models.We then theoretically analyze the convergence of FedPSE under the non-convex setting.Moreover, extensive experiments on four benchmark tasks demonstrate that our FedPSE outperforms the state-of-the-art methods on Non-IID datasets in terms of efficiency and accuracy.

Acoustic sensing for heartbeat monitoring has become a prevailing research topic in wireless sensing. Existing acoustic sensing systems have two limitations---limited sensing range, and heartbeat monitoring for a single user only, hindering the large-scale deployment of applications. In this paper, we present DF-Sense, a Dual Forming based multi-user acoustic Sensing system for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namely Dualforming, based on the constructive superposition across multiple subcarriers and microphones, and further build the quantitative relationship between critical factors and SSNR enhancement to optimize sensing performance. To enable Dualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method to identify multiple subjects with subtle motions. We implement DF-Sense using commercial acoustic devices and conduct extensive experiments in a home setting. Results show that DF-Sense achieves high precision measurement of instantaneous heart rate within the range of 10 m, which is sufficient for most daily space requirements, and is able to monitor heartbeat for up to 6 subjects in a 2-D space simultaneously.

Secure multi-party computation-based machine learning, referred to as multi-party learning (MPL for short), has become an important technology to utilize data from multiple parties with privacy preservation. While MPL provides rigorous security guarantees for the computation process, the models trained by MPL are still vulnerable to attacks that solely depend on access to the models. Differential privacy could help to defend against such attacks. However, the accuracy loss brought by differential privacy and the huge communication overhead of secure multi-party computation protocols make it highly challenging to balance the 3-way trade-off between privacy, efficiency, and accuracy.In this paper, we are motivated to resolve the above issue by proposing a solution, referred to as PEA (Private, Efficient, Accurate), which consists of a secure differentially private stochastic gradient descent (DPSGD for short) protocol and two optimization methods. First, we propose a secure DPSGD protocol to enforce DPSGD, which is a popular differentially private machine learning algorithm, in secret sharing-based MPL frameworks. Second, to reduce the accuracy loss led by differential privacy noise and the huge communication overhead of MPL, we propose two optimization methods for the training process of MPL: (1) the data-independent feature extraction method, which aims to simplify the trained model structure; (2) the local data-based global model initialization method, which aims to speed up the convergence of the model training. We implement PEA in two open-source MPL frameworks: TF-Encrypted and Queqiao. The experimental results on various datasets demonstrate the efficiency and effectiveness of PEA. E.g. when ϵ = 2, we can train a differentially private classification model with an accuracy of 88% for CIFAR-10 within 7 minutes under the LAN setting. This result significantly outperforms the one from CryptGPU, one state-of-the-art MPL framework: it costs more than 16 hours to train a non-private deep neural network model on CIFAR-10 with the same accuracy.

Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force. Under such situation, Federated Learning (FL) appears to facilitate privacy-preserving joint modeling among multiple parties. Although many federated algorithms have been extensively studied, there is still a lack of secure and practical gradient tree boosting models (e.g., XGB) in literature. In this paper, we aim to build large-scale secure XGB under vertically federated learning setting. We guarantee data privacy from three aspects. Specifically, (1) we employ secure multi-party computation techniques to avoid leaking intermediate information during training, (2) we store the output model in a distributed manner in order to minimize information release, and (3) we provide a novel algorithm for secure XGB predict with the distributed model. Furthermore, by proposing secure permutation protocols, we can improve the training efficiency and make the framework scale to large dataset. We conduct extensive experiments on both public datasets and real-world datasets, and the results demonstrate that our proposed XGB models provide not only competitive accuracy but also practical performance.

Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for scalability requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.

Gesture tracking is a basic Human-Computer Interaction mechanism to control devices, such as IoT and VR/AR devices. However, prior OFDM signal based systems focus on gesture recognition and provide results with insufficient accuracy, and thus, cannot be applied for high-precision gesture tracking. In this paper, we propose a CSI based device-free gesture tracking system, called WiTrace, which leverages the CSI values extracted from OFDM signals to enable accurate gesture tracking. For 1D tracking, WiTrace derives the phase of the signals reflected by the hand from the composite signals, and measures the phase changes to obtain the movement distance. For 2D tracking, WiTrace proposes the first CSI based scheme to accurately estimate the initial position, and adopts the Kalman Filter based on continuous Wiener process acceleration model to further filter out tracking noise. Our results show that WiTrace achieves an average accuracy of 6.23 cm for initial position estimation and achieves cm-level accuracy with average tracking errors of 1.46 cm and 2.09 cm for 1D tracking and 2D tracking, respectively.

With the rapid growth of data volume, data-driven machine learning models have become a necessary part of many industrial applications. Intuitively, the more high-quality data used for training leads to better model performance. However, in reality, data are usually scattered and isolated in different organizations or companies. Such a "data isolation" problem stimulates both academia and industry to explore the collaborative learning paradigm to build better models jointly with multiple data sources. Despite the potential performance gains, this learning paradigm inevitably faces privacy issues, especially for the Fintech domain where data are sensitive by nature. In this paper, we present a privacy-preserving collaborative learning system in Ant Financial, named Nebula. Our system aims to facilitate privacy-preserving collaborative model training for industrial-scale applications. Our system is built upon a ring-allreduce MPI based distributed framework. On top of that, with some optimization strategies and novel sharing scheme, our system is able to scale up to tens of millions of data samples with hundreds of thousands of features and achieve more than 100x speedup compared with the existing state-of-the-art implementations.

Gesture tracking is a basic Human-Computer Interaction mechanism to control devices such as electronic Internet of Things and VR/AR devices. However, prior WiFi signal based systems focus on gesture recognition and provide results with insufficient accuracy, and thus cannot be applied for highprecision gesture tracking. In this paper, we propose a CSI based device-free gesture tracking system, called WiTrace, which leverages the CSI values extracted from WiFi signals to enable accurate gesture tracking. For 1D tracking, WiTrace derives the phase of the signals reflected by the hand from the composite signals, and measures the phase changes to obtain the movement distance. For 2D tracking, WiTrace proposes the first CSI based scheme to accurately estimate the initial position, and adopts the Kalman filter based on Continuous Wiener Process Acceleration model to further filter out tracking noise. Our results show that WiTrace achieves the estimated accuracy of 3.91 cm for initial position on average, and achieves cm-level accuracy, with mean tracking errors of 1.46 cm and 2.09 cm for 1D tracking and 2D tracking, respectively.