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With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we present VocalPrint, a resilient mmWave interrogation system that directly captures and analyzes the vocal vibrations for user authentication. Specifically, VocalPrint exploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the vocal tract and vocal source features and input them into an ensemble classifier for authentication. VocalPrint is practical as it allows the effortless transition to a smartphone while having sufficient usability due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show that VocalPrint achieves over 96% authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.

In mountainous area, earthquake is an inevitable factor during the construction of tunnel. Earthquake can cause great damage to faults and mountain slopes, and at the same time, the deformation and failure of mountain tunnels are closely related to faults and landslides. This study analyzes the impacts of faults and landslides on the mountain tunnel by numerical modelling, model test and field investigation, and discusses the corresponding engineering countermeasures. Mountain tunnel through fault tends to be damaged severely because it may be shorn by fault dislocation. However, the fractured tunnel may undergo even severer damage due to earthquake wave. Various factors, including earthquake wave, condition of surrounding rock, width of fault, relative position of fault and tunnel, fault friction velocity, fault activity and lining section type, affect the seismic performance of tunnel through fault. In addition, buffer layer, grouting, shock absorption gap, sectional tunnel lining with flexible joints, fiber reinforced concrete lining, and ultra-excavating are all available aseismic or anti-dislocation measures. The damage mode and degree of landslide to tunnel are related to the type of tunnel, and the different relative positions of the tunnel and the sliding surface controls damages. Landslide prevention and control engineering measures mainly include weight loss, drainage, construction of retaining works, improvement and reinforcement of soil and rock properties of sliding zone, etc.

Brain health attracts more recent attention as the population ages. Smartphone-based gait sensing and analysis can help identify the risks of brain diseases in daily life for prevention. Existing gait analysis approaches mainly hand-craft temporal gait features or developing CNN-based feature extractors, but they are either prone to lose some inconspicuous pathological information or are only dedicated to a single brain disease screening. We discover that the relationship between gait segments can be used as a principle and generic indicator to quantify multiple pathological patterns. In this paper, we propose NeuralGait, a pervasive smartphone-cloud system that passively captures and analyzes principle gait segments relationship for brain health assessment. On the smartphone end, inertial gait data are collected while putting the smartphone in the pants pocket. We then craft local temporal-frequent gait domain features and develop a self-attention-based gait segment relationship encoder. Afterward, the domain features and relation features are fed to a scalable RiskNet in the cloud for brain health assessment. We also design a pathological hot update protocol to efficiently add new brain diseases in the RiskNet. NeuralGait is practical as it provides brain health assessment with no burden in daily life. In the experiment, we recruit 988 healthy people and 417 patients with a single or combination of PD, TBI, and stroke, and evaluate the brain health assessment using a set of specifically designed metrics including global accuracy, exact accuracy, sensitivity, and false alarm rate. We also demonstrate the generalization (e.g., analysis of feature effectiveness and model efficiency) and inclusiveness of NeuralGait.

Due to the premise of uniqueness and acceptance, fingerprint has been the most adopted biometric technologies in high-impact applications (e.g., smartphone security, monetary transactions and international-border verification). Although there are an array of commercial fingerprint scanners across different sensing modalities including optical, capacitive, thermal and ultrasonic, existing fingerprint technologies are vulnerable to spoofing attacks via fake-finger. In this paper, we investigate a new dimension of fingerprint sensing based on the friction-excited sonic wave (in simpler words, voice of fingerprint) from a user swiping his fingertip on everyday surfaces. Specifically, we develop SonicPrint to leverage the intrinsic fingerprint ridge information in sonic wave for user identification. First, the complex ambient noise is isolated from the sonic wave using background isolation and adaptive segmentation models. Afterward, a series of multi-level friction descriptors that highlight the target fingerprint information is extracted. These descriptors are fed to a specially designed ensemble classifier for user identification. SonicPrint is practical as it leverages in-built microphones in smart devices, requiring no hardware modifications. As the first exploratory study, our experimental results with 31 participants over three different swipe actions on 12 different types of materials show up to a 98% identification accuracy.

Treatment for multiple sclerosis (MS) focuses on managing its symptoms (e.g., depression, fatigue, poor sleep quality), varying with specific symptoms experienced. Thus, for optimal treatment, there arises the need to track these symptoms. Towards this goal, there is great interest in finding their relevant phenotypes. Prior research suggests links between activities of daily living (ADLs) and MS symptoms; therefore, we hypothesize that the behavioral phenotype (revealed through ADLs) is closely related to MS symptoms. Traditional approaches to finding behavioral phenotypes which rely on human observation or controlled clinical settings are burdensome and cannot account for all genuine ADLs. Here, we present MSLife, an end-to-end, burden-free approach to digital behavioral phenotyping of MS symptoms in the wild using wearables and graph-based statistical analysis. MSLife is built upon (1) low-cost, unobtrusive wearables (i.e., smartwatches) that can track and quantify ADLs among MS patients in the wild; (2) graph-based statistical analysis that can model the relationships between quantified ADLs (i.e., digital behavioral phenotype) and MS symptoms. We design, implement, and deploy MSLife with 30 MS patients across a one-week home-based IRB-approved clinical pilot study. We use the GENEActiv smartwatch to monitor ADLs and clinical behavioral instruments to collect MS symptoms. Then we develop a graph-based statistical analysis framework to model phenotyping relationships between ADLs and MS symptoms, incorporating confounding demographic factors. We discover 102 significant phenotyping relationships (e.g., later rise times are related to increased levels of depression, history of caffeine consumption is associated with lower fatigue levels, higher relative levels of moderate physical activity are linked with decreased sleep quality). We validate their healthcare implications, using them to track MS symptoms in retrospective analysis. To our best knowledge, this is one of the first practices to digital behavioral phenotyping of MS symptoms in the wild.

Using wireless signals to monitor human vital signs, especially heartbeat information, has been intensively studied in the past decade. This non-contact sensing modality can drive various applications from cardiac health, sleep, and emotion management. Under the circumstance of the COVID-19 pandemic, non-contact heart monitoring receives increasingly market demands. However, existing wireless heart monitoring schemes can only detect limited heart activities, such as heart rate, fiducial points, and Seismocardiography (SCG)-like information. In this paper, we present CardiacWave to enable a non-contact high-definition heart monitoring. CardiacWave can provide a full spectrum of Electrocardiogram (ECG)-like heart activities, including the details of P-wave, T-wave, and QRS complex. Specifically, CardiacWave is built upon the Cardiac-mmWave scattering effect (CaSE), which is a variable frequency response of the cardiac electromagnetic field under the mmWave interrogation. The CardiacWave design consists of a noise-resistant sensing scheme to interrogate CaSE and a cardiac activity profiling module for extracting cardiac electrical activities from the interrogation response. Our experiments show that the CardiacWave-induced ECG measures have a high positive correlation with the heart activity ground truth (i.e., measurements from a medical-grade instrument). The timing difference of P-waves, T-waves, and QRS complex is 0.67%, 0.71%, and 0.49%, respectively, and a mean cardiac event difference is within a delay of 5.3 milliseconds. These results indicate that CaridacWave offers high-fidelity and integral heart clinical characteristics. Furthermore, we evaluate the CardiacWave system with participants under various conditions, including heart and breath rates, ages, and heart habits (e.g., tobacco use).

Although fingerprint technology holds great promise for user authentication, commercial scanners face significant challenges in terms of security (e.g., fake finger) and adoptability (e.g., wearables). SonicPrint pushes the boundary of fingerprint biometrics beyond smartphones to any smart devices without the need for specialized hardware. To achieve this, it listens for fingerprintinduced sonic effect (FiSe) caused when a user swipes his/her fingertip on smart device surface. Compared to other biometrics including physiological patterns and passive sensing, SonicPrint is a low-cost, privacyoriented and secure approach to identify users across smart devices of unique form-factors.

Electroencephalogram (EEG) activity from the brain is a promising biological marker that can serve as personal identification. Despite substantial efforts, it remains an unsolved problem to quantify EEG feature distribution for brain biometrics due to the complexity of the brain. In this study, we attempt to tackle EEG-based identification challenges by exploiting a novel distribution model. The distribution dissimilarity is measured by Mallows distance, a cluster similarity sensitive distance that is robust to signal noises. Specifically, EEG signals are decomposed through several statistical feature extraction methods, autoregressive model, discrete wavelet transform, and fast Fourier transform. With the dataset obtained from the real-world application, our proposed system achieves the f score accuracy of $$96.18\%$$ and half total error rate of $$2.223\%$$ , which demonstrates the feasibility and effectiveness of utilizing EEG biometrics for personal identification applications.

In the age of big data, companies tend to deploy their services in data centers rather than their own servers. The demands of big data analytics grow significantly, which leads to an extremely high electricity consumption at data centers. In this paper, we investigate the cost minimization problem of big data analytics on geo-distributed data centers connected to renewable energy sources with unpredictable capacity. To solve this problem, we propose a Reinforcement Learning (RL) based job scheduling algorithm by combining RL with neural network (NN). Moreover, two techniques are developed to enhance the performance of our proposal. Specifically, Random Pool Sampling (RPS) is proposed to retrain the NN via accumulated training data, and a novel Unidirectional Bridge Network (UBN) structure is designed for further enhancing the training speed by using the historical knowledge stored in the trained NN. Experiment results on real Google cluster traces and electricity price from Energy Information Administration show that our approach is able to reduce the data centers’ cost significantly compared with other benchmark algorithms.