Xiaojing Yu

Intelligent voice systems are widely utilized to control smart home applications, which raises significant privacy and security concerns. Recent studies have revealed their vulnerability to adversarial attacks, replay attacks, and so on. However, these attacks rely on the victim’s voice data. In our work, we investigate a stealthy and command-independent attack that does not necessitate collecting victims’ voices. Our proposed attack, IUAC, misleads the voice system to go against the victim’s will, regardless of the commands delivered. Our core concept is to train highly robust attack commands through the construction of diverse data, rendering the user’s commands negligible. To achieve stealthy attacks, we leverage a high-frequency carrier to construct an inaudible universal adversarial command. Extensive experiments conducted with real-world datasets demonstrate that our attack system attains an average attack success rate of 96% while resisting environmental interference. Moreover, our attack success rate against real-world voice systems is 4.52× higher than the state-of-the-art. Finally, we propose an effective defense mechanism and provide experimental tests to validate its efficacy.

Distributed online convex optimization (DOCO) has emerged as a promising approach in scenarios where multiple learners collaboratively serve sequential (possibly untrusted) clients using AI models. However, ensuring clients' privacy and minimizing regret while keeping the communication cost reasonable poses a significant challenge. To address this issue, we propose private DOCO algorithms, termed PDOM, for both oblivious and stochastic settings. Our approach involves a mini-batch strategy that optimally balances the effects of slower model updates and differential privacy (DP) perturbation. Our theoretical analysis shows that compared to state-of-the-art algorithms, PDOM reduces the regret bounds and communication cost by an O(d/ε) factor for the oblivious setting. For the stochastic setting, the impact of DP perturbation becomes negligible if the learning time T = Ω(d⁴/(nε⁴)) provided that the loss functions are Lipschitz, convex, and smooth, where d is the model dimension, n is the number of learners, and ε is the privacy budget. Our evaluations validate these results, demonstrating that PDOM can reduce classification error rates of the state-of-the-art methods by up to 20% in distributed online logistic regression tasks while achieving communication savings of above 90%.

The development of creative applications and intelligent gadgets requires a secure and straightforward interface with human users. We propose, design, and implement ThumbUp, a smartwatch-based two-factor real-time identification and authentication system in which smartwatch users can identify and authenticate themselves using some simple hand and finger movements, such as thumb-up. ThumbUp leverages the signal from the Inertial Measurement Unit (IMU) in in Commercial-Off-The-Shelf (COTS) smart devices to discover the unique pattern generated by each user's simple gestures using a carefully constructed deep learning model. Smart homes provide a comfortable, safe, and efficient living environment, epecially help the sick and aged. We propose strategies for convenient and reliable control in smart homes with gesture command recognition. We build an Auto-Encoder-based filter that reconstructs the raw data to improve the representation of gesture features. Moreover, we adopt the random forest method to analyse the contextual command correlation. And we employ the authentication system based on smartwatch for personalized command feedback and ensure that illegals cannot use the device. We implement our system and undertake rigorous studies to determine its usefulness and efficiency over a three-month period with 65 users. It achieves a 97% accuracy for user classification and an EER of 0.014 for authentication task with a single simple gesture. And our method achieves 91% accuracy for command recognition and 96% command accuracy with contextual informations. Additionally, we conduct a study of user acceptability of our system and explain how gesture proficiency influences authentication accuracy.

Traditional fundamental frequency extraction algorithms mainly include cepstrum method, short-time autocorrelation method. Some end-point extraction methods can't correctly select the part of effective value due to the particularity of dialect audio and the noise influence. And, some fundamental frequency extraction methods cannot get the data consistent with the trend of tuning values. Against the above, this paper proposes an improved double threshold method for the extraction of the basic frequency according to the characteristics of the vowels of the initials of the one-word tone, which extracts the continuous vowels of the one-word tone, and looks for the basic frequency extraction algorithm that meets the classification of the tones of one-word. Finally, the effective basic frequency data is obtained. This method is more effective than other methods in speech tone recognition. It is of great significance to the study of speech tone, especially the study of dialect tone.

Angiotensin (Ang)-(1–7) is an important biologically-active peptide of the renin-angiotensin system. This study was designed to determine whether inhibition of Ang-(1–7) in the hypothalamic paraventricular nucleus (PVN) attenuates sympathetic activity and elevates blood pressure by modulating pro-inflammatory cytokines (PICs) and oxidative stress in the PVN in salt-induced hypertension. Rats were fed either a high-salt (8% NaCl) or a normal salt diet (0.3% NaCl) for 10 weeks, followed by bilateral microinjections of the Ang-(1–7) antagonist A-779 or vehicle into the PVN. We found that the mean arterial pressure (MAP), renal sympathetic nerve activity (RSNA), and plasma norepinephrine (NE) were significantly increased in salt-induced hypertensive rats. The high-salt diet also resulted in higher levels of the PICs interleukin-6, interleukin-1beta, tumor necrosis factor alpha, and monocyte chemotactic protein-1, as well as higher gp91^phox expression and superoxide production in the PVN. Microinjection of A-779 (3 nmol/50 nL) into the bilateral PVN of hypertensive rats not only attenuated MAP, RSNA, and NE, but also decreased the PICs and oxidative stress in the PVN. These results suggest that the increased MAP and sympathetic activity in salt-induced hypertension can be suppressed by blockade of endogenous Ang-(1–7) in the PVN, through modulation of PICs and oxidative stress.