Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems

Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands - audio obscured by noise that is correctly recognized by a VPS but not by human beings. Such attacks, though, are often highly dependent on white-box knowledge of a specific machine learning model and limited to specific microphones and speakers, making their use across different acoustic hardware platforms (and thus their practicality) limited. In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems. Specifically, we exploit the fact that multiple source audio samples have similar feature vectors when transformed by acoustic feature extraction algorithms (e.g., FFTs). We develop four classes of perturbations that create unintelligible audio and test them against 12 machine learning models, including 7 proprietary models (e.g., Google Speech API, Bing Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful attacks against all targets. Moreover, we successfully use our maliciously generated audio samples in multiple hardware configurations, demonstrating effectiveness across both models and real systems. In so doing, we demonstrate that domain-specific knowledge of audio signal processing represents a practical means of generating successful hidden voice command attacks

A Model for Circuit Execution Runtime And Its Implications for Quantum Kernels At Practical Data Set Sizes

Quantum machine learning (QML) is a fast-growing discipline within quantum computing. One popular QML algorithm, quantum kernel estimation, uses quantum circuits to estimate a similarity measure (kernel) between two classical feature vectors. Given a set of such circuits, we give a heuristic, predictive model for the total circuit execution time required, based on a recently-introduced measure of the speed of quantum computers. In doing so, we also introduce the notion of an "effective number of quantum volume layers of a circuit", which may be of independent interest. We validate the performance of this model using synthetic and real data by comparing the model's predictions to empirical runtime data collected from IBM Quantum computers through the use of the Qiskit Runtime service. At current speeds of today's quantum computers, our model predicts data sets consisting of on the order of hundreds of feature vectors can be processed in order a few hours. For a large-data workflow, our model's predictions for runtime imply further improvements in the speed of circuit execution -- as well as the algorithm itself -- are necessary.Comment: 8.5 pages of main text + 1.5 pages of appendices. 7 figures & 3 table

Key Topics on End-of-Life Care for African Americans

Racial classifications of human populations are politically and socially determined. There is no biological or genetic basis for these racial classifications. Health behaviors may be influenced by culture and poverty. Disparities in health outcomes, sometimes resulting in higher mortality rates for African-Americans appear to influence end of life decision-making attitudes and behaviors. To improve the quality of end of life care in African-American communities, health care professionals must better understand and work to eliminate disparities in health care, increase their own skills, knowledge and confidence in palliative and hospice care, and improve awareness of the benefits and values of hospice and palliative care in their patients and families

The Effects of Hydration Status on Heart Rate Variability Following Supramaximal Intensity Exercise

Heart rate variability (HRV) is a non-invasive method used to monitor physiological stress via assessment of sympathetic and parasympathetic regulations and can indicate an individual’s recovery and readiness to exercise. Evidence suggests dehydration negatively impacts HRV; however, the influence of hydration status on HRV following supramaximal resistance exercise (RE) is unknown. PURPOSE: To investigate the effect of hydration status on HRV indices following supramaximal intensity RE. METHODS: 14 recreationally resistance-trained men (age, 21 ± 2 years; height, 176.25 ± 5.84 cm; weight, 81.31 ± 12.77 kg) participated in this study. In a randomized, counterbalanced order, participants performed a supramaximal intensity RE protocol in a euhydrated (EUH; urine specific gravity [USG] \u3c 1.020) and a dehydrated (DEH; USG \u3e 1.020) state, with conditions separated by 2 weeks. HRV indices (standard deviation of normal sinus beats [SDNN], root mean square of successive differences between normal heartbeats [RMSSD], high frequency power [HF], low frequency power [LF], LF:HF ratio, standard deviation of Poincaré plot perpendicular to [SD1] and along the line of identity [SD2]) were measured with participants lying in a supine position for 5 minutes in a dark room at baseline, immediately post-, 1hr-, 2hr-, and 3hr post-RE. Repeated measure analysis of variance was used to determine the effect of hydration status on HRV indices at each timepoint, with Bonferroni corrections for post-hoc analysis. RESULTS: RMSSD was significantly higher 1hr post-exercise in EUH (30.69 ± 7.09 ms) compared to DEH (16.31 ± 2.44 ms; p = 0.04). Similarly, HF power was significantly higher 1hr post-exercise in EUH (32.49 ± 4.12 %) compared to DEH (16.63 ± 2.71 %; p \u3c 0.01). In contrast, LF power was lower 1hr post-exercise in EUH (57.74 ± 3.62 %) compared to DEH (75.95 ± 3.42 %; p = 0.02), with LF:HF ratio significantly lower in EUH (2.36 ± 0.62) than DEH (6.21 ± 1.34; p = 0.01). SD1 was significantly greater 1hr post-exercise in EUH (21.74 ± 5.03 ms) than DEH (11.54 ± 1.73 ms; p = 0.04). No significant condition by time effects were observed for SDNN and SD2, or at remaining timepoints. CONCLUSION: These findings indicate that recovery and readiness to exercise are impaired 1hr following supramaximal intensity RE in a dehydrated state. However, impairments were ameliorated 2-3hrs proceeding the RE bout

Soft balancing in the Americas : Latin American opposition to U.S. intervention, 1898–1936

In the aftermath of the 2003 U.S.-led invasion of Iraq, scholars of international relations debated how to best characterize the rising tide of global opposition. The concept of “soft balancing” emerged as an influential, though contested, explanation of a new phenomenon in a unipolar world: states seeking to constrain the ability of the United States to deploy military force by using multinational organizations, international law, and coalition building. Soft balancing can also be observed in regional unipolar systems. Multinational archival research reveals how Argentina, Mexico, and other Latin American countries responded to expanding U.S. power and military assertiveness in the early twentieth century through coordinated diplomatic maneuvering that provides a strong example of soft balancing. Examination of this earlier case makes an empirical contribution to the emerging soft-balancing literature and suggests that soft balancing need not lead to hard balancing or open conflict

A framework for intelligent policy decision making based on a government data hub

Author ProofThe e-Oman Integration Platform is a data hub that enables data exchanges across government in response to transactions. With millions of transactions weekly, and thereby data exchanges, we propose to investigate the potential of gathering intelligence from these linked sources to help government officials make more informed decisions. A key feature of this data is the richness and accuracy, which increases the value of the learning outcome when augmented by other big and open data sources. We consider a high-level framework within a government context, taking into account issues related to the definition of public policies, data privacy, and the potential benefits to society. A preliminary, qualitative validation of the framework in the context of e-Oman is presented. This paper lays out foundational work into an ongoing research to implement government decision-making based on big data.“SmartEGOV: Harnessing EGOV for Smart Governance (Foundations, Methods, Tools)/NORTE-01-0145-FEDER-000037”, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (EFDR

Comparison of outpatient health care utilization among returning women and men Veterans from Afghanistan and Iraq

<p>Abstract</p> <p>Background</p> <p>The number of women serving in the United States military increased during Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF), leading to a subsequent surge in new women Veterans seeking health care services from the Veterans Administration (VA). The objective of this study was to examine gender differences among OEF/OIF Veterans in utilization of VA outpatient health care services.</p> <p>Methods</p> <p>Our retrospective cohort consisted of 1,620 OEF/OIF Veterans (240 women and 1380 men) who enrolled for outpatient healthcare at a single VA facility. We collected demographic data and information on military service and VA utilization from VA electronic medical records. To assess gender differences we used two models: use versus nonuse of services (logistic regression) and intensity of use among users (negative binomial regression).</p> <p>Results</p> <p>In our sample, women were more likely to be younger, single, and non-white than men. Women were more likely to utilize outpatient care services (odds ratio [OR] = 1.47, 95% confidence interval [CI]:1.09, 1.98), but once care was initiated, frequency of visits over time (intensity) did not differ by gender (incident rate ratio [IRR] = 1.07; 95% CI: 0.90, 1.27).</p> <p>Conclusion</p> <p>Recently discharged OEF/OIF women Veterans were more likely to seek VA health care than men Veterans. But the intensity of use was similar between women and men VA care users. As more women use VA health care, prospective studies exploring gender differences in types of services utilized, health outcomes, and factors associated with satisfaction will be required.</p

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics