356 research outputs found
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 Impact of Exchange Rate on FDI and the Interdependence of FDI Over Time
The paper examines the impact of exchange rates on foreign direct investment (FDI) inflows into the United States in the context of a model that allows for the interdependence of FDI over time. Interdependence is modeled as a two-state Markov process where the two states can be interpreted as either a favorable or an unfavorable environment for FDI in an industry. Unbalanced industry-level panel data from the US wholesale trade sector are used in the analysis and yield two main results. First, the paper finds evidence that FDI is interdependent over time. Second, under a favorable FDI environment, the exchange rate has a positive and significant effect on the average rate of FDI inflows
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
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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
Salsalate treatment following traumatic brain injury reduces inflammation and promotes a neuroprotective and neurogenic transcriptional response with concomitant functional recovery
Neuroinflammation plays a critical role in the pathogenesis of traumatic brain injury (TBI). TBI induces rapid activation of astrocytes and microglia, infiltration of peripheral leukocytes, and secretion of inflammatory cytokines. In the context of modest or severe TBI, such inflammation contributes to tissue destruction and permanent brain damage. However, it is clear that the inflammatory response is also necessary to promote post-injury healing. To date, anti-inflammatory therapies, including the broad class of non-steroidal anti-inflammatory drugs (NSAIDs), have met with little success in treatment of TBI, perhaps because these drugs have inhibited both the tissue-damaging and repair-promoting aspects of the inflammatory response, or because inhibition of inflammation alone is insufficient to yield therapeutic benefit. Salsalate is an unacetylated salicylate with long history of use in limiting inflammation. This drug is known to block activation of NF-jB, and recent data suggest that salsalate has a number of additional biological activities, which may also contribute to its efficacy in treatment of human disease. Here, we show that salsalate potently blocks pro-inflammatory gene expression and nitrite secretion by microglia in vitro. Using the controlled cortical impact (CCI) model in mice, we find that salsalate has a broad antiinflammatory effect on in vivo TBI-induced gene expression, when administered post-injury. Interestingly, salsalate also elevates expression of genes associated with neuroprotection and neurogenesis, including the neuropeptides, oxytocin and thyrotropin releasing hormone. Histological analysis reveals salsalate-dependent decreases in numbers and activation-associated morphological changes in microglia/macrophages, proximal to the injury site. Flow cytometry data show that salsalate changes the kinetics of CCI-induced accumulation of various populations of CD11b-positive myeloid cells in the injured brain. Behavioral assays demonstrate that salsalate treatment promotes significant recovery of function following CCI. These pre-clinical data suggest that salsalate may show promise as a TBI therapy with a multifactorial mechanism of action to enhance functional recovery
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