130 research outputs found
On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems
Speaker verification systems have been widely used in smart phones and
Internet of things devices to identify legitimate users. In recent work, it has
been shown that adversarial attacks, such as FAKEBOB, can work effectively
against speaker verification systems. The goal of this paper is to design a
detector that can distinguish an original audio from an audio contaminated by
adversarial attacks. Specifically, our designed detector, called MEH-FEST,
calculates the minimum energy in high frequencies from the short-time Fourier
transform of an audio and uses it as a detection metric. Through both analysis
and experiments, we show that our proposed detector is easy to implement, fast
to process an input audio, and effective in determining whether an audio is
corrupted by FAKEBOB attacks. The experimental results indicate that the
detector is extremely effective: with near zero false positive and false
negative rates for detecting FAKEBOB attacks in Gaussian mixture model (GMM)
and i-vector speaker verification systems. Moreover, adaptive adversarial
attacks against our proposed detector and their countermeasures are discussed
and studied, showing the game between attackers and defenders
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Confluence and Classification: Towards a Philosophy of Descriptive-Set-Theoretic Practice
The dissertation presents a collection of interrelated works in the philosophy of mathematics. They are roughly unified by their focus on descriptive set theory, which is investigated through the lens of mathematical practice. Chapters 2 and 3 examine the roles that confluence plays in mathematical practice, such as providing justification for the Church-Turing Thesis. An extensive survey of the technical literature will attest to the ubiquity of justification by confluence, and it will be shown to serve a wide variety of justificational purposes that are largely orthogonal to each other. Chapter 4 presents a series of theorems and proofs that involve increasingly substantial use of metamathematical methods. Reflecting on the question of whether the metamathematical elements can be translated away without loss of insight, it attempts to shed light on our practical taxonomy of proofs by their methodology, as well as on the specific question of whether a proof can be said to make substantial use of metamathematical methods. Chapter 5 traces the pre-history of the theory of Borel equivalence relations, with the specific aim of identifying the early ancestors to this theory prior to its sudden emergence in the 1990s
Compression and denoising of time-resolved light transport
Exploiting temporal information of light propagation captured at ultra-fast frame rates has enabled applications such as reconstruction of complex hidden geometry and vision through scattering media. However, these applications require high-dimensional and high-resolution transport data, which introduces significant performance and storage constraints. Additionally, due to different sources of noise in both captured and synthesized data, the signal becomes significantly degraded over time, compromising the quality of the results. In this work, we tackle these issues by proposing a method that extracts meaningful sets of features to accurately represent time-resolved light transport data. Our method reduces the size of time-resolved transport data up to a factor of 32, while significantly mitigating variance in both temporal and spatial dimensions
Effects of Different Shading Rates on the Photosynthesis and Corm Weight of Konjac Plant
To study the effects of shading level on the photosynthesis and corm weight of konjac plant, the chlorophyll fluorescence parameters, daily variation of relative electron transport rate (rETR), net photosynthetic rate (Pn), and corm weight of konjac plants under different treatments were measured and comparatively analyzed through covered cultivation of biennial seed corms with shade nets at different shading rates (0%, 50%, 70%, and 90%). The results showed that with the increase in shading rate, the maximum photochemical efficiency, potential activity, and non-photochemical quenching of photosystem â…ˇ (PSâ…ˇ) of konjac leaves constantly increased, whereas the actual photosynthetic efficiency, rETR, and photochemical quenching of PSâ…ˇ initially increased and then decreased. This result indicated that moderate shading could enhance the photosynthetic efficiency of konjac leaves. The daily variation of rETR in konjac plants under unshaded treatment showed a bimodal curve, whereas that under shaded treatment displayed a unimodal curve. The rETR of plants with 50% treatment and 70% treatment was gradually higher than that under unshaded treatment around noon. The moderate shading could increase the Pn of konjac leaves. The stomatal conductance and transpiration rate of the leaves under shaded treatment were significantly higher than those of the leaves under unshaded treatment. Shading could promote the growth of plants and increase corm weight. The comprehensive comparison shows that the konjac plants had strong photosynthetic capacity and high yield when the shading rate was 50%-70% for the area
Universal Murray's law for optimised fluid transport in synthetic structures
Materials following Murray's law are of significant interest due to their
unique porous structure and optimal mass transfer ability. However, it is
challenging to construct such biomimetic hierarchical channels with perfectly
cylindrical pores in synthetic systems following the existing theory. Achieving
superior mass transport capacity revealed by Murray's law in nanostructured
materials has thus far remained out of reach. We propose a Universal Murray's
law applicable to a wide range of hierarchical structures, shapes and
generalised transfer processes. We experimentally demonstrate optimal flow of
various fluids in hierarchically planar and tubular graphene aerogel structures
to validate the proposed law. By adjusting the macroscopic pores in such
aerogel-based gas sensors, we also show a significantly improved sensor
response dynamic. Our work provides a solid framework for designing synthetic
Murray materials with arbitrarily shaped channels for superior mass transfer
capabilities, with future implications in catalysis, sensing and energy
applications.Comment: 19 pages, 4 figure
xPath: Human-AI Diagnosis in Pathology with Multi-Criteria Analyses and Explanation by Hierarchically Traceable Evidence
Data-driven AI promises support for pathologists to discover sparse tumor
patterns in high-resolution histological images. However, from a pathologist's
point of view, existing AI suffers from three limitations: (i) a lack of
comprehensiveness where most AI algorithms only rely on a single criterion;
(ii) a lack of explainability where AI models tend to work as 'black boxes'
with little transparency; and (iii) a lack of integrability where it is unclear
how AI can become part of pathologists' existing workflow. Based on a formative
study with pathologists, we propose two designs for a human-AI collaborative
tool: (i) presenting joint analyses of multiple criteria at the top level while
(ii) revealing hierarchically traceable evidence on-demand to explain each
criterion. We instantiate such designs in xPath -- a brain tumor grading tool
where a pathologist can follow a top-down workflow to oversee AI's findings. We
conducted a technical evaluation and work sessions with twelve medical
professionals in pathology across three medical centers. We report quantitative
and qualitative feedback, discuss recurring themes on how our participants
interacted with xPath, and provide initial insights for future physician-AI
collaborative tools.Comment: 31 pages, 11 figure
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