Weighted-Sampling Audio Adversarial Example Attack

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory. In this paper, we propose~\textit{weighted-sampling audio adversarial examples}, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the first in the field to generate audio adversarial examples with low noise and high audio robustness at the minute time-consuming level.Comment: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuXL.9260.pd

Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation L1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i) each channel in a color image is contaminated independently, and contaminative components are independent and identically distributed; (ii) in a natural image the gradients of different components of a pixel are similar to one another. This method divides components into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the components are divided into the corrupted and the noise-free components; if the image is corrupted by random-valued impulses, the components are divided into the corrupted, noise-free, and the possibly corrupted components. Components falling into different categories are processed differently. If a component is corrupted, modified total variation diffusion is applied; if it is possibly corrupted, scaled total variation diffusion is applied; otherwise, the component is left unchanged. Simulation results demonstrate its effectiveness

Adversarial Samples on Android Malware Detection Systems for IoT Systems

Many IoT(Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a \textbf{t}esting framework for \textbf{l}earning-based \textbf{A}ndroid \textbf{m}alware \textbf{d}etection systems(TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android Application with a success rate of nearly 100\% and can perform black-box testing on the system

Superconductivity in a new layered cobalt oxychalcogenide Na6_{6}Co3_{3}Se6_{6}O3_{3} with a 3d5d^{5} triangular lattice

Unconventional superconductivity in bulk materials under ambient pressure is extremely rare among the 3$d$ transition-metal compounds outside the layered cuprates and iron-based family. It is predominantly linked to highly anisotropic electronic properties and quasi-two-dimensional (2D) Fermi surfaces. To date, the only known example of the Co-based exotic superconductor was the hydrated layered cobaltate, Na$_{x}$CoO$_{2}\cdot$ yH$_{2}$O, and its superconductivity is realized in the vicinity of a spin-1/2 Mott state. However, the nature of the superconductivity in these materials is still an active subject of debate, and therefore, finding new class of superconductors will help unravel the mysteries of their unconventional superconductivity. Here we report the discovery of unconventional superconductivity at $\sim$ 6.3 K in our newly synthesized layered compound Na$_{6}$Co$_{3}$Se$_{6}$O$_{3}$, in which the edge-shared CoSe$_{6}$ octahedra form [CoSe$_{2}$] layers with a perfect triangular lattice of Co ions. It is the first 3$d$ transition-metal oxychalcogenide superconductor with distinct structural and chemical characteristics. Despite its relatively low $T_{c}$, material exhibits extremely high superconducting upper critical fields, $\mu_{0}H_{c2}(0)$, which far exceeds the Pauli paramagnetic limit by a factor of 3 - 4. First-principles calculations show that Na$_{6}$Co$_{3}$Se$_{6}$O$_{3}$ is a rare example of negative charge transfer superconductor. This new cobalt oxychalcogenide with a geometrical frustration among Co spins, shows great potential as a highly appealing candidate for the realization of high-$T_{c}$ and/or unconventional superconductivity beyond the well-established Cu- and Fe-based superconductor families, and opened a new field in physics and chemistry of low-dimensional superconductors

Microbial Community Succession and Response to Environmental Variables During Cow Manure and Corn Straw Composting

In composting system, the composition of microbial communities is determined by the constant change in the physicochemical parameters. This study explored the dynamics of bacterial and fungal communities during cow manure and corn straw composting using high throughput sequencing technology. The relationships between physicochemical parameters and microbial community composition and abundance were also evaluated. The sequencing results revealed the major phyla included Proteobacteria, Bacteroidetes, Firmicutes, Chloroflexi and Actinobacteria, Ascomycota, and Basidiomycota. Linear discriminant analysis effect size (LEfSe) illustrated that Actinomycetales and Sordariomycetes were the indicators of bacteria and fungi in the maturation phase, respectively. Mantel test showed that NO3--N, NH4+-N, TN, C/N, temperature and moisture content significantly influenced bacterial community composition while only TN and moisture content had a significant effect on fungal community structure. Structural equation model (SEM) indicated that TN, NH4+-N, NO3--N and pH had a significant effect on fungal abundance while TN and temperature significantly affected bacterial abundance. Our finding increases the understanding of microbial community succession in cow manure and corn straw composting under natural conditions

On the optimal search problem: The case when the target distribution is unknown

We consider the problem of searching for an object in a set of N locations (or bins) {C1,...CN}. The probability of the object being in the location Ci is p(i). Also, the probability of locating the object in the bin within a specified time, given that it is in the bin, is given by a function called the detection function. This is typically specified by an exponential function. The intention is to allocate the available resources so as to maximize the probability of locating the object. This problem has applications in searching large databases and in developing various military and strategic policies. All of the research done in this area has assumed the knowledge of the {p(i)} - the target distribution. In this paper we consider the problem of obtaining error bounds and estimating the target distribution. To our knowledge these are the first available results in this area, and are particularly interesting because the target distribution, in itself, is unobservable

Equivalent Characterizations of Some Graph Problems by Covering-Based Rough Sets

Covering is a widely used form of data structures. Covering-based rough set theory provides a systematic approach to this data. In this paper, graphs are connected with covering-based rough sets. Specifically, we convert some important concepts in graph theory including vertex covers, independent sets, edge covers, and matchings to ones in covering-based rough sets. At the same time, corresponding problems in graphs are also transformed into ones in covering-based rough sets. For example, finding a minimal edge cover of a graph is translated into finding a minimal general reduct of a covering. The main contributions of this paper are threefold. First, any graph is converted to a covering. Two graphs induce the same covering if and only if they are isomorphic. Second, some new concepts are defined in covering-based rough sets to correspond with ones in graph theory. The upper approximation number is essential to describe these concepts. Finally, from a new viewpoint of covering-based rough sets, the general reduct is defined, and its equivalent characterization for the edge cover is presented. These results show the potential for the connection between covering-based rough sets and graphs