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.

TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems.

With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great need for an automated testing framework to help security analysts to detect errors in learning-based IoT traffic detection systems.

Suspect screening and target quantification of human pharmaceutical residues in the surface water of Wuhan, China, using UHPLC-Q-Orbitrap HRMS.

In this study we developed a systematic method for suspect screening and target quantification of the human pharmaceutical residues in water, via solid phase extraction (SPE) followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). We then proceeded to study the occurrences and distribution of the pharmaceuticals in the surface waters of Wuhan, China, by analyzing water samples from lakes, rivers and municipal sewage. Initially, 33 human pharmaceuticals were identified from East Lake without using purchasing standards.

Identification of Organic Iodine Compounds and Their Transformation Products in Edible Iodized Salt Using Liquid Chromatography-High Resolution Mass Spectrometry.

The consumption of edible iodized salt is a key strategy to control and eliminate iodine deficiency disorders worldwide. We herein report the identification of the organic iodine compounds present in different edible iodized salt products using liquid chromatography combined with high resolution mass spectrometry. A total of 38 organic iodine compounds and their transformation products (TPs) were identified in seaweed iodine salt from China.

A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal.

Impulsive noise removal usually employs median filtering, switching median filtering, the total variation 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 new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics.

Rapid Identification of Unknown Organic Iodine in Small-Volume Complex Biological Samples Based on Nanospray Mass Spectrometry Coupled with in-Tube Solid Phase Microextraction.

A new method for rapid screening of unknown organic iodine (OI) in small-volume complex biological samples was developed using in-tube solid phase microextraction (SPME) nanospray mass spectrometry (MS). The method proposed a new identification scheme for OI based on nanospray high-resolution mass spectrometry (HR-MS). The mass ranges of OI ions were confirmed using the t-MS scan mode first; then, the possible precursor ions of OI were selected and identified orderly in full MS/ddMS and t-MS scan modes.

Extended dissipative state estimation for uncertain discrete-time Markov jump neural networks with mixed time delays.

This paper is concerned with the problem of extended dissipativity-based state estimation for uncertain discrete-time Markov jump neural networks with finite piecewise homogeneous Markov chain and mixed time delays. The aim of this paper is to present a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative. A triple-summable term is introduced in the constructed Lyapunov function and the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term.

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization.

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii) L 2 and L 1 regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can make L 1 regularization easy to implement.

A Poisson-based adaptive affinity propagation clustering for SAGE data.

Serial analysis of gene expression (SAGE) is a powerful tool to obtain gene expression profiles. Clustering analysis is a valuable technique for analyzing SAGE data. In this paper, we propose an adaptive clustering method for SAGE data analysis, namely, PoissonAPS.