Qingxin Zhu's research while affiliated with University of Electronic Science and Technology of China and other places

Publications (87)

Article
This paper addresses the problems of exponential mean‐square stability and reliable sampling data control design for nonlinear switched systems. First, a common model of actuator fault is used to give a description of the nonlinear of controller. The packet loss is assumed to be affected by multiple sampling periods. By input delay method, probabil...
Article
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 sa...
Article
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In the past few years, deep learning has become a research hotspot and has had a profound impact on computer vision. Deep CNN has been proven to be the most important and effective model for image processing, but due to the lack of training samples and huge number of learning parameters, it is easy to tend to overfit. In this work, we propose a new...
Article
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In recent years, cost-sensitive feature selection has drawn much attention. However, some issues still remain to be investigated. Particularly, most existing work deals with single-typed data, while only a few studies deal with hybrid data; moreover, both the test cost of a feature and the misclassification cost of an object are often assumed to be...
Article
This paper is concerned with the problem of stochastic synchronization for semi-Markovian jump chaotic Lur’e systems. Firstly, packet dropouts and multiple sampling periods are both considered. By input-delay approach and then fully considering the probability distribution characteristic of packet dropouts in the modeling, the original system is tr...
Article
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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 testi...
Article
This paper investigates the distributed state estimation problem for a class of sensor networks described by discrete-time stochastic systems with stochastic switching topologies. In the sensor network, the redundant channel and the randomly varying nonlinearities are considered. The stochastic Brownian motions affect both the dynamical plant and t...
Preprint
Full-text available
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 testin...
Preprint
Full-text available
Neural networks play an increasingly important role in the field of machine learning and are included in many applications in society. Unfortunately, neural networks suffer from adversarial samples generated to attack them. However, most of the generation approaches either assume that the attacker has full knowledge of the neural network model or a...
Article
People are accustomed to using an anonymous network to protect their private information. The Profile HMM (Hidden Markov Model) Website Fingerprinting Detection algorithm can detect the website that the data stream accesses by pattern matching the captured data traffic. This makes the anonymous network lose its effect. In order to bypass the detect...
Article
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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...
Article
In real applications of data mining, machine learning and granular computing, measurement errors, test costs and misclassification costs often occur. Furthermore, the test cost of a feature is usually variable with the error range, and the variability of the misclassification cost is related to the object considered. Recently, some approaches based...
Article
Rough set theory is an important branch of data mining and granular computing, among which neighborhood rough set is presented to deal with numerical data and hybrid data. In this paper, we propose a new concept called inconsistent neighborhood, which extracts inconsistent objects from a traditional neighborhood. Firstly, a series of interesting pr...
Article
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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 red...
Article
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A shrinkage curve optimization is proposed for weighted nuclear norm minimization and is adapted to image denoising. The proposed optimization method employs a penalty function utilizing the difference between a latent matrix and its observation and uses odd polynomials to shrink the singular values of the observation matrix. As a result, the coeff...
Article
Full-text available
Impulsive noise removal usually employs median filtering, switching median filtering, 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 new method to remove noise is proposed in...
Article
Image re-ranking is effective in improving text-based image retrieval performance. However, to achieve such a target is often constrained by two important issues: one is that any one visual feature of images is usually too superficial to represent the whole information of images; the other is that the corresponding textual information often mismatc...
Article
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 exten...
Conference Paper
This paper investigates the problem of the extended dissipativity analysis for discrete-time Markov jump neural networks with finite piecewise homogeneous Markov chain and mixed time delays. By introducing a triple-summable term in Lyapunov function, the reciprocally convex approach is utilized to bound the forward difference of the triple-summable...
Conference Paper
Traditional least-squares temporal difference (LSTD) algorithms provide an efficient way for policy evaluation, but their performance is greatly influenced by the manual selection of state features and their approximation ability is often limited. To overcome these problems, we propose a multikernel recursive LSTD algorithm in this paper. Different...
Article
Full-text available
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 wide...
Article
Full-text available
Optimizating the deployment of wireless sensor networks, which is one of the key issues in wireless sensor networks research, helps improve the coverage of the networks and the system reliability. In this paper, we propose an evolutionary algorithm based on modified t-distribution for the wireless sensor by introducing a deployment optimization ope...
Conference Paper
Image re-ranking is effective in improving text-based image retrieval experience. However, to construct an efficacious algorithm to achieve such a target is limited by two important issues: one is that visual features extracted for image re-ranking from images are too superficial to represent the whole information contained within images; the other...
Article
This paper investigates the problem of state estimation for uncertain Markovian jump neural networks(NNs) with additive time-varying discrete delay components and distributed delay. By constructing a novel Lyapunov–Krasovskii function with multiple integral terms and using an improved inequality, several sufficient conditions are derived. Some impr...
Article
Rough set reduction has been used as an important preprocessing tool for pattern recognition, machine learning and data mining. As the classical Pawlak rough sets can just be used to evaluate categorical features, a neighborhood rough set model is introduced to deal with numerical data sets. Three-way decision theory proposed by Yao comes from Pawl...
Article
Rough set theory is one of the effective methods to feature selection which can preserve the characteristics of the original features by deleting redundant information. The main idea of rough set approach to feature selection is to find a globally minimal reduct, the smallest set of features keeping important information of the original set of feat...
Article
Dimensionality reduction is an important and challenging task in machine learning and data mining. It can facilitate data clustering, classification and information retrieval. As an efficient technique for dimensionality reduction, feature selection is about finding a small feature subset preserving the most relevant information. In this paper, we...
Article
Dimensionality reduction is an important and challenging task in machine learning and data mining. Feature selection and feature extraction are two commonly used techniques for decreasing dimensionality of the data and increasing efficiency of learning algorithms. Specifically, feature selection realized in the absence of class labels, namely unsup...
Article
Uncertainty measures can supply new points of view for analyzing data and help us to disclose the substantive characteristics of data sets. Accuracy and roughness proposed by Pawlak are mainly two important measures to deal with uncertainty information in rough set theory. However, these measures are constructed by set operations, which bring about...
Article
A novel method is proposed in this paper to store and retrieve images in entangled quantum systems, i.e., we employ a 2-qubit (or 3-qubit) entangled quantum state to represent the colors of 3 pixels (or 5 pixels) of color or gray images, and design quantum circuits to store images in entangled quantum systems, in addition, illustrate how to retriev...
Article
Rough sets are efficient for attribute reduction and rule extraction in data mining. However, many important problems including attribute reduction in rough sets are NP-hard, therefore the algorithms to solve them are often greedy. Matroids, generalized from linear independence in vector spaces, provide well-established platforms for greedy algorit...
Article
Rough sets are efficient to extract rules from information systems. Matroids generalize the linear independency in vector spaces and the cycle in graphs. Specifically, matroids provide well-established platforms for greedy algorithms, while most existing algorithms for many rough set problems including attribute reduction are greedy ones. Therefore...
Article
Uncertainty measures can provide us with principled methodologies to analyze uncertain data and unveil the substantive characteristics of the data sets. Accuracy and roughness proposed by Pawlak are two main tools to deal with uncertainty measurement issue in rough set theory. Many uncertainty measure methodologies for discrete-valued information s...
Article
Full-text available
It is crucial to segment characters correctly and improve rate of correct character recognition when processing automobile license plates corrections. In this paper, two algorithms are proposed to obtain the horizontal tilt and vertical shear angles. The transformation matrix for images rectification is given and the subpixel issue is solved. Some...
Article
In this study, we propose a new representation method for multidimensional color images, called an n -qubit normal arbitrary superposition state (NASS), where n qubits represent the colors and coordinates of 2n2n pixels (e.g., a three-dimensional color image of 1024×1024×10241024×1024×1024 using only 30 qubits). Based on NASS, we present an (n+1n+1...
Article
Multi-dimensional color image processing has two difficulties: One is that a large number of bits are needed to store multi-dimensional color images, such as, a three-dimensional color image of 1024 × 1024 × 1024 needs 1024 × 1024 × 1024 × 24 bits. The other one is that the efficiency or accuracy of image segmentation is not high enough for some im...
Article
Full-text available
In recent years, the theory of decision-theoretic rough set and its applications have been studied, including the attribute reduction problem. However, most researchers only focus on decision cost instead of test cost. In this paper, we study the attribute reduction problem with both types of costs in decision-theoretic rough set models. A new d...
Article
Covering is a common form of data representation, and covering-based rough sets serve as an efficient technique to process this type of data. However, many important problems such as covering reduction in covering-based rough sets are NP-hard so that most algorithms to solve them are greedy. Matroids provide well-established platforms for greedy al...
Article
Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solution. Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. In this paper, we applied an improved hybridization of BBO with DE approach...
Article
Coverings are a useful form of data, while covering-based rough sets provide an effective tool for dealing with this data. Covering-based rough sets have been widely used in attribute reduction and rule extraction. However, few quantitative analyses for covering-based rough sets have been conducted, while many advances for classical rough sets have...
Article
Full-text available
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...
Article
Rough sets are efficient for data pre-processing in data mining. However, some important problems such as attribute reduction in rough sets are NP-hard, and the algorithms to solve them are almost greedy ones. As a generalization of the linear independence in vector spaces, matroids provide well-established platforms for greedy algorithms. In this...
Article
As a pure Lagrange method, Smoothed Particle Hydrodynamics (SPH) receives the immense attention recently and becomes one of the most important methods in physically-based simulation of fluid. We propose a new approach of Fast Particle Hydrodynamics, which integrates several features into a single framework, involving numerical calculation, neighbor...
Article
Large crowd simulation shows different level of detail in different granularity. We present a novel approach for simulating such phenomenon, using a progressive-level framework which combines continuous system smoothly with agents steering. Through multi-levels of detail of the crowd, we can dramatically reduce the cost of agents' interaction and m...
Conference Paper
Matroids generalize the linear independence in vector spaces, and they have many applications in diverse fields, especially in greedy algorithms. In this paper, 2-circuit matroids are defined and their several axioms are obtained through rough sets. First, we induce a matroid from a symmetric and transitive relation, and characterize it through gen...
Article
Covering is an important type of data structure while covering-based rough sets provide an efficient and systematic theory to deal with covering data. In this paper, we use boolean matrices to represent and axiomatize three types of covering approximation operators. First, we define two types of characteristic matrices of a covering which are essen...
Article
Covering is a type of widespread data representation. In covering-based rough sets, a pair of approximation operators are used to describe any object subset. Dozens of pairs of approximation operators have been defined to meet diverse application demands. Therefore there is much need to build connections among these approximation operators. This pa...
Article
Full-text available
During last few years, research communities paid much attention to interference in wireless sensor network, lots of work has been done. However, all of those approaches suffer from some kind of defects. Inspired by the unsolved problems, we propose a definition of interference, which considering interference under physical model, meanwhile, can be...
Article
This paper proposes an efficient clustering method for protein sequences, using Affinity propagation algorithm (AP) and post-processing. In order to optimize the clustering result, post-processing is used to improve the clustering result of AP. To measure the similarity between two protein sequences, an improved alignment-free similarity measure is...
Article
Spaced seeds technology, which was proposed by PatternHunter, has been proven to be more sensitive and faster than continuous seeds, and it is now widely used for bio-sequence local alignments. However, finding optimal spaced seeds is an NP-hard problem. A seed digraph model is proposed to find good spaced seeds, which are very close to optimal, in...
Article
Large scale crowd simulation can be difficult using existing techniques due to the high computational cost of the update to large number of crowd. We present a novel technique for simulating detailed groups quickly. Coarse grid is used to represent the macroscopic crowd distribution and motion tendency consistent with fluid dynamics, allowing for a...
Article
The software reliability is the ability of the software to perform its required function under stated conditions for a stated period of time In this paper a hybrid methodology that combines both ARIMA and fractal models is proposed to take advantage of unique strength of ARIMA and fractal in linear and nonlinear modeling Based on the experiments pe...
Article
Multiple sequence alignment is one of the basic techniques in bioinformatics, and it plays a vital role in structure modeling, functional site prediction, and phylogenetic analysis. In this paper, we review the methodologies and recent advances in the multiple protein sequence alignment, e.g., speeding up the calculation of distances among sequence...
Article
DNA and protein motif detection is one of the basic problems in computing biology. In this paper we propose an novel approach for motif discovery. We consider how to combine the existing motif detection tools to achieve better results under the constraint of resource such as computing time, in this situation any single method may fail to find the m...
Article
To detect the natural clusters for irregularly shaped data distribution is a difficult task in pattern recognition. In this study, we propose an efficient clustering algorithm for irregularly shaped clusters based on the advantages of spectral clustering and Affinity Propagation (AP) algorithm. We give a new similarity measure based on neighborhood...
Article
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. The method incorporates a novel clustering algorithm, Affinity Propagation (AP). Whi...
Article
Fractals are mathematical or natural objects that are made of parts similar to the whole in certain ways. In this paper a software reliability forecasting method of software failure is proposed based on predictability of fractal time series. The empirical failure data (three data sets of Musa's) are used to demonstrate the performance of the reliab...
Article
As modern high-throughput sequencing technologies continue to improve, there is an overwhelming amount of protein sequences un-annotated in the biomedical databases. Clustering protein sequences into homologous groups can help to annotate uncharacterized protein sequences. In this paper, we introduce an online cluster analysis method for large-scal...
Article
The sizes of the protein databases are growing rapidly nowadays thus clustering protein sequences based only on sequence information becomes increasingly important. In this paper, we analyze the limitation of Affinity propagation (AP) algorithm when clustering a dataset generated randomly. Then we propose a post-processing method to improve the AP...
Conference Paper
Software-reliability models are used for the assessment and improvement of reliability in software systems. It is one of the most important qualities of software, and failure analysis is an important part of the research of software reliability. An underlying assumption of these models is that software failures occur randomly in time. This assumpti...
Article
Designing good or optimal seeds is a key factor for local homology search in bioinformatics. Continuous seeds have existed for nearly 20 years used by BLAST series programs. Recently, spaced seeds, which were introduced by PattenHunter program, were shown to be more sensitive and faster than continuous seeds under the same similarity level. However...
Conference Paper
With the development of sequencing technologies, more and more protein sequences are uncharacterized. Clustering protein sequences into homologous groups can help to annotate uncharacterized protein sequences. In recent years, many clustering algorithms have been proposed to analyze protein sequences. It may be necessary to perform a comparative st...
Article
Node localization in wireless sensor networks is essential to many applications such as routing protocol, target tracking and environment surveillance. Many localization schemes have been proposed in the past few years and they can be classified into two categories: range-based and range-free. Since range-based techniques need special hardware, wh...
Article
We consider the problem of estimating the parameters of a distribution when the underlying events are themselves unobservable. The aim of the exercise is to perform a task (for example, search a web-site or query a distributed database) based on a distribution involving the state of nature, except that we are not allowed to observe the various “sta...
Article
eliability is one of the most important qualities of software, and failure analysis is an important part of the research of software reliability. Fractals are mathematical or natural objects that are made of parts similar to the whole in certain ways. A fractal has a self-similar structure that occurs at different scales. In this paper the failure...
Conference Paper
The contribution of metrics to the overall objective of software quality is understood and fully recognized by the software engineering community in general. In the design and development of object-oriented software we should keep information such as attributes and methods in a module or class invisible to external environment as possible. Although...
Article
A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization schemes are not well competent for localization in mobile sensor networks, while the probabilistic approach of Bayesian filtering with p...
Article
The MPEG-4 fine granularity scalability (FGS) video coding standard offers flexible adaptation to varying network bandwidths and different application needs. This paper presents a MPEG-4 FGS video CODEC based watermarking scheme to embed watermark during encoding. Watermark is embedded into base layer, and can be extracted from both base layer and...
Conference Paper
Brain-computer interface (BCI) is a communication system that connects the brain with the computer and the peripheral equipment. In classification experiment of single-trial electroencephalogram (EEG) for left and right finger movement task, common spatial patterns (CSP) are employed to extract feature for EEG signals, and support vector machines (...
Conference Paper
Monte Carlo method has been widely used in many fields in the past few years. Currently, for mobile object localizing and tracking, Monte Carlo method has been practically proved a successful solution to solve these non-Gaussian, non-nonlinear and multi-dimensional systems. Recently, several Monte Carlo localization algorithms have been proposed fo...
Article
Target detection is a basic requisition in wireless sensor networks for several applications such as topology formation, route establishment etc. Traditionally, flooding strategies are used to find the target. However, flooding is not energy effective for low cost low energy sensor networks. Moreover, considering the imperfect data transmission env...
Conference Paper
To date, much work has been focused on localization in wireless sensor networks and lots of algorithms have been proposed. Most of these algorithms use the reference points to aid the location estimation of unknown nodes. References such as beacon nodes are those whose positions are previously configured or can be acquired via location sensing devi...
Article
The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the network. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new distributed energy-efficient clustering scheme fo...
Conference Paper
In this paper, we present a non-adaptive optimal plan that minimizes the mean search efforts based on the optimal search theory. A non-adaptive plan makes no use of the feedback generated by the search except the query object is found. In particular, we apply our search algorithm on PSB database to retrieve different classes of 3D models. Experimen...