This paper investigates the problem of control with prescribed performance tracking for a class of nonlinear systems in the presence of quantized input. A novel state feedback control scheme by self-scrambling gain is proposed, with the first merit that it is computationally inexpensive, since no linearly-parameterized approximators are used, and the second merit that it is self-adjustable with respect

Fuzzy approximation spaces (FA-spaces) can be seen as approximation spaces (A-spaces) in a fuzzy environment. This article studies relationships between FA-spaces and their uncertainty measures. Dependence, partial dependence and independence between FA-spaces, as characterized by the inclusion degree, are first proposed. Then, the distance between FA-spaces is considered in terms of the angle of difference

There has been considerable interest in transforming unstructured social tagging data into structured knowledge for semantic-based retrieval and recommendation. Research in this line mostly exploits data co-occurrence and often overlooks the complex and ambiguous meanings of tags. Furthermore, there have been few comprehensive evaluation studies regarding the quality of the discovered knowledge. We

This paper addresses the challenge of providing trustworthy recommendations on newly deployed cloud services/resources for which little or no evidence about their trustworthiness is available. We also provide a two-level dishonesty discouragement mechanism to fight against unfair recommendations at both the collection and aggregation levels. Our solution consists of a (1) mechanism to allow users to

With the development of intelligent driving technology, human-machine cooperative driving is significant to improve driving safety in abnormal situations, such as distraction or incorrect operations of drivers. For human-machine cooperative driving, the capacity of pedestrian collision avoidance is fundamental and important. This paper proposes a novel learning-based human-machine cooperative driving

Multi-objective evolutionary algorithm based on the inverse model (IM-MOEA) is a popular method to solve multi-objective optimization problems (MOPs). However, IM-MOEA has some drawbacks such as low accuracy and difficulty in dealing with MOPs with irregular PFs. To address these issues, adaptive reference vector mechanism and nonrandom grouping strategy are employed in IM-MOEA, which enhances the

In recent years, more and more machine learning algorithms depend on the cloud computing. When a machine learning system is trained or classified in the cloud environment, the cloud server obtains data from the user side. Then, the privacy of the data depends on the service provider, it is easy to induce the malicious acquisition and utilization of data. On the other hand, the attackers can detect

Creating fake pictures becomes more accessible than ever, but tampered images are more harmful because the Internet propagates misleading information so rapidly. Reliable digital forensic tools are therefore strongly needed. Traditional methods based on hand-crafted features are only useful when tampered images meet specific requirements, and the low detection accuracy prevents them from using in realistic

Code-based cryptography has aroused wide public concern as one of the main candidates for post quantum cryptography to resist attacks against cryptosystems from quantum computation. However, the large key size becomes a drawback that prevents it from wide practical applications although it performs pretty well on the speed of both encryption and decryption. The use of algebraic geometry codes is considered

Recently, the security issues in networked control systems have become a major challenge, since networked control systems (NCSs) are vulnerable to malicious attacks. In this paper, a new event-triggered mechanism (ETM) is proposed, under which data packets could be actively dropped within consecutive steps, saving more communication resource than the existing ETM. Moreover, the effect of Denial-of-Service

Subspace clustering groups a set of data into their underlying subspaces according to the low-dimensional subspace structure of data. The performance of spectral clustering-based approaches heavily depends on the learned block diagonal structure of the affinity matrix. However, this structure is fragile in the presence of noise within data. As such, the clustering performance is degraded significantly

Community detection is a challenging issue because most existing methods are not well suited for complex social networks with ambiguous structures. In this paper, we propose a novel community detection method named Stacked Autoencoder-Based Community Detection Method via Ensemble Clustering (CDMEC). This is the first time that we have attempted to apply four different complex network similarity representations

A perceptron-like network based on integer domain was proposed, and then a new image cryptosystem based on the network was proposed in this paper. Different from the structure of classical image cryptosystem, the proposed image cryptosystem employed the perceptron-like network to achieve the diffusion of image information. It firstly generated the pseudo-random key streams from the secret key through

In the smart grid, efficient power supplies require near-real-time users’ electricity usage metering data, but these data might leak users’ private information, e.g., living habits. To address this problem, a number of privacy-preserving data aggregation schemes have been proposed in the literature. In this paper, we present a new type of attack, called malicious data mining attack, by which the adversary

Existing robust non-negative matrix factorization methods fail to achieve data recovery and learn a robust representation. This is because these methods suppose that outliers and noise of the original data are the Gaussian distribution. In this paper, we propose a robust non-negative matrix model, called robust Manhattan non-negative matrix factorization, which can handle various noise (e.g. Gaussian

This paper proposes two methods to investigate the problem of finite-time Mittag-Leffler synchronization for the systems of fractional-order quaternion-valued neural networks (FQVNNs) with two kinds of activation functions, respectively. Generally, the first method mainly reflects in the new establishment of Lyapunov-Krasovskii functionals (LKFs) and the novel application of a new fractional-order

Genetic Programming (GP) is an evolutionary algorithm for the automatic generation of symbolic models expressed as syntax trees. GP has been successfully applied in many domain, but most research in this area has not considered the presence of outliers in the training set. Outliers make supervised learning problems difficult, and sometimes impossible, to solve. For instance, robust regression methods

Mobile crowdsensing (MCS) has shown great potential in addressing large-scale data sensing problem by allocating sensing tasks to pervasive mobile users (MU). The MUs will participate in the MCS if they can receive sufficient compensation. Existing work has designed lots of incentive mechanisms for MCS, but ignores the MUs’ resource demand uncertainties that is critical for resource-constrained mobile

Recently, cryptocurrencies have attracted a growing interest from investors, practitioners and researchers. Nevertheless, few studies have focused on the predictability of them. In this paper we propose a new and comprehensive study about cryptocurrency market, evaluating the forecasting performance for three of the most important cryptocurrencies (Bitcoin, Ethereum and Litecoin) in terms of market

Threads running on different cores of chip multiprocessors (CMP) can cause thread performance degradation due to contention for shared resources such as shared L2 cache. Some studies have shown that thread co-scheduling can effectively reduce contention for shared resources. However, in a multi-core system with shared caches, mutual interference between threads is unpredictable. As the number of cores

Bus lines are suffering from serious decline in passenger volume due to the rapid development of urban rail transit and shared transport, and big data intelligence may help them change the status quo. However, the tremendous amount of travel data collected in recent years have not got effectively utilization. In order to improve passenger volume for bus lines, this paper devotes to develop a data-driven

Due to the wide applications, spam detection has long been a hot research topic in both academia and industry. Existing studies show that behavior features are effective in distinguishing the spam and legitimate reviews. However, it usually takes a long time to collect such features and thus is hard to apply them to cold-start spam review detection tasks. Recent advances leveraged the neural network

Stratifying personalized treatment for patients has been one of the main challenges for modern medicine. To solve this problem, various clustering algorithms have been proposed for patient stratification in both quantification and biological ways meaningfully. However, most of the existing clustering algorithms still suffer from many realistic algorithm limitations such as low diagnostic ability and

Overlap functions are aggregation functions that express the overlapping degree between two values. They have been used both as a conjunction in several practical problems (e.g., image processing and decision making), and to generate overlap indices between two fuzzy sets, which can be used to construct fuzzy confidence values to be applied in fuzzy rule based classification systems. Some generalizations

In this paper, we present the problem formulation and methodology framework of Super Resolution Perception (SRP) on smart meter data. With the widespread use of smart meters, a massive amount of electricity consumption data can be obtained. Smart meter data is the basis of automated billing and pricing, appliance identification, demand response, etc. However, the provision of high-quality data may

Burrows-Wheeler Transform, followed by Move-To-Front Transform, are often used transformation techniques in data compression. They may reduce the information entropy of the input sequence, which becomes more compressible in this way. This paper suggests an alternative, a Prediction-based Move-To-Front Transform, which may replace the aforementioned transformations. According to the context, consisting

This paper studies the fault tolerant control (FTC) problem for nonlinear multi-agent systems (MASs), in the presence of denial-of-service (DoS) attacks and actuator faults. In order to compensate for the effect of actuator faults, nonlinear dynamics and uncertainties, a distributed control strategy is presented by designing adaptive schemes and state-feedback control gains. The uncertainties are assumed

Remote data integrity checking (RDIC) enables clients to verify whether the outsourced data is intact without keeping a copy locally or downloading it. Nevertheless, the existing RDIC schemes do not support the pay-as-you-go (PAYG) payment model, where the payment is decided by the volume and duration of the outsourced data. Specifically, none of the existing works have considered the client’s control

Nowadays rumors spread by virtual identities of people in online social networks. In order to study the influence of forcing silence on spreaders, we propose a rumor propagation model with a silence-forcing function in online social networks. The theoretical analysis of the rumor propagation model reveals the existence of the equilibrium points, the backward bifurcation and the local stability. Meanwhile

In order to show high dynamic range (HDR) images by traditional displays, various tone-mapping operators have been designed to convert HDR images into low dynamic range (LDR) images recently. However, how to estimate the visual quality of LDR images effectively is still challenging. In this paper, we propose a novel blind quality assessment method for tone-mapped images with the consideration of naturalness

The Tartarus Problem is one of the candidate benchmark problems in evolutionary algorithms. We take advantage of the graphical processing unit (GPU) to improve the results of the software agents that use finite state machines (FSMs) for this benchmark. While doing so we also contribute to the study of the problem on several grounds. Similar to existing studies we use genetic algorithms to evolve FSMs

In this paper, a novel event-triggered fuzzy robust fault-tolerant control approach is designed to handle the offshore ship crane nonlinear system with actuator fault and external disturbance. The main objective of this study is to realize the tracking control ability of the outputs while saving the transmission resources, which is achieved by constructing the integrated adaptive sliding mode controller

The maximization of generalized modularity performs well on networks in which the members of all communities are statistically indistinguishable from each other. However, there is no theory bounding the maximization performance in more realistic networks where edges are heterogeneously distributed within and between communities. Using the random graph properties, we establish asymptotic theoretical

The problem of influence maximization has grown in popularity and interest in recent decades due to its valuable application in various fields. This problem mainly focuses on identifying Top-K influential users that, when selected, the influence spread will be maximized. Thus, the identification of such nodes is pivotal in increasing the adoption of promoted information and behavior within the network

Distributed deep learning (DDL) naturally provides a privacy-preserving solution to enable multiple parties to jointly learn a deep model without explicitly sharing the local datasets. However, the existing privacy-preserving DDL schemes still suffer from severe information leakage and/or lead to significant increase of the communication cost. In this work, we design a privacy-preserving DDL framework

The analytic network process framework is investigated to solve multi-criteria decision making problems in interval-valued intuitionistic fuzzy environments. First, a new approximate multiplicative consistent interval-valued intuitionistic fuzzy preference relation is defined, and the equivalent condition of approximate consistency is given by using the chance-constraint programming model of the Me

Currently, companies and individuals tend to use social media to publish information for promoting products and options. Although an increasing body of research has focused on promotional skills on social media, the role of post-publication time is rarely studied. However, publication time of a post plays an important role in its popularity. To select suitable publication times, an effective approach

Nowadays, computer science has the ability of assisting experts in different application areas. Recently there has been increasing attention in the health area, and there exist different approaches based on artificial intelligence that have been proposed in the diagnosis of several kinds of diseases. In particular, fuzzy systems have been successfully used as Diagnosis Systems, in this way helping

In todays digital era, a massive amount of streaming data is automatically and continuously generated. To learn such data streams, many algorithms have been proposed during the last decade. Due to the dynamic nature of streaming data, the learning algorithms must be adaptive to handle concept drift and work under limited memory and time. Currently, most existing works assume that the true class labels

Considering a class of skid-to-turn (STT) missiles with stochastic disturbances, non-affine aerodynamic characteristics and full state constraints, a novel full state constrained stochastic adaptive integrated guidance and control (IGC) scheme is proposed in this paper. By introducing several four-order Barrier Lyapunov functions (BLFs), the required state constraints of the stochastic closed-loop

This paper investigates the tracking control issue of nonlinear stochastic systems subject to time-varying full state constraints and input saturation. By employing both neural network-based approximator and backstepping technique, a novel adaptive neural network (NN) control approach is presented on the basis of the time-varying barrier Lyapunov function. To surmount the influence of saturation nonlinearity

In this paper, we present an incremental variant of the Twin Support Vector Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with large datasets and to learn from data streams. We combine the TWSVM with a fuzzy membership function, so that each input has a different contribution to each hyperplane in a binary classifier. To solve the pair of quadratic programming problems

Replacing Computed tomography (CT) with magnetic resonance image (MRI), MRI- positron electron tomography (PET) or MRI- single photon emission computed tomography (SPECT) imaging might have further advantages due to higher soft-tissue contrast of brain structure and lower dose absorbed by the patient. In this paper, a new three-layer (intensity, detail, and base layers) medical image fusion method

In cross-language question retrieval (CLQR), users employ a new question in one language to search the community question answering (CQA) archives for similar questions in another language. In addition to the ranking problem in monolingual question retrieval, one needs to bridge the language gap in CLQR. The existing adversarial models for cross-language learning normally rely on a single adversarial

The concept of hybrid random type-2 uncertainty is yet to be used in decision-making problems. Here, under this category, random type-2 triangular fuzzy, random type-2 Gaussian fuzzy and random type-2 zigzag uncertain variables are presented. For illustration, a fixed charge four dimension transportation problem (FC4D-TP) for a breakable item is formulated with its selling prices, purchasing costs

This paper investigates global fuzzy leader-follower consensus problem of mixed-order nonlinear multi-agent systems (MAS) with partially unknown direction control. The follower system is described by a first-order or second-order unknown nonlinear system model with partially unknown direction control and the leader system is described by a second-order time-varying nonlinear system model. Leader can

Intelligent education systems have enabled personalized learning (PL). In PL, students are presented with educational contents that are consistent with their personal knowledge states (KS), and the critical task is accurately estimating these states through data. Knowledge tracing (KT) infers KS (latent) through historical student interactions (observed) with the knowledge components (KCs). A wide

This paper addresses the problem of the quantization-based event-triggered sliding mode tracking control for mechanical systems. Only the quantized values of measurement outputs and control inputs are transmitted to the controller and actuator sides, respectively. Furthermore, the event-triggering mechanism is located in the controller side and specific events are monitored by using quantized states

Privacy-preserving set operations have important practical and theoretical significance and have always been a research focus of secure multiparty computation. Most existing studies focus on single set operations which cannot express many complicated set operations. However, the studies on mixed set operations are highly limited. The available protocols for mixed set operations can only be implemented

The rich source of online reports and discussions on social media can be leveraged to investigate the widespread cyber-attacks. In this paper, we study the problem of cybersecurity event mining based on continuous tweet streams. In contrast to traditional static methods that do not consider event evolution, we explore relevance among historical and online events for cyber-attack event discovery and

Addition-min fuzzy relation inequalities are introduced to characterize the Peer-to-Peer (P2P) network system. In order to decrease the network congestion, considering both efficiency and fairness, we define the leximax minimum solution for system of addition-min fuzzy relation inequalities. The leximax-optimality refines both Pareto-optimality and max-optimality. For solving the leximax minimum solution

The ultra-supercritical (USC) coal fired boiler-turbine unit is an advanced power generation system with low emissions and high efficiency. It is also a typical multivariable nonlinear system with great inertia. Generally, building an accurate analytic model using the conventional system identification methods are quite difficult. However, the big data generated by the monitoring system can reflect

The structural properties of network system components often display strikingly similar behavior when probed at a macroscopic perspective. Those structural properties, largely determining their dynamic behavior, are revealed by the mesoscopic structure of the underlying networks. To demonstrate this empirical conclusion, it is necessary to develop a set of tools to accurately quantify the structural

Formal concept analysis (FCA) has been widely studied as an important tool for data processing and knowledge discovery. The present work focuses on FCA under uncertainty while the attributes are described with linguistic terms or attribute description are incomplete. Accordingly, a linguistic concept formal context is introduced first. With an attempt of knowledge reduction, the multi-granularity similarity

This paper visits the design of event-triggered static and dynamic feedback controllers for discrete-time linear systems. For the efficiency of energy, we make the first attempt to devise a switching event-triggered mechanism (ETM) which is characterized by a switching between the discrete-time sampled-data control and the common continuous ETM. In this sense, the event detector has a waiting time

This paper deals with group decision making (GDM) with hesitant fuzzy preference relations (HFPRs) based on the acceptable multiplicative consistency and the consensus analysis. We first offer a multiplicative consistency index for fuzzy preference relations (FPRs) and then use the Monte Carlo simulation method to derive the average multiplicative consistency value. After that, a model-based interactive

This paper analyzes the existing zeroing neural network (ZNN) models from the perspective of control theory. It proposes an exclusive ZNN model for solving the dynamic complex-valued matrix Moore-Penrose inverse problem: the complex-valued zeroing neural network (CVZNN). Then, a method of constructing a special type of saturation-allowed activation function is defined, which relaxes the convex constraint

Contour detection is a salient operation in many computer vision applications as it extracts features that are important for distinguishing objects in scenes. It is believed to be a primary role of simple cells in visual cortex of the mammalian brain. Many of such cells receive push-pull inhibition or surround suppression. We propose a computational model that exhibits a combination of these two phenomena

Spatiotemporal safety forecasting has various applications in the neuroscience, climate and transportation domains. It is challenging due to (1) the complex spatial dependency on networks, (2) non-linear temporal dynamics with changing conditions and (3) the inherent difficulty of long-term forecasting. To address these challenges, a safety prediction model called the Spatial-Temporal Mixed Attention