Missing values are inherent in multivariate time series because of multiple reasons, such as collection errors, which deteriorate the performance of follow-up analytic applications on the multivariate time series. Numerous missing value imputation methods have been proposed to mitigate the influence of missing values on multivariate time series analysis. Recently, inspired by the success of generative

Canonical Polyadic Decomposition (CPD) is one of the most popular tensor decomposition methods and plays an important role in big data analysis. For sparse tensor, the major computation procedure in CPD, which is known as matricized tensor times Khatri-Rao product (MTTKRP), exhibits discontinuous memory access and turns to be the performance bottleneck from achieving high performance on emerging processor

Multiview clustering partitions a set of data into groups by exploring complementary information of multiple views. The mainstream tries to project the multiview data into a commonly shared subspace and further discover the true data structure. Ideally, clusters in the subspace should share less semantics with each other so that distinct groups can be obtained while this exclusivity is not guaranteed

The complex behaviors in cellular automata have been widely developed in recent years to generate and analyze automata that produce space-moving patterns or gliders that interact in a periodic background. This type of automata has been frequently found either by conducting an exhaustive search or through a meticulous construction of the evolution rule. In this study, the specification of cellular automata

We present an approach to efficiently construct stepwise regression models in a very high dimensional setting using a multidimensional index. The approach is based on an observation that the collections of available predictor variables often remain relatively stable and many models are built based on the same predictors. Example scenarios include data warehouses against which multiple ad-hoc analytical

In this work, we show how to hide a maximum of 8 bits of secret data into the pixel difference value. Adjacent pixel pairs are chosen in a non-sequential manner in the spatial domain without using any seed or key. In spite of having high average embedding capacity and embedding efficiency, our algorithm can withstand visual, structural and statistical attacks. We compare our work with state-of-the-art

In this paper, three novel secret image sharing schemes based on homogeneous linear recursion are introduced. Simple sharing, various methods for the recovery, small shadow image size and high resolution are some of the merits of our schemes. Moreover, shadow images generated in schemes 2 and 3 are unrevealing and are secured against attacks based on correlation of adjacent pixels, without any need

Hard C-means (HCM) is one of the most widely used partitive clustering methods and was extended to rough C-means (RCM) by referencing to the perspective of rough set theory to deal with the certain, possible, and uncertain belonging of object to clusters. Furthermore, rough set C-means (RSCM) and rough membership C-means (RMCM) have been proposed as clustering models on an approximation space considering

In this paper, output consensus for a series of heterogeneous systems under semi-markov switching topology is investigated. The scenario under consideration is that none of the graph is assumed to be connected and the mode-switching delay is existed. The main technical challenge is model and controller dismatch because of time delay. Assuming every individual system is stabilizable and detectable and

A new control strategy with networked event-triggered force domain controller and valve opening predictive management local controller is proposed for nonlinear hysteretic leaf spring suspension system with asymmetric cylinder hydraulic actuator over actuated by multiple servo-valves. Takagi–Sugeno fuzzy approach is employed to describe this uncertainty system with experimentally evaluated nonlinear

The formation of public opinions is a complex phenomenon that revolves around the aggregation of individuals’ beliefs. To accurately capture this phenomenon, one needs to build links from individualistic experiences to personal beliefs, which evolve in a social space through information exchange and belief revision. Despite many efforts to model opinion dynamics, the role of personal experiences and

This paper proposes a new multiattribute decision making (MADM) method based on the proposed score function of connection numbers (CNs) and the set pair analysis (SPA) theory in the interval-valued intuitionist fuzzy (IVIF) context. Firstly, we develop a score function for ranking CNs. The various notable characteristics of the proposed score function of CNs are also presented. Then, we propose a new

Data representation aims at learning an efficient low-dimensional representation, which is always a challenging task in machine learning and computer vision. It can largely improve the performance of specific learning tasks. Unsupervised methods are extensively applied to data representation, which considers the internal connection among data. Most of existing unsupervised models usually use a specific

During the past years, network dynamics has been widely investigated in various disciplines. As a practical and convenient description for social networks, signed networks have also garnered significant attention. In this work, we study information spreading with relative attributes on signed networks, where edges are assigned positive or negative labels, describing friendly or hostile relationships

Based on mating and environmental selections employed, multi-objective evolutionary algorithms (MOEAs) are classified as Pareto-based, decomposition-based and indicator-based approaches that are associated with their own advantages and disadvantages. To benefit from the advantages of different MOEAs, we propose an ensemble framework (ENMOEA) in which mating and environmental selections of diverse MOEAs

Although the learning speed in twin support vector regression (TWSVR) is four times that in support vector regression (SVR), the computing time and fitting precision of TWSVR are limited. This paper develops multiple birth support vector regression (MBSVR), motivated by the multiple birth support vector machine (MBSVM) formulation. MBSVR constructs the final regressor from K hyperplanes, each of which

In this article, we introduce different methods for constructing nullnorms on some special classes of bounded lattices by using closure and interior operators. As a by-product, we obtain new classes of idempotent nullnorms. Furthermore, we give some interesting examples for a better understanding of these new classes of nullnorms. In particular, the results presented here provide different approaches

The existing watermarking algorithms invariant wavelet domain are weak at resisting geometric attacks and have small embedding capacities. In this paper, a robust double-encrypted watermarking algorithm based on the fractional Fourier transform (FRFT) and discrete cosine transform (DCT) in invariant wavelet domain is proposed. A hybrid domain is obtained by applying the redistributed invariant wavelet

The satellite must continue working properly under different working environments and working loads. The power system is an essential component. Due to different working tasks, loads, and attitudes, a power system has many diverse working states. Therefore, it is necessary to accurately recognize the working state online for fault diagnostics and health management. However, under different working

A flexible image cipher based on orthogonal arrays is proposed in this paper. Orthogonal array is a typical combinatorial configuration. It has discreteness and uniformity, which makes orthogonal array a natural candidate for cryptography. In particular, an orthogonal array can provide multiple substitution sequences and permutation maps for image cipher. To control the selection of substitution sequences

This paper is concerned with the co-clustering of distribution-valued data, that is, the simultaneous partitioning of rows and columns of an input data table, the elements of which are distributions (or histograms) representing aggregate data. The first proposed method extends the double k-means algorithm to distributional data. The L2 Wasserstein distance, also known as Mallow’s distance, is used

The fuzzy rough set is one of the most effective methods for dealing with the fuzziness and uncertainty of data. However, in most cases this model only considers the information provided by the lower approximation of a decision when it is used to attribute reduction. In a realistic environment, the uncertainty of information is related to lower approximation as well as upper approximation. In this

Based on the conventional attentional encoder-decoder framework, multi-modal neural machine translation (NMT) further incorporates spatial visual features through a separate visual attention mechanism. In this aspect, most current multi-modal NMT models first separately learn the semantic representations of text and image and then independently produce two modalities of context vectors for word predictions

Currently, Deep Convolutional Neural Networks (DCNNs) are used to solve all kinds of problems in the field of machine learning and artificial intelligence due to their learning and adaptation capabilities. However, most successful DCNN models have a high computational complexity making them difficult to deploy on mobile or embedded platforms. This problem has prompted many researchers to develop algorithms

This study considers the problem of hybrid community detection in attributed networks based on the information of network topology and attributes with the aim to address the following two shortcomings of existing hybrid community detection methods. First, many of these methods are based on the assumption that network topology and attributes carry consistent information but ignore the intrinsic mismatch

The presence of anomalous information in a business process event log, such as missing, duplicated or swapped events, hampers the possibility of extracting useful insights from event log analysis. A number of approaches exist in the literature to detect anomalous cases in event logs based on different paradigms, such as probabilistic, distance-based or reconstruction-based anomaly detection. This paper

The research of dynamic community detection is becoming increasingly popular since it can disclose how the community structures change over time in dynamic networks. Evolutionary clustering is often utilized for the goal and has achieved some success, which, however, still has some major drawbacks: (1) The absence of error correction may lead to the result-drifting problem and the error accumulation

In recent years, many different differential evolution (DE) variants have been proposed to solve real-world optimization problems. However, the performance of them is largely determined by the selection of the mutation strategy, an approach to choose favorable mutation strategy when solving various optimization problems has attracted increasing attention recently. In this paper, we propose a DE with

The capacitated arc routing problem is a very important problem with many practical applications. This paper focuses on the large scale capacitated arc routing problem. Traditional solution optimization approaches usually fail because of their poor scalability. The divide-and-conquer strategy has achieved great success in solving large scale optimization problems by decomposing the original large problem

We study the operational risk classification problem, a critical yet challenging problem in the banking industry. In practice, banks build supervised multi-label classification models to identify the pre-defined risks using financial news sources. However, the models are often suboptimal due to the lack of labeled data and diverse combinations of risk types. To address these practical issues, we re-frame

The performance of differential evolution (DE) mainly depends on its breeding offspring strategy (i.e., trial vector generation strategies and associated control parameters). To take full advantage of several effective breeding offspring strategies proposed in recent years, a fitness-based adaptive differential evolution algorithm (FADE) is proposed in this paper. In FADE, the entire population is

We propose a novel co-evolutionary algorithm for interactive multiple objective optimization, named CIEMO/D. It aims at finding a region in the Pareto front that is highly relevant to the Decision Maker (DM). For this reason, CIEMO/D asks the DM, at regular intervals, to compare pairs of solutions from the current population and uses such preference information to bias the evolutionary search. Unlike

In consensus-based social network group decision making (SN-GDM) problems, experts tend to refuse modifications if they exceed the limit of their maximum compromise, which may lead to a failed consensus. However, few consensus models have addressed the compromise limit behaviors in SN-GDM. In this paper, we propose a minimum adjustment consensus framework with compromise limits for SN-GDM under incomplete

In recent years, among most approaches for few-shot learning, there exists a default premise that a big homogeneous-annotated dataset is applied to pre-train the few-shot learning model. However, since few-shot learning approaches are always used in the domain where annotated samples are rare, it would be difficult to collect another big annotated dataset in the same domain. Therefore, we propose Splicing

The popularity of apps makes smartphones rapidly become the most widespread form of communication. Due to the impact of resource constraints on mobile phones, users prefer to outsource data from the local device to the cloud. Access control of outsourced data drives the research for protecting sensitive data from all the possibly malicious software access or cloud service provider misbehavior. The

Stratification is a feature of the type-reduced set of the general type-2 fuzzy set, from which a new technique for general type-2 defuzzification, Stratic Defuzzification, may be derived. Existing defuzzification strategies are summarised. The stratified structure is described, after which the Stratic Defuzzifier is presented and contrasted experimentally for accuracy and efficiency with both the

Blockchain technologies have attracted a large amount of attention recently, with immutability as a basic property. However, it is often desired to allow editing a transaction or a block in a controlled way. Chameleon hash function, with enhanced collision-resistance property, has recently found to be an important tool to construct redactable blockchain. This means that the traditional key-exposure

Recent studies indicate that a classifier is vulnerable in an adversarial environment. The label flipping attack aims to mislead the training process. Some countermeasures have been proposed, but are usually designed for a particular classifier only, or may cause information loss. This study aims to investigate a generic model which fully utilizes the contaminated samples in learning. We assume a small

Traditional prediction error expansion (PEE)-based reversible data hiding (RDH) schemes focus on generating a sharp prediction error histogram (PEH) in order to increase embedding performance, but they neglect the influence of local complexity on embedding performance. When the PEH is partitioned into multiple sub-histograms by means of the local complexity, the embedding performance can be increased

To deal with multicriteria decision making (MCDM) problems with interaction criteria, the Choquet integral (CI) is one of effective tools. This paper first proposes the reverse Choquet integral (RCI), which defines the importance of the ordered elements in an opposite principle to the CI. To show the principle of the RCI, we offer its concrete expression in view of the Möbius representation by which

As a rule-based expert system, the belief rule base (BRB) exhibits tremendous advantages in modeling nonlinearity for complex systems. Present BRB learning studies can be classified into three categories: BRB structure learning, BRB parameter learning, and BRB joint optimization but only in an iterative and separate fashion. In this study, a novel Parallel Multipopulation optimization approach for

Merging operators represent a significant tool to extract a consistent and informative view from a set of agents. The consideration of practical scenarios where someagents can be more credible than others has contributed to substantially increase the interest in developing systems working with trust models. In this context, we propose an approach to the problem of merging knowledge in a multiagent

Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a much higher computational burden. A lot of work has been done to reduce the time complexity of information fusion with Dempster’s rule, which is a pointwise multiplication of two zeta transforms, and optimal general algorithms have been found to get the complete definition

Uncovering the underlying community structure of a social network is an important task in social network analysis. To solve this problem, many community detection algorithms for the full topology of an original social network have been proposed. However, these algorithms are not very effective at analyzing large-scale social networks. To overcome this deficiency, this paper proposes a community detection

This article investigates a new type of impulsive effect, namely the heterogeneous impulsive effect on the synchronization of complex-valued neural networks (CVNNs) with mixed time-varying delays. Heterogeneous impulsive effect is a unified framework of non-identical and time-varying impulses, i.e., the impulsive effect to state of each neuron is not only non-identical to each other but also distinct

Feature selection is facing great challenges brought by the enlarging label space and the inevitable noisy data. Flat feature selection methods fail to obtain a compact feature subset because of the numerous classes. In addition, these data-driven methods are sensitive to the data outliers. Fortunately, many practical tasks usually organize the classes by a hierarchical structure in a coarse-to-fine

This paper is concerned with the protocol-based finite-horizon H∞ estimation problem for discrete-time memristive neural networks (MNNs) subject to time-delays and energy-bounded disturbances. With the purpose of effectively alleviating data collisions and saving energy, the stochastic communication protocol (SCP) is adopted to regulate the data transmission procedure in the sensor-to-estimator communication

Much attention has been paid to evolutionary multi-objective optimization approaches to efficiently solve real-world engineering problems with multiple conflicting objectives. However, the loss of selection pressure and the non-uniformity in the distribution of the Pareto optimal solutions in the objective space can impede both dominance-based and decomposition-based multi-objective optimizers when

This article investigates the event-triggered consensus strategy (ETCS) for uncertain topological fractional-order multiagent systems based on Takagi-Sugeno fuzzy models. First, a novel distributed ETCS, in which the event generator only depends on the information from neighboring agents at event-triggering instants, is designed to reduce the frequency of information transmission among all agents and

This paper focuses on investigates the leader-follower consensus problem for linear multi-agent systems (MASs). In order to reduce the frequency of system network communication, this paper first designs three kinds of triggering mechanisms, for the leader, the edges composed of the leader and informed followers, and the edges composed of uninformed followers, respectively. All agents in the MASs do

Transfer learning builds models for the target domain by leveraging the information from another related source domain, in which the distributions of two domains are usually quite distinct. Real-world data are often characterized by multiple representations known as multi-view features. In the multi-view transfer learning field, existing methods aim to address the following two issues. Firstly, due

Probabilistic linguistic term sets (PLTSs) can express the qualitative information of decision makers more accurately in the complicated linguistic setting. However, the existing comparison methods for PLTSs cannot compare some special PLTSs. To tackle this problem, a novel score function based on the concentration degree of an PLTS, called ScoreC-PLTS, is proposed. Additionally, the existing distance

The state feedback fuzzy controller synthesis for nonlinear neutral state-delayed systems with impulsive effects is addressed. A methodology for stability analysis of the underlying systems is proposed in the framework of system augmentation. New impulse-timer-dependent Lyapunov functions based analysis techniques, including a Razumikhin-type technique and a Lyapunov functional technique, are developed

Owing to the presence of complex noise, it is extremely challenging to learn a low-dimensional subspace structure directly from the original data. In addition, the nonlinear structure of the data makes multi-view subspace clustering more difficult. In this paper, we propose a multiple kernel low-rank representation-based robust multi-view subspace clustering method (MKLR-RMSC) that combines a learnable

Automatic facial expression recognition (FER) has been extensively studied owing to its wide range of applications, such as in e-learning platforms used to automatically collect the feedback of students regarding a particular content and to help children with autism have a better understanding of their environment. Owing to the advances made in the fields of machine learning and computational devices

Traditional object recognition algorithms are based on a commonly adopted closed-set hypothesis, assuming that the knowledge given in training is complete. However, real situations are often open and nonstatic, in which case the models only obtain incomplete knowledge during the training phase. This paper proposes a new type of conditional random field (CRF) model to solve a special case of incomplete

Despite recent remarkable progress, person re-identification (ReID) still suffers from a shortage of annotated training data. To deal with this problem, there has been a boost of interest in developing various data augmentation methods. In this paper, we are devoted to developing an end-to-end joint representation learning framework for the ReID task on the basis of a novel data augmentation strategy

Transfer learning addresses the problem of how to leverage acquired knowledge from a source domain to improve the learning efficiency and accuracy of the target domain that has insufficient labeled data. Instead of one source domain, multiple domains could be the source domains that are available for knowledge transfer in practice. However, there are large differences between the source and target

Although there exist various adversarial attacking methods, most of them are performed by generating adversarial noises. Inspired by the fact that people usually set different camera parameters to obtain diverse visual styles when taking a picture, we propose the adversarial attributes, which generate adversarial examples by manipulating the image attributes like brightness, contrast, sharpness, chroma

In this paper, we study first order interval-valued differential equations with the boundary value condition x(0)=αx(T). By constructing proper operators, some sufficient conditions for the existence of solutions are provided for α∈R⧹{0,1}, corresponding to non-periodic boundary value problems. At the end of this paper, some examples are shown to illustrate the theorems proved.