Conventional subspace clustering algorithms group the data samples by optimizing the objective function which aggregates different clustering criteria using the linear combination. However, the performance is sensitive to the user-defined coefficients. Besides, the widely used Euclidean distance metric falls short of handling the linear indivisible problems. Some composite kernel metrics are proposed

The estimation of quantiles is one of the most fundamental data mining tasks. As most real-time data streams vary dynamically over time, there is a quest for adaptive quantile estimators. The most well-known type of adaptive quantile estimators is the incremental one which documents the state-of-the art performance in tracking quantiles. However, the absolute vast majority of incremental quantile estimators

This work deals with the design of fuzzy controllers for stabilization of continuous-time nonlinear systems subject to L2 disturbances, which are represented by nonlinear Takagi–Sugeno fuzzy models, i.e., Takagi–Sugeno fuzzy models with nonlinear consequents. A nonquadratic Lyapunov function is used to derive sufficient design conditions based on linear matrix inequality constraints as well as to reduce

Recently, image feature extraction algorithms based on 2D discriminant local preserving projection (2DDLPP) algorithms have been successfully applied in many fields. The 2DDLPP can maintain the discrimination information of the local intrinsic manifold structure using two-dimensional image representation data. However, the 2DDLPP algorithm encounters the problem of the sensitivity of overlapping points

We propose a new paradigm for describing complex networks in terms of the spectrum of the adjacency matrix and its submatrices. We show that a variety of basic node information, such as degree, clique, and subgraph centrality, can be calculated analytically. Moreover, we find that energy of spectrum series can uncover randomness and complexity of network structure. Interestingly, it presents an universal

Despite achieving superior performance for many graph-related tasks, recent works have shown that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks on graph structures. In particular, by adding or removing a small number of carefully selected edges in a graph, an adversary can maliciously manipulate a GNNs-based classifier. The vulnerability to adversarial attacks poses numerous concerns

Volatility is an important issue for companies, policy-makers, and researches. Autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) models are frequently used to study volatility. However, forecasting efficiency tends to fail when complex data is used. This paper proposes the use of ordered weighted average (OWA) operators in combination with ordinary least squares (OLS)

With the advent of online social networks, people became more eager to express and share their opinions and sentiment about all kinds of targets. The overwhelming amount of opinion texts soon attracted the interest of many entities (industry, e-commerce, celebrities, etc.) that were interested in analyzing the sentiment people express about what they produce or communicate. This interest has led to

As an important class of aggregation operators, the notion of overlap functions was first presented in 2009 in order to be considered for applications in image processing context. Later, many other researches arised bringing some variations of those functions for different purposes. Here, our main goal is defining overlap functions on lattices and discuss how a weakned version of it, named quasi-overlaps

Rumor propagation on complex networks is rapidly affecting people’s life. As we all know, the regulatory control of rumors by regulators has a specific impact on the spread of rumors. Limited regulatory resources may saturate the regulatory level. Therefore, in this paper, we have introduced a saturation treatment function to model this phenomenon and further establish an SIS rumor propagation model

In the field of recommender systems, diversity as the measure of recommendation quality has gained much attention recently. Unfortunately, many researchers have shown that it has a trade-off relation with accuracy. Meanwhile, tensor factorization has been used as a useful technique that considers multi-correlations between user-item-other factors directly. However, it generally suffers from the model

With the development and the progress of science and technology, the spread of rumors presents new characteristics. The spreaders publish false and malicious rumors on public social networks media, and individuals visit these networks and share them with their friends through the friendship network, which provides a convenient hotbed for the indiscriminate spread of rumors. In this paper, an improved

Many supply chain stakeholders rely on the cross-docking concept, according to which products delivered in specific transportation management units to the cross-docking terminal (CDT) undergo decomposition, sorting based on the end customer preferences, consolidation, and then transported to the final destinations. Scheduling of the inbound and outbound trucks for service at the CDT doors is considered

Fuzzy rule-based models and the extension of classical fuzzy models have been widely used in many domains. From a holistic perspective, regardless of the design methods and rules adopted in a fuzzy model, the determination of fuzzy sets is a pivotal issue. In the proposed methods, instead of traditional data clustering with no directional tendency, we introduce an optimization algorithm that can adjust

The average convergence rate (ACR) measures how fast the approximation error of an evolutionary algorithm converges to zero per generation. It is defined as the geometric average of the reduction rate of the approximation error over consecutive generations. This paper makes a theoretical analysis of the ACR in continuous optimization. The obtained results are summarized as follows. According to the

Binary decision trees (BDTs), where each node of the tree is split into two child nodes, are among the most popular classifiers. An alternative type of classification tree, namely, the multi-layer classifier (MLC), has been proposed to split the parent node into 1 or 2 classified child nodes and an unclassified child node at each layer. In contrast to the nodes in a BDT, only the unclassified node

Multibiometric recognition has become one of the most important solutions for enhancing overall personal recognition performance due to several inherent limitations of unimodal biometrics, such as nonuniversality and unacceptable reliability. However, most existing multibiometrics fuse completely different biometric traits based on addition schemes, which usually require several sensors and make the

Multimode feature is widely adopted in complex continuous chemical processes to meet changes in market demand. However, conducting effective process monitoring in a multimode chemical process is challenging because data usually have multimodal distribution. In this study, DMF, a novel model based on a deep network, is proposed for learning new feature spaces; the multimodality of the mix data is eliminated

This paper studies the event-triggered adaptive fuzzy control (AFC) problem for switched nonlinear systems with state constraints. Fuzzy logic systems are explored to handle the lumped unknown dynamics. The barrier Lyapunov function is deployed to solve state constraints. In addition, event-triggered mechanism is incorporated into the backstepping framework to mitigate the communication burden. The

Differential Evolution (DE) was a powerful population-based evolutionary algorithm for global optimization, and it achieved great success in both evolutionary computation competitions and engineering applications. Despite the excellent performance of the state-of-the-art DE variants, there are still two main weaknesses existing within them: one is the weakness in a given mutation strategy and the other

In recent years, with the proliferation of rich attribute information available for entities in real-world networks and the increasing demand for more personalized community searches, attributed community search (ACS), an upgraded version of the community search problem, has attracted great attention from the both academic and industry areas. Some algorithms have been proposed to solve this novel research

Explainable recommendation systems (ERSs) have attracted increasing attention from researchers, which generate high-quality recommendations with intuitive explanations to help users make appropriate decisions. However, most of the existing ERSs are designed with an offline setting, which can hardly adjust their models using the online feedback instantly for improved performance. To overcome the limitations

Existing recommender systems rely on user and item representations in a fixed continuous low-dimensional latent space. To predict ratings, they use only an implicit feedback matrix, whereas user and item side information is ignored. Furthermore, they use the same arbitrary priors for the user and item latent vectors, reducing the ability of the model to identify the actual latent vectors. Currently

Detecting communities or clusters in networks becomes a decisive issue in various interdisciplinary areas in recent years. Numerous methods are proposed to uncover community in networks, although the fundamental problem of most of the methods is the evolving nature of the networks and the presence of the imprecise number of communities. Since, real-world networks are scale-free networks and due to

Currently, the demand for covert communication in open social networks brings new opportunities and challenges to existing image steganography technology in terms of robustness and security. To this end, an image robust adaptive steganography is proposed with robustness against multiple image processing attacks and detection resistance. First, a robust embedding domain with theoretical foundation and

In this paper we introduce notions of distortion function and aggregated distortion function. Applying some extended aggregation function on the triangle of distortion functions, a new extended aggregation function is obtained. Properties of the aggregation function constructed in this way depend on the properties of applied aggregation function and the distortion functions used. Its properties as

Person re-identification (Re-ID) is an important but challenging task in for video surveillance applications. In Re-ID tasks, pose is an extremely useful cue to identify a person, even from the back view. Therefore, pose-detection models may learn the features that are beneficial to the Re-ID task and improve the Re-ID performance by fusing the feature maps into the Re-ID model. Two key problems in

In classification problems, normally there exists a large number of features, but not all of them contributing to the improvement of classification performance. These redundant features make the classification problem time consuming and often result in poor performance. Feature selection methods have been proposed to reduce the number of features, minimize computational cost, and maximize classification

An integral sliding mode control method for uncertain Takagi-Sugeno fuzzy systems is investigated in this paper. Considering the time-varying property of the fuzzy system control matrix, a dimensional-varying integral sliding mode controller is proposed. With a membership function piecewise linearization technique, the gain matrices of equivalent control law are derived. Then a dimension switching

This study focuses on the fixed-time event-triggered time-varying formation tracking issue for a class of nonlinear multi-agent systems with multi-dimensional dynamics, uncertain disturbances and non-zero control input of leader. Firstly, a distributed fixed-time event-triggered control scheme is proposed such that the time-varying formation tracking can be achieved with intermittent controller updates

It is a challenging problem to explore the capability of multi-sensor networks due to the identity of the underlying information space across modalities. In this paper, the information space for multi-sensor networks is developed from information geometry. The relationship between information space and the performance of multi-sensor networks is investigated. Different sensor information obtained by

As semi-supervised feature selection is becoming much more popular among researchers, many related methods have been proposed in recent years. However, many of these methods first compute a similarity matrix prior to feature selection, and the matrix is then fixed during the subsequent feature selection process. Clearly, the similarity matrix generated from the original dataset is susceptible to the

Clone theory plays an important role in studying aggregation functions on bounded posets or bounded lattices. Several important classes of aggregation functions on a bounded lattice L form a clone, particularly the set of all aggregation functions on L, the so-called full aggregation clone on L. For any finite lattice L, this clone is known to be finitely generated and various generating sets and their

High dimensionality is an issue that affects most classification algorithms. This factor implies that the predictive performance of many traditional classifiers decreases considerably as the number of features increases. Therefore, there are numerous proposals that try to mitigate the effects of this issue. This study proposes ClEnDAE, a new classifier based on ensembles whose components incorporate

With the extensive use of structural health monitoring technologies, vibration-based structural damage detection becomes a crucial task in both academic and industrial communities. Following the noteworthy trends of data-driven paradigms in recent years, some solutions have been released to identify, localize, and classify damages via deep neural networks. However, some deficiencies still exist for

Nowadays, due to the mass production of uncertain data, numerous methods have been proposed for mining frequent patterns from uncertain data; however, none of them are proper for dynamic data environments. In many real-world applications, transactions are constantly being updated. After incremental updates, the validity of the uncertain patterns changes. The existing static algorithms to handle this

The paper addresses the preset performance synchronization (PPS) issue for complex dynamical networks (CDNs)using an event-triggered communication mechanism and PPS scheme with event-based protocols is designed for the CDNs. The designed synchronization control strategy can reduce the overshoot in transient processes, and stabilize synchronous errors at the origin. Furthermore, by introducing event-based

This paper presents the development and verification of a new multi-stage genetic programming (MSGP) technique, called MSGP-LASSO, which was applied for univariate streamflow forecasting in the Sedre River, an intermittent river in Turkey. The MSGP-LASSO is a practical and cost-neutral improvement over classic genetic programming (GP) that increases modelling accuracy, while decreasing its complexity

As a generative model, probabilistic linear discriminant analysis (PLDA) has achieved good performance in supervised learning tasks. The model incorporates both within-individual and between-individual variation, and remaining unexplained data variation is assumed to follow Gaussian distribution. However, the assumption of Gaussian distribution makes the model sensitive to the presence of noise and

Despite the consistent recommendation to scale-up the testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), comprehensive analysis on determining the desirable testing capacity (TC) is limited. This study aims to investigate the daily TC and the percentage of positive cases over the tested population (PPCTP) to evaluate the novel coronavirus disease 2019 (COVID-19) trajectory phase

Many vehicle re-identification (Re-ID) problems require the robust recognition of vehicle instances across multiple viewpoints. Existing approaches for dealing with the vehicle re-ID problem are insufficiently robust because they cannot distinguish among vehicles of the same type nor recognize high-level representations in deep networks for identical vehicles with various views. To address these issues

The algorithm of C4.5 decision tree has the advantages of high classification accuracy, fast calculation speed and comprehensible classification rules, so it is widely used for medical data analysis. However, for imbalanced medical data, the classification accuracy of decision trees-based models is not ideal. Therefore, this paper proposes a cluster-based oversampling algorithm (KNSMOTE) combining

The robust guaranteed cost control problem for uncertain discrete-time systems with state and input quantizations has been studied in this paper. The polytope type uncertainties are considered in the plants. Different from previous related works, a novel guaranteed cost control strategy has been put forward in this paper. The novelty and challenge lie in that the quantized state and quantized input

In this article, an adaptive fuzzy control scheme based on command filtering is proposed for the position tracking control of permanent magnet synchronous motor (PMSM) stochastic system with full state constraints. Firstly, fuzzy logic systems are employed to approximate unknown stochastic nonlinear functions in PMSM stochastic system. Then, the barrier Lyapunov functions are constructed to ensure

To solve constrained optimization problems (COPs), it is crucial to guide the infeasible solution to a feasible region. Gradient-based repair (GR) is a successful repair strategy, where the forward difference is often used to estimate the gradient. However, GR has major deficiencies. First, it is difficult to deal with individuals falling into the local optima. Second, large amounts of fitness evaluations

A non-reciprocal fuzzy preference relation (NrFPR) can express partial relations of alternatives, including indifference relations, preference relations and incomparability relations, but cannot depict linguistic preference intensities. A probabilistic linguistic preference relation (PLPR) can represent preference intensities in forms of probabilities and linguistic terms, but the incomparability relations

Multicriteria group decision-making (MCGDM), with the strong uncertainty and randomness, has always been a hotspot in the world. The chief purpose of the paper is to address the problem with Atanassov’s interval-valued intuitionistic fuzzy sets (AIVIFSs), Z-numbers, and trapezium clouds. First, some related concepts and former operators of AIVIFSs, Z-numbers, and trapezium clouds are reviewed, meanwhile

Covering salesman problem (CSP) is an extension of the popular traveling salesman problem (TSP) arising from a number of real-life applications. Given a set of vertices and a predetermined coverage radius associated with each vertex, the goal of CSP is to find a minimum cost Hamiltonian cycle across a subset of vertices, such that each unvisited vertex must be within the coverage radius of at lis NP-hard

In this paper, we consider two parametric dominance-based rough set approaches (DRSA) proposed in the literature: variable precision DRSA (VP-DRSA) and variable consistency DRSA (VC-DRSA). They were introduced to cope with classification data encountered in practice for which the original definition of lower approximations is too restrictive. Both these extensions allow an augmentation of lower approximations

Offline map matching is a crucial step to facilitate many trajectory-based services in urban areas by finding vehicles’ travel paths from recorded and stored trajectory data. This paper proposes a novel turning point-based offline map matching algorithm, which introduces the concept of vehicle turning points to implement map matching piecewisely. The algorithm first separates the entire trajectory

High-precision short-term inbound passenger flow prediction is of great significance to the daily crowd management and line rescheduling in urban rail systems. Although current models have been applied to prediction, most methods need optimization to meet refined passenger flow management demand. In order to predict the passenger flow, a novel Wave-LSTM model, based on long short-term memory network

Multi-view clustering seeks an underlying partition of the data from multiple views. Organizing the data into a tensor and then learning a self-expressive latent one to exploit cross-view information has attracted much attention. Most of the recent works mainly focus on the tensor representation, but they fail to directly extract the task-driving affinity matrix for clustering. Such method is typically

This paper proposes an adaptive denoising methodology for noisy signals that employs a novel adaptive complementary ensemble empirical mode decomposition (NACEEMD) and a peak statistics (PS)-based thresholding technique. The key idea in this paper is the peak statistics (PS)-based thresholding technique,which breaks the traditional strategy with respect to selecting more accurate and more adaptive

Outlier detection is of great importance in industry as unexpected errors or faults, abnormal behaviours or phenomena, etc. can occur due to a variety of human, system, and environmental reasons. To identify and analyse these rare items, events or observations can find either anomalies or novelties and, as a result, can help avoid potential unexpected consequences or improve industrial system performance

Low-rate denial-of-service (DoS) attacks, which can exploit vulnerabilities in Internet protocols to deteriorate the quality of service, are variants of DoS attacks. It is challenging to identify low-rate DoS attacks using traditional DoS defence mechanisms due to their low attack rate and stealthy nature. Most of the existing attack detection techniques are based on statistical analysis and signal

Velocity planning serves as an important issue in motion planing for autonomous vehicles. The presented paper proposes a novel velocity planning method with minimum moving time on the basis of path curvature which is accomplished in three steps. First, the assigned path is divided into some elementary parts based on the path curvature. Second, the velocity planning is transformed into an unconstrained

This paper presents sufficient stability conditions for distributed prediction-based control of interconnected systems subject to network-induced time-varying delay. Due to the flexibility of the proposed criteria, continuous-time, sampled-data control and output-feedback problems can be handled by the same framework. A detuning procedure is combined with a distributed LQR design in order to ensure

Data streams applications generate a continuous stream of data in a high rate that it is not possible to store all data in available memory. Hence, it is important to apply techniques that are capable of learning concepts according to data presentation, taking into consideration available time, processing and memory resources. This paper presents a new ensemble-based approach to detect concept drift

Event causality identification is an important research task in natural language processing. Existing methods largely focus on identifying explicit causal relations, and give poor performance in implicit causalities, especially in the document level. In this paper, we formalize event causality identification as a graph-based edge prediction problem and propose a novel document-level context-based graph

The enhancement of anonymity for blockchain users received much attention. However, in some cases tracing user’s identity is also very important, especially to expose illegal transactions. In this paper, we propose an identity tracing scheme and implement it using a simple and efficient proof method. In particular, we design a new signature scheme that is used to generate a user’s certificate, and