As a new category of randomized neural networks (RNNs), stochastic configuration networks (SCNs) have demonstrated great potential for data analytics. Unlike conventional randomized learning techniques, e.g., random vector functional-link (RVFL) networks, SCNs provide a stochastic configuration mechanism on the assignment of input parameters which guarantees the universal approximation capability of

In modern era, the graphical information is presented in the form of web images. As the dependency of human beings on web information is increasing day-by-day, so the spammers are injecting spam by adopting new spamming techniques. Image spam is a spamming technique that integrates spam text contents into graphical images in order to bypass conventional text-based spam filters. The spam images are

For online service platforms such as Netflix, it is important to propose a list of high quality movies to their users. This type of problem can be regarded as a ranking problem in a bipartite network. This is a well-known problem, that can be solved by a ranking algorithm. However, many classical ranking algorithms share a common drawback: they tend to rank higher older movies rather than newer ones

With an increase in the number of devices involved in the Industrial Internet, effectively combining the characteristics of industrial scenarios with an edge computing methodology for computation-intensive applications poses a critical challenge. This paper proposes an integrated architecture that allows industrial devices to offload tasks to cloud or edge servers. An offloading problem is also formulated

In modern textile industrial processes, fast and efficient detection of textile defects plays a crucial role in textile quality control. Recently, as a critical machine-learning method, faster region-based convolutional neural networks (Faster RCNNs) have arisen as a promising framework, providing competitive performance for object detection. However, detecting small-scale objects, such as micro-defects

Product recommendation has played an important role in improving user experiences and obtaining more profits. To optimize recommendation models, pairwise learning has become a mainstream method for modeling user preferences from implicit feedback. Nevertheless, most existing pairwise methods optimize the relative order among products from only the user perspective. In fact, the purchase decision making

Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced performance for various natural language processing tasks, such as machine translation and document classification. Thus far, various sentence embedding models have been proposed,

Computer-aided breast mass classification is an effective and widely used technology to assist pathologists in formulating clinical diagnoses and improving working efficiencies. Existing studies usually use a single image feature to perform breast mass classification. Herein, we propose a simple, yet effective, model called the DE-Ada*, which is an organic integration of multi-feature fusions, for

The mold level is the key to stable continuous casting production process, and its fluctuation determines the quality of the slab. It is of great economic and scientific value to stabilize the mold level in numerous disturbances and complicated operating modes. To achieve high-precision and fast-response control of mold level control in engineering applications, a fuzzy PID controller with nonlinear

The use of social network theory and methods of analysis have been applied to different domains in recent years, including public health. The complete procedure for carrying out a social network analysis (SNA) is a time-consuming task that entails a series of steps in which the expert in social network analysis could make mistakes. This research presents a multi-domain knowledge model capable of automatically

This paper investigates the network-based modeling and distributed active disturbance rejection control (ADRC) to address the Ackermann steering problem of a four-in-wheel motor drive electric vehicle with deception attacks on controller area networks (CAN). The distributed ADRC can achieve the independent steering, ensure a small steering radius and improve the stability and robustness of the vehicle

Hardware Trojan detection has been becoming an attentive research subject since the first Trojan in real-world hardware was found. A common way to activate a hardware Trojan is to send a command, and detecting those command-activated Trojan is one of the significant dimensions in securing hardware. In this paper, we propose a novel chip-free detection method, called Pruning Bytes Command Search (PBCS)

Distributed and pervasive web services have become a major platform for sharing information. However, the hypertext transfer protocol secure (HTTPS), which is a crucial web encryption technology for protecting the information security of users, creates a supervisory burden for network management (e.g., quality-of-service guarantees and traffic engineering). Identifying various types of encrypted traffic

In this paper, an event-triggered strategy and an optimality scheme are presented to design an output-feedback controller for nonlinear discrete-time systems. Firstly, owing to the unavailability of the system states, a coordinate transformation is executed instead of state observers. Then, an action neural network and a specified critic neural network are presented to obtain the optimal controller

In practical applications, there generally exist incomplete hybrid data with heterogeneous and missing features. The complex structures and the fast update of incomplete hybrid data bring a series of challenges for decision making in dynamic data environments. Three-way decisions, as an important cognitive method for analyzing uncertain problems, have been extensively applied into various fields. However

Despite that existing knowledge graphs embedding (KGE) based methods can achieve better recommendation performance compared with deep learning based ones, such improvement is limited due to lack of capturing the shared information between user-item interaction and item-item relation encoded in knowledge graph (KG) by fully exploit implicit and explicit relationship. To address this issue, in this paper

In vehicular social networks(VSNs), edge stations or cloud service provider can support the traffic services or location services to vehicles. Moreover, the information sharing among vehicles can assist vehicles to avoid traffic accidents and guarantee driving safety. However, it is easy to be threatened in the communication of vehicle-to-edge station, vehicle-to-cloud service provider and vehicle-to-vehicle

In the last decade, the increasing video traffic, especially illegal videos brought big challenges for Internet management. Generally, abnormal videos, such as illegal videos only account for a small percentage which makes the detection of such videos to be a typical imbalanced classification problem. In this study, we propose a new imbalanced learning method, namely, the imbalanced data gravitation

This paper investigates the H∞ control of uncertain lineype without known upper bounds. To reduce the redundant transmission of control signals, three triggering strategies are developed, namely, the absolute threshold triggering manner is built firstly to deal with the small control input variation. By revisiting the first one, the large control input is monitored by the second triggering scheme.

Based on multiple levels of granularity, the notion of sequential three-way granular computing focuses on a multiple stages of thinking, problem-solving, and information processing in threes. This paper interprets, represents, and implements sequential three-way granular computing by a framework of temporal-spatial multi-granularity learning, which is described with the temporality of data and the

In this paper, a chaotic image encryption algorithm based on the matrix semi-tensor product (STP) with a compound secret key is designed. First, a new scrambling method is designed. The pixels of the initial plaintext image are randomly divided into four blocks. The pixels in each block are then subjected to different numbers of rounds of Arnold transformation, and the four blocks are combined to generate

Domain adaptation is one of the fundamental challenges in transfer learning. How to effectively transfer knowledge from labeled source domain to unlabeled target domain is critical for domain adaptation, as it benefits to reduce the considerable performance gap due to domain shift. Existing methods of domain adaptation address this issue via matching the global features across domains. However, not

In this paper, the H∞ state estimation problem is studied for a class of multi-rate artificial neural networks with integral measurements. A novel method, rather than the widely used lifting technique, is proposed to transform the multi-rate artificial neural networks to single-rate switched ones. The purpose of the addressed H∞ state estimation problem is to design an estimator such that the estimation

This paper describes an effective and relatively simple design method for an adaptive multi-input single-output (MISO) fuThis work is financed by the Polish MinistInitiative of Excellence” program from 2019 – 2022. Project number 027/RID/2018/19, funding amount 11 999 900 PLN.zzy controller for a linear plant with partially known parameters including transport delay. The model reference adaptive control

In the recent years, several hashing methods have been proposed for multi-label image retrieval. However, general methods quantify the similarities of image pairs roughly, which only consider the similarities based on category labels. In addition, general pairwise loss functions are not sensitive to the relative order of similar images. To address above problems, we present a deep semantic-aware ranking

A Bloom filter is a probabilistic data structure for approximate membership filtering. It is applied in diverse Network systems to enhance a system’s performance and reduce the memory consumption, for instance, Named-Data Networking, Software-Defined Networks, and Wireless Sensor Networking. Bloom filter consumes a tiny amount of RAM space to store information of large sets of data. On the contrary

The digital signature scheme, which underpins most of the existing distributed ledgers, is generally based on non-quantum-resilient algorithms (e.g. elliptic curve digital signature algorithm). This highlights the need for quantum-secure signature schemes in future distributed ledgers (and other products). Therefore, in this paper, we propose a novel quantum-secure digital signature scheme designed

Scene classification of high-spatial resolution (HSR) images has a wide range of potential applications in various fields, and it has become a research hotspot in remote sensing community. Recently, deep transfer learning-based methods have attracted tremendous attention due to powerful ability of feature extraction. In this paper, a novel architecture termed two-stream feature aggregation deep neural

Recently nullnorms on bounded lattices have been frequently investigated by many researchers. In this paper, we give the full characterization for quite a general class of nullnorms on bounded lattices and represent them by their underlying t-norms and t-conorms. An example is given to show the relationship between each block of the nullnorms. We also fully determine the values of arbitrary nullnorms

As perception-related applications are of great importance in industrial production and daily life nowadays, solutions for understanding given images semantically receive numerous attention from the literature. To this end, significant accomplishments have been reached for such pixel-wise segmentation problem thanks to novel manipulations of integrating global context into local details in convolutional

With the rapid development of cloud computing, the practical applications such as the machine learning based on the outsourced data have been investigated in the data sharing setting. In machine learning, the inner product is a necessary primitive to analyze the description statistics. However, the inner product computation in selective data sharing setting has not been fully considered. For this fact

Minimal and maximal descriptions of concepts are two important notions in covering-based rough sets. Many issues in covering-based rough sets (e.g., reducts, approximations, etc.) are related to them. It is well known that, it is time-consuming and error-prone when set representations are used to compute minimal and maximal descriptions in a large scale covering approximation space. To address this

Nowadays, multi-core architectures are being more and more used in the design of real-time systems. Those systems react usually to their environment that requires to amend their behaviors by applying reconfiguration scenarios. This paper deals with multi-core reconfigurable real-time systems that should be adapted to their environment under real-time constraints. Meanwhile, their synthesis induces

The Quadratic Assignment Problem (QAP) is one of the most studied classical combinatorial optimization problems. QAP has many practical applications. Designing enhanced meta-heuristic approaches for the QAP is an active research area. In this work, we propose a hybrid algorithm (EGATS) that combines an elite genetic algorithm and tabu search to solve the QAP. In the optimization process, EGATS employs

The framework of interval-valued fuzzy preference relations (IVFPRs) is adequate and effective to model human preference evaluations under indeterminacy. This paper analyzes three recently presented multiplicative transitivity models of IVFPRs and exposes their drawbacks. An and-like-uninorm-based functional transitivity equation is developed to introduce a multiplicative consistency notion for IVFPRs

In this paper, we firstly study two pairs of covering-based fuzzy rough set models and propose the TOPSIS-WAA method based on a covering-based fuzzy rough set. Subsequently, we design a rating scheme based on a multi-criteria decision-making method in a finite fuzzy covering approximation space. The rating scheme is implemented based on the preset subjective ratio and the calculated objective ranking

Despite that the reference point based preference articulation plays a vital role in evolutionary multi- and many-objective optimization, three issues remain challenging. First, the performance of reference point based preference articulation largely depends on the location of the reference point. Second, the parameter settings for controlling the region of interest are not robust to the Pareto optimal

In this paper we investigate the mathematical foundations of the notion of similarity between objects in relation to the granulations on a decision table D. First of all, we compare the endogenous granulation induced by Pawlak’s indiscernibility with the exogenous granulation induced by a similarity measure ζ defined on pairs of objects and assuming values in the unit interval. To this aim, the starting

We prove that important properties describing complex behaviours as ergodicity, chaos, topological transitivity, and topological mixing, are decidable for one-dimensional linear cellular automata (LCA) over (Z/mZ)n (Theorem 6 and Corollary 7), a large and important class of cellular automata (CA) which are able to exhibit the complex behaviours of general CA and are used in applications. In particular

As high-resolution remote-sensing (HRRS) images have become increasingly widely available, scene classification focusing on the smart classification of land cover and land use has also attracted more attention. However, mainstream methods encounter a severe problem in that many annotation samples are required to obtain an ideal model for scene classification. In the remote sensing community, there

The heterogeneous gap among cross modalities is a critical problem in many applications (e.g., retrieval). Considering that the main purpose of cross-modal learning is to learn a common representation while there also exist specific components across different modalities, a similarity and diversity induced paired projection (SDPP) method is proposed in this paper. SDPP not only extracts the correlation

The axiomatic distance-based method is a powerful tool to aggregate individual preferences, and the extant axiomatic distance-based aggregation methods are with regard to individual numerical preferences. However, in some real-world decision problems with qualitative aspects, it is more convenient and natural for individuals to express their preferences through linguistic terms rather than through

In smart cities, region-based prediction (e.g. traffic flow and electricity flow) is of great importance to city management and public safety, and it remains a daunting challenge that involves complicated spatial-temporal-related factors such as weather, holidays, events, etc. Region-based forecasting aims to predict the future situation for regions in a city based on historical data. In the existing

In this study, a novel metaheuristic optimization algorithm, gradient-based optimizer (GBO) is proposed. The GBO, inspired by the gradient-based Newton’s method, uses two main operators: gradient search rule (GSR) and local escaping operator (LEO) and a set of vectors to explore the search space. The GSR employs the gradient-based method to enhance the exploration tendency and accelerate the convergence

In this paper, a heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators (HCLDMS-PSO) is presented. In addition, a comprehensive learning (CL) strategy with the global optimal experience of the whole population is conducted to generate an exploitation subpopulation exemplar. However, a modified dynamic multi-swarm (DMS) strategy is specially

The Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) overcomes the limitation of evolutionary algorithm based on a Pareto dominant relationship in dealing with the problem of super multi-objective optimization and has wide application prospects, but there are also some problems, such as the lack of diversity and slow convergence speed in the later-stage evolution species. This

Regression tasks aim to determine accurate and simple relationship expressions between variables, which can be regarded as bi-objective optimization problems. As GP (genetic programming) can use expression trees as representation, it is popularly-used for regression. Introducing multi-objective techniques into GP enables it to solve bi-objective tasks, and the success of memetic algorithms show the

Finding dense subgraphs from sparse graphs is a fundamental graph mining task that has been applied in various domains, such as social networks, biology, and spam detection. Because the standard formulation of this problem is difficult to solve owing to connections with the Maximum Clique Problem, some tractable formulations have been proposed. These formulations find a dense subgraph by optimizing

Decision-theoretic rough sets (DTRSs), which can be considered as generalized rough set models produced by Bayesian risk minimum and three-way decisions (3WD) theories, have achieved fruitful results in risk decision-making problems. Nevertheless, the parameter determination of decision-theoretic rough sets is a challenging problem in practical applications, which narrows the generalization and development

Differential evolution (DE) is one of the most popular paradigms of evolutionary algorithms. Numerous variants of basic DE have been developed in the past two decades after it was first proposed. However, very few works focused on re-exploring the neighborhood area of the elite solutions, which is definitely a promising area according to the proximate optimality principle. Here, a simple yet efficient

The increasing complexity and dynamical property in stock markets are key challenges of the financial industry, in which inflexible trading strategies designed by experienced financial practitioners fail to achieve satisfactory performance in all market conditions. To meet this challenge, adaptive stock trading strategies with deep reinforcement learning methods are proposed. For the time-series nature

User-generated trajectories (e.g. during traveling) can be leveraged to offer value-added services (e.g. smart city policy formulation), but there are also privacy implications. For example, information about the routes or destinations obtained from such published trajectories can be used to profile and identify users, including during contact tracing in pandemics (e.g., COVID-19) or the monitoring

Quantum machine learning has recently attracted much attention from the community of quantum computing. In this paper, we explore the ability of generative adversarial networks (GANs) based on quantum computing. More specifically, we propose a quantum GAN for generating classical discrete distribution, which has a classical-quantum hybrid architecture and is composed of a parameterized quantum circuit

The intuitionistic multiplicative preference set is one of the replacements to the intuitionistic fuzzy preference set, where the preferences related to the object is asymmetrical distribution about 1. In it, Saaty’s 1–9 scale has been used to represent the uncertain and imprecise information. Meanwhile, an aggregation operator by using general operational laws for some fuzzy sets is an important task

For an ordered pair of real numbers, if both of its two entries are contained in the open interval (0,1), it is called an Ordered Pair of Normalized real numbers (OPN). In this paper, a comprehensive theory is presented, which provides a novel mathematical tool for dealing with OPNs. Specifically, the eight arithmetic operations and trigonometric functions of OPNs are given. The set of OPNs is closed

This paper proposes a reflexive fuzzy β-neighborhood operator by modifying Ma’s fuzzy β-neighborhood operators. Then, we use such an operator to build a covering-based variable precision fuzzy rough set (CVPFRS) model that can deal with the issue of misclassifications and perturbations in decision-making problems. By combining the CVPFRS model with two traditional decision-making methods (the PROMETHE

Granularity measures and rough approximation measures are two basic methods for dealing with uncertainty in rough set theory. Interval-valued information tables are a generalization of single-valued information tables. In this paper, we study granularity measures and rough approximation measures in interval-valued information tables based on kernel similarity relations. More specifically, we propose

Answer selection with deep neural networks has been studied extensively owing to its strong capability of encoding semantic features. Most previous work has treated the candidates as independent individuals for ranking but ignored the relations between these. In this study, we propose a ranking method via partial ordering for answer selection, which is applicable to various tasks, such as question

In this paper, we study the learning rate of generalized Bayes estimators in a general setting where the hypothesis class can be uncountable and have an irregular shape, the loss function can have heavy tails, and the optimal hypothesis may not be unique. We prove that under the multi-scale Bernstein’s condition, the generalized posterior distribution concentrates around the set of optimal hypotheses

User interactions are generally utilized to impose the hard constraint and estimate the label prior probability in the interactive image segmentation methods. The conventional interactive approaches cannot work well when the user inputs contain incorrect marks. The existing error tolerance methods mainly focus on the interaction itself and try to eliminate the influence of incorrect hard constraint