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This paper deals with the synchronization issue of multiweighted and directed coupled reaction-diffusion neural networks with hybrid coupling (MDCRDNNHC) based on boundary control. Spatial information is used for synchronization accompanied by state information, which is called hybrid coupling. By constructing appropriate controllers located in the boundary, synchronization matter for MDCRDNNHC with nonzero boundary values is solved. Inner coupling matrices (IMs) and outer coupling matrices (OMs) can be directed, competitive and even not connected in this paper. We propose a novel strategy to fuse current two technical routes for directed networks with multiweights. For MDCRDNNHC with diagonal IMs, we prove that if the weighted (added) combinations of multiple OMs for each dimension are strongly connected, and the Chebyshev distance among their normalized left eigenvectors of added OMs corresponding to zero eigenvalue for each dimension is less than a threshold, then synchronization can be attained. In addition, it is also applicable for positive definite but non-diagonal IMs under this fusion technique. Numerical examples are finally provided to demonstrate the effectiveness of these theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers the distributed prescribed-time optimization problem of multi-agent systems (MASs). Considering the strongly convex function of time-invariant for each agent, the two-stage distributed prescribed-time optimization algorithm is designed based on the idea of zero-gradient-sum. Meanwhile, in order to save system resources, the event-triggered control mechanism is introduced into the algorithm in this paper. In the first stage, the distributed prescribed-time event-triggered algorithm is proposed to minimize the local objective functions of each agent at the prescribed-time interval. In the second stage, the algorithm is driven to optimize the global cost function while maintaining the gradient sum of all local cost functions to zero. The criteria for achieving the consensus and optimization of MASs are obtained by using Lyapunov stability theory and optimization theory. Moreover, it is proved in detail that using the two triggering functions will not result in Zeno behavior. The numerical example is given to demonstrate the correctness of the theoretical analysis and the effectiveness of the control algorithms. [ABSTRACT FROM AUTHOR]

As a first attempt, the L 2 - L ∞ state estimation issue of the high-order inertial neural networks with time-varying delay is put forward in this paper. A more direct method, non-reduced order method, is adopted here rather than reducing the order of the original second-order dynamics via substitution of variables. Our main objective is to construct an appropriate state estimator, which can not only guarantee the global h-stability of the undisturbed error dynamics, but also ensure the peak value of the estimation error is kept within a certain range. Through utilizing Lyapunov theory and some simple matrix calculation, a delay-dependent criterion is presented to elaborate the state estimator conforms to the expectation. In the end, an illustrative simulation example shows the correctness of the estimation technique proposed in this paper. [ABSTRACT FROM AUTHOR]

Designing a Convolutional Neural Networks (CNN) is a complex task and requires expert knowledge to optimize the performance and network architecture. In this paper, a novel data-driven approach is proposed to determine the architecture of CNN models. The proposed Autonomous Convolutional Neural Networks (AutoCNNThe executable code and original numerical results can be downloaded from (https://tinyurl.com/AutoCNN)) algorithm introduces data driven strategies for addition of new convolutional layers, pruning of redundant filters and training cycle optimization. AutoCNN is evaluated using MNIST, MNIST-rot-back-image, Fashion MNIST and the ADHD200 datasets to measure the performance on small datasets with varied feature distributions. The results indicate that AutoCNN optimizes the CNN network architecture and helps maximise the classification performance. The data-driven network determination approach introduced in this paper was found to not only provides competitive performance similar to existing evolutionary computation based network determination algorithms in literature, but was found to be an effective optimization tool to improve the performance of existing CNN architectures. Further, the AutoCNN was found to highly immune to noise in the dataset and has proven to be effective method to transfer knowledge between related datasets. Therefore, the AutoCNN is a highly versatile CNN architecture determination tool that has a wide range of applications in the field of autonomous driving, medical image analysis, image enhancement, camera based security monitoring and image based fault detection. [ABSTRACT FROM AUTHOR]

In this paper, we develop an improved linguistic interval-valued Atanassov intuitionistic fuzzy weighted averaging (ILIVAIFWA) aggregation operator (AO) of linguistic interval-valued Atanassov intuitionistic fuzzy numbers (LIVAIFNs). The ILIVAIFWA AO of LIVAIFNs presented in this paper can conquer the drawbacks of the linguistic interval-valued Atanassov intuitionistic fuzzy weighted averaging (LIVAIFWA) AO, the linguistic interval-valued Atanassov intuitionistic fuzzy ordered weighted averaging (LIVAIFOWA) AO, the linguistic interval-valued Atanassov intuitionistic fuzzy weighted geometric (LIVAIFWG) AO and the linguistic interval-valued Atanassov intuitionistic fuzzy ordered weighted geometric (LIVAIFOWG) AO of LIVAIFNs. We also develop a novel group decision making (GDM) method on the basis of the proposed ILIVAIFWA AO of LIVAIFNs. The GDM method presented in this paper can oconquer the drawbacks of the existing GDM methods in the context of LIVAIFNs. [ABSTRACT FROM AUTHOR]

Optimal alignments are the basis of conformance checking. For long, researchers have been devoted to the efficiency issue of computing optimal alignments. This paper focuses on the optimality issue. Specifically, we aim to find alignments with maximum synchronous moves and minimum deviations. This paper introduces a coordinate-plane search space, which allows enumerating all the possible alignments. The alignments with maximum synchronous moves are translated into the lowest-cost paths, such that heuristic strategies (such as the Dijkstra algorithm) can be applied. Both theoretical proof and experimental results show that 100% optimality can be achieved. [ABSTRACT FROM AUTHOR]

Difficult and expensive financing has always been a problem for domestic and foreign enterprises, and how to effectively improve financing efficiency and improve the financing environment is a key issue to be studied. LightGBM is an advanced machine learning algorithm, which uses histogram algorithm and Leaf-wise strategy with depth limitation to improve the accuracy of the model. However, there are almost no cases of applying this method to corporate financing risk prediction. Therefore, the paper establishes the LightGBM model to predict the financing risk profile of 186 enterprises. In order to compare the prediction performance of LightGBM for enterprise financing risk, the paper conducted comparison experiments using k-nearest-neighbors algorithm, decision tree algorithm, and random forest algorithm on the same data set. The experiments show that LightGBM has better prediction results than the other three algorithms for several metrics in corporate financing risk prediction. Therefore, we believe that the LightGBM algorithm can be used as an effective tool to predict the financing risk of enterprises. [ABSTRACT FROM AUTHOR]

• Research about the intuitionistic fuzzy set theory is analyzed systematically with quantitative analysis. • The evolution of hot topics and knowledge diffusion path are investigated. • The self-citations phenomenon is deeply discussed when performing main path analysis. • The application of the intuitionistic fuzzy set in the domain of decision making is becoming more and more dominant. Since the intuitionistic fuzzy set theory was proposed, it has received widespread attention. To get a deeper understand of this field, this paper analyzes a total of 2913 papers downloaded from the Web of Science (WoS) from 1984 to 2019 from different perspectives. On the one hand, this research identifies important themes and their interrelationships, showing the thematic evolution of this field vividly. On the other hand, this research digs out the knowledge diffusion paths of this domain with the help of global and key-route main paths. From the perspective of the thematic evolution, five thematic area are detected including Intuitionistic fuzzy relation , IVIFS-Similarity , MCDM/MADM-GDM , Aggregation operator and Extensions of IFS. Among them, MCDM/MADM-GDM has evolved from an emerging theme to a motor theme being the most important topic and GDM integrating themes MADM 、 MCDM and Aggregation operator shows continuous growth. From the perspective of knowledge diffusion paths, research topic has gradually translated from theorical construction to practical application, and several factors such as self-citation, indirect citation, hierarchy and references that may influence the result of the main path are discussed. In general, this research systematically provides scholars with the development process in this domain, which is conductive to them to fully grasp the state-of-the-art research. [ABSTRACT FROM AUTHOR]

In the paper, a class of unknown fractional-order nonlinear systems suffering from actuator faults are investigated. Meanwhile, an adaptive finite-time sliding mode control (SMC) approach based on approximation principle of fuzzy logic system (FLS) and backstepping layout is proposed. It is well known that the standard backstepping control has inherent computational complexity. Therefore, a type of fractional-order command filter (CF) is introduced to overcome such a shortcoming, that is, by means of fractional-order CF, the virtual input signal and its fractional derivative can be estimated properly as anticipated. Fractional-order sliding mode surfaces are constructed to diminish the filtering errors such that more better performance is guaranteed. Besides, compared to the conventional backstepping control, the CF-based fuzzy backstepping SMC approach presented in the paper not only shows the superior robustness, but also facilitates to accomplish the desired tracking control objective in finite time. The finite-time stability analysis is established on the basis of fractional-order Lyapunov method. Finally, the effectiveness of the proposed methodology is identified by numerical simulations. [ABSTRACT FROM AUTHOR]

In most previous opinion dynamics research, the opinion evolution is usually defaulted to evolving over a single topic; however, in more general scenarios, agents often discuss several topics at the same time. This paper adopts a multidimensional opinion dynamics model in which the opinions are represented by vectors to reveal the multi-topic opinion pattern. To be more in line with reality, this paper employs a multidimensional version of the Friedkin-Johnsen (FJ) model, where each stubborn agent has a time-evolving stubbornness level. The theoretical analysis finds that the proposed model is convergent if the interpersonal influence matrix is stochastic indecomposable and aperiodic. To achieve unified consensus, opinion leaders are introduced, after which the consensus conditions for the proposed model are given. The theoretical conclusions suggest that in the proposed model, the stubborn agents are as prominent as the opinion leaders in the opinion formation process. Simulations are then conducted in two types of artificial networks, from which it is found that compared with the scalar version of the proposed model and standard multidimensional FJ models in which agents' stubbornness levels remain unchanged, the proposed model produce a more compact final opinion space. The results show that to generate smaller opinion distance and higher opinion correctness degree, it is necessary to ensure a lower proportion of stubborn agents and higher network connectivity. This is consistent with people's intuition. The population size is found to have little effect on the results, but larger number of topics result in larger opinion distances. These findings are enlightening and helpful to opinion managers. [ABSTRACT FROM AUTHOR]

• Fuzzy knowledge graph is a more general description of classical knowledge graph. • The fuzzy semantic scalability between multiple associative predicates is analyzed. • The mathematical model of semantic relationship in fuzzy knowledge graph is designed. • Some fuzzy reasoning rules are presented to realize fuzzy semantic extension. • Performance of the strategy discovers more implicit valid knowledge with fuzzy semantic. As the latest achievement of the development in semiotics, knowledge graph has been recognized and widely used by more and more researchers for its rich semantic information and clear logical structure. How to discovery the deep relevant knowledge from the massive graph-structured data has become a hot spot of artificial intelligence. Considering that some predicates in knowledge graph express fuzzy relationships whose semantics are not certain, the basic schema of classical knowledge graph in the form of RDF triple cannot describe the fuzzy semantic information effectively. To counter above problems, in this paper, we present a new semantic representation and reasoning model for multiple associative predicates by introducing fuzzy theory. Concretely, the presented method defines a new fuzzy annotating strategy to represent the fuzzy semantics between associative predicates in different RDF triples. On this basis, some fuzzy reasoning rules are presented to realize fuzzy semantic extension for classical knowledge graph. Lastly, the experimental results show that our proposal can discover more implicit valid knowledge with fuzzy semantic and have a good consistency with the intuition of human judgments. Overall, the methods proposed in this paper constitute some effective ways of knowledge discovery of structured semantic data. [ABSTRACT FROM AUTHOR]

Genesis and geometrical properties of fuzzy line are explored in this study. The existing studies on fuzzy geometry suggest that fuzzy lines can be characterized as a union of crisp lines with varying membership values but this characterization is not capable to identify the nature of the fuzzy point situated on the fuzzy line. Expressing fuzzy line as a collection of fuzzy points in the geometrical plane is an intended goal of this paper. In the first part, a fuzzy line has been interpreted as a fuzzification function that produces a collection of fuzzy numbers. The equivalency between the union of lines with different membership grades and collection of fuzzy numbers is also shown. In the next part, the possible nature of a fuzzy points situated on a generic fuzzy line is explored. Analogous to the concept of a line in classical geometry, this paper constructs and approximates a fuzzy line as a collection of fuzzy points. All the proposals and results are illustrated numerically and geometrically in a fuzzy geometrical plane. [ABSTRACT FROM AUTHOR]

In this paper, the fully distributed event-triggered time-varying formation control of heterogeneous linear multiagent systems is studied. To make the reference trajectory controllable and flexible, the bounded input is introduced to leader agent. The intention of this paper is to decrease the unnecessary information transmission of agents via communication topology by designing the intermittent transmission control protocol. Firstly, the adaptive state compensator is designed to evaluate the state information of leader agent based on the estimation value of leader state and two kinds of event-triggered mechanisms. Then, two types of output-feedback time-varying formation controllers are presented based on different formation feasible conditions. By theoretical analysis, the estimation error of event-triggered state compensator is proved to be uniformly ultimately bounded and the event-triggered mechanisms prevent Zeno behavior, and the formation tracking errors converge to the small region of zero. Finally, some simulation experiments are provided to support the theoretical results. [ABSTRACT FROM AUTHOR]

• To handle the uncertainty decision problems in incomplete hybrid data, a generalized three-way neighborhood decision model is proposed by distributing the interval-valued loss function to each object and averaging the interval-valued loss functions of all objects in the data-driven neighborhood class. • A matrix-based approach for representing three-way regions in the generalized three-way neighborhood decision model is presented by introducing the matrix forms of related concepts and the matrix operators. • An efficient framework for dynamically updating the three-way regions is provided when objects and attributes increase simultaneously. • An incremental algorithm based on matrix is designed for maintaining the three-way regions. • Experimental results demonstrate that the proposed incremental algorithm has an advantage in improving the computational performance. The theory of three-way decisions, as a powerful methodology of granular computing, has been widely used in making decision under uncertainty environments. Decision tasks in incomplete hybrid data including heterogeneous and missing features are of abundance in realistic situations. To deal with these tasks, some work based on three-way decisions has been investigated. However, the losses used for evaluating objects are precise real numbers, which makes these decision models have some limitations in applications when there exist missing values in incomplete hybrid data. Thus, this paper constructs a generalized three-way neighborhood decision model by assigning the interval-valued loss function to each object and further adopting an average strategy to integrate the interval-valued loss functions of objects in each data-driven neighborhood class. Moreover, considering that the objects and attributes of incomplete hybrid data will simultaneously change over time, this paper also provides an efficient framework to dynamically maintain three-way regions of the proposed model. An approach based on matrix to compute the three-way regions is first presented by introducing the matrix operations and the matrix forms of related concepts. Then, with the simultaneous variation of objects and attributes, the matrix-based incremental mechanism and algorithm are proposed for updating the three-way regions, respectively. Experimental results on nine datasets indicate that the proposed incremental algorithm can effectively improve the computational performance for evolving data in comparison with the static algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with finite-time stabilization (FTS) of linear systems by bounded event-triggered control (ETC) and self-triggered control (STC). A bounded linear ETC is firstly designed, where the time-varying control gain is only scheduled on a specified time determined by an event-triggered mechanism, such that the FTS of the closed-loop system is achieved and the communication resources are saved. Moreover, the corresponding STC, in which the updates of control law are determined by a self-triggered mechanism that only needs to monitor the previous triggered states, is designed. Specially, by exploring the properties of the parametric Lyapunov equation, the designable minimal inter-event time of the established ETC and STC is obtained, such that the Zeno phenomenon is avoided. In addition, the finite-time semi-global stabilization and the fixed-time (prescribed finite-time) stabilization of linear systems are achieved by bounded ETC and STC. What needs to be emphasized is that the finite-time stabilization in this paper can be called as the practical finite-time stabilization since the state converges to zero exponentially with a fast convergence rate after a designable time. Finally, the established algorithms are used to the design of the spacecraft rendezvous control system and their effectiveness is verified by simulations. [ABSTRACT FROM AUTHOR]

• To handle the uncertainty decision problems in incomplete hybrid data, a generalized three-way neighborhood decision model is proposed by distributing the interval-valued loss function to each object and averaging the interval-valued loss functions of all objects in the data-driven neighborhood class. • A matrix-based approach for representing three-way regions in the generalized three-way neighborhood decision model is presented by introducing the matrix forms of related concepts and the matrix operators. • An efficient framework for dynamically updating the three-way regions is provided when objects and attributes increase simultaneously. • An incremental algorithm based on matrix is designed for maintaining the three-way regions. • Experimental results demonstrate that the proposed incremental algorithm has an advantage in improving the computational performance. The theory of three-way decisions, as a powerful methodology of granular computing, has been widely used in making decision under uncertainty environments. Decision tasks in incomplete hybrid data including heterogeneous and missing features are of abundance in realistic situations. To deal with these tasks, some work based on three-way decisions has been investigated. However, the losses used for evaluating objects are precise real numbers, which makes these decision models have some limitations in applications when there exist missing values in incomplete hybrid data. Thus, this paper constructs a generalized three-way neighborhood decision model by assigning the interval-valued loss function to each object and further adopting an average strategy to integrate the interval-valued loss functions of objects in each data-driven neighborhood class. Moreover, considering that the objects and attributes of incomplete hybrid data will simultaneously change over time, this paper also provides an efficient framework to dynamically maintain three-way regions of the proposed model. An approach based on matrix to compute the three-way regions is first presented by introducing the matrix operations and the matrix forms of related concepts. Then, with the simultaneous variation of objects and attributes, the matrix-based incremental mechanism and algorithm are proposed for updating the three-way regions, respectively. Experimental results on nine datasets indicate that the proposed incremental algorithm can effectively improve the computational performance for evolving data in comparison with the static algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with finite-time stabilization (FTS) of linear systems by bounded event-triggered control (ETC) and self-triggered control (STC). A bounded linear ETC is firstly designed, where the time-varying control gain is only scheduled on a specified time determined by an event-triggered mechanism, such that the FTS of the closed-loop system is achieved and the communication resources are saved. Moreover, the corresponding STC, in which the updates of control law are determined by a self-triggered mechanism that only needs to monitor the previous triggered states, is designed. Specially, by exploring the properties of the parametric Lyapunov equation, the designable minimal inter-event time of the established ETC and STC is obtained, such that the Zeno phenomenon is avoided. In addition, the finite-time semi-global stabilization and the fixed-time (prescribed finite-time) stabilization of linear systems are achieved by bounded ETC and STC. What needs to be emphasized is that the finite-time stabilization in this paper can be called as the practical finite-time stabilization since the state converges to zero exponentially with a fast convergence rate after a designable time. Finally, the established algorithms are used to the design of the spacecraft rendezvous control system and their effectiveness is verified by simulations. [ABSTRACT FROM AUTHOR]

Three-way decision, an outstanding method to handle decision-making uncertainties, relies on essentially the loss functions derived from the Bayesian risk decision process. Actually, there are plentiful loss functions that depend on the subjective judgment of decision-makers under different decision scenarios, lacking uniform and objective measurement frameworks. This study pays attention to the real clinical diagnosis, and constructs a weighted probability kernel multi-granularity three-way decision method (WKMG-TWD) integrating gray relation analysis (GRA) over a multi-source heterogeneous decision information system (MHDIS). The method establishes a standardized data-driven calculation framework of loss functions. Foremost, the multi-kernel probabilistic similarity is defined and granularity's weights with knowledge consistency are explored. Subsequently, a weighted probability kernel multi-granularity rough set (WKMGRS) is constructed in this paper. Secondly, to introduce the three-way decision, this study proposes the cost-sensitive individual loss functions considering the correlation determined by GRA between decision objects and different decision classes. Ultimately, this study establishes and applies a three-way iterative classification model to hypertension diagnosis. The experimental results confirm the effectiveness and superiority of the model. The main contribution of this paper is twofold. One is to offer a uniform calculation framework for loss functions and granularity's weights. The other is to furnish invaluable guidance for solving complex medical decision-making problems. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce, more and more people are willing to post their reviews and opinions about the products they buy online. Therefore, the use of online review data for decision-making appears to be more practical and universal, and how to effectively use this kind of data to support large-scale group decision-making (LSGDM) is a worthy research direction. In this paper, we firstly use sentiment analysis to analyze online review data to derive the decision maker's (DM's) sentiment value, and apply the sentiment value to construct a social network based on a dual trust relationship, which considers both familiarity-based trust and similarity-based trust. Secondly, a directed Louvain clustering algorithm in light of dual trust relationships and a method for solving the DM's intra-group weights and the group's weights are proposed based on this network. A two-stage clustering based consensus model in light of dual trust relationships is then proposed, in which the DMs in the agreement cluster can communicate with other DMs outside of such a cluster and dynamically update the grouping using the clustering algorithm. Finally, the practicality and effectiveness of the LSGDM method proposed in this paper are verified through a real case. [ABSTRACT FROM AUTHOR]

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high-dimensional datasets. First, SMNNs have precomputed fixed weight and no SMNN training process has been defined so far, so they lack generalization ability. Second, SMNNs require the construction of a convex polytope surrounding the input dataset. In this paper, we overcome these issues by proposing an SMNN training procedure based on a support subset of the given dataset and replacing the construction of the convex polytope by a method based on projections to a hypersphere. In addition, the explainability capacity of SMNNs and effective implementation are also newly introduced in this paper. [ABSTRACT FROM AUTHOR]

Robust deadlock supervisory control techniques for automated manufacturing systems under resource failures that need additional central buffers may lead to supererogatory cost in control implementation. To mitigate this issue, this paper reports a Petri net-based low-cost adaptive supervisory control policy that does not require extra buffers. If an unreliable resource fails, three classes of buffer spaces can be temporarily used to store parts that require the failed resource in their impending processing routes. The proposed adaptive supervisory control comprises of control places and switch controllers. If an unreliable resource fails, the switch controllers are activated to move the part types that require the failed resource in their subsequent processing stages into borrowed buffer spaces. After the failed resource is recovered, the system returns to its normal operating mode. The part types in the borrowed buffer spaces will be returned to their last processing stages before the occurrence of the failure and continued along their processing route. We propose criteria for selecting buffer space borrowers and buffer space lenders. Furthermore, redundant control places are removed by a new technique proposed in the paper in order to reduce the complexity of the supervisory control structure. We demonstrate the proposed method using examples. [ABSTRACT FROM AUTHOR]

With the development of the Internet and social media, rumors can spread not only through word-of-mouth but also rapidly through the network. In this paper, a dynamic model of rumor propagation with time delay is proposed separately for non-network and network scenarios. Additionally, we analyze the equilibrium points and their existence conditions for rumor propagation. After the linear approximation of the model, the necessary conditions for Turing instability are derived. Furthermore, the amplitude equation corresponding to the model in this paper is derived. Finally, through numerical simulations in the non-network model, we validate the aforementioned theories and find that changing the removed coefficient and cross-diffusion coefficient have a significant impact on Turing patterns, while time delay and periodic diffusion have a smaller impact. In the network model, we compare the effects of two network structures, the WS network and the BA network, through numerical simulations, verifying the feasibility of the Monte Carlo simulation method. [ABSTRACT FROM AUTHOR]

Selecting discriminative features to build effective learning models is a significant research work in machine learning. In practical applications, the data distribution characteristics are diverse, and the uncertainties pose challenges for building learning models with robustness and generalization capabilities. Since one-hot encoding is good at representing independent labels, the label matrix of regression-based feature selection (FS) methods is usually encoded with one-hot encoding. However, it's not well adapted to the different data distributions. This paper proposes a sparse orthogonal supervised FS model with global redundancy minimization, label scaling, and robustness (GRMLSRSOFS) to address the above problems. This model uses the label scaling technique proposed in this paper to better adapt to different data distributions. An iterative optimization method is given, and its convergence is demonstrated theoretically and experimentally. Further, experimental results on 12 public datasets show that 1) The GRMLSRSOFS can achieve higher classification accuracy with fewer features in most cases than several state-of-the-art FS methods. For example, the GRMLSRSOFS achieves 100% classification accuracy using only 20 features on the warpPIE10P dataset and obtains nearly 6% improvement over other methods on the Yale dataset. 2) The convergence speed of the GRMLSRSOFS will be faster after label scaling. [ABSTRACT FROM AUTHOR]

E-commerce order fulfillment service (E-COFS) plays a pivotal role in shaping consumer behavior in online marketplaces. The strategic outsourcing of the service allows e-commerce sellers to prioritize their core business areas, enhance customer satisfaction, and minimize fulfillment costs. However, a critical challenge lies in appraising the potential quality of E-COFS provided by third parties, especially when lacking historical information. To address this, this paper first designs a generalized framework for guiding the construction of the quantitative model for evaluating the potential quality of E-COFS. The proposed framework unfolds in three stages: (1) evaluating potential effectiveness of an E-COFS through quantifying stakeholders' potential satisfaction from the E-COFS plan tailored by its provider, (2) evaluating its potential feasibility by quantifying the potential performance of the E-COFS quality management system (E-COFS-QMS) built by the provider on supporting the plan, and (3) integrating the above two parts to gauge the potential quality of the E-COFS. Building upon this framework, this paper then designs a novel quantitative model. Specifically, this model adopts the linguistic subjective judgment representation method and introduces basic uncertain linguistic information to achieve computing with words. Multiple stakeholders within e-commerce sellers are tasked with articulating their requirements, their preferences and expectations, and consensus reaching process is conducted to obtain the acceptable consensus among these stakeholders. Multiple experts from various domains are tasked with giving their subjective judgements on the performances of E-COFS and E-COFS-QMS, and a method of weighting individual judgments, which respects the reliabilities of individual judgements and the overall similarity in knowledge structures among the experts, is adopted to effectively tap into collective intelligence. Finally, a case study is conducted to validate the validity and feasibility of the proposed quantitative model. [ABSTRACT FROM AUTHOR]

This paper proposes a new intuitionistic fuzzy best-worst method (IFBWM) for group decision making (GDM) with intuitionistic fuzzy (IF) preference relations (IFPRs). IF values (IFVs) are used to express reference comparisons of criteria. Based on the additive consistency of IFPRs, this paper proposes the definition of additive consistency of IF reference comparisons (IFRCs). Based on the deviation minimization, a linear goal programming model is established to calculate the optimal IF priority weights. By the additive consistency, the consistency index in the closed form is computed by the score function of IFVs. A new approach is devised to enhance the additive consistency of IFRCs. Thus, an IFBWM with the additive consistency of IFRCs is proposed. For GDM with IFPRs, the best criterion and the worst criterion are identified for each decision maker by constructing the score matrix of the IFPR. The individual ranking order of the criteria is obtained by using the proposed IFBWM. Then, the collective ranking order of the criteria is obtained via building a 0–1 integer programming model. Therefore, a GDM method based on the developed IFBWM with IFPRs is proposed. Four examples are analyzed to demonstrate the effectiveness and the advantages of the proposed IFBWM for GDM. [ABSTRACT FROM AUTHOR]

Sequential databases have wide applications, such as market basket analysis, medical prediction, and sign language recognition. Most prior research is based on pointed-based sequential databases, which assume each item/event occurs instantaneously. However, in many real-world scenarios, events persist over intervals of varying durations, such as varying time intervals of a symptom or a gesture of sign language. Assigning the same weight to different times of events and neglecting the duration of events can hinder the recognition of interesting patterns, such as concurrent symptoms preceding a disease. To address these issues, this paper integrates duration with temporal patterns in interval-based sequential databases, introduces the concept of temporal duration-based patterns (TDPs), and designs two algorithms called FTDPMiner-EP (Frequent TDPMiner based on endpoint representation) and FTDPMiner-TM (Frequent TDPMiner based on triangular matrix representation) by using different extension methods to mine frequent TDPs. Due to the complex relationships between events, temporal pattern mining is more challenging than sequential pattern mining. Strategies are used in this paper to accelerate the algorithms' search process. Experiments are conducted on both real and synthetic databases, which show good performance of the two algorithms. [ABSTRACT FROM AUTHOR]

• Solve the imbalance problem in cardiovascular disease data. • A novel method named ADWGKFO is proposed to transform the unbalanced data sets. • A more targeted sampling weight determination method is proposed. • A sample testing method based on Gaussian kernel is proposed to filter new samples. • Empirical analysis shows the proposed method performs better than similar methods. In risk assessment of cardiovascular disease (CVD), the classification error caused by unbalanced data is a significant challenge, which has sparked widespread concern and research upsurge in the field of data mining. Therefore, in view of the imbalance of CVD data sets, an oversampling method via adaptive double weights and Gaussian kernel function (ADWGKFO) is proposed, which converts the unbalanced data sets into balanced data sets. Firstly, clustering algorithm is utilized to cluster minority samples, boundary samples are identified by Borderline-Synthetic Minority Over-sampling Technique (Borderline-SMOTE), K nearest neighbor and support vector machine algorithms, and the number of samples synthesized in each group is calculated based on the double weights of boundary points and majority distribution. Secondly, in order to clearly define the classification boundary, the mutual class potential of new samples in each cluster is calculated by Gaussian kernel function, and new samples are filtered according to the mutual class potential until the data set is balanced. Finally, taking the data sets from Kaggle platform as the research samples, the proposed method is empirically analyzed. In order to validate the efficacy and universality of the proposed method, this paper selects CatBoost that is a new integrated algorithm to test the effect of the ADWGKFO method, and compares it with different sampling methods and different classifiers using performance evaluation indexes such as accuracy, F1-score and area under the curve (AUC). Compared with the combinations of other methods, the accuracy, F1-score and AUC are significantly improved. It is concluded that the ADWGKFO method described in this paper can successfully improve the data quality, and increases the reliability of CVD risk assessment. [ABSTRACT FROM AUTHOR]

In the subjective evaluation process, the hesitant fuzzy set (HFS), as a convenient and robust presentation tool, cannot only suitably address the decision makers' (DMs') or experts' hesitant and uncertain issues but also can arise the dimension curse puzzle. Furthermore, the decision-making result is just derived according to the given objective and subjective information, without considering the DM's subjective evaluation and the environment's dynamic influence. Unlike the previous studies, this paper tries to address them from the deep learning viewpoint. To this end, we first define the non-equidimensional HFS (NHFS) and then introduce the equidimensional and classification characters into the NHFS to further develop the equidimensional HFS (EHFS) and the EHFS with the optimal classification result. Then, the equidimensional hesitant fuzzy-generative adversarial network (EHF-GAN) model is proposed to transform the hesitant fuzzy information from the non-equidimensional to the equidimensional form. The generalization and the convergence of the new model are proven to show the models' reasonability. In addition, the double-learning algorithm of the EHF-GAN model is designed, which can fuse the DMs' dynamic judgments and derive the optimal decision-making results. Lastly, this paper applies the proposed model and algorithm to an illustrative example of the new smart city enterprises and then shows their feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

There has been limited research on considering decision-makers psychological factors and risk attitudes in the early three-way decision theory and methods. Behavioral three-way decision models can better describe and analyze decision-makers risk preferences and behaviors, with prospect theory being prominent. This paper introduces a third-generation prospect theory-based three-way decision model. It characterizes uncertain reference points for decision-maker preferences through positive/negative ideal points and medians. Due to nonlinear weighting functions in prospect theory, closed-form solutions for three-way decision thresholds are absent. The paper introduces α -model and β -model optimizations for numerical threshold solutions, simplifying three-way decision rules. Additionally, the proposed optimized three-way decision model is applied to conflict analysis, considering multi-scale and Z-number information's impact on conflict evaluation. Effective methods for deriving three-way conflict analysis rules and outcomes from multi-scale Z-number information systems are studied. Audit team conflict analysis examples validate the validity and feasibility of the proposed models and methods. Finally, several experimental results with data sets confirm overall performance. [ABSTRACT FROM AUTHOR]

In this paper, the containment control problem is investigated for a class of non-strict feedback nonlinear multi-agent systems with arbitrary initial states. The state observation disturbance, defined as the deviation between the observed and true values of the state, is taken into account in the addressed system. Fuzzy logic systems and adaptive techniques are accordingly introduced to handle the unknown nonlinear terms and unknown disturbances. Based on a transformation function and a new Lyapunov function, an improved fuzzy adaptive control protocol is developed, which ensures that the output tracking error of each follower converges to a predefined region within a predefined settling time and allows the initial states of the agents to be arbitrarily bounded. Moreover, the error (between the input and output of the filter inherent to the dynamic surface control) can be accurately compensated under the newly designed Lyapunov function. The simultaneous consideration of the asymmetric input saturation and unknown time-varying control gain coefficients in the model makes the results established in this paper more general. Finally, the effectiveness of the proposed control protocol is verified by two simulation experiments. • Agent's system is the more general non-strict feedback system. • The initial value of the agent's state can be arbitrary. • State observation disturbance is considered. • This paper employs adaptive control techniques. [ABSTRACT FROM AUTHOR]

Z-numbers are efficient tools to represent uncertain information through restriction and reliability measurement. Z-numbers and their variants have been integrated with diverse decision-analysis methods to solve practical decision-making problems. To make researchers understand the research status and challenges in this area, this paper provides an overview of publications related to Z-number-based decision analysis methods and applications. Firstly, a bibliometric analysis is conducted to present the trends and hotspots in this research domain. To uncover theoretical developments of Z-numbers, concepts and operation rules of Z-numbers and their variants are then recalled. Furthermore, decision analysis methods regarding multiple criteria decision analysis, optimization, prediction, and reasoning within the context of Z-numbers are summarized. Applications of Z-number-based decision analysis methods are categorized into six different fields including business and financial management, industrial engineering and management, energy management, medical and healthcare management, environment and sustainable development, and others. Findings, challenges, and future research directions are further discussed. It is hoped that this paper can provide insights for scholars and practitioners in the fields of Z-number-based decision analysis and applications. [ABSTRACT FROM AUTHOR]

In this paper, we discuss a rough set approach to missing attribute values. Among many ways of interpreting missing values, in this paper we focus on two interpretations, lost values and "do not care" conditions. Using these interpretations, global and saturated probabilistic approximations are constructed with two types of granules: characteristic sets and maximal consistent blocks. We compare eight approaches, combining two interpretations of missing attribute values, two types of probabilistic approximations with two types of granules using an error rate that is computed as a result of ten-fold cross-validation. Using a 5% level of statistical significance, we present the experimental results for these eight approaches, showing statistically significant differences between all approaches to mining incomplete data. The results also show that no one method and approach is the best for every data set and that all eight approaches should be attempted. The final section of the paper presents the idea of concept-compatible data sets. We show that for these types of data sets, global and saturated probabilistic approximations for a concept are identical to the concept. We also show that for an incomplete data set with no duplicate rows using the lost interpretation of missing attribute values, the data set is concept-compatible. • Two interpretations of missing attribute values: lost values and "do not care" conditions are considered • Global and saturated probabilistic approximations are constructed from characteristic sets and maximal consistent blocks • Eight approaches to mining incomplete data sets are compared and significant differences between them are indicated • A novel idea of the concept-compatible data sets is introduced [ABSTRACT FROM AUTHOR]

Human behavior may be one of the most challenging phenomena to model and validate. This paper proposes a method for automatically extracting and compiling evidence on human behavior determinants into a knowledge graph. The method (1) extracts associations of behavior determinants and choice options in relation to study groups and moderators from published studies using Natural Language Processing and Deep Learning, (2) synthesizes the extracted evidence into a knowledge graph, and (3) sub-selects the model components and relationships that are relevant and robust. The method can be used to either (4a) construct a structurally valid simulation model before proceeding with calibration or (4b) to validate the structure of existing simulation models. To demonstrate the feasibility of the method, we discuss an example implementation with mode of transport as behavior choice. We find that including non-frequently studied significant behavior determinants drastically improves the model's explanatory power in comparison to only including frequently studied variables. The paper serves as a proof-of-concept which can be reused, extended or adapted for various purposes. • The structure of behavior models should be validated using existing evidence. • Deep Learning can help automatize behavior evidence extraction and synthesis. • Infrequently studied significant variables improve model performance drastically. [ABSTRACT FROM AUTHOR]

This paper presents a novel and robust framework for state estimation of switched finite-field networks (SFFNs), which are a class of multi-valued logical networks that can model complex systems. The paper uses the semi-tensor product (STP) method to transform the dynamical model of SFFNs into an algebraic form, which can be easily manipulated and analyzed. The paper then applies a Monte-Carlo-based sequential importance sampling (SIS) filter to estimate the state of SFFNs from noisy and incomplete observations. The paper also introduces a resampling algorithm (RA) to prevent the particle degeneracy problem (PDP), which can affect the accuracy and efficiency of the filter. Finally, the paper demonstrates the performance and advantages of the proposed multi-valued particle filter (MVPF) framework through three examples. [ABSTRACT FROM AUTHOR]

Surrogate-assisted evolutionary algorithms (SAEAs) have gained considerable attention owing to their ability of tackling expensive optimization problems (EOPs). The surrogate model can be used to replace real fitness value with approximated one, thus greatly reducing computational cost in expensive function evaluations. However, most existing SAEAs are designed for expensive optimization with low or medium dimensions owing to the curse of dimensionality. To improve the performance for solving high-dimensional expensive optimization problems (HEOPs), a surrogate-assisted Differential Evolution with fitness-independent parameter adaptation (SADE-FI) is proposed in the paper. The SADE-FI algorithm consists of a global surrogate-assisted prescreening strategy (GSA-PS) and a local surrogate-assisted DE with fitness-independent parameter adaptation (LSA-FIDE). The main highlights of the paper can be summarized as follows: First, both global and local surrogates are employed to approximate the fitness value of candidate offspring in GSA-PS and LSA-FIDE, respectively. Second, a fitness-independent parameter adaptation mechanism is firstly incorporated into the framework of surrogate-assisted DE as an efficient parameter adaptation for surrogate-assisted search. Third, both the kernel space determination mechanism and linear population size reduction strategy are implemented to enhance the exploitation capability of LSA-FIDE. To validate the performance of SADE-FI, it was tested on expensive benchmark functions on 30D, 50D, 100D, and 200D, as well as a real-world antenna array design problem. The optimization results were compared with state-of-the-art algorithms, and the results indicate that SADE-FI has a significant performance advantage in solving HEOPs. [ABSTRACT FROM AUTHOR]

As deep learning models become increasingly integral to various 3D applications, concerns about their vulnerability to adversarial attacks grow in tandem. This paper addresses the challenge of enhancing the transferability of 3D adversarial attacks, a critical aspect for evaluating model robustness across diverse scenarios. We propose a novel approach leveraging scale and shear transformations to generate adversarial examples that exhibit improved transferability across multiple 3D models. Our methodology involves carefully integrating scale and shear transformations into the adversarial perturbation generation process with only a marginal increase in computational time. The proposed attack method operates within the Carlini-Wagner (CW) optimization framework. For each iteration, it employs two hyperparameters: p a , determining the probability of transforming the input point cloud, and p s , deciding whether to shear or scale the point cloud. Limited to scaling and shearing transformations, Scale and Shear (SS) attack seamlessly integrates with established attack methods, enhancing flexibility and compatibility in adversarial attacks on 3D models. Extensive experiments show that the SS attack proposed in this paper can be seamlessly combined with the existing state-of-the-art (SOTA) 3D point cloud attack methods to form more powerful attack methods, and the SS attack improves the transferability over 3.6 times compared to the baseline. Moreover, while substantially outperforming the baseline methods, the SS attack achieves SOTA transferability under various defenses. Our code will be available online at: https://github.com/cuge1995/SS-attack. [ABSTRACT FROM AUTHOR]

This paper explores the stabilization of continuous-time stochastic systems by a sampled-state feedback controller (SFC) placed in either drift or diffusion terms. Different from the traditionally state feedback controllers, the controller of a stochastic system in this paper is connected through a communication network, whose state needs to be sampled. Because of the controller state being sampled and Brownian motion existing simultaneously, it will be a challenge to ensure the stabilization of the closed-loop stochastic system. Particularly, it is very difficult to analyze the sampled-state error. To address this issue, an approach based on the exact integral solution to the stochastic error system is developed, in which some novel enlarging techniques are presented. Then, several stabilization conditions of stochastic systems based on the SFC added in the drift and diffusion terms respectively are established, whose upper sampling bound can be deduced and some of which are less conservative in terms of providing a larger sampling bound. Finally, the effectiveness and advantages of the proposed methods are illustrated with several numerical examples. [ABSTRACT FROM AUTHOR]

This paper formulates two novel theoretical designs of input-to-state stabilizing control for a class of recurrent neural networks with multiple proportional delays. The analysis tool developed in this paper is based on Lyapunov function and inverse optimality method, which does not require solving Hamilton-Jacobi-Bellman equations. Two inverse optimal feedback laws are constructed via the dimensions of state and input, which ensure the input-state stability for the considered system. When the dimensions of state and input are different, we establish a scalar function and give one of the control laws by Sontag's formula. Furthermore, the designs of inverse optimal control reach both global inverse optimality and global asymptotic stability of the system for some meaningful cost functional. Four numerical examples are provided to show the effectiveness of the inverse optimal control. [ABSTRACT FROM AUTHOR]

To address the challenge of facilitating overall problem solving and improving the overall efficiency of intelligent systems efforts through group decision making (GDM) by experts at all stages of the systems, this paper proposes an approach that focuses on improving consistency and enhancing local consensus. The proposed approach specifically considers the probabilistic linguistic preference relations (PLPRs) and incorporates personalized individual semantics (PISs) of decision makers (DMs). For the consistency procedure, we construct an expectation-additive consistency-driven semantic model to acquire PISs of different DMs. Secondly, the preference relation (PR) which does not satisfy the consistency is improved based on a minimum adjustment model. In particular, according to the expected value with PISs this paper, DMs offer PLPRs can be transformed into fuzzy preference relations (FPRs) correspondently for making decisions. For the FPRs, a virtue consensus measure is explored for the consensus reaching process (CRP) from two levels to combine the average value and variance of pair similarities, which effectively improves the accuracy of the consensus measurement. Next, a collective consensus level is calculated to replace the consensus threshold objectively, and then the DMs and alternative pairs that do not reach consensus are identified locally from two levels. Subsequently, an optimisation model that combines the two objectives is developed to improve the consensus level while ensuring consistency. Finally, our method is applied to a publicly available air quality dataset, and the consensus and ranking results are discussed in comparison with other advanced methods to confirm the superiority of the established method. [ABSTRACT FROM AUTHOR]

Location factor of recommender systems has been extensively studied in the past decade. However, there is no research thoroughly analyzing location's role in news recommendation. In this paper, a comprehensive exploration on role of location in news recommendation is presented. First of all, based on analysis of real news datasets, we find that news recommendation differs from spatial item recommendation. Location affects news consumption behaviors of users with two-fold aspects including geographic feature and semantic feature. Regarding geographic feature, location influences news recommendation according to region rather than latitude-longitude level. Furthermore, interesting news topics are also impacted by semantic feature of location. Semantic feature may play a more positive role than geographic feature. The novel findings consistently manifest that, as non-spatial items, news differ from spatial items in that location influences users' selection in terms of different pattern and degree. In summary, geographic and semantic features influence reading preference through mapping locations into special topics. Changing of location topics leads to varying of reading preference. The news datasets in this paper belong to check in data. NewsREEL dataset is from a company, and it is provided by German researcher. The location data in Twitter dataset is also check in data. NetEase news dataset are collected from NetEase news websites, and the type of location data is city or region. [ABSTRACT FROM AUTHOR]

This paper considers an optimal bipartite formation control problem for high-order nonlinear multiagent systems (MASs) with the consideration of obstacle/collision avoidance. Contrary to existing literature, the control strategy designed in this paper can render the agents in a bipartite formation to complete the obstacle/collision avoidance tasks without requiring the obstacles to be located on the coordinate axis. To save resources, an identifier-critic-actor-based optimized formation control scheme is designed for MASs. An obstacle/collision avoidance approach based on dynamical systems (DS) is designed for high-order nonlinear MASs. Different from the existing related results, the proposed DS-based obstacle/collision avoidance approach can avoid the phenomenon that the control quantities approach infinity near obstacles or other agents, and eliminate the dependence on global information. Finally, a simulation example is given to validate the proposed control method. [ABSTRACT FROM AUTHOR]

• Based on the Euclidean distance between the input layer and the fuzzification layer, two sets of indicators are incorporated to assess the effectiveness and redundancy of current fuzzy rules. Rules determined as redundant are deleted. Meanwhile, new fuzzy rules are added when the current fuzzy rules become insufficient. Furthermore, a significance index is set to judge the essentiality of each fuzzy rule and remove unimportant rules. So that the algorithm can delete unimportant rules while deleting redundant rules. • The antecedent parameters of fuzzy rules are optimized by the AdaBound algorithm. By limiting the learning rate under a boundary value based on the Adam algorithm, this method has a higher training speed than the traditional gradient algorithm and simultaneously avoids the non-convergence caused by the extreme learning rate. • The adaptive gradient method is used to learn the consequences of fuzzy rules. By adjusting its learning rate with RMSE, this method can speed up the training speed while ensuring generalization capacity. • The proposed SIT2FWNN model is used to predict short-term traffic flow, Mackey-Glass chaotic time series, and the opening index of the Shanghai stock index and compared with similar studies. The simulation results demonstrate the superior network performance of the proposed SIT2FWNN in this paper. This paper presents a self-organizing interval type-2 fuzzy wavelet neural network (SIT2FWNN) model for predicting and identifying nonlinear systems. Based on traditional TSK interval type-2 fuzzy neural network (IT2FNN), the proposed SIT2FWNN utilizes the wavelet basis function with the locating abilities of time-domain and frequency-domain as the consequent of fuzzy rules, combining the capacity of IT2FNN to handle the uncertainty and the great learning potentiality of wavelet neural network (WNN). For the structural adjustment of SIT2FWNN, fuzzy rules are added and deleted according to the Euclidean distance between the input layer and the fuzzification layer. The self-organizing algorithm can delete redundant and unimportant fuzzy rules, thus optimizing the structure of SIT2FWNN and simplifying the calculation. In parameter learning, the AdaBound algorithm and self-adaptive gradient learning algorithm are used to find optimal values of unknown parameters of the SIT2FWNN model. Finally, the designed SIT2FWNN model is applied in the predictions of short-term traffic flow, Mackey-Glass time series, and the opening index of the Shanghai stock index. The evaluation comparison between the proposed model and similar studies proves that SIT2FWNN has higher prediction accuracy and speed. [ABSTRACT FROM AUTHOR]

This paper proposes an interactive forest algorithm for planning single-tuned harmonic filters for electric power distribution systems. The planning of harmonic filters is a complex problem that must consider multiple objectives, including minimizing the total cost of installed filters and the individual and total harmonic distortion while adhering to operational constraints, such as the voltage drop, the current loading and the harmonic limits. Moreover, system perturbations must be considered, such as frequency variations. This paper proposes a new method for multi-objective optimization of planning single-tuned harmonic filters utilizing the proposed interactive forest algorithm with a non-differentiable multi-objective function involving continuous and discrete variables. A multi-objective optimization technique based on the proposed interactive forest algorithm is proposed. In the interactive approach, planners set expectations for each objective and use the forest algorithm to figure out the optimization problems. The results of the Pareto Front are analyzed and the answers are searched through the interactive process to identify a solution that meets the planner's expectations, allowing for practical compromise or satisfactory solutions. A case study is reported to show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The foundation of intelligent computing and expert systems relies on the data processing conducted within the realm of multi-scale information systems (MSISs). Data processing within MSISs caters to diverse analytical needs, where the preference hierarchy among various factors plays a vital role in the consensus reaching process (CRP). Additionally, the three-way decisions (TWD) theory serves as an efficient tool, while regret theory (RT) quantifies decision-makers' risk inclinations associated with various psychological behaviors. Thus, the paper aims to introduce an innovative approach known as the CRP-TWD-RT-MSIS method. The study draws inspiration from spatial geometry, incorporating concepts like points, lines, surfaces, and bodies to create MSISs. This process generates a matrix of fuzzy preference relations (FPRs) among distinct decision-makers through data preprocessing. In addition, the paper explores feedback mechanisms based on identification and directional rules, as well as those rooted in minimal adjustments or cost considerations. Simultaneously, it takes negotiation and discussion time into account, culminating in the development of a personalized adjustment optimization model that focuses on minimizing time costs. In the end, the method's effectiveness and superiority are validated through a case study and a comparative analysis of real data from the Chinese Weather and Meteorological Bureau's website. • An MSIS with the ideas of points, lines, surfaces, and bodies is designed. • Regret theory is considered in the consensus measure. • A method with the consensus threshold is computed by classification. • A novel personalized optimization model is put forward. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient and powerful population-based search algorithm for solving numerical optimization problems in continuous spaces. It has been proven that multi-strategy DE algorithms are more effective than single-strategy DE algorithms in addressing benchmark and real-world problems. However, most multi-strategy DE variants focus on maintaining population diversity and balancing exploitation and exploration, ignoring the dynamic allocation of computational resources. Moreover, the success of these algorithms often depends on additional designed techniques, leading to increased computational complexity. In this paper, the Collaborative Resource Allocation-based Differential Evolution (CRADE) is introduced. It involves a collaborative resource allocation mechanism that utilizes the historical performance ranking of three mutation strategies to automatically allocate computational resources to various subpopulations during the search process. The parameter adaptation technique is used to adjust the associated control parameters of different mutation strategies. As a result, the most efficient mutation strategy consumes the majority of computational resources at different search stages to mitigate inefficient search under constrained resources. The performance of CRADE is evaluated on the well-known CEC2013 benchmark function set. The paper also investigates its application in the parameter identification of photovoltaic solar cells and modules. The overall results show that CRADE exhibits superior and competitive performance compared to other state-of-the-art algorithms. Consequently, CRADE has emerged as a novel and effective approach for addressing numerical optimization problems, distinguished by its excellence, practicality, and unwavering reliability. [ABSTRACT FROM AUTHOR]

In a linear stochastic bandit model, each arm corresponds to a vector in Euclidean space, and the expected return observed at each time step is determined by an unknown linear function of the selected arm. This paper addresses the challenge of identifying the optimal arm in a linear stochastic bandit model, where latent biases corrupt each arm's expected reward. Unlike traditional linear bandit problems, where the observed return directly represents the reward, this paper considers a scenario where the unbiased reward at each time step remains unobservable. This model is particularly relevant in situations where the observed return is influenced by latent biases that need to be carefully excluded from the learning model. For example, in recommendation systems designed to prevent racially discriminatory suggestions, it is crucial to ensure that the users' race does not influence the system. However, the observed return, such as click-through rates, may have already been influenced by racial attributes. In the case where there are finitely many arms, we develop a strategy to achieve O (| D | log ⁡ n) regret, where | D | is the number of arms and n is the number of time steps. In the case where each arm is chosen from an infinite compact set, our strategy achieves O (n 2 / 3 (log ⁡ n) 1 / 2) regret. Experiments verify the efficiency of our strategy. [ABSTRACT FROM AUTHOR]

The property of reversibility is quite meaningful for the classic theoreticabl computer science model, cellular automata. This paper focuses on the reversibility of general one-dimensional (1D) linear cellular automata (LCA), under null boundary conditions over the finite field Z p . Although the existing approaches have split the reversibility challenge into two sub-problems: calculate the period of reversibility first, then verify the reversibility in a period, they are still exponential in the size of the CA's neighborhood. In this paper, we use two efficient algorithms with polynomial complexity to tackle these two challenges, making it possible to solve large-scale reversible LCA, which substantially enlarge its applicability. Finally, we provide an interesting perspective to inversely generate a 1D LCA from a given period of reversibility. [ABSTRACT FROM AUTHOR]

Currently, more and more researchers use the idea and strategy of three-way decision to guide scientific research. In this paper, we propose two comprehensive fuzzy concepts and apply them to construct a new three-way decision model. Firstly, with the TOPSIS method, this paper regards the optimal distance and the worst distance as the cost fuzzy concept and the benefit fuzzy concept, respectively. Then, based on the fuzzy concepts with opposite characteristics, two comprehensive fuzzy concepts (i.e., the comprehensive benefit fuzzy concept and the comprehensive cost fuzzy concept) are proposed, and the relationship between the decision-making regions corresponding to the two comprehensive fuzzy concepts is discussed. Further, four fuzzy neighborhood operators are used to describe the relationship between any two schemes and the relative loss function is used to compute the loss value of each scheme with different behaviors in different states. Afterwards, we introduce a new three-way decision model to solve the multi-criteria decision-making (MCDM) problem. Comparing analysis, Kendall analysis and Spearman analysis show that our method is feasible and stable. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid elementary cellular automata (HECA) composed of two chaotic global rules is designed to enhance the chaotic properties of the pseudo-random coupled map lattices (PRCML) model based on the Chirikov standard map (CSM). Theory analyses and simulations indicate that the PRCML-HECA model exhibits superior chaos. Subsequently, a novel image encryption scheme based on this model is implemented. The scheme consists of key seed generation, encryption, and decryption. A series-wound PRCML-HECA model is designed to generate the key seed related to the plain-image, initial key, and timestamp. This process is nonlinear and irreversible, ensuring the proposed scheme can resist the chosen-plaintext/ciphertext attacks. Furthermore, a dynamic substitution box changed with the key seed is also included in the proposed scheme. Moreover, plenty of statistical and security analyses, such as diffusion, histogram, correlation analyses, etc., are introduced in this paper. The results prove that the proposed image encryption scheme based on the PRCML-HECA model can resist differential and statistical attacks. And the scheme also possesses strong robustness and high efficiency. [ABSTRACT FROM AUTHOR]

• A survey of probabilistic rough set methods. • A parameterised definition of probabilistic approximation spaces. • A ProbLog based framework for reasoning about probabilistic rough sets. • Case studies using the ProbLog based framework. Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. More recently, combining logic with probability has been of major interest. Rough set methods have been proposed for modeling incompleteness and imprecision based on indiscernibility and its generalizations and there is a large body of work in this direction. More recently, the classical theory has been generalized to include probabilistic rough set methods of which there are also a great variety of proposals. Pragmatic, easily accessible, and easy to use tools for specification and reasoning with this wide variety of methods is lacking. It is the purpose of this paper to fill in that gap where the focus will be on probabilistic rough set methods. A landscape of (probabilistic) rough set reasoning methods and the variety of choices involved in specifying them is surveyed first. While doing this, an abstract generalization of all the considered approaches is derived which subsumes each of the methods. One then shows how, via this generalization, one can specify and reason about any of these methods using ProbLog , a popular and widely used probabilistic logic programming language based on Prolog. The paper also considers new techniques in this context such as the use of probabilistic target sets when defining rough sets and the use of partially specified base relations that are also probabilistic. Additionally, probabilistic approaches using tolerance spaces are proposed. The paper includes a rich set of examples and provides a framework based on a library of generic ProbLog relations that make specification of any of these methods, straightforward, efficient and compact. Complete, ready to run ProbLog code is included in the Appendix for all examples considered. [ABSTRACT FROM AUTHOR]