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The ranking of decision makers (DMs)' preferences for feasible states in the graph model for conflict resolution (GMCR) is crucial for accurately determining stability results. This paper addresses the issue of subjective ranking methods lacking theoretical foundation and causing ambiguity when the number of feasible states is high by proposing the implementation of the multi-attribute decision-making (MADM) method in the GMCR. The paper utilizes the average level to choose evaluation based on distance from average solution (EDAS) method for determining the DM's preference ranking, which can effectively reduce the impact of anomalous evaluations. Further, the PUL-EDAS method based on probabilistic uncertainty linguistics (PUL) is developed, which overcomes the shortcomings of the traditional EDAS method, which only applies to the simple evaluation of information. The PUL aligns with DMs' daily evaluation practice by providing an interval for the quality of qualitative linguistic evaluations. Furthermore, it utilizes an objective aggregation method to calculate comprehensive evaluation information from all DMs. In addition, the four fundamental stability definitions, applicable solely under crisp preferences, are extended to the PUL context, providing related extended definitions. Finally, to ensure the scientific validity and practicality of the proposed theory, this paper selects digital rural governance as the research context for conflict calculus analysis, comparing it with other MADM methods in the preference ranking section. [ABSTRACT FROM AUTHOR]

This paper is concerned with the security problem of distributed state estimation for stochastic multi-agent systems (MASs) under directed topology. First, in the absence of attacks, an optimal distributed filter is proposed to reconstruct the states of MASs by minimizing the mean square error, which makes full use of the measurable local output information and relative state estimates of agents. Moreover, instead of using a single detection mechanism to measure the stealthiness of attacks, based on the structure of the derived filter, a composite attack detection mechanism capable of simultaneously detecting anomalies in the sensor channels of the agents themselves and the network topologies between different agents is utilized in this paper. Then, from the perspective of potential adversaries, two stealthy attack strategies are established through a joint design of sensor and topology attacks, enabling attackers to fulfill diverse attack objectives while evading the given composite detection mechanisms. Finally, simulation results are provided to verify the availability of the designed filter and attack generation schemes. [ABSTRACT FROM AUTHOR]

In this paper, we are devoted to solving the problems of designing an estimator, and determining its estimator stability and estimation performance for a system with unknown measurement losses (UML). The solutions to these problems include three steps: first, we design two measurement-loss detectors to detect the measurement losses; Then, we design a detector-based estimator for UML systems. Finally, by analyzing the upper and lower bounds of the covariances of the proposed estimator, we establish a stability condition and determine the estimation performance. Detailed findings and main contributions of this paper are summarized as follows: (i) from the estimator stability perspective, we obtain a necessary and sufficient stability condition. Specifically, the estimator is stable almost surely if and only if the measurement-loss rate is less than a critical value. (ii) From the estimation performance perspective, we prove that under some conditions, the performance of the proposed estimator is almost surely the same as that of the optimal estimator for the system with known measurement losses. • A detector-based estimator is designed for systems with unknown measurement losses (UML). • A necessary and sufficient estimator stability condition is obtained for UML systems. • The proposed estimator has almost surely the same performance as the optimal estimator with known measurement losses. [ABSTRACT FROM AUTHOR]

Sequential recommendations play a crucial role in recommender systems. Existing methods commonly focus on extracting sequential patterns within individual item sequences to make personalized recommendations. However, this paper argues that such a closure strategy has limitations. Firstly, the inherent uncertainty and sparsity within these sequences pose considerable challenges in accurately capturing sequential patterns. Secondly, ignoring group behaviors beyond the sequence hinders the modeling of user similarities, which are essential for recommendations. To address these limitations, this paper proposes a novel collaborative sequential recommendation approach, called G roup- a ware G raph N eural N etworks (GaGNN). GaGNN seeks to incorporate group behaviors within sequential recommendations, thereby enhancing the modeling of user preferences through the assimilation of collective wisdom. GaGNN constructs two transition graphs, each offering a distinct behavioral perspective. The group transition graph is designed to capture the collaborative effects and learn collaborative information, whereas the individual transition graph aims to articulate individual behaviors, thereby extracting sequential information. Additionally, a multi-source information fusion module is designed to bridge semantic gaps between the two types of information and yield a holistic understanding of user preferences. Through rigorous experimental comparisons and ablation studies, the superiority of GaGNN and the beneficial integration of collaborative information are emphatically validated. • Two types of transition graphs are introduced to depict group behaviors and individual behaviors. • Two information encoders are proposed to model collaborative and sequential information. • A multi-source information fusion module is proposed to achieve information complementarily. • The proposed GaGNN model aims to incorporate group behaviors into sequential recommendations. [ABSTRACT FROM AUTHOR]

This paper is devoted to investigate the dynamical behaviors of malware attack on a familiar kind of complex heterogeneous networks, namely Wireless Sensor Network, and discuss an effective immunization treatment based on fractional interconnected Takagi-Sugeno (T-S) fuzzy systems. Our approach is based on the mathematical modeling to establish a controlled fractional network-based SIRS malware propagation model that better describes the attacking behavior of malicious objects on Wireless Sensor Network. After that, we point out some qualitative properties of the proposed network-based SIRS malware propagation model such as the existence of positively invariant set, backward bifurcation and asymptotic behavior. Especially, in order to study the model's stability, we evaluate an epidemiological threshold value R 0 , namely basic reproductive ratio, which ensures the existence of at least one endemic equilibrium P ⁎ and the local asymptotic stability of malware-free equilibrium P 0. As a consequence of theoretical result, the malware-free equilibrium P 0 is unstable when R 0 > 1 and hence, the rest of this paper is to address a stabilization problem for the proposed controlled network-based model and establish some sufficient conditions related to linear matrix inequalities and positive definite matrices. Finally, we illustrate the obtained theoretical results by a computational example. [ABSTRACT FROM AUTHOR]

Large-scale multiobjective optimization problems (LSMOPs), which optimize multiple conflicting objectives with hundreds or even thousands of decision variables, demand increasing computational resources to assure satisfactory performance as the decision variables increase. Multiobjective evolutionary algorithms are naturally scalable, but as the dimension increases, the conflict between analyzing enough solutions and the limited number of function evaluations has hindered further improvements in scalability. In this paper, we first define the scalability of multiobjective evolutionary algorithms and design an indicator to quantitatively measure the scalability. Second, to boost scalability when solving LSMOPs, we propose a scalable multiobjective optimization algorithm by transferring weights between solutions to reduce dependency on ever-increasing computational resources as the problem dimension increases. The proposed framework entails constructing a latent decision space to determine evolutionary weights for chosen representative solutions. These computed weights are then transferred to the remaining solutions, enabling evolutionary optimization to proceed without requiring additional evaluations, even as the dimensionality increases. By utilizing the knowledge gained from the source solutions, each solution is customized with an evolutionary weight scheme that not only preserves computational resources but also enhances optimization performance, thereby boosting scalability. We have conducted experiments on LSMOPs to verify the effectiveness and scalability of the proposed algorithm. The proposed method outperforms selected state-of-the-art algorithms and gains scalability boosts in situations where the dimensions increase while the function evaluation remains constant. • This paper defines the scalability of large-scale multiobjective evolutionary algorithms and introduces a novel performance indicator to measure. • We propose a scalable algorithm for LSMOPs to ensure consistent performance within specified function evaluations, regardless of dimensionality. • We design a technique to transfer learned weights between solutions without additional evaluations to enhance scalability while conserving resources. [ABSTRACT FROM AUTHOR]

This paper introduces a prediction model rooted in sparse representation and transfer learning, with the primary objective of predicting user behavior during rumor propagation. Users' behavior is dynamic, and rumor, rumor-refuting, and rumor-promoting messages interact dynamically, according to the model. Firstly, this paper proposes to compensate for the low performance of propagation prediction models due to data sparsity at the beginning of rumor topics' lifes. Transfer learning is used to compensate for the data sparsity problem at the beginning of the topic. To map the rumor topic space effectively, a low-rank dense vectorization algorithm based on sparse representation is proposed. Finally, to mine the potential impact of multiple types of information on users, a model based on three-party game theory is constructed. It considers the complex interaction between rumor, rumor-refuting, and rumor-promoting information in the propagation of rumor. Additionally, this paper develops a model of dynamic user behavior prediction using Rumor-Attention-Mechanism-Graph-Attention (RAM-GAT) to predict rumor propagation. Experiments demonstrate that our model can effectively mine the interaction influence between multiple types of information. It can accurately predict user behavior when initial data is insufficient. In addition, rumor propagation patterns and trends are revealed. • A user behavior prediction model is proposed. • Sparse representation can be used to express the rumor feature space. • The complex influences that influence user behavior are quantified. [ABSTRACT FROM AUTHOR]

Regarding the defects of Ruspini partition-based clustering in revealing the intrinsic correlation between classes, this paper proposes a series of generalized multiplicative fuzzy possibilistic product partition clustering algorithms. First, based on the existing concept of generalized multiplicative intuitionistic fuzzy sets, this paper introduces a new concept of generalized multiplicative fuzzy sets and further defines the corresponding multiplicative fuzzy partition. Then, based on the concept of multiplicative fuzzy partition, a novel generalized multiplicative fuzzy possibilistic product partition C-means (GMFPCM) clustering algorithm is presented, and its local convergence is strictly proved using Zangwill's theorem. Meanwhile, a robust Gaussian-base radial kernel based on the M-estimator is introduced into the GMFPCM algorithm to improve its robustness against noise and outliers in numerical data. Additionally, a multiplicative fuzzy possibilistic local information factor is constructed and embedded into the GMFPCM algorithm to strengthen its ability to suppress noise in images. Finally, the comparison with existing fuzzy possibilistic clustering algorithms in the literature confirms the competitiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper considers a data-driven method to detect stealthy replay attacks on cyber-physical systems (CPSs) whose models are unknown. Most existing attack detection methods assume the system dynamic is known. However, due to the large scale and complexity of the system, it is difficult to get an accurate model of a CPS. This paper uses a moving window subspace identification method to construct a linear discrete time-varying model of CPS. The controllability and observability of the obtained model are proved. On this basis, the replay attack is transformed into a detectable additive attack using the output coding strategy. Then, considering modeling errors, a time-varying H ∞ filter is designed, and a detection function based on the output residual is constructed. When the system works without any attacks, the detection function approaches a limited threshold. On the contrary, that function will increase extremely larger than the threshold when the replay attack occurs. The detectability of our scheme for attacks is also mathematically proven. Finally, to verify the effectiveness of the proposed scheme, we provide simulation results on a linear motor system and a nonlinear robotic system. [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]

• 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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 rapid development of online social networks (OSNs) has enabled seamless cyberbullying activities, which not only cause psychological harm to victims but also bring disharmony to society. From the perspective of OSN platform administrators, strategic decision making towards when/how to launch an anti-cyberbullying campaign is the key to mitigating its negative impact. Hence, this paper develops a dynamic anti-cyberbullying investment solution to mitigate the adverse impact of strategic cyberbullying phenomena cost-effectively. Specifically, this paper first quantifies the payoffs of the attacker/bullying instigator and the defender/platform administrator based on a newly proposed propagation model. Then, considering the budget-constrained factor for both sides, a differential game-theoretical model is proposed to simulate each party's optimal trade-off between cost and benefit. Next, by utilizing Pontryagin Maximum/Minimum Principle, a set of necessary conditions for a Nash equilibrium strategy profile have been proposed. Based on the forward-backward sweep method, an iterative algorithm (SCR optimization algorithm) is tailored to solve the conditions numerically. Through experiments conducted under Facebook, Youtube and Twitter sub-networks, the algorithm's performance has been evaluated by inspecting its convergence speed and payoff compared with randomized profiles. Consequently, the resulting strategies are verified to exhibit superior performance in dealing with cyberbullying spreading. [ABSTRACT FROM AUTHOR]

A Boolean function in n variables is rotation symmetric (RS) if it is invariant under powers of ρ (x 1 , ... , x n) = (x 2 , ... , x n , x 1). An RS function is called monomial rotation symmetric (MRS) if it is generated by applying powers of ρ to a single monomial. Completing earlier research on special cases, the author showed in 2018 that for any RS function f n in n variables, the sequence of Hamming weights w t (f n) for all values of n satisfies a linear recurrence relation. It was also proved that the associated recursion polynomial could be explicitly calculated as the minimal polynomial of a rules matrix and an algorithm for computing the rules matrix was explained. Examples showed that the usual formula which gives the values of w t (f n) − 2 n − 1 as a linear combination of powers of the irrational roots of the minimal polynomial has simple coefficients which are all 1/2 if the multiset made up of the roots of the characteristic polynomial of the rules matrix is used instead, no matter what the degree of the Boolean function is. The conjecture that this is always true is called the Easy Coefficients Conjecture (ECC). The special case of MRS quadratics was proved in an earlier paper. The present paper gives some ECC applications valid for any function for which the ECC is true, even though there is a proof only for the quadratic MRS case so far. [ABSTRACT FROM AUTHOR]

The fishing behavior in a high-voltage environment may lead to electric shock accidents. This paper proposes a fishing risky behavior recognition method based on adaptive Transformer, reinforcement learning and stochastic configuration networks (SCNs). Firstly, the fishing behavior image sample training set is evaluated using the actor network based on linear entropy in the reinforcement learning module to select suitable training samples that possess abundant straight-line elements. Subsequently, the selected training samples are fed into a multi-scale spatial deep convolution to extract continuous spatial features of elongated objects. The deformable Transformer network, enhanced with adaptive encoding and decoding layers through SCNs, is used to obtain the position and detection results of the fishing rod. The trained Transformer model is evaluated by a defined credibility evaluation metric for acquiring rewards to update the training sample set iteratively. Then, an adaptive adjustment mechanism is constructed for the encoding and decoding layers of the adaptive Transformer network to establish a library of adaptive Transformer models with different encoding and decoding layer levels to accommodate the feature requirements of training samples in various scenarios. Finally, the blending learning method for combining the detection results from the model library is integrated with human body object detection and pose estimation methods, and a predefined expert system is utilized for logic reasoning on fishing risky behavior. Experimental results demonstrate the effectiveness of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Multiattribute group decision-making (MAGDM) is a decision analysis method used to address decision-making problems that have multiple attributes and multiple decision alternatives. It is generally believed that multigranulation rough sets (MGRSs) are a twofold scheme to solve MAGDM problems. Thus, in this paper, we aim to generalize MGRSs and decision-theoretic rough sets (DTRSs) to intuitionistic fuzzy β -covering (IF β C) information systems and propose a novel MAGDM method to handle decision-making problems, which not only deepens the cognition of the three-way decision (3WD) model but also expands its application in decision-making problems. The paper first introduces a novel conditional probability using the ideal positive degree of intuitionistic fuzzy numbers (IFNs) and then investigates its properties. Second, in the IF β C environment, we utilize the proposed conditional probabilities to construct two types of multigranulation decision-theoretic rough set (MGDTRS) models, an optimistic strategy and a pessimistic strategy, discuss their theoretical properties and explore the relationship between the two strategies. Third, a novel MAGDM method for multi-intuitionistic β -neighborhoods is established in accordance with the proposed MGDTRS model. We also establish an algorithm for the proposed MAGDM method. Fourth, we apply the proposed method to address several MAGDM problems. By conducting comparative analysis with existing methods, we demonstrate the effectiveness of the proposed method and discuss the advantages and limitations of the proposed method. Furthermore, we conduct extensive parameter experimentation analysis on the proposed method from different perspectives. The experimental results demonstrate the stability and effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

The process of adaptive assessment of individuals' knowledge by knowledge space theory has been implemented in deterministic finite automata. However, such automata are not suitable for polytomous situations, which limits the application of automata for knowledge assessment. To overcome this limitation, this paper combines the structure of observed learning outcome (SOLO) taxonomy with the response scale and proposes an automata knowledge assessment process that can assess individuals' knowledge in polytomous situations. Specifically, an automaton suitable for assessing knowledge in the SOLO taxonomy situation is proposed, and a questioning rule is provided based on the quasi-order relation between items. Then, relying on the automaton state update and the questioning rule, an automata knowledge assessment process for knowledge assessment in the SOLO taxonomy situation is given. Finally, empirical research and simulation experiments show that it is necessary to provide a questioning rule for the automaton and that the automata knowledge assessment process proposed in this paper is feasible. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for predicting and explaining instances of Information Disorder. The paper reports two significant findings: i) the use of structures of opposition to describe relationships between instances of Information Disorder, and ii) the development of an explainable prediction method that combines Fuzzy Rough Sets and TOPSIS with these structures. The findings have the potential to assist analysts and decision-makers in gaining a deeper understanding of the phenomenon of Information Disorder. The results are based on real data and demonstrate promising applications for future research. [ABSTRACT FROM AUTHOR]

This paper shows that a bipolar-valued fuzzy set and a special interval-valued fuzzy set, whose cut-off point of left and right ends of the interval-valued membership is λ (0 < λ < 1) , are identical from a mathematical point of view. All results on bipolar-valued fuzzy sets can be obtained by corresponding results of interval-valued fuzzy sets or fuzzy sets. This paper does not completely reject bipolar-valued fuzzy sets, but points the way for such research. [ABSTRACT FROM AUTHOR]

Dimensional collapse in graph contrastive learning (GCL) confines node embeddings to their lower-dimensional subspace, diminishing their distinguishability. However, the causes and solutions of this curse remain relatively underexplored. In statistics, whitening presents a powerful tool to eliminate correlations among multiple variables. This motivates us to relieve the dimensional collapse of GCL from a whitening perspective. In this paper, we propose an intuitive analysis suggesting that high similarity scores of node embeddings may cause dimensional collapse, providing more evidence for its presence. Considering the success of whitening in statistics, we introduce a new plug-and-play module called the ▪hitening ▪raph ▪ontrastive ▪earning (WGCL) to address the dimensional collapse issue in existing GCL methods. WGCL plugin standardises the covariance matrices of dimensions, eliminating correlations among node embeddings' dimensions. Additionally, we enhance the conventional GCL training objective by introducing a mutual information maximisation loss between input features and node embeddings to maintain information capacity. Our experiments demonstrate that WGCL effectively addresses dimensional collapse, leading to an average improvement of 0.93% (up to 2.0%) in classification accuracy across three GCL backbones on nine widely-used datasets. The code to reproduce the experiments is available at https://github.com/acboyty/WGCL. • The paper provides an intuitive analysis, indicating the dimensional collapse in GCL is inevitable. • The paper introduces a novel plug-and-play module (WGCL), effectively addressing the dimensional collapse issue. • WGCL module significantly improves the performance of GCL backbones on various datasets. [ABSTRACT FROM AUTHOR]

Many black-box adversarial attack algorithms perform attacks on machine learning models based on the transferability of adversarial examples, and the input transformation-based attack is one of the most effective methods. However, existing input transformation methods ignore that each pixel contributes differently to the output of the model, and the focus regions of different models on the same images are similar. Therefore, this paper proposes a targeted data augmentation-based adversarial attack algorithm named MixCam, which augments the input data based on the contribution of each pixel to the prediction result. This is done to enhance the transferability of the crafted adversarial example from the perspective of shifting the regions to which the model pays most of its attention. In addition, this paper proposes further boosting the transferability of the crafted adversarial examples by fusing the class activation maps of multiple models for the input image. Furthermore, the MixCam can integrate other input transformation methods to further boost the transferability of crafted adversarial examples. Extensive experiments on ImageNet demonstrate that MixCam outperforms other state-of-the-art methods in black-box attacks against considered adversarially trained models, with an average increase of 11.7% and 10.7% in attack success rates for single and ensemble attack settings, respectively. • A targeted data augmentation-based black-box adversarial attack method. • Performing adversarial attacks from the perspective of moving the focus region of DNN. • Boosting the transferability of the crafted adversarial examples. • Extensive experiments have demonstrated the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The concept of bipolar-valued fuzzy set was introduced by Zhang. Can we discuss three-way decisions based on bipolar-valued fuzzy sets over three-way decision spaces? In response to this problem, firstly, the paper discusses partially orders on truth values of bipolar-valued fuzzy sets and three-way decision spaces on this basis. Secondly, this paper presents construction method of decision evaluation functions based on bipolar-valued fuzzy sets over three-way decision spaces. Thirdly, this paper makes three-way decisions based on bipolar-valued fuzzy sets over three-way decision spaces and uses an example on an evaluation problem of credit card applicants to illustrate some notions of the (semi-, quasi-)three-way decision spaces and benefits of constructing methods from semi-(quasi-)decision evaluation functions to decision evaluation functions. Finally, the conclusions are summed up. [ABSTRACT FROM AUTHOR]

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm—Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data-driven optimization approach proposed in this paper is effective, practical, and scalable. • Provide a unified solution for optimisation problems with redundant variables and indefinite objective functions. • Propose a data-driven multi-objective optimisation algorithm. • Designing an adaptive optimiser to enhance the algorithm's depth search capability. [ABSTRACT FROM AUTHOR]

In this paper, the zonotopic set-membership estimation (SME) problem is studied for time-varying systems subject to dynamical biases and uniform quantization. The considered dynamical bias evolves according to a certain dynamic process disturbed by unknown-but-bounded noises. The main objective of the paper is to propose a zonotopic SME method such that the real states are included in zonotopes described by linear image of an unit hypercube. By analyzing the dynamics of biases and states, the intersection set containing the state are first obtained based on the quantization measurements. Then, an auxiliary zonotope is constructed to bound the intersection where the center and generator matrix are presented recursively. Through orchestrating the correlation matrix, the F -Radius of such an auxiliary zonotope is further minimized. To reduce the computational burden, the desired zonotope with finite-dimensional generator matrix is constructed to externally approximate the auxiliary zonotope. Furthermore, the boundedness stability is analyzed for the developed zonotopic SME approach in the simulations presence of dynamical biases and uniform quantization. Finally, an example is provided to display the effectiveness of the SME method. [ABSTRACT FROM AUTHOR]