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A novel grade assessment method for cybersecurity situation of online retailing with decision makers’ bounded rationality Inform. Sci. (IF 8.1) Pub Date : 2024-03-13 Gao-Feng Yu, Wen-Jin Zuo
The online retailing cybersecurity situations grade assessment (GS) is a key issue in cybersecurity management, which can be regarded as a type of multi-attributes GS problems. However, traditional GS methods rarely discuss the boundary fuzziness and hesitation in the grade classification of attributes, as well as the monotony relation and interrelation between entropy fuzziness and intuitionism. A
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Dual auto-weighted multi-view clustering via autoencoder-like nonnegative matrix factorization Inform. Sci. (IF 8.1) Pub Date : 2024-03-13 Si-Jia Xiang, Heng-Chao Li, Jing-Hua Yang, Xin-Ru Feng
Multi-view clustering (MVC) can exploit the complementary information among multi-view data to achieve the satisfactory performance, thus having extensive potentials for practical applications. Although Nonnegative Matrix Factorization (NMF) has emerged as an effective technique for MVC, the exsiting NMF-based methods still have two main limitations: 1) They solely focus on the reconstruction of original
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A Robust One-stage Detector for SAR Ship Detection with Sequential Three-way Decisions and Multi-granularity Inform. Sci. (IF 8.1) Pub Date : 2024-03-13 Li Ying, Duoqian Miao, Zhifei Zhang
Synthetic Aperture Radar (SAR) images are widely used in ship detection because of their all-weather and all-day imaging characteristics. However, there are two challenges for SAR ship detection. One is coherent speckle noise, causing ship confusion with similar objects and raising false alarms. The other is multi-scale ship detection, particularly in small ships, which suffers from insufficient accuracy
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DSTCNN: Deformable spatial-temporal convolutional neural network for pedestrian trajectory prediction Inform. Sci. (IF 8.1) Pub Date : 2024-03-13 Wangxing Chen, Haifeng Sang, Jinyu Wang, Zishan Zhao
Pedestrian trajectory prediction holds significant research value in service robots, autonomous driving, and intelligent monitoring. Currently, most pedestrian trajectory prediction methods focus on data-driven models based on recurrent neural networks, but there is insufficient research on data-driven models based on convolutional neural networks. In this study, we first analyze the two problems in
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Practical finite-time synchronization of delayed fuzzy cellular neural networks with fractional-order Inform. Sci. (IF 8.1) Pub Date : 2024-03-12 Feifei Du, Jun-Guo Lu, Qing-Hao Zhang
The practical finite-time (PFT) synchronization of fractional-order delayed fuzzy cellular neural networks (FODFCNNs) is presented in this article. Initially, a useful practical finite time (FT) stable lemma is developed, serving as an efficient instrument for the PFT synchronization of fractional-order systems. Subsequently, a new PFT synchronization criterion for FODFCNNs is derived using the designed
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Meta-path and Hypergraph Fused Distillation Framework for Heterogeneous Information Networks Embedding Inform. Sci. (IF 8.1) Pub Date : 2024-03-12 Beibei Yu, Cheng Xie, Hongming Cai, Haoran Duan, Peng Tang
Heterogeneous Information Networks (HINs) are crucial in various intelligent systems. The latest advancements in HIN learning aim to combine meta-paths and hypergraphs, capitalizing on their strengths for further success. However, existing methods typically transform meta-paths into hypergraphs by simply removing the original edges from the meta-paths to integrate two semantics. This will inevitably
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Sparse orthogonal supervised feature selection with global redundancy minimization, label scaling, and robustness Inform. Sci. (IF 8.1) Pub Date : 2024-03-12 Huming Liao, Hongmei Chen, Yong Mi, Chuan Luo, Shi-Jinn Horng, Tianrui Li
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
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Robust hyperspectral image classification using generative adversarial networks Inform. Sci. (IF 8.1) Pub Date : 2024-03-12 Ziru Yu, Wei Cui
This paper introduces Sill-Rgan, a novel Generative Adversarial Network (GAN) designed to improve hyperspectral image (HSI) classification under varying lighting conditions. Sill-Rgan uniquely maps different light condition domains, enhancing sample classification robustness and generating new virtual samples. Addressing challenges like high spectral dimensionality and noise in HSI classification,
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Variable precision fuzzy rough sets based on overlap functions with application to tumor classification Inform. Sci. (IF 8.1) Pub Date : 2024-03-12 Xiaohong Zhang, Qiqi Ou, Jingqian Wang
Overlap functions, which can be characterized as a type of non-associative binary aggregation operators, have emerged as one of the most extensively utilized aggregation operators in numerous applications, including image processing, information fusion, and classification problems. At the same time, fuzzy rough sets have also been widely used in these fields due to their excellent ability to handle
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A method of data analysis based on division-mining-fusion strategy Inform. Sci. (IF 8.1) Pub Date : 2024-03-12 Qingzhao Kong, Wanting Wang, Weihua Xu, Conghao Yan
With the advancement of data technology and storage services, the scale and complexity of data are rapidly growing. Consequently, promptly analyzing data and deriving precise insights have become urgent. Nevertheless, traditional methods struggle to balance the speed and accuracy of data mining. This paper proposes a data analysis technique called the Division-Mining-Fusion (DMF) strategy to tackle
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Z-number based neural network structured inference system Inform. Sci. (IF 8.1) Pub Date : 2024-03-11 Rafik A. Aliev, M.B. Babanli, Babek G. Guirimov
Z-number based Neural Network structured Inference System (ZNIS) with rule-base consisting of linguistic Z-terms trainable with Differential Evolution with Constraints (DEC) optimization algorithm is suggested. The inference mechanism of the multi-layered ZNIS consists of a fuzzifier, fuzzy rule base, inference engine, and output processor. Due to the use of extended fuzzy terms, each processing layer
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Distributed robust scheduling optimization for energy system of steel industry considering prediction uncertainties Inform. Sci. (IF 8.1) Pub Date : 2024-03-11 Zhiyuan Wang, Zhongyang Han, Jun Zhao, Wei Wang
Predictive scheduling is commonly deployed for the energy systems in the steel industry, while the uncertainties caused by the predictions can lead to under-optimization or over-adjustment. In order to solve this problem, a novel distributed robust optimization framework is proposed in this study. A Robust Optimization (RO) model is established at first to mathematically address the prediction uncertainties
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Turing instability analysis of a rumor propagation model with time delay on non-network and complex networks Inform. Sci. (IF 8.1) Pub Date : 2024-03-09 Yi Ding, Linhe Zhu
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
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Evaluating potential quality of e-commerce order fulfillment service: A collective intelligence-driven approach Inform. Sci. (IF 8.1) Pub Date : 2024-03-09 Jian-Peng Chang, Yan Su, Mirosław J. Skibniewski, Zhen-Song Chen
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
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Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints Inform. Sci. (IF 8.1) Pub Date : 2024-03-08 Michał Antkiewicz, Paweł B. Myszkowski
The paper presents a new balanced selection operator applied to the proposed Balanced Non-dominated Tournament Genetic Algorithm (B-NTGA) that actively uses archive to solve multi- and many-objective NP-hard combinatorial optimization problems with constraints. The primary motivation is to make B-NTGA more efficient in exploring Pareto Front Approximation (PFa), focusing on “gaps” and reducing some
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An XGBoost-assisted evolutionary algorithm for expensive multiobjective optimization problems Inform. Sci. (IF 8.1) Pub Date : 2024-03-08 Feiqiao Mao, Ming Chen, Kaihang Zhong, Jiyu Zeng, Zhengping Liang
Many expensive optimization problems exist in various real-world applications. However traditional evolutionary algorithms are inadequate for solving these problems directly. Surrogate-assisted evolutionary algorithm (SAEA) can effectively solve expensive optimization problems using computationally inexpensive surrogate models. However, both the Kriging and ensemble models most SAEAs adopted have limited
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Meta-path aware dynamic graph learning for friend recommendation with user mobility Inform. Sci. (IF 8.1) Pub Date : 2024-03-08 Ding Ding, Jing Yi, Jiayi Xie, Zhenzhong Chen
Recently, friend recommendation has gained widespread popularity in location-based social networks (LBSNs), which provides more opportunities for users to forge new friendships. Most existing studies exploit user trajectories or check-ins of Point-Of-Interests (POIs) to predict friendships based on geographic homophily. However, the dynamics of social relationships are left insufficiently considered
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Multi-hyperplane twin support vector regression guided with fuzzy clustering Inform. Sci. (IF 8.1) Pub Date : 2024-03-08 Zichen Zhang, Wei-Chiang Hong, Yongquan Dong
In recent years, twin support vector regression has become a hot research topic because of its low computing time and excellent performance. It can be observed, however, that either the support vector regression or twin support vector regression have no more than two regression hyperplanes. Many research studies have ignored the potential of multiple hyperplanes regression algorithms. In this paper
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Lightweight privacy-preserving authentication mechanism in 5G-enabled industrial cyber physical systems Inform. Sci. (IF 8.1) Pub Date : 2024-03-08 Xinyin Xiang, Jin Cao, Weiguo Fan
With the deep integration of informatization and industrialization, cyber-physical system (CPS), which integrates computing, communication and control technologies, comes into being and has been widely used in industrial applications. A large number of information physical system devices and control systems are based on open internet connections, and security and privacy protection issues are gradually
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Structured collaborative sparse dictionary learning for monitoring of multimode processes Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Yi Liu, Jiusun Zeng, Bingbing Jiang, Weiguo Sheng, Zidong Wang, Lei Xie, Li Li
In this paper, a novel structured collaborative sparse dictionary learning approach is proposed to improve the monitoring performance of discriminative dictionary learning for multimode processes. The mode discriminability and data reconstruction are first balanced by decomposing the dictionary coefficients into between- and within-class parts and introducing a within-class self-expression regularization
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A socio-technical approach to trustworthy semantic biomedical content generation and sharing Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Asim Abbas, Tahir Hameed, Fazel Keshtkar, Seifedine Kadry, Syed Ahmad Chan Bukhari
The rapid growth of online biomedical content has presented a notable challenge in delivering timely and precise semantic annotations. Semantic annotations play a crucial role in contextually indexing data, thereby enhancing search accuracy. This intricate process involves the utilization of multiple coded ontologies, requiring extensive technical expertise and domain knowledge. While automated ontologies
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An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Seyed Hamid Hosseini, Mahdi Imani
Interventions in gene regulatory networks (GRNs) aim to restore normal functions of cells experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic, uncertain, and complex nature of cellular processes poses significant challenges in determining the best interventions. Most existing intervention methods assume that cells are unresponsive to therapies, resulting in stationary
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Multi-perspective knowledge graph completion with global and interaction features Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Duantengchuan Li, Fobo Shi, Xiaoguang Wang, Chao Zheng, Yuefeng Cai, Bing Li
Knowledge graphs are multi-relation heterogeneous graphs. Thus, the existence of numerous multi-relation entities imposes a tough challenge to the modelling of the knowledge graph. Some recent works represent the property of corresponding entities and relations by generating embeddings. They attempted to identify the missing entities by translation operations or semantic matching. However, the expressiveness
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Supplement data in federated learning with a generator transparent to clients Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Xiaoya Wang, Tianqing Zhu, Wanlei Zhou
Federated learning is a decentralized learning approach that shows promise for preserving users' privacy by avoiding local data sharing. However, the heterogeneous data in federated learning limits its applications in wider scopes. The data heterogeneity from diverse clients leads to weight divergence between local models and degrades the global performance of federated learning. To mitigate data heterogeneity
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Quantifying opacity of discrete event systems modeled with probabilistic Petri nets Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Sian Zhou, Li Yin, Zhiwu Li
The verification and enforcement problem of opacity that falls into the category of security properties of information flows in a cyber-physical system has been extensively studied from the view of discrete event systems. Recent years have witnessed growing interest in the quantitative analysis of opacity. However, documented results on quantifying opacity in the literature are not formulated within
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Multi-criteria assessment of climate change due to green house effect based on Sugeno Weber model under spherical fuzzy Z-numbers Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Shahzaib Ashraf, Maria Akram, Chiranjibe Jana, LeSheng Jin, Dragan Pamucar
Using the multi-criteria decision-making (MCDM) approach, this research piece addresses the urgent problems of environmental degradation and climate change. The method provides a structured way to examine and compare different criteria and options, which improves the precision of decision-making. In order to make this method even better, we combine Zadeh's -numbers with limitations and reliability
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DCGNN: Adaptive deep graph convolution for heterophily graphs Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Yang Wu, Yu Wang, Liang Hu, Juncheng Hu
Graph neural networks (GNNs) have demonstrated significant efficacy in addressing graph learning tasks by leveraging both node features and graph topology. Prevalent GNN architectures often implicitly or explicitly rely on the homophily assumption, which presupposes that neighboring nodes tend to share similar features. Despite their efficacy, GNNs may prove inadequate in modeling graphs characterized
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Dealing with congestion in the optimization of locating single-server battery swapping stations Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Bowen Zhang, Xiang Li, Francisco Saldanha-da-Gama
This paper presents a study on the location problem of single-server battery swap stations, identifying instances of excessively long waiting times at certain stations during their operation in a real-world company scenario. This study innovatively transforms the problem into an extended version of the classic maximal covering location problem, incorporating technology selection and three sets of additional
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A regionally coordinated allocation strategy for medical resources based on multidimensional uncertain information Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Xinxin Wang, Yangyi Li, Ke Yang, Zeshui Xu, Jian Zhang
In the face of an emergency, regionally coordinated allocation is an important prerequisite for maintaining the normal order of production and living. Considering the complexity and uncertainty of the emergencies in local governments, this paper establishes a two-stage process for allocating medical resources. In the first stage, cooperative regions with incomplete weights and multidimensional uncertain
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A novel three-way classification and ranking approach based on regret theory and TOPSIS Inform. Sci. (IF 8.1) Pub Date : 2024-03-06 Ke-Ya Yan, Hai-Long Yang, Zhi-Lian Guo
In multi-attribute decision-making (MADM) problems, attribute types typically include benefit attributes and cost attributes. In most existing MADM studies, attribute types are converted to the same type to reduce the effect of diverse attribute types on decision results, inevitably leading to the loss of original information. In view of this, this paper presents a novel three-way decision (TWD) approach
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Density peak clustering by local centers and improved connectivity kernel Inform. Sci. (IF 8.1) Pub Date : 2024-03-06 Wenjie Guo, Wei Chen, Xinggao Liu
Similarity calculation is one of the most critical steps of clustering analysis, especially for arbitrarily formed elongated structures. When it comes to Density Peak Clustering (DPC), using Euclidean distance solely to calculate the similarity also makes it suffer arbitrarily formed data clustering. To tackle this deficiency of DPC, an improved Connectivity Kernel (ICK) was presented to accelerate
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SANe: Space Adaptation Network for Temporal Knowledge Graph Completion Inform. Sci. (IF 8.1) Pub Date : 2024-03-06 Yancong Li, Xiaoming Zhang, Bo Zhang, Feiran Huang, Xiaopeng Chen, Ming Lu, Shuai Ma
Temporal Knowledge Graphs (TKGs) model time-dependent facts as relations between entities at specific timestamps, making them well-suited for real-world scenarios. However, TKGs are susceptible to incompleteness, necessitating Temporal Knowledge Graph Completion (TKGC) to predict missing facts. Prior methods often struggle to effectively handle two critical properties of TKGs, time-variability and
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A spherical evolution algorithm with two-stage search for global optimization and real-world problems Inform. Sci. (IF 8.1) Pub Date : 2024-03-06 Yirui Wang, Zonghui Cai, Lijun Guo, Guoqing Li, Yang Yu, Shangce Gao
This paper proposes a spherical evolution algorithm with two-stage search. Spherical search and hypercube search are combined to achieve individuals' evolution. A self-adaptive Gaussian scale factor and a variable scale factor are designed to adaptively control individuals' spherical and hypercube search area according to their search situations. Two search stages frequently switch with four search
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Adaptive T-S fuzzy synchronization for uncertain fractional-order chaotic systems with input saturation and disturbance Inform. Sci. (IF 8.1) Pub Date : 2024-03-06 Yilin Hao, Zhiming Fang, Heng Liu
This paper proposes an adaptive Takagi-Sugeno (T-S) fuzzy control scheme to synchronize two uncertain fractional-order (FO) chaotic systems with input saturations and external disturbances. Under the FO Lyapunov criterion, a novel adaptive T-S FO controller with a simple architecture is developed. System constraints are handled by modeling a saturator as a fraction of unknown functions. Due to the
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Real-time chaotic video encryption based on multi-threaded parallel confusion and diffusion Inform. Sci. (IF 8.1) Pub Date : 2024-03-06 Dong Jiang, Tao Chen, Zhen Yuan, Wen-xin Li, Hai-tao Wang, Liang-liang Lu
Due to the strong correlation among adjacent pixels, image encryption schemes typically perform multiple rounds of confusion and diffusion to protect images against various attacks. This is time-consuming and cannot meet the real-time requirements for video encryption. Existing works, therefore, realize video encryption by simplifying encryption process or selectively encrypting specific pixels, resulting
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Performance-oriented design and analysis for direct data-driven control of multi-agent systems Inform. Sci. (IF 8.1) Pub Date : 2024-03-06 Ronghu Chi, Na Lin, Biao Huang, Zhongsheng Hou
There exists a relationship between the consensus performance and the control protocol in the coordination of multiple agents. This article proposes a novel direct data-driven control (DirDDC) using such a relationship directly by establishing a performance-oriented design and analysis framework without relying on any model information. A consensus error is defined by considering the topology information
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Context CVGN: A conditional multimodal trajectory prediction network based on scene semantic modeling Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Xin Yang, Shiyu Wang, Yitian Zhu, Dake Zhou, Tao Li
Pedestrian behavior and trajectory prediction in highly dynamic and interactive scenes have emerged as among the most daunting challenges in the realm of autonomous driving. In addressing the modeling of pedestrian interaction and the generation of multimodal trajectories for pedestrian trajectory prediction, we present a novel approach: a context-based conditional variational generative adversarial
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An adaptive population size based Differential Evolution by mining historical population similarity for path planning of unmanned aerial vehicles Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Zijian Cao, Kai Xu, Zhenyu Wang, Ting Feng, Feng Tian
Various variants have been proposed to improve the search ability and efficiency of Differential Evolution (DE). However, the variants ignore the impact caused by the use of accumulated historical information, resulting in unpromising performance of the search. Furthermore, the global exploration and local exploitation ability of population is affected by the population number (), which commonly adapts
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Mining frequent temporal duration-based patterns on time interval sequential database Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Fuyin Lai, Guoting Chen, Wensheng Gan, Mengfeng Sun
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
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A novel intuitionistic fuzzy best-worst method for group decision making with intuitionistic fuzzy preference relations Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Shu-Ping Wan, Jiu-Ying Dong, Shyi-Ming Chen
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
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Granular structure evaluation and selection based on justifiable granularity principle Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Lei-Jun Li, Mei-Zheng Li, Ju-Sheng Mi
Granular structures are fundamental components of human granulation intelligence and different views or scales of granulation result in different granular structures. Therefore, the evaluation and selection of optimal granular structures can lay the foundation for problem-solving. Information granules are basic components of granular structures. The principle of justifiable granularity presents a coherent
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Adaptive memory event-triggered observer-based pinning synchronization control for complex dynamical networks under asynchronous attacks Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Li Shu, Shengyuan Xu
This article addresses the security synchronization issue of complex dynamical networks under asynchronous cyber and physical attacks within the context of cyber-physical systems. An observer is devised to estimate the system's state using the measured outputs of partial nodes. The integration of a buffer for storing historical information enhances the accuracy of state estimation under asynchronous
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Asynchronous H∞ control for IT2 fuzzy networked system subject to hybrid attacks via improved event-triggered scheme Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Mourad Kchaou, M. Mubeen Tajudeen, M. Syed Ali, Grienggrai Rajchakit, G. Shanthi, Jinde Cao
This investigation focuses on the asynchronous control of interval type-2 (IT2) fuzzy networked systems under hybrid attacks employing a novel, improved event-triggered scheme. An IT2 fuzzy model is proposed to represent the nonlinear system with external disturbances and parametric uncertainties. A new hybrid attack involving two popular attacks-denial-of-service (DoS) attacks and deception attacks
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Spiking autoencoder for nonlinear industrial process fault detection Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Bochun Yue, Kai Wang, Hongqiu Zhu, Xiaofeng Yuan, Chunhua Yang
In recent years, artificial neural networks have been found successful applications in process monitoring within metallurgy, chemical engineering and mechanical manufacturing owing to their superior ability to construct flexible models with varying degrees of nonlinearity and effectively handle large-scale data. However, the model's distinctiveness diminishes due to the high cost associated with neural
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Generalisation of a synchronous solution of the parity problem on cyclic configurations over a non-circulant graph Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Fernando Faria, Eurico Ruivo, Pedro Paulo Balbi
The parity problem is a classical binary benchmark for addressing the computational ability and limitations of automata networks. It refers to conceiving a local rule to allow deciding whether the number of 1-states in the nodes of an arbitrary network is an odd or even number, without global access to the nodes. In its standard formulation, the automata network has an odd number of nodes whose states
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SVD enclosure of a class of interval matrices Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Sarishti Singh, Geetanjali Panda
This paper develops the concept of the singular value decomposition (SVD) of a class of interval matrices. The methodology relies on tighter outer estimations of eigenvalues and their corresponding eigenvectors of an interval matrix. The interval enclosure of every eigenvalue of an interval matrix is determined using an iterative procedure, and an algorithm is proposed for determining the corresponding
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An extension framework for creating operators and functions for intuitionistic fuzzy sets Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Shing-Chung Ngan
Intuitionistic fuzzy sets, introduced by Atanassov in the mid-1980′s, represent a key extension of Zadeh's fuzzy sets. Zadeh's extension principle, a fundamental concept in fuzzy set theory, has been utilized to extend functions from the classical mathematics setting to the fuzzy setting. In this article, we propose an extension framework for intuitionistic fuzzy sets. The proposed approach offers
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Improving adversarial transferability through frequency enhanced momentum Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Changfei Zhao, Xinyang Deng, Wen Jiang
The emergence of adversarial examples seriously affects the practical security deployment of convolutional neural networks. The existing attack algorithms perform brilliantly under white-box scenarios, but they show weak transferability when faced with unknown black-box models. Recent studies have revealed that models have different interests in different frequency components of images, and the low-frequency
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Improving adversarial robustness using knowledge distillation guided by attention information bottleneck Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Yuxin Gong, Shen Wang, Tingyue Yu, Xunzhi Jiang, Fanghui Sun
Deep neural networks (DNNs) have recently been found to be vulnerable to adversarial examples, which raises concerns about their reliability and poses potential security threats. Adversarial training has been extensively studied to counter adversarial attacks. However, the limited attack types incorporated during the training phase will restrict the defense performance of models against unknown attacks
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Fuzzy control of singular fractional order multi-agent systems with actuator saturation Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Yuying Wang, Jin-Xi Zhang, Xuefeng Zhang
In this paper, a T-S fuzzy model is established to describe nonlinear fuzzy singular fractional order multi-agent systems (SFOMASs) exhibiting actuator saturation when the order is between 0 and 2. We focus on the case where the system state is difficult to measure or cannot be directly measured, so it needs to be estimated based on other measurable signals. To this end, a fuzzy observer is designed
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Wearable-based behaviour interpolation for semi-supervised human activity recognition Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Haoran Duan, Shidong Wang, Varun Ojha, Shizheng Wang, Yawen Huang, Yang Long, Rajiv Ranjan, Yefeng Zheng
While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-and-error process, deep learning has emerged as a preferred method for high-level representations of sensor-based human activities. However, most deep learning-based HAR requires a large amount of labelled data and extracting HAR features from unlabelled data for effective deep learning training remains challenging
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MRI-CE: Minimal rare itemset discovery using the cross-entropy method Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Wei Song, Zhen Sun, Philippe Fournier-Viger, Youxi Wu
Rare itemsets have been studied less extensively than frequent itemsets, but have important potential applications in black swan events, like detecting anomalies. Mining rare itemsets poses two challenges: too many results may be obtained, and the process may incur a high computational overhead. To overcome these two challenges, we can attempt to mine minimal rare itemsets (MRIs) and use heuristic
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On the granular representation of fuzzy quantifier-based fuzzy rough sets Inform. Sci. (IF 8.1) Pub Date : 2024-03-05 Adnan Theerens, Chris Cornelis
Rough set theory is a well-known mathematical framework that can deal with inconsistent data by providing lower and upper approximations of concepts. A prominent property of these approximations is their granular representation: that is, they can be written as unions of simple sets, called granules. The latter can be identified with “if…, then…” rules, which form the backbone of rough set rule induction
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A framework for constrained large-scale multi-objective white-box problems based on two-scale optimization through decision transfer Inform. Sci. (IF 8.1) Pub Date : 2024-03-04 Qingzhu Wang, Tianyang Li, Fanqi Meng, Bin Li
Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To overcome white-box CLSMOPs with definitive objective functions, a two-scale optimization framework based on decision transfer, which integrates dimensionality reduction of large-scale decision variables and constraint handling technology
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A fast spatial high utility co-location pattern mining approach based on branch-and-depth-extension Inform. Sci. (IF 8.1) Pub Date : 2024-03-04 Peizhong Yang, Lizhen Wang, Lihua Zhou, Hongmei Chen
Mining co-location patterns hidden in spatial data is crucial for spatial association discovery, and it has broad prospects in many applications. igh tility o-location attern ining (HUCPM) further takes the utility factor of spatial features into consideration, so it is more realistic compared with the traditional co-location pattern mining. However, HUCPM is more difficult, since the Apriori-like
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Knowledge-Guided Communication Preference Learning Model for Multi-agent Cooperation Inform. Sci. (IF 8.1) Pub Date : 2024-03-04 Han Zhang, Hang Yu, Xiaoming Wang, Mengke Wang, Zhenyu Zhang, Yang Li, Shaorong Xie, Xiangfeng Luo
In partially observable scenarios such as distributed multi-agent systems with limited perceptual ranges, information sharing among agents is particularly crucial. Existing research, however, mostly focuses on broadcast communication, which not only depends heavily on bandwidth efficiency, it also causes information redundancy that may have a detrimental impact on collaboration. To solve these problems
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A two-view deep interpretable TSK fuzzy classifier under mutually teachable classification criterion Inform. Sci. (IF 8.1) Pub Date : 2024-03-04 Ta Zhou, Guanjin Wang, Kup Sze Choi, Shitong Wang
Most of the existing classification techniques generally requires the consistent distribution assumption between training and testing samples. However, recent results theoretically reveal that enhanced classification performance may be achieved by breaking this assumption and meanwhile managing to satisfy a subtle assumption between a prediction function, training and testing samples. Although such
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Adaptive fuzzy-evidential classification based on association rule mining Inform. Sci. (IF 8.1) Pub Date : 2024-03-04 Xiaojiao Geng, Qingxue Sun, Zhi-Jie Zhou, Lianmeng Jiao, Zongfang Ma
As one of the most promising classification approaches, association classification (AC) integrates data classification and association discovery techniques for generating a compact set of classification association rules. Recently, the fuzzy set and evidence theories are successively applied into AC in order to improve the classification performance in terms of accuracy and interpretability. However
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Adaptive robust control for fuzzy underactuated mechanical systems: A Stackelberg game-theoretic optimization approach Inform. Sci. (IF 8.1) Pub Date : 2024-03-04 Yuanjie Xian, Jun Ma, Kang Huang, Xiaolong Chen, Wenxin Wang, Abdullah Al Mamun, Tong Heng Lee
This article proposes a Stackelberg game-theoretic optimization approach for adaptive robust controller design of fuzzy underactuated mechanical systems (UMSs). As a commonly encountered challenge, several factors render it difficult to further improve the control performance of these UMSs, such as the coupling effects and nonlinearities resulting from the underactuated structure. Meanwhile, various
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Differential evolution with proration-based mutation strategy and multi-segment mixed parameter setting for numerical optimization Inform. Sci. (IF 8.1) Pub Date : 2024-03-04 Xueqing Yan, Mengnan Tian, Yongming Li
In this paper, a new differential evolution (DE) algorithm is presented for solving global optimization problems by guiding the individuals adaptively to explore different decision regions and setting the control parameters properly for individuals. To consolidate algorithm efficiency and preserve population diversity, we first introduce a proration-based mutation strategy. This strategy dynamically