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The strategy of consensus and consistency improving considering bounded confidence for group interval-valued intuitionistic multiplicative best-worst method Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Xiao-Yun Lu, Jiu-Ying Dong, Shu-Ping Wan, He-Cheng Li
To significantly and reasonably enhance the individual consistency and group consensus in group best-worst method (GBWM) under interval-valued intuitionistic multiplicative environment, this study proposes a strategy of consensus and consistency improving considering bounded confidence. Firstly, based on the consistency definition of of interval-valued intuitionistic multiplicative preference relations
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Adaptive Fuzzy Prescribed-Time Control of High-Order Nonlinear Systems with Actuator Faults Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Yu Gao, Wei Sun, Xiangpeng Xie
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MOFS-REPLS: A Large-Scale Multi-Objective Feature Selection Algorithm Based on Real-Valued Encoding and Preference Leadership Strategy Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Qiyong Fu, Qi Li, Xiaobo Li, Hui Wang, Jiapin Xie, Qian Wang
Multi-objective feature selection (MOFS) has emerged as a crucial step in constructing efficient machine-learning models. While multi-objective evolutionary algorithms often yield satisfactory sub-optimal solutions, enhancing these algorithms' global optimization capacity remains a central challenge in the field of engineering optimization. To improve the quality of solutions to problems, there is
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FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Rasha Al-Huthaifi, Tianrui Li, Zaid Al-Huda, Chongshou Li
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Secure and efficient federated learning via novel multi-party computation and compressed sensing Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Lvjun Chen, Di Xiao, Zhuyang Yu, Maolan Zhang
Federated learning (FL) enables the full utilization of decentralized training without raw data. However, various attacks still threaten the training process of FL. To address these concerns, differential privacy (DP) and secure multi-party computation (SMC) are applied, but these methods may result in low accuracy and heavy training load. Moreover, the high communication consumption of FL in resource-constrained
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Landmark-based k-Factorization Multi-view Subspace Clustering Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Yuan Fang, Geping Yang, Xiang Chen, Zhiguo Gong, Yiyang Yang, Can Chen, Zhifeng Hao
Multi-view subspace clustering (MSC) has gained significant popularity due to its ability to overcome noise and bias present in single views by fusing information from multiple views. MSC enhances the accuracy and robustness of clustering. However, many existing MSC methods suffer from high computational costs and sub-optimal performance on large-scale datasets, since they often construct a fused graph
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Random Subsequence Forests Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Zengyou He, Jiaqi Wang, Mudi Jiang, Lianyu Hu, Quan Zou
The random forest classifier is widely used in different fields due to its accuracy and robustness. Since its invention, the random forest algorithm is naturally developed for multi-dimensional vectorial data since features can be directly sampled during the decision tree construction procedure. In the context of discrete sequence classification, an explicit feature set is not readily available and
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E3WD: A three-way decision model based on ensemble learning Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Jin Qian, Di Wang, Ying Yu, XiBei Yang, Shang Gao
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Incremental information fusion in the presence of object variations for incomplete interval-valued data based on information entropy Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Xiuwei Chen, Maokang Luo
Information fusion technology plays a crucial role in integrating data from multiple sources or sensors to generate comprehensive representation, which can eliminate uncertainty in multi-source information systems (Ms-IS). Incomplete interval-valued data, a generalized form of single-valued data, is commonly encountered in real-world scenarios and effectively represents uncertain information. This
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Contribution-wise Byzantine-robust aggregation for Class-Balanced Federated Learning Inform. Sci. (IF 8.1) Pub Date : 2024-03-19 Yanli Li, Weiping Ding, Huaming Chen, Wei Bao, Dong Yuan
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Identification of Labeled Petri Nets From Finite Automata Inform. Sci. (IF 8.1) Pub Date : 2024-03-18 Guanghui Zhu, Li Yin, Yaohui Li, Zhiwu Li, Naiqi Wu
Automata and Petri nets are two typical models of discrete event systems. The paper studies the problem of converting a finite automaton into a Petri net that satisfies the specified structural features. More specifically, we identify a labeled Petri net from a nondeterminstic finite automaton such that the reachability graph of the identified net is isomorphic to the given automaton, meaning that
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Active Domain Adaptation with Mining Diverse Knowledge: An Updated Class Consensus Dictionary Approach Inform. Sci. (IF 8.1) Pub Date : 2024-03-18 Qing Tian, Liangyu Zhou, Yanan Zhu, Lulu Kang
Domain adaptation (DA) has recently emerged as an effective paradigm for training the target model with labeled source knowledge. When knowledge transfer in DA encounters the bottleneck, one effective crack way is to introduce the labeled data to guide the DA process. Along this line, many active learning-based DA approaches are emerging to improve the quality of samples selection at the decision border
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MVQS: Robust multi-view instance-level cost-sensitive learning method for imbalanced data classification Inform. Sci. (IF 8.1) Pub Date : 2024-03-18 Zhaojie Hou, Jingjing Tang, Yan Li, Saiji Fu, Yingjie Tian
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Spatiotemporal knowledge graph completion via diachronic and transregional word embedding Inform. Sci. (IF 8.1) Pub Date : 2024-03-18 Xiaobei Xu, Wei Jia, Li Yan, Xiaoping Lu, Chao Wang, Zongmin Ma
Knowledge Graph Completion (KGC) is an essential application in the field of knowledge graphs (KGs) that attempts to fill in the missing information in the process of KG modelling. With the popularity of temporal knowledge graphs (TKGs), a wide range of techniques based on temporal knowledge graph completion (TKGC) have appeared, solving the issue of real-world knowledge with temporal properties. However
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Trainable and explainable simplicial map neural networks Inform. Sci. (IF 8.1) Pub Date : 2024-03-18 Eduardo Paluzo-Hidalgo, Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo
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
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A large-scale graph partition algorithm with redundant multi-order neighbor vertex storage Inform. Sci. (IF 8.1) Pub Date : 2024-03-18 Huanqing Cui, Di Yang, Chuanai Zhou
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A log-based non-convex relaxation regularized regression for robust face recognition Inform. Sci. (IF 8.1) Pub Date : 2024-03-18 Ruonan Liu, Yitian Xu
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Neural collapse inspired semi-supervised learning with fixed classifier Inform. Sci. (IF 8.1) Pub Date : 2024-03-18 Zhanxuan Hu, Yichen Wang, Hailong Ning, Yonghang Tai, Feiping Nie
Pseudo-labeling-based approaches are gaining prominence in Semi-Supervised Learning (SSL). Recent studies have identified that the key bottleneck in this methodology is addressing insufficient, incorrect, and imbalanced pseudo-labels. In this paper, we argue that the intrinsic problem behind this bottleneck is classifier bias, i.e., the classifier's prototypes suffer from poor uniformity. Further,
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Topological numbers of fuzzy soft graphs and their application Inform. Sci. (IF 8.1) Pub Date : 2024-03-18 Muhammad Azeem, Shabana Anwar, Muhammad Kamran Jamil, Muhammad Saeed, Muhammet Deveci
The diagram kind of a graph is used to show accumulated data. Graphs can be utilized for a variety of purposes because this data can be either quantitative or qualitative. Graphs can be used to model different relationships and processes in physical, biological, and social media marketing systems, and in finding directions on a map. A graph with properties attached to its nodes and edges that emphasize
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Emergency scheduling based on event triggering and multi-hierarchical planning for space Surveillance network Inform. Sci. (IF 8.1) Pub Date : 2024-03-17 Xi Long, Leping Yang, Chenyuan Qiao
Space Surveillance Network (SSN) task scheduling plays a crucial role in maintaining the catalog of Resident Space Objects (RSO). However, various emergencies, such as RSO maneuvering, collisions, or rocket launch, may disrupt the original scheduled scheme. Therefore, it is essential to rapidly regenerating emergency schemes while minimizing disturbance to the initial scheduling scheme. This paper
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Adaptive integral sliding-mode finite-time control with integrated extended state observer for uncertain nonlinear systems Inform. Sci. (IF 8.1) Pub Date : 2024-03-16 Zhen Zhang, Yinan Guo, Song Zhu, Jianxing Liu, Dunwei Gong
For the typical uncertain nonlinear systems subjected to controller gain variation and disturbances, an integrated adaptive robust controller, called integral sliding-mode finite-time controller with an integrated extended state observer (ISFC-IESO), is proposed. The designed controller mainly consists of an integrated ESO (IESO) unit and an integral sliding-mode control (ISMC) unit. The IESO unit
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MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion Inform. Sci. (IF 8.1) Pub Date : 2024-03-15 Ming Xu, Jing Zhang
The identification of important nodes with strong propagation capabilities in road networks is a vital topic in urban planning. Existing methods for evaluating the importance of nodes in traffic networks only consider topological information and traffic volumes, the diversity of the traffic characteristics in road networks, such as the number of lanes and average speed of road segments, are ignored
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Learning to Walk with Logical Embedding for Knowledge Reasoning Inform. Sci. (IF 8.1) Pub Date : 2024-03-15 Ruinan Liu, Guisheng Yin, Zechao Liu
The path-based model has remarkably succeeded in the knowledge graph (KG) multi-hop reasoning task. It employs all available resources to accomplish various complex path reasoning tasks and continuously explores new graph paths. However, existing multi-hop reasoning methods rely heavily on the high reward, which is fed back to the model when the agent searches for the target. In contrast, most previous
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StreamliNet: Cost-aware Layer-wise Neural Network Linearization for Fast and Accurate Private Inference Inform. Sci. (IF 8.1) Pub Date : 2024-03-15 Zhi Pang, Lina Wang, Fangchao Yu, Kai Zhao, Bo Zeng
Private inference (PI) allows a client and a server to perform cryptographically-secure deep neural network inference without disclosing their sensitive data to each other. Despite the strong security guarantee, existing models are ill-suited for PI since their overused non-linear operations such as ReLUs are computationally expensive in the regime of ciphertext and therefore dominate the PI latency
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Adaptive Supervisory Control for Automated Manufacturing Systems Using Borrowed-Buffer Slots Inform. Sci. (IF 8.1) Pub Date : 2024-03-15 Umar Suleiman Abubakar, Gaiyun Liu
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
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A belief rule-based classification system using fuzzy unordered rule induction algorithm Inform. Sci. (IF 8.1) Pub Date : 2024-03-15 Yangxue Li, Ignacio Javier Pérez, Francisco Javier Cabrerizo, Harish Garg, Juan Antonio Morente-Molinera
A rule-based system is a widely used artificial intelligence system that employs a set of rules to make decisions. The belief rule-based (BRB) classification system is an extension of fuzzy rule-based (FRB) system that handles uncertainty and imprecision in classification tasks by incorporating Dempster-Shafer evidence theory and fuzzy set theory. However, the BRB classification system suffers from
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A novel regularization method for decorrelation learning of non-parallel hyperplanes Inform. Sci. (IF 8.1) Pub Date : 2024-03-15 Wen-Zhe Shao, Yuan-Hai Shao, Chun-Na Li
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A nondominated sorting genetic model for co-clustering Inform. Sci. (IF 8.1) Pub Date : 2024-03-15 Wuchun Yang, Hongjun Wang, Yinghui Zhang, Zhipeng Luo, Tanrui Li
Co-clustering aims to cluster the rows and columns of data simultaneously and can be often formulated as a two-objective optimization problem (one objective for rows and the other for columns) and the solution is a Pareto-optimal solution set in principle. Existing methods usually convert the co-clustering problem into a single-objective optimization problem by setting a hyper-parameter between the
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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 existing 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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SecureTLM: Private inference for transformer-based large model with MPC Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Yuntian Chen, Xianjia Meng, Zhiying Shi, Zhiyuan Ning, Jingzhi Lin
Transformer-based Large Models (TLM), such as generative pre-trained models (GPT), have become increasingly popular for practical applications through Deep Learning as a Service (DLaaS). They have been extensively used in natural language processing and computer vision. However, concerns regarding potential private data leakage arise with this type of inference service. While some private inference
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Hierarchical bottleneck for heterogeneous graph representation Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Yunfei He, Li Meng, Jian Ma, Yiwen Zhang, Qun Wu, Weiping Ding, Fei Yang
Heterogeneous graphs (HGs) contain many nodes and their interaction relationships, which can model complex systems and provide rich semantic and structural information for task execution. Among these, HG representation stands as the fundamental and pivotal component. Existing HG representation methods primarily employ graph neural networks to acquire the semantics of nodes along various meta-paths
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Unsupervised feature extraction based on uncorrelated approach Inform. Sci. (IF 8.1) Pub Date : 2024-03-07 Jayashree, Shiva Prakash T., Venugopal K.R.
In high-dimensional spaces, mathematically driven data processing methods have recently attracted a lot of attention. We consider the situation when information is obtained by sampling a probability distribution with support on or close to a sub-manifold of Euclidean space. In this paper, we provide an innovative unsupervised learning method called Uncorrelated Neighborhood Preserving Embedding (UNPE)
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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