Information Sciences最新文献_第8页

A quality-relevant deep rule-based system with complementary lifelong learning for adaptive quality prediction in industrial semi-supervised process data streams

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-01 DOI: 10.1016/j.ins.2025.122036

Yu Gao , Huaiping Jin , Zhiqiang Wang , Bin Wang , Bin Qian , Biao Yang

{"title":"A quality-relevant deep rule-based system with complementary lifelong learning for adaptive quality prediction in industrial semi-supervised process data streams","authors":"Yu Gao , Huaiping Jin , Zhiqiang Wang , Bin Wang , Bin Qian , Biao Yang","doi":"10.1016/j.ins.2025.122036","DOIUrl":"10.1016/j.ins.2025.122036","url":null,"abstract":"<div><div>Deep learning techniques have been widely applied for industrial quality prediction. However, industrial process data are often generated as data streams, which typically exhibit characteristics such as strong nonlinearity, time-varying behavior, and low sampling rates of quality variables. Conventional offline-trained deep learning models often fail to provide accurate predictions for such semi-supervised data streams. Therefore, a quality-relevant deep rule-based system with complementary lifelong learning (QDRSCLL) is proposed to enable adaptive prediction of critical quality variables in streaming data environments. QDRSCLL comprises a deep backbone network and a shallow predictor. The former utilizes a semi-supervised quality-relevant stacked autoencoder (SQSAE) for feature extraction, while the latter employs a hierarchical fuzzy rule system (HFRS) to perform fuzzy inference on hierarchical hidden features. Furthermore, a novel complementary lifelong learning mechanism is proposed to enable QDRSCLL with online incremental learning capabilities. Additionally, semi-supervised learning is integrated into the online learning process to further enhance its deep feature extraction capabilities and the prediction performance. The feasibility and superiority of the proposed method are demonstrated through two real-world processes and four synthetic datasets. Compared to the traditional evolving fuzzy system (EFS), the RMSE of QDRSCLL is reduced by more than 25% in all application scenarios.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122036"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep core node information embedding on networks with missing edges for community detection

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-28 DOI: 10.1016/j.ins.2025.122039

Rong Fei , Yuxin Wan , Bo Hu , Aimin Li , Yingan Cui , Hailong Peng

{"title":"Deep core node information embedding on networks with missing edges for community detection","authors":"Rong Fei , Yuxin Wan , Bo Hu , Aimin Li , Yingan Cui , Hailong Peng","doi":"10.1016/j.ins.2025.122039","DOIUrl":"10.1016/j.ins.2025.122039","url":null,"abstract":"<div><div>The incomplete network is defined as the network with missing edges, which forms incomplete network topology by missing real information because of multiple-factor such as personal privacy security and threats, etc. Academic interest in incomplete network studies is increasing. Some methods solving community detection problem in the incomplete network, as link prediction, show low ACC or NMI. To address those, there is a need for approaches less affected by missing edges and easy to obtain communities. We propose a deep core node information embedding(DCNIE) algorithm on network with missing edges for community detection, aiming to obtain core node information rather than the influence of edges. First, by edge augmentation, the network with missing edges is integrated into complete networks. Second, the <em>k</em>-core algorithm is used to obtain core node information and build a similarity matrix, followed by an unsupervised deep method that implements network embedding to obtain a low-dimensional feature matrix. Finally, Gaussian mixture model is used for clustering to obtain the community division. We compare eleven state-of-the-art methods on eleven real networks by using eight evaluation metrics. Experiments demonstrate that DCNIE is superior in performance and efficiency while gaining accurate community division in incomplete network.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122039"},"PeriodicalIF":8.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interval-valued fuzzy predicates from labeled data: An approach to data classification and knowledge discovery

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-28 DOI: 10.1016/j.ins.2025.122033

Diego S. Comas , Gustavo J. Meschino , Virginia L. Ballarin

{"title":"Interval-valued fuzzy predicates from labeled data: An approach to data classification and knowledge discovery","authors":"Diego S. Comas , Gustavo J. Meschino , Virginia L. Ballarin","doi":"10.1016/j.ins.2025.122033","DOIUrl":"10.1016/j.ins.2025.122033","url":null,"abstract":"<div><div>Interpretable data classifiers play a significant role in providing transparency in the decision-making process by ensuring accountability and auditability, enhancing model understanding, and extracting new information that expands the field of knowledge in a discipline while effectively handling large datasets. This paper introduces the Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC) method, in which interval-valued fuzzy predicates are used for interpretable data classification. The proposed approach begins by clustering the data within each class, associating clusters with collections of common attributes, and identifying class prototypes. Interval-valued membership functions and predicates are then derived from these prototypes, leading to the creation of an interpretable classifier. Empirical evaluations on 14 datasets, both public and synthetic, are presented to demonstrate the superior performance of T2-LFPC based on the accuracy and Jaccard index. The proposed method enables linguistic descriptions of classes, insight into attribute semantics, class property definitions, and an understanding of data space partitioning. This innovative approach enhances knowledge discovery by addressing the challenges posed by the complexity and size of modern datasets.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122033"},"PeriodicalIF":8.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated heterogeneous graph and reinforcement learning enabled efficient scheduling for surface mount technology workshop

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-28 DOI: 10.1016/j.ins.2025.122023

Biao Zhang , Hongyan Sang , Chao Lu , Leilei Meng , Yanan Song , Xuchu Jiang

{"title":"Integrated heterogeneous graph and reinforcement learning enabled efficient scheduling for surface mount technology workshop","authors":"Biao Zhang , Hongyan Sang , Chao Lu , Leilei Meng , Yanan Song , Xuchu Jiang","doi":"10.1016/j.ins.2025.122023","DOIUrl":"10.1016/j.ins.2025.122023","url":null,"abstract":"<div><div>Timely scheduling is crucial for manufacturing workshops to adapt swiftly to changing conditions. This paper introduces a novel deep heterogeneous graph and reinforcement learning approach to address real-time challenges in a surface mount technology (SMT) workshop. The scheduling problem in SMT workshop can be modeled as a reconfigurable distributed flowshop group scheduling problem (RDFGSP), involving assignment of family operations to cells for their flows, sequencing of family operations on the cells, and sequencing of job operations in the family operations for their flows. By mapping the problem to a heterogeneous graph with distinct node and edge types, an end-to-end learning model is developed. The model integrates a heterogeneous graph neural network (HGNN) and sequential Q networks to effectively represent the key scheduling elements and the Markov decision-making process. HGNN is employed to extract meaningful features and representations from the heterogeneous graph. These representations are then fed into the sequential Q networks to select two cooperated actions to be taken. A weighted sum approach is proposed to provide more reasonable evaluation of the selected actions. Experimental comparisons with exact and heuristic methods from the literature demonstrate the superior performance and effectiveness of the proposed model.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122023"},"PeriodicalIF":8.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computation of synchronous diagnosis bases of discrete-event systems

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-27 DOI: 10.1016/j.ins.2025.122031

Lucas N.R. Reis, Marcos V. Moreira

{"title":"Computation of synchronous diagnosis bases of discrete-event systems","authors":"Lucas N.R. Reis, Marcos V. Moreira","doi":"10.1016/j.ins.2025.122031","DOIUrl":"10.1016/j.ins.2025.122031","url":null,"abstract":"<div><div>Several works have been proposed to address fault diagnosis of Discrete-Event Systems (DES) considering different approaches and architectures. In the vast majority, the fault diagnoser is constructed based on the complete system model, which may have a huge number of states, due to the parallel composition of several modules. The implementation of diagnosers with a large number of states consumes a large amount of computer memory, and may become, in some cases, unfeasible. Recently, synchronous diagnosis of DES has been proposed, where state observers of fault-free models of system modules are used for fault diagnosis. The method provides a diagnoser that is not based on the composed plant model, which leads to a diagnoser with fewer states and transitions than the classical diagnoser. In the synchronous diagnosis approach, all the subsystem models are assumed to contribute to fault detection. However, in practice, certain subsystems may not provide useful information on fault occurrences, or redundant information may be available from other modules. Consequently, these redundant modules are not necessary in the synchronous diagnosis scheme and can be discarded, leading to reduced diagnosers. In this paper, we present a method for computing a synchronous diagnoser that uses only part of the subsystem models. It is also shown that the fault can be diagnosed using modules where the fault event is not even modeled. To do so, we present an algorithm for computing all the sets of modules that ensure the synchronous diagnosability of a DES. These sets are called synchronous diagnosis bases (SDB). We prove that the complexity of the problem of finding an SDB with cardinality less than or equal to a given natural number is NP-complete. Thus, the algorithm proposed in this work has the objective of mitigating the computational efforts to find all the SDB of a DES. Two examples are used to illustrate the efficiency of the proposed method.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122031"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal knowledge graph link predictions with query-guided temporal representation learning

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-27 DOI: 10.1016/j.ins.2025.122035

Linhua Dong , Xiaofei Zhou , Bo Wang , Qiannan Zhu , Fan Meng

{"title":"Temporal knowledge graph link predictions with query-guided temporal representation learning","authors":"Linhua Dong , Xiaofei Zhou , Bo Wang , Qiannan Zhu , Fan Meng","doi":"10.1016/j.ins.2025.122035","DOIUrl":"10.1016/j.ins.2025.122035","url":null,"abstract":"<div><div>Temporal Knowledge Graph (TKG) records real-life events using timestamped facts and is used for the TKG link prediction task which is to answer an incomplete timestamped fact called the query. Existing works predict by learning entity embeddings where they represent entities with entity-related facts guided by queries to emphasize important ones. Although they generalize well, their learning with queries is limited since they guide learning with the average query which merges all queries without considering that queries in TKG represent diverse meanings. Merging diverse queries generates a vague averaged query which will mislead embedding learning and further confuse predictions. To resolve the limitation, we propose individual-query-guided learning (IndiQ) to learn clearer embeddings which faithfully realizes the nature of TKG that its records are diverse and should be modeled individually rather than averaging. Specifically, IndiQ formulates embedding learning as a weighted sum of entity-related facts and calculates weights using queries individually following the total probability theorem. Then, with the novel formulation, IndiQ guides the learning of entity embeddings using queries individually to identify important facts. Finally, IndiQ predicts future links using learned entity embeddings. Experimental results show that we achieve better performance. Visualizations further demonstrate the effectiveness of our IndiQ.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122035"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed conflict analysis across varying analysis levels based on fuzzy formal contexts 基于模糊形式语境，在不同分析层面进行分布式冲突分析

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-27 DOI: 10.1016/j.ins.2025.122038

Zhenhao Qi , Huilai Zhi , Weiping Ding

{"title":"Distributed conflict analysis across varying analysis levels based on fuzzy formal contexts","authors":"Zhenhao Qi , Huilai Zhi , Weiping Ding","doi":"10.1016/j.ins.2025.122038","DOIUrl":"10.1016/j.ins.2025.122038","url":null,"abstract":"<div><div>Conflict analysis aims to understand the causes of conflicts and identify effective solutions. While existing studies have thoroughly examined conflict information within individual information systems, the conflict analysis across different systems remains underexplored. In this paper, we utilize fuzzy formal concept analysis to investigate conflict analysis in multi-source information. First, conflict information fusion strategies for distributed fuzzy formal contexts are proposed, including object set extension (vertical merging) and attribute set extension (horizontal merging). Second, algorithms for updating conflict analysis results when adjusting analysis levels are introduced, considering the varying conflict analysis levels inherent in multi-source information. Finally, the fusion strategies for conflict analysis results at varying analysis levels are evaluated, and the selection of analysis levels is analyzed to optimize computational efficiency. The experimental results demonstrate that fusing conflict information is significantly more efficient than recalculation, and it allows for the selection of varying analysis levels to balance time consumption and information volume. This work enhances the efficiency of conflict analysis in fuzzy formal contexts, providing practical methods for managing multi-source information and adjusting analysis levels to meet specific requirements.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122038"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Information diffusion prediction via meta-knowledge learners

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-27 DOI: 10.1016/j.ins.2025.122034

Zhangtao Cheng , Jienan Zhang , Xovee Xu , Wenxin Tai , Fan Zhou , Goce Trajcevski , Ting Zhong

{"title":"Information diffusion prediction via meta-knowledge learners","authors":"Zhangtao Cheng , Jienan Zhang , Xovee Xu , Wenxin Tai , Fan Zhou , Goce Trajcevski , Ting Zhong","doi":"10.1016/j.ins.2025.122034","DOIUrl":"10.1016/j.ins.2025.122034","url":null,"abstract":"<div><div>Information diffusion prediction is a fundamental task for a vast range of applications, including viral marketing identification and precise recommendation. Existing works focus on modeling limited contextual information from independent cascades while overlooking the diverse user behaviors during the information diffusion: First, users typically have diverse social relationships and pay more attention to their social neighbors, which significantly influences the process of information diffusion. Second, complex temporal influence among different cascade sequences leads to unique and dynamic diffusion patterns between users. To tackle these challenges, we propose MetaCas, a novel cascade meta-knowledge learning framework for enhancing information diffusion prediction in an adaptive and dynamic parameter generative manner. Specifically, we design two meta-knowledge-aware topological-temporal modules – Meta-GAT and Meta-LSTM – to extract cascade-specific topological and temporal user interdependencies inherent within the information diffusion process. Model parameters of topological-temporal modules are adaptively generated by the constructed meta-knowledge from three important perspectives: user social structure, user preference, and temporal diffusion influence. Extensive experiments conducted on four real-world social datasets demonstrate that MetaCas outperforms state-of-the-art information diffusion models across several settings (up to 16.6% in terms of Hits@100).</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122034"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conjunction subspaces test for conformal and selective classification

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-27 DOI: 10.1016/j.ins.2025.122037

Zengyou He, Zerun Li, Junjie Dong, Xinying Liu, Mudi Jiang, Lianyu Hu

引用次数: 0

A novel similarity-based taste features-extracted emotions-aware music recommendation algorithm

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-27 DOI: 10.1016/j.ins.2025.122001

Yu Gao , Shu-Ping Wan , Jiu-Ying Dong

{"title":"A novel similarity-based taste features-extracted emotions-aware music recommendation algorithm","authors":"Yu Gao , Shu-Ping Wan , Jiu-Ying Dong","doi":"10.1016/j.ins.2025.122001","DOIUrl":"10.1016/j.ins.2025.122001","url":null,"abstract":"<div><div>Human music tastes are subjective and difficult to measure, with existing recommendation algorithms often failing to consider music similarity, taste features, and emotions simultaneously. This paper proposes a novel music recommendation algorithm that integrates music similarity, taste features, and emotions, organized into five modules. Motivated by the probabilistic linguistic term set (PLTS), we establish attribute feature vectors of songs by associating attribute values with corresponding probabilities. Module 1 establishes behavior matrix of users to calculate comprehensive behavioral feeling score for obtaining the original music interests of users. Module 2 designs two improved collaborative filtering algorithms to alleviate data sparsity of intuitionistic fuzzy music taste matrix. Module 3 extracts taste features from user listening behavior and the favorite songs of users. Module 4 integrates subjective and objective music emotions to obtain the comprehensive music emotions of user. Considering the dynamic change in users’ music taste features, we incorporate the latest taste features in Module 5 to reorder the song list obtained by the above four modules. The experiment results verify the effectiveness of this recommendation algorithm. It significantly outperforms three popular music recommendation systems in accuracy, excels in ranking quality, new song accuracy, and richness metrics, marginally surpasses them in listening duration.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122001"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0