Information Sciences最新文献

{"title":"Multi-attribute intuitionistic fuzzy twin support vector machine based on data distribution","authors":"Jianxiang Qiu,&nbsp;Jialiang Xie","doi":"10.1016/j.ins.2026.123203","DOIUrl":"10.1016/j.ins.2026.123203","url":null,"abstract":"<div><div>Twin support vector machine (TSVM) is sensitive to noise due to its inability to differentiate sample contributions. How to construct the fuzzy weight assignment strategy to describe the sample contribution is the key to solving the noise-sensitive problem of TSVM. However, the existing strategies still face the challenges in describing the nonlinear characteristics of the complex sample distribution, over-reliance on specific parameter settings and neglecting the global distribution information. To address the above challenges, this paper constructs a weight assignment strategy based on multi-attribute intuitionistic fuzzy sets (IFSs) and further proposes a noise robust multi-attribute intuitionistic fuzzy TSVM based on data distribution (MIFTSVM). First, MIFTSVM constructs the multi-attribute IFS for each training sample based on data distribution and the generalized bell function. Then, inspired by the concepts of fuzzy absolute deviation and feature weighting, a novel multi-attribute IFS distance measure is developed. The proposed weight assignment strategy assigns fuzzy weights to training samples based on the distance measure which integrates data distribution information and is capable of accurately identifying noise. Numerical experiments show that MIFTSVM outperforms state-of-the-art baseline models in generalization performance and noise resistance, demonstrating promising applicability in brain tumor classification.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"740 ","pages":"Article 123203"},"PeriodicalIF":6.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147250","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}

{"title":"LPCLNet: Leveraging local pixel-wise contrastive learning for image tampering localization","authors":"Jun Sang ,&nbsp;Xiaowen Chen ,&nbsp;Wenhui Gong ,&nbsp;Sergey Gorbachev ,&nbsp;Shanjun Zhang","doi":"10.1016/j.ins.2026.123205","DOIUrl":"10.1016/j.ins.2026.123205","url":null,"abstract":"<div><div>To address poor generalization caused by the scarcity of real samples in image tampering localization, this paper proposes a Local Pixel-level Contrastive Learning Network (LPCLNet). The main contributions are: (1) a contour patch-oriented contrastive learning mechanism that categorizes patches into tampered, authentic, and contour classes, applying pixel-level and patch-level contrastive losses alongside binary cross-entropy loss to leverage boundary information and reduce dependence on synthetic data; (2) an LPCLNet architecture that integrates a multi-scale feature fusion module and an Atrous Spatial Pyramid Pooling module to aggregate fine-grained features and embed contextual information for multi-scale representation of tampered regions; (3) a joint optimization strategy combining InfoNCE contrastive loss with binary cross-entropy loss to enhance feature discriminability and localization accuracy. Experiments on the Columbia, NIST16, CASIA v1, and Coverage datasets demonstrate that LPCLNet achieves comparable or superior performance to mainstream methods without requiring synthetic data pre-training. Specifically, it attains leading F1 scores of 0.529 and 0.369 on CASIA v1 and NIST16, respectively, as well as the highest average IoU of 0.500 and AUC of 0.830 across benchmarks, validating its stable and highly generalizable performance with limited real samples.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"740 ","pages":"Article 123205"},"PeriodicalIF":6.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147253","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}

{"title":"Post-hoc explainability of graph neural networks: A comprehensive survey","authors":"Wenzheng Ma ,&nbsp;Xiaofeng Liu ,&nbsp;Yihu Liu ,&nbsp;Yinglong Ma","doi":"10.1016/j.ins.2026.123202","DOIUrl":"10.1016/j.ins.2026.123202","url":null,"abstract":"<div><div>Graph Neural Networks (GNNs) are powerful tools for analyzing graph-structured data and are widely applied in areas such as molecular structure prediction and social network analysis. However, GNN models are inherently nonlinear and opaque, making their internal mechanisms and the rationale behind their predictions difficult to understand. To address this issue, numerous explainability methods have been proposed to uncover the underlying decision-making mechanisms of GNNs. Among these, post-hoc explanation techniques offer significant flexibility, as they can be applied to any pre-trained GNN model without requiring modifications to the model itself. In this paper, we provide a comprehensive survey of existing post-hoc explainability methods for GNNs and propose a technology-oriented taxonomy based on the theoretical techniques they rely on. We analyze the strengths and limitations of each method and review commonly used datasets and evaluation protocols in the field. We further conduct a quantitative comparison of representative methods on selected datasets using commonly adopted evaluation metrics. Moreover, we outline promising research directions to advance the field. Altogether, this survey aims to provide researchers with a comprehensive understanding of the current landscape of post-hoc GNN explainability methods, identify their technical limitations, and facilitate the further advancement of explainable graph-based machine learning.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"740 ","pages":"Article 123202"},"PeriodicalIF":6.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147248","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}

{"title":"UHTS-DRL: A deep reinforcement learning framework for integrated agile satellite observation and data transmission scheduling","authors":"Jiaqi Cheng ,&nbsp;Mingfeng Fan ,&nbsp;Yi Gu ,&nbsp;Wei Tang ,&nbsp;Qizhang Luo ,&nbsp;Yalin Wang ,&nbsp;Xinwei Wang ,&nbsp;Guohua Wu","doi":"10.1016/j.ins.2026.123200","DOIUrl":"10.1016/j.ins.2026.123200","url":null,"abstract":"<div><div>Efficient Earth observation satellite scheduling requires integrated management of both observation and data transmission tasks to optimize the overall system efficiency in practice. However, conventional approaches typically treat these processes separately, leading to suboptimal resource utilization. This paper formulates the Earth observation satellite scheduling as a Multi-agile Satellite and Multi-ground Station Integrated Observation and Data Transmission Scheduling Problem (MSIODTSP) and proposes a Unified Hierarchical Two-stage Scheduling Deep Reinforce-ment Learning (UHTS-DRL) framework for solving MSIODTSP. The UHTS-DRL approach formulates MSIODTSP as a unified Markov Decision Process (MDP), enabling joint optimization of observation and transmission scheduling while accounting for multi-orbit time windows and complex operational constraints. The framework employs a hierarchical policy network comprising a time window encoder, a state encoder, and a dual-decoder for two-stage scheduling, facilitating end-to-end optimization without the need for handcrafted heuristics. Experimental results demonstrate that UHTS-DRL consistently outperforms existing approaches across various problem scales, resource configurations, and geographical target distributions, achieving up to 12.5% relative improvement in total profit while maintaining computational efficiency.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"740 ","pages":"Article 123200"},"PeriodicalIF":6.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147426","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}

{"title":"Machine learning on dynamic functional connectivity: Promise, pitfalls, and interpretations","authors":"Jiaqi Ding ,&nbsp;Tingting Dan ,&nbsp;Ziquan Wei ,&nbsp;Paul J. Laurienti ,&nbsp;Guorong Wu","doi":"10.1016/j.ins.2026.123184","DOIUrl":"10.1016/j.ins.2026.123184","url":null,"abstract":"<div><div>An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand how functional fluctuations relate to human cognition/behavior using data-driven approaches. To this end, tremendous efforts have been made in machine learning to decode cognitive states from evolving volumetric images of blood-oxygen-level-dependent (BOLD) signals. However, due to the complex nature of brain function, the performance and findings of current deep learning models remain inconsistent across tasks, datasets, and evaluation settings. In this work, by capitalizing on large-scale existing neuroimaging data (39,784 fMRI samples from seven databases), we seek to establish a well-founded empirical guideline for designing deep models in functional neuroimaging by linking the methodology underpinning with neuroscientific understanding. Specifically, we put the spotlight on (1) What is the performance landscape of various models in cognitive task recognition and disease diagnosis? (2) What are the key limitations and trade-offs of current deep models? and (3) What is the general guideline for selecting the suitable machine learning backbone for specific neuroimaging applications? We have conducted comprehensive evaluations and statistical analyses across cognitive and clinical scenarios, to answer the above outstanding questions. Our findings demonstrate that no universal model dominates all scenarios; instead, model effectiveness depends on factors such as demographics, task type, and disease stage. Furthermore, we introduce an attention-based interpretability method to reveal spatial patterns of brain activation associated with tasks and disorders.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"740 ","pages":"Article 123184"},"PeriodicalIF":6.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147251","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}

{"title":"Mathematical guarantees for trust region policy optimization","authors":"Li Li ,&nbsp;Xiangyu Luo ,&nbsp;Xiaoyu Song","doi":"10.1016/j.ins.2026.123190","DOIUrl":"10.1016/j.ins.2026.123190","url":null,"abstract":"<div><div>Policy gradient methods have achieved remarkable success in reinforcement learning, yet their performance critically depends on the step size selection during policy updates. Inappropriate step sizes can lead to drastic performance degradation or even training collapse. To mitigate this challenge, the trust region mechanism in TRPO formally guarantees stable policy gradient training through a bounded total variation divergence in consecutive policy iterations. This work establishes a tighter performance difference bound for the discounted return <span><math><mo>|</mo><mi>η</mi><mo>(</mo><mrow><mover><mi>π</mi><mo>ˇ</mo></mover></mrow><mo>)</mo><mo>−</mo><msub><mi>L</mi><mrow><mi>π</mi></mrow></msub><mo>(</mo><mrow><mover><mi>π</mi><mo>ˇ</mo></mover></mrow><mo>)</mo><mrow><mo>|</mo></mrow><mo>≤</mo><mfrac><mrow><mn>2</mn><mi>ϵ</mi><mi>γ</mi></mrow><msup><mrow><mo>(</mo><mn>1</mn><mo>−</mo><mi>γ</mi><mo>)</mo></mrow><mn>2</mn></msup></mfrac><msup><mi>α</mi><mn>2</mn></msup></math></span>, where <span><math><mi>α</mi></math></span> measures policy divergence and <span><math><mi>ϵ</mi></math></span> bounds advantage estimation errors. Leveraging mathematical induction, we rigorously analyze the total variation divergence between policy pairs, systematically quantifying the relationship between state advantage disparities and trajectory probability discrepancies. This formal proof reveals the fundamental mechanisms underlying policy improvement constraints, addressing key gaps in the intuitive proof of TRPO theory. Furthermore, our generalized framework demonstrates that any divergence metric satisfying specific axiomatic properties preserves the structural form of the monotonic improvement guarantee. These theoretical advances translate into practical engineering benefits, enabling more precise trust region sizing for safety-critical applications, including autonomous driving, robotic control, and large language model alignment. The tighter bounds provide concrete mathematical guidance for algorithm designers to balance the stability-efficiency tradeoff, minimizing reliance on exhaustive hyperparameter search.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"740 ","pages":"Article 123190"},"PeriodicalIF":6.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147425","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}

{"title":"A large-scale multi-objective optimization algorithm integrating multi-directional fuzzy sampling and multi-source learning competitive swarm optimizer","authors":"Wenyan Guo,&nbsp;Shenglong Li,&nbsp;Fang Dai,&nbsp;Junfeng Wang,&nbsp;Mengzhen Zhang","doi":"10.1016/j.ins.2026.123194","DOIUrl":"10.1016/j.ins.2026.123194","url":null,"abstract":"<div><div>Existing directional sampling-based methods for large-scale multi-objective optimization problems (LSMOPs) show promise but are often constrained by their reliance on singular representative solutions and insufficient diversity in search directions. To overcome these limitations, this paper proposes LSMDCSO, a novel algorithm integrating multi-directional fuzzy sampling (MDFS) and a multi-source learning competitive swarm optimizer (MLCSO). First, LSMDCSO utilizes angle-penalized distance to select representative solutions as guiding anchors. It then constructs a comprehensive set of search directions by synergizing approximate gradient-based sampling for convergence and orthogonal sampling for diversity. A fuzzy variable operator is incorporated to further enhance solution adaptability in high-dimensional spaces. Additionally, an MLCSO is designed to perform fine-grained exploitation, compensating for sampling imprecision. Experimental evaluations on the LSMOP, ZCAT, and real-world TREE benchmarks demonstrate that LSMDCSO outperforms state-of-the-art algorithms, exhibiting superior capabilities in balancing convergence and diversity for solving complex LSMOPs.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"740 ","pages":"Article 123194"},"PeriodicalIF":6.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147252","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}

{"title":"An effective and robust deep clustering approach for time series with spatial information","authors":"Ze Deng ,&nbsp;Haibo Zeng","doi":"10.1016/j.ins.2026.123201","DOIUrl":"10.1016/j.ins.2026.123201","url":null,"abstract":"<div><div>Deep clustering of time series with spatial information is a challenging task, as the representation learning in the clustering process needs to embed both temporal and spatial features of time series data. The existing deep clustering models fail to achieve a high quality of clustering due to the lack of support for complex time patterns and spatial entities in the representation learning process. Meanwhile, these deep clustering models ignore the effects of noise and outliers. Therefore, in this paper, we propose an effective and robust deep clustering model for time series data with spatial information called ER-Spatial-DEC. Our clustering model can effectively embed complex temporal patterns and spatial entities through a representation learning method by combining a spatial transformer and a quantum representational network. We further enhance the robustness of our clustering model by utilizing a collaborative multi-contrastive learning method. The experimental results demonstrate that ER-Spatial-DEC achieves superior clustering performance compared with all baseline methods across different sequence lengths. In addition, ER-Spatial-DEC exhibits strong robustness under various levels of noise and outlier perturbations, maintaining stable clustering quality compared with baselines.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"740 ","pages":"Article 123201"},"PeriodicalIF":6.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147247","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}

{"title":"A framework for technological bottleneck detection and collaborative optimization in heterogeneous parallel networks","authors":"Zhanxin Ma ,&nbsp;Chuanzhe Zhang ,&nbsp;Meixia Sun","doi":"10.1016/j.ins.2026.123189","DOIUrl":"10.1016/j.ins.2026.123189","url":null,"abstract":"<div><div>Accurately identifying internal technological bottlenecks that constrain system efficiency and effectively coordinating interactions among system components represent critical challenges in contemporary social science research. However, studies on parallel network systems with structural heterogeneity in economic and social contexts remain limited, and several existing models still suffer from issues such as the infeasibility of projection-point techniques. To address these limitations, this study makes three main contributions. First, based on the dual feasibility framework, a DEA model is proposed for evaluating heterogeneous parallel networks. Second, to uncover latent technological bottlenecks within parallel network systems, a method for identifying internal technological bottlenecks is developed. Third, to promote the collaborative optimization of members within the system, a re-adjustment model considering pressure dispersion of subunits is presented. Finally, the proposed approach is applied to evaluate the research and development (R&amp;D) innovation efficiency of 11 regions in western China. The empirical results indicate that the proposed method is capable of assessing parallel network systems with more complex structures while effectively overcoming the limitations of existing models. In particular, it not only identifies technological bottlenecks within decision-making units (DMUs) but also explicitly accounts for the feasibility and balance of each subunit in task allocation.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"740 ","pages":"Article 123189"},"PeriodicalIF":6.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147249","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}

{"title":"Temporal knowledge graph completion via global structural representation and deep interaction","authors":"Jingbin Wang,&nbsp;Yumeng Zhang,&nbsp;Zeyuan Lin,&nbsp;Jinsong Lai,&nbsp;Kun Guo","doi":"10.1016/j.ins.2026.123139","DOIUrl":"10.1016/j.ins.2026.123139","url":null,"abstract":"<div><div>Temporal knowledge graphs (TKGs) comprise timestamped facts and are widely used in intelligent systems. However, large-scale TKGs are often incomplete, therefore Temporal Knowledge Graph Completion (TKGC) is an important task. Existing approaches mostly use local neighborhoods to learn entity and relation representations, ignoring query-aware global semantics and semantic linkages between quadruples. Furthermore, timestamps are frequently considered as independent features, ignoring their periodicity and interactions with the graph structure. We propose <strong>T-GRIN</strong> (<strong>T</strong>emporal <strong>G</strong>raph completion via <strong>R</strong>epresentation and <strong>IN</strong>teraction) to incorporate query-aware global semantic representations and deep interaction between entities and relations. T-GRIN employs a dynamic time encoder to capture periodic temporal patterns, an entity encoder with relation-enhanced mechanisms to highlight query-relevant contexts, and a relation encoder with multi-head attention to model diverse semantics under temporal and entity contexts. Furthermore, an interactive convolutional decoder is designed to improve feature interaction and high-order semantic composition. Extensive experiments on benchmark datasets demonstrate the effectiveness of T-GRIN. In ICEWS05-15, T-GRIN outperforms the previous best model by 8.9% MRR and 10.9% Hit@1.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123139"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081915","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}