Pub Date : 2026-05-25Epub Date: 2026-01-31DOI: 10.1016/j.ins.2026.123180
Yang Gao , Gang Quan , Wujie Wen , Scott Piersall , Qian Lou , Liqiang Wang
Sparse matrix–vector multiplication (SpMV) is a fundamental operation in scientific computing, data analysis, and machine learning. When the data being processed are sensitive, preserving privacy becomes critical, and homomorphic encryption (HE) has emerged as a leading approach for addressing this challenge. Although HE enables privacy-preserving computation, its application to SpMV has remained largely unaddressed. To the best of our knowledge, this paper presents the first framework that efficiently integrates HE with SpMV, addressing the dual challenges of computational efficiency and data privacy. In particular, we introduce a novel compressed matrix format, named Compressed Sparse Sorted Column (CSSC), which is specifically designed to optimize encrypted sparse matrix computations. By preserving sparsity and enabling efficient ciphertext packing, CSSC significantly reduces storage and computational overhead. Our experimental results on real-world datasets demonstrate that the proposed method achieves significant gains in both processing time and memory usage. This study advances privacy-preserving SpMV and lays the groundwork for secure applications in federated learning, encrypted databases, and scientific computing, beyond.
{"title":"Efficient privacy-preserving sparse matrix-vector multiplication using homomorphic encryption","authors":"Yang Gao , Gang Quan , Wujie Wen , Scott Piersall , Qian Lou , Liqiang Wang","doi":"10.1016/j.ins.2026.123180","DOIUrl":"10.1016/j.ins.2026.123180","url":null,"abstract":"<div><div>Sparse matrix–vector multiplication (SpMV) is a fundamental operation in scientific computing, data analysis, and machine learning. When the data being processed are sensitive, preserving privacy becomes critical, and homomorphic encryption (HE) has emerged as a leading approach for addressing this challenge. Although HE enables privacy-preserving computation, its application to SpMV has remained largely unaddressed. To the best of our knowledge, this paper presents the first framework that efficiently integrates HE with SpMV, addressing the dual challenges of computational efficiency and data privacy. In particular, we introduce a novel compressed matrix format, named Compressed Sparse Sorted Column (CSSC), which is specifically designed to optimize encrypted sparse matrix computations. By preserving sparsity and enabling efficient ciphertext packing, CSSC significantly reduces storage and computational overhead. Our experimental results on real-world datasets demonstrate that the proposed method achieves significant gains in both processing time and memory usage. This study advances privacy-preserving SpMV and lays the groundwork for secure applications in federated learning, encrypted databases, and scientific computing, beyond.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123180"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191066","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}
In today’s economic climate, enterprises face a variety of internal and external risks, making bankruptcy prediction critical for risk management. The traditional statistical and machine learning-based methods mainly rely on economic indicators, which are insufficient for deducing the risk propagation among enterprises. Recently, researchers have begun to explore the use of graph neural networks, utilizing their message-passing mechanisms to simulate the risk propagation process. However, existing graph-based methods often neglect degree imbalances, leading to high misjudgment rates for sparsely connected nodes. Furthermore, existing methods typically use a risk-oriented decision model to evaluate the likelihood of bankruptcy, which may lead to the overestimation of bankruptcy probabilities.
To address these issues, we propose a novel bankruptcy prediction model which consists of several key components, including a data-driven explicit risk encoding module, a global multihead attention-based implicit risk encoding module, a hierarchical hypergraph-based external risk enhancement module, and a dual-decision expert-based risk assessment module. We extend the traditional graph structure to a hierarchical hypergraph structure and design a corresponding information propagation strategy to alleviate the degree imbalance issue. Furthermore, a dual-decision assessment module is designed to integrate the perspectives of both risk and non-risk experts to prevent the overestimation of bankruptcy probabilities. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness of the proposed model, which achieves an accuracy of 77.64% and an AUC of 0.8270, significantly outperforming existing methods.
{"title":"BPHD: Enterprise bankruptcy prediction with a hierarchical hypergraph and dual-decision experts","authors":"Boyuan Ren , Hongrui Guo , Hongzhi Liu , Xudong Tang , Jingming Xue , Zhonghai Wu","doi":"10.1016/j.ins.2026.123142","DOIUrl":"10.1016/j.ins.2026.123142","url":null,"abstract":"<div><div>In today’s economic climate, enterprises face a variety of internal and external risks, making bankruptcy prediction critical for risk management. The traditional statistical and machine learning-based methods mainly rely on economic indicators, which are insufficient for deducing the risk propagation among enterprises. Recently, researchers have begun to explore the use of graph neural networks, utilizing their message-passing mechanisms to simulate the risk propagation process. However, existing graph-based methods often neglect degree imbalances, leading to high misjudgment rates for sparsely connected nodes. Furthermore, existing methods typically use a risk-oriented decision model to evaluate the likelihood of bankruptcy, which may lead to the overestimation of bankruptcy probabilities.</div><div>To address these issues, we propose a novel bankruptcy prediction model which consists of several key components, including a data-driven explicit risk encoding module, a global multihead attention-based implicit risk encoding module, a hierarchical hypergraph-based external risk enhancement module, and a dual-decision expert-based risk assessment module. We extend the traditional graph structure to a hierarchical hypergraph structure and design a corresponding information propagation strategy to alleviate the degree imbalance issue. Furthermore, a dual-decision assessment module is designed to integrate the perspectives of both risk and non-risk experts to prevent the overestimation of bankruptcy probabilities. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness of the proposed model, which achieves an accuracy of 77.64% and an AUC of 0.8270, significantly outperforming existing methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"738 ","pages":"Article 123142"},"PeriodicalIF":6.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081294","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}
Pub Date : 2026-05-15Epub Date: 2026-01-23DOI: 10.1016/j.ins.2026.123131
Aijun Yan , Xin Liu
In the modeling process of model predictive control (MPC), the model typically exhibits non-convex characteristics, which make the optimization problem complex and prone to local optima. To address this, an MPC modeling method based on input convex stochastic configuration networks (SCN) is proposed. The method imposes convexity constraints on both network architecture and activation functions. A Sparsemax-based activation function selection mechanism is developed to adaptively choose convex activation functions for each configuration node. Output weights are determined using the alternating direction method of multipliers to solve least-squares problems with non-negative constraints. Two architectures are constructed: fully input convex and partially input convex SCN. Through a dynamic supervision mechanism, it is theoretically proven that the proposed model approximates convex functions to arbitrary accuracy under weight constraints. Experimental results demonstrate improved fitting accuracy with convex approximation guarantees, and control examples show enhanced closed-loop performance by ensuring MPC optimization convexity.
{"title":"Input convex stochastic configuration networks modeling method for predictive control","authors":"Aijun Yan , Xin Liu","doi":"10.1016/j.ins.2026.123131","DOIUrl":"10.1016/j.ins.2026.123131","url":null,"abstract":"<div><div>In the modeling process of model predictive control (MPC), the model typically exhibits non-convex characteristics, which make the optimization problem complex and prone to local optima. To address this, an MPC modeling method based on input convex stochastic configuration networks (SCN) is proposed. The method imposes convexity constraints on both network architecture and activation functions. A Sparsemax-based activation function selection mechanism is developed to adaptively choose convex activation functions for each configuration node. Output weights are determined using the alternating direction method of multipliers to solve least-squares problems with non-negative constraints. Two architectures are constructed: fully input convex and partially input convex SCN. Through a dynamic supervision mechanism, it is theoretically proven that the proposed model approximates convex functions to arbitrary accuracy under weight constraints. Experimental results demonstrate improved fitting accuracy with convex approximation guarantees, and control examples show enhanced closed-loop performance by ensuring MPC optimization convexity.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"738 ","pages":"Article 123131"},"PeriodicalIF":6.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081297","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}
Pub Date : 2026-05-15Epub Date: 2026-01-23DOI: 10.1016/j.ins.2026.123143
Nikolay I. Kalmykov , Razan Dibo , Kaiyu Shen , Zhonghan Xu , Anh-Huy Phan , Yipeng Liu , Ivan Oseledets
Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of classifiers. Existing adversarial attacks on NICs are often naive adaptations of pixel-space methods, overlooking the unique, structured nature of the compression pipeline. In this work, we propose a more advanced class of vulnerabilities by introducing T-MLA, the first targeted multiscale log–exponential attack framework. We introduce adversarial perturbations in the wavelet domain that concentrate on less perceptually salient coefficients, improving the stealth of the attack. Extensive evaluation across multiple state-of-the-art NIC architectures on standard image compression benchmarks reveals a large drop in reconstruction quality while the perturbations remain visually imperceptible. On standard NIC benchmarks, T-MLA achieves targeted degradation of reconstruction quality while improving perturbation imperceptibility (higher PSNR/VIF of the perturbed inputs) compared to PGD-style baselines at comparable attack success, as summarized in our main results. Our findings reveal a critical security flaw at the core of generative and content delivery pipelines.
{"title":"T-MLA: A targeted multiscale log–exponential attack framework for neural image compression","authors":"Nikolay I. Kalmykov , Razan Dibo , Kaiyu Shen , Zhonghan Xu , Anh-Huy Phan , Yipeng Liu , Ivan Oseledets","doi":"10.1016/j.ins.2026.123143","DOIUrl":"10.1016/j.ins.2026.123143","url":null,"abstract":"<div><div>Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of classifiers. Existing adversarial attacks on NICs are often naive adaptations of pixel-space methods, overlooking the unique, structured nature of the compression pipeline. In this work, we propose a more advanced class of vulnerabilities by introducing T-MLA, the first targeted multiscale log–exponential attack framework. We introduce adversarial perturbations in the wavelet domain that concentrate on less perceptually salient coefficients, improving the stealth of the attack. Extensive evaluation across multiple state-of-the-art NIC architectures on standard image compression benchmarks reveals a large drop in reconstruction quality while the perturbations remain visually imperceptible. On standard NIC benchmarks, T-MLA achieves targeted degradation of reconstruction quality while improving perturbation imperceptibility (higher PSNR/VIF of the perturbed inputs) compared to PGD-style baselines at comparable attack success, as summarized in our main results. Our findings reveal a critical security flaw at the core of generative and content delivery pipelines.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"738 ","pages":"Article 123143"},"PeriodicalIF":6.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081295","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}
Pub Date : 2026-05-15Epub Date: 2026-01-21DOI: 10.1016/j.ins.2026.123140
Francesco Villani , Dario Lazzaro , Antonio Emanuele Cinà , Matteo Dell’Amico , Battista Biggio , Fabio Roli
Data poisoning attacks on clustering algorithms have received limited attention, with existing methods struggling to scale efficiently as dataset sizes and feature counts increase. These attacks typically require re-clustering the entire dataset multiple times to generate predictions and assess the attacker’s objectives, significantly hindering their scalability. This paper addresses these limitations by proposing Sonic, a novel genetic data poisoning attack that leverages incremental and scalable clustering algorithms, e.g., FISHDBC, as surrogates to accelerate poisoning attacks against graph-based and density-based clustering methods, such as HDBSCAN. We empirically demonstrate the effectiveness and efficiency of Sonic in poisoning the target clustering algorithms. We then conduct a comprehensive analysis of the factors affecting the scalability and transferability of poisoning attacks against clustering algorithms, and we conclude by examining the robustness of hyperparameters in our attack strategy Sonic.
{"title":"Sonic: Fast and transferable data poisoning on clustering algorithms","authors":"Francesco Villani , Dario Lazzaro , Antonio Emanuele Cinà , Matteo Dell’Amico , Battista Biggio , Fabio Roli","doi":"10.1016/j.ins.2026.123140","DOIUrl":"10.1016/j.ins.2026.123140","url":null,"abstract":"<div><div>Data poisoning attacks on clustering algorithms have received limited attention, with existing methods struggling to scale efficiently as dataset sizes and feature counts increase. These attacks typically require re-clustering the entire dataset multiple times to generate predictions and assess the attacker’s objectives, significantly hindering their scalability. This paper addresses these limitations by proposing <span>Sonic</span>, a novel genetic data poisoning attack that leverages incremental and scalable clustering algorithms, e.g., FISHDBC, as surrogates to accelerate poisoning attacks against graph-based and density-based clustering methods, such as HDBSCAN. We empirically demonstrate the effectiveness and efficiency of <span>Sonic</span> in poisoning the target clustering algorithms. We then conduct a comprehensive analysis of the factors affecting the scalability and transferability of poisoning attacks against clustering algorithms, and we conclude by examining the robustness of hyperparameters in our attack strategy <span>Sonic</span>.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"738 ","pages":"Article 123140"},"PeriodicalIF":6.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081293","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}
Pub Date : 2026-05-15Epub Date: 2025-12-28DOI: 10.1016/j.ins.2025.123047
Shakir Khan , Arfat Ahmad Khan , Rakesh Kumar Mahendran , Mohd Fazil , Ateeq Ur Rehman , Weiwei Jiang , Ahmed Farouk
Cervical cancer (CC) is the major common cancers among women, and detecting earlier critical for successful treatment. Traditional methods, includes as Pap smear tests, are highly contagious to manual error which paves the way for Artificial Intelligence (AI) solutions for improved detection. Whereas the conventional AI enabled models faced with poor reliability and accuracy respectively. In order to overcome the issue mentioned, this research develops AI enabled model named C2DEEP-OT which is coined as Cervical Cancer Detection through Deep Reinforcement Learning and Optimized Transformers. Our models employ coloscopy and histopathology images for diagnosing the cervical cancer for enabling normalization and noise removal. After that, major features were extracted from Multi Agent Deep Reinforcement Learning (MA-DRL) named Enhanced Deep Q-network (EDQN) that effectively manage the color, contextual, and spectral, and spatial information with better accuracy. In parallel, the extracted features are then provided to the Optimized Attention based Transformer (OAT) which is improved by Rat Swarm Optimization (RSO) for categorize cervical cancer in accurate manner into three classes includes malignant, benign, and normal. From the results, it is seen that C2DEEP-OT gains 98.63% of accuracy which superiors state of the art models.
宫颈癌（CC）是妇女中主要的常见癌症，早期发现对成功治疗至关重要。包括巴氏涂片检查在内的传统方法对人工错误具有高度传染性，这为人工智能（AI）解决方案改善检测铺平了道路。而传统的人工智能模型分别面临着较差的可靠性和准确性。为了克服上述问题，本研究开发了名为C2DEEP-OT的人工智能支持模型，该模型被称为“通过深度强化学习和优化变压器检测宫颈癌”。我们的模型采用结肠镜和组织病理学图像来诊断宫颈癌，从而实现归一化和去噪。之后，从多Agent深度强化学习（MA-DRL）中提取主要特征，称为Enhanced Deep Q-network (EDQN)，有效地管理颜色、上下文、光谱和空间信息，精度更高。同时，将提取的特征提供给优化的基于注意力的转换器（OAT），该转换器通过大鼠群算法（RSO）进行改进，将宫颈癌准确地分为恶性、良性和正常三种类型。从结果中可以看出，C2DEEP-OT获得了98.63%的准确率，优于目前最先进的模型。
{"title":"C2DEEP-OT: Utilizing Multi-Agent Deep Reinforcement Learning Algorithm and Optimized Attentive Transformer Network for Cervical Cancer Detection","authors":"Shakir Khan , Arfat Ahmad Khan , Rakesh Kumar Mahendran , Mohd Fazil , Ateeq Ur Rehman , Weiwei Jiang , Ahmed Farouk","doi":"10.1016/j.ins.2025.123047","DOIUrl":"10.1016/j.ins.2025.123047","url":null,"abstract":"<div><div>Cervical cancer (CC) is the major common cancers among women, and detecting earlier critical for successful treatment. Traditional methods, includes as Pap smear tests, are highly contagious to manual error which paves the way for Artificial Intelligence (AI) solutions for improved detection. Whereas the conventional AI enabled models faced with poor reliability and accuracy respectively. In order to overcome the issue mentioned, this research develops AI enabled model named C2DEEP-OT which is coined as Cervical Cancer Detection through Deep Reinforcement Learning and Optimized Transformers. Our models employ coloscopy and histopathology images for diagnosing the cervical cancer for enabling normalization and noise removal. After that, major features were extracted from Multi Agent Deep Reinforcement Learning (MA-DRL) named Enhanced Deep Q-network (EDQN) that effectively manage the color, contextual, and spectral, and spatial information with better accuracy. In parallel, the extracted features are then provided to the Optimized Attention based Transformer (OAT) which is improved by Rat Swarm Optimization (RSO) for categorize cervical cancer in accurate manner into three classes includes malignant, benign, and normal. From the results, it is seen that C2DEEP-OT gains 98.63% of accuracy which superiors state of the art models.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"738 ","pages":"Article 123047"},"PeriodicalIF":6.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081292","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}
Pub Date : 2026-05-15Epub Date: 2026-01-23DOI: 10.1016/j.ins.2026.123141
Rongna Cai , Haibin Ouyang , Steven Li , Gaige Wang , Weiping Ding
To tackle challenges in industrial image defect detection, guided by three core hypotheses: dataset representativeness, continuous differentiable NAS search space, and GPU-based computing environment, this study presents an enhanced particle swarm optimization (PSO)-based neural architecture search (NAS) method designated as DNE-PSO-NAS. Firstly, it employs a two-level binary particle encoding scheme for network layer configurations and connectivity, transforming architecture search into a multi-dimensional optimization problem. Secondly, an improved MBConv module with CBAM is developed to reinforce the model’s ability to perceive local and global features of defects, thereby raising the signal-to-noise ratio for tiny defect regions. Additionally, dynamic ring neighborhood velocity topology and swarm entropy-driven mutation are proposed to balance exploration and exploitation, boosting PSO’s optimization efficiency. Finally, a low-fidelity evaluation strategy is incorporated, forming a three-stage framework that reduces input space via image downsampling, compresses convolutional layer parameters to lower spatial complexity, and adopts a dynamic training termination mechanism based on fitness tracking. Experiments on NEU-DET and WM-811 K datasets demonstrate that its discovered architectures surpass traditional CNNs and SOTA methods, with classification accuracy reaching 100% on NEU-DET and 93% on WM-811K. Meanwhile, our algorithm cuts computational costs significantly and the results highlight major benefits for real-time industrial quality inspection.
{"title":"Neural architecture search using an enhanced particle swarm optimization algorithm for industrial image classification","authors":"Rongna Cai , Haibin Ouyang , Steven Li , Gaige Wang , Weiping Ding","doi":"10.1016/j.ins.2026.123141","DOIUrl":"10.1016/j.ins.2026.123141","url":null,"abstract":"<div><div>To tackle challenges in industrial image defect detection, guided by three core hypotheses: dataset representativeness, continuous differentiable NAS search space, and GPU-based computing environment, this study presents an enhanced particle swarm optimization (PSO)-based neural architecture search (NAS) method designated as DNE-PSO-NAS. Firstly, it employs a two-level binary particle encoding scheme for network layer configurations and connectivity, transforming architecture search into a multi-dimensional optimization problem. Secondly, an improved MBConv module with CBAM is developed to reinforce the model’s ability to perceive local and global features of defects, thereby raising the signal-to-noise ratio for tiny defect regions. Additionally, dynamic ring neighborhood velocity topology and swarm entropy-driven mutation are proposed to balance exploration and exploitation, boosting PSO’s optimization efficiency. Finally, a low-fidelity evaluation strategy is incorporated, forming a three-stage framework that reduces input space via image downsampling, compresses convolutional layer parameters to lower spatial complexity, and adopts a dynamic training termination mechanism based on fitness tracking. Experiments on NEU-DET and WM-811 K datasets demonstrate that its discovered architectures surpass traditional CNNs and SOTA methods, with classification accuracy reaching 100% on NEU-DET and 93% on WM-811K. Meanwhile, our algorithm cuts computational costs significantly and the results highlight major benefits for real-time industrial quality inspection.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"738 ","pages":"Article 123141"},"PeriodicalIF":6.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043324","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}
Pub Date : 2026-05-15Epub Date: 2026-01-22DOI: 10.1016/j.ins.2026.123130
Jun Wan , Xinyu Xiong , Ning Chen , Zhihui Lai , Jie Zhou , Wenwen Min
Recently, deep learning based facial landmark detection (FLD) methods have achieved considerable success. However, in challenging scenarios such as large pose variations, illumination changes, and facial expression variations, they still struggle to accurately capture the geometric structure of the face, resulting in performance degradation. Moreover, the limited size and diversity of existing FLD datasets hinder robust model training, leading to reduced detection accuracy. To address these challenges, we propose a Frequency-Guided Task-Balancing Transformer (FGTBT), which enhances facial structure perception through frequency-domain modeling and multi-dataset unified training. Specifically, we propose a novel Fine-Grained Multi-Task Balancing loss (FMB-loss), which moves beyond coarse task-level balancing by assigning weights to individual landmarks based on their occurrence across datasets. This enables more effective unified training and mitigates the issue of inconsistent gradient magnitudes. Additionally, a Frequency-Guided Structure-Aware (FGSA) model is designed to utilize frequency-guided structure injection and regularization to help learn facial structure constraints. Extensive experimental results on popular benchmark datasets demonstrate that the integration of the proposed FMB-loss and FGSA model into our FGTBT framework achieves performance comparable to state-of-the-art methods. The code is available at https://github.com/Xi0ngxinyu/FGTBT.
{"title":"FGTBT: Frequency-guided task-balancing transformer for unified facial landmark detection","authors":"Jun Wan , Xinyu Xiong , Ning Chen , Zhihui Lai , Jie Zhou , Wenwen Min","doi":"10.1016/j.ins.2026.123130","DOIUrl":"10.1016/j.ins.2026.123130","url":null,"abstract":"<div><div>Recently, deep learning based facial landmark detection (FLD) methods have achieved considerable success. However, in challenging scenarios such as large pose variations, illumination changes, and facial expression variations, they still struggle to accurately capture the geometric structure of the face, resulting in performance degradation. Moreover, the limited size and diversity of existing FLD datasets hinder robust model training, leading to reduced detection accuracy. To address these challenges, we propose a Frequency-Guided Task-Balancing Transformer (FGTBT), which enhances facial structure perception through frequency-domain modeling and multi-dataset unified training. Specifically, we propose a novel Fine-Grained Multi-Task Balancing loss (FMB-loss), which moves beyond coarse task-level balancing by assigning weights to individual landmarks based on their occurrence across datasets. This enables more effective unified training and mitigates the issue of inconsistent gradient magnitudes. Additionally, a Frequency-Guided Structure-Aware (FGSA) model is designed to utilize frequency-guided structure injection and regularization to help learn facial structure constraints. Extensive experimental results on popular benchmark datasets demonstrate that the integration of the proposed FMB-loss and FGSA model into our FGTBT framework achieves performance comparable to state-of-the-art methods. The code is available at <span><span>https://github.com/Xi0ngxinyu/FGTBT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"738 ","pages":"Article 123130"},"PeriodicalIF":6.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081299","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}
Pub Date : 2026-05-15Epub Date: 2026-01-22DOI: 10.1016/j.ins.2026.123133
Muhammad Imran Khalid , Jian-Xun Mi , Ghulam Ali , Tariq Ali , Mohammad Hijji , Muhammad Ayaz , Zia-ur-Rehman
The complexity and opaque internal mechanisms of deep learning models, particularly modern object detectors like DETR, make them challenging to interpret. Existing explainability methods, such as ODAM, produce spatial heatmaps but often fail to distinguish overlapping objects or convey semantic meaning. Concept-based methods, while interpretable, typically lack precise instance-level localization. To overcome these limitations, we propose IntegraXAI (Integrated Explainable AI), a novel framework that, for the first time, integrates two distinct XAI modalities for object detection: (1) gradient-based, instance-specific heatmaps (inspired by ODAM) for spatial localization, and (2) semantic concept discovery via Non-negative Matrix Factorization (NMF) with concept importance quantification using Sobol sensitivity analysis. The proposed three-stage framework provides insight not only into where the detector focuses its attention, but also into the semantic cues that ultimately guide its predictions. The effectiveness of IntegraXAI is validated across multiple object detection architectures, including DETR, YOLOv5, and Faster R-CNN, using the COCO benchmark dataset. Experimental findings show that the proposed method consistently outperforms existing explainability techniques, including Grad-CAM++, D-RISE, and individual ODAM or CRAFT variants, achieving higher spatial localization accuracy and clearer semantic interpretation. At the same time, IntegraXAI maintains stable, practical computational requirements, producing explanations in approximately 1 s per image, which is substantially more efficient than perturbation-based approaches like D-RISE. By jointly integrating spatial, semantic, and quantitative explanation mechanisms, the proposed framework improves the interpretability and trustworthiness of object detection systems, particularly in safety–critical domains such as autonomous driving and video surveillance.
{"title":"Interpretable object detection via integrated heatmap, concept attribution, and sobol sensitivity analysis","authors":"Muhammad Imran Khalid , Jian-Xun Mi , Ghulam Ali , Tariq Ali , Mohammad Hijji , Muhammad Ayaz , Zia-ur-Rehman","doi":"10.1016/j.ins.2026.123133","DOIUrl":"10.1016/j.ins.2026.123133","url":null,"abstract":"<div><div>The complexity and opaque internal mechanisms of deep learning models, particularly modern object detectors like DETR, make them challenging to interpret. Existing explainability methods, such as ODAM, produce spatial heatmaps but often fail to distinguish overlapping objects or convey semantic meaning. Concept-based methods, while interpretable, typically lack precise instance-level localization. To overcome these limitations, we propose IntegraXAI (Integrated Explainable AI), a novel framework that, for the first time, integrates two distinct XAI modalities for object detection: (1) gradient-based, instance-specific heatmaps (inspired by ODAM) for spatial localization, and (2) semantic concept discovery via Non-negative Matrix Factorization (NMF) with concept importance quantification using Sobol sensitivity analysis. The proposed three-stage framework provides insight not only into where the detector focuses its attention, but also into the semantic cues that ultimately guide its predictions. The effectiveness of IntegraXAI is validated across multiple object detection architectures, including DETR, YOLOv5, and Faster R-CNN, using the COCO benchmark dataset. Experimental findings show that the proposed method consistently outperforms existing explainability techniques, including Grad-CAM++, D-RISE, and individual ODAM or CRAFT variants, achieving higher spatial localization accuracy and clearer semantic interpretation. At the same time, IntegraXAI maintains stable, practical computational requirements, producing explanations in approximately 1 s per image, which is substantially more efficient than perturbation-based approaches like D-RISE. By jointly integrating spatial, semantic, and quantitative explanation mechanisms, the proposed framework improves the interpretability and trustworthiness of object detection systems, particularly in safety–critical domains such as autonomous driving and video surveillance.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"738 ","pages":"Article 123133"},"PeriodicalIF":6.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081296","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}
Pub Date : 2026-05-15Epub Date: 2026-01-21DOI: 10.1016/j.ins.2026.123127
Wenli Chen , Xiaojian Li
This study investigates the problem of data-driven fault-tolerant output synchronization control for heterogeneous multi-agent systems with unknown dynamics. Unlike the existing approaches that rely on prior knowledge of system matrices, this work proposes a novel method to design distributed data-driven fault-tolerant output synchronization controllers using data and output regulation theory. The output regulator equations are essential for fault-tolerant output synchronization control, whereas exact solutions from noise-corrupted data are challenging to obtain. To address this issue, a data-driven optimization problem is formulated to seek approximate solutions by minimizing the output regulation error matrices. Stability conditions are then derived in the form of data-dependent programs, whose solutions directly yield stabilizing feedback gains for agents. This approach ensures the achievement of output synchronization by utilizing data. Furthermore, a data-driven fault-tolerant controller is constructed by integrating adaptive control techniques with approximate solutions to the output regulator equations and stabilizing feedback gains learned from data, equipping agents with fault-tolerant capabilities. Theoretical analysis demonstrates that the proposed controller ensures the output synchronization errors are globally ultimately bounded (GUB). To validate the theoretical results, simulation examples are presented to demonstrate their efficacy.
{"title":"Fault-tolerant control for output synchronization of multi-agent systems: A data-driven approach","authors":"Wenli Chen , Xiaojian Li","doi":"10.1016/j.ins.2026.123127","DOIUrl":"10.1016/j.ins.2026.123127","url":null,"abstract":"<div><div>This study investigates the problem of data-driven fault-tolerant output synchronization control for heterogeneous multi-agent systems with unknown dynamics. Unlike the existing approaches that rely on prior knowledge of system matrices, this work proposes a novel method to design distributed data-driven fault-tolerant output synchronization controllers using data and output regulation theory. The output regulator equations are essential for fault-tolerant output synchronization control, whereas exact solutions from noise-corrupted data are challenging to obtain. To address this issue, a data-driven optimization problem is formulated to seek approximate solutions by minimizing the output regulation error matrices. Stability conditions are then derived in the form of data-dependent programs, whose solutions directly yield stabilizing feedback gains for agents. This approach ensures the achievement of output synchronization by utilizing data. Furthermore, a data-driven fault-tolerant controller is constructed by integrating adaptive control techniques with approximate solutions to the output regulator equations and stabilizing feedback gains learned from data, equipping agents with fault-tolerant capabilities. Theoretical analysis demonstrates that the proposed controller ensures the output synchronization errors are globally ultimately bounded (GUB). To validate the theoretical results, simulation examples are presented to demonstrate their efficacy.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"738 ","pages":"Article 123127"},"PeriodicalIF":6.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081298","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号