Applied Intelligence最新文献

{"title":"StealthMask: Highly stealthy adversarial attack on face recognition system","authors":"Jian-Xun Mi,&nbsp;Mingxuan Chen,&nbsp;Tao Chen,&nbsp;Xiao Cheng","doi":"10.1007/s10489-025-06511-4","DOIUrl":"10.1007/s10489-025-06511-4","url":null,"abstract":"<div><p>Convolutional Neural Networks (CNNs) based on deep learning algorithms are widely used in real-world scenarios. However, these networks are vulnerable to adversarial examples-maliciously crafted inputs that can cause the model to make incorrect predictions. The existence of adversarial examples presents a significant challenge to the field of deep learning, with profound implications for various aspects of our lives. In face recognition technology, adversarial examples pose a substantial security risk. In this paper, we propose a novel method for generating adversarial patches designed to be worn as masks. The perturbed mask is crafted to deceive face recognition models, thereby highlighting the security vulnerabilities inherent in this technology. Our experimental results demonstrate that the mask generated by the proposed method effectively misleads the face recognition system, achieving high attack success rates while maintaining necessary stealthiness and transferability. Moreover, our method successfully attacks commercial face recognition systems and real-world access control systems, exposing the vulnerabilities of existing face recognition technologies in security-critical applications. Notably, compared to traditional methods, our proposed method emphasizes the stealthiness of the adversarial mask more than traditional methods. To account for physical-world factors, such as distortion, rotation, and deformations, we integrate a specifically designed loss function, thereby enhancing the method’s stability and reliability in practical scenarios.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Unsupervised learning with physics informed graph networks for partial differential equations","authors":"Lin Lu,&nbsp;Yiye Zou,&nbsp;Jingyu Wang,&nbsp;Shufan Zou,&nbsp;Laiping Zhang,&nbsp;Xiaogang Deng","doi":"10.1007/s10489-025-06479-1","DOIUrl":"10.1007/s10489-025-06479-1","url":null,"abstract":"<div><p>Natural physical phenomena are commonly expressed using partial differential equations (PDEs), in domains such as fluid dynamics, electromagnetism, and atmospheric science. These equations typically require numerical solutions under given boundary conditions. There is a burgeoning interest in the exploration of neural network methodologies for solving PDEs, mainly based on automatic differentiation methods to learn the PDE-solving process, which means that the model needs to be retrained when the boundary conditions of PDE are changed. However, automatic differentiation requires substantial memory resources to facilitate the training regimen. Moreover, a learning objective that is tailored to the solution process often lacks the flexibility to extend to boundary conditions; thereby limiting the solution’s overall precision. The method proposed in this paper introduces a graph neural network approach, embedded with physical information, mainly for solving Poisson’s equation. An approach is introduced that reduces memory usage and enhances training efficiency through an unsupervised learning methodology based on numerical differentiation. Concurrently, by integrating boundary conditions directly into the neural network as supplementary physical information, this approach ensures that a singular model is capable of solving PDEs across a variety of boundary conditions. To address the challenges posed by more complex network inputs, the introduction of graph residual connections serves as a strategic measure to prevent network overfitting and to elevate the accuracy of the solutions provided. Experimental findings reveal that, despite having 30 times more training parameters than the Physics-Informed Neural Networks (PINN) model, the proposed model consumes 2.2% less memory than PINN. Additionally, generalization in boundary conditions has been achieved to a certain extent. This enables the model to solve partial differential equations with different boundary conditions, a capability that PINN currently lacks. To validate the solving capability of the proposed method, it has been applied to the model equation, the Sod shock tube problem, and the two-dimensional inviscid airfoil problem. In terms of the solution accuracy of the model equations, the proposed method outperforms PINN by 30% to four orders of magnitude. Compared to the traditional numerical method, the Finite Element Method (FEM), the proposed method also shows an order of magnitude improvement. Additionally, when compared to the improved version of PINN, TSONN, our method demonstrates certain advantages. The forward problem of the Sod shock tube, which PINN is currently unable to solve, is successfully handled by the proposed method. For the airfoil problem, the results are comparable to those of PINN.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Topology-preserving and structure-aware (hyper)graph contrastive learning for node classification","authors":"Minhao Zou,&nbsp;Zhongxue Gan,&nbsp;Yutong Wang,&nbsp;Junheng Zhang,&nbsp;Chun Guan,&nbsp;Siyang Leng","doi":"10.1007/s10489-025-06491-5","DOIUrl":"10.1007/s10489-025-06491-5","url":null,"abstract":"<div><p>Recently, graph contrastive learning (GCL) has attracted considerable attention, establishing a new paradigm for learning graph representations in the absence of human annotations. While notable advancements have been made, simultaneous consideration of both graphs and hypergraphs remains rare. This limitation arises because graphs and hypergraphs encode connectivity differently, making it challenging to develop a unified structure augmentation strategy. Conventional structure augmentation methods like adding or removing edges risk imperiling intrinsic topological traits and introducing adverse distortions such as disconnected subgraphs or isolated nodes. In this work, we propose a framework of contrastive learning on graphs and hypergraphs, named as UniGCL, to address these challenges by leveraging a unified adjacency representation that enables simultaneous modeling of pairwise and higher-order relationships. In particular, two structure augmentation methods are developed to perturb graph structure weights instead of altering connectivity, thereby preserving both graph and hypergraph topology while generating diverse augmented views. Furthermore, a structure-aware contrastive loss is proposed, which incorporates gradient perturbation techniques to enhance the model’s ability to capture fine-grained structural dependencies in (hyper)graphs. Extensive experiments are conducted on six real-world graph datasets and nine representative hypergraph datasets to evaluate the performance of the proposed framework. The results demonstrate that UniGCL achieves superior node classification performance compared to the advanced graph and hypergraph contrastive learning methods, across datasets with different homophilic extents and limited annotations. Additionally, ablation studies validate the effectiveness of our structure-preserving augmentations and structure-aware contrastive loss in enhancing performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Intra-frame scan-free video state spaces model for video moment retrieval","authors":"Fengzhen Yu,&nbsp;Xiaodong Gu","doi":"10.1007/s10489-025-06517-y","DOIUrl":"10.1007/s10489-025-06517-y","url":null,"abstract":"<div><p>With the increasing complexity of video moment retrieval tasks, effectively handling temporal and spatial information in video data has become a central challenge. This paper proposes a novel Intra-frame Scan-free Video State Spaces Model to address the spatiotemporal modeling problem in video moment retrieval. The model eliminates the dependency on the scanning order of intra-frame patches, overcoming the dual temporal limitations of frame order and within-frame patch sequence, which enhances the flexibility and efficiency of video understanding. To better model temporal information, we introduce the concept of video moment boundaries and propose the Weighted Relative Center Difference Loss, which ensures that the predicted center regions are closer to the ground truth, thereby improving retrieval accuracy. Extensive experiments on three public video datasets (ActivityNet Captions, TACoS, and Charades-STA) show that the model achieves superior or near-optimal performance across multiple metrics. The ablation study compares the performance loss when removing different components, the effect of different scanning methods on performance and inference throughput, and the effect of hyperparameters such as the number of SSM layers and the weighted relative centre difference loss threshold on retrieval performance. These results validate the effectiveness and robustness of our approach for video moment retrieval.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Multiple disease diagnoses using heterogeneous EHR curated knowledge graph and machine learning models","authors":"Shivani Dhiman,&nbsp;Anjali Thukral,&nbsp;Punam Bedi","doi":"10.1007/s10489-024-05952-7","DOIUrl":"10.1007/s10489-024-05952-7","url":null,"abstract":"<div><p>Artificial Intelligence (AI) can play a significant role by assisting healthcare professionals in disease diagnosis, which is a critical step towards a patient’s treatment. Most of the research work in disease diagnosis systems predicts the presence or absence of a given single disease in a patient. However, there are only a few studies on multiple disease diagnoses, i.e., on detecting the presence of more than one disease at the same time. In this paper, we propose a framework for diagnosing multiple diseases using Knowledge Graph (KG), Knowledge embeddings and Machine Learning (ML). KG is created to semantically organize heterogeneous clinical details extracted from Electronic Health Records (EHRs). Additionally, we present a detailed comparison and analysis of three disease diagnosis systems, Single Disease Single Diagnosis (SDSD), Multiple Disease Single Diagnosis (MDSD), and Multiple Disease Multiple Diagnosis (MDMD) using the MIMIC-III dataset on Chronic Heart Failure (CHF), Acute Respiratory Failure (ARF) and Acute Kidney Failure (AKF) diseases. The above disease diagnosis systems have been implemented and analysed with different ML algorithms, such as Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB), and Support Vector Machine (SVM). Besides, detecting the probability of having multiple diseases at a time, the MDMD shows comparable results in comparison to SDSD and MDSD. This is being evaluated by using the Area Under Receiver Operating Characteristic (AUROC) and the Area Under Precision-Recall Curve (AUPRC) metrics. The MDMD system based on the proposed framework for multiple disease diagnosis predicts CHF, ARF and AKF in 91%, 74% and 79% of positive cases, respectively.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 modified SimRank++ approach for searching crash simulation data","authors":"Anahita Pakiman,&nbsp;Jochen Garcke,&nbsp;Axel Schumacher","doi":"10.1007/s10489-024-05945-6","DOIUrl":"10.1007/s10489-024-05945-6","url":null,"abstract":"<div><p>Data searchability has been utilized for decades and is now a crucial ingredient of data reuse. However, data searchability in industrial engineering is essentially still at the level of individual text documents, while for finite element (FE) simulations no content-based relations between FE simulations exist so far. Additionally, the growth of data warehouses with the increase of computational power leaves companies with a vast amount of engineering data that is rarely reused. Search techniques for FE data, which are in particular aware of the engineering problem context, is a new research topic. We introduce the prediction of similarities between simulations using graph algorithms, which for example allows the identification of outliers or ranks simulations according to their similarities. With that, we address searchability for FE-based crash simulations in the automotive industry. Here, we use SimRank-based methods to predict the similarity of crash simulations using unweighted and weighted bipartite graphs. Motivated by requirements from the engineering application, we introduce SimRankTarget++ an alternative formulation of SimRank++ that performs better for FE simulations. To show the generality of the graph approach, we compare component-based similarities with part-based ones. For that, we introduce a method for automatically detecting components in the vehicle. We use a car sub-model to illustrate the similarity ansatz and present results on data from real-life development stages of an automotive company.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-05945-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cutting-edge framework for industrial intrusion detection: Privacy-preserving, cost-friendly, and powered by federated learning","authors":"Lingzi Zhu,&nbsp;Bo Zhao,&nbsp;Jiabao Guo,&nbsp;Minzhi Ji,&nbsp;Junru Peng","doi":"10.1007/s10489-025-06404-6","DOIUrl":"10.1007/s10489-025-06404-6","url":null,"abstract":"<div><p>With the networking of industrially deployed facilities in distributed environments, industrial control systems (ICS) are facing an escalating number of attacks, emphasizing the criticality of intrusion detection systems. Currently, machine learning-based intrusion detection systems have been extensively researched. However, the sensitivity of ICS data poses a challenge of scarce labeled data for these systems. Additionally, distributed ICS necessitate privacy-preserving collaborative detection. To address these challenges, some solutions combining federated learning and transfer learning have been proposed. Nonetheless, these solutions often overlook the clustering characteristics of factory equipment and the constraints posed by limited computational and communication resources. Therefore, we propose GC-FADA, a chained cross-domain collaborative intrusion detection framework, to effectively address the interplay between labeled data scarcity, privacy protection, and resource constraints in ICS intrusion detection techniques. Firstly, GC-FADA used the adversarial domain adaptation scheme to train the local model to alleviate the performance limitation of intrusion detection model caused by labeled data scarcity. Then, to reduce the communication overhead between the nodes in the factory communication network and protect client privacy, GC-FADA utilizes the geographical clustering characteristics of the factory devices and proposes a FL-based grouped chain learning structure to achieve collaborative training. Finally, GC-FADA achieves privacy protection with low computational overhead by utilizing patterns from lightweight pseudo-random generators instead of complex cryptographic primitives. Extensive experiments conducted on real industrial SCADA datasets validate the effectiveness and rationality of the proposed approach, proving that GC-FADA outperforms major domain adaptation methods in terms of accuracy while reducing computation and communication costs. In the cross-domain learning task on the two data sets, the detection accuracy of our GC-FADA reaches 88.7% and 98.29% respectively, and the detection accuracy of various network attacks is mostly more than 90%.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Large-scale stochastic sparse subspace representation with consensus anchor guidance","authors":"Ge Yang,&nbsp;Tingquan Deng,&nbsp;Ming Yang,&nbsp;Changzhong Wang","doi":"10.1007/s10489-025-06392-7","DOIUrl":"10.1007/s10489-025-06392-7","url":null,"abstract":"<div><p>Subspace clustering (SC) is a hotspot in data analysis and machine learning. There exists much literature addressing this topic and most of which cannot handle large scale data. Although anchor graph learning is introduced to SC, there is still a problem that anchors cannot preserve the subspace structure of original data and spectral clustering process is still implemented slowly. To address these issues, an Anchor Graph Regularization based Large-Scale Stochastic Sparse Subspace Representation with Consensus Anchor Guidance (AGLS<span>(^4)</span>RA) is proposed in this paper, which integrates three modules, including sparse self-representation, anchor graph regularization, and sparse coding into a unified framework. These modules are collaboratively worked to learn an optimal, high-quality anchor matrix under the row sparse constraint. Furthermore, the random sampling and label propagation techniques are also introduced to accelerate the clustering task. AGLS<span>(^4)</span>RA is capable of processing data in linear time, which is beneficial to the execution of large-scale tasks. A series of comparative experiments on benchmark datasets verify the effectiveness of the proposed method.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Multimodal feature adaptive fusion for anchor-free 3D object detection","authors":"Yanli Wu,&nbsp;Junyin Wang,&nbsp;Hui Li,&nbsp;Xiaoxue Ai,&nbsp;Xiao Li","doi":"10.1007/s10489-025-06454-w","DOIUrl":"10.1007/s10489-025-06454-w","url":null,"abstract":"<div><p>LiDAR and camera are two key sensors that provide mutually complementary information for 3D detection in autonomous driving. Existing multimodal detection methods often decorate the original point cloud data with camera features to complete the detection, ignoring the mutual fusion between camera features and point cloud features. In addition, ground points scanned by LiDAR in natural scenes usually interfere significantly with the detection results, and existing methods fail to address this problem effectively. We present a simple yet efficient anchor-free 3D object detection, which can better adapt to complex scenes through the adaptive fusion of multimodal features. First, we propose a fully convolutional bird’s-eye view reconstruction module to sense ground map geometry changes, for improving the interference of ground points on detection results. Second, a multimodal feature adaptive fusion module with local awareness is designed to improve the mutual fusion of camera and point cloud features. Finally, we introduce a scale-aware mini feature pyramid networks (Mini-FPN) that can directly regress 3D bounding boxes from the augmented dense feature maps, boosting the network’s ability to detect scale-varying objects, and we additionally construct a scene-adaptive single-stage 3D detector in an anchor-free manner. Extensive experiments on the KITTI and nuScenes datasets validate our method’s competitive performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"SiamYOLOv8: a rapid conditional detection framework for one-shot object detection","authors":"Matthieu Desmarescaux,&nbsp;Wissam Kaddah,&nbsp;Ayman Alfalou,&nbsp;Isabelle Badoc","doi":"10.1007/s10489-025-06513-2","DOIUrl":"10.1007/s10489-025-06513-2","url":null,"abstract":"<div><p>Deep learning networks typically require vast amounts of labeled data for effective training. However, recent research has introduced a challenging task called One-Shot Object Detection, which addresses scenarios where certain classes are novel and unseen during training and represented by only a single labeled example. In this paper, we propose a novel One-Shot Object Detection model applicable to Conditional Detection without over-training on novel classes. Our approach leverages the strengths of YOLOv8 (You Only Look Once v8), a popular real-time object detector. Specifically, we incorporate a Siamese network and a matching module to enhance One-Shot Object Detection capabilities. Our proposed model, SiamYOLOv8, enables exploration of new applications without being limited by its training data. To evaluate the performance, we introduce a novel methodology for using the Retail Product Checkout (RPC) dataset “(https://github.com/MatD3mons/Conditional-Detection-datasets/tree/main/RPC)”, and extend our evaluation using the Grozi-3.2k dataset “(https://github.com/MatD3mons/Conditional-Detection-datasets/tree/main/GROZI-3.2k)”. In such contexts, new products often lack sufficient data for continuous Deep Learning methods, making individual case identification difficult. Our model outperforms SOTA models, achieving a significant performance improvement of 20.33% increase in Average Precision (+12.41 AP) on the Grozi-3.2k dataset and 25.68% increase (+17.37 AP) on the RPC dataset.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}