Complex & Intelligent Systems最新文献_第10页

Quantum theory-inspired inter-sentence semantic interaction model for textual adversarial defense 量子理论启发的句间语义交互模型用于文本对抗防御

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01733-4

Jiacheng Huang, Long Chen, Xiaoyin Yi, Ning Yu

{"title":"Quantum theory-inspired inter-sentence semantic interaction model for textual adversarial defense","authors":"Jiacheng Huang, Long Chen, Xiaoyin Yi, Ning Yu","doi":"10.1007/s40747-024-01733-4","DOIUrl":"https://doi.org/10.1007/s40747-024-01733-4","url":null,"abstract":"Deep neural networks have a recognized susceptibility to diverse forms of adversarial attacks in the field of natural language processing and such a security issue poses substantial security risks and erodes trust in artificial intelligence applications among people who use them. Meanwhile, quantum theory-inspired models that represent word composition as a quantum mixture of words have modeled the non-linear semantic interaction. However, modeling without considering the non-linear semantic interaction between sentences in the current literature does not exploit the potential of the quantum probabilistic description for improving the robustness in adversarial settings. In the present study, a novel quantum theory-inspired inter-sentence semantic interaction model is proposed for enhancing adversarial robustness via fusing contextual semantics. More specifically, it is analyzed why humans are able to understand textual adversarial examples, and a crucial point is observed that humans are adept at associating information from the context to comprehend a paragraph. Guided by this insight, the input text is segmented into subsentences, with the model simulating contextual comprehension by representing each subsentence as a particle within a mixture system, utilizing a density matrix to model inter-sentence interactions. A loss function integrating cross-entropy and orthogonality losses is employed to encourage the orthogonality of measurement states. Comprehensive experiments are conducted to validate the efficacy of proposed methodology, and the results underscore its superiority over baseline models even commercial applications based on large language models in terms of accuracy across diverse adversarial attack scenarios, showing the potential of proposed approach in enhancing the robustness of neural networks under adversarial attacks.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"114 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905451","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}

引用次数: 0

CDR-Detector: a chronic disease risk prediction model combining pre-training with deep reinforcement learning CDR-Detector：一种结合预训练和深度强化学习的慢性病风险预测模型

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01697-5

Shaofu Lin, Shiwei Zhou, Han Jiao, Mengzhen Wang, Haokang Yan, Peng Dou, Jianhui Chen

{"title":"CDR-Detector: a chronic disease risk prediction model combining pre-training with deep reinforcement learning","authors":"Shaofu Lin, Shiwei Zhou, Han Jiao, Mengzhen Wang, Haokang Yan, Peng Dou, Jianhui Chen","doi":"10.1007/s40747-024-01697-5","DOIUrl":"https://doi.org/10.1007/s40747-024-01697-5","url":null,"abstract":"Chronic disease risk prediction based on electronic health record (EHR) is an important research direction of Internet healthcare. Current studies mainly focused on developing well-designed deep learning models to predict the disease risk based on large-scale and high-quality longitudinal EHR data. However, in real-world scenarios, people’s medical habits and low prevalence of diseases often lead to few-shot and imbalanced longitudinal EHR data. This has become an urgent challenge for chronic disease risk prediction based on EHR. Aiming at this challenge, this study combines EHR based pre-training and deep reinforcement learning to develop a novel chronic disease risk prediction model called CDR-Detector. The model adopts the Q-learning architecture with a custom reward function. In order to improve the few-shot learning ability of model, a self-adaptive EHR based pre-training model with two new pre-training tasks is developed to mine valuable dependencies from single-visit EHR data. In order to solve the problem of data imbalance, a dual experience replay strategy is realized to help the model select representative data samples and accelerate model convergence on the imbalanced EHR data. A group of experiments have been conducted on real personal physical examination data. Experimental results show that, compared with the existing state-of-art methods, the proposed CDR-Detector has better accuracy and robustness on the few-shot and imbalanced EHR data.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"23 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905140","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}

引用次数: 0

Mining label-free consistency regularization for noisy facial expression recognition 基于无标签一致性正则化的噪声面部表情识别

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01722-7

Yumei Tan, Haiying Xia, Shuxiang Song

{"title":"Mining label-free consistency regularization for noisy facial expression recognition","authors":"Yumei Tan, Haiying Xia, Shuxiang Song","doi":"10.1007/s40747-024-01722-7","DOIUrl":"https://doi.org/10.1007/s40747-024-01722-7","url":null,"abstract":"Noisy labels are unavoidable in facial expression recognition (FER) task, significantly hindering FER performance in real-world scenarios. Recent advances tackle this problem by leveraging uncertainty for sample partitioning or constructing label distributions. However, these approaches primarily depend on labels, leading to confirmation bias issues and performance degradation. We argue that mining both label-independent features and label-dependent information can mitigate the confirmation bias induced by noisy labels. In this paper, we propose MCR, that is, mining simple yet effective label-free consistency regularization (MCR) to learn robust representations against noisy labels. The proposed MCR incorporates three label-free consistency regularizations: instance-level embedding consistency regularization, pairwise distance consistency regularization, and neighbour consistency regularization. Initially, we employ instance-level embedding consistency regularization to learn instance-level discriminative information from identical facial samples under perturbations in an unsupervised manner. This facilitates the efficacy of mitigating inherent noise in data. Subsequently, a pairwise distance consistency regularization is constructed to regularize the classifier and alleviate bias induced by noisy labels. Finally, we use the neighbour consistency regularization to further strengthen the discriminative capability of the model against noise. Benefiting from the advantages of these three label-free consistency regularizations, MCR can learn discriminative and robust representations against noise. Extensive experimental results demonstrate the superior performance of MCR on three popular in-the-wild facial expression datasets, including RAF-DB, FERPlus, and AffectNet. Moreover, MCR demonstrates superior generalization capability on other datasets with noisy labels, such as CIFAR100 and Tiny-ImageNet.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"54 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905142","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}

引用次数: 0

Backward chained behavior trees with deliberation for multi-goal tasks 考虑多目标任务的反向链式行为树

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01731-6

Haotian Zhou, Yunhan Lin, Huasong Min

{"title":"Backward chained behavior trees with deliberation for multi-goal tasks","authors":"Haotian Zhou, Yunhan Lin, Huasong Min","doi":"10.1007/s40747-024-01731-6","DOIUrl":"https://doi.org/10.1007/s40747-024-01731-6","url":null,"abstract":"Backward chained behavior trees (BTs) are an approach to generate BTs through backward chaining. Starting from the goal conditions for a task, this approach recursively expands unmet conditions with actions, aiming to achieve those conditions. It provides disturbance rejection for robots at the task level in the sense that if a disturbance changes the state of a condition, this condition will be expanded with new actions in the same way. However, backward chained BTs fail to handle disturbances optimally in multi-goal tasks. In this paper, we address this by formulating it as a global optimization problem and propose an approach termed BCBT-D, which endows backward chained BTs with the ability to achieve globally optimal disturbance rejection. Firstly, we define Implicit Constraint Conditions (ICCs) as the subsequent goals of nodes in BTs. In BCBT-D, ICCs act as global constraints on actions to optimize their execution and as global heuristics for selecting optimal actions that can achieve unmet conditions. We design various multi-goal tasks with time limits and disturbances for comparison. The experimental results demonstrate that our approach ensures the convergence of backward chained BTs and exhibits superior robustness compared to existing approaches.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"180 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905549","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}

引用次数: 0

OMSF2: optimizing multi-scale feature fusion learning for pneumoconiosis staging diagnosis through data specificity augmentation OMSF2：通过数据特异性增强优化多尺度特征融合学习用于尘肺分期诊断

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01729-0

Xueting Ren, Surong Chu, Guohua Ji, Zijuan Zhao, Juanjuan Zhao, Yan Qiang, Yangyang Wei, Yan Wang

{"title":"OMSF2: optimizing multi-scale feature fusion learning for pneumoconiosis staging diagnosis through data specificity augmentation","authors":"Xueting Ren, Surong Chu, Guohua Ji, Zijuan Zhao, Juanjuan Zhao, Yan Qiang, Yangyang Wei, Yan Wang","doi":"10.1007/s40747-024-01729-0","DOIUrl":"https://doi.org/10.1007/s40747-024-01729-0","url":null,"abstract":"Diagnosing pneumoconiosis is challenging because the lesions are not easily visible on chest X-rays, and the images often lack clear details. Existing deep detection models utilize Feature Pyramid Networks (FPNs) to identify objects at different scales. However, they struggle with insufficient perception of small targets and gradient inconsistency in medical image detection tasks, hindering the full utilization of multi-scale features. To address these issues, we propose an Optimized Multi-Scale Feature Fusion learning framework, OMSF2, which includes the following components: (1) Data specificity augmentation module is introduced to capture intrinsic data representations and introduce diversity by learning morphological variations and lesion locations. (2) Multi-scale feature learning module is utilized that refines micro-feature localization guided by heatmaps, enabling full extraction of multi-directional features of subtle diffuse targets. (3) Multi-scale feature fusion module is employed that facilitates the fusion of high-level and low-level features to better understand subtle differences between disease stages. Notably, this paper innovatively proposes a method for fine learning of low-resolution micro-features in pneumoconiosis, addressing the issue of maintaining cross-layer gradient consistency under multi-scale feature fusion. We established an enhanced pneumoconiosis X-ray dataset to optimize the lesion detection capability of the OMSF2 model. We also introduced an external dataset to evaluate other chest X-rays with complex lesions. On the AP-50 and R-50 evaluation metrics, OMSF2 improved by 3.25% and 3.31% on the internal dataset, and by 2.28% and 0.24% on the external dataset, respectively. Experimental results show that OMSF2 achieves significantly better performance than state-of-the-art baselines in medical image detection tasks.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"33 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905453","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}

引用次数: 0

FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value FSPPCFs：一种基于模糊c均值和Shapley值的隐私保护协同过滤推荐方案

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01758-9

Weiwei Wang, Wenping Ma, Kun Yan

{"title":"FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value","authors":"Weiwei Wang, Wenping Ma, Kun Yan","doi":"10.1007/s40747-024-01758-9","DOIUrl":"https://doi.org/10.1007/s40747-024-01758-9","url":null,"abstract":"Collaborative filtering recommendation systems generate personalized recommendation results by analyzing and collaboratively processing a large numerous of user ratings or behavior data. The widespread use of recommendation systems in daily decision-making also brings potential risks of privacy leakage. Recent literature predominantly employs differential privacy to achieve privacy protection, however, many schemes struggle to balance user privacy and recommendation performance effectively. In this work, we present a practical privacy-preserving scheme for user-based collaborative filtering recommendation that utilizes fuzzy C-means clustering and Shapley value, FSPPCFs, aiming to enhance the recommendation performance while ensuring privacy protection. Specifically, (i) we have modified the traditional recommendation scheme by introducing a similarity balance factor integrated into the Pearson similarity algorithm, enhancing recommendation system performance; (ii) FSPPCFs first clusters the dataset through fuzzy C-means clustering and Shapley value, grouping users with similar interests and attributes into the same cluster, thereby providing more accurate data support for recommendations. Then, differential privacy is used to achieve the user’s personal privacy protection when selecting the neighbor set from the target cluster. Finally, it is theoretically proved that our scheme satisfies differential privacy. Experimental results illustrate that our scheme significantly outperforms existing methods.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"13 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905452","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}

引用次数: 0

Calibration between a panoramic LiDAR and a limited field-of-view depth camera 全景激光雷达和有限视场深度相机之间的校准

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01710-x

Weijie Tang, Bin Wang, Longxiang Huang, Xu Yang, Qian Zhang, Sulei Zhu, Yan Ma

{"title":"Calibration between a panoramic LiDAR and a limited field-of-view depth camera","authors":"Weijie Tang, Bin Wang, Longxiang Huang, Xu Yang, Qian Zhang, Sulei Zhu, Yan Ma","doi":"10.1007/s40747-024-01710-x","DOIUrl":"https://doi.org/10.1007/s40747-024-01710-x","url":null,"abstract":"Depth cameras and LiDARs are commonly used sensing devices widely applied in fields such as autonomous driving, navigation, and robotics. Precise calibration between the two is crucial for accurate environmental perception and localization. Methods that utilize the point cloud features of both sensors to estimate extrinsic parameters can also be extended to calibrate limited Field-of-View (FOV) LiDARs and panoramic LiDARs, which holds significant research value. However, calibrating the point clouds from two sensors with different fields of view and densities presents challenges. This paper proposes methods for automatic calibration of the two sensors by extracting and registering features in three scenarios: environments with one plane, two planes, and three planes. For the one-plane and two-plane scenarios, we propose constructing feature histogram descriptors based on plane constraints for the remaining points, in addition to planar features, for registration. Experimental results on simulation and real-world data demonstrate that the proposed methods in all three scenarios achieve precise calibration, maintaining average rotation and translation calibration errors within 2 degrees and 0.05 meters respectively for a (360^{circ }) linear LiDAR and a depth camera with a field of view of (100^{circ }) vertically and (70^{circ }) degrees horizontally.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"48 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905138","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}

引用次数: 0

Semantic-enhanced panoptic scene graph generation through hybrid and axial attentions 基于混合关注和轴向关注的语义增强全景场景图生成

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01746-z

Xinhe Kuang, Yuxin Che, Huiyan Han, Yimin Liu

{"title":"Semantic-enhanced panoptic scene graph generation through hybrid and axial attentions","authors":"Xinhe Kuang, Yuxin Che, Huiyan Han, Yimin Liu","doi":"10.1007/s40747-024-01746-z","DOIUrl":"https://doi.org/10.1007/s40747-024-01746-z","url":null,"abstract":"The generation of panoramic scene graphs represents a cutting-edge challenge in image scene understanding, necessitating sophisticated predictions of both intra-object relationships and interactions between objects and their backgrounds. This complexity tests the limits of current predictive models' ability to discern nuanced relationships within images. Conventional approaches often fail to effectively combine visual and semantic data, leading to predictions that are semantically impoverished. To address these issues, we propose a novel method of semantic-enhanced panoramic scene graph generation through hybrid and axial attentions (PSGAtten). Specifically, a series of hybrid attention networks are stacked within both the object context encoding and relationship context encoding modules, enhancing the refinement and fusion of visual and semantic information. Within the hybrid attention networks, self-attention mechanisms facilitate feature refinement within modalities, while cross-attention mechanisms promote feature fusion across modalities. The axial attention model is further applied to enhance the integration ability of global information. Experimental validation on the PSG dataset confirms that our approach not only surpasses existing methods in generating detailed panoramic scene graphs but also significantly improves recall rates, thereby enhancing the ability to predict relationships in scene graph generation.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"65 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905454","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}

引用次数: 0

DCTnet: a double-channel transformer network for peach disease detection using UAVs DCTnet：利用无人机进行桃病检测的双通道变压器网络

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01749-w

Jie Zhang, Dailin Li, Xiaoping Shi, Fengxian Wang, Linwei Li, Yibin Chen

{"title":"DCTnet: a double-channel transformer network for peach disease detection using UAVs","authors":"Jie Zhang, Dailin Li, Xiaoping Shi, Fengxian Wang, Linwei Li, Yibin Chen","doi":"10.1007/s40747-024-01749-w","DOIUrl":"https://doi.org/10.1007/s40747-024-01749-w","url":null,"abstract":"The use of unmanned aerial vehicle (UAV) technology to inspect extensive peach orchards to improve fruit yield and quality is currently a major area of research. The challenge is to accurately detect peach diseases in real time, which is critical to improving peach production. The dense arrangement of peaches and the uneven lighting conditions significantly hamper the accuracy of disease detection. To overcome this, this paper presents a dual-channel transformer network (DCTNet) for peach disease detection. First, an Adaptive Dual-Channel Affine Transformer (ADCT) is developed to efficiently capture key information in images of diseased peaches by integrating features across spatial and channel dimensions within blocks. Next, a Robust Gated Feed Forward Network (RGFN) is constructed to extend the receptive field of the model by improving its context aggregation capabilities. Finally, a Local–Global Network is proposed to fully capture the multi-scale features of peach disease images through a collaborative training approach with input images. Furthermore, a peach disease dataset including different growth stages of peaches is constructed to evaluate the detection performance of the proposed method. Extensive experimental results show that our model outperforms other sophisticated models, achieving an ({AP}_{50}) of 95.57% and an F1 score of 0.91. The integration of this method into UAV systems for surveying large peach orchards ensures accurate disease detection, thereby safeguarding peach production.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"160 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905455","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}

引用次数: 0

A disturbance suppression second-order penalty-like neurodynamic approach to distributed optimal allocation 分布式最优分配的干扰抑制二阶类惩罚神经动力学方法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-12-30 DOI: 10.1007/s40747-024-01732-5

Wenwen Jia, Wenbin Zhao, Sitian Qin

引用次数: 0