Complex & Intelligent Systems最新文献

{"title":"Small sample smart contract vulnerability detection method based on multi-layer feature fusion","authors":"Jinlin Fan, Yaqiong He, Huaiguang Wu","doi":"10.1007/s40747-025-01782-3","DOIUrl":"https://doi.org/10.1007/s40747-025-01782-3","url":null,"abstract":"<p>The identification of vulnerabilities in smart contracts is necessary for ensuring their security. As a pre-trained language model, BERT has been employed in the detection of smart contract vulnerabilities, exhibiting high accuracy in tasks. However, it has certain limitations. Existing methods solely depend on features extracted from the final layer, thereby disregarding the potential contribution of features from other layers. To address these issues, this paper proposes a novel method, which is named multi-layer feature fusion (MULF). Experiments investigate the impact of utilizing features from other layers on performance improvement. To the best of our knowledge, this is the first instance of multi-layer feature sequence fusion in the field of smart contract vulnerability detection. Furthermore, there is a special type of patched contract code that contains vulnerability features which need to be studied. Therefore, to overcome the challenges posed by limited smart contract vulnerability datasets and high false positive rates, we introduce a data augmentation technique that incorporates function feature screening with those special smart contracts into the training set. To date, this method has not been reported in the literature. The experimental results demonstrate that the MULF model significantly enhances the performance of smart contract vulnerability identification compared to other models. The MULF model achieved accuracies of 98.95% for reentrancy vulnerabilities, 96.27% for timestamp dependency vulnerabilities, and 87.40% for overflow vulnerabilities, which are significantly higher than those achieved by existing methods.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"5 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143532575","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":"Decentralized non-convex online optimization with adaptive momentum estimation and quantized communication","authors":"Yunshan Lv, Hailing Xiong, Fuqing Zhang, Shengying Dong, Xiangguang Dai","doi":"10.1007/s40747-025-01818-8","DOIUrl":"https://doi.org/10.1007/s40747-025-01818-8","url":null,"abstract":"<p>In this work, we consider the decentralized non-convex online optimization problem over an undirected network. To solve the problem over a communication-efficient manner, we propose a novel quantized decentralized adaptive momentum gradient descent algorithm based on the adaptive momentum estimation methods, where quantified information is exchanged between agents. The proposed algorithm not only can effectively reduce the data transmission volume but also contribute to improved convergence. Theoretical analysis proves that the proposed algorithm can achieve sublinear dynamic regret under appropriate step-size and quantization level, which matches the convergence of the decentralized online algorithm with exact-communication. Extensive simulations are given to demonstrate the efficacy of the algorithm.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"66 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143532577","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":"SDGANets: a semantically enhanced dual graph-aware network for affine and registration of remote sensing images","authors":"Xie Zhuli, Wan Gang, Liu Jia, Bu Dongdong","doi":"10.1007/s40747-025-01792-1","DOIUrl":"https://doi.org/10.1007/s40747-025-01792-1","url":null,"abstract":"<p>Remote sensing image pairs of different time phases have complex and changeable semantic contents, and traditional convolutional registration methods are challenging in modeling subtle local changes and global large-scale deformation differences in detail. This results in poor registration performance and poor feature representation. To address these problems, a semantically enhanced dual-graph perception framework is proposed. This framework aims to gradually achieve semantic alignment and precise registration of remote sensing image pairs of different time phases via coarse to fine stages. On the one hand, a newly designed large-selection kernel convolution attention module is used to learn affine transformation parameters. Attention to global semantics perceives the large pixel displacement deviation caused by large-scale deformation, and the association relationship is established between remote sensing image pairs of different time phases. At the same time, dual-graph perception modules are embedded in multiple subspace structures, and the subtle local changes of remote sensing image pairs are modeled through the dynamic aggregation ability of graph perception nodes to achieve coarse registration of remote sensing images. On the other hand, a U-shaped module guided by global attention with deformable convolution is used to refine the local spatial structural features and global contextual semantic information of the rough registration, establish dependencies between channels, and correct the pixel displacement deviation of remote sensing image pairs of different phases through position encoding. It is worth noting that the newly designed weighted loss function supervises the learning of each module and the entire network structure from the perspective of inverse consistency, promoting the network’s optimal performance. Finally, the experimental results on the AerialData and GFRS datasets show that the proposed framework has good registration performance, with mean absolute error (MAE) of 3.64 and 3.81, respectively.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"86 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518617","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 multilevel attention for semi-supervised skeleton-based gesture recognition","authors":"Jinting Liu, Minggang Gan, Yuxuan He, Jia Guo, Kang Hu","doi":"10.1007/s40747-025-01807-x","DOIUrl":"https://doi.org/10.1007/s40747-025-01807-x","url":null,"abstract":"<p>Although skeleton-based gesture recognition using supervised learning has achieved promising results, the reliance on extensive annotated data poses significant costs. This paper addresses the challenge of semi-supervised skeleton-based gesture recognition, to effectively learn feature representations from labeled and unlabeled data. To resolve this problem, we propose a novel multimodal multilevel attention network designed for semi-supervised learning. This model utilizes the self-attention mechanism to polymerize multimodal and multilevel complementary semantic information of the hand skeleton, designing a multimodal multilevel contrastive loss to measure feature similarity. Specifically, our method explores the relationships between joint, bone, and motion to learn more discriminative feature representations. Considering the hierarchy of the hand skeleton, the skeleton data is divided into multilevel to capture complementary semantic information. Furthermore, the multimodal contrastive loss measures similarity among these multilevel representations. The proposed method demonstrates improved performance in semi-supervised skeleton-based gesture recognition tasks, as evidenced by experiments on the SHREC-17 and DHG 14/28 datasets.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"25 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518621","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":"$$text {H}^2text {CAN}$$ : heterogeneous hypergraph attention network with counterfactual learning for multimodal sentiment analysis","authors":"Changqin Huang, Zhenheng Lin, Qionghao Huang, Xiaodi Huang, Fan Jiang, Jili Chen","doi":"10.1007/s40747-025-01806-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01806-y","url":null,"abstract":"<p>Multimodal sentiment analysis (MSA) has garnered significant attention for its immense potential in human-computer interaction. While cross-modality attention mechanisms are widely used in MSA to capture inter-modality interactions, existing methods are limited to pairwise interactions between two modalities. Additionally, these methods can not utilize the causal relationship to guide attention learning, making them susceptible to bias information. To address these limitations, we introduce a novel method called Heterogeneous Hypergraph Attention Network with Counterfactual Learning <span>((text {H}^2text {CAN}).)</span> The method constructs a heterogeneous hypergraph based on sentiment expression characteristics and employs Heterogeneous Hypergraph Attention Networks (HHGAT) to capture interactions beyond pairwise constraints. Furthermore, it mitigates the effects of bias through a Counterfactual Intervention Task (CIT). Our model comprises two main branches: hypergraph fusion and counterfactual fusion. The former uses HHGAT to capture inter-modality interactions, while the latter constructs a counterfactual world using Gaussian distribution and additional weighting for the biased modality. The CIT leverages causal inference to maximize the prediction discrepancy between the two branches, guiding attention learning in the hypergraph fusion branch. We utilize unimodal labels to help the model adaptively identify the biased modality, thereby enhancing the handling of bias information. Experiments on three mainstream datasets demonstrate that <span>(text {H}^2text {CAN})</span> sets a new benchmark.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"33 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518813","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 new representation in genetic programming with hybrid feature ranking criterion for high-dimensional feature selection","authors":"Jiayi Li, Fan Zhang, Jianbin Ma","doi":"10.1007/s40747-025-01784-1","DOIUrl":"https://doi.org/10.1007/s40747-025-01784-1","url":null,"abstract":"<p>Feature selection is a common method for improving classification performance. Selecting features for high-dimensional data is challenging due to the large search space. Traditional feature ranking methods that search for top-ranked features cannot remove redundant and irrelevant features and may also ignore interrelated features. Evolutionary computation (EC) techniques are widely used in feature selection due to their global search capability. However, EC can easily fall into local optima when dealing with feature selection for high-dimensional applications. The top-ranked features are more likely to construct effective feature subsets and help EC reduce the search space. This paper proposes a feature selection method based on Genetic Programming (GP) with hybrid feature ranking criterion called GPHC, which combines multiple feature ranking methods into the GP structure using a novel GP representation to search for effective feature subsets. Experiments on eight high-dimensional datasets show that GPHC achieves significantly better classification performance compared to five feature ranking methods. Further comparisons between GPHC and other evolutionary algorithms demonstrate that GPHC has advantages in terms of classification performance, the number of features, and convergence speed.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"96 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518618","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":"Demonstration and offset augmented meta reinforcement learning with sparse rewards","authors":"Haorui Li, Jiaqi Liang, Xiaoxuan Wang, Chengzhi Jiang, Linjing Li, Daniel Zeng","doi":"10.1007/s40747-025-01785-0","DOIUrl":"https://doi.org/10.1007/s40747-025-01785-0","url":null,"abstract":"<p>This paper introduces DOAMRL, a novel meta-reinforcement learning (meta-RL) method that extends the Model-Agnostic Meta-Learning (MAML) framework. The method addresses a key limitation of existing meta-RL approaches, which struggle to effectively use suboptimal demonstrations to guide training in sparse reward environments. DOAMRL effectively combines reinforcement learning (RL) and imitation learning (IL) within the inner loop of the MAML framework, with dynamically adjusted weights applied to the IL component. This enables the method to leverage the exploration strengths of RL and the efficiency benefits of IL at different stages of training. Additionally, DOAMRL introduces a meta-learned parameter offset, which enhances targeted exploration in sparse reward settings, helping to guide the meta-policy toward regions with non-zero rewards. To further mitigate the impact of suboptimal demonstration data on meta-training, we propose a novel demonstration data enhancement module that iteratively improves the quality of the demonstrations. We provide a comprehensive analysis of the proposed method, justifying its design choices. A comprehensive comparison with existing methods in various stages (including training and adaptation), using both optimal and suboptimal demonstrations, along with results from ablation and sensitivity analysis, demonstrates that DOAMRL outperforms existing approaches in performance, applicability, and robustness.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"71 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518625","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":"Design of weighted based divided-search enhanced Karnik–Mendel algorithms for type reduction of general type-2 fuzzy logic systems","authors":"Yang Chen","doi":"10.1007/s40747-025-01798-9","DOIUrl":"https://doi.org/10.1007/s40747-025-01798-9","url":null,"abstract":"<p>General type-2 fuzzy logic systems (GT2 FLSs) based on the <span>(alpha)</span>-planes representation of general T2 fuzzy sets (FSs) have become more accessible to FL investigators in recent years. Type reduction (TR) is the most important block for GT2 FLSs. Here the weighted type-reduction algorithms based on the Newton and Cotes quadrature formulas of numerical methods of integration technique are first given, and the searching spaces are divided. Then a type of weighted divided search enhanced Karnik–Mendel (WDEKM) algorithms is shown to complete the centroid TR. In contrast to the WEKM algorithms, four simulation instances show that the WDEKM algorithms get lesser absolute errors and faster calculational speeds, which may offer the potentially application values for applying T2 FLSs.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"12 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518809","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":"Exact particle flow Daum-Huang filters for mobile robot localization in occupancy grid maps","authors":"Domonkos Csuzdi, Tamás Bécsi, Péter Gáspár, Olivér Törő","doi":"10.1007/s40747-025-01810-2","DOIUrl":"https://doi.org/10.1007/s40747-025-01810-2","url":null,"abstract":"<p>In this paper, we present a novel localization algorithm for mobile robots navigating in complex planar environments, a critical capability for various real-world applications such as autonomous driving, robotic assistance, and industrial automation. Although traditional methods such as particle filters and extended Kalman filters have been widely used, there is still room for assessing the capabilities of modern filtering techniques for this task. Building on a recent localization method that employs a chamfer distance-based observation model, derived from an implicit measurement equation, we explore its potential further by incorporating exact particle flow Daum–Huang filters to achieve superior accuracy. Recent advancements have spotlighted Daum–Huang filters as formidable contenders, outshining both the extended Kalman filters and traditional particle filters in various scenarios. We introduce two new Daum–Huang-based localization algorithms and assess their tracking performance through comprehensive simulations and real-world trials. Our algorithms are benchmarked against various methods, including the widely acclaimed Adaptive Monte–Carlo Localization algorithm. Overall, our algorithm demonstrates superior performance compared to the baseline models in simulations and exhibits competitive performance in the evaluated real-world application.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518620","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 random walk manifold contrastive hashing for multimedia retrieval","authors":"Yunfei Chen, Yitian Long, Zhan Yang, Jun Long","doi":"10.1007/s40747-025-01814-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01814-y","url":null,"abstract":"<p>With the rapid growth in both the variety and volume of data on networks, especially within social networks containing vast multimedia data such as text, images, and video, there is an urgent need for efficient methods to retrieve helpful information quickly. Due to their high computational efficiency and low storage costs, unsupervised deep cross-modal hashing methods have become the primary method for managing large-scale multimedia data. However, existing unsupervised deep cross-modal hashing methods still need help with issues such as inaccurate measurement of semantic similarity information, complex network architectures, and incomplete constraints among multimedia data. To address these issues, we propose an Unsupervised Random Walk Manifold Contrastive Hashing (<b>URWMCH</b>) method, designing a simple deep learning architecture. First, we build a random walk-based manifold similarity matrix based on the random walk strategy and modal-individual similarity structure. Second, we construct intra- and inter-modal similarity preservation and coexistent similarity preservation loss based on contrastive learning to constrain the training of hash functions, ensuring that the hash codes contain complete semantic association information. Finally, we designed comprehensive experiments on the MIRFlickr-25K, NUS-WIDE, and MS COCO datasets to demonstrate the effectiveness and superiority of the proposed <b>URWMCH</b> method.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"13 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518811","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}