Complex & Intelligent Systems最新文献

{"title":"GCN and GAT-based interpretable knowledge tracing model","authors":"Yujia Huo, Menghong He, Xue Tan, Kesha Chen","doi":"10.1007/s40747-025-01921-w","DOIUrl":"https://doi.org/10.1007/s40747-025-01921-w","url":null,"abstract":"<p>Knowledge tracing (KT) aims to predict students’ future performance by assessing their level of knowledge mastery from past problem-solving records. However, many existing methods fail to take full advantage of the potential relationship between questions and skills, or fail to effectively utilize students’ historical learning data, which makes it difficult to accurately capture individualized mastery for each question. In addition, redundancy in long sequential information often leads to model overfitting, and existing deep knowledge tracing models have significant limitations in terms of the interpretability of their predictions. To address these issues, we propose GCAKT, an interpretable KT model focuses on student problem mastery. GCAKT generates personalized problem representations by modeling students’ historical learning information at a fine-grained level, and learns these representations jointly through a problem-skill embedding module and a personalized problem mastery module. To cope with the redundancy generated by long sequences, GCAKT employs an attention-based knowledge evolution module that constructs a final hidden knowledge state by analyzing the attention relationship between the student’s hidden knowledge state and the problem at each point in time. Meanwhile, GCAKT utilizes the attention weights to construct interpretable paths, aiding to provide interpretable prediction results. Experimental results on three publicly available real-world educational datasets show that GCAKT outperforms traditional methods in terms of both prediction accuracy and interpretability.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"150 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088330","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":"Multiobjective integrated scheduling of disassembly and reprocessing operations considering product structures and stochastic processing time via reinforcement learning-based evolutionary algorithms","authors":"Yaping Fu, Fuquan Wang, Zhengyuan Li, Guangdong Tian, Duc Truong Pham, Hao Sun","doi":"10.1007/s40747-025-01907-8","DOIUrl":"https://doi.org/10.1007/s40747-025-01907-8","url":null,"abstract":"<p>Remanufacturing has become a mainstream sustainable manufacturing paradigm for energy conservation and environmental protection. Disassembly and reprocessing operations are two main activities in remanufacturing. This work proposes multiobjective integrated scheduling of disassembly and reprocessing operations considering product structures and random processing time. First, a stochastic programming model is developed to minimize maximum completion time and total tardiness. Second, a reinforcement learning-based multiobjective evolutionary algorithm is devised considering problem-specific knowledge. Three search strategy combinations are formed: crossover and mutation, crossover and key product-based iterated local search, mutation and key product-based iterated local search. At each iteration, a Q-learning method is devised to intelligently choose a combination of premium strategies. A stochastic simulation is incorporated to evaluate the objective values of the searched solutions. Finally, the formulated model and method are compared with an exact solver, CPLEX, and three well-known metaheuristics from the literature on a set of test instances. The results confirm the excellent competitiveness of the developed model and algorithm for solving the considered problem.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"18 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067262","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":"Graph-based multi-attribute decision-making method with new fuzzy information measures","authors":"Lili Zhang, Shu Sun, Ruping Wang, Chunfeng Suo","doi":"10.1007/s40747-025-01879-9","DOIUrl":"https://doi.org/10.1007/s40747-025-01879-9","url":null,"abstract":"<p><i>n</i>-intuitionistic polygonal fuzzy sets have significant advantages over traditional fuzzy sets in handling uncertain information. Due to the fact that information measure is an effective tool for handling uncertain information, this paper proposes distance measures, symmetric cross entropies and knowledge measures for <i>n</i>-intuitionistic polygonal fuzzy sets. First, this paper initially formulates distance measure models, which is based on theoretical underpinnings of t-conorms. Then, we explore a conversion mechanism between the distance measure and symmetric cross entropies, in conjunction with the conversion path from these measures to knowledge measures. These transformations can elicit a series of formula for delineating symmetric cross entropy and knowledge measure. Moreover, this study affords a compendium of precise mathematical formulations to support the interconvertibility relationships delineated, this conversion can enable the selection of appropriate measures for optimization according to different needs. Subsequently, we consider constructing a fuzzy graph by graph theory and information measures for the decision model. Graph theory can visually depict diverse attributes and their intrinsic interconnections, which possesses significant practical utility in facilitating a more precise assessment of alternatives. Ultimately, we provide a case study of purchasing a house, which demonstrates the effectiveness and practical application value of the proposed method through detailed comparative evaluation, rigorous sensitivity assessment and robustness analysis.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"16 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067430","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":"Multi-granularity feature intersection learning for visible-infrared person re-identification","authors":"Sixian Chan, Jie Wang, Jiaao Cui, Jie Hu, Zhuorong Li, Jiafa Mao","doi":"10.1007/s40747-025-01853-5","DOIUrl":"https://doi.org/10.1007/s40747-025-01853-5","url":null,"abstract":"<p>This paper proposes a multi-granularity feature intersection network (MGFINet) for visible-infrared person re-identification (VI-ReID). VI-ReID aims to retrieve images of the same pedestrian from different spectral cameras. The key challenge is to extract pedestrian descriptions with both inter-class discriminability and intra-class similarity. Previous methods ignore the potential loss of details during representation extraction and the presence of data bias in the metric function, limiting further improvements in retrieval performance. Meanwhile, the discrepancy regarding how to calculate the loss for representation learning and metric learning also affects the model’s training. To address the above issues, MGFINet consists of three components: a hierarchical part pooling method (HPP), a hierarchical part restriction method (HPC), and a feature intersection (FI) loss. HPP adopts a hierarchical framework to extract multi-granularity pedestrian representations, and it performs an inter-layer fusion operation to exploit the high-resolution information from shallow layers and the semantic representability from deep layers. Meanwhile, HPP employs part pooling with different step sizes to capture pedestrian details in each layer. Next, HPC spreads the identity loss across all layers to reduce the distance for gradient backpropagation and further optimize fine-grained features in shallow layers. Besides, FI loss combines representation and metric learning by incorporating hyperparameters of classifiers into metric learning, mitigating data bias and reducing the gap between the two learning processes. Finally, extensive experiments evaluated on two public datasets, SYSU-MM01 and RegDB demonstrate the effectiveness of the proposed method.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"42 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945864","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":"Joint optimization of communication rates for multi-UAV relay systems","authors":"Chenghua Wen, Guifen Chen, Xinglong Gu, Wenzhe Wang","doi":"10.1007/s40747-025-01910-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01910-z","url":null,"abstract":"<p>The multi-UAV relay system can rapidly deploy a temporary communication network in disaster emergency communication scenarios to enhance communication coverage and stability in the affected area and ensure the efficient transmission of rescue information. Aiming at the problems of insufficient real-time performance, low user fairness, and low utilization of communication resources in multi-UAV relay systems, this paper proposes a joint optimization method of communication rate based on Lexicographic Optimization (LO). The multi-UAV relay scenario using multi-carrier non-orthogonal multiple access (MC-NOMA) technology is modeled as a multi-objective optimization problem and solved in stages using the LO method. Specifically, the first stage maximizes the minimum user communication rate by jointly optimizing the user association, relay UAV transmit power and flight trajectory; the second stage optimizes the modulation order of the relay UAVs to maximize the communication rate between UAV base stations. Finally, the proposed algorithm is compared under different optimization schemes and different optimization algorithms. The simulation results show that the user communication rate of the proposed algorithm is <span>(6.8times {10}^5bps/s)</span> higher than that of the static user association scheme, <span>(6.7times {10}^6bps/s)</span> higher than that of the static UAV trajectory scheme, and <span>(2.0times {10}^5bps/s)</span> higher than that of the traditional Jara algorithm, and <span>(6.7times {10}^6bps/s)</span> higher than that of the K-mean based optimization algorithm. Moreover, this algorithm performs well in convergence and stability, effectively improves the user communication rate, the communication rate between UAV base stations, and the energy utilization of relay UAV, and enhances the adaptability of the system to the dynamic environment.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"29 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940384","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":"DEBPIR: enhancing information privacy in decentralized business modeling","authors":"Gulshan Kumar, Rahul Saha, Mauro Conti, Tai Hoon Kim","doi":"10.1007/s40747-025-01868-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01868-y","url":null,"abstract":"<p>Business modelling often involves extensive data collection and analysis, raising concerns about privacy infringement. Integrating Privacy Information Retrieval (PIR) mechanisms within business models is crucial to address privacy concerns, ensure compliance with regulations, safeguard sensitive data, and maintain trust with stakeholders; however, PIR is not included in the existing business models yet. In this paper, we propose the first decentralized business model that uses the PIR. We call our proposed model <i>DEcentralized Business model with PIR (DEBPIR)</i>. DEBPIR uses a smart contract for PIR and encryption to share the privacy of classified information. We execute a thorough set of experiments on DEBPIR and evaluate the results based on privacy attainment, latency, and throughput. We also perform a comparative analysis between our proposed DEBPIR and the existing models; we observe that DEBPIR outperforms the existing models and provides <span>(95%)</span> privacy attainment. The latency and throughput of our proposed DEBPIR do not outgrow compared to the existing models. Thus, DEBPIR is an efficient solution for business models.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"51 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940385","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":"Eye contact based engagement prediction for efficient human–robot interaction","authors":"Magnus Jung, Ahmed Abdelrahman, Thorsten Hempel, Basheer Al-Tawil, Qiaoyue Yang, Sven Wachsmuth, Ayoub Al-Hamadi","doi":"10.1007/s40747-025-01902-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01902-z","url":null,"abstract":"<p>This paper introduces a new approach to predict human engagement in human–robot interactions (HRI), focusing on eye contact and distance information. Recognising engagement, particularly its decline, is essential for successful and natural interactions. This requires early, real-time user behavior detection. Previous HRI engagement classification approaches use various audiovisual features or adopt end-to-end methods. However, both approaches face challenges: the former risks error accumulation, while the latter suffer from small datasets. The proposed class-sensitive model for capturing engagement in HRI is based on eye contact detection. By analyzing eye contact intensity over time, the model provides a more robust and reliable measure of engagement levels, effectively capturing both temporal dynamics and subtle behavioral changes. Direct eye contact detection, a crucial social signal in human interactions that has not yet been explored as a standalone indicator in HRI, offers a significant advantage in robustness over gaze detection and incorporates additional facial features into the assessment. This approach reduces the number of features from up to over 100 to just two, enabling real-time processing and surpassing state-of-the-art results with 80.73% accuracy and 80.68% F1-Score on the UE-HRI dataset, the primary resource in current engagement detection research. Additionally, cross-dataset testing on a newly recorded dataset with the Tiago robot from Pal Robotics achieved an accuracy of 86.8% and an F1-score of 87.9%. The model employs a sliding window approach and consists of just three fully connected layers for feature fusion and classification, offering a minimalistic yet effective architecture. The study reveals that engagement, traditionally relying on extensive feature sets, can be inferred reliably from temporal eye contact dynamics. The results include a detailed analysis of established engagement levels on the UE-HRI dataset using the proposed model. Additionally, models for more nuanced engagement classification are introduced, showcasing the effectiveness of this minimalistic feature set. These models provide a robust foundation for future research, advancing robotic systems and deepening understanding of HRI, for example by improving real-time social cue detection and creating adaptive engagement strategies in HRI.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"123 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933550","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":"Siamese network with squeeze-attention for incomplete multi-view multi-label classification","authors":"Mengqing Wang, Jiarui Chen, Lian Zhao, Yinghao Ye, Xiaohuan Lu","doi":"10.1007/s40747-025-01909-6","DOIUrl":"https://doi.org/10.1007/s40747-025-01909-6","url":null,"abstract":"<p>Multi-view multi-label classification (MvMLC) has garnered significant interest because of its ability to handle complex datasets. However, the inherent complexity of real-world data often results in incomplete views and missing labels, which limit the richness of data and hinder the accurate association of features with their corresponding categories. Additionally, the MvMLC task is intricate due to the need for diverse views to coherently represent the same entity, thus demanding the creation of stable and consistent multi-view representations that can ensure a reliable feature alignment process across heterogeneous perspectives. To address these challenges, we propose a model based on a Siamese network with squeeze attention (SSA) for incomplete multi-view multi-label classification (iMvMLC). Specifically, to capture the shared semantic information across different views, we combine cross-view collaborative synthesis (CCS) and viewwise representation calibration (VRC) mechanisms. CCS enhances the semantic interaction between views by introducing directive blocks and stacked autoencoders on top of the Siamese network, thereby improving the ability to extract shared semantic representations. The VRC mechanism uses contrastive learning with positive and negative sample pairs to refine the shared semantic space, ensuring higher feature consistency and better alignment across views. Furthermore, considering the task-specific importance variation exhibited by each view, we apply the squeeze attention-weighted fusion (SWF) strategy, which performs feature dimensionality reduction to amplify the key characteristics from each view and enables the model to flexibly adjust the influence of each perspective. Extensive evaluations conducted across five datasets demonstrate that the SSA method outperforms many existing approaches.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"13 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933514","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":"PLGNN: graph neural networks via adaptive feature perturbation and high-way links","authors":"Meixia He, Peican Zhu, Yang Liu, Keke Tang","doi":"10.1007/s40747-025-01929-2","DOIUrl":"https://doi.org/10.1007/s40747-025-01929-2","url":null,"abstract":"<p>Graph neural networks (GNNs) have exhibited remarkable performance in addressing diverse graph learning tasks. However, inevitable missing information in graph networks hinders GNNs from aggregating more abundant feature information, limiting GNNs’ performance. Moreover, missing information further exacerbates the risk of overfitting in GNNs. In this manuscript, we devote to presenting a novel framework, i.e., <b>G</b>raph <b>N</b>eural <b>N</b>etworks via Adaptive Feature <b>P</b>erturbation and High-way <b>L</b>inks (PLGNN), to tackle these challenges. We introduce an efficient high-way links strategy to augment the graph, which enhances the features aggregation of GNNs, thereby improving the performance of PLGNN. Subsequently, an adaptive feature perturbation strategy is proposed to reduce model’s overfitting and also improve robustness of PLGNN. Then, we perform experiments on ten real-world datasets to reveal the superiority of PLGNN, with the corresponding performance being compared with that of state-of-the-art ones. Specifically, the <i>Accuracy</i> improved by an average of 2.6% on five node classification datasets, and an average of 2.1% on five graph classification datasets.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"38 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933517","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":"Graph attention based on contextual reasoning and emotion-shift awareness for emotion recognition in conversations","authors":"Juan Yang, Puling Wei, Xu Du, Jun Shen","doi":"10.1007/s40747-025-01903-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01903-y","url":null,"abstract":"<p>Emotion recognition in conversations has recently emerged as a hot research topic owing to its increasingly important role in developing intelligent empathy services. Thoroughly exploring the conversational context and accurately capturing emotion-shift information are highly crucial for accurate emotion recognition in conversations. However, existing studies generally failed to fully understand the complex conversational context due to their insufficient capabilities in extracting and integrating emotional cues. Moreover, they mainly focused on the speaker’s emotion inertia while paying less attention to explore multi-perspective emotion-shift patterns. To address these limitations, this study proposes a novel multimodal approach, namely, GAT-CRESA (Graph ATtention based on Contextual Reasoning and Emotion-Shift Awareness). Specifically, the multi-turn global contextual reasoning module iteratively performs contextual perception and cognitive reasoning for efficiently understanding the global conversational context. Then, GAT-CRESA explores emotion-shift information among utterances from both the speaker-dependent and the global context-based perspectives. Next, the emotion-shift awareness graphs are constructed for extracting significant local-level conversational context, where edge relations are determined by the learnt emotion-shift labels. Finally, the outputs of graphs are concatenated for final emotion recognition. The loss of emotion prediction task is combined together with those of two perspective’s emotion-shift learning for guiding the training process. Experimental results show that our GAT-CRESA achieves new state-of-art records with 72.77% ACC and 72.81% wa-F1 on IEMOCAP, and 65.44% ACC and 65.04% wa-F1 on MELD, respectively. The ablation results also indicate the effectiveness and rationality of each component in our approach.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"51 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933516","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}