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Intelligent integrated sensing and communication:a survey

Science China(information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Coarse-to-fine lightweight meta-embedding for ID-based recommendations

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Science China(information Sciences) 2025年第4期68卷 82-97页

作者： Yang WANG Haipeng LIU Zeqian YI Biao QIAN Meng WANG School of Computer Science and Information Engineering Hefei University of Technology College of Information and Intelligence Hunan Agricultural University

State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.

关键词： lightweight meta-embedding coarse-to-fine learning ID-based recommendations

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Federated Learning for Short-term Load Forecasting

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第1期52卷 52-58页

作者： Meng, Zhaorui Xie, Xiaozhu Xie, Yanqi Sun, Jinhua School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen361024 China

Deep learning architectures have exhibited robust performance in short-term load forecasting tasks, contingent upon access to substantial training datasets. However, the acquisition of such datasets presents significant challenges, as load operators may be reluctant to share their data due to privacy protection policies. Consequently, the accurate prediction of loads becomes increasingly complex. This study proposes a novel approach to short-term load forecasting, leveraging federated learning and local tuning techniques. The proposed two-stage methodology commences with the training of a generic model through decentralized learning and central aggregation, aimed at preserving data privacy. Subsequently, the resulting generic model undergoes local fine-tuning on each operator's specific load data for enhanced forecasting accuracy. Empirical evaluations conducted on multiple regional load datasets from the GEFCom2012 and AEMO datasets demonstrate the trained model's capacity to improve short-term load forecasting accuracy while maintaining the confidentiality of individual operators’ data. © (2025), (International Association of Engineers). All rights reserved.

关键词： Federated learning

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Collaborative Trajectory Planning for Stereoscopic Agricultural Multi-UAVs Driven by the Aquila Optimizer

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computers, Materials & Continua 2025年第1期82卷 1349-1376页

作者： Xinyu Liu Longfei Wang Yuxin Ma Peng Shao School of Computer and Information Engineering Jiangxi Agricultural UniversityNanchang330045China

Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture,a multi-task trajectory planning model and algorithm(IEP-AO)that synthesizes flight safety and flight efficiency is *** on the requirements of stereoscopic agricultural geomorphological features and operational characteristics,the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects,including the path,slope,altitude,corner,energy and obstacle threat,to improve the effectiveness of the trajectory planning *** combined with the path optimization algorithm,an Aquila optimizer(IEP-AO)based on the interference-enhanced combination model is proposed,which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks,and jump out of the locally optimal trajectory path region timely,to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight ***,four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene,and the experimental results can show that the proposedmulti-task trajectory planning method canmeet themulti-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV.

关键词： Stereoscopic agriculture unmanned aerial vehicle multi-task interference model Aquila optimizer

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Research on Optimization of Hierarchical Quantum Circuit Scheduling Strategy

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computers, Materials & Continua 2025年第3期82卷 5097-5113页

作者： Ziao Han Hui Li Kai Lu Shujuan Liu Mingmei Ju School of Computer and Information Engineering Harbin University of CommerceHeilongjiang150028China

Traditional quantum circuit scheduling approaches underutilize the inherent parallelism of quantum computation in the Noisy Intermediate-Scale Quantum(NISQ)era,overlook the inter-layer operations can be further *** on this,two quantum circuit scheduling optimization approaches are designed and integrated into the quantum circuit compilation ***,we introduce the Layered Topology Scheduling Approach(LTSA),which employs a greedy algorithm and leverages the principles of topological sorting in graph *** allocates quantum gates to a layered structure,maximizing the concurrent execution of quantum gate ***,the Layerwise Conflict Resolution Approach(LCRA)is *** focuses on utilizing directly executable quantum gates within *** the insertion of SWAP gates and conflict resolution checks,it minimizes conflicts and enhances parallelism,thereby optimizing the overall computational *** findings indicate that LTSA and LCRA individually achieve a noteworthy reduction of 51.1%and 53.2%,respectively,in the number of inserted SWAP ***,they contribute to a decrease in hardware gate overhead by 14.7%and 15%,*** the intricate nature of quantum circuits and the temporal dependencies among different layers,the amalgamation of both approaches leads to a remarkable 51.6%reduction in inserted SWAP gates and a 14.8%decrease in hardware gate *** results underscore the efficacy of the combined LTSA and LCRA in optimizing quantum circuit compilation.

关键词： Quantum circuit scheduling layered topology scheduling approach(LTSA) layerwise conflict resolu-tion approach(LCRA) quantum computing quantum circuit compilation

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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Partial multi-label learning via label-specific feature corrections

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Science China(information Sciences) 2025年第3期68卷 95-109页

作者： Jun-Yi HANG Min-Ling ZHANG School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education

Partial multi-label learning（PML） allows learning from rich-semantic objects with inaccurate annotations, where a set of candidate labels are assigned to each training example but only some of them are valid. Existing approaches rely on disambiguation to tackle the PML problem, which aims to correct noisy candidate labels by recovering the ground-truth labeling information ahead of prediction model induction. However, this dominant strategy might be suboptimal as it usually needs extra assumptions that cannot be fully satisfied in real-world scenarios. Instead of label correction, we investigate another strategy to tackle the PML problem, where the potential ambiguity in PML data is eliminated by correcting instance features in a label-specific manner. Accordingly, a simple yet effective approach named PASE, i.e., partial multi-label learning via label-specific feature corrections, is proposed. Under a meta-learning framework, PASElearns to exert label-specific feature corrections so that potential ambiguity specific to each class label can be eliminated and the desired prediction model can be induced on these corrected instance features with the provided candidate labels. Comprehensive experiments on a wide range of synthetic and real-world data sets validate the effectiveness of the proposed approach.

关键词： machine learning multi-label learning partial multi-label learning label-specific features feature correction

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Summary Graph Induced Invariant Learning for Generalizable Graph Learning

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3739-3752页

作者： Ning, Xuecheng Wang, Yujie Yu, Kui Miao, Jiali Cao, Fuyuan Liang, Jiye Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei China Shanxi University School of Computer and Information Technology Taiyuan China

As a promising strategy to achieve generalizable graph learning tasks, graph invariant learning emphasizes identifying invariant subgraphs for stable predictions on biased unknown distribution by selecting the important edges/nodes based on their contributions to the predictive tasks (i.e., subgraph predictivity). However, the existing approaches solely relying on subgraph predictivity face a challenge: the learned invariant subgraph often contains numerous spurious nodes and shows poor connectivity, undermining the generalization power of Graph Neural Networks (GNNs). To tackle this issue, we propose a summary graph-induced Invariant Learning (SIL) model that innovatively adopts a summary graph to leverage both the subgraph connectivity and predictivity for learning strong connected and accurate invariant subgraphs. Specifically, SIL first learns a summary graph containing multiple strongly connected supernodes while maintaining structure consistency with the original graph. Secondly, the learned summary graph is disentangled into an invariant supernode and spurious counterparts to eliminate the interference of highly predictive edges and nodes. Finally, SIL identifies a potential invariant subgraph from the invariant supernode to accomplish generalization tasks. Additionally, we provide a theoretical analysis of the summary graph learning mechanism, guaranteeing that the learned summary graph is consistent with the original graph. Experimental results validate the effectiveness of the SIL model. © 2025 IEEE. All rights reserved.

关键词： Contrastive Learning

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Knowledge-aware Recommendation with Attention Mechanism and Contrastive Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第3期52卷 615-624页

作者： Chen, Cuihong Sun, Gang School of Information Engineering Anhui Business And Technology College Hefei China School of Computer and Information Engineering Fuyang Normal University Fuyang China

Knowledge graph (KG) is important in recommendation algorithms. For the past few years, graph neural networks (GNNs) models applied to knowledge-aware recommendation (KGR) have been a current research hotspot. However, GNN-based KGR models have some problems, eg. data sparsity, insufficient supervision indication, unbalanced information utilization, and insufficient knowledge extraction. This paper presents a knowledge-aware recommendation algorithm that integrates an attention mechanism with contrastive learning, thereby make the most of the rich entity information available in the KG. Through contrastive learning, the algorithm aims to derive more discriminative expressions for users and items, and the attention mechanism helps to identify key relationships between them. This dual way improves the accuracy and efficiency of recommendations. Firstly, by contrasting hierarchical structures within the KG, non-local graphs are constructed for users and items, facilitating the extraction of additional KG facts in KGR. Secondly, Intra-graph-level interactive contrastive learning is enforced within both non-local graphs to evaluate the levels of collaborative filtering alongside the KG part, so as to obtain more consistent information utilization. In addition, inter-graph-level interactive contrastive learning is implemented between non-local graphs to fully and effectively extract non-local KG indication. Finally, the attention mechanism is integrated to determine the consequence of node neighbors and adaptively propagate the embeddings from the neighbors of nodes. The proposed knowledge-aware recommendation algorithm (KGAC) in this paper, along with other recommendation algorithms, has been subjected to extensive experimentation across three benchmark data sets. The results of these tests confirm that KGAC outperforms other algorithms in terms of recommendation performance. © (2025), (International Association of Engineers). All rights reserved.

关键词： Contrastive Learning

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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