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A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network

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IEEE Sensors Journal 2025年第9期25卷 15548-15563页

作者： Ye, Miao Jiang, Zhibang Xue, Xingsi Li, Xingwang Wen, Peng Wang, Yong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo454099 China

Attention-based transformers have played an important role in wireless sensor network (WSN) timing anomaly detection due to their ability to capture long-term dependencies. However, there are several issues that must be addressed, such as the fact that their ability to capture long-term dependencies is not completely reliable, their computational complexity levels are high, and the spatiotemporal features of WSN timing data are not sufficiently extracted for detecting the correlation anomalies of multinode WSN timing data. To address these limitations, this article proposes a WSN anomaly detection method that integrates frequency-domain features with dynamic graph neural networks (GNNs) under a designed self-encoder reconstruction framework. First, the discrete wavelet transform (DWT) effectively decomposes trend and seasonal components of time series to solve the poor long-term reliability of transformers. Second, a frequency-domain attention mechanism (FDAM) is designed to make full use of the difference between the amplitude distributions of normal data and anomalous data in this domain. Finally, a multimodal fusion-based dynamic graph convolutional network (MFDGCN) is designed by combining an attention mechanism and a graph convolutional network (GCN) to adaptively extract spatial correlation features. A series of experiments conducted on public datasets and their results demonstrate that the anomaly detection method designed in this article exhibits superior precision and recall than the existing methods do, with an F1 score of 93.5%, representing an improvement of 2.9% over that of the existing models. © 2025 IEEE. All rights reserved.

关键词： Normal distribution

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data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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Computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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Can satellite observations detect global ocean heat content change with high resolution by deep learning?

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 52-68页

作者： Su, Hua Teng, Jianchen Zhang, Feiyan Wang, An Huang, Zhanchao Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China Fuzhou University Fuzhou350108 China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China Department of Geography Ghent University Ghent9000 Belgium

The development of in situ observations has significantly improved ocean heat content (OHC) estimation. However, high-resolution OHC data remain limited, hindering detailed studies on mesoscale oceanic warming variability. This study used a deep learning method-Densely Deep Neural Network (DDNN) to reconstruct a high-resolution (0.25° × 0.25°) global OHC dataset for the upper 2000m ocean from 1993 to 2023, named the Ocean Projection and Extension Neural Network 0.25° (OPEN0.25°) product. This deep ocean remote sensing approach integrates multi-source remote sensing data, including Sea Surface Temperature (SST), Absolute Dynamic Topography (ADT), and Sea Surface Wind (SSW), alongside spatiotemporal coordinates and in situ observations. The DDNN model was trained using Argo-based gridded data and EN4-profile data, initially undergoing pre-training to assimilate large-scale oceanic features, followed by fine-tuning to enhance its accuracy in capturing mesoscale thermal structures. Our results demonstrate that the DDNN model achieves high accuracy across various depths. Particularly, OPEN0.25° can effectively capture detailed thermal variations in regions with complex dynamics, as well as the heat transfer processes within the ocean interior, outperforming traditional methods in resolution. The research highlights that, influenced by strong El Niño-Southern Oscillation (ENSO) events, OHC in the upper 700m of the Pacific Ocean potentially far exceeding expectations over the past decade. Through this study, OPEN0.25° has demonstrated its critical role in detecting and monitoring long-term changes in global OHC at high resolution. © 2025 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

关键词： Submarine geophysics

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Fair Personalized Learner Modeling Without Sensitive Attributes 34

Fair Personalized Learner Modeling Without Sensitive Attribu...

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34th ACM Web Conference, WWW 2025

作者： Xu, Hefei Hou, Min Wu, Le Liu, Fei Yang, Yonghui Bai, Haoyue Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei China Institute of Dataspace Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9798400712746

Personalized learner modeling uses learners’ historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD). This is essential for web-based learning services such as learning resource recommendation and adaptive testing. However, prior studies have shown that CD models may unfairly correlate learners’ abilities with sensitive attributes (e.g., gender, region), leading to biased outcomes. While existing approaches mitigate this issue by decorrelating sensitive attributes from the modeling process, privacy concerns make collecting such attributes impractical. Furthermore, the presence of multiple sensitive attributes complicates fairness improvements. In this paper, we explore how to achieve fair personalized learner modeling without relying on any sensitive attribute input. We introduce a novel fairness objective tailored for personalized learner modeling and design a max-min strategy to facilitate both sensitive information inference and fair CD modeling. In the max step, we infer pseudo-labels by maximizing the fairness objective, while in the min step, we retrain the CD model by minimizing it. Additionally, we provide a theoretical guarantee that our framework reduces the upper bound of fairness generalization error. Extensive experiments demonstrate that the proposed framework significantly outperforms existing methods. © 2025 Copyright held by the owner/author(s).

关键词： Cognitive Diagnosis Debiasing Fairness Out-of-distribution User Modeling

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Enhancing Continuous Cognitive Diagnosis with Fuzzy Strategy-Based Hybrid Genetic Algorithm 3rd

Enhancing Continuous Cognitive Diagnosis with Fuzzy Strateg...

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3rd International Conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： He, Chenlong Hu, Xuegang Cao, Zhiyong Bu, Chenyang Luo, Wenjian Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819645053

Continuous cognitive diagnosis models (CDMs) are vital tools for assessing students’ mastery of knowledge points. However, traditional probability-based CDMs are prone to falling into local optima due to their use of single-point search methods, which can affect the accuracy of the models. To address this issue, we propose a hybrid genetic algorithm (HGA) enhanced with a fuzzy strategy to improve continuous cognitive diagnosis. This approach introduces the multidimensional item response theory (MIRT) as a local search operator to boost diagnostic precision. Additionally, considering the limitation on the number of local searches within a finite time, we introduce a fuzzy strategy that dynamically adjusts the number of local searches by evaluating the similarity between the current population and the elite set, thus balancing global and local search. Experimental results on three real-world datasets demonstrate that our method significantly outperforms six existing comparison models, validating the effectiveness of the fuzzy strategy and continuous CDM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Continuous cognitive diagnosis Educational data mining Evolutionary algorithm Local search

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KAN v.s. MLP for Offline Reinforcement Learning

KAN v.s. MLP for Offline Reinforcement Learning

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (纸本)9798350368741

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements. © 2025 IEEE.

关键词： KAN Kolmogorov-Arnold networks MLP multilayer perceptrons offline reinforcement learning

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Federated Incremental Named Entity Recognition 31

Federated Incremental Named Entity Recognition

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Zesheng Zhu, Qiannan Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application MOE China

ISBN: (纸本)9798891761964

Federated learning-based Named Entity Recognition (FNER) has attracted widespread attention through decentralized training on local clients. However, most FNER models assume that entity types are pre-fixed, so in practical applications, local clients constantly receive new entity types without enough storage to access old entity types, resulting in severe forgetting on previously learned knowledge. In addition, new clients collecting only new entity types may join the global training of FNER irregularly, further exacerbating catastrophic forgetting. To overcome the above challenges, we propose a Forgetting-Subdued Learning (FSL) model which solves the forgetting problem on old entity types from both intra-client and inter-client two aspects. Specifically, for intra-client aspect, we propose a prototype-guided adaptive pseudo labeling and a prototypical relation distillation loss to surmount catastrophic forgetting of old entity types with semantic shift. Furthermore, for inter-client aspect, we propose a task transfer detector. It can identify the arrival of new entity types that are protected by privacy and store the latest old global model for relation distillation. Qualitative experiments have shown that our model has made significant improvements compared to several baseline methods. © 2025 Association for Computational Linguistics.

关键词： Federated learning

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Predicting microRNA-Disease Associations Through Multi-View Feature Fusion and Matrix Completion on Graph Convolutional Networks

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 151-159页

作者： Shifei Ding Chuangui Cao Fan Sun Xuan Li Xindong Wu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of Ministry of Education Xuzhou China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China

Since the regulatory roles of microRNAs in complex human diseases have been gradually demonstrated, more and more enlightening models were developed for predicting microRNA-disease associations. These models can inform bio-logical studies on the differential expression of microRNAs. In this paper, a graph convolutional network-based model using multi-view feature fusion and matrix completion, FMCGCN for brevity, is proposed. First of all, graph autoencoders are used to learn multiple embeddings from different networks. For convenience, attention mechanisms are used to fuse them. As a result, multi-view features constructed from different perspectives are aggregated. Then, the fused embedding is fed into the graph convolutional network to aggregate local information. This fused embedding is thought to facilitate feature extraction for graph convolutional networks. Finally, feature and nuclear norm minimization, a method for matrix completion, is used to obtain the prediction matrix. Additionally, evaluation results under 5-fold cross-validation prove that FMCGCN outperforms current models in several metrics. Furthermore, case studies for esophageal neoplasms and pancreatic neoplasms also demonstrate the validity of our model.

关键词： Diseases Semantics Predictive models Kernel Topology Graph convolutional networks Feature extraction Learning systems Biological system modeling Reliability

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Dual-Aspect Noise-Based Regularization for Multi-Modal Relation Extraction in Media Posts

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1324-1336页

作者： Kai Sun Bin Shi Samuel Mensah Wenjian Liu Bo Dong School of Computer Science and Technology and Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Technology The University of Sheffield Sheffield U.K. Faculty of Data Science City University of Macau Macau China School of Continuing Education and Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

Multi-Modal Relation Extraction (MMRE) plays a key role in various multimedia applications including, recommendation and information retrieval systems. MMRE aims to extract the semantic relation between entities by leveraging context from a text-image pair. By utilizing context from images, the challenge of learning from noisy images in MMRE emerges as a research problem by itself. For instance, subtle variations in similar images can act as noise and potentially impact the predictions made by MMRE models. To tackle this problem, current work utilizes attention mechanisms to fuse relevant text and image features or devise data augmentation techniques (e.g., via generative models) to improve generalization. However, the current performance still remains unsatisfactory. In an effort to improve upon the performance, we propose a Dual-Aspect Noise-based Regularization framework that encompasses two techniques: 1) noise removal through an adaptive gating mechanism, 2) fighting noise with noise to improve feature stability in the learning process. We find that combining these techniques encourages the model to focus on more relevant image features for MMRE. We carry out extensive experiments and demonstrate that our proposed model is further enhanced by exploring data augmentation techniques. This additional improvement leads the model to achieve state-of-the-art performance on the widely-used Multi-modal Neural Relation Extraction (MNRE) dataset, and show its effectiveness and generalizability on the Multi-Modal Named Entity Recognition task.

关键词： Feature extraction Noise data mining Noise measurement Social networking (online) Adaptation models Transformers Training Predictive models data models

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Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Li, Zhenyu Wu, Ruixin Wang, Jin Hu, Chuanqi Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture. © 2025 IEEE.

关键词： Motion planning

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