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Novel Sm3+ and Eu3+ co-doped Sr2YNbO6 red-emitting phosphor for white LED applications

Ceramics International 2025年第15期51卷 19901-19909页

作者： Zhang, Yuanfeng Shi, Youzhen Deng, Chaoyong Zhang, Min College of Big Data and Information Engineering Guizhou University Guiyang550025 China Key Laboratory of Electronic Functional Composite Materials of Guizhou Province Guiyang550025 China School of Science China University of Geosciences Beijing100083 China Guizhou Academy of Sciences Guiyang550001 China

A series Sm3+ and Eu3+ co-doped Sr2YNbO6 phosphors were prepared by high temperature solid state reaction method in this work. The photoluminescence properties and energy transfer process were studied, and the results revealed that the photoluminescence intensity was significantly enhanced by co-doped with Eu3+, with the optimal concentration of 0.15mol, and the energy transfer efficiency between Sm3+ and Eu3+ ions was as high as 94.25%. In addition, the diffuse reflectance spectrum results confirmed the excitation wavelength of the sample, and the optical band gap showed concentration dependence. Finally, the lower color temperature CCT=2502K and the higher color rendering index Ra=76.4 indicate that the material has potential application value in the fields of LED and anti-counterfeiting display. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Photoluminescence

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NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning 34

NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on ...

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34th ACM Web Conference, WWW 2025

作者： Li, Ken Shi, Bin Wei, Jiazhe Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China School of Distance Education Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9798400712746

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client’s local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom d

关键词： Backdoor Attacks Federated Graph Learning

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Contactless Vital Signs Monitoring for Animals

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IEEE Internet of Things Journal 2025年第11期12卷 16789-16796页

作者： Pitafi, Zaid Farooq Song, Yingjian Xie, Zaipeng Brainard, Benjamin Song, Wenzhan University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing211100 China University of Georgia College of Veterinary Medicine AthensGA30602 United States

Monitoring vital signs such as Heart Rate (HR) and Respiratory Rate (RR) is critical for veterinary medicine. The existing contact based systems are difficult to use on animals for longer periods as they may cause movement restriction. Contactless solutions have recently gained more popularity due to their ease-of-use. However, there are very few validated systems for animals. In this study, we propose a contactless vital signs monitoring system, CageDot for animals. Our system provides continuous real-time monitoring of HR and RR during hospitalization. The CageDot is placed under the animal cage and detects the heart vibrations using a geophone sensor. The CageDot also has a signal quality control algorithm to address the problem of obtaining high-quality cardiac data in real-life noisy environments such as hospitals. Compared to the existing works that use controlled environments, this is especially significant. The algorithm includes several steps that include background noise/movement removal, subject movement detection, heartbeat extraction and vital signs estimation. The experimental results on 16 hospitalized dogs and cats show that this system can achieve high accuracy for vital signs monitoring with a Mean Absolute Error (MAE) of 4.71 for Heart Rate (3.8 percent error rate) and 1.28 (8 percent error rate) for Respiratory Rate. © 2014 IEEE.

关键词： Heart

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UARC:Unsupervised Anomalous Traffic Detection with Improved U-Shaped Autoencoder and RetNet Based Multi-clustering 26th

UARC:Unsupervised Anomalous Traffic Detection with Improved ...

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26th International Conference on Information and Communications Security, ICICS 2024

作者： Xie, Yunyang Chen, Kai Li, Shenghui Li, Bingqian Zhang, Ning Hubei Key Laboratory of Distributed System Security Hubei Engin eering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University Technology Wuhan430074 China

ISBN: (纸本)9789819787975

With the ongoing advancement of deep learning, modern network intrusion detection systems increasingly favor utilizing deep learning networks to improve their ability to learn traffic characteristics. To address the challenge of obtaining a substantial amount of labeled training data, many intrusion detection systems now focus on unsupervised anomaly detection methods. Despite this shift, researchers still face the daunting task of distinguishing a significant volume of anomalous traffic and dealing with data imbalance. To address these real-world challenges, we introduce UARC, a system capable of achieving unsupervised anomaly traffic detection through multi-clustering. UARC utilizes an enhanced U-shaped autoencoder and a feature fusion method incorporating Masked Retnet to effectively extract spatiotemporal features from network traffic. It combines these techniques with the HDBSCAN algorithm for multi-clustering of traffic, providing a form of reverse guidance for network learning. Experimental results on multiple datasets demonstrate that UARC can cluster various types of traffic with an impressive accuracy rate of up to 97.96%, while achieving a 99.70% AUC value for anomaly detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model

arXiv

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arXiv 2025年

作者： Hu, Yuxuan Zhang, Jing Chen, Xiaodong Zhao, Zhe Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China Engineering Research Center of Database and Business Intelligence Beijing China Tencent AI Lab Beijing China

Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity. Recent works introduced methods that maintain sparsity by augmenting LoRA techniques with additional masking mechanisms. Despite these successes, such approaches suffer from an increased memory and computation overhead, which affects efficiency of LoRA methods. In response to this limitation, we introduce LoRS, an innovative method designed to achieve both memory and computation efficiency when fine-tuning sparse LLMs. To mitigate the substantial memory and computation demands associated with preserving sparsity, our approach incorporates strategies of weight recompute and computational graph rearrangement. In addition, we also improve the effectiveness of LoRS through better adapter initialization. These innovations lead to a notable reduction in memory and computation consumption during the fine-tuning phase, all while achieving performance levels that outperform existing LoRA approaches. © 2025, CC BY.

关键词： Problem oriented languages

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key Clues Guided Video Character Social Relationship Recognition Enhanced by LLM

Key Clues Guided Video Character Social Relationship Recogni...

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

作者： Dong, Wenlong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large Language Model Enhanced key Clues Selection (LEKCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large Language Model to VCSRR for understanding the selected clues. We evaluated LEKCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance. © 2025 IEEE.

关键词： large language model Social relationship recognition video understanding

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A leaded sine–cosine artificial protozoa optimizer algorithm for solving multi-objective optimization problems

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Cluster Computing 2025年第5期28卷 1-65页

作者： Liao, Junjie Melethil Sethumadhavan, Syam Ke, Rui Gao, Zheng-Ming Zhao, Juan College of Computer and Information Technology China Three Gorges University Yichang443002 China School of Computer Engineering Jingchu University of Technology Jingmen448000 China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang443002 China Big Data Research Center Jingchu University of Technology Jingmen448000 China School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China

Many real-world engineering problems require simultaneous optimization of multiple objectives. The artificial protozoa optimizer (APO), a recently proposed and efficient optimization method, has demonstrated potential in solving multi-objective problems. In order to solve multi-objective problems, this paper proposes a novel algorithm based on the APO framework, the Leaded Sine Cosine Multi-objective APO (LSCMOAPO) algorithm. LSCMOAPO mainly integrates the sine–cosine algorithm (SCA) and a leader selection strategy to guide the population towards the true Pareto front. The algorithm was validated through extensive simulations involving 41 benchmark test functions (ZDT-series, UF-series, CF-series, and CEC2020-series) and five practical engineering problems. Performance was evaluated using the inverted generational distance, generational distance, Hypervolume, and spacing metrics. Comparative analysis with ten other multi-objective optimization algorithms (MOPSO, MOSSA, MOGWO, MODA, MOCryStAl, MOALO, MOGOA, MOSMA, MOGNDO, and MOAPO) also validated the better performance of LSCMOAPO in more than 86% occasions (The average of the evaluation indicators) in realizing high-quality solutions to all multi-objective problems, including linear, nonlinear, continuous, and discrete Pareto optimal front. It will set a new benchmark for new algorithm proposed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Multiobjective optimization

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Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks 34

Dual-level Mixup for Graph Few-shot Learning with Fewer Task...

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34th ACM Web Conference, WWW 2025

作者： Liu, Yonghao Li, Mengyu Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education China

ISBN: (纸本)9798400712746

Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled samples for training, which unavoidably leads to overfitting in few-shot scenarios. Recent research has sought to alleviate this issue by simultaneously leveraging graph learning and meta-learning paradigms. However, these graph meta-learning models assume the availability of numerous meta-training tasks to learn transferable meta-knowledge. Such assumption may not be feasible in the real world due to the difficulty of constructing tasks and the substantial costs involved. Therefore, we propose a SiMple yet effectIve approach for graph few-shot Learning with fEwer tasks, named SMILE. We introduce a dual-level mixup strategy, encompassing both within-task and across-task mixup, to simultaneously enrich the available nodes and tasks in meta-learning. Moreover, we explicitly leverage the prior information provided by the node degrees in the graph to encode expressive node representations. Theoretically, we demonstrate that SMILE can enhance the model generalization ability. Empirically, SMILE consistently outperforms other competitive models by a large margin across all evaluated datasets with in-domain and cross-domain settings. Our anonymous code can be found here. © 2025 Copyright held by the owner/author(s).

关键词： Few-shot learning Graph neural network Node classification

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SSMamba: Superpixel Segmentation With Mamba

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IEEE Signal Processing Letters 2025年 32卷 1715-1719页

作者： Jia, Xiaohong Li, Yonghui Jiao, Jianjun Zhao, Yao Xia, Zhiwei Lanzhou Jiaotong University School of Electronic and Information Engineering Lanzhou730070 China Beijing Jiaotong University Key Laboratory of Big Data and Artiffcial Intelligence in Transportation Ministry of Education Beijing100044 China Beijing Jiaotong University Institute of Information Science Beijing100044 China

Deep convolutional networks have achieved remarkable success in superpixel segmentation. However, they only focus on local features ignoring global attributes. The visual Mamba demonstrates an exceptional capability to capture long-range dependencies and offers a lower computational cost compared to the Transformer. Building on this inspiration, we propose a novel superpixel segmentation with Mamba, termed SSMamba. In SSMamba, Mamba is integrated into a global-local architecture, enabling efficient interaction between global attributes and local features to produce high-quality superpixels. The designed activation function further enhances the effectiveness of SSMamba. Extensive experiments on four public datasets demonstrate that SSMamba outperforms existing state-of-the-art methods, achieving competitive average values of ASA = 0.9541, BR = 0.8768, BP = 0.2124, UE = 0.0910, and CO = 0.3698. © 1994-2012 IEEE.

关键词： Convolutional neural networks

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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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