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SU-YOLO: Spiking neural network for efficient underwater object detection

Neurocomputing 2025年 644卷

作者： Li, Chenyang Liu, Wenxuan Gong, Guoqiang Ding, Xiaobo Zhong, Xian College of Computer and Information Technology China Three Gorges University Yichang443002 China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing100091 China State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China

Underwater object detection is critical for oceanic research and industrial safety inspections. However, the complex optical environment and the limited resources of underwater equipment pose significant challenges to achieving high accuracy and low power consumption. To address these issues, we propose Spiking Underwater YOLO (SU-YOLO), a Spiking Neural Network (SNN) model. Leveraging the lightweight and energy-efficient properties of SNNs, SU-YOLO incorporates a novel spike-based underwater image denoising method based solely on integer addition, which enhances the quality of feature maps with minimal computational overhead. In addition, we introduce Separated Batch Normalization (SeBN), a technique that normalizes feature maps independently across multiple time steps and is optimized for integration with residual structures to capture the temporal dynamics of SNNs more effectively. The redesigned spiking residual blocks integrate the Cross Stage Partial Network (CSPNet) with the YOLO architecture to mitigate spike degradation and enhance the model's feature extraction capabilities. Experimental results on URPC2019 underwater dataset demonstrate that SU-YOLO achieves mAP of 78.8% with 6.97M parameters and an energy consumption of 2.98 mJ, surpassing mainstream SNN models in both detection accuracy and computational efficiency. These results underscore the potential of SNNs for engineering applications. The code is available in https://***/lwxfight/snn-underwater. © 2025 Elsevier B.V.

关键词： Image denoising

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Multifunctional metasurface based on vanadium dioxide in the terahertz frequency

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Applied Optics 2025年第17期64卷 4692-4700页

作者： Zou Long Zhengji Xu School of Microelectronics Science and Technology Sun Yat-Sen University Zhuhai 519000 China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-Sen University Zhuhai 519000 China

As metasurface technology has been developing rapidly over the past decades, multi-multiplexing and tunability are evolving into hot spots in its development. Here, a metasurface fit for vortex beam generation, beam focusing, linear-to-circular polarization conversion, and absorption is proposed in this study, contingent upon the phase change properties of vanadium dioxide. When the temperature is 25°C, the vortex beam and focusing generated when the circularly polarized wave is incident on the metasurface, the calculated outcomes demonstrate that the cross-polarization reflective coefficient is close to the co-polarization coefficient at the frequency range of 0.5–0.9 THz and 1.2–1.5 THz, implementing the linear-to-circular polarization conversion. When heated up to 68°C, it functions as an absorber at 1.55 THz when the y-polarized wave is incident vertically on the metasurface. The designed metasurface is likely to be used in the fields of terahertz communication and imaging systems.

关键词： Bandpass filters Circular polarization Imaging systems Polarization Surface plasmon polaritons Terahertz imaging

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FGCM: Modality-behavior Fusion Model Integrated with Graph Contrastive Learning for Multimodal Recommendation

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IEEE Multimedia 2025年

作者： Mo, Feng Xiao, Lin Song, Qiya Gao, Xieping Song, Wenzhuo Wang, Shoujin Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China Northeast Normal University Changchun China University of Technology Sydney Sydney Australia

Multimodal recommender systems (MRSs) aim to integrate information from multiple modalities, for better capturing users' preferences. However, existing MRSs usually face the challenge of data sparsity, especially for the limited user-item interactions. It is not uncommon that a user interacts with only a few items out of millions. More importantly, the interaction information is easy to be overwhelmed, leading to poor recommendation performance. To tackle these significant gaps for further improving the performance of MRSs, we propose a Modality-behavior Fusion Model Integrated with Graph Contrastive Learning for Multimodal Recommendation (FGCM). First, through the introduction of random noise into graph contrastive learning, FGCM adeptly mitigates data sparsity. Then, a modality-behavior fuser is proposed to effectively fuse interaction data and multimodal side information data by taking cooperative signals and multimodal signals as the guidance. Extensive experiments on three datasets demonstrate that our proposed approach outperforms several state-of-the-art methods. © 2025 IEEE.

关键词： Contrastive Learning

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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DiffuSyn: A Diffusion-Driven Framework With Syntactic Dependency for Aspect Sentiment Triplet Extraction

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 842-854页

作者： Qiuhua Yi Xiangjie Kong Linan Zhu Chenwei Zhang Guojiang Shen College of Computer Science and Technology Zhejiang University of Technology Hangzhou China Zhejiang Key Laboratory of Visual Information Intelligent Processing Hangzhou China School of Faculty of Education University of Hong Kong Hong Kong

Aspect Sentiment Triplet Extraction (ASTE) is a fine-grained sentiment analysis task that involves identifying aspect and opinion terms and conducting sentiment analysis for each aspect-opinion pair. We categorize existing methods into tagging-based, span-based, and generation methods. However, despite notable achievements, these methods still face certain challenges: span-based methods struggle to distinguish similar spans, and generation methods are prone to slower decoding speeds. To address these challenges, we propose a novel ASTE framework, called DiffuSyn, which leverages diffusion models and syntactic dependency parsing to improve the performance of sentiment analysis. Specifically, we first add Gaussian noise to the indices at the beginning and end of aspect words and opinion words in sentences based on a non-autoregressive diffusion model. We obtain accurate boundaries of aspect and opinion terms through boundary denoising, and thus identify precise spans. Secondly, we introduce a syntactic dependency parser to capture the syntactic dependencies within review sentences, thereby providing effective syntactic information for sentiment analysis. Finally, we complete the matching of aspects and opinions by leveraging the extracted boundaries and syntactic semantic information, facilitating the prediction of sentiment relationships. We conduct experiments on four public datasets (ASTE-Data-V2), and the results indicate the effectiveness of our approach in the ASTE task. Furthermore, our method achieves state-of-the-art performance in both Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks.

关键词： Diffusion models Syntactics Sentiment analysis Fish Speech processing Data mining Transforms Noise reduction Decoding Tagging

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Knowledge Graph Entity Typing with Curriculum Contrastive Learning 31

Knowledge Graph Entity Typing with Curriculum Contrastive Le...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Hao Nuo, Minghua Jiang, Shan College of Computer Science Inner Mongolia University Inner Mongolia Hohhot010021 China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Hohhot010021 China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Inner Mongolia Hohhot010021 China

ISBN: (纸本)9798891761964

The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Most recent studies only focus on the structural information from an entity's neighborhood or semantic information from textual representations of entities or relations. In this paper, inspired by curriculum learning and contrastive learning, we propose the CCLET model using the Curriculum Contrastive Learning strategy for KGET, which uses the Pre-trained Language Model (PLM) and the graph model to fuse the entity related semantic and the structural information of the Knowledge Graph (KG) respectively. Our CCLET model consists of two main parts. In the Knowledge Fusion part, we design an Enhanced-MLP architecture to fuse the text of the entity's description, related triplet and tuples;In the Curriculum Contrastive Learning part, we define the difficulty of the training course by controlling the level of added noise, we aim to accurately learn with curriculum contrastive learning strategy from easy to difficult. Our extensive experiments demonstrate that the CCLET model outperforms recent state-of-the-art models, verifying its effectiveness in the KGET task. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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A text dependent copy-paste plagiarism and text-rewriting plagiarism model

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Journal of Intelligent and Fuzzy Systems 2025年第6期48卷 849-858页

作者： Guo, Qingkai Huang, Guimin Wang, Yabing Zhou, Ya School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin China

Plagiarism is common in English writing exams. Researchers classify plagiarism into copy-paste and text-rewriting plagiarism, but existing models need help with problems such as the single way of checking and unsatisfactory results. Aiming at the copy-paste problem in English writing plagiarism, this paper proposes a digital fingerprint model based on the N-Gram window jumping mechanism. The model incorporates a sliding window and an improved matching tool to solve the problems of excessive fingerprint density and low checking efficiency in text extraction. Meanwhile, the model adds a Fisher-Yates shuffle algorithm with a salt parameter to crack the hash collision in text matching. The experiments show that the model can detect copy-paste plagiarism in English composition. For text rewriting plagiarism, this paper designs a TextCNN-BiGRU-based model, which combines TextCNN and BiGRU so that the extracted text semantic information considers the text’s local and contextual features. The experiments show that the model improves the accuracy by 1.9 percentage points and the F1 value by 1.2 percentage points on the MRPC dataset compared with other models. © 2025 – IOS Press. All rights reserved.

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Robust Self-Localization of Wireless Acoustic Sensor Networks

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IEEE Internet of Things Journal 2025年

作者： Wang, Xu Hu, De Liu, Rui Bao, Feilong Inner Mongolia University College of Computer Science Hohhot010021 China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot 010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot 010021 China

Wireless acoustic sensor networks (WASNs), or the so-called internet of audio things (IoAuTs), have attracted increasing attention in the IoT community. As the geometry structure of WASNs is required in audio/speech processing tasks like source localization or acoustic beamforming, automatic self-localization of sensors is necessary. However, most of the existing approaches suffer from poor stability, as their constructed cost functions involve non-convex programming. To address this issue, we investigate the robust self-localization (or geometry calibration) of WASNs in this paper. Specifically, a rough self-localization (RSL) method is first presented based on measurements including time-difference-of-arrivals (TDoAs), direction-of-arrivals (DoA), and energy-rates (ERs), and its closed-form solution is further derived. As ER estimates are sensitive to acoustic environments, the performance of the RSL method is somewhat limited. Therefore, a precise self-localization (PSL) method is then developed by building a weighted (and non-convex) TDoA-DoA cost function, after regarding the RSL approach as an initialization step. As the RSL offers better initial values compared with existing initialization strategies, the combination of RSL and PSL methods (named as RSL-PSL method) shows stronger robustness and stability. In addition, computational complexity of both RSL and PSL methods are analyzed in details. Finally, the Cramér-Rao Bound (CRB) of the PSL method is derived to show its theoretical lower bound. The proposed RSL-PSL method outperforms the state-of-the-arts in terms of stability and accuracy, which is confirmed by numerical real-world and simulation experiments. © 2014 IEEE.

关键词： Cost functions

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