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Achieving Enhanced Bi-Linear Attention Network for Teaching Manner Analysis over Edge Cloud-assisted AIoT: Voice-Body Coordination Perspective

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IEEE Transactions on Cloud Computing 2025年

作者： Zhou, Yu Zou, Sai Wu, Bochun Ni, Wei Du, Xiaojiang Guizhou University Micro-nano and Intelligent Manufacturing Engineering Research Centre of Ministry of Education College of Big Data and Information Engineering China Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai200433 China Data61 CSIRO Australia Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Edge computing, an advanced extension of cloud computing, provides superior computational capabilities and lowlatency processing at the network edge, facilitating its availability for real-time data analysis in resource-limited settings. When applied to the analysis of teaching methodologies, edge computing enables the seamless integration of vocal and physical cues, facilitating collaborative, dynamic, and real-time evaluations of teaching quality. However, the inherent complexity of human perception and multimodal interactions impose great challenges to the analysis of these aspects in Artificial Intelligence of Things (AIoT). This paper introduces an innovative mathematical model and a measurement index specifically designed to assess changes in voice-body coordination over time. To achieve this, we propose a cloud-enabled enhanced Bi-Linear Attention Network incorporating entropy and Fourier transforms (BAN-E-FT), which leverages both temporal and frequencydomain features. Specifically, by harnessing the computational and storage capabilities of edge computing, BAN-E-FT facilitates distributed training, expedites large-scale data processing, and enhances model scalability, where entropy measures and Fourier transforms capture modality dynamics, enhancing BAN's fusion capabilities. Moreover, a conditional domain adversarial network is embedded to address regional teaching variations, improving model generalizability. We also verify the robustness of BAN-EFT with accuracy and convergence through convex optimization analysis. Experiments on the eNTERFACE'05 dataset demonstrate 81% accuracy in assessing teaching adaptability, while real-world test at Guizhou University confirms 78% accuracy when using BAN-E-FT, matching human expert assessments. © 2013 IEEE.

关键词： data integration

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SAHSD: Enhancing Hate Speech Detection in LLM-Powered Web Applications via Sentiment Analysis and Few-Shot Learning 34

SAHSD: Enhancing Hate Speech Detection in LLM-Powered Web Ap...

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34th ACM Web Conference, WWW 2025

作者： Wang, Yulong Li, Hong Wei, Ni State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China College of Big Data and Information Engineering Guizhou University Guiyang China School of Communication Science and Engineering Fudan University Shanghai China

ISBN: (纸本)9798400712746

As large language models (LLMs) increasingly power web applications, including social networks, the challenge of moderating hate speech has become a critical concern for the Web. These LLM-powered applications, while offering near-human interaction capabilities, are vulnerable to harmful or biased content due to imperfect training data scraped from the Web. Current hate speech detection methods often struggle with limited annotated data, especially for real-time moderation on these platforms. This paper introduces Sentiment-Aided Hate Speech Detection (SAHSD), a novel approach designed to enhance hate speech detection specifically in LLM-powered web applications. By treating hate speech detection as a few-shot learning task, SAHSD utilizes sentiment analysis to refine pre-trained language models (LM) for improved accuracy in recognizing harmful content. SAHSD first employs publicly available sentiment datasets to train a sentiment analysis model, which is then fine-tuned by merging sentiment prompts with hate speech prompts, enabling efficient and accurate detection even with limited training samples. The effectiveness of SAHSD is demonstrated through experiments on widely used web-sourced datasets like SBIC and HateXplain. SAHSD achieves an exceptional F1-score of 0.99 with only 64 training samples and outperforms advanced techniques such as ToKen, MRP, and HARE, with significant improvements of 33% on SBIC and 95% on HateXplain. SAHSD surpasses GPT-4 in generalization performance across multiple datasets, showing an 8% improvement when trained on equal-sized samples. These results underscore SAHSD’s potential to enhance content moderation in LLM-driven web platforms, contributing to a safer, more inclusive and accountable Web ecosystem. © 2025 Copyright held by the owner/author(s).

关键词： Hate Speech Large Language Model Machine Learning Security

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SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding

arXiv

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arXiv 2025年

作者： Shi, Liangtao Liu, Ting Hu, Xiantao Hu, Yue Yin, Quanjun Hong, Richang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of systems engineering National University of Defense Technology Hunan Province Changsha410073 China Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge separately by fully fine-tuning uni-modal pre-trained models, followed by a simple stack of visual-language transformers for multimodal fusion. However, these approaches not only limit adequate interaction between visual and linguistic contexts, but also incur significant computational costs. Therefore, to address these issues, we explore a step-wise multimodal fusion and adaption framework, namely SwimVG. Specifically, SwimVG proposes step-wise multimodal prompts (Swip) and cross-modal interactive adapters (CIA) for visual grounding, replacing the cumbersome transformer stacks for multimodal fusion. Swip can improve the alignment between the vision and language representations step by step, in a token-level fusion manner. In addition, weight-level CIA further promotes multimodal fusion by cross-modal interaction. Swip and CIA are both parameter-efficient paradigms, and they fuse the cross-modal features from shallow to deep layers gradually. Experimental results on four widely-used benchmarks demonstrate that SwimVG achieves remarkable abilities and considerable benefits in terms of efficiency. Our code is available at https://***/liuting20/SwimVG. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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From leaf to lab-on-cloth: Spatial DNA nanorobotics and 2D graphyne synergy enable ultra-precise electrochemical tracking of sugarcane pokkah boeng disease

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Biosensors and Bioelectronics 2025年 283卷 117548页

作者： Wu, Qingnian Ya, Yu Jin, Chenchen Zhao, Yinxia Yan, Feiyan Feng, Defen Huang, Ke-Jing Xie, Shengyu Tan, Xuecai Education Department of Guangxi Zhuang Autonomous Region Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition Engineering Research Center of Low-carbon and High-quality Utilization of Forest Biomass Guangxi Key Laboratory of Chemistry and Engineering of Forest Products Key Laboratory of Chemistry and Engineering of Forest Products State Ethnic Affairs Commission School of Chemistry and Chemical Engineering Guangxi Minzu University Nanning530006 China Guangxi Academy of Agricultural Sciences Nanning530007 China School of Big Data Guangxi Vocational and Technical College Nanning530226 China

As a vital cash crop and bioenergy feedstock, sugarcane plays a pivotal role in global agriculture and renewable energy systems. The emergence of pokkah boeng disease has become a critical threat to sugarcane productivity. Current diagnostic methods face challenges in field-applicable early detection due to time-consuming procedures and insufficient sensitivity. This study pioneers a "Lab-on-Cloth" electrochemical biosensor that synergizes DNA nanorobotics with 2D graphyne for ultra-precise detecting of pathogen. The biosensor integrates three synergistic innovation mechanisms: a spatially confined DNA Walker system enabling programmable strand displacement cascades upon target recognition, sulfur-doped graphyne (S-GDY) nanoarrays providing enhanced electron transfer efficiency and catalytic current density, and a dual-signal readout strategy for self-verifying detection accuracy. The biosensor fabrication involves in fixing of AuNPs/S-GDY heterostructures on flexible carbon cloth, creating a hierarchical 3D conductive network. Upon pathogen DNA binding, the DNA Walker initiates dual strand displacement amplification cycles, generating distinct current responses through potential-resolved signal decoupling. This dual-signal readout achieves an ultra-low limit of detection (16.6 aM, S/N = 3) with a dynamic range spanning six orders of magnitude (0.1 fM-10 nM), outperforming conventional qPCR in field tests. This spatial DNA nanorobotics-graphyne synergy establishes a new paradigm for plant disease monitoring, providing real-time phyto-diagnostic capabilities. The technology's cost-effectiveness and operational simplicity position it as a transformative tool for precision agriculture and sustainable bioenergy production. © 2025 Elsevier B.V.

关键词： Sugar cane

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T3: Multi-modal Tailless Triple-Flapping-Wing Robot for Efficient Aerial and Terrestrial Locomotion

arXiv

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arXiv 2025年

作者： Xu, Xiangyu Zheng, Zhi Wang, Jin Chen, Yikai Huang, Jingyang Wu, Ruixin Yu, Huan Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Flapping-wing robots offer great versatility;however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of T3, a novel tailless flapping-wing robot with three pairs of independently actuated wings. Inspired by juvenile water striders, T3 incorporates bio-inspired elastic passive legs that effectively transmit vibrations generated during wing flapping, enabling ground movement without additional motors. This novel mechanism facilitates efficient multi-modal locomotion while minimizing actuator usage, reducing complexity, and enhancing performance. An SE(3)-based controller ensures precise trajectory tracking and seamless mode transition. To validate T3’s effectiveness, we developed a fully functional prototype and conducted targeted modeling, real-world experiments, and benchmark comparisons. The results demonstrate the robot’s and controller’s outstanding performance, underscoring the potential of multi-modal flapping-wing technologies for future aerial-ground robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

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IEEE Transactions on Signal Processing 2025年

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China

This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via ratelimited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users' signal-to-interference-plus-noise ratio (SINR) constraints and all BSs' fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. © 1991-2012 IEEE.

关键词： Lagrange multipliers

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Causal-Inspired Multitask Learning for Video-Based Human Pose Estimation

arXiv

引用

arXiv 2025年

作者： Chen, Haipeng Wu, Sifan Wang, Zhigang Yin, Yifang Jiao, Yingying Lyu, Yingda Liu, Zhenguang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China A*STAR Singapore Public Computer Education and Research Center Jilin University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies. However, they overlook the causal relationships in the joints, leading to models that may be overly tailored and thus estimate poorly to challenging scenes. Therefore, adequate causal reasoning capability, coupled with good interpretability of model, are both indispensable and prerequisite for achieving reliable results. In this paper, we pioneer a causal perspective on pose estimation and introduce a causal-inspired multitask learning framework, consisting of two stages. In the first stage, we try to endow the model with causal spatio-temporal modeling ability by introducing two self-supervision auxiliary tasks. Specifically, these auxiliary tasks enable the network to infer challenging keypoints based on observed keypoint information, thereby imbuing causal reasoning capabilities into the model and making it robust to challenging scenes. In the second stage, we argue that not all feature tokens contribute equally to pose estimation. Prioritizing causal (keypoint-relevant) tokens is crucial to achieve reliable results, which could improve the interpretability of the model. To this end, we propose a Token Causal Importance Selection module to identify the causal tokens and non-causal tokens (e.g., background and objects). Additionally, non-causal tokens could provide potentially beneficial cues but may be redundant. We further introduce a non-causal tokens clustering module to merge the similar non-causal tokens. Extensive experiments show that our method outperforms state-of-the-art methods on three large-scale benchmark datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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An enhanced CLKAN-RF framework for robust anomaly detection in unmanned aerial vehicle sensor data

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knowledge-Based Systems 2025年 319卷

作者： Li, Chuanjiang Xie, Wenhui Zheng, Bing Yi, Qian Yang, Lei Hu, Bingtao Deng, Chengxin State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China The State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310000 China School of Business Administration Zhejiang Gongshang University Hangzhou310000 China Collage of Machine and Vehicle Engineering Chongqing University Chongqing400044 China

Autonomous flight and real-time control of unmanned aerial vehicles (UAVs) critically rely on onboard sensors, which are susceptible to mechanical and environmental disruptions. Sensor anomalies pose substantial risks to UAV safety, emphasizing the importance of anomaly detection (AD) methods. However, AD remains challenging due to the scarcity of real anomaly data and the intricate spatiotemporal dependencies in sensor readings, often obscured by random noise and interference. This paper presents an enhanced framework based on one-dimensional convolutional neural network (1D CNN), long short-term memory network (LSTM) and Kolmogorov-Arnold network (KAN) with residual filtering (CLKAN-RF), utilizing multivariate sensor data without labeled information. First, a correlation analysis is employed to avoid the negative impact of irrelevant parameters on model training. Second, a multiple regression model is designed to comprehensively extract spatial-temporal relationships using 1D CNN and LSTM, while KAN is incorporated to non-linearly process the complex patterns and optimize the learned features with high accuracy. To address the issue of random noise, a bi-directional adaptive exponentially weighted moving average (Bi-AEWMA) scheme is introduced to smooth residuals, complemented by an adaptive dynamic thresholding mechanism to further enhance detection performance. Finally, extensive experiments on real UAV sensor data highlight the superiority of the proposed CLKAN-RF framework, which improves the true positive rate and overall accuracy by an average of 6.43 % and 7.63 %, respectively, while reducing the false positive rate by an average of 11.96 % compared to existing methods, demonstrating its potential application in UAV prognostics and health management. © 2025

关键词： Drones

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Machine learning strategies for small sample size in materials science

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Science China Materials 2025年第2期68卷 387-405页

作者： Qiuling Tao JinXin Yu Xiangyu Mu Xue Jia Rongpei Shi Zhifu Yao Cuiping Wang Haijun Zhang Xingjun Liu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhenShenzhen 518055China School of Computer Science&Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China Advanced Institute for Materials Research(WPI-AIMR) Tohoku UniversitySendai 980-8577Japan Department of Materials Science and Engineering College of MaterialsXiamen UniversityXiamen 361005China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhenShenzhen 518055China

Machine learning (ML) has been widely used todesign and develop new materials owing to its low computational cost and powerful predictive capabilities. In recentyears, the shortcomings of ML in materials science have gradually emerged, with a primary concern being the scarcity ofdata. It is challenging to build reliable and accurate ML modelsusing limited data. Moreover, the small sample size problemwill remain long-standing in materials science because of theslow accumulation of material data. Therefore, it is importantto review and categorize strategies for small-sample learningfor the development of ML in materials science. This reviewsystematically sorts the research progress of small-samplelearning strategies in materials science, including ensemblelearning, unsupervised learning, active learning, and transferlearning. The directions for future research are proposed, including few-shot learning, and virtual sample *** importantly, we emphasize the significance of embedding material domain knowledge into ML and elaborate on thebasic idea for implementing this strategy.

关键词： material design machine learning small sample size few-shot learning material domain knowledge

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