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A knowledge-Enhanced Contrastive Learning Network for Weakly-Supervised Aspect Detection

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 506-517页

作者： Zhuoming Zheng Yi Cai Liuwu Li School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou China

Aspect detection, aiming at identifying the aspects of review segments, is a fundamental task in opinion mining and aspect-based sentiment analysis. Due to the high cost and time consuming of human-annotation for massive reviews, several unsupervised and weakly-supervised methods are proposed recently. However, existing weakly-supervised models are mostly seed-driven methods based on co-occurrence of words, which suffer from lacking the ability of detecting the aspects with infrequent aspect terms and identifying Misc aspect. To tackle these problems, we leverage external knowledge to enhance the representation of aspects and segments by a weakly-supervised method. In this paper, we propose an aspect knowledge-enhanced contrastive learning (AKECL) network with two powerful knowledge-enhanced encoders for aspects and reivew segments to enhance weakly-supervised aspect detection task. Experiments in seven different domains show that AKECL outperforms the competitive baselines, and demonstrate the effectiveness of our proposed method, as well as the improvement by introducing external knowledge.

关键词： Reviews Contrastive learning Vectors Feature extraction knowledge graphs Speech processing Sentiment analysis knowledge engineering Hardware Touch sensitive screens

HVIS: A Human-like Vision and Inference System for Human Motion Prediction

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arXiv 2025年

作者： Lyu, Kedi Chen, Haipeng Liu, Zhenguang Yin, Yifang Lin, Yukang Jiao, Yingying College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China A*STAR Singapore Tsinghua Shenzhen International Graduate School Nanshan District Shenzhen China

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion. While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements. HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module. The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally, a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses. The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning. Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8% on Human3.6M, 15.7% on CMU Mocap, and 11.1% on G3D. © 2025, CC BY.

关键词： Motion estimation

Blockchain-Enhanced Federated Learning for Secure and Intelligent Consumer Electronics : An Overview

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IEEE Consumer Electronics Magazine 2025年

作者： Zhu, Chenlu Wang, Shuilong Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao He, Yuanyuan Wu, Celimuge Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China The University of Electro-Communications China

With the rapid advancement of Artificial Intelligence (AI) and Internet of Things (IoT), consumer electronics have become essential in daily life. However, these devices generate and store vast amounts of personal data, posing significant security threats. Federated learning, a distributed machine learning method, protects user privacy by training models collaboratively without sharing raw data. However, it still faces numerous challenges. To further enhance data security, the integration of blockchain technology is crucial. Blockchain's decentralized and immutable characteristics enable secure data sharing and privacy protection in consumer electronics. This paper analyzes the application of federated learning in consumer electronics, examines how blockchain can enhance federated learning, and discusses future research directions. The primary contributions of this paper include a detailed application of blockchain-enhanced federated learning in consumer electronics, intended to serve as a reference for future research. © 2012 IEEE.

关键词： Federated learning

Optimizing Human Pose Estimation Through Focused Human and Joint Regions

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arXiv 2025年

作者： Jiao, Yingying Wang, Zhigang Liu, Zhenguang Fan, Shaojing Wu, Sifan Wu, Zheqi Xu, Zhuoyue 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 The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China School of Computing National University of Singapore Singapore

Human pose estimation has given rise to a broad spectrum of novel and compelling applications, including action recognition, sports analysis, as well as surveillance. However, accurate video pose estimation remains an open challenge. One aspect that has been overlooked so far is that existing methods learn motion clues from all pixels rather than focusing on the target human body, making them easily misled and disrupted by unimportant information such as background changes or movements of other people. Additionally, while the current Transformer-based pose estimation methods has demonstrated impressive performance with global modeling, they struggle with local context perception and precise positional identification. In this paper, we try to tackle these challenges from three aspects: (1) We propose a bilayer Human-keypoint Mask module that performs coarse-to-fine visual token refinement, which gradually zooms in on the target human body and keypoints while masking out unimportant figure regions. (2) We further introduce a novel deformable cross attention mechanism and a bidirectional separation strategy to adaptively aggregate spatial and temporal motion clues from constrained surrounding contexts. (3) We mathematically formulate the deformable cross attention, constraining that the model focuses solely on the regions centered at the target person body. Empirically, our method achieves state-of-the-art performance on three large-scale benchmark datasets. A remarkable highlight is that our method achieves an 84.8 mean Average Precision (mAP) on the challenging wrist joint, which significantly outperforms the 81.5 mAP achieved by the current state-of-the-art method on the PoseTrack2017 dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

Highly crystalline SiC fibers with enhanced microwave absorption properties via boron doping and electron beam irradiation oxidation curing

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Ceramics International 2025年

作者： Huang, Linying Wang, Hui Ouyang, Qin Mo, Gaoming Huang, Jing Zheng, Yueqing Huang, Qing School of Materials Science and Chemical Engineering Ningbo University Ningbo315211 China Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Advanced Nuclear Materials Laboratory Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China

Polymer-derived silicon carbide (SiC) fibers are excellent structural and functional materials for extreme environments. However, the low efficiency of the curing process and the inferior stability of the amorphous SiCxOy phase have limited their large-scale preparation and widespread application. In this study, phenylboronic acid (PBA)-modified polyaluminocarbosilane (PACS) electrospun fibers were cured efficiently via an electron beam irradiation oxidation technique, and then converted into highly crystalline SiC fibers with low oxygen content by high-temperature sintering. The combination of irradiation oxidation and subsequent annealing treatment facilitated the effective, rapid, and sufficient curing of PACS fibers. Notably, boron played a significant role in inhibiting the growth of SiC grains and promoting the graphitization of free carbon. The SiC fibers obtained from PBA-modified PACS through irradiation oxidation curing exhibited superior electrical conductivity and microwave absorption performance, achieving a minimal reflection loss (RL) of −41.8 dB at 12 GHz with a thickness of 1.5 mm. This work provides an effective method for preparing SiC fibers with tunable carbon structures and enhanced microwave absorption properties, thereby paving a way for expanding their applications. © 2025

关键词： Silicon carbide

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

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arXiv 2025年

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 The Ministry of Education China

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. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport

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arXiv 2025年

作者： Liu, Yonghao Giunchiglia, Fausto Li, Ximing Huang, Lan 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 The Ministry of Education China

Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among researchers. Recent advancements in graph few-shot learning models have exhibited superior performance across diverse applications. Despite their successes, several limitations still exist. First, existing models in the meta-training phase predominantly focus on instance-level features within tasks, neglecting crucial set-level features essential for distinguishing between different categories. Second, these models often utilize query sets directly on classifiers trained with support sets containing only a few labeled examples, overlooking potential distribution shifts between these sets and leading to suboptimal performance. Finally, previous models typically require necessitate abundant labeled data from base classes to extract transferable knowledge, which is typically infeasible in real-world scenarios. To address these issues, we propose a novel model named STAR, which leverages Set funcTions and optimAl tRansport for enhancing unsupervised graph few-shot learning. Specifically, STAR utilizes expressive set functions to obtain set-level features in an unsupervised manner and employs optimal transport principles to align the distributions of support and query sets, thereby mitigating distribution shift effects. Theoretical analysis demonstrates that STAR can capture more task-relevant information and enhance generalization capabilities. Empirically, extensive experiments across multiple datasets validate the effectiveness of STAR. Our code can be found here. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

SpatioTemporal Learning for Human Pose Estimation in Sparsely-Labeled Videos

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arXiv 2025年

作者： Jiao, Yingying Wang, Zhigang Wu, Sifan Fan, Shaojing Liu, Zhenguang Xu, Zhuoyue Wu, Zheqi 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 School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Human pose estimation in videos remains a challenge, largely due to the reliance on extensive manual annotation of large datasets, which is expensive and labor-intensive. Furthermore, existing approaches often struggle to capture long-range temporal dependencies and overlook the complementary relationship between temporal pose heatmaps and visual features. To address these limitations, we introduce STDPose, a novel framework that enhances human pose estimation by learning spatiotemporal dynamics in sparsely-labeled videos. STDPose incorporates two key innovations: 1) A novel Dynamic-Aware Mask to capture long-range motion context, allowing for a nuanced understanding of pose changes. 2) A system for encoding and aggregating spatiotemporal representations and motion dynamics to effectively model spatiotemporal relationships, improving the accuracy and robustness of pose estimation. STDPose establishes a new performance benchmark for both video pose propagation (i.e., propagating pose annotations from labeled frames to unlabeled frames) and pose estimation tasks, across three large-scale evaluation datasets. Additionally, utilizing pseudo-labels generated by pose propagation, STDPose achieves competitive performance with only 26.7% labeled data. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

Nonlinear Interference-Aware Routing,Wavelength and Power Allocation in C+L+S Multi-Band Optical Networks

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China Communications 2025年第4期22卷 129-142页

作者： Zhang Xu Xie Wang Feng Chuan Zeng Hankun Zhou Shanshan Zhang Fan Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China

Multi-band optical networks are a potential technology for increasing network ***,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical *** overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength ***,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical *** objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not ***,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation ***,we conduct simulations under different test *** simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.

关键词： multiband optical communications multiband optical networks power allocation wavelength assignment