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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

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 Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China

ISBN: (纸本)157735897X

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, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Semi-autoregressive transformer for image captioning

arXiv

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arXiv 2021年

作者： Zhou, Yuanen Zhang, Yong Hu, Zhenzhen Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Tencent AI Lab Key Laboratory of Knowledge Engineering with Big Data Ministry of Education

Current state-of-the-art image captioning models adopt autoregressive decoders, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel. However, these non-autoregressive models inevitably suffer from large generation quality degradation since they remove words dependence excessively. To make a better trade-off between speed and quality, we introduce a semi-autoregressive model for image captioning (dubbed as SATIC), which keeps the autoregressive property in global but generates words parallelly in local. Based on Transformer, there are only a few modifications needed to implement SATIC. Experimental results on the MSCOCO image captioning benchmark show that SATIC can achieve a good trade-off without bells and whistles. Code is available at https://***/YuanEZhou/satic. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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Meta-Path Based Graph Contrastive Learning for Micro-Video Recommendation

SSRN

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SSRN 2022年

作者： He, Ying Wu, Gongqing Cai, Desheng Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China

Nowadays, micro-video sharing platforms have become popular tools for people creating and viewing micro-videos in daily life. Micro-video recommendation task has attracted significant attention from researchers, recently. The key to a high-quality micro-video recommendation system is learning meaningful user and micro-video representations. Recently, many graph neural networks (GNNs) are proposed to learn node representations in graphs, which could be useful for recommendation tasks. However, we argue that directly utilizing existing GNNs in micro-video recommendations is ill-posed. The reasons are: (1) most previous GNNs fail to capture the heterogeneity of heterogeneous graphs since they are designed for homogeneous graphs;(2) they overemphasize node proximity and could hurt the robustness of node embeddings. In contrast to previous work, we propose a meta-path-based graph contrastive learning network (MPGCL) that learns more meaningful user and video embeddings for recommendation. Specifically, we yield homogeneous graphs for user type and video type to better capture heterogeneity based on a well-designed meta-path-based random walk strategy. Furthermore, to learn more robust node embeddings that are less sensitive to noise, we propose a graph contrastive learning network on different types of homogeneous graphs, which maximizes the consistency between graph representations with different views. We do extensive experiments on three real-world datasets and the results show that our model can learn more meaningful embeddings for user and micro-videos and outperforms the strong baselines. © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Model updating for real time dynamic substructures based on UKF algorithm

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Earthquake engineering and engineering Vibration 2020年第2期19卷 413-421页

作者： Su Tingli Tang Zhenyun Peng Lingyun Bai Yuting Jin Xuebo Kong Jianlei School of Computer Information and Engineering Beijing Technology and Business UniversityBeijing 100048China The Key Laboratory of Urban Security and Disaster Engineering Ministry of EducationBeijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048China China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business UniversityBeijing 100048China

Combining the advantages of numerical simulation with experimental testing,real-time dynamic substructure(RTDS)testing provides a new experimental method for the investigation of engineered ***,not all unmodeled parts can be physically tested,as testing is often limited by the capacity of the test *** updating is a good option to improve the modeling accuracy for numerical substructures in *** this study,a model updating method is introduced,which has great performance in describing this *** order to determine the optimal parameters in this model,an Unscented Kalman Filter(UKF)-based algorithm was applied to extract the knowledge contained in the sensors *** the parameters that need to be identified are listed as the extended state variables,and the identification was achieved via the step-by-step state prediction and state update *** of the proposed method was verified through a group of experimental data,and results showed good ***,the proposed method was compared with the Extended Kalman Filter(EKF)-based method,and better accuracy was easily *** proposed parameter identification method has great applicability for structural objects with nonlinear behaviors and could be extended to research in other engineering fields.

关键词： dynamic substructure complex nonlinear model nonlinear estimation adaptive updating civil infrastructures

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Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension 2

Extremely Short Chinese Text Classification Method Based on ...

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2019 2nd International Symposium on big data and Applied Statistics, ISBDAS 2019

作者： Yue, Yongzeng Zhang, Yuhong Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology] Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei23061 China

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness;secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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A Revocable Fast and Lightweight Parallel Encryption Scheme for IIoT

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IEEE Internet of Things Journal 2025年

作者： Lu, Junze Hu, Chunqiang Chen, Jiajun Xia, Hui Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China Chongqing University School of Big Data and Software Engineering Chongqing 400030 China Ocean University of China College of Computer Science and Technology China

As the Industrial Internet of Things (IIoT) expands, the number of stakeholders increases. Many entities require significant amounts of data from industrial devices. These data streams improve information sharing and optimize the industrial chain. However, IIoT’s enormous data volumes pose challenges to traditional encryption methods, which are unable to meet efficiency and energy consumption requirements. Thus, a new, efficient encryption algorithm is essential for managing data flows among IIoT subscribers. In this paper, we propose a fast, low-energy, high-security encryption scheme to manage multi-entity data streams. First, we introduce a novel encryption scheme based on the Subset Sum Problem (SSP), which improves energy efficiency and speed. Second, to meet subscription requirements, we employ attribute-based encryption (ABE) for key forwarding. Finally, we handle subscription revocations with device key updates. The device owner utilizes their secret value to generate an identity proof with a key update request. © 2014 IEEE.

关键词： attribute based encryption data sharing Industrial Internet of Things Subset sum problem

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A robust three-way classifier with shadowed granular-balls based on justifiable granularity

arXiv

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arXiv 2024年

作者： Yang, Jie Xiaodiao, Lingyun Wang, Guoyin Pedrycz, Witold Xia, Shuyin Zhang, Qinghua Wu, Di The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China The School of Physics and Electronic Science Zunyi Normal University Zunyi563002 China The Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Chongqing400065 China The Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada College of Computer and Information Science Southwest University Chongqing400715 China

The granular-ball (GB)-based classifier introduced by Xia, exhibits adaptability in creating coarse-grained information granules for input, thereby enhancing its generality and flexibility. Nevertheless, the current GB-based classifiers rigidly assign a specific class label to each data instance and lacks of the necessary strategies to address uncertain instances. These far-fetched certain classification approachs toward uncertain instances may suffer considerable risks. To solve this problem, we construct a robust three-way classifier with shadowed GBs for uncertain data. Firstly, combine with information entropy, we propose an enhanced GB generation method with the principle of justifiable granularity. Subsequently, based on minimum uncertainty, a shadowed mapping is utilized to partition a GB into Core region, Important region and Unessential region. Based on the constructed shadowed GBs, we establish a three-way classifier to categorize data instances into certain classes and uncertain case. Finally, extensive comparative experiments are conducted with 2 three-way classifiers, 3 state-of-the-art GB-based classifiers, and 3 classical machine learning classifiers on 12 public benchmark datasets. The results show that our model demonstrates robustness in managing uncertain data and effectively mitigates classification risks. Furthermore, our model almost outperforms the other comparison methods in both effectiveness and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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NOBGP: A Novel Optimized Balanced Graph Partitioning Algorithm 19th

NOBGP: A Novel Optimized Balanced Graph Partitioning Algorit...

引用

19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Chen, Jiebin Hu, Ziqiang Ye, Renjie Zhang, Qishan Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai China

ISBN: (纸本)9789819623723

Large-scale graphs have become prevalent with the advent of the big data era. Distributed graph computing systems are commonly used for processing and analyzing large-scale graphs, with graph partitioning being a key prerequisite for their efficient computation. Graph partitioning aims to balance the load across partitions while minimizing the number of cut-edges. Moreover, it should achieve high efficiency and scalability. However, the existing popular graph partitioning algorithms do not fully take into account the internal topology of real-world graphs, which affects the final partition quality and convergence. Meanwhile, they easily fall into the local optimum due to partition load constraints. This paper introduces a Novel Optimized Balanced Graph Partitioning algorithm (NOBGP). First, we propose an initialization strategy based on label propagation of core vertices to achieve initial partitions with good locality and accelerate convergence. Second, we optimize the label propagation process to ensure balanced partitions and propose a probability-based disruption strategy to avoid the local optimum. We implement NOBGP on the distributed graph computing framework GraphX. Extensive experimental results on real-world graphs show that the proposed algorithm is scalable and performs better than the existing algorithms. We also run PageRank and Louvain applications using the graph partitioning results to demonstrate the efficiency of our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph algorithms

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Local Entropy Based Fuzzy Connectedness Segmentation for Thyroid Ultrasound Images

Local Entropy Based Fuzzy Connectedness Segmentation for Thy...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Yixuan Fu Kai Li Junying Chen Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education School of Software Engineering South China University of Technology Guangzhou China Department of Ultrasound Third Affiliated Hospital of Sun Yat-sen University Guangzhou China

Although deep learning methods are popular in recent years to achieve dominant image segmentation performance, the carefully annotated training images are very difficult to obtain in clinical medicine, and the deep learning model training demands a lot of computing resources which are not always available. To overcome these difficulties, it is very necessary to investigate segmentation algorithms which do not require any training data. In this work, we improve a semi-automatic conventional segmentation algorithm, fuzzy connectedness segmentation, by introducing the local information entropy of the ultrasound image into the fuzzy affinity membership function. The proposed semi-automatic local entropy based fuzzy connectedness (LEFC) segmentation algorithm is further modified by incorporating the popular deep learning methods to make the LEFC algorithm fully automatic. The proposed semi-automatic LEFC algorithm achieves better performance than other representative fuzzy connectedness segmentation algorithms with the accuracy and IoU values increased by 5.9~14% and 5~10.5%, and achieves very close segmentation performance as compared with the best deep learning model in the experiments (0.2% lower accuracy and 0.2% lower IoU compared to CE-Net). Besides, the automatic LEFC algorithm improves the segmentation results obtained by the corresponding deep learning based coarse segmentation modules with the accuracy and IoU increased by 0.2~8% and 0.1~3.8%, respectively. Furthermore, the experiment results demonstrate that the proposed automatic LEFC algorithm obtains stably good segmentation performance even with limited training data, achieving the accuracy and IoU values (at the stable state) 23.6 % and 18.5% higher than those of the corresponding U-Net model which is trained with the samesized dataset.

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