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International Conference on Advanced Cloud and Big Data (CBD)

作者： Xuanqi Liu Sen Xu Xiufang Xu Naixuan Guo Xuesheng Bian Chaofeng Chen Yancheng Institute of Technology (School of Information Engineering) Yancheng China Yancheng Institute of Technology (School of Information Engineering) Key Laboratory of Computer Network and Information Integration (Southeast University) Yancheng China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Recently, graph clustering methods that integrate structured information into deep clustering have become one of the important research directions. However, current methods still lack an efficient and flexible fusion strategy when combining auto encoders and Graph Convolutional network (GCN) to learn feature representation. Accordingly, we propose a network named Structural Attention-based Graph Clustering network (SAGC). Firstly, we use the attention coefficient as weighted values to dynamically allocate the contribution of node attribute features and structural features. Secondly, an enhanced delivery operator is designed to efficiently convey the node features learned by the autoencoder. Lastly, a dual self-supervised mechanism is employed to unify the optimization of the two types of node features, thereby enhancing the stability of the model. Experiments on various types of datasets validate that our method outperforms state-of-the-art methods.

关键词： Graph convolutional networks Clustering methods Autoencoders Big Data Stability analysis Optimization

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Semantic-Aware Prompt Learning for Multimodal Sarcasm Detection

Semantic-Aware Prompt Learning for Multimodal Sarcasm Detect...

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

作者： Wang, Guangjin Wang, Bao Xu, Fuyong Zhu, Zhenfang Wang, Peipei Wang, Ru Liu, Peiyu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model's "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model's ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： KL divergence multi-view semantic representation multimodal sarcasm detection semantic representation enhancement

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DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing 29

DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Pa...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Bin Gao, Miao Fan, Yunlong Sataer, Yikemaiti Gao, Zhiqiang Gui, Yaocheng School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing210096 China School and Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing210003 China

A recent success in semantic dependency parsing shows that graph neural networks can make significant accuracy improvements, owing to its powerful ability in learning expressive graph representations. However, this work learns graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone (e.g., noisy or incomplete), and (2) graph construction stage and graph representation learning stage are disjoint, the errors introduced in the graph construction stage cannot be corrected and might be accumulated to later stages. To address these two drawbacks, we propose a dynamic graph learning framework and apply it to semantic dependency parsing, for jointly learning graph structure and graph representations. Experimental results show that our parser outperforms the previous parsers on the SemEval-2015 Task 18 dataset in three languages (English, Chinese, and Czech). © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Graph neural networks

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DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer 37

DPText-DETR: Towards Better Scene Text Detection with Dynami...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Ye, Maoyuan Zhang, Jing Zhao, Shanshan Liu, Juhua Bo, Du Tao, Dacheng Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University China The University of Sydney Australia JD Explore Academy National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

ISBN: (纸本)9781577358800

Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling. In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation. To address these challenges, this paper proposes a concise Dynamic Point Text DEtection TRansformer network, termed DPText-DETR. In detail, DPText-DETR directly leverages explicit point coordinates to generate position queries and dynamically updates them in a progressive way. Moreover, to improve the spatial inductive bias of non-local self-attention in Transformer, we present an Enhanced Factorized Self-Attention module which provides point queries within each instance with circular shape guidance. Furthermore, we design a simple yet effective positional label form to tackle the side effect of the previous form. To further evaluate the impact of different label forms on the detection robustness in real-world scenario, we establish an Inverse-Text test set containing 500 manually labeled images. Extensive experiments prove the high training efficiency, robustness, and state-of-the-art performance of our method on popular benchmarks. The code and the Inverse-Text test set are available at https://***/ymy-k/DPText-DETR. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Efficiency

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Adaptive group personalization for federated mutual transfer learning 24

Adaptive group personalization for federated mutual transfer...

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Proceedings of the 41st International Conference on Machine Learning

作者： Haoqing Xu Dian Shen Meng Wang Beilun Wang School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China College of Design and Innovation Tongji University Shanghai China

Mutual transfer learning aims to improve prediction with knowledge from related domains. Recently, federated learning is applied in this field to address the communication and privacy concerns. However, previous clustered federated learning (CFL) solutions lack theoretical guarantee of learnability recovery and require time-consuming hyper-parameter tuning, while centralized mutual transfer learning methods lack adaptability to concept drifts. In this paper, we propose the Adaptive Group Personalization method (AdaGrP) to overcome these challenges. We adaptively decide the recovery threshold with a non-parametric method, adaptive threshold correction, for tuning-free solution with relaxed condition. Theoretical results guarantee the perfect learnability recovery with the corrected threshold. Empirical results show AdaGrP achieves 16.9% average improvement in learnability structure recovery compared with state-of-the-art CFL baselines.

关键词：

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Constructing and Generating a Time-varying State- Space Vector against Cloud Service Event Interactions 4

Constructing and Generating a Time-varying State- Space Vect...

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2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021

作者： Jiang, Yiyi Li, Xiaojian Xia, Chunhe Hu, Hangping Zhang, Zhen Wang, Hailan Guangxi Normal University School of Computer Science Engineering Guilin China Beihang University Beijing Key Laboratory of Network Technology School of Computer Science and Engineering Beijing China

ISBN: (纸本)9781665412704

the abnormal interaction between cloud service events is an essential factor leading to failure. Using neural networks explores the interaction between cloud service events, which needs a method to transform the cloud service events into a vector. However, cloud service events exist as a log with time- varying characteristics. The dataset of cloud service is vast, including numerous strings that cannot be directly involved in calculations. There is a lack of pathway transform data semantics to interaction semantics. Thus, this paper provides a method to construct cloud service logs into vector. Regarding cloud service events as natural language, the method characterizes cloud service events as vector without building a corpus. In addition, we use the semi-supervised neural networks to predict the interaction between cloud service events. The results show that the effectiveness of our method, indicating that it meets the needs of project. This work also provides a foundation for security traceability. © 2021 IEEE.

关键词： Vectors

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Detecting Fake-Normal Pornographic and Gambling Websites through one Multi-Attention HGNN 26

Detecting Fake-Normal Pornographic and Gambling Websites thr...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ma, Xiaoqing Zheng, Chao Li, Zhao Yin, Jiangyi Liu, Qingyun Chen, Xunxun Institute of Information Engineering Chinese Academy of Sciences Beijing China National Engineering Laboratory for Information Security Technologies Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Geedge Networks Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (纸本)9798350331684

The rapid development of pornographic and gambling websites, fueled by the widespread abuse of information technology, has become a growing concern. They pose a serious threat to the physical and mental health of children and can also endanger personal property. Therefore, it is necessary to detect them. However, pornographic and gambling websites become more and more tricky, which shows fake-normal to evade censorship and challenges traditional content-based detection methods. Therefore, it is essential to rely on information about relationships between *** propose HMAN, one Multi-Attention Heterogeneous Graph Neural network (HGNN) model to detect pornographic and gambling websites by integrating content features and structural information, even if they present fake-normal. By one multi-attention mechanism consisting of explicit weight, self-attention and attention mechanism, content features can be selectively utilized with the assistance of structural information. The experimental results show that our method achieves the best 95.1% Macro-Avg-F1 and outperforms all baselines. We also illustrate that all extracted metapaths do contribute to the detection, where the hyperlink, title/meta terms and IP address are relatively important. © 2023 IEEE.

关键词： Websites

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基于侧基多通道和对称性破缺效应的分子电导调控

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Science China Materials 2024年第6期67卷 1994-1999页

作者：郝杰汪博宇赵璁霍雅妮王进莹贾传成郭雪峰 Center of Single-Molecule Sciences Institute of Modern OpticsFrontiers Science Center for New Organic MatterTianjin Key Laboratory of Micro-scale Optical Information Science and TechnologyCollege of Electronic Information and Optical EngineeringNankai UniversityTianjin 300350China Network for Computational Nanotechnology School of Electrical and Computer EngineeringPurdue UniversityIndiana 47907USA Beijing National Laboratory for Molecular Sciences National Biomedical Imaging CenterCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China

化学取代是有效调节分子电导,产生多通道输运、量子干涉效应和其他机制的关键方法.但分子骨架和侧基之间存在复杂的相互作用,目前仍未被研究清楚.在本工作中,我们结合扫描隧道显微镜裂结实验技术和第一性原理计算方法,深入研究了具有不... 详细信息

化学取代是有效调节分子电导,产生多通道输运、量子干涉效应和其他机制的关键方法.但分子骨架和侧基之间存在复杂的相互作用,目前仍未被研究清楚.在本工作中,我们结合扫描隧道显微镜裂结实验技术和第一性原理计算方法,深入研究了具有不同σ和π侧基的分子电导.实验结果及理论分析表明,σ侧基的加入引起了分子前线轨道的对称性破缺并加剧了分子内散射,从而导致电导的降低.相反,π侧基引入了额外的电荷传输通道,实现了电荷传输的多通道效应,增加了分子的电导.这些发现为未来分子功能化和分子电子器件的设计提供了坚实的基础.

关键词： side substituent multiple-channel effect symmetry-breaking effect single-molecule junction

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Balancing Privacy and Performance: A Many-in-One Approach for Image Anonymization 39

Balancing Privacy and Performance: A Many-in-One Approach fo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jia, Xuemei Du, Jiawei Wei, Hui Xue, Ruinian Wang, Zheng Zhu, Hongyuan Chen, Jun National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China A*STAR Singapore A*STAR Singapore

ISBN: (纸本)157735897X

The effective utilization of data through Deep Neural networks (DNNs) has profoundly influenced various aspects of society. The growing demand for high-quality, particularly personalized, data has spurred research efforts to prevent data leakage and protect privacy in recent years. Early privacy-preserving methods primarily relied on instance-wise modifications, such as erasing or obfuscating essential features for de-identification. However, this approach highlights an inherent trade-off: minimal modification offers insufficient privacy protection, while excessive modification significantly degrades task performance. In this paper, we propose a novel Recombining for Obfuscation (FRO) approach to address this trade-off. Unlike existing methods that generate one anonymized instance by perturbing the original data on a one-to-one basis, our FRO approach generates an anonymized instance by reassembling mixed id-related features from multiple original data sources on a many-in-one basis. Instead of introducing additional noise for de-identification, our approach leverages the existing non-polluted features from other instances to anonymize data. Extensive experiments on identity identification tasks demonstrate that FRO outperforms previous state-of-the-art methods, not only in utility performance but also in visual anonymization. Copyright © 2025, Association for the Advancement of Artificial Intelligence.

关键词： Deep neural networks

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Wavelet Convolutional Neural network for Low-Resolution Brain MRI Images 22

Wavelet Convolutional Neural Network for Low-Resolution Brai...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Oh, Soo Min Li, Yifan Chong, Jo Woon Massachusetts Institute of Technology Wireless Information and Network Sciences Laboratory CambridgeMA02139 United States Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA02139 United States Texas A&m University Department of Statistics College StationTX77843 United States Texas Tech University Department of Electrical and Computer Engineering LubbockTX79409 United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9798331520526

Low-resolution images inherently contain less information, making effective feature extraction more challenging and posing difficulties for training neural networks. However, if neural networks can be trained successfully on low-resolution images, this could significantly reduce memory storage requirements and computational costs. In this study, we address the limitations of low-resolution brain MRI images by enhancing the available information through wavelet transform techniques. Specifically, we leverage the high-frequency coefficients obtained from wavelet transforms and the Hurst exponent to improve feature representation and optimize model training for convolutional neural networks (CNN), which is referred to as the wavelet CNN (WCNN). We demonstrate that WCNN outperforms standard CNNs in multi-class classification tasks, distinguishing among four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This approach highlights the potential to achieve high classification accuracy even with low-resolution data, ultimately reducing the memory and computational resources required for data processing and model training. © 2025 IEEE.

关键词： Convolutional neural networks

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