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Knowledge Graph Augmented network Towards Multiview Representation Learning for Aspect-Based Sentiment Analysis

IEEE Transactions on Knowledge and Data engineering 2023年第10期35卷 10098-10111页

作者： Zhong, Qihuang Ding, Liang Liu, Juhua Du, Bo Jin, Hua Tao, Dacheng Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Wuhan430072 China Jd Explore Academy at *** Beijing100101 China Wuhan University Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Hubei Wuhan430072 China Hospital of Kunming University of Science and Technology Kunming650032 China

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets. © 1989-2012 IEEE.

关键词： Semantics

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A Shortest Path Query Approach for Encrypted Graphs Based on Padding Dictionary Structure

A Shortest Path Query Approach for Encrypted Graphs Based on...

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2023 China Automation Congress, CAC 2023

作者： Yang, Ming Zhang, Kaiyang Mu, Chao Wang, Xin Liu, Yuanlong Zhang, Heng Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China School of Computer Engineering Jiangsu Ocean University Lianyungang China

ISBN: (纸本)9798350303759

Hybrid intelligence has emerged as an innovative approach that synergistically combines the strengths of human intelligence and artificial intelligence (AI) to address complex problems. In this paradigm, knowledge graphs play a pivotal role by supplying vital background information and contextual understanding to hybrid intelligence systems. However, due to limitations in local storage capacity, managing large-scale graph data often requires outsourcing to cloud servers. Nevertheless, the use of untrusted cloud servers poses a risk of potential user data leaks. To address these concerns, this paper presents an encryption scheme specifically designed to protect graph data, enabling queries involving intermediate nodes in the shortest path between two nodes within the graph. The proposed scheme ensures the privacy and security of outsourced data by employing a novel method that utilizes padding dictionary structure encryption. This method ensures strict limitations on the cloud server's capabilities, restricting it solely to query operations, it effectively prevents any compromise of user data or unauthorized access to sensitive information, users can securely store and process their graph data on servers, significantly reducing the potential risk of data leakage and unauthorized breaches. To validate the feasibility and effectiveness of the proposed solution, practical experiments were conducted using real-world data. © 2023 IEEE.

关键词： Cryptography

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Error analysis of spherically constrained least squares reformulation in solving the stackelberg prediction game 24

Error analysis of spherically constrained least squares refo...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiyuan Li Weiwei Liu School of Computer Science and National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9798331314385

The Stackelberg prediction game (SPG) is a popular model for characterizing strategic interactions between a learner and an adversarial data provider. Although optimization problems in SPGs are often NP-hard, a notable special case involving the least squares loss (SPG-LS) has gained significant research attention recently [1, 2, 3]. The latest state-of-the-art method for solving the SPG-LS problem is the spherically constrained least squares reformulation (SCLS) method proposed in the work of [3]. However, the paper [3] lacks theoretical analysis on the error of the SCLS method, which limits its large-scale applications. In this paper, we investigate the estimation error between the learner obtained by the SCLS method and the actual learner. Specifically, we reframe the estimation error of the SCLS method as a Primary Optimization (PO) problem and utilize the Convex Gaussian min-max theorem (CGMT) to transform the PO problem into an Auxiliary Optimization (AO) problem. Subsequently, we provide a theoretical error analysis for the SCLS method based on this simplified AO problem. This analysis not only strengthens the theoretical framework of the SCLS method but also confirms the reliability of the learner produced by it. We further conduct experiments to validate our theorems, and the results are in excellent agreement with our theoretical predictions.

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The reliability of OKRidge method in solving sparse ridge regression problems 24

The reliability of OKRidge method in solving sparse ridge re...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiyuan Li Youjun Wang Weiwei Liu School of Computer Science and National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9798331314385

Sparse ridge regression problems play a significant role across various domains. To solve sparse ridge regression, [1] recently proposes an advanced algorithm, Scalable Optimal K-Sparse Ridge Regression (OKRidge), which is both faster and more accurate than existing approaches. However, the absence of theoretical analysis on the error of OKRidge impedes its large-scale applications. In this paper, we reframe the estimation error of OKRidge as a Primary Optimization (PO) problem and employ the Convex Gaussian min-max theorem (CGMT) to simplify the PO problem into an Auxiliary Optimization (AO) problem. Subsequently, we provide a theoretical error analysis for OKRidge based on the AO problem. This error analysis improves the theoretical reliability of OKRidge. We also conduct experiments to verify our theorems and the results are in excellent agreement with our theoretical findings.

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A boosting-type convergence result for *** with factorized multi-class classifiers 24

A boosting-type convergence result for *** with factorized m...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xin Zou Zhengyu Zhou Jingyuan Xu Weiwei Liu School of Computer Science and National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9798331314385

ADABOOST is a well-known algorithm in boosting. Schapire and Singer propose, an extension of ADABOOST, named ***, for multi-class classification problems. Kégl shows empirically that *** works better when the classical one-against-all base classifiers are replaced by factorized base classifiers containing a binary classifier and a vote (or code) vector. However, the factorization makes it much more difficult to provide a convergence result for the factorized version of ***. Then, Kégl raises an open problem in COLT 2014 to look for a convergence result for the factorized ***. In this work, we resolve this open problem by presenting a convergence result for *** with factorized multi-class classifiers.

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Data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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Dual-Domain Multi-Model GAN Fingerprint Restoration for Compressed Fake Face Attribution

Dual-Domain Multi-Model GAN Fingerprint Restoration for Comp...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chengxiang Fan Aohong Shen Zhen Han Cai Tong Zhongyuan Wang Dekang Yi National Engineering Research Center for Multimedia Software and Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Recent advances in GAN fingerprint have shown increasing success in fake face attribution. However, the fake faces are usually compressed during network transmission, which causes the degradation of GAN fingerprint and the decrease of attribution accuracy. To this issue, a dual-domain multi-model GAN fingerprint restoration method for compressed fake face attribution is proposed in this paper. Firstly, considering that image-domain and fingerprint-domain are directly and indirectly affected by compression respectively, we propose a dual-domain parallel restoration architecture that enhances GAN fingerprint using direct image-domain and indirect fingerprint-domain restoration, thereby improving attribution performance by mining the cross-domain complementarity. Secondly, since real and fake GAN-speciffc restoration models can describe GAN fingerprint from different aspects, we first enhance GAN fingerprint by multiple restoration models, and then improve attribution performance by exploiting the cross-model complementarity through the multi-model restoration fusion strategy. Experiments demonstrate the superiority of our method under different compression qualities.

关键词： Degradation Image coding Accuracy Fingerprint recognition Generative adversarial networks Image restoration Faces

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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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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with state-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

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

作者： Zhang, Haodi Wang, Yichi Jian, Yifan Jiang, Jiahui Bai, Zhaohai Ma, Lin College of Computer Science and Software Engineering Shenzhen University China Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences China State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University China

ISBN: (纸本)9798350368741

Climate downscaling is crucial for detailed small-scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a state-Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-ofthe-art performance and provides strong support for climate downscaling and the planning of climate-related strategies. © 2025 IEEE.

关键词： Climate downscaling Deep Learning Multimodal fusion Operator learning Time series forecasting

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A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

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computers, Materials & Continua 2023年第1期74卷 1279-1302页

作者： Yu Fan Jingbing Li Uzair Aslam Bhatti Chunyan Shao Cheng Gong Jieren Cheng Yenwei Chen School of Information and Communication Engineering Hainan UniversityHaikou570100China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570100China School of Computer Science and Technology Hainan UniversityHaikou570100China Graduate School of Information Science and Engineering Ritsumeikan UniversityKyoto5258577Japan Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570100China TJ-YZ School of Network Science Haikou University of EconomicsHaikou571127China

Medical images are a critical component of the diagnostic process for *** the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical *** watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient *** addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are *** paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)Inception V3 and the discrete cosine transform(DCT)to address above mentioned ***,the medical image is input into the Inception V3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution ***,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical *** last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third *** encrypted watermarks and the original image features are performed logical operations to realize the embedding of *** the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed *** NC values for all the images are more than 90%accurate which shows the robustness of the *** experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

关键词： Inception V3 multi-watermarking DCT watermark encryption robustness

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