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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Geng, Xiwen Zhao, Suyun Yu, Yixin Peng, Borui Du, Pan Chen, Hong Li, Cuiping Wang, Mengdie Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China China School of Information Renmin University of China China School of Statistics Remin University of China China

ISBN: (纸本)157735897X

Clustering traditionally aims to reveal a natural grouping structure within unlabeled data. However, this structure may not always align with users’ preferences. In this paper, we propose a personalized clustering method that explicitly performs targeted representation learning by interacting with users via modicum task information (e.g., must-link or cannot-link pairs) to guide the clustering direction. We query users with the most informative pairs, i.e., those pairs most hard to cluster and those most easy to miscluster, to facilitate the representation learning in terms of the clustering preference. Moreover, by exploiting attention mechanism, the targeted representation is learned and augmented. By leveraging the targeted representation and constrained contrastive loss as well, personalized clustering is obtained. Theoretically, we verify that the risk of personalized clustering is tightly bounded, guaranteeing that active queries to users do mitigate the clustering risk. Experimentally, extensive results show that our method performs well across different clustering tasks and datasets, even when only a limited number of queries are available. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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CCAC: Contrastive Learning with Channel Attention and Contour Loss for Brain Image Registration

CCAC: Contrastive Learning with Channel Attention and Contou...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Ying Shi, Dantong Wang, Jiaqi Xiang, Jinhai Zhang, Wen Huazhong Agricultural University College of Informatics Wuhan China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big Data Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Wuhan China

ISBN: (纸本)9798350337488

In medical image analysis, image registration plays a key role in establishing the deformation field between image pairs. A good deformation model is essential for high-quality estimates. However, most existing approaches still face great challenges in extracting better image features and generating detailed information on deformation fields. In this paper, we present a brain image registration model based on contrastive learning with channel attention and contour loss (CCAC). To integrate lower-level features more effectively, we propose a channel attention-based skip connection to obtain weight information of features at different encoder layers. To enhance the feature consistency of semantics in different stages, we then introduce multi-stage contrastive learning to capture semantics at different stages. Moreover, in order to reduce the influence caused by the disparities of inter-image gray value in registered images, contour feature loss is employed. Both qualitative and quantitative experiments on the popular LPBA40 dataset demonstrate that our method can achieve much better performance than the baseline methods. © 2023 IEEE.

关键词： Brain MR image Channel attention Contour features Medical image registration Multistage contrastive learning

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenyang Bu Jiacheng Liu Jiaxuan Liu Shengwei Ji Hongbin Yang Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China School of Artificial Intelligence and Big Data Hefei University China

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets.

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Innovative remote sensing methodologies and applications in coastal and marine environments

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地球空间信息科学学报（英文版） 2024年第3期27卷 836-853页

作者： Qing Zhao Antonio Pepe Virginia Zamparelli Pietro Mastro Francesco Falabella Saygin Abdikan Caglar Bayik Fusun Balik Sanli Mustafa Ustuner Nevin Betul Avşar Jingjing Wang Peng Chen Zhengjie Li Adam T.Devlin Fabiana Calò Key Laboratory of Geographical Information Science Ministry of EducationEast China Normal UniversityShanghaiChina School of Geographic Sciences East China Normal UniversityShanghaiChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural ResourcesShanghaiChina Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly School of Engineering University of BasilicataPotenzaItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute of Methodologies for Environmental Analysis(IMAA) Italian National Research CouncilTito ScaloItaly Department of Geomatics Engineering Hacettepe UniversityAnkaraTurkey Department of Geomatics Engineering Zonguldak Bulent Ecevit UniversityZonguldakTurkey Department of Geomatic Engineering Yildiz Technical UniversityIstanbulTurkey Department of Geomatic Engineering Artvin Çoruh UniversityArtvinTurkey Department of Geomatics Engineering Izmir Katip Celebi UniversityİzmirTurkey School of Ocean and Earth Science and Technology University of Hawaii at MānoaHonoluluHIUSA

Remote sensing(RS)technologies are extensively exploited by scientists and a vast audience of local authorities,urban managers,and city *** regions,geohazard-prone areas,and highly populated cities represent natural laboratories to apply RS technologies and test new *** the last decades,many efforts have been spent on improving Earth's surface monitoring,including intensifying Earth Observation(EO)operations by the major national space *** oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development oppor-tunities in the geoscience *** instance,within this framework,the European Space Agency(ESA)and the Ministry of Science and Technology of China(MOST)have sponsored,since the early 2000s,the DRAGON initiative jointly carried out by the European and Chinese RS scientific *** manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further *** main findings are related to ⅰ)the analysis of flood risk in China,ⅱ)the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar(SAR)methods,ⅲ)the analy-sis of the inundation risk in low-lying regions using coherent and incoherent SAR methods;andⅳ)the use of SAR-based technologies for marine applications.

关键词： Disaster risk management Remote Sensing(RS) Earth Observation(EO) Synthetic Aperture Radar(SAR) flooding subsidence coastal/marine environments

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Vehicle attribute recognition methods incorporating attention mechanisms

Vehicle attribute recognition methods incorporating attentio...

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2024 International Conference on Computer Application and Information Security, ICCAIS 2024

作者： Hu, Junzhu Lu, Ying Xu, Quanyuan Li, Caiyi Wang, Mingyang Zhou, Hao Yang, Peidong Jiang, Feng College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650024 China Key Laboratory of Forestry and Ecological Big Data State Forestry Administration on Southwest Forestry University Kunming650024 China Qujing Tobacco Company Qujing655000 China

ISBN: (纸本)9781510689312

To address the challenges of vehicle attribute recognition in traffic management, intelligent parking systems, and road surveillance, this paper proposes an algorithm that integrates the Squeeze-and-Excitation (SE) attention mechanism into ResNet34. The SE mechanism is added after each BasicBlock, with a total of 16 SE modules incorporated to train the network. The optimal model is achieved after 100 iterations. The SE mechanism enhances the network’s ability to focus on key features by adaptively adjusting the importance of features across channels, thereby improving the effectiveness of feature representation. This dynamic adjustment allows the model to better adapt to complex and varying image structures, demonstrating strong adaptability in vehicle attribute recognition tasks. Experimental results show a significant improvement in recognition accuracy, with the enhanced ResNet34 model achieving a 5.1% increase in vehicle attribute recognition accuracy compared to the original ResNet34. The proposed algorithm effectively improves recognition performance, offering valuable technical support for the application of intelligent transportation systems. © The Authors.

关键词： Highway administration

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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A distributed Kalman filter based fault detection scheme incorporating weighted average consensus algorithm for large-scale interconnected systems ⁎

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IFAC-PapersOnLine 2024年第14期58卷 586-591页

作者： Haozhou Yuan Jingjing Gao Jian Huang Xu Yang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China

Stimulated by the increasing demands for system safety and process reliability in complex industrial processes, this paper investigates a weighted average consensus algorithm based distributed fault detection scheme for large-scale interconnected systems, using a sensor network where each node is equipped with a Kalman filter (KF). To reduce the communication and computation efforts, the proposed distributed fault detection scheme is splitted into two phases: distributed offline training and online fault detection. To this end, the Expectation-Maximization (EM) algorithm is firstly addressed to identify the unknown measurement matrices and covariance matrices of noise vectors. It is followed by an average consensus algorithm so that the identical Kalman filters can be designed in parallel at all sensor nodes. On this basis, distributed residual generators and test statistics are constructed for fault detection purpose using the average consensus algorithm. Considering that there exist some special conditions, such as the occurrence of node failures, a variation of the distributed Kalman filter based fault detection scheme is proposed by dynamically adjusting the consensus weight. Finally, the feasibility and effectiveness of the proposed scheme are demonstrated through a case study on the waste water treatment plants (WWTPs).

关键词： Distributed fault detection Large-scale systems Distributed Kalman filter Weighted average consensus Expectation-Maximization algorithm

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Effective In-Context Learning for Named Entity Recognition

Effective In-Context Learning for Named Entity Recognition

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Jin Guo, Qian Liang, Jiaqing Li, Zhixu Xiao, Yanghua Fudan University Shanghai Key Laboratory of Data Science School of Computer Science Shanghai China Fuyang Normal University College of Information Engineering Anhui China Renmin University of China School of Information Suzhou China

ISBN: (纸本)9798350386226

Named entity recognition aims to identify meaningful words or phrases from text. Traditional NER methods depend heavily on training datasets to learn entity features, which often leads to poor performance in recognizing low-resource entities. Large language models offer a promising solution in low-resource scenarios, but they struggle with entity boundary recognition and often suffer from hallucination issues due to their reliance on general-purpose corpora. In this paper, we propose a novel approach that enhances LLMs' performance in NER by integrating a boundary regularity extraction algorithm and an explanatory path extraction method. The boundary regularity algorithm leverages ChatGPT's summarization capabilities to articulate entity boundaries in natural language, improving boundary detection accuracy. The explanatory path method constructs logical reasoning steps from text to entity recognition, guiding LLMs to generate more accurate predictions and mitigating hallucination problems. Extensive experiments on three public datasets demonstrate that our method outperforms current mainstream LLMs in accurately recognizing entities from unstructured text. These findings validate the effectiveness of our approach and its potential to address key challenges in NER. © 2024 IEEE.

关键词： Large language models Low-resource Named entity recognition

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Real-time Dense 3D Semantic Mapping Using RGB-D Camera 42

Real-time Dense 3D Semantic Mapping Using RGB-D Camera

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42nd Chinese Control Conference, CCC 2023

作者： Yang, Guanci Wang, Xiaoyuan Zhu, Dongying Li, Yang Guizhou University Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guiyang550025 China School of Mechanical Engineering Guizhou University Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China

ISBN: (纸本)9789887581543

Focusing on the poor real-time performance of 3D semantic mapping using RGB-D cameras in indoor environments, this paper investigates the Real-time Dense 3D Semantic Mapping (RD3DSM) using RGB-D camera. Firstly, a lightweight semantic point cloud acquisition algorithm based on keyframe is designed, which is used to segment scene images and obtain the semantic point cloud real-timely. Secondly, to address the loss of inter-frame tracking in the semantic mapping process, ORB-SLAM3 system is employed as the underlying architecture of the visual 3D semantic mapping system to provide stable camera pose. Thirdly, an information fusion mechanism of the visual 3D semantic mapping system is designed, and the OctoMap 3D scene reconstruction module is adopted to implement dense 3D map representation with semantic information, and then a visual 3D semantic mapping algorithm based on ORB-SLAM3 is proposed. Finally, the designed RD3DSM system is integrated into a robot platform. The tests were conducted on the TUM dataset and natural indoor scenes. The results show the effectiveness and real-time performance of the RD3DSM system. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Cameras

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Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer

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Science China(Information Sciences) 2024年第2期67卷 20-40页

作者： Peng YANG Laoming ZHANG Haifeng LIU Guiying LI Department of Statistics and Data Science Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Guangdong OPPO Mobile Telecommunications Corp. Ltd Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.

关键词： evolutionary reinforcement learning evolutionary multi-objective optimization load balance cloud computing

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