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41st International Conference on Machine Learning, ICML 2024

作者： Li, Xudong Gao, Timin Hu, Runze Zhang, Yan Zhang, Shengchuan Zheng, Xiawu Zheng, Jingyuan Shen, Yunhang Li, Ke Liu, Yutao Dai, Pingyang Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China School of Information and Electronics Beijing Institute of Technology Beijing100080 China School of Medicine Xiamen University China Tencent Youtu Lab China School of Computer Science and Technology Ocean University of China China

The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection. Empirically, we find that many image pairs with small feature spatial distances can have vastly different quality scores, indicating that the extracted features may contain quality-irrelevant noise. To address this issue, we propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) that employs an adversarial perspective to remove harmful semantic noise features from the upstream task. Specifically, QFM-IQM enhances the semantic noise distinguish capabilities by matching image pairs with similar quality scores but varying semantic features as adversarial semantic noise and adaptively adjusting the upstream task's features by reducing sensitivity to adversarial noise perturbation. Furthermore, we utilize a distillation framework to expand the dataset and improve the model's generalization ability. Extensive experiments conducted on eight standard IQA datasets have demonstrated the effectiveness of our proposed QFM-IQM. Copyright 2024 by the author(s)

关键词： Feature Selection

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An Efficient Spatial-Temporal Representation Method for EEG Emotion Recognition 19

An Efficient Spatial-Temporal Representation Method for EEG ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Weng, Weining Gu, Yang Chen, Yiqiang Wang, Guoqiang Shi, Nianfeng Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China School of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China

ISBN: (纸本)9798350346558

Affective computing based on electroencephalogram (EEG) has been widely studied due to the high correlation between the brain and emotions. Neurological research shows that emotions are generated by the interaction of neural circuits separated in different brain regions. Recently, many deep learning methods have been demonstrated to recognize different emotions. However, these methods extract features at the data level, ignoring the brain's neural mechanisms of emotion generation, which may result in the features' weak ability to express emotion. To solve this problem and extract more discriminant features according to the neural mechanism of emotion generation, this article proposes an attention based spatial-temporal representation method (ASTR) for emotion recognition. The ASTR method constructs spatial-temporal representations of emotion from neural circuits' activity in different brain regions. In the model, the cascade attention quantifies the multi-granularity correlation and information exchange pattern between brain regions as the spatial features, which measure neural circuits' activation and information interaction in different brain regions. The one-way transformer introduces temporal order in spatial features, calculates temporal variation features from the long-term activity of neural circuits and generates the emotion representation. The representation incorporates spatial-temporal features of neural circuits' activity during emotion generation, which is highly related to emotion. We conducted experiments on SEED and DEAP datasets. The experimental results show that the emotion recognition accuracy of our model is about 3.0% higher than that of the SOTA algorithms, reaching more than 98%. The results also visualize the emotion generation process, revealing how the brain works under different emotions. © 2022 IEEE.

关键词： Emotion Recognition

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Research on named entity recognition method for high-speed railway technology transformation project text 4

Research on named entity recognition method for high-speed r...

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2024 4th International Conference on Artificial Intelligence and Industrial technology Applications, AIITA 2024

作者： Lian, Xiaoqin Bao, Kexin Gao, Chao Gong, Yonggang Wu, Yanhua Cheng, Zhibo School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100081 China

High-speed railway technology transformation projects are crucial to the layout of high-speed rail transportation production, high-speed rail safety, and high-speed rail operation and management. However, the data of high-speed railway technology transformation projects is mainly stored in the form of unstructured text, and the relevant personnel of high-speed railroad often need to manually go through the form to extract the key information of the project data and enter it into the system, with a low degree of automation and intelligence. Therefore, to improve the efficiency and quality of processing the data of high-speed railway technology transformation projects, and help the intelligent development of high-speed railways, this paper constructs the RoBERTa-BiLSTM-CRF fusion model to automate the identification of key information entities in the data of high-speed railway technology transformation project. The experimental data were selected as technology transformation project documents of a railway bureau from 2013 to 2022, and six defined entity types were identified. Experimental results show that the constructed model can effectively identify six types of defined entities, and the F1 value of each type of entity is above 90%. In addition, compared with other commonly used models, the F1 value has been significantly improved. The fusion model proposed in this paper can be used as a method to automatically extract key information for high-speed rail technology transformation projects. © Published under licence by IOP Publishing Ltd.

关键词： Railroads

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Integrating Global Context Contrast and Local Sensitivity for Blind Image Quality Assessment 41

Integrating Global Context Contrast and Local Sensitivity fo...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Xudong Hu, Runze Zheng, Jingyuan Zhang, Yan Zhang, Shengchuan Zheng, Xiawu Li, Ke Shen, Yunhang Liu, Yutao Dai, Pingyang Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China School of Information and Electronics Beijing Institute of Technology Beijing100080 China School of Medicine Xiamen University China Tencent Youtu Lab China School of Computer Science and Technology Ocean University of China China

Blind Image Quality Assessment (BIQA) mirrors subjective made by human observers. Generally, humans favor comparing relative qualities over predicting absolute qualities directly. However, current BIQA models focus on mining the "local" context, i.e., the relationship between information among individual images and the absolute quality of the image, ignoring the "global" context of the relative quality contrast among different images in the training data. In this paper, we present the Perceptual Context and Sensitivity BIQA (CSIQA), a novel contrastive learning paradigm that seamlessly integrates "global" and "local" perspectives into the BIQA. Specifically, the CSIQA comprises two primary components: 1) A Quality Context Contrastive Learning module, which is equipped with different contrastive learning strategies to effectively capture potential quality correlations in the global context of the dataset. 2) A Quality-aware Mask Attention Module, which employs the random mask to ensure the consistency with visual local sensitivity, thereby improving the model's perception of local distortions. Extensive experiments on eight standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Relationship Prompt Learning is Enough for Open-Vocabulary Semantic Segmentation 38

Relationship Prompt Learning is Enough for Open-Vocabulary S...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Jiahao Lu, Yang Xie, Yuan Qu, Yanyun School of Informatics Xiamen University China Institute of Artificial Intelligence Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computer Science and Technology East China Normal University China Chongqing Institute of East China Normal University China

Open-vocabulary semantic segmentation (OVSS) aims to segment unseen classes without corresponding labels. Existing Vision-Language Model (VLM)based methods leverage VLM's rich knowledge to enhance additional explicit segmentation-specific networks, yielding competitive results, but at the cost of extensive training cost. To reduce the cost, we attempt to enable VLM to directly produce the segmentation results without any segmentation-specific networks. Prompt learning offers a direct and parameter-efficient approach, yet it falls short in guiding VLM for pixel-level visual classification. Therefore, we propose the Relationship Prompt Module (RPM), which generates the relationship prompt that directs VLM to extract pixel-level semantic embeddings suitable for OVSS. Moreover, RPM integrates with VLM to construct the Relationship Prompt Network (RPN), achieving OVSS without any segmentation-specific networks. RPN attains state-of-the-art performance with merely about 3M trainable parameters (2% of total parameters). © 2024 Neural information processing systems foundation. All rights reserved.

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High Performance computing for CS-InSAR Data Processing

High Performance Computing for CS-InSAR Data Processing

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2021 SAR in Big Data Era, BIGSARDATA 2021

作者： Zhang, Weikang You, Haihang Tang, Yixian Wang, Chao Zhang, Hong Chinese Academy of Sciences State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China Chinese Academy of Sciences Key Laboratory of Digital Earth Science Aerospace Information Research Institute Beijing China

ISBN: (纸本)9781665401234

In this paper, we have developed a highperformance data processing system for big SAR data based on parallel and distributed computation using supercomputer. Aiming at the pipeline of InSAR, we mainly focus on the parallelization and optimization of computing hotspot to accelerate the operation. Furthermore, we take advantage of the technology of distributed computation and multitask automation to get higher performance - process more data in less time. With the high-performance system, we obtained the nationwide measurement of China's surface movement from September 2018 to February 2020 and from January to December in 2020 using 20-30 computing nodes, processing nearly 1PB of data in total. The calculation time of the two images of national scale surface movement is completed in only 7 and 5 days, respectively. © 2021 IEEE.

关键词： Supercomputers

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Effective Entry-Wise Flow for Molecule Generation 40

Effective Entry-Wise Flow for Molecule Generation

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhang, Qifan Yao, Junjie Yang, Yuquan Shi, Yizhou Gao, Wei Wang, Xiaoling School of Computer Science and Technology East China Normal University China Software Engineering Institute East China Normal University China Shanghai Key Laboratory of Trustworthy Computing China University of Massachusetts Department of Chemistry Boston United States University of Michigan Ann Arbor United States

ISBN: (纸本)9798350317152

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts. © 2024 IEEE.

关键词： Complex networks

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IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

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Journal of Genetics and Genomics 2024年第4期51卷 443-453页

作者： Shuyu Liang Sicheng Xu Shichong Zhou Cai Chang Zhiming Shao Yuanyuan Wang Sheng Chen Yunxia Huang Yi Guo Department of Electronic Engineering School of Information Science and TechnologyFudan UniversityShanghai 200433China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention(MICCAI)of Shanghai Shanghai 200032China Shanghai Key Laboratory of Metabolic Remodeling and Health Institute of Metabolism and Integrative BiologyFudan UniversityShanghai 200433China Department of Ultrasound Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China Department of Breast Surgery Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China

Investigating correlations between radiomic and genomic profiling in breast cancer(BC)molecular subtypes is crucial for understanding disease mechanisms and providing personalized *** present a well-designed radiogenomic framework image–gene–gene set(IMAGGS),which detects multi-way associations in BC subtypes by integrating radiomic and genomic *** dataset consists of 721 patients,each of whom has 12 ultrasound(US)images captured from different angles and gene mutation *** better characterize tumor traits,12 multi-angle US images are fused using two distinct ***,we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm,deviating from the prevalent one-to-one relationship pattern observed in previous *** radiomic and genomic features are screened using these *** addition,gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC *** further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and *** together,IMAGGS provides a comprehensive characterization for diseases by associating imaging,genes,and gene sets,paving the way for biological interpretation of radiomics and development of targeted therapy.

关键词： IMAGGS Radiogenomic framework "lmage-gene-geneset"associations Molecular subtypes Breast cancer

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