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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Zesheng Zhu, Qiannan Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application MOE China

ISBN: (纸本)157735897X

Catastrophic forgetting is a key challenge in incremental named entity recognition (INER). Existing methods often address this issue through distillation-based approaches, which involve transferring previously learned knowledge from the old model to the new one. However, these methods may not fully equip the new model with an adequate understanding of the characteristics about old entity types, leading to confusion when classifying tokens associated with these entity types. To address this challenge, we propose a novel method called Prototypical Replay with Old-class Focusing knowledge Distillation (POF) for INER. Our approach focuses on preserving the main characteristics of each previous entity type by storing compact prototypes and replaying them with appropriate frequency. This replay strategy makes the new model review the knowledge of old entity types while minimizing storage needs. Additionally, we introduce an old-class focusing knowledge distillation (OFKD) loss, which distills features only in old-class regions to maintain the quality of old-class prototypes and prevent ineffective prototypical replay while preserving sufficient plasticity for learning new entity types. We conduct experiments on three benchmark datasets (i.e., Few-NERD, I2B2 and OntoNotes5), and the results demonstrate that our method outperforms all previous state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Fault Estimation for Nonlinear Systems with Disturbance Based on Reinforcement Iterative Learning Strategy 13

Fault Estimation for Nonlinear Systems with Disturbance Base...

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

作者： Chen, Zhengquan Huang, Ruirui Zhang, Zhiheng Hou, Yandong Henan University School of Computer and Information Engineering Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Artificial Intelligence Henan University Zhengzhou450000 China

ISBN: (纸本)9798350361674

This paper addresses the problem of low fault estimation accuracy in Lipschitz nonlinear systems with external disturbances, presenting an innovative strategy based on a Reinforcement Iterative Learning (RIL) framework. Initially, we conducted precise mathematical modeling, comprehensively considering the impact of Lipschitz non-linearity and external disturbances. Subsequently, an iterative learning fault observer was designed, and the optimization problem for its parameters was reformulated into a reinforcement learning task, clearly defining the states, actions, and reward functions. Employing the Advantage Actor-Critic (A2C) reinforcement learning method, we optimized the gain parameters of the iterative learning fault estimator to approximate non-linearity. Simultaneously, with the increase in iteration count, the neural network weights were continually optimized, achieving accurate fault estimation. Experimental results demonstrate a significant improvement in fault estimation using the proposed method. © 2024 IEEE.

关键词： Adversarial machine learning

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knowledge-Guided Cross-Topic Visual Question Generation 30

Knowledge-Guided Cross-Topic Visual Question Generation

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Hongfei Wang, Guohua Xie, Jiayuan Chen, Jiali Fang, Wenhao Cai, Yi School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9782493814104

Visual question generation (VQG) task aims to generate high-quality questions based on the input image. Current methods primarily focus on generating questions containing specified content utilizing answers or question types as constraints. However, these constraints make it challenging to control the topic of generated questions (e.g., conversation or test subject topics) for various applications. Thus, it is necessary to utilize topics as constraints to guide question generation. Considering that there are many topics and it is almost impossible for human annotations to cover them, we propose the cross-topic learning VQG (CTL-VQG) task, which aims to generate questions related to unseen topics in cross-topic scenarios. In this paper, we propose a knowledge-guided cross-topic visual question generation (KC-VQG) model to extract unseen topic-related information for question generation. Specifically, an image-topic feature extractor is introduced in our model to extract topic-related intuitive visual features;an image-topic knowledge extractor is used to extract and select the most appropriate topic-related implicit knowledge from large language models for generating questions. Extensive experiments show that our model outperforms baselines and can effectively generate unseen topic-related questions in cross-topic scenarios. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： data mining

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Spatial-temporal graph attention network for traffic flow forecasting 5

Spatial-temporal graph attention network for traffic flow fo...

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2022 5th International Conference on Advanced Algorithms and Control engineering, ICAACE 2022

作者： Wang, Ling Jia, Gaofeng School of Automation and Electrical Engineering Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education University of Science and Technology Beijing Beijing100083 China

Traffic flow forecasting is one of the most challenging tasks in intelligent transportation system, which is very important and critical for many traffic services. Although there are existing works on forecasting the future traffic flow, the majority of them have certain limitations on modelling spatial-temporal correlations: they usually use different modules for spatial and temporal dependencies, and lack abilities of utilizing the historical periodic data. In order to overcome these problems, a novel Spatial-Temporal Graph Attention network (STGAT) is proposed. Considering the periodic characteristic of traffic data, the raw data is transformed into a three-dimensional tensor corresponded to the weekly period and recent period before inputting into the STGAT model. The random walk theory is used to construct a localized spatial-temporal graph to model the spatial-temporal dependencies, and an elaborately designed spatial-temporal graph attention mechanism is used to synchronously capture the complex spatial-temporal dynamic correlations. By calculating the similarity of spatial-temporal features of weekly and recent period input, the model integrates the periodic features into the target. Experimental results on two public real-world datasets demonstrate that STGAT achieves superior performance and consistently outperforms other baselines. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Optimization Scheme of Active Reconfigurable Intelligent Surface Assisted Internet of Vehicles System

Optimization Scheme of Active Reconfigurable Intelligent Sur...

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2024 International Workshop on Automation, Control, and Communication engineering, IWACCE 2024

作者： Xu, Zhuoran Ni, Yiyang Sun, Wenxue Liu, Yaxuan Mo, Zhaoying Zhao, Haitao Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal University Nanjing210013 China

ISBN: (数字)9781510685420

ISBN: (纸本)9781510685413

In recent years, the Integrated Satellite Aerial Terrestrial (I-SAT) network has emerged as an innovative and comprehensive communication system and has gained wide attention. However, in the face of a complex external environment, it will still be affected by other factors. This paper considers the integration of Unmanned Aerial Vehicle (UAV) with Reconfigurable Intelligent Surfaces (RIS) and introduces a novel sub-connected active RIS architecture under the energy consumption constraints of the system. Firstly, a joint RIS phase shift, amplification, and UAV trajectory optimization algorithm is proposed to achieve a high achievable sum rate. Secondly, a Deep Deterministic Policy Gradient (DDPG) algorithm is utilized to solve the optimization problem and converge in a continuous action space. Finally, simulation results confirm that the proposed approach significantly enhances the system performance compared to the other schemes. © 2024 SPIE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Ship Target Detection Algorithm Based on Improved YOLOv5 3

Ship Target Detection Algorithm Based on Improved YOLOv5

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3rd International Conference on Computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Zhu, Louyu Bi, Zhenbo School of Information Engineering Zhejiang Ocean University Zhoushan China Zhejiang Ocean University Department of Physics Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9798350372052

In view of the low accuracy and low efficiency of traditional ship target detection methods, this paper introduces an improved ship target detection method based on YOLOv5. Firstly, we preprocess the ship target data set, which includes graph denoising, graph enhancement and so on. Secondly, based on the benchmark YOLOv5 object detection algorithm, SimAM attention mechanism module is introduced. Then, a pyramid feature fusion strategy is added to filter conflicting information in airspace to suppress inconsistent features and improve the network's feature fusion capability for targets of different scales. Finally, the trained model is tested to achieve accurate evaluation of ship target detection. Experimental results show that the proposed ship target detection method is compared with YOLOv5s in accuracy rate, recall rate, mAP@0.5 and mAP@0.5: 0.95 increased by 1.9, 3.8, 2.6 and 7.3 percentage points. Respectively, which can meet the requirements of detection speed and obtain better detection accuracy, effectively realizing high-speed and high-precision ship detection. © 2023 IEEE.

关键词： Object detection

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Two-dimensional transition metal chalcogenide nanomaterials for cancer diagnosis and treatment

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Chinese Chemical Letters 2022年第10期33卷 4437-4448页

作者： Jingjing Wu Tingting Hu Guoping Zhao Anran Li Ruizheng Liang State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing 100029China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Department of Orthopedics Guilin People's HospitalGuilin 541002China

For more than a decade,the exfoliation of graphene and other layered materials has led to a tremendous amount of research in two-dimensional(2D)materials,among which 2D transition metal chalcogenides(TMCs)nanomaterials have attracted much attention in a wide range of applications including photoelectric devices,lithium-ion batteries,catalysis,and energy conversion and storage owing to their unique photoelectric physical *** such large specific surface area,strong near-infrared(NIR)absorption and abundant chemical element composition,2D TMCs nanomaterials have become good candidates in biomedical imaging and cancer *** review systematically summarizes recent progress on 2D TMCs nanomaterials,which includes their synthesis methods and applications in cancer *** the end of this review,we also highlight the future prospects and challenges of 2D TMCs *** is expected that this work can provide the readers with a detailed overview of the synthesis of 2D TMCs and inspire more novel functional biomaterials based on 2D TMCs for cancer treatment in the future.

关键词： Two-dimensional(2D)nanomaterials Transition metal chalcogenides(TMCs) Synthesis method Biomedical imaging Cancer treatment

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A Highly Selective Wideband Filtering Stacked DDPA 4

A Highly Selective Wideband Filtering Stacked DDPA

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4th IEEE International Conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Tian, Kang Zhang, JiaSheng La, Dongsheng Northeastern University at Qinhuangdao Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Qinhuangdao066000 China An Hui Ming Bian Electronic Science & Technology Co. Ltd. China

ISBN: (纸本)9798350363586

In this paper, a compact and highly selective stacked filtering dense dielectric patch (DDP) antenna (DDPA) is proposed. By placing a pair of thin DDP with high dielectric constant along the y axis on the DDP with low dielectric constant, the resonant frequency of the TE12 mode is adjusted to approach low frequency and form a passband with the TE11 mode. By loading a U-shaped microstrip feeding line on the microstrip feeding line, the bandwidth can be further broadened and the TE11, TE11 + δ and TE12 modes can be successfully excited in the passband. The radiation nulls are generated by the U-shaped microstrip line and non-radiative TE12 mode, respectively. By adjusting the length of the U-shaped microstrip line and the size of DDP with high dielectric constant, the independent adjustment of radiation nulls can be achieved. Finally, the proposed filtering antenna achieves a relative bandwidth of 11.66 % (4.04-4.54 GHz). The average realized gain and peak gain are 6.89 dBi and 7.1 dBi, respectively. The realized gain in the passband is very flat, and the fluctuation is less than 0.7 dBi. The proposed antenna has the advantages of compactness, high selectivity and flat gain, and it can be applied to modern satellite communication and wireless communication systems. © 2024 IEEE.

关键词： Bandwidth

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Adaptive Gaussian Process Regression Based Remaining Useful Life Prediction of PEMFC Incorporating An Improved Health Indicator 11

Adaptive Gaussian Process Regression Based Remaining Useful ...

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11th IEEE data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Tang, Lin Yang, Xu Gao, Jing Jing Huang, Jian Cui, Jia Rui School of Automation and Electrical Engineering University of Science and Technology Beijing Ministry of Education Key Laboratory of Knowledge Automation for Industrial Processes Beijing100083 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Considering the importance of the proton exchange membrane fuel cells(PEMFC) to daily life and industry, this paper makes the remaining useful life(RUL) prediction of the PEMFC based on two different environments. To this end, the improved health indicator is proposed to describe the health state of PEMFC. On this basis, a data-driven method, namely the adaptive Gaussian process regression(GPR) method, is proposed to predict the RUL of PEMFC. The effectiveness of the proposed life prediction method is demonstrated in the aging data set of PEMFC provided by the prognostic and health management(PHM) challenge by a case study, the artificial neural network(ANN) method, and the adaptive GPR method are used to predict the PEMFC's RUL. Results show that the adaptive GPR method achieves better prediction results and provides the probability distribution of the results compared with the ANN method. © 2022 IEEE.

关键词： Proton exchange membrane fuel cells (PEMFC)

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Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education

arXiv

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arXiv 2024年

作者： Shao, Pengyang Yang, Yonghui Gao, Chen Chen, Lei Zhang, Kun Zhuang, Chenyi Wu, Le Li, Yong Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei China BNRist Tsinghua University Beijing China Department of Electronic Engineering BNRist Tsinghua University Beijing China Ant Group Zhejiang Hangzhou China

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students’ proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence Criterion. Finally, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD. © 2024, CC BY.

关键词： knowledge graph

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