检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Peng, Hao Zhang, Jingyun Huang, Xiang Hao, Zhifeng Li, Angsheng Yu, Zhengtao Yu, Philip S. School of Cyber Science and Technology Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China State Key Laboratory of Public Big Data Guizhou University No. 2708 South Section of Huaxi Avenue Huaxi District Guizhou Province Guiyang City550025 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China College of Science University of Shantou No. 243 University Road Shantou515063 China School of Computer Science and Engineering Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, e.g., Graph Neural Network, Transformer, etc., which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: Posting Type Distribution, Posting Influence, and Follow-to-follower Ratio. Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Thirdly, a new community labeling method is presented to distinguish social bot communities by computing the user's stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with ten representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on four real social network datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

来源：评论

学校读者我要写书评

暂无评论

Bipartite Containment Control of Nonuniform Delayed Fractional-Order Multi-Agent Systems Over Signed Networks

Bipartite Containment Control of Nonuniform Delayed Fraction...

引用

IEEE Conference on Decision and Control

作者： Weihao Li Kaiyu Qin Jinliang Shao Lei Shi Mengji Shi Wei Xing Zheng School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China School of Artificial Intelligence Henan University Zhengzhou China School of Computer Data and Mathematica Sciences Western Sydney University Sydney NSW Australia

In this study, the bipartite containment control problem of fractional-order multi-agent systems with nonuni-form time delays is addressed. An in-depth analysis of the system stability and bipartite containment control performance from a delay margin perspective is provided. Theoretically, the corresponding delay margin (maximum allowable time delay) over undirected and directed signed networks is obtained in the presence of nonuniform time delays, respectively. In addition, numerical relationships between the delay margin and the control coefficients, fractional order, and topology parameters are established, thus enabling easy and direct calculation of the maximum allowable time delay and facilitating distributed controller design and controller parameter tuning. Finally, some simulation examples are given to verify the effectiveness of the proposed bipartite containment controller and the obtained delay margin.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness

arXiv

引用

arXiv 2024年

作者： Huang, Bin Zhao, Changchen Liu, Zimeng Hong, Shenda Zhang, Baochang Lu, Hao Liu, Zhijun Wang, Wenjin Liu, Hui AI Research Center Hangzhou Innovation Institute Beihang University Zhejiang Hangzhou China School of Computer Science Hangzhou Dianzi University China School of Automation Science and Electrical Engineering Beihang University China Institute of Medical Technology Health Science Center of Peking University National Institute of Health Data Science Peking University China Institute of Artificial Intelligence Beihang University China Information Hub Hong Kong University of Science and Technology China Department of Biomedical Engineering City University of Hong Kong China Department of Biomedical Engineering Southern University of Science and Technology China Institute of Medical Information Chinese Academy of Medical Sciences Peking Union Medical College China

Good health and well-being is among key issues in the United Nations 2030 Sustainable Development Goals. The rising prevalence of large-scale infectious diseases and the accelerated aging of the global population are driving the transformation of healthcare technologies. In this context, establishing large-scale public health datasets, developing medical models, and creating decision-making systems with a human-centric approach are of strategic significance. Recently, by leveraging the extraordinary number of accessible cameras, groundbreaking advancements have emerged in AI methods for physiological signal monitoring and disease diagnosis using camera sensors. These approaches, requiring no specialized medical equipment, offer convenient manners of collecting large-scale medical data in response to public health events. Therefore, we outline a prospective framework and heuristic vision for a camera-based public health (CBPH) framework utilizing visual physiological monitoring technology. The CBPH can be considered as a convenient and universal framework for public health, advancing the United Nations Sustainable Development Goals, particularly in promoting the universality, sustainability, and equity of healthcare in low- and middle-income countries or regions. Furthermore, CBPH provides a comprehensive solution for building a large-scale and human-centric medical database, and a multi-task large medical model for public health and medical scientific discoveries. It has a significant potential to revolutionize personal monitoring technologies, digital medicine, telemedicine, and primary health care in public health. Therefore, it can be deemed that the outcomes of this paper will contribute to the establishment of a sustainable and fair framework for public health, which serves as a crucial bridge for advancing scientific discoveries in the realm of AI for medicine (AI4Medicine). Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Offline evolutionary optimization with problem-driven model pool design and weighted model selection indicator

引用

Swarm and Evolutionary Computation 2025年 97卷

作者： Huixiang Zhen Bing Xue Wenyin Gong Mengjie Zhang Ling Wang School of Computer Science China University of Geosciences Wuhan 430074 China Center for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington 6140 New Zealand Department of Automation Tsinghua University Beijing 100084 China

Offline data-driven evolutionary algorithms aim to provide a promising solution based on the collected historical data, without online real fitness evaluations. However, the suitability of surrogate models varies significantly across different problem types, and current research often overlooks the relationship between problem characteristics and model performance. To address this gap, we propose a novel offline data-driven evolutionary algorithm, termed MSEA, which integrates a problem-driven model pool design and a weighted indicator-based model selection mechanism. The model pool is carefully designed, incorporating four distinct surrogate models tailored for various optimization landscapes to align with diverse problem characteristics. A weighted selection indicator, derived from both model evaluation and solution quality assessment, is employed to dynamically select the most suitable model for the optimization problem. Extensive experimental results demonstrate that MSEA effectively identifies and utilizes the optimal model from the pool for specific offline optimization tasks. Compared to five state-of-the-art offline data-driven methods, MSEA achieved optimal results for 26 out of 32 functions across dimensions ranging from 10 to 100 and also exhibited faster running times. Furthermore, in high-dimensional spaces, MSEA achieved the best optimization results in dimensions ranging from 200 to 500. Our code is available at https://***/zhenhuixiang/MSEA .

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

引用

IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

来源：评论

学校读者我要写书评

暂无评论

Tipping Prediction of a Class of Large-Scale Radial-Ring Neural Networks

SSRN

引用

SSRN 2024年

作者： Lu, Yunxiang Xiao, Min Wu, Xiaoqun Karimi, Hamid Reza Xie, Xiangpeng Cao, Jinde Zheng, Wei Xing College of Automation College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China College of Computer Science and Software Engineering Shen Zhen University Shen Zhen518060 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China School of Mathematics Southeast University Nanjing210096 China School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

Understanding the emergence of collective dynamics in large-scale neural networks remains a challenging endeavor. This paper aims to address this gap by utilizing dynamic systems theory, particularly emphasizing tipping mechanisms. First of all, we introduce a novel $(n+mn)$-scale radial-ring neural network and utilize the topological approach of Coates' flow graph to determine the characteristic equation of the linearized network. Secondly, by deriving stability conditions and predicting the tipping point using the algebraic approach based on the integral element idea, we identify significant factors such as synaptic transmission delay, self-feedback coefficient, and network topology. Finally, we validate the effectiveness of the methodology in predicting the tipping point through numerical simulations. The simulations provide a thorough portrayal of the dynamics exhibited by large-scale neural networks, while also integrating robustness tests. This research contributes to a deeper understanding of the mechanisms underlying collective dynamics in large-scale neural networks, offering valuable insights for both theoretical frameworks and practical applications. © 2024, The Authors. All rights reserved.

关键词： Flow graphs

来源：评论

学校读者我要写书评

暂无评论

Asynchronous control for Markov jump systems subject to actuator saturation

Asynchronous control for Markov jump systems subject to actu...

引用

第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

来源：评论

学校读者我要写书评

暂无评论

Study of facial generation methods after orthodontic treatment

Study of facial generation methods after orthodontic treatme...

引用

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Jia– Liang Tian Qin– Yan Zhang Hai– Zhen Li Qing Wang Yi Lei Lin Zang Xue– Mei Gao Ji– Jiang Yang College of artificial intelligence Beijing University of Posts and Telecommunications Beijing Department of Orthodontics Peking University School and Hospital of Stomatology Beijing Department of Automation Tsinghua University Beijing Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing Pharmacovigilance Research Center for information technology and Data Science Xiamen Fujian

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

As the medical aesthetic market is growing rapidly in China, orthodontic treatment is becoming very common among the adolescent population. However, there are countless doctor-patient disputes due to treatment results that do not meet patients' expectations, so there is an urgent need for a method to predict treatment results. With the development of artificial intelligence technology, generative adversarial network has provided us with a new way of thinking. The purpose of this paper is to accurately predict the face of patients after orthodontic treatment by using generative adversarial network. Therefore, we designed an evaluation index to reflect the difference between the algorithm predicted image and the patient's real image. After that, we designed a network based on Encoder-Decoder architecture to transform the vectors in StyleGAN latent space. Finally, we carried out experiments to verify the effectiveness of the evaluation index design and the advantages of the algorithm.

关键词： Measurement Technological innovation Medical services Transforms Predictive models Prediction algorithms Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler

arXiv

引用

arXiv 2023年

作者： Lin, Shaohui Huang, Wenxuan Xie, Jiao Zhang, Baochang Shen, Yunhang Yu, Zhou Han, Jungong Doermann, David The School of Computer Science and Technology East China Normal University Shanghai China The Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China The Department of Automation School of Aerospace Engineering Xiamen University Xiamen China The Institute of Artificial Intelligence Beihang University Beijing China Zhongguancun Laboratory Beijing China The Youtu Lab Tencent Shanghai China The School of Statistics East China Normal University Shanghai China The Department of Computer Science University of Sheffield United Kingdom The University at Buffalo BuffaloNY United States

Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs, which can be effectively applied to edge devices and cloud services. In this paper, we propose a novel Knowledge-driven Differential Filter Sampler (KDFS) with Masked Filter Modeling (MFM) framework for filter pruning, which globally prunes the redundant filters based on the prior knowledge of a pre-trained model in a differential and non-alternative optimization. Specifically, we design a differential sampler with learnable sampling parameters to build a binary mask vector for each layer, determining whether the corresponding filters are redundant. To learn the mask, we introduce masked filter modeling to construct PCA-like knowledge by aligning the intermediate features from the pre-trained teacher model and the outputs of the student decoder taking sampling features as the input. The mask and sampler are directly optimized by the Gumbel-Softmax Straight-Through Gradient Estimator in an end-to-end manner in combination with global pruning constraint, MFM reconstruction error, and dark knowledge. Extensive experiments demonstrate the proposed KDFS’s effectiveness in compressing the base models on various datasets. For instance, the pruned ResNet-50 on ImageNet achieves 55.36% computation reduction, and 42.86% parameter reduction, while only dropping 0.35% Top-1 accuracy, significantly outperforming the state-of-the-art methods. The code is available at https://***/Osilly/KDFS. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Parallel Distance: A New Paradigm of Measurement for Parallel Driving

引用

IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1169-1178页

作者： Teng Liu Hong Wang Bin Tian Yunfeng Ai Long Chen the Vehicle Intelligence Pioneers Inc. Qingdao 266109 the Department of Automotive Engineering Chongqing UniversityChongqing 400044China the Mechanical and Mechatronics Engineering Department Waterloo UniversityN2L 3G1Canada the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China

In this paper, a new paradigm named parallel distance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluated in the parallel distance framework. First, the parallel driving 3.0 system included control and management platform, intelligent vehicle platform and remote-control platform is introduced. Then,Markov chain(MC) is utilized to model the transition probability matrix of control commands in these systems. Furthermore, to distinguish the control variables in artificial and physical driving conditions, different distance calculation methods are enumerated to specify the differences between the virtual and real signals. By doing this, the real system can be guided and the virtual system can be im-proved. Finally, simulation results exhibit the merits and multiple applications of the proposed parallel distance framework.

关键词： artificial and physical system parallel distance parallel driving 3.0 parallel system rotational and accelerator signal

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：