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arXiv 2022年

作者： Ma, Pengfei Wu, Youxi Li, Yan Guo, Lei Jiang, He Zhu, Xingquan Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjing300401 China Hebei Key Laboratory of Big Data Computing Tianjing300401 China School of Economics and Management Hebei University of Technology Tianjing300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjing300401 China School of Software Dalian University of Technology Dalian116023 China Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Hefei University of Technology Hefei230009 China Mininglamp Academy of Sciences Mininglamp Technology Beijing100084 China

As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vectors, and this increases the time cost of the model. To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening. HW-Forest employs perceptual hashing algorithm to calculate the similarity between feature vectors in hashing screening strategy, which is used to remove the redundant feature vectors produced by multi-grained scanning and can significantly decrease the time cost and memory consumption. Furthermore, we adopt a self-adaptive instance screening strategy to improve the performance of our approach, called window screening, which can achieve higher accuracy without hyperparameter tuning on different datasets. Our experimental results show that HW-Forest has higher accuracy than other models, and the time cost is also reduced. Copyright © 2022, The Authors. All rights reserved.

关键词： Scanning

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Understanding collective behaviors in reinforcement learning evolutionary games via a belief-based formalization

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Physical Review E 2020年第4期101卷 042402-042402页

作者： Ji-Qiang Zhang Si-Ping Zhang Li Chen Xu-Dong Liu Beijing Advanced Innovation Center for Big Data and Brain Computing School of Comuter Science and Engineering Beihang University Beijing 100191 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710062 China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing 100191 China

Collective behaviors by self-organization are ubiquitous in nature and human society and extensive efforts have been made to explore the mechanisms behind them. Artificial intelligence (AI) as a rapidly developing field is of great potential for these tasks. By combining reinforcement learning with evolutionary game (RLEG), we numerically discover a rich spectrum of collective behaviors—explosive events, oscillation, and stable states, etc., that are also often observed in the human society. In this work, we aim to provide a theoretical framework to investigate the RLEGs systematically. Specifically, we formalize AI-agents' learning processes in terms of belief switches and behavior modes defined as a series of actions following beliefs. Based on the preliminary results in the time-independent environment, we investigate the stability at the mixed equilibrium points in RLEGs generally, in which agents reside in one of the optimal behavior modes. Moreover, we adopt the maximum entropy principle to infer the composition of agents residing in each mode at a strictly stable point. When the theoretical analysis is applied to the 2×2 game setting, we can explain the uncovered collective behaviors and are able to construct equivalent systems intuitively. Also, the inferred composition of different modes is consistent with simulations. Our work may be helpful to understand the related collective emergence in human society as well as behavioral patterns at the individual level and potentially facilitate human-computer interactions in the future.

关键词： Collective dynamics Complex systems Evolutionary dynamics

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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OPP-Miner: Order-preserving sequential pattern mining

arXiv

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arXiv 2022年

作者： Wu, Youxi Hu, Qian Li, Yan Guo, Lei Zhu, Xingquan Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Key Laboratory of Big Data Computing Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China School of Economics and Management Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China Department of Computer Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Mininglamp Academy of Sciences Mininglamp Technology Beijing100084 China Ministry of Education Hefei230009 China

A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values. Moreover, existing methods mainly neglect the order relationships between time series values. To tackle these issues, inspired by order-preserving matching, this paper proposes an Order-Preserving sequential Pattern (OPP) mining method, which represents patterns based on the order relationships of the time series data. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath the time series data. To obtain frequent trends in time series, we propose the OPP-Miner algorithm to mine patterns with the same trend (sub-sequences with the same relative order). OPP-Miner employs the filtration and verification strategies to calculate the support and uses pattern fusion strategy to generate candidate patterns. To compress the result set, we also study finding the maximal OPPs. Experiments validate that OPP-Miner is not only efficient and scalable but can also discover similar sub-sequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https://***/wuc567/Pattern-Mining/tree/master/OPP-Miner. Copyright © 2022, The Authors. All rights reserved.

关键词： Time series

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Large AI Models in Health Informatics: Applications, Challenges, and the Future

arXiv

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arXiv 2023年

作者： Qiu, Jianing Li, Lin Sun, Jiankai Peng, Jiachuan Shi, Peilun Zhang, Ruiyang Dong, Yinzhao Lam, Kyle Lo, Frank P.-W. Xiao, Bo Yuan, Wu Wang, Ningli Xu, Dong Lo, Benny The Department of Computing Imperial College London United Kingdom The Department of Biomedical Engineering The Chinese University of Hong Kong Hong Kong Ltd. Hong Kong The Department of Informatics King’s College London United Kingdom The School of Engineering Stanford University United States The Department of Engineering Science University of Oxford United Kingdom The Faculty of Engineering The University of Hong Kong Hong Kong The Department of Surgery and Cancer Imperial College London United Kingdom The Hamlyn Centre for Robotic Surgery Imperial College London United Kingdom Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University China Beijing Ophthalmology & Visual Sciences Key Laboratory Beijing China The Department of Electrical Engineering and Computer Science United States The Christopher S. Bond Life Sciences Center University of Missouri United States The Facualty of Medicine Imperial College London United Kingdom

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people’s imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including 1) bioinformatics;2) medical diagnosis;3) medical imaging;4) medical © 2023, CC BY-NC-ND.

关键词： Medical imaging

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JGC-IAGCL: Fusing joint graph convolution and intent-aware graph contrastive learning for explainable recommendation

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Information Fusion 2025年 123卷

作者： Yang, Zhi Lin, Chuan Qin, Yongbin Huang, Ruizhang Chen, Yanping Qin, Jiwei State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang Guizhou550025 China College of Information Science and Engineering Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

Graph contrastive learning (GCL) enhances recommendation accuracy by leveraging self-supervised features to refine node representations from large-scale unlabeled data. Traditional GCL-based recommendation models typically construct contrastive views via graph augmentation (e.g., stochastic node/edge dropout) or embedding-space perturbation, aiming to maximize representation consistency. However, these methods struggle to effectively model and interpret user preferences and consumption intents, limiting explainability and recommendation performance. To address these challenges, we propose JGC-IAGCL (Joint Graph Convolution and Intent-Aware Graph Contrastive Learning), an explainable recommendation framework. JGC-IAGCL integrates joint graph convolution to capture implicit user preferences and employs intent-aware graph contrastive learning to extract explicit user intents from user–item interactions. By fusing these features, our method generates evenly distributed, intent-propensity-aware user/item representations. Theoretical analysis shows that JGC-IAGCL mitigates popularity bias while enhancing the exposure of long-tail items. Extensive experiments on four highly sparse public datasets validate its effectiveness, demonstrating superior recommendation accuracy and improved interpretability. © 2025

关键词： Self-supervised learning

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A Causal Adversarial Graph Neural Network for Multi-Center Autism Spectrum Disorder Identification

SSRN

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SSRN 2025年

作者： Zhang, Zhuan Wang, Lihui Chen, Qijian Wang, Li Jian, Caiqing Zhu, Yue-Min Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Due to the heterogeneous distributions of multi-center rs-fMRI data, currently it is still challenging to identify accurately autism spectrum disorder (ASD) patients from these heterogenous data. To deal with the influence of heterogeneous distributions on multi-center ASD diagnosis, a novel causal adversarial graph neural network (CAG) for multi-center ASD prediction is proposed in this work. CAG mainly consists of a graph filtering (GF) module and a learnable causal graph predictor (CGP). Specifically, the brain connection graph is first constructed from rs-fMRI data, after passing a GF module, the filtered graph is further divided by a causal subgraph and causally irrelevant subgraph. The ground-truth environmental labels and ASD labels are predicted from causal subgraph by a clustering method and a classification block, respectively. In addition, from the filtered graph and causally irrelevant subgraph, the environmental labels are predicted, while from the causally irrelevant subgraph the ASD labels are predicted. Assuming that the predicted environmental labels are independent to causal subgraph, while the predicted ASD labels are independent to causally irrelevant subgraph, accordingly, several adversarial losses are implemented by reversing the gradient. Through this adversarial learning strategy, the derived causal graph can predict multi-center ASD. The comparison experiments on both ABIDE-I and ABIDE-II datasets demonstrate that, the ACC and AUC of CAG has been improved by 3.78\% and 7.38\% at least comparing against the existing methods. Moreover, some abnormal functional connectivities in ASD group are identified from the causal subgraph which may provide useful imaging biomarkers for the early diagnosis of ASD. © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area 9th

OSAF_e: One-Stage Anchor Free Object Detection Method Consid...

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Zhang, Yong Zhang, Lizong Rao, Zhihong Duan, Guiduo Wang, Chunyu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China China Electronic Technology Cyber Security Co. Ltd. Chengdu610000 China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9783030596040

The task of object detection is to identify the bounding box of the object and its corresponding category in images. In this paper, we propose a new one-stage anchor free object detection algorithm OSAF_e, with the consideration of effective mapping area. A feature extraction network is used to obtain high level feature, and the true bounding box of the object in the original image is mapped to the grid of feature map, in order to perform category prediction and bounding box regression. The proposed algorithm is evaluated with the Pascal Voc dataset, and the experiments indicate that it has a better result. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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Multi-Grained Selection and Fusion for Fine-Grained Image Representation

Multi-Grained Selection and Fusion for Fine-Grained Image Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jianrong Jiang Hongxing Wang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

How to learn a good fine-grained image representation is a key problem for fine-grained tasks. Most previous supervised methods suffer from insufficient training data, which require laborious annotations of fine-grained objects. In this paper, we propose an annotation-free method for fine-grained image representation, dubbed Multi-Grained Selection and Fusion (MGSF). The proposed MGSF extracts two types of visual features, i.e., fine-grained discriminative features that highlight informative convolutional parts by spatial selection and channel selection, and coarse-grained scene-level features that provide context information for fine-grained objects. Extensive experiments in fine-grained image retrieval demonstrate the superiority of our proposed representation compared with the state-of-the-art approaches on several fine-grained datasets.

关键词： Feature extraction Image retrieval Image representation Semantics visualization Image classification Task analysis

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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第32届中国控制与决策会议

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang University School of Engineering RMIT University

This brief explores the approximation properties of a unique basis expansion based on Pascal’s triangle,which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time *** roles of certain parameters,such as sampling time interval or model order,and signal characteristics,i.e.,its curvature,on the approximation are *** errors in one and multiple-step predictions are ***,time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Signal approximation Sampling Discretization

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