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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Zhang, Zixian Zhao, Zhixian Yang, Xiaobao Hou, Zhiqiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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MULTI-GRANULARITY REPRESENTATION LEARNING FOR SKETCH-BASED DYNAMIC FACE IMAGE RETRIEVAL

arXiv

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arXiv 2023年

作者： Wang, Liang Dai, Dawei Fu, Shiyu Wang, Guoyin Key Laboratory of Big Data Intelligent Computing School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

In specific scenarios, face sketch can be used to identify a person. However, drawing a face sketch often requires exceptional skill and is time-consuming, limiting its widespread applications in actual scenarios. The new framework of sketch less face image retrieval (SLFIR)[1] attempts to overcome the barriers by providing a means for humans and machines to interact during the drawing process. Considering SLFIR problem, there is a large gap between a partial sketch with few strokes and any whole face photo, resulting in poor performance at the early stages. In this study, we propose a multigranularity (MG) representation learning (MGRL) method to address the SLFIR problem, in which we learn the representation of different granularity regions for a partial sketch, and then, by combining all MG regions of the sketches and images, the final distance was determined. In the experiments, our method outperformed state-of-the-art baselines in terms of early retrieval on two accessible datasets. Codes are available at https://***/ddw2AIGROUP2CQUPT/MGRL. Copyright © 2023, The Authors. All rights reserved.

关键词： Image retrieval

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Diabetic retinopathy identification based on multi-sourcefree domain adaptation

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International Journal of Ophthalmology(English edition) 2024年第7期17卷 1193-1204页

作者： Guang-Hua Zhang Guang-Ping Zhuo Zhao-Xia Zhang Bin Sun Wei-Hua Yang Shao-Chong Zhang School of Big Data Intelligent Diagnosis&Treatment Industry Taiyuan UniversityTaiyuan 030032Shanxi ProvinceChina Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology Taiyuan Normal UniversityTaiyuan 030619Shanxi ProvinceChina Shanxi Eye Hospital Taiyuan 030002Shanxi ProvinceChina College of Computer Science and Technology Taiyuan Normal UniversityTaiyuan 030619Shanxi ProvinceChina Shenzhen Eye Institute Shenzhen Eye HospitalJinan UniversityShenzhen 518040Guangdong ProvinceChina

AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled ***:A multi-SFDA method was proposed for DR *** method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target ***,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class *** is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification *** demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target ***:The multi-SFDA method provides an effective approach to overcome the challenges in DR *** method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.

关键词： diabetic retinopathy multisource-free domain adaptation pseudo-label generation softmaxconsistence minimization

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Deep Learning-Based Recurrence Risk Prediction for Differentiated Thyroid Cancer Using Local Clustering Contrastive Learning

Deep Learning-Based Recurrence Risk Prediction for Different...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Junhao Zhu Pengbo Gao Feng Hu Hong Yu Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

Thyroid cancer incidence in women has risen sharply in China from 2000 to 2016. Differentiated thyroid cancer has a relatively low mortality rate but a higher recurrence rate. The American Thyroid Association (ATA) risk model, based on traditional clinical and pathological parameters, may not fully capture all factors affecting recurrence risk. Deep learning technology has shown potential in predicting recurrence in patients with well-differentiated thyroid cancer, but its application to heterogeneous tabular medical data remains challenging. We propose a local clustering contrastive learning method to address the challenges of applying deep learning to tabular medical data. This method divides features into random subsets, performs clustering within each subset, generates positive and negative sample pairs, and trains encoders and a decoder to maximize the InfoNCE output. The learned feature representation is used for downstream classification tasks. The proposed method was applied to a retrospective cohort of 383 patients diagnosed with thyroid cancer at a medical center. Experimental verification on this dataset and ten public tabular datasets demonstrated the effectiveness of the method in risk-stratifying patients with well-differentiated thyroid cancer. The proposed local clustering contrastive learning method effectively leverages the complexity of tabular medical data, enhancing the learned feature representation and enabling accurate recurrence risk prediction for well-differentiated thyroid cancer. This approach can assist doctors in making personalized treatment decisions, such as the necessity, dosage, and frequency of radioactive iodine treatment, ultimately improving patient care.

关键词： Deep learning Pathology Thyroid cancer Decision making Contrastive learning Predictive models data models Risk management Medical diagnostic imaging Thyroid

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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Inter-Layer Explainable Variational Autoencoder Model for Multivariate Time Series Anomaly Detection

SSRN

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SSRN 2024年

作者： Zhang, Xiaoxia Wang, Guangyao Chen, Yi Liu, Qun Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

The wide application of big data and artificial intelligence has dramatically increased the dimension and quantity of monitoring data in various industries. As one of the most used common data in various industries, multivariate time series data often contains anomalies, which can be caused by human error or electromagnetic interference. Thus, it is important and necessary to detect anomalies from multivariate time series data. While the sheer size of the data makes it impractical to manually label it. And supervised methods face the inability to handle unknown anomalies. Additionally, the existing deep learning detection methods are black box models, resulting the distrust of test results and opacity of the testing process. Based on these considerations, we propose an inter-layer explainable variational autoencoder model to detect the anomalies for multivariate time series data. Notably, our model measures the contributions of the nodes at previous layer to those of their successor by shapley values, and identifies the cause root from the dimensionality by anomaly score. The optimization of the model can be executed by employing a two-phase training method with adversarial style, wherein the random Gaussian noise is introduced to reduce the interference of noise in the data. Extensive experiments are implemented on several large datasets demonstrate our method outperforms others in terms of accuracy and robustness. Thus, we establish the framework of inter-layer explainable variational autoencoder, which can be applied to anomaly detection of multivariate time series in various industries. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Variable projection algorithms with sparse constraint for separable nonlinear models

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Control Theory and Technology 2024年第1期22卷 135-146页

作者： Hui-Lang Xu Guang-Yong Chen Si-Qing Cheng Min Gan Jing Chen College of Computer and Data Science Fuzhou UniversityFuzhou350116FujianChina Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350116FujianChina Key Laboratory of Intelligent Metro of Universities in Fujian Fuzhou UniversityFuzhou350116FujianChina Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou UniversityFuzhou350116FujianChina School of Science Jiangnan UniversityWuxi214122JiangsuChina

Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.

关键词： Variable projection(VP) Non-smooth constraint Separable nonlinear models

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Task Prioritization in Multiagent Environments: A Novel Approach Using Nash Q-Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Kexin Hu, Yu Wang, Jielei Li, Lin Zhang, Shuyuan Luo, Guangchun Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

—With the rapid proliferation of smartphones and various terminal devices, edge computing has gained increasing importance in overcoming computational limitations and enhancing service quality through task offloading. However, most existing approaches handle tasks on a first-come, first-served basis, neglecting task prioritization, which is critical in real-world scenarios where tasks have different levels of urgency and resource requirements. This paper addresses the challenge of effective task prioritization in multi-agent task offloading by proposing a Nash Q-learning-based strategy. Our methodology involves optimizing task allocation decisions using Nash Q-learning to integrate Nash equilibrium principles, thereby enhancing adaptability and dynamic decision-making capabilities within a multi-agent environment. The proposed algorithm is evaluated through extensive simulations, comparing total delay, resource utilization, and task success rates across various task offloading strategies. The results demonstrate that our Nash Q-learning approach outperforms conventional techniques by dramatically lowering overall latency, optimizing resource allocation, and maintaining high task success rates. This work demonstrates the efficacy of Nash Q-learning for dynamically setting priorities for tasks in multi-agent edge computing environments, providing a robust framework for effective resource management and collaborative task completion. And also highlight the potential of Nash Q-learning in advancing intelligent task management in edge computing, suggesting its applicability in real-world, resource-constrained environments. © 2025 IEEE. All rights reserved,

关键词： Computation offloading

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Chronic Disease Detection Via Non-negative Latent Feature Analysis 18

Chronic Disease Detection Via Non-negative Latent Feature An...

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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Zhou, Leming Li, Qing Shang, Mingsheng Academy of Sciences College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chongqing China Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Chongqing China

ISBN: (纸本)9781665440486

Chronic diseases such as coronary heart disease (CHD) are of significant harm to human health. However, chronic disease detection commonly relies on many examinations to implement reliable diagnosis, resulting in a fatal delay in therapy. On the other hand, obtained examination data are often incomplete with numerous missing data, which further increases the difficulty in detection. To address this issue, this paper develops an inherently non-negative latent feature analysis-incorporating into a chronic disease detection (INC) model. The main idea are as follows: 1) adopting an inherently non-negative latent feature analysis model with a non-linear function to extract the non-negative latent features from incomplete examination data for accurate prediction of the unknown missing ones;2) detecting the chronic disease based on the high-dimensional and different types of inspection features via classifiers. Experiments on actual CHD datasets demonstrate that the proposed INC model outperforms state-of-the-art models in detection accuracy, which well fits the desire of preliminary screening on coronary heart disease (CHD). © 2021 IEEE.

关键词： Diagnosis

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Cosmology with fast radio bursts in the era of SKA

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Science China(Physics,Mechanics & Astronomy) 2023年第12期66卷 37-55页

作者： Ji-Guo Zhang Ze-Wei Zhao Yichao Li Jing-Fei Zhang Di Li Xin Zhang Department of Physics College of SciencesNortheastern UniversityShenyang 110819China Key Laboratory of Cosmology and Astrophysics(Liaoning) Northeastern UniversityShenyang 110819China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China

We present a forecast of the cosmological parameter estimation using fast radio bursts(FRBs)from the upcoming Square Kilometre Array(SKA),focusing on the issues of dark energy,the Hubble constant,and baryon *** simulate 105and 106localized FRBs from a 10-year SKA observation,and find that:(1)using 106FRB data alone can tightly constrain dark-energy equation of state parameters better than CMB+BAO+SNe,providing an independent cosmological probe to explore dark energy;(2)combining the FRB data with gravitational-wave standard siren data from 10-year observation with the Einstein Telescope,the Hubble constant can be constrained to a sub-percent level,serving as a powerful low-redshift probe;(3)using 106FRB data can constrain the baryon density?bh to a precision of~0.1%.Our results indicate that SKA-era FRBs will provide precise cosmological measurements to shed light on both dark energy and the missing baryon problem,and help resolve the Hubble tension.

关键词： fast radio burst cosmology dark energy the Hubble constant baryon density Square Kilometre Array

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