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4th International Conference on Computer, big data and Artificial Intelligence, ICCBD+AI 2023

作者： Yan, Jianzhuo Jiao, Han Zhang, Hang Yu, Yongchuan Chen, Jianhui Zhou, Shiwei Beijing University of Technology Faculty of Information Technology Beijing China Beijing Institute of Water Science and Technology Institute of Big Data Analysis and Applications Beijing China

ISBN: (纸本)9798350373233

A key issue in the management of water resources in China is the adoption of a water supply total quantity control framework at the regional level based on socioeconomic development. This necessitates more effective management of water resource demand to achieve a balance between supply and demand. This paper aims to conduct an in-depth analysis of the industrial water usage structure, identify the primary influencing factors, and thereby provide insights for the design of demand management policies. Using a typical application scenario of "water extraction and supply"in a subsidiary of a water supply company in Beijing, a water knowledge graph incorporating multiple data sources is constructed. The study proposes a knowledge graph embedding model based on graph convolutional neural networks. Over the 36 months from 2019 to 2021, the water units in the accommodation, office, urban resident, healthcare, retail, education, catering, and residential services industries are clustered, and the water usage is analyzed. The results of this study are expected to provide references for optimizing urban water resource utilization. © 2023 IEEE.

关键词： Convolutional neural networks

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Intelligent Microclimate Control System Using PLC and SCADA 7

Intelligent Microclimate Control System Using PLC and SCADA

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7th International Conference on Computer Science and Engineering, UBMK 2022

作者： Assiya, Boltaboyeva Ardak, Akhmetova Madina, Mansurova Gulshat, Amirkhanova Ramzat, Abdikarov Faculty of Information Technology Department of Artificial Intellegence and Big Data Almaty Kazakhstan

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Automation of various types of production is an important direction in the scientific and technical development of society. Automation brings the quality of labor productivity, freeing a person from the production process, improving the quality of products and better meeting needs to a higher level in society. The basis of modern automation is the concept of technology, flexible and without human help. technology without human help is considered as a mode of highly automated production processes without human involvement (or minimal). Flexible technology contributes to the removal of restrictions on the nature of products (or their significant reduction) and a sharp reduction in the required volume of preparatory work during the transition to a new technology. Flexible technology leads to high automation of the production of many products, which allows you to master the production of new products in a short time and in an automated mode. Therefore, the system development architecture is an urgent scientific task. © 2022 IEEE.

关键词： Remote control

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Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Runuo Lu Shoubin Dong Jianxin Jia Xusheng Wang Kai Liu Jinsong Chen Shanxin Guo Xiaorou Zheng Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou 510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute Espoo FI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen 518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS 1 . ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) Geographic location variation. (II) Geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios.

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A federated learning scheme meets dynamic differential privacy

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CAAI Transactions on Intelligence technology 2023年第3期8卷 1087-1100页

作者： Shengnan Guo Xibin Wang Shigong Long Hai Liu Liu Hai Toong Hai Sam State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyangChina School of Big Data Key Laboratory of Electric Power Big Data of Guizhou ProvinceGuizhou Institute of TechnologyGuiyangChina School of Information Guizhou University of Finance and EconomicsGuiyangChina Faculty of Business and Communication INTI International UniversityNilaiMalaysia

Federated learning is a widely used distributed learning approach in recent years,however,despite model training from collecting data become to gathering parameters,privacy violations may occur when publishing and sharing models.A dynamic approach is pro-posed to add Gaussian noise more effectively and apply differential privacy to federal deep ***,it is abandoning the traditional way of equally distributing the privacy budget e and adjusting the privacy budget to accommodate gradient descent federation learning dynamically,where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created *** also incorporates adaptive threshold cropping to control the sensitivity,and finally,moments accountant is used to counting the∈consumed on the privacy‐preserving,and learning is stopped only if the∈_(total)by clients setting is reached,this allows the privacy budget to be adequately explored for model *** experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy,which is significantly better than the differential privacy method used by TensorFlow.

关键词： data privacy machine learning security of data

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Pointnet-based channel attention VLAD network 2nd

Pointnet-based channel attention VLAD network

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2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Fan, Rongrong Shuai, Hui Liu, Qingshan Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing211800 China

ISBN: (纸本)9783030317256

With the upgrading of application scenarios, computer vision is progressively expanded to 3D. Many methods that process point cloud directly provide a new paradigm for 3D understanding. Most of these methods employ maxpooling to handle the sparsity and disorder of point cloud. However, maxpooling layer extracts the global feature of the entire point cloud without learnable parameters, which is heuristics and insufficient. In this paper, we propose a VLAD enhanced Feature Aggregate Module to aggregate local features adaptively. In addition, a Channel Attention Module is applied to the features to reassemble the elements in high-dimension feature space. The experiments in both classification and segmentation demonstrate that the proposed method can improve the capacity of the baseline to extract more informative features. Specifically, we improve the accuracy from 88.5% to 89.8% for classification in ModelNet40 and improve the accuracy from 78.94% to 82.07% for semantic segmentation in S3DIS. © Springer Nature Switzerland AG 2019.

关键词： Aggregates

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Advancing iterative quantization hashing using isotropic prior 22nd

Advancing iterative quantization hashing using isotropic pri...

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Li, Lai Liu, Guangcan Liu, Qingshan Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783319276731

It is prevalent to perform hashing on the basis of the well-known Principal Component analysis (PCA), e.g., [1-4]. Of all those PCA-based methods, Iterative Quantization (ITQ) [1] is probably the most popular one due to its superior performance in terms of retrieval accuracy. However, the optimization problem in ITQ is severely under-deterministic, thereby the quality of the produced hash codes may be depressed. In this paper, we propose a new hashing method, termed Isotropic Iterative Quantization (IITQ), that extends the formulation of ITQ by incorporating properly the isotropic prior proposed by [3]. The optimization problem in IITQ is complicate, non-convex in nature and therefore not easy to solve. We devise a proximal method that can solve problem in a practical fashion. Extensive experiments on two benchmark datasets, CIFAR-10 [5] and 22K-LabelMe [6], show the superiorities of our IITQ over several existing methods. © Springer International Publishing Switzerland 2016.

关键词： Principal component analysis

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Robust Matrix Discriminative analysis for Feature Extraction From Hyperspectral Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第5期10卷 2002-2011页

作者： Hang, Renlong Liu, Qingshan Sun, Yubao Yuan, Xiaotong Pei, Hucheng Plaza, Javier Plaza, Antonio Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China Beijing Electro-Mechanical Engineering Institute Beijing China Hyperspectral Computing Laboratory University of Extremadura Caceres Spain

Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibration errors, and other issues, HSIs can be noisy. These corrupted data easily degrade the performance of the MDA. In this paper, a robust MDA (RMDA) model is proposed to address this important issue. Specifically, based on the prior knowledge that the pixels in a small spatial neighborhood of the HSI lie in a low-rank subspace, a denoising model is first employed to recover the intrinsic components from the noisy HSI. Then, the MDA model is used to extract discriminative spatial-spectral features from the recovered components. Besides, different HSIs exhibit different spatial contextual structures, and even a single HSI may contain both large and small homogeneous regions simultaneously. To sufficiently describe these multiscale spatial structures, a multiscale RMDA model is further proposed. Experiments have been conducted using three widely used HSIs, and the obtained results show that the proposed method allows for a significant improvement in the classification performance when compared to other LDA-based methods.

关键词： Hyperspectral image (HSI) classification multiscale fusion robust matrix discriminative analysis (RMDA) spatial-spectral feature extraction (FE)

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The association between clinicians’ legal literacy and the service quality of primary healthcare - evidence from the Greater Bay Area study, China

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BMC Medical Education 2025年第1期25卷 1-11页

作者： Xie, Fang Liu, Ruqing Sun, Peng Zheng, Lingling Wang, Kaixuan Chen, Jianli Hu, Ruwei School of Public Health Sun Yat-Sen University Guangdong Guangzhou 510080 China Shenzhen Nanshan District Maternity & Child Healthcare Hospital Shenzhen 518052 China Department of Computer Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Macao Department of Computational Biology and Medical Big Data Shenzhen University of Advanced Technology Shenzhen China Faculty of Social Science Macao Polytechnic University Macao School of Public Health Southeast University Jiangsu Nanjing 210009 China School of Law Sun Yat-Sen University Guangzhou Guangdong 510275 China

Background: In healthcare facilities, clinicians’ legal literacy may influence the implementation of the law, which is one of the determinants of service quality. Studies regarding the association between clinicians’ legal literacy and the quality of services provided by medical facilities were insufficient. Objective: To evaluate the association between clinicians’ legal literacy and the quality of primary healthcare services in the Greater Bay Area, China. Methods: This study was a cross-sectional design. A general linear model was employed to assess the association between clinicians’ legal literacy and the service quality of community health service institutions (CHSIs), controlling for confounding variables. From September 2023 to April 2024, 477 physicians and nurses from 58 CHSIs in the Greater Bay Area, China, engaged in this study. A self-administered questionnaire was utilized to evaluate the clinicians’ legal literacy, encompassing legal knowledge, legal attitude, and legal compliance, which was validated by the Delphi method. The National Committee for Quality Assurance Patient-Centered Medical Home (NCQA-PCMH) was employed to assess the service quality of CHSIs. Results: In the adjusting models, higher scores of legal literacy were related to higher scores of NCQA-PCMH significantly (aβ = 1.360, 95%CI = 0.091–2.630, P = 0.036). Among the six domains of PCMH, PCMH1 (aβ = 0.184, 95%CI = 0.080–0.287, P = 0.001), PCMH3 (aβ = 0.330, 95%CI = 0.052–0.608, P = 0.020), and PCMH4 (aβ = 0.660, 95%CI = 0.256–1.063, P = 0.001) were found to be related to clinicians’ legal literacy. Conclusion: This study demonstrated an association between clinicians’ legal literacy and the quality of services provided by primary healthcare institutions. The findings offered novel evidence for policymakers and administrators of medical institutions to enhance clinicians’ legal literacy with continuing medical education programs, which could improve the overall management framework o

关键词： Community health service institutions Legal literacy Primary healthcare Service quality

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Evaluating quality of motion for unsupervised video object segmentation

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Optoelectronics Letters 2024年第6期20卷 379-384页

作者： CHENG Guanjun SONG Huihui Jiangsu Key Laboratory of Big Data Analysis Technology Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyNanjing University of Information Science&TechnologyNanjing 210044China12

Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.

关键词： Evaluating quality of motion for unsupervised video object segmentation

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Illuminating Recommendation by Understanding the Explicit Item Relations

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Journal of Computer Science & technology 2018年第4期33卷 739-755页

作者： Qi Liu Hong-Ke Zhao Le Wu Zhi Li En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027 China School of Computer and Information Hefei University of Technology Hefei 230009 China School of Software Engineering University of Science and Technology of China Hefei 230051 China

Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from ＂implicit＂ to ＂explicit＂ views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.

关键词： recommender system item relation recommendation interpretability

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