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NeurDB: an AI-powered autonomous data system

science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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FC-MIDTR-WCCA: A Machine Learning Framework for PM2.5 Prediction

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第5期51卷 544-552页

作者： Tu, Tianyi Su, Ye Ren, Sheng School of Computer and Electrical Engineering Hunan University of Arts and Science Chang De415000 China School of Computer and Electrical Engineering Hunan University of Arts and Science Chang De China School of Computer and Electrical Engineering Hunan University of Arts and Science Chang De China

The rapid acceleration of urbanization and industrialization has led to a significant increase in PM2.5 pollution, making it a critical global concern. The accurate prediction of PM2.5 concentrations is of utmost importance for the effective implementation of protective measures and environmental management. This study presents a machine learning framework for PM2.5 prediction called FC-MIDTR-WCCA. The framework is composed of three main components. The first component involves conducting an analysis of air quality PM2.5 data to identify features highly correlated with PM2.5 and to examine seasonal patterns. This approach facilitates feature crossing (FC) by combining different relevant features. The second component utilizes a feature selection algorithm known as the mutual information decision tree regressor (MIDTR) to effectively account for correlations and contributions among features. This algorithm identifies the optimal feature dataset. The third component involves the adoption of a weighted arithmetic mean fusion algorithm that combines canonical correlation analysis (WCCA) for PM2.5 prediction. This algorithm considers the correlations between prediction models and addresses collinearity issues to achieve stable model weight vectors. We experimentally assessed the performance of four ensemble tree models and the stacking algorithm. The results demonstrated that the FC-MIDTR-WCCA model outperformed all the other methods evaluated in terms of R2 and MAE. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Decision trees

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FACNet: Feature alignment fast point cloud completion network

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Computational Visual Media 2025年第1期11卷 141-157页

作者： Xinxing Yu Jianyi Li Chi-Chong Wong Chi-Man Vong Yanyan Liang School of Computer Science and Engineering Faculty of Innovation EngineeringMacao University of Science and TechnologyTaipaMacaoChina Faculty of Science and Technology University of MacaoTaipaMacaoChina

Point cloud completion aims to infer complete point clouds based on partial 3D point cloud *** previous methods apply coarseto-fine strategy networks for generating complete point ***,such methods are not only relatively time-consuming but also cannot provide representative complete shape features based on partial *** this paper,a novel feature alignment fast point cloud completion network(FACNet)is proposed to directly and efficiently generate the detailed shapes of *** aligns high-dimensional feature distributions of both partial and complete point clouds to maintain global information about the complete *** its decoding process,the local features from the partial point cloud are incorporated along with the maintained global information to ensure complete and time-saving generation of the complete point *** results show that FACNet outperforms the state-of-theart on PCN,Completion3D,and MVP datasets,and achieves competitive performance on ShapeNet-55 and KITTI ***,FACNet and a simplified version,FACNet-slight,achieve a significant speedup of 3–10 times over other state-of-the-art methods.

关键词： 3D point clouds shape completion geometry processing deep learning

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Multiple-event-triggered adaptive fuzzy output-feedback control for MIMO uncertain nonlinear systems

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science China(Technological sciences) 2025年第2期68卷 216-226页

作者： Shuai SUI Yu YANG Shaocheng TONG C.L.Philip CHEN School of Science Liaoning University of TechnologyJinzhou 121001China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China

The article addresses the output-feedback control issue for a class of multi-input multi-output(MIMO)uncertain nonlinear systems with multiple event-triggered mechanisms(ETM).Compared to previous event-triggering studies,this paper aims to trigger both the output and filtered *** nonlinear dynamics are approximated using fuzzy logic systems(FLSs).Then,a novel kind of state observer has been designed to deal with unmeasurable state problems using the triggered output *** sampled estimated state,the triggered output signal,and the filtered signal are utilized to propose an event-triggering mechanism that consists of sensor-to-observer(SO)and observer-to-controller(OC).An event-triggered output feedback control approach is given inside backstepping control,whereby the filter may be employed to circumvent the issue of the virtual control function not being differentiable at the trigger *** is testified that,according to the Lyapunov stability analysis scheme,all closed-loop signals and the system output are ultimately uniformly constrained by our control ***,the simulation examples are performed to confirm the theoretical findings.

关键词： event-triggered control(ETC) dynamic surface control(DSC) output-feedback control fuzzy adaptive control

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Hybrid concurrency control protocol for data sharing among heterogeneous blockchains

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Frontiers of computer science 2024年第3期18卷 1-12页

作者： Tiezheng GUO Zhiwei ZHANG Ye YUAN Xiaochun YANG Guoren WANG School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100029China School of Computer Science and Engineering Northeastern UniversityShenyang 110167China

With the development of information technology and cloud computing,data sharing has become an important part of scientific *** traditional data sharing,data is stored on a third-party storage platform,which causes the owner to lose control of the *** a result,there are issues of intentional data leakage and tampering by third parties,and the private information contained in the data may lead to more significant ***,data is frequently maintained on multiple storage platforms,posing significant hurdles in terms of enlisting multiple parties to engage in data sharing while maintaining *** this work,we propose a new architecture for applying blockchains to data sharing and achieve efficient and reliable data sharing among heterogeneous *** design a new data sharing transaction mechanism based on the system architecture to protect the security of the raw data and the processing *** also design and implement a hybrid concurrency control protocol to overcome issues caused by the large differences in blockchain performance in our system and to improve the success rate of data sharing *** took Ethereum and Hyperledger Fabric as examples to conduct crossblockchain data sharing *** results show that our system achieves data sharing across heterogeneous blockchains with reasonable performance and has high scalability.

关键词： data sharing blockchain concurrency control protocol crosschain

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An Approach for Privacy-Aware Mobile App Package Recommendation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6240-6252页

作者： Liu, Shanpeng Cao, Buqing Liu, Jianxun Kang, Guosheng Shi, Min Li, Xiong Fletcher, Kenneth K. Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University of Technology School of Computer Zhuzhou412007 China Harvard University Harvard Medical School BostonMA02138 United States School of Computer Science and Engineering Chengdu611731 China University of Massachusetts School of Computer Science BostonMA02125 United States

With the prosperity of the mobile Internet, the abundance of data makes it difficult for users to choose their favorite app. Thus, mobile app recommendation as an emerging topic attracts lots of attention. However, existing methods for app recommendation rarely consider recommendation accuracy under the privacy representation of user preferences. To address this problem, we propose a privacy-aware app package recommendation method named APP-Rec. Specifically, in this method: 1) treat an app and its associated heterogeneous entities (APP-Rec considers not only the apps themselves but also a variety of related factors-collectively referred to as heterogeneous entities, such as app category and app neighbors) as an app package and extract comprehensive features from the app package using an intrapackage attention network and an interpackage attention network to improve app recommendation;and 2) design a privacy module utilizing Laplace noise to achieve privacy preservation of user preferences. Experimental results show that APP-Rec outperforms the state-of-the-art methods in terms of area under the curve (AUC). Moreover, the privacy preservation of user preferences in APP-Rec is proved by theoretical analysis and experimental results. © 2024 IEEE.

关键词： Differential privacy

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Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 405-414页

作者： Hao Liu Fengwei Liang Tianyu Hu Jichao Hong Huimin Ma School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Mechanical Engineering University of Science and Technology BeijingBeijingChina IEEE

Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical *** correlation analysis is first employed to identify SOC-related *** parameters are then input into a multi-layer GRU for point-wise feature ***,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time ***,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are *** extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.

关键词： Electric vehicle battery energy storage system(BESS) state-of-charge(SOC)prediction gated recurrent unit(GRU) multi-scale fusion(MSF).

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Gallium-induced structural modification over gel-derived CuZnGaAlOx catalysts for methanol steam reforming

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Ceramics International 2025年第14期51卷 18775-18785页

作者： Huang, Ziru Guo, Chenxu Guo, Wenming Zhang, Yi Huang, Shaoqin Qin, Hang Xie, Wen Gao, Pengzhao Xiao, Hanning College of Materials Science and Engineering Hunan University Changsha410082 China School of Materials Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

Methanol steam reforming (MSR) is an attractive way to supply hydrogen applied for proton exchange membrane fuel cells. However, there is still a challenge in designing high-activity, low CO selectivity and durable copper-based catalysts. Herein, Ga-promoted CuZnAlOx catalysts are prepared by the phase separation-assisted sol-gel method with nanosheet structures, which provides a desirable space for reactants to contact the catalyst surface. It is demonstrated that the incorporation of Ga3+ can modify the defective chemistry characteristics and reducibility of ZnO, thus increasing the oxygen vacancy defects and Cu+ species for facilitating MSR reaction. Experimental results prove that CZA-7.5 Ga catalyst attains 100 % methanol conversion and 0.015 % CO selectivity at 260 °C, exhibiting higher MSR conversion and lower CO selectivity compared with the Ga-free catalyst. CZA just reached a methanol conversion of 96.7 % with 0.027 % CO selectivity at 260 °C. Additionally, CZA-7.5 Ga catalyst displayed the catalytic stability with methanol conversion rate of 85.41 % and CO selectivity ranging from 0 to 0.04 % after the 100 h reaction, while the methanol conversion of CZA decreased to 69.43 %. The process of reactant and intermediate evolution on the catalyst surface was briefly discussed using FTIR spectroscopy. In this work, a design strategy for Ga-promoted CuZnAlOx catalysts synthesized via the method is proposed, which provides insights into designing the copper catalysts and the selection of structural promoters. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Sol-gel process

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Augmented FCN: rethinking context modeling for semantic segmentation

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science China(Information sciences) 2023年第4期66卷 193-211页

作者： Dong ZHANG Liyan ZHANG Jinhui TANG School of Computer Science and Engineering Nanjing University of Science and Technology College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

The effectiveness of modeling contextual information has been empirically shown in numerous computer vision tasks. In this paper, we propose a simple yet efficient augmented fully convolutional network(AugFCN) by aggregating content-and position-based object contexts for semantic ***, motivated because each deep feature map is a global, class-wise representation of the input,we first propose an augmented nonlocal interaction(AugNI) to aggregate the global content-based contexts through all feature map interactions. Compared to classical position-wise approaches, AugNI is more efficient. Moreover, to eliminate permutation equivariance and maintain translation equivariance, a learnable,relative position embedding branch is then supportably installed in AugNI to capture the global positionbased contexts. AugFCN is built on a fully convolutional network as the backbone by deploying AugNI before the segmentation head network. Experimental results on two challenging benchmarks verify that AugFCN can achieve a competitive 45.38% mIoU(standard mean intersection over union) and 81.9% mIoU on the ADE20K val set and Cityscapes test set, respectively, with little computational overhead. Additionally, the results of the joint implementation of AugNI and existing context modeling schemes show that AugFCN leads to continuous segmentation improvements in state-of-the-art context modeling. We finally achieve a top performance of 45.43% mIoU on the ADE20K val set and 83.0% mIoU on the Cityscapes test set.

关键词： semantic segmentation context modeling long-range dependencies attention mechanism

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E2E-LANet: Unleashing the Potential of Linear Attention for High-Resolution Medical Image Segmentation via Pretraining Distillation 24

E2E-LANet: Unleashing the Potential of Linear Attention for ...

引用

5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Tang, Hui Yu, Gang Cao, Lu School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9798400717826

Medical image segmentation plays an important role in computer-aid diagnosis. In the past years, convolutional neural networks, especially the UNet-based architectures with symmetric U-shape encoder-decoder structure and skip connection, have been widely used in a variety of medical imaging segmentation tasks. However, such methods are difficult to capture global context. In contrast, Vision Transformers (ViTs) utilize the self-attention mechanism to obtain long-range dependencies in images. Despite these advantages, the huge computational costs of the self-attention mechanism reduce the inference speed of models, thereby limiting their real-world applications. Meanwhile, hierarchical pixel-level decoding of U-shape architectures increases the complexity of large-size segmentation decoding. To address these limitations, in this paper we propose a linear attention-based end-to-end model for high-resolution medical image segmentation, named E2E-LANet. It consists of two modules: an image encoder based on constructed Efficient Vision Transformer (EVT) that reduces the high quadratic complexity of self-attention to linear complexity and a Query-Supported Mask Decoder (QSMD) that employs independent mask query for each type of medical images in segmentation mask predictions. It focuses on the detailed segmentation decoding of patch-level features to further improve the efficiency of large-size mask predictions. Especially, we leverage the advantages of pretraining distillation to improve the transferability of our model in various segmentation tasks. Experimental results on the nucleus segmentation task and the polyp segmentation task display the important role of pretraining distillation in improving the performance of our framework. E2E-LANet demonstrates superior performance compared to CNNs, ViTs, and latest Mamba-based U-shape architectures. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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