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IEEE International Conference on Cloud Computing and Big data Analysis (ICCCBDA)

作者： Dongju Yang Dan He Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Data Engineering Institute North China University of Technology Beijing China

The increasing number of Web services brings challenges for accurate and efficient service discovery. Service clustering technology can narrow the retrieval range of service discovery and improve the efficiency of service discovery. However, due to the short text features with few words and difficult feature extraction in service description text, the traditional text clustering model can not get effective clusters. In this paper, a web service clustering method based on word vector and BTM (Biterm Topic Model) is proposed. Firstly, the sparse feature problem of short text is alleviated by expanding the word vector. Secondly, the extended service description text is modeled by the BTM based on Gibbs sampling. Finally, the k-means algorithm is used to cluster web services. Compared with LDA (Latent Dirichlet Allocation) and BTM, this method improves the F-Measure of clustering index. Experimental results show that the proposed method can effectively alleviate the problem of sparse service description text features and improve the clustering effect of services.

关键词： Analytical models Web services Clustering methods Computational modeling Clustering algorithms Feature extraction data models

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Efficient Test-Time Model Adaptation without Forgetting

arXiv

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arXiv 2022年

作者： Niu, Shuaicheng Wu, Jiaxiang Zhang, Yifan Chen, Yaofo Zheng, Shijian Zhao, Peilin Tan, Mingkui School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Tencent AI Lab China National University of Singapore Singapore Pazhou Laboratory China

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important for deep models when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two practical challenges: 1) existing methods have to perform backward computation for each test sample, resulting in unbearable prediction cost to many applications;2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). In this paper, we point out that not all the test samples contribute equally to model adaptation, and high-entropy ones may lead to noisy gradients that could disrupt the model. Motivated by this, we propose an active sample selection criterion to identify reliable and non-redundant samples, on which the model is updated to minimize the entropy loss for test-time adaptation. Furthermore, to alleviate the forgetting issue, we introduce a Fisher regularizer to constrain important model parameters from drastic changes, where the Fisher importance is estimated from test samples with generated pseudo labels. Extensive experiments on CIFAR-10-C, ImageNet-C, and ImageNet-R verify the effectiveness of our proposed method. Code is available at https://***/mr-eggplant/EATA. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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High-Resolution Natural Image Matting by Refining Low-resolution Alpha Mattes

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Xianmin Ye Yihui Liang Mian Tan Fujian Feng Lin Wang Han Huang College of Data Science and Information Engineering Guizhou Minzu University Guiyang China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Key Laboratory of Pattern Recognition and Intelligent System and the College of Data Science and Information Engineering Guizhou Minzu University Guiyang China School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot (SCUT) MOE of China Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology Guangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

High-resolution natural image matting plays an important role in image editing, film-making and remote sensing due to its ability of accurately extract the foreground from a natural background. However, due to the complexity brought about by the proliferation of resolution, the existing image matting methods cannot obtain high-quality alpha mattes on high-resolution images in reasonable time. To overcome this challenge, we introduce a high-resolution image matting framework based on alpha matte refinement from low-resolution to high-resolution (HRIMF-AMR). The proposed framework transforms the complex high-resolution image matting problem into low-resolution image matting problem and high-resolution alpha matte refinement problem. While the first problem is solved by adopting an existing image matting method, the latter is addressed by applying the Detail Difference Feature Extractor (DDFE) designed as a part of our work. The DDFE extracts detail difference features from high-resolution images by measuring the image feature difference between high-resolution images and low-resolution images. The low-resolution alpha matte is refined according to the extracted detail difference feature, providing the high-resolution alpha matte. In addition, the Matte Detail Resolution Difference (MDRD) loss is introduced to train the DDFE, which imposes an additional constraint on the extraction of detail difference features with mattes. Experimental results show that integrating HRIMF-AMR significantly enhances the performance of existing matting methods on high-resolution images of Transparent-460 and Alphamatting. Project page: https://***/yexianmin/HRAMR-Matting.

关键词： Feature extraction Image resolution Image color analysis Optimization Remote sensing Clustering algorithms Accuracy Visualization Transforms Training

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Oxygen Vacancy and Van Der Waals Heterojunction Modulated Interfacial Chemical Bond Over Mo2c/Bi4o5br2 for Boosting Photocatalytic Co2 Reduction

SSRN

引用

SSRN 2022年

作者： Peng, Haiyan Deng, Xiaoxu Li, Gen Wang, Qiuchen Song, Meiyang Chen, Peng Yin, Shuang-Feng Provincial Guizhou Key Laboratory of Green Chemical and Clean Energy Technology School of Chemistry and Chemical Engineering Guizhou University Guizhou Guiyang550025 China College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China State Key Laboratory of Chemo/Biosensing and Chemometrics Provincial Hunan Key Laboratory for Cost-effective Utilization of Fossil Fuel Aimed at Reducing Carbon-dioxide Emissions College of Chemistry and Chemical Engineering Hunan University Hunan Changsha410082 China

The establishment of heterojunctions between phase interfaces is an important strategy to enhance catalytic activity, but the behavior of charge transfer and exciton dissociation is unsharpness and significant. Here, a Van der Waals heterojunction (VDWH) of Mo2C/Bi4O5Br2 composite with oxygen vacancies (BM) was synthesized via an extremely facile method. Various experimental results and density functional theory calculations demonstrated the compact VDWH and unsaturated oxygen vacancy induced the interfacial Mo-O bonds for modulating surface electronic structure. Consequently, the electron extracted from the exciton formed a strong internal electric field, which boosted the excitons dissociation and charge transfer via electron transfer channel of the Mo-O bond. Therefore, the BM shows excellent CO2 reduction performance (1277.4 µmol g-1 h-1) and stability without using sacrificial agents and cocatalysts, which is about 6 times of Mo2C/Bi4O5Br2. © 2022, The Authors. All rights reserved.

关键词： Heterojunctions

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ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations

arXiv

引用

arXiv 2025年

作者： Mei, Xiyao Zhang, Yu Yang, Chaofan Shi, Rui Yuan, Xiaoru National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Department of Computer Science University of Oxford Oxford United Kingdom Center for Research on Ancient Chinese History Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China PKU-WUHAN Institute for Artificial Intelligence Wuhan China

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： data collection

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EAPDS: Efficient Auditable and Privacy-preservation data Sharing Scheme Based on Attribute-Based Encryption for IoMT

引用

IEEE Internet of Things Journal 2025年

作者： Wang, Hui Xie, Yong Luo, Min Liu, Yining Shirazi, Syed Hamad Qinghai University Department of Information Technology Xining810016 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining810016 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China Hazara University Department of Information Technology Mansehra21300 Pakistan

data sharing schemes based on the Internet of Medical Things (IoMT) have emerged as a more convenient way to monitor and manage individuals’ health. However, this scenario faces challenges such as privacy preservation, effectiveness, and practicality, which hinder its further development. To the best of our knowledge, there is no agreed-upon data-sharing method that addresses all of these problems. In this paper, we make a step ahead by designing an Efficient and Auditable Privacy-preservation data Sharing scheme (EAPDS) based on multi-authority attribute-based encryption. EAPDS designs an auditable anonymous authentication mechanism to realize identity privacy protection, as well as an efficient multi-authority attribute-based encryption mechanism to achieve the efficiency and practicability of data-sharing. Formal security analysis demonstrates EAPDS can resist replayable chosen-ciphertext attack. Many performance evaluation experiments and functional analyses show that EAPDS not only performs better than existing medical data-sharing schemes in terms of efficiency, but also in terms of privacy protection and feasibility. Consequently, our EAPDS scheme holds promising application prospects. © 2014 IEEE.

关键词： data consistency

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Bridges engineering Manipulated Exciton Dissociation and Charge Separation in Small Acceptors of Pdi Supramolecular for Boosting Photocatalytic Nitrogen Fixation

SSRN

引用

SSRN 2022年

作者： Yang, Shilian Deng, Xiaoxu Chen, Peng Li, Gen Wang, Qiuchen Wang, Qian Yin, Shuang-Feng Provincial Guizhou Key Laboratory of Green Chemical and Clean Energy Technology School of Chemistry and Chemical Engineering Guizhou University Guizhou Guiyang550025 China College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China State Key Laboratory of Chemo/Biosensing and Chemometrics Provincial Hunan Key Laboratory for Cost-effective Utilization of Fossil Fuel Aimed at Reducing Carbon-dioxide Emissions College of Chemistry and Chemical Engineering Hunan University Hunan Changsha410082 China

Constructing acceptor–donor units (D/A) of polymer is an efficient method for promoting carrier separation to optimize photoreactivity. However, the carriers across the D/A interface of micromolecule was relatively hindered by the Coulomb potential. In this work, the polarity-bridged acceptor–donor units of PDI composite (BOPDI) has been constructed using a simple strategy. The improved BOPDI achieves excellent photocatalytic nitrogen fixation rate (74.0 μmol g-1 h-1) as well as stability without using any co-catalyst and sacrificial agent, which is 11 times that of the original PDI group. DFT calculations and experimental results show that the polarity-bridges reasonably adjusts the HOMO and LOMO bands and absorption characteristics, ensuring the BOPDI absorbed near-infrared light and activated N2 molecules. In addition, the polarity bridged units strengthens the construction of the built-in electric field for overcoming the Coulomb potential, resulting in a higher carrier separation and exciton dissociation efficiency. This work not only provides an tactics for enhancing the electronic transmission of D/A units, but also expanding the application research of PDI composite materials in the photocatalytic reduction of nitrogen. © 2022, The Authors. All rights reserved.

关键词： Electric fields

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Building Cooling load prediction based on LightGBM

引用

IFAC-PapersOnLine 2022年第11期55卷 114-119页

作者： RuoChen Zhao Dong Wei YiBing Ran Guang Zhou YuChen Jia ShiLun Zhu YouQuan He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Xicheng District Beijing 100032 China Beijing Municipal Science and Technology Commission Key Laboratory of Intelligent Processing for Building Big Data Xicheng District Beijing 100032 China Siemens Ltd. China 100020 China Zhongkai University of Agriculture and Engineering Guangzhou Tevo Energy Management Co. Ltd.

In building Heating Ventilation and Air Conditioning (HVAC) control systems, the HVAC must be controlled in real-time according to the actual building cooling load demand while minimizing energy consumption. Given that the building cooling load is affected by several factors and that historical data are sparse and have outliers due to sensor errors, existing prediction methods cannot accurately calculate the building's cooling load demand. Therefore, this paper first pre-processes the historical building cooling load data, fills in the missing data utilizing the KNNImputer algorithm, and then locates and replaces the outlier data through the 3-sigma method, solving the poor data availability problem. Moreover, to address the problem of small feature variety in historical data, this paper analyzes the time features and the climatic features separately. Specifically, our method splits the time features into several independent features for describing the impact of pedestrian flow on the building cooling load and expands the climatic features to increase the variety of the features that can be used for training and improving the model's prediction accuracy. Finally, the LightGBM algorithm is exploited to train the processed data and build a building cooling load prediction model. The proposed data pre-processing method and model training algorithm are validated through Python programming, with the results highlighting that the proposed method effectively improves the prediction accuracy of building cooling loads.

关键词： Building cooling load prediction LightGBM data pre-processing KNN 3Sigma

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knowledge Fragment Cleaning in a Genealogy knowledge Graph

Knowledge Fragment Cleaning in a Genealogy Knowledge Graph

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Guliu Liu Lei Li Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

As an important topic of artificial intelligence, knowledge graphs have a wide range of applications such as semantic search, intelligent question answering, and visual decision support. Among them, the genealogy knowledge graph, as a kind of domain knowledge graph, has important application value in genetic disease analysis, population behavior analysis, etc. In the case of multiple data sources and multi-person collaboration, the construction of a genealogy knowledge graph involves the techniques of knowledge representation, knowledge acquisition, and knowledge fusion. In the knowledge fusion process, there are many situations such as the lack and chaos of a relationship, redundant entities, the isolation of some entities and knowledge fragments. How to effectively detect and process these problematic knowledge fragments is significant to the construction of a genealogy knowledge graph. In response to this problem, we propose a method for cleaning the problematic knowledge fragments in a genealogy knowledge graph. The method consists of three phases. In phase 1, we propose a method for detecting and analyzing the problematic knowledge fragments. In phase 2, we design a method for supplementing the entity-relationship of people for different error patterns and a method fusion method for the aligned entity. In phase 3, for the cleaning of isolated knowledge fragments, we propose an entity alignment method based on the father-son relationship and people's names and a connection method of isolated knowledge fragments. Finally, we conduct experiments on a family tree dataset of the Huapu System, and the experimental results indicate the effectiveness and practicality of the method.

关键词： Cleaning Big data knowledge acquisition Redundancy Organizations databases

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The Development Trends of Fuel Cell Technologies Based on Patent Analysis

The Development Trends of Fuel Cell Technologies Based on Pa...

引用

2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control, ICBTE 2019

作者： Tan Xinyu Zhang Yingxuan Deng Sanhong School of Information Management Nanjing university Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Province

Fuel cells are made from fuel and *** of its low pollution,high energy conversion efficiency and high reliability,fuel cell has become the future direction of new energy application,the technological development path in the field of fuel cell research has great significance to the development of technological and energy *** the patent analysis method,this paper analyses the patent data from Derwent Innovation Index quantitively to study the state of application for patents,core technologies,highly cited patents and the main *** shows that auxiliary device and related methods were a research hotspot in recent years;as the biggest patent holder of fuel cell technologies,Toyota,Honda motor *** Nissan motor *** an *** paper has discovered some potential problems behind the phenomena and some suggestions are put forward finally.

关键词： Fuel cells

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