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7th International Conference on data Science and Information Technology, DSIT 2024

作者： Lu, Jiamin Liu, Jiahao Fan, Zhendong Zhou, Zhenyu Kong, Xu Feng, Jun Hohai University Key Laboratory of Water Big Data Technology Ministry of Water Resources Nanjing China Hohai University College of Computer and Software Nanjing China PowerChina Huadong Engineering Corporation Limited Hangzhou China

ISBN: (纸本)9798350384093

Traditional sorting-based semantic place retrieval methods may face the problem of a large retrieval space. An improved method is proposed in this paper, based on Learning-based Clustering. It has three components: encoding, clustering and retrieval. Specifically, we craft a spatial entity encoder that integrates both spatial and semantic dimensions to embed spatial entities. Then, we utilize a cascaded Siamese network architecture for deep clustering, thereby minimizing the retrieval space for downstream tasks. Finally, we employ the SSPR method to assess the impact of our approach on retrieval efficiency. © 2024 IEEE.

关键词： Content based retrieval

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Travel Activity Characteristics Identification Based on Driving Trajectory data 24

Travel Activity Characteristics Identification Based on Driv...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Xie, Kun Xing, Xinyuan Dong, Honghui Dong, Chunjiao Ma, Jihui Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong Univ. Beijing China College of Transportation Engineering Tongji Univ. Shanghai China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Residence and work place are the most frequent places in all the starting and ending points of residents’ travel. The commuting trip with residence as the starting and ending point has a high regularity, which is an important travel feature of residents. Based on the GPS data of travel trajectory, this paper establishes an important stop identification method based on the clustering algorithm of mean shift, and a work residence identification method based on the double constraints of time and space, so as to identify important places of residence and work in travel activities. The research results can be described as accurate identification of residents’ occupational residence and excavation of the spatial and temporal distribution of residents’ occupational residence to provide data support for urban occupational residence planning, public resource allocation, and traffic management decisions. © ASCE.

关键词： Resource allocation

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A Feature Distribution Smoothing Network Based on Gaussian Distribution for QoS Prediction

A Feature Distribution Smoothing Network Based on Gaussian D...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Lu, Tongxin Zhang, Xiaohong Wang, Ziliang Yan, Meng Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China Chongqing University School of Big Data and Software Engineering Chongqing China

ISBN: (纸本)9798350304855

With the increasing number of services and their homogenization, the use of Quality of Service (QoS) for recommendations has become necessary. However, existing QoS prediction solutions have limitations in solving the noise and label imbalance problems of dataset, which greatly limit the improvement of QoS prediction accuracy. In this paper, we propose FSNet that contains a feature distribution smoothing module and an improved W-Huber loss function. The feature distribution smoothing module mitigates the effect of noise problem by fitting potential Gaussian distribution of known features with a supervised feedforward neural network. W-Huber loss function mitigates the impact of label imbalance problem on QoS prediction by reweighting the two components of Huber loss function. We conduct extensive experiments on real large-scale QoS dataset, and the results demonstrate that the proposed FSNet method outperforms existing QoS prediction methods. © 2023 IEEE.

关键词： Forecasting

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Image denoising based on residual network 3

Image denoising based on residual network

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3rd International Conference on Electronic Information engineering, big data, and Computer Technology, EIBDCT 2024

作者： Ma, Lihe Wang, Yan Sha, Ning Ma, Lin School of Information Engineering Harbin University Harbin China Heilongjiang Government Affairs Big Data Center3 Harbin China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin China

ISBN: (纸本)9781510680449

Image denoising has always been a hot research topic in the field of computer vision, playing an important role in improving image quality and accuracy. In this paper, a method for image denoising based on residual network is proposed, which uses deep learning technology to improve the effect of image processing. This method can automatically learn useful features in the image and effectively remove noise in the image, achieving automatic removal of image noise. Finally, experiments were conducted on standard image datasets for evaluation. The experimental results show that the image denoising method based on residual network has better performance compared to traditional methods. It can effectively remove various types of noise, improve image quality and clarity, and has broad application prospects in image processing. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image denoising

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Interpretable Machine Learning Method for Compressive Strength Prediction and Analysis of Pure Fly Ash-based Geopolymer Concrete

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Journal of Wuhan University of Technology(Materials Science) 2025年第1期40卷 65-78页

作者： SHI Yuqiong LI Jingyi ZHANG Yang LI Li Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education Northwest A&F Uni-versityYangling 712100China College of Water Resources and Architectural Engineering Northwest A&F UniversityYangling 712100China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing 401420China Chongqing College of Mobile Communication Chongqing 401520China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina National Rail Transit Electrification and Automation Engineering Technology Research Center(Hong Kong Branch) The Hong Kong Polytechnic UniversityKowloonHung HomHong KongChina

In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive *** this study,505 groups of data were collected,and a new database of compressive strength of PFGC was *** order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative *** five machine learning models all showed good compressive strength prediction performance on *** them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,*** R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,*** two models have high prediction accuracy and outstanding generalization *** order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 *** 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide *** temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement *** importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.

关键词： machine learning pure fly ash geopolymer compressive strength feature perception

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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Differentially Private Discrete Multi-Attributed Network Releasing

Differentially Private Discrete Multi-Attributed Network Rel...

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2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Lin, Yinghao Wang, Yuye Zhao, Shihao Qiao, Baojun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University ShenZhen China

ISBN: (纸本)9781510664616

In the area of social network, different attributes have different effects on the structure of network. Most of the existing privacy protection methods for attributed networks ignore the situation which different attributes have different effects on the network structure. They protect the privacy of the attributes indiscriminately. In respect of the issues above, a differentially private discrete multi-attributed network releasing method is proposed. Firstly, a probability model of discrete multi-attributed network is structured and the correlation parameter between multiple attributes and network structure is defined. The factor with different effects of different attributes on network structure is added into the model. Then, the algorithm uses the correlation parameter to establish the partition model of metadata and divides the metadata into different groups. As the group has different network model and attribute between each other, the groups are independence. The differential privacy of discrete multi-attributed network is realized through sanitizing parameters of the model and allocating metadata using exponential mechanism. Finally, experiment on real datasets verifies that the algorithm can satisfy the characteristics of the discrete multi-attributed network. It can also improve the efficiency and data availability. © 2023 SPIE.

关键词： Metadata

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Classification of Table Cells Based on LLM Prompts

Classification of Table Cells Based on LLM Prompts

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IEEE International Conference on Systems, Man and Cybernetics

作者： Mengjie Liu Chenyang Bu Shengxing Bai Bingbing Dong Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Tables, as an important means of data storage, are widely used in spreadsheets, web tables, and PDFs. By integrating information from table data with knowledge re-trieved from an external knowledge base, and examining the correspondences between cell values in the table and instances in the knowledge base, we can extract knowledge from the table to augment and enrich the knowledge base. To achieve this goal, we first need to classify table cells based on their functions in the layout. Due to the diverse structures arising from the arrangements of rows and columns, as well as the complexity of content resulting from concise data storage, current automation techniques heavily rely on stylistic features of table cells, such as font or color. Moreover, these methods are rarely experimented with or validated on tables without style features. Recent literature indicates that large language models (LLMs) demonstrate an ability to understand the structure and content of tables in tasks such as table judgment reasoning. Even without extensive feature inputs or pre-training, LLMs still show comparable results to machine learning and deep learning in these tasks. Therefore, this paper attempts to apply LLMs to table cell classification without using other stylistic features. We have designed a 4-component prompt paradigm (Classification Definition, Instruction, Table, Com-pletion), representing respectively the classification definition, task instructions, table data, and result output. We conduct experiments on three datasets CIUS, SAUS, and DEEX for table cell classification with one-shot learning. Our experimental results show that with the assistance of LLMs, better results can be achieved without utilizing stylistic features.

关键词： Deep learning Image color analysis Large language models knowledge based systems Layout Memory One shot learning Benchmark testing Probability density function data mining

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PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering

arXiv

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arXiv 2024年

作者： Xu, Fangzhi Lin, Qika Zhao, Tianzhe Han, Jiawei Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture PathReasoner. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. © 2024, CC BY.

关键词： Atoms

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Partial Label Learning via Conditional-Label-Aware Disambiguation

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Journal of Computer Science & Technology 2021年第3期36卷 590-605页

作者： Peng Ni Su-Yun Zhao Zhi-Gang Dai Hong Chen Cui-Ping Li Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China) Ministry of Education Beijing 100087China School of Information Renmin University of ChinaBeijing 100087China

Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth *** paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing *** existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints,our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise *** experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.

关键词： disambiguation partial label learning similarity and dissimilarity weak supervision

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