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Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy

Journal of Computer Science & Technology 2023年第6期38卷 1403-1422页

作者：赵丹赵素云陈红刘睿瑄李翠平张晓莹 Institute of Scientific and Technical Information of China Beijing 100038China Key Laboratory of Data Engineering and Knowledge Engineering(Ministry of Education) School of InformationRenmin University of ChinaBeijing 100872China

Local differential privacy(LDP)approaches to collecting sensitive information for frequent itemset mining(FIM)can reliably guarantee *** current approaches to FIM under LDP add"padding and sampling"steps to obtain frequent itemsets and their frequencies because each user transaction represents a set of *** current state-of-the-art approach,namely set-value itemset mining(SVSM),must balance variance and bias to achieve accurate ***,an unbiased FIM approach with lower variance is highly *** narrow this gap,we propose an Item-Level LDP frequency oracle approach,named the Integrated-with-Hadamard-Transform-Based Frequency Oracle(IHFO).For the first time,Hadamard encoding is introduced to a set of values to encode all items into a fixed vector,and perturbation can be subsequently applied to the *** FIM approach,called optimized united itemset mining(O-UISM),is pro-posed to combine the padding-and-sampling-based frequency oracle(PSFO)and the IHFO into a framework for acquiring accurate frequent itemsets with their ***,we theoretically and experimentally demonstrate that O-UISM significantly outperforms the extant approaches in finding frequent itemsets and estimating their frequencies under the same privacy guarantee.

关键词： local differential privacy frequent itemset mining frequency oracle

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A Reference Process for Assessing the Reliability of Predictive Analytics Results

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SN Computer Science 2024年第5期5卷 563页

作者： Staudinger, Simon Schuetz, Christoph G. Schrefl, Michael Institute of Business Informatics-Data and Knowledge Engineering Johannes Kepler University Linz Altenberger Straße 69 Linz 4040 Austria

Organizations employ data mining to discover patterns in historic data in order to learn predictive models. Depending on the predictive model the predictions may be more or less accurate, raising the question about the reliability of individual predictions. This paper proposes a reference process aligned with the CRISP-DM to enable the assessment of reliability of individual predictions obtained from a predictive model. The reference process describes activities along the different stages of the development process required to establish a reliability assessment approach for a predictive model. The paper then presents in more detail two specific approaches for reliability assessment: perturbation of input cases and local quality measures. Furthermore, this paper describes elements of a knowledge graph to capture important metadata about the development process and training data. The knowledge graph serves to properly configure and employ the reliability assessment approaches. © The Author(s) 2024.

关键词： Business analytics Business intelligence CRISP-DM data mining Decision support systems

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ViGT: proposal-free video grounding with a learnable token in the transformer

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Science China(Information Sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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Multi-Cluster Feature Selection Based on Isometric Mapping

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 570-572页

作者： Yadi Wang Zefeng Zhang Yinghao Lin Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004 Institute of Data and Knowledge Engineering School of Computer and Information EngineeringHenan UniversityKaifeng 475004China

Dear editor,This letter presents an unsupervised feature selection method based on machine *** selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of d... 详细信息

关键词： problem letter dimensionality

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knowledge Graph for China's Genealogy1

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IEEE Transactions on knowledge and data engineering 2023年第1期35卷 634-646页

作者： Wu, Xindong Jiang, Tingting Zhu, Yi Bu, Chenyang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei230009 China Hefei University of Technology Research Institute of Big Knowledge Anhui Hefei230009 China Mininglamp Technology Mininglamp Academy of Sciences Beijing100102 China Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei230009 China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230009 China

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descendant paths, and explore the origins of a family. However, the heterogenous, autonomous, complex, and evolving natures of genealogical data bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical data may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical datasets verify the feasibility and effectiveness of our proposed model. © 1989-2012 IEEE.

关键词： History

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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Performance Analysis of Health Care Expense Anticipation using Computational Intelligence 1

Performance Analysis of Health Care Expense Anticipation usi...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Gopalakrishnan, A. Kumarganesh, S. Divya Bharathi, G. Anthoniraj, S. Natarajan, Suresh Kumar Gochhait, Saikat Knowledge Institute of Technology Department of Artificial Intelligence and Data Science Salem India Knowledge Institute of Technology Department of Electronics and Communication Engineering Salem India Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India School of Computer Science and Engineering Ramanagara India Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Maharashtra Pune India

ISBN: (纸本)9798350348637

The escalating costs of healthcare globally necessitate the development of accurate prediction models to address the financial strain on individuals, families, businesses, and governments. This research employs linear regression models to forecast healthcare expenditures, analyzing the intricate relationship between various factors and costs. A comprehensive literature survey explores topics such as the impact of environmental pollution on healthcare spending, prediction of surgical expenses, budget impact analysis for cancer treatment, and the integration of big data in healthcare infrastructure investments. The methodology involves gathering a diverse dataset encompassing patient demographics, medical history, treatment methods, and associated expenditures. Exploratory data analysis employs Pearson's correlation coefficient to identify trends and correlations between variables. Two linear regression models are proposed and compared, with the second model excluding non-significant variables for enhanced accuracy and interpretability. Results indicate that the second model, with refined feature selection, outperforms the first in terms of mean squared error and R-squared values. A comparative analysis of different studies underscores the diverse findings in healthcare economics, ranging from the impact of CO2 emissions on health to predicting surgery costs and budgetary savings in cancer treatment. The research contributes to the growing body of knowledge in healthcare economics, providing decision-makers with a valuable tool for precise expenditure estimation. The emphasis on linear regression models aids stakeholders in understanding factors influencing healthcare costs, guiding resource allocation, policy creation, and financial planning for more sustainable and effective healthcare systems. © 2024 IEEE.

关键词： Budget control

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Optimizing mechanical strength and thermal stability of sisal fiber-plastic composites by high-temperature treatment

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Interactions 2025年第1期246卷 1-15页

作者： Mungle, Nischal P. Jadhav, Varsha D. Kulkarni, Sumant Khanderao D.S.Vijayan Saminathan, Rajasekaran Satishkumar, P. Department of Mechanical Engineering Yeshawantrao Chavan College of Engineering Maharashtra Wanadongari Nagpur India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune India School of Civil Engineering REVA University Rukmini Knowledge Park Karnataka Kattigenahalli Bengaluru India Tamilnadu Chennai India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India

In this experiment, high-temperature polyethylene terephthalate (PT) was mixed with epoxy resin (ER) that had been thinned with acetone. Sisal fibers were coated with the resulting product. Composites of Coated treated sisal fibre-reinforced PT (CTSF-PT) were made using a constant fiber loading of 10 wt% and processing temperatures of 230, 250, and 270ºC. The fibers were treated with NaOH. These composites were then characterized for thermal, mechanical and morphological performances. When comparing findings obtained at 250 and 270ºC, the optimal processing temperature was found to be 230ºC. This temperature resulted in the best thermal and mechanical properties, and excellent interfacial adhesion among the coated fibers and the PT. In comparison to the uncoated SF, the coated composites showed a 9.5% reduction in degradation onset temperature and a 13% improvement in decomposition temperature following resin coating, according to thermogravimetric analysis. The CSF-PT composites surpassed the uncoated composites by 2.18% and 2.8% in terms of glass transition (Tg) and melting temperature (Tm) measured by differential scanning calorimetry (DSC), with peak values at 122.2 °C and 265.1 °C, respectively. Coated Sisal-reinforced PT (CSF-PT) and coated-treated PT (CTSF-PT) composites outperformed raw Sisal (SF-PT) composites in terms of overall qualities and morphological properties, regardless of processing temperature. Applying resin to natural fibers enhances their heat resistance and the bond between the coated fibers and the plastic matrix, making them better suited for engineering plastic composites. Applications involving high temperatures, such as construction and automotive industries are well-suited to the resulting composites. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Automotive industry

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Towards A knowledge Graph-based Framework for Integrated Security and Safety Analysis in Digital Production Systems 22

Towards A Knowledge Graph-based Framework for Integrated Sec...

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22nd International Semantic Web Conference on Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters-Demos-Industry 2023

作者： Kropatschek, Sebastian Kurniawan, Kabul Boshale, Pusparaj Hollerer, Siegfried Kiesling, Elmar Winkler, Dietmar TU Wien Institute of Computer Engineering Vienna Austria WU Wien Institute for Data Process and Knowledge Management Vienna Austria Vienna Austria SBA Research Vienna Austria

The increasing interconnection of Information Technology and Operational Technology in Industry 4.0 creates new challenges and requires new approaches to ensure that production processes are executed safely and securely. Production system safety and security have therefore become critical aspects as security incidents can lead to serious problems such as production failure, equipment damage, or human injury. This paper introduces a knowledge-graph-based framework for safety and security analysis that integrates prior work on product, process, and resources (PPR) as well as cause-effect modeling. To identify possible attack chains and their impact on safety issues, we leverage Bayesian Belief Networks to estimate failure probabilities and propagate them through the knowledge graph. We evaluate our approach by means of a real-world manufacturing use-case. © 2022 Copyright for this paper by its authors.

关键词： Bayesian networks

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