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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Jing Yang Gaojin Fan Anbo Shen Aiguo Wang Ministry of Education Key Laboratory of Knowledge Engineering With Big Data(Hefei University of Technology) School of Computer Science and Information Engineering Hefei University of Technology Hefei P. R. China School of Electronic and Information Engineering Foshan University China

ISBN: (纸本)9781665424288

The current streaming feature structure learning needs to be improved in the processing of nonlinear continuous data and the dynamic acquisition of causal structures. In this paper, we propose a causal structure learning algorithm, CANSF, based on the streaming feature of additive noise models. We have made three contributions. First, by using the information carried by the noise of nonlinear continuous data, we propose a real correlation identification method based on logarithmic likelihood, which can identify the real correlation and redundant features of target features, and dynamically select parent and child nodes for each feature. Second, based on regression analysis, a method to determine the causal direction is proposed, which can be used for dynamic orientation. Third, a learning method of causal structure based on streaming features is proposed, which can obtain the Causal structure diagram directly and dynamically.

关键词： Additive noise Learning systems Correlation Heuristic algorithms Conferences data models Regression analysis

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Rule-enhanced Noisy knowledge Graph Embedding via Low-quality Error Detection

Rule-enhanced Noisy Knowledge Graph Embedding via Low-qualit...

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IEEE International Conference on Big knowledge (ICBK)

作者： Yan Hong Chenyang Bu Tingting Jiang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

knowledge graphs (KGs) have been widely applied in many fields such as recommendation systems and knowledge reasoning. Embedding KGs into a continuous vector space has quickly gained significant attention. However, most traditional KG embedding models assume that all the facts in the existing KGs are completely correct, ignoring that KG construction usually involves automatic mechanisms. These automatic construction processes inevitably generate a lot of noises and conflicts, including low-quality errors (e.g., entity type errors). Moreover, these low-quality noises could greatly influence the quality of rule extraction, which may reduce the efficiency of Rule-Guided Embedding model (RUGE). To address this problem, an efficient method to eliminate those entity type errors in triples is proposed and applied to RUGE. Experimental results demonstrate that the filtering of low-quality noises can greatly improve the accuracy of knowledge representation learning as well as the quality of rules, further illustrating the effectiveness of our method.

关键词： Noise measurement Semantics knowledge representation data mining Predictive models data models

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Personalized Recommendation Based On Entity Attributes and Graph Features

Personalized Recommendation Based On Entity Attributes and G...

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IEEE International Conference on Big knowledge (ICBK)

作者： Yi Zhu Bingbing Dong Zhiqing Sha School of Information Engineering Yangzhou University Yangzhou China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Instituse of Big Knowledge Science Hefei University of Technology Hefei China

ISBN: (纸本)9781665438599

With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods.

关键词： Deep learning Privacy Laplace equations Conferences Predictive models Feature extraction Complexity theory

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Gaussian Model-Based Fully Convolutional Networks for Multivariate Time Series Classification

Gaussian Model-Based Fully Convolutional Networks for Multiv...

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IEEE International Conference on Big knowledge (ICBK)

作者： Changyang Tai Ze Yang Huicheng Zhang Gongqing Wu Junwei Lv Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dimensional MTS modeling has a large time and space consumption. This paper proposes a novel method, Gaussian Model-based Fully Convolutional Networks (GM-FCN), to improve the performance of high-dimensional MTS classification. Each original MTS is converted into multivariate Gaussian model parameters as the input of FCN. These parameters effectively capture the correlation be-tween MTS variables and significantly reduce the data scale by aligning an MTS size to its dimension. FCN is designed to learn more in-depth features of MTS based on these parameters for modeling the correlation between samples. Thus, GM-FCN can not only model the correlation between variables, but also the correlation between samples. We compare GM-FCN with nine state-of-the-art MTS classification methods, INN-ED, INN-DTW-i, INN-DTW-D, KLD-GMC, MLP, ResNet, Encoder, MCNN, and MCDCNN, on four high-dimensional public datasets, experimen-tal results show that the accuracy of G M - FCN is significantly superior to the others. Besides, the training time of GM-FCN is dozens of times faster than FCN using the original equal-length MTS data as input.

关键词： Training knowledge engineering Deep learning Correlation Conferences Time series analysis data models

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An integrated classification model for massive short texts with few words 19

An integrated classification model for massive short texts w...

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2019 International Conference on Robotics Systems and Vehicle Technology, RSVT 2019

作者： Tang, Xuetao Zhu, Yi Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China

ISBN: (纸本)9781450362429

The excellent performance of short texts classification has emerged in the past few years. However, massive short texts with few words like invoice data are different with traditional short texts like tweets in its no contextual and less semantic information, which hinders the application of conventional classification algorithms. To address these problems, we propose an integrated classification model for massive short texts with few words. More specifically, the word embedding model is introduced to train the word vectors of massive short texts with few words to form the feature space, and then the vector representation of each instance in texts is trained based on sentence embedding. With this integrated model, higher level representations are learned from massive short texts with few words. It can boost the performance of the base subsequent classifiers such as K-Nearest Neighbor. Extensive experiments conducted on dataset including 16 million real data demonstrate the superior classification performance of our proposed model compared with all competing state-of-the-art models. © 2019 Association for Computing Machinery.

关键词： Embeddings

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Research on Crowdsourcing Truth Inference Method Based on Graph Embedding

Research on Crowdsourcing Truth Inference Method Based on Gr...

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IEEE International Conference on Big knowledge (ICBK)

作者： Liangzhu Zhou Xingrui Zhuo Gongqing Wu Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Crowdsourcing is a cheap and popular method to solve problems that are difficult for computers to handle. Due to the differences in ability among workers on crowdsourcing platforms, existing research use aggregation strategies to deal with the labels of different workers to improve the utility of crowdsourcing data. However, most of these studies are based on probabilistic graphical models, which have problems such as difficulty in setting initial parameters. This paper proposes a novel crowdsourcing method Truth Inference based on Graph Embedding (TIGE) for single-choice questions, the method draws on the idea of graph autoencoder, constructs feature vectors for each crowdsourcing task, embeds the relationship between crowdsourcing tasks and workers in graphs, then uses graph neural networks to convert crowdsourcing problems into graph node prediction problems. The feature vectors are continuously optimized in the convolutional layer to obtain the final result. Compared with the six state-of-the-art algorithms on real-world datasets, our method has significant advantages in accuracy and F1-score.

关键词： Crowdsourcing Training Graphical models Probabilistic logic Prediction algorithms Graph neural networks Robustness

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Ontology Revision based on Pre-trained Language Models

arXiv

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arXiv 2023年

作者： Ji, Qiu Zhang, Xiaoping Ye, Yuxin Qi, Guilin Li, Jiaye Li, Site Ren, Jianjie Lu, Songtao School of Modern Posts Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National Data Center ofTraditional Chinese MedicineChina Academy of ChineseMedical Sciences Beijing100700 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Mathematics Southeast University Nanjing China Chien-Shiung Wu College Southeast University Nanjing China

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers. To deal with this problem, various ontology revision approaches have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. In addition, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years. Therefore, in this paper, we study how to apply pre-trained models to revise ontologies. We first define four scoring functions to rank axioms based on a pre-trained model by considering various information from an ontology. Based on the functions, an ontology revision algorithm is then proposed to deal with unsatisfiable concepts at once. To improve efficiency, an adapted revision algorithm is designed to deal with unsatisfiable concepts group by group. We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, our algorithms could achieve promising performance. The adapted revision algorithm could improve the efficiency largely, and at most about 90% of the time could be saved for some ontology pairs. Some of our scoring functions like reliableOnt cos could help a revision algorithm obtain better results in many cases, especially for those challenging ontology pairs like OM8. We also provide discussion about the overall experimental results and guidelin

关键词： Ontology

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Computed tomography-based delta radiomics of tumor core_edge combination for systemic treatment response evaluation in pancreatic cancer

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Journal of Pancreatology 2024年第3期7卷 181-190页

作者： Xiang Li Na Lu Peijun Hu Yiwen Chen Liying Liu Xinyuan Liu Chengxiang Guo Wenbo Xiao Ke Sun Jingsong Li Xueli Bai Tingbo Liang Department of Hepatobiliary and Pancreatic Surgery The First Affiliated HospitalZhejiang University School of MedicineHangzhouChina Zhejiang Provincial Key Laboratory of Pancreatic Disease HangzhouChina Research Center for Healthcare Data Science Zhejiang LabHangzhou China Department of Radiology The First Affiliated HospitalZhejiang University School of MedicineHangzhouChina Department of Pathology The First Affiliated HospitalZhejiang University School of MedicineHangzhouChina Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument ScienceZhejiang UniversityHangzhouChina Zhejiang University Cancer Center HangzhouChina

Background:As a systemic disease,pancreatic cancer(PC)can be treated systemically to raise the R0 resection rate and enhance patient *** best ways to assess the treatment response to systemic treatment of patients with PC are still ***:A total of 122 PC patients were enrolled;25 of these patients were used as an independent testing *** to the pathologic response,PC patients were classified into the responder and nonresponder *** whole tumor,core,edge,and peritumoral were segmented from the enhanced computed tomography(CT)***-learning models were created by extracting the variations in radionics features before and after therapy(delta radiomics features).Finally,we compared the performance of models based on radiomics features,changes in tumor markers,and radiologic ***:The model based on the core(area under curve[AUC]=0.864)and edge features(AUC=0.853)showed better performance than that based on the whole tumor(AUC=0.847)or peritumoral area(AUC=0.846).Moreover,the tumor core_edge combination model(AUC=0.899)could better increase confidence in treatment response than using either of them *** accuracies of models based on changes in tumor markers and radiologic evaluation were relatively poorer than of the radiomics ***,Patients predicted to respond to therapy using the radiomics model showed a relatively longer overall survival(43 vs 27 months),although there were no significant differences(P=.063).Conclusions:The tumor core_edge combination delta radiomics model is an effective approach to evaluate pathologic response in PC patients with systemic treatment.

关键词： Computed tomography Delta radiomics Pancreatic cancer Pathologic response Systemic treatment

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MTSC-GE: A Novel Graph based Method for Multivariate Time Series Clustering

MTSC-GE: A Novel Graph based Method for Multivariate Time Se...

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IEEE International Conference on Big knowledge (ICBK)

作者： Ze Yang Changyang Tai Gongqing Wu Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance.

关键词： Deep learning Conferences Clustering methods Time series analysis Clustering algorithms Benchmark testing Stability analysis

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Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey

arXiv

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arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Liu, Jiamou Zhang, Ziang Li, Meng Zhu, Liehuang Russello, Giovanni The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China The School of Computer Science The University of Auckland Auckland New Zealand Southeast Institute of Information Technology Beijing Institute of Technology Fujian China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China

With the rapid advancement of artificial intelligence technology, the usage of machine learning models is gradually becoming part of our daily lives. High-quality models rely not only on efficient optimization algorithms but also on the training and learning processes built upon vast amounts of data and computational power. However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally. This has led them to explore alternative approaches such as outsourced learning and federated learning. While these methods address the feasibility of model training effectively, they introduce concerns about the trustworthiness of the training process since computations are not performed locally. Similarly, there are trustworthiness issues associated with outsourced model inference. These two problems can be summarized as the trustworthiness problem of model computations: How can one verify that the results computed by other participants are derived according to the specified algorithm, model, and input data? To address this challenge, verifiable machine learning (VML) has emerged. This paper presents a comprehensive survey of zero-knowledge proof-based verifiable machine learning (ZKP-VML) technology. We first analyze the potential verifiability issues that may exist in different machine learning scenarios. Subsequently, we provide a formal definition of ZKP-VML. We then conduct a detailed analysis and classification of existing works based on their technical approaches. Finally, we discuss the key challenges and future directions in the field of ZKP-based VML. © 2023, CC BY.

关键词： Machine learning

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