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Counterfactual Faithful data Generation Based on Disentangled Representation for Compound Fault Diagnosis of Rolling Bearings 3

Counterfactual Faithful Data Generation Based on Disentangle...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Ding, Xu Zhu, Qiang Wang, Song Zhang, Yiqi Wang, Hui Xu, Juan Institute of Industry and Equipment Technology Hefei University of Technology Hefei China School of Mechanical Engineering Hefei University of Technology Hefei China Bengbu Triumph Engineering Technology Co. Ltd Bengbu China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Recently, deep learning and human-out-of-the-loop methods enjoy their prosperous applications in mechanical fault diagnosis. Nonetheless, the None-IID(independent and identically distributed) issue radicated in acquired data severely limits the stability and accuracy of compound fault diagnosis of rolling bearings. This paper proposes a sample augmentation method for generating simulated signals based on the concept of counterfactuals. Firstly, disentangled representations and counterfactual faithful theory are applied to classify the original signal into two categories of properties. One is the fault semantics encoded from the original vibration signal. And the other is the sample attribute encoded by the encoder of Variational Autoencoders (VAEs). Secondly, the counterfactual faithful pseudo-samples are conjured through the Generative Adversarial Network(GAN) using the seeds of the 'factual' sample attributes and 'counterfactual' fault semantics to compensate for the drawback of distribution shift. Finally, the original samples and pseudo-samples are used as the CNN classifier dataset to realize bearing fault diagnosis. Experiments show that this method can generate counterfactual signals that are highly consistent with the original data distribution and can achieve better classification accuracy after balancing the dataset. © 2022 IEEE.

关键词： counterfactual faithfulness fault diagnosis rolling bearing structural causal model VAEGAN

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A New Dynamic Hash Index for Flash-Based Storage

A New Dynamic Hash Index for Flash-Based Storage

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International Conference on Web-Age Information Management Workshops (WAIM)

作者： Xiang Li Zhou Da Xiaofeng Meng Renmin University of China Beijing CN Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China China

Compared with traditional magnetic disks, flash memory has many advantages and has been used as external storage media for a wide spectrum of electronic devices (such as PDA, MP3, digital camera and mobile phone). As the capacity increases and price drops, it looks like a perfect alternative for magnetic disks. However, due to hardware limitations of flash memory, techniques including storage subsystem and indexing originally designed for magnetic disks can not run smoothly in a flash memory without any modification. In this paper we explore problems of indexing flash-resided data and present a new dynamical hash index for flash memory in two schemas. The analysis and experimental results validate the efficiency of our design.

关键词： Indexes Flash memory Algorithm design and analysis Writing Tuning Indexing Arrays

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Graph Neural Networks with Learnable and Optimal Polynomial Bases

arXiv

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arXiv 2023年

作者： Guo, Yuhe Wei, Zhewei Gaoling School of Articial Intelligence Renmin University of China China Peng Cheng Laboratory China Beijing Key Laboratory of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard’s Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis from Wang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is available at https://***/yuziGuo/FarOptBasis. © 2023, CC BY.

关键词： Graph neural networks

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A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction 24

A Symbolic Rule Integration Framework with Logic Transformer...

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33rd ACM Web Conference, WWW 2024

作者： Pan, Yudai Liu, Jun Zhao, Tianzhe Zhang, Lingling Lin, Yun Dong, Jin Song School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'an China Shanghai Jiao Tong University Shanghai China National University of Singapore Singapore Singapore

ISBN: (纸本)9798400701719

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities. This situation is called an inductive setting, which is more common in the real-world scenario. To handle this issue, implicit symbolic rules have shown great potential in capturing the inductive capability. However, it is still challenging to obtain precise representations of logic rules from KGs. The argument variability and predicate non-commutativity in symbolic rule integration make the modeling of component symbols difficult. To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. SymRITa firstly extracts the subgraph, whose embeddings are captured by a graph network. Meanwhile, symbolic rule graphs in the subgraph can be generated. Then, the symbolic rules are modeled by a proposed logic transformer. Specifically, the input format based on the subgraph-based embeddings is to focus on the argument variability in symbolic rules. In addition, a conjunction attention mechanism in the logic transformer can resolve predicate non-commutativity in the symbolic rule integration process. Finally, the subgraph-based and symbol-based embeddings obtained from the previous steps are combined for the training regime, and prediction results as well as rules explaining the reasoning process are explicitly output. Extensive experiments on twelve inductive datasets show that SymRITa achieves outstanding effectiveness compared to state-of-the-art inductive baselines. Moreover, the logic rules with corresponding confidences provide an interpretable paradigm. © 2024 ACM.

关键词： Forecasting

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Predicting ICU Pressure Injuries with Historical data: A Multivariate Time Series Approach

Predicting ICU Pressure Injuries with Historical Data: A Mul...

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IEEE International Conference on Big knowledge (ICBK)

作者： Lintai Cui Liuqi Jin Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Hospital-acquired pressure injury (HAPI) is a se-rious healthcare problem for intensive care unit (ICU) patients, which significantly affects their quality of life and prognosis, and increases hospitalization time and medical expenses. The current prediction models cannot accurately predict the pressure injury (PI) in ICU wards, as these models usually only predict based on the patient's current physical condition and electronic health record data, resulting in poor recall and precision, which affects the prediction performance. To solve this problem, we applied the multivariate time series data of I CU patients from admission to discharge and established a PI prediction model for ICU patients by utilizing the bidirectional long short-term memory neural network (Bi-LSTM) model. Experiments on the MIMIC-III (Medical Information Mart for Intensive Care) dataset show that the Bi-LSTM model has an F1 score of 0.24 and an AUC (Area Under Curve) value of 0.81, which are better than other models. This validates the effectiveness of the Bi-LSTM on the multivariate time series data to predict the occurrence of PI in ICU wards and assist nursing staff to allocate nursing resources more effectively.

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Lawyer information integration and recommendation by multi-source information validation

Lawyer information integration and recommendation by multi-s...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Tao Liu Biao Fan He Hu Xiao-Yong Du Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China School of Information Renmin University of China Beijing China

On the internet, all-round lawyer information is located at separated information sources, which prevent web users from effective information acquisition. In order to build a unified view of separated, heterogeneous, and often redundant lawyer information, we propose a new information integration method using multi-source information cross-validation. Based on the unified integrated data, a lawyer recommendation system is built. Several key technologies are presented and evaluated, including the multi-source information acquisition and validation. Experimental results indicate the key techniques used in the system are effective for lawyer information integration and recommendation.

关键词： Organizations Crawlers Search engines data mining Web pages databases Reliability

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A Hybrid Model for Explaining Older Adults' Continuance Intention Toward SNSs

A Hybrid Model for Explaining Older Adults' Continuance Inte...

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International Conference on Management of e-Commerce and e-Government, ICMECG

作者： Wei Liu Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance & Economics Nanchang P. R. China

Due to the rapid growth of social net services (SNSs), research into SNSs continuance usage has recently emerged as an important issue in information systems adaption. This study develops an integrated model based on the expectation-confirmation model (ECM), the technology acceptance model (TAM), and the theory of planned behavior (TPB), and apply them into the context of older adults' continuance intention toward SNSs. The hypothesized model is validated empirically using a sample collected from 250 older adults who had prior experience with SNSs and was tested against the proposed research model using structure equation modeling. Analysis results demonstrate that satisfaction has the most significant effect on older adults' continuance intention, followed by perceived usefulness, attitude, subject norm and perceived behavioral control. The results of these findings for SNSs practitioners are discussed at the end of this work.

关键词： Electronic countermeasures Adaptation models Reliability Mathematical model Attitude control Loading Load modeling

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Parallel Aggregation Queries over Star Schema: A Hierarchical Encoding Scheme and Efficient Percentile Computing as a Case

Parallel Aggregation Queries over Star Schema: A Hierarchica...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Xiongpai Qin Huiju Wang Xiaoyong Du Shan Wang Ministry of Education Key Laboratory of Data Engineering and Knowledge Engineering RUC Beijing China School of Information Renmin University of China Beijing China

Big data analysis is a main challenge we meet recently. Cloud computing is attracting more and more big data analysis applications, due to its well scalability and fault-tolerance. Some aggregation functions, like SUM, can be computed in parallel, because they satisfy distributive law of addition. Unfortunately, some of statistical functions are not naturally parallelizable. That means they do not satisfy distributive law of addition. In this paper, we focus on percentile computing problem. We proposed an iterative-style prediction-based parallel algorithm in a distributed system. Prediction is done through a sampling technique. Experiment results verify the efficiency of our algorithm.

关键词： Prediction algorithms Convergence Algorithm design and analysis Histograms Encoding Query processing Indexes

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Generating multi-page mirror site from ajax interfaces

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Journal of Information and Computational Science 2009年第2期6卷 985-991页

作者： Xia, Tian Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Resource Management Renmin University of China Beijing 100872 China

Ajax is an important approach for improving rich interactivity between web server and end users during Web 2.0 eras. At the same time, AJAX web pages can not be indexed by search engines due to its asynchronous loading. In this paper, we propose a technique for generating the traditional multi-page mirror site from the AJAX based web pages. Firstly, a page state structure to represent any dynamic state of Ajax page is defined, and a states repository is proposed to store the different states. Secondly, each state is fetched from repository and rendered by an embedded browser to get the dynamic DOM, and the click events of candidate clickable elements are fired, then new states are created and stored to repository. Finally, page states are linked and transformed into static HTML files. Experimental results show that mirroring Ajax sites is feasible, though there still have some limitations need to be improved later. Copyright ©2009 Binary Information Press.

关键词： Websites

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Meta-path Enhanced knowledge Graph Convolutional Network for Recommender Systems

Meta-path Enhanced Knowledge Graph Convolutional Network for...

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IEEE International Conference on Big knowledge (ICBK)

作者： Ru Wang Meng Wu Shengwei Ji Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665438599

knowledge Graph (KG) is a directed heterogeneous information network that contains a large number of entities and relations, which is widely used as effective side information in rec-ommender systems. Moreover, in recommender systems, the Graph Convolutional Network (GCN) model is introduced to mine the relatedness between entities in a KG because of its efficiency in extracting spatial features on topological graphs. The knowledge Graph Convolutional Network (KGCN) model up-dates the embedding of a currently positioned entity by aggregating the information of adjacent entities selected randomly. Never-theless, it has two limititations: 1) the information of neighbors se-lected randomly cannot accurately represent the current entity in the KG; 2) the model is hard to converge as graph features (i.e. The spatial relation features and semantic information features of en-tities in the KG) grow. To solve these limitations, in this paper, a meta-path (i.e., a sequence of artificially constructed relationships) is introduced into the selection of neighbors in the KGCN model to enhance the representation of each entity. Furthermore, two construction methods of the meta-path - constructing a meta-path based on the same relation (KGCN-SP) and the characteris-tics of KG (KGCN-MP) -are proposed. The experiments based on three real-world datasets demonstrate that the neighbor selection based on the meta-path is able to collect more accurate infor-mation from a KG and improve the recommendation performance effectively.

关键词： knowledge engineering Adaptation models Computational modeling Semantics Diversity reception Feature extraction data models

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