A holistic analysis of problem and incident tickets in a real production cloud service environment is presented in this paper.By extracting different bags of words,we use principal component analysis(PCA)to examine th...A holistic analysis of problem and incident tickets in a real production cloud service environment is presented in this paper.By extracting different bags of words,we use principal component analysis(PCA)to examine the clustering characteristics of these tickets.Then Kmeans and latent Dirichlet allocation(LDA)are applied to show the potential clusters within this Cloud environment.The second part of our study uses a pre-trained bidirectional encoder representation from transformers(BERT)model to classify the tickets,with the goal of predicting the optimal dispatching department for a given ticket.Experimental results show that due to the unique characteristics of ticket description,pre-processing with domain knowledge turns out to be critical in both clustering and classification.Our classification model yields 86%accuracy when predicting the target dispatching department.展开更多
A modified multisurface ＂proximal support vector machine classifier via generalized eigenvalues （GEPSVM for short）＂ was proposed. By defining a new principle, we designed a new classification approach via GEPSVM, ...A modified multisurface ＂proximal support vector machine classifier via generalized eigenvalues （GEPSVM for short）＂ was proposed. By defining a new principle, we designed a new classification approach via GEPSVM, namely, maximum or minimum plane distance GEPSVM （MPDGEPSVM）. Unlike GEPSVM, our approach obtains two planes by solving two simple eigenvalue problems, such that it can avoid occurrence of singular problems. Our approach, compared with GEPSVM, has better classification performalce. Moreover, MPDGEPSVM is over one order of magnitude faster than GEPSVM, and almost two orders of magnitude faster than SVM. Computational results on public datasets from UCI database illustrated the efficiency of MPDGEPSVM.展开更多
Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the...Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique;second,it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model;third,it extracts problem phrases from generated titles according to regular expression rules;fourth,it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm;finally,it identifies multidisciplinary problems based on the disciplinary labels of papers.Findings:Experiments in the“Carbon Peaking and Carbon Neutrality”field show that the proposed method can effectively identify multidisciplinary research problems.The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.Research limitations:It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.Practical implications:Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments,fund valuable multidisciplinary problems for research management authorities,and borrow ideas from other disciplines for researchers.Originality/value:This approach proposes a novel multidisciplinary problem identification method based on text generation,which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.展开更多
In this paper,a multi-point boundary value problems for a three order nonlinear deferential equation is considered.With the help of coincidence theorem due to Mawhin,a existence theorem is obtained.
文摘A holistic analysis of problem and incident tickets in a real production cloud service environment is presented in this paper.By extracting different bags of words,we use principal component analysis(PCA)to examine the clustering characteristics of these tickets.Then Kmeans and latent Dirichlet allocation(LDA)are applied to show the potential clusters within this Cloud environment.The second part of our study uses a pre-trained bidirectional encoder representation from transformers(BERT)model to classify the tickets,with the goal of predicting the optimal dispatching department for a given ticket.Experimental results show that due to the unique characteristics of ticket description,pre-processing with domain knowledge turns out to be critical in both clustering and classification.Our classification model yields 86%accuracy when predicting the target dispatching department.
基金The National Defence Basic Research Pro-gram in China(No.S0500A001)the National High Technol-ogy Research and Development Program of China(863 Pro-gram) (No.2002AA411030)the Scientific and Techno-logical Innovation Foundation of Jiangsu Province in China
文摘A modified multisurface ＂proximal support vector machine classifier via generalized eigenvalues （GEPSVM for short）＂ was proposed. By defining a new principle, we designed a new classification approach via GEPSVM, namely, maximum or minimum plane distance GEPSVM （MPDGEPSVM）. Unlike GEPSVM, our approach obtains two planes by solving two simple eigenvalue problems, such that it can avoid occurrence of singular problems. Our approach, compared with GEPSVM, has better classification performalce. Moreover, MPDGEPSVM is over one order of magnitude faster than GEPSVM, and almost two orders of magnitude faster than SVM. Computational results on public datasets from UCI database illustrated the efficiency of MPDGEPSVM.
基金supported by the General Projects of ISTIC Innovation Foundation“Problem innovation solution mining based on text generation model”(MS2024-03).
文摘Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique;second,it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model;third,it extracts problem phrases from generated titles according to regular expression rules;fourth,it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm;finally,it identifies multidisciplinary problems based on the disciplinary labels of papers.Findings:Experiments in the“Carbon Peaking and Carbon Neutrality”field show that the proposed method can effectively identify multidisciplinary research problems.The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.Research limitations:It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.Practical implications:Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments,fund valuable multidisciplinary problems for research management authorities,and borrow ideas from other disciplines for researchers.Originality/value:This approach proposes a novel multidisciplinary problem identification method based on text generation,which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.
基金Supported by Nature Science Foundation of Education Department of Henan Province（2010A110023）
文摘In this paper,a multi-point boundary value problems for a three order nonlinear deferential equation is considered.With the help of coincidence theorem due to Mawhin,a existence theorem is obtained.
