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2017 IEEE 2nd International Conference on big data Analysis（ICBDA 2017）

作者： Kai Lei Lizhu Zhang Ying Shen Xiaohui Huang Jincheng Wu Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology and Applications School of Electronic and Computer Engineering Peking University

ISBN: (纸本)9781509036202

Clinical decision support is a probabilistic and quantitative method learning used for problem modeling in situations of ***,mathematical methods such as probability learning and statistical analysis is increasingly used in the medical field to assure the accuracy of the diagnoses and optimize the *** study describes the design and implementation of an improved weighted Na?ve Bayes classifier based on the idea that there is a certain level of dependency between *** the coronary artery disease,clinical data and patients' medical records were used to train the improved Na?ve Bayes classifier to compute the possible disease manifestations,the priori probability and the posterior probability and to carry out the disease ***,we were able to improve the precision of the improved weighted Na?ve Bayes classifier to 79%(increase of 12% compared to Na?ve Bayes).The suitability of the improved Na?ve Bayes classifiers for carrying out diagnosis allows them to be utilized to distinguish syndromes and signs based on unobserved evidence.

关键词： Na?ve Bayes decision support diagnostic classification attribute relevancy negative symptoms coronary artery disease

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Topic Representation:A Novel Method of Tag Recommendation for Text 2

Topic Representation:A Novel Method of Tag Recommendation fo...

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2017 IEEE 2nd International Conference on big data Analysis（ICBDA 2017）

作者： Shangru Zhong Kai Lei Xiaohui Huang Jincheng Wu Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications School of Electronic and Computer Engineering Peking University

ISBN: (纸本)9781509036202

With tags widely used in organizing and searching contents in massive data era,how to automatically generate appropriate tags of resource for users became a hot issue on social networks *** recommendation for text resource can be modeled as a keyword extraction problem,hence topic modeling such as LDA which extracts latent semantic topics from text is suitable for tag ***,latent topics are too coarse-grained to describe ***,LDA trains corpus globally without considering context ***,topics generated are difficult to be quantifiably *** problems lead to the poor quality of tag recommendation in topic model based *** this paper,we propose topic representation method,which introduces embedding semantic representation into topic *** results of evaluation on real social networks show that the proposed method improves the quality of tag recommendation for Chinese text resource,when comparing with traditional LDA-based method,which demonstrates the effectiveness of modifying topic modeling.

关键词： Tag recommendation topic model LDA word embedding

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Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

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IEEE Transactions on Knowledge and data engineering 2022年第3期34卷 1427-1445页

作者： Qiang, Jipeng Qian, Zhenyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou University Jiangsu225009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230026 China Mininglamp Academy of Sciences Minininglamp Beijing China

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm. © 2020 IEEE.

关键词： Surveys

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Correction: FragGen: towards 3D geometry reliable fragment-based molecular generation

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Chemical science 2025年第3期16卷 1467页

作者： Odin Zhang Yufei Huang Shicheng Chen Mengyao Yu Xujun Zhang Haitao Lin Yundian Zeng Mingyang Wang Zhenxing Wu Huifeng Zhao Zaixi Zhang Chenqing Hua Yu Kang Sunliang Cui Peichen Pan Chang-Yu Hsieh Tingjun Hou College of Pharmaceutical Sciences Zhejiang University Hangzhou 310058 Zhejiang China slcui@*** panpeichen@*** kimhsieh@*** tingjunhou@***. Zhejiang University Hangzhou 310058 Zhejiang China. Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Hefei Anhui China. Montreal Institute for Learning Algorithms McGill University Montreal QC Canada.

[This corrects the article DOI: 10.1039/D4SC04620J.].

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A Robust Distance Regularized Potential Function for Level Set Image Segmentation 17th

A Robust Distance Regularized Potential Function for Level S...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Zou, Le Huang, Qian-Jing Wu, Zhi-Ze Song, Liang-Tu Wang, Xiao-Feng Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure School of Artificial Intelligence and Big Data Hefei University Anhui Hefei230601 China Hefei Institutes of Physical Science Chinese Academy of Sciences P. O. Box 1130 Anhui Hefei230031 China University of Science and Technology of China Anhui Hefei230026 China College of Bioengineering Food and Environmental Sciences Hefei University Anhui Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Anhui Hefei230601 China

ISBN: (纸本)9783030845216

The level set is a classical image segmentation method, but during the evolution of the level set, it can produce evolutionary problems such as local spikes and deep valleys, or overly flat regions, making the iterative process of final segmentation unstable and segmentation results inaccurate. In order to ensure the stability and validity of the level set evolution during the evolution process, the level set function must be periodically initialized so that the level set is always kept as a signed distance function. We construct a new distance regularization potential function based on logarithmic and power function and give a specific analysis. During the evolution process, the level set function always approximates the signed distance function, which is stable and efficient for level set image segmentation. Experimental analyses are conducted to compare the segmentation performance of various distance regularization potential functions when combining with the classical Chan Vese model. © 2021, Springer Nature Switzerland AG.

关键词： Image segmentation

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A method for correcting characteristic X-ray net peak count from drifted shadow peak

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Nuclear Science and Techniques 2023年第11期34卷 155-167页

作者： Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

关键词： Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of Computer Science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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An Enhanced Computational Feature Selection Method for Medical Synonym Identification via Bilingualism and Multi-corpus Training 2

An Enhanced Computational Feature Selection Method for Medic...

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2017 IEEE 2nd International Conference on big data Analysis（ICBDA 2017）

作者： Kai Lei Shangchun Si Desi Wen Ying Shen Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology and Applications School of Electronic and Computer Engineering(SECE) Peking University

ISBN: (纸本)9781509036202

Medical synonym identification has been an important part of medical natural language processing(NLP).However,in the field of Chinese medical synonym identification,there are problems like low precision and low recall *** solve the problem,in this paper,we propose a method for identifying Chinese medical *** first selected 13 features including Chinese and English *** we studied the synonym identification results of each feature alone and different combinations of the *** the comparison among identification results,we present an optimal combination of features for Chinese medical synonym *** show that our selected features have achieved 97.37% precision rate,96.00% recall rate and 97.33% F1 score.

关键词： medical synonym feature selection bilingual multi-corpus

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer 23

Tracing Knowledge Instead of Patterns: Stable Knowledge Trac...

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2023 World Wide Web Conference, WWW 2023

作者： Yin, Yu Dai, Le Huang, Zhenya Shen, Shuanghong Wang, Fei Liu, Qi Chen, Enhong Li, Xin Anhui Key Lab. of Big Data Analysis and Application University of Science and Technology of China State Key Lab. of Cognitive Intelligence Anhui Hefei China University of Science and Technology of China IFLYTEK Co. Ltd China

ISBN: (纸本)9781450394161

Knowledge Tracing (KT) aims at tracing the evolution of the knowledge states along the learning process of a learner. It has become a crucial task for online learning systems to model the learning process of their users, and further provide their users a personalized learning guidance. However, recent developments in KT based on deep neural networks mostly focus on increasing the accuracy of predicting the next performance of students. We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner's learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. With DTransformer, we build the architecture from question-level to knowledge-level, explicitly diagnosing learner's knowledge proficiency from each question mastery states. We also propose a novel training algorithm based on contrastive learning that focuses on maintaining the stability of the knowledge state diagnosis. Through extensive experiments, we will show that with its understanding of knowledge state evolution, DTransformer achieves a better performance prediction accuracy and more stable knowledge state tracing results. We will also show that DTransformer is less sensitive to specific patterns with case study. We open-sourced our code and data at https://***/yxonic/DTransformer. © 2023 ACM.

关键词： Deep neural networks

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