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Correction to: MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

Multimedia Tools and Applications 2021年第9期80卷 13019-13019页

作者： Rui Zhu Xiongfei Li Xiaoli Zhang Xiaowei Xu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Department of Information Sciences University of Arkansas at Little Rock Little Rock USA

A Correction to this paper has been published: https://***/10.1007/s11042-021-10782-7

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NETS: Extremely fast outlier detection from a data stream via set-based processing 45th

NETS: Extremely fast outlier detection from a data stream vi...

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45th International Conference on Very Large Data Bases, VLDB 2019

作者： Yoon, Susik Lee, Jae-Gil Lee, Byung Suk Graduate School of Knowledge Service Engineering KAIST Daejeon34141 Korea Republic of Department of Computer Science University of Vermont BurlingtonVT05405 United States

This paper addresses the problem of efficiently detecting outliers from a data stream as old data points expire from and new data points enter the window incrementally. The proposed method is based on a newly discovered characteristic of a data stream that the change in the locations of data points in the data space is typically very insignificant. This observation has led to the finding that the existing distance-based outlier detection algorithms perform excessive unnecessary computations that are repetitive and/or canceling out the effects. Thus, in this paper, we propose a novel set-based approach to detecting outliers, whereby data points at similar locations are grouped and the detection of outliers or inliers is handled at the group level. Specifically, a new algorithm NETS is proposed to achieve a remarkable performance improvement by realizing set-based early identification of outliers or inliers and taking advantage of the "net effect" between expired and new data points. Additionally, NETS is capable of achieving the same efficiency even for a high-dimensional data stream through two-level dimensional filtering. Comprehensive experiments using six real-world data streams show 5 to 25 times faster processing time than state-of-the-art algorithms with comparable memory consumption. We assert that NETS opens a new possibility to real-time data stream outlier detection. © 2019, is held by the owner/author(s).

关键词： Anomaly detection

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Development of a Novel Chemoinformatic Tool for Natural Product Databases

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Future Drug Discovery 2023年第2期5卷

作者： Paulo Ricardo Viviurka do Carmo Ricardo Marcacini Marilia Valli João Victor Silva-Silva Leonardo Luiz Gomes Ferreira Alan Cesar Pilon Vanderlan da Silva Bolzani Adriano D Andricopulo Edgard Marx 1 Agile Knowledge Engineering & Semantic Web (AKSW) Institute of Computer Science Leipzig University Leipzig04109 Germany 2 Computer Science & Mathematics Institute University of São Paulo São Carlos SP13566-590 Brazil 3 Laboratory of Medicinal & Computational Chemistry São Carlos Institute of Physics University of São Paulo São Carlos SP13563-120 Brazil 4 Nuclei of Bioassays Biosynthesis & Ecophysiology of Natural Products (NuBBE) Department of Organic Chemistry Institute of Chemistry São Paulo State University (UNESP) Araraquara SP14800-901 Brazil

Aim:This study aimed to develop a chemoinformatic tool for extracting natural product information from academic *** & methods:Machine learning graph embeddings were used to extract knowledge from a knowledge graph, connecting properties, molecular data and BERTopic ***:Metapath2Vec performed best in extracting compound names and showed improvement over evaluation stages. Embedding Propagation on Heterogeneous Networks achieved the best performance in extracting bioactivity information. Metapath2Vec excelled in extracting species information, while DeepWalk and Node2Vec performed well in one stage for species location extraction. Embedding Propagation on Heterogeneous Networks consistently improved performance and achieved the best overall scores. Unsupervised embeddings effectively extracted knowledge, with different methods excelling in different ***:This research establishes a foundation for frameworks in knowledge extraction, benefiting sustainable resource *** language summaryIn this study, a tool to extract relevant information on natural products from scientific papers was developed. Advanced machine learning techniques were used to create a knowledge graph by connecting different information sources. Several methods were tested, with some showing better performance in specific tasks such as the extraction of compound names and bioactivity information. The incorporation of additional data associated with the studied resources proved to improve the results of the models. This study provides a foundation for the development of future tools that can assist researchers in extracting valuable knowledge from scientific literature. Such tools have the potential to facilitate drug discovery efforts and promote the sustainable utilization of natural resources.

关键词： chemoinformatics databases drug discovery knowledge extraction knowledge graphs machine learning natural products NuBBEDB

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Which scores to predict in sentence regression for text summarization?

Which scores to predict in sentence regression for text summ...

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2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018

作者： Zopf, Markus Menćia, Eneldo Loza Fürnkranz, Johannes Research Training Group AIPHES / Knowledge Engineering Group Department of Computer Science Technische Universität Darmstadt Hochschulstraße 10 Darmstadt64289 Germany

ISBN: (纸本)9781948087278

The task of automatic text summarization is to generate a short text that summarizes the most important information in a given set of documents. Sentence regression is an emerging branch in automatic text summarizations. Its key idea is to estimate the importance of information via learned utility scores for individual sentences. These scores are then used for selecting sentences from the source documents, typically according to a greedy selection strategy. Recently proposed state-ofthe-art models learn to predict ROUGE recall scores of individual sentences, which seems reasonable since the final summaries are evaluated according to ROUGE recall. In this paper, we show in extensive experiments that following this intuition leads to suboptimal results and that learning to predict ROUGE precision scores leads to better results. The crucial difference is to aim not at covering as much information as possible but at wasting as little space as possible in every greedy step. © 2018 The Association for Computational Linguistics.

关键词： Forecasting

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A model for early prediction of diabetes

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Informatics in Medicine Unlocked 2019年 16卷

作者： Mahboob Alam, Talha Iqbal, Muhammad Atif Ali, Yasir Wahab, Abdul Ijaz, Safdar Imtiaz Baig, Talha Hussain, Ayaz Malik, Muhammad Awais Raza, Muhammad Mehdi Ibrar, Salman Abbas, Zunish Computer Science and Engineering Department University of Engineering and Technology Lahore Pakistan School of Systems and Technology University of Management and Technology Lahore Pakistan Knowledge Units of Systems & Technology University of Management and Technology Sialkot Pakistan Department of Computer Science Lahore College for Women University Pakistan

Diabetes is a common, chronic disease. Prediction of diabetes at an early stage can lead to improved treatment. Data mining techniques are widely used for prediction of disease at an early stage. In this research paper, diabetes is predicted using significant attributes, and the relationship of the differing attributes is also characterized. Various tools are used to determine significant attribute selection, and for clustering, prediction, and association rule mining for diabetes. Significant attributes selection was done via the principal component analysis method. Our findings indicate a strong association of diabetes with body mass index (BMI) and with glucose level, which was extracted via the Apriori method. Artificial neural network (ANN), random forest (RF) and K-means clustering techniques were implemented for the prediction of diabetes. The ANN technique provided a best accuracy of 75.7%, and may be useful to assist medical professionals with treatment decisions. © 2019

关键词： Artificial neural network (ANN) Association rule mining Data mining Diabetes K-means clustering Random forest

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Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

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A Quantized Kernel Least Mean Square Scheme with Entropy-Guided Learning for Intelligent Data Analysis

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China Communications 2017年第7期14卷 127-136页

作者： Xiong Luo Jing Deng Ji Liu Weiping Wang Xiaojuan Ban Jenq-Haur Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science Department of Computer Science and Information Engineering Taipei University of Technology

Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.

关键词： quantized kernel least mean square （QKLMS） consecutive square entropy data analysis

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Edge-centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Weiping Wang Ruiying Du Zhen Wang Xiong Luo Haiyan Zhao Ping Luan Jipeng Ouyang Song Liu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School Beijing University of Science and Technology Shunde Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Investigation Writing - original draft Writing - review & editing The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Weiping Wang Zhen Wang and Haiyan Zhao. Contribution: Conceptualization Formal analysis Contribution: Formal analysis Supervision Department of Neurology Peking University Third Hospital Beijing China Contribution: Data curation Formal analysis Guangdong Second People's Hospital Guangzhou Guangdong China Department of Neurology Shunde Hospital Southern Medical University Shunde Guangdong China Contribution: Conceptualization Data curation Southeast Asia Project Manager Department of China Petroleum Pipeline Engineering Co. Ltd. Langfang Hebei China Contribution: Software Supervision

Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI) rely on a node-centric network model, which only calculates correlations between brain regions. Considering the interaction of low-order correlations between pairs of brain regions, we use an edge-centric network model to study high-order functional network correlations. Here, we compute edge time series (eTS) to obtain overlapping communities and study the relationship between subnetworks and communities in space. Then, based on the overlapping communities, we calculate the normalized entropy to measure the diversity of each node. Next, we compute the high-amplitude co-fluctuation of the eTS to explore the pattern of brain activity with temporal precision. Our results show that the normal control and EMCI patients differ in brain regions, subnetworks, and the whole brain. In particular, entropy values show a gradual decrease, and brain network co-fluctuation increases with disease progression. Our study is the first to investigate the pathogenesis of EMCI from the perspective of spatiotemporal flexibility and cognitive diversity based on high-order edge connectivity, further characterizing brain dynamics and providing new insights into the search for biomarkers of EMCI.

关键词： co-fluctuation edge-centric functional flexibility overlapping community spatiotemporal

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Towards Extractive Text Summarization using Multidimensional knowledge Representation

Towards Extractive Text Summarization using Multidimensional...

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IEEE International Conference on Electro Information Technology

作者： Johannes Zenkert André Klahold Madjid Fathi Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management

Multidimensional knowledge representation (MKR) is the result of integrative text mining. Analysis results from individual text mining methods such as named entity recognition, sentiment analysis, or topic detection are represented as dimensions in a knowledge base to support knowledge discovery, visualization or complex computer-aided writing tasks. Extractive text summarization is a content-oriented task which uses available information from text to shorten its length in order to summarize it. In this regard, a MKR knowledge base provides a structure which is applicable as an innovative selection instrument for text summarization. This paper introduces cross-dimensional text summarization based on dimensional selection and filtering of results retrieved from MKR knowledge base.

关键词： Text Summarization Integrative Text Mining knowledge Representation

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Adaptive structure-constrained robust latent low-rank coding for image recovery

arXiv

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arXiv 2019年

作者： Zhang, Zhao Wang, Lei Li, Sheng Wang, Yang Zhang, Zheng Zha, Zhengjun Wang, Meng School of Computer Science and Technology Soochow University Suzhou215006 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia 549 Boyd GSRC AthensGA30602 Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2,1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery. Copyright © 2019, The Authors. All rights reserved.

关键词： Recovery

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