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IEEE International Conference on Big knowledge (ICBK)

作者： Yuxin Jin Ze Yang Ying He Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

Classification is a hot topic in such fields as machine learning and data mining. The traditional approach of machine learning is to find a classifier closest to the real classification function, while ensemble classification is to integrate the results of base classifiers, then make an overall prediction. Compared to using a single classifier, ensemble classification can significantly improve the generalization of the learning system in most cases. However, the existing ensemble classification methods rarely consider the weight of the classifier, and there are few methods to consider updating the weights dynamically. In this paper, we are inspired by the idea of truth discovery and propose a new ensemble classification method based on the truth discovery (named ECTD). As far as we know, we are the first to apply the idea of truth discovery in the field of ensemble learning. Experimental results demonstrate that the proposed method performs well in ensemble classification.

关键词： Reliability Machine learning Classification algorithms Task analysis Buildings Computational modeling knowledge engineering

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Consensus with Voting Theory in Blockchain Environments

Consensus with Voting Theory in Blockchain Environments

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IEEE International Conference on Big knowledge (ICBK)

作者： Lei Li Yongkang Jiang Guanfeng Liu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Computing Department Macquarie University Sydney Australia

A blockchain can be taken as a decentralized and distributed public database. In order to achieve data consistency of the system nodes, the execution of a consensus algorithm is necessary and required in the case of decentralized environments. Simply speaking, the consensus is that every node agrees on some record in the blockchain. There are many kinds of consensus algorithms in blockchain environments, and each consensus algorithm has its own proper application scenario. Here we firstly analysis and compare various popular consensus algorithms in blockchain environments, and then as voting theory has systematically studied the decision-making in a group, the traditional methods of voting theory is summarized and listed, including (Position) scoring rules, Copeland, Maximin, Ranked pairs, Voting trees, Bucklin, Plurality with runoff, Single transferable vote, Baldwin rule, and Nanson rule. Finally, we introduce the voting methods from voting theory to consensus algorithms in the blockchain to improve its performance.

关键词： Blockchain Consensus algorithm Bitcoin Voting knowledge engineering Computer science Decision making

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Single Object Tracking for Dynamic Programming based on Fine-grained Network

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Journal of Physics: Conference Series 2020年第1期1576卷

作者： Jianguo Jiang Kaiyuan Jin Jingjing Wu Meibin Qi Qian Wang 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 Hefei 230601 China

Single target tracking has always been a key and challenging research field in computer vision. Currently, an increasing number of researchers are focusing on extracting better tracking features and designing the best tracker. This paper proposes a new single target tracking network that uses fine-grained features and dynamic programming (DPFNet). In order to extract superior features, we added an attention module to the regression network enabling us to extract finer-grained and discriminative features to achieve regression. Besides, we did observe that different objects have varying moving rates; for different moving targets, the magnitude of the changes in target position within two adjacent frames is not the same either. Although an area search of 4 times the target's size can be applied to most objects, targets with large position changes may appear in other image areas outside the search area and the target would not be located as a result. Aiming at solving this problem, when designing the tracker, this paper analyzes some of the indicators for predicting the location and uses the analysis results to determine whether the search area is appropriate, so as to dynamically adjust the extent of the search area thereby significantly improving the tracking function. In other words, the size of the search area can be dynamically recalibrated for different images. Subsequent experiments prove that the method put forward in this paper achieves State-of-the-Art results.

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Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension

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Journal of Physics: Conference Series 2020年第1期1437卷

作者： Yongzeng Yue Yuhong Zhang Xuegang Hu Peipei Li 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 Hefei 23061 China

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness; secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method.

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A Large-Scale Attribute dataset for Zero-Shot Learning

A Large-Scale Attribute Dataset for Zero-Shot Learning

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Bo Zhao Yanwei Fu Rui Liang Jiahong Wu Yonggang Wang Yizhou Wang National Engineering Laboratory for Video Technology Key Laboratory of Machine Perception (MoE) Cooperative Medianet Innovation Center Shanghai School of EECS Peking University Beijing China School of Data Science Fudan University Sinovation Ventures

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Zero-Shot Learning (ZSL) has attracted huge research attention over the past few years; it aims to learn the new concepts that have never been seen before. Each concept (class) is embedded in two or more modalities, e.g., the image features and semantic embeddings. Attributes are introduced as the intermediate semantic representation to realize the knowledge transfer from seen to unseen classes. Previous ZSL algorithms are tested on several benchmark datasets, which are defective in terms of the image distribution and attribute diversity. In addition, we argue that the "co-occurrence bias problem" of existing datasets, which is caused by the biased co-occurrence of objects, significantly hinders models from correctly learning the concept. To overcome these problems, we propose a Large-scale Attribute dataset (LAD) with 78,017 images of 230 classes. 359 attributes of visual, semantic and subjective properties are defined and annotated in instance-level. Seven state-of-the-art ZSL algorithms are tested on this new dataset. The experimental results reveal the challenge of implementing ZSL on our dataset. Based on the proposed dataset, Zero-shot Learning Competition of AI Challenger (>110 teams attended) has been organized for promoting ZSL research.

关键词： Semantics Visualization Feature extraction Image color analysis Sun Dogs

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Automated vision inspection of rail surface cracks: A double-layer data driven framework 48

Automated vision inspection of rail surface cracks: A double...

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48th International Conference on Computers and Industrial engineering, CIE 2018

作者： Zhuang, Li Wang, Long Zhang, Zijun School of Data Science City University of Hong Kong Hong Kong Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

A double-layer data-driven framework for the automated vision inspection of the rail surface cracks is proposed in this paper. Based on images of rails, the proposed framework is capable to detect the location of cracks firstly and next automatically obtain the boundary of cracks via a feature-based linear iterative crack aggregation (FLICA). Extended Haar-like features are applied to develop significant features for identifying cracks in images. Built on extended Haar-like features, a cascading classifier ensemble integrating three single cascading classifiers with a major voting scheme is proposed to decide the presence of cracks in the image. Each single cascading classifier is composed of a sequence of stage classifiers trained by the LogitBoost algorithm. A scalable sliding window carrying the cascading classifier ensemble is applied to scan images of rail tracks, which is identified by the Otsu’s method, and detect cracks. After completing the crack registration in the first layer, the FLICA is developed to discover boundaries of cracks. The effectiveness of the proposed data-driven framework for identifying rail surface cracks is validated with the rail images provided by the China Railway Corporation and Hong Kong Mass Transit Railway (MRT). Six benchmarking methods, the Otsu’s method, mean shift, the visual detection system, the geometrical approach, fully convolutional networks and the U-net, are utilized to prove advantages of the proposed framework. Results of the validation and comparative analyses demonstrate that the proposed framework is most effective in the rail surface crack detection. © 2018, Curran Associates Inc. All rights reserved.

关键词： Crack detection

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CitationAS： A Tool of Automatic Survey Generation Based on Citation Content

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Journal of data and Information Science 2018年第2期3卷 20-37页

作者： jie wang chengzhi zhang mengying zhang sanhong deng School of Information Management Nanjing University Nanjing 210023 China Department of Information Management Nanjing University of Science and Technology Nanjing 210094 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing 210023 China

Purpose： This study aims to build an automatic survey generation tool, named CitationAS, based on citation content as represented by the set of citing sentences in the original ***/methodology/approach： Firstly, we apply LDA to analyse topic distribution of citation content. Secondly, in CitationAS, we use bisecting K-means, Lingo and STC to cluster retrieved citation content. Then Word2Vec, Word Net and combination of them are applied to generate cluster labels. Next, we employ TF-IDF, MMR, as well as considering sentence location information, to extract important sentences, which are used to generate surveys. Finally, we adopt manual evaluation for the generated ***： In experiments, we choose 20 high-frequency phrases as search terms. Results show that Lingo-Word2Vec, STC-Word Net and bisecting K-means-Word2Vec have better clustering effects. In 5 points evaluation system, survey quality scores obtained by designing methods are close to 3, indicating surveys are within acceptable limits. When considering sentence location information, survey quality will be improved. Combination of Lingo, Word2Vec, TF-IDF or MMR can acquire higher survey *** limitations： The manual evaluation method may have a certain subjectivity. We use a simple linear function to combine Word2Vec and Word Net that may not bring out their strengths. The generated surveys may not contain some newly created knowledge of some articles which may concentrate on sentences with no *** implications： CitationAS tool can automatically generate a comprehensive, detailed and accurate survey according to user’s search terms. It can also help researchers learn about research status in a certain ***/value： Citaiton AS tool is of practicability. It merges cluster labels from semantic level to improve clustering results. The tool also considers sentence location information when calculating sentence score by TF-IDF and MMR.

关键词： Automatic survey system Citation content Clustering algorithms Labelgeneration approaches Sentence extraction methods

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Digital personal health libraries: A systematic literature review

arXiv

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arXiv 2020年

作者： Ding, Huitong Zhang, Chi An, Ning Zhang, Lingling Xie, Ning Alterovitz, Gil School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Computer Science University of California Davis CA United States College of Nursing and Health Sciences University of Massachusetts Boston BostonMA United States Biomedical Cybernetics Laboratory Division of General Internal Medicine and Primary Care Department of Medicine Brigham and Women's Hospital BostonMA United States Department of Medicine Harvard Medical School BostonMA United States

Objective: This paper gives context on recent literature regarding the development of digital personal health libraries (PHL) and provides insights into the potential application of consumer health informatics in diverse clinical specialties. Materials and Methods: A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Here, 2,850 records were retrieved from PubMed and EMBASE in March 2020 using search terms: personal, health, and library. Information related to the health topic, target population, study purpose, library function, data source, data science method, evaluation measure, and status were extracted from each eligible study. In addition, knowledge discovery methods, including co-occurrence analysis and multiple correspondence analysis, were used to explore research trends of PHL. Results: After screening, this systematic review focused on a dozen articles related to PHL. These encompassed health topics such as infectious diseases, congestive heart failure, electronic prescribing. data science methods included relational database, information retrieval technology, ontology construction technology. Evaluation measures were heterogeneous regarding PHL functions and settings. At the time of writing, only one of the PHLs described in these articles is available for the public while the others are either prototypes or in the pilot stage. Discussion: Although PHL researches have used different methods to address problems in diverse health domains, there is a lack of an effective PHL to meet the needs of older adults. Conclusion: The development of PHLs may create an unprecedented opportunity for promoting the health of older consumers by providing diverse health information. Copyright © 2020, The Authors. All rights reserved.

关键词： Population statistics

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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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The Second-order h-type Indicators for Identifying Top Units

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data and Information Management 2018年第1期2卷 49-56页

作者： Ye, Fred Y. Bornmann, Lutz School of Information Management Nanjing University 210023 Nanjing China Jiangsu Key Laboratory of Data Engineering and Knowledge Service 210023 Nanjing China Administrative Headquarters of the Max Planck Society Division for Science and Innovation Studies Hofgartenstr. 8 D-80539 Munich Germany

The second-order h-type indicators are suggested to identify top units in scientometrics. Basically, the re-ranking of h-type series leads to the second-order h-type indicator. The second-order h-type indicators provide an interesting and natural method to identify top units, yielding fixed h-top. Differentiating from the series of artificially defined highly cited percentile classes, the h-top contributes a natural definite top in the series of highly cited classes. When studying theoretically, the second-order h-index concerns 3% of the h-top whereas the first-order h-index refers to 10% of the h-core. The ratio of the first- and second-order h-index, hT/h, is 30%. When studying empirically, the ratio of the first- and second-order h-index, hT/h, is © 2018 © 2018 Fred Y. Ye, Lutz Bornmann, published by Sciendo

关键词： Indexing (of information)

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