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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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Review of bankruptcy prediction using machine learning and deep learning techniques

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Procedia Computer Science 2019年 162卷 895-899页

作者： Yi Qu Pei Quan Minglong Lei Yong Shi School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Faculty of Information Technology Beijing University of Technology Beijing 100124 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Bankruptcy prediction has long been a significant issue in finance and management science, which attracts the attention of researchers and practitioners. With the great development of modern information technology, it has evolved into using machine learning or deep learning algorithms to do the prediction, from the initial analysis of financial statements. In this paper, we will review the machine learning or deep learning models used in bankruptcy prediction, including the classical machine learning models such as Multivariant Discriminant Analysis (MDA), Logistic Regression (LR), Ensemble method, Neural Networks (NN) and Support Vector Machines (SVM), and major deep learning methods such as Deep Belief Network (DBN) and Convolutional Neural Network (CNN). In each model, the specific process of experiment and characteristics will be summarized through analyzing some typical articles. Finally, possible innovative changes of bankruptcy prediction and its future trends will be discussed.

关键词： Bankruptcy prediction Machine learning Deep learning

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Overview of the balancing pervasive information systems project and a call for research collaborations 10

Overview of the balancing pervasive information systems proj...

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10th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2018

作者： Xie, Sherry L. School of Information Resource Management Renmin University of China 59 Zhongguancun Ave. Beijing China Center for Digital Records Management Research Beijing China Key Laboratory of Knowledge and Data Engineering Ministry of Education of China China

ISBN: (纸本)9789897583308

The Research on Balancing Theories and Mechanisms to Pervasive Information Systems (B2P) is one of the component studies of the Records-centered Digital Information Management Theory and Mechanisms (DI{R}Mtm) Project, which aims pointedly at synchronizing records/information related research interests with the evolution of information technologies and their joint impact on society. Due to the breakthroughs in cloud computing, big data, the Internet of Things and artificial intelligence, ubiquitous/pervasive computing, conceived in 1988, has now been rapidly advanced. As its materialization in organizations, pervasive information systems (PISs) converges information technologies, data, digital operations and human actors and functions as the main channel for digital decisions. Digital decisions, therefore, are autonomous, algorithmic and penetrating, possessing influential abilities that are much greater than any single technology in human history, including the Internet. Such abilities make it a strong force for societal advancement, yet, at the same time, a potential hurdle – even harm – for decision recipients who are unaware of the decision-making methods. The B2P study is conceived as a reaction to this phenomenon. This paper gives an overview of the B2P study and calls for collaborations among researchers from the various fields relevant to the aims of the study, i.e., archival science, records management, information science, pubic administration, law, social sciences, and computer sciences. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Information management

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New Discoveries of Domination Between Traffic Matrices

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 561-566页

作者： Peng-fei LIU Wen-guo YANG Tian-de GUO School of Mathematics Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100049China

In this paper the definition of domination is generalized to the case that the elements of the traffic matrices may have negative values. It is proved that D3 dominates D3 ＋ λ（D2 - D1） for any λ ≥0 if D1 dominates D2. Let u（D） be the set of all the traffic matrices that are dominated by the traffic matrix D. It is shown that u （ D∞） and u （D ∈） are isomorphic. Besides, similar results are obtained on multi-commodity flow problems. Fhrthermore, the results are the generalized to integral flows.

关键词： robust network design multi-commodity flows domination

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous Information. All rights reserved.

关键词： Semantics

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Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition

arXiv

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arXiv 2019年

作者： Zhao, Meixiang Jia, Zhigang Cai, Yunfeng Chen, Xiao Gong, Dunwei School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Mathematics and Statistics Jiangsu Key Laboratory of Education Big Data Science and Engineering Jiangsu Normal University Xuzhou221116 China Cognitive Computing Lab Baidu Research Beijing100193 China

The two-dimensional principal component analysis (2DPCA) has become one of the most powerful tools of artificial intelligent algorithms. In this paper, we review 2DPCA and its variations, and propose a general ridge regression model to extract features from both row and column directions. To enhance the generalization ability of extracted features, a novel relaxed 2DPCA (R2DPCA) is proposed with a new ridge regression model. R2DPCA generates a weighting vector with utilizing the label information, and maximizes a relaxed criterion with applying an optimal algorithm to get the essential features. The R2DPCA-based approaches for face recognition and image reconstruction are also proposed and the selected principle components are weighted to enhance the role of main components. Numerical experiments on well-known standard databases indicate that R2DPCA has high generalization ability and can achieve a higher recognition rate than the state-of-the-art methods, including in the deep learning methods such as CNNs, DBNs, and DNNs. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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Importance of Small Probability Events in Big data: Information Measures, Applications, and Challenges

arXiv

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arXiv 2019年

作者： She, Rui Liu, Shanyun Wan, Shuo Xiong, Ke Fan, Pingyi Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

In many applications (e.g., anomaly detection and security systems) of smart cities, rare events dominate the importance of the total information of big data collected by Internet of Things (IoTs). That is, it is pretty crucial to explore the valuable information associated with the rare events involved in minority subsets of the voluminous amounts of data. To do so, how to effectively measure the information with importance of the small probability events from the perspective of information theory is a fundamental question. This paper first makes a survey of some theories and models with respect to importance measures and investigates the relationship between subjective or semantic importance and rare events in big data. Moreover, some applications for message processing and data analysis are discussed in the viewpoint of information measures. In addition, based on rare events detection, some open challenges related to information measures, such as smart cities, autonomous driving, and anomaly detection in IoTs, are introduced which can be considered as future research directions. Copyright © 2019, The Authors. All rights reserved.

关键词： data Analytics

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Unsupervised keyword Extraction Method Based on Chinese Patent Clustering

Unsupervised Keyword Extraction Method Based on Chinese Pate...

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IEEE International Conference on Big knowledge (ICBK)

作者： Yuxin Xie Xuegang Hu Yuhong Zhang Shi 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 China Intelligent Connected Vehicle Research Institute (JAC Technical Center) Anhui Jianghuai Automobile Group Corp.Ltd Hefei China

Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic information. In this paper, we propose an Unsupervised keyword Extraction Method (UKEM) based on Chinese patent clustering. More specifically, we use a Skip-gram model to train word embeddings based on a Chinese patent corpus. Then each patent is represented as a vector called patent vector. These patent vectors are clustered to obtain several cluster centroids. Next, the distance between each word vector in patent abstract and cluster centroid is computed to indicate the semantic importance of this word. The experimental results on several Chinese patent datasets show that the performance of our proposed method is better than several competitive methods.

关键词： Patents Semantics data mining knowledge engineering Neural networks Big data Computer science

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Multi-Stage Enhanced Rain Image Restoration Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Quanqing Wang Yuanshun Cheng Yueting Yang Chao Wang Chuansheng Yang School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang 316022 China Tianfu College of SWUFE Mianyang Sichuan 621000 China School of Mathematics and Statistics Beihua University Jilin Jilin 132013 P.R. China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan Zhejiang 316022 China

The restoration task of rain images is beneficial to the further development of computer vision tasks. However, recent rain removal methods have difficulty coping with dense rain streaks. In this paper, we propose a Multi-stage Enhanced Rain Image Restoration Network (MERNet) for the task of rain removal. Our main idea is to design a three-stage network architecture, which makes full use of the enhanced information of the previous stage and complements the information of the next stage through transmission step by step. We give consideration to information enhancement at the same time of information transmission, fully improve the information utilization rate and the overall network effect.

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Attention-based Feature Enhanced Dehazing Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Yuanshun Cheng Anhui Tan Chuansheng Yang Yueting Yang Chao Wang Tianfu College of SWUFE Mianyang Sichuan 621000 China School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang 316022 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan Zhejiang 316022 China School of Mathematics and Statistics Beihua University Jilin Jilin 132013 P.R. China

Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should enhance the feature information of the background while weakening the feature information of haze. In this paper, we propose an end-to-end attention-based feature enhanced dehazing network (AEDNet), which integrates enhancement strategy and attention mechanism, to achieve haze removal. The network is based on U-Net, which has the advantages of retaining information, obtaining multi-scale features and so on. In the training of the network, pixel loss and perceptual loss are used to preserve feature information and improve the overall quality of results. The extensive evaluation shows that the proposed model performs significantly better than previous dehazing methods on various benchmarks.

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