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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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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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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

MedComm

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MedComm 2025年第6期6卷 e70247页

作者： Ruoyun Wang Fanxuan Chen Haoman Chen Chenxing Lin Jincen Shuai Yutong Wu Lixiang Ma Xiaoqu Hu Min Wu Jin Wang Qi Zhao Jianwei Shuai Jingye Pan Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China. Wenzhou Medical University Wenzhou China. UCSC Baskin School of Engineering University of California Santa Cruz California USA. Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China. Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston Massachusetts USA. Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China. Stony Brook University Stony Brook New York USA. School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China. Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China. Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China. Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China.

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging.

关键词： deep learning digital breast tomosynthesis early breast cancer screening medical image analysis public database

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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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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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Assessing Mental Stress Based on Smartphone Sensing data: An Empirical Study

Assessing Mental Stress Based on Smartphone Sensing Data: An...

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Feng Wang Yasha Wang Jiangtao Wang Haoyi Xiong Junfeng Zhao Daqing Zhang Key Laboratory of High Confdence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Peking University Information Technology Institute Tianjin Binhai China National Engineering Research Center of Software Engineering Beijing China Big Data Lab Baidu Research Beijing

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mental stress is a critical factor affecting one's physical and mental well-being. At the early stage, the effect of stress is often underestimated, while it usually leads to serious issue Lateran. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones embedded with a rich set of sensors, which can capture people's context, such as movement, sound, location and so on, it is an alternative way to access people's behavior by smartphones. Through an empirical study, this paper proposes an automatic and non-intrusive stress detection framework based on smartphone sensing data. First, we construct various discriminative features from multi-modality phone sensing data, in which both absolute and relative features are considered to make the model more personalized. Then, to tackle the challenge of label insufficiency, we further develop a co-training based method for stress level classification. Finally, we evaluate our model based on an open dataset, and the experimental results verify its advantages over other baselines.

关键词： Sensors Stress Feature extraction Psychology Mobile handsets data models Biomedical monitoring

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Fake Colorized Image Detection with Channel-wise Convolution based Deep-learning Framework

Fake Colorized Image Detection with Channel-wise Convolution...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Long Zhuo Shunquan Tan Jishen Zeng Bin Lit Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781728102436;9789881476852

Colorization is one remarkable emerging image manipulating technique, which maybe potentially used for illegal purpose. In this paper, we introduce WISERNet (Wider Separate-then-reunion Network), a recently proposed deep-learning based data-driven color image steganalyzer in the field of fake colorized image detection. We believe that statistical inconsistencies introduced by different automatic colorization methods can be captured by advanced deep-learning based data-driven color-image steganalyzers such as WISERNet. Experimental evidences reported in this paper supports our claims: the detection performance of our proposed detector obviously outperforms FCID-HIST and FCID-FE, two state-of-the-art hand-crafted features specific to fake colorized image detection. Please note that in our approach we have never explicitly utilized information from the specific channels other than the ordinary red, green, and blue color channel, which is completely different from prior works in this field.

关键词： Detectors Training Image color analysis Error analysis Convolution Forensics

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Deep learning based super resolution using significant and general regions 25

Deep learning based super resolution using significant and g...

引用

25th IEEE International Conference on Image Processing, ICIP 2018

作者： Liling, Zhao Zelin, Zhang Quansen, Sun School of Information and Control Engineering NUIST Nanjing Jiangsu210044 China Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing Jiangsu210044 China School of Cornputer Science and Technology NUST Nanjing Jiangsu210044 China

ISBN: (纸本)9781479970612

Today, big data brings benefits to many areas of scientific research. However, processing these large amounts of data often requires extensive computing time and a large storage space. Global feature analysis is considered to be universal in traditional super resolution methods, but it is not applicable to big data. There remains a viewpoint that it is not necessary to address all data equally and impartially. Focusing on useful information can make the massive data analysis possible and more effective. In this paper, we consider the significant regions, and thus, we propose a new super resolution approach that uses significant and general information. Under the framework of a convolutional neural network, the training process is performed on the significant parts of the training data set, and the reconstruction process considers significant parts separately;then, a super resolution image will be obtained according to each different demand. This concept is easy to understand, but it can be achieved only via the big data approach with many similar images on the Internet and the effective deep learning algorithm. Experiments show that our new approach can reduce the testing time and obtain a high-quality reconstructed image. © 2018 IEEE.

关键词： big data

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Efficient EK-means: Extended K-means Clustering for Categorical data with High Processing Speed

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Journal of Physics: Conference Series 2020年第1期1584卷

作者： Chen Xi Peng Shuo HunanProvincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of computer and communication engineering Changsha University of Science&technology No. 960 Wanjiali South Road Hunan Province China

The typical representative of the hard clustering algorithm, K-means, is one of the fastest processing algorithms with good scalability. However, it cannot deal with categorical attributes, which is one of the important indicators to measure the pros and cons. Due to the lack of processing capabilities on categorical attributes, k-means has a large limit on data processing capabilities. This paper proposes a clustering algorithm extends from K-means. This algorithm introduces the concept of a Pseudo-mean distance calculation formula and a counting-table so that categorical attributes can be processed while reducing the time cost as much as possible. Experimental results illustrate the proposed Pseudo-means can extend the processing range of k-means to category-type data, and the counting table also effectively reduces the time cost.

关键词：

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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...

引用

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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