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30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018

作者： Liu, Chiyu Liu, Zheng Li, Tao Xia, Bin Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)1891706446

Understanding contents in social networks by inferring high-quality latent topics from short texts is a significant task in social analysis, which is challenging because social network contents are usually extremely short, noisy and full of informal vocabularies. Due to the lack of sufficient word co-occurrence instances, well-known topic modeling methods such as LDA and LSA cannot uncover high-quality topic structures. Existing research works seek to pool short texts from social networks into pseudo documents or utilize the explicit relations among these short texts such as hashtags in tweets to make classic topic modeling methods work. In this paper, we explore this problem by proposing a topic model for noisy short texts with multiple relations called MRTM (Multiple Relational Topic Modeling). MRTM exploits both explicit and implicit relations by introducing a document-Attribute distribution and a two-step random sampling strategy. Extensive experiments, compared with stateof-the-Art topic modeling approaches, demonstrate that MRTM can alleviate the word co-occurrence sparsity and uncover highquality latent topics from noisy short texts. © 2018 Universitat zu Koln. All rights reserved.

关键词： Quality control

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A multi-branch hybrid transformer network for corneal endothelial cell segmentation

arXiv

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arXiv 2021年

作者： Zhang, Yinglin Higashita, Risa Fu, Huazhu Xu, Yanwu Zhang, Yang Liu, Haofeng Zhang, Jian Liu, Jiang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Cixi Institute of Biomedical Engineering Chinese Academy of Sciences China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Tomey Corporation Nagoya451-0051 Japan Global Big Data Technologies Centre University of Technology SydneyNSW Australia Inception Institute of Artificial Intelligence United Arab Emirates Intelligent Healthcare Unit Baidu Beijing100085 China

Corneal endothelial cell segmentation plays a vital role in quantifying clinical indicators such as cell density, coefficient of variation, and hexagonality. However, the corneal endothelium’s uneven reflection and the subject’s tremor and movement cause blurred cell edges in the image, which is difficult to segment, and need more details and context information to release this problem. Due to the limited receptive field of local convolution and continuous downsampling, the existing deep learning segmentation methods cannot make full use of global context and miss many details. This paper proposes a Multi-Branch hybrid Transformer Network (MBT-Net) based on the transformer and body-edge branch. Firstly, We use the convolutional block to focus on local texture feature extraction and establish long-range dependencies over space, channel, and layer by the transformer and residual connection. Besides, We use the body-edge branch to promote local consistency and to provide edge position information. On the self-collected dataset TM-EM3000 and public Alisarine dataset, compared with other State-Of-The-Art (SOTA) methods, the proposed method achieves an improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Endothelial cells

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Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

Deep Learning for Password Guessing and Password Strength Ev...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Tao Zhang Zelei Cheng Yi Qin Qiang Li Lin Shi Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China Department of Computer and Information Technology Purdue University West Lafayette USA PLA Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.

关键词： Deep learning Training Transfer learning Security Gallium nitride Password Text processing

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Anomaly detection for drinking water quality via deep bilSTM ensemble 18

Anomaly detection for drinking water quality via deep bilSTM...

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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Chen, Xingguo Feng, Fan Wu, Jikai Liu, Wenyu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu Province China

ISBN: (纸本)9781450357647

In this paper, a deep BiLSTM ensemble method was proposed to detect anomaly of drinking water quality. First, a convolutional neural network (CNN) is utilized as a feature extractor in order to process the raw data of water quality. Second, bidirectional Long Short Term Memory (BiLSTM) is employed to handle the time series prediction problem. Then, a linear combination of t-time-step predictions weighted by a discount factor was utilized to ensemble the final output of event. Finally, cost-sensitive learning combined with Adam optimization was applied to learn the model according to the imbalance property of the event label. © 2018 Copyright held by the owner/author(s).

关键词： Long short-term memory

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Theory-screened Prussian blue analogues-based nanozymes for promoting diabetic wound healing via ferroptosis inhibition

Materials Today Bio

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Materials Today Bio 2025年 32卷 101839页

作者： Dong, Qingrong Shen, Xiaomei Fang, Ge Shi, Jia Zhu, Xiafeng Du, Jiangfeng Zhang, Hui Ge, Cuicui Department of Medical Imaging First Hospital of Shanxi Medical University Taiyuan030001 China State Key Laboratory of Radiation Medicine and Protection School of Radiation Medicine and Protection Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions Soochow University Suzhou215123 China College of Chemistry and Materials Jiangxi Normal University Nanchang330022 China Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province Taiyuan030001 China College of Pharmacy Academy of Medical Sciences Shanxi Medical University Jinzhong030619 China

Diabetes mellitus (DM) induced wound healing impairment remains a serious health problem, which can lead to limb amputation and even shorten life span. Inhibiting ferroptosis of endothelial cells has shown promise in promoting tissue repair and regeneration. However, a majority of known ferroptosis inhibitors belong to either antioxidants or iron-chelators but with poor pharmacological adherence and even serious side effects. Herein, we construct a series of Prussian blue analogues-based nanozymes as ferroptosis nano-inhibitors to simultaneously achieve highly efficient intracellular iron capture and antioxidant properties. Through computational and experimental methods, the optimized Prussian blue analogues (MPBs) with highest iron chelating efficiency and efficacy (CuPBs) are screened out and both the CuPBs and the post-chelated products have been demonstrated to exhibit reactive oxygen species (ROS)-scavenging activities. As expected, the CuPBs successfully inhibit ferroptosis in high glucose-cultured skin repair cells, thereby repairing their proliferation, migration and angiogenesis. Mechanistically, the CuPBs regulate ferroptosis by inhibiting iron accumulation and improving the antioxidant capacity of the Xc−-GPX4 system. Moreover, in a murine diabetic wound model, the CuPBs remarkably promote the wound healing by inhibiting ferroptosis and increasing the M2/M1 macrophage ratio, showing a 2-fold higher wound closure rate than deferoxamine (DFO). Collectively, our study presents a general design strategy on developing ferroptosis nano-inhibitors and provides a promising approach for treating ferroptosis-related diseases. © 2025 The Authors

关键词： Endothelial cells

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A personalized privacy protection data uploading scheme for mobile crowdsensing 2

A personalized privacy protection data uploading scheme for ...

引用

2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Ma, Rong Chen, Lei Xiong, Jinbo Tian, Youliang Li, Qi Liu, Ximeng Fujian Provincial Key Laboratory of Network Security and Cryptology College of Mathematics and Informatics Fujian Normal University Fuzhou350117 China College of Engineering and Computing Georgia Southern University GA30458 United States Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Fujian Provincial Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China

ISBN: (纸本)9781728129778

Mobile crowdsensing is a typical application of the Industrial Internet of hings and brings a promising opportunity to construct powerful industrial systems and services. The existing privacy protection schemes usually adopt a unified privacy protection policy for all sensing data due to lack of effective privacy metrics, thereby resulting in excessive or insufficient protection of privacy information and low accuracy of sensing data. To tackle these issues, in this paper, we propose a personalized privacy protection data uploading (PPDU) scheme based on game theory and sensing user's historical spatio-temporal data. In PPDU, a personalized privacy measurement algorithm is designed to obtain the personalized privacy degree of user sensing data. Based on the privacy degree, a user uploading behavior game is designed by employing an incomplete information game, and a game-based spatio-temporal sensing data uploading method is proposed for user privacy protection. Simulation experiments with real trajectory data show that the PPDU scheme makes a reasonable balance between retaining high quality of crowdsensing service and protecting user's privacy. © 2019 IEEE.

关键词： Game theory

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Research on Video Recommendation Algorithm Based on Knowledge Reasoning of Knowledge Graph

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and big data, APWeb-WAIM 2018

作者： Xu, Zhihong Zhao, Xing Dong, Yongfeng Yan, Wenjie Yu, Ziqi School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Computing Tianjin300401 China

ISBN: (纸本)9783030012977

Since collaborative filtering algorithm cannot make full use of video attribute information, mining implicit information of video, this paper proposes a video recommendation algorithm based on knowledge reasoning of knowledge graph. The algorithm uses a knowledge graph with powerful semantic processing capabilities and open interconnection capabilities. The ontology model is used to formalize the implicit semantics in the data. By using the knowledge inference method of the knowledge graph, the existing triplet information is used to establish new relationships between entities and assign corresponding weights to the paths. All the semantic information is embedded in the low-dimensional vector space, and the potential semantic similarity of the video is calculated by combining the path weights. And then, integrate semantic similarity into collaborative filtering for recommendation. Experiments show that this algorithm can make up for the deficiency that collaborative filtering algorithms cannot fully utilize the hidden information of video, and enhance the effectiveness of recommendation at the semantic level, what is more, the recommendation results are interpretable. To a certain extent, it can solve the problem of data sparseness. © 2018, Springer Nature Switzerland AG.

关键词： Collaborative filtering

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Automatic Recognition of Business Process Images

Automatic Recognition of Business Process Images

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第一届计算机科学与教育信息化国际会议

作者： Qingyi Kang Yong Yu Fei Dai School of Software Yunnan University School of Software Key Laboratory in Software Engineering of Yunnan Province Yunnan University School of Big Data and Intelligent Engineering Southwest Forestry University

The Business Process Modelling Notation(BPMN) is a defacto standard for modelling business processes in the early phases of systems development. The increased adoption of business process management approaches has led organizations to model large collections of BPMN business process models. The mix of constructs in BPMN business process models can lead to semantic error. The ability to take a picture of BPMN business process models and analyze the semantic correctness of BPMN business process images is thus a desirable feature. In this paper, we propose an automatic recognition method to transform BPMN business process images into Petri nets, for which efficient analysis techniques are available. The proposed recognition has been implemented as a tool, in conjunction with existing Petri net-based tools, that enables the soundness analysis of business process images. This transformation can led to the identification of deficiencies in the BPMN business process images.

关键词： business process business process recognition Petri nets

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Matching Biomedical Ontologies with Long Short-Term Memory Networks

Matching Biomedical Ontologies with Long Short-Term Memory N...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chao Jiang Xingsi Xue Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian China School of Computer Science and Mathematics Fujian University of Technology Fuzhou Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin Guangxi China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Semantic Web (SW) has attracted the increasing attention of researchers, which facilitates people to link and handle various data. Ontology is the kernel technique of SW, and biomedical ontology is a state-of-art biomedical knowledge modeling technique, which formally defines the biomedical concepts and their relationships. However, the same biomedical concepts in different biomedical ontologies could be defined in various contexts or with different terms, which yields the biomedical ontology heterogeneity problem. It is crucial to find mapping among heterogeneity concepts of different biomedical ontologies for bridging the semantic gaps, which is the so-called biomedical ontology matching. Biomedical ontology matching problem is an open challenge due to the rich semantic meaning and the flexible representation on a biomedical concept. To address this challenging problem, in this work, it is regarded as a binary classification problem, and a Long Short-Term Memory Networks (LSTM)-based ontology matching technique is proposed to solve it. Our proposal improves the quality of the alignment by introducing the char-embedding technique, which takes into account the semantic and context information of concepts. The comparing results with OAEI's participants show the effectiveness of our proposal.

关键词： Ontologies Semantics Logic gates Training Human anatomy Proposals Biomedical measurement

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Multi-view ontology alignment visualization (MOAV): A human-cognition based ontology alignment visualization technique

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第4期5卷 198-210页

作者： Chen, Jie Zhang, Jie Xue, Xingsi Huang, Yikun Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guangxi Guilin541004 China Department of Information Technology Concord University College Fujian Normal University No.68 Xueyuan Road University Town Minhou FuzhouFujian350117 China

To further improve the quality of ontology alignment, it is necessary for an ontology matcher to introduce a user’s knowledge into its automatic matching process, which yields the development of interactive ontology matching techniques. Since validating problematic entity correspondences is a difficult cognition task, user interaction based on Ontology Alignment Visualization (OAV) has become the critical component of an interactive ontology matcher, which directly affects the quality of validating result and the efficiency of validating process. The existing OAV tools are mainly developed based on their designers’ subjective feelings and experiences, which do not take into consideration the law of human cognition. To improve the efficiency of the ontology alignment validating process, in this paper, a Multi-view OAV (MOAV) is proposed, which synthetically utilizes the human cognitive theory-information visualization and human-computer inter-action. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test the performance of our proposal, and the experimental results show that MOAV can effectively improve a user’s validating efficiency. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Visualization

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