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A method for translating belief function models to probability models

Journal of Information and Computational Science 2011年第10期8卷 1817-1823页

作者： Xu, Peida Wu, Jiyi Li, Ya Cong, Jiang Sun, Xiaohong Kang, Bingyi Deng, Yong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Hangzhou Key Laboratory of E-Business and Information Security Hangzhou Normal University Zhejiang 310036 China College of Food Science and Technology Shanghai Ocean University Shanghai 201306 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China Key Subject Laboratory of National defense for Radioactive Waste and Environmental Security Southwest University of Science and Technology Mianyang 621010 China College of Computer and Information Sciences Southwest University Chongqing 400715 China

One of the key issues in the applications of Dempster-Shafer evidence theory is how to make a decision based on Basic Probability Assignment (BPA). The conventional method is Pignistic Probability Transformation (PPT), which transforms BPA to probability distribution with equally assignment. To solve the existing problems of pignistic probability transformation, a new transformation method using an optimization algorithm based on the conflict coefficient k is proposed in this paper. The obtained probability has the minimum conflict coefficient with the original BPA. Numerical examples are used to illustrate the efficiency of the proposed method. © 2011 by Binary Information Press.

关键词： Decision making

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Risk analysis of physical protection system based on evidence theory

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Journal of Information and Computational Science 2010年第13期7卷 2871-2878页

作者： Xu, Peida Su, Xiaoyan Wu, Jiyi Sun, Xiaohong Zhang, Yajuan Deng, Yong College of Computer and Information Sciences Southwest University Chongqing 400715 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University Zhejiang 310036 China College of Food Science and Technology Shanghai Ocean University Shanghai 201306 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China Key Subject Laboratory of National defense for Radioactive Waste and Environmental Security Southwest University of Science and Technology Mianyang 621010 China School of Electronics and Information Technology Shanghai Jiao Tong University Shanghai 200240 China

Risk evaluation is very important to the design and improvement of physical protection systems. In this paper, an evaluation method of multi-source information fusion is proposed based on the D-S evidence theory. In the proposed method, each individual component of the protection system in the simulated plane is modeled. Then, the threat report of each component according to the specific tactics is determined based on its real environment. Finally, the comprehensive threat distribution is obtained based on through the D-S evidence theory to combine multi-sources information. The proposed method can easily applied to the evaluation of the effectiveness of the protection system. We make the total threat of the protection system lowest through changes of the protection resources allocation. A numerical example is used to illustrate the efficiency of the proposed method. Copyright © 2010 Binary Information Press.

关键词： Information fusion

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Ed-Tgan: A Methodology on Temporal Generative Gan for Imbalanced Data for Predicting Academic Performance of Students

SSRN

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SSRN 2022年

作者： Waheed, Hajra Zhou, Jia Aljohani, Naif R. Tuarob, Suppawong Nawaz, Raheel Herrera, Francisco Hassan, Saeed-Ul Information Technology University 346-B Ferozepur Road Lahore Pakistan Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Faculty of Information and Communication Technology Mahidol University Thailand Staffordshire University Stoke-on-Trent United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Manchester Metropolitan University Manchester United Kingdom

With the increase in the self-paced learning phenomenon, class imbalance in the learning analytics community has become more prevalent, with lesser students dropping out than students who intend to pursue. Similarly, an imbalance in the behavioral performances of students is also observed. This imbalance problem becomes more complicated for temporal data, where each time sequence represents a particular student, and several sequences formulate the overall engagement of students throughout a course. The conventional methods for generating synthetic data do not account for this association of sequences, where each synthetically generated student is associated with its previous sequence. Generative Adversarial Networks (GANs) have been rapidly adopted in the machine learning discipline for synthetic data generation to reduce the class imbalance. In this study, we propose a deep learning-powered methodology called Educational Temporal Generative Adversarial Networks (Ed-TGANs) methodology comprising the following steps: a) Smart Data Processing for Actionable Data, b) GANs for Temporal Data Generation, and c) Learning Approach. Furthermore, the Open University Learning Analytics dataset was deployed as a case study comprising 69% pass and 31% fail students to validate the effectiveness of our proposed methodology, encompassing discrete and continuous features. The results are compared with GAN-based and non-GAN upsampling approaches to demonstrate the proposed methodology's effectiveness, which shows an increase in accuracy of up to 5.33% compared to classic GANs, resulting in improved understating of student behavior. We believe that the presented methodology can be used as a starting point for various areas for upsampling jobs with temporal longitudinal data. © 2022, The Authors. All rights reserved.

关键词： Students

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Research on the Evolution Mechanism of Friction Wear Based on Macroscopic Dynamics

SSRN

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SSRN 2024年

作者： Jia, Bowen Pei, Shixun Yang, Fangchao Li, Honggang Chen, Bingxue Zhao, Menghao Chen, Yuanhang Wang, Liang Hong, Wei MOE Key Laboratory of Fundamental Physical Quantities Measurement Hubei Key Laboratory of Gravitation and Quantum Physics PGMF School of Physics Huazhong University of Science and Technology Wuhan430074 China School of Aero-engine Engineering Zhengzhou University of Aeronautics Zhengzhou450046 China Intelligent Computing Hardware Center Zhejiang Lab Hangzhou311121 China CYG Sunri Co. Ltd. Shenzhen518057 China

This paper models the macroscopic dynamics of wear based on the linear damage mechanism, abstracting state variables of loss power, friction pair characteristics and debris characteristics. The wear evolution trend is then analyzed from system stability perspective by the Routh criterion. Furthermore, wear evolution mechanism is studied by Monte Carlo simulation within reasonable parameter ranges. Finally, the effects of multiple factors on wear evolution are analyzed by critical parameter perturbation. The results show that the action and correlation coefficients between the state variables would influence the evolution direction of friction wear. The reasonable combination of these parameters can effectively reduce loss power, decrease roughness and fluctuation of friction pairs, and slow growth rate of debris mass. © 2024, The Authors. All rights reserved.

关键词： Wear of materials

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METS-CoV: a dataset of medical entity and targeted sentiment on COVID-19 related tweets 22

METS-CoV: a dataset of medical entity and targeted sentiment...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Peilin Zhou Zeqiang Wang Dading Chong Zhijiang Guo Yining Hua Zichang Su Zhiyang Teng Jiageng Wu Jie Yang School of Public Health and the Second Affiliated Hospital Zhejiang University China and The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province China School of Electronic and Computer Engineering Peking University China Department of Computer Science and Technology University of Cambridge UK Department of Biomedical Informatics Harvard Medical School School of Public Health and the Second Affiliated Hospital Zhejiang University China and Chu Kochen Honors College Zhejiang University China School of Engineering Westlake University China

ISBN: (纸本)9781713871088

The COVID-19 pandemic continues to bring up various topics discussed or debated on social media. In order to explore the impact of pandemics on people's lives, it is crucial to understand the public's concerns and attitudes towards pandemic-related entities (e.g., drugs, vaccines) on social media. However, models trained on existing named entity recognition (NER) or targeted sentiment analysis (TSA) datasets have limited ability to understand COVID-19-related social media texts because these datasets are not designed or annotated from a medical perspective. This paper releases METS-CoV, a dataset containing medical entities and targeted sentiments from COVID-19-related tweets. METS-CoV contains 10,000 tweets with 7 types of entities, including 4 medical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity types (Person, Location, and Organization). To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9,101 entities (in 5,278 tweets). To the best of our knowledge, METS-CoV is the first dataset to collect medical entities and corresponding sentiments of COVID-19-related tweets. We benchmark the performance of classical machine learning models and state-of-the-art deep learning models on NER and TSA tasks with extensive experiments. Results show that the dataset has vast room for improvement for both NER and TSA tasks. With rich annotations and comprehensive benchmark results, we believe METS-CoV is a fundamental resource for building better medical social media understanding tools and facilitating computational social science research, especially on epidemiological topics. Our data, annotation guidelines, benchmark models, and source code are publicly available (https://***/YLab-Open/METS-CoV) to ensure reproducibility.

关键词：

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Reciprocating Magnetic Fields in the Pulsar Wind Observed from the Black Widow Pulsar J1720-0534

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Research in Astronomy and Astrophysics 2023年第10期23卷 255-266页

作者： Chen-Chen Miao Victoria Blackmon Wei-Wei Zhu Dong-Zi Li Ming-Yu Ge Xiao-Peng You Maura McLaughlin Di Li Na Wang Pei Wang Jia-Rui Niu M.Cruces Jian-Ping Yuan Jun-Tao Bai D.J.Champion Yu-Tong Chen Ming-Min Chi P.C.C.Freire Yi Feng Zhen-Ye Gan M.Kramer Fei-Fei Kou Yu-Xi Li Xue-Li Miao Ling-Qi Meng Chen-Hui Niu Sheng-Nan Sun Zhong-Yi Sun H.M.Tedila Shuang-Qiang Wang Qing-Dong Wu Jing-Bo Wang Zhi-Gang Wen Shen Wang Ya-Biao Wang Cheng-Jie Wang Meng-Yao Xue You-Ling Yue Mao Yuan Ju-Mei Yao Wen-Ming Yan Ru-Shuang Zhao Lei Zhang De Zhao National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China West Virginia University 1500 University AveMorgantownWV 26506USA Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Cahill Center for Astronomy and Astrophysics California Institute of Technology1216 E California BoulevardPasadenaCA 91125USA Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China School of Physical Science and Technology Southwest UniversityChongqing 400715China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China Max-Planck Institut für Radioastronomie Auf dem Hügel 69D-53121 BonnGermany School of Computer Science Fudan UniversityShanghai 200433China Tencent Youtu Lab Shanghai 200233China College of Physical Science and Technology Central China Normal UniversityWuhan 430079China Institute of Optoelectronic Technology Lishui UniversityLishui 323000China Guizhou Normal University Guiyang 550001China

We report the radio observations of the eclipsing black widow pulsar J1720-0534, a 3.26 ms pulsar in orbit with a low mass companion of mass 0.029 to 0.034 M⊙. We obtain the phase-connected timing ephemeris and polarization profile of this millisecond pulsar(MSP) using the Five-hundred-meter Aperture Spherical radio Telescope(FAST), the Green Bank Telescope(GBT), and the Parkes Telescope. For the first time from such a system, an oscillatory polarization angle change was observed from a particular eclipse egress with partial depolarization, indicating 10-milliGauss-level reciprocating magnetic fields oscillating in a length scale of 5 ×10^(3)km(assuming an orbital inclination angle of 90°) outside the companion's magnetosphere. The dispersion measure variation observed during the ingresses and egresses shows the rapid raising of the electron density in the shock boundary between the companion's magnetosphere and the surrounding pulsar wind. We suggest that the observed oscillatory magnetic fields originate from the pulsar wind outside the companion's magnetosphere.

关键词： (stars:)pulsars:general (stars:)pulsars:individual(J1720-0534) (stars:)binaries:eclipsing

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A Hybrid Blockchain Scheme for Tracing Manufacturing Processes and Trading Transactions

A Hybrid Blockchain Scheme for Tracing Manufacturing Process...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Ye Yuan Mingxing Yang Ruoting Xiong Yani Sun Xianchao Zhang Wei Ren HuBei Land Resources Vocational College China Gemological Institute China University of Geosciences Wuhan China School of Computing Science University of East Anglia UK Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Institute of Beihang University Kunming China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Beijing China Ministry of Education Key Laboratory of Data Protection and Intelligent Management (Sichuan University) China School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Blockchain can be envisioned as an enabling framework in terms of cryptography and distributed computing to construct trusted data records among un-trusted users. It can be applied in two typical scenarios - historical process provenance and transaction tracing, by immutably sequentialized blocks. In the former there exists no tokens; but in the latter token handover among accounts should be cryptographically guaranteed. In some situations, above two applications may be both required. E.g., in the manufacture of a jewel, processing flow by different operators should be recorded for quality guaranteeing, and in its trading stage, the ownership handover should be recorded for value confirmation. To tackle above two requirements, two chains are usually thus demanded. In this paper, we propose a single chain solution to avoid cross-chain burden, by a hybrid block structure. The process data can be imported from the legacy (non-anonymous) information processing system, and transaction data stems from anonymous payment demand for ownership handover. We formally describe and prove token handover model with cryptographical authenticity. We also propose several security enhancements in the design for shortening the length of the chain, and protecting the data confidentiality in the chain. Our design is general and not limited to single application. The experimental results and extensive analysis justified that the performance and security of our proposed scheme is efficient.

关键词： Distributed processing Data privacy Manufacturing processes Information processing Handover Throughput Data structures Blockchains Delays Periodic structures

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METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets

arXiv

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arXiv 2022年

作者： Zhou, Peilin Wang, Zeqiang Chong, Dading Guo, Zhijiang Hua, Yining Su, Zichang Teng, Zhiyang Wu, Jiageng Yang, Jie School of Public Health The Second Affiliated Hospital Zhejiang University China The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province China School of Electronic and Computer Engineering Peking University China Department of Computer Science and Technology University of Cambridge United Kingdom Department of Biomedical Informatics Harvard Medical School United States Chu Kochen Honors College Zhejiang University China School of Engineering Westlake University China

The COVID-19 pandemic continues to bring up various topics discussed or debated on social media. In order to explore the impact of pandemics on people’s lives, it is crucial to understand the public’s concerns and attitudes towards pandemic-related entities (e.g., drugs, vaccines) on social media. However, models trained on existing named entity recognition (NER) or targeted sentiment analysis (TSA) datasets have limited ability to understand COVID-19-related social media texts because these datasets are not designed or annotated from a medical perspective. This paper releases METS-CoV, a dataset containing medical entities and targeted sentiments from COVID-19-related tweets. METS-CoV contains 10,000 tweets with 7 types of entities, including 4 medical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity types (Person, Location, and Organization). To further investigate tweet users’ attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9,101 entities (in 5,278 tweets). To the best of our knowledge, METS-CoV is the first dataset to collect medical entities and corresponding sentiments of COVID-19-related tweets. We benchmark the performance of classical machine learning models and state-of-the-art deep learning models on NER and TSA tasks with extensive experiments. Results show that the dataset has vast room for improvement for both NER and TSA tasks. With rich annotations and comprehensive benchmark results, we believe METS-CoV is a fundamental resource for building better medical social media understanding tools and facilitating computational social science research, especially on epidemiological topics. Our data, annotation guidelines, benchmark models, and source code are publicly available (https://***/YLab-Open/METS-CoV) to ensure reproducibility. Copyright © 2022, The Authors. All rights reserve

关键词： COVID-19

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Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging

arXiv

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arXiv 2024年

作者： Yang, Jing Cheng, Jian Li, Cheng Fan, Wenxin Zou, Juan Wu, Ruoyou Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Peng Cheng Laboratory Guangdong Shenzhen China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Yuhang District Hangzhou China

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation *** experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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FAST Observations of an Extremely Active Episode of FRB 20201124A.Ⅳ.Spin Period Search

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Research in Astronomy and Astrophysics 2022年第12期22卷 99-122页

作者： Jia-Rui Niu Wei-Wei Zhu Bing Zhang Mao Yuan De-Jiang Zhou Yong-Kun Zhang Jin-Chen Jiang J.L.Han Di Li Ke-Jia Lee Pei Wang Yi Feng Dong-Zi Li Rui Luo Fa-Yin Wang Zi-Gao Dai Chen-Chen Miao Chen-Hui Niu Heng Xu Chun-Feng Zhang Wei-Yang Wang Bo-Jun Wang Jiang-Wei Xu National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Institute for Frontier in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Department of Physics and Astronomy University of NevadaLas VegasNV 89154USA Department of Astronomy Peking UniversityBeijing 100871China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100871China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China Cahill Center for Astronomy and Astrophysics MC 249-17 California Institute of TechnologyPasadena CA 91125USA CSIRO Space and Astronomy PO Box 76EppingNSW 1710Australia School of Astronomy and Space Science Nanjing UniversityNanjing 210093China Key Laboratory of Modern Astronomy and Astrophysics Nanjing UniversityMinistry of EducationNanjing 210093China Department of Astronomy University of Science and Technology of ChinaHefei 230026China

We report the properties of more than 800 bursts detected from the repeating fast radio burst(FRB)source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio Telescope during an extremely active episode on UTC 2021 September 25th-28th in a series of four *** this fourth paper of the series,we present a systematic search of the spin period and linear acceleration of the source object from both 996 individual pulse peaks and the dedispersed time *** credible spin period was found from this data *** rule out the presence of significant periodicity in the range between 1 ms and 100 s with a pulse duty cycle<0.49±0.08(when the profile is defined by a von-Mises function,not a boxcar function)and linear acceleration up to 300 m s^(-2)in each of the four one-hour observing sessions,and up to 0.6 m s^(-2)in all 4 *** searches contest theoretical scenarios involving a 1 ms–100 s isolated magnetar/pulsar with surface magnetic field<10^(15)G and a small duty cycle(such as in a polar-cap emission mode)or a pulsar with a companion star or black hole up to 100 M_(⊙)and P_(b)>10 *** also perform a periodicity search of the fine structures and identify 53 unrelated millisecond-timescale“periods”in multicomponents with the highest significance of 3.9σ.The“periods”recovered from the fine structures are neither consistent nor harmonically *** they are not likely to come from a spin *** caution against claiming spin periodicity with significance below~4σwith multi-components from one-off *** discuss the implications of our results and the possible connections between FRB multi-components and pulsar microstructures.

关键词： pulsars:general methods:observational radio continuum:general

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